Effect of Skiing on Physical Performance, Pain, and Quality of Life Based on Gender
Bibliographic record
Abstract
CONTEXT: Identifying gender-specific differentiation in each sport type is significant. In this way, sport- and gender-specific gains can be predicted. Therefore, this study aimed to examine the effects of skiing on physical performance, pain, quality of life, and gender-based differentiation. DESIGN: Crossover trial. METHODS: Sixty-eight volunteers, between 18 and 25 years of age, with no history of severe trauma in the spine and extremities, participated in our study. The skiers group consisted of licensed athletes (17 males and 17 females) who did active skiing for at least the past 2 years, the control group consisted of nonathletic and age-matched participants with no skiing experience (17 males and 17 females). Step test, vertical jump test, Flamingo balance test, hand-grip strength, and back-leg-chest strength measurements were performed to evaluate physical performance. Visual Analog Scale, McGill Pain Questionnaire, and Oswestry Low Back Pain Disability Questionnaire were used to evaluate pain experience. Quality of life was evaluated with Short Form-36 (SF-36). RESULTS: Compared to the sedentary controls, VAS-activity and Oswestry Disability Index scores were lower; and aerobic performance, balance, hand-grip strength, back-leg-chest strength, and quality of life (SF-36-general health, SF-36-vitality, SF-36-mental health, and SF-total score) were higher in skiers. Skiing was found to be effective in eliminating gender-based differentiation of the variables of hand-grip strength, pain, and quality of life; however, back-leg-chest strength and anaerobic performance variables were found to be ineffective in eliminating gender differentiation. CONCLUSIONS: Skiing allows that the individual increases physical performance and quality of life. It can also minimize gender-based differentiation of certain variables, such as muscular force, pain, and quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".