Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE)

Springer Science and Business Media LLC - Tập 12 - Trang 1-9 - 2015
Catrine Tudor-Locke1, Tiago V Barreira1,2, John M Schuna1,3, Emily F Mire1, Jean-Philippe Chaput4, Mikael Fogelholm5, Gang Hu1, Rebecca Kuriyan6, Anura Kurpad6, Estelle V Lambert7, Carol Maher8, José Maia9, Victor Matsudo10, Tim Olds8, Vincent Onywera11, Olga L Sarmiento12, Martyn Standage13, Mark S Tremblay4, Pei Zhao14, Timothy S Church1, Peter T Katzmarzyk1
1Pennington Biomedical Research Center, Baton Rouge, USA
2Syracuse University, Syracuse, USA
3Oregon State University, Corvallis, USA
4Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada
5University of Helsinki, Helsinki, Finland
6St. Johns Research Institute, Bangalore, India
7University of Cape Town, Cape Town, South Africa
8University of South Australia, Adelaide, Australia
9CIFI2D, Faculdade de Desporto, University of Porto, Porto, Portugal
10Center of Studies of the Physical Fitness Research Laboratory from Sao Caetano do Sul (CELAFISCS), Sao Paulo, Brazil
11Kenyatta University, Nairobi, Kenya
12School of Medicine, Universidad de los Andes, Bogota, Colombia
13University of Bath, Bath, UK
14Tianjin Women's and Children's Health Center, Tianjin, China

Tóm tắt

We compared 24-hour waist-worn accelerometer wear time characteristics of 9–11 year old children in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) to similarly aged U.S. children providing waking-hours waist-worn accelerometer data in the 2003–2006 National Health and Nutrition Examination Survey (NHANES). Valid cases were defined as having ≥4 days with ≥10 hours of waking wear time in a 24-hour period, including one weekend day. Previously published algorithms for extracting total sleep episode time from 24-hour accelerometer data and for identifying wear time (in both the 24-hour and waking-hours protocols) were applied. The number of valid days obtained and a ratio (percent) of valid cases to the number of participants originally wearing an accelerometer were computed for both ISCOLE and NHANES. Given the two surveys’ discrepant sampling designs, wear time (minutes/day, hours/day) from U.S. ISCOLE was compared to NHANES using a meta-analytic approach. Wear time for the 11 additional countries participating in ISCOLE were graphically compared with NHANES. 491 U.S. ISCOLE children (9.92±0.03 years of age [M±SE]) and 586 NHANES children (10.43 ± 0.04 years of age) were deemed valid cases. The ratio of valid cases to the number of participants originally wearing an accelerometer was 76.7% in U.S. ISCOLE and 62.6% in NHANES. Wear time averaged 1357.0 ± 4.2 minutes per 24-hour day in ISCOLE. Waking wear time was 884.4 ± 2.2 minutes/day for U.S. ISCOLE children and 822.6 ± 4.3 minutes/day in NHANES children (difference = 61.8 minutes/day, p < 0.001). Wear time characteristics were consistently higher in all ISCOLE study sites compared to the NHANES protocol. A 24-hour waist-worn accelerometry protocol implemented in U.S. children produced 22.6 out of 24 hours of possible wear time, and 61.8 more minutes/day of waking wear time than a similarly implemented and processed waking wear time waist-worn accelerometry protocol. Consistent results were obtained internationally. The 24-hour protocol may produce an important increase in wear time compliance that also provides an opportunity to study the total sleep episode time separate and distinct from physical activity and sedentary time detected during waking-hours. ClinicalTrials.gov NCT01722500 .

Tài liệu tham khảo

Katzmarzyk PT, Barreira TV, Broyles ST, Champagne CM, Chaput J-P, Fogelholm M, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health. 2013;13:900.

Tudor-Locke C, Johnson WD, Katzmarzyk PT. U.S. population profile of time-stamped accelerometer outputs: impact of wear time. J Phys Act Health. 2011;8:693–8.

Herrmann SD, Barreira TV, Kang M, Ainsworth BE. How many hours are enough? Accelerometer wear time may provide bias in daily activity estimates. J Phys Act Health. 2012;10(5):742–9.

Matthews CE, Hagstromer M, Pober DM, Bowles HR. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc. 2012;44(1 Suppl 1):S68–76. doi: 10.1249/MSS.0b013e3182399e5b.

Rosenberger ME, Haskell WL, Albinali F, Mota S, Nawyn J, Intille S. Estimating activity and sedentary behavior from an accelerometer on the hip or wrist. Med Sci Sports Exerc. 2013;45(5):964–75. doi:10.1249/MSS.0b013e31827f0d9c.

Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44(10):2009–16. doi:10.1249/MSS.0b013e318258cb36.

Tudor-Locke C, Camhi SM, Troiano RP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and Nutrition Examination Survey, 2003–2006. Prev Chronic Dis. 2012;9:E113.

Tudor-Locke C, Johnson WD, Katzmarzyk PT. Accelerometer-determined steps per day in US adults. Med Sci Sports Exerc. 2009;41(7):1384–91. doi:10.1249/MSS.0b013e318199885c.

Scholle S, Beyer U, Bernhard M, Eichholz S, Erler T, Graness P, et al. Normative values of polysomnographic parameters in childhood and adolescence: quantitative sleep parameters. Sleep Med. 2011;12(6):542–9. doi:10.1016/j.sleep.2010.11.011.

Tudor-Locke C, Barreira TV, Schuna Jr JM, Mire EF, Katzmarzyk PT. Fully automated waist-worn accelerometer algorithm for detecting children’s sleep period time separate from 24-hour physical activity or sedentary behaviors. Appl Physiol Nutr Metabol. 2014;39(1):53–7.

Barreira TV, Schuna JM, Jr., Mire EF, Katzmarzyk PT, Chaput JP, Leduc G et al. Identifying children’s nocturnal sleep using 24-hour waist accelerometry. Med Sci Sports Exerc. in press.

Mark AE, Janssen I. Dose–response relation between physical activity and blood pressure in youth. Med Sci Sports Exerc. 2008;40(6):1007–12. doi:10.1249/MSS.0b013e318169032d.

Masse LC, Fuemmeler BF, Anderson CB, Matthews CE, Trost SG, Catellier DJ, et al. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005;37(11 Suppl):S544–54.

Herrmann SD, Barreira TV, Kang M, Ainsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semisimulation data approach. Br J Sports Med. 2012. doi:10.1136/bjsports-2012-091410

Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64. doi:10.1249/MSS.0b013e3181ed61a3.

Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8. doi:10.1249/mss.0b013e31815a51b3.

Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br J Sports Med. 2014;48(13):1019–23. doi:10.1136/bjsports-2014-093546.

Kinder JR, Lee KA, Thompson H, Hicks K, Topp K, Madsen KA. Validation of a hip-worn accelerometer in measuring sleep time in children. J Pediatr Nurs. 2012;27(2):127–33. doi:10.1016/j.pedn.2010.11.004.

Rowlands AV, Olds TS, Hillsdon M, Pulsford R, Hurst TL, Eston RG et al. Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere. Med Sci Sports Exerc. 2013. doi:10.1249/MSS.0000000000000224.

Swartz AM, Strath SJ, Bassett Jr DR, O’Brien WL, King GA, Ainsworth BE. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc. 2000;32(9 Suppl):S450–6.

Tudor-Locke C, Barreira TV, Schuna JM, Jr. Comparison of step outputs for waist and wrist accelerometer attachment sites. Med Sci Sports Exerc. in press. doi:10.1249/MSS.0000000000000476.

Thông tin
: []
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 12

1-9

Thông tin tác giả