Determinants of sedentary behavior in community-dwelling older adults with type 2 diabetes based on the behavioral change wheel: a path analysis
Bibliographic record
Abstract
Abstract Background Sedentary behavior (SB) is deeply ingrained in the daily lives of community-dwelling older adults with type 2 diabetes mellitus (T2DM). However, the specific underlying mechanisms of the determinants associated with SB remain elusive. We aimed to explore the determinants of SB based on the behavior change wheel framework as well as a literature review. Methods This cross-sectional study recruited 489 community-dwelling older adults with T2DM in Jinan City, Shandong Province, China. Convenience sampling was used to select participants from relevant communities. This study used the Measure of Older Adults’ Sedentary Time-T2DM, the Abbreviated-Neighborhood Environment Walkability Scale, the Social Support Rating Scale, the Lubben Social Network Scale 6, the Subjective Social Norms Questionnaire for Sedentary Behavior, the Functional Activities Questionnaire, the Numerical Rating Scale, the Short Physical Performance Battery, and the Montreal Cognitive Assessment Text to assess the levels of and the determinants of SB. Descriptive statistical analysis and path analysis were conducted to analyze and interpret the data. Results Pain, cognitive function, social isolation, and social support had direct and indirect effects on SB in community-dwelling older adults with T2DM (total effects: β = 0.426, β = -0.171, β = -0.209, and β = -0.128, respectively), and physical function, walking environment, and social function had direct effects on patients’ SB (total effects: β = -0.180, β = -0.163, and β = 0.127, respectively). All the above pathways were statistically significant ( P < 0.05). The path analysis showed that the model had acceptable fit indices: RMSEA = 0.014, χ 2 /df = 1.100, GFI = 0.999, AGFI = 0.980, NFI = 0.997, RFI = 0.954, IFI = 1.000, TLI = 0.996, CFI = 1.000. Conclusion Capability (physical function, pain, and cognitive function), opportunity (social isolation, walking environment, and social support), and motivation (social function) were effective predictors of SB in community-dwelling older adults with T2DM. Deeper knowledge regarding these associations may help healthcare providers design targeted intervention strategies to decrease levels of SB in this specific population.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".