Using mHealth for HIV/TB Treatment Support in Lesotho: Enhancing Patient–Provider Communication in the START Study
Bibliographic record
Abstract
BACKGROUND: mHealth is a promising means of supporting adherence to treatment. The Start TB patients on ART and Retain on Treatment (START) study included real-time adherence support using short-text messaging service (SMS) text messaging and trained village health workers (VHWs). We describe the use and acceptability of mHealth by patients with HIV/tuberculosis and health care providers. METHODS: Patients and treatment supporters received automated, coded medication and appointment reminders at their preferred time and frequency, using their own phones, and $3.70 in monthly airtime. Facility-based VHWs were trained to log patient information and text message preferences into a mobile application and were given a password-protected mobile phone and airtime to communicate with community-based VHWs. The use of mHealth tools was analyzed from process data over the study course. Acceptability was evaluated during monthly follow-up interviews with all participants and during qualitative interviews with a subset of 30 patients and 30 health care providers at intervention sites. Use and acceptability were contextualized by monthly adherence data. FINDINGS: From April 2013 to August 2015, the automated SMS system successfully delivered 39,528 messages to 835 individuals, including 633 patients and 202 treatment supporters. Uptake of the SMS intervention was high, with 92.1% of 713 eligible patients choosing to receive SMS messages. Patient and provider interviews yielded insight into barriers and facilitators to mHealth utilization. The intervention improved the quality of health communication between patients, treatment supporters, and providers. HIV-related stigma and technical challenges were identified as potential barriers. CONCLUSIONS: The mHealth intervention for HIV/tuberculosis treatment support in Lesotho was found to be a low-tech, user-friendly intervention, which was acceptable to patients and health care providers.
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".