An Exploratory Study Of Time-segmented Patterns Of Physical Activity Among Young Children With Down Syndrome

PURPOSE: Physical activity (PA), at any intensity, is essential for healthy growth and development, especially for young children with Down syndrome (DS) who are at greater risk for developing secondary health conditions. Yet, little is known about typical PA patterns among this population. This pilot study aims to investigate the time-segmented distribution of total PA (TPA) among 2-6 year old children with DS. METHODS: A convenience sample of 12 ambulatory children with DS and their parents were recruited through community partners. Children wore an ActiGraph GT3X+ accelerometer on the non-dominant wrist for 7 consecutive days and devices were initialized to sample acceleration at 60 Hz across 1-second epochs. Choi (2011) algorithms were applied to determine periods of non-wear time during waking hours (6 am - 9 pm), and Johanssen (2016) wrist-worn cut-points were applied to determine time spent in TPA. Waking hours were further segmented into 5 distinct categories: Early Morning (6 am-9 am), Late Morning (9 am-12 pm), Early Afternoon (12 pm-3 pm), Late Afternoon (3 pm-6 pm), and Evening (6 pm-9 pm). Children with ≥5 hours of wear time·day-1 for ≥1 weekend day and ≥ 3 weekdays were included in analyses. RESULTS: Most participating children (n = 12, mean age = 4.2 ± 1.3 years, 50% female) were non-Hispanic white (83.3%) and spent their days at home with a parent rather than in a childcare setting (66.7%). Four children were excluded from analyses due to inadequate wear time. Among included children (n = 8), average wear time was 732.95 ± 141.51 min·day-1 and TPA was 286.73 ± 70.19 min·day1. An ANOVA revealed a fixed effect between TPA and time (F(4,264) = 12.20, p < 0.0001). Children with DS engaged in significantly more TPA (76.08 ± 19.22 min) during the Late Morning (9 am-12 pm) compared with Early Morning (46.48 ± 19.33 min, p < 0.0001), Early Afternoon (51.20 ± 28.93 min, p < 0.0001) and Evening (62.35 ± 27.12 min, p < 0.05) time segments. CONCLUSION: Young children with DS were significantly more active in the late morning compared to other time-segmented periods of the day. Further research on relevant social and environmental factors that influence TPA during specific time periods may inform opportunities for intervention. Supported by: This study was supported by a 2024 University of Nebraska at Omaha’s University Committee on Research and Creative Activity (UCRCA) minigrant.

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2025-09-16
DOI
https://doi.org/10.1249/01.mss.0001155516.64013.d4
Primary Topic
Down syndrome and intellectual disability research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Exploratory Study Of Time-segmented Patterns Of Physical Activity Among Young Children With Down Syndrome

Michaela A. Schenkelberg, Heather Wasser, Kelsey Thompson, Erik A. Willis et al.
Medicine & Science in Sports & Exercise
Down syndrome and intellectual disability research
article

An Exploratory Study Of Time-segmented Patterns Of Physical Activity Among Young Children With Down Syndrome

Michaela A. Schenkelberg, Heather Wasser, Kelsey Thompson, Erik A. Willis, Emily K. Clarke‐Rubright, Phoebe P. Tchoua, Seema Agrawal, Derek Hales
article en

Abstract

PURPOSE: Physical activity (PA), at any intensity, is essential for healthy growth and development, especially for young children with Down syndrome (DS) who are at greater risk for developing secondary health conditions. Yet, little is known about typical PA patterns among this population. This pilot study aims to investigate the time-segmented distribution of total PA (TPA) among 2-6 year old children with DS. METHODS: A convenience sample of 12 ambulatory children with DS and their parents were recruited through community partners. Children wore an ActiGraph GT3X+ accelerometer on the non-dominant wrist for 7 consecutive days and devices were initialized to sample acceleration at 60 Hz across 1-second epochs. Choi (2011) algorithms were applied to determine periods of non-wear time during waking hours (6 am - 9 pm), and Johanssen (2016) wrist-worn cut-points were applied to determine time spent in TPA. Waking hours were further segmented into 5 distinct categories: Early Morning (6 am-9 am), Late Morning (9 am-12 pm), Early Afternoon (12 pm-3 pm), Late Afternoon (3 pm-6 pm), and Evening (6 pm-9 pm). Children with ≥5 hours of wear time·day-1 for ≥1 weekend day and ≥ 3 weekdays were included in analyses. RESULTS: Most participating children (n = 12, mean age = 4.2 ± 1.3 years, 50% female) were non-Hispanic white (83.3%) and spent their days at home with a parent rather than in a childcare setting (66.7%). Four children were excluded from analyses due to inadequate wear time. Among included children (n = 8), average wear time was 732.95 ± 141.51 min·day-1 and TPA was 286.73 ± 70.19 min·day1. An ANOVA revealed a fixed effect between TPA and time (F(4,264) = 12.20, p < 0.0001). Children with DS engaged in significantly more TPA (76.08 ± 19.22 min) during the Late Morning (9 am-12 pm) compared with Early Morning (46.48 ± 19.33 min, p < 0.0001), Early Afternoon (51.20 ± 28.93 min, p < 0.0001) and Evening (62.35 ± 27.12 min, p < 0.05) time segments. CONCLUSION: Young children with DS were significantly more active in the late morning compared to other time-segmented periods of the day. Further research on relevant social and environmental factors that influence TPA during specific time periods may inform opportunities for intervention. Supported by: This study was supported by a 2024 University of Nebraska at Omaha’s University Committee on Research and Creative Activity (UCRCA) minigrant.

Medicine & Science in Sports & ExerciseVol. 57(10S)
University of North Carolina at Chapel Hill (US), University of Nebraska at Omaha (US)
Openalex Percentile: Top 66%
Down syndrome and intellectual disability research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.