A - 01 Baseline Executive Functioning and Mood in Older Adults before a Remote Physical Exercise Training Program
Bibliographic record
Abstract
Abstract Objective Physical exercise shows benefits to executive functioning (EF), a cognitive process that is relevant to goal-directed planning, application of complex rules, and dynamic control of action. However, many older adults have not engaged in exercise due to social distancing measures implemented to combat the Covid-19 pandemic. The present study aims to administer a remote physical exercise training program to older adults in Canada and examine its effects on EF and mood, beginning with the collection of the following baseline data. Methods So far, nine older adults (78% females, 66–78 years-old) have completed a remote assessment that examines current physical exercise engagement, Covid-related distress, general mood, and executive behavior. In addition, the assessment included computerized tasks measuring various aspects of EF. Pattern analyses were used to characterize trends in baseline data. Results 67% of participants reported undergoing no moderate-vigorous physical exercise in a typical week. Compared to other participants, more individuals in this subgroup performed at a lower rate on a higher-order EF task (i.e., the Balloon Analogue Risk Task). Similarly, more participants who endorsed higher Covid-related psychological distress performed worse in the same task compared to others (40% in the lower third of performance vs. 0%), and also endorsed more difficulties with organization. Conclusions These results suggest that those who do not engage in physical exercise are potential candidates to experience the mental health and cognitive benefits of a physical exercise training program.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".