Evidence map›Paper›PMID 41909497›Full record

ArticleJournal for the measurement of physical behaviour2025

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.

Olivia L Finnegan, James W White, Bridget Armstrong, Elizabeth L Adams, Sarah Burkart, Michael W Beets, Srihari Nelakuditi, Zifei Zhong, Hongpeng Yang, Keagan P Kiely and 4 more

Abstract read
In one paragraph

Article in Journal for the measurement of physical behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Olivia L FinneganUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
James W WhiteUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Bridget ArmstrongUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Elizabeth L AdamsUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Sarah BurkartUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Michael W BeetsUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Srihari NelakuditiUniversity of South Carolina, Department of Computer Science and Engineering, Columbia, SC, USA.
Zifei ZhongUniversity of South Carolina, Department of Computer Science and Engineering, Columbia, SC, USA.
Hongpeng YangUniversity of South Carolina, Department of Computer Science and Engineering, Columbia, SC, USA.
Keagan P KielyUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.
Rahul GhosalUniversity of South Carolina, Department of Epidemiology and Biostatistics, Columbia, SC, USA.
Stuart J FaircloughEdge Hill University, Department of Sport and Physical Activity.
Gregory J WelkIowa State University, Department of Kinesiology, Ames, Iowa, USA.
R Glenn WeaverUniversity of South Carolina, Department of Exercise Science, Columbia, SC, USA.

Funding

Biomedical-Behavioral Interface: Prevention and Developmental Sciences TrainingT32GM081740 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI PRINZ, RONALD J · 2008 to 2022
$4.5M
Validity and Utility of Consumer-based Wearable Fitness Trackers to Monitor Free-Living Physical Activity Energy Expenditure and Sleep in Children 5-12 Years OldR01DK129215 · NIDDK · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI Robert G Weaver · 2022 to 2026
$3.0M
Behavioral-Biomedical Interface: Translational and Prevention Sciences TrainingT32GM145226 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI MICHAEL W BEETS, RONALD J PRINZ · 2023 to 2026
$1.2M
Utility and feasibility of activity type to augment consumer wearable-based physical activity energy expenditure prediction equations using heartrate and movement in childrenF31DK136205 · NIDDK · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI WHITE III, JAMES W. · 2023 to 2024
$83k
Leveraging Embedded Mobile Device Sensors for Detection of Children's Mobile Screen Use: Examining Feasibility and PerformanceF31HD115296 · NICHD · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI Olivia Finnegan · 2025 to 2026
$76k
NICHD NIH HHS F31 HD115296NIDDK NIH HHS F31 DK136205NIDDK NIH HHS R01 DK129215NIGMS NIH HHS T32 GM081740NIGMS NIH HHS T32 GM145226
6 · The paper itself

Abstract

Background: Epoch-level accelerometry summary metrics have the potential to be device-agnostic, meaning that similar estimates should be obtained regardless of the device used. The objective of this study was to identify which metric (ENMO, MAD, MIMS) best harmonizes data across devices in an applied setting measuring children's activity. Methods: Children (n=239; 9.3 ± 2.1 years, 47% female, 30% Black) wore ActiGraph GT9X (+/-8g, 50Hz) accelerometers and were randomized to wear two of three consumer wearables including Apple Watch Series 7 (+/-16g, 50Hz), Garmin Vivoactive 4S (+/-8g, 25Hz), and Fitbit Sense (+/-4g, 50Hz) on their non-dominant wrist, while participating in 60 minutes of simulated free-living activities (i.e., walking, running, soccer). The standard deviation (SD) across z-scores for ENMO, MAD and MIMS of each device was calculated at the second-level to quantify variability across devices. Lin's Concordance Correlation Coefficient (LCCC) was calculated for each combination of devices by metric to determine agreement. Multi-level intra-class correlation coefficients (ICC) were additionally used to quantify harmonization. Results: The SD of z-score across devices was the lowest, indicating better harmonization, for MAD (0.13±0.23), followed by ENMO (0.24±0.50), and then MIMS (0.26±0.39). LCCC was strongest for MAD, with coefficients ranging from 0.89 to 0.96. LCCC for MIMS ranged from 0.70 to 0.83, and was lowest for ENMO, ranging from 0.62 to 0.76. Overall ICC was highest for MAD (0.88), followed by MIMS (0.73), and ENMO (0.62). Conclusions: MAD appears to perform best at harmonizing across all the sampled research-grade and consumer devices in children.

Indexed as

AccelerometerENMOMADMIMS

Identifiers

PMID41909497
PMCPMC13029015

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.