Providing 2 Types of mHealth Interventions to Support Self-Management Among People Living With HIV: Randomized Clinical Trial
Bibliographic record
Abstract
Background: Mobile health (mHealth) has been continuously developed to support the HIV care continuum for people living with HIV. Considering the practical needs and acceptability of digital health solutions, it is essential to explore effective content and diverse delivery methods for self-management support. Objective: This study aimed to assess the effectiveness of 2 non-face-to-face mHealth interventions for people living with HIV. We compared the impact on HIV self-management of (1) a link group, which received access to information via mobile link, and (2) an app group, which used a mobile app enabling information exploration and self-recording of health outcomes, including medication adherence, symptoms, mental health score, and sexual safety. Methods: A 2-arm, prospective, randomized clinical trial was conducted, involving 83 people living with HIV aged 19 years or older, who were assigned to the app group (n=42) or link group (n=41). The primary outcome was self-reported self-efficacy for HIV management (HIV-SE), which comprised 6 domains: managing depression or mood, medication, symptoms, and fatigue; communicating with health care providers; and getting support or help. A paired t test and generalized estimating equation were used to analyze the outcomes at baseline, 4 weeks postintervention, and 8 weeks after an additional 4-week voluntary use period. Results: Both groups demonstrated improvements in total HIV-SE scores at 4 weeks compared with baseline. All domain scores improved in the app group, with a significant increase in total HIV-SE and managing fatigue. The link group significantly improved in managing depression or mood, fatigue, and getting support or help domains. The generalized estimating equation analysis indicated that, compared with the link group, the app group had significant group-by-time interaction with a positive effect on managing symptoms at 4 weeks (β=0.635, 95% CI 0.023 to 1.247; P=.04) but a negative effect on managing depression or mood at 8 weeks (β=-0.824, 95% CI -1.448 to -0.200; P=.01). Only 9.5% (4/42) of app group participants maintained daily visits during the voluntary use period of 4 to 8 weeks. Conclusions: Both types of informational mHealth interventions, through mobile apps or link access, contributed to improving HIV-SE. Delivering information via direct text message links could be suitable for individuals who are hesitant to use HIV-related apps. While mobile apps promote self-monitoring and symptom management through self-recording and reflection, strategies are needed to sustain long-term app engagement. In addition, user-customized psychiatric content beyond mental health recordings has been suggested for managing depressed moods in mHealth interventions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".