Brodie Matthew A, Okubo Yoshiro, Annegarn Janneke, Wieching Rainer, Lord Stephen R, Delbaere Kim
Neuroscience Research Australia, UNSW, Randwick, Sydney, Australia.
Physiol Meas. 2017 Jan;38(1):45-62. doi: 10.1088/1361-6579/38/1/45. Epub 2016 Dec 12.
Falls and physical deconditioning are two major health problems for older people. Recent advances in remote physiological monitoring provide new opportunities to investigate why walking exercise, with its many health benefits, can both increase and decrease fall rates in older people. In this paper we combine remote wearable device monitoring of daily gait with non-linear multi-dimensional pattern recognition analysis; to disentangle the complex associations between walking, health and fall rates. One week of activities of daily living (ADL) were recorded with a wearable device in 96 independent living older people prior to completing 6 months of exergaming interventions. Using the wearable device data; the quantity, intensity, variability and distribution of daily walking patterns were assessed. At baseline, clinical assessments of health, falls, sensorimotor and physiological fall risks were completed. At 6 months, fall rates, sensorimotor and physiological fall risks were re-assessed. A non-linear multi-dimensional analysis was conducted to identify risk-groups according to their daily walking patterns. Four distinct risk-groups were identified: The Impaired (93% fallers), Restrained (8% fallers), Active (50% fallers) and Athletic (4% fallers). Walking was strongly associated with multiple health benefits and protective of falls for the top performing Athletic risk-group. However, in the middle of the spectrum, the Active risk-group, who were more active, younger and healthier were 6.25 times more likely to be fallers than their Restrained counterparts. Remote monitoring of daily walking patterns may provide a new way to distinguish Impaired people at risk of falling because of frailty from Active people at risk of falling from greater exposure to situations were falls could occur, but further validation is required. Wearable device risk-profiling could help in developing more personalised interventions for older people seeking the health benefits of walking without increasing their risk of falls.
跌倒和身体机能衰退是老年人面临的两大主要健康问题。远程生理监测的最新进展为研究步行锻炼为何在带来诸多健康益处的同时,却既能增加又能降低老年人的跌倒率提供了新机遇。在本文中,我们将日常步态的远程可穿戴设备监测与非线性多维度模式识别分析相结合,以厘清步行、健康和跌倒率之间的复杂关联。在96名独立生活的老年人完成6个月的运动游戏干预之前,使用可穿戴设备记录了他们一周的日常生活活动(ADL)。利用可穿戴设备数据,评估了日常步行模式的数量、强度、变异性和分布情况。在基线时,完成了对健康、跌倒、感觉运动和生理跌倒风险的临床评估。在6个月时,重新评估了跌倒率、感觉运动和生理跌倒风险。进行了非线性多维度分析,以根据他们的日常步行模式识别风险组。识别出了四个不同的风险组:受损组(93%的跌倒者)、受限组(8%的跌倒者)、活跃组(50%的跌倒者)和运动组(4%的跌倒者)。步行与多种健康益处密切相关,对表现最佳的运动风险组具有预防跌倒的作用。然而,在范围的中间部分,更活跃、更年轻且更健康的活跃风险组跌倒的可能性是其受限对应组的6.25倍。对日常步行模式的远程监测可能提供一种新方法,以区分因身体虚弱而有跌倒风险的受损人群和因更多暴露于可能发生跌倒的情况而有跌倒风险的活跃人群,但还需要进一步验证。可穿戴设备风险评估有助于为寻求步行健康益处而又不增加跌倒风险的老年人制定更个性化的干预措施。