Multiple routes to help you roam: A comparison of training interventions to improve cognitive-motor dual-tasking in healthy older adults
Bibliographic record
Abstract
Cognitive-motor dual-tasking is a complex activity that predicts falls risk and cognitive impairment in older adults. Cognitive and physical training can both lead to improvements in dual-tasking; however, less is known about what mechanisms underlie these changes. To investigate this, 33 healthy older adults were randomized to one of three training arms: Executive function (EF; n = 10), Aerobic Exercise (AE; n = 10), Gross Motor Abilities (GMA; n = 13) over 12 weeks (1 h, 3×/week). Single and dual-task performance (gait speed, m/s; cognitive accuracy, %) was evaluated before and after training, using the 2-back as concurrent cognitive load. Training arms were designed to improve cognitive and motor functioning, through different mechanisms (i.e., executive functioning – EF, cardiorespiratory fitness – CRF, and energy cost of walking – ECW). Compared to baseline, we observed few changes in dual-task gait speed following training (small effect). However, dual-task cognitive accuracy improved significantly, becoming facilitated by walking (large effect). There were no differences in the magnitude of improvements across training arms. We also found that older adults with lower cognitive ability (i.e., MoCA score < 26; n = 14) improved more on the dual-task cognitive accuracy following training, compared to older adults with higher cognitive ability (i.e., MoCA ≥26; n = 18). Taken together, the results suggest that regardless of the type of intervention, training appears to strengthen cognitive efficiency during dual-tasking, particularly for older adults with lower baseline cognitive status. These gains appear to occur via different mechanisms depending on the form of intervention. Implications of this research are paramount, as we demonstrate multiple routes for improving cognitive-motor dual-tasking in older adults, which may help reduce risk of cognitive impairment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".