Prevalence and Correlates of Accelerometer-Based Physical Activity and Sedentary Time Among Kidney Transplant Recipients
Bibliographic record
Abstract
Background: Physical activity is recommended for kidney transplant recipents as it may improve outcomes including mortality, exercise capacity, muscle strength, and health-related quality of life. Objective: The objective of this study was to examine accelerometer-based physical activity and sedentary time profiles among kidney transplant recipients and examine possible demographic and clinical correlates of physical activity and sedentary time. Design: Cross-sectional. Setting: Edmonton, Alberta, Canada. Patients: Kidney transplant recipients were recruited (N = 1,284) from the Northern Alberta Renal Program’s Nephrology Information System database (1993-2016). Measurements: Participants wore an ActiGraph GT3X+ accelerometer on their hip during waking hours for seven consecutive days. Methods: Kidney transplant recipients (1993-2016) recruited from the Northern Alberta Renal Program’s Nephrology Information System database wore an accelerometer and completed a self-reported questionnaire. Multiple linear regression was used to determine associations between activity level, demographic, and clinical characteristics. Results: Participants’ (n = 133; 11% response rate) mean age (SD) was 58 (14) years and 56% were female. Mean total sedentary time was 9.4 (1.4) hours per day; total moderate-to-vigorous physical activity (MVPA) time was 20.7 (19.6) minutes per day. MVPA was significantly associated with age where each additional year was associated with 0.48 fewer min/day (ie, ~30 seconds) (unstandardized beta: B = −0.48 min/day, 95% confidence interval [95% CI]: −0.75, −0.22). Sedentary time was significantly associated with age ( B = 1.0 min/day, 95% CI: 0.03, 1.9), body mass index ( B = 2.7 min/day, 95% CI: 0.2, 5.13), education ( B = 39.1 min/day, 95% CI: 12.3, −65.8), and inversely associated with income ( B = −44.9 min/day, 95% CI: −73.1, −16.8). Limitations: Limitations include the cross-sectional design, poor response rate, and limited generalizability of the results. Conclusions: Kidney transplant recipients showed high volumes of sedentary time and low volumes of health-enhancing physical activity. Understanding correlates of these behaviors may aid in the development of interventions to favorably change these behaviors.
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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 | 0.001 |
| 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 | 0.000 | 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".