Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review

Abstract

Background The number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively. Objective The aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. Methods We systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed. Results None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms. Conclusions Promising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.

Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis

“…These reviews generally found DBCIs to be effective in improving diabetes-related outcomes, particularly HbA 1c [ 51 , 52 , 61 - 64 ], which is in line with our findings; nevertheless, they also concur that the current evidence is limited and there is a need for adequately powered, rigorous trials with long-term follow-ups to determine the clinical and economic impact of such interventions [ 52 , 65 ]. In terms of JITAIs, a recent systematic review investigating popular mental health apps for individuals with depression concluded that JITAI mechanisms have not yet been translated into mainstream depression apps [ 66 ], which also aligns with our findings.…”

Digital Phenotyping Data to Predict Symptom Improvement and App Personalization: Protocol for a Prospective Study

“…Beyond their predictive ability, the results around the validity of the digital phenotyping biomarkers hold potential for advancing adaptive interventions [ 25 ]. A key component of adaptive interventions is the tailoring variable that is used to customize treatment at each decision time point [ 26 ].…”

Receptivity to mobile health interventions

“…Given the causal structure of proximal and distal outcomes, which is usually derived from meta-analyses, process research, optimization trials, or randomized controlled trials (Collins, 2018;Collins et al, 2011), an "ideal" MHI uses information about a target person (e.g., age, gender, location, genome, epigenome, microbiome, blood count results, personality trait, physiological data, specific adverse health behavior, or condition) and that target person's context (e.g., time of the day, weather condition, family or friends close-by) in the most unobtrusive way to predict vulnerable and receptive states. Unobtrusive prediction of these states often requires the use of-in a best-case scenario even noninvasive and contactless-sensors and other technologies to minimize any burden related to MHI use (Jakob et al, 2022;Keller et al, 2022;Teepe et al, 2021).…”

“…To this end, receptivity to MHIs is an important aspect to consider that bridges the detection of vulnerable states on the one side (Bent et al, 2021b;Bent, Lu, Kim, & Dunn, 2021a;Coravos, Khozin, & Mandl, 2019;Lekkas, Price, & Jacobson, 2022;Sahandi Far, Stolz, Fischer, Eickhoff, & Dukart, 2021;Sieberts et al, 2021;Teepe et al, 2021), and precision support on the other side (Haug et al, 2020;Hekler, Tiro, Hunter, & Nebeker, 2020;Kramer et al, 2020;Mishra et al, 2021;Schembre et al, 2018).…”