Designing Digital Mental Health Interventions to Meet the Needs of Older Adolescents: Qualitative Interview and Group Discussion Study
Bibliographic record
Abstract
BACKGROUND: Anxiety and depression are common in adolescents, but adolescents are often uninterested in formal mental health treatments or are unable to access them. Digital interventions can be delivered at scale to bridge critical gaps in mental health care but must address the needs and preferences of adolescents. OBJECTIVE: This study aims to conduct qualitative research involving adolescents aged 18 years to inform both the design of digital mental health interventions for adolescents broadly and new features and refinements to incorporate in an automated SMS text messaging intervention, Small Steps SMS, that was originally designed for young adults. METHODS: We recruited non-treatment-engaged older adolescents who were aged 18 years, lived in the United States, and had experienced depression or anxiety. In total, 12 participants were recruited through social media advertising and online self-screeners hosted by Mental Health America, a mental health advocacy organization. For 24 days, participants answered researcher prompts and engaged with one another in an asynchronous online discussion group, with a new discussion prompt released every 3 days. In parallel, partway through the discussion group, participants received interactive messages from Small Steps SMS, an automated SMS text messaging intervention that delivers daily dialogues supporting mental health self-management. Questions in the discussion group pertained to mental health challenges, help-seeking attitudes, perceptions of Small Steps SMS, and ways the program and other digital mental health interventions could meet the needs of older adolescents. A subset of participants (n=4, 33%) also completed interviews to elaborate on their responses. Thematic analysis was applied to transcripts of the discussion group and interviews to characterize user needs and design priorities when making Small Steps SMS and similar interventions available to adolescents. RESULTS: Participants reported factors that contributed to their experience of mental health symptoms, including the transition from adolescence to adulthood, fears that the world is unstable and their futures are uncertain, and ineffective use of social media to cope with symptoms. Participants were proud of their generation's mental health acceptance but also observed a generational divide in mental health stigma and literacy that could impede seeking help from parents and other adults. Participants appreciated that Small Steps SMS allowed them to pursue mental health self-management conveniently and independently. They suggested that the program and similar interventions address adolescent-specific challenges and facilitate intergenerational communication about mental health. They also recommended possible ways to increase engagement through peer-to-peer communication, gamification, and greater explanation of self-management strategies. CONCLUSIONS: Major life transitions affected adolescent participants' mental health needs and preferences for digital mental health tools. While interactive automated messaging programs have the potential to support self-management in this population, program content and features should be adapted to adolescents' needs.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".