Combined influence of epoch length, cut-point and bout duration on accelerometry-derived physical activity

International Journal of Behavioral Nutrition and Physical Activity

Soren Brage²  Ulf Ekelund¹  Kate Westgate²  Stephen J Sharp²  Katrien Wijndaele²  Mark Orme³ 

[1] Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway;Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrookes Hospital, Box 285, Cambridge, CB2 0QQ, UK;School of Sport, Exercise and Health Sciences, Loughborough University, Leicestershire, UK

关键词: Objective;    Actigraph;    Wear-time;    Measurement;    Adults;    Moderate-to-vigorous;   

Others  :  803980
DOI  :  10.1186/1479-5868-11-34

 received in 2013-10-04, accepted in 2014-02-17,  发布年份 2014

【 摘 要 】

Background

It is difficult to compare accelerometer-derived estimates of moderate-to-vigorous physical activity (MVPA) between studies due to differences in data processing procedures. We aimed to evaluate the effects of accelerometer processing options on total and bout-accumulated time spent in MVPA in adults.

Methods

267 participants from the ProActive Trial provided 1236 days of valid physical activity (PA) data, collected using a 5-s epoch with ActiGraph GT1M accelerometers. We integrated data over 5-s to 60-s epoch lengths (EL) and applied two-level mixed effects regression models to MVPA time, defined using 1500 to 2500 counts/minute (cpm) cut-points (CP) and bout durations (BD) from 1 to 15 min.

Results

Total MVPA time was lower on longer EL and higher CP (47 vs 26 min/day and 26 vs 5 min/day on 1500 vs 2500 cpm on 5-s and 60-s epoch, respectively); this could be approximated as MVPA = exp[2.197 + 0.279*log(CP) + 6.120*log(EL) - 0.869*log(CP)*log(EL)] with an 800 min/day wear-time. In contrast, EL was positively associated with time spent in bout-accumulated MVPA; the approximating equation being MVPA = exp[54.679 - 6.268*log(CP) + 6.387*log(EL) - 10.000*log(BD) - 0.162*log(EL)*log(BD)- 0.626*log(CP)*log(EL) + 1.033*log(CP)*log(BD)]. BD and CP were inversely associated with MVPA, with higher values attenuating the influence of EL.

Conclusions

EL, CP and BD interact to influence estimates of accelerometer-determined MVPA. In general, higher CP and longer BD result in lower MVPA but the direction of association for EL depends on BD. Reporting scaling coefficients for these key parameters across their frequently used ranges would facilitate comparisons of population-level accelerometry estimates of MVPA.

【 授权许可】

   
2014 Orme et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT: Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012, 380:219-229.
• [2]Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U: Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 2012, 380:247-257.
• [3]Knuth AG, Hallal PC: Temporal trends in physical activity: a systematic review. J Phys Act Health 2009, 6:548-559.
• [4]Sallis JF, Saelens BE: Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport 2000, 71:S1-S14.
• [5]Corder K, van Sluijs EM: Invited commentary: comparing physical activity across countries–current strengths and weaknesses. Am J Epidemiol 2010, 171:1065-1068.
• [6]Ainsworth BE, Bassett DR Jr, Strath SJ, Swartz AM, O’Brien WL, Thompson RW, Jones DA, Macera CA, Kinsey CD: Comparison of three methods for measuring the time spent in physical activity. Med Sci Sports Exerc 2000, 32:S457-S464.
• [7]Rowlands AV, Powell SN, Humpries R, Eston RG: The effect of accelerometer epoch on physical activity output measures. J Exerc Sci Fitness 2006, 4:51-57.
• [8]Nilsson A, Ekelund U, Agneta Y, Sjostrom M: Assessing physical activity among children with accelerometers using different time sampling intervals and placements. Ped Exerc Sci 2002, 14:87-96.
• [9]Bailey RC, Olson J, Pepper SL, Porszasz J, Barstow TJ, Cooper DM: The level and tempo of children’s physical activities: an observational study. Med Sci Sports Exerc 1995, 27:1033-1041.
• [10]Gabriel KP, McClain JJ, Schmid KK, Storti KL, High RR, Underwood DA, Kuller LH, Kriska AM: Issues in accelerometer methodology: the role of epoch length on estimates of physical activity and relationships with health outcomes in overweight, post-menopausal women. Int J Behav Nutr Phys Act 2010, 7:53-57. doi: 10.1186/1479-5868-7-53
• [11]Matthew CE: Calibration of accelerometer output for adults. Med Sci Sports Exerc 2005, 37:S512-S522.
• [12]Freedson PS, Melanson E, Sirard J: Calibration of the Computer Science and Applications. Inc. accelerometer. Med Sci Sports Exerc 1998, 30:777-781.
• [13]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, 40:181-188.
• [14]Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A: Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007, 39:1423-1434.
• [15]Cook I, Alberts M, Lambert EV: Influence of cut-points on patterns of accelerometry-measured free-living physical activity in rural and urban black South African women. J Phys Act Health 2012, 9:300-310.
• [16]Nicaise V, Marshall S, Ainsworth BE: Domain-specific physical activity and self-report bias among low-income Latinas living in San Diego County. J Phys Act Health 2011, 8:881-890.
• [17]Strath SJ, Holleman RG, Ronis DL, Swartz AM, Richardson CR: Objective physical activity accumulation in bouts and nonbouts and relation to markers of obesity in US adults. Prev Chronic Dis 2008, 5:A131.
• [18]Ayabe M, Kumahara H, Morimura K, Ishii K, Sakane N, Tanaka H: Very short bouts of non-exercise physical activity associated with metabolic syndrome under free-living conditions in Japanese female adults. Eur J Appl Physiol 2012, 112:3525-3532.
• [19]Williams K, Prevost AT, Griffin S, Hardeman W, Hollingworth W, Spiegelhalter D, Sutton S, Ekelund U, Wareham N, Kinmonth AL: The ProActive trial protocol - a randomised controlled trial of the efficacy of a family-based, domiciliary intervention programme to increase physical activity among individuals at high risk of diabetes [ISRCTN61323766]. BMC Public Health 2004, 4:48.
• [20]Catenacci VA, Grunwald GK, Ingebrigtsen JP, Jakicic JM, McDermott MD, Phelan S, Wing RR, Hill JO, Wyatt HR: Physical activity patterns using accelerometry in the National Weight Control Registry. Obesity (Silver Spring) 2011, 19:1163-1170.
• [21]Evenson KR, Buchner DM, Morland KB: Objective measurement of physical activity and sedentary behavior among US adults aged 60 years or older. Prev Chronic Dis 2012, 9:E26. Epub@2011 Dec 15.: E26
• [22]Pettee Gabriel K, McClain JJ, High RR, Schmid KK, Whitfield GP, Ainsworth BE: Patterns of accelerometer-derived estimates of inactivity in middle-age women. Med Sci Sports Exerc 2012, 44:104-110.
• [23]Schmidt MD, Blizzard CL, Venn AJ, Cochrane JA, Dwyer T: Practical considerations when using pedometers to assess physical activity in population studies: lessons from the Burnie Take Heart Study. Res Q Exerc Sport 2007, 78:162-170.
• [24]Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P: An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009, 107:1300-1307.