Usability and Quality of the JoyPop App: Prospective Evaluation Study
Bibliographic record
Abstract
Background: Mental health difficulties are increasing among Canadian postsecondary students, and many face barriers to accessing mental health care. Mobile health smartphone apps for mental health reduce common barriers to care and improve student mental health outcomes. However, students' engagement and use of mental health apps is low. Evaluating the usability and quality of mental health apps is essential not only for user engagement but also for safety and overall utility. Few mental health apps have undergone usability and quality evaluations, especially with measures explicitly designed for these apps. The JoyPop app is a resilience-building mental health app with evidence supporting its effectiveness for student mental health. It has yet to be evaluated using standardized measures of mental health app usability and quality, and the influence of usability and quality on use is unknown. Objective: We evaluated the usability and quality of the JoyPop app and the predictive importance of usability and quality, compared to other relevant user characteristics, in predicting intentions to use the app in the future (usage intentions). Methods: Participants (N=183) completed preapp measures assessing demographics and personality traits, then used the app for 1 week, and then completed postapp measures assessing the usability, quality, and use of the JoyPop app. Usability (overall; and subscales: ease of use, interface and satisfaction, and usefulness) and quality (objective, subjective, and perceived impact) were assessed with descriptive statistics. Multiple regression analyses tested the predictive importance of usability and quality on usage intentions after controlling for other user characteristics. Results: Participants rated the JoyPop app's overall usability as "very good" (mean 5.63, SD 0.85). Participants rated the JoyPop app's overall objective quality as "excellent" (mean 4.06, SD 0.54). Subjective quality ratings were good, with many participants (135/183, 73.8%) indicating they would recommend the app to others. Participants rated the app as having a moderate and helpful impact on their mental health and coping skills (mean 3.48, SD 0.88). In each regression model, usability (β=.56, P<.001) and quality (β=.52, P<.001) were the strongest predictors and predicted usage intentions over and above other user characteristics. Conclusions: Results align with prior research evaluating the JoyPop app and maintain that it is an engaging and high-quality mental health app that can support students. Findings provide important insight into the optimal design of mental health apps for students and inform adaptations to future iterations of the JoyPop app.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".