Trial reportFrontiers in public health2021
Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate.
Trial report in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Feasibility of energy expenditure estimation during outdoor walking using multimodal in-ear sensors: a preliminary validation study against indirect calorimetry.Scientific reports · 2026Article
- Energy-Expenditure Estimation During Aerobic Training Sessions for Badminton Players.Sensors (Basel, Switzerland) · 2025Article
- Promoting physical activity during recess in small sized elementary school: a pilot study.BMC public health · 2025Article
- Two Tabata cycles in a single training set maximize fat oxidation after exercise in male college students with overweight/obesity.Scientific reports · 2025Article
- Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction.Sensors (Basel, Switzerland) · 2024Article
- The effect of acute high-intensity interval training and Tabata training on inhibitory control and cortical activation in young adults.Frontiers in neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tabata training plays an important role in health promotion. Effective monitoring of exercise energy expenditure is an important basis for exercisers to adjust their physical activities to achieve exercise goals. The input of acceleration combined with heart rate data and the application of machine learning algorithm are expected to improve the accuracy of EE prediction. This study is based on acceleration and heart rate to build linear regression and back propagate neural network prediction model of Tabata energy expenditure, and compare the accuracy of the two models. Participants (
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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.