Understanding physical activity in individuals with prediabetes: an application of social cognitive theory
Bibliographic record
Abstract
Despite well-documented evidence implicating physical activity (PA) in the prevention of type 2 diabetes, the overwhelming majority of individuals with prediabetes are not physically active enough. The purpose of this study was to investigate the applicability of the social cognitive theory (SCT) in understanding PA behaviour in individuals with prediabetes. Individuals with prediabetes (N = 232) completed a mailed questionnaire assessing demographics, self-reported PA (MET.min/wk) and SCT constructs for PA MET.min/wk. For PA MET.min/wk, scheduling and task efficacy both had significant effects on PA (β = .30 and .22, respectively). Goal formation also had a direct effect on PA for scheduling, coping and task efficacy (β = .20, .34 and .30, respectively). Task, coping and scheduling efficacy explained a significant portion of the variance in PA behaviour. Overall, SCT appears to have merit as a model for understanding PA in individuals with prediabetes. Further evaluative inquiry is needed to establish support for the use of the SCT as a framework for developing, implementing and evaluating PA behaviour change interventions in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".