Leisure-Time Physical Activity Trajectories across Adulthood and Cardiometabolic Risk at the Beginning of Late Adulthood: A Prospective Cohort Study
Bibliographic record
Abstract
PURPOSE: Physical activity (PA) is a key strategy in preventing and treating metabolic syndrome (MetS). The purpose is to investigate the associations of leisure-time PA (LTPA) trajectories across adulthood and current PA with MetS at age 61. METHODS: Participants were 159 Finnish adults (52% women). LTPA frequency was assessed at ages 27, 42, 50, and 61 with a single question. Current PA at age 61 included self-reported vigorous, muscle-strengthening, commuting, and occupational PA. Cardiometabolic risk factors at age 61 included waist circumference, blood pressure, high-density lipoprotein cholesterol, triglycerides, and glucose. MetS was defined based on the Adult Treatment Panel III criteria. LTPA trajectories were conducted using k-means for longitudinal data. RESULTS: Of the three LTPA trajectories found, consistently inactive ( N = 34) and increasingly active ( N = 58) had a higher risk of MetS compared with consistently active ( N = 67) (odds ratio [95% confidence interval]: 3.93 [1.55-10.01] and 2.39 [1.14-4.99], respectively). Only the difference between consistently inactive and consistently active remained statistically significant when the current PA indicators were included in the model. Considering the individual components of MetS, those who were consistently inactive and increasingly active had higher waist circumference, lower high-density lipoprotein, and higher triglyceride levels compared with consistently active. These differences did not remain statistically significant when current PA was included. CONCLUSIONS: Although consistently inactive and increasingly active individuals had higher cardiometabolic risk at age 61 compared with those who were consistently active across adulthood, current PA participation at the beginning of late adulthood attenuated these risks. These findings emphasize the importance of promoting and sustaining PA throughout life to reduce the burden of MetS in the aging population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".