An Approach to a Novel Device Agnostic Model Illustrating the Relative Change in Physical Behavior Over Time to Support Behavioral Change

Åkerberg, Anna; Arwald, Jan; Söderlund, Anne; Lindén, María

doi:10.1007/s41347-022-00246-6

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…An alternative, device-agnostic or monitor-independent approach may address these limitations by enabling data from any device to be processed using the same algorithm or processing methodology [13,14]. A device-agnostic approach is a realistic possibility as consumer wearables have released application programming interfaces (API) that allow access to the raw accelerometry data (i.e., x, y, z axis readings collected by these devices [15].…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

White,

Finnegan,

Tindall

et al. 2023

Preprint

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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 the criterion accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). For reliability testing, identical devices were oscillated at 0.6 and 3.2 Hz for 5 trials that lasted 2 minutes each. For validity testing, devices were oscillated for 1 trial across 7 speeds that lasted 2 minutes each. The intraclass correlation coefficient (ICC) was calculated to test inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, and mean bias were calculated to assess the validity between the raw estimates from the devices and the criterion metric. Estimates produced by the raw accelerometry data from Apple and ActiGraph were more reliable ICCs=0.99 and 0.97 than Garmin and Fitbit ICCs=0.88 and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the criterion 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 criterion based on the mean aggregation method. ActiGraph produced the lowest mean bias 0.0mg (95%CI=-40.0, 41.0) based on the mean aggregation method. Raw accelerometry data collected from Apple and Fitbit are comparable to ActiGraph. However, raw accelerometry data from Garmin appears to be different. Future studies may be able to develop algorithms using device-agnostic methods for estimating physical activity from consumer wearables.

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Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

White,

Finnegan,

Tindall

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An alternative, device-agnostic or monitor-independent approach may address these limitations by enabling data from any device to be processed using the same algorithm or processing methodology [15,16]. A device-agnostic approach is a realistic possibility as consumer wearables have released application programming interfaces (API) that allow access to the raw accelerometry data (i.e., x, y, z axis readings collected by these devices) [17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

White,

Finnegan,

Tindall

et al. 2024

PLoS ONE

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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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“…Adopting a device-agnostic approach offers one solution to this problem (7,8). A device-agnostic approach relies on the underlying data collected via sensors rather than the metrics produced by the proprietary algorithms on these devices.…”

mentioning

confidence: 99%

A Device Agnostic Approach to Predict Children’s Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study

WEAVER,

WHITE,

FINNEGAN

et al. 2023

Medicine &Amp; Science in Sports &Amp; Exercise

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Introduction This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared to a research-grade accelerometry. Methods Seventy-five 5–12-year-olds (58% male, 63% White) participated in a 60-minute protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (Cosmed K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity[MVPA]) were estimated via indirect calorimetry (criterion) and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. Results Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95CI = 67.1%, 69.3%), 73.0% (95CI = 71.8%, 74.3%), and 66.6% (95CI = 65.7%, 67.5%), respectively; and weighted specificity = 84.4% (95CI = 83.6%, 85.2%), 82.0% (95CI = 80.6%, 83.4%), and 75.3% (95CI = 74.7%, 75.9%), respectively. Apple Watch produced the lowest mean bias (inactive = -4.0 ± 4.5, light activity = 2.1 ± 4.0) and absolute error (inactive = 4.9 ± 3.4, light activity = 3.6 ± 2.7) for inactive and light physical activity minutes. For MVPA, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. Conclusions Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than a research-grade device, when compared to indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

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An Approach to a Novel Device Agnostic Model Illustrating the Relative Change in Physical Behavior Over Time to Support Behavioral Change

Cited by 3 publications

References 45 publications

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

A Device Agnostic Approach to Predict Children’s Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study

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