National Institute for Health and Care Research (NIHR) Older People and Frailty Policy Research Unit, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, UK.
Division of Nursing, Midwifery and Social Work, School of Health Sciences, The University of Manchester, Manchester M13 9PT, UK.
Age Ageing. 2024 Jan 2;53(1). doi: 10.1093/ageing/afad238.
OBJECTIVE: Falls are a common cause of potentially preventable death, disability and loss of independence with an annual estimated cost of £4.4bn. People living with dementia (PlwD) or mild cognitive impairment (MCI) have an increased fall risk. This overview evaluates evidence for technologies aiming to reduce falls and fall risk for PlwD or MCI. METHODS: In October 2022, we searched five databases for evidence syntheses. We used standard methods to rapidly screen, extract data, assess risk of bias and overlap, and synthesise the evidence for each technology type. RESULTS: We included seven systematic reviews, incorporating 22 relevant primary studies with 1,412 unique participants. All reviews had critical flaws on AMSTAR-2: constituent primary studies were small, heterogeneous, mostly non-randomised and assessed as low or moderate quality. Technologies assessed were: wearable sensors, environmental sensor-based systems, exergaming, virtual reality systems. We found no evidence relating to apps. Review evidence for the direct impact on falls was available only from environmental sensors, and this was inconclusive. For wearables and virtual reality technologies there was evidence that technologies may differentiate PlwD who fell from those who did not; and for exergaming that balance may be improved. CONCLUSIONS: The evidence for technology to reduce falls and falls risk for PlwD and MCI is methodologically weak, based on small numbers of participants and often indirect. There is a need for higher-quality RCTs to provide robust evidence for effectiveness of fall prevention technologies. Such technologies should be designed with input from users and consideration of the wider implementation context.
目的:跌倒会导致潜在可预防的死亡、残疾和丧失独立性,每年估计造成 44 亿英镑的损失。患有痴呆症(PlwD)或轻度认知障碍(MCI)的人跌倒风险增加。本综述评估了旨在降低 PlwD 或 MCI 跌倒和跌倒风险的技术的证据。
方法:2022 年 10 月,我们在五个数据库中搜索证据综合报告。我们使用标准方法快速筛选、提取数据、评估风险偏倚和重叠,并对每种技术类型的证据进行综合。
结果:我们纳入了 7 项系统评价,纳入了 22 项相关的原始研究,涉及 1412 名独特的参与者。所有综述在 AMSTAR-2 上都存在严重缺陷:组成的原始研究规模小、异质性大、大多数为非随机且评估为低或中等质量。评估的技术包括:可穿戴传感器、基于环境传感器的系统、健身游戏、虚拟现实系统。我们没有发现与应用程序相关的证据。关于直接影响跌倒的证据仅来自环境传感器,且结果不确定。对于可穿戴设备和虚拟现实技术,有证据表明这些技术可能可以区分跌倒的 PlwD 和未跌倒的 PlwD;对于健身游戏,平衡可能会得到改善。
结论:针对 PlwD 和 MCI 减少跌倒和跌倒风险的技术的证据在方法学上很薄弱,基于参与者人数少且往往间接的证据。需要进行更高质量的 RCT 来提供预防跌倒技术有效性的可靠证据。此类技术的设计应考虑到用户的意见和更广泛的实施背景。
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