Reaching Patients With Noncommunicable Diseases in Rural Tanzania Using Mobile Devices and Community Trust: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: A health service using mobile devices, mobile health (mHealth), has been widely applied to programs focusing on maternal and child health and communicable diseases in sub-Saharan African countries. However, mHealth apps for noncommunicable disease (NCD) services remain limited. OBJECTIVE: This study aimed to explore the acceptability and potential usability of SMS text messaging for patients and health care providers for the management of NCDs as part of an implementation research in rural Tanzania. METHODS: Nine focus group discussions were conducted with 56 participants (21 community health workers [CHWs], 17 patients, and 18 health care professionals [HPs]) in 3 districts in the Dodoma region, Tanzania. The interview guides were prepared in Swahili, and each session was recorded, transcribed, and translated into English. The focus group discussions consisted of the following topics: (1) perceptions of the participants about the possible use of mobile devices and SMS text messages as an mHealth platform in community health services; and (2) experiences of mobile device use in health activities or receiving health services via a mobile phone in the past. RESULTS: CHWs and HPs reported having familiarity using mobile devices to provide health services, especially for reaching or tracing patients in remote settings; however, patients with NCDs were less familiar with the use of mobile devices compared with the other groups. Hesitation to receive health services via SMS text messaging was seen in the patient group, as they wondered who would send health advice to them. Some patients expected services beyond what mHealth could do, such as aiding in recovery from a disease or sending notifications about the availability of prescription medications. CHWs showed interest in using text messaging to provide health services in the community; however, the concerns raised by CHWs included the cost of using their own mobile devices. Moreover, they demanded training about NCD management before engaging in such an activity. CONCLUSIONS: This study explored views and experiences regarding the possible installation of an mHealth intervention for managing NCDs in rural Tanzania. Although HPs and CHWs had experience using mobile devices to provide health services in non-NCD projects, only a few patients (3/17, 17%) had heard about the use of mobile devices to receive health services. To improve the suitability and acceptability of the intervention design for patients with NCDs, their trust must be earned. Involving CHWs in the intervention is recommended because they have already been appointed in the community and already know how to communicate effectively with patients in the area.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".