Introduction : Reducing sedentary time is associated with improved postprandial glucose regulation. However, it is not known if the timing of sedentary behavior (i.e., pre- vs. postmeal) differentially impacts postprandial glucose in older adults with overweight or obesity. Methods : In this secondary analysis, older adults (≥65 years) with overweight and obesity (body mass index ≥ 25 kg/m2) wore a continuous glucose monitor and a sedentary behavior monitor continuously in their real-world environments for four consecutive days on four separate occasions. Throughout each 4-day measurement period, participants followed a standardized eucaloric diet and recorded mealtimes in a diary. Glucose, sedentary behavior, and meal intake data were fused using sensor and diary timestamps. Mixed-effect linear regression models were used to evaluate the impact of sedentary timing relative to meal intake. Results : Premeal sedentary time was significantly associated with both the increase from premeal glucose to the postmeal peak (ΔG) and the percent of premeal glucose increase that was recovered 1-hr postmeal glucose peak (%Baseline Recovery; p < .05), with higher levels of premeal sedentary time leading to both a larger ΔG and a smaller %Baseline Recovery. Postmeal sedentary time was significantly associated with the time from meal intake to glucose peak (ΔT; p < .05), with higher levels of postmeal sedentary time leading to a longer time to peak. Conclusions : Pre- versus postmeal sedentary behavior differentially impacts postprandial glucose response in older adults with overweight or obesity, suggesting that the timing of sedentary behavior reductions might play an influential role on long-term glycemic control.
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Pre- Versus Postmeal Sedentary Duration—Impact on Postprandial Glucose in Older Adults With Overweight or Obesity
Elizabeth Chun, Irina Gaynanova, Edward L. Melanson, and Kate Lyden
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.
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.
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.
To the Editor
Sarah Kozey-Keadle, Kate Lyden, Dr. John Staudenmayer, and Dr. Patty Freedson
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.
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.
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.
An Evaluation of Participant Perspectives and Wear-Time Compliance for a Wrist-Worn Versus Thigh-Worn Accelerometer in Cancer Survivors
Mary C. Hidde, Mary E. Crisafio, Emma Gomes, Kate Lyden, and Heather J. Leach
Background: Accelerometers are frequently used to measure free-living physical activity and sleep in cancer survivors. To obtain valid data, participants must adhere to wear-time guidelines; therefore, understanding survivor’s preference may be critical when selecting an accelerometer. This study compared cancer survivors’ reported discomfort and interference, and wear-time compliance between a wrist-worn accelerometer and a thigh-worn accelerometer. Methods: This was a secondary data analysis. Cancer survivors (N = 52, mean age = 51.8 [13.0], 82.3% female) wore the Actiwatch-2 (wrist) and the activPAL (thigh) for 7 days, 24 hours per day. On day 7, participants completed a questionnaire to evaluate each accelerometer using a 1 to 5 Likert scale and open-ended questions. The Kolmogorov–Smirnov test evaluated differences in discomfort and interference. Paired samples t test evaluated differences in wear-time compliance. Open-ended responses were analyzed using thematic analysis methods. Results: No differences were observed in discomfort, interference, or wear-time compliance (P = .08). Qualitative analysis resulted in 2 themes: discomfort and ease of use and interference and adverse reaction. Interferences were primarily reported with the Actiwatch-2, whereas discomfort and ease were primarily reported with the activPAL. Conclusion: No significant differences were observed regarding discomfort, interference, and compliance. Results of this study can prepare researchers for common issues regarding accelerometer compliance, allowing researchers to offer resources to alleviate discomforts or interferences that may affect wear-time compliance.
Comparison of activPAL and Actiwatch for Estimations of Time in Bed in Free-Living Adults
Mary C. Hidde, Kate Lyden, Josiane L. Broussard, Kim L. Henry, Julia L. Sharp, Elizabeth A. Thomas, Corey A. Rynders, and Heather J. Leach
Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p < .001) were observed. There was not a significant interaction between accelerometer and day of wear (p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.
