Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

Abstract

Background: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail).Methods: 133 participants (≥ 60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index.Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity.Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls

“…The use of inertial sensors has emerged as a technology useful in different gait analysis studies [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. The main studies and innovations found in the literature about inertial sensing have already been reviewed in several surveys [ 19 , 20 , 21 ].…”

How wearable sensors have been utilised to evaluate frailty in older adults: a systematic review

“…Five further articles were identified from manual search of references of eligible studies. One paper [33] was published after the updated search but was included when discovered incidentally. Of the 40 articles reviewed, 11 were excluded (See Appendix 2).…”

“…One study [17] was a mixed methods design but only the objective quantitative results were included in the report. The studies were carried out in varying settings; home: n = 14 [11,17,24,[34][35][36][37][38][39][40][41][42][43][44], laboratory: n = 8 [42,[45][46][47][48][49][50][51], hospital: in-patient n = 2 [52,53], outpatient n = 2 [34,54], community centre n = 1 [55] and not specified: n = 4 [33,[56][57][58]. Participant numbers ranged from n = 30 to n = 718.…”

“…Sensor placement included the lumbar spine (LSp) (n = 8), chest (n = 7), shin/ankle (n = 7), wrist and upper-limb combination (n = 3), wrist (n = 2), waist (n = 3), hip (n = 3), thigh (n = 3), foot (n = 1) and not specified (n = 3). Nineteen studies used just one body location [11, 17, 34-37, 40-42, 45-48, 50, 51, 54-56, 58], three studies, measuring elbow kinetics specifically, used a combination of above elbow and wrist [39,52,53] while six others used multiple body-locations of LSp and shin [57], and chest, LSp, thigh, shin and foot [24,33,38,43,49].…”

“…Seven different measures of mobility and PA were reported. Mobility measures included temporal-spatial gait parameters of speed, total steps, double support, stride length, time and variability [24,33,38,47,49,50,54,56], postural transitions: acceleration counts of sit to stand (STS), stand to walk, stand to sit [24,40,41,46,48,49,58], trunk angular velocity [47,50], upper limb kinematics [39,52,53], intensity of PA and percentage of time in walking, standing, sitting and lying [11, 17, 24, 35-38, 40-43, 55]. Two studies examined PA intensity with the aim to objectively define and compare with the low PA criterion of a frailty classification tool [34,44].…”