Characterising walking behaviours in aged residential care using accelerometery: a cross-sectional comparison of care level, cognitive status and physical function
Bibliographic record
Abstract
Background: Walking is important for maintaining physical and mental wellbeing in aged residential care (ARC). Walking behaviours are not well characterised in ARC due to inconsistencies in assessment methods and metrics, and limited research regarding the impact of care environment, cognition or physical capacity on these behaviours. It is recommended that walking behaviours in ARC are assessed using validated digital methods which can capture low volumes of walking activity. Objective: This study aims to characterise and compare accelerometry-derived walking behaviours in ARC residents across different care levels, cognitive abilities, and physical capacities. Methods: 306 ARC residents were recruited from the Staying Upright RCT from three care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants’ cognitive status was classified as mild (n=87), moderate (n=128) or severe impairment (n=61), and physical capacity as moderate (n=60), low (n=107) or very low (n=115) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cut-off scores respectively. To assess walking, participants wore an accelerometer (Axivity AX3, York, UK; 23x32.5x7.6mm, 11g; sampling rate: 100Hz, range ± 8 g, memory: 512 M) on their lower back for seven days. Outcomes included volume (daily time spent walking, steps, bouts), pattern (mean walking bout duration, alpha) and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviours between groups as categorised level of care, cognition, or physical capacity, while controlling for age and sex. Tukey HSD tests for multiple comparisons were used to determine where significant differences occurred. Effect size of group differences were calculated using Hedges’ G. Results: Dementia care residents showed greater volumes of walking (p<.01), with longer (p<.01), more variable bouts (p<.01) compared to other care levels, with moderate-large effect sizes. Residents with severe cognitive impairment took longer (p<.01), more variable (p<.01) bouts with moderate-large effect sizes compared to those with mild cognitive impairment and small-moderate effect sizes compared to moderate cognitive impairment. Residents with very low physical capacity had lower walking volumes compared to moderate capacity (p<.001) with moderate effect sizes. Conclusions: ARC residents across different levels of care, cognition and physical capacity demonstrate different walking behaviours. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multi-morbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern and variability of walking behaviours in ARC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".