PURPOSE: The purpose of this study was to compare the accuracy of physical activity energy expenditure (PAEE)-prediction models using accelerometry alone (ACC) and accelerometry combined with heart rate monitoring (HR+ACC) to estimate PAEE during six common activities in children (lying, sitting, slow and brisk walking, hop-scotch, running). Three PAEE-prediction models derived using the current data, and five previously published prediction models were cross-validated to estimate PAEE in this sample. METHODS: PAEE was assessed using ACC, HR+ACC, and indirect calorimetry during six activities in 145 children (12.4 +/- 0.2 yr). One ACC and two HR+ACC PAEE-prediction models were derived using linear regression on data from the current study. These three new models were cross-validated using a jackknife approach, and a modified Bland-Altman method was used to assess the validity of all eight models. RESULTS: PAEE predictions using the one ACC and two HR+ACC models derived in the current study correlated strongly with measured values (RMSE = 97.3-118.0 J.min.kg). All five previously published models agreed well overall (RMSE = 115.6-245.3 J.min.kg), but systematic error was present for most of these, to a greater extent for ACC. CONCLUSIONS: ACC and HR+ACC can both be used to predict overall PAEE during these six activities in children; however, systematic error was present in all predictions. Although both ACC and HR+ACC provide accurate predictions of overall PAEE, according to the activities in this study, PAEE-prediction models using HR+ACC may be more accurate and widely applicable than those based on accelerometry alone.
PURPOSE: The purpose of this study was to compare the accuracy of physical activity energy expenditure (PAEE)-prediction models using accelerometry alone (ACC) and accelerometry combined with heart rate monitoring (HR+ACC) to estimate PAEE during six common activities in children (lying, sitting, slow and brisk walking, hop-scotch, running). Three PAEE-prediction models derived using the current data, and five previously published prediction models were cross-validated to estimate PAEE in this sample. METHODS: PAEE was assessed using ACC, HR+ACC, and indirect calorimetry during six activities in 145 children (12.4 +/- 0.2 yr). One ACC and two HR+ACC PAEE-prediction models were derived using linear regression on data from the current study. These three new models were cross-validated using a jackknife approach, and a modified Bland-Altman method was used to assess the validity of all eight models. RESULTS: PAEE predictions using the one ACC and two HR+ACC models derived in the current study correlated strongly with measured values (RMSE = 97.3-118.0 J.min.kg). All five previously published models agreed well overall (RMSE = 115.6-245.3 J.min.kg), but systematic error was present for most of these, to a greater extent for ACC. CONCLUSIONS: ACC and HR+ACC can both be used to predict overall PAEE during these six activities in children; however, systematic error was present in all predictions. Although both ACC and HR+ACC provide accurate predictions of overall PAEE, according to the activities in this study, PAEE-prediction models using HR+ACC may be more accurate and widely applicable than those based on accelerometry alone.
Authors: N C Harvey; Z A Cole; S R Crozier; M Kim; G Ntani; L Goodfellow; S M Robinson; H M Inskip; K M Godfrey; E M Dennison; N Wareham; U Ekelund; C Cooper Journal: Osteoporos Int Date: 2011-05-12 Impact factor: 4.507
Authors: K Corder; E M F van Sluijs; R M Steele; A M Stephen; V Dunn; D Bamber; I Goodyer; S J Griffin; U Ekelund Journal: Br J Nutr Date: 2011-01 Impact factor: 3.718
Authors: Kirsten Corder; Esther Mf van Sluijs; Ian Goodyer; Charlotte L Ridgway; Rebekah M Steele; Diane Bamber; Valerie Dunn; Simon J Griffin; Ulf Ekelund Journal: Arch Pediatr Adolesc Med Date: 2011-07-01