Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

Journal of Nutrition

Volumn 143, Issue 1, 2013, Pages 114-122

  Zakeri, Issa ^a   Adolph, Anne ^b   Puyau, Maurice ^b   Vohra, Firoz A ^b   Butte, Nancy F ^b  

^a DREXEL UNIVERSITY   (United States)

^b BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELEROMETRY; ANTHROPOMETRIC PARAMETERS; ARTICLE; CALORIMETRY; CHILD; ENERGY EXPENDITURE; FEMALE; HEART RATE; HUMAN; MALE; MATHEMATICAL MODEL; NORMAL HUMAN; PHYSICAL ACTIVITY; PRESCHOOL CHILD; RECREATION; SLEEP; TIME SERIES ANALYSIS;

ACCELEROMETRY; BODY MASS INDEX; CALORIMETRY; CHILD BEHAVIOR; CHILD DEVELOPMENT; CHILD, PRESCHOOL; CROSS-SECTIONAL STUDIES; ENERGY METABOLISM; FEMALE; HEART RATE; HUMANS; MALE; MODELS, BIOLOGICAL; MONITORING, AMBULATORY; MOTOR ACTIVITY; MULTIVARIATE ANALYSIS; OXYGEN CONSUMPTION; REPRODUCIBILITY OF RESULTS; RESPIRATION; TEXAS;

EID: 84872189980     PISSN: 00223166     EISSN: 15416100     Source Type: Journal    
DOI: 10.3945/jn.112.168542     Document Type: Article

References (28)

1. Pate RR, Almeida MJ, McIver KL, Pfeiffer KA, Dowda M. Validation and calibration of an accelerometer in preschool children. Obesity (Silver Spring). 2006;14:2000-6.
2. Pfeiffer KA, McIver KL, Dowda M, Almeida MJ, Pate RR. Validation and calibration of the Actical accelerometer in preschool children. Med Sci Sports Exerc. 2006;38:152-7.
3. Tanaka C, Tanaka S, Kawahara J, Midorikawa T. Triaxial accelerometry for assessment of physical activity in young children. Obesity (Silver Spring). 2007;15:1233-41.
4. Adolph AL, Puyau MR, Vohra FA, Nicklas TA, Zakeri IF, Butte NF. Validation of uniaxial and triaxial accelerometers for the assessment of physical activity in preschool children. J Phys Act Health. 2012;7:944-53.
5. Lopez-Alarcon M, Merrifield J, Fields DA, Hilario-Hailey T, Franklin FA, Shewchuk RM, Oster RA, Gower BA. Ability of the Actiwatch accelerometer to predict free-living energy expenditure in young children. Obes Res. 2004;12:1859-65.
6. Montgomery C, Reilly JJ, Jackson DM, Kelly LA, Slater C, Paton JY, Grant S. Relation between physical activity and energy expenditure in a representative sample of young children. Am J Clin Nutr. 2004;80: 591-6.
7. Reilly JJ, Jackson DM, Montgomery C, Kelly LA, Slater C, Grant S, Paton JY. Total energy expenditure and physical activity in young Scottish children: mixed longitudinal study. Lancet. 2004;363:211-2.
8. Reilly JJ, Kelly LA, Montgomery C, Jackson DM, Slater C, Grant S, Paton JY. Validation of Actigraph accelerometer estimates of total energy expenditure in young children. Int J Pediatr Obes. 2006;1:161-7.
9. Oliver M, Schofield GM, Kolt GS. Physical activity in preschoolers: understanding prevalence and measurement issues. Sports Med. 2007;37:1045-70.
10. 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;27:1033-41.
11. Finn KJ, Specker BL. Comparison of Actiwatch activity monitor and Children's Activity Rating Scale in children. Med Sci Sports Exerc. 2000;32:1794-7.
12. van Cauwenberghe E, Labarque V, Trost SG, de Bourdeaudhuij I, Cardon G. Calibration and comparison of accelerometer cut points in preschool children. Int J Pediatr Obes. 2011;6:e582-9.
13. Reilly JJ, Coyle J, Kelly L, Burke G, Grant S, Paton JY. An objective method for measurement of sedentary behavior in 3- to 4-year olds. Obes Res. 2003;11:1155-8.
14. Trost SG, Fees BS, Haar SJ, Murray AD, Crowe LK. Identification and validity of accelerometer cut-points for toddlers. Obesity (Silver Spring). 2012;20:2317-9.
15. Zakeri I, Adolph AL, Puyau MR, Vohra FA, Butte NF. Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry. J Appl Physiol. 2008;104: 1665-73.
16. Zakeri IF, Adolph AL, Puyau MR, Vohra FA, Butte NF. Multivariate adaptive regression splines (MARS) models for the prediction of energy expenditure in children and adolescents. J Appl Physiol. 2010;108:128-36.
17. Butte NF, Wong WW, Adolph AL, Puyau MR, Vohra FA, Zakeri IF. Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water. J Nutr. 2010;140: 1516-23.
18. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, Mei Z, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Advance data from vital and health statistics. Adv Data. 2000;314:1-27.
19. Moon JK, Vohra FA, Valerio Jimenez OS, Puyau MR., Butte NF. Closedloop control of carbon dioxide concentration and pressure improves response of room respiration calorimeters. J Nutr. 1995;125:220-8.
20. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol. 1949;109:1-9.
21. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307-10.
22. Krippendorff K. Bivariate agreement coefficients for reliability of data. 2nd ed. San Francisco: Jossey Bass; 1970.
23. Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255-68.
24. Lin L. A note on the concordance correlation coefficient. Biometrics. 2000;56:324-5.
25. Eston RG, Rowlands AV, Ingledew DK. Validity of heart rate pedometry, and accelerometry for predicting the energy cost of children's activities. J Appl Physiol. 1998;84:362-71.
26. Diggle PJ. An approach to the analysis of repeated measurements. Biometrics. 1988;44:959-71.
27. Hsiao C. Analysis of panel data. Cambridge (UK): Cambridge University Press; 2004.
28. Friedman JH. Multivariate adaptive regression splines (with discussion). Ann Stat. 1991;19:1-67