Background: Mental health conditions are considered the leading cause of disability, sickness absence, and long-term work incapacity. eHealth interventions provide employees with access to psychological assistance. There has been widespread implementation and provision of eHealth interventions in the workplace as an inexpensive and anonymous way of addressing common mental disorders. Objective: This updated review aimed to synthesize the literature on the efficacy of eHealth interventions for anxiety, depression, and stress outcomes in employee samples in organizational settings and evaluate whether their effectiveness has improved over time. Methods: Systematic searches of relevant articles published from 2004 to July 2020 of eHealth intervention trials (app- or web-based) focusing on the mental health of employees were conducted. The quality and bias of all studies were assessed. We extracted means and SDs from publications by comparing the differences in effect sizes (Hedge g) in standardized mental health outcomes. We meta-analyzed these data using a random-effects model. Results: We identified a tripling of the body of evidence, with 75 trials available for meta-analysis from a combined sample of 14,747 articles. eHealth interventions showed small positive effects for anxiety (Hedges g=0.26, 95% CI 0.13-0.39; P<.001), depression (Hedges g=0.26, 95% CI 0.19-0.34; P<.001), and stress (Hedges g=0.25, 95% CI 0.17-0.34; P<.001) in employees’ after intervention, with similar effects seen at the medium-term follow-up. However, there was evidence of no increase in the effectiveness of these interventions over the past decade. Conclusions: This review and meta-analysis confirmed that eHealth interventions have a small positive impact on reducing mental health symptoms in employees. Disappointingly, we found no evidence that, despite the advances in technology and the enormous resources in time, research, and finance devoted to this area for over a decade, better interventions are being produced. Hopefully, these small effect sizes do not represent optimum outcomes in organizational settings. Trial Registration: PROSPERO CRD42020185859; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=185859

Background: Mobile health (mHealth) apps demonstrate promise for improving sleep at scale. End-user engagement is a prerequisite for sustained use and effectiveness. Objective: We assessed the needs and preferences of those with poor sleep and insomnia to inform the development of an engaging sleep app. Methods: We triangulated results from qualitative (focus groups and app reviews) and quantitative (online survey) approaches. A total of 2 focus groups were conducted (N=9). An online survey tested themes identified from the focus groups against a larger population (N=167). In addition, we analyzed 434 user reviews of 6 mobile apps available on app stores. Results: Common focus group themes included the need to account for diverse sleep phenotypes with an adaptive and tailored program, key app features (alarms and sleep diaries), the complex yet condescending nature of existing resources, providing rationale for information requested, and cost as a motivator. Most survey participants (156/167, 93%) would try an evidence-based sleep app. The most important app features reported were sleep diaries (148/167, 88%), sharing sleep data with a doctor (116/167, 70%), and lifestyle tracking (107/167, 64%). App reviews highlighted the alarm as the most salient app feature (43/122, 35%) and data synchronization with a wearable device (WD) as the most commonly mentioned functionality (40/135, 30%). Conclusions: This co-design process involving end users through 3 methods consistently highlighted sleep tracking (through a diary and WD), alarms, and personalization as vital for engagement, although their implementation was commonly criticized in review. Engagement is negatively affected by poorly designed features, bugs, and didactic information which must be addressed. Other needs depend upon the type of user, for example, those with severe insomnia.

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.

This pilot trial aimed to provide evidence for whether the integration of a wearable device with digital behavioral therapy for insomnia (dBTi) improves treatment outcomes and engagement.One hundre...

Insomnia is a major public health concern. Sleep restriction therapy (SRT) is an effective behavioral treatment but its delivery is impeded by a shortage of trained clinicians. We developed a mobile app delivering SRT to individuals with insomnia. This feasibility study employed a mixed-methods design to examine the engagement, acceptability, and potential efficacy of the mobile app. Fifteen participants diagnosed with insomnia disorder used the mobile app synchronized with a wearable device for 3 weeks. Those who persisted with the study (n = 12) found the mobile app to be highly acceptable and engaging, logging on average 19 nightly sleep diary entries across the 21 day period. Significant improvements were observed for sleep measures (insomnia severity and sleep efficiency) and daytime symptoms (fatigue and sleepiness). The results suggest that a mobile app delivering SRT to individuals with insomnia is engaging, acceptable, and potentially efficacious. Further, a full-scale effectiveness study is warranted.

Sleep-related fears (i.e. fears related to sleep, nighttime and/or occurring before sleep) are linked to the development of anxiety and sleep problems in children. Parent- and child-report measurement tools are key to increasing understanding and facilitating better identification for treatment to prevent disorder development. We conducted a systematic review with the aim of identifying the breadth of parent- and child-report measures that include an assessment of sleep-related fears in children from 7 to 12 years old (stage 1) and reviewing the psychometric properties of measures that more comprehensively assess sleep-related fears (i.e. 3 or more items) using the COSMIN checklist (stage 2) to provide recommendations for measures (stage 3). A systematic search was performed using MEDLINE, EMBASE, ERIC and PsycINFO in accordance with PRISMA guidelines. At stage 1, we retrieved 14,495 records. Of these, 66 papers met eligibility and included 43 distinct measures. At stage 2, 11 out of the 43 measures met criteria for more comprehensive measurement of sleep-related fears. Findings demonstrated mixed and incomplete data and very little high-quality evidence. No measures met criteria for recommendation and the majority of measures require further validation studies.