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Abstract Background Neurodevelopmental disorders such as 16p11.2 syndrome are frequently associated with motor impairments including locomotion. The lack of precise measures of gait, combined with the challenges inherent in studying children with neurodevelopmental disorders, hinders quantitative motor assessments. Gait and balance are quantifiable measures that may help to refine the motor phenotype in 16p11.2. The characterization of motor profile is useful to study the trajectories of locomotion performance of children with genetic variants and may provide insights into neural pathway dysfunction based on genotype/phenotype model. Methods Thirty-six children (21 probands with 16p11.2 deletion and duplication mutation and 15 unaffected siblings), with a mean age of 8.5 years (range 3.2–15.4) and 55% male, were enrolled. Of the probands, 23% (n = 6) had a confirmed diagnosis of autism spectrum disorder (ASD) and were all male. Gait assessments included 6-min walk test (6MWT), 10-m walk/run test (10MWR), timed-up-and-go test (TUG), and spatio-temporal measurements of preferred- and fast-paced walking. The Pediatric Evaluation of Disability Inventory-Computer Adaptive Tests (PEDI-CAT), a caregiver-reported functional assessment, was administered. Measures of balance were calculated using percent time in double support and base of support. Analyses of the six children with ASD were described separately. Results Thirty-six participants completed the protocol. Compared with sibling controls, probands had significantly lower scores on the 6MWT (p = 0.04), 10MWR (p = 0.01), and TUG (p = 0.005). Group differences were also identified in base of support (p = 0.003). Probands had significantly lower PEDI-CAT scores in all domains including the mobility scale (p

The purpose of this study was to determine the validity and reliability of the Exercise Vital Sign (EVS) questionnaire in an ethnically diverse sample. Participants (N = 39) were asked to wear an accelerometer at the hip for at least 7 days and to complete the EVS at the beginning (T1) and end (T2) of the wear period. The EVS questionnaire validity was determined against accelerometry, and bias was calculated as the mean difference between measures. The sensitivity and specificity of the EVS questionnaire were also evaluated. The reliability of the questionnaire was calculated using intraclass correlation coefficient (ICC) between EVS responses at T1 and T2. The mean difference in EVS- and accelerometer-determined time in MVPA was 24 min/wk. The reliability for the questionnaire was excellent (ICC = 0.98). The EVS specificity and sensitivity at T2 were 56% and 78%, respectively. The EVS questionnaire may be an acceptable measure of weekly MVPA time compared to accelerometry in an ethnically diverse sample; however, further research is needed to confirm these findings.

PurposeTo provide empirically-supported thresholds for step-based intensity (i.e., peak 30-min cadence; average of the top 30 steps/min in a day) and steps/day in relation to cardiometabolic health outcomes.MethodsReceiver operating characteristic curve analysis was applied to the National Health and Nutrition Examination Survey (NHANES) 2005-2006 accelerometer-derived step data to determine steps/day and peak 30-min cadence as risk screening values (i.e., thresholds) for fasting glucose, body mass index, waist circumference, high blood pressure, triglycerides, and HDL cholesterol. Thresholds for peak 30-min cadence and steps/day were derived that, when exceeded, classify the absence of each cardiometabolic risk factor. Additionally, logistic regression models that included the influence of age and smoking were developed using the sample weights, primary sampling units (PSUs), and stratification variables provided by the NHANES survey. Finally, a decision tree analysis was performed to delineate criteria for at-risk versus healthy populations using cadence bands.ResultsPeak 30-min cadence thresholds across cardiometabolic outcomes ranged from 66-72 steps/min. Steps/day thresholds ranged from 4325-6192 steps/day. Higher thresholds were observed in men compared to women. In men, higher steps/day thresholds were observed in age ranges of 30-39, while in women, higher thresholds were observed in the age-range 50-59 years. Decision trees for classifying being at low risk for metabolic syndrome contained one risk-free leaf at higher cadence bands, specifically for any time accumulated at ≥120 steps/min.ConclusionsMinimum thresholds representing absence of cardiometabolic risk range from 4325-6192 steps/day and 66-72 steps/min for peak 30-min cadence. Any time accumulated at ≥120 steps/min was associated with an absence of cardiometabolic risk. Although based on cross-sectional data, these thresholds represent potentially important and clinically interpretable daily physical activity goals.

The purpose of this study was to determine the feasibility and validity of using three-dimensional (3D) video data and computer vision to estimate physical activity intensities in young children. Families with children (2−5-years-old) were invited to participate in semi-structured 20-minute play sessions that included a range of indoor play activities. During the play session, children’s physical activity (PA) was recorded using a 3D camera. PA video data were analyzed via direct observation, and 3D PA video data were processed and converted into triaxial PA accelerations using computer vision. PA video data from children (n = 10) were analyzed using direct observation as the ground truth, and the Receiver Operating Characteristic Area Under the Curve (AUC) was calculated in order to determine the classification accuracy of a Classification and Regression Tree (CART) algorithm for estimating PA intensity from video data. A CART algorithm accurately estimated the proportion of time that children spent sedentary (AUC = 0.89) in light PA (AUC = 0.87) and moderate-vigorous PA (AUC = 0.92) during the play session, and there were no significant differences (p > 0.05) between the directly observed and CART-determined proportions of time spent in each activity intensity. A computer vision algorithm and 3D camera can be used to estimate the proportion of time that children spend in all activity intensities indoors.

This study aimed to characterize daily physical activity (PA) behaviors in 2-year-old girls and boys and their parents, with and without an objective measure of dyadic spatial proximity. Urban-dwelling parent⁻toddler dyads (N = 110) wore accelerometers for 7 days, and parents completed a sociodemographic questionnaire. Accelerometers were initialized to collect PA and Bluetooth-based proximity data. After applying wear-time algorithms, n = 65 dyads were further analyzed using a dyadic analysis statistical methodology. Toddler⁻parent sedentary and light PA time were respectively interdependent, conditional on child sex and child-parent proximity, but moderate⁻vigorous physical activity (MVPA) time was not. Toddlers were significantly more active on weekdays and weekends than their parents, and no differences were found in daily PA volumes between girls and boys. In dyads with proximity data (n = 34), analyses of joint (i.e., proximal and mutual) PA time showed that girls participated in significantly more joint PA with their mothers than boys. Children who engaged in ≥60 min of MVPA/day participated in ~2 h of joint PA/day, on average, while children with

The purpose of this study was to estimate distances from accelerometer-derived Bluetooth signals as a measure of interpersonal spatial proximity. Accelerometer-derived proximity data were collected indoors and outdoors over a 10m range to calibrate simulation models. Proximity data were simulated over 20m (indoor) and 50m (outdoor) ranges. Competing statistical and machine learning models were used to predict simulated distances; the Root-Mean-Square-Error (RMSE) was calculated. Simulation estimates were validated under conditions wherein a single beacon-receiver (SBR) and multiple beacons-receivers (MBR) collected proximity data indoors and outdoors within a =10m range. Simulation data showed that a Random Forest (RF) model performed optimally. The validated RF RMSE was =2.7 for SBR, and =90% of predicted distances were accurately classified as =10m. For MBR, =67% of predicted distances were accurately classified as =10m. Simulation and validation data suggest that distances can be estimated from accelerometer-derived proximity data within a 20m range using a SBR.

Background: The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies. Purpose: To catalog studies validating step-counting wearable technologies during treadmill ambulation. Methods: The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. Where reported, speed- and wear location-specific mean absolute percentage error (MAPE) values were tabulated. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment. Results: Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8m/s; their weighted medianMAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and =1% for thigh-worn devices. Conclusions: Despite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies. [ABSTRACT FROM AUTHOR]

Background: A walking cadence of ≥100 steps/min corresponds to minimally moderate intensity, absolutely defined as ≥3 metabolic equivalents (METs). This threshold has primarily been calibrated during treadmill walking. There is a need to determine the classification accuracy of this cadence threshold to predict intensity during overground walking. Methods: In this laboratory-based cross-sectional investigation, participants (N = 75, 49.3% women, age 21-40 y) performed a single 5-minute overground (hallway) walking trial at a self-selected preferred pace. Steps accumulated during each trial were hand tallied and converted to cadence (steps/ min). Oxygen uptake was measured using indirect calorimetry and converted to METs. The classification accuracy (sensitivity, specificity, overall accuracy, and positive predictive value) of ≥100 steps/min to predict ≥3METs was calculated. Results: A cadence threshold of ≥100 steps/min yielded an overall accuracy (combined sensitivity and specificity) of 73.3% for predicting minimally moderate intensity. Moreover, for individuals walking at a cadence ≥100 steps/min, the probability (positive predictive value) of achieving minimally moderate intensity was 80.3%. Conclusions: Although primarily developed using treadmill-based protocols, a cadence threshold of ≥100 steps/min for young adults appears to be a valid heuristic value (evidence-based, rounded, practical) associated with minimally moderate intensity during overground walking performed at a self-selected preferred pace. [ABSTRACT FROM AUTHOR]