Lu Wei, Silvera-Tawil David, Yoon Hwan-Jin, Higgins Liesel, Zhang Qing, Karunanithi Mohanraj, Bomke Julia, Byrnes Joshua, Hewitt Jennifer, Smallbon Vanessa, Freyne Jill, Prabhu Deepa, Varnfield Marlien
Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.
Research Center for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, ZheJiang, China.
J Med Internet Res. 2025 Jan 22;27:e59921. doi: 10.2196/59921.
An increasingly aging population, accompanied by a shortage of residential aged care homes and workforce and consumer feedback, has driven a growing interest in enabling older people to age in place through home-based care. In this context, smart home technologies for remote health monitoring have gained popularity for supporting older people to live in their own homes.
This study aims to investigate the impact of smart home monitoring on multiple outcomes, including quality of life, activities of daily living, and depressive symptoms among older people living in their own homes over a 12-month period.
We conducted an open-label, parallel-group randomized controlled trial. The control group continued to receive their existing care from aged care service providers. Meanwhile, the intervention group, in addition to receiving their usual aged care services, had their activities of daily living monitored using a smart home platform. Surveys including the Adult Social Care Outcomes Toolkit (ASCOT), EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Katz Index of Independence in Activities of Daily Living (Katz ADL), Lawton Instrumental Activities of Daily Living Scale (IADL), and Geriatric Depression Scale (GDS) were conducted at baseline and 6 and 12 months from baseline. Linear mixed-effects models were used to compare the difference between the intervention and control groups, with the ASCOT as the primary outcome measure.
Data from 130 participants were used in the analysis, with no significant differences in baseline characteristics between the control group (n=61) and the intervention group (n=69). In comparison to the control group, the intervention group had a higher ASCOT score at the 6-month assessment (mean difference 0.045, 95% CI 0.001 to 0.089; Cohen d=0.377). However, this difference did not persist at the 12-month assessment (mean difference 0.031, 95% CI -0.014 to 0.076; Cohen d=0.259). There were no significant differences in EQ-5D-5L, Katz ADL, IADL, and GDS observed between the intervention and control groups at the 6-month and 12-month assessments.
The study demonstrates that smart home monitoring can improve social care-related quality of life for older people living in their own homes. However, the improvement was not sustained over the long term. The lack of statistically significant findings and diminished long-term improvements may be attributed to the influence of the COVID-19 pandemic during the later stage of the trial. Further research with a larger sample size is needed to evaluate the effect of smart home monitoring on broader quality-of-life measures.
Australian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/31970.
人口老龄化日益加剧，同时伴有养老院及工作人员短缺以及消费者反馈等问题，这使得人们越来越关注如何通过居家护理让老年人能够在家中养老。在此背景下，用于远程健康监测的智能家居技术因能支持老年人居家生活而受到欢迎。
本研究旨在调查智能家居监测对多种结果的影响，包括居家老年人在12个月期间的生活质量、日常生活活动能力及抑郁症状。
我们进行了一项开放标签、平行组随机对照试验。对照组继续接受老年护理服务提供者提供的现有护理。与此同时，干预组除接受常规老年护理服务外，还通过智能家居平台对其日常生活活动进行监测。在基线以及基线后6个月和12个月进行了包括成人社会护理结果工具包（ASCOT）、欧洲五维度健康量表（EQ-5D-5L）、日常生活活动能力的Katz指数（Katz ADL）、Lawton日常生活活动能力量表（IADL）和老年抑郁量表（GDS）在内的调查。使用线性混合效应模型比较干预组和对照组之间的差异，以ASCOT作为主要结局指标。
分析使用了130名参与者的数据，对照组（n = 61）和干预组（n = 69）的基线特征无显著差异。与对照组相比，干预组在6个月评估时ASCOT得分更高（平均差异0.045，95%CI 0.001至0.089；Cohen d = 0.377）。然而，这种差异在12个月评估时未持续存在（平均差异0.031，95%CI -0.014至0.076；Cohen d = 0.259）。在6个月和12个月评估时，干预组和对照组在EQ-5D-5L、Katz ADL、IADL和GDS方面均未观察到显著差异。
该研究表明，智能家居监测可以改善居家老年人与社会护理相关的生活质量。然而，这种改善并未长期持续。缺乏具有统计学意义的结果以及长期改善效果减弱可能归因于试验后期COVID-19大流行的影响。需要进行更大样本量的进一步研究，以评估智能家居监测对更广泛生活质量指标的影响。
澳大利亚新西兰临床试验注册中心ACTRN126180008
