Active Travel and Mild Cognitive Impairment among Older Adults from Low- and Middle-Income Countries
Bibliographic record
Abstract
Active travel may be an easily achievable form of physical activity for older people especially in low- and middle-income countries (LMICs), but there are currently no studies on how this form of physical activity is associated with a preclinical state of dementia known as mild cognitive impairment (MCI). Therefore, we aimed to investigate the association between active travel and MCI among adults aged ≥50 years from six LMICs. Cross-sectional, community-based data from the World Health Organization's Study on Global Ageing and Adult Health were analyzed. The definition of MCI was based on the National Institute on Ageing-Alzheimer's Association criteria. Active travel (minutes/week) was assessed with questions of the Global Physical Activity Questionnaire (GPAQ) and presented in tertiles. Multivariable logistic regression analysis was conducted to assess the association between active travel and MCI. Data on 32715 people aged ≥50 years (mean age 62.4 years; 52.1% females) were analyzed. Compared to the highest tertile of active travel, the lowest tertile was associated with 1.33 (95%CI = 1.14-1.54) times higher odds for MCI overall. This association was particularly pronounced among those aged ≥65 years (OR = 1.70; 95%CI = 1.32-2.19) but active travel was not associated with MCI among those aged 50-64 years. In conclusion, low levels of active travel were associated with a significantly higher odds of MCI in adults aged ≥65 years in LMICs. Promoting active travel among people of this age group in LMICs via tailored interventions and/or country-wide infrastructure investment to provide a safe environment for active travel may lead to a reduction in MCI and subsequent dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".