N Lapierre1, N Neubauer2, A Miguel-Cruz3, A Rios Rincon4, L Liu5, J Rousseau6. 1. Faculty of Medicine, Université de Montréal, C.P. 6128 Centre-ville, Montréal, Québec, H3C 3J7, Canada; Research Center, Institut universitaire de gériatrie de Montréal (Pavillon André-Roch Lecours), 4565 chemin Queen-Mary, Montréal, Québec, H3W 1W5, Canada. Electronic address: nolwenn.lapierre@umontreal.ca. 2. Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta. 2-64 Corbett Hall, Edmonton, AB, T6G 2G4, Canada. Electronic address: noelanna@ualberta.ca. 3. School of Medicine and Health Sciences, Universidad del Rosario. Calle 63D # 24-31, 7 de Agosto, Bogotá D.C, Colombia, Colombia; Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta. 2-64 Corbett Hall, Edmonton, AB, T6G 2G4, Canada. Electronic address: antonio.miguel@urosario.edu.co. 4. School of Medicine and Health Sciences, Universidad del Rosario. Calle 63D # 24-31, 7 de Agosto, Bogotá D.C, Colombia, Colombia; Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta. 2-64 Corbett Hall, Edmonton, AB, T6G 2G4, Canada. Electronic address: adriana.rios@urosario.edu.co. 5. Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta. 2-64 Corbett Hall, Edmonton, AB, T6G 2G4, Canada. Electronic address: lili.liu@ualberta.ca. 6. Research Center, Institut universitaire de gériatrie de Montréal (Pavillon André-Roch Lecours), 4565 chemin Queen-Mary, Montréal, Québec, H3W 1W5, Canada; Faculty of Medicine, Université de Montréal, School of Rehabilitation, Site Pavillon Parc, C.P. 6128 Centre-ville, Montréal, Québec, H3C 3J7, Canada. Electronic address: Jacqueline.Rousseau@umontreal.ca.
Abstract
BACKGROUND: Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. OBJECTIVES: To examine the extent and the diversity of current technologies for fall detection in older adults. METHODS: A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. RESULTS: The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. CONCLUSIONS: Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults.
BACKGROUND: Globally, populations are aging with increasing life spans. The normal aging process and the resulting disabilities increase fall risks. Falls are an important cause of injury, loss of independence and institutionalization. Technologies have been developed to detect falls and reduce their consequences but their use and impact on quality of life remain debatable. Reviews on fall detection technologies exist but are not extensive. A comprehensive literature review on the state of knowledge of fall detection technologies can inform research, practice, and user adoption. OBJECTIVES: To examine the extent and the diversity of current technologies for fall detection in older adults. METHODS: A scoping review design was used to search peer-reviewed literature on technologies to detect falls, published in English, French or Spanish since 2006. Data from the studies were analyzed descriptively. RESULTS: The literature search identified 3202 studies of which 118 were included for analysis. Ten types of technologies were identified ranging from wearable (e.g., inertial sensors) to ambient sensors (e.g., vision sensors). Their Technology Readiness Level was low (mean 4.54 SD 1.25; 95% CI [4.31, 4.77] out of a maximum of 9). Outcomes were typically evaluated on technological basis and in controlled environments. Few were evaluated in home settings or care units with older adults. Acceptability, implementation cost and barriers were seldom addressed. CONCLUSIONS: Further research should focus on increasing Technology Readiness Levels of fall detection technologies by testing them in real-life settings with older adults.
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