Katherine L Hsieh1, Jacob J Sosnoff2. 1. Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, 906 S. Goodwin Ave, Urbana, IL 61801, USA; Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, 901 W. University Avenue, Suite 201 Urbana, IL 61801, USA; Department of Internal Medicine-Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC 27109, USA. Electronic address: klhsieh3@illinois.edu. 2. Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, 906 S. Goodwin Ave, Urbana, IL 61801, USA; Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, 901 W. University Avenue, Suite 201 Urbana, IL 61801, USA.
Abstract
BACKGROUND: Falls are a major health concern for people with Multiple Sclerosis (pwMS), and impaired postural control is an important predictor of falls. Lab-based technology to measure posture is precise but expensive, and clinical tests may not capture underlying impairments. An alternative solution is to leverage smartphone accelerometry as it is affordable, ubiquitous, and portable. RESEARCH QUESTION: Can smartphone accelerometry measure postural control compared to a force plate and research grade accelerometer in pwMS, and can smartphone accelerometry discriminate between assisted device and non-assisted device users? METHODS: 27 pwMS (12 assisted device users, 15 non-assisted device users) stood on a force plate while holding a smartphone with an attached research grade accelerometer against their chest. Participants performed two, 30 s trials of: eyes open, eyes closed, semi-tandem, tandem, and single leg. Acceleration and center of pressure were extracted, and Root Mean Square (RMS) and 95 % confidence ellipse were calculated. Spearman's correlations were performed, and receiving operating characteristic (ROC) curves and the Area Under the Curve (AUC) were calculated. RESULTS: There were moderate to high correlations between the smartphone and accelerometer for RMS (ρ = 0.85 - 1.0; p = 0.001 - <0.001) and 95 % area ellipse (ρ = 0.92 - 0.99; p = <0.001). There were weak to moderate correlations between the smartphone and force plate for RMS (ρ = 0.38 - 0.92; p = 0.06 - <0.001) and 95 % area ellipse (ρ = 0.69 - 0.90 p = 0.002 - <0.001). To discriminate between assisted device usage, ROC curves for smartphone outputs were constructed, the AUC was high and statistically significant (p < 0.001 - 0.02). SIGNIFICANCE: There is potential to leverage smartphone accelerometery to measure postural control in pwMS. These finding provide preliminary results to support the development of a mobile health application to measure fall risk for pwMS.
BACKGROUND: Falls are a major health concern for people with Multiple Sclerosis (pwMS), and impaired postural control is an important predictor of falls. Lab-based technology to measure posture is precise but expensive, and clinical tests may not capture underlying impairments. An alternative solution is to leverage smartphone accelerometry as it is affordable, ubiquitous, and portable. RESEARCH QUESTION: Can smartphone accelerometry measure postural control compared to a force plate and research grade accelerometer in pwMS, and can smartphone accelerometry discriminate between assisted device and non-assisted device users? METHODS: 27 pwMS (12 assisted device users, 15 non-assisted device users) stood on a force plate while holding a smartphone with an attached research grade accelerometer against their chest. Participants performed two, 30 s trials of: eyes open, eyes closed, semi-tandem, tandem, and single leg. Acceleration and center of pressure were extracted, and Root Mean Square (RMS) and 95 % confidence ellipse were calculated. Spearman's correlations were performed, and receiving operating characteristic (ROC) curves and the Area Under the Curve (AUC) were calculated. RESULTS: There were moderate to high correlations between the smartphone and accelerometer for RMS (ρ = 0.85 - 1.0; p = 0.001 - <0.001) and 95 % area ellipse (ρ = 0.92 - 0.99; p = <0.001). There were weak to moderate correlations between the smartphone and force plate for RMS (ρ = 0.38 - 0.92; p = 0.06 - <0.001) and 95 % area ellipse (ρ = 0.69 - 0.90 p = 0.002 - <0.001). To discriminate between assisted device usage, ROC curves for smartphone outputs were constructed, the AUC was high and statistically significant (p < 0.001 - 0.02). SIGNIFICANCE: There is potential to leverage smartphone accelerometery to measure postural control in pwMS. These finding provide preliminary results to support the development of a mobile health application to measure fall risk for pwMS.
Authors: Katherine L Hsieh; Mikaela L Frechette; Jason Fanning; Lingjun Chen; Aileen Griffin; Jacob J Sosnoff Journal: Front Digit Health Date: 2022-04-27
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