13 research outputs found
Neuromotor Changes in Participants with a Concussion History can be Detected with a Custom Smartphone App
Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture
Neuromotor Changes in Participants With a Concussion History Can Be Detected With a Custom Smartphone App
Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture
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Does Physical Activity Confound Race Differences in Osteoarthritis-Related Functional Limitation
Objective: This study sought to determine the extent to which physical activity confounds the relation between race and the incidence of osteoarthritis (OA)-related functional limitation. Methods: OA Initiative study participants with or at increased risk of knee OA who wore an accelerometer were included. Race was self-reported. Average time spent in moderate to vigorous physical activity (minutes per day) based on ActiGraph uniaxial accelerometer data was assessed. Functional limitation was based on the following: (1) inability to achieve a community walking speed (1.2 m/s) standard, (2) slow walking speed (<1.0 m/s), and (3) low physical functioning based on a Western Ontario and McMaster Universities OA Index (WOMAC) physical function score greater than 28 of 68. Results: African American (AA) participants (n = 226), compared with White participants (n = 1348), had a higher likelihood of developing functional limitation based on various measures. When adjusted for time in moderate to vigorous physical activity, the association between AA race and inability to walk a community walking speed slightly decreased (from relative risk [RR] 2.15, 95% confidence interval [95% CI] 1.64–2.81, to RR 1.99, 95% CI 1.51–2.61). Association between AA race and other measures of functional limitation mildly decreased (slow walking speed: from RR 2.06, 95% CI 1.40–3.01, to RR 1.82, 95% CI 1.25–2.63; low physical functioning: from RR 3.44, 95% CI 1.96–6.03, to RR 3.10, 95% CI 1.79–5.39). When further adjusted for demographic and other clinical variables, only the association between race and low physical functioning (WOMAC) significantly decreased and no longer met statistical significance. Conclusion: Greater physical activity is unlikely to completely make up for race differences in OA-related functional limitation, and other barriers to health equity need to be addressed.12 month embargo; first published: 30 July 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Data processing and model specification flow chart.
Abbreviations: HS = head shake, EC = Eyes-closed conditions. (A) Flow chart of the data processing. The two boxes in the bottom are the sample size submitted for the statistical analyses for all variables except CV Stride time. *1 n = the number of subjects, nt = the number of trials. *2 for CV stride time, n = 138 healthy and n = 61 concussed participants for the EC condition and n = 141 healthy and n = 60 concussed participants for the HS condition were submitted for analyses. (B) the model specification process: Fixed effects coefficients are B0, B1, B2, and B3, j = j-th group of i-th individual. u0i represents the random effect of the individual intercept, and e0i represents the residuals, where both are assumed to be normally distributed.</p
Smartphone app.
(A) Placement of the phone on the thigh and the illustration of stepping movement. (B) Representative time series of the thigh flexion angle in the sagittal plane during the stepping in place task. (C) Study design and dependent variables extracted from the smartphone app.</p
Association between time from concussion and SD Max Vel.
Data showing no association between the number of days since the concussion event and neuromotor performance as assessed with SD Max Vel.</p
Final model and statistical results of each movement variability dependent variable.
Final model and statistical results of each movement variability dependent variable.</p
Estimated marginal means of SD knee maximum angular velocity.
EC = Eyes-closed, HS = Head Shake conditions; Bars = Standard error of the mean; the values are estimated marginal means from the final model.</p