Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Abstract

Background Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. Results Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). Conclusion Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment. Trial registration This systematic review is registered in PROSPERO (CRD42020171118).

Cross-sectional associations of physical frailty with fall, multiple falls and fall-injury among older Indian adults: Findings from LASI, 2018

“…The findings from this study are consistent with prior research [54,55], suggesting the higher rate of falls by increasing age. Another important finding of this study points to the strong statistically significant association between richest individuals and fall as well as fall related injuries as compared to other individuals in lower wealth quintiles.…”

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.…”

Effectiveness and Acceptability of e- and m-Health Interventions to Promote Physical Activity and Prevent Falls in Nursing Homes—A Systematic Review

“…Regarding training frequency, interventions consisting of three training sessions per week seem to be more effective than interventions with a lower training frequency which might not be able to achieve sufficient stimulus. Overall, none of the included studies used a sensor-based fall risk assessment, which has been shown to be feasible and able to successfully distinguish between groups of faller status ( Bezold, Krell-Roesch et al, 2021 ).…”

“…E- and m-health technologies offer new opportunities for a more person-centered care that more effectively considers individual abilities and impairments ( Goh et al, 2017 ). Given the high heterogeneity of the NH population in terms of their cognitive and physical functioning, it is difficult to adjust conventional intervention programs (i.e., that are not based on e- or m-health) to individual needs ( Trautwein et al, 2020 ; Bezold, Trautwein et al, 2021 ; Barisch-Fritz et al, 2022a ). Furthermore, the interactive nature and implementation of game elements in digital applications can provide an enjoyable experience for elderly users ( Syed-Abdul et al, 2019 ).…”