Distributional regression data analysis across different race/ethnic groups and poverty levels in the US adult population

doi:10.21203/rs.3.rs-1986131/v1

Disparities in physical activity may contribute to the well-known racial/ethnic and poverty gaps in health and well-being (Winkleby et al. 1998). Recent developments in wearable technologies enable the continuous recording, at a high resolution, of the amount and intensity of physical activity performed by an individual over a period of time. Unlike previous self-reported (mostly leisure time) physical activity evidence (Saffer et al. 2013; Dogra, Meisner, and Ardern 2010), monitoring the full continuum of device-based physical activity intensity can help uncover critical information to plan, implement, and evaluate public health initiatives that promote evidence-based practice and policy, particularly among at-risk populations that would most benefit. Capitalizing on a representative sample of the US population who wore wrist accelerometers, this study examined the impact of race/ethnicity and poverty on detailed functional representations of device-based physical activity.

Health Policy

accelerometer data

NHANES

distributional representations

Disparities in physical activity may contribute to the well-known racial/ethnic and poverty gaps in health and well-being (Winkleby et al. 1998). Recent developments in wearable technologies enable the continuous recording, at a high resolution, of the amount and intensity of physical activity performed by an individual over a period of time. Unlike previous self-reported (mostly leisure time) physical activity evidence (Saffer et al. 2013; Dogra, Meisner, and Ardern 2010), monitoring the full continuum of device-based physical activity intensity can help uncover critical information to plan, implement, and evaluate public health initiatives that promote evidence-based practice and policy, particularly among at-risk populations that would most benefit. Capitalizing on a representative sample of the US population who wore wrist accelerometers, this study examined the impact of race/ethnicity and poverty on detailed functional representations of device-based physical activity.

Data for this study were taken from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. The analysis was restricted to adults aged \(18-80\) years old who provided 4 or more days of valid accelerometer data (at least 10 hours of valid wear time per day). Participants were categorized into three groups based on their age: \(a:18-39\), \(b:40-59,\) \(c:60-80\). Pregnant participants and those with self-reported severe mobility issues were excluded. The final analytical sample consisted of \(9502\) individuals. The global Fréchet regression model with the \(2-\) Wasserstein distance (Petersen and Müller 2019) was used to examine age-group specific associations of race/ethnicity (Non-Hispanic White—reference—, Non-Hispanic Black, Non-Hispanic Asian, Mexican American, Other Hispanic, other race - including multi-racial) and poverty level (0–5) with the time- and intensity-based quantile (continuous) distribution of physical activity (Matabuena and Petersen 2021; Ghosal et al. 2021). All models were adjusted for age, gender, sex, body mass index, and self-reported physician diagnosis of cancer, diabetes, stroke, heart disease, and asthma. Recalibrated sample weights were used to account for NHANES’ complex design. The R Software (version 3.6.1) was used for all computations.

Figure 1 presents the age-group specific associations of accelerometer-derived physical activity levels with different race/ethnic groups and poverty levels in the US adult population in terms of functional slope coefficients. Young and middle-aged adults from poorer backgrounds spent more time being active across the full range of physical activity intensities except for that of vigorous intensity. In contrast, wealthier older adults were more active overall. Compared with Non-Hispanic White, Mexican-American and Other Hispanic races moved more across the full physical activity intensity spectrum. Minor differences were detected between young and middle-age Non-Hispanic White adults and their Non-Hispanic Black counterparts. However, older Non-Hispanic White spent more time in higher intensities compared with Non-Hispanic Black. Younger Non-Hispanic Asians were less active than Non-Hispanic White. The opposite was found for older individuals. Similarly, young, and middle-aged adults from a Multiracial background were less active than Non-Hispanic White, but older adults were more active on average.

This study has revealed important racial/ethnic and poverty level discrepancies in the physical activity levels of the US adult population. Our results have clear implications for public health policy and practice. Individuals from poorer backgrounds, and particularly older adults should be a priority target when planning strategies to decrease socioeconomic inequalities in health through increasing physical activity. Younger and middle-aged adults may be most responsive to interventions that promote high-intensity physical activities. Our results also indicate the need to focus on interventions culturally tailored particularly to communities with large numbers of Black and Asian older adults. Non-Hispanic White and wealthier young and middle-aged adults may benefit from interventions aimed at boosting light and moderate physical activity throughout the day.

This study uses a cross-sectional design and may describe only association, not causation. The sample is representative of the US adult population. Together with the novel time- and intensity-based quantile distribution representation of accelerometer-physical activity (Matabuena and Petersen 2021; Ghosal et al. 2021), our study findings may be used to assist effective decision-making and strategies to reduce disparities in physical activity participation and potentially close the racial/ethnic and socioeconomic health gaps in the US population.

Dogra, Shilpa, Brad A Meisner, and Chris I Ardern. 2010. “Variation in Mode of Physical Activity by Ethnicity and Time Since Immigration: A Cross-Sectional Analysis.” International Journal of Behavioral Nutrition and Physical Activity 7 (1): 1–11.

Ghosal, Rahul, Vijay R Varma, Dmitri Volfson, Inbar Hillel, Jacek Urbanek, Jeffrey M Hausdorff, Amber Watts, and Vadim Zipunnikov. 2021. “Distributional Data Analysis via Quantile Functions and Its Application to Modelling Digital Biomarkers of Gait in Alzheimer’s Disease.” arXiv Preprint arXiv:2102.10783.

Matabuena, Marcos, and Alex Petersen. 2021. “Distributional Data Analysis with Accelerometer Data in a Nhanes Database with Nonparametric Survey Regression Models.” arXiv Preprint arXiv:2104.01165.

Petersen, Alexander, and Hans-Georg Müller. 2019. “Fréchet Regression for Random Objects with Euclidean Predictors.” The Annals of Statistics 47 (2): 691–719.

Saffer, Henry, Dhaval Dave, Michael Grossman, and Leigh Ann Leung. 2013. “Racial, Ethnic, and Gender Differences in Physical Activity.” Journal of Human Capital 7 (4): 378–410.

Winkleby, Marilyn A, Helena C Kraemer, David K Ahn, and Ann N Varady. 1998. “Ethnic and Socioeconomic Differences in Cardiovascular Disease Risk Factors: Findings for Women from the Third National Health and Nutrition Examination Survey, 1988-1994.” Jama 280 (4): 356–62.

Competing interests: The authors declare no competing interests.

Supplementalmaterial.docx

Distributional regression data analysis across different race/ethnic groups and poverty levels in the US adult population

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

References

Declarations

Supplementary Files

Status:

Version 1