Cell Phone Availability and Usage for mHealth and Intervention Delivery to Persons Living With HIV in a Low-Resource Setting: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: HIV/AIDS is now a manageable chronic illness owing to effective antiretroviral therapy (ART), which involves routine follow-up care, including regular physical visits to the clinic. In the recent past, and in wake of the COVID-19 pandemic, there has been increased need for virtual care and intervention delivery, a modality known as mobile health (mHealth), which includes cell phone-delivered services for medical and public health practice. OBJECTIVE: Here we describe cell phone use and its relationship with alcohol use in a cohort of persons living with HIV and latent tuberculosis (TB). METHODS: We performed a cross-sectional analysis of baseline data from a cohort of persons living with HIV and latent TB in HIV care in southwestern Uganda. We estimated proportions of cell phone and text message use and evaluated their associations with alcohol use-a common modifiable behavior among persons living with HIV. Cell phone use (primary outcome) was defined as owning a cell phone that is turned on at least half of the day. Any alcohol use was defined as any self-reported alcohol use in the prior 3 months or a phosphatidylethanol (an alcohol biomarker) level of ≥8 ng/mL. RESULTS: A total of 300 participants (median age 40 years; n=146, 48.7% male) were included in the analysis. Most (n=267, 89.0%) participants had access to a phone and of them, 26 (9.7%) shared the phone with someone else. In total, 262/300 (87.3%) of participants owned a cell phone that is turned on at least half of the time; the majority (n=269, 89.7%) rarely or never sent text messages, and over two-thirds (n=200, 66.9%) rarely or never received text messages. Most (n=214, 71.3%) had any alcohol use in the prior 3 months. In adjusted analyses, any alcohol use was not significantly associated with cell phone use (adjusted odds ratio [aOR] 0.48, 95% CI 0.18-1.25; P=.13) or sending (aOR 0.82, 95% CI 0.28-2.37; P=.71) or receiving (aOR 1.31, 95% CI 0.70-2.47; P=.40) text messages. CONCLUSIONS: There is hope that mHealth interventions in this population can be carried out using cell phones owing to their popularity; however, the interventions may need to employ methods that do not rely on the sending and receiving of text messages only.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".