ArticlePloS one2024
Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07512687 (Impact of Ambulatory Oxygen Delivery on Real-world Activity and Quality-of-life in Patients With Interstitial Lung Disease), which is not on this map. Cited by 10 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.
Impact of Ambulatory Oxygen Delivery on Real-world Activity and Quality-of-life in Patients With Interstitial Lung Disease (ILD) and Chronic Obstructive Pulmonary Disease (COPD)
Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Examining the feasibility and preliminary efficacy of a group-based physical activity intervention integrating strength training among pre- and perimenopausal women: A randomized pilot trial.Women's health (London, England)Trial
- Comparison of Consumer Smartwatch and Research-Grade Accelerometer-Derived Step Counts in Amyotrophic Lateral Sclerosis.Muscle & nerve · 2026Observational
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- Wearable technology-derived digital biomarkers for non-invasive assessment of erectile dysfunction: a cross-sectional pilot study.International urology and nephrology · 2026Article
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- Sleep Quality and Delirium Occurrence in Intensive Care Unit Patients: A Prospective Observational Study.Nursing in critical care · 2026Observational
- Use of sensing technologies to assess sleep quality and physical activity levels in nursing home residents with dementia taking nightly psychotropic drugs for sleep disturbance: a cross-sectional study.Frontiers in aging · 2026Article
- ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.Sensors (Basel, Switzerland) · 2025Article
- Construct validity and responsiveness of clinical upper limb measures and sensor-based arm use within the first year after stroke: a longitudinal cohort study.Journal of neuroengineering and rehabilitation · 2025Observational
- The Ability of Monitor-Independent Movement Summary Units, Euclidean Norm Minus One, and Mean Amplitude Deviation to Harmonize Accelerometry Data Across Research-Grade and Consumer Wearable Devices During Simulated Free-Living Physical Activity in Children.Journal for the measurement of physical behaviour · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
Abstract
The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.
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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.