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

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]

Understanding Recruitment Yield From Social Media Advertisements and Associated Costs of a Telehealth Randomized Controlled Trial: Descriptive Study

BackgroundRecruiting study participants for clinical research is a challenging yet essential task. Social media platforms, such as Facebook, offer the opportunity to recruit participants through paid advertisements. These ad campaigns may be a cost-effective approach to reaching and recruiting participants who meet specific study criteria. However, little is known about the extent to which clicks on social media advertisements translate to the actual consent and enrollment of participants who meet the study criteria. Understanding this is especially important for clinical trials conducted remotely, such as telehealth-based studies, which open the possibility to recruit over large geographical areas and are becoming more common for the treatment of chronic health conditions, such as osteoarthritis (OA). ObjectiveThe aim of this study was to report on the conversion of clicks on a Facebook advertisement campaign to consent to enrollment in an ongoing telehealth physical therapy study for adults with knee OA, and the costs associated with recruitment. MethodsThis was a secondary analysis using data collected over the first 5 months of an ongoing study of adults with knee OA. The Delaware Physical Exercise and Activity for Knee Osteoarthritis program compares a virtually delivered exercise program to a control group receiving web-based resources among adults with knee OA. Advertisement campaigns were configured on Facebook to reach an audience who could be potentially eligible. Clicking on the advertisement directed potential participants to a web-based screening form to answer 6 brief questions related to the study criteria. Next, a research team member called individuals who met the criteria from the screening form and verbally asked additional questions related to the study criteria. Once considered eligible, an electronic informed consent form (ICF) was sent. We described the number of potential study participants who made it through each of these steps and then calculated the cost per participant who signed the ICF. ResultsIn sum, between July and November 2021, a total of 33,319 unique users saw at least one advertisement, 9879 clicks were made, 423 web-based screening forms were completed, 132 participants were successfully contacted, 70 were considered eligible, and 32 signed the ICF. Recruitment costed an average of US $51.94 per participant. ConclusionsWhile there was a low conversion from clicks to actual consent, 32$90 to US $1000 per participant. Trial RegistrationClinicaltrails.gov NCT04980300; https://clinicaltrials.gov/ct2/show/NCT0498030

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

Raw data.

Dependent variable and demographic data for each participant. (XLSX)</p