Enhancing Mental Health and Medication Adherence Among Men Who Have Sex With Men Recently Diagnosed With HIV With a Dialectical Behavior Therapy–Informed Intervention Incorporating mHealth, Online Skills Training, and Phone Coaching: Development Study Using Human-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Mental health problems are common among men who have sex with men (MSM) living with HIV and may negatively affect medication adherence. Psychosocial interventions designed to address these urgent needs are scarce in China. Incorporating behavioral health theories into intervention development strengthens the effectiveness of these interventions. The absence of a robust theoretical basis for interventions may also present challenges to identify active intervention ingredients. OBJECTIVE: This study aims to systematically describe the development of a mobile health-based intervention for MSM recently diagnosed with HIV in China, including the theoretical basis for the content and the considerations for its technological delivery. METHODS: We used intervention mapping (IM) to guide overall intervention development, a behavioral intervention technology model for technological delivery design, and a human-centered design and cultural adaptation model for intervention tailoring throughout all steps of IM. RESULTS: The dialectical behavior therapy (DBT)-informed intervention, Turning to Sunshine, comprised 3 components: app-based individual skills learning, group-based skills training, and on-demand phone coaching. The theoretical basis for the intervention content is based on the DBT model of emotions, which fits our conceptualization of the intervention user's mental health needs. The intervention aims to help MSM recently diagnosed with HIV (1) survive moments of high emotional intensity and strong action urges, (2) change emotional expression to regulate emotions, and (3) reduce emotional vulnerability, as well as (4) augment community resources for mental health services. Technological delivery considerations included rationale of the medium, complexity, and esthetics of information delivery; data logs; data visualization; notifications; and passive data collection. CONCLUSIONS: This study laid out the steps for the development of a DBT-informed mobile health intervention that integrated app-based individual learning, group-based skills training, and phone coaching. This intervention, Turning to Sunshine, aims to improve mental health outcomes for MSM newly diagnosed with HIV in China. The IM framework informed by human-centered design principles and cultural adaptation considerations offered a systematic approach to develop the current intervention and tailor it to the target intervention users. The behavioral intervention technology model facilitated the translation of behavioral intervention strategies into technological delivery components. The systematic development and reporting of the current intervention can serve as a guide for similar intervention studies. The content of the current intervention could be adapted for a broader population with similar emotional struggles to improve their mental health outcomes.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".