Associations between physical activity and sedentary behaviour patterns and cognition: A cross‐sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Background Engaging in regular physical activity (PA) and limited sedentary behaviour (SB) are critical for healthy cognitive aging. However, it is unclear what combination of PA and SB patterns (e.g., high PA and low SB, high PA and high SB, etc.) is most beneficial for cognition. We thus examined how different combinations of PA and SB were associated with cognitive performance in community‐dwelling middle‐aged and older adults. Method A cross‐sectional analysis using baseline data from the Comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA; 2010‐2015) among participants with complete PA and SB data (n = 23,125; age range 45‐86 years). PA and SB were indexed using the Physical Activity Scale for the Elderly (PASE) and cognition using a three‐factor structural equation model (i.e., memory, executive function, and verbal fluency). Participants' PA and SB levels were classified as 1) low PA and high SB; 2) low PA and low SB; 3) high PA and high SB; and 4) high PA and low SB. Linear regressions assessed associations between PA and SB combinations and cognition, adjusted for age, sex, activities of daily living, income level, educational attainment, and depression. Contrasts with Bonferroni correction tested associations of combinations of PA and SB with cognition. Result High PA was associated with better cognition whether combined with low or high SB. A high PA and low SB combination was associated with better executive function (Estimated mean difference = ‐0.24, 95%CI ‐0.39 to ‐0.10, p<0.001) and verbal fluency (Estimated mean difference = ‐0.29, 95%CI ‐0.42 to ‐0.16, p<0.001) vs. low PA and low SB. A combination of high PA and high SB was associated with better executive function (Estimated mean difference = 0.17, 95%CI 0.01 to 0.33, p = 0.034) and verbal fluency (Estimated mean difference = 0.20, 95%CI 0.05 to 0.34, p = 0.002) vs. high PA and low SB. Surprisingly, low PA and high SB was associated with better executive function vs. low PA and low SB (Estimated mean difference = 0.26, 95%CI 0.11 to 0.41, p<0.001) (Table 1 and Figure 1). Conclusion High PA, irrespective of SB, is associated with better cognition. Low PA levels may be more detrimental to cognition than high SB levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".