A Mobile Health App to Improve HIV Medication Adherence: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Adherence to antiretroviral therapy (ART) is essential for allowing persons living with HIV to live longer, healthier lives. However, a large portion of this population has suboptimal adherence and are not virally suppressed. Conventional interventions aimed at improving ART adherence lack portability and scalability, and improvements in adherence are not often sustained. Mobile health (mHealth) ART interventions offer a low-cost and accessible method of improving adherence, but many have limited functionality and do not offer comprehensive support. The combination of an mHealth intervention with a face-to-face adherence intervention and interactive health coaching feature may offer sufficient support in a manner that is sensitive to resource limitations that are often found in HIV treatment settings. This paper details the protocol of a study designed to evaluate the potential of an enhanced mHealth intervention for improving ART adherence. OBJECTIVE: The primary objective of this study is to assess the feasibility and acceptability of the Fitbit Plus app enhanced with a face-to-face LifeSteps session (Fitbit Plus condition) for improving ART adherence. In addition, we will determine the preliminary efficacy of the intervention by calculating treatment effect sizes. METHODS: This study will be conducted in 2 phases. The intervention will be developed and piloted with a small group of participants during phase 1. Pilot participants will provide feedback that will be used to refine the intervention for phase 2. In phase 2, a preliminary randomized controlled trial (RCT) comparing Fitbit Plus with a condition that approximates the standard of care (SOC) will be conducted with 60 persons living with HIV. Interviews will be conducted with RCT participants at baseline, and follow-up interviews will be conducted at 1, 3, 6, and 12 months. ART adherence is the primary outcome and will be monitored throughout the study via electronic pill boxes. Effect sizes will be generated using a fractional logit model estimated by generalized estimating equations. RESULTS: Phase 1 of this trial is complete; data collection for phase 2 is ongoing. Follow-ups with enrolled participants will conclude in January 2020. CONCLUSIONS: This study will contribute to the literature on ART adherence and may produce an efficacious intervention. Owing to a small sample size, there may be insufficient power to detect statistically significant differences between Fitbit Plus and SOC. However, if Fitbit Plus is found to be acceptable and feasible and yields promising effect size estimates, this pilot study could serve as the foundation for a larger, fully powered trial of Fitbit Plus. TRIAL REGISTRATION: ClinicalTrials.gov NCT02676128; https://clinicaltrials.gov/ct2/show/NCT02676128. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15356.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.126 | 0.019 |
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".