Self-care practices in diabetes among populations in India: a systematic review and meta-analysis
Bibliographic record
Abstract
Diabetes mellitus is a chronic condition that requires consistent self-management to prevent complications and maintain quality of life. This meta-analysis estimated the pooled prevalence of various self-care components in Indian populations with diabetes and their regional distribution. A literature search was conducted for studies published between January 2014 and November 2024 in PubMed, EMBASE, Scopus, Web of Science, and Google Scholar. Studies reporting prevalence of various self-care practices among diabetic populations in India were included. Data was extracted based on study details, demographics, setting, and self-care practices. Study quality and bias risk were assessed using the Newcastle-Ottawa Scale. Statistical analysis was done in Stata version 18. A mixed method model was used to estimate the pooled prevalence. Heterogeneity was explored through sub-group analysis, sensitivity analysis and meta-regression. A total of 29 articles were included in this review with 7863 populations. Most studies belonged to southern India ( n = 19, 65.5%), and community settings ( n = 15, 51.7%). The pooled prevalence of drug adherence was highest (71%, 95% CI: 63–79). This was followed by glucose monitoring (60, 50–70), dietary compliance (51, 42–59), healthy coping (43, 22–63), physical activity (41, 34–47), risk reduction (28, 20–36), and problem solving (22, 12–33). Meta-regression showed that the prevalence of physical activity is notably higher in the West (1.02, 95% CI: 0.57–1.48), East (0.77, 0.36–1.18), North (0.78, 0.36–1.18), and South (0.65, 0.31–1.0) regions compared to Central region. Glucose monitoring was significantly higher in East (0.7, 0.2–1.2) and South (0.57, 0.15–1.0) regions compared to Central region. Self-care practices among diabetic populations in India remain suboptimal, with considerable variation across regions. These findings underscore the urgent need for context-specific, scalable interventions and strengthened health system support to promote comprehensive diabetes self-management across the country.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".