Daily-Life Walking Characteristics of Older Adults in Relation to Age, Sex, and Physical Function: the HUNT4 Trondheim 70+ Observational Study
Notice bibliographique
Résumé
Background: Knowledge about how older adults walk is crucial for the effective prevention and treatment of various mobility issues as well as treatment evaluation, but it is currently largely limited to laboratory-based measurements. Although laboratory-based data provide relevant information about what older adults can do under standardized conditions, they do not provide insight into how they actually walk in their daily life, a gap that needs to be addressed urgently. Objective: The objective of this study was to describe how older adults walk in daily life, in relation to age, sex, and level of physical function, using wearable sensor data from a large sample of older adults with a wide range of age and function from the HUNT4 Trondheim 70+ study. Methods: The current study is based on 1-week accelerometer data (Axivity AX3) from 1289 older adults (mean age 77.41, SD 6.06 years; age range 70-105 years; n=705, 54.7% women). Physical function was assessed using the Short Physical Performance Battery (SPPB). To investigate the effect of age and SPPB score on gait metrics (daily number of steps, 95th percentile speed, mode speed, 95th percentile cadence, mode cadence, and maximum walking bout [WB] distance) for women and men, univariate gamma regression models with log link were used for each outcome measure, with age and SPPB score in separate models. Sex differences were investigated using Mann-Whitney U tests. Results: Older adults showed a large variation in how and how much they walked in daily life across age, sex, and physical function, particularly younger participants and those with better physical function. Most gait metrics decreased at an increasing rate with higher age, with men maintaining their levels up to higher ages than women. Poorer physical function led to an exponential or close-to-linear decrease in all gait metrics apart from habitual cadence, which remained stable up to a high age. Women had a lower daily number of steps, gait speed, and maximum distance but higher cadence than men (P<.001 for all). On average, 63% of all WBs lasted <10 seconds, corresponding to a median accumulated time of 99 (IQR 66-128) minutes. For WBs lasting 10 to 30 seconds, 30 to 60 seconds, and >60 seconds, the median accumulated time was 105 (IQR 65-154) minutes, 31 (IQR 18-47) minutes, and 113 (IQR 37-219) minutes, respectively. Conclusions: Daily-life walking performance was affected more by functional ability than by age itself, except for the highest ages, and differed significantly between sexes. Although most WBs were very short, the total accumulated walking time in WBs shorter than 30 seconds was longer than that in longer WBs. Future research can build upon our findings by considering both the impact of short WBs and relevant group and sex differences when implementing daily-life mobility assessment in both clinical studies and patient follow-up.
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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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».