The purpose of this study was to calibrate and cross-validate the Youth Activity Profile (YAP), a self-report tool designed to capture physical activity (PA) and sedentary behaviors (SB) in youth.
...Eight schools in the Midwest part of the U.S. were involved and a total of 291 participants from grades 4-12 agreed to wear an accelerometer (SWA Armband) and complete the YAP in two separate weeks (5-7 days apart). Individual YAP items capture PA behavior during specific segments of the week and these items were combined with temporally matched estimates of moderate-to-vigorous PA (MVPA) and sedentary time from the SWA to enable calibration. Quantile regression procedures yielded YAP prediction algorithms that estimated MVPA at School, MVPA at Out-of-School, MVPA on Weekend, as well as time spent in SB. The YAP estimates of time spent in MVPA and SB were cross-validated using Pearson product correlations and limits of agreement, as indicative of individual error and, equivalence testing techniques as indicative of group-level error.
Following calibration, the correlations between YAP and SWA estimates of MVPA were low to moderate (rrange = .19 to .58) and individual-level YAP estimates of MVPA ranged from -134.9% to +110.0% of SWA MVPA values. Differences between aggregated YAP and SWA MVPA ranged from -3.4 to 21.7 minutes of MVPA at the group-level and predicted YAP MVPA estimates were within 15%, 20%, and 30%, of values from the SWA for the School, Out-of-School, and Weekend time periods, respectively. Estimates of time spent in SB were highly correlated with each other (r = .75). The individual estimates of SB ranged from -54.0% to +44.0% of SWA sedentary time, and the aggregated group-level estimates differed by 49.7 minutes (within 10% of the SWA aggregated estimates).
This study provides preliminary evidence that the calibration procedures enabled the YAP to provide estimates of MVPA and SB that approximated values from an objective monitor. The YAP provides a simple, low-cost and educationally sound method to accurately estimate children's MVPA and SB at the group level.
Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to ...substantiate their validity.
This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions.
Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing.
For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate.
The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.
Accelerometer-based activity monitors are widely used in research and surveillance applications for quantifying sedentary behavior (SB) and physical activity (PA). Considerable research has been done ...to refine methods for assessing PA, but relatively little attention has been given to operationalizing SB parameters (i.e., sedentary time and breaks) from accelerometer data - particularly in relation to health outcomes. This study investigated: (a) the accrued patterns of sedentary time and breaks; and (b) the associations of sedentary time and breaks in different bout durations with cardiovascular risk factors.
Accelerometer data on 5,917 adults from the National Health Examination and Nutrition Survey (NHANES) 2003-2006 were used. Sedentary time and breaks at different bout durations (i.e., 1, 2-4, 5-9, 10-14, 15-19, 20-24, 25-29, and ≥ 30-min) were obtained using a threshold of < 100 counts per minute. Sedentary time and breaks were regressed on cardiovascular risk factors (waist circumference, triglyceride, and high-density lipoprotein cholesterol) and body mass index across bout durations.
The results revealed that the majority of sedentary time occurred within relatively short bout durations (≈ 70% and ≈ 85% for < 5-min and < 10-min, respectively). The associations of sedentary time and breaks with health outcomes varied depending on how bout time was defined. Estimates of SB parameters based on bout durations of 5 min or shorter were associated with reduced cardiovascular risk factors while durations longer than 10-min were generally associated with increased risk factors.
The present study demonstrates that the duration of sedentary bouts should be further considered when operationalizing the SB parameters from accelerometer data. The threshold of 5 minutes to define a bout is defensible, but a 10 minute threshold would provide a more conservative estimate to clearly capture the prolonged nature of sedentary behavior. Additional research is needed to determine the relative sensitivity and specificity of these thresholds.
Statistical equivalence testing is more appropriate than conventional tests of difference to assess the validity of physical activity (PA) measures. This article presents the underlying principles of ...equivalence testing and gives three examples from PA and fitness assessment research.
The three examples illustrate different uses of equivalence tests. Example 1 uses PA data to evaluate an activity monitor's equivalence to a known criterion. Example 2 illustrates the equivalence of two field-based measures of physical fitness with no known reference method. Example 3 uses regression to evaluate an activity monitor's equivalence across a suite of 23 activities.
The examples illustrate the appropriate reporting and interpretation of results from equivalence tests. In the first example, the mean criterion measure is significantly within ±15% of the mean PA monitor. The mean difference is 0.18 METs and the 90% confidence interval of -0.15 to 0.52 is inside the equivalence region of -0.65 to 0.65. In the second example, we chose to define equivalence for these two measures as a ratio of mean values between 0.98 and 1.02. The estimated ratio of mean V˙O2 values is 0.99, which is significantly (P = 0.007) inside the equivalence region. In the third example, the PA monitor is not equivalent to the criterion across the suite of activities. The estimated regression intercept and slope are -1.23 and 1.06. Neither confidence interval is within the suggested regression equivalence regions.
When the study goal is to show similarity between methods, equivalence testing is more appropriate than traditional statistical tests of differences (e.g., ANOVA and t-tests).
The purpose of this investigation was to examine the validity of energy expenditure (EE), steps, and heart rate measured with the Apple Watch 1 and Fitbit Charge HR. Thirty-nine healthy adults wore ...the two monitors while completing a semi-structured activity protocol consisting of 20 minutes of sedentary activity, 25 minutes of aerobic exercise, and 25 minutes of light intensity physical activity. Criterion measures were obtained from an Oxycon Mobile for EE, a pedometer for steps, and a Polar heart rate strap worn on the chest for heart rate. For estimating whole-trial EE, the mean absolute percent error (MAPE) from Fitbit Charge HR (32.9%) was more than twice that of Apple Watch 1 (15.2%). This trend was consistent for the individual conditions. Both monitors accurately assessed steps during aerobic activity (MAPE
: 6.2%; MAPE
: 9.4%) but overestimated steps in light physical activity. For heart rate, Fitbit Charge HR produced its smallest MAPE in sedentary behaviors (7.2%), followed by aerobic exercise (8.4%), and light activity (10.1%). The Apple Watch 1 had stronger validity than the Fitbit Charge HR for assessing overall EE and steps during aerobic exercise. The Fitbit Charge HR provided heart rate estimates that were statistically equivalent to Polar monitor.
To date, no study has objectively measured physical activity levels among U.S. adults according to the 2008 Physical Activity Guidelines for Americans (PAGA).
The purpose of this study was to assess ...self-reported and objectively measured physical activity among U.S. adults according to the PAGA.
Using data from the NHANES 2005-2006, the PAGA were assessed using three physical activity calculations: moderate plus vigorous physical activity ≥150 minutes/week (MVPA); moderate plus two instances of vigorous physical activity ≥150 minutes/week (M2VPA); and time spent above 3 METs ≥500 MET-minutes/week (METPA). Self-reported physical activity included leisure, transportation, and household activities. Objective activity was measured using Actigraph accelerometers that were worn for 7 consecutive days. Analyses were conducted in 2009-2010.
U.S. adults reported 324.5 ± 18.6 minutes/week (M ± SE) of moderate physical activity and 73.6 ± 3.9 minutes/week of vigorous physical activity, although accelerometry estimates were 45.1 ± 4.6 minutes/week of moderate physical activity and 18.6 ± 6.6 minutes/week of vigorous physical activity. The proportion of adults meeting the PAGA according to M2VPA was 62.0% for self-report and 9.6% for accelerometry.
According to the NHANES 2005-2006, fewer than 10% of U.S. adults met the PAGA according to accelerometry. However, physical activity estimates vary substantially depending on whether self-reported or measured via accelerometer.
This study evaluated the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise.
...Fifty-two (28 male and 24 female) participants age 18-65 yr performed 20 min of self-selected sedentary activity, 25 min of aerobic exercise, and 25 min of resistance exercise, with 5 min of rest between each activity. Each participant wore five wrist-worn consumer monitors Fitbit Flex, Jawbone Up24, Misfit Shine (MS), Nike+ Fuelband SE (NFS), and Polar Loop and two research monitors ActiGraph GT3X+ on the waist and BodyMedia Core (BMC) on the arm while being concurrently monitored with Oxycon Mobile (OM), a portable metabolic measuring system. Energy expenditure (EE) on different activity sessions was measured by OM and estimated by all monitors.
Mean absolute percent error (MAPE) values for the full 80-min protocol ranged from 15.3% (BMC) to 30.4% (MS). EE estimates from ActiGraph GT3X+ were found to be equivalent to those from OM (± 10% equivalence zone, 285.1-348.5). Correlations between OM and the various monitors were generally high (ranged between 0.71 and 0.90). Three monitors had MAPE values lower than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values lower than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values lower than 25% for resistance exercise.
Overall, the research monitors and Fitbit Flex, Jawbone Up24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities as well as under real-world conditions.
The purpose of the study is to examine the associations of youth physical activity and screen time with weight status and cardiorespiratory fitness in children and adolescents, separately, utilizing ...a nationally representative sample. A total of 1,113 participants (692 children aged 6-11 yrs; 422 adolescents aged 12-15 yrs) from the 2012 NHANES National Youth Fitness Survey. Participants completed physical activity and screen time questionnaires, and their body mass index and cardiorespiratory fitness (adolescents only) were assessed. Adolescents completed additional physical activity questions to estimate daily MET minutes. Children not meeting the screen time guideline had 1.69 times the odds of being overweight/obese compared to those meeting the screen time guideline, after adjusting for physical activity and other control variables. Among adolescent, screen time was significantly associated with being overweight/obese (odds ratio = 1.82, 95% confidence interval: 1.06-3.15), but the association attenuated toward the borderline of being significant after controlling for physical activity. Being physically active was positively associated with cardiorespiratory fitness, independent of screen time among adolescents. In joint association analysis, children who did not meet physical activity nor screen time guidelines had 2.52 times higher odds of being overweight/obese than children who met both guidelines. Adolescents who did not meet the screen time guideline had significantly higher odds ratio of being overweight/obese regardless of meeting the physical activity guideline. Meeting the physical activity guideline was also associated with cardiorespiratory fitness regardless of meeting the screen time guideline in adolescents. Screen time is a stronger factor than physical activity in predicting weight status in both children and adolescents, and only physical activity is strongly associated with cardiorespiratory fitness in adolescents.
The term "calibration" in accelerometry research has come to mean the conversion of counts into other established measurement units. In this article, two types of calibration research are described. ...Unit calibration (or interinstrument variability) is described as a reliability issue, whereas value calibration is described as more of a validity issue. Principles for design of accelerometry-based validation studies are described to provide a guide for future calibration research. The population must be representative of the intended population in terms of demographics and size, monitors must be representative of the population of monitors that would be available, and activities in the protocol must be representative of the types of activities performed by the intended population. It is also important to employ appropriate analytical strategies in this type of research. A case study employing the appropriate design principles is included to demonstrate how results can vary depending on the type of analyses that are used. Direct comparisons are made between a mixed model regression approach and an approach based on receiver operator characteristic curves.
The purpose of this study was to examine the comparative and criterion validity of the three activity monitors in relation to a portable metabolic analyzer (Oxycon Mobile (OM)) in adults.
A total of ...52 adults age 18-40 yr each performed a series of 15 activities for 5 min each, with 1-min resting intervals between different activities. Participants completed the trials while wearing the three activity monitors and while being measured with the OM. Estimates of energy expenditure (EE) were obtained from the ActiGraph (one based on the vertical axis and the other from vector magnitude) as well as from the activPAL (AP) and the Core Armband (CA). The EE estimates were converted into MET(RMR) values by standardizing EE values with each person's resting metabolic rate and then temporarily matched to facilitate minute-by-minute comparisons. Equivalence testing and mean absolute percent errors (MAPE) were used to evaluate the agreement.
MET(RMR) values from the CA were significantly equivalent to those from the OM for the overall group comparison (90% confidence interval (CI), 3.65 and 3.85 MET(RMR)) and vigorous intensity (90% CI, 8.27 and 10.10 MET(RMR)). The CA had the smallest MAPE for moderate (20.7%) and vigorous (14.5%) intensity, but the AP had smaller MAPE for sedentary activities (27.4%) and light (24.7%) intensity activities.
The CA showed good agreement relative to the OM for the overall group comparison and for moderate and vigorous activities. The AP, in contrast, was the most accurate for sedentary and light activities. The combined use of the CA and AP may yield more accurate estimates of EE than using a single monitor.
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