Apps for Mental Health: An Evaluation of Behavior Change Strategies and Recommendations for Future Development
Bibliographic record
Abstract
Mobile applications have shown promise in supporting people with mental health issues to adopt healthy lifestyles using various persuasive strategies. However, the extent to which mental health apps successfully employ various persuasive strategies remains unknown. Hence, it is important to understand the persuasive strategies integrated into mental health applications (apps) and how they are implemented to promote mental health. This paper aims to achieve three main objectives. First, we review 103 mental health apps and identify distinct persuasive strategies incorporated in them using the Persuasive Systems Design (PSD) model and Behaviour Change Techniques (BCTs). We further classify the persuasive strategies based on the type of mental health issues the app is focused on. Second, we reveal the various ways that the persuasive strategies are implemented/operationalized in mental health apps to achieve their intended objectives. Third, we examine the relationship between apps effectiveness (measured by user ratings) and the persuasive strategies employed. To achieve this, two researchers independently downloaded and used all identified apps to identify the persuasive strategies using the PSD model and BCTs. Next, they also examine the various ways that these strategies are implemented in mental health apps. The results show that the apps employed 26 distinct persuasive strategies and a range of 1 to 10 strategies per app. Self-monitoring (n=59), personalization (n=55), and reminder (n=49) were the most frequently employed strategies. We also found that anxiety, stress, depression, and general mental health issues were the common mental health issues targeted by the apps. Finally, we offer some design recommendations for designing mental health apps based on our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".