Body shape and performance on the US Army Combat Fitness Test: Insights from a 3D body image scanner.

OBJECTIVE: To identify relationships between body shape, body composition, sex and performance on the new US Army Combat Fitness Test (ACFT). METHODS: Two hundred and thirty-nine United States Military Academy cadets took the ACFT between February and April of 2021. The cadets were imaged with a Styku 3D scanner that measured circumferences at 20 locations on the body. A correlation analysis was conducted between body site measurements and ACFT event performance and evaluated using Pearson correlation coefficients and p-values. A k-means cluster analysis was performed over the circumference data and ACFT performance were evaluated between clusters using t-tests with a Holm-Bonferroni correction. RESULTS: The cluster analysis resulted in 5 groups: 1. "V" shaped males, 2. larger males, 3. inverted "V" shaped males and females, 4. "V" shaped smaller males and females, and 5. smallest males and females. ACFT performance was the highest in Clusters 1 and 2 on all events except the 2-mile run. Clusters 3 and 4 had no statistically significant differences in performance but both clusters performed better than Cluster 5. CONCLUSIONS: The association between ACFT performance and body shape is more detailed and informative than considering performance solely by sex (males and females). These associations may provide novel ways to design training programs from baseline shape measurements.

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.

Phenotypic differences between people varying in muscularity.

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.

Food for thought: A natural language processing analysis of the 2020 Dietary Guidelines publice comments.

BACKGROUND: The Administrative Procedure Act of 1946 guarantees the public an opportunity to view and comment on the 2020 Dietary Guidelines as part of the policymaking process. In the past, public comments were submitted by postal mail or public hearings. The convenience of public comment through the Internet has generated increased comment volume, making manual analysis challenging. OBJECTIVES: To apply natural language processing (NLP NLP is natural language processing.) to identify sentiment, emotion, and themes in the 2020 Dietary Guidelines public comments. METHODS: Written comments to the Scientific Report of the 2020 Dietary Guidelines Advisory Committee that were uploaded and visible at https://beta.regulations.gov/docket/FNS-2020-0015 were extracted using a computer program and retained for analysis. All comments were filtered, and duplicates were removed. A 2-round latent Dirichlet analysis (LDA) was used to identify 3 overarching topics as well as subtopics addressed in the comments. Sentiment analysis was applied to categorize emotion and overall positive and negative sentiment within each topic. RESULTS: Three different topics were identified by LDA. The first topic involved negative sentiment surrounding removing dairy from the guidelines because the commenters felt dairy is unnecessary. The second topic focused on positive sentiment involved in restricting added sugars. The third topic was too diverse to characterize under 1 theme. A second LDA within the third topic had 3 subtopics containing positive sentiment. The first subtopic valued the inclusion of dairy in the recommendations, the second involved the health benefits of consuming beef, and the third indicated that the recommendations lead to overall good health outcomes. CONCLUSIONS: Public comments were diverse, held conflicting viewpoints, and often did not base comments on personal anecdotes or opinions without citing scientific evidence. Because the volume of public comments has grown dramatically, NLP has promise to assist in objective analysis of public comment input.

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.

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research.

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.

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.

Bite count rates in free-living individuals: new insights from a portable sensor.

BACKGROUND: Conclusions regarding bite count rates and body mass index (BMI) in free-living populations have primarily relied on self-report. The objective of this exploratory study was to compare the relationship between BMI and bite counts measured by a portable sensor called the Bite Counter in free-living populations and participants eating in residence. METHODS: Two previously conducted studies were analyzed for relationships between BMI and sensor evaluated bite count/min, and meal duration. Participants from the first study (N = 77) wore the bite counter in a free-living environment for a continuous period of 14 days. The second study (N = 214) collected bite count/min, meal duration, and total energy intake in participants who consumed one meal in a cafeteria. Linear regression was applied to examine relationships between BMI and bite count/min. RESULTS: There was no significant correlation in the free-living participants average bite counts per second and BMI (R2 = 0.03, p = 0.14) and a significant negative correlation in the cafeteria participants (R 2 = 0.04, p = 0.03) with higher bite count rates observed in lean versus obese participants. There was a significant correlation between average meal duration and BMI in the free-living participants (R 2 = 0.08, p = 0.01). Total energy intake in the cafeteria participants was also significantly correlated to meal duration (R 2 = 0.31, p < 0.001). CONCLUSIONS: With additional novel applications of the Bite Counter, insights into free-living eating behavior may provide avenues for future interventions that are sustainable for long term application.