ArticleScientific reports2020
Evaluating the performance of raw and epoch non-wear algorithms using multiple accelerometers and electrocardiogram recordings.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
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- Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.Muscle & nerve · 2026Observational
- Optimizing accelerometer implementation in a gerotherapeutic trial: Feasibility, adherence, and operational insights of ABLE.PLOS digital health · 2026Article
- Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.Statistics in medicine · 2026Article
- Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development.PloS one · 2026Article
- Beyond the (Log)book: Comparing Accelerometer Nonwear Detection Techniques in Toddlers.Child: care, health and development · 2025Article
- Association of Cut-Point Free Metrics and Common Clinical Tests Among Older Adults After Proximal Femoral Fracture.Sensors (Basel, Switzerland) · 2025Article
- Accelerometer-Measured Physical Activity, Fitness and Indicators of Cardiometabolic Risk among Rural Adolescents: A Cross-Sectional Study at 15-Year Follow-up of the MINIMat Cohort.Journal of epidemiology and global health · 2024Article
- Acute Response of Different High-Intensity Interval Training Protocols on Cardiac Auto-Regulation Using Wearable Device.Sensors (Basel, Switzerland) · 2024Article
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- A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study.Scientific reports · 2023Article
- Using GPS Technologies to Examine Community Mobility in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- Generalizability and performance of methods to detect non-wear with free-living accelerometer recordings.Scientific reports · 2023Article
- How people wake up is associated with previous night's sleep together with physical activity and food intake.Nature communications · 2022Article
- Simulation-Based Evaluation of Methods for Handling Nonwear Time in Accelerometer Studies of Physical Activity.Journal for the measurement of physical behaviour · 2022Article
- Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature.BMC medical research methodology · 2022Article
- Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease.Journal of neurology · 2022Article
- 24-hour movement behaviour profiles and their transition in children aged 5.5 and 8 years - findings from a prospective cohort study.The international journal of behavioral nutrition and physical activity · 2021Article
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of accelerometer non-wear time is crucial for calculating physical activity summary statistics. In this study, we evaluated three epoch-based non-wear algorithms (Hecht, Troiano, and Choi) and one raw-based algorithm (Hees). In addition, we performed a sensitivity analysis to provide insight into the relationship between the algorithms' hyperparameters and classification performance, as well as to generate tuned hyperparameter values to better detect episodes of wear and non-wear time. We used machine learning to construct a gold-standard dataset by combining two accelerometers and electrocardiogram recordings. The Hecht and Troiano algorithms achieved poor classification performance, while Choi exhibited moderate performance. Meanwhile, Hees outperformed all epoch-based algorithms. The sensitivity analysis and hyperparameter tuning revealed that all algorithms were able to achieve increased classification performance by employing larger intervals and windows, while more stringently defining artificial movement. These classification gains were associated with the ability to lower the false positives (type I error) and do not necessarily indicate a more accurate detection of the total non-wear time. Moreover, our results indicate that with tuned hyperparameters, epoch-based non-wear algorithms are able to perform just as well as raw-based non-wear algorithms with respect to their ability to correctly detect true wear and non-wear episodes.
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