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

Abstract

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.

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

“…e next were learnability and errors (9 times each); effectiveness (7 times) and operability (6 times); memorability, usefulness, flexibility, consistency, and understandability (5 times); privacy and acceptability (4 times); simplicity, interactivity, and aesthetics (3 times); accuracy and universality (2 times); and reliability and objectivity (1 time). Ease of use ese studies compared content with the targeted users to assess ease of use and acceptability of mobile health apps using a rating scale Acceptability [48][49][50][51][52] is shows that, in order to further enhance the capabilities of mobile eHealth applications, it is essential to enhance the accuracy and reliability of the eHealth applications.…”

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

“…There is a growing concern that apps should not be used for sleep because they can cause orthosomnia: an individual’s unhealthy obsession with achieving perfect sleep [ 8 ]. However, there are other reports that technology has the potential to improve sleep outcomes [ 9 - 11 ]. Possibly, the lack of consensus for the viability of mHealth apps with sleep is because of the components making up the intervention.…”

“…Despite several systematic reviews examining mHealth interventions for chronic disease management and other health behaviors [ 2 ], there has not been a systematic review dedicated to examining BCTs in fully automated mHealth apps for sleep. One systematic review [ 9 ] evaluated the design engineering and implementation of mHealth apps for sleep disturbances but included papers with no quantitative evaluations of sleep (eg, apps that only measured and tracked sleep) and apps that required clinician input (ie, did not function autonomously). There have also been promising reviews on the efficacy of internet-delivered interventions for insomnia [ 31 ] but they notably did not review mobile apps that offer unique features including portability, touchscreen interactivity, and notifications and alerts [ 32 ].…”

“…Given the rapidly expanding public health issue of inadequate sleep [ 33 ] and the vast number of commercially available sleep apps compared with the small number of peer-reviewed studies of sleep apps [ 9 ], conducting a systematic review of fully automated mHealth apps for sleep in the peer-reviewed literature is required. In this systematic review, we aim to identify when and how BCTs were used in mHealth apps for sleep, with the hope of informing future app development and providing areas to focus on in future meta-analyses.…”

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

“…Maintaining functioning mental health services during future lockdowns, especially for vulnerable populations, should be a priority for governments and may require innovative solutions. Telemedicine may represent an opportunity to maintain services, as some therapies and healthcare support are adaptable to new technologies (e.g., teleconsultation services and health care apps) some of which target sleep (Aji et al, 2021) (e.g., online cognitive and behavioral therapy for insomnia).…”