A population-based survey on physical inactivity and leisure time physical activity among adults in Chiang Mai, Thailand, 2014
Bibliographic record
Abstract
Reducing physical inactivity among the population is a challenge for many nations. Targeting leisure time physical activity (LTPA) may be useful in increasing overall physical activity as it is assumed it is associated with a higher degree of free choice and personal preference than physical activity at work and during travel. The study explored the prevalence of physical inactivity and focused on the overall level of energy expenditure and energy level spent during leisure time among those who were physically inactive and assessed the stages of change for LTPA among those who were physically inactive. A population-based survey was conducted in 2014 in Chiang Mai, Thailand using a stratified two-stage cluster sampling technique. The Global Physical Activity Questionnaire (GPAQ) was used to collect the data on physical activity. Sufficient levels of physical activity (PA) were defined as ≥150 min/week of moderate-intensity PA or ≥75 min/week of vigorous-intensity PA or ≥600 metabolic equivalent of task (MET)-minutes/week. Weighted analyses were used to estimate the prevalence of physical inactivity, the total energy expenditure and expenditure during LTPA as well as stages of change among the physically inactive population. A total of 1744 people (808 men and 936 women), aged 15 to 64 years, participated in the study. We estimated that a quarter (26%) of the population were physically inactive. Physical inactivity was more commonly found among women than men in most age groups. LTPA contributed a small proportion of overall PA. On average, physically inactive men spent 132.8 MET-minutes/week and inactive women spent 208.2 MET-minutes/week in overall PA which is well below the 600 MET-minutes/week recommend by the World Health Organization. Around 75% of physically inactive people had no intention of engaging in regular LTPA. About a quarter of the investigative population were physically inactive. Most physically inactive members of the population participate in low levels of LTPA, but the majority has no intention of increasing PA during leisure time. A large-scale health promotion program is needed, and it should focus on an approach for the pre-contemplated population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".