Does lifestyle matter? Individual lifestyle factors and their additive effects associated with cognitive function in older men and women
Bibliographic record
Abstract
Objectives: This study investigated the association between healthy lifestyle comprised of multiple domains, gender, and cognitive function in older Chinese people in Hong Kong.Methods: We conducted a cross-sectional analysis with data from 1,831 community-dwellers aged 65 years and above. Participants’ basic demographics, comorbidity, and six lifestyle factors: diet; smoking; alcohol drinking; and physical, mental, and social activities were surveyed. Cognitive function was assessed using the Cantonese Chinese Montreal Cognitive Assessment (CC-MoCA). Linear regressions were performed to examine the associations between lifestyle, gender, and cognitive performance.Results: There were gender differences in lifestyle: men smoked (χ2(1) = 159.4) and drank more (χ2(1) = 85.9) and were more active in mentally stimulating activities (χ2(1) = 14.3, all p<.001); while women were more socially active (χ2(1) = 28.0). Age, gender and education explained the greatest variance in cognition (R2=.32). Being active/healthy in more domains further contributed to better cognitive function, although the effect was small (ΔR2=0.03 in women; ΔR2=0.01 in men, both p<.05). Among the lifestyle domains, physical activity showed the strongest effects on cognitive function (ΔR2=0.004 in men and ΔR2=0.02 in women, both p<.05).Conclusions: Naturalistically, a physically active lifestyle and being active/healthy in more domains is associated with better cognitive function in older people after controlling for non-modifiable and early-life factors. The effects are however small. There are gender differences in lifestyle and the impact of lifestyle on cognitive function. Preventive strategies targeting lifestyle domains for cognitive health in older people may consider these naturalistic associations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".