Physical Activity Related Information Sources Predict Physical Activity Behaviors in Adults with Type 2 Diabetes
Bibliographic record
Abstract
Physical activity (PA) is a key management strategy for type 2 diabetes. Despite the known benefits, PA levels are low. Whether the low level of PA is related to lack of knowledge or support is not fully understood. This study was conducted to describe where and how often adults with type 2 diabetes receive and seek information related to PA and examine the relationships between the source and quality of PA information with PA behaviors. A series of questions related to the source and quality of PA information were added to a baseline survey distributed to the participants (N = 244) of the Canadian Aerobic and Resistance Training in Diabetes (CARED) study. Physicians and television were found to be the main sources of PA-related information. In our cross-sectional model, sources of PA-related information other than that from health care professionals explained 14% (p = .05) and 16% (p < .05) of the variance for aerobic-based and resistance training behaviors and 22% (p < .01) and 15% (p < .05) for these behaviors in our longitudinal model. Physical activity (PA)-related information is widely available to adults with type 2 diabetes. Neither the quantity nor the quality of the PA information provided by health care professionals predicted PA behavior. These data provide further insight into the modes with which PA can be promoted to adults with type 2 diabetes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".