The purpose of this study was to conduct a comprehensive evaluation of the ActiGraph GT3X+ (AG) and activPAL (AP) for assessing time spent in sedentary behaviors (SB) in youth using structured and free-living activities. Forty-four participants (M age, 12.7±0.8 yrs) completed up to eight structured activities and approximately 2 hrs of free-living activity while wearing an AG (right hip) and AP (right thigh). A Cosmed K4b2 was used for measured energy expenditure (METy; activity VO2 ÷ resting VO2). Direct observation was used during the structured activities. SB time was estimated using the inclinometer function of the AP and AG, and count thresholds with AG (<75 vector magnitude [VM] counts/10-s; <25 vertical axis [VA] counts/10-s; and <50, 100, 150, and 200 VA counts/min). For the structured activities, the AG inclinometer and AP correctly classified supine rest about 45% of the time, seated activities 54.6% and 65.1% of the time, respectively, and walking and running >96% of the time. For the free-living measurement, the VA <25 counts/10-s had the lowest RMSE (20.6 min), while the VM <75 counts/10-s had the lowest MAPE (69.2%). The AG inclinometer was within 0.2 minutes of measured time, but had the highest MAPE (107.1%). The AP was within 1.6 minutes of measured time, but had the highest RMSE (28.5 minutes). Compared to measured SB time, the VA <25 counts/10-s and VM <75 counts/10-s provided the most precise estimates of SB during free-living activity. Further refinement is needed to improve the AP and AG posture estimates.
Scott E. Crouter, Paul R. Hibbing and Samuel R. LaMunion
Natalie Jayne Taylor, Scott E. Crouter, Rebecca J. Lawton, Mark T. Conner and Andy Prestwich
Precise measurement of physical activity (PA) is required to identify current levels and changes in PA within a population, and to gauge effectiveness of interventions.
The Online Self-reported Walking and Exercise Questionnaire (OSWEQ) was developed for monitoring PA via the Web. Forty-nine participants (mean ± SD; age = 27 ± 11.9yrs) completed the OSWEQ and International PA Questionnaire (IPAQ) short form 3 times [T1/T2/T3 (separated by 7-days)] and wore an Actigraph-GT3X-accelerometer for 7-days between T2-T3. For each measure, estimates of average MET·min·day−1 and time spent in moderate PA (MPA), vigorous PA (VPA) and moderate and vigorous PA (MVPA) were obtained.
The OSWEQ and IPAQ demonstrated test-retest reliability for MPA, VPA, and MVPA minutes and average MET·min·day−1 between T1-T2 (OSWEQ range, r = .71–.77; IPAQ range, r = .59–.79; all, P < .01). The OSWEQ and IPAQ, compared with the GT3X, had lower estimates (mean error ± 95% PI) of MVPA MET·min·day−1 by 150.4 ± 477.6 and 247.5 ± 477.5, respectively.
The OSWEQ demonstrates good test-retest reliability over 7-days and better group level estimates of MET·min·day−1 than the IPAQ, compared with the GT3X. These results suggest that the OSWEQ is a reliable and valid measure among young/working age adults and could be useful for monitoring PA trends over time.
Scott E. Crouter, Diane M. DellaValle, Jere D. Haas, Edward A. Frongillo and David R. Bassett
The purpose of this study was to compare the 2006 and 2010 Crouter algorithms for the ActiGraph accelerometer and the NHANES and Matthews cut-points, to indirect calorimetry during a 6-hr free-living measurement period.
Twenty-nine participants (mean ± SD; age, 38 ± 11.7 yrs; BMI, 25.0 ± 4.6 kg·m-2) were monitored for 6 hours while at work or during their leisure time. Physical activity (PA) data were collected using an ActiGraph GT1M and energy expenditure (METs) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared with the Cosmed for METs and time spent in sedentary behaviors, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA).
The 2010 Crouter algorithm overestimated time spent in LPA, MPA, and VPA by 9.0%−44.5% and underestimated sedentary time by 20.8%. The NHANES cut-points overestimated sedentary time and LPA by 8.3%−9.9% and underestimated MPA and VPA by 50.4%−56.7%. The Matthews cut-points overestimated sedentary time (9.9%) and MPA (33.4%) and underestimated LPA (25.7%) and VPA (50.1%). The 2006 Crouter algorithm was within 1.8% of measured sedentary time; however, mean errors ranged from 34.4%−163.1% for LPA, MPA, and VPA.
Of the ActiGraph prediction methods examined, none of them was clearly superior for estimating free-living PA compared with indirect calorimetry.
David R. Bassett, John Pucher Jr., Ralph Buehler, Dixie L. Thompson and Scott E. Crouter
This study was designed to examine the relationship between active transportation (defined as the percentage of trips taken by walking, bicycling, and public transit) and obesity rates (BMI ≥ 30 kg · m−2) in different countries.
National surveys of travel behavior and health indicators in Europe, North America, and Australia were used in this study; the surveys were conducted in 1994 to 2006. In some cases raw data were obtained from national or federal agencies and then analyzed, and in other cases summary data were obtained from published reports.
Countries with the highest levels of active transportation generally had the lowest obesity rates. Europeans walked more than United States residents (382 versus 140 km per person per year) and bicycled more (188 versus 40 km per person per year) in 2000.
Walking and bicycling are far more common in European countries than in the United States, Australia, and Canada. Active transportation is inversely related to obesity in these countries. Although the results do not prove causality, they suggest that active transportation could be one of the factors that explain international differences in obesity rates.
Lindsay P. Toth, Susan Park, Whitney L. Pittman, Damla Sarisaltik, Paul R. Hibbing, Alvin L. Morton, Cary M. Springer, Scott E. Crouter and David R. Bassett
Purpose: To examine the effect of brief, intermittent stepping bouts on step counts from 10 physical activity monitors (PAMs). Methods: Adults (N = 21; M ± SD, 26 ± 9.0 yr) wore four PAMs on the wrist (Garmin Vivofit 2, Fitbit Charge, Withings Pulse Ox, and ActiGraph wGT3X-BT [AG]), four on the hip (Yamax Digi-Walker SW-200 [YX], Fitbit Zip, Omron HJ-322U, and AG), and two on the ankle (StepWatch [SW] with default and modified settings). AG data were processed with and without the low frequency extension (AGL) and with the Moving Average Vector Magnitude algorithm. In Part 1 (five trials), walking bouts were varied (4–12 steps) and rest intervals were held constant (10 s). In Part 2 (six trials), walking bouts were held constant (4 steps) and rest intervals were varied (1–10 s). Percent of hand-counted steps and mean absolute percentage error were calculated. One sample t-test was used to compare percent of hand-counted steps to 100%. Results: In Parts 1 and 2, the SWdefault, SWmodified, YX, and AGLhip captured within 10% of hand-counted steps across nearly all conditions. In Part 1, estimates of most methods improved as the number of steps per bout increased. In Part 2, estimates of most methods decreased as the rest duration increased. Conclusion: Most methods required stepping bouts of >6–10 consecutive steps to record steps. Rest intervals of 1–2 seconds were sufficient to break up walking bouts in many methods. The requirement for several consecutive steps in some methods causes an underestimation of steps in brief, intermittent bouts.
Susan Park, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter and David R. Bassett
It has become common to wear physical activity monitors on the wrist to estimate steps per day, but few studies have considered step differences between monitors worn on the dominant and non-dominant wrists. Purpose: The purpose of this study was to compare four step counting methods on the dominant versus non-dominant wrist using the Fitbit Charge (FC) and ActiGraph GT9X (GT9X) across all waking hours of one day. Methods: Twelve participants simultaneously wore two monitors (FC and GT9X) on each wrist during all waking hours for an entire day. GT9X data were analyzed with three step counting methods: ActiLife algorithm with default filter (AG-noLFE), ActiLife algorithm with low-frequency extension (AG-LFE), and the Moving Average Vector Magnitude (AG-MAVM) algorithm. A 2-way repeated measures ANOVA (method × wrist) was used to compare step counts. Results: There was a significant main effect for wrist placement (F(1,11) = 11.81, p = .006), with the dominant wrist estimating an average of 1,253 more steps than the non-dominant wrist. Steps differed between the dominant and non-dominant wrist for three of the step methods: AG-noLFE (1,327 steps), AG-LFE (2,247 steps), AG-MAVM (825 steps), and approached statistical significance for FC (613 steps). No significant method x wrist placement interaction was found (F(3,9) = 2.62, p = .115). Conclusion: Findings suggest that for step counting algorithms, it may be important to consider the placement of wrist-worn monitors since the dominant wrist location tended to yield greater step estimates. Alternatively, standardizing the placement of wrist-worn monitors could help to reduce the differences in daily step counts across studies.
Peter T. Katzmarzyk, Kara D. Denstel, Kim Beals, Christopher Bolling, Carly Wright, Scott E. Crouter, Thomas L. McKenzie, Russell R. Pate, Brian E. Saelens, Amanda E. Staiano, Heidi I. Stanish and Susan B. Sisson
The 2016 United States (U.S.) Report Card on Physical Activity for Children and Youth provides a comprehensive evaluation of physical activity levels and factors influencing physical activity among children and youth.
The report card includes 10 indicators: Overall Physical Activity, Sedentary Behavior, Active Transportation, Organized Sport Participation, Active Play, Health-related Fitness, Family and Peers, School, Community and the Built Environment, and Government Strategies and Investments. Nationally representative data were used to evaluate the indicators using a standard grading rubric.
Sufficient data were available to assign grades to 7 of the indicators, and these ranged from B- for Community and the Built Environment to F for Active Transportation. Overall Physical Activity received a grade of D- due to the low prevalence of meeting physical activity guidelines. A grade of D was assigned to Health-related Fitness, reflecting the low prevalence of meeting cardiorespiratory fitness standards. Disparities across age, gender, racial/ethnic and socioeconomic groups were observed for several indicators.
Continued poor grades suggest that additional work is required to provide opportunities for U.S. children to be physically active. The observed disparities indicate that special attention should be given to girls, minorities, and those from lower socioeconomic groups when implementing intervention strategies.
Karin A. Pfeiffer, Kathleen B. Watson, Robert G. McMurray, David R. Bassett, Nancy F. Butte, Scott E. Crouter, Stephen D. Herrmann, Stewart G. Trost, Barbara E. Ainsworth, Janet E. Fulton, David Berrigan and For the CDC/NCI/NCCOR Research Group
Purpose: This study compared the accuracy of physical activity energy expenditure (PAEE) prediction using 2 methods of accounting for age dependency versus 1 standard (single) value across all ages. Methods: PAEE estimates were derived by pooling data from 5 studies. Participants, 6–18 years (n = 929), engaged in 14 activities while in a room calorimeter or wearing a portable metabolic analyzer. Linear regression was used to estimate the measurement error in PAEE (expressed as youth metabolic equivalent) associated with using age groups (6–9, 10–12, 13–15, and 16–18 y) and age-in-years [each year of chronological age (eg, 12 = 12.0–12.99 y)] versus the standard (a single value across all ages). Results: Age groups and age-in-years showed similar error, and both showed less error than the standard method for cycling, skilled, and moderate- to vigorous-intensity activities. For sedentary and light activities, the standard had similar error to the other 2 methods. Mean values for root mean square error ranged from 0.2 to 1.7 youth metabolic equivalent across all activities. Error reduction ranged from −0.2% to 21.7% for age groups and −0.23% to 18.2% for age-in-years compared with the standard. Conclusions: Accounting for age showed lower errors than a standard (single) value; using an age-dependent model in the Youth Compendium is recommended.
Peter T. Katzmarzyk, Kara D. Denstel, Kim Beals, Jordan Carlson, Scott E. Crouter, Thomas L. McKenzie, Russell R. Pate, Susan B. Sisson, Amanda E. Staiano, Heidi Stanish, Dianne S. Ward, Melicia Whitt-Glover and Carly Wright
Kara N. Dentro, Kim Beals, Scott E. Crouter, Joey C. Eisenmann, Thomas L. McKenzie, Russell R. Pate, Brian E. Saelens, Susan B. Sisson, Donna Spruijt-Metz, Melinda S. Sothern and Peter T. Katzmarzyk
The National Physical Activity Plan Alliance partnered with physical activity experts to develop a report card that provides a comprehensive assessment of physical activity among United States children and youth.
The 2014 U.S. Report Card on Physical Activity for Children and Youth includes 10 indicators: overall physical activity levels, sedentary behaviors, active transportation, organized sport participation, active play, health-related fitness, family and peers, school, community and the built environment, and government strategies and investments. Data from nationally representative surveys were used to provide a comprehensive evaluation of the physical activity indicators. The Committee used the best available data source to grade the indicators using a standard rubric.
Approximately one-quarter of children and youth 6 to 15 years of age were at least moderately active for 60 min/day on at least 5 days per week. The prevalence was lower among youth compared with younger children, resulting in a grade of D- for overall physical activity levels. Five of the remaining 9 indicators received grades ranging from B- to F, whereas there was insufficient data to grade 4 indicators, highlighting the need for more research in some areas.
Physical activity levels among U.S. children and youth are low and sedentary behavior is high, suggesting that current infrastructure, policies, programs, and investments in support of children’s physical activity are not sufficient.