CHAP-Adult : A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+
Journal article
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 and Natarajan, Loki. (2022). CHAP-Adult : A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+. Journal for the Measurement of Physical Behaviour. 5(4), pp. 215-223. https://doi.org/10.1123/jmpb.2021-0062
Authors | 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 and Natarajan, Loki |
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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 |
Year | 01 Jan 2022 |
Journal | Journal for the Measurement of Physical Behaviour |
Journal citation | 5 (4), pp. 215-223 |
Publisher | Human Kinetics, Inc. |
ISSN | 2575-6605 |
Digital Object Identifier (DOI) | https://doi.org/10.1123/jmpb.2021-0062 |
Web address (URL) | https://journals.humankinetics.com/view/journals/jmpb/5/4/article-p215.xml?content=abstract |
Open access | Published as non-open access |
Research or scholarly | Research |
Page range | 215-223 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 21 Sep 2022 |
Publication process dates | |
Deposited | 06 Jan 2023 |
Supplemental file | License All rights reserved File Access Level Controlled |
Additional information | © 2022 Human Kinetics, Inc. |
Place of publication | United States |
https://acuresearchbank.acu.edu.au/item/8y920/chap-adult-a-reliable-and-valid-algorithm-to-classify-sitting-and-measure-sitting-patterns-using-data-from-hip-worn-accelerometers-in-adults-aged-35
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