ReviewMedicine and science in sports and exercise2012
Computational methods for estimating energy expenditure in human physical activities.
Review 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 22 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Non-Invasive Physiological Monitoring for Physical Exertion and Fatigue Assessment in Military Personnel: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study.The international journal of behavioral nutrition and physical activity · 2018Pooled it
- Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations.Sports medicine (Auckland, N.Z.) · 2017Pooled it
- Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate.Frontiers in public health · 2021Trial
- Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables.Scientific reports · 2025Article
- Effects of Sampling Frequency on Human Activity Recognition with Machine Learning Aiming at Clinical Applications.Sensors (Basel, Switzerland) · 2025Article
- Associations Between Physical Activity and Preserved Ratio Impaired Spirometry: A Cross-Sectional NHANES Study.International journal of chronic obstructive pulmonary disease · 2024Article
- A theory-based model of cumulative activity.Scientific reports · 2022Article
- Predicting lying, sitting and walking at different intensities using smartphone accelerometers at three different wear locations: hands, pant pockets, backpack.BMJ open sport & exercise medicine · 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
- Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury.Sensors (Basel, Switzerland) · 2021Article
- Predicting lying, sitting, walking and running using Apple Watch and Fitbit data.BMJ open sport & exercise medicine · 2021Article
- Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation.PloS one · 2020Article
- Defining accelerometer cut-points for different intensity levels in motor-complete spinal cord injury.Spinal cord · 2020Article
- Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.Journal of neuroengineering and rehabilitation · 2018Article
- Article
- Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications.Parkinson's disease · 2017Review
- Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study.Sensors (Basel, Switzerland) · 2016Article
- Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies.JMIR mHealth and uHealth · 2015Article
- Validation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settings.Sensors (Basel, Switzerland) · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Accurate and reliable methods for assessing human physical activity (PA) energy expenditure (PAEE) are informative and essential for understanding individual behaviors and quantifying the effect of PA on disease, for PA surveillance, and for examining determinants of PA in different populations. This article reviews recent advances in the estimation of PAEE in three interrelated areas: 1) types of sensors worn by human subjects, 2) features extracted from the measured sensor signals, and 3) modeling techniques to estimate the PAEE using these features. The review illustrates three directions in the PAEE studies and provides recommendations for future research, with the aim to produce valid, reliable, and accurate assessment of PAEE from wearable sensors.
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What OpenQuestion holds
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.