The Sensor Technology and Rehabilitative Timing (START) Protocol: A Randomized Controlled Trial for the Rehabilitation of Mild Traumatic Brain Injury

Parrington, Lucy; Jehu, Deborah A.; Fino, Peter C.; Stuart, Samuel; Wilhelm, Jennifer; Pettigrew, Natalie C.; Murchison, Charles; El‐Gohary, Mahmoud; VanDerwalker, Jess; Pearson, Sean; Hullar, Timothy E.; Chesnutt, J. C.; Peterka, Robert J.; Horak, Fay B.; King, Laurie A.

doi:10.1093/ptj/pzaa007

Cited by 21 publications

(23 citation statements)

References 51 publications

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“…Data collection details for each study have been previously reported. 8,40,41 Common demographic details that were collected included participant age (yrs), sex, height (m), mass (kg), and body mass index (BMI). Symptom severity and the total number of symptoms as derived by the second Sports Concussion Assessment Tool (SCAT-2) were also collected during each study.…”

Section: Data Collectionmentioning

confidence: 99%

Instrumented balance assessment in mild traumatic brain injury: Normative values and descriptive data for acute, sub-acute and chronic populations

Parrington

Popa

Martini

et al. 2020

Journal of Concussion

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Often the Balance Error Scoring System (BESS) is used to assess balance during a clinical evaluation of a patient presenting with mild Traumatic Brain Injury (mTBI). Although recent research has shown the benefits of using inertial sensor measures such as the Root Mean Square (RMS) of the acceleration in place of clinical scoring, few normative data are available for clinicians to reference. The purpose of this paper was to provide normative data collected using wearable sensors for healthy controls across three age groups, as well as providing cohort data for mTBI participants across three stages following injury (acute, sub-acute and chronic). The RMS in the Medio-Lateral direction (ML RMS sway) of each condition (double stance – DS; single stance – SS; and tandem stance – TS) was extracted per participant for analysis. The average ML RMS sway across all conditions was also calculated (ML RMS-Av). Percentiles were calculated to provide normative data, and two multivariate general linear models were used to evaluate differences between 1) non-athlete controls, athlete controls, and athletes with acute mTBI, and 2) non-athletic cohorts of control, sub-acute and chronic mTBI groups across young, middle-aged, and older adults. Model 1 revealed athletes with acute mTBI had more ML RMS sway than athlete controls the for the DS condition ( p < 0.001), but no differences with non-athlete controls. Athlete controls also had less ML RMS sway for the SS condition and ML RMS-Av ( p ≤ 0.022) compared with non-athlete controls. Model 2 revealed less ML RMS sway in the control group than the sub-acute and chronic mTBI groups for DS ( p ≤ 0.004), but no differences between the sub-acute and chronic group, while more ML RMS sway occurred in the chronic group compared with the control and sub-acute groups for the TS condition and ML RMS-Av ( p ≤ 0.013). Older adults had more ML RMS sway than young and middle-aged adults for SS, TS and ML RMS-Av ( p ≤ 0.019), while there were no differences between the young and middle-aged adults. Normative values presented here can help increase the practical application of instrumented balance assessment of mTBI patients through wearable sensors. ML RMS sway in the DS condition provided the clearest distinction between control and mTBI groups, but we caution that young adult athletes need to be assessed against athletic peers in the absence of baseline normative values. In non-athlete cohorts, age and gender norms may not be necessary to consider when assessing DS performance; however, age may be an important factor to consider when accessing norms for other stance conditions or the average performance across all conditions.

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Section: Data Collectionmentioning

confidence: 99%

Instrumented balance assessment in mild traumatic brain injury: Normative values and descriptive data for acute, sub-acute and chronic populations

Parrington

Popa

Martini

et al. 2020

Journal of Concussion

Self Cite

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“…In recent years, small sensors that can be attached to the body to acquire biological data, called "wearable devices," have been used extensively worldwide [1] . Wearable devices are used in clinical research and many medical and healthcare elds including neurology [2] , cardiology [3] , respiratory medicine [4] , and rehabilitation medicine [5] . In the eld of neurology, for example, applications that collect data from accelerometers in mobile devices have been used to quantify postural instability in Parkinson's disease [6] .…”

Section: Introductionmentioning

confidence: 99%

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

Yamanobe

Fujioka

Ohashi

et al. 2022

Preprint

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Background Many studies have reported the use of wearable devices to acquire biological data for the diagnosis and treatment of various diseases. Balance dysfunction, however, is difficult to evaluate in real time because the equilibrium function is conventionally examined using a stabilometer installed on the ground. Results Total trajectory length (r = 0.818, p -false discovery rate [FDR] = 0.004) and outer peripheral area (r = 0.691, p -FDR = 0.026) values measured using the wearable device and stabilometer were significantly positively correlated.Root mean square area values were not significantly correlated with wearable device stabilometry, but were comparable. Conclusion These results indicate that wearable, widely available, non-medical devices may be used to assess balance outside the hospital setting, and new approaches for testing balance function should be considered. Methods Using a wearable accelerometer that measures head motion to evaluate balance and to examine whether it performs comparably to a conventional stabilometer. We constructed a simplified physical head-feet model that simultaneously records “head” motion measured using an attached wearable accelerometer and center-of-gravity motion at the “feet”, which is measured using an attached stabilometer.

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“…In recent years, small sensors that can be attached to the body to acquire biological data, called "wearable devices," have been used extensively worldwide [1] . Wearable devices are used in clinical research and many medical and healthcare fields including neurology [2] , cardiology [3] , respiratory medicine [4] , and rehabilitation medicine [5] . In the field of neurology, for example, applications that collect data from accelerometers in mobile devices have been used to quantify postural instability in Parkinson's disease [6] .…”

Section: Introductionmentioning

confidence: 99%

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

Yamanobe

Fujioka

Ohashi

et al. 2022

Preprint

View full text Add to dashboard Cite

Many studies have reported the use of wearable devices to acquire biological data for the diagnosis and treatment of various diseases. Balance dysfunction, however, is difficult to evaluate in real time because the equilibrium function is conventionally examined using a stabilometer installed on the ground. Here, we used a wearable accelerometer that measures head motion to evaluate balance and examined whether it performs comparably to a conventional stabilometer. We constructed a simplified physical head-feet model that simultaneously records “head” motion measured using an attached wearable accelerometer and center-of-gravity motion at the “feet”, which is measured using an attached stabilometer. Total trajectory length (r = 0.818, p -false discovery rate [FDR] = 0.004) and outer peripheral area (r = 0.691, p -FDR = 0.026) values measured using the wearable device and stabilometer were significantly positively correlated. Root mean square area values were not significantly correlated with wearable device stabilometry but were comparable. These results indicate that wearable, widely available, non-medical devices may be used to assess balance outside the hospital setting, and new approaches for testing balance function should be considered.

show abstract

The Sensor Technology and Rehabilitative Timing (START) Protocol: A Randomized Controlled Trial for the Rehabilitation of Mild Traumatic Brain Injury

Cited by 21 publications

References 51 publications

Instrumented balance assessment in mild traumatic brain injury: Normative values and descriptive data for acute, sub-acute and chronic populations

Instrumented balance assessment in mild traumatic brain injury: Normative values and descriptive data for acute, sub-acute and chronic populations

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

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