Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review

Cognitive Behavioral Therapy for Insomnia Self-Management Mobile Apps: A Review of Efficacy and Quality

Cognitive behavioral therapy for insomnia (CBT-I) is the recommended treatment for insomnia, yet multiple barriers limit utilization. Digital CBT-I may present a solution, though related reviews have focused on Internet-based delivery rather than app use. The high utilization of health apps and prevalence of sleep apps indicate the need to equip clinicians with app-specific research. Toward this end, we reviewed efficacy and quality data on self-management CBT-I smartphone apps, revealing efficacy research on eleven apps, five of which were publicly available. While preliminary, these efficacy studies showed consistent positive findings. When examining quantitative quality indicators for the five publicly available apps, two had consistent data. Overall, two apps, CBTi Coach and Insomnia Coach, had positive, empirical findings across all efficacy and quality assessment approaches. We provide recommendations to guide clinician decision making regarding CBT-I self-management apps based on the literature and publicly available methods of app evaluations.

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Experience with the use of a digital sleep diary in symptom management by individuals with insomnia -a pilot mixed method study

Background

Insomnia is the most common sleep disorder. The recommended treatment is cognitive behavioural therapy for insomnia (CBTi). A sleep diary is a core tool in CBTi. We have developed a digital sleep diary with a standardised feedback function.

Aim

To study feasibility of the digital sleep diary in participants of the Akershus Sleep Apnea (ASAP) cohorts with difficulties falling asleep or maintaining sleep. To describe sleep diary engagement and explore experiences with the digital sleep diary with potential influences in insomnia symptom management.

Material and methods

Twenty participants were recruited from the ASAP. All filled out a digital sleep diary up to 12 weeks. Treatment options provided were a self-help book (N = 11) or electroencephalography neurofeedback (N = 9) in addition to the sleep diary standardised feedback function. We collected quantitative data from the sleep diary reports and we sub-divided insomnia by sleep onset insomnia and non-sleep onset insomnia. Finally, we performed qualitative interviews.

Results

The median number of entries to the sleep diary was 81 (25th quartile: 26, 75th quartile 84). In the qualitative analysis, we identified two main themes; "structure and overview" and "usability and digital features".

Conclusion

The sleep diary was found to be feasible when distributed in combination with a self-help book or electroencephalography neurofeedback. The qualitative results emphasised the importance of a timely graphical overview and visualisations of self-recorded sleep.

Clinical guidelines for the use of lifestyle-based mental health care in major depressive disorder: World Federation of Societies for Biological Psychiatry (WFSBP) and Australasian Society of Lifestyle Medicine (ASLM) taskforce

OBJECTIVES

The primary objectives of these international guidelines were to provide a global audience of clinicians with (a) a series of evidence-based recommendations for the provision of lifestyle-based mental health care in clinical practice for adults with Major Depressive Disorder (MDD) and (b) a series of implementation considerations that may be applicable across a range of settings.

METHODS

Recommendations and associated evidence-based gradings were based on a series of systematic literature searches of published research as well as the clinical expertise of taskforce members. The focus of the guidelines was eight lifestyle domains: physical activity and exercise, smoking cessation, work-directed interventions, mindfulness-based and stress management therapies, diet, sleep, loneliness and social support, and green space interaction. The following electronic bibliographic databases were searched for articles published prior to June 2020: PubMed, EMBASE, The Cochrane Library (Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL), Cochrane Methodology Register), CINAHL, PsycINFO. Evidence grading was based on the level of evidence specific to MDD and risk of bias, in accordance with the World Federation of Societies for Biological Psychiatry criteria.

RESULTS

Nine recommendations were formed. The recommendations with the highest ratings to improve MDD were the use of physical activity and exercise, relaxation techniques, work-directed interventions, sleep, and mindfulness-based therapies (Grade 2). Interventions related to diet and green space were recommended, but with a lower strength of evidence (Grade 3). Recommendations regarding smoking cessation and loneliness and social support were based on expert opinion. Key implementation considerations included the need for input from allied health professionals and support networks to implement this type of approach, the importance of partnering such recommendations with behaviour change support, and the need to deliver interventions using a biopsychosocial-cultural framework.

CONCLUSIONS

Lifestyle-based interventions are recommended as a foundational component of mental health care in clinical practice for adults with Major Depressive Disorder, where other evidence-based therapies can be added or used in combination. The findings and recommendations of these guidelines support the need for further research to address existing gaps in efficacy and implementation research, especially for emerging lifestyle-based approaches (e.g. green space, loneliness and social support interventions) where data are limited. Further work is also needed to develop innovative approaches for delivery and models of care, and to support the training of health professionals regarding lifestyle-based mental health care.

Help for insomnia from the app store? A standardized rating of mobile health applications claiming to target insomnia

A large number of mobile health applications claiming to target insomnia are available in commercial app stores. However, limited information on the quality of these mobile health applications exists. The present study aimed to systematically search the European Google Play and Apple App Store for mobile health applications targeting insomnia, and evaluate the quality, content, evidence base and potential therapeutic benefit. Eligible mobile health applications were evaluated by two independent reviewers using the Mobile Application Rating Scale-German, which ranges from 1 - inadequate to 5 - excellent. Of 2236 identified mobile health applications, 53 were included in this study. Most mobile health applications (68%) had a moderate overall quality. Concerning the four main subscales of the Mobile Application Rating Scale-German, functionality was rated highest (M = 4.01, SD = 0.52), followed by information quality (M = 3.49, SD = 0.72), aesthetics (M = 3.31, SD = 1.04) and engagement (M = 3.02, SD = 1.03). While scientific evidence was identified for 10 mobile health applications (19%), only one study employed a randomized controlled design. Fifty mobile health applications featured sleep hygiene/psychoeducation (94%), 27 cognitive therapy (51%), 26 relaxation methods (49%), 24 stimulus control (45%), 16 sleep restriction (30%) and 24 sleep diaries (45%). Mobile health applications may have the potential to improve the care of insomnia. Yet, data on the effectiveness of mobile health applications are scarce, and this study indicates a large variance in the quality of the mobile health applications. Thus, independent information platforms are needed to provide healthcare seekers and providers with reliable information on the quality and content of mobile health applications.

Understanding the research on tracking, diagnosing, and intervening in sleep disorders using mHealth apps: Bibliometric analysis and systematic reviews

Objectives

In solving the global challenge of sleep disorders, Mobile Health app is one of the important means to monitor, diagnose, and intervene in sleep disorders. This study aims to (1) summarize the status and trends of research in this field; (2) assess the production and usage of sleep mHealth apps; (3) calculate the conversion rate of grants that the proportion of newly developed apps from being funded and developed to published on application stores.

Methods

Using bibliometric and content analysis methods, based on "Research Paper-Product Output-Product Application" chain and considering the "Research Grants" of articles, we conducted a systematic review of eight databases, to identify relevant studies over the last decade.

Results

Over the past decade, 1399 authors published 313 papers in 182 journals and conferences. The number of publications increased with an average annual growth of 41.6%. The current focus area is research using cognitive behavioral therapy to intervene in sleep. Sleep-staging tracking is a shortcoming of this field. A total 368 sleep mHealth apps (233 newly developed and 135 existing) were examined in 313 papers; 323 grants supported 178 articles (56.9%). Only 12 of the newly developed apps are used in the real world, resulting in a 9% grant conversion rate.

Conclusions

In the last decade, the field of tracking, diagnosing, and intervening in sleep disorders using mHealth apps has shown a trend of rapid development. However, the conversion rate of products from being funded and developed for use by end-users is low.

A framework for design and usability testing of telerehabilitation system for adults with chronic diseases: A panoramic scoping review

Objective

This scoping review aimed to identify the design and usability testing of a telerehabilitation (TR) system, and its characteristics and functionalities that are best-suited for rehabilitating adults with chronic diseases.

Methods

Searches were conducted in PubMed, EBSCO, Web of Science, and Cochrane library for studies published between January 2017 and December 2022. We followed the Joanna Briggs Institute guidelines and the framework by Arksey and O'Malley. Screening was undertaken by two reviewers, and data extraction was undertaken by the first author. Then, the data were further reviewed and discussed thoroughly with the team members.

Results

A total of 31 results were identified, with the core criteria of developing and testing a telerehabilitation system, including a mobile app for cardiovascular diseases, cancer, diabetes, and chronic respiratory disorders. All developed systems resulted from multidisciplinary teams and employed mixed-methods research. We proposed the "input-process-output" framework that identified phases of both system design and usability testing. Through system design, we reported the use of user-centered design, iterative design, users' needs and characteristics, theory underpinning development, and the expert panel in 64%, 75%, 86%, 82%, and 71% of the studies, respectively. We recorded the application of moderated usability testing, unmoderated testing (1), and unmoderated testing (2) in 74%, 63%, and 15% of the studies, respectively. The identified design and testing activities produced a matured system, a high-fidelity prototype, and a released system in 81.5%, 15%, and 3.5%, respectively.

Conclusion

This review provides a framework for TR system design and testing for a wide range of chronic diseases that require prolonged management through remote monitoring using a mobile app. The identified "input-process-output" framework highlights the inputs, design, development, and improvement as components of the system design. It also identifies the "moderated-unmoderated" model for conducting usability testing. This review illustrates characteristics and functionalities of the TR systems and healthcare professional roles.

The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review

Background

Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep.

Objective

This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep?

Methods

We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020.

Results

A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes.

Conclusions

Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although 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 (eg, self-efficacy, social norms, and attitudes).

Differential effects of COVID-related lockdown on sleep-wake rhythms in adults with autism spectrum disorder compared to the general population

COVID‐related lockdown led to a radical modification of daily activities and routines which are known to affect sleep. Compared to the general population, participants with autism may be particularly vulnerable to the repercussions of lockdown on sleep, given their intrinsic inflexible adherence to routines and the high overall prevalence of sleep disturbances in this population. The study is a French nation‐wide online survey assessing sleep–wake rhythms and behaviors known to affect sleep (daily screen time, daylight exposure, and physical activity), before and during COVID‐related lockdown. Respondents were 207 adults with autism (56% female) and 1652 adults of the general population (77% female), with a mean age 35.3 years (SD 11.3). Before lockdown, the adults with autism displayed on average later bedtime and waking hours, lower sleep quality, more evening screen time, less exposure to daylight, and less exercise (all p < 0.01). Lockdown affected all studied measures of sleep and related exposures in a similar way in both groups: poorer self‐rated sleep quality as well as a less regular and delayed sleep–wake rhythm, longer screen time in the evening and less exposure to daylight (all p < 0.001). Adults with autism displayed significantly higher levels of sleep and circadian rhythm disturbances and less favorable daily routines known to regulate sleep. While the effect of confinement on sleep and sleep related behaviors was similar in both groups, the results highlight that the pre‐existing shift in circadian rhythms and lifestyles in adults with ASD further deteriorated during lockdown.

Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications

Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.