Nurse-Led Mobile Phone Intervention to Promote Self-Management in Type 2 Diabetes in Ghana: A Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to test the effectiveness of a nurse-led mobile phone intervention (NMPI) on glycemic variability and self-management among people living with type 2 diabetes (T2DM) in Ghana. METHODS: In this randomized controlled trial, the intervention group received a 3-month NMPI program plus standard care, and the control group received standard care alone in a tertiary health care setting. Ninety-eight participants (baseline A1C > 7%) were randomized 1:1 to either NMPI or standard care group. The primary study outcomes were changes in A1C testing and self-management assessed using the Summary of Diabetes Self-Care Activities tool at baseline and end of the study. RESULTS: The intervention group had statistically significant improvement in their mean A1C level from baseline to the end of the study. In comparison, the control group also had improvement in their mean A1C level but was not statistically significant. Consistently, the intervention participants had better statistically significant improvements in self-management behaviors than the control group. There was a medium, negative correlation between A1C changes and overall self-care changes for the intervention group, whereas that of the control group was smaller. CONCLUSIONS: Study findings have shown that a tailored NMPI program in addition to standard care could improve glycemic variability and self-management among people living with poorly managed T2DM in Ghana better than standard care alone.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".