Investigating the influence of various types of recreation physical activity on joint position sense
Bibliographic record
Abstract
Maintaining good joint position sense (JPS) is vital for many activities of daily living. Unfortunately, throughout the aging process JPS tends to decline as a function of age. Recently research has been looking into types physical activity to prevent this decline, specifically tai chi and dance (Li, 2008-2009; Kiefer, 2013). These studies have shown improvements in joint position sense when looking at professional athletes or intense intervention programs. Therefore, the purpose of the current study was to determine if improvements in JPS can be seen in recreational athletes who participate in a variety of sports and physical activity. Joint position sense was measured in 55 young adults (m=19, f=36 ) using a Vernier goniometer in the elbows, wrists, knees, and ankles at 2 mid-range angles. A background questionnaire was used to collect information on the types and amount of physical activity each participant practiced within the last 5 years. Participants were categorized into 4 physical activity groups based on where the joint positioning is most required: upper limb (hockey, golf, racquet sports), lower limb (soccer, jogging), and full body (yoga, martial arts, dance, gymnastics). Participants were grouped based on the type of physical activity in which they dedicated the most time per month. The results show that there is no significant difference in JPS between the groups at any of the joints measured (p=0.332). The results suggest that exercises designed specifically for improving joint position sense may be more beneficial for older adults than more general forms of physical activity.
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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) | 0.000 | 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) | 0.001 | 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".