Cited by 25Physical Activity and HealthObesity, Physical Activity, DietContext-Aware Activity Recognition Systems

Longitudinal Changes in Children’s Accelerometer-derived Activity Pattern Metrics

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DUNTON, GENEVIEVE F., YANG, CHIH-HSIANG, ZINK, JENNIFER, DZUBUR, ELDIN, BELCHER, BRITNI R.·2020·Medicine & Science in Sports & Exercise

Abstract

Purpose: The objective of this study was to quantify age-related changes in accelerometer-derived day-level physical activity and sedentary behavior pattern metrics (i.e., number, length, and temporal dispersion of bouts and breaks) across 3 yr of middle childhood. Differences by child sex and weekend versus weekday were examined. Method: Children ( N = 169, 54% female, 56% Hispanic; 8–12 yr old at enrollment) participated in a longitudinal study with six assessments across 3 yr. Day-level moderate-to-vigorous physical activity (MVPA; i.e., total minutes, number of short (<10 min) bouts, proportion of long (≥20 min) bouts, temporal dispersion) and sedentary behavior (i.e., total minutes, number of breaks, proportion of long (≥60 min) bouts, temporal dispersion) pattern metrics were measured using a waist-worn accelerometer (Actigraph GT3X). Results: Random intercept multilevel linear regression models showed that age-related decreases in the number of short MVPA bouts per were steeper for girls than for boys ( b = −1.28; 95% confidence interval (CI), −1.93 to −0.64; P < 0.01) and on weekend days than on weekdays ( b = −1.82; 95% CI, −2.36 to −1.29; P < 0.01). The evenness of the temporal dispersion of MVPA across the day increased more on weekend days than on weekdays as children got older ( b = −0.02; 95% CI, −0.02 to −0.01; P < 0.01). Girls had steeper age-related decreases in the number of sedentary breaks per day ( b = −2.89; 95% CI, −3.97 to −1.73; P < 0.01) and the evenness of the temporal dispersion of sedentary behavior across the day ( b ≤ 0.01; 95% CI, <0.01 to 0.01; P < 0.01) than did boys. Changes in sedentary behavior metrics did not differ between weekend days and weekdays. Conclusion: Strategies to protect against declines in short physical activity bouts and promote sedentary breaks, especially among girls and on weekend days, could reduce cardiometabolic risks.

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  1. [28]Belcher BR, Berrigan D, Dodd KW, Emken BA, Chou C-P, Spuijt-Metz D. Physical Activity in US Youth: Impact of Race/Ethnicity, Age, Gender, & Weight Status. Medicine and Science in Sports and Exercise. 2010; 42(12): 2211– 21. doi: 10.1249/MSS.0b013e3181e1fba9. 21084930 PMC3242154 · DOI
  2. [29]Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Medicine and science in sports and exercise. 2008; 40(1): 181– 8. Epub 2007/12/20. doi: 10.1249/mss.0b013e31815a51b3. 18091006 · DOI · Paper page

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Authors
DUNTON, G. F., YANG, C.-H., ZINK, J., DZUBUR, E., & BELCHER, B. R.
Affiliations
University of Southern California (US)
Venue
Medicine & Science in Sports & Exercise
Published
2019-12-31
Volume
52
Issue
6
Pages
1307-1313
ISSN
  • 0195-9131 (ISSN-L)
  • 1530-0315
OpenAlex
W2997920469
Cited by
25
References
39
Citations by year
2026: 4 · 2025: 2 · 2024: 1 · 2022: 7 · 2021: 4 · 2020: 7
FWCI
3.09
Type
journal article
Language
en
Open access
Locations
  • Medicine & Science in Sports & Exercise (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Heart, Lung, and Blood Institute (R01HL119255, R01HL121330)
  • National Cancer Institute (T32CA009492)
Topics
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
physical activity patterns, sedentary behavior, moderate-to-vigorous physical activity, temporal dispersion, sedentary breaks, age-related changes, middle childhood, sex differences, children
MeSH terms
Fitness Trackers, Age Factors, Child, Female, Humans, Longitudinal Studies, Male, Sex Factors, Exercise, Sedentary Behavior, Accelerometry

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