Does Change Predict Change in the CHANGE Intervention Study?
Bibliographic record
Abstract
Abstract Does change in one lifestyle factor (e.g., exercise/fitness) help explain change in another factor or process (i.e., dietary behavior or cardiovascular risk)? The current modeling was a secondary analysis of a primary care feasibility study of individualized lifestyle (diet and exercise) treatment of metabolic syndrome (n=293; mean age = 59yrs) that achieved 19% reversal over one year. Diet quality was assessed by the Healthy Eating Index (HEI) (2005 Canada); while fitness was assessed by several measures (VO2max, flexibility, curl-ups, push-ups). Three occasions (i.e., baseline, 3-, and 12-month) were examined using latent change score and latent growth curve models (in AMOS) to assess whether changes in one domain predicted changes in the remaining domains: (1) diet (measured by HEI or latent construct); (2) fitness (measured by VO2max percentiles or latent construct); and, (3) 10-year risk of cardiovascular disease (by Procam Risk score). Results showed significant improvement in all three domains separately during the intervention, with greater change between baseline and 3-month assessment and continued change between 3- and 12-months. Initial status variables on observed constructs were moderately positively correlated and change in dietary behavior was significantly related to change in fitness levels, but neither were significantly related to change in the 10-year risk of cardiovascular disease. In addition, the associations between change in diet and changes in fitness were inconsistent baseline to 3 months, and 3-12 months. These results offered new insight on relationships among interventions in a behavioural counselling program which can inform future programming.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".