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. 2019 Feb 28;19(1):244.
doi: 10.1186/s12889-019-6568-9.

Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X

Affiliations

Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X

Raphael Knaier et al. BMC Public Health. .

Abstract

Background: Wrist-worn accelerometers are increasingly used in epidemiological studies to record physical activity. The accelerometer data are usually only analyzed if the convention for compliant wear time is met (i.e. ≥ 10 h per day) but the algorithms to detect wear time have been developed based on data from hip-worn devices only and have not been tested in a free-living setting. The aim of this study was to validate the automatic wear time detection algorithms of one of the most frequently used devices in a free-living setting.

Methods: Sixty-eight adults wore one ActiGraph GT3X+ accelerometer on the wrist and one on the hip and additionally recorded wear times for each device separately in a diary. Monitoring phase was during three consecutive days in a free-living setting. Wear time was computed by the algorithms of Troiano and Choi and compared to the diary recordings.

Results: Mean wear time was over 1420 min per day for both devices on all days. Lin's concordance correlation coefficient for the wrist-worn wear time was 0.73 (0.60; 0.82) when comparing the diary with Troiano and 0.78 (0.67; 0.86) when comparing the diary with Choi. For hip-worn devices the respective values were 0.23 (0.13; 0.33) for Troiano and 0.92 (0.88; 0.95) for Choi. Mean and standard deviation values for absolute percentage errors for wrist-worn devices were - 1.3 ± 8.1% in Troiano and 0.9 ± 7.7% in Choi. The respective values for hip-worn devices were - 17.5 ± 10% in Troiano and - 0.8 ± 4.6% in Choi.

Conclusions: Hip worn devices may be preferred due to their higher accuracy in physical activity measurement. Automatic wear-time detection can show high errors in individuals, but on a group level, type I, type II, and total errors are generally low when the Choi algorithm is used. In a real-life setting and participants with a high compliance, the algorithm by Choi is sufficient to distinguish wear time from non-wear time on a group level.

Keywords: Accelerometer; Free-living; Physical activity.

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Conflict of interest statement

Ethics approval and consent

This study was approved by the ethics committee “Ethikkommission Nordwest- und Zentralschweiz”, Switzerland (EKNZ 2014–056) and complied with the declaration of Helsinki. Written informed consent was obtained from all study participants.

Consent for publication

not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Bland–Altman plot of the total wear time (minutes) from the diaries and wrist-worn ActiGraph calculated by Troiano (a), wrist-worn ActiGraph calculated by Choi (b), hip-worn ActiGraph calculated by Troiano (c), hip-worn ActiGraph calculated by Choi (d). The dashed lines denote the bias (i.e. the mean difference) and the dotted lines denote the 95% limits of agreement

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References

    1. Lipert A, Jegier A. Comparison of different physical activity measurement methods in adults aged 45 to 64 years under free-living conditions. Clin J Sport Med 2016;0:1–9. - PubMed
    1. Freedson PS, John D. Comment on “Estimating Activity and Sedentary Behavior from an Accelerometer on the Hip and Wrist”. Med Sci Sports Exerc. 2013;45(5):962–963. doi: 10.1249/MSS.0b013e31827f024d. - DOI - PubMed
    1. Kerr J, Marinac CR, Ellis K, Godbole S, Hipp A, Glanz K, et al. Comparison of Accelerometry methods for estimating physical activity. Med Sci Sports Exerc. 2017;49(3):617–624. doi: 10.1249/MSS.0000000000001124. - DOI - PMC - PubMed
    1. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. Buchowski M, editor. PLoS One 2017;12(2):e0169649. - PMC - PubMed
    1. White T, Westgate K, Wareham NJ, Brage S. Estimation of physical activity energy expenditure during free-living from wrist Accelerometry in UK adults. Song H, editor. PLoS One 2016;11(12):e0167472. - PMC - PubMed