Telenursing Health Education and Lifestyle Modification Among Patients With Diabetes in Bangladesh: Protocol for a Pilot Study With a Quasi-experimental Pre- and Postintervention Design
Bibliographic record
Abstract
Background The global burden of chronic diseases is increasing and becoming a public health issue throughout the world. The use of telenursing is increasing significantly during and after the COVID-19 pandemic to treat and prevent chronic diseases. Telenursing is growing in many countries to reduce health care costs, increase the number of aging and chronically ill populations, and increase health care coverage to distant, rural, small, or sporadically populated regions. Among its many benefits, telenursing may help to solve increasing shortages of nurses, reduce distances, save travel time, and keep patients out of the hospital. Objective The objective of this study is to apply the self-management telenursing program and telenursing system developed by the researchers to Bangladesh and to evaluate its feasibility and efficacy (improved diabetes control in participants). Methods This is a pilot, quasi-experimental pre- and post-intervention study. Diabetes patients who will attend the Grameen Primary Health Centers (PHCs) in Bangladesh will be enrolled between September 2024 and August 2025. We include patients who have been diagnosed with type 2 diabetes, both sexes, ages 18-75 years old, all types of treatment, willing to participate and give us consent. We exclude patients who have been diagnosed with gestational diabetes, diabetes as a secondary cause, complication of chronic kidney disease (CKD) stage 5, Hemoglobin A1c (HbA1c) is less than 7% for the past 1 year with CKD stage 1 or 2, no complications or complications with good control, having enough knowledge (had education before) and implemented good practice regarding diabetes management assessed by the research nurses, and disabled persons who need other person’s support for daily living. The sample size was calculated and found 70. Written informed consent will be obtained from all the participants. The study protocol got approval from the National Research Ethics Committee of the Bangladesh Medical Research Council (BMRC/NREC/2022-2025/336) on September 08, 2024. The outcome of this study is to evaluate the effects of telenursing intervention by controlling HbA1c. Results The project was funded in 2024. The enrollment of the participants started on October 26, 2024, and the required sample (n=70) enrollment was completed in February 2025. Data analysis will be started after completion of data collection and results will be expected to be submitted for publication in 2026. Conclusions Diabetic patients will acquire disease-specific management skills. Setting and monitoring goals ensures the continuation of the desired behavior and gives the patients control over their lifestyle. After developing self-management skills, patients assess their lab data and lifestyles including diet, and understand their condition so that they can work with their physiological data by acquiring knowledge of both the disease and self-care. By making self-supported decisions, the patients will be able to manage their diet, exercise, and medication. Trial Registration ClinicalTrials.gov NCT06632652; https://clinicaltrials.gov/study/NCT06632652 International Registered Report Identifier (IRRID) DERR1-10.2196/71849
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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".