Evaluating the Effect of Using Mobile Phone Reminders on the Adherence to Children and Young Adults With Type 1 Diabetes Attending Outreach Appointments
Bibliographic record
Abstract
Effective management of Type 1 Diabetes Mellitus (T1DM) requires that people living with the condition attend regular clinical visits. The Rwanda Diabetes Association (RDA) asks young T1DM patients to attend quarterly outreach visits, and prior to the visits, RDA issues reminders via local radio stations. However, adherence in attending clinical appointments has remained low.Since Rwanda has a high mobile phone penetration rate, a pilot intervention study was conducted exploring the use of mobile phone call reminders and Short Message Service (SMS) messages to increase T1DM patients’ attendance of RDA’s quarterly outreach visits. The control group was exposed to only the regular radio broadcast, while the intervention group received reminder phone calls or SMS messages 72 hours prior to their appointments in addition to the regular radio broadcast.The attendance rate was significantly different between the 14 control patients and 35 intervention patients, with 23.3% (3/14) and 76.7% (27/35) attending visits, respectively (P=0.048). The results suggest that using mHealth methods (phone call/SMS reminders) can be effective in improving health outcomes, improving the adherence of T1DM patients to follow-up visits with minimal added cost. The total cost was 0.37 USD per person, compared to potential 672.40 USD for each lost treatment, indicating the intervention is cost-effective in that it minimizes loss to follow up in resource-limited settings. Further research is needed to evaluate the feasibility of scaling up the pilot project and to understand whether improved attendance is sustained long-term.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".