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 ).

中文翻译：

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对有或没有认知障碍的老年人进行基于传感器的跌倒风险评估：系统评价


较高的年龄和认知障碍与较高的跌倒风险相关。可穿戴传感器技术可能有助于客观评估运动跌倒风险因素，以改善预防跌倒的体育锻炼干预措施。本系统综述旨在提供可穿戴传感器当前研究的最新概述，用于评估有或没有认知障碍的老年人跌倒风险。因此，我们解决了两个具体的研究问题：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，通过区分有或没有认知能力的老年人样本中的跌倒者和非跌倒者损伤？； 2) 对于 CI 患者应用基于传感器的跌倒风险评估，可以给出哪些实用建议？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）。包括基于社区的研究或在老年环境中进行的研究，这些研究检查有或没有认知障碍的老年人（年龄≥60岁）跌倒危险因素。预先确定的纳入标准产生了 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是在实验室环境中的步行测试期间收集的。主要传感器位置在下背部，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的日常生活中收集的从坐到步行和从步行到坐的转换数据（平均准确度 = 88.0%）。 28 项纳入研究中的 9 项揭示了可能存在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差于没有认知障碍的老年人（平均准确度 = 79.0%）。无论老年人的认知状况如何，使用可穿戴传感器评估跌倒风险都是可行的。准确性可能会有所不同，具体取决于传感器位置、传感器附件以及为记录传感器数据而选择的评估类型。需要更多关于使用传感器对老年人进行客观跌倒风险评估的研究，特别是对于患有认知障碍的老年人。该系统评价已在 PROSPERO 中注册（CRD42020171118）。