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. 2022 Sep 8;19:117. doi: 10.1186/s12966-022-01350-9

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — Effect of wear location on overall accuracy (mean absolute percentage error, MAPE) of wearable technologies’ step counting ability. MAPE and corresponding 95% confidence intervals (CIs; estimated using mixed effect models) of each wear location are presented at slow, normal, and fast walking speeds. Slow speed bouts: 0.8, 1.6, 2.4, 3.2 km/h (0.5, 1.0, 1.5, 2.0 mph); normal speed bouts: 4.0, 4.8, 5.6, 6.4 km/h (2.5, 3.0, 3.5, 4.0 mph); fast speed bouts: 7.2, 8.0 km/h (4.5, 5.0 mph). MAPE values were averaged across devices respective to each wear location for slow, normal, and fast walking speeds. MAPE values closer to 0 indicate greater accuracy. The 95% CIs bars were not drawn when they were shorter than the height of the symbol. Further, where 95% CIs do not overlap, there are significant differences between locations. Likelihood ratio test P value is reported for the effect of wear location on MAPE for each specific speed level. Ankle-worn wearable: StepWatch (N = 253). Thigh-worn wearable: activPAL (N = 249). Waist-worn wearables: Actical (N = 250), ActiGraph GT9X (N = 254), Digi-Walker SW-200 (N = 258), Fitbit One (N = 160), Fitbit Zip (N = 98), GENEActiv (N = 224), NL-1000 (N = 258), PiezoRx (N = 98). Wrist-worn wearables: ActiGraph GT9X (N = 254), Apple Watch Series 1 (N = 174), Fitbit Ionic (N = 98), Garmin vivoactive 3 (N = 96), Garmin vivoactive HR (N = 77), Garmin vivofit 2 (N = 80), Garmin vivofit 3 (N = 77), GENEActiv (N = 217), Polar M600 (N = 97), Samsung Gear Fit2 (N = 80), Samsung Gear Fit2 Pro (N = 98). See Additional file 2 for a graphical classification of wearable technologies by age groups