智能系统评估衰老变化：基于家庭、非侵入式、持续评估衰老。
Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging.
机构信息
Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, Oregon 97239-3098, USA.
出版信息
J Gerontol B Psychol Sci Soc Sci. 2011 Jul;66 Suppl 1(Suppl 1):i180-90. doi: 10.1093/geronb/gbq095.
OBJECTIVES
To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community.
METHODS
Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sensors include total daily activity, time out of home, and walking speed. Participants were given a computer as well as training, and computer usage was monitored. Participants are assessed annually with health and function questionnaires, physical examinations, and neuropsychological testing.
RESULTS
Mean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day.
DISCUSSION
These results demonstrate for the first time the feasibility of engaging seniors in a large-scale deployment of in-home activity assessment technology and the successful collection of these activity metrics. We plan to use this platform to determine if continuous unobtrusive monitoring may detect incident cognitive decline.
目的
描述一项纵向社区队列研究，即智能评估衰老变化系统，该研究已在现有社区的许多老年人家庭中部署了一个不引人注目的基于家庭的评估平台。
方法
为 265 名老年人的家庭安装了几种类型的传感器，平均安装时间为 33 个月。传感器评估的指标包括每日总活动量、离家时间和行走速度。参与者配备了电脑，并接受了培训，同时还对电脑使用情况进行了监测。参与者每年接受健康和功能问卷、身体检查和神经心理学测试评估。
结果
平均年龄为 83.3 岁，平均受教育年限为 15.5 年，73%的参与者为女性。在为期 4 周的快照期间，参与者平均每天离家两次，每天总计外出 208 分钟。平均室内行走速度为 61.0cm/s。参与者每天有 43%的时间在使用电脑，平均每天使用 76 分钟。
讨论
这些结果首次证明了让老年人参与大规模的家庭活动评估技术部署并成功收集这些活动指标的可行性。我们计划使用这个平台来确定连续的非侵入性监测是否可以检测到认知能力下降的发生。
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