A feasibility study of a mobile app to treat insomnia

The effect of single-component sleep restriction therapy on depressive symptoms: A systematic review and meta-analysis

Sleep restriction therapy is a behavioural component within cognitive behavioural therapy for insomnia and is an effective standalone treatment for insomnia, but its effect on depressive symptoms remains unclear. This review aimed to synthesise and evaluate the impact of single-component sleep restriction therapy on depressive symptoms relative to a control intervention. We searched electronic databases and sleep-related journals for randomised controlled trials and uncontrolled clinical trials, published from 1 January 1986 until 19 August 2023, that delivered sleep restriction therapy to adults with insomnia. Random-effects meta-analysis of standardised mean differences and Cochrane risk of bias assessment were performed on randomised controlled trials, while uncontrolled clinical trials were discussed narratively. The meta-analysis was pre-registered on PROSPERO (ID: CRD42020191803). We identified seven randomised controlled trials (N = 1102) and two uncontrolled clinical trials (N = 22). Findings suggest that sleep restriction therapy is associated with a medium effect for improvement in depressive symptoms at post-treatment (Nc  = 6, g = -0.45 [95% confidence interval = -0.70 to -0.21], p < 0.001) and a small effect at follow-up (Nc  = 4, g = -0.31 [95% confidence interval = -0.45 to -0.16], p < 0.001). Five of the seven included randomised controlled trials were judged to have a high risk of bias. Standalone sleep restriction therapy appears to be efficacious for improving depressive symptoms at post-treatment and follow-up. However, conclusions are tentative due to the small number of trials and because none of the trials was performed in a population with clinically defined depression. Large-scale trials are needed to test the effect of sleep restriction therapy in patients experiencing depression and insomnia. Findings also highlight the need to improve the standardisation and reporting of sleep restriction therapy procedures, and to design studies with more rigorous control arms to reduce potential bias.

Diagnosis and management of sleep disorders in shift workers, with patient informed solutions to improve health services research and practice

BACKGROUND

The combination of shift work and an unmanaged sleep disorder carries health and safety risks. Yet, diagnosis rates for sleep disorders are low in shift workers. The aim of this study was to understand the experience of sleep disorder diagnosis and treatment in shift workers, and consider patient informed solutions to improve access to health services.

METHODS

Semi-structured interviews were conducted with 16 Australian shift workers with a diagnosed sleep disorder. Patient journey mapping and reflexive thematic analysis were used to understand diagnosis and management experiences.

RESULTS

There were highly variable experiences with diagnosis and management, often taking >5 years to seek help from a health care provider (HCP) after noticing symptoms of a sleep disorder. Three themes were constructed, including 'the cause of the problem', 'prioritising work', and '(dis)satisfaction and (dis)connection'. Extent of patient and HCP awareness of sleep disorders, and a prevailing attitude of shift work being 'the problem' impacted diagnosis, as did organisational needs (including rostering, which had both positive and negative influences on help seeking). Relationships with HCPs were important, and living on non-standard time was both a barrier and an enabler to sleep disorder care. Participants identified the need for education and awareness, prompts and easy access to screening and referral pathways, and tailored models of care.

CONCLUSION

Education and awareness initiatives for shift workers, their employers and HCPs, together with development of a model of care for shift workers with sleep disorders may address some of the unique barriers to diagnosis and management.

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.

Emerging applications of objective sleep assessments towards the improved management of insomnia

Self-reported sleep difficulties are the primary concern associated with diagnosis and treatment of chronic insomnia. This said, in-home sleep monitoring technology in combination with self-reported sleep outcomes may usefully assist with the management of insomnia. The rapid acceleration in consumer sleep technology capabilities together with their growing use by consumers means that the implementation of clinically useful techniques to more precisely diagnose and better treat insomnia are now possible. This review describes emerging techniques which may facilitate better identification and management of insomnia through objective sleep monitoring. Diagnostic techniques covered include insomnia phenotyping, better detection of comorbid sleep disorders, and identification of patients potentially at greatest risk of adverse outcomes. Treatment techniques reviewed include the administration of therapies (e.g., Intensive Sleep Retraining, digital treatment programs), methods to assess and improve treatment adherence, and sleep feedback to address concerns about sleep and sleep loss. Gaps in sleep device capabilities are also discussed, such as the practical assessment of circadian rhythms. Proof-of-concept studies remain needed to test these sleep monitoring-supported techniques in insomnia patient populations, with the goal to progress towards more precise diagnoses and efficacious treatments for individuals with insomnia.

"Leave your smartphone out of bed": quantitative analysis of smartphone use effect on sleep quality

Smartphones have become an integral part of people's everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one's sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one's sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7-456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87-55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

Background

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

Objective

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

Methods

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

Results

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

Conclusions

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app’s intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

The Effects of Objective Push-type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: Mobile Health Intervention Trial using a Healthcare IoT System (Preprint)

Background

Sleep is beneficial for physical and mental health. Several mobile and wearable sleep-tracking devices have been developed, and personalized sleep feedback is the most common functionality among these devices. To date, no study has implemented an objective push-type feedback message and investigated the characteristics of habitual sleep behavior and diurnal symptoms when receiving sleep feedback.

Objective

We conducted a mobile health intervention trial to examine whether sending objective push-type sleep feedback changes the self-reported mood, physical symptoms, and sleep behavior of Japanese office workers.

Methods

In total, 31 office workers (mean age 42.3, SD 7.9 years; male-to-female ratio 21:10) participated in a 2-arm intervention trial from November 30 to December 19, 2020. The participants were instructed to indicate their momentary mood and physical symptoms (depressive mood, anxiety, stress, sleepiness, fatigue, and neck and shoulder stiffness) 5 times a day using a smartphone app. In addition, daily work performance was rated once a day after work. They were randomly assigned to either a feedback or control group, wherein they did or did not receive messages about their sleep status on the app every morning, respectively. All participants wore activity monitors on their nondominant wrists, through which objective sleep data were registered on the web on a server. On the basis of the estimated sleep data on the server, personalized sleep feedback messages were generated and sent to the participants in the feedback group using the app. These processes were fully automated.

Results

Using hierarchical statistical models, we examined the differences in the statistical properties of sleep variables (sleep duration and midpoint of sleep) and daily work performance over the trial period. Group differences in the diurnal slopes for mood and physical symptoms were examined using a linear mixed effect model. We found a significant group difference among within-individual residuals at the midpoint of sleep (expected a posteriori for the difference: −15, 95% credible interval −26 to −4 min), suggesting more stable sleep timing in the feedback group. However, there were no significant group differences in daily work performance. We also found significant group differences in the diurnal slopes for sleepiness (P<.001), fatigue (P=.002), and neck and shoulder stiffness (P<.001), which was largely due to better scores in the feedback group at wake-up time relative to those in the control group.

Conclusions

This is the first mobile health study to demonstrate that objective push-type sleep feedback improves sleep timing of and physical symptoms in healthy office workers. Future research should incorporate specific behavioral instructions intended to improve sleep habits and examine the effectiveness of these instructions.

A scoping review of mobile apps for sleep management: User needs and design considerations

Sleep disorders are prevalent nowadays, leading to anxiety, depression, high blood pressure, and other health problems. Due to the proliferation of mobile devices and the development of communication technologies, mobile apps have become a popular way to deliver sleep disorder therapy or manage sleep. This scoping review aims to conduct a systematic investigation of mobile apps and technologies supporting sleep, including the essential functions of sleep apps, how they are used to improve sleep and the facilitators of and barriers to using apps among patients and other stakeholders. We searched articles (2010 to 2022) from Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore using the keyword sleep apps. In total, 1,650 peer-reviewed articles were screened, and 51 were selected for inclusion. The most frequently provided functions by the apps are sleep monitoring, measuring sleep, providing alarms, and recording sleep using a sleep diary. Several wearable devices have been used with mobile apps to record sleep duration and sleep problems. Facilitators and barriers to using apps were identified, along with the evidence-based design guidelines. Existing studies have proved the initial validation and efficiency of delivering sleep treatment by mobile apps; however, more research is needed to improve the performance of sleep apps and devise a way to utilize them as a therapy tool.

Sleepy Psychotherapists: How Clinicians' Biological Factors May Affect the Conduct of Psychotherapy

OBJECTIVE

Numerous therapist variables and cognitive biases can affect the quality of the therapeutic alliance and the conduct and outcomes of psychotherapy. This article aims to examine factors that potentially affect clinician performance, including chronobiological variables of clinicians and patients.

METHODS

The author reviewed literature pertaining to biological influences on human cognitive performance and considered how these factors may apply to the practice of psychotherapy.

RESULTS

Biological factors potentially affecting the conduct and quality of psychotherapy were identified. These factors include decision fatigue, hunger, sleep deficit, shift work, and several chronobiological issues related to circadian rhythms and episodic ultradian rhythms. In addition, misaligned scheduling of psychotherapy sessions in relation to therapist and patient evening-morning chronotypes may impede the effectiveness of psychotherapy.

CONCLUSIONS

The practice of psychotherapy is cognitively demanding, requiring that clinicians remain constantly alert and in command of their executive functions. Decreases in clinician alertness resulting from homeostatic depletion, chronobiologically misaligned schedules, and illness-associated factors may reduce the quality and benefit of psychotherapy sessions. Mitigation strategies are available. Investigations of these factors are needed.

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

Background

Mobile health (mHealth) apps offer a scalable option for treating sleep disturbances at a population level. However, there is a lack of clarity about the development and evaluation of evidence-based mHealth apps.

Objective

The aim of this systematic review was to provide evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance.

Methods

A systematic search of studies published from the inception of databases through February 2020 was conducted using 5 databases (MEDLINE, Embase, Cochrane Library, PsycINFO, and CINAHL).

Results

A total of 6015 papers were identified using the search strategy. After screening, 15 papers were identified that examined the design engineering and clinical implementation and evaluation of 8 different mHealth apps for sleep disturbance. Most of these apps delivered cognitive behavioral therapy for insomnia (CBT-I, n=4) or modified CBT-I (n=2). Half of the apps (n=4) identified adopting user-centered design or multidisciplinary teams in their design approach. Only 3 papers described user and data privacy. End-user acceptability and engagement were the most frequently assessed implementation metrics. Only 1 app had available evidence assessing all 4 implementation metrics (ie, acceptability, engagement, usability, and adherence). Most apps were prototype versions (n=5), with few matured apps. A total of 6 apps had supporting papers that provided a quantitative evaluation of clinical outcomes, but only 1 app had a supporting, adequately powered randomized controlled trial.

Conclusions

This is the first systematic review to synthesize and examine evidence for the design engineering and clinical implementation and evaluation of mHealth apps for sleep disturbance. The minimal number of apps with published evidence for design engineering and clinical implementation and evaluation contrasts starkly with the number of commercial sleep apps available. Moreover, there appears to be no standardization and consistency in the use of best practice design approaches and implementation assessments, along with very few rigorous efficacy evaluations. To facilitate the development of successful and evidence-based apps for sleep disturbance, we developed a high-level framework to guide researchers and app developers in the end-to-end process of app development and evaluation.