Social epidemiology of Fitbit daily steps in early adolescence

Jason M Nagata; Sana Alsamman; Natalia Smith; Jiayue Yu; Kyle T Ganson; Erin E Dooley; David Wing; Fiona C Baker; Kelley Pettee Gabriel

doi:10.1038/s41390-023-02700-4

Social epidemiology of Fitbit daily steps in early adolescence

Pediatr Res. 2023 Nov;94(5):1838-1844. doi: 10.1038/s41390-023-02700-4. Epub 2023 Jun 23.

Authors

Jason M Nagata¹, Sana Alsamman², Natalia Smith², Jiayue Yu², Kyle T Ganson³, Erin E Dooley⁴, David Wing^{5

6

7}, Fiona C Baker^{8

9}, Kelley Pettee Gabriel⁴

Affiliations

¹ Department of Pediatrics, University of California, San Francisco, CA, USA. [email protected].
² Department of Pediatrics, University of California, San Francisco, CA, USA.
³ Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.
⁴ Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
⁵ Exercise and Physical Activity Resource Center, University of California, San Diego, CA, USA.
⁶ Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, CA, USA.
⁷ Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla University of California, San Diego, La Jolla, CA, USA.
⁸ Center for Health Sciences, SRI International, Menlo Park, CA, USA.
⁹ School of Physiology, University of the Witwatersrand, Johannesburg, South Africa.

Abstract

Background: Sociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d^-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US.

Methods: We analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d^-1 over the course of 21 days.

Results: Participants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d^-1. In mixed-effects models, 1543.6 more steps d^-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d^-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d^-1), >$200,000 versus <$25,000 household income (1003.3 more steps d^-1), and having married/partnered parents versus unmarried/unpartnered parents (326.3 more steps d^-1). We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Conclusions: Given sociodemographic differences in adolescent steps d^-1, physical activity guidelines should focus on key populations and adopt strategies optimized for adolescents from diverse backgrounds.

Impact: Sociodemographic disparities in physical activity have been documented but mostly rely on self-reported data, which can be limited by reporting and prevarication bias. In this demographically diverse sample of 10-14-year-old early adolescents in the U.S., we found notable and nuanced sociodemographic disparities in Fitbit steps per day. More daily steps were recorded for male versus female sex, Black versus White race, heterosexual versus sexual minority, >$100,000 versus <$25,000 household income, and having married/partnered versus unmarried/unpartnered parents. We found effect modification by household income for Black adolescents and by sex for Asian adolescents.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Adolescent
Child
Ethnicity*
Exercise*
Female
Fitness Trackers*
Humans
Male
Minority Groups
Prospective Studies

Abstract

Publication types

MeSH terms

Grants and funding