Enhancement of Self-Management of Metabolic Syndrome Among Adults in Urban, Low-Income Settings of India Using Digital Health Interventions: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome (MetS) is a growing concern among adult populations in India, particularly among those living in urban, low-income settings. This group is challenged by a combination of risk factors, including an urbanized lifestyle, poor access to health care, and financial limitations, leading to high levels of obesity, diabetes, and hypertension. OBJECTIVE: This study aims to address this challenge by designing, developing, and piloting a tailored, mobile-enabled, interactive, digital health intervention to enhance self-management of MetS among individuals living in urban, low-income settings in New Delhi, India. METHODS: The study uses mixed methods, including both quantitative and qualitative data collection, to design and evaluate the effectiveness of the intervention built on a multifactorial model in improving the self-management of MetS. Data will be collected at baseline and 12 months from adults living in urban, low-income settings in New Delhi. The results will contribute to our understanding of the interplay of risk factors in MetS and the impact of tailored digital health interventions in addressing this challenge. The findings will be disseminated to both national and international audiences through peer-reviewed publications. RESULTS: This study was funded in March 2022 for 3 years. The project started in April 2022. Data collection began in June 2022. The results are expected to be published in 2025. CONCLUSIONS: The study is expected to provide valuable insights into the role of digital health interventions in enhancing the self-management of MetS among urban, low-income populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40144.
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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 | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".