ArticleSensors (Basel, Switzerland)2013
Comparison of raw acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors.
Article in Sensors (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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Who cites it
18 citing papers in PubMed, 70 citations in OpenAlex.
- Preconception to postpartum accelerometry-based 24-hour movement behaviors: a prospective cohort study.BMC public health · 2026Article
- Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration.Medicine and science in sports and exercise · 2024Article
- Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.PloS one · 2024Article
- A Transparent Method for Step Detection using an Acceleration Threshold.Journal for the measurement of physical behaviour · 2021Article
- Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera.Sensors (Basel, Switzerland) · 2020Article
- Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults.Scientific reports · 2019Article
- Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data.Sensors (Basel, Switzerland) · 2019Article
- Establishing Raw Acceleration Thresholds to Classify Sedentary and Stationary Behaviour in Children.Children (Basel, Switzerland) · 2018Article
- "What Is a Step?" Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research.Sensors (Basel, Switzerland) · 2018Article
- Motion sensors in multiple sclerosis: Narrative review and update of applications.Expert review of medical devices · 2017Review
- Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water.Medicine and science in sports and exercise · 2017Article
- Understanding physical activity in cancer patients and survivors: new methodology, new challenges, and new opportunities.Cold Spring Harbor molecular case studies · 2017Review
- Article
- Assessing Daily Physical Activity in Older Adults: Unraveling the Complexity of Monitors, Measures, and Methods.The journals of gerontology. Series A, Biological sciences and medical sciences · 2016Review
- Evaluation of four sensor locations for physical activity assessment.Applied ergonomics · 2016Article
- Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior.AIMS public health · 2016Article
- Measuring Physical Activity in Free-Living Conditions-Comparison of Three Accelerometry-Based Methods.Frontiers in physiology · 2016Article
- Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.PloS one · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors.
methodsA GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model.
resultsGENEA produced significantly higher (p < 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p < 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors.
conclusionsIt may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration.
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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.