Longitudinal Analysis of Physical Performance, Functional Status, Physical Activity, and Mood in Relation to Executive Function in Older Adults Who Fall
Bibliographic record
Abstract
OBJECTIVES: To examine whether good executive function (EF; the cognitive processes important for goal-oriented and controlled behavior) at baseline and maintenance of EF over time predict maintenance of physical performance, functional status, physical activity, and mood over a 1-year period, and conversely, to examine whether baseline functioning in these noncognitive domains predicts maintenance of EF over the same period of time. DESIGN: 12-month prospective cohort study. SETTING: Vancouver Falls Prevention Clinic. PARTICIPANTS: Community-dwelling older adults (N = 199; mean age 81.6 ± 6.5; 63% female) referred to the clinic after a fall. MEASURMENTS: At each time point, structural equation modeling created a latent EF variable from performance on five EF tasks. Physical performance (physiological falls risk and gait speed), instrumental activities of daily living (IADLs), physical activity, and depressive symptoms were also assessed at each time point. RESULTS: Higher baseline EF predicted decreases in depressive symptoms (P = .005) and maintenance of IADLs (P = .006) from baseline to follow-up. Improvements in EF correlated with increases in gait speed (P = .005) and physical activity (P = .03) and with the maintenance of IADLs (P = .002) over follow-up. All effects were independent of demographic characteristics and global cognitive function. Baseline performance in the noncognitive domains did not predict changes in EF. CONCLUSION: In older fallers, EF is a marker of resiliency in several noncognitive domains and should therefore be assessed. Furthermore, interventions to improve EF should be tested in older fallers with EF deficits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".