A novel smartphone App-based assessment of standing postural control: Demonstration of reliability and sensitivity to aging and task constraints

摘要

Laboratory-based assessments of standing posture provide important insights into balance, mobility, and many other factors. We created a smartphone-based assessment of standing posture-completed with the phone placed in the user's pocket-for use in remote, non-laboratory settings. We here tested the reliability and sensitivity of App-derived metrics of postural sway to participant age and standing condition. Fifteen healthy younger and 15 older adults completed two laboratory visits separated by one week. On each visit, they followed multi-media instructions provided by the App to complete three 30-second trials each of standing with eyes open (EO), eyes closed (EC), and eyes open while performing a serial subtraction dual task (DT). Sway data were simultaneously collected with the App and a gold-standard force plate. Participants also completed App-based tests within their own homes on three separate days. To characterize sway, path length and root-mean-square (RMS) were derived from the acceleration and angular velocity signal acquired from the phone's internal motion sensor, and from the center of pressure (COP) signal acquired by the force plate. Across repeated trials conducted on the same day within the laboratory, App-derived path length and RMS of acceleration and angular velocity across conditions demonstrated moderate to excellent test-retest reliability (Intraclass Correlation Coefficients (ICCs)=0.69-0.97) for both the younger and older adults. Force plate-derived metrics demonstrated low to excellent test-retest reliability (ICCs=0.44-0.93). Compared to within-day laboratory testing, App-derived sway outcomes generally exhibited greater variability when tested across days within the home (ICCs=0.14-0.79). All sway metrics derived from the App were sensitive to age group (F>43.8, p<0.001) and task condition (F>31.8, p<0.001) in both laboratory and home settings. The smartphone App we created enabled reliable and sensitive assessment of standing postural sway within different task conditions, in both relatively healthy younger and older adults.