PURPOSE: To derive a regression equation that estimates metabolic equivalent (MET) from accelerometer counts, and to define thresholds of accelerometer counts that can be used to delineate sedentary, light, moderate, and vigorous activity in adolescent girls. METHODS: Seventy-four healthy 8th grade girls, age 13 - 14 yr, were recruited from urban areas of Baltimore, MD, Minneapolis/St. Paul, MN, and Columbia, SC, to participate in the study. Accelerometer and oxygen consumption (.-)VO(2)) data for 10 activities that varied in intensity from sedentary (e.g., TV watching) to vigorous (e.g., running) were collected. While performing these activities, the girls wore two accelerometers, a heart rate monitor and a Cosmed K4b2 portable metabolic unit for measurement of (.-)VO(2). A random-coefficients model was used to estimate the relationship between accelerometer counts and (.-)VO(2). Activity thresholds were defined by minimizing the false positive and false negative classifications. RESULTS: The activities provided a wide range in (.-)VO(2) (3 - 36 mL x kg x min) with a correspondingly wide range in accelerometer counts (1- 3928 counts x 30 s). The regression line for MET score versus counts was MET = 2.01 +/- 0.00171 (counts x 30 s) (mixed model R = 0.84, SEE = 1.36). A threshold of 1500 counts x 30 s defined the lower end of the moderate intensity (approximately 4.6 METs) range of physical activity. That cutpoint distinguished between slow and brisk walking, and gave the lowest number of false positive and false negative classifications. The threshold ranges for sedentary, light, moderate, and vigorous physical activity were found to be 0 - 50, 51- 1499, 1500 - 2600, and >2600 counts x 30 s, respectively. CONCLUSION: The developed equation and these activity thresholds can be used for prediction of MET score from accelerometer counts and participation in various intensities of physical activity in adolescent girls.
PURPOSE: To derive a regression equation that estimates metabolic equivalent (MET) from accelerometer counts, and to define thresholds of accelerometer counts that can be used to delineate sedentary, light, moderate, and vigorous activity in adolescent girls. METHODS: Seventy-four healthy 8th grade girls, age 13 - 14 yr, were recruited from urban areas of Baltimore, MD, Minneapolis/St. Paul, MN, and Columbia, SC, to participate in the study. Accelerometer and oxygen consumption (.-)VO(2)) data for 10 activities that varied in intensity from sedentary (e.g., TV watching) to vigorous (e.g., running) were collected. While performing these activities, the girls wore two accelerometers, a heart rate monitor and a Cosmed K4b2 portable metabolic unit for measurement of (.-)VO(2). A random-coefficients model was used to estimate the relationship between accelerometer counts and (.-)VO(2). Activity thresholds were defined by minimizing the false positive and false negative classifications. RESULTS: The activities provided a wide range in (.-)VO(2) (3 - 36 mL x kg x min) with a correspondingly wide range in accelerometer counts (1- 3928 counts x 30 s). The regression line for MET score versus counts was MET = 2.01 +/- 0.00171 (counts x 30 s) (mixed model R = 0.84, SEE = 1.36). A threshold of 1500 counts x 30 s defined the lower end of the moderate intensity (approximately 4.6 METs) range of physical activity. That cutpoint distinguished between slow and brisk walking, and gave the lowest number of false positive and false negative classifications. The threshold ranges for sedentary, light, moderate, and vigorous physical activity were found to be 0 - 50, 51- 1499, 1500 - 2600, and >2600 counts x 30 s, respectively. CONCLUSION: The developed equation and these activity thresholds can be used for prediction of MET score from accelerometer counts and participation in various intensities of physical activity in adolescent girls.
Authors: S Y Kimm; N W Glynn; A M Kriska; S L Fitzgerald; D J Aaron; S L Similo; R P McMahon; B A Barton Journal: Med Sci Sports Exerc Date: 2000-08 Impact factor: 5.411
Authors: Daniel A Rodriguez; Gi-Hyoug Cho; John P Elder; Terry L Conway; Kelly R Evenson; Bonnie Ghosh-Dastidar; Elizabeth Shay; Deborah Cohen; Sara Veblen-Mortenson; Julie Pickrell; Leslie Lytle Journal: J Phys Act Health Date: 2011-05-11
Authors: Charlotte Pratt; Larry S Webber; Chris D Baggett; Dianne Ward; Russell R Pate; David Murray; Timothy Lohman; Leslie Lytle; John P Elder Journal: Res Q Exerc Sport Date: 2008-12 Impact factor: 2.500
Authors: Russell R Pate; June Stevens; Larry S Webber; Marsha Dowda; David M Murray; Deborah R Young; Scott Going Journal: J Adolesc Health Date: 2008-10-29 Impact factor: 5.012
Authors: Daniel R Taber; June Stevens; David M Murray; John P Elder; Larry S Webber; Jared B Jobe; Leslie A Lytle Journal: Ann Epidemiol Date: 2009-02-20 Impact factor: 3.797
Authors: Robert G McMurray; Kimberly B Ring; Margarita S Treuth; Gregory J Welk; Russell R Pate; Kathryn H Schmitz; Julie L Pickrel; Vivian Gonzalez; M Jaoa C A Almedia; Deborah Rohm Young; James F Sallis Journal: Med Sci Sports Exerc Date: 2004-12 Impact factor: 5.411
Authors: Deborah A Cohen; J Scott Ashwood; Molly M Scott; Adrian Overton; Kelly R Evenson; Lisa K Staten; Dwayne Porter; Thomas L McKenzie; Diane Catellier Journal: Pediatrics Date: 2006-11 Impact factor: 7.124