mHealth Interventions to Promote Anti-Retroviral Adherence in HIV: Narrative Review
Bibliographic record
Abstract
BACKGROUND: Antiretrovirals (ARVs) are key in the management of HIV. Although no cure exists, ARVs help patients live healthy lives and prevent transmission to others. Adherence to complex regimens is paramount to outcomes and in avoiding the emergence of drug-resistant viruses. The goal of therapy is to reach an undetectable viral load. However, adherence is a common problem, stemming from issues such as mental health, chaotic home situations, and busy work schedules. Mobile health (mHealth) represents a new approach in improving medication adherence, and multiple studies have been performed in this area. OBJECTIVE: This study aims to review the current implementation of mHealth in the management of HIV among different groups of patients. METHODS: We used PubMed, Academic Search Elite, and 1 journal database with various search terms to review the current implementation of mHealth in HIV care. RESULTS: Titles and abstracts were screened, and 61 papers were identified and fully reviewed. The literature was divided into lower- and higher-income nations, as defined by the United Nations. A total of 20 studies with quantitative results were identified, with 10 being text- and SMS-based interventions (the majority of these being in lower-income countries) and 8 being smartphone-based apps (primarily in higher-income countries). The majority of these studies determined whether there was an effect on adherence or biochemical parameters (viral load and CD4 count). Various qualitative studies have also been conducted, and many have focused on determining the specific design of interventions that were successful (frequency of messaging, types of messages, etc) as well as priorities for patients with regard to mHealth interventions. CONCLUSIONS: There seems to be a role of mHealth in the management of HIV in lower-income nations; however, the optimal design of an intervention needs to be delineated. In higher-income countries, where the 2 significant risk factors were injection drugs and men who have sex with men, the benefit was less clear, and more research is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".