Exploring Adolescents’ Attitudes Toward Mental Health Apps: Concurrent Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Adolescence is a critical time in which many psychological disorders develop. Mental health promotion is important, especially during this period. In recent years, an increasing number of mobile apps geared toward mental health promotion and preventing mental illness have been developed specifically for adolescents, with the goal of strengthening their mental health and well-being. OBJECTIVE: This study aims to explore adolescents' attitudes toward mental health apps, as well as the perceived usefulness of mental health apps. METHODS: In this mixed methods study, a total of 183 adolescents (mean age 15.62, SD 3.21 years) answered a cross-sectional questionnaire, with 10 questions (eg, "What do you think about mental health apps in general?"). To complement the quantitative findings, individual interviews were conducted with 9 adolescents, during which they could elaborate on their opinions about mental health apps. RESULTS: A total of 30% (56/183) of the adolescents in the quantitative study had used a mental health app. Over half of the respondents (77/126, 61.1%) reported that they would use a mental health app if they had a mental health problem as well as that they thought mental health apps were somewhat or very useful (114/183, 62.3%). Availability was the most frequently reported advantage of mental health apps (107/183, 58.8%). Possible associated costs of mental health apps were the most frequently mentioned barrier to their use (87/183, 47.5%). Findings from the interviews also pointed to the importance of the availability of mental health apps as well as their credibility and potential to provide adolescents with autonomy when seeking mental health advice and help. CONCLUSIONS: Overall, the results indicate that adolescents have a positive attitude toward and an interest in mental health apps. However, adolescents are also more or less unaware of such apps, which might be one reason why they are often not used. The findings of this study have important implications for future research on mental health apps and for developers of mental health apps that target young people. The insights gained from this study can inform the development of more effective mental health apps that better meet the needs and preferences of adolescents.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".