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.

Disparities in Follow-Up After Low-Dose Lung Cancer Screening.

The National Lung Cancer Screening Trial (NLST) demonstrated an improvement in overall survival with lung cancer screening. Achieving follow-up for a positive screen is essential to impact early intervention for lung cancer. The objective of this study was to determine predictors of follow-up after a positive lung cancer screening test. The NLST database was queried for participants with a positive lung cancer screening exam. This cohort was then subdivided into patients who had follow-up and those who did not. Pairwise comparison was performed within different subgroups. A logistic regression model was then utilized to identify predictive factors associated with follow-up. Of the 53,454 patients who participated in the study, we identified 14,000 patients who had a positive lung cancer screening test. Of those patients, 12,503 followed up appropriately (89.3%). Women had a statistically higher follow-up rate compared to men (90% vs 88.8%, P ≤ 0.05). Patients reported as married or living as married also showed a higher rate of follow-up compared to patients reported as never married, divorced, separated, or widowed (90.2% vs 87.5%, P ≤ 0.05). The rate of follow-up among African-American patients was 82.8%, while those in white patients was 89.6%, this was statistically significant (P ≤ 0.05). Education level was not a significant factor in follow-up rates. Current smokers followed up at lower rates compared to former smokers (87.9 % vs 90.6%, P ≤ 0.05). Logistic regression determined gender, marital status, race, and smoking status to be predictors of follow-up. Follow-up rates after a positive lung cancer screening test were associated with a patient's gender, marital status, race, and smoking status.