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User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity.

ABSTRACT: OBJECTIVE:Despite the potential benefits of mobile mental health apps, real-world results indicate engagement issues because of low uptake and sustained use. This review examined how studies have measured and reported on user engagement indicators (UEIs) for mental health apps. METHODS:A systematic review of multiple databases was performed in July 2018 for studies of mental health apps for depression, bipolar disorder, schizophrenia, and anxiety that reported on UEIs, namely usability, user satisfaction, acceptability, and feasibility. The subjective and objective criteria used to assess UEIs, among other data, were extracted from each study. RESULTS:Of 925 results, 40 studies were eligible. Every study reported positive results for the usability, satisfaction, acceptability, or feasibility of the app. Of the 40 studies, 36 (90%) employed 371 indistinct subjective criteria that were assessed with surveys, interviews, or both, and 23 studies used custom subjective scales, rather than preexisting standardized assessment tools. A total of 25 studies (63%) used objective criteria-with 71 indistinct measures. No two studies used the same combination of subjective or objective criteria to assess UEIs of the app. CONCLUSIONS:The high heterogeneity and use of custom criteria to assess mental health apps in terms of usability, user satisfaction, acceptability, or feasibility present a challenge for understanding real-world low uptake of these apps. Every study reviewed claimed that UEIs for the app were rated highly, which suggests a need for the field to focus on engagement by creating reporting standards and more carefully considering claims.

SUBMITTER: Ng MM

PROVIDER: S-EPMC6839109 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity.

Ng Michelle M MM Firth Joseph J Minen Mia M Torous John J

Psychiatric services (Washington, D.C.) 20190327 7

<h4>Objective</h4>Despite the potential benefits of mobile mental health apps, real-world results indicate engagement issues because of low uptake and sustained use. This review examined how studies have measured and reported on user engagement indicators (UEIs) for mental health apps.<h4>Methods</h4>A systematic review of multiple databases was performed in July 2018 for studies of mental health apps for depression, bipolar disorder, schizophrenia, and anxiety that reported on UEIs, namely usab ...[more]

PMID: 30914003

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Project description:BACKGROUND:Understanding patterns of real-world usage of mental health apps is key to maximizing their potential to increase public self-management of care. Although developer-led studies have published results on the use of mental health apps in real-world settings, no study yet has systematically examined usage patterns of a large sample of mental health apps relying on independently collected data. OBJECTIVE:Our aim is to present real-world objective data on user engagement with popular mental health apps. METHODS:A systematic engine search was conducted using Google Play to identify Android apps with 10,000 installs or more targeting anxiety, depression, or emotional well-being. Coding of apps included primary incorporated techniques and mental health focus. Behavioral data on real-world usage were obtained from a panel that provides aggregated nonpersonal information on user engagement with mobile apps. RESULTS:In total, 93 apps met the inclusion criteria (installs: median 100,000, IQR 90,000). The median percentage of daily active users (open rate) was 4.0% (IQR 4.7%) with a difference between trackers (median 6.3%, IQR 10.2%) and peer-support apps (median 17.0%) versus breathing exercise apps (median 1.6%, IQR 1.6%; all z?3.42, all P<.001). Among active users, daily minutes of use were significantly higher for mindfulness/meditation (median 21.47, IQR 15.00) and peer support (median 35.08, n=2) apps than for apps incorporating other techniques (tracker, breathing exercise, psychoeducation: medians range 3.53-8.32; all z?2.11, all P<.05). The medians of app 15-day and 30-day retention rates were 3.9% (IQR 10.3%) and 3.3% (IQR 6.2%), respectively. On day 30, peer support (median 8.9%, n=2), mindfulness/meditation (median 4.7%, IQR 6.2%), and tracker apps (median 6.1%, IQR 20.4%) had significantly higher retention rates than breathing exercise apps (median 0.0%, IQR 0.0%; all z?2.18, all P?.04). The pattern of daily use presented a descriptive peak toward the evening for apps incorporating most techniques (tracker, psychoeducation, and peer support) except mindfulness/meditation, which exhibited two peaks (morning and night). CONCLUSIONS:Although the number of app installs and daily active minutes of use may seem high, only a small portion of users actually used the apps for a long period of time. More studies using different datasets are needed to understand this phenomenon and the ways in which users self-manage their condition in real-world settings.

Project description:BackgroundWhile many digital mental health interventions (DMHIs) have been found to be efficacious, patient engagement with DMHIs has increasingly emerged as a concern for implementation in real-world clinical settings. To address engagement, we must first understand what standard engagement levels are in the context of randomized controlled trials (RCTs) and how these compare with other treatments.ObjectiveThis scoping review aims to examine the state of reporting on intervention engagement in RCTs of mobile app-based interventions intended to treat symptoms of depression. We sought to identify what engagement metrics are and are not routinely reported as well as what the metrics that are reported reflect about standard engagement levels.MethodsWe conducted a systematic search of 7 databases to identify studies meeting our eligibility criteria, namely, RCTs that evaluated use of a mobile app-based intervention in adults, for which depressive symptoms were a primary outcome of interest. We then extracted 2 kinds of information from each article: intervention details and indices of DMHI engagement. A 5-element framework of minimum necessary DMHI engagement reporting was derived by our team and guided our data extraction. This framework included (1) recommended app use as communicated to participants at enrollment and, when reported, app adherence criteria; (2) rate of intervention uptake among those assigned to the intervention; (3) level of app use metrics reported, specifically number of uses and time spent using the app; (4) duration of app use metrics (ie, weekly use patterns); and (5) number of intervention completers.ResultsDatabase searching yielded 2083 unique records. Of these, 22 studies were eligible for inclusion. Only 64% (14/22) of studies included in this review specified rate of intervention uptake. Level of use metrics was only reported in 59% (13/22) of the studies reviewed. Approximately one-quarter of the studies (5/22, 23%) reported duration of use metrics. Only half (11/22, 50%) of the studies reported the number of participants who completed the app-based components of the intervention as intended or other metrics related to completion. Findings in those studies reporting metrics related to intervention completion indicated that between 14.4% and 93.0% of participants randomized to a DMHI condition completed the intervention as intended or according to a specified adherence criteria.ConclusionsFindings suggest that engagement was underreported and widely varied. It was not uncommon to see completion rates at or below 50% (11/22) of those participants randomized to a treatment condition or to simply see completion rates not reported at all. This variability in reporting suggests a failure to establish sufficient reporting standards and limits the conclusions that can be drawn about level of engagement with DMHIs. Based on these findings, the 5-element framework applied in this review may be useful as a minimum necessary standard for DMHI engagement reporting.

Project description:BackgroundThe use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement.ObjectiveThis systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes.MethodsWe conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed.ResultsOf the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results.ConclusionsIncreased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

Project description:BackgroundMobile health (mHealth) apps offer great opportunities to deliver large-scale, cost-efficient digital solutions for implementing lifestyle changes. Furthermore, many mHealth apps act as medical devices. Yet, there is little research on how to assess user satisfaction with an mHealth solution.ObjectiveThis study presents the development of the mHealth Satisfaction Questionnaire and evaluates its measurement properties.MethodsRespondents who took part in the Health Integrator Study and were randomized to use the Health Integrator smartphone app for lifestyle changes (n=112), with and without additional telephone coaching, rated their satisfaction with the app using the new 14-item mHealth Satisfaction Questionnaire. The ratings were given on a 5-point Likert scale and measurement properties were evaluated using Rasch measurement theory (RMT).ResultsOptimal scoring was reached when response options 2, 3, and 4 were collapsed, giving three response categories. After omitting two items that did not fit into the scale, fit residuals were within, or close to, the recommended range of ±2.5. There was no differential item functioning between intervention group, age group, or sex. The Person Separation Index was 0.79, indicating that the scale's ability to discriminate correctly between person leniency was acceptable for group comparisons but not for individual evaluations. The scale did not meet the criterion of unidimensionality; 16.1% (18/112) of the respondents were outside the desired range of -1.96 to 1.96. In addition, several items showed local dependency and three underlying dimensions emerged: negative experiences, positive experiences, and lifestyle consequences of using the mHealth solution.ConclusionsIn times where mHealth apps and digital solutions are given more attention, the mHealth Satisfaction Questionnaire provides a new possibility to measure user satisfaction to ensure usability and improve development of new apps. Our study is one of only a few cases where RMT has been used to evaluate the usability of such an instrument. There is, though, a need for further development of the mHealth Satisfaction Questionnaire, including the addition of more items and consideration of further response options. The mHealth Satisfaction Questionnaire should also be evaluated in a larger sample and with other mHealth apps and in other contexts.Trial registrationClinicalTrials.gov NCT03579342; http://clinicaltrials.gov/ct2/show/NCT03579342.

Project description:BackgroundMobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps.ObjectiveThis study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention.MethodsWe conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention.ResultsOf the 389 identified studies, 62 (15.9%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained.ConclusionsRetaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention.

Dataset Information

User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity.

Publications

User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity.

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