Dataset Information

Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

ABSTRACT: The objective of this study is to determine the effect of body mass index (BMI) on level of agreement between six previously established prediction equations for three commonly used accelerometers to predict summary measures of energy expenditure (EE) in youth.One hundred and thirty-one youth between the ages of 10-17 yr and BMI from 15 to 44 kg·m were outfitted with hip-worn ActiGraph GT1M (Pensacola, FL), Actical (MiniMiter/Respironics, Bend, OR), and RT3 (StayHealthy, Monrovia, CA) accelerometers and spent approximately 24 h in a whole-room indirect calorimeter while performing structured and self-selected activities. Five commonly used regression and one propriety equations for each device were used to predict the minute-to-minute EE (normalized to METs), daily physical activity level (PAL), and time spent in sedentary, light, moderate, and vigorous physical activity intensity categories. The calculated values were compared with criterion measurements obtained from the room calorimeter.All predictive equations, except RT3, significantly over- or underpredicted daily PAL (P < 0.001), with large discrepancies observed in the estimate of sedentary and light activity. Discrepancies between actual and estimated PAL ranged from 0.05 to 0.68. In addition, BMI represented a modifier for two ActiGraph predictive equations (AG1 and AG2), affecting the accuracy of physical activity-related EE predictions.ActiGraph (AG3) and the RT3 closely predicted overall PAL (within 4.2% and 6.8%, respectively) as a group. When adjusting for age, sex, and ethnicity, Actical (AC1 and AC2) and ActiGraph (AG3) were not influenced by BMI. However, a gap between some hip-worn accelerometer predictive and regression equations was demonstrated compared with both criterion measurement and each other, which poses a potential difficulty for interstudy (e.g., different accelerometers) and intrastudy (e.g., BMI and adiposity) comparisons.

SUBMITTER: Warolin J

PROVIDER: S-EPMC3501581 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

Warolin Joshua J Carrico Amanda R AR Whitaker Lauren E LE Wang Li L Chen Kong Y KY Acra Sari S Buchowski Maciej S MS

Medicine and science in sports and exercise 20121201 12

<h4>Purpose</h4>The objective of this study is to determine the effect of body mass index (BMI) on level of agreement between six previously established prediction equations for three commonly used accelerometers to predict summary measures of energy expenditure (EE) in youth.<h4>Methods</h4>One hundred and thirty-one youth between the ages of 10-17 yr and BMI from 15 to 44 kg·m were outfitted with hip-worn ActiGraph GT1M (Pensacola, FL), Actical (MiniMiter/Respironics, Bend, OR), and RT3 (StayH ...[more]

PMID: 22776880

Similar Datasets

Project description:BackgroundMany large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion.MethodsMeasurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m-2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation.ResultsMean TEE and AEE derived from DLW were 11.6 (2.3) MJ day-1 and 49.8 (16.3) kJ day-1 kg-1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day-1 kg-1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE ~12.2 kJ day-1 kg-1, r ~ 0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSE ~1.0 MJ day-1, r ~ 0.90).ConclusionsIn UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.

Project description: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.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.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.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.

Dataset Information

Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

Publications

Effect of BMI on prediction of accelerometry-based energy expenditure in youth.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets