Developing a co-designed sleep wearable to support adherence to behavioural sleep interventions with a wearable component
开发共同设计的睡眠可穿戴设备，以支持使用可穿戴组件坚持行为睡眠干预

Danyang Wang ________________ Content
王丹阳 ________________ 内容

Exploring potential enhancements of adherence on sleep wearable interventions from User experience aspect 1 Content 2 Outline for Anna 3 Introduction 5 Background Literature 8 2.1 Sleep Problems 8 2.2 Sleep Behavioural Interventions 9 2.3 Behavior Change Techniques 14 2.4 The roles of wearable technology for sleep interventions 18 2.5 Adherence in sleep intervention 25 2.6 Promoting adherence from user experience (UX) research 27 Research Questions 31 3.1 Research Plan 31 3.2 Proposed contribution of new knowledge and understanding 34 Methodology 35 4.1 Methods 35 4.2 Ontology 39 4.3 Epistemological Stance 40 4.4 Statement of Positionality 40 Methods Implementation 42 5.1 BCTs Mapping (Discover and Define Phase) - The 1st Diamond 42 5.2 Sleep App Designing (Develop and Deliver Phase) - The 2nd Diamond 49 5.3 Timetable 53 5.4 Thesis chapter plan 53 Ethics and Data Management 55 6.1 Ethical Considerations 55 6.2 Data Management Plan 58 Challenges and Mitigations 60 TNA 62 References 68
从用户体验方面探索睡眠可穿戴干预措施依从性的潜在增强 1 内容 2 Anna 大纲 3 简介 5 背景文献 8 2.1 睡眠问题 8 2.2 睡眠行为干预 9 2.3 行为改变技术 14 2.4 可穿戴技术在睡眠干预中的作用 18 2.5睡眠干预的依从性 25 2.6 促进用户体验（UX）研究的依从性 27 研究问题 31 3.1 研究计划 31 3.2 新知识和理解的拟议贡献 34 方法论 35 4.1 方法 35 4.2 本体论 39 4.3 认识论立场 40 4.4 立场陈述 40 方法实施 42 5.1 BCT 映射（发现和定义阶段） - 第一颗钻石 42 5.2 睡眠应用程序设计（开发和交付阶段） - 第二颗钻石 49 5.3 时间表 53 5.4 论文章节计划 53 道德和数据管理 55 6.1 道德考虑 55 6.2 数据管理计划 58 挑战和缓解措施 60 TNA 62 参考资料 68

________________ Outline for Anna
________________ 安娜的大纲

List of outputs in this project[a]
该项目的产出列表[a]

* RQ1: In sleep behavioural interventions, how many BCTs are provided by wearable devices? How many of them are from CBT-i?
* RQ1：在睡眠行为干预中，可穿戴设备提供了多少BCT？其中有多少来自 CBT-i？

* RQ2: Which BCTs would contribute and affect adherence in sleep wearable behavioural interventions?
* RQ2：哪些 BCT 会有助于并影响睡眠可穿戴行为干预的依从性？

* A list of BCTs which have effect on sleep intervention and adherence to the intervention;
* 对睡眠干预和干预依从性有影响的 BCT 列表；

* RQ3: What’s the user experience of these BCTs under the scenario of wearable behavioural interventions?
* RQ3：这些BCT在可穿戴行为干预场景下的用户体验如何？

* A group of interview data about user’s experience when they were using their wearable on their sleep self management, whether it is positive or negative; * Series of theme coded from user experience interview and mapping with BCTs from systematic reviews;
* 一组关于用户使用可穿戴设备进行睡眠自我管理的体验的访谈数据，无论是积极的还是消极的； * 根据用户体验访谈编码的系列主题，并根据系统评论与 BCT 进行映射；

* RQ4: In sleep wearable behavioural interventions, how to improve adherence by combining sleep BCTs and adherence BCTs?
* RQ4：在睡眠可穿戴行为干预中，如何通过结合睡眠 BCT 和依从性 BCT 来提高依从性？

* Use the UX themes and BCTs map to design a personalised app prototype or a personalise framework prototype protocol
* 使用UX主题和BCT地图来设计个性化应用程序原型或个性化框架原型协议

Relevant paper with similar prototype outcome
具有类似原型结果的相关论文

I don’t mind using either to describe the concept in this CR, but I wonder which you might think would be the most clear or easy word?
我不介意使用其中任何一个来描述此 CR 中的概念，但我想知道您可能认为哪个词是最清晰或最简单的？

* Healthcare app * Self management app * Personalise framework * Health dashboard * Gamification mHealth[b]
* 医疗保健应用程序 * 自我管理应用程序 * 个性化框架 * 健康仪表板 * 游戏化 mHealth[b]

BCT-Intervention mapping：https://pediatrics.jmir.org/2022/3/e34588
BCT-干预映射：https://pediatrics.jmir.org/2022/3/e34588

Concepts to clear: 需要明确的概念：

BCT - under the scenario of wearable sleep behavioural intervention / why? - because more wearable is popular and mostly involved in traditional interventions (at least industries gives fundings and biosignal data), BCTs could be different with f2f scenario as it’s human computer(watch) interaction not human-human interaction;
BCT——可穿戴场景下的睡眠行为干预/为什么？ - 由于更多可穿戴设备很受欢迎，并且主要涉及传统干预措施（至少行业提供资金和生物信号数据），BCT 可能与面对面场景不同，因为它是人机（手表）交互而不是人与人交互；

Adherence - to intervention / why? - because adherence to intervention could including adherence to wearables
坚持——干预/为什么？ - 因为坚持干预可能包括坚持使用可穿戴设备

Aim - this study aims to solve the adherence as its a general question under this scenario. The pain points of each fields: intervention - adherence, individual difference, long-term effect, accuracy, limited resource; monitoring - adherence, accuracy, database not enough, privacy, standardise, Device updates; self-management - adherence, data is hard to understand, accuracy, comfort, data safety, result anxiety) my aim is to solve the adherence in Intervention design, but self-management app seems easier to solve ________________
目的 - 本研究旨在解决这种情况下的普遍问题——依从性。各领域痛点：干预——依从性、个体差异、长期效果、准确性、资源有限；监控 - 依从性、准确性、数据库不足、隐私、标准化、设备更新；自我管理-依从性、数据难以理解、准确性、舒适性、数据安全性、结果焦虑）我的目标是解决干预设计中的依从性，但自我管理应用程序似乎更容易解决________________

Introduction[c][d][e] 简介[c][d][e]

Behavioural interventions in non-pharmacological treatment encompass a broad spectrum and serve as an affordable primary care approach for most individuals experiencing sleep issues. Cognitive Behavioral Therapy for Insomnia (CBT-I), as a first-line treatment for insomnia, is considered the most effective non-medical intervention to manage chronic insomnia, as there is now an overwhelming preponderance of evidence that it is as effective as sedative-hypnotics during acute treatment (4-8 weeks) and is more effective than sedative-hypnotics in the long term (more than 3 months after treatment) (Muench et al., 2022; Wisner, 2023). Apart from the widely acknowledged CBT-I (Miller et al., 2014; Mitchell et al., 2012; Montgomery & Dennis, 2003; Morin et al., 1994; Murtagh & Greenwood, 1995; Okajima et al., 2010; Smith et al., 2002; Van Straten et al., 2018; Irwin et al., 2006; Koffel et al., 2015), interventions like Brief Behavioral Treatment for Insomnia (BBTI) (Germain et al., 2006; Buysse et al., 2011; Troxel et al., 2012) target the alteration of behavioural and cognitive patterns that could contribute to or exacerbate insomnia. The limitations of CBT-I primarily involve issues of accessibility and availability. This therapy faces challenges due to a shortage of skilled therapists and its comparatively high clinical costs, which limit patient access to this form of treatment. Furthermore, digital behavioural therapy, especially when integrated with technological product design, has proven effective. The combination of wearable devices with digital insomnia therapy has shown to enhance user engagement and improve sleep parameters more than digital Behavioral Therapy for Insomnia (dBTi) alone (Aji et al., 2021). Moreover, current online behavioural interventions also have significant shortcomings, including low adherence rates and the absence of therapist guidance. Other concerns with these online interventions encompass problems like overly generalised advice, technical difficulties, and privacy issues (Uyumaz et al., 2021). These intervention units, known as Behavior Change Techniques (BCTs), are fundamental components utilised to facilitate changes in behaviour (Michie et al., 2013). The BCT Taxonomy V1, commonly recognized as the standard in behaviour change research for both design and reporting, classifies BCTs into 16 clusters, each representing a key method of behaviour change (Michie et al., 2013; Michie et al., 2009; Abraham & Michie, 2008). The implementation of these evidence-based BCTs in interventions is advised due to their proven effectiveness in modifying health behaviours (NICE, 2014), like sleep. Behavioural intervention has demonstrated efficacy in ameliorating sleep problems. [f]
非药物治疗中的行为干预涵盖范围广泛，对于大多数遇到睡眠问题的人来说，这是一种负担得起的初级保健方法。失眠认知行为疗法（CBT-I）作为失眠的一线治疗方法，被认为是治疗慢性失眠最有效的非医疗干预措施，因为现在有压倒性的证据表明它与镇静剂一样有效- 急性治疗期间（4-8 周）使用催眠药，长期（治疗后 3 个月以上）比镇静催眠药更有效（Muench 等，2022；Wisner，2023）。除了广泛认可的 CBT-I 之外（Miller 等人，2014；Mitchell 等人，2012；Montgomery 和 Dennis，2003；Morin 等人，1994；Murtagh 和 Greenwood，1995；Okajima 等人，2010；Smith等人，2002；Irwin 等人，2006；Koffel 等人，2015），失眠短期行为治疗 (BBTI) 等干预措施（Germain 等人，2006；Buysse 等人） ., 2011; Troxel et al., 2012）的目标是改变可能导致或加剧失眠的行为和认知模式。 CBT-I 的局限性主要涉及可访问性和可用性问题。由于缺乏熟练的治疗师以及相对较高的临床成本，这种疗法面临着挑战，这限制了患者获得这种形式的治疗。此外，数字行为疗法，尤其是与技术产品设计相结合时，已被证明是有效的。事实证明，可穿戴设备与数字失眠疗法的结合比单独使用数字失眠行为疗法 (dBTi) 更能增强用户参与度并改善睡眠参数（Aji 等人，2021）。 此外，目前的在线行为干预也存在明显的缺点，包括依从率低和缺乏治疗师指导。对这些在线干预措施的其他担忧包括过于笼统的建议、技术困难和隐私问题等问题（Uyumaz 等人，2021）。这些干预单元被称为行为改变技术（BCT），是用于促进行为改变的基本组成部分（Michie 等，2013）。 BCT 分类法 V1 被普遍认为是设计和报告行为改变研究的标准，它将 BCT 分为 16 个簇，每个簇代表一种行为改变的关键方法（Michie 等人，2013 年；Michie 等人，2009 年；Abraham）和米奇，2008）。建议在干预措施中实施这些基于证据的 BCT，因为它们在改变睡眠等健康行为方面已被证明有效（NICE，2014 年）。行为干预已被证明可以有效改善睡眠问题。 [f]

Wearable technology consists of noninvasive devices which perform monitoring functions (Hemapriya et al., 2017) and support facilitating health behaviour change (Lehrer et al., 2021). Wearables have integrated sensors that continuously measure body functions (Mettler & Wulf, 2019), which are displayed to the user. Wearables continuously monitor physiological parameters remotely and provide real-time objective feedback (Channa et al., 2021). Current reports indicate the usage of wearables will increase by 18% in 2022 (Vijayan et al., 2021), suggesting that wearable technology will continue to be widely implemented among individuals to improve sleep outcomes. With the emergence of commercially available sleep trackers, interventions to enhance sleep health are often delivered using wearables alone or as an adjunct to digital insomnia therapy (Luik et al., 2018). Compared to the traditional sleep measuring scale, e.g. Pittsburgh Sleep Quality Index (PSQI), wearable devices provide more accurate and instant bio-data to reference.The development of consumer sleep technology provides a wider range of sleep datasets and a more comfortable measurement scenario than the sleeping lab. Sleep-tracking wearables, including devices like wristbands, armbands, smartwatches, headbands, rings, and sensor clips, belong to the broader category of consumer sleep technology (CST). This category also encompasses smartphones, sensors placed in beds, and contactless sensors, along with various other devices aimed at enhancing sleep quality or modifying sleep behaviours. Examples of these additional devices are neurostimulators, biofeedback devices, and systems for brainwave entrainment (De Zambotti et al., 2019). In the research and development of wearable sleep intervention technologies, Artificial Intelligence (AI) technologies, particularly machine learning, deep learning, natural language processing, and expert systems, demonstrate substantial application potential. Machine learning algorithms can be employed to extract patterns from sleep data collected by wearable devices, such as identifying potential sleep disorders by analysing heart rate, movement, and breathing frequency. Additionally, deep learning, influenced by biological decision-making models, especially in the analysis of sound and physiological signals, can recognize anomalous patterns during sleep, such as snoring or apnea, providing a basis for personalised sleep interventions. The application of natural language processing lies in developing intelligent systems capable of processing user sleep data through voice inputs, which can parse everyday language descriptions from users and provide feedback and suggestions accordingly (Mohammad, 2022; Rahman et al., 2022). Combining with expert systems incorporate sleep medicine expertise, analysing users' sleep patterns and offering customised sleep improvement strategies based on established rule sets (Goldstein et al., 2020; Hwang et al., 2022; Ohayon, 1999). Especially, when it comes to full therapy protocols, like CBT-I, large language model(LLM) applications for interventions that are highly structured, behavioural, and protocolized may be available sooner than applications delivering highly flexible or personalised interventions (Stade et al., 2024). Th[g][h]e integration of these technologies could potentially promote a more personalised user experience, thereby having chances to enhance the efficacy of sleep intervention strategies. Despite facing numerous challenges in practical applications, including data accuracy, privacy concerns, and the generality of algorithms, their capability to analyse vast data sets and learn therefrom indicates a broad prospect in the field of wearable sleep interventions. However, increased user adoption and collection of personal longitudinal data is urgently needed by the academic community. [i]The [j][k]balance between adherence, performance, versatility, cost, and ease of use needs to be found to suit the consumer (Yin et al., 2023).
可穿戴技术由非侵入性设备组成，这些设备执行监测功能（Hemapriya 等人，2017 年）并支持促进健康行为改变（Lehrer 等人，2021 年）。可穿戴设备具有集成传感器，可以连续测量身体功能（Mettler & Wulf，2019），并将其显示给用户。可穿戴设备持续远程监测生理参数并提供实时客观反馈（Channa 等人，2021）。目前的报告表明，到 2022 年，可穿戴设备的使用量将增加 18%（Vijayan 等人，2021），这表明可穿戴技术将继续在个人中广泛应用，以改善睡眠结果。随着商用睡眠追踪器的出现，增强睡眠健康的干预措施通常单独使用可穿戴设备或作为数字失眠疗法的辅助手段来实施（Luik 等，2018）。与传统的睡眠测量量表相比，例如匹兹堡睡眠质量指数（PSQI），可穿戴设备提供更准确、即时的生物数据参考。消费者睡眠技术的发展提供了比睡眠实验室更广泛的睡眠数据集和更舒适的测量场景。睡眠跟踪可穿戴设备，包括腕带、臂带、智能手表、头带、戒指和传感器夹等设备，属于更广泛的消费者睡眠技术 (CST) 类别。此类别还包括智能手机、放置在床上的传感器和非接触式传感器，以及旨在提高睡眠质量或改变睡眠行为的各种其他设备。这些附加设备的示例包括神经刺激器、生物反馈设备和脑电波夹带系统（De Zambotti 等人，2019）。 在可穿戴睡眠干预技术的研发中，人工智能技术，特别是机器学习、深度学习、自然语言处理、专家系统等展现出巨大的应用潜力。机器学习算法可用于从可穿戴设备收集的睡眠数据中提取模式，例如通过分析心率、运动和呼吸频率来识别潜在的睡眠障碍。此外，受生物决策模型影响的深度学习，特别是在声音和生理信号分析方面，可以识别睡眠期间的异常模式，例如打鼾或呼吸暂停，为个性化睡眠干预提供基础。自然语言处理的应用在于开发能够通过语音输入处理用户睡眠数据的智能系统，该系统可以解析用户的日常语言描述并相应地提供反馈和建议（Mohammad，2022；Rahman et al.，2022）。结合睡眠医学专业知识的专家系统，分析用户的睡眠模式，并根据既定的规则集提供定制的睡眠改善策略（Goldstein et al., 2020；Hwang et al., 2022；Ohayon, 1999）。特别是，当涉及到完整的治疗方案时，例如 CBT-I，用于高度结构化、行为化和协议化的干预措施的大型语言模型 (LLM) 应用程序可能比提供高度灵活或个性化的应用程序更早可用干预措施（Stade 等，2024）。这些技术的整合可能会促进更加个性化的用户体验，从而有机会提高睡眠干预策略的功效。 尽管在实际应用中面临着数据准确性、隐私问题和算法通用性等诸多挑战，但它们分析大量数据集并从中学习的能力表明了可穿戴睡眠干预领域的广阔前景。然而，学术界迫切需要增加用户采用和收集个人纵向数据。 [i]需要找到依从性、性能、多功能性、成本和易用性之间的[j][k]平衡，以适合消费者（Yin 等人，2023）。

Adherence remains a challenge for online Sleep Behavioural Interventions practice. While the integration with wearable devices has an effect on higher adherence, it is still insufficient. Though few current sleep self-management apps meet the pre-specified criteria for quality, content, and functionality (Choi et al., 2018). However, due to its large quantity and variety, some recent studies provide options for analysing the effect by using BCTs (Antezana et al., 2018; Arroyo & Zawadzki, 2022; Lancaster et al., 2023), which also provides a feasible classification to resolve online Sleep Behavioural Interventions into units. Insights from user research based on their experience on BCTs could possibly help identify potential mechanisms and methods to enhance adherence.
依从性仍然是在线睡眠行为干预实践的一个挑战。虽然与可穿戴设备的集成可以提高依从性，但仍然不够。尽管目前很少有睡眠自我管理应用程序能够满足预先指定的质量、内容和功能标准（Choi et al., 2018）。然而，由于其数量大、种类多，最近的一些研究提供了使用 BCT 分析效果的选择（Antezana et al., 2018; Arroyo & Zawadzki, 2022; Lancaster et al., 2023），这也提供了可行的分类解决单位的在线睡眠行为干预问题。基于 BCT 经验的用户研究的见解可能有助于确定提高依从性的潜在机制和方法。

[l] ________________ Literature Review
[l]________________文献综述

2.1 Sleep and Sleep Problems
2.1 睡眠和睡眠问题

Sleep is a fundamental human behaviour that plays a critical role in every aspect of our health and daily life. Sleep is a healthy behaviour influenced by various factors such as lifestyle, environment, and health conditions (Johnson et al., 2018; Liu et al., 2022). [m]Biologically, sleep is a complex physiological process essential for health and well-being. It involves several stages (Patel et al., 2024), including REM (rapid eye movement) and non-REM sleep, each playing a critical role in brain function and overall health. During sleep, the body repairs tissues (Everson et al., 2014), consolidates memories (Born et al., 2006; Born & Wilhelm, 2011), and processes information (Smith, 1995; Hoedlmoser et al., 2022). Hormones that regulate growth, appetite, and metabolism are also released. Psychologically, sleep affects mood (Baum et al., 2013; Short et al., 2020), cognitive function (De Bruin et al., 2017), and mental health (Scott et al., 2021). Lack of sleep can lead to irritability, cognitive impairment, and increased stress. Chronic sleep deprivation has been linked to depression (Conklin et al., 2018), anxiety (Suresh et al., 2022), and other mental health disorders (Freeman et al., 2020). Sleep and mental health are interconnected, influencing and being influenced by each other, better sleep quality correlated with better mental health(Scott et al., 2021; Poon et al., 2024). Socially, sleep behaviours vary widely across different cultures and lifestyles. Social norms and practices around sleep—such as the timing and duration of sleep, siesta cultures, and the use of technology before bed (Zhang & Wu, 2020)—significantly influence sleep patterns. Moreover, work schedules (Geiger-Brown et al., 2011), family responsibilities (Maume et al., 2010), and social engagements (Carney et al., 2006) can impact one’s ability to get sufficient sleep, affecting overall health and productivity.
睡眠是人类的一项基本行为，在我们健康和日常生活的各个方面都发挥着至关重要的作用。睡眠是一种健康行为，受生活方式、环境和健康状况等多种因素影响（Johnson et al., 2018；Liu et al., 2022）。 [m]从生物学角度来说，睡眠是一个对健康和福祉至关重要的复杂生理过程。它涉及多个阶段（Patel et al., 2024），包括快速眼动睡眠和非快速眼动睡眠，每个阶段都在大脑功能和整体健康中发挥着关键作用。在睡眠期间，身体修复组织（Everson et al., 2014）、巩固记忆（Born et al., 2006；Born & Wilhelm, 2011）并处理信息（Smith, 1995；Hoedlmoser et al., 2022）。调节生长、食欲和新陈代谢的激素也会被释放。从心理上讲，睡眠影响情绪（Baum et al., 2013；Short et al., 2020）、认知功能（De Bruin et al., 2017）和心理健康（Scott et al., 2021）。睡眠不足会导致烦躁、认知障碍和压力增加。长期睡眠不足与抑郁症（Conklin 等人，2018 年）、焦虑症（Suresh 等人，2022 年）和其他精神健康障碍（Freeman 等人，2020 年）有关。睡眠和心理健康是相互影响、相互影响的，更好的睡眠质量与更好的心理健康相关(Scott et al., 2021; Poon et al., 2024)。从社会角度来看，不同文化和生活方式的睡眠行为差异很大。围绕睡眠的社会规范和实践——例如睡眠时间和持续时间、午睡文化以及睡前技术的使用（Zhang & Wu，2020）——显着影响睡眠模式。此外，工作时间表（Geiger-Brown 等，2011）、家庭责任（Maume 等，2011）。，2010）和社交活动（Carney et al.，2006）会影响一个人获得充足睡眠的能力，从而影响整体健康和生产力。

Sleep quality and quantity are typically measured with self-report questionnaires, Sleep quality when measured this way is a highly subjective experience. Sleep is usually reported as slow quality if trouble falling asleep, wake up frequently during the night, lie awake for extended periods when they do wake up, or have low sleep efficiency (Suni & Suni, 2023; Thensf, 2024). Additionally, poor sleep quality can negatively impact overall health, contributing to the development of conditions like cardiovascular and metabolic diseases. Sleep has been notably influenced by the COVID-19 pandemic (Huang & Zhao, 2020; Holst et al., 2021; Mohammadi et al., 2021; Iranzo, 2022; Neculicioiu et al., 2022). Post-pandemic observations suggest an increase in sleep duration among many individuals, yet, paradoxically, this has not translated into better sleep quality. In fact, there has been a noticeable deterioration in sleep quality and alterations in sleep patterns (Neculicioiu et al., 2022), for example 18.2% of the population in China is experiencing poor sleep quality (Huang & Zhao, 2020). Healthcare workers, in particular, have seen a rise in sleep disturbances and mental health challenges (Huang & Zhao, 2020). Patients with both acute and long-term manifestations of COVID-19 are reporting increased instances of sleep deprivation. Moreover, the pandemic has influenced dream patterns (Margherita & Caffieri[n], 2022) and post-vaccination experiences have also been noted to affect sleep quality (Holst et al., 2021).[o]
睡眠质量和数量通常通过自我报告问卷来测量，以这种方式测量的睡眠质量是一种高度主观的体验。如果入睡困难、夜间频繁醒来、醒来后长时间保持清醒或睡眠效率低，则睡眠质量通常被报告为低（Suni & Suni，2023；Thensf，2024）。此外，睡眠质量差会对整体健康产生负面影响，导致心血管和代谢疾病等疾病的发生。睡眠受到了 COVID-19 大流行的显着影响（Huang & Zhu，2020；Holst 等，2021；Mohammadi 等，2021；Iranzo，2022；Neculicioiu 等，2022）。大流行后的观察表明，许多人的睡眠时间有所增加，但矛盾的是，这并没有转化为更好的睡眠质量。事实上，睡眠质量和睡眠模式的改变已经明显恶化（Neculiciiou et al., 2022），例如，中国有 18.2% 的人口睡眠质量较差（Huang & Zhao, 2020）。尤其是医护人员，他们的睡眠障碍和心理健康挑战有所增加（Huang＆Zhao，2020）。患有 COVID-19 急性和长期症状的患者报告睡眠不足的情况有所增加。此外，大流行影响了梦境模式（Margherita & Caffieri[n]，2022），并且疫苗接种后的经历也被指出会影响睡眠质量（Holst 等人，2021）。[o]

The financial impact of insomnia encompasses both the direct expenses (such as costs for medical visits, consultations, and products) and indirect expenses (like workplace absenteeism and lower productivity levels). There is a common belief that effectively treating insomnia could significantly reduce these costs. However, current research providing evidence for this is limited (Baka et al., 2021; Wickwire et al., 2019; Wickwire et al., 2016). Even if treatment does lead to notable savings in health-care costs, the widespread adoption of insomnia treatment faces hurdles. The primary challenges include a shortage of CBT-I practitioners, leading to a supply and demand issue, and the lack of universal insurance coverage for CBT-I services (Muench et al., 2022).
失眠的财务影响包括直接费用（例如就诊、咨询和产品费用）和间接费用（例如工作场所缺勤和生产力水平降低）。人们普遍认为，有效治疗失眠可以显着降低这些成本。然而，目前为此提供证据的研究有限（Baka 等人，2021 年；Wickwire 等人，2019 年；Wickwire 等人，2016 年）。即使治疗确实可以显着节省医疗费用，失眠治疗的广泛采用也面临障碍。主要挑战包括 CBT-I 从业者短缺，导致供需问题，以及 CBT-I 服务缺乏全民保险（Muench 等，2022）。

Modern society frequently experiences insufficient, inconsistent, or low-quality sleep. Factors such as occupational stress, societal obligations, mental and physical health issues, sleep conditions, ethnicity, age, marital status, sex, and the experience of hospital stays contribute to this lack of sleep (Tracy et al., 2021; Gohari et al., 2022). The repercussions of sleep deficiency or disorders include diminished cognitive function, reduced alertness, mood instability, cardiovascular issues, diabetes, metabolic and immune dysfunctions, and in severe cases, mortality (Yamazaki et al., 2020; Grandner, 2022).[p]
现代社会经常出现睡眠不足、不一致或质量低下的情况。职业压力、社会义务、精神和身体健康问题、睡眠条件、种族、年龄、婚姻状况、性别和住院经历等因素导致睡眠不足（Tracy 等人，2021；Gohari 等人., 2022)。睡眠不足或疾病的影响包括认知功能下降、警觉性降低、情绪不稳定、心血管问题、糖尿病、代谢和免疫功能障碍，严重时甚至会导致死亡（Yamazaki et al., 2020；Grandner, 2022）。 [p]

Specific demographics face unique sleep challenges. For instance, adolescents often sleep late and wake up early on school days, compensating for lost sleep on weekends, leading to a sleep pattern discrepancy between weekdays and weekends (Armstrong et al., 2020; Kwon et al., 2022). Aging can also deteriorate the robustness of circadian rhythms and sleep homeostasis, leading to sleep disruptions in older adults (Li et al., 2018). Women with severe premenstrual syndrome (PMS) often suffer from poorer sleep quality, potentially linked to disrupted melatonin cycles (Baker & Lee, 2022). Additionally, a range of sleep disorders, like obstructive sleep apnea, chronic insomnia, narcolepsy, delayed sleep-wake phase disorder, and Kleine-Levin syndrome, can significantly impact life quality (Iranzo, 2022).
特定人群面临着独特的睡眠挑战。例如，青少年经常在上学日睡得晚、起得早，以补偿周末失去的睡眠，导致工作日和周末之间的睡眠模式差异（Armstrong 等，2020；Kwon 等，2022）。衰老还会降低昼夜节律和睡眠稳态的稳健性，导致老年人的睡眠中断（Li et al., 2018）。患有严重经前综合症 (PMS) 的女性通常睡眠质量较差，这可能与褪黑激素周期中断有关 (Baker & Lee, 2022)。此外，一系列睡眠障碍，如阻塞性睡眠呼吸暂停、慢性失眠、发作性睡病、睡眠-觉醒时相延迟障碍和克莱恩-莱文综合征，可能会显着影响生活质量（Iranzo，2022）。

Evaluating human sleep, a complex physiological behaviour, can be quite challenging, but a common criterion for assessment is the presence of multiple complete and healthy sleep cycles during a night's sleep. Human consciousness is categorised into three states: wakefulness, non-rapid eye movement (NREM or non-REM) sleep, and rapid eye movement (REM) sleep (Feinsilver, 2021; figure x). NREM sleep itself is further classified into three or four distinct stages. These stages typically alternate in a pattern known as the sleep cycle throughout the night. Each cycle lasts approximately 2 to 3 hours (De Fazio et al., 2022). Sleep that encompasses several complete cycles is generally considered healthy.
评估人类睡眠这一复杂的生理行为可能相当具有挑战性，但评估的一个常见标准是在夜间睡眠期间存在多个完整且健康的睡眠周期。人类意识分为三种状态：清醒、非快速眼动（NREM 或非 REM）睡眠和快速眼动（REM）睡眠（Feinsilver，2021；图 x）。 NREM 睡眠本身进一步分为三个或四个不同的阶段。这些阶段通常以称为睡眠周期的模式在整个晚上交替。每个周期持续约 2 至 3 小时（De Fazio 等人，2022）。包含几个完整周期的睡​​眠通常被认为是健康的。

figure x: Sleep cycles and REM sleep
图 x：睡眠周期和快速眼动睡眠

2.2 Behavioural Sleep Interventions
2.2 行为睡眠干预

Sleep regulation is influenced by several key factors, including circadian rhythms, sleep-wake homeostasis, and cognitive-behavioural influences (Schwartz & Roth, 2008). Behavioural factors will be mainly focused on in this study. Behavioural intervention is a series of interventions aiming to improve sleep through behavioural change, is commonly indicating: CBT-I and Brief behavioural therapy for insomnia (BBTI), and other derived interventions such as digital CBT-I (dBTI) developed on the principle of CBT-I. A scoping review also includes “wellness interventions with behaviour change” under this categorisation (Baron et al., 2021).
睡眠调节受到几个关键因素的影响，包括昼夜节律、睡眠-觉醒稳态和认知行为影响（Schwartz & Roth，2008）。本研究将主要关注行为因素。行为干预是一系列旨在通过行为改变来改善睡眠的干预措施，通常指：CBT-I和失眠简短行为疗法（BBTI），以及其他衍生的干预措施，例如基于以下原则开发的数字CBT-I（dBTI） CBT-I。范围界定审查还包括此分类下的“健康干预措施与行为改变”（Baron 等人，2021）。

2.1.1 Cognitive Behavioral Therapy for Insomnia (CBT-I)[q]
2.1.1 失眠认知行为疗法（CBT-I）[q]

CBT-I is a short-term, evidence-based treatment for chronic insomnia. It focuses on restructuring thoughts, feelings, and behaviours that contribute to insomnia (Wisner, 2023; Sleep Foundation, 2024). CBT-I has several components, including cognitive therapy, behavioural techniques like stimulus control and sleep restriction, and educational components such as sleep hygiene.
CBT-I 是一种针对慢性失眠的短期、循证治疗方法。它侧重于重组导致失眠的思想、感觉和行为（Wisner，2023；睡眠基金会，2024）。 CBT-I 有几个组成部分，包括认知治疗、刺激控制和睡眠限制等行为技术，以及睡眠卫生等教育组成部分。

CBT-I operates by addressing the interplay among our thoughts, actions, and sleep patterns. This therapeutic approach, usually delivered over 6-8 sessions, involves a trained specialist working to pinpoint and modify the cognitive (thoughts and beliefs about sleep), behavioural (activities that precede sleep), and emotional factors that impede restful sleep. Through techniques like cognitive restructuring, stimulus control, and sleep education, CBT-I aims to correct misconceptions and instil beneficial pre-sleep routines, thereby improving sleep quality and alleviating symptoms of insomnia. The length and specific content of these sessions can be adapted based on individual needs, encompassing cognitive, behavioural, and educational strategies to foster a better understanding of how thoughts and behaviours influence sleep.
CBT-I 通过解决我们的思想、行动和睡眠模式之间的相互作用来发挥作用。这种治疗方法通常需要 6-8 个疗程，需要训练有素的专家来查明和修改阻碍安宁睡眠的认知（关于睡眠的想法和信念）、行为（睡眠前的活动）和情绪因素。通过认知重建、刺激控制和睡眠教育等技术，CBT-I 旨在纠正错误观念并灌输有益的睡前常规，从而提高睡眠质量并减轻失眠症状。这些课程的长度和具体内容可以根据个人需求进行调整，包括认知、行为和教育策略，以促进更好地理解思想和行为如何影响睡眠。

figure x: The structure of CBT-I components in in-person treatment.
图 x：面对面治疗中 CBT-I 组件的结构。

Cognitive Restructuring (Thakral et al., 2020; Sleep Foundation, 2024): This aspect of CBT-I targets the negative thought patterns that contribute to insomnia. Individuals often harbour inaccurate beliefs about sleep, such as fearing the consequences of not getting enough sleep, which exacerbates their anxiety about sleeping. Cognitive restructuring involves identifying these disruptive thoughts and systematically challenging and modifying them. By addressing and reframing such beliefs, individuals can reduce anxiety associated with sleep, helping to break the cycle of insomnia.
认知重构（Thakral 等人，2020；睡眠基金会，2024）：CBT-I 的这一方面针对导致失眠的消极思维模式。人们常常对睡眠抱有不准确的信念，例如担心睡眠不足的后果，这加剧了他们对睡眠的焦虑。认知重组涉及识别这些破坏性想法并系统地挑战和修改它们。通过解决和重新构建这些信念，个人可以减少与睡眠相关的焦虑，有助于打破失眠的循环。

Stimulus Control (Thakral et al., 2020; Sleep Foundation, 2024): This strategy helps individuals reassociate their bedroom with sleep instead of wakefulness and frustration. Many insomnia sufferers develop negative associations with their sleep environment due to activities that are not conducive to sleep, like watching TV or using smartphones in bed. Stimulus control involves using the bed exclusively for sleep and sex. If unable to sleep, individuals are advised to leave the bed and only return when genuinely tired, establishing a stronger mental connection between the bedroom and sleep.
刺激控制（Thakral et al., 2020；Sleep Foundation, 2024）：这种策略可以帮助个人将卧室与睡眠重新联系起来，而不是清醒和沮丧。许多失眠症患者由于不利于睡眠的活动（例如在床上看电视或使用智能手机）而与睡眠环境产生负面联系。刺激控制包括仅将床用于睡眠和性行为。如果无法入睡，建议人们离开床，只有在真正疲倦时才返回，从而在卧室和睡眠之间建立更牢固的精神联系。

Sleep Restriction and Compression (Thakral et al., 2020; Sleep Foundation, 2024): These techniques aim to enhance sleep efficiency. Sleep restriction involves limiting the amount of time spent in bed to closely match the actual time spent sleeping. This approach creates a mild sleep deprivation that increases sleep drive. Sleep compression, a less intensive method typically used with older adults, gradually reduces the time spent in bed until it aligns more closely with the actual sleep duration. Both methods are designed to consolidate sleep and improve overall sleep quality over time.
睡眠限制和压缩（Thakral et al., 2020；Sleep Foundation, 2024）：这些技术旨在提高睡眠效率。睡眠限制包括限制在床上的时间，使其与实际睡眠时间紧密匹配。这种方法会造成轻微的睡眠剥夺，从而增加睡眠动力。睡眠压缩是一种通常用于老年人的强度较低的方法，它逐渐减少在床上的时间，直到与实际睡眠时间更接近。这两种方法都旨在随着时间的推移巩固睡眠并提高整体睡眠质量。

Relaxation training (Thakral et al., 2020; Sleep Foundation, 2024) targeting the reduction of racing thoughts and tension often experienced when lying awake in bed. These relaxation techniques enhance the body's natural relaxation response (Relaxation Techniques for Health, n.d.), beneficial for both physical and mental well-being. Effective relaxation methods that can be easily integrated into one’s daily routine include breathing exercises (Zaccaro et al., 2018), progressive muscle relaxation (PMR), autogenic training, biofeedback (De Melo et al., 2019), hypnosis, and meditation (Meditation and Mindfulness: What You Need to Know, n.d.).
放松训练（Thakral 等，2020；睡眠基金会，2024）旨在减少清醒躺在床上时经常出现的思绪和紧张。这些放松技巧增强了身体的自然放松反应（健康放松技巧，n.d.），有益于身心健康。可以轻松融入日常生活的有效放松方法包括呼吸练习（Zaccaro et al., 2018）、渐进式肌肉放松（PMR）、自体训练、生物反馈（De Melo et al., 2019）、催眠和冥想（冥想和正念：你需要知道什么，n.d.）。

CBT-I has proven highly effective, with evidence suggesting that 70% to 80% of patients with primary insomnia see significant improvements when employing a multi-component approach. According to a study published in the Annals of Internal Medicine, these benefits encompass reduced time to fall asleep, increased duration of sleep, and fewer awakenings during the night (Trauer et al., 2015), with many patients maintaining these results over time.
CBT-I 已被证明非常有效，有证据表明 70% 至 80% 的原发性失眠患者在采用多成分方法时看到了显着改善。根据发表在《内科医学年鉴》上的一项研究，这些益处包括缩短入睡时间、延长睡眠时间以及减少夜间醒来次数（Trauer 等，2015），并且许多患者长期保持这些结果。

Recognizing its effectiveness, the American College of Physicians recommends CBT-I as the primary treatment strategy for all adults with insomnia (American College of Physicians, n.d.). This endorsement is based on robust research, including findings reported by the National Library of Medicine and other sources, which highlight the efficacy of CBT-I among high-risk groups such as pregnant individuals (Manber et al., 2019), those with post-traumatic stress disorder (PTSD; Talbot et al., 2014), and cancer survivors dealing with post-treatment insomnia (Johnson et al., 2016).
美国内科医师学会认识到其有效性，建议将 CBT-I 作为所有成人失眠症的主要治疗策略（美国内科医师学会，n.d.）。这一认可基于强有力的研究，包括国家医学图书馆和其他来源报告的研究结果，这些研究强调了 CBT-I 在高危人群中的功效，例如孕妇（Manber 等人，2019 年）、术后人群-创伤性应激障碍（PTSD；Talbot 等人，2014），以及处理治疗后失眠的癌症幸存者（Johnson 等人，2016）。

CBT-I is typically administered by trained healthcare professionals such as doctors, therapists, or psychiatrists, with practitioners accessible through entities like the Society of Behavioral Sleep Medicine and the American Board of Sleep Medicine. Due to a significant demand for CBT-I that exceeds the availability of trained professionals, innovative solutions like digital formats of CBT-I and Brief CBT-I are being developed to make this effective treatment more accessible to a broader audience. Although CBT-I is effective for many, it is not an immediate cure; mastering the techniques requires time and practice. Tracking progress over sessions can be beneficial, as small improvements can motivate continued adherence to the therapy (Sleep Foundation, 2024).
CBT-I 通常由经过培训的医疗保健专业人员（例如医生、治疗师或精神科医生）进行管理，可以通过行为睡眠医学协会和美国睡眠医学委员会等实体联系从业者。由于对 CBT-I 的巨大需求超出了训练有素的专业人员的能力，因此正在开发 CBT-I 数字格式和简短 CBT-I 等创新解决方案，以使更广泛的受众更容易获得这种有效的治疗。尽管 CBT-I 对许多人有效，但它并不是立竿见影的治愈方法。掌握这些技术需要时间和练习。跟踪疗程的进展可能是有益的，因为微小的改进可以激励继续坚持治疗（睡眠基金会，2024）。

Obstacles impede access to CBT-I include (Bramoweth et al., 2020): (a) a shortage of clinicians trained in CBT-I, unable to meet the extensive demand; (b) the frequent location of CBT-I services within mental health clinics, which may carry stigmas associated with mental health treatment; (c) a prevalent belief among healthcare providers that insomnia is merely symptomatic of other conditions like depression or pain, potentially leading to misdiagnosis or inadequate referral to specialised care; (d) insufficient knowledge about CBT-I among both patients and healthcare providers; and (e) dissatisfaction among medical providers concerning the available treatment options. Additionally, logistical challenges like travelling long distances, conflicting work schedules, and caregiving responsibilities further complicate attendance at medical appointments for many individuals (Ulmer et al., 2017).
阻碍获得 CBT-I 的障碍包括（Bramoweth 等，2020）： (a) 接受过 CBT-I 培训的临床医生短缺，无法满足广泛的需求； (b) CBT-I 服务经常位于精神卫生诊所内，这可能会带来与精神卫生治疗相关的耻辱； (c) 医疗保健提供者普遍认为，失眠只是抑郁或疼痛等其他病症的症状，可能导致误诊或转诊不充分； (d) 患者和医疗保健提供者对 CBT-I 的了解不足； (e) 医疗服务提供者对现有治疗方案的不满。此外，长途旅行、工作安排冲突和护理责任等后勤挑战进一步使许多人的医疗预约变得更加复杂（Ulmer 等，2017）。

2.1.2 Brief Behavioural therapy for insomnia (BBTI)
2.1.2 失眠简短行为疗法（BBTI）

Addressing the barriers to accessing insomnia care necessitates the adoption of alternative, flexible, and evidence-supported treatment modalities (Bramoweth et al., 2020). The Brief Behavioral Treatment for Insomnia (BBTI) is a streamlined adaptation of CBT-I that simplifies the training process for providers and allows for versatile treatment delivery methods, including both telephone and in-person sessions. BBTI focuses on key behavioural components such as stimulus control and sleep restriction, packaged into a succinct four-session approach over a span of 4 to 5 weeks (Troxel, Germain, & Buysse, 2012). This model was specifically developed to expand the availability and distribution of evidence-based insomnia treatments in accessible healthcare environments like primary care settings. Compared with CBT-I, BBTI is typically administered by non psychologist health professionals in medical settings (Gunn et al., 2019). Additionally, studies have shown that such brief interventions, consisting of four or fewer sessions, can achieve outcomes comparable to the traditional eight-session CBT-I format (Edinger et al., 2007). A meta-analysis indicates that BBTI may be deemed preliminarily effective and is suitable for use among middle-aged and older adult populations (Kwon et al., 2021).
要解决获得失眠护理的障碍，就必须采用替代的、灵活的、有证据支持的治疗方式（Bramoweth 等，2020）。失眠简短行为治疗 (BBTI) 是 CBT-I 的精简版，它简化了提供者的培训过程，并允许采用多种治疗方法，包括电话和面对面治疗。 BBTI 侧重于刺激控制和睡眠限制等关键行为组成部分，并打包成为期 4 至 5 周的简洁的四次会议方法（Troxel、Germain 和 Buysse，2012 年）。该模型是专门开发的，旨在扩大初级保健机构等可及的医疗环境中基于证据的失眠治疗的可用性和分布。与 CBT-I 相比，BBTI 通常由非心理学家健康专业人员在医疗环境中实施（Gunn 等人，2019）。此外，研究表明，这种由四次或更少的会议组成的简短干预措施可以达到与传统的八次 CBT-I 格式相当的结果（Edinger 等人，2007 年）。荟萃分析表明，BBTI 可能被认为初步有效，适合中老年人群使用（Kwon 等，2021）。

2.1.3 CBT-I with mHealth
2.1.3 CBT-I 与移动医疗

Muench et al. (2022) provided insights into Online Cognitive Behavioral Therapy for Insomnia (iCBT-I), including automated, self-contained applications developed in the past decade, such as Bmedi, Shuti, Sleepio, Sleepful, and Sleepstation. The rationale behind the creation of these CBT-I applications is multifaceted: firstly, CBT-I is data-driven and thus lends itself well to programming; secondly, the online platform facilitates prospective data collection and automated adherence assessment; thirdly, it allows for real-time reminders, queries, and feedback; fourthly, being algorithm-based, it can be delivered without a therapist and outside traditional state practice regulations; and fifthly, as an automated app, it is highly scalable. iCBT-I, while a form of self-help, differs significantly from written guidelines by incorporating visual aids such as expert videos, patient testimonials, animated explanations, and necessitates proactive data collection and algorithm-based treatment recommendations. Studies by Bramoweth et al. (2020) and Lee et al. (2021) suggest that iCBT-I may serve as a viable option when in-person or telemedicine options are unavailable. However, Taylor et al. (2017) found that in-person therapy consistently yielded better results than internet treatments, with effect sizes similar to those observed in civilian populations. Muench et al. (2022) raise concerns regarding iCBT-i, including its potential limitations in screening for a range of sleep disorders, conducting differential diagnoses, determining when treatment may be inappropriate, referring patients, customising therapy components, and replicating the dynamic between patient and therapist found in traditional settings. They propose that more experienced therapists may be more adept at tailoring therapy, managing patient resistances, and ensuring adherence. This aspect remains a topic for empirical investigation. In the absence of definitive data, one interpretation of the comparative outcomes in clinical case series is that therapist experience may be more influential than patient complexity, particularly in cases with significant comorbidities.
慕尼黑等人。 (2022) 提供了对失眠在线认知行为疗法 (iCBT-I) 的见解，包括过去十年开发的自动化、独立的应用程序，例如 Bmedi、Shuti、Sleepio、Sleepful 和 Sleepstation。创建这些 CBT-I 应用程序背后的基本原理是多方面的：首先，CBT-I 是数据驱动的，因此非常适合编程；其次，在线平台有利于前瞻性数据收集和自动依从性评估；第三，实时提醒、查询、反馈；第四，基于算法，​​它可以在没有治疗师的情况下进行，并且可以在传统的国家实践法规之外进行；第五，作为一个自动化应用程序，它具有高度可扩展性。 iCBT-I 虽然是一种自助形式，但与书面指南有很大不同，它结合了专家视频、患者感言、动画解释等视觉辅助工具，并且需要主动收集数据和基于算法的治疗建议。 Bramoweth 等人的研究。 （2020）和李等人。 （2021）建议，当现场或远程医疗选项不可用时，iCBT-I 可能是一个可行的选择。然而，泰勒等人。 （2017）发现，面对面治疗始终比互联网治疗产生更好的效果，其效果大小与在平民中观察到的效果相似。慕尼黑等人。 (2022) 提出了对 iCBT-i 的担忧，包括其在筛查一系列睡眠障碍、进行鉴别诊断、确定何时治疗可能不合适、转诊患者、定制治疗成分以及复制患者和治疗师之间的动态方面的潜在局限性在传统环境中。 他们认为，经验丰富的治疗师可能更擅长调整治疗、管理患者的抵抗力和确保依从性。这方面仍然是实证研究的主题。在缺乏明确数据的情况下，对临床病例系列比较结果的一种解释是，治疗师的经验可能比患者的复杂性更具影响力，特别是在患有严重合并症的病例中。

figure x: Interface of CBT-I Coach (2016) - need update The 2021 study by Uyumaz et al. concentrates on digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) applications that offer a comprehensive set of features, in alignment with the stepped care model proposed by Espie et al. (2013). This model highlights the necessity of both extensive and brief therapeutic interventions in app development. The study acknowledges its focus on full-package apps while suggesting that the principles of design-for-engagement they investigated are also relevant to brief therapy apps, indicating an area for future research. The study identifies and implements several contemporary design principles derived from interaction design, experience design, and serious gaming. These principles are integrated into the dCBT-I applications, enhancing them with supportive and motivational features. The empirical impact of these applications, as established by CBT-I studies, is predominantly on the improvement of sleep time and quality. However, their study posits that the true potential of dCBT-I platforms extends beyond these metrics, with a significant focus on features aimed at increasing user engagement and adherence. In addressing the treatment of insomnia, the study emphasises the necessity of behavioural change, a challenging aspect of insomnia therapy. It points out that the efficacy of recommendations and exercises designed to induce behavioural changes, ultimately improving sleep, depends significantly on user adherence and engagement. The study provides a comprehensive overview of the content and interaction styles in commercially available dCBT-I apps, examining engagement-related factors. The findings regarding design elements and interaction styles are presented as potential guidelines for future researchers and designers in the field of digital behavioural change interventions, especially those targeting high user engagement.
图 x：CBT-I Coach 界面（2016 年）- 需要更新 Uyumaz 等人的 2021 年研究。专注于失眠数字认知行为疗法 (dCBT-I) 应用程序，该应用程序提供了一套全面的功能，与 Espie 等人提出的阶梯式护理模型相一致。 （2013）。该模型强调了应用程序开发中广泛和简短的治疗干预的必要性。该研究承认其重点是全包应用程序，同时表明他们研究的参与设计原则也与简短的治疗应用程序相关，这表明了未来研究的一个领域。该研究确定并实施了源自交互设计、体验设计和严肃游戏的几项当代设计原则。这些原则已集成到 dCBT-I 应用程序中，并通过支持性和激励性功能来增强它们。根据 CBT-I 研究确定，这些应用的实证影响主要在于睡眠时间和质量的改善。然而，他们的研究认为 dCBT-I 平台的真正潜力超出了这些指标，重点关注旨在提高用户参与度和依从性的功能。在解决失眠的治疗方面，该研究强调了行为改变的必要性，这是失眠治疗的一个具有挑战性的方面。它指出，旨在引起行为改变并最终改善睡眠的建议和练习的功效在很大程度上取决于用户的坚持和参与。该研究全面概述了市售 dCBT-I 应用程序的内容和交互风格，研究了与参与度相关的因素。 有关设计元素和交互风格的研究结果为未来数字行为改变干预领域的研究人员和设计师提供了潜在的指导方针，特别是那些针对高用户参与度的干预措施。

A research (Aji et al., 2019) focusing on the topic of "Exploring User Needs and Preferences for Mobile Apps for Sleep Disturbance," found that involving end users in the co-design process through three distinct methods consistently emphasised the importance of features such as sleep tracking (via a diary and wearable devices), alarms, and personalization for user engagement. However, these features often faced criticism in their practical implementation as observed in reviews. The study noted that engagement levels are adversely impacted by poorly crafted features, software bugs, and overly didactic content, which are areas needing improvement. Additionally, it was observed that specific user needs vary depending on the user category, such as those suffering from severe insomnia.
一项以“探索针对睡眠障碍的移动应用程序的用户需求和偏好”为主题的研究（Aji 等人，2019）发现，通过三种不同的方法让最终用户参与协同设计过程始终强调功能的重要性例如睡眠跟踪（通过日记和可穿戴设备）、闹钟和用户参与的个性化。然而，正如评论中所观察到的，这些功能在实际实施中经常面临批评。该研究指出，参与度受到制作不良的功能、软件错误和过于说教的内容的不利影响，这些都是需要改进的领域。此外，据观察，特定用户需求因用户类别而异，例如患有严重失眠的用户。

伊杰夫 18 02929 G002 550

figure x: Advantages and disadvantages of digital CBT-I (Uyumaz et al., 2021)
图 x：数字 CBT-I 的优点和缺点（Uyumaz 等人，2021）

2.3 Behavior Change Techniques
2.3 行为改变技巧

Behaviour change techniques (BCTs) are the smallest components of behaviour change interventions that retain the proposed mechanisms of change (Michie et al., 2015; Marques et al., 2023). They are specific techniques or processes that have been shown to change one or more determinants of behaviour (Kok et al., 2015). The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions (figure x).
行为改变技术 (BCT) 是行为改变干预措施的最小组成部分，保留了所提出的改变机制（Michie 等人，2015 年；Marques 等人，2023 年）。它们是已被证明可以改变一个或多个行为决定因素的特定技术或流程（Kok 等，2015）。行为改变技术分类法 v1 (BCTTv1) 指定了行为改变干预措施的潜在活跃内容（图 x）。

figure x: The ‘periodic table’ of behaviour change techniques, version 1. (Armitage et al., 2020)
图 x：行为改变技术的“周期表”，版本 1。（Armitage 等人，2020）

The contents of all interventions, including mHealth apps for sleep, consist of BCTs (Arroyo & Zawadzki, 2022). BCTs can be used alone or in combination with other BCTs (Michie et al., 2015). The Behavior Change Technique Taxonomy (BCTT) is a formal ontology that specifies the potentially active content of behaviour change interventions (Marques et al., 2023). BCTs are used in mobile health (mHealth) apps to improve sleep. These techniques are designed to change or redirect the determinants that regulate behaviour (Arroyo & Zawadzki, 2022). A meta-analysis indicated that the pooled effect size for sleep duration change was approximately 45 minutes, on average, using a variety of behaviour change methods and in a variety of populations (Baron et al., 2021).
所有干预措施的内容，包括用于睡眠的移动健康应用程序，都由 BCT 组成（Arroyo & Zawadzki，2022）。 BCT 可以单独使用，也可以与其他 BCT 结合使用（Michie 等，2015）。行为改变技术分类法 (BCTT) 是一种正式的本体论，指定了行为改变干预措施的潜在活跃内容（Marques 等人，2023）。 BCT 用于移动健康 (mHealth) 应用程序以改善睡眠。这些技术旨在改变或重定向调节行为的决定因素（Arroyo & Zawadzki，2022）。一项荟萃分析表明，在不同人群中使用各种行为改变方法，睡眠持续时间变化的汇总效应大小平均约为 45 分钟（Baron 等人，2021）。

BCT Taxonomy v1 (Michie et al., 2013) is the most widely used version, developed by Professor Susan Michie and her team. It categorises 93 distinct behaviour change techniques into 16 categories. Now explain how BCTs can be applied to cBTI (see my comment below)
BCT Taxonomy v1（Michie et al., 2013）是使用最广泛的版本，由 Susan Michie 教授和她的团队开发。它将 93 种不同的行为改变技术分为 16 类。现在解释一下 BCT 如何应用于 cBTI（请参阅下面我的评论）

Even the effectiveness in other areas of research can be used as a reference, there is not enough evidence to prove the effectiveness of BCTs in sleep interventions[r]. For example, A Meta-analysis of interventions to enhance adherence to physical activity in patients with chronic musculoskeletal disorders shows higher effect for interventions using a greater number of BCTs (Eisele et al., 2019). However, Arroyo and Zawadzki (2022) systematically reviewed numbers of BCTs that are used on average in sleep apps, and increasing or decreasing the number of BCTs did not seem to produce a discernible pattern in the proportion of positive sleep outcomes. [s]
即使其他领域研究的有效性可以作为参考，但没有足够的证据证明BCT在睡眠干预方面的有效性[r]。例如，一项针对增强慢性肌肉骨骼疾病患者身体活动依从性的干预措施的荟萃分析显示，使用更多 BCT 进行的干预措施效果更好（Eisele 等人，2019）。然而，Arroyo 和 Zawadzki（2022）系统地审查了睡眠应用程序中平均使用的 BCT 数量，增加或减少 BCT 数量似乎并没有在积极睡眠结果的比例方面产生明显的模式。 [s]

Lancaster et al. (2023) suggested 11 sleep BCTs have consistently demonstrated efficacy in improving sleep outcomes and have subsequently been recommended for use in sleep management interventions. They are: 1) behaviour-health link, psychoeducation regarding the connection between sleep and the user’s health; 2) consequences, psychoeducation that informs the user of the potential outcomes of engaging or not engaging in a health behaviour; 3) prompt intention formation, setting a general goal; 4) prompt barrier identification, the user identifying and planning ways to overcome barriers for the health behaviour; 5) instruction, providing directions on how to perform a health behaviour; 6) prompt specific goal setting, includes thorough planning of a specific goal (e.g., frequency and intensity) and specification of a strategy to increase practice of the behaviour change (e.g., where, when, and how); 7) self-monitoring, includes tracking of a specific behaviour; 8) feedback, involves providing data about a tracked behaviour or evaluation of performance in relation to a goal; 9) prompt self-talk, encouraging the person to talk to themselves to support target behaviours; 10) stress management, includes relaxation training to reduce stress/anxiety in an effort to facilitate achievement of their behaviour change goal; and 11) stimulus control, reducing environmental cues that promote deleterious health behaviours and increasing those that enhance health behaviour.
兰卡斯特等人。 (2023) 提出 11 种睡眠 BCT 已一致证明在改善睡眠结果方面有效，并随后被推荐用于睡眠管理干预措施。它们是：1）行为与健康的联系，关于睡眠与用户健康之间联系的心理教育； 2) 后果、心理教育，告知用户参与或不参与健康行为的潜在结果； 3）迅速形成意图，设定总体目标； 4）及时识别障碍，用户识别并规划克服健康行为障碍的方法； 5) 指导，提供如何执行健康行为的指导； 6) 及时设定具体目标，包括对具体目标（例如频率和强度）的全面规划以及增加行为改变实践的策略规范（例如，地点、时间和方式）； 7) 自我监控，包括跟踪特定行为； 8) 反馈，涉及提供有关跟踪行为的数据或与目标相关的绩效评估； 9) 及时自言自语，鼓励患者自言自语以支持目标行为； 10) 压力管理，包括放松训练以减轻压力/焦虑，以促进实现行为改变目标； 11）刺激控制，减少促进有害健康行为的环境因素，增加促进健康行为的环境因素。

All three studies [t]involved analyses of BCTs for mHealth sleep interventions, but the results appear to be less consistent: A Lancaster et al. (2023) study shows that sleep management apps included 0--15 BCTs (M = 6.89) and 0--9 sleep BCTs (M = 4.87) (Lancaster et al., 2023). Different from CBT-I, The most commonly used BCT was “teach to use prompts/cues”, however, stimulus control, self-talk, and prompt barrier identification were not often included. Despite the literature identifying stimulus control as an effective strategy to improve sleep (Murawski et al., 2018; Taylor & Roane, 2010), only 16% of the apps included stimulus control. Despite the use of different BCTs Taxonomy, the result is consistent with Antezana et al.'s (2018) findings that sleep tracking apps relied on movement sensors embedded within smartphones to provide ‘Biofeedback’ (50%) and ‘Feedback on outcomes of behaviour’ (50%) expressed as sleep cycle reports. These apps also used ‘action planning’ (60%) and ‘Prompts/cues’ (70%) in the form of ‘relaxing sounds’ and ‘smart alarms’ designed to help sleep conciliation as well as prompting the wake-up cycle. However, Arroyo and Zawadzki (2022) study shows the average number of different BCTs used across interventions (N=12) was 7.67 of 16 clusters on BCT taxonomy v1. Most interventions implemented several different BCTs, with 75% (9/12) of studies reporting using ≥7 BCTs in their mHealth app intervention for sleep. BCTs that appeared most often across mHealth app interventions for sleep were feedback and monitoring (contains biofeedback and feedback on outcomes of behaviour), and shaping knowledge. Other BCTs that were frequently implemented by most (≥75%) of the interventions were goals and planning (contains action planning), antecedents, associations (contains prompts/cues), repetition and substitution, and regulation. Conversely, some BCTs were rarely or never used: natural consequences, comparison of behaviour, reward and threat, scheduled consequences, self-belief, and covert learning.
所有三项研究都涉及对移动健康睡眠干预的 BCT 进行分析，但结果似乎不太一致：A Lancaster 等人。 (2023) 研究表明，睡眠管理应用程序包括 0--15 个 BCT (M = 6.89) 和 0--9 个睡眠 BCT (M = 4.87)（Lancaster 等人，2023）。与CBT-I不同，最常用的BCT是“教使用提示/线索”，但通常不包括刺激控制、自言自语和提示障碍识别。尽管有文献将刺激控制视为改善睡眠的有效策略（Murawski 等人，2018；Taylor & Roane，2010），但只有 16% 的应用程序包含刺激控制。尽管使用了不同的 BCT 分类法，但结果与 Antezana 等人 (2018) 的发现一致，即睡眠跟踪应用程序依赖智能手机中嵌入的运动传感器来提供“生物反馈”(50%) 和“行为结果反馈” ' (50%) 以睡眠周期报告的形式表示。这些应用程序还以“放松声音”和“智能闹钟”的形式使用“行动计划”（60%）和“提示/提示”（70%），旨在帮助安抚睡眠并提示唤醒周期。然而，Arroyo 和 Zawadzki (2022) 研究表明，在 BCT 分类法 v1 中，跨干预措施使用的不同 BCT 的平均数量 (N=12) 为 16 个簇中的 7.67 个。大多数干预措施实施了几种不同的 BCT，其中 75% (9/12) 的研究报告在其 mHealth 应用程序睡眠干预中使用了 ≥7 个 BCT。在 mHealth 应用睡眠干预措施中最常出现的 BCT 是反馈和监测（包含生物反馈和行为结果反馈）以及塑造知识。 大多数（≥75%）干预措施经常实施的其他 BCT 包括目标和计划（包含行动计划）、前因、关联（包含提示/线索）、重复和替代以及调节。相反，一些 BCT 很少或从未使用过：自然后果、行为比较、奖励和威胁、预定后果、自信和秘密学习。

Arroyo and Zawadzki (2022) analysis revealed a significant presence effect: specific Behavior Change Techniques (BCTs) within interventions correlated with differing rates of positive sleep outcomes. These BCTs are in order Comparison of behaviour 78% (n=1); Regulation 63% (n=9); Feedback and monitoring 60% (n=11); Goals and planning 60% (n=10); Comparison of outcomes 60% (n=8); Associations 61% (n=9); Repetition and substitution 61% (n=9); Antecedents 54% (n=10); Social support 56% (n=7); Shaping knowledge 57% (n=11); Identity 48% (n=4); Natural consequences 40% (n=2); Reward and threat 50% (n=1); Scheduled consequences — (n=0); Self-belief — (n=0); Covert learning — (n=0) Notably, the BCT comparison of behaviour was associated with a higher efficacy, showing a 78% rate (n=1) of positive sleep outcomes, markedly above the average 50%-60% observed with other BCTs. Conversely, the inclusion of the BCT natural consequences in studies was linked to a lower effectiveness, with only 40% (n=2) positive outcomes, suggesting its impact was less beneficial compared to other techniques. However, it is important to note that the BCT comparison of behaviour only had 1 study that used it and the BCT natural consequences only had 2 studies, and thus, future studies need more evidence to know if this is a reliable effect of the BCT or random chance.
Arroyo 和 Zawadzki (2022) 的分析揭示了显着的存在效应：干预措施中的特定行为改变技术 (BCT) 与不同的积极睡眠结果率相关。这些 BCT 的行为比较顺序为 78% (n=1)；调节 63% (n=9)；反馈和监控 60% (n=11)；目标和计划 60% (n=10)；结果比较 60% (n=8)；关联 61% (n=9)；重复和替换 61% (n=9)；前因 54% (n=10)；社会支持 56% (n=7)；塑造知识 57% (n=11)；同一性 48% (n=4)；自然后果 40% (n=2)；奖励和威胁 50% (n=1)；预定的后果 - (n=0);自信 — (n=0);隐蔽学习 — (n=0) 值得注意的是，BCT 的行为比较与更高的功效相关，显示出 78% 的积极睡眠结果率 (n=1)，明显高于其他 BCT 观察到的平均 50%-60% 。相反，在研究中纳入 BCT 自然后果与较低的有效性相关，只有 40% (n=2) 的积极结果，表明与其他技术相比，其影响较小。然而，值得注意的是，BCT 行为比较只有 1 项研究使用它，而 BCT 自然后果只有 2 项研究，因此，未来的研究需要更多证据来知道这是否是 BCT 的可靠效果或随机的机会。

Comparing with the CBT-I, the results of reviews might indicate that most mHealth apps were not designed under the standard therapy trail. In fact, basing on the top BCTs (feedback and monitoring, Prompts/cues), it seems more designed according to the characteristics of mHealth and the functionality of the device. While evidence suggests that mHealth apps are effective in improving sleep, more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (Arroyo & Zawadzki, 2022). Which BCTs are more suitable for sleep interventions and the interactions between BCTs need to be answered by further research. 2.4 The roles of wearable technology for sleep interventions
与 CBT-I 相比，审查结果可能表明大多数 mHealth 应用程序并不是在标准治疗试验下设计的。事实上，基于最上面的BCT（反馈和监控，Prompts/cues），似乎更多是根据mHealth的特点和设备的功能来设计的。虽然有证据表明移动医疗应用程序可有效改善睡眠，但仍需要更多研究来了解如何有效实施 BCT 以利用移动医疗改善睡眠以及其有效的作用机制（Arroyo & Zawadzki，2022）。哪些BCT更适合睡眠干预以及BCT之间的相互作用需要进一步研究来解答。 2.4 可穿戴技术在睡眠干预中的作用

Many of the benefits of using commercially available wearable devices in research studies are clearly apparent: the technology is of relatively low cost, readily available, and continues to improve in accuracy, convenience, and impact on care (Henriksen et al., 2018). Furthermore, wearables passively collect data without the need for substantial levels of participant interaction, and without the need to travel to a sleep lab for polysomnography (PSG), the gold standard of sleep measurement. One of the greatest potential benefits is in the ability of wearables to evaluate multiple nights (sometimes years) of sleep, thereby providing longitudinal data that may be more reflective of a participant’s normal sleep patterns compared to what can generally be obtained with more traditional methods. Longitudinal assessments, especially when gathered from large, diverse populations, can help us better understand how sleep variability and different sleep patterns might impact human health outcomes. Wearables also provide value to participants since they can return personalised health data in a user-friendly, real-time way via data visualisations, which may also help with engagement and retention in sleep-related research. Furthermore, wearables capture other data (e.g., steps, exercise) that might affect sleep, which cannot be done using PSG.
在研究中使用市售可穿戴设备的许多好处是显而易见的：该技术成本相对较低、容易获得，并且在准确性、便利性和对护理的影响方面不断提高（Henriksen 等人，2018）。此外，可穿戴设备被动地收集数据，不需要大量的参与者互动，也不需要前往睡眠实验室进行多导睡眠图（PSG）——睡眠测量的黄金标准。最大的潜在好处之一是可穿戴设备能够评估多个夜晚（有时是几年）的睡眠，从而提供纵向数据，与通常使用更传统的方法获得的数据相比，这些数据可能更能反映参与者的正常睡眠模式。纵向评估，尤其是从大量不同人群中收集的数据，可以帮助我们更好地了解睡眠变异性和不同的睡眠模式如何影响人类健康结果。可穿戴设备还为参与者提供了价值，因为它们可以通过数据可视化以用户友好的实时方式返回个性化健康数据，这也可能有助于睡眠相关研究的参与和保留。此外，可穿戴设备捕获可能影响睡眠的其他数据（例如步数、锻炼），这是使用 PSG 无法完成的。

Although in Aji et al.'s (2021) study using wearable device-assisted dBTi suggests this adjunctive wearable treatment was positive, it is possible shown to be effective and led to conversative results.[u] There is an expectation in recent research to focus on the creation of participatory data to enhance the precision of sleep monitoring. As with the earlier mentioned bio-signal scenario, the arbitrary mixing of unrelated sensors or interpreting individual sensor data in multiple ways may lead to improved classification but with limited overall outcomes. In practice, there is often a discrepancy between biological data and self-reported sleep data. Strictly speaking, the data collected by existing wearable devices is not recognised by the medical community as a substitute for self-reporting. Efforts to substitute self-reported measures with wearables in tracking sleep-related health issues will likely necessitate research to evaluate the alignment of such data with patients' daily perceptions of illness severity, as noted by Muench et al. (2022). Nevertheless, these devices could be valuable supplementary tools. For instance, data from wearables can verify patient adherence to recommended sleep schedules or the practice of stimulus control therapy (SCT). They are particularly useful in cases where there are concerns about sleep/wake irregularities linked to circadian rhythm disturbances, the need to detect or confirm paradoxical insomnia, or situations where self-report data are unattainable (e.g., in children, those with severe cognitive impairments, or dementia patients). Obviously, one potential benefit of using wearable devices is reducing the burden of completing sleep logs. Baron et al. (2021) systematic review on behavioural sleep medicine interventions to suggest that the automated nature of wearable devices did not interfere with treatment outcomes.Therefore, using a wearable device may reduce barriers to treatment for patients who prefer automated tracking rather than keeping a sleep log. Additionally, "wearables and nearables" might enhance compliance and rapport—the former due to a perceived surveillance effect leading to more honest reporting by patients, and the latter due to a halo effect where integrating device data into treatment boosts patient confidence in the therapy. Enhancing user wear rates and personal data collection could contribute further evidence in this field. The utilisation of wearable devices for sleep behaviour measurement is increasingly widespread, and experiences indicate that actigraphy is generally well-received by patients with sleep disorders. However, individualised databases are necessary to comprehend patient preferences based on factors like the type of sleep disorder, age, and other variables. Studies (Smith et al., 2018) have suggested that future research should also investigate statistical models that take advantage of micro-longitudinal data. This will involve evaluating day-to-day variations in sleep parameters and trajectories over time, rather than solely depending on aggregated, average-level data.
尽管 Aji 等人 (2021) 使用可穿戴设备辅助 dBTi 的研究表明这种辅助可穿戴治疗是积极的，但它可能被证明是有效的并导致了有争议的结果。[u] 最近的研究预计注重创建参与式数据，提升睡眠监测的精准度。与前面提到的生物信号场景一样，任意混合不相关的传感器或以多种方式解释单个传感器数据可能会改进分类，但总体结果有限。在实践中，生物数据和自我报告的睡眠数据之间经常存在差异。严格来说，现有可穿戴设备收集的数据并不能被医学界认可作为自我报告的替代品。 Muench 等人指出，在跟踪睡眠相关健康问题时，用可穿戴设备替代自我报告的测量结果可能需要进行研究，以评估这些数据与患者对疾病严重程度的日常看法的一致性。 （2022）。尽管如此，这些设备可能是有价值的补充工具。例如，来自可穿戴设备的数据可以验证患者是否遵守推荐的睡眠时间表或刺激控制疗法（SCT）的实践。当担心睡眠/觉醒不规律与昼夜节律紊乱有关、需要检测或确认矛盾性失眠或无法获得自我报告数据的情况（例如，儿童、患有严重认知障碍的儿童）时，它们特别有用，或痴呆症患者）。显然，使用可穿戴设备的一个潜在好处是减轻完成睡眠日志的负担。巴伦等人。 (2021) 对行为睡眠医学干预措施的系统回顾表明，可穿戴设备的自动化特性不会干扰治疗结果。因此，使用可穿戴设备可能会减少那些喜欢自动跟踪而不是保留睡眠日志的患者的治疗障碍。此外，“可穿戴设备和近设备”可能会增强依从性和融洽关系——前者是由于感知到的监视效应导致患者更诚实地报告，后者是由于光环效应，即将设备数据集成到治疗中可以增强患者对治疗的信心。提高用户佩戴率和个人数据收集可以为该领域提供进一步的证据。可穿戴设备用于睡眠行为测量的应用越来越广泛，经验表明体动记录仪普遍受到睡眠障碍患者的欢迎。然而，需要个性化数据库来根据睡眠障碍类型、年龄和其他变量等因素了解患者的偏好。研究（Smith 等人，2018）表明，未来的研究还应该调查利用微观纵向数据的统计模型。这将涉及评估睡眠参数和轨迹随时间的每日变化，而不是仅仅依赖于汇总的平均水平数据。

26 different BCTs were incorporated in the 5 wearables out of the 93 BCTs[v] analysed by the BCTTv1. On average, 19 BCTs (range 15-24) were incorporated in the wearables (Düking et al., 2020). Wearable-delivered interventions contain various components to help improve sleep outcomes such as goal setting, real-time feedback, reminders, information, coaching, and social support (Crowley et al., 2016; Gay & Leijdekkers, 2015).
BCTTv1 分析的 93 个 BCT[v] 中，5 个可穿戴设备中纳入了 26 个不同的 BCT。平均而言，可穿戴设备中包含 19 个 BCT（范围 15-24）（Düking 等人，2020）。可穿戴设备提供的干预措施包含各种有助于改善睡眠结果的组成部分，例如目标设定、实时反馈、提醒、信息、指导和社会支持（Crowley 等人，2016 年；Gay & Leijdekkers，2015 年）。

Although there is not enough study summarising how many BCTs are used in sleep wearable interventions, the existing research can provide a framework. A recent systematic review (Lai et al., 2023) summarised three potential mechanisms of how wearable-delivered interventions act, targeted on Spielman's diathesis–stress (3-P) model, comprising predisposing, precipitating, and perpetuating factors, via sleep or activity tracker, sleep position trainer, wearable respiratory monitoring device (Spielman et al., 1987).
尽管没有足够的研究总结睡眠可穿戴干预措施中使用了多少 BCT，但现有研究可以提供一个框架。最近的一项系统综述（Lai 等人，2023）总结了可穿戴设备干预的三种潜在机制，针对 Spielman 的素质-压力（3-P）模型，包括通过睡眠或活动诱发、诱发和持续的因素跟踪器、睡眠姿势训练器、可穿戴呼吸监测设备（Spielman 等，1987）。

figure x, Potential mechanisms of wearable-delivered intervention on sleep outcomes based on concepts of social cognitive theory and targets factors in 3-P model (Lai et al., 2023) First, wearables provide education about good sleep hygiene habits and give individualised tailored messages (Balbim et al., 2021). Health coaching helps users to achieve sleep goals (Greiwe & Nyenhuis, 2020; Liddy et al., 2015). These target predisposing factors by encouraging sleep hygiene habits (Drake et al., 2014).
图x，基于社会认知理论概念和3-P模型中的目标因素的可穿戴设备对睡眠结果进行干预的潜在机制（Lai等人，2023）首先，可穿戴设备提供有关良好睡眠卫生习惯的教育，并提供个性化定制消息（Balbim 等人，2021）。健康指导可帮助用户实现睡眠目标（Greiwe & Nyenhuis，2020；Liddy 等人，2015）。这些措施通过鼓励睡眠卫生习惯来针对诱发因素（Drake 等，2014）。

Second, wearables increase self-efficacy by providing real-time feedback (Gowin et al., 2019). They become bedtime reminders that serve as verbal persuasion (Rieder et al., 2021) and as social support by connecting users to work together toward the same sleep goals (Rieder et al., 2021). Targeting precipitating factors by increasing users' self-efficacy in managing their sleep behaviour can improve sleep (Xiao et al., 2020). For providing real-time feedback, in particular, a wearable sleep position trainer gently vibrates when a supine position is detected to prompt a change in body position while asleep (de Ruiter et al., 2018), thereby improving sleep outcomes.
其次，可穿戴设备通过提供实时反馈来提高自我效能（Gowin et al., 2019）。它们成为就寝提醒，起到口头说服的作用（Rieder et al., 2021），并通过连接用户共同努力实现相同的睡眠目标来提供社会支持（Rieder et al., 2021）。通过提高用户管理睡眠行为的自我效能来针对诱发因素可以改善睡眠（Xiao 等人，2020）。特别是为了提供实时反馈，当检测到仰卧位置时，可穿戴睡眠姿势训练器会轻轻振动，以提示睡眠时身体姿势的变化（de Ruiter et al., 2018），从而改善睡眠结果。

Third, wearables facilitate outcome expectations through individual sleep goal setting to compare sleep behaviour with goals (Lyons et al., 2014), such that change can be made (Bailey, 2019). Perpetuating factors are targeted by redirecting users away from poor sleep habits that maintain sleep difficulty toward consciously practising healthy sleeping habits (Kwasnicka et al., 2016). Wearables allow for self-regulation through self-monitoring sleep parameters to give users a sense of control over their sleep health (Gowin et al., 2019). This targets perpetuating factors by making users more aware of their current maladaptive sleep behaviours (Todd & Mullan, 2014) and encouraging conscious effort to improve sleep quality (Mairs & Mullan, 2015).
第三，可穿戴设备通过设定个人睡眠目标来将睡眠行为与目标进行比较，从而促进结果预期（Lyons 等人，2014 年），从而可以做出改变（Bailey，2019 年）。通过将用户从导致睡眠困难的不良睡眠习惯转向有意识地养成健康的睡眠习惯（Kwasnicka et al., 2016），可以针对持久性因素。可穿戴设备允许通过自我监测睡眠参数进行自我调节，让用户有一种掌控自己睡眠健康的感觉（Gowin et al., 2019）。这通过让用户更加意识到他们当前的适应不良睡眠行为（Todd & Mullan，2014）并鼓励有意识地努力改善睡眠质量（Mairs & Mullan，2015）来针对长期存在的因素。

Although the above BCTs are of interest to other health care fields and have been shown to have a positive effect, however, in the context of the topic of sleep, the mechanism of the effect on sleep, when viewed in conjunction with the sleep BCTs (Lancaster et al., 2023) and the corresponding BCTs commonly used in mHealth, is not yet stable. Current limitations of current sleep wearable interventions revolves around the following points: Adherence Issues, Accuracy and Reliability, User Experience and Design, Privacy Concerns and Generalisation of Findings.
尽管上述 BCT 受到其他医疗保健领域的关注，并且已被证明具有积极作用，但是，在睡眠主题的背景下，当与睡眠 BCT 结合起来时，其对睡眠的影响机制（ Lancaster et al., 2023）以及 mHealth 中常用的相应 BCT 尚未稳定。当前睡眠可穿戴干预措施的局限性主要围绕以下几点：依从性问题、准确性和可靠性、用户体验和设计、隐私问题和研究结果的概括。

Adherence Issues address that effective data collection requires long-term wear, yet user adherence is often low, which compromises the effectiveness of interventions. Despite their benefits, a major challenge with wearable sleep interventions is ensuring consistent use by individuals. Discomfort, the inconvenience of wearing devices nightly, and the perceived intrusion of privacy can deter regular use.[w] Poor user experience design can affect the usability and acceptability of wearable devices. Factors such as device comfort, interface design, and battery life are critical in determining long-term usage. Furthermore, a balance must be struck between signal quality and wearing comfort, with battery life further limiting the portability of these devices. * Accuracy and Reliability of sleep data from wearables can vary significantly. Issues such as device sensitivity, algorithm efficacy, and user variability often lead to discrepancies in sleep stage identification and overall sleep quality assessment compared to standard polysomnography. Relying solely on physiological data from wearables may not provide a comprehensive assessment of sleep disorders. A multidimensional approach that includes subjective perceptions is necessary for a thorough evaluation and intervention.
依从性问题解决了有效的数据收集需要长期佩戴，但用户依从性往往较低，这会损害干预措施的有效性。尽管有这些好处，可穿戴式睡眠干预措施的一个主要挑战是确保个人持续使用。夜间佩戴设备的不适、不便以及侵犯隐私的感觉可能会阻碍正常使用。[w]不良的用户体验设计可能会影响可穿戴设备的可用性和可接受性。设备舒适度、界面设计和电池寿命等因素对于决定长期使用至关重要。此外，必须在信号质量和佩戴舒适度之间取得平衡，电池寿命进一步限制了这些设备的便携性。 * 可穿戴设备的睡眠数据的准确性和可靠性可能存在很大差异。与标准多导睡眠图相比，设备灵敏度、算法功效和用户变异性等问题通常会导致睡眠阶段识别和整体睡眠质量评估存在差异。仅依靠可穿戴设备的生理数据可能无法对睡眠障碍提供全面的评估。包括主观感知在内的多维方法对于彻底的评估和干预是必要的。

Privacy and security concerns are significant, as wearable devices collect sensitive physiological data. Users may be hesitant to adopt wearable technologies due to fears of data misuse or breaches. Poor data management can lead to breaches of privacy.
由于可穿戴设备会收集敏感的生理数据，因此隐私和安全问题非常重要。由于担心数据滥用或泄露，用户可能会犹豫是否采用可穿戴技术。数据管理不善可能导致隐私泄露。

Generalisation of Findings means that among those factors that affect sleep, the current field of research may not have been able to give a comprehensive result. The data collected from wearable devices often lack contextual information about environmental or psychological factors that may affect sleep. This limitation can hinder the holistic understanding and treatment of sleep disorders. For example, the term Orthosomnia indicates those participants’ perfectionist quest to achieve perfect sleep as sleep trackers may pose unique challenges in CBT-I and reinforce sleep-related anxiety or perfectionism for some patients (Baron et al., 2017).
研究结果的泛化意味着，在那些影响睡眠的因素中，目前的研究领域可能还无法给出全面的结果。从可穿戴设备收集的数据通常缺乏可能影响睡眠的环境或心理因素的背景信息。这种限制可能会阻碍对睡眠障碍的整体理解和治疗。例如，“正交睡眠”一词表明这些参与者对实现完美睡眠的完美主义追求，因为睡眠追踪器可能会对 CBT-I 带来独特的挑战，并强化某些患者与睡眠相关的焦虑或完美主义（Baron 等，2017）。

Designing clearly defined and evidence-based BCTs into intervention adherence apps confers a number of advantages, including an increased likelihood of app efficacy to improve medication adherence, a clearer theoretical understanding of potential mechanisms of action, an increased capacity to refine and combine complementary BCTs, and an enhanced ability to compare apps with similar BCT content (Morrissey et al., 2016). Future research analysing sleep wearable intervention with BCT as a basic unit that can be observed could shed some light on the process and dosage of specific interventions, and could also fill a gap in this area.
将明确定义和基于证据的 BCT 设计到干预依从性应用程序中具有许多优势，包括提高应用程序功效改善药物依从性的可能性、对潜在作用机制的更清晰的理论理解、提高完善和组合补充 BCT 的能力、以及增强比较具有类似 BCT 内容的应用程序的能力（Morrissey 等人，2016）。未来研究以BCT为可观察的基本单位来分析睡眠可穿戴干预，可以为特定干预的过程和剂量提供一些线索，也可以填补该领域的空白。

2.5 Adherence in sleep interventions
2.5 睡眠干预的依从性

Formerly, the term compliance has been used in the literature instead of the term adherence in relation to interventions. However, by definition, compliance implies a degree of medical authority and passiveness of the patient. Because this is at odds with the interpersonal behaviour of most clinicians/therapists and psychotherapy clients, the term adherence is currently favoured in the literature emphasising the active engagement of patients (Steinmetz et al., 2023). Three meanings of the term adherence can be distinguished (Mellor et al., 2022), as follows:
以前，文献中使用术语依从性而不是与干预相关的术语依从性。然而，根据定义，依从性意味着一定程度的医疗权威和患者的被动性。由于这与大多数临床医生/治疗师和心理治疗客户的人际行为不一致，因此“依从”一词目前在强调患者积极参与的文献中受到青睐（Steinmetz 等，2023）。术语“依从性”可以区分为三种含义（Mellor 等人，2022），如下所示：

* Therapy adherence: the patient persistently enacts the given treatment recommendations. * Study adherence: the patient fills in all questionnaires and takes all study measures asked for. * Therapist adherence: the therapist follows the treatment manual of the study (this is more often called treatment fidelity in the literature).
* 治疗依从性：患者坚持执行给定的治疗建议。 * 研究依从性：患者填写所有调查问卷并采取要求的所有研究措施。 * 治疗师依从性：治疗师遵循研究的治疗手册（这在文献中通常称为治疗保真度）。

In the context of sleep interventions, adherence refers to the extent to which individuals consistently follow the prescribed use of therapeutic devices, techniques, or procedures designed to improve sleep quality and duration. Adherence encompasses the regularity and accuracy with which a patient applies the intervention according to the recommendations provided by healthcare professionals. This includes, but is not limited to, the use of wearable devices, participation in behavioural therapy sessions (like CBT-I), and the practice of prescribed sleep hygiene and relaxation techniques.
在睡眠干预的背景下，依从性是指个人始终遵循旨在改善睡眠质量和持续时间的治疗设备、技术或程序的规定使用的程度。依从性包括患者根据医疗保健专业人员提供的建议应用干预措施的规律性和准确性。这包括但不限于使用可穿戴设备、参与行为治疗课程（如 CBT-I）以及实践规定的睡眠卫生和放松技巧。

Adherence in sleep research related to wearable devices is defined as the consistent use of the device as intended by the researchers (Jaiswal et al., 2024). [x]The adherence is usually measured by the duration of device usage or the number of nights the device is worn. In sleep interventions via wearable technology, adherence specifically refers to the extent to which individuals consistently use wearable devices as prescribed to monitor or improve sleep. This includes not only wearing the device according to the recommended schedule, typically every night during sleep, but also correctly maintaining the device, syncing data with associated apps or platforms, and engaging with any feedback or recommendations provided by the technology. Effective adherence in this context is essential to ensure accurate data collection and to maximise the therapeutic benefits of the wearable intervention for sleep enhancement and management.
与可穿戴设备相关的睡眠研究中的依从性被定义为按照研究人员的预期持续使用设备（Jaiswal 等人，2024）。 [x]依从性通常通过设备使用的持续时间或佩戴设备的夜晚数来衡量。在通过可穿戴技术进行睡眠干预中，依从性特指个人按照规定持续使用可穿戴设备来监测或改善睡眠的程度。这不仅包括按照建议的时间表（通常是每天晚上睡觉时）佩戴设备，还包括正确维护设备、与相关应用程序或平台同步数据，以及参与技术提供的任何反馈或建议。在这种情况下，有效的坚持对于确保准确的数据收集并最大限度地发挥可穿戴干预措施对睡眠增强和管理的治疗效果至关重要。

The measure of adherence in sleep wearable interventions has no consensus among researchers, further research could analyse and summarise these measurements in conjunction with the corresponding intervention components. A review suggested one possible approach, using sleep diaries, is to quantify daily deviations from prescribed Time to Bed and Time out of Bed (TOB-TTB) (Muench et al., 2022). Meanwhile, in some studies, other parameters like daytime naps or alcohol use were also examined indirectly via sleep diary . Adherence measures in the literature were retrieved either directly from patients, the clinician/therapist, or a person closely related to the patient (e.g. spouse). Direct measures ask questions such as “Did you adhere to treatment recommendation xy?” (Steinmetz et al., 2023); Furthermore, Adherence may be evaluated not only with sleep diaries but also with wearables, especially those that have event markers, for example, In my master's dissertation project, based on the sleep intervention provided by the online webpage, a check point was set before and after the daily 10-minute intervention, and the researcher could access the participants' arrival time at the checkpoint through the backend of the Gorilla platform, and I could make a rough judgement of adherence by the ratio of the time span in time between the participants' arrival time among two checkpoints to the 10-minute intervention: the ratio closer to 1, the higher the adherence.
研究人员对睡眠可穿戴干预措施的依从性衡量标准尚未达成共识，进一步的研究可以结合相应的干预措施来分析和总结这些测量结果。一项评论提出了一种可能的方法，即使用睡眠日记来量化每日与规定的就寝时间和离床时间 (TOB-TTB) 的偏差 (Muench 等人，2022)。与此同时，在一些研究中，还通过睡眠日记间接检查了其他参数，例如白天小睡或饮酒。文献中的依从性测量直接从患者、临床医生/治疗师或与患者密切相关的人（例如配偶）处检索。直接测量会提出诸如“您是否遵守 xy 治疗建议？”之类的问题。 （斯坦梅茨等人，2023）；此外，依从性不仅可以通过睡眠日记来评估，还可以通过可穿戴设备来评估，尤其是那些带有事件标记的可穿戴设备，例如，在我的硕士论文项目中，基于在线网页提供的睡眠干预，之前设置了一个检查点，然后再进行评估。每天10分钟的干预结束后，研究者可以通过Gorilla平台后端获取参与者到达检查点的时间，我可以通过参与者之间时间跨度的比例来粗略判断遵守情况两个检查点到达时间与10分钟干预时间的比值越接近1，依从性越高。

Enhancing adherence in sleep wearable interventions is key to enhancing intervention effectiveness. Users with high adherence are more likely to fully follow the intervention plan, thereby realising the anticipated health benefits of the devices and intervention strategies. If users do not continue to use or correctly use the devices, even the most technologically advanced solutions will not achieve their intended effects. Customization and optimization of intervention measures By studying users' adherence behaviours, it is possible to better understand which features are frequently used, which are ignored, and the specific needs of the users. This allows researchers and developers to adjust and optimise device functionalities and intervention programs based on actual usage and user feedback.
提高睡眠可穿戴干预措施的依从性是提高干预效果的关键。依从性高的用户更有可能完全遵循干预计划，从而实现设备和干预策略的预期健康益处。如果用户不继续使用或不正确使用设备，即使是技术最先进的解决方案也无法达到其预期效果。干预措施的定制和优化通过研究用户的依从行为，可以更好地了解哪些功能被频繁使用、哪些被忽视，以及用户的具体需求。这使得研究人员和开发人员能够根据实际使用情况和用户反馈来调整和优化设备功能和干预程序。

Low adherence can lead to missing or misleading data, which may affect the accuracy and reliability of research results. In sleep studies using wearable devices, the quality of data collection directly depends on the frequency and correctness of use by users. Data donation faces all of the issues that traditional research faces around issues of recruitment and representation, but also presents several specific challenges to representativeness of the sample (Bietz et al., 2019). As healthcare gradually shifts towards personalization and precision medicine, understanding individual users' behavioural patterns in using wearable devices becomes particularly important. Adherence research helps reveal how different groups respond to specific health interventions, providing a basis for designing more personalised intervention strategies.
低依从性可能导致数据丢失或误导，从而可能影响研究结果的准确性和可靠性。在使用可穿戴设备的睡眠研究中，数据收集的质量直接取决于用户使用的频率和正确性。数据捐赠面临传统研究在招募和代表性方面面临的所有问题，但也对样本代表性提出了一些具体挑战（Bietz 等人，2019）。随着医疗保健逐渐转向个性化和精准医疗，了解个人用户使用可穿戴设备的行为模式变得尤为重要。依从性研究有助于揭示不同群体对特定健康干预措施的反应，为设计更个性化的干预策略提供基础。

Research against adherence also contributes to wearable device user retention and long-term benefits. The effectiveness of wearable devices largely depends on the user's ability to use these devices over the long term. Studies show that many users are initially enthusiastic about new technologies, but usage often declines over time. Understanding and addressing the factors that affect long-term use is crucial for improving the success rate of interventions. Increasing adherence can enhance the cost-effectiveness of wearable devices. If users stop using devices after purchase, it is a waste of resources from both healthcare providers and consumers' perspectives. By ensuring continued use of the devices, resources can be used more efficiently, and the economic returns of treatments can be improved.
针对依从性的研究也有助于可穿戴设备用户的保留和长期利益。可穿戴设备的有效性很大程度上取决于用户长期使用这些设备的能力。研究表明，许多用户最初对新技术充满热情，但随着时间的推移，使用率往往会下降。了解并解决影响长期使用的因素对于提高干预措施的成功率至关重要。提高依从性可以提高可穿戴设备的成本效益。如果用户在购买后停止使用设备，从医疗保健提供者和消费者的角度来看，这都是资源的浪费。通过确保设备的持续使用，可以更有效地利用资源，并提高治疗的经济回报。

Researching adherence issues with wearable sleep interventions can benefit from an interdisciplinary approach, drawing knowledge from several academic fields. Behavioral Science provides insights into human behaviours and motivations and understanding why people may or may not adhere to using wearable devices consistently (e.g., comfort, perceived benefits, ease of use) is crucial. Behavioural science can help design interventions that are more user-friendly and engaging to improve adherence; Human-Computer Interaction (HCI) researchers study the design and use of computer technology, focused particularly on the interfaces between people and computers, can provide insights into how wearable devices can be designed to be more intuitive and less intrusive, enhancing user adherence; Knowledge from Data Science can analyse usage patterns to identify when and why users may be more likely to discontinue use, this can inform the development of tailored intervention strategies to increase long-term adherence; Ethics and Privacy Law areas of study address concerns around data privacy and security, significant factors influencing user trust and willingness to use health monitoring devices continuously.
研究可穿戴睡眠干预的依从性问题可以受益于跨学科方法，汲取多个学术领域的知识。行为科学提供了对人类行为和动机的洞察，并且理解为什么人们可能会或可能不会坚持使用可穿戴设备（例如舒适度、感知的好处、易用性）至关重要。行为科学可以帮助设计更加用户友好且更具吸引力的干预措施，以提高依从性；人机交互（HCI）研究人员研究计算机技术的设计和使用，特别关注人与计算机之间的界面，可以深入了解如何将可穿戴设备设计得更直观、更少侵入性，从而提高用户的依从性；来自数据科学的知识可以分析使用模式，以确定用户何时以及为何更有可能停止使用，这可以为制定量身定制的干预策略提供信息，以提高长期依从性；道德和隐私法的研究领域解决了对数据隐私和安全的担忧，这是影响用户信任和持续使用健康监测设备意愿的重要因素。

2.6 Promoting adherence from user experience (UX) research
2.6 促进用户体验（UX）研究的坚持

As term adherence is emphasising the active engagement of patients (Steinmetz et al., 2023), studies focusing on user experience will be a necessary entry point. User experience studies can play a significant role in understanding and promoting adherence, particularly in the context of health apps (Pérez‐Jover et al., 2019). This is because a positive user experience can lead to increased engagement and, in turn, greater adherence to the app's recommendations (Kaveladze et al., 2022). In the field of health care, the provider-patient relationship is an important factor to consider in achieving patient adherence to a provider's treatment recommendations. UX researchers can utilise this framework to better understand patients' treatment needs and improve adherence (Panahi et al., 2022). Some mHealth programs like Shut-I, Sleepio, CBTi Coach (Espie et al., 2012; Ritterband et al., 2017)allow for linkage between wearable consumer targeted sleep wearables with the online program, but currently no data is available on whether the use of these technologies affects outcomes or user experience.
由于术语依从性强调患者的积极参与（Steinmetz et al., 2023），因此关注用户体验的研究将是一个必要的切入点。用户体验研究可以在理解和促进依从性方面发挥重要作用，特别是在健康应用程序的背景下（Pérez‐Jover 等人，2019）。这是因为积极的用户体验可以提高参与度，进而更好地遵守应用程序的建议（Kaveladze 等人，2022）。在医疗保健领域，提供者与患者的关系是实现患者遵守提供者的治疗建议时需要考虑的重要因素。用户体验研究人员可以利用该框架更好地了解患者的治疗需求并提高依从性（Panahi 等人，2022）。 Shut-I、Sleepio、CBTi Coach（Espie 等人，2012 年；Ritterband 等人，2017 年）等一些 mHealth 计划允许将可穿戴消费者目标睡眠可穿戴设备与在线计划联系起来，但目前没有数据表明这些技术的使用会影响结果或用户体验。

Moreover, adherence is a complex and multifaceted concept that is influenced by various factors, including environmental, technological, and support variables, as well as individual user demographics and psychological characteristics (Ryan et al., 2017). UX studies can help identify and address these integrating factors to enhance adherence. According to Radomski et al. (2020), the only UX questionnaire constructs that did not differ between the two intervention groups were the adherence and usage constructs, with study participants expressing few concerns about technology or Internet accessibility or functionality. This suggests that providing a sleep intervention that is easy to use and free of technological issues can improve adherence. In addition, Zhu et al. (2023) research has shown that qualitative research using the Capability, Opportunity, Motivation-Behavior (COM-B) model can explore the barriers and accelerators that promote adherence. In this model, capability represents an individual's skills and knowledge, opportunity represents the environment and opportunities, and motivation represents an individual's motivation and preferences. By understanding these factors, it is possible to design a sleep intervention that is easier to follow and favours adherence. Additionally, long-term studies (Anderson et al., 2016; Biduski et al., 2020) can better capture trends in how individuals experience change during participation in sleep interventions. According to the literature in Ng et al. (2019), while mobile mental health apps have potential benefits, the actual results suggest a stickiness problem due to low engagement and sustained usage rates. A better understanding of UEIs (User Engagement Indicators) is needed in order to increase engagement and sustained usage rates. The study in Fatima et al. (2023) suggests that gamification promotes movement, but adherence is a key factor in gamification. More insights are necessary to understand the drivers responsible for adherence. This study aims to focus on adherence and encourage participants to lead a more energetic lifestyle. Scholars at the Human-Computer Interaction - User Experience and Behavior (2022) conference have begun to focus on the importance of UX in healthcare settings. This suggests that it may be beneficial to apply the methods and theories of UX research in sleep interventions.
此外，依从性是一个复杂且多方面的概念，受到各种因素的影响，包括环境、技术和支持变量，以及个人用户人口统计和心理特征（Ryan et al., 2017）。用户体验研究可以帮助识别和解决这些整合因素，以提高依从性。根据拉多姆斯基等人的说法。 （2020），两个干预组之间唯一没有差异的用户体验问卷构造是依从性和使用构造，研究参与者对技术或互联网可访问性或功能很少表示担忧。这表明提供易于使用且无技术问题的睡眠干预措施可以提高依从性。此外，朱等人。 (2023) 研究表明，使用能力、机会、动机-行为 (COM-B) 模型的定性研究可以探索促进依从性的障碍和加速器。在这个模型中，能力代表个人的技能和知识，机会代表环境和机会，动机代表个人的动机和偏好。通过了解这些因素，可以设计出更容易遵循且有利于坚持的睡眠干预措施。此外，长期研究（Anderson 等人，2016 年；Biduski 等人，2020 年）可以更好地捕捉个人在参与睡眠干预期间经历变化的趋势。根据 Ng 等人的文献。 (2019)，虽然移动心理健康应用程序具有潜在的好处，但实际结果表明，由于参与度低和持续使用率，存在粘性问题。 为了提高参与度和持续使用率，需要更好地了解 UEI（用户参与度指标）。法蒂玛等人的研究。 （2023）表明游戏化促进运动，但坚持是游戏化的关键因素。需要更多的见解来了解负责遵守的驱动因素。这项研究旨在关注坚持并鼓励参与者过上更有活力的生活方式。人机交互 - 用户体验和行为（2022）会议的学者们已经开始关注用户体验在医疗保健环境中的重要性。这表明将用户体验研究的方法和理论应用于睡眠干预可能是有益的。

According to the study by Aji et al. (2019), participants who experienced sleep difficulties exhibited a strong interest in sleep tracking and data visualisation. The study highlighted several ideas that were particularly appealing to these participants. These included the implementation of alarms with challenging turn-off sequences to prevent oversleeping and a variety of alarm sounds, as well as audio features like meditation, podcasts, and relaxing sounds. Additionally, the participants valued encouragement in the form of progress signs and rewards. Interestingly, they preferred Frequently Asked Questions (FAQs) over chatbots for seeking information, as chatbots required keyboard input which was less convenient. Recommendations for helpful apps and websites were also favoured, along with social features that allowed sharing information within a community of individuals with sleep difficulties. The participants expressed interest in receiving tips from others who had successfully completed sleep improvement programs. The study also revealed some areas of frustration among the participants. They found existing resources to be condescending, long-winded, and complex. There was also a sense of annoyance in filling out lengthy questionnaires, especially when the rationale behind these questionnaires was not clear or when immediate feedback on the collected data was not provided. This feedback underscores the need for more user-friendly and straightforward resources in sleep intervention programs.
根据 Aji 等人的研究。 （2019），经历过睡眠困难的参与者对睡眠跟踪和数据可视化表现出浓厚的兴趣。该研究强调了对这些参与者特别有吸引力的几个想法。其中包括实施具有挑战性的关闭顺序的警报，以防止睡过头和各种警报声音，以及冥想、播客和放松声音等音频功能。此外，参与者还重视进步标志和奖励形式的鼓励。有趣的是，他们更喜欢通过常见问题（FAQ）而不是聊天机器人来寻找信息，因为聊天机器人需要键盘输入，这不太方便。有用的应用程序和网站的推荐以及允许在睡眠困难的个人社区内共享信息的社交功能也受到青睐。参与者表示有兴趣从其他成功完成睡眠改善计划的人那里获得建议。该研究还揭示了参与者的一些沮丧之处。他们发现现有资源居高临下、冗长且复杂。填写冗长的调查问卷也会让人感到烦恼，特别是当这些调查问卷背后的理由不清楚或没有对收集的数据立即提供反馈时。这一反馈强调了睡眠干预计划中需要更加用户友好和直接的资源。

In examining user experience barriers for wearable sleep devices, compiled from reviews and social media for each sleep related wearable app, several key issues have been identified across three main categories: accurate data feedback, wearing experience, and sleep intervention products. Firstly, accurate data feedback often demands a significant learning cost as users must become acquainted with various physiological indicators to effectively interpret the data. Moreover, the data provided frequently lacks actionable steps and scientific guidance, making it difficult for users to improve based on the data alone. Additionally, receiving negative sleep data can induce psychological stress, further complicating the user's experience; Regarding the wearing experience, user willingness to wear these devices during sleep varies significantly. Devices that offer more accurate measurements tend to be bulkier and heavier, which can deter usage. Furthermore, the design of these devices often limits their application in different scenarios, restricting their versatility and overall practicality; Lastly, sleep intervention products present their own set of challenges. While these products may offer programmed interventions, individual variations in sleep quality can reduce user willingness to adhere to the prescribed routines. The lack of an interdisciplinary approach in these products means that even well-designed guidance systems may not align with actual physiological data, undermining their effectiveness. Additionally, interface designs often fail to engage users long-term, and the resources provided can appear condescending, overly complex, or verbose. Users also express frustration with having to complete lengthy questionnaires that lack clear rationale or immediate feedback, further diminishing their experience and satisfaction with these products.
在检查可穿戴睡眠设备的用户体验障碍时，根据每个与睡眠相关的可穿戴应用程序的评论和社交媒体整理，发现了三个主要类别的几个关键问题：准确的数据反馈、佩戴体验和睡眠干预产品。首先，准确的数据反馈通常需要大量的学习成本，因为用户必须熟悉各种生理指标才能有效地解释数据。此外，提供的数据往往缺乏可操作的步骤和科学指导，使得用户很难仅根据数据进行改进。此外，接收负面睡眠数据可能会引发心理压力，使用户的体验进一步复杂化；在佩戴体验方面，用户在睡眠期间佩戴这些设备的意愿差异很大。提供更准确测量的设备往往体积更大、更重，这会阻碍使用。此外，这些设备的设计往往限制了它们在不同场景中的应用，限制了它们的多功能性和整体实用性；最后，睡眠干预产品也面临着一系列挑战。虽然这些产品可能提供程序化干预，但睡眠质量的个体差异可能会降低用户遵守规定程序的意愿。这些产品缺乏跨学科方法，意味着即使设计良好的引导系统也可能与实际生理数据不一致，从而削弱其有效性。此外，界面设计通常无法长期吸引用户，并且提供的资源可能显得居高临下、过于复杂或冗长。 用户还对必须完成冗长的调查问卷表示沮丧，而这些调查问卷缺乏明确的理由或即时反馈，进一步降低了他们对这些产品的体验和满意度。

Involving the consideration of BCTs are essential to the effectiveness of sleep wearable interventions, as they can drive user adherence and motivation through personalised feedback and objectives (Mercer et al., 2016). Studying the user experience of behaviour change techniques (BCTs) in sleep is important because previous research has shown that mHealth apps using BCTs can be effective in promoting healthy behaviours, but their efficacy with sleep is unclear (Arroyo & Zawadzki, 2022). Some studies have reported success in promoting sleep through mHealth, while others have noted that sleep apps can lead to unhealthy obsessions with achieving perfect sleep (Arroyo & Zawadzki, 2022). However, there is a lack of research on BCTs in relation to changes in sleep in self-monitoring systems or intervention studies (Duncan et al., 2017). Therefore, examining BCTs in sleep apps can provide novel insights (Duncan et al., 2017) and help optimise the use of BCTs in mHealth apps for sleep.
考虑 BCT 对于睡眠可穿戴干预措施的有效性至关重要，因为它们可以通过个性化的反馈和目标来推动用户的依从性和动机（Mercer 等，2016）。研究睡眠中行为改变技术 (BCT) 的用户体验非常重要，因为之前的研究表明，使用 BCT 的移动医疗应用程序可以有效促进健康行为，但其对睡眠的功效尚不清楚（Arroyo & Zawadzki，2022）。一些研究报告称，通过移动健康在促进睡眠方面取得了成功，而另一些研究则指出，睡眠应用程序可能会导致对完美睡眠的不健康痴迷（Arroyo & Zawadzki，2022）。然而，自我监测系统或干预研究中缺乏关于 BCT 与睡眠变化相关的研究（Duncan 等，2017）。因此，检查睡眠应用程序中的 BCT 可以提供新颖的见解（Duncan 等，2017），并有助于优化 BCT 在移动医疗睡眠应用程序中的使用。

BCT is a systematic procedure that is included as an active component of an intervention designed to change behaviour (Michie et al., 2020). The Theory & Techniques Tool is an interactive resource providing information about links between behaviour change techniques [y](BCTs) and their mechanisms of action (MoAs). The Theory & Techniques of Behaviour Change Project identified BCT-MoA links in a heat map through published scientific literature and expert consensus exercises, producing a final 'triangulation' between these two sources of evidence (Carey et al., 2018).
BCT 是一个系统程序，是旨在改变行为的干预措施的积极组成部分（Michie 等人，2020）。理论与技术工具是一种交互式资源，提供有关行为改变技术 [y](BCT) 及其作用机制 (MoA) 之间联系的信息。行为改变理论与技术项目通过已发表的科学文献和专家共识练习在热图中确定了 BCT-MoA 链接，从而在这两个证据来源之间进行了最终的“三角测量”（Carey 等人，2018）。

In this study, it is argued that researchers design BCTs for wearable devices based on the principles of sleep behavioural intervention, and that wearable devices and mobile apps serve as interfaces for direct interaction with the user to deliver the intervention content, the user obtains the content of the intervention through interaction to make behavioural changes, and the entire user experience that affects the user's adherence, and the user's adherence directly affects the effectiveness of the intervention.
本研究认为，研究人员基于睡眠行为干预原理为可穿戴设备设计BCT，以可穿戴设备和移动应用程序作为与用户直接交互的界面来传递干预内容，用户获取内容干预的效果是通过交互来做出行为改变，而整个用户体验影响着用户的依从性，而用户的依从性直接影响干预的有效性。

figure x: Relationship of factors in this study (draft)
图x：本研究中各因素的关系（草稿）

figure x: Relationship of factors in this study (draft) ________________ Research Questions[z]
图x：本研究中因素的关系（草稿）________________研究问题[z]

To summarise briefly, the research gaps targeted by this study is as follows:
简单总结一下，本研究针对的研究空白如下：

* More research is needed to understand how BCTs (Behavior Change Techniques) can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes) (Arroyo & Zawadzki, 2022) * Few apps meet prespecified criteria for quality, content, and functionality for sleep self-management (Choi et al, 2018)
* 需要更多的研究来了解如何有效实施 BCT（行为改变技术），以利用移动医疗改善睡眠，以及它们有效的作用机制（例如，自我效能、社会规范和态度）（Arroyo & Zawadzki） ，2022）* 很少有应用程序能够满足睡眠自我管理的质量、内容和功能的预先指定标准（Choi 等人，2018）

This PhD project is designed to guide a series of user studies on sleep behaviour interventions delivered through wearable devices, especially focusing on the aspect of adherence. The following research questions were asked, based upon the literature review synthesis:
该博士项目旨在指导一系列通过可穿戴设备提供的睡眠行为干预的用户研究，特别关注依从性方面。根据文献综述提出了以下研究问题：

* RQ1: In sleep behavioural interventions, how many BCTs are provided by wearable devices? How many of them are from CBT-I?
* RQ1：在睡眠行为干预中，可穿戴设备提供了多少BCT？其中有多少来自 CBT-I？

* RQ2: Which BCTs contribute and affect adherence in sleep wearable behavioural interventions?
* RQ2：哪些 BCT 有助于并影响睡眠可穿戴行为干预的依从性？

* RQ3: What’s the user experience of these BCTs under the scenario of wearable behavioural interventions?
* RQ3：这些BCT在可穿戴行为干预场景下的用户体验如何？

3.1 Research Plan 3.1 研究计划

Under the structure of Double Diamond Design Model, Discover, Define, Develop and Deliver, The model has 4 stages (Banbury et al., 2021): The Double Diamond design process comprises four key phases. The first phase, "Discover," involves exploring problems and generating ideas by diverging to gather broad insights and user needs. Next, in the "Define" phase, these problems are refined and clearly framed, converging the focus to prepare for solution development. The third phase, "Develop" diverges again to create and explore various solutions. Finally, the "Deliver" phase converges by testing and refining these solutions, ensuring only the most effective parts are retained for the final service or product. This methodical approach ensures a balanced exploration and refinement, leading to robust, user-centred designs. Each diamond includes 2 phases, the 1st diamond including the phase discover and define, aiming for: designing the right thing; whereas the 2nd diamond with phase develop and deliver, is targeting: designing the things right (figure x). In this study design, 1st diamond is aiming for understanding the BCTs under the scenario of wearable sleep behavioural intervention and mapping those BCTs with intervention and user experience; 2nd diamond is working with stakeholders to design a sleep self-management app with higher adherence and under the CBT-I treatment standard.
在双钻石设计模型的结构下，发现、定义、开发和交付，该模型有 4 个阶段（Banbury 等人，2021）： 双钻石设计过程包括四个关键阶段。第一阶段“发现”涉及通过分歧收集广泛的见解和用户需求来探索问题并产生想法。接下来，在“定义”阶段，将这些问题细化并明确框架，汇聚焦点，为解决方案开发做好准备。第三阶段“开发”再次发散，创建和探索各种解决方案。最后，“交付”阶段通过测试和完善这些解决方案来收敛，确保只为最终服务或产品保留最有效的部分。这种有条不紊的方法确保了平衡的探索和改进，从而形成稳健的、以用户为中心的设计。每颗钻石包括两个阶段，第一个钻石包括发现和定义阶段，目标是：设计正确的东西；而第二个钻石阶段的开发和交付目标是：设计正确的东西（图x）。在本研究设计中，第一颗钻石旨在了解可穿戴睡眠行为干预场景下的 BCT，并将这些 BCT 与干预和用户体验进行映射； 2nd Diamond 正在与利益相关者合作，设计一款具有更高依从性并符合 CBT-I 治疗标准的睡眠自我管理应用程序。

Figure x: Double Diamond
图 x：双菱形

In this project, each parts aims are: * Discover: Discover the knowledge of latest wearable technology and sleep behaviour intervention; * Define: Collecting and summarising the positive and negative UX feature in self-management and online sleep intervention practice and categorised under each BCTs, provide reference for the optimization of online CBT-I or sleep management app; * Develop: Collecting high adherence strategies of the sleep self-management app from Stakeholders; * Deliver: Sleep Self-Management Prototype protocol with prompting on AI tool
在这个项目中，每个部分的目标是： * 发现：发现最新可穿戴技术和睡眠行为干预的知识； * 定义：收集和总结自我管理和在线睡眠干预实践中的正负UX特征并分类到每个BCT下，为在线CBT-I或睡眠管理应用程序的优化提供参考； * 开发：从利益相关者那里收集睡眠自我管理应用程序的高依从性策略； * 交付：带有人工智能工具提示的睡眠自我管理原型协议

1st Diamond: BCTs Mapping The purpose of the Discover Phase is to explore the broad context of sleep interventions using wearable technology, particularly focusing on adherence measurements and finalising the research question of the PhD thesis. This part of work is done by searching literature and communicating with experts from sleep intervention and wearable devices.
第一颗钻石：BCT 映射发现阶段的目的是探索使用可穿戴技术进行睡眠干预的广泛背景，特别关注依从性测量并最终确定博士论文的研究问题。这部分工作是通过检索文献以及与睡眠干预和可穿戴设备方面的专家交流来完成的。

This the Define Phase is aiming to solve the following research questions: * How many BCTs are used in current sleep wearable behavioural interventions? * Which BCTs have effects on sleep intervention, adherence or both? * How do current sleep behavioural interventions studies via wearable technology measure adherence? * While designing sleep behavioural interventions among wearable devices, what BCTs have researchers adopted in response to increased adherence? * What’s the user experience of those sleep BCTs and adherence BCTs?
这个定义阶段旨在解决以下研究问题： * 当前睡眠可穿戴行为干预中使用了多少 BCT？ * 哪些 BCT 对睡眠干预、依从性或两者都有影响？ * 当前通过可穿戴技术进行的睡眠行为干预研究如何衡量依从性？ * 在设计可穿戴设备的睡眠行为干预措施时，研究人员采用了哪些 BCT 来应对依从性的提高？ * 这些睡眠 BCT 和依从性 BCT 的用户体验如何？

This phase contains a systematic review of sleep behaviour interventions via wearable. A thorough analysis of selected articles and studies will be conducted to identify and categorise BCTs used by researchers to enhance effect and adherence in wearable device interventions. This will involve focusing on understanding the implications of these BCTs and how they relate to user experience, crucial for informing the subsequent development phase. Should the literature review reveal gaps or raise new questions that cannot be addressed through published studies alone, interviews with wearable sleeping app users will be planned. Accordingly, a set of interview questions will be developed, aimed at exploring a comprehensive collection of user experience with both sleep BCTs and adherence BCTs. Using thematic analysis, common user experiences and BCTs mentioned by users will be categorised and understood, combining these insights with the findings from the initial systematic literature review to create a list of BCTs that could be used for designing higher adherence sleep wearable behavioural interventions that meet with the criteria for CBT-I or BBTI quality.
此阶段包含对通过可穿戴设备进行睡眠行为干预的系统回顾。将对选定的文章和研究进行彻底分析，以确定研究人员使用的 BCT 并对其进行分类，以增强可穿戴设备干预措施的效果和依从性。这将涉及重点了解这些 BCT 的含义以及它们与用户体验的关系，这对于为后续开发阶段提供信息至关重要。如果文献综述揭示了差距或提出了仅通过已发表的研究无法解决的新问题，我们将计划对可穿戴睡眠应用程序用户进行采访。因此，将开发一套访谈问题，旨在探索睡眠 BCT 和依从性 BCT 的用户体验的全面集合。使用主题分析，对常见用户体验和用户提到的 BCT 进行分类和理解，将这些见解与最初系统文献综述的结果相结合，创建 BCT 列表，可用于设计更高依从性的睡眠可穿戴行为干预措施，以满足符合 CBT-I 或 BBTI 质量标准。

Figure x, Possible mapping between interventions contents - BCTs - UX
图 x，干预内容 - BCT - 用户体验之间的可能映射

2nd Diamond: Sleep App Design The research aim of the Develop Phase is to develop the threshold of user experience and intervention design, based on the list of BCTs and their UX gained from the define phase. The following research questions will be focused on this phase: Whether sleep behavioural intervention can be developed into users daily routine via wearables? In self sleep management, can users choose BCTs as their interventions based on their preference? Whether users' understanding of their experience preferences is enough to help them choose BCTs for self sleep management? If not, do existing demographic definitions help categorization and selection of sleep BCTs and adherence BCTs? What kind of interaction design enables this personalisation?
第二颗钻石：睡眠应用程序设计 开发阶段的研究目标是根据从定义阶段获得的 BCT 列表及其用户体验来开发用户体验和干预设计的门槛。这一阶段将重点关注以下研究问题：睡眠行为干预是否可以通过可穿戴设备发展到用户的日常生活中？在自我睡眠管理中，用户可以根据自己的喜好选择BCT作为干预措施吗？用户对自己体验偏好的了解是否足以帮助他们选择BCT进行自我睡眠管理？如果不是，现有的人口统计定义是否有助于睡眠 BCT 和依从性 BCT 的分类和选择？什么样的交互设计可以实现这种个性化？

The methods to be employed include Participatory Design Workshops. Participatory design sessions will be conducted, involving CBT-I experts, app developers and wearable users. These workshops are designed to co-create solutions, allowing users to contribute their ideas, preferences, and feedback directly into the prototype design process. Following these sessions, initial prototypes will be developed, incorporating the feedback received.
采用的方法包括参与式设计研讨会。将举办参与式设计会议，参与者包括 CBT-I 专家、应用程序开发人员和可穿戴用户。这些研讨会旨在共同创建解决方案，允许用户将他们的想法、偏好和反馈直接贡献到原型设计过程中。在这些会议之后，将开发初始原型，并结合收到的反馈。

The purpose of the Deliver Phase will be to finalise a protocol for the co-designed sleep app prototype.
交付阶段的目的是最终确定共同设计的睡眠应用程序原型的协议。

In the protocol of the prototype, custom versions of ChatGPT tools will be used with the prompting engineering. The method of dissertation compilation will involve documenting the entire process, starting from the initial literature review through to the expert and user-involved development process and protocol for the co-designed prototype. The doctoral dissertation will include detailed sections on methodology, the development process, findings, and implications. In conclusion, the outcomes of the research will be summarised, and recommendations for future developments in the field of wearable sleep technology interventions will be provided. 3.2 Proposed contribution of new knowledge and understanding
在原型协议中，将使用自定义版本的ChatGPT工具来进行提示工程。论文编写方法将涉及记录整个过程，从最初的文献综述到专家和用户参与的共同设计原型的开发过程和协议。博士论文将包括有关方法论、开发过程、研究结果和影响的详细部分。最后，将对研究结果进行总结，并对可穿戴睡眠技术干预领域的未来发展提出建议。 3.2 新知识和理解的拟议贡献

This study addresses the gap in understanding the specific efficacy of integrated BCTs in wearable technology for sleep improvement. Results will expand the empirical evidence for technology-driven behavioural interventions in sleep health. This project is aiming for: Collecting and summarising the positive and negative UX feature in self-management and online sleep intervention practice and categorised under each BCTs; Mapping interventional components and behaviour change techniques used in sleep behavioural interventions among wearable devices; Mapping interventional components and behaviour change techniques used to suggesting a promotion of self-management.
这项研究解决了人们对可穿戴技术中集成 BCT 对改善睡眠的具体功效的理解上的差距。结果将扩大技术驱动的睡眠健康行为干预的经验证据。该项目的目的是： 收集和总结自我管理和在线睡眠干预实践中的积极和消极的用户体验特征，并按每个 BCT 进行分类；绘制可穿戴设备睡眠行为干预中使用的干预成分和行为改变技术；绘制干预措施和行为改变技术，用于建议促进自我管理。

________________ Methodology[aa]
________________ 方法[aa]

This study is a mixed methods study under the context of understanding existing wearable and sleeping behaviour intervention, discovering collecting and promoting the potential enhancement based on the aspect of user experience. It draws from techniques used in design and user experience research. This study will[ab] integrate up to date technical and medical perspectives, adopt Phenomenology Typology and Cognitive Science experience, understand user needs, sort and categorise problem types, explain phenomena, and provide actionable strategies.[ac]
本研究是在了解现有可穿戴设备和睡眠行为干预的背景下进行的混合方法研究，基于用户体验方面发现收集和促进潜在的增强。它借鉴了设计和用户体验研究中使用的技术。这项研究将[ab]整合最新的技术和医学观点，采用现象学类型学和认知科学经验，了解用户需求，对问题类型进行排序和分类，解释现象并提供可行的策略。[ac]

4.1 Methods 4.1 方法

4.1.1 Double Diamond design process[ad]
4.1.1 双钻石设计流程[广告]

The double diamond model, developed by the UK’s Design Council, is a process for creating successful products that involves understanding a domain, selecting a problem to solve, and exploring potential solutions (Design Council, n.d.; Kochanowska & Gagliardi, 2021). This model has been applied in the design of a wearable orientation guidance device for blind and visually impaired travellers (Zhang et al., 2019).
双钻石模型由英国设计委员会开发，是一个创造成功产品的过程，涉及理解领域、选择要解决的问题以及探索潜在的解决方案（设计委员会，n.d.；Kochanowska & Gagliardi，2021）。该模型已应用于盲人和视障旅行者的可穿戴定向引导设备的设计（Zhang et al., 2019）。

Double Diamond is sometimes used as a tool or process for Co-Design. Co-Design is a collaborative approach to design that actively involves all stakeholders, especially end users, in the design process to ensure the final product meets their needs and is usable (Butler et al., 2022). This approach is rooted in the belief that involving users and other stakeholders in the design process leads to more innovative, effective, and sustainable solutions. By leveraging the diverse perspectives and expertise of all participants, co-design fosters creativity and ensures that the design outcomes are more user-centric, accessible, and practical. In the context of health-related research, policy, and practice, the application of co-design methodology has grown significantly over the past decade (Blomkamp, 2018; Butler et al., 2022; Dillon, 2021).
双钻石有时被用作协同设计的工具或流程。协同设计是一种协作设计方法，积极让所有利益相关者，特别是最终用户参与设计过程，以确保最终产品满足他们的需求并且可用（Butler 等人，2022）。这种方法植根于这样的信念：让用户和其他利益相关者参与设计过程可以带来更具创新性、更有效和更可持续的解决方案。通过利用所有参与者的不同观点和专业知识，协同设计可以培养创造力，并确保设计结果更加以用户为中心、易于理解和实用。在与健康相关的研究、政策和实践的背景下，协同设计方法的应用在过去十年中显着增长（Blomkamp，2018；Butler 等人，2022；Dillon，2021）。

As a research methodology, Co-Design primarily focuses on integrating users, designers, stakeholders, and other participants into the design process to foster innovation and achieve more inclusive design solutions. Particularly in the context of this study, as already been commented as "Existing Resources Can Feel Condescending" (Aji et al., 2019), behavioural interventions tailored to the user need to be tailored to the user's everyday behaviour and provide appropriate guidance. Exploring participation in the research and design of information and communication technology (ICT) has been central to participatory design since this research community first began to emerge in the early 1990s (Greenbaum and Kyng Citation1991; Schuler and Namioka Citation1993). Central to the pursuit of how to involve end-users in the development of ICT were core ideals of democracy that were rooted in the North American and Scandinavian political landscapes of the time as these underwent fundamental change (Smith et al., 2017).
作为一种研究方法，协同设计主要侧重于将用户、设计师、利益相关者和其他参与者整合到设计过程中，以促进创新并实现更具包容性的设计解决方案。特别是在本研究的背景下，正如已经评论为“现有资源可能会让人感到居高临下”（Aji 等人，2019），为用户量身定制的行为干预措施需要根据用户的日常行为进行定制，并提供适当的指导。自 20 世纪 90 年代初首次出现这一研究团体以来，探索参与信息和通信技术 (ICT) 的研究和设计一直是参与式设计的核心（Greenbaum 和 Kyng Citation1991；Schuler 和 Namioka Citation1993）。追求如何让最终用户参与信息通信技术发展的核心是民主的核心理想，民主的核心理想植根于当时经历根本性变化的北美和斯堪的纳维亚政治格局（Smith et al., 2017）。

This PhD project will draw upon the principles of co-design adopting the Double Diamond Design Model to guide the co-design process, which has also been adopted to wearable participatory studies (Zhang et al., 2019). [ae]
该博士项目将借鉴协同设计的原则，采用双钻石设计模型来指导协同设计过程，该模型也已应用于可穿戴参与式研究（Zhang et al., 2019）。 [AE]

4.1.2 Systematic Review[af]
4.1.2 系统审查[af]

Systematic literature reviews are the gold standard for locating, selecting, and synthesising published material to answer a predetermined research question (Turnbull et al., 2023). This research begins with a systematic review to provide a structured and comprehensive assessment of existing research, ensuring that all relevant studies are identified and evaluated in a consistent and unbiased manner (Buchholz et al., 2023; Ferguson et al., 2022). This method ensures that all relevant studies are identified and included in the analysis, which is particularly important in the field of wearable technology where there is a rapid growth in the number of available devices and studies (De Zambotti et al., 2019). This is particularly important in the field of sleep wearable behavioural intervention studies, where a growing body of literature in recent years, and where it can be challenging to keep track of all relevant studies (Chow et al., 2023). [ag]Systematic reviews can help to identify gaps in the existing research, inform the design of future studies, and provide evidence-based recommendations for the use of wearable devices in healthcare settings (Buchholz et al., 2023). Additionally, systematic reviews can help to assess the validity and reliability of wearable devices, as well as their effectiveness in improving health outcomes (Aekanth & Tillinghast, 2022; Chow et al., 2023).
系统的文献综述是定位、选择和综合已发表材料以回答预定研究问题的黄金标准（Turnbull 等人，2023）。这项研究首先进行系统回顾，对现有研究进行结构化和全面的评估，确保以一致和公正的方式识别和评估所有相关研究（Buchholz 等人，2023 年；Ferguson 等人，2022 年）。这种方法可确保识别所有相关研究并将其纳入分析中，这在可用设备和研究数量快速增长的可穿戴技术领域尤为重要（De Zambotti 等人，2019）。这在睡眠可穿戴行为干预研究领域尤为重要，近年来该领域的文献不断增加，并且跟踪所有相关研究可能具有挑战性（Chow 等人，2023）。 [ag]系统评价可以帮助确定现有研究中的差距，为未来研究的设计提供信息，并为在医疗保健环境中使用可穿戴设备提供基于证据的建议（Buchholz 等人，2023）。此外，系统评价可以帮助评估可穿戴设备的有效性和可靠性，以及它们在改善健康结果方面的有效性（Aekanth & Tillinghast，2022；Chow 等人，2023）。

Systematic reviews are also a powerful tool for synthesising and advancing knowledge in the field of sleep BCTs. Firstly, the field of sleep BCTs is expanding rapidly, and a systematic review can help to synthesise and organise the existing evidence (Salari et al., 2020). Secondly, a systematic review follows a rigorous and transparent methodology, which ensures that all relevant studies are identified and included in the analysis (Arroyo & Zawadzki, 2022). This is important because the quality and generalizability of the evidence base for sleep BCTs can be improved by including a larger and more diverse sample of studies. Thirdly, a systematic review can help to identify gaps in the existing evidence and suggest directions for future research (Albakri et al., 2021). Finally, a systematic review can provide a comprehensive and nuanced understanding of the effects of sleep BCTs on sleep outcomes, as well as any potential adverse effects or moderators of these effects (Mitchell et al., 2012).
系统评价也是综合和推进睡眠 BCT 领域知识的强大工具。首先，睡眠 BCT 领域正在迅速扩大，系统综述有助于综合和整理现有证据（Salari 等，2020）。其次，系统评价遵循严格和透明的方法，确保所有相关研究都得到识别并纳入分析（Arroyo & Zawadzki，2022）。这一点很重要，因为通过纳入更大、更多样化的研究样本可以提高睡眠 BCT 证据基础的质量和普遍性。第三，系统评价有助于找出现有证据中的差距，并为未来的研究提出方向（Albakri et al., 2021）。最后，系统评价可以全面、细致地了解睡眠 BCT 对睡眠结果的影响，以及任何潜在的不利影响或这些影响的调节因素（Mitchell 等，2012）。

4.1.3 Stakeholder Interviews
4.1.3 利益相关者访谈

A user, or stakeholder, interview is a conversation between a researcher and a person who has a vested interest in a project (Gibbons, 2023; Fard, 2022). In the topic of this project of interviewing sleep app user existing experience of BCTs, on one hand, interviewing users about their experiences with sleep-related BCTs can provide valuable insights for future mHealth app intervention development (Arroyo & Zawadzki, 2022; Michaelsen & Esch, 2022). This is because users' feedback can help identify effective BCTs that may be underutilised or novel in this area (Reid et al., 2022). This information can help identify best practices for future intervention development, as well as reveal potential barriers or facilitators to behaviour change (Arroyo & Zawadzki, 2022; Schroé et al., 2020). Understanding users' perspectives can inform the refinement of existing BCTs or the creation of new ones tailored to sleep-related contexts (Bohlen et al., 2020). This user-centred approach is essential for developing engaging and effective mHealth interventions that address the unique challenges of promoting sleep (Arroyo & Zawadzki, 2022). Moreover, understanding users' experiences will inform the selection of resources and tailored approaches that engage patients in their health, ultimately leading to the formation of new sleep habits (Michaelsen & Esch, 2022).. Therefore, interviewing users on their sleep BCTs' experience is a crucial step in developing more effective mHealth interventions for sleep; On the other hand, Interviewing users about their experience with BCT adherence can provide valuable insights into the usability and effectiveness of the gamified app (Fatima et al., 2023). User interviews are a crucial method in UX research, helping to understand users' perspectives, preferences, and pain points (Lobo, 2023; “How to Conduct User Interviews,” 2024). By gathering data on participants' experiences, opinions, attitudes, and needs regarding BCT adherence, researchers can make informed decisions to improve the application (User Interviews: The Complete Guide for 2024 | Great Question, n.d.; Kao, 2019). Furthermore, user interviews can serve as a starting point in the discovery phase of product development, assisting in identifying potential challenges and opportunities (User Interviews for UX Research: What, Why & How, n.d.; UserInput, 2023) To ensure accurate and insightful data, it is essential to prepare for user interviews by establishing a positive mood and planning thoughtful, guided questions. This will enable me to effectively communicate and observe user interactions with BCT adherence, leading to a deeper understanding of the user experience.[ah]
用户或利益相关者访谈是研究人员与项目既得利益者之间的对话（Gibbons，2023；Fard，2022）。在采访睡眠应用程序用户现有 BCT 体验的项目主题中，一方面，采访用户与睡眠相关的 BCT 体验可以为未来移动医疗应用程序干预开发提供有价值的见解（Arroyo & Zawadzki，2022；Michaelsen & Esch） ，2022）。这是因为用户的反馈可以帮助识别该领域可能未得到充分利用或新颖的有效 BCT（Reid 等人，2022）。这些信息可以帮助确定未来干预措施发展的最佳实践，并揭示行为改变的潜在障碍或促进因素（Arroyo & Zawadzki，2022；Schroé et al.，2020）。了解用户的观点可以为现有 BCT 的完善或针对睡眠相关环境创建新的 BCT 提供信息（Bohlen 等人，2020）。这种以用户为中心的方法对于开发有吸引力且有效的移动医疗干预措施至关重要，以应对促进睡眠的独特挑战（Arroyo & Zawadzki，2022）。此外，了解用户的体验将为选择资源和量身定制的方法提供信息，使患者参与其健康，最终形成新的睡眠习惯（Michaelsen & Esch，2022）。因此，采访用户的睡眠 BCT 体验是开发更有效的睡眠移动健康干预措施的关键一步；另一方面，采访用户了解 BCT 遵守情况的体验可以为游戏化应用程序的可用性和有效性提供有价值的见解（Fatima 等人，2023）。 用户访谈是用户体验研究中的重要方法，有助于了解用户的观点、偏好和痛点（Lobo，2023；“如何进行用户访谈”，2024）。通过收集参与者关于 BCT 依从性的经验、意见、态度和需求的数据，研究人员可以做出明智的决定来改进应用程序（用户访谈：2024 年完整指南 | Great Question，n.d.；Kao，2019）。此外，用户访谈可以作为产品开发发现阶段的起点，帮助识别潜在的挑战和机遇（UX 研究的用户访谈：内容、原因和方式，日期不详；UserInput，2023）确保数据准确且富有洞察力，通过建立积极的情绪和计划深思熟虑的引导性问题来准备用户访谈是至关重要的。这将使我能够有效地沟通和观察用户与 BCT 遵守情况的交互，从而更深入地了解用户体验。[啊]

4.1.4 Thematic analysis[ai]
4.1.4 主题分析[ai]

Thematic analysis will be used to describe the user experience data set in detail. However, frequently goes further than this, and interprets various aspects of the research topic (Boyatzis, 1998), in this topic, user experience of BCTs. This method identifies, analyses, and reports patterns or themes within data, providing a detailed description of the dataset (Braun & Clarke, 2008).[aj][ak]
将使用主题分析来详细描述用户体验数据集。然而，经常比这更进一步，并解释研究主题的各个方面（Boyatzis，1998），在这个主题中，BCT 的用户体验。该方法识别、分析和报告数据中的模式或主题，提供数据集的详细描述（Braun & Clarke，2008）。[aj][ak]

There are different approaches to thematic analysis, including inductive and deductive methods (Caulfield, 2023). An inductive approach will be used in “user experience” coding which involves allowing the data to determine user experience themes, while a deductive approach will be used on BCTs coding as it involves coming to the data with preconceived BCT themes. When conducting thematic analysis on BCT user experience data, it is important to base the analysis on the data, consistently and accurately interpret the data, and zero in on the most relevant and important information (Caulfield, 2023).[al][am]
主题分析有不同的方法，包括归纳法和演绎法（Caulfield，2023）。归纳方法将用于“用户体验”编码，其中涉及允许数据确定用户体验主题，而演绎方法将用于 BCT 编码，因为它涉及到具有预先设想的 BCT 主题的数据。在对 BCT 用户体验数据进行主题分析时，重要的是要以数据为基础进行分析，一致且准确地解释数据，并将最相关和最重要的信息归零（Caulfield，2023）。[al][am]

4.1.5 Participatory design workshop
4.1.5 参与式设计研讨会

Participatory design is an approach to design that actively involves all stakeholders, such as experts, app developers and end-users, in the design process(Frauenberger et al., 2015; Van Gemert-Pijnen et al., 2011). Stakeholder participation is considered to play an important role in developing electronic health interventions (eHealth), of which mHealth is included. This method, also known as co-design, co-creation, or cooperative design, aims to ensure that the final product aligns with the needs and usability expectations of the end-users. By directly involving users in the design process, participatory prototype design ensures that sleep wearables meet real user needs and preferences. This approach facilitates continuous feedback, leading to iterative improvements, which are invaluable in refining devices to enhance the user experience. Active user participation yields deeper insights into specific needs, challenges, and expectations, thereby increasing user satisfaction, as involvement in the design process typically fosters a sense of ownership and satisfaction with the final product.[an][ao][ap]
参与式设计是一种让所有利益相关者（例如专家、应用程序开发人员和最终用户）积极参与设计过程的设计方法（Frauenberger 等，2015；Van Gemert-Pijnen 等，2011）。利益相关者的参与被认为在制定电子健康干预措施（eHealth）（其中包括移动医疗）方面发挥着重要作用。这种方法也称为共同设计、共同创造或合作设计，旨在确保最终产品符合最终用户的需求和可用性期望。通过直接让用户参与设计过程，参与式原型设计可确保睡眠可穿戴设备满足真实的用户需求和偏好。这种方法有利于持续反馈，从而实现迭代改进，这对于改进设备以增强用户体验非常有价值。用户的积极参与可以更深入地了解特定需求、挑战和期望，从而提高用户满意度，因为参与设计过程通常会培养对最终产品的所有权和满意度。[an][ao][ap]

Participatory prototype design offers several advantages: it leads to more customised and user-friendly designs due to the integral role of user input in the design process. This approach typically results in products with higher usability and acceptance rates, as they are directly informed by user feedback. Additionally, the collaborative nature of participatory design fosters enhanced creativity by incorporating diverse ideas and perspectives into the design process, enriching the final product.
参与式原型设计有几个优点：由于用户输入在设计过程中的不可或缺的作用，它可以带来更多定制和用户友好的设计。这种方法通常会产生具有更高可用性和接受率的产品，因为它们直接受到用户反馈的影响。此外，参与式设计的协作性质通过将不同的想法和观点融入设计过程来增强创造力，丰富最终产品。

Participatory prototype design, while beneficial, has its limitations. It is resource-intensive, as involving users in the design process can be time-consuming and costly. The diversity of user perspectives may lead to conflicting opinions, posing challenges in reaching a consensus on design aspects. Tailoring designs to specific user groups can limit the scalability or generalizability of the product to a wider audience. Additionally, there is a risk of over-design, where trying to meet varied user demands may result in adding too many features, potentially complicating the product unnecessarily.[aq][ar]
参与式原型设计虽然有益，但也有其局限性。它是资源密集型的，因为让用户参与设计过程可能既耗时又昂贵。用户观点的多样性可能会导致意见冲突，从而给设计方面达成共识带来挑战。针对特定用户组定制设计可能会限制产品对更广泛受众的可扩展性或通用性。此外，还存在过度设计的风险，试图满足不同的用户需求可能会导致添加太多功能，从而可能使产品不必要地复杂化。[aq][ar]

There is a broad range of outcome measures available for assessing the outputs of the participatory design (PD) process as well as the resulting eHealth technology outputs (Benefits of Co-design in Service Design Projects, n.d.; Langley et al., 2018). Tools describe the actions that take place between participants , and PD scholars have categorised these tools into make, tell, and enact tools (Sanders, Brandt, & Binder, 2010; Sanders & Stappers, 2013). Make tools are material components such as a prototype to facilitate the embodiment of thoughts in physical artefacts (Brandt, Binder, & Sanders, 2012). Tell tools facilitate the telling of stories to capture implicit information about the use of a technology and how people may wish to use it in the future (Brandt, Binder, & Sanders, 2012). Enacting refers to the activities where one or more people act out possible futures by physically trying things out in settings that resemble the possible futures (Brandt, Binder, & Sanders, 2012). Finally, PD toolkits can involve make, tell, and enact tools and are used to push people to start thinking about their experiences so that using the tools in the PD process can yield better results (Vandekerckhove et al., 2020). These tools can be used in both in-person and remote settings. Some common participatory design tools include sketching, mapping, modelling, and prototyping(Sanders et al., 2010).[as]
有多种结果衡量标准可用于评估参与式设计 (PD) 过程的输出以及由此产生的电子医疗技术输出（服务设计项目中协同设计的好处，n.d.；Langley 等人，2018）。工具描述了参与者之间发生的行为，PD 学者将这些工具分为制造工具、讲述工具和制定工具（Sanders、Brandt 和 Binder，2010；Sanders 和 Stappers，2013）。制造工具是材料组件，例如原型，以促进物理制品中思想的体现（Brandt、Binder 和 Sanders，2012）。讲述工具有助于讲述故事，以捕获有关技术使用以及人们未来希望如何​​使用它的隐含信息（Brandt、Binder 和 Sanders，2012）。表演是指一个或多个人通过在类似于可能的未来的环境中实际尝试事物来表演可能的未来的活动（Brandt、Binder 和 Sanders，2012）。最后，PD 工具包可以涉及制作、讲述和制定工具，并用于推动人们开始思考他们的经验，以便在 PD 过程中使用这些工具可以产生更好的结果（Vandekerckhove 等人，2020）。这些工具可用于现场和远程设置。一些常见的参与式设计工具包括草图、绘图、建模和原型制作（Sanders 等人，2010）。[as]

Figue x, The stages of co-design (Ashley, 2022)
图 x，协同设计的阶段（Ashley，2022）

In the early development of participatory design, the novel idea of integrating users directly into technology design marked a significant breakthrough, introducing both opportunities and challenges. This approach necessitated a collaborative space for researchers, software companies, managers, and end-users to exchange ideas and learn collaboratively. New techniques were crucial for facilitating these interactions, with the inaugural conference in this field emphasising the importance of mutual learning among diverse stakeholders. The role of civic engagement in Information and Communication Technology (ICT) has been recognized as crucial for success, offering new democratic opportunities and fostering participatory cultures in areas like the sharing economy and digital media. Distinct from the 'Human in the loop' concept prevalent in AI, which focuses on the role of humans in automated systems, participatory design emphasises user involvement and collaboration throughout the design process. While previous work has often separated user engagement from algorithmic implementation, future design interventions in AI and machine learning will increasingly consider the user experience of human participants, aiming to integrate their feedback directly into system development. [at][au] 4.2 Ontology
在参与式设计的早期发展中，将用户直接融入技术设计的新颖理念标志着重大突破，带来了机遇和挑战。这种方法需要为研究人员、软件公司、管理人员和最终用户提供一个协作空间，以交流想法和协作学习。新技术对于促进这些互动至关重要，该领域的首次会议强调了不同利益相关者之间相互学习的重要性。公民参与在信息和通信技术（ICT）领域的作用被认为是成功的关键，它提供了新的民主机会，并在共享经济和数字媒体等领域培育参与性文化。与人工智能中流行的“人在循环”概念不同，该概念侧重于人类在自动化系统中的作用，参与式设计强调用户在整个设计过程中的参与和协作。虽然以前的工作通常将用户参与与算法实现分开，但未来人工智能和机器学习的设计干预将越来越多地考虑人类参与者的用户体验，旨在将他们的反馈直接整合到系统开发中。 [at][au] 4.2 本体论

This study aims to enhance the user’s experience of the scenario “when they use wearable for sleep intervention”, therefore to gain better sleep quality. I believe that the reality of user experience, particularly with sleep wearable devices, is subjective and constructed through individual interactions and perceptions. In the realm of wearable technology, this translates into an understanding that the reality of user experiences with wearable devices is not a fixed entity but is shaped by the users' interactions with the technology, their personal contexts, and cultural backgrounds. This perspective leads to the acknowledgment that user experience is a construct that can vary significantly among individuals. The study's design reflects a commitment to capturing this diversity, seeking to understand how different users perceive and interact with the technology in their unique ways. Equivalently, Sleep quality as a concept is defined as one’s satisfaction with the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening(Kline, 2013).
本研究旨在增强用户“使用可穿戴设备进行睡眠干预时”场景的体验，从而获得更好的睡眠质量。我相信用户体验的现实，尤其是睡眠可穿戴设备，是主观的，是通过个人交互和感知构建的。在可穿戴技术领域，这意味着可穿戴设备的用户体验的现实并不是一个固定的实体，而是由用户与技术的交互、个人背景和文化背景决定的。这种观点导致人们承认用户体验是一种结构，在个体之间可能存在很大差异。该研究的设计体现了捕捉这种多样性的承诺，试图了解不同用户如何以独特的方式感知技术并与之互动。同样，睡眠质量作为一个概念被定义为一个人对睡眠体验的满意度，综合了睡眠启动、睡眠维持、睡眠数量和觉醒后的恢复等方面（Kline，2013）。

4.3 Epistemological Stance
4.3 认识论立场

This project incorporates aspects of both subjectivist and constructivist epistemologies. This study will use a mixed-methods approach, combining quantitative systematic review data analysis and qualitative user interviews to gain a comprehensive understanding of possible user experience of sleep wearable interventions.
该项目结合了主观主义和建构主义认识论的各个方面。本研究将采用混合方法，结合定量系统评价数据分析和定性用户访谈，以全面了解睡眠可穿戴干预措施的可能用户体验。

The qualitative approach emphasises the depth and richness of subjective experiences. My research predominantly employs methodologies such as systematic reviews, interviews, thematic studies, and participatory prototype design. These methods allow for an in-depth exploration of individual perceptions and behaviours, highlighting how users interpret, react to, and integrate sleep wearable technologies into their daily lives. This approach is particularly suited to uncovering the multifaceted nature of user experience, which encompasses interaction, perception, emotional, and behavioural responses. Qualitative analyses appear in the part of systematic reviews, such as in studies of the correlation between sleep behavioural intervention BCTs and sleep outcomes. This includes employing a systematic review to quantify the impact of various Behavior Change Techniques (BCTs) on sleep outcomes, such as duration and quality of sleep. By coding and categorising these BCTs, I can apply statistical methods to assess correlations, trends, and effectiveness across different studies.
定性方法强调主观经验的深度和丰富性。我的研究主要采用系统评价、访谈、专题研究和参与式原型设计等方法。这些方法可以深入探索个人的看法和行为，突出用户如何解释、反应并将睡眠可穿戴技术融入他们的日常生活。这种方法特别适合揭示用户体验的多方面性质，其中包括交互、感知、情感和行为反应。定性分析出现在系统评价的一部分中，例如睡眠行为干预 BCT 与睡眠结果之间相关性的研究。这包括采用系统评价来量化各种行为改变技术 (BCT) 对睡眠结果（例如睡眠持续时间和质量）的影响。通过对这些 BCT 进行编码和分类，我可以应用统计方法来评估不同研究之间的相关性、趋势和有效性。

By integrating these quantitative findings with the qualitative insights from interviews and thematic analyses, the research will not only explore the depth and richness of user experiences but also establish a robust empirical foundation. This dual approach allows for a thorough examination of how well the interventions work in quantifiable terms while also capturing the subjective nuances that influence user interactions with the technology.
通过将这些定量研究结果与访谈和主题分析的定性见解相结合，该研究不仅将探索用户体验的深度和丰富性，还将建立坚实的实证基础。这种双重方法可以以可量化的方式彻底检查干预措施的效果，同时还可以捕获影响用户与技术交互的主观细微差别。

4.4 Statement of Positionality
4.4 立场声明

As the principal investigator of this user experience study on the adoption of wearable technology among sleep behavioural intervention, I acknowledge my background and experiences that influence my approach to this research.
作为这项关于在睡眠行为干预中采用可穿戴技术的用户体验研究的主要研究者，我承认我的背景和经历影响了我的研究方法。

As a 29-year-old Chinese woman engaged in interdisciplinary research at the intersection of psychology, education, and user experience design, my diverse academic and professional experiences have profoundly shaped my research perspective and methodology. My journey from an undergraduate education in Architecture to the research of sleeping and wellness interventions, and to my current doctoral research in User Experience of Sleep Wearable Intervention has equipped me with a unique lens through which I view both the environment and the nuanced interactions between technology and human behaviour.
作为一名29岁的中国女性，从事心理学、教育学和用户体验设计交叉领域的跨学科研究，我多样化的学术和专业经历深刻地塑造了我的研究视角和方法。从建筑学本科教育到睡眠和健康干预研究，再到目前睡眠可穿戴干预用户体验的博士研究，我的经历为我提供了独特的视角，通过它我可以观察环境和技术之间微妙的相互作用和人类行为。

As a researcher, my identity as a young Asian woman shapes both how I am perceived by study participants and how I engage with them. This demographic characteristic may influence the dynamics of interviews and interactions, particularly with participants from different cultural or age backgrounds. My previous roles as a researcher and curriculum designer in architecture design workshops have honed my skills in observing and interpreting user interactions in both physical and digital realms, which are critical in studying wearable technologies.
作为一名研究人员，我作为一名年轻的亚洲女性的身份决定了研究参与者对我的看法以及我与他们互动的方式。这种人口统计特征可能会影响访谈和互动的动态，特别是与来自不同文化或年龄背景的参与者。我之前在建筑设计研讨会中担任研究员和课程设计师，磨练了我在物理和数字领域观察和解释用户交互的技能，这对于研究可穿戴技术至关重要。

Being aware of these perspectives allows me to critically reflect on how my background might influence the research process. For example, my training in architecture often leads me to focus on the structural and design elements of the wearable devices, which might affect how I interpret user feedback related to device aesthetics and ergonomics. Acknowledging this, I strive to maintain a balanced view by integrating diverse viewpoints and methodologies, ensuring that my research findings are comprehensive and well-rounded.
了解这些观点使我能够批判性地反思我的背景可能如何影响研究过程。例如，我的建筑学培训经常使我关注可穿戴设备的结构和设计元素，这可能会影响我如何解释与设备美学和人体工程学相关的用户反馈。认识到这一点，我努力通过整合不同的观点和方法来保持平衡的观点，以确保我的研究结果是全面和全面的。

Through my research, I am committed to maintaining a reflexive stance that acknowledges the influences of my background and biases while rigorously exploring the user experience of sleep wearables. This commitment is essential not only for the integrity of my research but also for the broader goal of designing technologies that are truly user-centred and effective in enhancing health and well-being.
通过我的研究，我致力于保持一种反思性的立场，承认我的背景和偏见的影响，同时严格探索睡眠可穿戴设备的用户体验。这一承诺不仅对于我研究的完整性至关重要，而且对于设计真正以用户为中心、有效促进健康和福祉的技术这一更广泛的目标至关重要。

________________ Methods Implementation
________________ 方法实施

5.1 BCTs Mapping (Discover and Define Phase) - The 1st Diamond * Thesis Chapter 5
5.1 BCT 映射（发现和定义阶段）-第一颗钻石 * 论文第 5 章

This Discover and Define phase identifies the research gap and defines the research direction of wearable device-based sleep interventions, especially on the aspect of user experience.
这个发现和定义阶段确定了研究差距，并定义了基于可穿戴设备的睡眠干预的研究方向，特别是在用户体验方面。

To discover the latest research around the topic, 5854 relevant studies has been screened after broad search of literature around theme search term: adult / sleep hygiene / technology and limited to RCT and recent 5 years on 6 databases Medline, Web of Science, Scopus, CINAHL, Cochrane and ProQuest. 152 abstracts and 21 full text has been read. This phase of the work expanded my understanding of the topic and focused on the fact that the topic of “adherence”, as mentioned in the background literature, is receiving a lot of attention from researchers and experts in this area.
为了发现围绕该主题的最新研究，在围绕主题搜索词“成人/睡眠卫生/技术”广泛检索文献后，筛选了 5854 项相关研究，并仅限于 RCT 和最近 5 年的 6 个数据库 Medline、Web of Science、Scopus、 CINAHL、Cochrane 和 ProQuest。已阅读 152 篇摘要和 21 篇全文。这一阶段的工作扩展了我对该主题的理解，并重点关注背景文献中提到的“依从性”主题正在受到该领域研究人员和专家的大量关注。

However, on the one hand, my understanding of this topic is not comprehensive enough to control the inclusion and exclusion criteria rigorously, and on the other hand, similar study (Lai et al., 2023) on this topic have been published, and the results of the search are no longer sufficient to support the purpose of the study. With my knowledge of sleep monitoring technology and extensive wearable device behavioural intervention research, I designed a systematic review of sleep behavioural interventions more specific to the wearable to define the way the wearable is used and the participants’ adherence to the intervention.
然而，一方面，我对这个主题的理解还不够全面，无法严格控制纳入和排除标准，另一方面，关于这个主题的类似研究（Lai et al., 2023）已经发表，并且搜索结果不再足以支持研究目的。凭借我对睡眠监测技术的了解和广泛的可穿戴设备行为干预研究，我设计了针对可穿戴设备的睡眠行为干预的系统回顾，以定义可穿戴设备的使用方式以及参与者对干预的依从性。

5.1.1 Systematic Review 5.1.1 系统评价

A systematic review is partially complete and will form the first study in the thesis _ Chapter 5. The systematic review aims to answer the following 2 research questions:
系统综述已部分完成，将构成论文第 5 章的第一项研究。系统综述旨在回答以下 2 个研究问题：

* RQ1: In sleep behavioural interventions, how many BCTs are provided by wearable devices? How many of them are from CBT-I?
* RQ1：在睡眠行为干预中，可穿戴设备提供了多少BCT？其中有多少来自 CBT-I？

* RQ2: Which BCTs contribute and affect adherence in sleep wearable behavioural interventions?
* RQ2：哪些 BCT 有助于并影响睡眠可穿戴行为干预的依从性？

Based on these two RQs, the subdivided RQs are presented even further: Which types of BCT of the wearable device are involved among sleep behaviour interventions? What is the user experience reported? Which BCTs give a better user adherence to the entire intervention flow? Which BCTs have consistently demonstrated efficacy in improving sleep outcomes?
基于这两个RQ，进一步提出细分的RQ：睡眠行为干预涉及可穿戴设备的哪些类型的BCT？报告的用户体验如何？哪些 BCT 可以让用户更好地遵守整个干预流程？哪些 BCT 已持续证明在改善睡眠结果方面有效？

The inclusion and exclusion criteria were structured using the PICOS (ie, population, intervention, comparison, outcome, and study design) framework (Methley et al., 2014) as follows: * Population (P): Adults. * Intervention (I): Wearable technology interventions that incorporate BCTs based on CBT-I. These interventions could include features like sleep tracking, personalised feedback, sleep education, relaxation techniques, and cognitive restructuring prompts. * Comparator (C): Any study. * Outcomes (O): Primary outcomes will focus on improvements in sleep quality and duration, reduction in sleep onset latency, and improvement in insomnia severity index scores. Secondary outcomes might include improvements in daytime functioning, mood (e.g., reduced symptoms of depression and anxiety), and adherence to CBT-I principles; user experience results. * Study Design (S): Randomised controlled trials (RCTs), quasi-experimental studies, and observational studies that provide comparative data on the effectiveness of wearable technology interventions versus standard treatments for insomnia.
纳入和排除标准采用 PICOS（即人口、干预、比较、结果和研究设计）框架（Methley 等人，2014 年）构建，如下： * 人口 (P)：成人。 * 干预 (I)：可穿戴技术干预，结合基于 CBT-I 的 BCT。这些干预措施可能包括睡眠跟踪、个性化反馈、睡眠教育、放松技巧和认知重建提示等功能。 * 比较器 (C)：任何研究。 * 结果 (O)：主要结果将侧重于睡眠质量和持续时间的改善、入睡潜伏期的缩短以及失眠严重程度指数评分的改善。次要结果可能包括白天功能、情绪的改善（例如抑郁和焦虑症状的减轻）以及对 CBT-I 原则的遵守；用户体验结果。 * 研究设计 (S)：随机对照试验 (RCT)、准实验研究和观察性研究，提供可穿戴技术干预措施与标准失眠治疗方法有效性的比较数据。

Referencing the systematic review papers (Baron et al., 2021; Lai et al., 2023) of relevant topics and meeting with experienced psychology specialist librarians, 3 search concepts: Sleep/Behaviour Intervention/ Wearable were identified as well as search terms (Table x). It is worth noting that the consideration of using behavioural interventions rather than CBT-I in the search terms is because some behavioural interventions fall into the sub-categories of CBT-I (Sleep Foundation, 2024; Walker et al., 2022), and in case these studies are missed, the search terms will be expanded to behavioural intervention related search terms.
参考相关主题的系统综述论文（Baron 等，2021；Lai 等，2023）并与经验丰富的心理学专家图书馆员会面，确定了 3 个搜索概念：睡眠/行为干预/可穿戴设备以及搜索术语（表X）。值得注意的是，在搜索词中考虑使用行为干预而不是 CBT-I 是因为某些行为干预属于 CBT-I 的子类别（Sleep Foundation，2024；Walker et al.，2022），并且如果错过这些研究，搜索词将扩展到行为干预相关搜索词。

Key Terms Search Terms Concept 1 Sleep sleep* OR insomnia OR nap OR napping OR dyssomnia* OR "daytime dysfunction" OR snoring OR "diurnal tiredness" AND Concept 2 Behaviour Intervention
关键术语 搜索术语 概念 1 睡眠* 或失眠或小睡或小睡或睡眠障碍* 或“日间功能障碍”或打鼾或“日间疲倦”以及概念 2 行为干预

"Behavio* Change Technique*" OR "Behavio* Intervention*" OR "Health Behavio* Change" OR cbt OR "cognitive behavio* therap*" OR "CBT-I" OR "Behavio* Sleep Medicine" OR "Insomnia Therap*" OR "Sleep Education" OR "Relaxation Technique*" OR "Cognitive Restructuring" OR "Sleep Hygiene" AND Concept 3 Wearable pedometer* OR "apple watch*" OR fitbit OR beddit OR fuelband OR jawbone OR garmin OR withings OR xiaomi OR "sleep position trainer" OR nightbalance OR "Wearable Technolog*" OR "Wearable Device*" OR "Smart Band*" OR "smartband*" OR "Smart Ring*" Table x, search terms under sleep / behavioural intervention / wearable concepts
“行为*改变技术*”或“行为*干预*”或“健康行为*改变”或cbt或“认知行为*治疗*”或“CBT-I”或“行为*睡眠医学”或“失眠治疗*”或“睡眠教育”或“放松技巧*”或“认知重建”或“睡眠卫生”和概念 3 可穿戴计步器*或“苹果手表*”或 Fitbit 或 beddit 或 Fuelband 或颚骨或 Garmin 或 withings 或小米或“睡眠”表 x，睡眠/行为干预/可穿戴概念下的搜索词

I searched the following databases: MEDLINE, EMBASE, PsycINFO, Cochrane Library and Scopus; Database search results were imported into Rayyan; 543 papers have been searched after removing the duplication. Next I will expand term search to include databases in engineering fields: EMBASE, IEEE Xplore Digital Library, ACM Digital Library, IET Digital Library, ASME Digital Collection, CINAHL, ScienceDirect, Science Citation Index Expanded, and Compendex.
我检索了以下数据库：MEDLINE、EMBASE、PsycINFO、Cochrane Library 和 Scopus；数据库检索结果导入Rayyan；去重后已检索到 543 篇论文。接下来，我将扩展术语搜索以包括工程领域的数据库：EMBASE、IEEE Xplore Digital Library、ACM Digital Library、IET Digital Library、ASME Digital Collection、CINAHL、ScienceDirect、Science Citation Index Expanded 和 Compendex。

The inclusion and exclusion criteria strictly followed the PICOS framework (Randles & Paul, 2023). Articles have been excluded from the upcoming research plan under the following conditions: if no wearable is involved, if no behavioural intervention is involved, if no sleep outcome is assessed (including sleep quality, duration, sleep onset latency, insomnia severity, daytime functioning, or mood), if the full text is unavailable, or if the article is not in English. This approach will ensure that the inclusion criteria are strictly adhered to, focusing only on studies that meet the specific requirements of the research objectives. To rigorously assess the effects of wearable technology interventions on sleep-related health outcomes, this study utilised a detailed data extraction method, which allowed for further BCT taxonomy analysis of each study included in the review. The data extraction focused on several critical aspects of the studies. The second reviewer will be my 1st supervisor Dr Anna Weighall, who has extensive experience with CBT-I and sleep research. We have created the data extraction template. Basic Information: The foundational data collection will include the title, author (s), publication year, and the country where each study was conducted, providing an initial context for the research focus and geographic diversity; Study Characteristics: We will catalogue demographic and clinical characteristics of the study populations, such as age, gender distribution, total number of participants, and health conditions. These health conditions included healthy individuals, those with insomnia, cardiovascular diseases, self-reported stress, depression, epilepsy, and cases where health status was unspecified; Study Design: The design of each study was classified into one of the following categories: randomised controlled trials (RCTs), quasi-experimental studies, or observational studies. This categorization was crucial as it provided comparative data on the effectiveness of wearable interventions versus standard treatments for insomnia; Interventions: We will look into the content of interventions used in the studies, noting whether they involved full Cognitive Behavioral Therapy (CBT), components of CBT, physical exercise, or mHealth app interventions. The comparator groups were also identified, which included non-wearable mHealth applications, non-wearable CBT-I, sham treatments, or the absence of a control group; Wearable Technology: The type of wearable technology used was meticulously recorded, including devices like Fitbit, Apple Watch, Beddit, Fuelband, Jawbone, Garmin, Withings, Xiaomi, Nightbalance, and Oura. We also noted whether the wearables were used merely for recording data or were actively involved in the trial settings; Outcome Measures: Outcome measures will be divided into objective and subjective categories. Objective measures included total sleep time, apnea-hypopnea index, sleep efficiency, wake after sleep onset, oxygen desaturation index, sleep latency, number of awakenings, respiratory distress index, and time in bed. Subjective outcomes covered aspects like daytime sleepiness, sleep quality scores, sleep disturbance scores, insomnia symptom severity scores, sleep-related impairment scores and the impact of sleepiness on daily functioning; Results and Feedback: Our review will evaluate the efficiency of CBT when used in conjunction with wearable technology, the accuracy of the wearable devices, and user feedback or adherence to the intervention. We will also explore limitations related to the use of CBT and wearable technologies; Additional Information: Further, we documented the source of funding, reported conflicts of interest, ethics approvals, other study limitations, and any other findings reported by the studies.
纳入和排除标准严格遵循 PICOS 框架（Randles & Paul，2023）。在以下条件下，文章已被排除在即将进行的研究计划之外：如果不涉及可穿戴设备，如果不涉及行为干预，如果没有评估睡眠结果（包括睡眠质量、持续时间、入睡潜伏期、失眠严重程度、日间功能、或心情），如果全文不可用，或者文章不是英文的。这种方法将确保严格遵守纳入标准，仅关注满足研究目标特定要求的研究。为了严格评估可穿戴技术干预措施对睡眠相关健康结果的影响，本研究采用了详细的数据提取方法，从而可以对审查中包含的每项研究进行进一步的 BCT 分类分析。数据提取集中于研究的几个关键方面。第二位审稿人将是我的第一位导师 Anna Weighall 博士，她在 CBT-I 和睡眠研究方面拥有丰富的经验。我们已经创建了数据提取模板。基本信息：基础数据收集将包括标题、作者、出版年份以及每项研究进行的国家/地区，为研究重点和地理多样性提供初步背景；研究特征：我们将对研究人群的人口统计和临床特征进行分类，例如年龄、性别分布、参与者总数和健康状况。 这些健康状况包括健康个体、患有失眠、心血管疾病、自我报告的压力、抑郁、癫痫以及健康状况未明确的病例；研究设计：每项研究的设计分为以下类别之一：随机对照试验（RCT）、准实验研究或观察性研究。这种分类至关重要，因为它提供了可穿戴干预措施与标准失眠治疗方法有效性的比较数据；干预措施：我们将研究研究中使用的干预措施的内容，注意它们是否涉及完整的认知行为疗法 (CBT)、CBT 的组成部分、体育锻炼或移动医疗应用程序干预措施。还确定了比较组，其中包括非可穿戴式 mHealth 应用、非可穿戴式 CBT-I、假治疗或不存在对照组；可穿戴技术：所使用的可穿戴技术类型被精心记录，包括 Fitbit、Apple Watch、Beddit、Fuelband、Jawbone、Garmin、Withings、小米、Nightbalance 和 Oura 等设备。我们还注意到可穿戴设备是否仅用于记录数据或积极参与试验设置；结果测量：结果测量将分为客观和主观两类。客观指标包括总睡眠时间、呼吸暂停低通气指数、睡眠效率、入睡后觉醒、氧饱和度下降指数、睡眠潜伏期、觉醒次数、呼吸窘迫指数和卧床时间。 主观结果涵盖白天嗜睡、睡眠质量评分、睡眠障碍评分、失眠症状严重程度评分、睡眠相关障碍评分以及嗜睡对日常功能的影响等方面；结果和反馈：我们的审查将评估 CBT 与可穿戴技术结合使用时的效率、可穿戴设备的准确性以及用户反馈或对干预的遵守情况。我们还将探讨与 CBT 和可穿戴技术的使用相关的局限性；附加信息：此外，我们记录了资金来源、报告的利益冲突、伦理批准、其他研究限制以及研究报告的任何其他发现。

The quality of the included studies will be assessed using validated tools such as the Cochrane Risk of bias 2 Tool for randomised controlled trials (RCTs) and the Newcastle-Ottawa Scale for observational studies. The evaluation will cover the risk of bias in selection, performance, detection, attrition, reporting, and other potential biases. Following the quality assessment, the plan for data synthesis will be implemented. Should the data allow, a meta-analysis will be performed using a random-effects model to account for variations across studies, with I² statistics employed to assess heterogeneity. For studies not amenable to meta-analysis, findings will be summarised either narratively or in tabular form, highlighting observed patterns, strengths, and limitations.
纳入研究的质量将使用经过验证的工具进行评估，例如用于随机对照试验 (RCT) 的 Cochrane 偏倚风险 2 工具和用于观察性研究的纽卡斯尔-渥太华量表。评估将涵盖选择、绩效、检测、人员流失、报告和其他潜在偏差方面的偏差风险。质量评估后，将实施数据合成计划。如果数据允许，将使用随机效应模型进行荟萃分析，以解释研究之间的差异，并使用 I² 统计数据来评估异质性。对于不适合荟萃分析的研究，将以叙述方式或表格形式总结研究结果，突出观察到的模式、优点和局限性。

Utilising the Behavior Change Technique (BCT) taxonomy as a standard framework allows for the classification of the roles that wearables play in interventions. This involves categorising and combining the BCTs employed in these interventions, and comparing them with those used in CBT-I and mobile health (mHealth) applications. Researchers will complete the training on the BCTTv1 website (https://www.bct-taxonomy.com/) before the analysis (Düking et al., 2020). The primary outcomes of this analysis will include: the types of BCTs used; how these BCTs are combined and implemented; the correlation between BCTs and intervention outcomes; and whether the design of the BCT process influences the results; Whether Demographic or other categories could be a possible way to predict whether those BCTs show the positive or negative effect. Coding disagreements will be resolved by a discussion between the researchers. In case of disagreement, a third researcher's opinion will be included to resolve the disagreement.
利用行为改变技术 (BCT) 分类法作为标准框架，可以对可穿戴设备在干预中发挥的作用进行分类。这涉及对这些干预措施中使用的 BCT 进行分类和组合，并将它们与 CBT-I 和移动医疗 (mHealth) 应用程序中使用的 BCT 进行比较。研究人员将在分析之前在 BCTTv1 网站 (https://www.bct-taxonomy.com/) 上完成培训（Düking et al., 2020）。该分析的主要结果将包括： 使用的 BCT 类型；如何组合和实施这些旅战斗队； BCT 与干预结果之间的相关性； BCT流程的设计是否影响结果；人口统计或其他类别是否可以作为预测这些 BCT 是否显示出积极或消极影响的可能方法。编码分歧将通过研究人员之间的讨论来解决。如有分歧，将纳入第三位研究者的意见来解决分歧。

5.2.2 Wearable User Interviews (planned, not yet conducted)
5.2.2 可穿戴用户访谈（计划中，尚未进行）

The content of the sleep app user interview will be informed by the findings of the systematic review. In this user interview, user experience of participants previous experiences on using wearable on sleeping enhancement purpose will be collected. Question list will be developed further basing on the sleep intervention relevant BCTs list from the Systematic Review, the draft question list is:
睡眠应用程序用户访谈的内容将根据系统审查的结果来确定。在本次用户访谈中，将收集参与者之前使用可穿戴设备来增强睡眠的用户体验。问题清单将根据系统审查中睡眠干预相关 BCT 清单进一步制定，问题清单草案为：

Basic Information and Usage Context: Can you describe which wearable device(s) you use to monitor your sleep? How long have you been using this wearable device for sleep tracking? Initial Impressions and Setup: What were your initial impressions when you first started using the wearable device for sleep? Can you walk me through the first time you set up the device for sleep tracking? What was that process like? Daily Usage: Describe a typical night of using the wearable device. What do you do before going to bed? How do you interact with the device right before you sleep and immediately after waking up? Specific Scenarios: Can you recall a specific night when the wearable device significantly impacted your sleep (either positively or negatively)? What happened? In what situations have you found the wearable device to be most helpful or most troublesome? Feelings and Reactions: What feelings or thoughts do you associate with using the wearable device during the night? Have there been any nights where the device's data or feedback altered how you felt about your sleep quality? Sleep Data Interaction: How do you usually review and interact with the sleep data collected by your device? Has the data from your wearable device led you to change any habits or routines related to your sleep? Device Features and Functionality: Which features of the wearable device do you find most useful for understanding or improving your sleep? Are there any features that you feel are missing or could be improved to better assist you with your sleep? Impact on Sleep Perception and Management: How has using the wearable device changed your perception of your sleep patterns? What actions have you taken based on the sleep data provided by the device? Recommendations and Feedback: Based on your experience, what advice would you give to someone new to using a wearable device for sleep? What feedback would you offer to the manufacturers of the wearable device to enhance its functionality or user experience? Overall Satisfaction and Future Use: Overall, how satisfied are you with the use of your wearable device in managing your sleep? Do you plan to continue using this device or explore other devices for sleep tracking? Why or why not?
基本信息和使用环境：您能描述一下您使用哪些可穿戴设备来监测您的睡眠吗？您使用这款可穿戴设备进行睡眠跟踪已有多久了？初始印象和设置：当您第一次开始使用可穿戴设备进行睡眠时，您的初始印象是什么？您能指导我完成第一次设置睡眠跟踪设备的过程吗？那个过程是怎样的？日常使用：描述使用可穿戴设备的典型夜晚。睡觉前你会做什么？您在睡觉前和醒来后如何与设备进行交互？具体场景：您能回忆起可穿戴设备对您的睡眠产生显着影响（积极或消极）的特定夜晚吗？发生了什么？您认为可穿戴设备在什么情况下最有帮助或最麻烦？感受和反应：您在夜间使用可穿戴设备时会产生什么感受或想法？是否有过某个夜晚，设备的数据或反馈改变了您对睡眠质量的感受？睡眠数据交互：您通常如何查看设备收集的睡眠数据并与之交互？来自可穿戴设备的数据是否导致您改变了与睡眠相关的习惯或惯例？设备特性和功能：您认为可穿戴设备的哪些功能对于了解或改善您的睡眠最有用？您是否觉得有哪些功能缺失或可以改进以更好地帮助您睡眠？对睡眠感知和管理的影响：使用可穿戴设备如何改变您对睡眠模式的感知？根据设备提供的睡眠数据，您采取了哪些行动？ 建议和反馈：根据您的经验，您会给刚开始使用可穿戴设备睡眠的人什么建议？您会向可穿戴设备制造商提供哪些反馈来增强其功能或用户体验？总体满意度和未来使用：总体而言，您对使用可穿戴设备管理睡眠的满意度如何？您打算继续使用此设备还是探索其他设备进行睡眠跟踪？为什么或者为什么不？

This user interview focus is on adults aged 18 to 65 who are willing to use wearable devices to improve their sleep. I would start to recruit after receiving the ethical approval. I will employ various recruitment methods including social media, university networks, community outreach, professional conferences, and snowball sampling.
本次用户访谈重点关注 18 至 65 岁愿意使用可穿戴设备改善睡眠的成年人。获得道德批准后我会开始招聘。我将采用各种招聘方法，包括社交媒体、大学网络、社区外展、专业会议和滚雪球抽样。

The interview data will be recorded by Google Meeting and stored in University of Sheffield Google Drive. Voice recognition software like Otter.ai will be employed to transcribe the content of the interviews, facilitating quick access to text records for subsequent analysis.
面试数据将由Google Meeting记录并存储在谢菲尔德大学Google Drive中。将使用Otter.ai等语音识别软件来转录采访内容，以便快速访问文本记录以进行后续分析。

As a non-native English Interviewer, there are possibilities I might not give a perfect performance in an English interview. Thus, following preparation will be prepared and adopted during the interview. A detailed interview outline with questions will be prepared in advance, ensuring the use of simple and clear language. Familiarity with relevant professional terminology and expressions will be established beforehand to minimise language barriers during the interviews. The entire interview process will be recorded using recording devices, allowing for a meticulous review and ensuring the accuracy of the information. Detailed notes will also be taken during the interviews, especially noting sections that require further clarification or translation. For highly formal or technical interviews, the engagement of professional simultaneous interpreters will be considered. Professional translation services will be used for the final analysis of the interview results to ensure accuracy and professionalism. Participation in language exchanges with native English speakers will improve English speaking and listening comprehension skills. Attendance at professional English courses or workshops, particularly those focusing on interview skills and academic English, will also be pursued. Through these methods, the impact of language barriers will be minimised, and the research quality and efficiency will be significantly enhanced.
作为一名非英语母语面试官，我可能无法在英语面试中表现完美。因此，在面试期间将准备并采用以下准备。我们将提前准备详细的采访大纲和问题，确保使用简单明了的语言。事先熟悉相关专业术语和表达方式，以尽量减少面试过程中的语言障碍。整个访谈过程将使用录音设备进行录音，以便进行细致的审查并确保信息的准确性。采访期间也会做详细的笔记，特别是需要进一步澄清或翻译的部分。对于高度正式或技术性的采访，将考虑聘请专业的同声传译员。面试结果将采用专业翻译服务进行最终分析，确保准确性和专业性。参与与英语为母语的人的语言交流将提高英语口语和听力理解能力。还将追求参加专业英语课程或研讨会，特别是那些关注面试技巧和学术英语的课程或研讨会。通过这些方法，将语言障碍的影响降到最低，研究质量和效率将显着提高。

Expert interviews are particularly advantageous in this study over other methodologies like surveys or observational studies for gathering in-depth, nuanced insights while they design interventions [av]that require detailed exploration, such as adherence to maximise the intervention effect to facilitate a valuable study. These interviews enable a deep examination of expert knowledge and experiences, offering flexibility to adapt conversations dynamically as new themes emerge, which is not feasible with more rigid methods like surveys. They also allow researchers to delve into contextual factors and specific gaps in existing research, providing high-quality data through direct engagement with experts. This method is invaluable for accessing specialised knowledge directly from experts, crucial for advancing understanding in specialised fields such as wearable technology.
在本研究中，与调查或观察性研究等其他方法相比，专家访谈特别有利于收集深入、细致的见解，同时设计需要详细探索的干预措施，例如坚持最大化干预效果以促进有价值的研究。这些访谈可以对专家知识和经验进行深入检查，并提供随着新主题的出现而动态调整对话的灵活性，而这对于调查等更严格的方法来说是不可行的。它们还允许研究人员深入研究现有研究中的背景因素和具体差距，通过与专家的直接接触提供高质量的数据。这种方法对于直接从专家那里获取专业知识非常宝贵，对于增进对可穿戴技术等专业领域的理解至关重要。

Meanwhile, using expert interviews as a methodology allows researchers to mitigate the biases that might arise from personal interpretations and opinions in their studies. By directly engaging with experts, researchers are provided with insights based on years of experience and deep understanding in the field, rather than relying solely on theoretical frameworks or their own preliminary conclusions. This approach ensures that the data collected is grounded in practical, real-world applications and expert consensus, which significantly enriches the reliability and validity of the research findings. Furthermore, the interactive nature of interviews enables researchers to clarify ambiguities and probe deeper into complex issues, thereby obtaining a more objective and comprehensive view that effectively reduces the influence of personal bias in the study.
同时，使用专家访谈作为一种方法可以让研究人员减少研究中个人解释和观点可能产生的偏见。通过直接与专家接触，研究人员可以获得基于多年经验和对该领域深刻理解的见解，而不是仅仅依赖理论框架或自己的初步结论。这种方法确保收集的数据基于实际、现实世界的应用和专家共识，从而显着丰富了研究结果的可靠性和有效性。此外，访谈的互动性使研究人员能够澄清歧义，更深入地探讨复杂问题，从而获得更客观、更全面的观点，有效减少个人偏见对研究的影响。

5.1.3 Thematic analysis (planned, not yet conducted)
5.1.3 专题分析（计划中，尚未进行）

Thematic analysis will be adopted to analyse the data collected on user interviews. At this point, as the interviewer in the user interview(5.1.2), I will have enough familiarity with the data[aw]. Initial labelling of meaningful information that may be relevant to the research question can be started and preliminary coding (Mortensen, 2024). With the preliminary coding, I will search for patterns or themes within the codes across the different UX interviews, review these themes in relation to the coded extracts and the entire data set. After forming initial themes, I will go back to the data and check that these themes are a true reflection of the data content. If not appropriate, I will readjust the definition of themes or merge some of them. Then, I will define and name the themes.[ax] In the research report, I will provide a detailed description of each user experience theme and how it will have been described under their BCT. I will cite data snippets to support the description of each theme to ensure that the reader can see the basis of the analysis.
将采用主题分析来分析用户访谈收集的数据。至此，作为用户访谈（5.1.2）中的访谈者，我对数据就有了足够的熟悉度[aw]。可以开始对可能与研究问题相关的有意义的信息进行初步标记并进行初步编码（Mortensen，2024）。通过初步编码，我将在不同的用户体验访谈的代码中搜索模式或主题，回顾这些与编码摘录和整个数据集相关的主题。形成初始主题后，我将返回数据并检查这些主题是否真实反映了数据内容。如果不合适，我会重新调整主题的定义或者合并一些主题。然后，我将定义并命名主题。[ax] 在研究报告中，我将详细描述每个用户体验主题以及如何在 BCT 下描述它。我会引用数据片段来支持每个主题的描述，以确保读者能够看到分析的基础。

As a non-native English coder, I will consider doing preliminary coding under the same content twice to reduce the bias of language habits: in a version of Chinese translation (Google translate) transcript and English original transcript.
作为一个非英语母语的编码员，我会考虑在相同的内容下进行两次初步编码，以减少语言习惯的偏差：在中文翻译（谷歌翻译）抄本和英文原始抄本的一个版本中。

In the upcoming thematic analysis of data from semi-structured interviews focused on wearable device-based sleep behaviour interventions, an in-depth, inductive initial coding of the transcribed interviews will be conducted. This step is essential for labelling segments of data with codes that succinctly capture their essence, thereby facilitating the emergence of themes relevant to user experience. Each transcript will be coded independently to ensure a comprehensive analysis, with a focus on identifying recurring patterns, unique perspectives, and insightful comments about the wearable devices. The process will then proceed to an iterative phase, where the initially identified codes will be grouped and categorised into potential themes. This will involve a meticulous comparison and contrast of the codes to understand how they interrelate or differ. Themes will be reviewed in the context of the entire data set to ensure they accurately represent the interviewees' perspectives. During this review, some themes might be merged, subdivided, or discarded, depending on their relevance and the data supporting them.
在即将对来自半结构化访谈的数据进行主题分析时，重点是基于可穿戴设备的睡眠行为干预，将对转录的访谈进行深入、归纳的初始编码。此步骤对于使用简洁地捕捉其本质的代码来标记数据片段至关重要，从而促进与用户体验相关的主题的出现。每个记录都将独立编码，以确保进行全面分析，重点是识别可穿戴设备的重复模式、独特视角和富有洞察力的评论。然后，该过程将进入迭代阶段，最初确定的代码将被分组并分类为潜在的主题。这将涉及对代码进行细致的比较和对比，以了解它们如何相互关联或不同。将在整个数据集的背景下审查主题，以确保它们准确地代表受访者的观点。在此审查期间，某些主题可能会被合并、细分或丢弃，具体取决于它们的相关性和支持它们的数据。

Following this, the themes will be further refined by defining and naming them descriptively to accurately convey their central concepts. The salience of these themes will be determined based on the frequency and depth of the insights shared, focusing on aspects critical to understanding the user experience with the wearable devices. Each theme will be validated for internal coherence and distinctiveness to ensure they are relevant and unique.
此后，将通过描述性定义和命名主题来进一步细化主题，以准确传达其中心概念。这些主题的重要性将根据共享见解的频率和深度来确定，重点关注对于理解可穿戴设备的用户体验至关重要的方面。每个主题都将经过内部一致性和独特性验证，以确保它们的相关性和独特性。

To assist in the organisation and analysis of the data, NVivo, a qualitative data analysis software, will be utilised. NVivo will facilitate efficient categorization and retrieval of data segments and enable the visualisation of relationships between themes and sub-themes. The software's query functions will be used to explore patterns within the data, enriching the thematic analysis. This approach is expected to provide nuanced insights into the user experience of wearable device-based sleep behaviour interventions, highlighting key areas for improvement and the overall impact of these interventions on sleep behaviour.
为了协助数据的组织和分析，将使用定性数据分析软件 NVivo。 NVivo 将促进数据片段的高效分类和检索，并实现主题和子主题之间关系的可视化。该软件的查询功能将用于探索数据中的模式，丰富主题分析。这种方法预计将为基于可穿戴设备的睡眠行为干预措施的用户体验提供细致入微的见解，突出需要改进的关键领域以及这些干预措施对睡眠行为的总体影响。

Based on the findings of the thematic analysis, design a questionnaire which is specifically tailored to encompass key elements related to user experience in wearable device-based sleep behaviour interventions, as identified in the qualitative research findings, in order to facilitate follow-up quantitative studies. In the development of the questionnaire, key considerations are integrated to ensure its effectiveness. The questionnaire, designed to reflect insights from qualitative research findings, is tailored with clear and precise wording suitable for the target audience's demographic and educational background. Scale selection, such as Likert scales, is aligned with capturing user opinions and preferences efficiently. Pre-testing is conducted to refine the questionnaire, ensuring a balance between comprehensiveness and respondent fatigue. Open-ended questions are included to garner deeper insights, and the design anticipates the data analysis methods to be employed. Ethical standards, particularly regarding respondent privacy and data confidentiality, are rigorously upheld, making this questionnaire a pivotal tool for extracting meaningful data on user experiences with sleep intervention wearable devices. [ay]
根据主题分析的结果，设计一份专门针对定性研究结果中确定的可穿戴设备睡眠行为干预中与用户体验相关的关键要素的调查问卷，以促进后续定量研究。在问卷的制定过程中，综合考虑了关键因素以确保其有效性。该调查问卷旨在反映定性研究结果的见解，采用适合目标受众人口统计和教育背景的清晰准确的措辞。量表选择（例如李克特量表）与有效捕获用户意见和偏好相一致。预测试旨在完善问卷，确保全面性和受访者疲劳度之间的平衡。包含开放式问题以获取更深入的见解，并且设计预期要采用的数据分析方法。严格遵守道德标准，特别是有关受访者隐私和数据保密性的道德标准，使该调查问卷成为提取有关睡眠干预可穿戴设备用户体验的有意义数据的关键工具。 [是]

5.2 Sleep App Design (Develop and Deliver Phase) - The 2nd Diamond * Thesis Chapter 6
5.2 睡眠应用程序设计（开发和交付阶段）-第二颗钻石 * 论文第 6 章

5.2.1 Participatory Design (planned, not yet conducted)
5.2.1 参与式设计（已计划，尚未实施）

In the upcoming study, an iterative design approach will be utilised to ensure a robust and well-rounded evaluation of the application's design and functionality. This approach will involve a diverse group of participants, comprising approximately 7 users, 2 to 4 researchers, 1 to 2 health professionals, and 1 to 2 designers. The researchers participating will have experience with Cognitive Behavioral Therapy for Insomnia (CBTi) or digital CBTi (dBTi), providing critical insights into the therapeutic aspects of the application. Health professionals involved will be medical or clinical experts with specific knowledge in CBTi, offering essential medical and clinical perspectives that will guide the health-related functionalities of the app. The designers will be professionals specialising in user experience or health app development, tasked with ensuring that the application is user-friendly and effectively integrates the desired features.Users recruiting will be focused on adults between the ages of 18 and 65 who are willing to use wearable devices to improve their sleep. Participants should be comfortable using digital technology, particularly smartphones, as the wearable device will be synchronised with a mobile application. Each participant group will bring a unique perspective to the design process, enhancing the overall development and assessment of the application. I will seek individuals who are willing to engage actively in the development process through various participatory design workshops. I would start to recruit after receiving the ethical approval. Similar to the interview, the recruitment will be through social media, university network, community outreach, professional conferences and snowball sampling.
在即将进行的研究中，将利用迭代设计方法来确保对应用程序的设计和功能进行稳健且全面的评估。这种方法将涉及不同的参与者群体，包括大约 7 名用户、2 至 4 名研究人员、1 至 2 名卫生专业人员和 1 至 2 名设计师。参与的研究人员将拥有失眠认知行为疗法 (CBTi) 或数字 CBTi (dBTi) 的经验，为该应用的治疗方面提供重要见解。参与的健康专业人员将是具有 CBTi 专业知识的医学或临床专家，他们提供基本的医学和临床观点来指导应用程序的健康相关功能。设计师将是专门从事用户体验或健康应用程序开发的专业人士，其任务是确保应用程序用户友好并有效地集成所需的功能。用户招募将集中于18岁至65岁之间愿意使用的成年人可穿戴设备来改善他们的睡眠。参与者应该能够轻松使用数字技术，尤其是智能手机，因为可穿戴设备将与移动应用程序同步。每个参与者小组都会为设计过程带来独特的视角，从而增强应用程序的整体开发和评估。我将寻找愿意通过各种参与式设计研讨会积极参与开发过程的个人。获得道德批准后我会开始招聘。与面试类似，招聘将通过社交媒体、大学网络、社区外展、专业会议和滚雪球抽样进行。

During this participatory design workshop, UX as design principle, experience as design subject. Considerations under this phase will be about the threshold of user experience and intervention design, based on the list of BCTs and their UX gained from the previous phase. Each Workshop durations will be no more than two hours, this approach is to conduct these sessions in phases:
在本次参与式设计工作坊中，以用户体验为设计原则，以体验为设计主题。这一阶段的考虑将是基于上一阶段获得的 BCT 列表及其用户体验，关于用户体验的门槛和干预设计。每个研讨会的持续时间不会超过两个小时，这种方法是分阶段进行：

Workshop 1: Establishing the Foundation The initial stage involves a Theory of Change workshop with expert (researcher and health professional) stakeholders. The goal here is to lay down a clear framework detailing how the wearable will influence behaviour and enhance sleep quality. This session will use the BCT taxonomy v1 to pinpoint the active ingredients necessary for effective insomnia management and explore how these can be incorporated into the self-management app design. The BCT list from the previous phase will be used to check with experts.
研讨会 1：建立基础 初始阶段涉及由专家（研究人员和卫生专业人员）利益相关者参加的变革理论研讨会。这里的目标是制定一个清晰的框架，详细说明可穿戴设备将如何影响行为并提高睡眠质量。本次会议将使用 BCT 分类法 v1 来查明有效失眠管理所需的活性成分，并探讨如何将这些成分纳入自我管理应用程序设计中。上一阶段的BCT名单将用于与专家核对。

Workshop 2: Engaging with Users Following the expert workshop, the focus will shift towards the device's end-users. I will conduct sessions with wearable user stakeholders to develop detailed personas that reflect the varied needs, experiences, and challenges faced by individuals struggling with their sleep. These personas will ensure that the design of the self-management app addresses a broad spectrum of user requirements.
研讨会 2：与用户互动 在专家研讨会之后，重点将转向设备的最终用户。我将与可穿戴用户利益相关者举行会议，以开发详细的角色，以反映睡眠困难的个人所面临的不同需求、经历和挑战。这些角色将确保自我管理应用程序的设计满足广泛的用户需求。

Workshop 3: Prioritisation of Features With a deep understanding of both therapeutic and user-centric needs, I will prioritise which features should be implemented in the wearable. This will involve critical discussions among stakeholders to determine which interventions are most critical based on the impact they are expected to have on user experience and therapeutic outcomes.
研讨会 3：功能的优先顺序通过对治疗和以用户为中心的需求的深入了解，我将优先考虑应在可穿戴设备中实现哪些功能。这将涉及利益相关者之间的批判性讨论，根据预期对用户体验和治疗结果的影响来确定哪些干预措施是最关键的。

Workshop 4: Design and Co-Design The final stages of the project will involve intensive co-design workshops where stakeholders, including designers, experts, and patients, will collaboratively develop and refine mock-ups of the wearable functionality. These sessions are crucial for translating the prioritised features into practical design elements that are both effective and user-friendly.
研讨会 4：设计和协同设计 该项目的最后阶段将包括密集的协同设计研讨会，包括设计师、专家和患者在内的利益相关者将共同开发和完善可穿戴功能的模型。这些会议对于将优先功能转化为既有效又用户友好的实用设计元素至关重要。

Hand Sketch, Google Jamboard, Canva Whiteboards(Figure x) and Figma(Figure x) will be used to generate, communicate and collect ideas,
Hand Sketch、Google Jamboard、Canva Whiteboards（图 x）和 Figma（图 x）将用于生成、交流和收集想法，

Figure x, Collaborate interface of Canva
图x，Canva的协作界面

Figure x, Collaborate interface of Figma
图x，Figma的协作界面

5.2.2 Prototype protocol (planned, not yet conducted)
5.2.2 原型协议（计划中，尚未实施）

This research will build on existing attempts, as well as experiences from interdisciplinary aspects, to propose prototypes of potential solutions for wearable device-based sleep intervention experiences. The sleep app prototype workflow, content, interface would be the result of participatory design. The design tool will be used to generate guiding tasks by prompting with ChatGPT, any other better AI tool would also take into consideration by then.
这项研究将建立在现有尝试以及跨学科方面的经验的基础上，为基于可穿戴设备的睡眠干预体验提出潜在解决方案的原型。睡眠应用程序原型的工作流程、内容、界面将是参与式设计的结果。该设计工具将用于通过ChatGPT提示来生成引导任务，届时也会考虑其他更好的AI工具。

Figure x, CBT-i prompting customised versions of ChatGPT
图x，CBT-i提示ChatGPT的定制版本

Figure x, Chat screenshot on ChatGPT after CBT-I and personalised language prompting
图x，CBT-I和个性化语言提示后ChatGPT上的聊天截图

5.3 Timetable 5.4 Thesis chapter plan
5.3 时间表 5.4 论文章节计划

Exploring potential enhancements of adherence on sleep wearable interventions from User experience aspect
从用户体验方面探索睡眠可穿戴干预措施依从性的潜在增强

Chapter 1: Introduction * Overview of the Research Problem * Importance of the topic * Objectives of the PhD Project * Structure of the Thesis
第一章：简介 * 研究问题概述 * 主题的重要性 * 博士项目的目标 * 论文结构

Chapter 2: Literature Review * Current Sleep Behaviour Interventions * Wearable Devices in Sleep Behaviour Intervention * Challenges in Adherence * User Experience in Wearable Sleep Studies
第 2 章：文献综述 * 当前睡眠行为干预措施 * 睡眠行为干预中的可穿戴设备 * 依从性方面的挑战 * 可穿戴睡眠研究中的用户体验

Chapter 3: Methodology * Design Framework: Detailed description of the Double Diamond Design Model and its studies designed under each phase * Ontology and Epidemiology * Statement of Positionality * Ethical Considerations in Research Design * Ethics Approval Process
第 3 章：方法论 * 设计框架：双钻石设计模型及其在每个阶段设计的研究的详细描述 * 本体论和流行病学 * 立场声明 * 研究设计中的伦理考虑 * 伦理审批流程

Chapter 4: BCT Mapping (Discover and Define Phases) * Systematic Review * User Interviews * Thematic Analysis
第 4 章：BCT 规划（发现和定义阶段） * 系统回顾 * 用户访谈 * 主题分析

Chapter 5: Sleep App Design (Develop and Deliver Phase) * Participatory Design Workshops * Prototype Development * Prototype protocol
第 5 章：睡眠应用程序设计（开发和交付阶段） * 参与式设计研讨会 * 原型开发 * 原型协议

Chapter 6: Integration and Conclusions * Integration Across Phases * Major Findings * Conclusions and Future Work ________________ Ethics and Data Management
第 6 章：整合和结论 * 跨阶段整合 * 主要发现 * 结论和未来工作 ________________ 道德与数据管理

6.1 Ethical Considerations Without adequate training and supervision, the neophyte researcher can unwittingly become an unguarded projectile bringing turbulence to the field, fostering personal trauma (for researcher and researcher), and even causing damage to the discipline(Punch, 1994). Ethics of this project has been treated carefully and comprehensively (figure X).
6.1 道德考虑 如果没有足够的培训和监督，新手研究员可能会不知不觉地成为一个无人看管的抛射物，给该领域带来动荡，造成个人创伤（对研究员和研究员而言），甚至对学科造成损害（Punch，1994）。该项目的道德规范得到了认真、全面的对待（图十）。

Figure x: Ethical training attended
图 x：参加的道德培训

6.1.1 Systematic review The initial phase involves conducting a systematic review where paramount importance is placed on ensuring data privacy and transparency. This is achieved by respecting the intellectual property rights of the original studies and maintaining transparency in the utilisation of data. A meticulous effort is made to mitigate biases in the selection of studies, ensuring objectivity and inclusivity in the review process. This is accomplished by incorporating a diverse range of research while clearly documenting the inclusion and exclusion criteria, thus providing a robust foundation for the study.
6.1.1 系统审查 初始阶段涉及进行系统审查，其中最重要的是确保数据隐私和透明度。这是通过尊重原始研究的知识产权并保持数据使用的透明度来实现的。我们竭尽全力减少研究选择中的偏见，确保审查过程的客观性和包容性。这是通过纳入各种研究，同时明确记录纳入和排除标准来实现的，从而为研究提供坚实的基础。

6.1.2 User Interview During the interview phase with users specialising in sleep wearable behaviour interventions, ethical considerations are meticulously adhered to. Recruited Interviewees will receive email with attached information sheet (with interview questions) and consent form. The participants' information and their user experience original data would be handled carefully employing methods such as anonymization or pseudonyms in any published works to safeguard identities and only ethic permitted researchers will be able to access. Participants will be fully informed about the purpose of the user interview, some questions might contain their description of their daily habit, such as wearable usage, their interview data would be used for thematic analysis and their data would contribute to draw a conclusion of user experience mapping, this mapping will be used in the next phase of the research. The result generated from their data may be used for publication. Consent will be obtained by they signing the consent form, and participants will be told that they can withdraw from the study before their data get analysed. Safeguarding the participants' privacy is crucial. I will ensure that any information that could identify a participant is kept confidential unless explicit consent has been given to disclose it. This includes securing the data in a manner that prevents unauthorised access. Participation will be voluntary, and make sure participants will not feel coerced into providing information. It's important to communicate that their participation, or lack thereof, will not affect any services or benefits they are currently receiving or might receive in the future.
6.1.2 用户访谈 在与专门从事睡眠可穿戴行为干预的用户访谈阶段，严格遵守道德考虑。招募的受访者将收到附有信息表（包含面试问题）和同意书的电子邮件。参与者的信息及其用户体验原始数据将在任何已发表的作品中采用匿名或假名等方法进行仔细处理，以保护身份，并且只有道德允许的研究人员才能访问。参与者将充分了解用户访谈的目的，一些问题可能包含他们对日常习惯的描述，例如可穿戴设备的使用情况，他们的访谈数据将用于主题分析，他们的数据将有助于得出用户体验的结论映射，该映射将用于下一阶段的研究。从他们的数据生成的结果可用于发布。参与者将通过签署同意书获得同意，并且参与者将被告知他们可以在数据分析之前退出研究。保护参与者的隐私至关重要。我将确保对任何可识别参与者身份的信息保密，除非已明确同意披露。这包括以防止未经授权的访问的方式保护数据。参与将是自愿的，并确保参与者不会感到被迫提供信息。重要的是要传达这样的信息：他们的参与或不参与不会影响他们目前正在接受或将来可能接受的任何服务或福利。

Since the topic involves personal habits and sleep self-management user experiences, it's essential to approach the interviews with sensitivity. Recognize that discussions about personal health and sleep problems can be intimate and potentially distressing. I will ensure that the questions asked are unbiased and formulated in a way that the responses accurately represent the participant's views and experiences. Avoid leading questions that could skew the data. After the interview, it's helpful to provide participants with a debriefing session where they can discuss their experience of the interview, ask questions, and be provided with information on where they can receive support if discussing their sleep issues has caused any distress. Interview audio recording would be stored in University of Sheffield Google Drive and will be keeped the access only among me and my supervisors. Raw data like audio recording will be deleted 3 years after the publication of the research findings, and the transcript will be saved. I would offer participants the opportunity to provide feedback on the interview process and suggest improvements. This can help refine the study and make it more participant-friendly.
由于该主题涉及个人习惯和睡眠自我管理用户体验，因此有必要敏感地进行采访。认识到有关个人健康和睡眠问题的讨论可能是亲密的，并且可能会令人痛苦。我将确保所提出的问题是公正的，并且其回答能够准确地代表参与者的观点和经验。避免可能扭曲数据的引导性问题。访谈结束后，为参与者提供一次情况汇报会很有帮助，他们可以在其中讨论他们的访谈经历、提出问题，并了解如果讨论他们的睡眠问题造成任何困扰，他们可以在哪里获得支持。采访录音将存储在谢菲尔德大学 Google Drive 中，并且只有我和我的主管可以访问。研究成果发表3年后，录音等原始数据将被删除，并保存笔录。我将为参与者提供机会提供有关面试过程的反馈并提出改进建议。这有助于完善研究并使其更加适合参与者。

6.1.3 Thematic analysis In the thematic analysis phase, there is a conscientious effort to maintain the integrity of data interpretation. This is crucial in ensuring that the representations of participants' responses are accurate and reflective of their true perspectives, thereby providing a genuine understanding of the experiences and viewpoints of the researchers involved. Given that thematic analysis often involves detailed discussions that can reveal personal or sensitive information, I will ensure confidentiality and anonymity. This means that any publications or presentations resulting from the research will not include any identifying information, and data will be securely stored to prevent unauthorised access. When coding and analysing data, I will make sure the data is accurately represented. This means being careful not to impose my personal biases or interpretations that are not supported by the data. The themes developed will accurately reflect the views and experiences of the participants. As a researcher, practising reflexivity involves acknowledging and reflecting upon my influence on the research, including the analysis and interpretation of data. This helps in maintaining objectivity and ensures that the research findings are a true representation of the data.
6.1.3 专题分析 在专题分析阶段，要认真努力保持数据解释的完整性。这对于确保参与者的反应准确并反映他们的真实观点至关重要，从而提供对所涉及研究人员的经验和观点的真正理解。鉴于主题分析通常涉及可能泄露个人或敏感信息的详细讨论，我将确保保密和匿名。这意味着研究产生的任何出版物或演示文稿都不会包含任何识别信息，并且数据将被安全存储以防止未经授权的访问。在编码和分析数据时，我会确保数据得到准确的表示。这意味着要小心，不要强加我的个人偏见或没有数据支持的解释。制定的主题将准确反映参与者的观点和经验。作为一名研究人员，实践反思性包括承认和反思我对研究的影响，包括数据的分析和解释。这有助于保持客观性并确保研究结果真实反映数据。

I will provide participants with a debriefing session after the analysis to address any questions or concerns they might have about the study. This is also an opportunity to inform them about any support services if the content of the interview or the themes analysed cause distress. I will offer participants the chance in debrief to review the themes or conclusions drawn from their interviews (also known as member checking) can enhance the validity of the research. This step ensures that the analysis is accurate and resonates with the participants' experiences. If the thematic analysis uncovers sensitive themes or potentially distressing information, it's important to handle these topics ethically. This includes considering the impact of these findings on the participants and the broader community. When reporting the findings, I will be transparent about the methods used for data collection (sleep app user interview) and analysis (thematic analysis). Clearly describe how themes were developed, how decisions were made during the analysis, and how interpretations were derived from the data.
分析后，我将为参与者提供一次汇报会，以解决他们对研究可能存在的任何问题或担忧。如果采访内容或分析的主题造成困扰，这也是一个告知他们任何支持服务的机会。我将为参与者提供汇报的机会，以审查从访谈中得出的主题或结论（也称为成员检查），可以提高研究的有效性。此步骤可确保分析准确并与参与者的体验产生共鸣。如果主题分析发现敏感主题或潜在令人痛苦的信息，以合乎道德的方式处理这些主题非常重要。这包括考虑这些发现对参与者和更广泛社区的影响。在报告调查结果时，我将对数据收集（睡眠应用程序用户访谈）和分析（主题分析）所使用的方法保持透明。清楚地描述如何开发主题、如何在分析过程中做出决策以及如何从数据中得出解释。

6.1.4 Participatory design workshops The final stage of the research involves participatory design methods, where a conscientious effort is made to engage participants in a manner that respects their contributions and time. This phase is approached with a keen awareness of the potential burden placed on participants and strives to ensure an inclusive and diverse participant group. Special attention is given to including underrepresented or marginalised groups to prevent the skewing of results and perspectives, thus enhancing the breadth and depth of the research findings.
6.1.4 参与式设计研讨会 研究的最后阶段涉及参与式设计方法，即以尊重参与者的贡献和时间的方式认真努力地吸引参与者。在进入这一阶段时，我们敏锐地意识到参与者的潜在负担，并努力确保参与者群体具有包容性和多样性。特别注意纳入代表性不足或边缘化群体，以防止结果和观点出现偏差，从而增强研究结果的广度和深度。

I will ensure that participants are fully informed about the purpose of the workshop, a timetable showing which part of the workshop their participation will involve, especially under different roles, they will be told their data they provide will be used for sleep app prototype design, and any potential risks like privacy experience information sharing during the workshop, potential stressful engaging in intense brainstorming sessions and potential social risks if their views or contributions are not received well by other group members or if they are subjected to criticism or conflict. I will obtain consent in a clear and understandable form, preferably in writing, and inform participants that they can withdraw at any time before data is started to be analysed.
我将确保参与者充分了解研讨会的目的，时间表显示他们参与研讨会的哪一部分，特别是在不同的角色下，他们将被告知他们提供的数据将用于睡眠应用程序原型设计，任何潜在风险，例如研讨会期间共享隐私体验信息、参与激烈的头脑风暴会议可能带来的压力，以及如果他们的观点或贡献没有被其他小组成员很好地接受或者受到批评或冲突，则可能产生潜在的社会风险。我将以清晰易懂的形式（最好是书面形式）获得同意，并告知参与者他们可以在开始分析数据之前随时退出。

I will protect the privacy of participants by keeping all personal data confidential and ensuring that participants are not identifiable in any reports or publications resulting from the workshop unless they give explicit consent to be identified. I will treat all participants with respect, ensuring that their views are heard and considered equally. This includes providing an environment where participants feel comfortable expressing their thoughts and opinions without fear of criticism or discrimination. I will make the workshop accessible to all participants, including those with disabilities, by providing materials in different formats, ensuring physical accessibility, and being mindful of language barriers or technological limitations.
我将通过对所有个人数据保密来保护参与者的隐私，并确保参与者在研讨会产生的任何报告或出版物中都不会被识别出来，除非他们明确同意被识别。我将尊重所有参与者，确保他们的意见得到平等听取和考虑。这包括提供一个让参与者能够轻松表达自己的想法和意见而不必担心批评或歧视的环境。我将通过提供不同格式的材料、确保物理无障碍性并注意语言障碍或技术限制，使包括残疾人在内的所有参与者都能参加研讨会。

I will carefully consider the potential benefits and harms of the workshop outcomes, aiming for designs and interventions that maximise benefits for users while minimising any potential harm. I will be transparent about the goals of the workshop and the subsequent steps, clearly informing participants about how their input will be used in the design process and how they can access the results of the work they helped to create. I will manage any conflicts of interest that may arise during the workshop, particularly with respect to commercial interests of the app developers or researchers. I will provide participants with feedback on how their contributions have been integrated into the project and offer opportunities for them to comment on and refine further iterations of the sleep app prototype. I will conduct the workshops in a culturally sensitive manner, respecting the different backgrounds, customs, and traditions of participants, which might influence their interaction with technology and their perceptions of sleep and health. In the end, I will provide support and a debriefing session for participants after the workshop to discuss any issues that arose and to ensure that participants do not feel used or misunderstood.
我将仔细考虑研讨会成果的潜在好处和危害，旨在设计和干预措施，最大限度地为用户带来好处，同时最大限度地减少任何潜在的危害。我将对研讨会的目标和后续步骤保持透明，清楚地告知参与者他们的意见将如何在设计过程中使用，以及他们如何获得他们帮助创建的工作结果。我将管理研讨会期间可能出现的任何利益冲突，特别是与应用程序开发人员或研究人员的商业利益有关的冲突。我将为参与者提供有关他们的贡献如何融入项目的反馈，并为他们提供评论和完善睡眠应用程序原型的进一步迭代的机会。我将以文化敏感的方式举办研讨会，尊重参与者的不同背景、习俗和传统，这些可能会影响他们与技术的互动以及他们对睡眠和健康的看法。最后，我将在研讨会结束后为参与者提供支持和汇报会，讨论出现的任何问题，并确保参与者不会感到被利用或误解。

6.2 Data Management Plan 1. Will you be processing (i.e. collecting, recording, storing, or otherwise using) personal data as part of this project?
6.2 数据管理计划 1. 作为本项目的一部分，您是否会处理（即收集、记录、存储或以其他方式使用）个人数据？

* Yes, this project will involve the processing of personal data, including collecting, recording, storing, and using information related to identified or identifiable individuals. This data will be used to analyse user experiences with wearable devices for sleep enhancement.
* 是的，该项目将涉及个人数据的处理，包括收集、记录、存储和使用与已识别或可识别个人相关的信息。这些数据将用于分析可穿戴设备的用户体验，以增强睡眠。

2. Which organisation(s) will act as Data Controller (i.e. the organisation which determines the purposes and means of processing the data) for personal data collected and used as part of the project?
2. 哪些组织将充当项目中收集和使用的个人数据的数据控制者（即确定数据处理目的和方式的组织）？

* The University of Sheffield will act as the Data Controller for all personal data collected and used in this project.
* 谢菲尔德大学将充当本项目中收​​集和使用的所有个人数据的数据控制者。

3. What is the legal basis for processing of personal data?
3. 处理个人数据的法律依据是什么？

* The legal basis for processing personal data in this project is “a task in the public interest,” as the research aims to contribute to public knowledge and health improvements. This basis is supported by the University's guidelines and complies with data protection legislation.
* 本项目处理个人数据的法律依据是“一项符合公共利益的任务”，因为该研究旨在促进公众知识和健康改善。这一基础得到大学指导方针的支持，并符合数据保护立法。

4. Will you be processing (i.e. collecting, recording, storing, or otherwise using) “Special Category” personal data?
4. 您是否会处理（即收集、记录、存储或以其他方式使用）“特殊类别”个人数据？

* Yes, this project will process Special Category personal data, particularly data concerning health, as it relates to user experiences with health-related wearable devices.
* 是的，该项目将处理特殊类别个人数据，特别是有关健康的数据，因为它与健康相关可穿戴设备的用户体验有关。

5. What is the condition in place for processing Special Category data?
5. 处理特殊类别数据的条件是什么？

* The condition for processing Special Category data is that it is necessary for scientific research purposes or statistical purposes, in the public interest. This condition is aligned with university guidelines and data protection legislation, ensuring the ethical handling of sensitive data. 6. What measures will be put in place to ensure confidentiality of personal data, where appropriate?
* 处理特殊类别数据的条件是出于科学研究目的或统计目的、为了公共利益所必需的。此条件符合大学指导方针和数据保护立法，确保敏感数据的道德处理。 6. 在适当情况下，将采取哪些措施来确保个人数据的机密性？

* To ensure confidentiality, personal data will be pseudonymised or anonymised wherever possible. Confidentiality levels will be clearly communicated to participants during the consent process. Additionally, all research data will be handled with care to avoid promising a level of confidentiality that cannot be guaranteed.
* 为确保机密性，个人数据将尽可能采用假名或匿名处理。在同意过程中将向参与者明确传达保密级别。此外，所有研究数据都将谨慎处理，以避免承诺无法保证的保密程度。

7. Who will have access to the data generated at each stage of the research, and in what form (e.g. identifiable, pseudonymised, anonymised)?
7. 谁将有权访问研究每个阶段生成的数据以及以何种形式（例如可识别、假名、匿名）？

* Access to data generated during the research will be restricted to the research team and specific service providers involved in data processing tasks such as transcription and anonymisation. Data will be managed in various forms, ranging from identifiable to anonymised, based on the sensitivity and the research phase. 8. What steps will be taken to ensure the security of data processed during the project, including any identifiable personal data? * Comprehensive data security measures will be implemented, including secure storage, controlled access, and encryption, to protect personal data. Data security practices will align with university guidelines, legislative requirements, and any specific stipulations from funding bodies. Additionally, participants will be informed of the data storage and security arrangements as part of the informed consent process.
* 研究期间生成的数据的访问权限仅限于研究团队和参与转录和匿名等数据处理任务的特定服务提供商。根据敏感性和研究阶段，数据将以各种形式进行管理，从可识别到匿名。 8. 将采取哪些措施来确保项目期间处理的数据（包括任何可识别的个人数据）的安全？ * 将实施全面的数据安全措施，包括安全存储、受控访问和加密，以保护个人数据。数据安全实践将符合大学指导方针、立法要求以及资助机构的任何具体规定。此外，作为知情同意流程的一部分，参与者将被告知数据存储和安全安排。

9. Outline when all identifiable personal data processed during the research will be destroyed.
9. 概述研究期间处理的所有可识别个人数据何时将被销毁。

* All identifiable personal data collected during this research will be destroyed within an appropriate timeframe, specifically three years after the publication of the research findings. This timeframe ensures that data is available for follow-up studies or audits while also minimising privacy risks to participants. ________________
* 本研究期间收集的所有可识别个人数据将在适当的时间内销毁，特别是研究结果发布后三年。这个时间范围确保数据可用于后续研究或审计，同时也最大限度地减少参与者的隐私风险。 ________________

Challenges and Mitigations
挑战和缓解措施

Systematic Review 系统审查

In the systematic review, a common challenge is the incomplete detailing of interventions within the articles, particularly specifics regarding BCTs, which complicates accurate classification and analysis. To address this, we plan to establish a standardised protocol to request additional intervention details directly from the study authors and broaden the inclusion criteria to accommodate studies with general descriptions of interventions. Additionally, the variability in study quality can significantly influence the analysis. To mitigate this, we will employ rigorous assessment tools like the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale to evaluate study quality, supplemented by conducting sensitivity analyses to determine the impact of lower-quality studies on the overall findings.
在系统评价中，一个常见的挑战是文章中干预措施的细节不完整，特别是有关 BCT 的细节，这使得准确的分类和分析变得复杂。为了解决这个问题，我们计划建立一个标准化协议，直接向研究作者请求额外的干预细节，并扩大纳入标准，以适应具有干预措施一般描述的研究。此外，研究质量的变异性也会显着影响分析。为了缓解这一问题，我们将采用严格的评估工具，如 Cochrane 偏差风险工具和纽卡斯尔-渥太华量表来评估研究质量，并辅之以进行敏感性分析，以确定较低质量的研究对总体结果的影响。

User Interviews 用户访谈

For the user interviews, recruiting enough participants who are willing to engage over time poses a significant challenge, risking insufficient data collection. We intend to utilise diverse recruitment channels such as social media and community outreach, enhanced by offering incentives to boost participation and retention, like providing sleep knowledge or sleep app recommendations. Another issue is the potential for data inconsistency due to the subjective nature of self-reported data. To counteract this, we will implement a semi-structured interview format, guided by a set of standardised open-ended questions, and engage multiple reviewers to ensure consistency in data interpretation and analysis.
对于用户访谈来说，招募足够多愿意长期参与的参与者是一项重大挑战，存在数据收集不足的风险。我们打算利用社交媒体和社区外展等多种招聘渠道，并通过提供激励措施来提高参与度和保留率，例如提供睡眠知识或睡眠应用程序推荐。另一个问题是，由于自我报告数据的主观性，可能会出现数据不一致的情况。为了解决这个问题，我们将实施半结构化访谈形式，以一组标准化的开放式问题为指导，并聘请多名审稿人以确保数据解释和分析的一致性。

Thematic Analysis 专题分析

Thematic analysis presents its own set of challenges, particularly in maintaining the integrity of data interpretation to ensure that the emerging themes accurately reflect participant views without researcher bias. To combat these issues, the analysis will be conducted in iterative phases to refine and validate themes continually. We will utilise qualitative data analysis software like NVivo to manage the large volumes of data effectively and ensure comprehensive coverage of all relevant themes. Furthermore, multiple coders will be involved in the initial coding to enhance the reliability of theme identification, and any disagreements will be resolved through discussion or consultation with a third researcher, ensuring a robust and unbiased analytical process.
主题分析提出了自己的一系列挑战，特别是在保持数据解释的完整性方面，以确保新出现的主题准确反映参与者的观点而没有研究人员的偏见。为了解决这些问题，分析将在迭代阶段进行，以不断完善和验证主题。我们将利用NVivo等定性数据分析软件来有效管理大量数据，并确保全面覆盖所有相关主题。此外，多个编码人员将参与初始编码，以提高主题识别的可靠性，任何分歧都将通过讨论或与第三位研究人员协商解决，确保分析过程稳健且公正。

Participatory Design 参与式设计

In the participatory design phase, participant dropout due to loss of interest or the demanding nature of active involvement is a significant risk. To keep participants engaged and aligned with the study goals, we will ensure the workshop content is engaging and directly relevant to their needs, supported by regular updates on the impact of their contributions. Clear communication of roles and expectations at the outset and regular checks for alignment will also help manage expectations and prevent conflicts. Additionally, the potential for data overload from qualitative feedback will be managed using data analysis software (like NVivo, MAXQDA, Atlas.ti, Dedoose, QDA Miner, HyperRESEARCH, Transana etc.) , allowing for efficient organisation and iterative analysis to prevent key insights from being overlooked.[az] ________________
在参与式设计阶段，参与者因失去兴趣或积极参与的要求而退出是一个重大风险。为了让参与者参与并与研究目标保持一致，我们将确保研讨会内容具有吸引力并与他们的需求直接相关，并定期更新他们的贡献的影响。从一开始就明确沟通角色和期望并定期检查一致性也将有助于管理期望并防止冲突。此外，将使用数据分析软件（如 NVivo、MAXQDA、Atlas.ti、Dedoose、QDA Miner、HyperRESEARCH、Transana 等）来管理定性反馈带来的数据过载的可能性，从而实现高效的组织和迭代分析，以防止关键洞察的出现免遭忽视。[az] ________________

TNA

Training Needs Analysis (TNA)
培训需求分析（TNA）

This TNA aims to enable you to up to 100 hours a year of training and development in transferable skills to enhance your employability. However, it is not a requirement to use all 100 hours. This is a living document and you are expected to continue to complete this throughout your time as a PGR student. The comments below each skill are just an example of the type of question you might ask yourself when reflecting on this area of development. Note: You do not have to undertake development in all eight competencies in each year of your programme. For instance, you could decide to prioritise two or three competencies per year. This means you may not need to complete every box on the TNA form in a single sitting. Please refer to the “TNA Tips” within the Training & Development Mapping document. Name: Danyang Wang Registration Number: 220265036 Department: Education Primary Supervisor: Anna R Weighall Second Supervisor: David Cameron Additional Supervisor (s): Lauren A Powell Start Date: 01-December-2022 Time Limit: 01-December-2026 Thesis Title: Exploring potential enhancements of adherence on sleep wearable interventions from User experience aspect
该 TNA 旨在让您每年接受最多 100 个小时的可转移技能培训和发展，以提高您的就业能力。然而，并不要求使用全部 100 小时。这是一份动态文件，您需要在作为 PGR 学生期间继续完成此文件。每项技能下方的评论只是您在反思该发展领域时可能会问自己的问题类型的示例。注意：您不必在课程的每一年都进行所有八项能力的发展。例如，您可以决定每年优先考虑两到三种能力。这意味着您可能不需要一次性填写 TNA 表格上的每个方框。请参阅培训和发展规划文件中的“TNA 提示”。姓名：王丹阳 注册号：220265036 部门：教育 主要导师：Anna R Weighall 第二导师：David Cameron 其他导师：Lauren A Powell 开始日期：2022 年 12 月 1 日 时限：2026 年 12 月 1 日 论文标题：从用户体验方面探索睡眠可穿戴干预措施依从性的潜在增强

1. Personal Skills Time management How do you capture and prioritise your work tasks?
1. 个人技能 时间管理 您如何把握工作任务并确定其优先顺序？

Perseverance Do you persevere in the face of obstacles and set-backs?
毅力 面对障碍和挫折，你是否能坚持下去？

Problem-solving Can you effectively analyse and interpret research results?
解决问题 你能有效地分析和解释研究结果吗？

Critical thinking How do you evaluate the quality of your work and the work of others?
批判性思维 您如何评价自己和他人的工作质量？

Reflect on your current ability/experience in these areas
反思您当前在这些领域的能力/经验

* Weak implementation of the plan * New ideas always interfere with existing plans
* 计划实施不力 * 新想法总是干扰现有计划

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

* Organise a PGR co-learning group; * Setting up of office monthly self-attendance; * Set up a supervision meeting and email plan and report progress on a regular basis What (if anything) will you do to further develop in these areas?
* 组织PGR共同学习小组； * 设立办公室每月自助考勤； * 设立监督会议和电子邮件计划并定期报告进展情况，您将采取什么措施（如果有的话）来进一步发展这些领域？

Working on effective time management strategies * I will continue to use to-do lists, calendars, and project management software to help me stay organised. * I will ask for feedback from others on my time management skills. * Practice working habits for everyday.
制定有效的时间管理策略 * 我将继续使用待办事项列表、日历和项目管理软件来帮助我保持井井有条。 * 我会询问其他人对我的时间管理技能的反馈。 * 练习每天的工作习惯。

2. Communication, networking and collaboration Communication Media and Methods Do you use a variety of communication means including face-to-face, online and media?
2. 沟通、网络和协作 沟通媒体和方法 您是否使用多种沟通方式，包括面对面、在线和媒体？

Networking Have you engaged individuals at conferences to make new working relationships outside of your institution?
网络 您是否在会议上与个人进行交流，以在机构之外建立新的工作关系？

Collaboration Are you aware of your own behaviour and how it impacts on others in the team? Reflect on your current ability/experience in these areas (using examples if you wish):
协作 您是否了解自己的行为以及它对团队中其他人的影响？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* Participation in PGR events, British Sleep Society conferences, Royal Society of Medicine wearable related event, research group and willingness to discuss with potential research partners; * Help research group organise away day events; * Help organise the Sleep Network among University of Sheffield
* 参加PGR活动、英国睡眠协会会议、英国皇家医学会可穿戴相关活动、研究小组并愿意与潜在研究合作伙伴讨论； * 帮助研究组组织外出活动； * 帮助组织谢菲尔德大学睡眠网络

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

* Very well done * Improvement of academic competence and gaining the trust of collaborators What (if anything) will you do to further develop in these areas?
* 做得很好 * 提高学术能力并获得合作者的信任，您将采取什么措施（如果有的话）来进一步发展这些领域？

* Maintain being in students’ seminars and group discussions with fellow PGRs. * Work on creating strong relationships with researchers with shared interests. * Dedicate time to using professional social media platforms such as LinkedIn to connect with researchers with similar interests. * To familiarise myself with key players in participatory methods and approach to see if I can arrange discussions with them about my work to help ensure methodological robustness and accessibility. colleagues may include and not be limited to Dr Claire Craig and Dr Joe Langley (Sheffield Hallam University).
* 继续参加学生的研讨会以及与 PGR 同事的小组讨论。 * 致力于与具有共同兴趣的研究人员建立牢固的关系。 * 花时间使用 LinkedIn 等专业社交媒体平台与具有相似兴趣的研究人员建立联系。 * 熟悉参与方法和方法的关键参与者，看看我是否可以安排与他们讨论我的工作，以帮助确保方法的稳健性和可访问性。同事可能包括但不限于克莱尔·克雷格博士和乔·兰利博士（谢菲尔德哈勒姆大学）。

3. Professional Skills Argument construction Are you clear about your research question?
3.专业技能论证构建您清楚您的研究问题吗？

Publication Do you know how to prepare research for publication?
发表 您知道如何准备发表研究成果吗？

Project planning and delivery Do you make the decision on what to do next in your research?
项目规划和交付 您是否决定下一步研究做什么？

Teaching Can you effectively teach/supervise undergraduate students? Reflect on your current ability/experience in these areas (using examples if you wish):
教学 你能有效地教学/监督本科生吗？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* Conference posters on psychology area * ChatGPT & trust study with 2 PGR and 2 lecturer * Psychology and Education MA dissertation supervision * GTA in Data visualisation, Communicating Data and Dissertation
* 心理学领域的会议海报 * ChatGPT 和 2 位 PGR 和 2 位讲师的信任研究 * 心理学和教育硕士论文指导 * 数据可视化、数据通信和论文领域的 GTA

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

* Attended user-centred design course * Read and learn more about the double diamond framework and participatory research methods. * Collaborating with CMU researchers for a LLM prompting engineering for K12 education systematic literature review
* 参加以用户为中心的设计课程 * 阅读并了解有关双钻石框架和参与式研究方法的更多信息。 * 与 CMU 研究人员合作进行 K12 教育系统文献综述LLM

What (if anything) will you do to further develop in these areas?
为了在这些领域进一步发展，你会做什么（如果有的话）？

* Attend Education@Sheffield lecture on co-design examples with Dr Lauren Powell * Attend CBT-I training
* 与 Lauren Powell 博士一起参加 Education@Sheffield 关于协同设计示例的讲座 * 参加 CBT-I 培训

4. Leadership Influence and leadership Can you recognise the implications of your own research for real life contexts?
4. 领导力 影响力和领导力 你能认识到你自己的研究对现实生活环境的影响吗？

Reputation and esteem/profile How might you develop your online research profile?
声誉和尊重/形象 您如何发展您的在线研究形象？

Research Funding Are you aware of how your research aligns with the focus of your department, institution or funding body? Reflect on your current ability/experience in these areas (using examples if you wish):
研究资助 您是否知道您的研究如何与您所在部门、机构或资助机构的重点相一致？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* Organised MSc dissertation intervention group design and collecting data * Have work experience as group leader in architecture design tool
* 组织硕士论文干预小组设计和收集数据 * 具有架构设计工具组长的工作经验

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

* Coordinate the division of labour using timetables and Gantt charts What (if anything) will you do to further develop in these areas?
* 使用时间表和甘特图协调分工 您将采取哪些措施（如果有的话）来进一步发展这些领域？

* Learn more about funding that fits the field of study
* 了解有关适合研究领域的资助的更多信息

5. Ownership and understanding of the scope for career development options Career Management Are you aware of career pathways within and outside of academia? Can you communicate convincingly about your research specific and transferable skills? How can you improve your employability? Reflect on your current ability/experience in these areas (using examples if you wish):
5. 对职业发展选择范围的所有权和理解 职业管理 您是否了解学术界内外的职业道路？您能令人信服地传达您的研究特定技能和可转移技能吗？如何提高你的就业能力？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* One year full time research assistant
* 一年全职研究助理

Reflection of ongoing development during your PGR studies: What (if anything) will you do to further develop in these areas?
反思 PGR 研究期间的持续发展：您将采取哪些措施（如果有的话）来进一步发展这些领域？

* Learn and meeting the HEA qualification * Finding research groups that fit into the sleep field and fostering cooperation
* 学习并达到 HEA 资格 * 寻找适合睡眠领域的研究小组并促进合作

6. Understanding the importance of impact and translation (if you are not engaged in this area, reflect on its importance instead) Impact Do you have an awareness of the impact of your research on wider society (culture, environment policy)?
6. 理解影响力和转化的重要性（如果你不从事这一领域，则反思其重要性） 影响力 你是否意识到你的研究对更广泛的社会（文化、环境政策）的影响？

Public engagement /Outreach Do you have experience in communicating your research to a non-specialist audience?
公众参与/外展 您是否有向非专业受众传达您的研究成果的经验？

Policy How could your research impact on policy?
政策 您的研究对政策有何影响？

Enterprise and IP Are you aware of the value to academia of establishing relationships in business/commercial context? Reflect on your current ability/experience in these areas (using examples if you wish):
企业和知识产权 您是否意识到在商业/商业环境中建立关系对学术界的价值？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

What (if anything) will you do to further develop in these areas?
为了在这些领域进一步发展，你会做什么（如果有的话）？

7. Responsible research and innovation Ethics Are you aware of ethical issues and/or good research practices relevant to your field?
7. 负责任的研究和创新道德规范您是否了解与您的领域相关的道德问题和/或良好的研究实践？

Data Management Do you have a data management plan that you have shared with your supervisor?
数据管理 您是否有与主管共享的数据管理计划？

Sustainability Do you ensure your research does not have a negative effect on the environment and society?
可持续性 您是否确保您的研究不会对环境和社会产生负面影响？

Reflect on your current ability/experience in these areas (using examples if you wish):
反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* Familiarise myself with research ethics including the SoE student ethics google site. * Learned more about data management and data management plans.
* 熟悉研究伦理，包括 SoE 学生伦理谷歌网站。 * 了解有关数据管理和数据管理计划的更多信息。

Reflection of ongoing development during your PGR studies:
对 PGR 研究期间持续发展的反思：

* Developed skills in endnote and Rayyan * developed literature searching skills * learned how to conduct systematic review
* 培养 Endnote 和 Rayyan 技能 * 培养文献检索技能 * 学会如何进行系统评价

What (if anything) will you do to further develop in these areas?
为了在这些领域进一步发展，你会做什么（如果有的话）？

* Practice defence  * 练习防守

8. Qualitative skills and/or quantitative and digital skills, depending on discipline
8. 定性技能和/或定量和数字技能，取决于学科

Do you know what research approaches/ techniques are generally used in your field and which are relevant to you?
您知道您的领域通常使用哪些研究方法/技术以及哪些与您相关？

Can you use relevant IT packages for effective analysis? Reflect on your current ability/experience in these areas (using examples if you wish):
您能使用相关IT包进行有效分析吗？反思您当前在这些领域的能力/经验（如果您愿意，可以使用示例）：

* Have experience on quantitative research; Can independently complete the experimental design, data collection and data processing
* 有定量研究经验；能够独立完成实验设计、数据采集和数据处理

Reflection of ongoing development during your PGR studies: What (if anything) will you do to further develop in these areas? * Attend as possible of the PhD students’ seminars and group discussions to exchange information and understand more about different methods used by my peers. * To attend MSc session on Qualitative Research methods about ontological and epistemological approaches to qualitative research * Read books and articles on qualitative/participatory research methods. * Watch videos and webinars on qualitative/participatory research methods.
反思 PGR 研究期间的持续发展：您将采取哪些措施（如果有的话）来进一步发展这些领域？ * 尽可能参加博士生的研讨会和小组讨论，以交流信息并更多地了解同行使用的不同方法。 * 参加有关定性研究本体论和认识论方法的定性研究方法理学硕士课程 * 阅读有关定性/参与式研究方法的书籍和文章。 * 观看有关定性/参与式研究方法的视频和网络研讨会。

________________ References Aekanth, S. G., & Tillinghast, D. J. (2022). The Emergence of Wearable Technologies in Healthcare: A Systematic Review. In Automation, collaboration, and e-services (pp. 43–59). https://doi.org/10.1007/978-3-031-10788-7_3 Aji, M., Glozier, N., Bartlett, D. J., Grunstein, R., Calvo, R. A., Marshall, N. S., . . . Gordon, C. J. (2021). The Effectiveness of Digital Insomnia Treatment with Adjunctive Wearable Technology: A Pilot Randomized Controlled Trial. Behavioral Sleep Medicine, 20(5), 570–583. https://doi.org/10.1080/15402002.2021.1967157 Aktaruzzaman, M., Rivolta, M. W., Karmacharya, R., Scarabottolo, N., Pugnetti, L., Garegnani, M., . . . Sassi, R. (2017). Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification. Computers in Biology and Medicine, 89, 212–221. https://doi.org/10.1016/j.compbiomed.2017.08.006 Albakri, U., Drotos, E., & Meertens, R. M. (2021). Sleep Health Promotion Interventions and Their Effectiveness: An Umbrella Review. International Journal of Environmental Research and Public Health/International Journal of Environmental Research and Public Health, 18(11), 5533. https://doi.org/10.3390/ijerph18115533 American College of Physicians. (n.d.). Management of Chronic Insomnia Disorder in Adults: A Clinical Practice Guideline From the American College of Physicians | Annals of Internal Medicine. Retrieved from https://www.acpjournals.org/doi/10.7326/M15-2175 Amir-Behghadami, M., & Janati, A. (2020). Population, Intervention, Comparison, Outcomes and Study (PICOS) design as a framework to formulate eligibility criteria in systematic reviews. Emergency Medicine Journal, 37(6), 387. https://doi.org/10.1136/emermed-2020-209567 Anderson, K., Burford, O., & Emmerton, L. (2016). Mobile Health Apps to Facilitate Self-Care: A Qualitative Study of User Experiences. PloS One, 11(5), e0156164. https://doi.org/10.1371/journal.pone.0156164 Armitage, C. J., Conner, M., Prestwich, A., De Bruin, M., Johnston, M., Sniehotta, F. F., & Epton, T. (2020). Investigating which behaviour change techniques work for whom in which contexts delivered by what means: Proposal for an international collaboratory of Centres for Understanding Behaviour Change (CUBiC). British Journal of Health Psychology, 26(1), 1–14. https://doi.org/10.1111/bjhp.12479 Arroyo, A. C., & Zawadzki, M. J. (2022). The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review. JMIR Mhealth and Uhealth, 10(4), e33527. https://doi.org/10.2196/33527 Ashley. (2022, June 3). The difference between co-design and participatory design. Medium. Retrieved from https://uxdesign.cc Baka, A., Van Der Zweerde, T., Lancee, J., Bosmans, J. E., & Van Straten, A. (2021). Cost-effectiveness of Guided Internet-Delivered Cognitive Behavioral Therapy in Comparison with Care-as-Usual for Patients with Insomnia in General Practice. Behavioral Sleep Medicine, 20(2), 188–203. https://doi.org/10.1080/15402002.2021.1901708 Baker, F. C., & Lee, K. A. (2022). Menstrual Cycle Effects on Sleep. Sleep Medicine Clinics, 17(2), 283–294. https://doi.org/10.1016/j.jsmc.2022.02.004 Bakker, J. P., Weaver, T. E., Parthasarathy, S., & Aloia, M. S. (2019). Adherence to CPAP. Chest, 155(6), 1272–1287. https://doi.org/10.1016/j.chest.2019.01.012 Banbury, A., Pedell, S., Parkinson, L., & Byrne, L. (2021). Using the Double Diamond model to co-design a dementia caregivers telehealth peer support program. Journal of Telemedicine and Telecare, 27(10), 667–673. https://doi.org/10.1177/1357633x211048980 Barclay, N. L., & Ellis, J. G. (2013). Sleep-related attentional bias in poor versus good sleepers is independent of affective valence. Journal of Sleep Research, 22(4), 414–421. https://doi.org/10.1111/jsr.12035 Baron, K. G., Abbott, S. M., Jao, N. C., Manalo, N., & Mullen, R. (2017). Orthosomnia: Are Some Patients Taking the Quantified Self Too Far? Journal of Clinical Sleep Medicine, 13(02), 351–354. https://doi.org/10.5664/jcsm.6472 Baron, K. G., Culnan, E., Duffecy, J., Berendson, M., Mason, I. C., Lattie, E. G., & Manalo, N. (2021). How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review. Behavioral Sleep Medicine, 20(2), 173–187. https://doi.org/10.1080/15402002.2021.1898397 Baron, K. G., Duffecy, J., Berendsen, M., Mason, I. C., Lattie, E. G., & Manalo, N. (2018). Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Medicine Reviews, 40, 151–159. https://doi.org/10.1016/j.smrv.2017.12.002 Baron, K. G., Duffecy, J., Reutrakul, S., Levenson, J. C., McFarland, M., Lee, S., & Qeadan, F. (2021). Behavioral interventions to extend sleep duration: A systematic review and meta-analysis. Sleep Medicine Reviews, 60, 101532. https://doi.org/10.1016/j.smrv.2021.101532 Baum, K. T., Desai, A., Field, J., Miller, L. E., Rausch, J. R., & Beebe, D. W. (2013). Sleep restriction worsens mood and emotion regulation in adolescents. Journal of Child Psychology and Psychiatry and Allied Disciplines, 55(2), 180–190. https://doi.org/10.1111/jcpp.12125 Beattie, Z., Pantelopoulos, A., Ghoreyshi, A., Oyang, Y., Statan, A., & Heneghan, C. (2017). 0068 ESTIMATION OF SLEEP STAGES USING CARDIAC AND ACCELEROMETER DATA FROM A WRIST-WORN DEVICE. Sleep, 40(suppl_1), A26. https://doi.org/10.1093/sleepj/zsx050.067 Benefits of Co-design in Service Design Projects. (n.d.). Retrieved from https://www.ijdesign.org/index.php/IJDesign/article/view/890/346 Biduski, D., Bellei, E. A., Rodriguez, J. P. M., Zaina, L. a. M., & De Marchi, A. C. B. (2020). Assessing long-term user experience on a mobile health application through an in-app embedded conversation-based questionnaire. Computers in Human Behavior, 104, 106169. https://doi.org/10.1016/j.chb.2019.106169 Bietz, M. J., Patrick, K., & Bloss, C. S. (2019). Data Donation as a Model for Citizen Science Health Research. Citizen Science, 4(1). https://doi.org/10.5334/cstp.178 Blomkamp, E. (2018). The Promise of Co‐Design for Public Policy. Australian Journal of Public Administration, 77(4), 729–743. https://doi.org/10.1111/1467-8500.12310 Bohlen, L. C., Michie, S., De Bruin, M., Rothman, A. J., Kelly, M. P., Groarke, H., . . . Johnston, M. (2020). Do Combinations of Behavior Change Techniques That Occur Frequently in Interventions Reflect Underlying Theory? Annals of Behavioral Medicine, 54(11), 827–842. https://doi.org/10.1093/abm/kaaa078 Born, J., Rasch, B., & Gais, S. (2006). Sleep to Remember. Neuroscientist, 12(5), 410–424. https://doi.org/10.1177/1073858406292647 Born, J., & Wilhelm, I. (2011). System consolidation of memory during sleep. Psychological Research, 76(2), 192–203. https://doi.org/10.1007/s00426-011-0335-6 Bramoweth, A. D., Lederer, L. G., Youk, A. O., Germain, A., & Chinman, M. (2020). Brief Behavioral Treatment for Insomnia vs. Cognitive Behavioral Therapy for Insomnia: Results of a Randomized Noninferiority Clinical Trial Among Veterans. Behavior Therapy, 51(4), 535–547. https://doi.org/10.1016/j.beth.2020.02.002 Brandt, E., Binder, T., & Sanders, E. (2012). Tools and techniques: ways to engage telling, making and enacting. In Routledge eBooks (pp. 165–201). https://doi.org/10.4324/9780203108543-14 Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., . . . Zhang, Y. (2023, March 22). Sparks of Artificial General Intelligence: Early experiments with GPT-4. Retrieved from https://arxiv.org/abs/2303.12712 Buchholz, S., Mowbray, F., Allman, G., Verboncoeur, J., Beam, L., & Small, L. (2023). Wearable Devices and Nurses’ Health: Protocol for an Integrative Review. JMIR Research Protocols, 12, e48178. https://doi.org/10.2196/48178 Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4 Cardoso, J. M. P., De Figueiredo Coutinho, J. G., & Diniz, P. C. (2017). Embedded computing for high performance: Efficient Mapping of Computations Using Customization, Code Transformations and Compilation. Morgan Kaufmann. Carey, R., Connell, L., Johnston, M., Rothman, A. J., De Bruin, M., Kelly, M. P., & Michie, S. (2018). Behavior Change Techniques and Their Mechanisms of Action: A Synthesis of Links Described in Published Intervention Literature. Annals of Behavioral Medicine. https://doi.org/10.1093/abm/kay078 Carney, C. E., Edinger, J. D., Meyer, B., Lindman, L., & Istre, T. (2006). Daily activities and sleep quality in college students. Chronobiology International, 23(3), 623–637. https://doi.org/10.1080/07420520600650695 Caulfield, J. (2023, June 22). How to Do Thematic Analysis | Step-by-Step Guide & Examples. Retrieved from https://www.scribbr.com/methodology/thematic-analysis/ Chambers, R., Gullone, E., & Allen, N. B. (2009). Mindful emotion regulation: An integrative review. Clinical Psychology Review, 29(6), 560–572. https://doi.org/10.1016/j.cpr.2009.06.005 Choi, Y. K., Demiris, G., Lin, S., Iribarren, S. J., Landis, C. R., Thompson, H. J., . . . Ward, T. M. (2018). Smartphone Applications to Support Sleep Self-Management: Review and Evaluation. Journal of Clinical Sleep Medicine, 14(10), 1783–1790. https://doi.org/10.5664/jcsm.7396 Chow, R., Drkulec, H., Im, J., Tsai, J., Nafees, A., Kumar, S., . . . Raman, S. (2023). The Use of Wearable Devices in Oncology Patients: A Systematic Review. ˜the œOncologist, 29(4), e419–e430. https://doi.org/10.1093/oncolo/oyad305 Conklin, A., Yao, C. A., & Richardson, C. G. (2018). Chronic sleep deprivation and gender-specific risk of depression in adolescents: a prospective population-based study. BMC Public Health, 18(1). https://doi.org/10.1186/s12889-018-5656-6 Craig, P., Dieppe, P., Macintyre, S., Michie, S., Nazareth, I., & Petticrew, M. (2008). Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ, a1655. https://doi.org/10.1136/bmj.a1655 De Bruin, E. J., Van Run, C., Staaks, J., & Meijer, A. M. (2017). Effects of sleep manipulation on cognitive functioning of adolescents: A systematic review. Sleep Medicine Reviews, 32, 45–57. https://doi.org/10.1016/j.smrv.2016.02.006 De Fazio, R., Mattei, V., Al-Naami, B., De Vittorio, M., & Visconti, P. (2022). Methodologies and wearable devices to monitor biophysical parameters related to sleep dysfunctions: an overview. Micromachines, 13(8), 1335. https://doi.org/10.3390/mi13081335 De Melo, D. L. M., De Carvalho, L. B. C., Prado, L. B. F. D., & Prado, G. F. D. (2019). Biofeedback Therapies for Chronic Insomnia: A Systematic Review. Applied Psychophysiology and Biofeedback, 44(4), 259–269. https://doi.org/10.1007/s10484-019-09442-2 De Zambotti, M., Cellini, N., Goldstone, A., Colrain, I. M., & Baker, F. C. (2019). Wearable sleep technology in clinical and research settings. Medicine and Science in Sports and Exercise, 51(7), 1538–1557. https://doi.org/10.1249/mss.0000000000001947 Design Council. (n.d.). History of the Double Diamond - Design Council. Retrieved from https://www.designcouncil.org.uk/our-resources/the-double-diamond/history-of-the-double-diamond/ Dillon, M. (2021, March 2). Codesign in the Indigenous policy domain: risks and opportunities. Retrieved from https://apo.org.au/node/311233 Doerr, J. M., Klaus, K., Troxel, W., Nater, U. M., Bodenmann, G., Heinrichs, M., . . . Ditzen, B. (2022). The Effect of Intranasal Oxytocin on the Association Between Couple Interaction and Sleep: A Placebo-Controlled Study. Psychosomatic Medicine, 84(6), 727–737. https://doi.org/10.1097/psy.0000000000001091 Düking, P., Tafler, M., Wallmann-Sperlich, B., Sperlich, B., & Kleih, S. C. (2020). Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis. JMIR Mhealth and Uhealth, 8(11), e20820. https://doi.org/10.2196/20820 Duncan, M. J., Murawski, B., Short, C. E., Rebar, A. L., Schöeppe, S., Alley, S., . . . Kirwan, M. (2017). Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors. Interactive Journal of Medical Research, 6(2), e13. https://doi.org/10.2196/ijmr.6685 Dunn, K. E., Finan, P. H., Huhn, A. S., Gamaldo, C. E., Bergeria, C. L., & Strain, E. C. (2022). Wireless electroencephalography (EEG) to monitor sleep among patients being withdrawn from opioids: Evidence of feasibility and utility. Experimental and Clinical Psychopharmacology, 30(6), 1016–1023. https://doi.org/10.1037/pha0000483 Eisele, A., Schagg, D., Krämer, L. V., Bengel, J., & Göhner, W. (2019). Behaviour change techniques applied in interventions to enhance physical activity adherence in patients with chronic musculoskeletal conditions: A systematic review and meta-analysis. Patient Education and Counseling, 102(1), 25–36. https://doi.org/10.1016/j.pec.2018.09.018 Elías, M. N. (2021). Assessment and monitoring of sleep in the intensive care unit. Critical Care Nursing Clinics of North America, 33(2), 109–119. https://doi.org/10.1016/j.cnc.2021.01.008 Espie, C. A. (2009). “Stepped Care”: A Health Technology Solution for Delivering Cognitive Behavioral Therapy as a First Line Insomnia Treatment. Sleep, 32(12), 1549–1558. https://doi.org/10.1093/sleep/32.12.1549 Espie, C. A., Hames, P., & McKinstry, B. (2013). Use of the Internet and Mobile Media for Delivery of Cognitive Behavioral Insomnia Therapy. Sleep Medicine Clinics, 8(3), 407–419. https://doi.org/10.1016/j.jsmc.2013.06.001 Everson, C. A., Henchen, C. J., Szabó, A., & Hogg, N. (2014). Cell Injury and Repair Resulting from Sleep Loss and Sleep Recovery in Laboratory Rats. Sleep, 37(12), 1929–1940. https://doi.org/10.5665/sleep.4244 Exelmans, L., & Van Den Bulck, J. (2016). Bedtime mobile phone use and sleep in adults. Social Science &Amp; Medicine, 148, 93–101. https://doi.org/10.1016/j.socscimed.2015.11.037 Fard, A. (2022a, January 11). The Guide to Stakeholder Interview Questions. Retrieved from https://adamfard.com/blog/stakeholder-interviews Fard, A. (2022b, January 11). The Guide to Stakeholder Interview Questions. Retrieved from https://adamfard.com/blog/stakeholder-interviews Fatima, S., Augusto, J. C., Moseley, R., Urbonas, P., Elliott, A., & Payne, N. (2023). Applying motivational techniques for user adherence to adopt a healthy lifestyle in a gamified application. Entertainment Computing, 46, 100571. https://doi.org/10.1016/j.entcom.2023.100571 Feinsilver, S. H. (2021). Normal and abnormal sleep in the elderly. Clinics in Geriatric Medicine, 37(3), 377–386. https://doi.org/10.1016/j.cger.2021.04.001 Ferguson, T., Olds, T., Curtis, R., Blake, H., Crozier, A., Dankiw, K. A., . . . Maher, C. (2022). Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. ˜the œLancet. Digital Health, 4(8), e615–e626. https://doi.org/10.1016/s2589-7500(22)00111-x Fonseca, P., Long, X., Radha, M., Haakma, R., Aarts, R. R., & Rolink, J. J. (2015). Sleep stage classification with ECG and respiratory effort. Physiological Measurement, 36(10), 2027–2040. https://doi.org/10.1088/0967-3334/36/10/2027 Frauenberger, C., Good, J., Fitzpatrick, G., & Iversen, O. S. (2015). In pursuit of rigour and accountability in participatory design. International Journal of Human-computer Studies, 74, 93–106. https://doi.org/10.1016/j.ijhcs.2014.09.004 Freeman, D., Sheaves, B., Waite, F., Harvey, A. G., & Harrison, P. J. (2020). Sleep disturbance and psychiatric disorders. ˜the œLancet. Psychiatry, 7(7), 628–637. https://doi.org/10.1016/s2215-0366(20)30136-x Geiger-Brown, J., Trinkoff, A. M., & Rogers, V. (2011). The Impact of Work Schedules, Home, and Work Demands on Self-Reported Sleep in Registered Nurses. Journal of Occupational and Environmental Medicine, 53(3), 303–307. https://doi.org/10.1097/jom.0b013e31820c3f87 Gibbons, S. (2023, January 3). Stakeholder Interviews 101. Retrieved from https://www.nngroup.com/articles/stakeholder-interviews/ Goldstein, C., Berry, R. B., Kent, D. T., Kristo, D. A., Seixas, A., Redline, S., & Westover, M. B. (2020). Artificial intelligence in sleep medicine: background and implications for clinicians. Journal of Clinical Sleep Medicine, 16(4), 609–618. https://doi.org/10.5664/jcsm.8388 Grandner, M. (2022). Sleep, Health, and Society. Sleep Medicine Clinics, 17(2), 117–139. https://doi.org/10.1016/j.jsmc.2022.03.001 Gross, J. J. (2015). Emotion Regulation: Current Status and Future Prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840x.2014.940781 Gunn, H. E., Tutek, J., & Buysse, D. J. (2019). Brief Behavioral Treatment of Insomnia. Sleep Medicine Clinics, 14(2), 235–243. https://doi.org/10.1016/j.jsmc.2019.02.003 Hebert, E. A., Vincent, N., Lewycky, S., & Walsh, K. T. (2010). Attrition and Adherence in the Online Treatment of Chronic Insomnia. Behavioral Sleep Medicine, 8(3), 141–150. https://doi.org/10.1080/15402002.2010.487457 Henriksen, A., Mikalsen, M. H., Woldaregay, A. Z., Mužný, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018). Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. JMIR. Journal of Medical Internet Research/Journal of Medical Internet Research, 20(3), e110. https://doi.org/10.2196/jmir.9157 Herlan, A., Ottenbacher, J., Schneider, J., Riemann, D., & Feige, B. (2018). Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification. Journal of Sleep Research, 28(2). https://doi.org/10.1111/jsr.12694 Hill, C. L. M., & Updegraff, J. A. (2012). Mindfulness and its relationship to emotional regulation. Emotion, 12(1), 81–90. https://doi.org/10.1037/a0026355 Hoedlmoser, K., Peigneux, P., & Rauchs, G. (2022). Recent advances in memory consolidation and information processing during sleep. Journal of Sleep Research, 31(4). https://doi.org/10.1111/jsr.13607 Holst, H. M. L. D., Lammers, G. J., Van Der Horst, G. T. J., Chaves, I., De Vries, R. D., GeurtsvanKessel, C. H., . . . Van Der Kuy, H. (2021). Understanding the association between sleep, shift work and COVID‐19 vaccine immune response efficacy: Protocol of the S‐CORE study. Journal of Sleep Research, 31(2). https://doi.org/10.1111/jsr.13496 How to Conduct User Interviews. (2024, April 30). Retrieved from https://www.interaction-design.org/literature/article/how-to-conduct-user-interviews How To Run The Right Kind Of Research Study With The Double-Diamond Model — Smashing Magazine. (2020, May 29). Retrieved from https://www.smashingmagazine.com/2020/05/research-study-double-diamond-model/ Huang, Y., & Zhao, N. (2020). Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Research, 288, 112954. https://doi.org/10.1016/j.psychres.2020.112954 Hubbling, A., Reilly-Spong, M., Kreitzer, M. J., & Gross, C. R. (2014). How mindfulness changed my sleep: focus groups with chronic insomnia patients. BMC Complementary and Alternative Medicine, 14(1). https://doi.org/10.1186/1472-6882-14-50 Human-Computer Interaction. User Experience and Behavior | Guide Proceedings. (n.d.). Retrieved from https://dl.acm.org/doi/proceedings/10.1007/978-3-031-05412-9 Hutka, P., Krivosova, M., Muchova, Z., Tonhajzerová, I., Hamrakova, A., Mlyncekova, Z., . . . Ondrejka, I. (2021). Association of Sleep Architecture and Physiology with Depressive Disorder and Antidepressants Treatment. International Journal of Molecular Sciences, 22(3), 1333. https://doi.org/10.3390/ijms22031333 Hwang, J., Lee, T., Lee, H., & Byun, S. (2022). A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study. Journal of Medical Internet Research, 24(1), e28659. https://doi.org/10.2196/28659 Iranzo, À. (2022). An Overview on Sleep Medicine. In Advances in Experimental Medicine and Biology (pp. 3–15). https://doi.org/10.1007/978-3-031-06413-5_1 Irwin, M. R., Cole, J. C., & Nicassio, P. M. (2006). Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychology, 25(1), 3–14. https://doi.org/10.1037/0278-6133.25.1.3 Jaiswal, S. J., Pawelek, J., Warshawsky, S., Quer, G., Trieu, M., Pandit, J. A., & Owens, R. L. (2024). Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies. Current Sleep Medicine Reports, 10(1), 82–92. https://doi.org/10.1007/s40675-023-00272-7 Johns, M. W. (1991). A New Method for Measuring Daytime Sleepiness: The Epworth Sleepiness Scale. Sleep, 14(6), 540–545. https://doi.org/10.1093/sleep/14.6.540 Johns, M. W. (1992). Reliability and Factor Analysis of the Epworth Sleepiness Scale. Sleep, 15(4), 376–381. https://doi.org/10.1093/sleep/15.4.376 Johnson, J. A., Rash, J. A., Campbell, T. S., Savard, J., Gehrman, P., Perlis, M. L., . . . Garland, S. N. (2016). A systematic review and meta-analysis of randomized controlled trials of cognitive behavior therapy for insomnia (CBT-I) in cancer survivors. Sleep Medicine Reviews, 27, 20–28. https://doi.org/10.1016/j.smrv.2015.07.001 Journal of Sleep Research. (2010). Journal of Sleep Research. https://doi.org/10.1111/(issn)1365-2869 Just a moment. . . (n.d.). Retrieved September 18, 2022, from https://linkinghub.elsevier.com/retrieve/pii/S0277953615302458 Kao, C. (2019, February 10). Effective User Interviews - Product Manager HQ - Medium. Medium. Retrieved from https://medium.com Kaveladze, B., Wasil, A. R., Bunyi, J., Ramirez, V., & Schueller, S. M. (2022). User Experience, Engagement, and Popularity in Mental Health Apps: Secondary Analysis of App Analytics and Expert App Reviews. JMIR Human Factors, 9(1), e30766. https://doi.org/10.2196/30766 Khalid, H., Shihab, E., Nagappan, M., & Hassan, A. E. (2015). What do mobile app users complain about? IEEE Software, 32(3), 70–77. https://doi.org/10.1109/ms.2014.50 Kline, C. (2013). Sleep Quality. In Springer eBooks (pp. 1811–1813). https://doi.org/10.1007/978-1-4419-1005-9_849 Koffel, E., Koffel, J., & Gehrman, P. (2015). A meta-analysis of group cognitive behavioral therapy for insomnia. Sleep Medicine Reviews, 19, 6–16. https://doi.org/10.1016/j.smrv.2014.05.001 Kok, G., Gottlieb, N. H., Peters, G., Mullen, P. D., Parcel, G. S., Ruiter, R. a. C., . . . Bartholomew, L. K. (2015). A taxonomy of behaviour change methods: an Intervention Mapping approach. Health Psychology Review, 10(3), 297–312. https://doi.org/10.1080/17437199.2015.1077155 Kwon, A., Choi, Y., Kim, S., Song, K., Suh, J., Chae, H. W., & Kim, H. (2022). Characteristic Sleep Patterns and Associated Obesity in Adolescents. Life, 12(9), 1316. https://doi.org/10.3390/life12091316 Lai, M. Y. C., Mong, M. S. A., Cheng, L. J., & Lau, Y. (2023). The effect of wearable‐delivered sleep interventions on sleep outcomes among adults: A systematic review and meta‐analysis of randomized controlled trials. Nursing & Health Sciences, 25(1), 44–62. https://doi.org/10.1111/nhs.13011 Lancaster, B., Sweenie, R., Noser, A. E., Roberts, C. M., & Ramsey, R. R. (2023). Sleep mHealth Applications and Behavior Change Techniques Evaluation. Behavioral Sleep Medicine, 21(6), 757–773. https://doi.org/10.1080/15402002.2022.2161548 Lancel, M., Krömer, S., & Neumann, I. D. (2003). Intracerebral oxytocin modulates sleep–wake behaviour in male rats. Regulatory Peptides, 114(2–3), 145–152. https://doi.org/10.1016/s0167-0115(03)00118-6 Langley, J., Wolstenholme, D., & Cooke, J. (2018). ‘Collective making’ as knowledge mobilisation: the contribution of participatory design in the co-creation of knowledge in healthcare. BMC Health Services Research, 18(1). https://doi.org/10.1186/s12913-018-3397-y Lee, M. R. G., Breitstein, J., Hoyt, T., Stolee, J. D., Baxter, T., Kwon, H. P., & Mysliwiec, V. (2021). Cognitive behavioral therapy for insomnia among active duty military personnel. Psychological Services, 18(1), 42–50. https://doi.org/10.1037/ser0000340 Li, J., Vitiello, M. V., & Gooneratne, N. S. (2018). Sleep in Normal Aging. Sleep Medicine Clinics, 13(1), 1–11. https://doi.org/10.1016/j.jsmc.2017.09.001 Lim, J., Jeong, C. Y., Lim, J. M., Chung, S., Kim, G., Noh, K. J., & Jeong, H. (2022). Assessing sleep quality using Mobile EMAs: Opportunities, practical consideration, and challenges. IEEE Access, 10, 2063–2076. https://doi.org/10.1109/access.2021.3140074 Lobo, A. (2023, December 4). Conducting user interviews: A comprehensive guide for UX researchers. Medium. Retrieved from https://bootcamp.uxdesign.cc Maher, C., Ryan, J., Ambrosi, C. C., & Edney, S. (2017). Users’ experiences of wearable activity trackers: a cross-sectional study. BMC Public Health, 17(1). https://doi.org/10.1186/s12889-017-4888-1 Margherita, G., & Caffieri, A. (2022). An observatory on changes in dreaming during a pandemic: a living systematic review (part 1). Journal of Sleep Research, 32(3). https://doi.org/10.1111/jsr.13742 Marino, M., Li, Y., Rueschman, M., Winkelman, J. W., Ellenbogen, J. M., Solet, J. M., . . . Buxton, O. M. (2013). Measuring sleep: Accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep, 36(11), 1747–1755. https://doi.org/10.5665/sleep.3142 Marques, M. M., Wright, A. J., Corker, E., Johnston, M., West, R., Hastings, J., . . . Michie, S. (2023). The Behaviour Change Technique Ontology: Transforming the Behaviour Change Technique Taxonomy v1. Wellcome Open Research, 8, 308. https://doi.org/10.12688/wellcomeopenres.19363.1 Maume, D. J., Sebastian, R. A., & Bardo, A. R. (2010). Gender, Work-Family Responsibilities, and Sleep. Gender & Society, 24(6), 746–768. https://doi.org/10.1177/0891243210386949 McCarthy, E., DeViva, J. C., Southwick, S. M., & Pietrzak, R. H. (2021). Self‐rated sleep quality predicts incident suicide ideation in US military veterans: Results from a 7‐year, nationally representative, prospective cohort study. Journal of Sleep Research, 31(1). https://doi.org/10.1111/jsr.13447 Meditation and Mindfulness: What You Need To Know. (n.d.). Retrieved from https://www.nccih.nih.gov/health/meditation-and-mindfulness-what-you-need-to-know Mercer, K., Giangregorio, L., Schneider, E. C., Chilana, P. K., Li, M., & Grindrod, K. (2016). Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. JMIR Mhealth and Uhealth, 4(1), e7. https://doi.org/10.2196/mhealth.4225 Mercer, K., Li, M., Giangregorio, L., Burns, C. M., & Grindrod, K. (2016). Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis. JMIR Mhealth and Uhealth, 4(2), e40. https://doi.org/10.2196/mhealth.4461 Michaelsen, M. M., & Esch, T. (2022). Functional Mechanisms of Health Behavior Change Techniques: A Conceptual Review. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.725644 Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., & French, D. P. (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychology & Health, 26(11), 1479–1498. https://doi.org/10.1080/08870446.2010.540664 Michie, S., Fixsen, D. L., Grimshaw, J., & Eccles, M. (2009). Specifying and reporting complex behaviour change interventions: the need for a scientific method. Implementation Science, 4(1). https://doi.org/10.1186/1748-5908-4-40 Michie, S., Johnston, M., & Carey, R. (2020). Behavior Change Techniques. In Springer eBooks (pp. 206–213). https://doi.org/10.1007/978-3-030-39903-0_1661 Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J. J., Hardeman, W., . . . Wood, C. (2013). The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions. Annals of Behavioral Medicine, 46(1), 81–95. https://doi.org/10.1007/s12160-013-9486-6 Michie, S., Wood, C., Johnston, M., Abraham, C., Francis, J. J., & Hardeman, W. (2015). Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). HTA on DVD/Health Technology Assessment, 19(99), 1–188. https://doi.org/10.3310/hta19990 Miller, C. H., Espie, C. A., Epstein, D. R., Friedman, L., Morin, C. M., Pigeon, W. R., . . . Kyle, S. D. (2014). The evidence base of sleep restriction therapy for treating insomnia disorder. Sleep Medicine Reviews, 18(5), 415–424. https://doi.org/10.1016/j.smrv.2014.01.006 Miller, M. M., Renn, B. N., Chu, F., & Torrence, N. D. (2019). Sleepless in the hospital: A systematic review of non-pharmacological sleep interventions. General Hospital Psychiatry, 59, 58–66. https://doi.org/10.1016/j.genhosppsych.2019.05.006 Mitchell, M. D., Gehrman, P., Perlis, M. L., & Umscheid, C. A. (2012). Comparative effectiveness of cognitive behavioral therapy for insomnia: a systematic review. BMC Family Practice, 13(1). https://doi.org/10.1186/1471-2296-13-40 Mohammadi, S., Enshaeifar, S., Hilton, A., Dijk, D., & Wells, K. (2021). Transfer learning for clinical sleep pose detection using a single 2D IR camera. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, 290–299. https://doi.org/10.1109/tnsre.2020.3048121 Montgomery, P., & Dennis, J. A. (2003). Cognitive behavioural interventions for sleep problems in adults aged 60+. The Cochrane Library. https://doi.org/10.1002/14651858.cd003161 Morcillo, L. F., Morer-Camo, P., Ferradas, M. I. R., & Cazón, A. (2020). The Wearable Co-Design Domino: A User-Centered Methodology to Co-Design and Co-Evaluate Wearables. Sensors, 20(10), 2934. https://doi.org/10.3390/s20102934 Morin, C. M., Culbert, J. P., & Schwartz, S. M. (1994). Nonpharmacological interventions for insomnia: a meta-analysis of treatment efficacy. American Journal of Psychiatry, 151(8), 1172–1180. https://doi.org/10.1176/ajp.151.8.1172 Morrissey, E., Corbett, T., Walsh, J., & Molloy, G. J. (2016). Behavior Change Techniques in Apps for Medication Adherence. American Journal of Preventive Medicine, 50(5), e143–e146. https://doi.org/10.1016/j.amepre.2015.09.034 Morrone, E., Giordano, A., Carli, S., Visca, D., Rossato, F., Godio, M., . . . Braghiroli, A. (2019). Something is changing in adherence to CPAP therapy: real world data after 1 year of treatment in patients with obstructive sleep apnoea. European Respiratory Journal/˜the œEuropean Respiratory Journal, 55(3), 1901419. https://doi.org/10.1183/13993003.01419-2019 Mortensen, D. H. (2024, April 26). How to Do a Thematic Analysis of User Interviews. Retrieved from https://www.interaction-design.org/literature/article/how-to-do-a-thematic-analysis-of-user-interviews Moul, D. E., Hall, M., Pilkonis, P. A., & Buysse, D. J. (2004). Self-report measures of insomnia in adults: rationales, choices, and needs. Sleep Medicine Reviews, 8(3), 177–198. https://doi.org/10.1016/s1087-0792(03)00060-1 Muench, A., Vargas, I., Grandner, M., Ellis, J., Posner, D., Bastien, C., . . . Perlis, M. L. (2022). We know CBT-I works, now what? Faculty Reviews, 11. https://doi.org/10.12703/r/11-4 Murtagh, D., & Greenwood, K. M. (1995). Identifying effective psychological treatments for insomnia: A meta-analysis. Journal of Consulting and Clinical Psychology, 63(1), 79–89. https://doi.org/10.1037/0022-006x.63.1.79 Naeem, M., Ozuem, W., Howell, K. E., & Ranfagni, S. (2023). A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231205789 Ng, M. M., Firth, J., Minen, M., & Torous, J. (2019). User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity. Psychiatric Services, 70(7), 538–544. https://doi.org/10.1176/appi.ps.201800519 NICE. (2014, January 2). Overview | Behaviour change: individual approaches | Guidance | NICE. Retrieved from https://www.nice.org.uk/guidance/ph49 Ohayon, M. M. (1999). Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders. Journal of Psychosomatic Research, 47(4), 297–311. https://doi.org/10.1016/s0022-3999(99)00010-0 Okajima, I., Komada, Y., & Inoue, Y. (2010). A meta-analysis on the treatment effectiveness of cognitive behavioral therapy for primary insomnia. Sleep and Biological Rhythms, 9(1), 24–34. https://doi.org/10.1111/j.1479-8425.2010.00481.x Ong, J. C., Manber, R., Segal, Z., Xia, Y., Shapiro, S., & Wyatt, J. K. (2014). A Randomized Controlled Trial of Mindfulness Meditation for Chronic Insomnia. Sleep, 37(9), 1553–1563. https://doi.org/10.5665/sleep.4010 Ong, J. C., Shapiro, S. L., & Manber, R. (2008). Combining Mindfulness Meditation with Cognitive-Behavior Therapy for Insomnia: A Treatment-Development Study. Behavior Therapy, 39(2), 171–182. https://doi.org/10.1016/j.beth.2007.07.002 Panahi, S., Rathi, N., Hurley, J., Sundrud, J., Lucero, M. T., & Kamimura, A. (2022). Patient Adherence to Health Care Provider Recommendations and Medication among Free Clinic Patients. Journal of Patient Experience, 9, 237437352210775. https://doi.org/10.1177/23743735221077523 Pataka, A., Kotoulas, S., Gavrilis, P. R., Karkala, A., Tzinas, A., & Stefanidou, A. (2023). Adherence to CPAP Treatment: Can Mindfulness Play a Role? Life, 13(2), 296. https://doi.org/10.3390/life13020296 Patel, A. K., Reddy, V., Shumway, K. R., & Araujo, J. F. (2024, January 26). Physiology, Sleep Stages. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK526132/ Pérez‐Jover, V., Sala-González, M., Guilabert, M., & Mira, J. J. (2019). Mobile Apps for Increasing Treatment Adherence: Systematic Review. JMIR. Journal of Medical Internet Research/Journal of Medical Internet Research, 21(6), e12505. https://doi.org/10.2196/12505 Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., García‐Gómez, J. M., . . . Fernández-Luque, L. (2020). The future of sleep health: a data-driven revolution in sleep science and medicine. Npj Digital Medicine, 3(1). https://doi.org/10.1038/s41746-020-0244-4 Poon, K., Ho, M. S. H., Tai, A. P., Leung, M. K. H., Chan, M., & Lau, W. K. (2024). Mediating roles of sleep quality and resilience in the relationships between chronotypes and mental health symptoms. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-56688-w Radomski, A., Bagnell, A., Curtis, S., Hartling, L., & Newton, A. S. (2020). Examining the Usage, User Experience, and Perceived Impact of an Internet-Based Cognitive Behavioral Therapy Program for Adolescents With Anxiety: Randomized Controlled Trial. JMIR Mental Health, 7(2), e15795. https://doi.org/10.2196/15795 Rahman, M., Nowakowski, S., Agrawal, R., Naik, A. D., Sharafkhaneh, A., & Razjouyan, J. (2022). Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports. Healthcare, 10(10), 1837. https://doi.org/10.3390/healthcare10101837 Randles, R., & Paul, A. (2023). Guidelines for writing a systematic review. Nurse Education Today, 125, 105803. https://doi.org/10.1016/j.nedt.2023.105803 Relaxation Techniques for Health. (n.d.). Retrieved from https://www.nccih.nih.gov/health/relaxation-techniques-what-you-need-to-know Rundo, J. V., & Downey, R. (2019). Polysomnography. In Handbook of Clinical Neurology (pp. 381–392). https://doi.org/10.1016/b978-0-444-64032-1.00025-4 Rusch, H. L., Rosario, M., Levison, L. M., Olivera, A., Livingston, W. S., Wu, T., & Gill, J. M. (2018). The effect of mindfulness meditation on sleep quality: a systematic review and meta‐analysis of randomized controlled trials. Annals of the New York Academy of Sciences, 1445(1), 5–16. https://doi.org/10.1111/nyas.13996 Ryan, C., Bergin, M., & Wells, J. (2017). Theoretical Perspectives of Adherence to Web-Based Interventions: a Scoping Review. International Journal of Behavioral Medicine, 25(1), 17–29. https://doi.org/10.1007/s12529-017-9678-8 Salari, N., Khazaie, H., Hosseinian-Far, A., Khaledi-Paveh, B., Ghasemi, H., Mohammadi, M., & Shohaimi, S. (2020). The effect of acceptance and commitment therapy on insomnia and sleep quality: A systematic review. BMC Neurology, 20(1). https://doi.org/10.1186/s12883-020-01883-1 Sanders, E., Brandt, E., & Binder, T. (2010). A framework for organizing the tools and techniques of participatory design. PDC ’10: Proceedings of the 11th Biennial Participatory Design Conference. https://doi.org/10.1145/1900441.1900476 Sanders, E., & Stappers, P. (2013). Convivial Toolbox: generative research for the front end of design. Schwartz, J., & Roth, T. (2008). Neurophysiology of Sleep and Wakefulness: Basic science and clinical implications. Current Neuropharmacology, 6(4), 367–378. https://doi.org/10.2174/157015908787386050 Scott, A., Webb, T. L., James, M. M., Rowse, G., & Weich, S. (2021). Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Medicine Reviews, 60, 101556. https://doi.org/10.1016/j.smrv.2021.101556 Short, M. A., Booth, S. A., Omar, O., Östlundh, L., & Arora, T. (2020). The relationship between sleep duration and mood in adolescents: A systematic review and meta-analysis. Sleep Medicine Reviews, 52, 101311. https://doi.org/10.1016/j.smrv.2020.101311 Sleep Foundation. (2024, March 27). Cognitive Behavioral Therapy for Insomnia (CBT-I): An Overview. Retrieved from https://www.sleepfoundation.org/insomnia/treatment/cognitive-behavioral-therapy-insomnia Smith, A. M., Kumar, V., Boyd-Graber, J., Seppi, K., & Findlater, L. (2018). Closing the Loop. IUI ’18: 23rd International Conference on Intelligent User Interfaces. https://doi.org/10.1145/3172944.3172965 Smith, C. (1995). Sleep states and memory processes. Behavioural Brain Research, 69(1–2), 137–145. https://doi.org/10.1016/0166-4328(95)00024-n Smith, M. T., McCrae, C. S., Cheung, J., Martin, J. L., Harrod, C. G., Heald, J. L., & Carden, K. A. (2018). Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Systematic Review, Meta-Analysis, and GRADE Assessment. Journal of Clinical Sleep Medicine, 14(07), 1209–1230. https://doi.org/10.5664/jcsm.7228 Smith, M. T., Perlis, M. L., Park, A., Smith, M. S., Pennington, J. M., Giles, D. E., & Buysse, D. J. (2002). Comparative Meta-Analysis of Pharmacotherapy and Behavior Therapy for Persistent Insomnia. American Journal of Psychiatry, 159(1), 5–11. https://doi.org/10.1176/appi.ajp.159.1.5 Spielman, A. J., Caruso, L. S., & Glovinsky, P. B. (1987). A Behavioral Perspective on Insomnia Treatment. Psychiatric Clinics of North America/˜the œPsychiatric Clinics of North America, 10(4), 541–553. https://doi.org/10.1016/s0193-953x(18)30532-x Stade, E. C., Stirman, S. W., Ungar, L. H., Boland, C. L., Schwartz, H. A., Yaden, D. B., . . . Eichstaedt, J. C. (2024). Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. Npj Mental Health Research, 3(1). https://doi.org/10.1038/s44184-024-00056-z Steinmetz, L., Simon, L., Feige, B., Riemann, D., Akram, U., Crawford, M. R., . . . Spiegelhalder, K. (2023). Adherence to sleep restriction therapy – An evaluation of existing measures. Journal of Sleep Research, 32(6). https://doi.org/10.1111/jsr.13975 Su, T., Calvo, R. A., Jouaiti, M. A., Daniels, S., Kirby, P., Dijk, D., . . . Vaidyanathan, R. (2023). Assessing a Sleep Interviewing Chatbot to Improve Subjective and Objective Sleep: Protocol for an Observational Feasibility Study. JMIR Research Protocols, 12, e45752. https://doi.org/10.2196/45752 Suni, E., & Suni, E. (2023, December 8). How To Determine Poor Sleep Quality. Retrieved from https://www.sleepfoundation.org/sleep-hygiene/how-to-determine-poor-quality-sleep Suresh, K., Raavi, V., Kumar, H., & Shankar, V. (2022). Effect of chronic sleep deprivation and sleep recovery on hippocampal CA3 neurons, spatial memory and anxiety-like behavior in rats. Neurobiology of Learning and Memory, 187, 107559. https://doi.org/10.1016/j.nlm.2021.107559 Talbot, L. S., Maguen, S., Metzler, T. J., Schmitz, M., McCaslin, S. E., Richards, A., . . . Neylan, T. C. (2014). Cognitive Behavioral Therapy for Insomnia in Posttraumatic Stress Disorder: A Randomized Controlled Trial. Sleep, 37(2), 327–341. https://doi.org/10.5665/sleep.3408 Taylor, D. J., Peterson, A. L., Pruiksma, K. E., Young‐McCaughan, S., Nicholson, K. L., & Mintz, J. (2017). Internet and In-Person Cognitive Behavioral Therapy for Insomnia in Military Personnel: A Randomized Clinical Trial. Sleep, 40(6). https://doi.org/10.1093/sleep/zsx075 Thakral, M., Von Korff, M., McCurry, S. M., Morin, C. M., & Vitiello, M. V. (2020). Changes in dysfunctional beliefs about sleep after cognitive behavioral therapy for insomnia: A systematic literature review and meta-analysis. Sleep Medicine Reviews, 49, 101230. https://doi.org/10.1016/j.smrv.2019.101230 Thensf. (2024, April 24). What Is Sleep Quality? - National Sleep Foundation. Retrieved from https://www.thensf.org/what-is-sleep-quality/ Thoma, M. V., La Marca, R., Brönnimann, R., Finkel, L., Ehlert, U., & Nater, U. M. (2013). The Effect of Music on the Human Stress Response. PLoS ONE, 8(8), e70156. https://doi.org/10.1371/journal.pone.0070156 Tracy, E. L., Reid, K. J., & Baron, K. G. (2021). The relationship between sleep and physical activity: the moderating role of daily alcohol consumption. Sleep, 44(10). https://doi.org/10.1093/sleep/zsab112 Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera. (2021). Retrieved from https://ieeexplore.ieee.org/abstract/document/9311169 Trauer, J. M., Qian, M., Doyle, J. S., Rajaratnam, S. M., & Cunnington, D. (2015). Cognitive Behavioral Therapy for Chronic Insomnia. Annals of Internal Medicine, 163(3), 191–204. https://doi.org/10.7326/m14-2841 Troxel, W., Germain, A., & Buysse, D. J. (2012). Clinical Management of Insomnia with Brief Behavioral Treatment (BBTI). Behavioral Sleep Medicine, 10(4), 266–279. https://doi.org/10.1080/15402002.2011.607200 User Interviews for UX Research: What, Why & How. (n.d.). Retrieved from https://www.userinterviews.com/ux-research-field-guide-chapter/user-interviews User interviews: The complete guide for 2024 | Great Question. (n.d.). Retrieved from https://greatquestion.co/blog/user-interviews-guide UserInput. (2023, June 6). Complete Guide to User Interviews: Definition, Benefits, and How to Conduct. Retrieved from https://userinput.io/customer-research/user-interviews/ Uyumaz, B. E., Feijs, L., & Hu, J. (2021). A Review of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I Apps): Are They Designed for Engagement? International Journal of Environmental Research and Public Health, 18(6), 2929. https://doi.org/10.3390/ijerph18062929 Van Gemert-Pijnen, J. E., Nijland, N., Van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. (2011). A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies. JMIR. Journal of Medical Internet Research/Journal of Medical Internet Research, 13(4), e111. https://doi.org/10.2196/jmir.1672 Van Straten, A., Van Der Zweerde, T., Kleiboer, A., Cuijpers, P., Morin, C. M., & Lancee, J. (2018). Cognitive and behavioral therapies in the treatment of insomnia: A meta-analysis. Sleep Medicine Reviews, 38, 3–16. https://doi.org/10.1016/j.smrv.2017.02.001 Vandekerckhove, M., Weiss, R., Schotte, C., Exadaktylos, V., Haex, B., Verbraecken, J., & Cluydts, R. (2011). The role of presleep negative emotion in sleep physiology. Psychophysiology, 48(12), 1738–1744. https://doi.org/10.1111/j.1469-8986.2011.01281.x Vandekerckhove, P., De Mul, M., Bramer, W. M., & De Bont, A. (2020). Generative Participatory Design Methodology to Develop Electronic Health Interventions: Systematic Literature Review. JMIR. Journal of Medical Internet Research/Journal of Medical Internet Research, 22(4), e13780. https://doi.org/10.2196/13780 Vincent, N., & Hameed, H. (2003). Relation Between Adherence and Outcome in the Group Treatment of Insomnia. Behavioral Sleep Medicine, 1(3), 125–139. https://doi.org/10.1207/s15402010bsm0103_1 Walker, J., Muench, A., Perlis, M. L., & Vargas, I. (2022). Cognitive Behavioral Therapy for Insomnia (CBT-I): A Primer. Kliničeskaâ I Specialʹnaâ Psihologiâ, 11(2), 123–137. https://doi.org/10.17759/cpse.2022110208 Walter, J., Lee, J. Y., Snoll, B., Park, J. B., Kim, D. H., Xu, S., & Barnhart, K. T. (2022). Pregnancy outcomes in infertility patients diagnosed with sleep disordered breathing with wireless wearable sensors. Sleep Medicine, 100, 511–517. https://doi.org/10.1016/j.sleep.2022.09.026 What do mobile app users complain about? (2015, June 1). Retrieved from https://ieeexplore.ieee.org/document/6762802 Wickwire, E. M., Shaya, F. T., & Scharf, S. M. (2016). Health economics of insomnia treatments: The return on investment for a good night’s sleep. Sleep Medicine Reviews, 30, 72–82. https://doi.org/10.1016/j.smrv.2015.11.004 Wickwire, E. M., Tom, S. E., Scharf, S. M., Vadlamani, A., Bulatao, I. G., & Albrecht, J. S. (2019). Untreated insomnia increases all-cause health care utilization and costs among Medicare beneficiaries. Sleep, 42(4). https://doi.org/10.1093/sleep/zsz007 Wideman, T. H., Sullivan, M. J., Inada, S., McIntyre, D., Kumagai, M., Yahagi, N., . . . Woltz, P. (2013). Behavior Change Techniques. In Springer eBooks (pp. 182–187). https://doi.org/10.1007/978-1-4419-1005-9_1661 Wisner, W. (2023, November 30). What Is Cognitive-Behavioral Therapy for Insomnia (CBT-i)? Retrieved from https://www.verywellmind.com/cognitive-behavioral-therapy-for-insomnia-cbti-definition-techniques-efficacy-7094606 Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022). A survey of human-in-the-loop for machine learning. Future Generation Computer Systems, 135, 364–381. https://doi.org/10.1016/j.future.2022.05.014 Wu, Y., Chen, C., Han, F., & Chen, W. (2022). [Research progress on the application of novel sensing technologies for sleep-related breathing disorder monitoring at home]. PubMed, 39(4), 798–805. https://doi.org/10.7507/1001-5515.202112066 Yamazaki, E., Antler, C., Lasek, C. R., & Goel, N. (2020). Residual, differential neurobehavioral deficits linger after multiple recovery nights following chronic sleep restriction or acute total sleep deprivation. Sleep, 44(4). https://doi.org/10.1093/sleep/zsaa224 Yang, X., Ma, L., Zhao, X., & Kankanhalli, A. (2020). Factors influencing user’s adherence to physical activity applications: A scoping literature review and future directions. International Journal of Medical Informatics, 134, 104039. https://doi.org/10.1016/j.ijmedinf.2019.104039 Yin, J., Xu, J., & Ren, T. (2023). Recent progress in Long-Term sleep monitoring technology. Biosensors, 13(3), 395. https://doi.org/10.3390/bios13030395 Yu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A. M., Dodds, N. E., . . . Pilkonis, P. A. (2012). Development of Short Forms From the PROMISTM Sleep Disturbance and Sleep-Related Impairment Item Banks. Behavioral Sleep Medicine, 10(1), 6–24. https://doi.org/10.1080/15402002.2012.636266 Zaccaro, A., Piarulli, A., Laurino, M., Garbella, E., Menicucci, D., Neri, B., & Gemignani, A. (2018). How Breath-Control Can Change Your Life: A Systematic Review on Psycho-Physiological Correlates of Slow Breathing. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00353 Zhang, M. X., & Wu, A. M. S. (2020). Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addictive Behaviors, 111, 106552. https://doi.org/10.1016/j.addbeh.2020.106552 Zhang, X., Zhang, H., Zhang, L., Zhu, Y., & Hu, F. (2019). Double-Diamond Model-Based Orientation Guidance in Wearable Human–Machine Navigation Systems for Blind and Visually Impaired People. Sensors, 19(21), 4670. https://doi.org/10.3390/s19214670
________________ 参考文献 Aekanth, S. G. 和 Tillinghast, D. J. (2022)。可穿戴技术在医疗保健中的出现：系统回顾。自动化、协作和电子服务（第 43-59 页）。 https://doi.org/10.1007/978-3-031-10788-7_3 Aji, M.、Glozier, N.、Bartlett, D. J.、Grunstein, R.、Calvo, R. A.、Marshall, N. S., 。 。 。戈登，C.J.（2021）。使用辅助可穿戴技术治疗数字失眠的有效性：一项随机对照试验。行为睡眠医学，20(5), 570–583。 https://doi.org/10.1080/15402002.2021.1967157 Aktaruzzaman, M.、Rivolta, M. W.、Karmacharya, R.、Scarabottolo, N.、Pugnetti, L.、Garegnani, M., 。 。 。萨西，R.（2017）。手腕和胸部体动记录仪与心率变异性相结合进行睡眠分类的性能比较。生物学和医学中的计算机，89, 212–221。 https://doi.org/10.1016/j.compbiomed.2017.08.006 Albakri, U.、Drotos, E. 和 Meertens, R. M. (2021)。睡眠健康促进干预措施及其有效性：总体回顾。国际环境研究与公共卫生杂志/国际环境研究与公共卫生杂志，18(11), 5533。https://doi.org/10.3390/ijerph18115533 美国内科医师学会。 （日期不详）。成人慢性失眠症的治疗：美国医师学会的临床实践指南内科医学年鉴。摘自 https://www.acpjournals.org/doi/10.7326/M15-2175 Amir-Behghadami, M. 和 Janati, A. (2020)。人口、干预、比较、结果和研究（PICOS）设计作为制定系统评价资格标准的框架。急诊医学杂志，37(6), 387。 https://doi.org/10.1136/emermed-2020-209567 Anderson, K., Burford, O.，＆埃默顿，L.（2016）。促进自我保健的移动健康应用程序：用户体验的定性研究。 PloS One，11(5)，e0156164。 https://doi.org/10.1371/journal.pone.0156164 Armitage, C. J.、Conner, M.、Prestwich, A.、De Bruin, M.、Johnston, M.、Sniehotta, F. F. 和 Epton, T.（ 2020）。调查哪种行为改变技术对谁有效，在哪种情况下通过什么方式提供：关于理解行为改变中心（CUBiC）国际合作的提案。英国健康心理学杂志，26（1），1-14。 https://doi.org/10.1111/bjhp.12479 Arroyo, A. C. 和 Zawadzki, M. J. (2022)。行为改变技术在睡眠移动健康应用程序中的实施：系统审查。 JMIR Mhealth 和 Uhealth，10(4)，e33527。 https://doi.org/10.2196/33527 阿什利。 （2022 年 6 月 3 日）。协同设计和参与式设计之间的区别。中等的。摘自 https://uxdesign.cc Baka, A.、Van Der Zweerde, T.、Lancee, J.、Bosmans, J. E. 和 Van Straten, A. (2021)。与常规护理相比，互联网引导的认知行为治疗对于全科失眠患者的成本效益。行为睡眠医学，20(2), 188–203。 https://doi.org/10.1080/15402002.2021.1901708 Baker, F. C., & Lee, K. A. (2022)。月经周期对睡眠的影响。睡眠医学诊所，17(2), 283–294。 https://doi.org/10.1016/j.jsmc.2022.02.004 Bakker, J. P.、Weaver, T. E.、Parthasarathy, S. 和 Aloia, M. S. (2019)。坚持持续气道正压通气 (CPAP)。胸部，155(6)，1272–1287。 https://doi.org/10.1016/j.chest.2019.01.012 Banbury, A.、Pedell, S.、Parkinson, L. 和 Byrne, L. (2021)。使用双钻石模型共同设计痴呆症护理人员远程医疗同伴支持计划。远程医疗和远程护理杂志，27(10), 667–673。 https://doi.org/10。1177/1357633x211048980 巴克莱，N.L.，＆埃利斯，J.G.（2013）。睡眠不佳者与睡眠良好者的睡眠相关注意力偏差与情感效价无关。睡眠研究杂志，22(4), 414–421。 https://doi.org/10.1111/jsr.12035 Baron, K. G.、Abbott, S. M.、Jao, N. C.、Manalo, N. 和 Mullen, R. (2017)。正交睡眠：有些患者对自我的量化是否太过分了？临床睡眠医学杂志，13(02), 351–354。 https://doi.org/10.5664/jcsm.6472 Baron, K. G.、Culnan, E.、Duffecy, J.、Berendson, M.、Mason, I.C.、Lattie, E. G. 和 Manalo, N. (2021)。如何使用消费者睡眠技术数据来提供行为睡眠医学干预措施？系统回顾。行为睡眠医学，20(2), 173–187。 https://doi.org/10.1080/15402002.2021.1898397 Baron, K. G.、Duffecy, J.、Berendsen, M.、Mason, I.C.、Lattie, E. G. 和 Manalo, N. (2018)。感觉得到验证了吗？对使用以消费者为目标的可穿戴和移动技术来测量和改善睡眠的范围审查。睡眠医学评论，40，151-159。 https://doi.org/10.1016/j.smrv.2017.12.002 Baron, K. G.、Duffecy, J.、Reutrakul, S.、Levenson, J. C.、McFarland, M.、Lee, S. 和 Qeadan, F. （2021）。延长睡眠时间的行为干预：系统评价和荟萃分析。睡眠医学评论，60, 101532。https://doi.org/10.1016/j.smrv.2021.101532 Baum, K. T.、Desai, A.、Field, J.、Miller, L. E.、Rausch, J. R. 和 Beebe, D. W.（ 2013）。睡眠限制会恶化青少年的情绪和情绪调节。儿童心理学和精神病学及相关学科杂志，55（2），180-190。 https://doi.org/10.1111/jcpp.12125 Beattie, Z.、Pantelopoulos, A.、Ghoreyshi, A.、Oyang, Y.、Statan, A. 和 Heneghan, C. (2017)。 0068 使用腕戴设备中的心脏和加速度计数据估计睡眠阶段。睡眠，40（suppl_1），A26。 https://doi.org/10.1093/sleepj/zsx050.067 服务设计项目中协同设计的好处。 （日期不详）。摘自 https://www.ijdesign.org/index.php/IJDesign/article/view/890/346 Biduski, D.、Bellei, E. A.、Rodriguez, J. P. M.、Zaina, L. a. M.，＆De Marchi，A.C.B.（2020）。通过应用内嵌入的基于对话的调查问卷来评估移动健康应用程序的长期用户体验。人类行为中的计算机，104, 106169。https://doi.org/10.1016/j.chb.2019.106169 Bietz, M. J.、Patrick, K. 和 Bloss, C. S. (2019)。数据捐赠作为公民科学健康研究的模型。公民科学，4(1)。 https://doi.org/10.5334/cstp.178 Blomkamp, E. (2018)。公共政策协同设计的承诺。澳大利亚公共管理杂志，77(4), 729–743。 https://doi.org/10.1111/1467-8500.12310 Bohlen, L. C.、Michie, S.、De Bruin, M.、Rothman, A. J.、Kelly, M. P.、Groarke, H., . 。 。约翰斯顿，M.（2020）。干预措施中经常出现的行为改变技术的组合是否反映了潜在的理论？行为医学年鉴，54(11), 827–842。 https://doi.org/10.1093/abm/kaaa078 Born, J.、Rasch, B. 和 Gais, S. (2006)。睡觉来记住。神经科学家，12(5), 410–424。 https://doi.org/10.1177/1073858406292647 Born, J. 和 Wilhelm, I. (2011)。睡眠期间的系统记忆巩固。心理学研究，76（2），192-203。 https://doi.org/10.1007/s00426-011-0335-6 Bramoweth, A. D.、Lederer, L. G.、Youk, A. O.、Germain, A. 和 Chinman, M. (2020)。失眠的简短行为治疗与失眠的认知行为治疗：退伍军人随机非劣效性临床试验的结果。 行为治疗，51(4), 535–547。 https://doi.org/10.1016/j.beth.2020.02.002 Brandt, E.、Binder, T. 和 Sanders, E. (2012)。工具和技术：参与讲述、制作和表演的方式。在 Routledge 电子书中（第 165-201 页）。 https://doi.org/10.4324/9780203108543-14 Bubeck, S.、Chandrasekaran, V.、Eldan, R.、Gehrke, J.、Horvitz, E.、Kamar, E., 。 。 。张勇（2023 年 3 月 22 日）。通用人工智能的火花：GPT-4 的早期实验。摘自 https://arxiv.org/abs/2303.12712 Buchholz, S.、Mowbray, F.、Allman, G.、Verboncoeur, J.、Beam, L. 和 Small, L. (2023)。可穿戴设备和护士健康：综合审查协议。 JMIR 研究协议，12，e48178。 https://doi.org/10.2196/48178 Buysse, D. J.、Reynolds, C. F.、Monk, T. H.、Berman, S. R. 和 Kupfer, D. J. (1989)。匹兹堡睡眠质量指数：精神病学实践和研究的新工具。精神病学研究，28(2), 193–213。 https://doi.org/10.1016/0165-1781(89)90047-4 Cardoso, J. M. P.、De Figueiredo Coutinho, J. G. 和 Diniz, P. C. (2017)。高性能嵌入式计算：使用定制、代码转换和编译进行高效计算映射。摩根·考夫曼。 Carey, R.、Connell, L.、Johnston, M.、Rothman, A. J.、De Bruin, M.、Kelly, M. P. 和 Michie, S. (2018)。行为改变技术及其作用机制：已发表的干预文献中描述的链接综合。行为医学年鉴。 https://doi.org/10.1093/abm/kay078 Carney, C. E.、Edinger, J. D.、Meyer, B.、Lindman, L. 和 Istre, T. (2006)。大学生日常活动与睡眠质量.国际时间生物学，23(3), 623–637。 https://doi.org/10.1080/07420520600650695 Caulfield, J.（2023 年，6 月 22 日）。 如何进行主题分析|分步指南和示例。摘自 https://www.scribbr.com/methodology/thematic-analysis/Chambers, R.、Gullone, E. 和 Allen, N. B. (2009)。正念情绪调节：综合回顾。临床心理学评论，29（6），560-572。 https://doi.org/10.1016/j.cpr.2009.06.005 Choi，Y.K.，Demiris，G.，Lin，S.，Iribarren，S.J.，Landis，C.R.，Thompson，H.J.，。 。 。沃德，T.M.（2018）。支持睡眠自我管理的智能手机应用程序：审查和评估。临床睡眠医学杂志，14(10)，1783–1790。 https://doi.org/10.5664/jcsm.7396 Chow, R.、Drkulec, H.、Im, J.、Tsai, J.、Nafees, A.、Kumar, S., 。 。 。拉曼，S.（2023）。可穿戴设备在肿瘤患者中的使用：系统评价。肿瘤学家，29(4)，e419–e430。 https://doi.org/10.1093/oncolo/oyad305 Conklin, A.、Yao, C. A. 和 Richardson, C. G. (2018)。青少年慢性睡眠不足和特定性别的抑郁症风险：一项基于人群的前瞻性研究。 BMC 公共卫生，18(1)。 https://doi.org/10.1186/s12889-018-5656-6 Craig, P.、Dieppe, P.、Macintyre, S.、Michie, S.、Nazareth, I. 和 Petticrew, M. (2008) 。制定和评估复杂的干预措施：新的医学研究委员会指南。英国医学杂志，a1655。 https://doi.org/10.1136/bmj.a1655 De Bruin, E. J.、Van Run, C.、Staaks, J. 和 Meijer, A. M. (2017)。睡眠操纵对青少年认知功能的影响：系统评价。睡眠医学评论，32，45-57。 https://doi.org/10.1016/j.smrv.2016.02.006 De Fazio, R.、Mattei, V.、Al-Naami, B.、De Vittorio, M. 和 Visconti, P. (2022)。监测与睡眠功能障碍相关的生物物理参数的方法和可穿戴设备：概述。微型机器，13(8), 1335。https://doi.org/10。3390/mi13081335 德梅洛，D. L. M.，德卡瓦略，L. B. C.，普拉多，L. B. F. D.，& 普拉多，G. F. D. (2019)。慢性失眠的生物反馈疗法：系统评价。应用心理生理学和生物反馈，44(4), 259–269。 https://doi.org/10.1007/s10484-019-09442-2 De Zambotti, M.、Cellini, N.、Goldstone, A.、Colrain, I. M. 和 Baker, F. C. (2019)。临床和研究环境中的可穿戴睡眠技术。运动和锻炼中的医学和科学，51(7), 1538–1557。 https://doi.org/10.1249/mss.0000000000001947 设计委员会。 （日期不详）。双钻的历史 - 设计委员会。摘自 https://www.designcouncil.org.uk/our-resources/the-double-diamond/history-of-the-double-diamond/Dillon, M.（2021 年，3 月 2 日）。原住民政策领域的协同设计：风险和机遇。摘自 https://apo.org.au/node/311233 Doerr, J. M.、Klaus, K.、Troxel, W.、Nater, U. M.、Bodenmann, G.、Heinrichs, M., 。 。 。迪岑，B.（2022）。鼻内催产素对夫妻互动和睡眠之间关系的影响：一项安慰剂对照研究。心身医学，84(6), 727–737。 https://doi.org/10.1097/psy.0000000000001091 Düking, P.、Tafler, M.、Wallmann-Sperlich, B.、Sperlich, B. 和 Kleih, S. C. (2020)。腕戴式可穿戴设备中促进身体活动的行为改变技术：内容分析。 JMIR Mhealth 和 Uhealth，8(11)，e20820。 https://doi.org/10.2196/20820 Duncan, M. J.、Murawski, B.、Short, C. E.、Rebar, A. L.、Schöeppe, S.、Alley, S., 。 。 。科万，M.（2017）。活动追踪器针对活动、睡眠和久坐行为实施不同的行为改变技术。医学研究互动杂志，6(2)，e13。 https://doi.org/10.2196/ijmr.6685 Dunn, K. E.、Finan, P. H.、Huhn, A. S.、Gamaldo, C. E.、Bergeria, C. L. 和 Strain, E. C.（2022）。无线脑电图（EEG）监测戒断阿片类药物患者的睡眠：可行性和实用性的证据。实验和临床精神药理学，30(6), 1016–1023。 https://doi.org/10.1037/pha0000483 Eisele, A.、Schagg, D.、Krämer, L. V.、Bengel, J. 和 Göhner, W. (2019)。行为改变技术应用于增强慢性肌肉骨骼疾病患者身体活动依从性的干预措施：系统评价和荟萃分析。患者教育和咨询，102(1), 25–36。 https://doi.org/10.1016/j.pec.2018.09.018 Elías，M.N.（2021）。重症监护病房睡眠的评估和监测。北美重症护理诊所，33(2), 109–119。 https://doi.org/10.1016/j.cnc.2021.01.008 Espie，C.A.（2009）。 “阶梯式护理”：一种将认知行为疗法作为一线失眠治疗的健康技术解决方案。睡眠，32(12)，1549–1558。 https://doi.org/10.1093/sleep/32.12.1549 Espie, C. A.、Hames, P. 和 McKinstry, B. (2013)。使用互联网和移动媒体提供认知行为失眠治疗。睡眠医学诊所，8(3), 407–419。 https://doi.org/10.1016/j.jsmc.2013.06.001 Everson, C. A.、Henchen, C. J.、Szabó, A. 和 Hogg, N. (2014)。实验室大鼠睡眠不足和睡眠恢复导致的细胞损伤和修复。睡眠，37(12)，1929–1940。 https://doi.org/10.5665/sleep.4244 Exelmans, L. 和 Van Den Bulck, J. (2016)。成人睡前手机使用和睡眠。社会科学与科学医学，148, 93–101。 https://doi.org/10.1016/j.socscimed.2015.11.037 Fard, A.（2022a，1 月 11 日）。利益相关者访谈问题指南。摘自 https://adamfard.com/blog/stakeholder-interviews Fard, A.（2022b，1 月 11 日）。 利益相关者访谈问题指南。摘自 https://adamfard.com/blog/stakeholder-interviews Fatima, S.、Augusto, J.C.、Moseley, R.、Urbonas, P.、Elliott, A. 和 Payne, N. (2023)。在游戏化应用程序中应用激励技术来促使用户坚持采用健康的生活方式。娱乐计算，46, 100571。https://doi.org/10.1016/j.entcom.2023.100571 Feinsilver, S. H. (2021)。老年人的正常和异常睡眠。老年医学诊所，37(3), 377–386。 https://doi.org/10.1016/j.cger.2021.04.001 Ferguson, T.、Olds, T.、Curtis, R.、Blake, H.、Crozier, A.、Dankiw, K. A.、。 。 。马赫，C.（2022）。可穿戴活动追踪器增加身体活动和改善健康的有效性：系统评价和荟萃分析的系统评价。 《柳叶刀》。数字健康，4(8)，e615–e626。 https://doi.org/10.1016/s2589-7500(22)00111-x Fonseca, P.、Long, X.、Radha, M.、Haakma, R.、Aarts, R. R. 和 Rolink, J. J. (2015) 。根据心电图和呼吸努力进行睡眠阶段分类。生理测量，36(10)，2027–2040。 https://doi.org/10.1088/0967-3334/36/10/2027 Frauenberger, C.、Good, J.、Fitzpatrick, G. 和 Iversen, O. S. (2015)。追求参与式设计的严谨性和责任感。国际人机研究杂志，74, 93–106。 https://doi.org/10.1016/j.ijhcs.2014.09.004 Freeman, D.、Sheaves, B.、Waite, F.、Harvey, A. G. 和 Harrison, P. J. (2020)。睡眠障碍和精神疾病。 《柳叶刀》。精神病学，7(7), 628–637。 https://doi.org/10.1016/s2215-0366(20)30136-x Geiger-Brown, J.、Trinkoff, A. M. 和 Rogers, V. (2011)。工作时间表、家庭和工作需求对注册护士自我报告睡眠的影响。职业与环境医学杂志，53（3），303-307。 https://doi.org/10.1097/jom.0b013e31820c3f87 Gibbons, S.（2023 年 1 月 3 日）。利益相关者访谈 101. 摘自 https://www.nngroup.com/articles/stakeholder-interviews/ Goldstein, C.、Berry, R. B.、Kent, D. T.、Kristo, D. A.、Seixas, A.、Redline, S. 和韦斯托弗，医学学士（2020）。睡眠医学中的人工智能：背景和对临床医生的影响。临床睡眠医学杂志，16(4), 609–618。 https://doi.org/10.5664/jcsm.8388 Grandner, M. (2022)。睡眠、健康和社会。睡眠医学诊所，17(2), 117–139。 https://doi.org/10.1016/j.jsmc.2022.03.001 Gross, J. J. (2015)。情绪调节：现状和未来前景。心理学探究，26（1），1-26。 https://doi.org/10.1080/1047840x.2014.940781 Gunn, H. E.、Tutek, J. 和 Buysse, D. J. (2019)。失眠的简短行为治疗。睡眠医学诊所，14(2), 235–243。 https://doi.org/10.1016/j.jsmc.2019.02.003 Hebert, E. A.、Vincent, N.、Lewycky, S. 和 Walsh, K. T. (2010)。慢性失眠在线治疗的消耗和依从性。行为睡眠医学，8(3), 141–150。 https://doi.org/10.1080/15402002.2010.487457 Henriksen, A.、Mikalsen, M. H.、Woldaregay, A. Z.、Mužný, M.、Hartvigsen, G.、Hopstock, L. A. 和 Grimsgaard, S. (2018)。在研究中使用健身追踪器和智能手表测量身体活动：消费者腕戴式可穿戴设备的分析。杰米尔。医学互联网研究杂志/医学互联网研究杂志，20(3)，e110。 https://doi.org/10.2196/jmir.9157 Herlan, A.、Ottenbacher, J.、Schneider, J.、Riemann, D. 和 Feige, B. (2018)。睡眠阶段的皮肤电活动模式及其在睡眠与清醒分类中的效用。睡眠研究杂志，28(2)。 https://doi.org/10.1111/jsr.12694 Hill, C. L. M., & Updegraff, J. A. （2012）。正念及其与情绪调节的关系。情感，12(1)，81-90。 https://doi.org/10.1037/a0026355 Hoedlmoser, K.、Peigneux, P. 和 Rauchs, G. (2022)。睡眠期间记忆巩固和信息处理的最新进展。睡眠研究杂志，31(4)。 https://doi.org/10.1111/jsr.13607 Holst，H.M.L.D.，Lammers，G.J.，Van Der Horst，G.T.J.，Chaves，I.，De Vries，R.D.，GeurtsvanKessel，C.H.，。 。 。范德库伊，H.（2021）。了解睡眠、轮班工作和 COVID-19 疫苗免疫反应功效之间的关联：S-CORE 研究方案。睡眠研究杂志，31(2)。 https://doi.org/10.1111/jsr.13496 如何进行用户访谈。 （2024 年 4 月 30 日）。摘自 https://www.interaction-design.org/literature/article/how-to-conduct-user-interviews 如何使用双钻石模型进行正确的研究 — Smashing 杂志。 （2020 年 5 月 29 日）。摘自 https://www.smashingmagazine.com/2020/05/research-study-double-diamond-model/ Huang, Y. 和赵 N. (2020)。中国 COVID-19 爆发期间的广泛性焦虑症、抑郁症状和睡眠质量：一项基于网络的横断面调查。精神病学研究，288, 112954。https://doi.org/10.1016/j.psychres.2020.112954 Hubbling, A.、Reilly-Spong, M.、Kreitzer, M. J. 和 Gross, C. R. (2014)。正念如何改变我的睡眠：慢性失眠患者的焦点小组。 BMC 补充和替代医学，14(1)。 https://doi.org/10.1186/1472-6882-14-50 人机交互。用户体验和行为|指南诉讼程序。 （日期不详）。摘自 https://dl.acm.org/doi/proceedings/10.1007/978-3-031-05412-9 Hutka, P.、Krivosova, M.、Muchova, Z.、Tonhajzerová, I.、Hamrakova, A ., 姆林切科娃, Z., . 。 。翁德雷卡，I.（2021）。 睡眠结构和生理学与抑郁症和抗抑郁药治疗的关联。国际分子科学杂志，22(3), 1333。https://doi.org/10.3390/ijms22031333 Hwang, J.、Lee, T.、Lee, H. 和 Byun, S. (2022)。具有人工智能解释的睡眠分期任务的临床决策支持系统：以用户为中心的设计和评估研究。医学互联网研究杂志，24(1)，e28659。 https://doi.org/10.2196/28659 伊朗佐，À。 （2022）。睡眠医学概述。实验医学和生物学进展（第 3-15 页）。 https://doi.org/10.1007/978-3-031-06413-5_1 Irwin, M. R.、Cole, J. C. 和 Nicassio, P. M. (2006)。失眠行为干预措施及其对中年人和 55 岁以上老年人疗效的比较荟萃分析。健康心理学，25（1），3-14。 https://doi.org/10.1037/0278-6133.25.1.3 Jaiswal, S. J.、Pawelek, J.、Warshawsky, S.、Quer, G.、Trieu, M.、Pandit, J. A. 和 Owens, R. L. (2024) 。在基于人群的研究中使用新技术和可穿戴设备来表征睡眠。当前睡眠医学报告，10(1), 82–92。 https://doi.org/10.1007/s40675-023-00272-7 约翰斯，M.W.（1991）。测量白天嗜睡的新方法：Epworth 嗜睡量表。睡眠，14(6)，540–545。 https://doi.org/10.1093/sleep/14.6.540 约翰斯，M.W. (1992)。 Epworth 嗜睡量表的可靠性和因素分析。睡眠，15(4)，376–381。 https://doi.org/10.1093/sleep/15.4.376 约翰逊，J.A.，拉什，J.A.，坎贝尔，T.S.，萨瓦德，J.，格尔曼，P.，玻璃市，M.L.，。 。 。加兰，S.N.（2016）。对癌症幸存者失眠认知行为疗法（CBT-I）随机对照试验的系统回顾和荟萃分析。睡眠医学评论，27，20-28。 https://doi.org/10.1016/j.smrv.2015.07.001 睡眠研究杂志。 （2010）。睡眠研究杂志。 https://doi.org/10.1111/(issn)1365-2869 请稍等。 。 。 （日期不详）。 2022 年 9 月 18 日检索自 https://linkinghub.elsevier.com/retrieve/pii/S0277953615302458 Kao, C.（2019 年 2 月 10 日）。有效的用户访谈 - 产品经理总部 - 中。中等的。摘自 https://medium.com Kaveladze, B.、Wasil, A. R.、Bunyi, J.、Ramirez, V. 和 Schueller, S. M. (2022)。心理健康应用程序的用户体验、参与度和受欢迎程度：应用程序分析和专家应用程序评论的二次分析。 JMIR 人为因素，9(1)，e30766。 https://doi.org/10.2196/30766 Khalid, H.、Shihab, E.、Nagappan, M. 和 Hassan, A. E. (2015)。移动应用程序用户抱怨什么？ IEEE 软件，32(3), 70–77。 https://doi.org/10.1109/ms.2014.50 Kline, C. (2013)。睡眠质量。在 Springer 电子书中（第 1811-1813 页）。 https://doi.org/10.1007/978-1-4419-1005-9_849 Koffel, E.、Koffel, J. 和 Gehrman, P. (2015)。失眠团体认知行为疗法的荟萃分析。睡眠医学评论，19，6-16。 https://doi.org/10.1016/j.smrv.2014.05.001 Kok, G.、Gottlieb, N. H.、Peters, G.、Mullen, P. D.、Parcel, G. S.、Ruiter, R. a. C。， 。 。 。巴塞洛缪，L.K.（2015）。行为改变方法的分类：干预映射方法。健康心理学评论，10（3），297-312。 https://doi.org/10.1080/17437199.2015.1077155 Kwon, A.、Choi, Y.、Kim, S.、Song, K.、Suh, J.、Chae, H. W. 和 Kim, H. (2022) 。青少年的典型睡眠模式和相关肥胖。 《生活》，12(9), 1316。 https://doi.org/10.3390/life12091316 Lai, M. Y. C.、Mong, M. S. A.、Cheng L. J. 和 Lau, Y. (2023)。 可穿戴设备提供的睡眠干预对成人睡眠结果的影响：随机对照试验的系统回顾和荟萃分析。护理与健康科学，25(1), 44–62。 https://doi.org/10.1111/nhs.13011 Lancaster, B.、Sweenie, R.、Noser, A. E.、Roberts, C. M. 和 Ramsey, R. R. (2023)。睡眠移动健康应用和行为改变技术评估。行为睡眠医学，21(6), 757–773。 https://doi.org/10.1080/15402002.2022.2161548 Lancel, M.、Krömer, S. 和 Neumann, I. D. (2003)。脑内催产素调节雄性大鼠的睡眠-觉醒行为。调节肽，114(2–3), 145–152。 https://doi.org/10.1016/s0167-0115(03)00118-6 Langley, J.、Wolstenholme, D. 和 Cooke, J. (2018)。作为知识动员的“集体创造”：参与式设计在医疗保健知识共同创造中的贡献。 BMC 健康服务研究，18(1)。 https://doi.org/10.1186/s12913-018-3397-y Lee, M. R. G.、Breitstein, J.、Hoyt, T.、Stolee, J. D.、Baxter, T.、Kwon, H. P. 和 Mysliwiec, V.（ 2021）。现役军人失眠的认知行为疗法。心理服务，18(1), 42–50。 https://doi.org/10.1037/ser0000340 Li, J.、Vitiello, M. V. 和 Gooneratne, N. S. (2018)。正常衰老时的睡眠。睡眠医学诊所，13(1), 1–11。 https://doi.org/10.1016/j.jsmc.2017.09.001 Lim, J.、Jeong, C. Y.、Lim, J. M.、Chung, S.、Kim, G.、Noh, K. J. 和 Jeong, H.（ 2022）。使用移动 EMA 评估睡眠质量：机遇、实际考虑和挑战。 IEEE 访问，10，2063–2076。 https://doi.org/10.1109/access.2021.3140074 Lobo, A.（2023 年，12 月 4 日）。进行用户访谈：用户体验研究人员的综合指南。中等的。摘自 https://bootcamp.uxdesign.cc Maher, C.、Ryan, J.、Ambrosi, C. C.，＆埃德尼，S.（2017）。可穿戴活动追踪器的用户体验：横断面研究。 BMC 公共卫生，17(1)。 https://doi.org/10.1186/s12889-017-4888-1 Margherita, G. 和 Caffieri, A. (2022)。大流行期间梦想变化的观察站：实时系统回顾（第 1 部分）。睡眠研究杂志，32(3)。 https://doi.org/10.1111/jsr.13742 Marino，M.，Li，Y.，Rueschman，M.，Winkelman，J.W.，Ellenbogen，J.M.，Solet，J.M.，。 。 。巴克斯顿，O.M.（2013）。测量睡眠：手腕体动记录仪与多导睡眠图相比的准确性、灵敏度和特异性。睡眠，36(11)，1747–1755。 https://doi.org/10.5665/sleep.3142 马克斯，M.M.，赖特，A.J.，科克，E.，约翰斯顿，M.，韦斯特，R.，黑斯廷斯，J.，。 。 。米奇，S.（2023）。行为改变技术本体：转变行为改变技术分类 v1。 Wellcome 开放研究，8, 308。https://doi.org/10.12688/wellcomeopenres.19363.1 Maume, D. J.、Sebastian, R. A. 和 Bardo, A. R. (2010)。性别、工作家庭责任和睡眠。性别与社会，24(6), 746–768。 https://doi.org/10.1177/0891243210386949 McCarthy, E.、DeViva, J. C.、Southwick, S. M. 和 Pietrzak, R. H. (2021)。自评睡眠质量可预测美国退伍军人的自杀意念：一项为期 7 年、具有全国代表性的前瞻性队列研究的结果。睡眠研究杂志，31(1)。 https://doi.org/10.1111/jsr.13447 冥想和正念：你需要知道什么。 （日期不详）。摘自 https://www.nccih.nih.gov/health/meditation-and-mindativity-what-you-need-to-know Mercer, K.、Giangregorio, L.、Schneider, E. C.、Chilana, P. K.、Li ，M.，＆Grindrod，K.（2016）。 50 岁以上患有慢性病的成年人对市售可穿戴活动追踪器的接受度：混合方法评估。 JMIR Mhealth 和 Uhealth，4(1)，e7。 https://doi.org/10.2196/mhealth.4225 Mercer, K.、Li, M.、Giangregorio, L.、Burns, C. M. 和 Grindrod, K. (2016)。可穿戴活动追踪器中存在的行为改变技术：批判性分析。 JMIR Mhealth 和 Uhealth，4(2)，e40。 https://doi.org/10.2196/mhealth.4461 Michaelsen, M. M. 和 Esch, T. (2022)。健康行为改变技术的功能机制：概念回顾。心理学前沿，13。https://doi.org/10.3389/fpsyg.2022.725644 Michie, S.、Ashford, S.、Sniehotta, F. F.、Dombrowski, S. U.、Bishop, A. 和 French, D. P. (2011)。帮助人们改变身体活动和健康饮食行为的行为改变技术的精细分类法：CALO-RE 分类法。心理学与健康，26（11），1479–1498。 https://doi.org/10.1080/08870446.2010.540664 Michie, S.、Fixsen, D. L.、Grimshaw, J. 和 Eccles, M. (2009)。指定和报告复杂的行为改变干预措施：需要科学方法。实施科学，4(1)。 https://doi.org/10.1186/1748-5908-4-40 Michie, S.、Johnston, M. 和 Carey, R. (2020)。行为改变技巧。在 Springer 电子书中（第 206-213 页）。 https://doi.org/10.1007/978-3-030-39903-0_1661 米奇，S.，理查森，M.，约翰斯顿，M.，亚伯拉罕，C.，弗朗西斯，J.J.，哈德曼，W.，. 。 。伍德，C.（2013）。 93 种分层聚类技术的行为改变技术分类法 (v1)：为报告行为改变干预措施建立国际共识。行为医学年鉴，46(1), 81–95。 https://doi.org/10.1007/s12160-013-9486-6 Michie, S.、Wood, C.、Johnston, M.、Abraham, C.、Francis, J. J.，＆哈德曼，W.（2015）。行为改变技术：开发和评估用于报告和描述行为改变干预措施的分类方法（一套五项研究，涉及共识方法、随机对照试验和定性数据分析）。 DVD 上的 HTA/卫生技术评估，19(99), 1–188。 https://doi.org/10.3310/hta19990 Miller, C. H.、Espie, C. A.、Epstein, D. R.、Friedman, L.、Morin, C. M.、Pigeon, W. R., 。 。 。凯尔，S.D.（2014）。睡眠限制疗法治疗失眠症的证据基础。睡眠医学评论，18(5)，415–424。 https://doi.org/10.1016/j.smrv.2014.01.006 Miller, M. M.、Renn, B. N.、Chu, F. 和 Torrence, N. D. (2019)。医院里失眠：非药物睡眠干预的系统评价。综合医院精神病学，59, 58–66。 https://doi.org/10.1016/j.genhosppsych.2019.05.006 Mitchell, M. D.、Gehrman, P.、Perlis, M. L. 和 Umscheid, C. A. (2012)。认知行为疗法治疗失眠的效果比较：系统评价。 BMC 家庭诊所，13(1)。 https://doi.org/10.1186/1471-2296-13-40 Mohammadi, S.、Enshaeifar, S.、Hilton, A.、Dijk, D. 和 Wells, K. (2021)。使用单个 2D 红外相机进行临床睡眠姿势检测的迁移学习。 IEEE 神经系统和康复工程汇刊，29, 290–299。 https://doi.org/10.1109/tnsre.2020.3048121 Montgomery, P. 和 Dennis, J. A. (2003)。针对 60 岁以上成年人睡眠问题的认知行为干预。科克伦图书馆。 https://doi.org/10.1002/14651858.cd003161 Morcillo, L. F.、Morer-Camo, P.、Ferradas, M. I. R. 和 Cazón, A. (2020)。可穿戴设备联合设计多米诺骨牌：以用户为中心的联合设计和联合评估可穿戴设备的方法。传感器，20(10), 2934。https://doi.org/10。3390/s20102934 Morin, C. M.、Culbert, J. P. 和 Schwartz, S. M. (1994)。失眠的非药物干预：治疗效果的荟萃分析。美国精神病学杂志，151（8），1172-1180。 https://doi.org/10.1176/ajp.151.8.1172 Morrissey, E.、Corbett, T.、Walsh, J. 和 Molloy, G. J. (2016)。药物依从性应用程序中的行为改变技术。美国预防医学杂志，50(5)，e143–e146。 https://doi.org/10.1016/j.amepre.2015.09.034 Morrone, E.、Giordano, A.、Carli, S.、Visca, D.、Rossato, F.、Godio, M., 。 。 。布拉吉罗利，A.（2019）。 CPAP 治疗的依从性正在发生变化：阻塞性睡眠呼吸暂停患者治疗一年后的真实世界数据。 《欧洲呼吸杂志》/《欧洲呼吸杂志》，55(3), 1901419。https://doi.org/10.1183/13993003.01419-2019 Mortensen, D. H.（2024 年，4 月 26 日）。如何对用户访谈进行主题分析。摘自 https://www.interaction-design.org/literature/article/how-to-do-a-thematic-analysis-of-user-interviews Moul, D. E.、Hall, M.、Pilkonis, P. A. 和 Buysse ，D.J.（2004）。成人失眠的自我报告测量：理由、选择和需求。睡眠医学评论，8(3)，177–198。 https://doi.org/10.1016/s1087-0792(03)00060-1 Muench, A.、Vargas, I.、Grandner, M.、Ellis, J.、Posner, D.、Bastien, C.、。 。 。玻璃市，M.L.（2022）。我们知道 CBT-I 有效，现在怎么办？教师评论，11。https://doi.org/10.12703/r/11-4 Murtagh, D. 和 Greenwood, K. M. (1995)。确定失眠的有效心理治疗方法：荟萃分析。咨询与临床心理学杂志，63（1），79-89。 https://doi.org/10.1037/0022-006x.63.1.79 Naeem, M.、Ozuem, W.、Howell, K. E. 和 Ranfagni, S. (2023)。 在定性研究中开发概念模型的主题分析的逐步过程。 《国际定性方法杂志》，22。https://doi.org/10.1177/16094069231205789 Ng, M. M.、Firth, J.、Minen, M. 和 Torous, J. (2019)。心理健康应用程序中的用户参与度：测量、报告和有效性的回顾。精神病学服务，70(7), 538–544。 https://doi.org/10.1176/appi.ps.201800519 很好。 （2014 年 1 月 2 日）。概述 |行为改变：个人方法|指导|好的。摘自 https://www.nice.org.uk/guidance/ph49 Ohayon, M. M. (1999)。在睡眠障碍流行病学中利用模糊逻辑方法改善决策过程。心身研究杂志，47(4), 297–311。 https://doi.org/10.1016/s0022-3999(99)00010-0 Okajima, I.、Komada, Y. 和 Inoue, Y. (2010)。认知行为疗法治疗原发性失眠疗效的荟萃分析。睡眠和生物节律，9(1), 24–34。 https://doi.org/10.1111/j.1479-8425.2010.00481.x Ong, J. C.、Manber, R.、Segal, Z.、Xia, Y.、Shapiro, S. 和 Wyatt, J.K. (2014) 。正念冥想治疗慢性失眠的随机对照试验。睡眠，37(9)，1553–1563。 https://doi.org/10.5665/sleep.4010 Ong, J. C.、Shapiro, S. L. 和 Manber, R. (2008)。正念冥想与认知行为疗法相结合治疗失眠：一项治疗开发研究。行为治疗，39(2), 171–182。 https://doi.org/10.1016/j.beth.2007.07.002 Panahi, S.、Rathi, N.、Hurley, J.、Sundrud, J.、Lucero, M. T. 和 Kamimura, A. (2022)。免费诊所患者对医疗保健提供者建议和药物治疗的遵守情况。 《患者体验杂志》，9，237437352210775。https://doi.org/10.1177/23743735221077523 Pataka, A.、Kotoulas, S.、Gavrilis, P. R.、Karkala, A.、Tzinas, A. 和 Stefanidou, A. (2023)。坚持 CPAP 治疗：正念可以发挥作用吗？ 《生活》，13(2), 296。 https://doi.org/10.3390/life13020296 Patel, A. K.、Reddy, V.、Shumway, K. R. 和 Araujo, J. F.（2024 年，1 月 26 日）。生理学，睡眠阶段。摘自 https://www.ncbi.nlm.nih.gov/books/NBK526132/Pérez‐Jover, V.、Sala-González, M.、Guilabert, M. 和 Mira, J. J. (2019)。用于提高治疗依从性的移动应用程序：系统审查。杰米尔。医学互联网研究杂志/医学互联网研究杂志，21(6)，e12505。 https://doi.org/10.2196/12505 Perez-Pozuelo, I.、Zhai, B.、Palotti, J.、Mall, R.、Aupetit, M.、García-Gómez, J. M., . 。 。费尔南德斯-卢克，L.（2020）。睡眠健康的未来：睡眠科学和医学的数据驱动革命。 Npj 数字医学，3(1)。 https://doi.org/10.1038/s41746-020-0244-4 Poon, K., Ho, M. S. H., Tai, A. P., Leung, M. K. H., Chan, M., & Lau, W. K. (2024)。睡眠质量和弹性在睡眠时间型和心理健康症状之间的关系中发挥中介作用。科学报告，14(1)。 https://doi.org/10.1038/s41598-024-56688-w Radomski, A.、Bagnell, A.、Curtis, S.、Hardling, L. 和 Newton, A. S. (2020)。检查针对青少年焦虑症的基于互联网的认知行为治疗计划的使用情况、用户体验和感知影响：随机对照试验。 JMIR 心理健康，7(2)，e15795。 https://doi.org/10.2196/15795 Rahman, M.、Nowakowski, S.、Agrawal, R.、Naik, A. D.、Sharafkhaneh, A. 和 Razjouyan, J. (2022)。用于从多导睡眠图报告中提取睡眠参数的自然语言处理算法的验证。医疗保健，10(10)，1837。https://doi.org/10.3390/healthcare10101837 Randles, R. 和 Paul, A. (2023)。 撰写系统评价的指南。今日护士教育，125, 105803。https://doi.org/10.1016/j.nedt.2023.105803 健康放松技巧。 （日期不详）。摘自 https://www.nccih.nih.gov/health/relaxation-techniques-what-you-need-to-know Rundo, J. V. 和 Downey, R. (2019)。多导睡眠图。见《临床神经病学手册》（第 381-392 页）。 https://doi.org/10.1016/b978-0-444-64032-1.00025-4 Rusch, H. L.、Rosario, M.、Levison, L. M.、Olivera, A.、Livingston, W. S.、Wu, T. 和 Gill ，J.M.（2018）。正念冥想对睡眠质量的影响：随机对照试验的系统回顾和荟萃分析。纽约科学院年鉴，1445(1)，5-16。 https://doi.org/10.1111/nyas.13996 Ryan, C.、Bergin, M. 和 Wells, J. (2017)。遵守基于网络的干预措施的理论观点：范围界定审查。国际行为医学杂志，25(1), 17–29。 https://doi.org/10.1007/s12529-017-9678-8 Salari, N.、Khazaie, H.、Hosseinian-Far, A.、Khaledi-Paveh, B.、Ghasemi, H.、Mohammadi, M. ，＆Shohaimi，S.（2020）。接受和承诺疗法对失眠和睡眠质量的影响：系统评价。 BMC 神经病学，20(1)。 https://doi.org/10.1186/s12883-020-01883-1 Sanders, E.、Brandt, E. 和 Binder, T. (2010)。用于组织参与式设计的工具和技术的框架。 PDC ’10：第 11 届双年度参与式设计会议论文集。 https://doi.org/10.1145/1900441.1900476 Sanders, E. 和 Stappers, P. (2013)。 Convivial Toolbox：设计前端的生成研究。施瓦茨，J. 和罗斯，T. (2008)。睡眠和觉醒的神经生理学：基础科学和临床意义。现代神经药理学，6(4), 367–378。 https://doi.org/10.2174/157015908787386050 斯科特，A.、Webb, T. L.、James, M. M.、Rowse, G. 和 Weich, S. (2021)。改善睡眠质量可以改善心理健康：随机对照试验的荟萃分析。睡眠医学评论，60, 101556。https://doi.org/10.1016/j.smrv.2021.101556 Short, M. A.、Booth, S. A.、Omar, O.、Östlundh, L. 和 Arora, T. (2020)。青少年睡眠持续时间与情绪之间的关系：系统评价和荟萃分析。睡眠医学评论，52, 101311。https://doi.org/10.1016/j.smrv.2020.101311 睡眠基金会。 （2024 年 3 月 27 日）。失眠认知行为疗法 (CBT-I)：概述。摘自 https://www.sleepfoundation.org/insomnia/treatment/cognitive-behavioral-therapy-insomnia Smith, A. M.、Kumar, V.、Boyd-Graber, J.、Seppi, K. 和 Findlater, L.（ 2018）。关闭循环。 IUI ’18：第 23 届智能用户界面国际会议。 https://doi.org/10.1145/3172944.3172965 史密斯，C. (1995)。睡眠状态和记忆过程。大脑行为研究，69（1-2），137-145。 https://doi.org/10.1016/0166-4328(95)00024-n Smith, M. T.、Mccrae, C. S.、Cheung, J.、Martin, J. L.、Harrod, C. G.、Heald, J. L. 和 Carden, K. A.（2018 年） ）。使用体动记录仪评估睡眠障碍和昼夜节律睡眠-觉醒障碍：美国睡眠医学会系统回顾、荟萃分析和 GRADE 评估。临床睡眠医学杂志，14(07)，1209–1230。 https://doi.org/10.5664/jcsm.7228 Smith, M. T.、Perlis, M. L.、Park, A.、Smith, M. S.、Pennington, J. M.、Giles, D. E. 和 Buysse, D. J. (2002)。持续性失眠药物治疗和行为治疗的比较荟萃分析。美国精神病学杂志，159(1)，5-11。 https://doi.org/10.1176/appi.ajp.159.1.5 Spielman, A. J.、Caruso, L. S. 和 Glovinsky, P. B. (1987)。 失眠治疗的行为视角。北美精神病诊所/北美精神病诊所，10(4), 541–553。 https://doi.org/10.1016/s0193-953x(18)30532-x Stade, E.C.、Stirman, S.W.、Ungar, L.H.、Boland, C.L.、Schwartz, H.A.、Yaden, D.B., 。 。 。艾希施塔特，J.C.（2024）。大型语言模型可以改变行为医疗保健的未来：负责任的开发和评估的提案。 Npj 心理健康研究，3(1)。 https://doi.org/10.1038/s44184-024-00056-z Steinmetz, L.、Simon, L.、Feige, B.、Riemann, D.、Akram, U.、Crawford, M. R., 。 。 。斯皮格哈尔德，K. (2023)。坚持睡眠限制疗法——对现有措施的评估。睡眠研究杂志，32(6)。 https://doi.org/10.1111/jsr.13975 Su, T.、Calvo, R. A.、Jouaiti, M. A.、Daniels, S.、Kirby, P.、Dijk, D.、。 。 。 Vaidyanathan，R.（2023）。评估睡眠访谈聊天机器人以改善主观和客观睡眠：观察可行性研究协议。 JMIR 研究协议，12，e45752。 https://doi.org/10.2196/45752 Suni, E. 和 Suni, E.（2023 年，12 月 8 日）。如何确定睡眠质量差。摘自 https://www.sleepfoundation.org/sleep-hygiene/how-to-determine-poor-quality-sleep Suresh, K.、Raavi, V.、Kumar, H. 和 Shankar, V. (2022) 。慢性睡眠剥夺和睡眠恢复对大鼠海马 CA3 神经元、空间记忆和焦虑样行为的影响。学习和记忆的神经生物学，187, 107559。 https://doi.org/10.1016/j.nlm.2021.107559 Talbot, L. S., Maguen, S., Metzler, T. J., Schmitz, M., McCaslin, S. E., Richards, A ., . 。 。内兰，T.C. (2014)。创伤后应激障碍失眠的认知行为疗法：随机对照试验。睡眠，37(2), 327–341。 https://doi.org/10。5665/sleep.3408 Taylor, D. J.、Peterson, A. L.、Pruiksma, K. E.、Young-McCaughan, S.、Nicholson, K. L. 和 Mintz, J. (2017)。互联网和面对面认知行为疗法治疗军事人员失眠：一项随机临床试验。睡觉，40(6)。 https://doi.org/10.1093/sleep/zsx075 Thakral, M.、Von Korff, M.、McCurry, S.M.、Morin, C.M. 和 Vitiello, M.V. (2020)。失眠认知行为治疗后对睡眠的功能失调信念的变化：系统文献综述和荟萃分析。睡眠医学评论，49, 101230。https://doi.org/10.1016/j.smrv.2019.101230 （2024 年 4 月 24 日）。什么是睡眠质量？ - 国家睡眠基金会。摘自 https://www.thensf.org/what-is-sleep-quality/ Thoma, M. V.、La Marca, R.、Brönnimann, R.、Finkel, L.、Ehlert, U. 和 Nater, U. M.（ 2013）。音乐对人类压力反应的影响。 《公共科学图书馆一》，8(8)，e70156。 https://doi.org/10.1371/journal.pone.0070156 Tracy, E. L.、Reid, K. J. 和 Baron, K. G. (2021)。睡眠与身体活动之间的关系：日常饮酒的调节作用。睡觉，44（10）。 https://doi.org/10.1093/sleep/zsab112 使用单个 2D 红外摄像头进行临床睡眠姿势检测的迁移学习。 （2021）。摘自 https://ieeexplore.ieee.org/abstract/document/9311169 Trauer, J. M.、Qian, M.、Doyle, J. S.、Rajaratnam, S. M. 和 Cunnington, D. (2015)。慢性失眠的认知行为疗法。内科年鉴，163(3), 191–204。 https://doi.org/10.7326/m14-2841 Troxel, W.、Germain, A. 和 Buysse, D. J. (2012)。通过简短行为治疗 (BBTI) 进行失眠的临床管理。行为睡眠医学，10(4), 266–279。 https://doi.org/10.1080/15402002.2011.607200 用户体验研究的用户访谈：什么、为什么和如何。 （日期不详）。 摘自 https://www.userinterviews.com/ux-research-field-guide-chapter/user-interviews 用户访谈：2024 年完整指南 |好问题。 （日期不详）。取自 https://greatquestion.co/blog/user-interviews-guide UserInput。 （2023 年 6 月 6 日）。用户访谈完整指南：定义、好处以及如何进行。摘自 https://userinput.io/customer-research/user-interviews/Uyumaz, B. E.、Feijs, L. 和 Hu, J. (2021)。失眠数字认知行为疗法（CBT-I 应用程序）综述：它们是为参与而设计的吗？ 《国际环境研究与公共卫生杂志》，18(6), 2929。https://doi.org/10.3390/ijerph18062929 Van Gemert-Pijnen, J. E.、Nijland, N.、Van Limburg, M.、Ossebaard, H.C.、Kelders 、S.M.、Eysenbach, G. 和 Seydel, E. (2011)。提高电子医疗技术的采用和影响的整体框架。杰米尔。医学互联网研究杂志/医学互联网研究杂志，13(4)，e111。 https://doi.org/10.2196/jmir.1672 Van Straten, A.、Van Der Zweerde, T.、Kleiboer, A.、Cuijpers, P.、Morin, C. M. 和 Lancee, J. (2018)。治疗失眠的认知和行为疗法：荟萃分析。睡眠医学评论，38，3-16。 https://doi.org/10.1016/j.smrv.2017.02.001 Vandekerckhove, M.、Weiss, R.、Schotte, C.、Exadaktylos, V.、Haex, B.、Verbraecken, J. 和 Cluydts, R.（2011）。睡前负面情绪在睡眠生理学中的作用。心理生理学，48（12），1738-1744。 https://doi.org/10.1111/j.1469-8986.2011.01281.x Vandekerckhove, P.、De Mul, M.、Bramer, W. M. 和 De Bont, A. (2020)。开发电子健康干预措施的生成参与式设计方法：系统文献综述。杰米尔。 医学互联网研究杂志/医学互联网研究杂志，22(4)，e13780。 https://doi.org/10.2196/13780 Vincent, N. 和 Hameed, H. (2003)。失眠团体治疗的依从性与结果之间的关系。行为睡眠医学，1(3), 125–139。 https://doi.org/10.1207/s15402010bsm0103_1 Walker, J.、Muench, A.、Perlis, M. L. 和 Vargas, I. (2022)。失眠认知行为疗法 (CBT-I)：入门。 Kliničeskaâ I Specialʹnaâ Psihologiâ，11(2), 123–137。 https://doi.org/10.17759/cpse.2022110208 Walter, J.、Lee, J. Y.、Snoll, B.、Park, J. B.、Kim, D. H.、Xu, S. 和 Barnhart, K. T. (2022)。使用无线可穿戴传感器诊断患有睡眠呼吸障碍的不孕症患者的妊娠结局。睡眠医学，100，511–517。 https://doi.org/10.1016/j.sleep.2022.09.026 移动应用程序用户抱怨什么？ （2015 年 6 月 1 日）。摘自 https://ieeexplore.ieee.org/document/6762802 Wickwire, E. M.、Shaya, F. T. 和 Scharf, S. M. (2016)。失眠治疗的健康经济学：良好睡眠的投资回报。睡眠医学评论，30，72-82。 https://doi.org/10.1016/j.smrv.2015.11.004 Wickwire, E. M.、Tom, S. E.、Scharf, S. M.、Vadlamani, A.、Bulatao, I. G. 和 Albrecht, J. S. (2019)。未经治疗的失眠会增加医疗保险受益人的全因医疗保健利用率和成本。睡觉，42（4）。 https://doi.org/10.1093/sleep/zsz007 Wideman, T. H.、Sullivan, M. J.、Inada, S.、McIntyre, D.、Kumagai, M.、Yahagi, N., 。 。 。沃尔茨，P.（2013）。行为改变技巧。在 Springer 电子书中（第 182-187 页）。 https://doi.org/10.1007/978-1-4419-1005-9_1661 Wisner, W.（2023 年，11 月 30 日）。什么是失眠认知行为疗法 (CBT-i)？检索自 https://www.verywellmind。com/cognitive-behavioral-therapy-for-insomnia-cbti-definition-techniques-efficacy-7094606 吴X.，肖L.，孙Y.，张J.，马T.，和何L （2022）。机器学习的人机交互调查。下一代计算机系统，135, 364–381。 https://doi.org/10.1016/j.future.2022.05.014 吴 Y.、陈 C.、韩 F. 和陈 W. (2022)。新型传感技术在家庭睡眠相关呼吸障碍监测中的应用研究进展公共医学，39(4), 798–805。 https://doi.org/10.7507/1001-5515.202112066 Yamazaki, E.、Antler, C.、Lasek, C. R. 和 Goel, N. (2020)。在慢性睡眠限制或急性完全睡眠剥夺后的多个恢复之夜后，残留的、差异性的神经行为缺陷仍然存在。睡觉，44（4）。 https://doi.org/10.1093/sleep/zsaa224 杨 X.、马 L.、赵 X. 和 Kankanhalli, A. (2020)。影响用户坚持体育活动应用的因素：范围界定文献综述和未来方向。国际医学信息学杂志，134, 104039。https://doi.org/10.1016/j.ijmedinf.2019.104039 Yin, J.、Xu, J. 和 Ren, T. (2023)。长期睡眠监测技术的最新进展。生物传感器，13(3), 395。https://doi.org/10.3390/bios13030395 Yu, L.、Buysse, D. J.、Germain, A.、Moul, D. E.、Stover, A. M.、Dodds, N. E., 。 。 。皮尔科尼斯，P.A.（2012）。从 PROROMISTM 睡眠障碍和睡眠相关障碍项目库中开发简短形式。行为睡眠医学，10(1)，6-24。 https://doi.org/10.1080/15402002.2012.636266 Zaccaro, A.、Piarulli, A.、Laurino, M.、Garbella, E.、Menicucci, D.、Neri, B. 和 Gemignani, A.（2018 年） ）。呼吸控制如何改变您的生活：对缓慢呼吸的心理生理相关性的系统回顾。人类神经科学前沿，12。 https://doi.org/10.3389/fnhum.2018.00353 张 M. X. 和吴 A. M. S. (2020)。智能手机成瘾对中国大学生睡眠质量的影响：自我调节与就寝拖延的中介作用成瘾行为, 111, 106552. https://doi.org/10.1016/j.addbeh.2020.106552 张 X.、张 H.、张 L.、朱 Y. 和胡 F. (2019) 。针对盲人和视障人士的可穿戴人机导航系统中基于双钻石模型的定向指导。传感器，19(21), 4670。https://doi.org/10。3390/s19214670

[a]what is the aim/are the aims? What are the research questions [b]you probably don't need to discuss these in the report - I just wanted to see some examples [c]Write this after you have finished other sections it will be easier [d]Finish the literature review first [e]Start by talking about sleep problems, then non pharmacological interventions inlding cBTI. Most CBTi [f]- 有哪些具体的睡眠情境下的应用？ [g]这里可以添加nature那篇reference 后续prompting on LLM [h]或许把这部分仍进穿戴设备的那部分 [i]why? [j]https://search.lepton.run/search?rid=eC5o1OYfYXBNzTYWc7_Sz [k]这种论述可以后面加到adhere里 - 但其实是adhere to 穿戴设备（这里有两种，adhere：一个是针对穿戴设备；一个是干预。 可以提本研究针对干预，其中对穿戴的adhere就是顺理成章？这里俩到底是什么关系？？？ [l]本研究分析CBT-i+wearable的BCT，然后mapping with UX， 然后use LLM prompting做prototype [m]I think you can make this clearer [n]This would be better at the beginning of your review. I would start with this why is sleep [o]I think add something about students here too - there is lots of relevant research and will probably be students who make up your sample [p]repetitive [q]This is from chatfpt - do not just copy and paste it, but you should include these details [r]i don't understand this sentence [s]You need to explain how this relates to your work - what do you mean here [t]which studies [u]I don't know what this means [v]why don't you have an appendix which explains the taxonomy? Tere is probaly a diagram or something [w]this is good - this is what your study can address [x]great! [y]repetitive [z]say something here about user x questions. I think some of the problem is that you need to explain in your introduction that your PhD will draw upon participatory methods to undertake user x research. Include a paragraph about what user x is [aa]I commented on this section before so I have not added further comments [ab]make sure you get the tense right. For your work it should be future tense (e.g., we). For research that has already been conducted (by yourself (e.g. if you talk about what you have done so far for the systematic review; and the literature which has already happened) [ac]label all tables and figures following APA guidance and explain what they are in the text [ad]repetitive [ae]good [af]this should be later - at the start of the section about your review. [ag]repetition here [ah]try to make this all more specific to your own research if you can. How will you use this method to develop and inform your design. Why is is important to involve stakeholders? [ai]解释一下why mapping这里 [aj]what will thematic analysis be used for? THematic analysis of what? [ak]this is the key reference [al]I don't understand this. How will you use thematic analysis for your BCT coding. I don't actually understand what you are doing with your BCT coding. EXplain what it is and how it will be used [am]maybe this will make more sense when youve written your discusison [an]This is the most relevant section [ao]all this needs to be in the introduction not here [ap]or method [aq]all this needs to be in the introduction not here [ar]or method [as]make this specific to your research [at]all this needs to be in the introduction not here [au]or method [av]who are the experts [aw]I don't know what this means [ax]look at Braun and Clarke for method of thematic analysis [ay]you can summarise this much more briefly - set it out like a research write up - start with participants [az]these are great!
[a]目标是什么？研究问题是什么 [b]你可能不需要在报告中讨论这些 - 我只是想看一些例子 [c]在完成其他部分后写这个会更容易 [d]完成文献综述首先[e]首先讨论睡眠问题，然后讨论 cBTI 中的非药物干预措施。大多数CBTi [f]-有哪些具体的睡眠防护下的应用？ [g]这里可以添加nature那篇参考后续提示LLM [h]可能把这部分仍然进穿戴设备的那部分[我]为什么？ [j]https://search.lepton.run/search?rid=eC5o1OYfYXBNzTYWc7_Sz [k]这种走势可以后面加到adhere里 - 但其实是坚持穿戴设备（这里有两种，adhere：一个是针对穿戴可以提本研究针对干预，其中对穿戴式的坚持就是顺理成章？这俩到底是什么关系？？？ [l]本研究分析CBT-i+可穿戴的BCT，与UX映射，然后使用LLM提示做原型[m]我认为你可以让这一点更清楚[n]这在你的评论开始时会更好。我将从这个为什么睡眠开始 [o]我想在这里也添加一些关于学生的内容 - 有很多相关研究，并且可能是学生组成你的样本 [p]重复 [q]这是来自 chatfpt - 不要只需复制并粘贴它，但您应该包含这些详细信息 [r]我不明白这句话 [s]您需要解释这与您的工作有何关系 - 您在这里是什么意思 [t]哪个研究 [u]I不知道这意味着什么 [v]为什么没有一个附录来解释分类法？可能有一个图表或其他东西[w]这很好 - 这就是你的研究可以解决的问题[x]太棒了！ [y]重复 [z]在这里说一些关于用户 x 的问题。我认为部分问题在于你需要在简介中解释你的博士学位将利用参与式方法来进行用户研究。 包含一段关于用户 x 的内容 [aa]我之前对此部分发表过评论，因此我没有添加更多评论 [ab]确保您的时态正确。对于你的工作，它应该是将来时（例如，我们）。对于已经进行的研究（您自己进行的研究（例如，如果您谈论到目前为止您为系统评价所做的工作；以及已经发生的文献）[ac]按照 APA 指南标记所有表格和图形，并解释它们的内容在文本中[ad]重复[ae]好[af]这应该稍后 - 在关于您的评论的部分的开头[ag]这里重复[ah]尝试使这一切更加具体到您自己的研究，如果。你将如何使用这种方法来开发和告知你的设计。为什么让利益相关者参与进来很重要？[aj]主题分析将用于什么？ ]这是关键参考资料 [al]我不明白你将如何使用 BCT 编码的主题分析。我实际上不明白你用 BCT 编码做什么。 [am]也许当你写完你的讨论后这会更有意义[an]这是最相关的部分[ao]所有这些都需要在介绍中而不是在这里[ap]或方法[aq]所有这些都需要放在介绍中，不在这里 [ar] 或方法 [as] 使其具体到您的研究 [at] 所有这些都需要放在介绍中，不在这里 [au] 或方法 [av] 谁是专家 [aw] 我不知道不知道这意味着什么[ax]看看布劳恩和克拉克的主题分析方法[ay]你可以更简短地总结这一点 - 像研究报告一样列出它 - 从参与者开始[az]这些太棒了！