Exploring the Patterns of Use and Acceptability of Mobile Phones Among People Living With HIV to Improve Care and Treatment: Cross-Sectional Study in Three Francophone West African Countries
Bibliographic record
Abstract
BACKGROUND: The use of mobile technology in health care (mobile health [mHealth]) could be an innovative way to improve health care, especially for increasing retention in HIV care and adherence to treatment. However, there is a scarcity of studies on mHealth among people living with HIV (PLHIV) in West and Central Africa. OBJECTIVE: The aim of this study was to assess the acceptability of an mHealth intervention among PLHIV in three countries of West Africa. METHODS: A cross-sectional study among PLHIV was conducted in 2017 in three francophone West African countries: Côte d'Ivoire, Burkina Faso, and Togo. PLHIV followed in the six preselected HIV treatment and care centers, completed a standardized questionnaire on mobile phone possession, acceptability of mobile phone for HIV care and treatment, preference of mobile phone services, and phone sharing. Descriptive statistics and logistic regression were used to describe variables and assess factors associated with mHealth acceptability. RESULTS: A total of 1131 PLHIV-643 from Côte d'Ivoire, 239 from Togo, and 249 from Burkina Faso-participated in the study. Median age was 44 years, and 76.1% were women (n=861). Almost all participants owned a mobile phone (n=1107, 97.9%), and 12.6% (n=140) shared phones with a third party. Acceptability of mHealth was 98.8%, with the majority indicating their preference for both phone calls and text messages. Factors associated with mHealth acceptability were having a primary school education or no education (adjusted odds ratio=7.15, 95% CI 5.05-10.12; P<.001) and waiting over one hour before meeting a medical doctor on appointment day (adjusted odds ratio=1.84, 95% CI 1.30-2.62; P=.01). CONCLUSIONS: The use of mHealth in HIV treatment and care is highly acceptable among PLHIV and should be considered a viable tool to allow West and Central African countries to achieve the Joint United Nations Programme on HIV/AIDS 90-90-90 goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".