Research Priorities and Indicators for Lifestyle Programs to Address Cardiometabolic Conditions
Bibliographic record
Abstract
Abstract Background: Cardiometabolic conditions are a major and growing health burden in many countries. At least one-third of middle-aged adults with overweight and obesity develop various combinations of type 2 diabetes, hypertension, dyslipidemia, and other cardiometabolic conditions. Currently, all relevant clinical and community guidelines recommend lifestyle (e.g. diet and physical activity) interventions, yet current evidence suggests limited and variable uptake by either primary care or public health services. New implementation research in lifestyle interventions is needed in multiple jurisdictions. As part of this effort, some agreement within the research community on priorities and core measures and indicators across studies would improve comparability and drive progress. Members of a new volunteer network undertook a first Delphi process to determine initial consensus.Methods: Network members were invited by email to participate and completed two rounds of a modified Delphi process delivered through online questionnaire and teleconferences. Results were sent back to participants at the end of each round of the survey. High priority with consensus was defined as the median and 25-75%ile range within the 7 to 9 range on a 9-point scale. Results: Fifty-three people were invited and provided with a link to the first questionnaire. Twenty-five (47%) and 18 (34%) participants completed the round 1 and round 2 surveys, respectively. Of 22 possible research priorities, four were rated high priority with consensus, including: evaluating the efficacy and effectiveness of interventions in place, improving existing interventions for sustainability, and clinical and public health research to advance existing knowledge to develop new capacities. Of the 93 measures and indicators proposed, 15 achieved consensus with an additional nine measures having high medians, but greater variance.Conclusions: This first effort suggests a wide range of research priorities within the group, but also broad agreement on a few core implementation research priorities. There is currently limited agreement on core indicators/measures for implementation studies and additional work is needed. The results provide a starting point for further development of common measures for implementation research in lifestyle interventions for cardiometabolic conditions.
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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 | 0.157 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".