Predictors of Physical Inactivity in Men and Women With Type 2 Diabetes From the Detection of Ischemia in Asymptomatic Diabetics (DIAD) Study
Bibliographic record
Abstract
PURPOSE: The purpose of this secondary analysis was to determine changes in physical inactivity from baseline to 5 years and to identify factors associated with and predictive of physical inactivity among individuals with type 2 diabetes enrolled in the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study. METHODS: DIAD was a prospective randomized screening trial that assessed the prevalence of silent ischemia in asymptomatic patients with type 2 diabetes. Subjects were recruited from diabetes and primary care practices at 14 centers throughout the United States and Canada. This is a secondary data analysis of the physical activity data (type and hours/week) collected. No intervention was conducted. RESULTS: In all subjects, physical inactivity rose from 24% at baseline to 33% at 5 years (S = 28.93; P < .0001). This change was significant in both men (S = 11.44; P < .0001), increasing from 23% to 31%, and women (S = 18.05; P < .0001), increasing from 25% to 36%. Gender differences were noted in several factors associated with baseline physical inactivity as well as in factors predictive of physical inactivity at 5 years. Important factors associated at both time points included lower level of education, current employment, presence of peripheral and autonomic neuropathy, and indicators of overweight/obesity. Baseline physical inactivity was strongly predictive of physical inactivity at 5 years (odds ratio, 3.27; 95% confidence interval, 2.36-4.54; P < .0001). CONCLUSIONS: Gender-related differences were noted in factors associated with and predictive of physical inactivity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".