ArticlePloS one2020
Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06169020 (Design of an Intelligent Wearable to Assess Physical Activity and Health Related Outcomes - the DIWAH Study), which is not on this map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Design of an Intelligent Wearable to Assess Physical Activity and Health Related Outcomes - the DIWAH Study
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old).The international journal of behavioral nutrition and physical activity · 2022Pooled it
- Development and evaluation of wrist- and thigh-worn accelerometer algorithms using self-training machine learning models for classification of activity type and posture: towards device placement-agnostic methods in the ProPASS consortium.The international journal of behavioral nutrition and physical activity · 2026Article
- The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- From past to future: a review of methods for assessing physical activity energy expenditure.Journal of health, population, and nutrition · 2025Article
- Evaluating machine learning approaches to predict the energy expenditure of cross-national preschool children: a study of preprocessing and feature effects.European journal of applied physiology · 2025Article
- Behaviour-based movement cut-off points in 3-year old children comparing wrist- with hip-worn actigraphs MW8 and GT3X.PloS one · 2025Article
- Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption.Sensors (Basel, Switzerland) · 2023Article
- Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique.The international journal of behavioral nutrition and physical activity · 2023Article
- Machine Learning in Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2022Article
- Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study.JMIR mHealth and uHealth · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data.
methods25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an "off the shelf" model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children.
resultsRoot mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63-0.67 kcals/min. In the hold out sample, RMSE's for the hip LAB (0.62-0.71), retrained LAB (0.58-0.62) and FL models (0.61-0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE (0.73 ± 0.29 kcals/min) than the retrained LAB SVM (0.63 ± 0.30 kcals/min) and LAB SVM (0.64 ± 0.18 kcals/min). The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy.
conclusionMachine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions.
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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.