Influence of daily physical activity on fine motor skills of adults around a Fitts task
Bibliographic record
Abstract
Introduction: Achieving our daily tasks depends on the speed-accuracy conflict. Physical activity plays a role in the development of our motor skills. However, the relationship between physical activity level (PAL) and fine motor skills remains largely unexplored. Aim: Our aim was to examine the relationship between the amount of daily physical activity and the performance of healthy adults in a reciprocal aiming task. Materials and methods: Eighty-seven healthy adults completed a reciprocal aiming task using a digital tablet. Four difficulty levels (3-6, determined by target width) and 50 scores for each level were performed using both hands. Movement time, error rate, and performance index were analyzed. PAL was measured using the Global Physical Activity Questionnaire. Spearman correlations and nparLD analysis were used in R Studio to explore the influence of physical activity level, difficulty index on individuals’ performances. Results: Apart from a correlation between PAL and motor performance at the easiest level (r=0.23, p=0.002), there was no correlation between PAL and fine motor performance. Conclusions: The results of our study did not indicate any significant major correlations between daily PAL and fine motor performance except when the constraints of a reciprocal aiming task are the lowest. Further work is needed to consider the use of the reciprocal Fitts task in a clinical setting.
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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 | 0.002 |
| 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 | 0.000 |
| 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".