A Digital Health Intervention to Lower Cardiovascular Risk
Bibliographic record
Abstract
IMPORTANCE: South Asian individuals have a high burden of premature myocardial infarction (MI). OBJECTIVES: To test whether a digital health intervention (DHI) designed to change diet and physical activity improves MI risk among a South Asian population. DESIGN, SETTING, AND PARTICIPANTS: This single-blind, community-based, randomized clinical trial with 1-year follow-up was performed among South Asian men and women 30 years or older and living in Ontario and British Columbia who were free of cardiovascular disease. Data analysis was by intention to treat. Data were collected from June 3, 2012, to October 27, 2013. Final follow-up was completed on December 2, 2014, and data were analyzed from April 2, 2015, to February 29, 2016. INTERVENTIONS: Participants were randomized 1:1 to the DHI or control condition. The goal-setting DHI used emails or text messages and focused on improving diet and physical activity that was tailored to the participant's self-reported stage of change. MAIN OUTCOMES AND MEASURES: The change in an MI risk score from baseline to 1 year was the primary outcome. Secondary outcomes included the change in each objectively measured component of the MI risk score (ie, blood pressure, waist to hip ratio, hemoglobin A1c level, and the ratio of apolipoprotein B to apolipoprotein A). Genetic risk for MI was determined by counting the 9p21 risk alleles; results were provided to each participant at baseline. RESULTS: A total of 343 South Asian men and women (178 men [51.9%]; mean [SD] age, 50.6 [11.4] years) who were free of cardiovascular disease were randomized to the control condition (n = 174) or the DHI (n = 169). The mean (SD) MI risk score was 13.3 (6.6) at baseline. No significant difference was found in the change in MI score after 1 year between the DHI and control groups (-0.27; 95% CI, -1.12 to 0.58; P = .53) after adjusting for baseline scores, and no difference was found in the fully adjusted model (-0.39; 95% CI, -1.24 to 0.45; P = .36). No association between knowledge of the genetic risk status at baseline and the change in MI risk score was found (0.19; 95% CI, -0.40 to 0.78; P = .53). CONCLUSIONS AND RELEVANCE: Among South Asian individuals, a DHI was not associated with a reduction in MI risk score after 12 months and was not influenced by knowledge of genetic risk status. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01841398.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".