Mediators of Intervention Effects on Depressive Symptoms Among People Living With HIV: Secondary Analysis of a Mobile Health Randomized Controlled Trial Using Latent Growth Curve Modeling
Bibliographic record
Abstract
BACKGROUND: Although several studies have investigated the effects of mobile health (mHealth) interventions on depression among people living with HIV, few studies have explored mediators of mHealth-based interventions to improve mental health in people living with HIV. Identifying influential mediators may enhance and refine effective components of mHealth interventions to improve mental health of people living with HIV. OBJECTIVE: This study aimed to examine mediating factors of the effects of a mHealth intervention, Run4Love, designed to reduce depression among people living with HIV using 4 time-point measurement data. METHODS: This study used data from a randomized controlled trial of a mHealth intervention among people living with HIV with elevated depressive symptoms in Guangzhou, China. A total of 300 patients were assigned to receive either the mHealth intervention (n=150) or a waitlist control group (n=150) through computer-generated block randomization. Depressive symptoms, coping, and HIV-related stigma were measured at baseline, 3-, 6-, and 9-month follow-ups. The latent growth curve model was used to examine the effects of the intervention on depressive symptoms via potential mediators. Mediating effects were estimated using bias-corrected 95% bootstrapped CIs (BCIs) with resampling of 5000. RESULTS: Enhanced positive coping and reduced HIV-related stigma served as effective treatment mediators in the mHealth intervention. Specially, there was a significant indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of positive coping (beta=-2.86; 95% BCI -4.78 to -0.94). The indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of HIV-related stigma was also statistically significant (beta=-1.71; 95% BCI -3.03 to -0.40). These findings indicated that enhancement of positive coping and reduction of HIV-related stigma were important mediating factors of the mHealth intervention in reducing depression among people living with HIV. CONCLUSIONS: This study revealed the underlying mediators of a mHealth intervention to reduce depression among people living with HIV using latent growth curve model and 4 time-point longitudinal measurement data. The study results underscored the importance of improving positive coping skills and mitigating HIV-related stigma in mHealth interventions to reduce depression among people living with HIV.
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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 | 0.044 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".