Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

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.

Development and Initial Evaluation of Web-Based Cognitive Behavioral Therapy for Insomnia in Rural Family Caregivers of People With Dementia (NiteCAPP): Mixed Methods Study

BACKGROUND

Informal caregivers of people with dementia frequently experience chronic insomnia, contributing to stress and poor health outcomes. Rural caregivers are particularly vulnerable but have limited access to cognitive behavioral therapy for insomnia (CBT-I), a recommended frontline treatment for chronic insomnia. Web-based delivery promises to improve insomnia, particularly for rural caregivers who have limited access to traditional in-person treatments. Our team translated an efficacious 4-session standard CBT-I content protocol into digital format to create NiteCAPP.

OBJECTIVE

This study aimed to (1) adapt NiteCAPP for dementia caregivers to create NiteCAPP CARES, a tailored digital format with standard CBT-I content plus caregiver-focused modifications; (2) conduct usability testing and evaluate acceptability of NiteCAPP CARES' content and features; and (3) pilot-test the adapted intervention to evaluate feasibility and preliminary effects on sleep and related health outcomes.

METHODS

We followed Medical Research Council recommendations for evaluating complex medical interventions to explore user needs and adapt and validate content using a stepwise approach: (1) a rural dementia caregiver (n=5) and primary care provider (n=5) advisory panel gave feedback that was used to adapt NiteCAPP; (2) caregiver (n=5) and primary care provider (n=7) focus groups reviewed the newly adapted NiteCAPP CARES and provided feedback that guided further adaptations; and (3) NiteCAPP CARES was pilot-tested in caregivers (n=5) for feasibility and to establish preliminary effects. Self-report usability measures were collected following intervention. Before and after treatment, 14 daily electronic sleep diaries and questionnaires were collected to evaluate arousal, health, mood, burden, subjective cognition, and interpersonal processes.

RESULTS

The stepped approach provided user and expert feedback on satisfaction, usefulness, and content, resulting in a new digital CBT-I tailored for rural dementia caregivers: NiteCAPP CARES. The advisory panel recommended streamlining content, eliminating jargon, and including caregiver-focused content. Focus groups gave NiteCAPP CARES high usefulness ratings (mean score 4.4, SD 0.79, scored from 1=least to 5=most favorable; score range 4.2-4.8). Multiple features were evaluated positively, including the intervention's comprehensive and engaging information, caregiver focus, good layout, easy-to-access intervention material, and easy-to-understand sleep graphs. Suggestions for improvement included the provision of day and night viewing options, collapsible text, font size options, tabbed access to videos, and a glossary of terms. Pilot-test users rated usefulness (mean score 4.3, SD 0.83; range 4.1-4.5) and satisfaction (mean score 8.4, SD 1.41, scored from 1=least to 10=most satisfied; range 7.4-9.0) highly. Preliminary effects on caregiver sleep, arousal, health, mood, burden, cognition, and interpersonal processes (all P<.05) were promising.

CONCLUSIONS

Adaptations made to standard digital CBT-I created a feasible, tailored digital intervention for rural dementia caregivers. Important next steps include further examination of feasibility and efficacy in a randomized controlled trial with an active control condition, a multisite effectiveness trial, and eventual broad dissemination.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04632628; https://clinicaltrials.gov/ct2/show/NCT04632628.

Reduction of Sleep Medications via a Combined Digital Insomnia and Pharmacist-Led Deprescribing Intervention: Protocol for a Feasibility Trial

BACKGROUND

Chronic insomnia is one of the most common health problems among veterans and negatively impacts their health, function, and quality of life. Although cognitive behavioral therapy for insomnia (CBT-I) is the first-line recommended treatment, sedative-hypnotic medications remain the most common. Sedative-hypnotics, however, have mixed effectiveness, are frequently prescribed longer than recommended, and are associated with numerous risks and adverse effects that negatively impact veteran function. Meeting the treatment needs of veterans impacted by insomnia requires delivering gold standard behavioral care, like CBT-I, and the reduction of sedative-hypnotics through innovative methods.

OBJECTIVE

The objective of this feasibility clinical trial is to test a digital CBT-I approach combined with deprescribing to improve the success of sedative-hypnotic reduction among veterans. The intervention combines Noctem Health Clinician Operated Assistive Sleep Technology (COAST), an effective and efficient, scalable, and adaptable digital platform to deliver CBT-I, with clinical pharmacy practitioner (CPP)-led deprescribing of sedative-hypnotic medications.

METHODS

In this nonrandomized single-group clinical trial, 50 veterans will be recruited and enrolled to receive CBT-I delivered via Noctem COAST and CPP-led deprescribing for up to 12 weeks. Assessments will occur at baseline, posttreatment, and 3-month follow-up. The aims are to (1) assess the feasibility of recruiting veterans with chronic sedative-hypnotic use to participate in the combined intervention, (2) evaluate veterans' acceptability and usability of the COAST platform, and (3) measure changes in veterans' sleep, sedative-hypnotic use, and function at baseline, posttreatment, and 3-month follow-up.

RESULTS

The institutional review board approved the study in October 2021 and the trial was initiated in May 2022. Recruitment and data collection began in September 2022 and is anticipated to be completed in April 2024. Aim 1 will be measured by tracking the response to a mail-centric recruitment approach using electronic medical records to identify potentially eligible veterans based on sedative-hypnotic use. Aim 2 will be measured using the Post-Study System Usability Questionnaire, assessing overall usability as well as system usefulness, information quality, and interface quality. Aim 3 will use the Insomnia Severity Index and sleep diaries to measure change in insomnia outcomes, the Patient-Reported Outcome Measurement Information System Profile to measure change in physical function, anxiety, depression, fatigue, sleep disturbance, participation in social roles, pain, cognitive function, and self-reported sedative-hypnotic use to measure change in dose and frequency of use.

CONCLUSIONS

Findings will inform the utility of a combined digital CBT-I and CPP-led deprescribing intervention and the development of an adequately powered clinical trial to test the effectiveness in a diverse sample of veterans. Further, findings will help inform potential new approaches to deliver care and improve access to care for veterans with insomnia, many of whom use sedative-hypnotics that may be ineffective and increase the risk for negative outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05027438; https://classic.clinicaltrials.gov/ct2/show/NCT05027438.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/47636.

A pragmatic methodical framework for the user-centred development of an electronic process support for the sleep laboratory patients' management

OBJECTIVE

Limited capacities and ineffective care pathways result in long waiting times for patients and sporadic treatment controls in sleep medicine. As one objective of the 'Telesleep Medicine' project, a portal should be developed, which supports sleep specialists in an efficient and resource-saving patient management. On account of the limited project timeframe, the 'classical' user-centred design and evaluation methods could not be comprehensively implemented. Therefore, a pragmatic methodical framework was developed.

METHODS

For the iterative development of the portal, a combination of low-cost and quick-to-implement methods was used. In chronological order, these were: context interviews, personas, the development of an as-is model, a web search of design standards and good design aspects of similar systems, the development of a to-be model, the creation of an overarching mind map, and the iterative creation of mockups with simplified usability walkthroughs.

RESULTS

The feasibility of the pragmatic methodological framework for the development of a prototype for the portal was demonstrated. The used method combination resulted in a prototype based on the needs and requirements of the sleep specialists, taking into account their specific workflow and the technical implementation conditions.

CONCLUSIONS

The presented pragmatic methodological framework can be a valuable resource for developers of comparable projects. The combination of methods worked well together regarding the limited timeframe and resources for concept development. For the future, we plan to implement and test the portal in the clinical field and thus enrich our framework with additional methods.

A Pilot Randomized Controlled Trial of the Insomnia Coach Mobile App to Assess Its Feasibility, Acceptability, and Potential Efficacy

Insomnia is highly prevalent among military veterans but access to cognitive-behavioral therapy for insomnia (CBT-I) is limited. Thus, this study examined the feasibility, acceptability, and potential efficacy of Insomnia Coach, a CBT-I-based, free, self-management mobile app. Fifty U.S. veterans, who were mostly male (58%) and mean age 44.5 (range = 28-55) years with moderate insomnia symptoms were randomized to Insomnia Coach (n = 25) or a wait-list control condition (n = 25) for 6 weeks. Participants completed self-report measures and sleep diaries at baseline, posttreatment, and follow-up (12 weeks postrandomization), and app participants (n = 15) completed a qualitative interview at posttreatment. Findings suggest that Insomnia Coach is feasible to use, with three quarters of participants using the app through 6 weeks and engaging with active elements. For acceptability, perceptions of Insomnia Coach were very favorable based on both self-report and qualitative interview responses. Finally, for potential efficacy, at posttreatment, a larger proportion of Insomnia Coach (28%) than wait-list control participants (4%) achieved clinically significant improvement (p = .049) and there was a significant treatment effect on daytime sleep-related impairment (d = -0.6, p = .044). Additional treatment effects emerged at follow-up for insomnia severity (d = -1.1, p = .001), sleep onset latency (d = -0.6, p = .021), global sleep quality (d = -0.9, p = .002), and depression symptoms (d = -0.8, p = .012). These findings provide preliminary evidence that among veterans with moderate insomnia symptoms, a CBT-I-based self-management app is feasible, acceptable, and promising for improving insomnia severity and other sleep-related outcomes. Given the vast unmet need for insomnia treatment in the population, Insomnia Coach may provide an easily accessible, convenient public health intervention for individuals not receiving care.

How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review

BACKGROUND

The rapid growth of consumer sleep technology demonstrates the population's interest in measuring sleep. However, the extent to which these devices can be used in the delivery of behavioral sleep interventions is currently unknown. The objectives of this systematic review were to evaluate the use of consumer sleep technology (wearable and mobile) in behavioral sleep medicine interventions, identify gaps in the literature and potential future directions.

METHODS

We completed a scoping review of studies conducted in adult populations that used consumer sleep tracking technology to deliver sleep-related interventions.

RESULTS

Our initial search returned 4,538 articles and 14 articles met our inclusion/exclusion criteria. Results demonstrated that wearable devices are being used for two main purposes: 1. To deliver treatment for insomnia and 2. Sleep monitoring as part of overall wellness programs. Half of the articles reviewed (n = 7) used consumer sleep technology in a cognitive behavioral therapy for insomnia. The majority of the studies reviewed (n = 10) were fully digital, without human intervention, and only two small studies evaluated interventions delivered with and without a sleep tracking device.

CONCLUSIONS

These studies demonstrate opportunities to utilize consumer sleep trackers in insomnia treatment and wellness programs, but most new and innovative interventions are in the early, feasibility stages. Future research is needed to determine how to leverage wearables to improve existing behavioral sleep treatments and determine how this technology can engage patients and reduce barriers to behavioral sleep medicine interventions.

Challenges and Opportunities for Applying Wearable Technology to Sleep

Wearable technology has a history in sleep research dating back to the 1970s. Because modern wearable technology is relatively cheap and widely used by the general population, this represents an opportunity to leverage wearable devices to advance sleep medicine and research. However, there is a lack of published validation studies designed to quantify device performance against accepted gold standards, especially across different populations. Recommendations for conducting performance assessments and using wearable devices are now published with the goal of standardizing wearable device implementation and advancing the field.

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

Investigating Group-Specific Models of Hospital Workers’ Well-Being: Implications for Algorithmic Bias

Hospital workers often experience burnout due to the demanding job responsibilities and long work hours. Data yielding from ambulatory monitoring combined with machine learning algorithms can afford us a better understanding of the naturalistic processes that contribute to this burnout. Motivated by the challenges related to the accurate tracking of well-being in real-life, prior work has investigated group-specific machine learning (GS-ML) models that are tailored to groups of participants. We examine a novel GS-ML for estimating well-being from real-life multimodal measures collected in situ from hospital workers. In contrast to the majority of prior work that uses pre-determined clustering criteria, we propose an iterative procedure that refines participant clusters based on the representations learned by the GS-ML models. Motivated by prior work that highlights the differential impact of job demands on well-being, we further explore the participant clusters in terms of demography and job-related attributes. Results indicate that the GS-ML models mostly outperform general models in estimating well-being constructs. The GS-ML models further depict different degrees of predictive power for each participant cluster, as distinguished upon age, education, occupational role, and number of supervisees. The observed discrepancies with respect to the GS-ML model decisions are discussed in association with algorithmic bias.

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.

Enhancing behavioral sleep care with digital technology: study protocol for a hybrid type 3 implementation-effectiveness randomized trial

BACKGROUND

Insomnia affects almost one in four military service members and veterans. The first-line recommended treatment for insomnia is cognitive-behavioral therapy for insomnia (CBTI). CBTI is typically delivered in-person or online over one-to-four sessions (brief versions) or five-to-eight sessions (standard versions) by a licensed doctoral or masters-level clinician with extensive training in behavioral sleep medicine. Despite its effectiveness, CBTI has limited scalability. Three main factors inhibit access to and delivery of CBTI including restricted availability of clinical expertise; rigid, resource-intensive treatment formats; and limited capacities for just-in-time monitoring and treatment personalization. Digital technologies offer a unique opportunity to overcome these challenges by providing scalable, personalized, resource-sensitive, adaptive, and cost-effective approaches for evidence-based insomnia treatment.

METHODS

This is a hybrid type 3 implementation-effectiveness randomized trial using a scalable evidence-based digital health software platform, NOCTEM™'s Clinician-Operated Assistive Sleep Technology (COAST™). COAST includes a clinician portal and a patient app, and it utilizes algorithms that facilitate detection of sleep disordered patterns, support clinical decision-making, and personalize sleep interventions. The first aim is to compare three clinician- and system-centered implementation strategies on the reach, adoption, and sustainability of the COAST digital platform by offering (1) COAST only, (2) COAST plus external facilitation (EF: assistance and consultation to providers by NOCTEM's sleep experts), or (3) COAST plus EF and internal facilitation (EF/IF: assistance/consultation to providers by NOCTEM's sleep experts and local champions). The second aim is to quantify improvements in insomnia among patients who receive behavioral sleep care via the COAST platform. We hypothesize that reach, adoption, and sustainability and the magnitude of improvements in insomnia will be superior in the EF and EF/IF groups relative to the COAST-only group.

DISCUSSION

Digital health technologies and machine learning-assisted clinical decision support tools have substantial potential for scaling access to insomnia treatment. This can augment the scalability and cost-effectiveness of CBTI without compromising patient outcomes. Engaging providers, stakeholders, patients, and decision-makers is key in identifying strategies to support the deployment of digital health technologies that can promote quality care and result in clinically meaningful sleep improvements, positive systemic change, and enhanced readiness and health among service members.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04366284 . Registered on 28 April 2020.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

INTRODUCTION

We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.

METHODS

We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain.

RESULTS

Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited.

CONCLUSION

There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.

Improving RNN Performance by Modelling Informative Missingness with Combined Indicators

Daily questionnaires from mobile applications allow large amounts of data to be collected with relative ease. However, these data almost always suffer from missing data, be it due to unanswered questions, or simply skipping the survey some days. These missing data need to be addressed before the data can be used for inferential or predictive purposes. Several strategies for dealing with missing data are available, but most are prohibitively computationally intensive for larger models, such as a recurrent neural network (RNN). Perhaps even more important, few methods allow for data that are missing not at random (MNAR). Hence, we propose a simple strategy for dealing with missing data in longitudinal surveys from mobile applications, using a long-term-short-term-memory (LSTM) network with a count of the missing values in each survey entry and a lagged response variable included in the input. We then propose additional simplifications for padding the days a user has skipped the survey entirely. Finally, we compare our strategy with previously suggested methods on a large daily survey with data that are MNAR and conclude that our method worked best, both in terms of prediction accuracy and computational cost.

Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study

Background

Although evidence-based cognitive behavioral sleep treatments have been shown to be safe and effective, these treatments have limited scalability. Mobile health tools can address this scalability challenge. iREST, or interactive Resilience Enhancing Sleep Tactics, is a mobile health platform designed to provide a just-in-time adaptive intervention (JITAI) in the assessment, monitoring, and delivery of evidence-based sleep recommendations in a scalable and personalized manner. The platform includes a mobile phone–based patient app linked to a clinician portal.

Objective

The first aim of the pilot study was to evaluate the effectiveness of JITAI using the iREST platform for delivering evidence-based sleep interventions in a sample of military service members and veterans. The second aim was to explore the potential effectiveness of this treatment delivery form relative to habitual in-person delivery.

Methods

In this pilot study, military service members and veterans between the ages of 18 and 60 years who reported clinically significant service-related sleep disturbances were enrolled as participants. Participants were asked to use iREST for a period of 4 to 6 weeks during which time they completed a daily sleep/wake diary. Through the clinician portal, trained clinicians offered recommendations consistent with evidence-based behavioral sleep treatments on weeks 2 through 4. To explore potential effectiveness, self-report measures were used, including the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), and the PSQI Addendum for Posttraumatic Stress Disorder.

Results

A total of 27 participants completed the posttreatment assessments. Between pre- and postintervention, clinically and statistically significant improvements in primary and secondary outcomes were detected (eg, a mean reduction on the ISI of 9.96, t₂₆=9.99, P<.001). At posttreatment, 70% (19/27) of participants met the criteria for treatment response and 59% (16/27) achieved remission. Comparing these response and remission rates with previously published results for in-person trials showed no significant differences.

Conclusion

Participants who received evidence-based recommendations from their assigned clinicians through the iREST platform showed clinically significant improvements in insomnia severity, overall sleep quality, and disruptive nocturnal disturbances. These findings are promising, and a larger noninferiority clinical trial is warranted.