TY - JOUR
T1 - A Device Agnostic Approach to Predict Children’s Activity from Consumer Wearable Accelerometer Data
T2 - A Proof-of-Concept Study
AU - Weaver, R. Glenn
AU - White, James
AU - Finnegan, Olivia
AU - Nelakuditi, Srihari
AU - Zhu, Xuanxuan
AU - Burkart, Sarah
AU - Beets, Michael
AU - Brown, Trey
AU - Pate, Russ
AU - Welk, Gregory J.
AU - de Zambotti, Massimiliano
AU - Ghosal, Rahul
AU - Wang, Yuan
AU - Armstrong, Bridget
AU - Adams, Elizabeth L.
AU - Reesor-Oyer, Layton
AU - Pfledderer, Christopher D.
AU - Bastyr, Meghan
AU - von Klinggraeff, Lauren
AU - Parker, Hannah
N1 - Publisher Copyright:
Copyright © 2023 by the American College of Sports Medicine.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - Introduction: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. Methods: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min 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) 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% (95% confidence interval (CI), 67.1%–69.3%), 73.0% (95% CI, 71.8%–74.3%), and 66.6% (95% CI, 65.7%–67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%–85.2%), 82.0% (95% CI, 80.6%–83.4%), and 75.3% (95% CI, 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 moderate-to-vigorous physical activity, 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 with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.
AB - Introduction: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. Methods: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min 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) 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% (95% confidence interval (CI), 67.1%–69.3%), 73.0% (95% CI, 71.8%–74.3%), and 66.6% (95% CI, 65.7%–67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%–85.2%), 82.0% (95% CI, 80.6%–83.4%), and 75.3% (95% CI, 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 moderate-to-vigorous physical activity, 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 with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.
KW - FREE-LIVING ACTIVITY MONITORING
KW - OPEN SOURCE
KW - PHYSICAL ACTIVITY TRACKING
KW - VALIDATION
UR - https://www.scopus.com/pages/publications/85182357103
UR - https://www.scopus.com/pages/publications/85182357103#tab=citedBy
U2 - 10.1249/MSS.0000000000003294
DO - 10.1249/MSS.0000000000003294
M3 - Article
C2 - 37707503
AN - SCOPUS:85182357103
SN - 0195-9131
VL - 56
SP - 370
EP - 379
JO - Medicine and Science in Sports and Exercise
JF - Medicine and Science in Sports and Exercise
IS - 2
ER -