Background Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution). Objectives To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research. Methods We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM. Results Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect. Conclusions The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higherlevel courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students "correlation does not imply causation" or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.
Background Body mass is the primary metabolic compartment related to a vast number of clinical indices and predictions. The extent to which skeletal muscle (SM), a major body mass component, varies between people of the same sex, weight, height, and age is largely unknown. The current study aimed to explore the magnitude of muscularity variation present in adults and to examine if variation in muscularity associates with other body composition and metabolic measures. Methods Muscularity was defined as the difference (residual) between a person's actual and model‐predicted SM mass after controlling for their weight, height, and age. SM prediction models were developed using data from a convenience sample of 492 healthy non‐Hispanic (NH) White adults (ages 18–80 years) who had total body SM and SM surrogate, appendicular lean soft tissue (ALST), measured with magnetic resonance imaging and dual‐energy X‐ray absorptiometry, respectively; residual SM (SMR) and ALST were expressed in kilograms and kilograms per square meter. ALST mass was also evaluated in a population sample of 8623 NH‐White adults in the 1999–2006 National Health and Nutrition Examination Survey. Associations between muscularity and variation in the residual mass of other major organs and tissues and resting energy expenditure were evaluated in the convenience sample. Results The SM, on average, constituted the largest fraction of body weight in men and women up to respective BMIs of 35 and 25 kg/m2. SM in the convenience sample varied widely with a median of 31.2 kg and an SMR inter‐quartile range/min/max of 3.35 kg/−10.1 kg/9.0 kg in men and 21.1 kg and 2.59 kg/−7.2 kg/7.5 kg in women; per cent of body weight as SM at 25th and 75th percentiles for men were 33.1% and 39.6%; corresponding values in women were 24.2% and 30.8%; results were similar for SMR indices and for ALST measures in the convenience and population samples. Greater muscularity in the convenience sample was accompanied by a smaller waist circumference (men/women: P < 0.001/=0.085) and visceral adipose tissue (P = 0.014/0.599), larger liver (P = 0.065/<0.001), kidneys (P = 0.051/<0.009), and bone mineral (P < 0.001/<0.001), and larger magnitude resting energy expenditure (P < 0.001/<0.001) than predicted for the same sex, age, weight, and height. Conclusions Muscle mass is the largest body compartment in most adults without obesity and is widely variable in mass across people of similar body size and age; and high muscularity is accompanied by distinct body composition and metabolic characteristics. This previously unrecognized heterogeneity in muscularity in the general population has important clinical and research implications.
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 dataset of U.S. older adults aged ≥ 60 years. Methods We used data from the 2011–2014 National Health and Nutrition Examination Survey (n = 2441, 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 tri-axial accelerometer worn on participants’ non-dominant wrist. PA was expressed as two 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 mins/day). 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 1000-unit increase in Daily MIMS, DSST score increased (β-coefficient [95% CIs]) by 0.67 (0.40, 0.93), while AFT score increased by 0.13 (0.04, 0.22); for each one-unit increase in Peak-30MIMS, DSST score increased by 0.56 (0.42, 0.70), while AFT score increased by 0.10 (0.05, 0.15), all p-values <0.001. When including both MIMS metrics in a single model, the association between Peak-30MIMS and cognitive scores remained significant (p-values <0.01), 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.
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