Measured and Perceived Exercise Intensity During the Performance of Single-Task, Cognitive-Motor Dual-Task, and Exergame Training: Transversal Study
Bibliographic record
Abstract
BACKGROUND: The physical and cognitive loads borne during exergaming may differ from more conventional cognitive-motor dual-task trainings. OBJECTIVE: The aim of this pilot transversal study was to compare objectively measured and perceived exercise intensity during exergame, cognitive-motor dual-task, and single-task training sessions. METHODS: We recruited apparently healthy young adults who carried out one session of each type of training: exergaming, cognitive-motor dual-tasking, and single-tasking. We used a custom-made exergame as support. The sessions lasted 30 minutes, were spaced at least 24 hours apart, and took place in random order for each group of 4 participants. We used heart rates to assess exercise intensity and the modified Borg scale to assess perception of intensity. In all, 16 apparently healthy young participants carried out all sessions. RESULTS: There was no difference between the different types of training in mean heart rates (P=.27), peak heart rates (P=.50), or Borg scale scores (P=.40). Our custom-made exergame's objectively measured and perceived physical load did not differ between cognitive-motor dual-task and single-task training. CONCLUSIONS: As a result, our exergame can be considered to be as challenging as more traditional physical training. Future studies should be conducted in older adults with or without cognitive impairments and incorporate an assessment of cognitive performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".