Characterising walking behaviours in aged residential care using accelerometery: a cross-sectional comparison of care level, cognitive status and physical function
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».