Self-reported sedentary time among masters and recreational athletes aged 55 years and older
Bibliographic record
Abstract
At the population level, it has been established that over 90% of older adults are sedentary for 8 or more hours per day. While most studies have controlled for moderate to vigorous intensity physical activity, none have looked at sedentary time related to sport participation among older adults. The purpose of this study was to determine whether there are differences between sedentary time of older masters athletes (MA) and recreational athletes (RA). A cross-sectional survey was created and included questions pertaining to demographics, sport participation, physical activity levels (International Physical Activity Questionnaire) and sedentary time (Measure of Older Adults' Sedentary Time Questionnaire). RA (men n=63; women n=65) and MA (men n=46; women n=33) were similar in age (RA men: 65.4±7.8 years, RA women 64.8±6.4 years, MA men 62.8±7.3 years, MA women 64.1±7.2 years). A larger portion of MA reported having a coach and competing at the national level compared to RA. Data indicate that MA were engaging in significantly more moderate-vigorous physical activity (4.1±1.9 times/week and 80.7±44.3 mins/session) than RA (2.6±2.2 times/week and 60.8±58.5 mins/session). Data also indicate that MA (818.1±796.0 mins/week) were spending significantly more time on the computer/internet than RA (619.3±645.1 mins/week); however, MA were spending less time watching TV and the computer compared to RA (MA: 3.2±1.7 hours/day; RA: 3.6±1.9 hours/day). These findings indicate that MA and RA accumulate a large amount of daily sedentary time; however, the type of sedentary behaviours they engage in may differ.
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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 | 0.001 | 0.001 |
| 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".