P2‐017: EFFECTS OF TECHNOLOGY‐ENABLED PHYSICAL ACTIVITY COUNSELLING ON INTRA‐INDIVIDUAL VARIABILITY AND OTHER MEASURES OF COGNITIVE PERFORMANCE AMONG ADULTS WITH KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Low physical activity (PA) is a risk factor for cognitive decline in later life. Adults with knee osteoarthritis (OA) are often inactive and are at increased risk for dementia. Not only does performance on cognitive tasks become slower and less accurate with increasing age, but also response time (RT) becomes less consistent, which is called intra-individual variability (IIV). A PA counselling program using wearable activity monitors is one potential strategy for promoting PA, and in turn cognition. We conducted a randomized controlled trial. The Immediate Group received a brief education session by a physical therapist (PT), a Fitbit Flex, and 4 bi-weekly phone calls for activity counselling. The Delayed Group received the same intervention two months later. Participants were assessed at baseline (T0), and the end of 2 months (T1), 4 months (T2), and 6 months (T3). Cognition was measured via 126 trials of the Stroop task, in which individuals have to indicate the color of words presented on a computer display using a button box. Outcomes included (1) RT standard deviation (a measure of IIV); (2) median overall RT; (3) difference in median RT on incongruent and congruent trials; and (4) overall response. For RT measures, only trials with correct responses were used. Intention-to-treat linear mixed models with random intercept and slopes assessed three contrasts: (1) Immediate T1–T0 vs. Delayed T1–T0; (2) Delayed T2–T1 vs. Delayed T1–T0; and (3) Average of Contrast 1 and Contrast 2. IIV has been linked to neural integrity and increases with age. A PA counselling program using wearable activity monitors might counteract negative changes to IIV among adults with knee OA .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".