Journal for the measurement of physical behaviour · 2025
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.
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Finnegan Olivia L, White James W, Armstrong Bridget, Adams Elizabeth L, Burkart Sarah, Beets Michael W, Nelakuditi Srihari, Zhong Zifei, Yang Hongpeng, Kiely Keagan P, Ghosal Rahul, Fairclough Stuart J, Welk Gregory J, Weaver R Glenn
Аннотация
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.
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.