CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children

Autor: Jordan A. Carlson, Nicola D. Ridgers, Supun Nakandala, Rong Zablocki, Fatima Tuz-Zahra, John Bellettiere, Paul R. Hibbing, Chelsea Steel, Marta M. Jankowska, Dori E. Rosenberg, Mikael Anne Greenwood-Hickman, Jingjing Zou, Andrea Z. LaCroix, Arun Kumar, Loki Natarajan
Přispěvatelé: Carlson, Jordan A, Ridgers, Nicola D, Nakandala, Supun, Zablocki, Rong, Tuz-Zahra, Fatima, Bellettiere, John, Hibbing, Paul R, Steel, Chelsea, Jankowska, Marta M, Rosenberg, Dori E, Greenwood-Hickman, Mikael Anne, Zou, Jingjing, LaCroix, Andrea Z, Kumar, Arun, Natarajan, Loki
Rok vydání: 2021
Předmět:
Zdroj: The international journal of behavioral nutrition and physical activity, vol 19, iss 1
ISSN: 1479-5868
Popis: Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. Methods Participants were 278, 8–11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). Results Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. Conclusions There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.
Databáze: OpenAIRE
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