Predicting energy expenditure from accelerometry counts in adolescents girls

Kathryn Schmitz, Margarita Treuth, Peter Hannan, Robert Mcmurray, Kimberly B. Ring, Diane Catellier, Russ Pate

Research output: Contribution to journalArticle

81 Citations (Scopus)

Abstract

Purpose: Calibration of accelerometer counts against oxygen consumption to predict energy expenditure has not been conducted in middle school girls. We concurrently assessed energy expenditure and accelerometer counts during physical activities on adolescent girls to develop an equation to predict energy expenditure. Methods: Seventy-four girls aged 13-14 yr performed 10 activities while wearing an Actigraph accelerometer and a portable metabolic measurement unit (Cosmed K4b2). The activities were resting, watching television, playing a computer game, sweeping, walking 2.5 and 3.5 mph, performing step aerobics, shooting a basketball, climbing stairs, and running 5 mph. Height and weight were also assessed. Mixed-model regression was used to develop an equation to predict energy expenditure (EE) (kJ·min -1 ) from accelerometer counts. Results: Age (mean [SD] = 14 yr [0.34]) and body-weight-adjusted correlations of accelerometer counts with EE (kJ·min -1 ) for individual activities ranged from -0.14 to 0.59. Higher intensity activities with vertical motion were best correlated. A regression model that explained 85% of the variance of EE was developed: [EE (kJ·m -1 ) = 7.6628 + 0.1462 [(Actigraph counts per minute - 3000)/100] + 0.2371 (body weight in kilograms) - 0.00216 [(Actigraph counts per minute - 3000)/100] 2 + 0.004077 [((Actigraph counts per minute - 3000)/100) x (body weight in kilograms)]. The MCCC = 0.85, with a standard error of estimate = 5.61 kJ·min -1 . Conclusions: We developed a prediction equation for kilojoules per minute of energy expenditure from Actigraph accelerometer counts. This equation may be most useful for predicting energy expenditure in groups of adolescent girls over a period of time that will include activities of broad-ranging intensity, and may be useful to intervention researchers interested in objective measures of physical activity.

Original languageEnglish (US)
Pages (from-to)155-161
Number of pages7
JournalMedicine and Science in Sports and Exercise
Volume37
Issue number1
DOIs
StatePublished - Jan 1 2005

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Accelerometry
Energy Metabolism
Body Weight
Exercise
Basketball
Video Games
Television
Oxygen Consumption
Running
Calibration
Walking
Research Personnel
Weights and Measures

All Science Journal Classification (ASJC) codes

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation

Cite this

Schmitz, Kathryn ; Treuth, Margarita ; Hannan, Peter ; Mcmurray, Robert ; Ring, Kimberly B. ; Catellier, Diane ; Pate, Russ. / Predicting energy expenditure from accelerometry counts in adolescents girls. In: Medicine and Science in Sports and Exercise. 2005 ; Vol. 37, No. 1. pp. 155-161.
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abstract = "Purpose: Calibration of accelerometer counts against oxygen consumption to predict energy expenditure has not been conducted in middle school girls. We concurrently assessed energy expenditure and accelerometer counts during physical activities on adolescent girls to develop an equation to predict energy expenditure. Methods: Seventy-four girls aged 13-14 yr performed 10 activities while wearing an Actigraph accelerometer and a portable metabolic measurement unit (Cosmed K4b2). The activities were resting, watching television, playing a computer game, sweeping, walking 2.5 and 3.5 mph, performing step aerobics, shooting a basketball, climbing stairs, and running 5 mph. Height and weight were also assessed. Mixed-model regression was used to develop an equation to predict energy expenditure (EE) (kJ·min -1 ) from accelerometer counts. Results: Age (mean [SD] = 14 yr [0.34]) and body-weight-adjusted correlations of accelerometer counts with EE (kJ·min -1 ) for individual activities ranged from -0.14 to 0.59. Higher intensity activities with vertical motion were best correlated. A regression model that explained 85{\%} of the variance of EE was developed: [EE (kJ·m -1 ) = 7.6628 + 0.1462 [(Actigraph counts per minute - 3000)/100] + 0.2371 (body weight in kilograms) - 0.00216 [(Actigraph counts per minute - 3000)/100] 2 + 0.004077 [((Actigraph counts per minute - 3000)/100) x (body weight in kilograms)]. The MCCC = 0.85, with a standard error of estimate = 5.61 kJ·min -1 . Conclusions: We developed a prediction equation for kilojoules per minute of energy expenditure from Actigraph accelerometer counts. This equation may be most useful for predicting energy expenditure in groups of adolescent girls over a period of time that will include activities of broad-ranging intensity, and may be useful to intervention researchers interested in objective measures of physical activity.",
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Predicting energy expenditure from accelerometry counts in adolescents girls. / Schmitz, Kathryn; Treuth, Margarita; Hannan, Peter; Mcmurray, Robert; Ring, Kimberly B.; Catellier, Diane; Pate, Russ.

In: Medicine and Science in Sports and Exercise, Vol. 37, No. 1, 01.01.2005, p. 155-161.

Research output: Contribution to journalArticle

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AB - Purpose: Calibration of accelerometer counts against oxygen consumption to predict energy expenditure has not been conducted in middle school girls. We concurrently assessed energy expenditure and accelerometer counts during physical activities on adolescent girls to develop an equation to predict energy expenditure. Methods: Seventy-four girls aged 13-14 yr performed 10 activities while wearing an Actigraph accelerometer and a portable metabolic measurement unit (Cosmed K4b2). The activities were resting, watching television, playing a computer game, sweeping, walking 2.5 and 3.5 mph, performing step aerobics, shooting a basketball, climbing stairs, and running 5 mph. Height and weight were also assessed. Mixed-model regression was used to develop an equation to predict energy expenditure (EE) (kJ·min -1 ) from accelerometer counts. Results: Age (mean [SD] = 14 yr [0.34]) and body-weight-adjusted correlations of accelerometer counts with EE (kJ·min -1 ) for individual activities ranged from -0.14 to 0.59. Higher intensity activities with vertical motion were best correlated. A regression model that explained 85% of the variance of EE was developed: [EE (kJ·m -1 ) = 7.6628 + 0.1462 [(Actigraph counts per minute - 3000)/100] + 0.2371 (body weight in kilograms) - 0.00216 [(Actigraph counts per minute - 3000)/100] 2 + 0.004077 [((Actigraph counts per minute - 3000)/100) x (body weight in kilograms)]. The MCCC = 0.85, with a standard error of estimate = 5.61 kJ·min -1 . Conclusions: We developed a prediction equation for kilojoules per minute of energy expenditure from Actigraph accelerometer counts. This equation may be most useful for predicting energy expenditure in groups of adolescent girls over a period of time that will include activities of broad-ranging intensity, and may be useful to intervention researchers interested in objective measures of physical activity.

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