Predicting Fall Risk Through Automatic Wearable Monitoring

Abstract

Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.

Health-Related Telemonitoring Parameters/Signals of Older Adults: An Umbrella Review

“…Accordingly, 18 systematic reviews were included in this umbrella review ( Figure 1 ). Nine of the reviews were developed in European countries [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], four in North American countries [ 26 , 27 , 28 , 29 ], three in Australia [ 30 , 31 , 32 ], one in Pakistan [ 33 ], and one in India [ 34 ]. The number of studies included in each review varied between 7 and 73, with an average of 32 studies, and a median of 25 studies.…”

“…The databases most commonly searched by the studies included MEDLINE (ten studies [ 17 , 19 , 21 , 22 , 23 , 26 , 28 , 30 , 31 , 33 ]), PubMed by nine [ 18 , 20 , 24 , 25 , 27 , 29 , 31 , 32 , 33 ]), EMBASE by eight [ 17 , 20 , 21 , 22 , 26 , 28 , 29 , 32 ], CINAHL by six [ 17 , 20 , 23 , 29 , 30 , 31 ], SCOPUS by five [ 19 , 23 , 24 , 25 , 34 ], COCHRANE by four [ 18 , 26 , 30 , 33 ], Web of Science by three [ 19 , 22 , 25 ], Science Direct [ 19 , 23 ], Google Scholar [ 29 , 33 ], ACM digital [ 19 , 24 ], IEE Xplorer [ 19 ,…”

“…Different types of studies were included in each review, varying between observational [ 19 , 21 , 22 , 23 , 24 , 26 , 28 , 30 , 32 ], CC [ 19 ], CSS [ 18 , 19 , 25 ], CO [ 19 ], RCT [ 17 , 21 , 22 , 28 , 32 , 33 ], nRCT [ 17 ], CS [ 28 ], PRO [ 27 ], and the development of a monitoring system [ 18 ].…”

“…The methodological quality of each systematic review is shown in Table 2 . Only four reviews were considered high quality [ 21 , 22 , 29 , 32 ], five presented moderate quality [ 17 , 23 , 25 , 26 , 30 ], eight presented low quality [ 18 , 19 , 20 , 24 , 27 , 28 , 31 , 33 ], and one showed critically low quality [ 34 ].…”

Uses of mHealth in Injury Prevention and Control: a Critical Review

“…Published works leveraging mHealth approaches to fall prevention primarily focus on the primary prevention of falls in older adults. Formative work includes using apps and wearables to track gait, fall risk, falls, and fitness over time [13,14]. There is a paucity of literature around the translation of wearable and tracking technologies into fall prevention interventions.…”