Document Type

Article

Publication Date

7-2018

Identifier

DOI: 10.1249/MSS.0000000000001578; PMCID: PMC6023581

Abstract

PURPOSE: This study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data.

METHODS: Thirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit-stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models.

RESULTS: Overall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts.

CONCLUSIONS: This is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted.

Journal Title

Medicine and science in sports and exercise

Volume

50

Issue

7

First Page

1518

Last Page

1524

MeSH Keywords

Accelerometry; Aged; Algorithms; Breast Neoplasms; Cross-Sectional Studies; Exercise; Female; Hip; Humans; Machine Learning; Middle Aged; Monitoring, Ambulatory; Pilot Projects; Sitting Position; Survivors; Thigh

Keywords

Accelerometry; Aged; Algorithms; Breast Neoplasms; Cross-Sectional Studies; Exercise; Female; Hip; Humans; Machine Learning; Middle Aged; Monitoring, Ambulatory; Pilot Projects; Sitting Position; Survivors; Thigh

Comments

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher's Link: https://journals.lww.com/acsm-msse/Fulltext/2018/07000/Improving_Hip_Worn_Accelerometer_Estimates_of.22.aspx

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