Can Digital Health Interventions Improve Adherence to Antiretroviral (ART) for Patients Living with HIV/AIDS in Sub-Saharan Africa? Systematic Review
Bibliographic record
Abstract
BACKGROUND: Human Immunodeficiency Virus (HIV) infection is one of the most devastating human pandemics in Sub-Saharan Africa (SSA) and this is the region most hit by pandemic. Adherence to Antiretroviral Therapy (ART remains challenging and varies between 27% and 80% compared to the required level of 95%. Lack of adherence is of one the major causes of treatment failures. Given the increase in the use of mobile phones in Africa, text messaging is seen as a potential strategy to improve medication adherence although there is little evidence to support this argument. The aim of this review is to evaluate the efficacy of text messaging interventions to improve adherence to antiretroviral treatment. METHODS: The Effective Public Health Practice Project (APHPP) tool was used to ensure that included Randomized Controlled Trials (RCT) studies follow vigorous methodological standards including selection bias, study design, confounders, blinding, data collection methods, and withdrawal and dropout. Selected bibliographic databases MEDLINE, Web of Science, and CINAHL Plus were searched for relevant articles published in English and dated between 2005 and 2018. Six trials met the inclusion criteria as set out in the protocol. Due to the inconsistency and the likely observed heterogeneity, narrative synthesis of evidence was carried out. RESULTS: The results from 2/3 of included studies provided evidence that text messages reminders improve adherence to antiretroviral treatment whereas 1/3 produced contradictory results. Nevertheless, weekly Short Messaging Service (SMS) reminders were more effective than daily (SMS) in achieving 95% self-reported adherence to antiretroviral treatment and in reducing the frequency of treatment interruptions. The results indicated that patients receiving text messages had their plasma HIV viral load suppressed, median CD4+ cell counts increased and were on 100% on time picking up monthly ART refills compared to the control. CONCLUSION: Included studies in this review provided evidence that simple SMS reminders were important in improving and sustaining optimal ART adherences. Text messaging is seen as potential strategy to improve medication adherence. Therefore, it should be included in health systems strategies to help improve sustainable development goals. The results suggest that preventing treatment failure can be achieved by SMS reminders in a resource limited setting.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".