Independent and inverse association of healthcare utilisation with physical activity in older adults with multiple chronic conditions
Bibliographic record
Abstract
OBJECTIVE: In this study, whether physical activity is independently associated with direct healthcare costs in community-dwelling older adults with multiple chronic conditions was examined. DESIGN: Cross-sectional analysis. SETTING: Research laboratory. PARTICIPANTS: 299 community-dwelling men and women volunteers aged 65 years and older with chronic conditions. INTERVENTION: None. MAIN OUTCOME MEASURES: Primary dependent variable was direct healthcare costs incurred in the previous 3 months. Participants completed the Health Resource Utilisation (HRU) questionnaire. To estimate HRU, direct costs in the previous 3 months were calculated using the three-party payer perspective of the British Columbia Ministry of Health, deemed representative of the Canadian healthcare system costs. For medications, the Retail Pharmacy Dispensed prescription cost tables were used. Primary independent variables were (1) self-report current level of physical activity as assessed by the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD) and (2) general balance and mobility as assessed by the National Institute on Aging Balance Scale. RESULTS: The mean number of chronic conditions per participant was six. Current level of physical activity was independently and inversely associated with HRU. Age, sex, number of chronic conditions, global cognitive function, body mass index, and general balance and mobility together accounted for 24.3% of the total variance. Adding the PASIPD score resulted in an R2 change of 3.3% and significantly improved the model. The total variance accounted by the final model was 27.6%. CONCLUSIONS: Physical activity promotion may reduce healthcare costs in older adults with chronic conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".