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Errors in MET Estimates of Physical Activities Using 3.5 ml·kg−1·min−1 as the Baseline Oxygen Consumption

Sarah Kozey, Kate Lyden, John Staudenmayer, and Patty Freedson

Purpose:

To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.

Methods:

RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.

Results:

The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.

Conclusion:

Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 7 (2010): Issue 4 (Jul 2010)

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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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Impact of Continuous Focal Sampling Time Thresholds on Physical Activity Metrics When Using Video-Recorded Direct Observation

Julian Martinez, John Staudenmayer, and Scott J. Strath

Purpose: To determine differences in physical activity metrics between 1-, 5-, and 10-s direct observation (DO) time thresholds and compare annotation completion time between different time thresholds. Methods: Participants (n = 10, mean age 40.7 ± 22.3 years, five males) were video recorded for 2 hr within a free-living setting. DO videos were annotated by one experienced annotator with a priori developed Posture and Behavior schemas. The annotation order of video, time threshold, and schema used was randomized. For analysis, annotations were collapsed into posture and behavior domains. Total video time is reported. Time to code videos, overall percent agreement, and statistical bias of each posture and behavior domain for the 5-s time threshold and 10-s time threshold were compared to 1-s time threshold output. Results: 19.7 hr of DO were recorded. On average, the 1-s time threshold took 183.9 ± 34.2 min to annotate with the Posture schema and 118.8 ± 23.6 min with the Behavior schema. Under the Posture schema, the 5-s time threshold was 31.7% faster, had 91.5% agreement, and all biases were <±5 min, while the 10-s time threshold was 43.6% faster, had 89.2% agreement, and had biases ranging from −7.59 to 5.21 min. Under the Behavior schema, the 5-s time threshold was 16.0% faster, had 92.0% agreement, and had all biases <±2.1 min, while the 10-s time threshold was 27.6% faster, had 88.3% agreement, and had all biases <±3.9 min. Conclusion: Longer DO annotation time thresholds are accurate and faster but less precise for certain posture and behavior domains when compared to criterion 1-s time threshold in healthy adults.

Cover Journal for the Measurement of Physical Behaviour

In Journal for the Measurement of Physical Behaviour Volume 6 (2023): Issue 3 (Sep 2023)

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Direct Observation is a Valid Criterion for Estimating Physical Activity and Sedentary Behavior

Kate Lyden, Natalia Petruski, Stephanie Mix, John Staudenmayer, and Patty Freedson

Background:

Physical activity and sedentary behavior measurement tools need to be validated in free-living settings. Direct observation (DO) may be an appropriate criterion for these studies. However, it is not known if trained observers can correctly judge the absolute intensity of free-living activities.

Purpose:

To compare DO estimates of total MET-hours and time in activity intensity categories to a criterion measure from indirect calorimetry (IC).

Methods:

Fifteen participants were directly observed on three separate days for two hours each day. During this time participants wore an Oxycon Mobile indirect calorimeter and performed any activity of their choice within the reception area of the wireless metabolic equipment. Participants were provided with a desk for sedentary activities (writing, reading, computer use) and had access to exercise equipment (treadmill, bike).

Results:

DO accurately and precisely estimated MET-hours [% bias (95% CI) = –12.7% (–16.4, –7.3), ICC = 0.98], time in low intensity activity [% bias (95% CI) = 2.1% (1.1, 3.2), ICC = 1.00] and time in moderate to vigorous intensity activity [% bias (95% CI) –4.9% (–7.4, –2.5), ICC = 1.00].

Conclusion:

This study provides evidence that DO can be used as a criterion measure of absolute intensity in free-living validation studies.

Cover Journal of Physical Activity and Health

In Journal of Physical Activity and Health Volume 11 (2014): Issue 4 (May 2014)

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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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Step Count and Sedentary Time Validation of Consumer Activity Trackers and a Pedometer in Free-Living Settings

Albert R. Mendoza, Kate Lyden, John Sirard, John Staudenmayer, Catrine Tudor-Locke, and Patty S. Freedson

We evaluated the accuracy and precision of wearable activity trackers and a pedometer (ATPs) in estimating steps and sedentary time (ST) in free-living settings. Thirty-two healthy men and women (M ± SD: age = 32.3 ± 13.3 years; BMI = 24.4 ± 3.3 kg·m−2) were directly observed during three, 2-hour sessions on different days while wearing 10 devices and a biometric shirt. A validated direct observation (DO) system provided criterion measures for steps and ST. For steps, bias ranged from −753 steps/2-hrs (Fitbit Flex) to −57 steps/2-hrs (Polar Loop) and CIs ranged from [−1,144, −365] (Fitbit Flex) to [−291,175] (Polar Loop) steps/2-hrs. For all devices, step estimates were strongly correlated (r = 0.90 [Fitbit Flex] to r = 0.97 [New Lifestyles pedometer model 1000]) with DO counted steps. Estimates of ST were not accurate and were weakly correlated (r = −0.06 and r = 0.06 for Fitbit Flex and Fitbit One, respectively) with DO ST. Most ATPs were not accurate and varied in precision in estimating steps and ST in free-living settings. Implications from this study are that although point estimates of steps from ATPs are not accurate, ATPs’ ranking of step counts among individuals was high. However, the Fitbit Flex and Fitbit One are not recommended for estimating ST. This study advances our understanding of the performance of ATPs in estimating steps and ST in free-living settings, and significantly advances activity tracker and pedometer validation studies.

Cover Journal for the Measurement of Physical Behaviour

In Journal for the Measurement of Physical Behaviour Volume 2 (2019): Issue 2 (Jun 2019)

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Sensitivity of the Misfit Shine™ to Detect Changes in Laboratory-Based and Free-Living Physical Activity

Greg Petrucci Jr., Patty Freedson, Brittany Masteller, Melanna Cox, John Staudenmayer, and John Sirard

Purpose: Determine the sensitivity of the Misfit Shine™ (MS) to detect changes in physical activity (PA) measures (steps, “points,” kCals) in laboratory (LAB) and free-living (FL) conditions. Methods: Twenty-one participants wore the MS and ActiGraph GT3X+™ accelerometer (AG) at the hip and dominant-wrist during three, one-hour LAB sessions: sedentary (SS), sedentary plus walking (SW), and sedentary plus jogging (SJ). Direct observation (DO) of steps served as the criterion measure. Devices were also worn during two FL conditions: 1) active week (ACT) and 2) inactive week (INACT). For LAB and FL, significant differences were examined using paired t-tests and linear mixed effects models, respectively. Linear mixed effects models were used to estimate differences between MS estimated steps and DO (α ≤ 0.05). Results: For all hip-worn MS measures and wrist-worn MS estimates of steps and “points,” there was a significant increase (p < .05) from SS to SJ. However, wrist-worn MS kCal estimates were greater for SJ, compared to SS and SW, which were similar to each other (95% CI [95.5, 152.8] and [141.1, 378.9], respectively). Compared with DO, MS hip significantly underestimated steps by 3.5%, while MS wrist significantly overestimated steps by 4.2%. During FL conditions, all MS measures were sensitive to changes between ACT and INACT (p < .0001). Conclusion: Although there were systematic errors in step estimates from the MS, it was sensitive to changes during LAB and FL, and may be a useful tool for interventionists where tracking changes in PA is an important exposure or outcome variable.

Cover Journal for the Measurement of Physical Behaviour

In Journal for the Measurement of Physical Behaviour Volume 1 (2018): Issue 1 (Mar 2018)

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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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A Novel Video-Based Direct Observation System for Assessing Physical Activity and Sedentary Behavior in Children and Young Adults

Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson, and John R. Sirard

Purpose: Develop a direct observation (DO) system to serve as a criterion measure for the calibration of models applied to free-living (FL) accelerometer data. Methods: Ten participants (19.4 ± 0.8 years) were video-recorded during four, one-hour FL sessions in different settings: 1) school, 2) home, 3) community, and 4) physical activity. For each setting, 10-minute clips from three randomly selected sessions were extracted and coded by one expert coder and up to 20 trained coders using the Observer XT software (Noldus, Wageningen, the Netherlands). The coder defines each whole-body movement which was further described with three modifiers: 1) locomotion, 2) activity type, and 3) MET value (used to categorize intensity level). Percent agreement was calculated for intra- and inter-rater reliability. For intra-rater reliability, the criterion coder coded all 12 clips twice, separated by at least one week between coding sessions. For inter-rater reliability, coded clips by trained coders were compared to the expert coder. Intraclass correlations (ICCs) were calculated to assess the agreement of intensity category for intra- and inter-rater comparisons described above. Results: For intra-rater reliability, mean percent agreement ranged from 91.9 ± 3.9% to 100.0 ± 0.0% across all variables in all settings. For inter-rater reliability, mean percent agreement ranged from 88.2 ± 3.5% to 100.0 ± 0.0% across all variables in all settings. ICCs for intensity category ranged from 0.74–1.00 and 0.81–1.00 for intra- and inter-rater comparisons, respectively. Conclusion: The DO system is reliable and feasible to serve as a criterion measure of FL physical activity in young adults to calibrate accelerometers, subsequently improving interpretation of surveillance and intervention research.

Cover Journal for the Measurement of Physical Behaviour

In Journal for the Measurement of Physical Behaviour Volume 3 (2020): Issue 1 (Mar 2020)

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Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions

Julian Martinez, Autumn E. Decker, Chi C. Cho, Aiden Doherty, Ann M. Swartz, John Staudenmayer, and Scott J. Strath

Purpose: To assess the convergent validity of body-worn wearable camera still images (IMGs) for determining posture compared with activPAL (AP) classifications. Methods: The participants (n = 16, mean age 46.7 ± 23.8 years, 9 F) wore an Autographer wearable camera and an AP during three 2-hr free-living visits. IMGs were on average 8.47 s apart and were annotated with output consisting of events, transitory states, unknown, and gaps. The events were annotations that matched AP classifications (sit, stand, and move), consisting of at least three IMGs; the transitory states were posture annotations fewer than three IMGs; the unknowns were IMGs that could not be accurately classified; and the gaps were the time between annotations. For the analyses, the annotation and AP output were converted to 1-s epochs. The total and average length of visits and events were reported in minutes. Bias and 95% confidence intervals for event posture times from IMGs to AP were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. Results: Forty-three visits were analyzed, with a total visit and event time of 5,027.73 and 4,237.23 min, respectively, and the average visit and event lengths being 116.92 and 98.54 min, respectively. Bias was not statistically significant for sitting, but was significant for standing and movement (0.84, −6.87, and 6.04 min, respectively). From confusion matrices, IMGs correctly classified sitting, standing, and movement (85.69%, 54.87%, and 69.41%, respectively) of total AP time. Conclusion: Wearable camera IMGs provide a good estimation of overall sitting time. Future work is warranted to improve posture classifications and examine the validity of IMGs in assessing activity-type behaviors.

Cover Journal for the Measurement of Physical Behaviour

In Journal for the Measurement of Physical Behaviour Volume 4 (2021): Issue 1 (Mar 2021)