Adolescents’ Assessment of Two Mental Health–Promoting Mobile Apps: Results of Two User Surveys
Bibliographic record
Abstract
BACKGROUND: The importance of mental health promotion is irrevocable and is especially important at a young age. More mental health-promoting mobile apps have been developed in the last few years. However, their usability and quality have been rarely assessed. OBJECTIVE: The aim of this study is to investigate how adolescents assess the usability, quality, and potential goal achievement of Opp and NettOpp. Opp is a universal mental health-promoting mobile app aimed at 13- to 19-year-olds, and NettOpp is a mobile app for children and adolescents between 11 to 16 years of age that have experienced negative incidents online. METHODS: A total of 45 adolescents tested either Opp (n=30) or NettOpp (n=15) for a period of 3 weeks and answered a questionnaire. The System Usability Scale (SUS) was used to measure the usability of the apps. A SUS score above 70 indicates acceptable usability. Items from the Mobile Application Rating Scale were adapted for study purposes and used to measure the quality and perceived goal achievement that Opp and NettOpp might have on adolescents' knowledge, attitudes, and intention to change behavior. Furthermore, adolescents could answer an open comment question. RESULTS: Opp had a mean SUS score of 80.37 (SD 9.27), and NettOpp's mean SUS score was 80.33 (SD 10.30). In the overall evaluation, Opp and NettOpp were given a mean score of 3.78 (SD 0.42) and 4.20 (SD 0.56), respectively, on a 5-point scale, where 5 was best. Most adolescents who evaluated Opp rated that the app would increase knowledge about mental health and help young people deal with stress and difficult emotions or situations. Most adolescents who evaluated NettOpp agreed that the app would increase awareness and knowledge about cyberbullying, change attitudes toward cyberbullying, and motivate them to address cyberbullying. Some adolescents stated that Opp was difficult to navigate and consisted of too much text. Some of the adolescents that tested NettOpp stated that the app had crashed and that the design was a bit childish. CONCLUSIONS: All in all, this study indicates that Opp and NettOpp have good usability and that adolescents are satisfied with both apps. It also indicates that the potential goal achievement of the apps, for example, increasing knowledge about mental health (Opp) or cyberbullying (NettOpp) is promising. While there are some comments from the users that are more difficult to solve (eg, Opp is too text-based), some comments helped improve the apps (eg, that the app crashed). Overall, the user evaluation provided valuable knowledge about how adolescents assess Opp and NettOpp. However, more extensive effectiveness studies are necessary to measure their actual goal achievement.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".