Abstract
Gait quality characteristics obtained from accelerometry during daily life are predictive of falls in older people but it is unclear how they relate to fall risk. Our aim was to test whether these gait quality characteristics are associated with the severity of fall risk. We collected one week of trunk accelerometry data from 279 older people (aged 65–95 years; 69.5% female). We used linear regression to investigate the association between six daily-life gait quality characteristics and categorized physiological fall risk (QuickScreen). Logarithmic rate of divergence in the vertical (VT) and anteroposterior (AP) direction were significantly associated with the level of fall risk after correction for walking speed (both p < 0.01). Sample entropy in VT and the mediolateral direction and the gait quality composite were not significantly associated with the level of fall risk. We found significant differences between the high fall risk group and the very low-and low-risk groups, the moderate-and very low-risk and the moderate and low-risk groups for logarithmic rate of divergence in VT and AP (all p ≤ 0.01). We conclude that logarithmic rate of divergence in VT and AP are associated with fall risk, making them feasible to assess the physiological fall risk in older people.
| Original language | English |
|---|---|
| Article number | 5580 |
| Pages (from-to) | 1-8 |
| Number of pages | 8 |
| Journal | Sensors (Switzerland) |
| Volume | 20 |
| Issue number | 19 |
| DOIs | |
| Publication status | Published - 29 Sept 2020 |
Funding
Funding: This research received no external funding. Sabine Schootemeijer received the AMS Innovation call and the FGB Travel Grant to visit NeuRA. Kim van Schooten is supported by a Human Frontier Science Program fellowship. Kim Delbaere is supported by the National Health and Medical Research Council (Australia).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Accelerometry
- Accidental falls
- Activity monitoring
- Aged
- Locomotion
- Mobility
- Wearable devices
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