Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review

被引:5
|
作者
Oyibo, Kiemute
Wang, Kang
Morita, Plinio Pelegrini [1 ,2 ,3 ,4 ,5 ,6 ]
机构
[1] York Univ, Dept Elect Engn & Comp Sci, Toronto, ON, Canada
[2] Univ Waterloo, Sch Publ Hlth Sci, Waterloo, ON, Canada
[3] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON, Canada
[4] Univ Hlth Network, Techna Inst, Ctr Digital Therapeut, Toronto, ON, Canada
[5] Univ Toronto, Inst Hlth Policy Management & Evaluat, Toronto, ON, Canada
[6] Univ Waterloo, Sch Publ Hlth Sci, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada
关键词
smart home; physical activity; aging population; activity of daily living; remote health care monitoring; health monitoring; health promotion; smart home technology; assisted living; mobile phone; OLDER-ADULTS; MANAGEMENT; FRAMEWORK; INTERNET; THERAPY; SENSOR; THINGS;
D O I
10.2196/41942
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. Objective: This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities. Methods: Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts. Results: This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naive Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome. Conclusions: Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.
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页数:23
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