Profiles of resistance training behavior and sedentary time among older adults: Associations with health-related quality of life and psychosocial health
Bibliographic record
Abstract
BACKGROUND: The primary objective of this study was to gain a better understanding of the associations of health-related quality of life (HRQoL) and psychosocial factors (e.g., satisfaction with life, level of self-esteem, anxiety, depression) with resistance training and sedentary behavior profiles. METHODS: For this cross-sectional study, 358 older adults (≥ 55 years of age) across Alberta, Canada, completed self-reported measures of resistance training behavior, sedentary time, HRQoL, and psychosocial health (e.g., depression, anxiety, self-esteem, satisfaction with life). Participants were placed into one of four profiles with respect to their sedentary and resistance training behaviors. Data were collected in Alberta, Canada between August 2013 and January 2014. RESULTS: Pairwise comparisons indicated that those in the low SED/low RT group had a higher mental health composite (MHC) score compared to those in the high SED/low RT group (M diff = 3.9, p = 0.008). Compared to those in the high SED/low RT group, those in the low SED/high RT groups had significantly higher MHC scores (M diff = 4.8, p < 0.001). Those in the low SED/high RT group reported significantly higher physical health composite scores (PHC) (M diff = 3.7, p = 0.019), compared to the high SED/low RT group. Lower depression symptom scores were observed in the low SED/high RT groups compared to the high SED/low RT group, (M diff = - 0.60, p < 0.001). CONCLUSION: Resistance training, regardless of sedentary time, was significantly associated with HRQoL and psychosocial health.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".