Increased regular levels of physical activity are associated with better balance performance
Bibliographic record
Abstract
Studies have determined that increased frequency of physical activity (PA) has a positive effect on balance. However, studies have not reached a consensus regarding what frequency results in the best balance performance. Therefore, the present study aims to investigate what type of PA and frequency is most beneficial to improve one’s balance. Seventeen undergraduate students (female: 41.2%; age: 20 ± 0.94) at Western University participated in this study. Participants completed a seven-day and six-month Leisure Time Exercise Questionnaire indicating the frequency of participation in mild, moderate, and strenuous PA. Afterwards, individuals completed balance trials with four stances (eyes-open [EO] and closed [EC] for both unilateral [U] or bilateral [B] stances). The total path length (PL) was calculated from the center of pressure location. Pearson Correlations were used to assess the relationship between PA and PL. One-way ANOVA assessed PLs between the strenuous intensity and the non-strenuous intensities (i.e. mild/moderate).The amount of exercise performed within seven-days was moderately correlated with ECU PL (r= -0.489, p=0.046). During the ECU stance, 23.95% of the variance in PL was explained by the amount of exercise performed within seven-days. Additionally, 26.45% of the variance in PL during EOU stance was explained by PA performed within a six-month period. The one-way ANOVA showed no significant difference between the intensity of PA and balance. The amount of seven-day and six-month PA was associated with standing balance performance. Approximately 25% of the variance in PL during EOU and ECU was explained by seven-day total physical activity. This dropped to approximately 12% for the six-month period. During both periods, on most stances, greater levels of regular exercise were associated with improved balance performance. Therefore, greater levels of regular PA may lead to better outcomes on balance tests and should be considered when diagnoses are made using batteries that include balance testing.
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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 | 0.001 | 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) | 0.003 | 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".