1
|
Hibbing PR, Carlson JA, Steel C, Greenwood-Hickman MA, Nakandala S, Jankowska MM, Bellettiere J, Zou J, LaCroix AZ, Kumar A, Katzmarzyk PT, Natarajan L. Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Obes (Lond) 2023; 47:1100-1107. [PMID: 37580374 PMCID: PMC10714872 DOI: 10.1038/s41366-023-01364-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND/OBJECTIVES Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.
Collapse
Affiliation(s)
- Paul R Hibbing
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA.
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA.
| | - Jordan A Carlson
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, University of Missouri Kansas City, Kansas City, MO, USA
| | - Chelsea Steel
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA
| | | | - Supun Nakandala
- Databricks Inc, San Francisco, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Marta M Jankowska
- Beckman Research Institute, Population Sciences, City of Hope, Duarte, CA, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Arun Kumar
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Loki Natarajan
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|
2
|
Carlson JA, Ridgers ND, Nakandala S, Zablocki R, Tuz-Zahra F, Bellettiere J, Hibbing PR, Steel C, Jankowska MM, Rosenberg DE, Greenwood-Hickman MA, Zou J, LaCroix AZ, Kumar A, Natarajan L. CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children. Int J Behav Nutr Phys Act 2022; 19:109. [PMID: 36028890 PMCID: PMC9419346 DOI: 10.1186/s12966-022-01349-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 08/15/2022] [Indexed: 11/29/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Jordan A Carlson
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA.
- Department of Pediatrics, University of Missouri - Kansas City, Kansas City, MO, USA.
| | - Nicola D Ridgers
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
- Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, Adelaide, Australia
| | - Supun Nakandala
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rong Zablocki
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Fatima Tuz-Zahra
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Paul R Hibbing
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA
| | - Chelsea Steel
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA
| | - Marta M Jankowska
- Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Jingjing Zou
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Arun Kumar
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Loki Natarajan
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|