Bezold Jelena, Krell-Roesch Janina, Eckert Tobias, Jekauc Darko, Woll Alexander
Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131, Karlsruhe, Germany.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Eur Rev Aging Phys Act. 2021 Jul 9;18(1):15. doi: 10.1186/s11556-021-00266-w.
Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design.
Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%).
Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment.
This systematic review is registered in PROSPERO ( CRD42020171118 ).
高龄和认知障碍与跌倒风险较高相关。可穿戴传感器技术可能有助于客观评估运动性跌倒风险因素,以改善预防跌倒的体育锻炼干预措施。本系统评价旨在提供关于可穿戴传感器在有或无认知障碍的老年人跌倒风险评估方面当前研究的最新综述。因此,我们提出了两个具体研究问题:1)可穿戴传感器能否提供有关运动表现的准确数据,这些数据可用于评估跌倒风险,例如,在有或无认知障碍的老年人样本中区分跌倒者和非跌倒者?2)对于在有认知障碍的个体中应用基于传感器的跌倒风险评估可给出哪些实用建议?使用PubMed、Scopus和Web of Science数据库进行了系统文献检索(2019年7月,2020年7月更新)。纳入了在社区或老年环境中进行的、研究有或无认知障碍的老年人(年龄≥60岁)跌倒风险因素的研究。预定义的纳入标准产生了16项横断面研究、10项前瞻性研究和2项混合设计研究。
总体而言,基于传感器的数据主要在实验室环境中的步行测试期间收集。传感器的主要放置位置是下背部,以提供佩戴舒适度并避免干扰参与者。最准确的跌倒风险分类模型包括在三天日常生活中收集的从坐起到行走和从行走至坐下转换的数据(平均准确率=88.0%)。28项纳入研究中有9项揭示了有关可能有认知障碍的老年人使用传感器的信息,但分类模型的表现略逊于无认知障碍的老年人(平均准确率=79.0%)。
无论认知状态如何,使用可穿戴传感器进行跌倒风险评估在老年人中都是可行的。准确性可能因传感器位置、传感器附着方式以及为记录传感器数据而选择的评估类型而异。需要对可穿戴传感器在老年人客观跌倒风险评估中的应用进行更多研究,尤其是在有认知障碍的老年人中。
本系统评价已在PROSPERO中注册(CRD42020171118)。