Using Cadence to Predict the Walk-to-Run Transition in Children and Adolescents: A Logistic Regression Approach.

The natural transition from walking to running occurs in adults at ≅140 steps/min. It is unknown when this transition occurs in children and adolescents. The purpose of this study was to develop a model to predict age- and anthropometry-specific preferred transition cadences in individuals 6-20 years of age. Sixty-nine individuals performed sequentially faster 5-min treadmill walking bouts, starting at 0.22 m/s and increasing by 0.22 m/s until completion of the bout during which they freely chose to run. Steps accumulated during each bout were directly observed and converted to cadence (steps/min). A logistic regression model was developed to predict preferred transition cadences using the best subset of parameters. The resulting model, which included age, sex, height, and BMI z-score, produced preferred transition cadences that accurately classified gait behaviour (k-fold cross-validated prediction accuracy =97.02%). This transition cadence ranged from 136-161 steps/min across the developmental age range studied. The preferred transition cadence represents a simple and practical index to predict and classify gait behaviour from wearable sensors in children, adolescents, and young adults. Moreover, herein we provide an equation and an open access online R Shiny app that researchers, practitioners, or clinicians can use to predict individual-specific preferred transition cadences.

Dose-Response Association Between Physical Activity (Daily MIMS, Peak 30-Minute MIMS) and Cognitive Function Among Older Adults: NHANES 2011-2014.

BACKGROUND: The purpose of this study was to determine the dose-response association between habitual physical activity (PA) and cognitive function using a nationally representative data set of U.S. older adults aged ≥60 years. METHODS: We used data from the 2011-2014 National Health and Nutrition Examination Survey (n = 2 441, mean [SE] age: 69.1 [0.2] years, 54.7% females). Cognitive function was assessed using the digit symbol substitution test (DSST) and animal fluency test (AFT). Habitual PA was collected using a triaxial accelerometer worn on participants' nondominant wrist. PA was expressed as 2 metrics using monitor-independent movement summary (MIMS) units: the average of Daily MIMS (MIMS/day) and peak 30-minute MIMS (Peak-30MIMS; the average of the highest 30 MIMS min/d). Sample weight-adjusted multivariable linear regression was performed to determine the relationship between each cognitive score and MIMS metric while adjusting for covariates. RESULTS: After controlling for covariates, for each 1 000-unit increase in Daily MIMS, DSST score increased (ß-coefficient [95% CIs]) by 0.67 (0.40, 0.93), whereas AFT score increased by 0.13 (0.04, 0.22); for each 1-unit increase in Peak-30MIMS, DSST score increased by 0.56 (0.42, 0.70), whereas AFT score increased by 0.10 (0.05, 0.15), all p < .001. When including both MIMS metrics in a single model, the association between Peak-30MIMS and cognitive scores remained significant (p < .01), whereas Daily MIMS did not. CONCLUSIONS: Our findings suggest that higher PA (both daily accumulated and peak effort) is associated with better cognitive function in the U.S. older adult population.

A machine learning approach relating 3D body scans to body composition in humans.

A long-standing question in nutrition and obesity research involves quantifying the relationship between body fat and anthropometry. To date, the mathematical formulation of these relationships has relied on pairing easily obtained anthropometric measurements such as the body mass index (BMI), waist circumference, or hip circumference to body fat. Recent advances in 3D body shape imaging technology provides a new opportunity for quickly and accurately obtaining hundreds of anthropometric measurements within seconds, however, there does not yet exist a large diverse database that pairs these measurements to body fat. Herein, we leverage 3D scanned anthropometry obtained from a population of United States Army basic training recruits to derive four subpopulations of homogenous body shape archetypes using a combined principal components and cluster analysis. While the Army database was large and diverse, it did not have body composition measurements. Therefore, these body shape archetypes were paired to an alternate smaller sample of participants from the Pennington Biomedical Research Center in Baton Rouge, LA that were not only similarly imaged by the same 3D scanning machine, but also had concomitant measures of body composition by dual-energy X-ray absorptiometry body composition. With this enhanced ability to obtain anthropometry through 3D scanning quickly of large populations, our machine learning approach for pairing body shapes from large datasets to smaller datasets that also contain state-of-the-art body composition measurements can be extended to pair other health outcomes to 3D body shape anthropometry.