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  • Author: Sarah Kozey Keadle x
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Energy Cost of Common Activities in Children and Adolescents

Kate Lyden, Sarah Kozey Keadle, John Staudenmayer, Patty Freedson, and Sofiya Alhassan

Background:

The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values.

Purpose:

To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium.

Methods:

Thirty-two children (8−11 years old) and 28 adolescents (12−16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer.

Results:

In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1−3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3−2.5 METs lower)] and 3 TRDs [2.24 m·s-1, 1.56 m·s-1, and 1.34 m·s-1 (0.4−0.8 METs lower)].

Conclusion:

The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 10 (2013): Issue 1 (Jan 2013)

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Combining Activity-Related Behaviors and Attributes Improves Prediction of Health Status in NHANES

Sarah Kozey Keadle, Shirley Bluethmann, Charles E. Matthews, Barry I. Graubard, and Frank M. Perna

Background:

This paper tested whether a physical activity index (PAI) that integrates PA-related behaviors (ie, moderate-to-vigorous physical activity [MVPA] and TV viewing) and performance measures (ie, cardiorespiratory fitness and muscle strength) improves prediction of health status.

Methods:

Participants were a nationally representative sample of US adults from 2011 to 2012 NHANES. Dependent variables (self-reported health status, multimorbidity, functional limitations, and metabolic syndrome) were dichotomized. Wald-F tests tested whether the model with all PAI components had statistically significantly higher area under the curve (AUC) values than the models with behavior or performance scores alone, adjusting for covariates and complex survey design.

Results:

The AUC (95% CI) for PAI in relation to health status was 0.72 (0.68, 0.76), and PAI-AUC for multimorbidity was 0.72 (0.69, 0.75), which were significantly higher than the behavior or performance scores alone. For functional limitations, the PAI AUC was 0.71 (0.67, 0.74), significantly higher than performance, but not behavior scores, while the PAI AUC for metabolic syndrome was 0.69 (0.66, 0.73), higher than behavior but not performance scores.

Conclusions:

These results provide empirical support that an integrated PAI may improve prediction of health and disease. Future research should examine the clinical utility of a PAI and verify these findings in prospective studies.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 14 (2017): Issue 8 (Jan 2017)

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Reexamining the Energy Cost of Sedentary Behaviors From the 2011 Adult Compendium

Rachel K. Barnett, Cory Greever, Karen Yagi, Brendan Rhoan, and Sarah Kozey Keadle

Background: This study reexamines the energy cost of lower intensity activities compared to the 2011 Adult Compendium of Physical Activities. Methods: Participants (n = 32, age = 35 [13.8] y, 16 females) wore a portable metabolic system (COSMED), during 5 different conditions: sitting quietly, watching TV, sitting while working, driving, and walking at 2.0 mph. The metabolic equivalent (MET) values (VO2 mL·kg−1·min−1/3.5 mL·kg−1·min−1) were calculated. Results: The mean (SD) MET value for driving (1.46 [0.24]) was significantly lower than the Adult Compendium value of 2.5 (P < .001). Driving and slow walking have similar Adult Compendium values, but driving METs were significantly lower than slow walking (P < .001). Driving was similar to sitting while working (1.32 [0.25] METs, P > .05) and yielded significantly higher MET values than quiet sitting (1.08 [0.23] METs, P < .001) and watching TV (1.12 [0.22] METs, P < .001), both of which were lower than their respective Adult Compendium MET values. Conclusion: Existing Adult Compendium METs are significantly higher than measured METs for driving, which more closely correspond to sedentary behaviors than slow walking. The TV and quiet sitting also differed from their Adult Compendium values, which should be updated to reflect these findings, given that researchers and practitioners rely on Adult Compendium MET values to estimate energy cost.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 18 (2021): Issue 2 (Feb 2021)

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Changes in Sedentary Time and Physical Activity in Response to an Exercise Training and/or Lifestyle Intervention

Sarah Kozey-Keadle, John Staudenmayer, Amanda Libertine, Marianna Mavilia, Kate Lyden, Barry Braun, and Patty Freedson

Background:

Individuals may compensate for exercise training by modifying nonexercise behavior (ie, increase sedentary time (ST) and decrease nonexercise physical activity [NEPA]).

Purpose:

To compare ST and NEPA during a 12-week exercise training and/or lifestyle intervention.

Methods:

Fifty-seven overweight/obese participants (19 M/39 F) completed the study (mean ± SD; age 43.6 ± 9.9 y, BMI 35.1 ± 4.6 kg/m2). There were no between-group differences in activity levels at baseline. Four-arm quasi-experimental intervention study 1) EX: exercise 5 days per week at a moderate intensity (40% to 65% VO2peak) 2) rST: reduce ST and increase NEPA, 3) EX-rST: combination of EX and rST and 4) CON: maintain habitual behavior.

Results:

For the EX group, ST did not decrease significantly (mean ((95% confidence interval) 0.48 (–2.2 to 3.1)% and there was no changes in NEPA at week-12 compared with baseline. The changes were variable, with approximately 50% of participants increasing ST and decreasing NEPA. The rST group decreased ST (–4.8 (0.8 to 7.9)% and increased NEPA. EX-rST significantly decreased ST (–5.1 (–2.2 to 7.9)% and increased time in NEPA at week-12 compared with baseline. The control group increased ST by 4.3 (0.8 to 7.9)%.

Conclusions:

Changes in nonexercise ST and NEPA are variable among participants in an exercise-training program, with nearly half decreasing NEPA compared with baseline. Interventions targeting multiple behaviors (ST and NEPA) may effectively reduce compensation and increase daily activity.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 11 (2014): Issue 7 (Sep 2014)

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Accuracy of Accelerometer Regression Models in Predicting Energy Expenditure and Mets in Children and Youth

Sofiya Alhassan, Kate Lyden, Cheryl Howe, Sarah Kozey Keadle, Ogechi Nwaokelemeh, and Patty S. Freedson

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8–16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (±95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

Cover Pediatric Exercise Science

In Pediatric Exercise Science Volume 24 (2012): Issue 4 (Jan 2012)

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To the Editor

Sarah Kozey-Keadle, Kate Lyden, Dr. John Staudenmayer, and Dr. Patty Freedson

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 8 (2011): Issue 1 (Jan 2011)

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Validation of the Fitbit Wireless Activity Tracker for Prediction of Energy Expenditure

Jeffer Eidi Sasaki, Amanda Hickey, Marianna Mavilia, Jacquelynne Tedesco, Dinesh John, Sarah Kozey Keadle, and Patty S. Freedson

Objective:

The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities.

Methods:

Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities.

Results:

Mean bias for estimated energy expenditure for all activities was −4.5 ± 1.0 kcals/6 min (95% limits of agreement: −25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities.

Conclusion:

The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 12 (2015): Issue 2 (Feb 2015)

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Energy Expenditure for 70 Activities in Children and Adolescents

Jeffer Eidi Sasaki, Cheryl A. Howe, Dinesh John, Amanda Hickey, Jeremy Steeves, Scott Conger, Kate Lyden, Sarah Kozey-Keadle, Sarah Burkart, Sofiya Alhassan, David Bassett Jr, and Patty S. Freedson

Background:

Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.

Methods:

Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.

Results:

Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.

Conclusion:

This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 13 (2016): Issue s1 (Jan 2016): New Data for an Updated Youth Energy Expenditure Compendium