Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

Abstract

Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods: DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results: One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step-and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty. Trial registration: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

Objectively measured physical activity is associated with frailty in community-dwelling older adults: A systematic review

“…Step counts were studied in 12 populations [50,49,45,54,51,47,55,48,56,62,46], with all finding that a higher number of steps was associated with lower frailty. The median [IQR] standardised effect size was β=-0.302 [-0.370, -0.153], with a range from β=-0.459 to β=-0.084.…”

“…outlines the measurement and characteristics of PA/SB and frailty. PA/SB measures included light physical activity (LPA)[44,42,49,45,37,62,46,50,52], moderate-to-vigorous physical activity (MVPA)[36,44,53,50,52,49,45,37,56,62,46], step count[49,45,47,48,62,46,50,54,51,55,56], accelerometer activity counts[40,48,62], total physical activity (TPA)[48], postural transitions (TRS)[63], energy expenditure (EE)[39,62,56], sedentary behaviour (SB)[36,44,43,38,45,37,62,46,52,…”

Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review

“…In over half of the studies relying on transparent activities (23 out of 37—please note that [ 71 , 73 ] are include in both activity and gait patterns), the participants were monitored while carrying out their daily activities at home over several days: 14 out of 15 studies on activity patterns [ 71 , 73 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]; all six studies on gait patterns [ 71 , 72 , 73 , 74 , 75 , 76 ]; all three studies on mobility patterns [ 106 , 107 , 108 ]; and two studies on STS transitions [ 55 , 56 ]. However, the remaining 14 studies monitored the participants during simulated activities in the lab.…”

“…Transparent studies relying on STS transitions measured parameters such as duration, acceleration (mean and maximal), velocity (mean and maximal), trunk angular velocity (mean and maximal), maximal jerk, peak power, vertical acceleration range, and number of postural transitions [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Transparent studies on gait patterns measured parameters such as gait speed, gait variability, gait asymmetry, gait irregularity, daily walking time, total walking duration, and number of walks per day [ 71 , 72 , 73 , 74 , 75 , 76 ]. Studies on activity patterns measured parameters such as sedentary behavior, time spent active, and step counts [ 71 , 73 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 ].…”

“…Transparent studies on gait patterns measured parameters such as gait speed, gait variability, gait asymmetry, gait irregularity, daily walking time, total walking duration, and number of walks per day [ 71 , 72 , 73 , 74 , 75 , 76 ]. Studies on activity patterns measured parameters such as sedentary behavior, time spent active, and step counts [ 71 , 73 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 ]. Studies on mobility measured parameters such as the number of transitions between different rooms, the time spent in each room, and some other features derived from them [ 106 , 107 , 108 ].…”