Socioeconomic status and health disparities drive differences in accelerometer-derived physical activity in fatty liver disease and significant fibrosis.

BACKGROUND AND AIMS: The cornerstone of clinical management of patients with nonalcoholic fatty liver disease (NAFLD) are lifestyle changes such as increasing physical activity (PA) aimed at improving cardiometabolic risk. To inform NAFLD prevention and treatment guidelines we aimed to: (i) quantify the role of PA on lowering the risk for NAFLD and fibrosis; (ii) characterize NAFLD and fibrosis association with PA in the context of socioeconomic environment. METHODS: A sample of 2648 participants from the NHANES 2003-2006 was selected to develop survey weighted multivariable logistic regression models for predicting NAFLD and significant fibrosis, diagnosed non-invasively via fatty liver index (FLI) and fibrosis-4 (FIB-4) index. The PA measures were obtained from a hip-worn accelerometer. RESULTS: The predictive model for NAFLD showed AUC of 0.687 and a decrease of 43% in NAFLD risk with moderate vigorous PA (MVPA) (OR = 0.569, p < 0.001). The predictive model for fibrosis had AUC of 0.755 and there was a 48% and a 70% decrease in significant fibrosis risk with MVPA (OR = 0.518, p = 0.022) and total log activity count (TLAC) (OR = 0.296, p = 0.017), respectively. Participants with NAFLD and NAFLD with fibrosis engage in declining PA. Despite having jobs with higher level of PA and participating in more moderate-to-vigorous PA, a larger proportion of Hispanics participants had NAFLD and significant fibrosis. CONCLUSIONS: These findings demonstrate the role of PA as a protective factor against the presence of NAFLD and significant fibrosis. Protective levels of PA in NAFLD differ by races.

Physical activity intensity and markers of inflammation in those with non-alcoholic fatty liver disease.

AIMS: To investigate associations between objectively measured light (LPA) and moderate-to-vigorous (MVPA) physical activity on plasma homocysteine and serum C-reactive protein (CRP) in individuals with Non-Alcoholic Fatty Liver Disease (NAFLD). METHODS: This study was a secondary analysis using data from 2003 to 2006 National Health and Nutrition Examination Survey including a total of 983 individuals with NAFLD. Physical activity was assessed over 7 days with accelerometers. Participants were split into tertiles based on average daily minutes of LPA or MVPA and CRP and homocysteine were assessed across tertiles. RESULTS: Adjusted plasma homocysteine and CRP were not different between groups regarding levels of LPA (Homocysteine: 1st tertile - 10.4 ± 0.7 µmol/L; 2nd tertile - 9.6 ± 0.4 µmol/L; 3rd tertile - 9.6 ± 0.4 µmol/L; p = 0.28; CRP: 1st tertile - 0.79 ± 0.12 mg/dL; 2nd tertile - 0.73 ± 0.09 mg/dL; 3rd tertile - 0.73 ± 0.09 mg/dL; p = 0.72). Adjusted CRP was significantly (p = 0.02) different across MVPA tertiles (1st: 0.87 ± 0.13 mg/dL; 2nd: 0.75 ± 0.10 mg/dL; 3rd:0.65 ± 0.09). CONCLUSIONS: LPA does not appear to be effective at improving homocysteine or CRP levels in individuals with NAFLD. However, MVPA may be an effective therapy for decreasing CRP in NAFLD patients.

Teachers' Familiarity, Confidence, Training, and Use of Problem Behavior Interventions for Learners with Autism Spectrum Disorder in School Settings.

Our main purpose in this study was to investigate the levels of and the relationship between familiarity, confidence, training, and use of problem behavior interventions by special education teachers working with learners with autism spectrum disorder (ASD) in school settings. A total of 80 special education teachers in South Carolina and Virginia completed an online survey. Results indicate a positive correlation between teachers' familiarity, confidence, training, and use of problem behavior interventions. Across all intervention categories, providing choices, prompting, modeling, and direct instruction received the highest rankings for familiarity, confidence, and use. In addition, our results reveal that familiarity and confidence in implementing these interventions differs across groups of special education teachers based on years of experience. The most frequently reported factors that limit the use of problem behavior interventions in school settings were competing responsibilities, the need to involve multiple people, the amount of time required, and the difficulty using interventions during typical routines. Implications for research and practice are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s10882-022-09885-2.

Cardiovascular mortality risk prediction using objectively measured physical activity phenotypes in NHANES 2003-2006.

Increased physical activity (PA) has been associated with a decreased risk of cardiovascular disease (CVD) and mortality. However, most previous studies use self-reported PA instead of objectively measured PA assessed by wearable accelerometers. To the best of our knowledge, there have not been studies that quantified the univariate and multivariate ability of objectively measured PA summaries to predict the risk of CVD mortality. We investigate the ability of objectively measured PA summary variables to predict CVD mortality: as individual predictors, as part of the best multivariate model incorporating traditional predictors, and as additions to the best multivariate model using only traditional CVD predictors. Data were collected in the National Health and Nutrition Examination Survey 2003-2006 waves for US participants aged 50-85. The predictive ability was measured using Concordance, sometimes referred to as the C-statistic. Specifically, we calculated 10-fold cross-validated concordance (CVC) in survey-weighted Cox proportional hazard models. The best univariate predictor of CVD mortality was total activity count (outperformed age). In multivariate models, two of the eight predictors identified using the improvement in CVC threshold of 0.001 were PA measures (CVC = 0.844). The best model without physical activity (7 predictors) had CVC of 0.830. The addition of PA measures to the best traditional model was significantly better at predicting CVD mortality (P < 0.001). Accelerometer-derived PA measures have excellent cardiovascular mortality prediction performance. Wearable accelerometers have a potential for assessment of individuals' CVD mortality risks.

Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003-2006.

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003-2004 and 2005-2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) was ranked first in all five horizons (AUC between 0.831 and 0.774) while the active to sedentary transition probability (ASTP) was ranked second for one- to four-year mortality models and fourth for the five-year all-cause mortality model (AUC between 0.825 and 0.735). In multivariate models age and ASTP were significant in all one- to five-year all-cause mortality prediction models. Physical activity measures are consistently among the top predictors, even after adjusting for demographic and lifestyle variables. Physical activity measures are strong stand-alone predictors and substantially improve the prediction performance of models based on traditional risk factors.

The Predictive Performance of Objective Measures of Physical Activity Derived From Accelerometry Data for 5-Year All-Cause Mortality in Older Adults: National Health and Nutritional Examination Survey 2003-2006.

BACKGROUND: Declining physical activity (PA) is a hallmark of aging. Wearable technology provides reliable measures of the frequency, duration, intensity, and timing of PA. Accelerometry-derived measures of PA are compared with established predictors of 5-year all-cause mortality in older adults in terms of individual, relative, and combined predictive performance. METHODS: Participants aged between 50 and 85 years from the 2003-2006 National Health and Nutritional Examination Survey (NHANES, n = 2,978) wore a hip-worn accelerometer in the free-living environment for up to 7 days. A total of 33 predictors of 5-year all-cause mortality (number of events = 297), including 20 measures of objective PA, were compared using univariate and multivariate logistic regression. RESULTS: In univariate logistic regression, the total activity count was the best predictor of 5-year mortality (Area under the Curve (AUC) = 0.771) followed by age (AUC = 0.758). Overall, 9 of the top 10 predictors were objective PA measures (AUC from 0.771 to 0.692). In multivariate regression, the 10-fold cross-validated AUC was 0.798 for the model without objective PA variables (9 predictors) and 0.838 for the forward selection model with objective PA variables (13 predictors). The Net Reclassification Index was substantially improved by adding objective PA variables (p < .001). CONCLUSIONS: Objective accelerometry-derived PA measures outperform traditional predictors of 5-year mortality, including age. This highlights the importance of wearable technology for providing reproducible, unbiased, and prognostic biomarkers of health.

Organizing and analyzing the activity data in NHANES.

The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. However, using the accelerometry data has proven daunting because: 1) currently, there are no agreed upon standard protocols for data storage and analysis; 2) data exhibit heterogeneous patterns of missingness due to varying degrees of adherence to wear-time protocols; 3) sampling weights need to be carefully adjusted and accounted for in individual analyses; 4) there is a lack of reproducible software that transforms the data from its published format into analytic form; and 5) the high dimensional nature of accelerometry data complicates analyses. Here, we provide a framework for processing, storing, and analyzing the NHANES accelerometry data for the 2003-2004 and 2005-2006 surveys. We also provide an NHANES data package in R, to help disseminate high quality, processed activity data combined with mortality and demographic information. Thus, we provide the tools to transition from "available data online" to "easily accessible and usable data", which substantially reduces the large upfront costs of initiating studies of association between physical activity and human health outcomes using NHANES. We apply these tools in an analysis showing that accelerometry features have the potential to predict 5-year all cause mortality better than known risk factors such as age, cigarette smoking, and various comorbidities.