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Introduction Hypnotics continue to be preferentially used in practice for the treatment of chronic insomnia, yet the comparative safety and efficacy of medication options are unclear. While several position statements and network meta-analyses have provided some guidance, none have provided a quantitative assessment of risk-benefit. In the present analysis, each therapeutic class commonly used for chronic insomnia is quantitatively ranked with respect to safety, efficacy, and risk-benefit. Methods Safety data for FDA-approved hypnotics were extracted from the package insert adverse event tables and standardized to form a placebo-adjusted adverse event rate per 1000 (AEr). Efficacy data were extracted from randomized controlled trials identified in professional society position statements and published systematic reviews. The efficacy metric was computed from placebo adjusted pre-post change scores for self-reported sleep continuity (i.e., sleep latency [SL], wake after sleep onset [WASO], and total sleep time [TST]) and represented as a summed composite effect size score (SWT). Risk-benefit (R/B) was represented as a ratio between AEr and SWT. Comparative safety, efficacy, and risk-benefit metrics were calculated for five therapeutic classes: Benzodiazepines (BZs), non-benzodiazepine benzodiazepine receptor agonists (BZRAs), dual orexin receptor antagonists (DORAs), melatonin receptor agonists (MELAs), and sedating antidepressants (SADs). Results With respect to safety, MELAs had the lowest adverse event rate (AEr=43.1) and BZRAs had the highest rate (AEr=255.0). With respect to efficacy, BZs were the most efficacious (SWT=1.94, Mean ES=0.59) and MELAs were least efficacious (SWT=0.11, Mean ES=0.02). Overall, with respect to risk-benefit, SADs had the most favorable profile (R/B=69.5), while MELAs had the least favorable profile (R/B=395.7). Conclusion The optimal selection of hypnotics requires consideration of both risk and benefit. Findings suggest that SADs can be considered the first-line pharmacotherapeutic option due to the superior risk-benefit profile. If treatment responses are inadequate or if SADs are contraindicated, risk-benefit rankings may serve as a decision tree as part of a medical algorithm protocol to guide treatment selection for patients with chronic insomnia. Support (If Any) Sleep Research Society Mentor-Mentee Award (Cheung); K24AG055602 & R01AG054521 (Perlis)

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. CLINICALTRIAL