C Tudor-Locke1, E F Mire2, T V Barreira3, J M Schuna4, J-P Chaput5, M Fogelholm6, G Hu2, A Kurpad7, R Kuriyan7, E V Lambert8, C Maher9, J Maia10, V Matsudo11, T Olds9, V Onywera12, O L Sarmiento13, M Standage14, M S Tremblay5, P Zhao15, T S Church2, P T Katzmarzyk2. 1. Department of Kinesiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA; Pennington Biomedical Research Center, Baton Rouge, LA, USA. 2. Pennington Biomedical Research Center , Baton Rouge, LA, USA. 3. Pennington Biomedical Research Center, Baton Rouge, LA, USA; Department of Exercise Science, Syracuse University, Syracuse, NY, USA. 4. Pennington Biomedical Research Center, Baton Rouge, LA, USA; Oregon State University, Corvallis, USA. 5. Children's Hospital of Eastern Ontario Research Institute , Ottawa, Ontario, Canada. 6. Department of Food and Environmental Sciences, University of Helsinki , Helsinki, Finland. 7. St Johns Research Institute , Bangalore, India. 8. Department of Human Biology, Division of Exercise Science and Sports Medicine, Faculty of Health Sciences, University of Cape Town , Cape Town, South Africa. 9. Alliance for Research in Exercise Nutrition and Activity (ARENA), School of Health Sciences, University of South Australia , Adelaide, South Australia, Australia. 10. CIFI2D, Faculdade de Desporto, University of Porto , Porto, Portugal. 11. Centro de Estudos do Laboratório de Aptidão Física de São Caetano do Sul (CELAFISCS) , Sao Paulo, Brazil. 12. Department of Recreation Management and Exercise Science, Kenyatta University , Nairobi, Kenya. 13. School of Medicine, Universidad de los Andes , Bogota, Colombia. 14. Department for Health, University of Bath , Bath, UK. 15. Tianjin Women's and Children's Health Center , Tianjin, China.
Abstract
OBJECTIVES: We describe the process of identifying and defining nocturnal sleep-related variables (for example, movement/non-movement indicators of sleep efficiency, waking episodes, midpoint and so on) using the unique 24-h waist-worn free-living accelerometer data collected in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). METHODS: Seven consecutive days of 24-h waist-worn accelerometer (GT3X+, ActiGraph LLC) data were collected from over 500 children at each site. An expert subgroup of the research team with accelerometry expertize, frontline data collectors and data managers met on several occasions to categorize and operationally define nocturnal accelerometer signal data patterns. The iterative process was informed by the raw data drawn from a sub set of the US data, and culminated in a refined and replicable delineated definition for each identified nocturnal sleep-related variable. Ultimately based on 6318 participants from all 12 ISCOLE sites with valid total sleep episode time (TSET), we report average clock times for nocturnal sleep onset, offset and midpoint in addition to sleep period time, TSET and restful sleep efficiency (among other derived variables). RESULTS: Nocturnal sleep onset occurred at 2218 hours and nocturnal sleep offset at 0707 hours. The mean midpoint was 0243 hours. The sleep period time of 529.6 min (8.8 h) was typically accumulated in a single episode, making the average TSET very similar in duration (529.0 min). The mean restful sleep efficiency ranged from 86.8% (based on absolute non-movement of 0 counts per minute) to 96.0% (based on relative non-movement of <100 counts per minute). CONCLUSIONS: These variables extend the potential of field-based 24-h waist-worn accelerometry to distinguish and categorize the underlying robust patterns of movement/non-movement signals conveying magnitude, duration, frequency and periodicity during the nocturnal sleep period.
OBJECTIVES: We describe the process of identifying and defining nocturnal sleep-related variables (for example, movement/non-movement indicators of sleep efficiency, waking episodes, midpoint and so on) using the unique 24-h waist-worn free-living accelerometer data collected in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). METHODS: Seven consecutive days of 24-h waist-worn accelerometer (GT3X+, ActiGraph LLC) data were collected from over 500 children at each site. An expert subgroup of the research team with accelerometry expertize, frontline data collectors and data managers met on several occasions to categorize and operationally define nocturnal accelerometer signal data patterns. The iterative process was informed by the raw data drawn from a sub set of the US data, and culminated in a refined and replicable delineated definition for each identified nocturnal sleep-related variable. Ultimately based on 6318 participants from all 12 ISCOLE sites with valid total sleep episode time (TSET), we report average clock times for nocturnal sleep onset, offset and midpoint in addition to sleep period time, TSET and restful sleep efficiency (among other derived variables). RESULTS:Nocturnal sleep onset occurred at 2218 hours and nocturnal sleep offset at 0707 hours. The mean midpoint was 0243 hours. The sleep period time of 529.6 min (8.8 h) was typically accumulated in a single episode, making the average TSET very similar in duration (529.0 min). The mean restful sleep efficiency ranged from 86.8% (based on absolute non-movement of 0 counts per minute) to 96.0% (based on relative non-movement of <100 counts per minute). CONCLUSIONS: These variables extend the potential of field-based 24-h waist-worn accelerometry to distinguish and categorize the underlying robust patterns of movement/non-movement signals conveying magnitude, duration, frequency and periodicity during the nocturnal sleep period.
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