The Importance of User Segmentation for Designing Digital Therapy for Adolescent Mental Health: Findings From Scoping Processes
Bibliographic record
Abstract
BACKGROUND: New Zealand youth, especially those of Māori and Pacific descent, have high rates of depression, anxiety, and self-harm, but have low rates of help-seeking from mental health professionals. Apps, computerized therapy, and other digital tools can be effective, highly scalable treatments for anxiety and depression. Co-design processes are often used to foster engagement with end users, but this does not always lead to high levels of engagement. OBJECTIVE: We aimed to carry out preliminary scoping to understand adolescents' current internet use and diversity of preferences to inform a planned co-design process for creating digital mental health tools for teenagers. METHODS: Interactive workshops and focus groups were held with young people. Data were analyzed using a general inductive approach. RESULTS: Participants (N=58) engaged in 2 whānau (extended family) focus groups (n=4 and n=5), 2 school- or community-based focus groups (n=9 each), and 2 workshops (n=11 and n=20). The authors identified 3 overarching themes: (1) Digital mental health tools are unlikely to be successful if they rely solely on youth help-seeking. (2) A single approach is unlikely to appeal to all. Participants had diverse, noncompatible preferences in terms of look or feel of an app or digital tool. The authors identified 4 user groups players or gamers, engagers, sceptics, and straight-talkers. These groups differed by age and degree of current mental health need and preferred gamified or fun approaches, were open to a range of approaches, were generally disinterested, or preferred direct-to-the-point, serious approaches, respectively. (3) Digital mental health tools should provide an immediate response to a range of different issues and challenges that a young person may face. CONCLUSIONS: Defining the preferences of different groups of users may be important for increasing engagement with digital therapies even within specific population and mental health-need groups. This study demonstrates the importance of scoping possible user needs to inform design processes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".