Popular Evidence-Based Commercial Mental Health Apps: Analysis of Engagement, Functionality, Aesthetics, and Information Quality
Bibliographic record
Abstract
BACKGROUND: There is a robust market for mobile health (mHealth) apps focused on self-guided interventions to address a high prevalence of mental health disorders and behavioral health needs in the general population. Disseminating mental health interventions via mHealth technologies may help overcome barriers in access to care and has broad consumer appeal. However, development and testing of mental health apps in formal research settings are limited and far outpaced by everyday consumer use. In addition to prioritizing efficacy and effectiveness testing, researchers should examine and test app design elements that impact the user experience, increase engagement, and lead to sustained use over time. OBJECTIVE: The aim of this study was to evaluate the objective and subjective quality of apps that are successful across both research and consumer sectors, and the relationships between objective app quality, subjective user ratings, and evidence-based behavior change techniques. This will help inform user-centered design considerations for mHealth researchers to maximize design elements and features associated with consumer appeal, engagement, and sustainability. METHODS: We conducted a user-centered design analysis of popular consumer apps with scientific backing utilizing the well-validated Mobile Application Rating Scale (MARS). Popular consumer apps with research support were identified via a systematic search of the App Store iOS (Apple Inc) and Google Play (Google LLC) and literature review. We evaluated the quality metrics of 19 mental health apps along 4 MARS subscales, namely, Engagement, Functionality, Aesthetics, and Information Quality. MARS total and subscale scores range from 1 to 5, with higher scores representing better quality. We then extracted user ratings from app download platforms and coded apps for evidence-based treatment components. We calculated Pearson correlation coefficients to identify associations between MARS scores, App Store iOS/Google Play consumer ratings, and number of evidence-based treatment components. RESULTS: The mean MARS score was 3.52 (SD 0.71), consumer rating was 4.22 (SD 0.54), and number of evidence-based treatment components was 2.32 (SD 1.42). Consumer ratings were significantly correlated with the MARS Functionality subscale (r=0.74, P<.001), Aesthetics subscale (r=0.70, P<.01), and total score (r=0.58, P=.01). Number of evidence-based intervention components was not associated with MARS scores (r=0.085, P=.73) or consumer ratings (r=-0.329, P=.16). CONCLUSIONS: In our analysis of popular research-supported consumer apps, objective app quality and subjective consumer ratings were generally high. App functionality and aesthetics were highly consistent with consumer appeal, whereas evidence-based components were not. In addition to designing treatments that work, we recommend that researchers prioritize aspects of app design that impact the user experience for engagement and sustainability (eg, ease of use, navigation, visual appeal). This will help translate evidence-based interventions to the competitive consumer app market, thus bridging the gap between research development and real-world implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".