Frailty, Quality of Life, Anxiety, and Other Factors Affecting Adherence to Physical Activity Recommendations by Hemodialysis Patients
Bibliographic record
Abstract
Hemodialysis patients perform little physical activity. We formulated a hypothesis that some factors, i.e., frailty, medical and functional factors, psychological factors, quality of life, awareness of recommendations, and sociodemographic factors influence the decisions of taking up physical activity. This prospective study comprised 72 dialysis patients aged 57.8 ± 16.0 ( x ¯ ± SD; in the range of 19-87 years of age). The following research tools were used: an interview about awareness of the physical activity recommendations, the Canadian Study of Health and Aging Scale (CSHA-CFS), scales for the assessment of functional status, State-Trait Anxiety Inventory (STAI), Acceptance of Illness Scale (AIS), and the questionnaire of Kidney Disease Quality of Life (KDQOL-SF 1.3). The majority of patients diagnosed with frailty did not follow the physical activity recommendations (79.3%). Quality of life was better in active patients compared to inactive patients, especially in the domains of sleep and physical performance. The severity of trait anxiety was significantly higher in patients who did not follow the recommendations compared to patients who adhered to physical activity recommendations (46.0 ± 10.5 vs. 40.0 ± 8.2; p = 0.021 ). The likelihood of adherence decreased by 1% after each subsequent month of dialysis (odds ratio = 0.99; 95% confidence interval = 0.972-0.999; p = 0.047 ). Adherence was most limited by frailty. Adherence to recommendations on physical activity was affected by: motivation, lower levels of trait anxiety, and better quality of life. Age modified the effect of awareness and acceptance of the disease on adherence to physical activity recommendations.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".