Loading...
CHAP-Adult : A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+
Bellettiere, John Nakandala, Supun Tuz-Zahra, Fatima Winkler, Elisabeth Hibbing, Paul Healy, Genevieve Dunstan, David Wayne Owen, Neville Greenwood-Hickman, Mikael Rosenberg, Dori
Bellettiere, John
Nakandala, Supun
Tuz-Zahra, Fatima
Winkler, Elisabeth
Hibbing, Paul
Healy, Genevieve
Dunstan, David Wayne
Owen, Neville
Greenwood-Hickman, Mikael
Rosenberg, Dori
Author
Bellettiere, John
Nakandala, Supun
Tuz-Zahra, Fatima
Winkler, Elisabeth
Hibbing, Paul
Healy, Genevieve
Dunstan, David Wayne
Owen, Neville
Greenwood-Hickman, Mikael
Rosenberg, Dori
Zou, Jingjing
Carlson, Jordan
Di, Chongzhi
Dillon, Lindsay
Jankowska, Marta
LaCroix, Andrea
Ridgers, Nicola
Zablocki, Rong
Kumar, Arun
Natarajan, Loki
Nakandala, Supun
Tuz-Zahra, Fatima
Winkler, Elisabeth
Hibbing, Paul
Healy, Genevieve
Dunstan, David Wayne
Owen, Neville
Greenwood-Hickman, Mikael
Rosenberg, Dori
Zou, Jingjing
Carlson, Jordan
Di, Chongzhi
Dillon, Lindsay
Jankowska, Marta
LaCroix, Andrea
Ridgers, Nicola
Zablocki, Rong
Kumar, Arun
Natarajan, Loki
Abstract
Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.
Keywords
sedentary behavior, activity classification, computational methods, neural networks, validation, machine learning
Date
2022
Type
Journal article
Journal
Book
Volume
5
Issue
4
Page Range
215-223
Article Number
ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
Faculty of Health Sciences
Collections
Files
Bellettiere_2021_CHAP-Adult_A_Reliable_and_Valid_Algorithm.pdf
Adobe PDF, 804 KB
All rights reserved
- Embargoed until 9999-12-31
Relation URI
Event URL
Open Access Status
License
File Access
Controlled
Controlled
Controlled
Notes
© 2022 Human Kinetics, Inc.