Associations Of Raw Accelerometer-derived Metrics With Body Composition, Physical Fitness, And Metabolic Biomarkers In Children

Advances in accelerometry now enable raw data analysis, improving cross-device comparability and facilitating the move beyond traditional population- and protocol-specific intensity cut-points that complicate study comparisons. Standardized metrics such as intensity gradient and average acceleration have been proposed to capture the distribution of physical activity intensity and overall activity volume, respectively. While these metrics show promise, research on their associations with health outcomes, especially in children, remains limited. PURPOSE: To assess the associations of intensity gradient and average acceleration with body composition, physical fitness, and metabolic biomarkers in children. METHODS: A sample of 156 children (53% male), aged 9.4 ± 0.9 years with a body mass index (BMI)-percentile for age and sex of 69.3 ± 25.0, was recruited. Physical activity was measured daily using waist-worn accelerometry, analyzing the intensity gradient and average acceleration metrics. Body composition (dual-energy X-ray absorptiometry), knee maximal strength (isokinetic dynamometer), handgrip maximal strength (hydraulic dynamometer), aerobic capacity (indirect calorimetry), and glucose/lipoprotein profile (fasted venipuncture) were measured. Associations were tested using linear regression with standardized coefficients, adjusting for sex and age. RESULTS: A higher intensity gradient was associated with a lower body mass index (β = −0.30; p < .001), lower body fat percentage (β = −0.24; p = .003), and higher peak oxygen uptake (β = 0.24; p = .002) but showed no association with maximal strength or metabolic biomarkers (p > .05). Higher average acceleration was associated only with higher peak oxygen uptake (β = 0.21; p = .004). The intensity gradient’s associations were independent of average acceleration, whereas the association between average acceleration and peak oxygen uptake was not significant after adjustment for intensity gradient. CONCLUSION: The intensity gradient was associated with body composition and aerobic capacity. Incorporating intensity gradient into physical activity assessments may improve understanding of health-relevant activity patterns in children, supporting the design of targeted interventions. Supported by: USDA-ARS Project 6026-10700-001-000D

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2025-09-16
DOI
https://doi.org/10.1249/01.mss.0001155132.92011.9c
Primary Topic
Body Composition Measurement Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Associations Of Raw Accelerometer-derived Metrics With Body Composition, Physical Fitness, And Metabolic Biomarkers In Children

Eva C. Diaz, Florêncio Diniz‐Sousa, Elisabet Børsheim, Timothy Edwards
Medicine & Science in Sports & Exercise
Body Composition Measurement Techniques
article

Associations Of Raw Accelerometer-derived Metrics With Body Composition, Physical Fitness, And Metabolic Biomarkers In Children

Eva C. Diaz, Florêncio Diniz‐Sousa, Elisabet Børsheim, Timothy Edwards
article en

Abstract

Advances in accelerometry now enable raw data analysis, improving cross-device comparability and facilitating the move beyond traditional population- and protocol-specific intensity cut-points that complicate study comparisons. Standardized metrics such as intensity gradient and average acceleration have been proposed to capture the distribution of physical activity intensity and overall activity volume, respectively. While these metrics show promise, research on their associations with health outcomes, especially in children, remains limited. PURPOSE: To assess the associations of intensity gradient and average acceleration with body composition, physical fitness, and metabolic biomarkers in children. METHODS: A sample of 156 children (53% male), aged 9.4 ± 0.9 years with a body mass index (BMI)-percentile for age and sex of 69.3 ± 25.0, was recruited. Physical activity was measured daily using waist-worn accelerometry, analyzing the intensity gradient and average acceleration metrics. Body composition (dual-energy X-ray absorptiometry), knee maximal strength (isokinetic dynamometer), handgrip maximal strength (hydraulic dynamometer), aerobic capacity (indirect calorimetry), and glucose/lipoprotein profile (fasted venipuncture) were measured. Associations were tested using linear regression with standardized coefficients, adjusting for sex and age. RESULTS: A higher intensity gradient was associated with a lower body mass index (β = −0.30; p < .001), lower body fat percentage (β = −0.24; p = .003), and higher peak oxygen uptake (β = 0.24; p = .002) but showed no association with maximal strength or metabolic biomarkers (p > .05). Higher average acceleration was associated only with higher peak oxygen uptake (β = 0.21; p = .004). The intensity gradient’s associations were independent of average acceleration, whereas the association between average acceleration and peak oxygen uptake was not significant after adjustment for intensity gradient. CONCLUSION: The intensity gradient was associated with body composition and aerobic capacity. Incorporating intensity gradient into physical activity assessments may improve understanding of health-relevant activity patterns in children, supporting the design of targeted interventions. Supported by: USDA-ARS Project 6026-10700-001-000D

Medicine & Science in Sports & ExerciseVol. 57(10S)
Arkansas Children's Hospital (US), Arkansas Children's Nutrition Center (US)
Openalex Percentile: Top 69%
Body Composition Measurement Techniques
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.