Cycling and cognition in middle-aged and older adults: A scoping review
Bibliographic record
Abstract
Cognitive decline with aging is a population health issue. Cycling may help maintain, cognitive function and prevent cognitive decline in older adulthood. We conducted a scoping review to summarize and identify gaps in the literature examining cycling for transportation and/or recreation and their association with cognition in middle-aged and older adults. Nine bibliographic databases were searched (inception-March 2024) to identify all articles that reported on cycling for transportation, leisure outdoors, or cycling indoors, along a virtual bike path, and any cognitive outcomes among adults ≥ 45 years of age.We summarized article findings and associations narratively. Searching identified 5171 citations, and 23 articles were included examining apparently healthy and clinical populations. Cycling interventions included virtual cycling indoors (randomized controlled trials=10, quasi-experimental=5), cycling outdoors for recreation (quasi-experimental=3, cross-sectional=1), any reason (cohort=1, crosssectional=1), or transportation (simulation modelling=2). The most frequently examined cognitive outcome was executive function (n=15), followed by memory (n=9) and, global cognition (n=9). Most articles (70%) reported a statistically significant positive association between cycling and a cognitive outcome. Identified literature gaps include, a lack of sex- and gender-based analyses; robust study designs exploring cycling • Positive association (p <0.05) between cycling and cognition in adults ≥ 45 years of age • Only simulation modelling studies have examined cycling for transportation and cognition • Lack of sex and gender-based analyses of cycling and cognition • Longitudinal and experimental studies needed to examine cycling outdoors and cognition • Must test cycling's extra cognitive benefit over other physical activity/transport modes
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".