Perceived Impact of Outdoor Swimming on Health: Web-Based Survey
Bibliographic record
Abstract
Background Outdoor swimming in lakes, lidos (outdoor pools), rivers, and the sea has grown in popularity in many countries, including the United Kingdom. Many anecdotal accounts indicate improvements in medical conditions, which are considered a consequence of outdoor swimming. Objective The aim of this study is to better understand outdoor swimmers’ perceptions of their health and the extent to which participation impacted their existing self-reported symptoms. Methods A survey was conducted to investigate outdoor swimming behaviors and reports of any diagnosed medical conditions. Medical conditions were coded into categories, and descriptive statistics were generated regarding the outdoor swimmers’ behaviors and the effect that outdoor swimming had on their medical symptoms if any. The medical categories were clustered into five larger categories based on their prevalence in the current sample: mental health; musculoskeletal and injury; neurological; cardiovascular and blood disease; and other, which comprises inflammatory, immune, endocrine, and respiratory conditions. Results In total, 722 outdoor swimmers responded, of whom 498 (68.9%) were female. The probability of outdoor swimming having some positive impact on health across all medical categories was 3.57 times higher compared with no impact (B=1.28, 95% CI 0.63-1.91; P<.001), 44.32 times higher for the mental health category (B=3.79, 95% CI 2.28-5.30; P<.001), 5.25 times higher for musculoskeletal and injury category (B=1.66, 95% CI 0.52-2.79; P=.004), and 4.02 times higher for the other category (B=1.39, 95% CI 0.27-2.51; P=.02). Overall, outdoor swimming was associated with perceived reductions in symptoms of poor mental health (χ22=25.1; P<.001), musculoskeletal and injury (χ22=8.2; P=.04), cardiovascular and blood (χ22=14.7; P=.006), and other conditions (χ22=18.2; P<.001). Conclusions Physical activity in the form of outdoor swimming is perceived to have positive impacts on health and is associated with perceived symptom reductions in mental health, musculoskeletal and injury, and cardiovascular and blood conditions. This study cannot provide causal relationships or provide mechanistic insights. However, it does provide a starting point for more targeted prospective intervention research into individual conditions or categories of conditions to establish the impact in those who choose to start outdoor swimming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".