| Title: | Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants |
| Journal: | International Journal of Behavioral Nutrition and Physical Activity |
| Published: | 24 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38001522/ |
| DOI: | https://doi.org/10.1186/s12966-023-01537-8 |
| URL: | https://ijbnpa.biomedcentral.com/counter/pdf/10.1186/s12966-023-01537-8 |
Publication 8261
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Abstract
BackgroundMovement behaviours, including physical activity, sedentary behaviour, and sleep have been shown to be associated with several chronic diseases. However, they have not been objectively measured in large-scale prospective cohort studies in low-and middle-income countries. We aim to describe the patterns of device-measured movement behaviours collected in the China Kadoorie Biobank (CKB) study.MethodsDuring 2020 and 2021, a random subset of 25,087 surviving CKB individuals participated in the 3rd resurvey of the CKB. Among them, 22,511 (89.7%) agreed to wear an Axivity AX3 wrist-worn triaxial accelerometer for seven consecutive days to assess their habitual movement behaviours. We developed a machine-learning model to infer time spent in four movement behaviours [i.e. sleep, sedentary behaviour, light intensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)]. Descriptive analyses were performed for wear-time compliance and patterns of movement behaviours by different participant characteristics.ResultsData from 21,897 participants (aged 65.4 ± 9.1 years; 35.4% men) were received for demographic and wear-time analysis, with a median wear-time of 6.9 days (IQR: 6.1-7.0). Among them, 20,370 eligible participants were included in movement behavior analyses. On average, they had 31.1 mg/day (total acceleration) overall activity level, accumulated 7.7 h/day (32.3%) of sleep time, 8.8 h/day (36.6%) sedentary, 5.7 h/day (23.9%) in light physical activity, and 104.4 min/day (7.2%) in moderate-to-vigorous physical activity. There was an inverse relationship between age and overall acceleration with an observed decline of 5.4 mg/day (17.4%) per additional decade. Women showed a higher activity level than men (32.3 vs 28.8 mg/day) and there was a marked geographical disparity in the overall activity level and time allocation.ConclusionsThis is the first large-scale accelerometer data collected among Chinese adults, which provides rich and comprehensive information about device-measured movement behaviour patterns. This resource will enhance our knowledge about the potential relevance of different movement behaviours for chronic disease in Chinese adults.</p>
18 Authors
- Yuanyuan Chen
- Shing Chan
- Derrick Bennett
- Xiaofang Chen
- Xianping Wu
- Yalei Ke
- Jun Lv
- Dianjianyi Sun
- Lang Pan
- Pei Pei
- Ling Yang
- Yiping Chen
- Junshi Chen
- Zhengming Chen
- Liming Li
- Huaidong Du
- Canqing Yu
- Aiden Doherty
1 Application
| Application ID | Title |
|---|---|
| 59070 | Statistical machine learning of wearable sensor data to predict disease outcomes |
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