Heterogeneity in Association Between Cognitive Function and Gait Speed Among Older Adults: An Integrative Data Analysis Study
Bibliographic record
Abstract
BACKGROUND: Increasing evidence shows that cognition and gait speed are associated and are important measures of health among older adults. However, previous studies have used different methods to assess these 2 outcomes and lack sufficient sample size to examine heterogeneity among subgroups. This study examined how the relationship between global cognitive function and gait speed are influenced by age, gender, and race utilizing an integrated data analysis approach. METHOD: Data on cognition (Montreal Cognitive Assessment [MoCA], Mini-Mental Status Examination [MMSE], and Modified Mini-Mental State Examination [3MSE]) and gait speed (range: 4-400 m) were acquired and harmonized from 25 research studies (n = 2802) of adults aged 50+ from the Wake Forest Older American Independence Center. Multilevel regression models examined the relationship between predicted values of global cognitive function (MoCA) and gait speed (4-m walk), including heterogeneity by age, race, and gender. RESULTS: Global cognitive function and gait speed exhibited a consistent positive relationship among whites with increasing age, while this was less consistent for African Americans. That is, there was a low correlation between global cognitive function and gait speed among African Americans aged 50-59, a positive correlation in their 60s and 70s, then a negative correlation thereafter. CONCLUSION: Global cognition and gait speed exhibited a curvilinear U-shaped relationship among whites; however, the association becomes inverse in African Americans. More research is needed to understand this racial divergence and could aid in identifying interventions to maintain cognitive and gait abilities across subgroups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".