Construct Validity of the Both Hands Assessment Using Wrist-Worn Accelerometers.

AIM: The aim of this study was to evaluate the construct validity of the Both Hands Assessment (BoHA) using activity of the upper limbs as detected by accelerometry in children with bilateral cerebral palsy (CP). METHODS: Observational study of children with CP (n = 44, n = 27 boys, aged 9.1 ± 1.6 years; Manual Ability Classification Scale I: n = 15, II: n = 22, III: n = 7) completing a BoHA assessment while wearing a triaxial accelerometer on each wrist. BoHA Each-Hand sub-scores, BoHA percentage difference between hands, BoHA Units, mean activity for each hand, mean activity asymmetry index and total mean activity were calculated. Linear regressions were used to analyze associations between measures. RESULTS: There were significant, positive associations between BoHA Units and total mean activity (B = 0.86, 95%CI: 0.32, 1.40), BoHA Percentage difference between hands and mean activity asymmetry index (B = 0.95, 95%CI: 0.75,1.15), and BoHA Each-Hand sub-score and mean activity for the non-dominant hand (B = 1.71, 95%CI: 1.16, 2.28), but not the dominant hand (B = 0.50, 95%CI: -0.45, 1.45). CONCLUSIONS: This study provides further evidence for the construct validity of the BoHA as a measure of upper limb performance. Wearable wrist sensors such as accelerometers capture and quantify gross upper limb movement in children with CP but cannot measure fine finger movements captured by the BoHA. CLINICAL TRIALS REGISTRATION: Australian New Zealand Clinical Trials Registry (ACTRN12616001488493 and ACTRN12618000164291).

Accelerometry as an objective measure of upper-extremity activity.

Most studies evaluating the effectiveness of treatments targeting shoulder pathologies use subjective outcome measures such as self-administered questionnaires. To date, there are no validated tools that objectively measure shoulder-specific functional activity. The purpose of this study was to validate wearable accelerometers as an objective proxy for shoulder activity. Ten healthy volunteers wore accelerometers placed at both wrists, the dominant upper arm and the chest while performing standardised shoulder and non-shoulder activities. Recorded tridimensional acceleration was computed into activity counts for epochs of 10 s. Receiver operating characteristics (ROC) curves were built to determine the optimal configuration to classify shoulder-type activities. For single accelerometer placement, the area under the ROC curve (AUC) was optimal for the 10-s epoch (AUC = 0.779) using the wrist placement, with a sensitivity of 94.1% and specificity of 67.5%. The combined upper arm and chest placement had an AUC of 0.985 (94.8% sensitivity, 94.8% specificity). Dual-accelerometer placement (upper arm and chest) is the optimal configuration to classify shoulder activity. However, a sole wrist-based accelerometer can be used as an objective proxy for shoulder activity in long-term unsupervised monitoring with excellent sensitivity and acceptable specificity.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Feasibility of using acceleration-derived jerk to quantify bimanual arm use.

BACKGROUND: Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts" - a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury. METHOD: We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (non-paretic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics. RESULTS: The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = - 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015. CONCLUSIONS: Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.

Wrist-Based Accelerometers and Visual Analog Scales as Outcome Measures for Shoulder Activity During Daily Living in Patients With Rotator Cuff Tendinopathy: Instrument Validation Study.

BACKGROUND: Shoulder pain secondary to rotator cuff tendinopathy affects a large proportion of patients in orthopedic surgery practices. Corticosteroid injections are a common intervention proposed for these patients. The clinical evaluation of a response to corticosteroid injections is usually based only on the patient's self-evaluation of his function, activity, and pain by multiple questionnaires with varying metrological qualities. Objective measures of upper extremity functions are lacking, but wearable sensors are emerging as potential tools to assess upper extremity function and activity. OBJECTIVE: This study aimed (1) to evaluate and compare test-retest reliability and sensitivity to change of known clinical assessments of shoulder function to wrist-based accelerometer measures and visual analog scales (VAS) of shoulder activity during daily living in patients with rotator cuff tendinopathy convergent validity and (2) to determine the acceptability and compliance of using wrist-based wearable sensors. METHODS: A total of 38 patients affected by rotator cuff tendinopathy wore wrist accelerometers on the affected side for a total of 5 weeks. Western Ontario Rotator Cuff (WORC) index; Short version of the Disability of the Arm, Shoulder, and Hand questionnaire (QuickDASH); and clinical examination (range of motion and strength) were performed the week before the corticosteroid injections, the day of the corticosteroid injections, and 2 and 4 weeks after the corticosteroid injections. Daily Single Assessment Numeric Evaluation (SANE) and VAS were filled by participants to record shoulder pain and activity. Accelerometer data were processed to extract daily upper extremity activity in the form of active time; activity counts; and ratio of low-intensity activities, medium-intensity activities, and high-intensity activities. RESULTS: Daily pain measured using VAS and SANE correlated well with the WORC and QuickDASH questionnaires (r=0.564-0.815) but not with accelerometry measures, amplitude, and strength. Daily activity measured with VAS had good correlation with active time (r=0.484, P=.02). All questionnaires had excellent test-retest reliability at 1 week before corticosteroid injections (intraclass correlation coefficient [ICC]=0.883-0.950). Acceptable reliability was observed with accelerometry (ICC=0.621-0.724), apart from low-intensity activities (ICC=0.104). Sensitivity to change was excellent at 2 and 4 weeks for all questionnaires (standardized response mean=1.039-2.094) except for activity VAS (standardized response mean=0.50). Accelerometry measures had low sensitivity to change at 2 weeks, but excellent sensitivity at 4 weeks (standardized response mean=0.803-1.032). CONCLUSIONS: Daily pain VAS and SANE had good correlation with the validated questionnaires, excellent reliability at 1 week, and excellent sensitivity to change at 2 and 4 weeks. Daily activity VAS and accelerometry-derived active time correlated well together. Activity VAS had excellent reliability, but moderate sensitivity to change. Accelerometry measures had moderate reliability and acceptable sensitivity to change at 4 weeks.

An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.

BACKGROUND: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep. PURPOSE: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices. METHODS: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X. RESULTS: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors. CONCLUSIONS: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

Hemispheric asymmetry in myelin after stroke is related to motor impairment and function.

The relationships between impairment, function, arm use and underlying brain structure following stroke remain unclear. Although diffusion weighted imaging is useful in broadly assessing white matter structure, it has limited utility in identifying specific underlying neurobiological components, such as myelin. The purpose of the present study was to explore relationships between myelination and impairment, function and activity in individuals with chronic stroke. Assessments of paretic upper-extremity impairment and function were administered, and 72-hour accelerometer based activity monitoring was conducted on 19 individuals with chronic stroke. Participants completed a magnetic resonance imaging protocol that included a high resolution T1 anatomical scan and a multi-component T2 relaxation imaging scan to quantify myelin water fraction (MWF). MWF was automatically parcellated from pre- and post-central subcortical regions of interest and quantified as an asymmetry ratio (contralesional/ipsilesional). Cluster analysis was used to group more and less impaired individuals based on Fugl-Meyer upper extremity scores. A significantly higher precentral MWF asymmetry ratio was found in the more impaired group compared to the less impaired group (p < 0.001). There were no relationships between MWF asymmetry ratio and upper-limb use. Stepwise multiple linear regression identified precentral MWF asymmetry as the only variable to significantly predict impairment and motor function in the upper extremity (UE). These results suggest that asymmetric myelination in a motor specific brain area is a significant predictor of upper-extremity impairment and function in individuals with chronic stroke. As such, myelination may be utilized as a more specific marker of the neurobiological changes that predict long term impairment and recovery from stroke.

Evaluation of a wireless activity monitoring system to quantify locomotor activity in horses in experimental settings.

REASONS FOR PERFORMING STUDY: Methods of evaluating locomotor activity can be useful in efforts to quantify behavioural activity in horses objectively. OBJECTIVES: To evaluate whether an accelerometric device would be adequate to quantify locomotor activity and step frequency in horses, and to distinguish between different levels of activity and different gaits. STUDY DESIGN: Observational study in an experimental setting. METHODS: Dual-mode (activity and step count) piezo-electric accelerometric devices were placed at each of 4 locations (head, withers, forelimb and hindlimb) in each of 6 horses performing different controlled activities including grazing, walking at different speeds, trotting and cantering. Both the activity count and step count were recorded and compared by the various activities. Statistical analyses included analysis of variance for repeated measures, receiver operating characteristic curves, Bland-Altman analysis and linear regression. RESULTS: The accelerometric device was able to quantify locomotor activity at each of the 4 locations investigated and to distinguish between gaits and speeds. The activity count recorded by the accelerometer placed on the hindlimb was the most accurate, displaying a clear discrimination between the different levels of activity and a linear correlation to speed. The accelerometer placed on the head was the only one to distinguish specifically grazing behaviour from standing. The accelerometer placed on the withers was unable to differentiate different gaits and activity levels. The step count function measured at the hindlimb was reliable but the count was doubled at the walk. CONCLUSIONS: The dual-mode accelerometric device was sufficiently accurate to quantify and compare locomotor activity in horses moving at different speeds and gaits. Positioning the device on the hindlimb allowed for the most accurate results. The step count function can be useful but must be manually corrected, especially at the walk.