2021
DOI: 10.2196/23938
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Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Abstract: Background Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some evidence indicates that these techniques can be applied to more scalable commercial devices. Objective This study aims to test the validity and out-of-sample generalizability of algorithms for the prediction o… Show more

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Cited by 14 publications
(9 citation statements)
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“…The triple recall bias can also be extended to other psychological EBT, which raises concerns over their construct validity and questions whether EBT measuring what they were designed to measure? Interestingly, new developments in real time digital tracking of energy balance behaviors, 148 , 149 , 150 , 151 , 152 digital ecological momentary assessments and mobile video recording 153 could be a new avenue for more ecological assessments of EI to capture EBT.…”
Section: Discussionmentioning
confidence: 99%
“…The triple recall bias can also be extended to other psychological EBT, which raises concerns over their construct validity and questions whether EBT measuring what they were designed to measure? Interestingly, new developments in real time digital tracking of energy balance behaviors, 148 , 149 , 150 , 151 , 152 digital ecological momentary assessments and mobile video recording 153 could be a new avenue for more ecological assessments of EI to capture EBT.…”
Section: Discussionmentioning
confidence: 99%
“…Generalizability (i.e., effective performance across multiple tasks and settings) is commonly identified as an important target in robotic control literature 45 , and a critical goal in the meat processing context 46 . In tests on generalizability in other contexts, RF approaches show strong performance among various machine learning algorithms tested 47 . However, superficial data segmentation for training and testing is acknowledged as a methodological pitfall likely to overestimate model performance 48 .…”
Section: Resultsmentioning
confidence: 99%
“…The methodological constraints to such designs are considerable but not necessarily unsurmountable. New technologies, approaches to data analytics and wearable sensors may improve our understanding of food intake [118][119][120][121], patterns of physical activity and EE [122][123][124][125][126] and changes in body composition (and hence EB) in the environmental contexts were they occur, e.g. [127][128][129] to provide comparative measures across experimental environments (laboratory and real world) and to aggregate data across time windows relevant to behavioural trajectories that affect longer-term EB.…”
Section: Discussionmentioning
confidence: 99%