A Smartphone App-Based Application Enabling Remote Assessments of Standing Balance During the COVID-19 Pandemic and Beyond
| Autor: | On-Yee Lo, Hao Zhu, Junhong Zhou, Thomas G. Travison, Natalia Gouskova, Wanting Yu, Lewis A. Lipsitz, Xin Jiang, Brad Manor, Alvaro Pascual-Leone |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) Computer Networks and Communications Computer science Intraclass correlation Computer Science Applications Postural control Acceleration Standing balance Physical medicine and rehabilitation Hardware and Architecture Signal Processing Smartphone app medicine Task analysis Reliability (statistics) Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 8:15818-15828 |
| ISSN: | 2372-2541 |
| Popis: | Assessments of postural control provide important insights into health. We created a smartphone App-based assessment of standing posture—completed with the phone placed in the user’s pocket—to enable remote monitoring of the function while minimizing the need for in-person assessment or contact. We tested the reliability of App-derived postural sway metrics, as well as their sensitivity to age and task conditions. Fifteen older and 15 younger adults completed two separate laboratory visits. They followed multimedia instructions provided by the App to complete three 30-s trials of eyes-open (EO), eyes-closed (EC), and dual-task (DT) standing. Sway data were recorded by the App and a force plate. Participants also used the App to complete the assessment in their homes on three separate days. Sway path length and root-mean square were derived from the angular velocity and acceleration acquired from phone’s internal motion sensor, and from the center-of-pressure signal from force plate. App-derived path length and root-mean-square of acceleration and angular velocity across conditions demonstrated moderate-to-excellent test–retest reliability (intraclass correlation coefficients (ICCs) = 0.69 − 0.97) for both younger and older adults. Reliability was comparable to metrics derived from the force plate (ICCs = 0.44 − 0.93). As expected, App-derived sway outcomes exhibited somewhat greater variability when tested across days at home (ICCs = 0.14 − 0.79). All App-derived metrics were sensitive to age ( $F>43.8$ , $p ) and testing condition ( $F>31.8$ , $p ) in both laboratory and home settings, revealing that the smartphone App enabled reliable and sensitive assessment of standing posture in both healthy younger and older adults. |
| Databáze: | OpenAIRE |
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