Literature DB >> 24195866

Prediction of energy expenditure and physical activity in preschoolers.

Nancy F Butte1, William W Wong, Jong Soo Lee, Anne L Adolph, Maurice R Puyau, Issa F Zakeri.   

Abstract

PURPOSE: Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for the prediction of EE using room calorimetry and doubly labeled water (DLW) and established accelerometry cut points for PA levels.
METHODS: Fifty preschoolers, mean ± SD age of 4.5 ± 0.8 yr, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+), and HR (Actiheart). Free-living 105 children, ages 4.6 ± 0.9 yr, completed the 7-d DLW procedure while wearing the devices. AC cut points for PA levels were established using smoothing splines and receiver operating characteristic curves.
RESULTS: On the basis of calorimetry, mean percent errors for EE were -2.9% ± 10.8% and -1.1% ± 7.4% for CSTS models and -1.9% ± 9.6% and 1.3% ± 8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. On the basis of DLW, mean percent errors were -0.5% ± 9.7% and 4.1% ± 8.5% for CSTS models and 3.2% ± 10.1% and 7.5% ± 10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut points were determined: 41, 449, and 1297 cpm for Actiheart x-axis; 820, 3908, and 6112 cpm for ActiGraph vector magnitude; and 240, 2120, and 4450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA), respectively. On the basis of confusion matrices, correctly classified rates were 81%-83% for sedentary PA, 58%-64% for light PA, and 62%-73% for MVPA.
CONCLUSIONS: The lack of bias and acceptable limits of agreement affirms the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut points are satisfactory for the classification of sedentary, light, and moderate/vigorous levels of PA in preschoolers.

Entities:  

Mesh:

Year:  2014        PMID: 24195866      PMCID: PMC4010568          DOI: 10.1249/MSS.0000000000000209

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  30 in total

1.  An objective method for measurement of sedentary behavior in 3- to 4-year olds.

Authors:  John J Reilly; Jennifer Coyle; Louise Kelly; Genevieve Burke; Stanley Grant; James Y Paton
Journal:  Obes Res       Date:  2003-10

2.  Validation of uniaxial and triaxial accelerometers for the assessment of physical activity in preschool children.

Authors:  Anne L Adolph; Maurice R Puyau; Firoz A Vohra; Theresa A Nicklas; Issa F Zakeri; Nancy F Butte
Journal:  J Phys Act Health       Date:  2011-12-27

3.  Validation and calibration of the Actical accelerometer in preschool children.

Authors:  Karin A Pfeiffer; Kerry L McIver; Marsha Dowda; Maria J C A Almeida; Russell R Pate
Journal:  Med Sci Sports Exerc       Date:  2006-01       Impact factor: 5.411

4.  A concordance correlation coefficient to evaluate reproducibility.

Authors:  L I Lin
Journal:  Biometrics       Date:  1989-03       Impact factor: 2.571

5.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

6.  Children's Activity Rating Scale (CARS): description and calibration.

Authors:  J Puhl; K Greaves; M Hoyt; T Baranowski
Journal:  Res Q Exerc Sport       Date:  1990-03       Impact factor: 2.500

7.  Combining accelerometry and HR for assessing preschoolers' physical activity.

Authors:  Freia De Bock; Jochen Menze; Simone Becker; David Litaker; Joachim Fischer; Ilka Seidel
Journal:  Med Sci Sports Exerc       Date:  2010-12       Impact factor: 5.411

8.  Evaluation of the Children's Activity Rating Scale (CARS) in young children.

Authors:  R H DuRant; T Baranowski; J Puhl; T Rhodes; H Davis; K A Greaves; W O Thompson
Journal:  Med Sci Sports Exerc       Date:  1993-12       Impact factor: 5.411

9.  Triaxial accelerometry for assessment of physical activity in young children.

Authors:  Chiaki Tanaka; Shigeho Tanaka; Junko Kawahara; Taishi Midorikawa
Journal:  Obesity (Silver Spring)       Date:  2007-05       Impact factor: 5.002

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

Authors:  Issa F Zakeri; Anne L Adolph; Maurice R Puyau; Firoz A Vohra; Nancy F Butte
Journal:  J Nutr       Date:  2012-11-28       Impact factor: 4.798

View more
  64 in total

1.  Both increases and decreases in energy density lead to sustained changes in preschool children's energy intake over 5 days.

Authors:  Alissa D Smethers; Liane S Roe; Christine E Sanchez; Faris M Zuraikat; Kathleen L Keller; Barbara J Rolls
Journal:  Physiol Behav       Date:  2019-03-01

2.  Revision of Dietary Reference Intakes for energy in preschool-age children.

Authors:  Nancy F Butte; William W Wong; Theresa A Wilson; Anne L Adolph; Maurice R Puyau; Issa F Zakeri
Journal:  Am J Clin Nutr       Date:  2014-05-07       Impact factor: 7.045

3.  Portion size has sustained effects over 5 days in preschool children: a randomized trial.

Authors:  Alissa D Smethers; Liane S Roe; Christine E Sanchez; Faris M Zuraikat; Kathleen L Keller; Samantha M R Kling; Barbara J Rolls
Journal:  Am J Clin Nutr       Date:  2019-05-01       Impact factor: 7.045

4.  European Obesity Summit (EOS) - Joint Congress of EASOand IFSO-EC, Gothenburg, Sweden, June 1 - 4, 2016: Abstracts.

Authors: 
Journal:  Obes Facts       Date:  2016-05-25       Impact factor: 3.942

5.  Design and methodology of a cluster-randomized trial in early care and education centers to meet physical activity guidelines: Sustainability via Active Garden Education (SAGE).

Authors:  Rebecca E Lee; Elizabeth Lorenzo; Jacob Szeszulski; Anel Arriola; Meg Bruening; Paul A Estabrooks; Jennie Hill; Flavio F Marsiglia; Teresia O'Connor; Kim Sellers Pollins; Gabriel Q Shaibi; Erica Soltero; Michael Todd
Journal:  Contemp Clin Trials       Date:  2018-12-12       Impact factor: 2.226

6.  Role of physical activity and sleep duration in growth and body composition of preschool-aged children.

Authors:  Nancy F Butte; Maurice R Puyau; Theresa A Wilson; Yan Liu; William W Wong; Anne L Adolph; Issa F Zakeri
Journal:  Obesity (Silver Spring)       Date:  2016-04-18       Impact factor: 5.002

7.  Evaluation of the wrist-worn ActiGraph wGT3x-BT for estimating activity energy expenditure in preschool children.

Authors:  C Delisle Nyström; J Pomeroy; P Henriksson; E Forsum; F B Ortega; R Maddison; J H Migueles; M Löf
Journal:  Eur J Clin Nutr       Date:  2017-07-26       Impact factor: 4.016

8.  Effect of a Behavioral Intervention for Underserved Preschool-Age Children on Change in Body Mass Index: A Randomized Clinical Trial.

Authors:  Shari L Barkin; William J Heerman; Evan C Sommer; Nina C Martin; Maciej S Buchowski; David Schlundt; Eli K Po'e; Laura E Burgess; Juan Escarfuller; Charlotte Pratt; Kimberly P Truesdale; June Stevens
Journal:  JAMA       Date:  2018-08-07       Impact factor: 56.272

9.  Sociodemographic Predictors of Adherence to National Diet and Physical Activity Guidelines at Age 5 Years: The Healthy Start Study.

Authors:  Traci A Bekelman; Katherine A Sauder; Bonny Rockette-Wagner; Deborah H Glueck; Dana Dabelea
Journal:  Am J Health Promot       Date:  2020-10-29

Review 10.  Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations.

Authors:  Jairo H Migueles; Cristina Cadenas-Sanchez; Ulf Ekelund; Christine Delisle Nyström; Jose Mora-Gonzalez; Marie Löf; Idoia Labayen; Jonatan R Ruiz; Francisco B Ortega
Journal:  Sports Med       Date:  2017-09       Impact factor: 11.136

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.