» Articles » PMID: 30477518

24-h Movement Behaviors from Infancy to Preschool: Cross-sectional and Longitudinal Relationships with Body Composition and Bone Health

Abstract

Background: New physical activity guidelines for children address all movement behaviors across the 24-h day (physical activity, sedentary behavior, sleep), but how each component relates to body composition when adjusted for the compositional nature of 24-h data is uncertain.

Aims: To i) describe 24-h movement behaviors from 1 to 5 years of age, ii) determine cross-sectional relationships with body mass index (BMI) z-score, iii) determine whether movement behaviors from 1 to 5 years of age predict body composition and bone health at 5 years.

Methods: 24-h accelerometry data were collected in 380 children over 5-7 days at 1, 2, 3.5 and 5 years of age to determine the proportion of the day spent: sedentary (including wake after sleep onset), in light (LPA) and moderate-to-vigorous physical activity (MVPA), and asleep (including naps). BMI was determined at each age and a dual-energy x-ray absorptiometry (DXA) scan measured fat mass, bone mineral content (BMC) and bone mineral density (BMD) at 5 years of age. 24-h movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes back-transformed.

Results: At age 1, children spent 49.6% of the 24-h day asleep, 38.2% sedentary, 12.1% in LPA, and 0.1% in MVPA, with corresponding figures of 44.4, 33.8, 19.8 and 1.9% at 5 years of age. Compositional time use was only related significantly to BMI z-score at 3.5 years in cross-sectional analyses. A 10% increase in mean sleep time (65 min) was associated with a lower BMI z-score (estimated difference, - 0.25; 95% CI, - 0.42 to - 0.08), whereas greater time spent sedentary (10%, 47 min) or in LPA (10%, 29 min) were associated with higher BMI z-scores (0.12 and 0.08 respectively, both p < 0.05). Compositional time use from 1 to 3.5 years was not related to future BMI z-score or percent fat. Although MVPA at 2 and 3.5 years was consistently associated with higher BMD and BMC at 5 years, actual differences were small.

Conclusions: Considerable changes in compositional time use occur from 1 to 5 years of age, but there is little association with adiposity. Although early MVPA predicted better bone health, the differences observed had little clinical relevance.

Trial Registration: ClinicalTrials.gov number NCT00892983 .

Citing Articles

The association between 24-h movement behaviours and adiposity among Australian preschoolers: a compositional data analysis.

Decraene M, Chong K, Stanford T, Dumuid D, Cross P, Cardon G BMC Public Health. 2025; 25(1):368.

PMID: 39881264 PMC: 11781000. DOI: 10.1186/s12889-024-21217-x.


Longitudinal Associations Between Movement Behaviours and Development Among Infants Using Compositional Data Analysis.

Carson V, Zhang Z, Boyd M, Pritchard L, Hesketh K Child Care Health Dev. 2024; 51(1):e70025.

PMID: 39704390 PMC: 11660523. DOI: 10.1111/cch.70025.


Longitudinal changes in preschoolers' adiposity indicators according to compliance with 24-hour movement behavior guidelines: results from the ToyBox-study.

De Craemer M, Cardon G, Decraene M, Androutsos O, Moreno L, Iotova V BMC Public Health. 2024; 24(1):3115.

PMID: 39528995 PMC: 11555805. DOI: 10.1186/s12889-024-20626-2.


Association between sedentary behavior and bone mass, microstructure and strength in children, adolescents and young adults: a systematic review.

Wang L, Peng F, Zhang X, Liang L, Chi H BMC Public Health. 2024; 24(1):2991.

PMID: 39472834 PMC: 11520865. DOI: 10.1186/s12889-024-20437-5.


Longitudinal Change in Physical Activity in Children 6 to 36 Months of Age.

Pate R, Dowda M, McLain A, Frongillo E, Saunders R, Inak N J Pediatr. 2024; 276:114358.

PMID: 39423909 PMC: 11645185. DOI: 10.1016/j.jpeds.2024.114358.


References
1.
Meredith-Jones K, Williams S, Galland B, Kennedy G, Taylor R . 24 h Accelerometry: impact of sleep-screening methods on estimates of sedentary behaviour and physical activity while awake. J Sports Sci. 2015; 34(7):679-85. DOI: 10.1080/02640414.2015.1068438. View

2.
Rich C, Geraci M, Griffiths L, Sera F, Dezateux C, Cortina-Borja M . Quality control methods in accelerometer data processing: defining minimum wear time. PLoS One. 2013; 8(6):e67206. PMC: 3691227. DOI: 10.1371/journal.pone.0067206. View

3.
Zemel B, Leonard M, Kelly A, Lappe J, Gilsanz V, Oberfield S . Height adjustment in assessing dual energy x-ray absorptiometry measurements of bone mass and density in children. J Clin Endocrinol Metab. 2010; 95(3):1265-73. PMC: 2841534. DOI: 10.1210/jc.2009-2057. View

4.
Pfeiffer K, McIver K, Dowda M, Almeida M, Pate R . Validation and calibration of the Actical accelerometer in preschool children. Med Sci Sports Exerc. 2006; 38(1):152-7. DOI: 10.1249/01.mss.0000183219.44127.e7. View

5.
Meredith-Jones K, Haszard J, Moir C, Heath A, Lawrence J, Galland B . Physical activity and inactivity trajectories associated with body composition in pre-schoolers. Int J Obes (Lond). 2018; 42(9):1621-1630. DOI: 10.1038/s41366-018-0058-5. View