Developing an Internet-Based Cognitive Behavioral Therapy Intervention for Adolescents With Anxiety Disorders: Design, Usability, and Initial Evaluation of the CoolMinds Intervention
Bibliographic record
Abstract
BACKGROUND: Digital mental health interventions may help increase access to psychological treatment for adolescents with anxiety disorders. However, many clinical evaluations of digital treatments report low adherence and engagement and high dropout rates, which remain challenges when the interventions are implemented in routine care. Involving intended end users in the development process through user-centered design methods may help maximize user engagement and establish the validity of interventions for implementation. OBJECTIVE: This study aimed to describe the methods used to develop a new internet-based cognitive behavioral therapy intervention, CoolMinds, within a user-centered design framework. METHODS: The development of intervention content progressed in three iterative design phases: (1) identifying needs and design specifications, (2) designing and testing prototypes, and (3) running feasibility tests with end users. In phase 1, a total of 24 adolescents participated in a user involvement workshop exploring their preferences on graphic identity and communication styles as well as their help-seeking behavior. In phase 2, a total of 4 adolescents attended individual usability tests in which they were presented with a prototype of a psychoeducational session and asked to think aloud about their actions on the platform. In phase 3, a total of 7 families from the feasibility trial participated in a semistructured interview about their satisfaction with and initial impressions of the platform and intervention content while in treatment. Activities in all 3 phases were audio recorded, transcribed, and coded using thematic analysis and qualitative description design. The intervention was continuously revised after each phase based on the feedback. RESULTS: In phase 1, adolescent feedback guided the look and feel of the intervention content (ie, color scheme, animation style, and communication style). Participants generally liked content that was relatable and age appropriate and felt motivating. Animations that resembled "humans" received more votes as adolescents could better "identify" themselves with them. Communication should preferably be "supportive" and feel "like a friend" talking to them. Statements including praise-such as "You're well on your way. How are you today?"-received the most votes (12 votes), whereas directive statements such as "Tell us how your day has been?" and "How is practicing your steps going?" received the least votes (2 and 0 votes, respectively). In phase 2, adolescents perceived the platform as intuitive and easy to navigate and the session content as easy to understand but lengthy. In phase 3, families were generally satisfied with the intervention content, emphasizing the helpfulness of graphic material to understand therapeutic content. Their feedback helped identify areas for further improvement, such as editing down the material and including more in-session breaks. CONCLUSIONS: Using user involvement practices in the development of interventions helps ensure continued alignment of the intervention with end-user needs and may help establish the validity of the intervention for implementation in routine care practice.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".