Trial reportJournal of applied physiology (Bethesda, Md. : 1985)2018
Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.
Trial report in Journal of applied physiology (Bethesda, Md. : 1985), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2026Pooled it
- Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation.Clinical Medicine Insights. Cardiology · 2026Review
- Article
- Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction.Sensors (Basel, Switzerland) · 2024Article
- The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning.Heliyon · 2024Article
- Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.PloS one · 2024Article
- Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.PloS one · 2023Article
- A Novel Score for mHealth Apps to Predict and Prevent Mortality: Further Validation and Adaptation to the US Population Using the US National Health and Nutrition Examination Survey Data Set.Journal of medical Internet research · 2022Review
- Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms.Sensors (Basel, Switzerland) · 2022Review
- Cardiac monitoring for patients with palpitations.World journal of cardiology · 2021Review
- Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.NPJ digital medicine · 2021Article
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
- EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient.BMC medical informatics and decision making · 2020Article
- Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2020Review
- Article
- Mean Normalized Gain: A New Method for the Assessment of the Aerobic System Temporal Dynamics during Randomly Varying Exercise in Humans.Frontiers in physiology · 2017Article
- Article
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
Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o
Indexed as
Identifiers
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.