ArticleMedicine and science in sports and exercise2012
Effect of BMI on prediction of accelerometry-based energy expenditure in youth.
Article in Medicine and science in sports and exercise, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Peer mentor versus teacher delivery of a physical activity program on the effects of BMI and daily activity: protocol of a school-based group randomized controlled trial in Appalachia.BMC public health · 2018Trial
- Identification of Activity Breaks Using Accelerometry.Sensors (Basel, Switzerland) · 2026Article
- Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.Statistics in medicine · 2026Article
- Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.Statistics in medicine · 2024Article
- PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS.Statistica Sinica · 2023Article
- Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.Biostatistics (Oxford, England) · 2022Article
- Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model.International journal of environmental research and public health · 2021Article
- Rates of Obesity and Obesogenic Behaviors of Rural Appalachian Adolescents: How Do They Compare to Other Adolescents or Recommendations?Journal of physical activity & health · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThe objective of this study is to determine the effect of body mass index (BMI) on level of agreement between six previously established prediction equations for three commonly used accelerometers to predict summary measures of energy expenditure (EE) in youth.
methodsOne hundred and thirty-one youth between the ages of 10-17 yr and BMI from 15 to 44 kg·m were outfitted with hip-worn ActiGraph GT1M (Pensacola, FL), Actical (MiniMiter/Respironics, Bend, OR), and RT3 (StayHealthy, Monrovia, CA) accelerometers and spent approximately 24 h in a whole-room indirect calorimeter while performing structured and self-selected activities. Five commonly used regression and one propriety equations for each device were used to predict the minute-to-minute EE (normalized to METs), daily physical activity level (PAL), and time spent in sedentary, light, moderate, and vigorous physical activity intensity categories. The calculated values were compared with criterion measurements obtained from the room calorimeter.
resultsAll predictive equations, except RT3, significantly over- or underpredicted daily PAL (P < 0.001), with large discrepancies observed in the estimate of sedentary and light activity. Discrepancies between actual and estimated PAL ranged from 0.05 to 0.68. In addition, BMI represented a modifier for two ActiGraph predictive equations (AG1 and AG2), affecting the accuracy of physical activity-related EE predictions.
conclusionActiGraph (AG3) and the RT3 closely predicted overall PAL (within 4.2% and 6.8%, respectively) as a group. When adjusting for age, sex, and ethnicity, Actical (AC1 and AC2) and ActiGraph (AG3) were not influenced by BMI. However, a gap between some hip-worn accelerometer predictive and regression equations was demonstrated compared with both criterion measurement and each other, which poses a potential difficulty for interstudy (e.g., different accelerometers) and intrastudy (e.g., BMI and adiposity) comparisons.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.