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  • Author: Gregory Welk x
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Yang Bai, Kelly Allums-Featherston, Pedro F. Saint-Maurice, Gregory J. Welk and Norma Candelaria

Purpose: The consensus is that physical activity (PA) and sedentary behavior (SB) are independent behaviors, but past findings suggest that they may be influenced by common underlying factors. To clarify this issue, we examined associations between enjoyment of PA and participation in both PA and SB in a large sample of 4th- to 12th-grade US youth. Methods: A total of 18,930 students from 187 schools completed the youth activity profile, a self-report 15-item survey that assesses time spent in PA and SB in school and home settings. Two additional items captured enjoyment of PA and physical education. Two-way (gender × enjoyment and grade × enjoyment) mixed analysis of variances were conducted. Results: Pearson correlation results revealed a positive relationship between enjoyment and PA (r = .38, P < .05) and an inverse correlation between enjoyment and SB (r = −.23, P < .05). Statistically significant main effects of enjoyment were found in the 2-way analysis of variance for both PA and SB. The simple main effect from analysis of variance indicated students with high enjoyment of PA reported higher levels of PA and lower levels of SB compared with students reporting moderate or low levels of enjoyment. Conclusion: The results provide new insights related to the relevance of enjoyment as a common underlying variable influencing both PA and SB across gender and grade levels.

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Matthew T. Mahar, Gregory J. Welk, David A. Rowe, Dana J. Crotts and Kerry L. McIver

Background:

The purpose of this study was to develop and cross-validate a regression model to estimate VO2peak from PACER performance in 12- to 14-year-old males and females.

Methods:

A sample of 135 participants had VO2peak measured during a maximal treadmill test and completed the PACER 20-m shuttle run. The sample was randomly split into validation (n = 90) and cross-validation (n = 45) samples. The validation sample was used to develop the regression equation to estimate VO2peak from PACER laps, gender, and body mass.

Results:

The multiple correlation (R) was .66 and standard error of estimate (SEE) was 6.38 ml·kg−1·min−1. Accuracy of the model was confirmed on the cross-validation sample. The regression equation developed on the total sample was: VO2peak = 47.438 + (PACER*0.142) + (Gender[m=1, f=0]*5.134) − (body mass [kg]*0.197), R = .65, SEE = 6.38 ml·kg–1·min–1.

Conclusions:

The model developed in this study was more accurate than the Leger et al. model and allows easy conversion of PACER laps to VO2peak.

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Adam Šimůnek, Jan Dygrýn, Lukáš Jakubec, Filip Neuls, Karel Frömel and Gregory John Welk

Purpose: Activity trackers are useful tools for physical activity promotion in adolescents, but robust validity evaluations have not been done under free-living conditions. This study evaluated the validity of the Garmin Vívofit 1 (G1) and Garmin Vívofit 3 (G3) in different settings and contexts. Methods: The participants (girls: 52%, age: 15.9 [1.9] y) wore the G1 and G3 on their nondominant wrist and the Yamax pedometer on their right hip for a period of 1 week. Validity was examined in 4 discrete segments (before school, in school, after school, and whole day). The criterion method was the Yamax pedometer. Results: Both the G1 and G3 could be considered equivalent to the Yamax pedometer regarding the before school, in school, and whole day segments. The G1 showed wider limits of agreement than G3. Conclusions: The G1 and G3 trackers exhibited acceptable validity for 3 of the 4 segments (before school, in school, and whole day measurements). The results were less accurate during the after-school segment. The evidence that the validity of the monitors varied depending on the setting and context is an important consideration for research on adolescent activity patterns.

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Bradley J. Cardinal, Minsoo Kang, James L. Farnsworth II and Gregory J. Welk

Kinesiology leaders were surveyed regarding their views of the (re)emergence of physical activity and public health. Their views were captured via a 25-item, online survey conducted in 2014. The survey focused on four areas: (a) types of affiliation with public health; (b) program options and course coverage; (c) outreach programming; and (d) perspectives on integration. Member and nonmember institutions of the American Kinesiology Association received the survey. Responses were received from 139 institutional leaders, resulting in an overall response rate of 21.4%. Key findings included that the combination of physical activity and public health was seen as both a stand-alone subdisciplinary area within kinesiology and also an area that has a great deal of potential for collaboration, the acquisition of external funding, and further strengthening of community outreach and engagement. The survey results are placed in historical context and interpreted with various caveats and limitations in mind.

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Richard P. Troiano, Kelley K. Pettee Gabriel, Gregory J. Welk, Neville Owen and Barbara Sternfeld

Context:

Advances in device-based measures have led researchers to question the value of reported measures of physical activity or sedentary behavior. The premise of the Workshop on Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods, held in July 2010, was that assessment of behavior by self-report is a valuable approach.

Objective:

To provide suggestions to optimize the value of reported physical activity and sedentary behavior, we 1) discuss the constructs that devices and reports of behavior can measure, 2) develop a framework to help guide decision-making about the best approach to physical activity and sedentary behavior assessment in a given situation, and 3) address the potential for combining reported behavior methods with device-based monitoring to enhance both approaches.

Process:

After participation in a workshop breakout session, coauthors summarized the ideas presented and reached consensus on the material presented here.

Conclusions:

To select appropriate physical activity assessment methods and correctly interpret the measures obtained, researchers should carefully consider the purpose for assessment, physical activity constructs of interest, characteristics of the population and measurement tool, and the theoretical link between the exposure and outcome of interest.

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Mark A. Sarzynski, Joey C. Eisenmann, Gregory J. Welk, Jared Tucker, Kim Glenn, Max Rothschild and Kate Heelan

The present study examined the association between the angiotensin converting enzyme (ACE) insertion/deletion (I/D) polymorphism, physical activity, and resting blood pressure (BP) in a sample of 132 children (48.4% female). Children attaining 60 min/day of moderate-to-vigorous physical activity (MVPA) possessed lower % body fat (29% vs 24%, p < .05). Resting BP did not significantly differ between genotypes. Furthermore, partial correlations between MVPA and BP were low and did not vary by ACE genotype. Thus, the ACE I/D genotype is not associated with BP and does not modify the relationship between physical activity and BP in this sample of children.

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Miguel A. Calabro, Gregory J. Welk, Alicia L. Carriquiry, Sarah M. Nusser, Nicholas K. Beyler and Charles E. Matthews

Purpose:

The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR).

Methods:

Participants (n = 20) wore 2 pattern-recognition activity monitors (an IDEEA and a SenseWear Pro Armband) for a 24-hour period and then completed the 24PAR the following morning. Participants completed 2 trials, 1 while maintaining a prospective diary of their activities and 1 without a diary. The trials were counterbalanced and completed within a week from each other. Estimates of energy expenditure (EE) and minutes of moderate-to-vigorous physical activity (MVPA) were compared with the criterion measures using 3-way (method by gender by trial) mixed-model ANOVA analyses.

Results:

For EE, pairwise correlations were high (r > .88), and there were no differences in estimates across methods. Estimates of MVPA were more variable, but correlations were still in the moderate to high range (r > .57). Average activity levels were significantly higher on the logging trial, but there was no significant difference in the accuracy of self-report on days with and without logging.

Conclusions:

The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.

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Sharon A. Plowman, Charles L. Sterling, Charles B. Corbin, Marilu D. Meredith, Gregory J. Welk and James R. Morrow Jr.

Initially designed by Charles L. Sterling as a physical fitness “report card” FITNESSGRAM ® / ACTIVITYGRAM ® is now an educational assessment and reporting software program. Based on physiological/epidemiological, behavioral, and pedagogical research, FITNESSGRAM is committed to health-related physical fitness, criterion-referenced standards, an emphasis on physical activity including behavioral based recognitions, and the latest in technology. The evolution of these major concepts is described in this history of FITNESSGRAM.

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Robin P. Shook, Nicole C. Gribben, Gregory A. Hand, Amanda E. Paluch, Gregory J. Welk, John M. Jakicic, Brent Hutto, Stephanie Burgess and Steven N. Blair

Background:

Subjective measures of moderate and vigorous physical activity (MVPA) rely on relative intensity whereas objective measures capture absolute intensity; thus, fit individuals and unfit individuals may perceive the same activity differently.

Methods:

Adults (N = 211) wore the SenseWear Armband (SWA) for 10 consecutive days to objectively assess sedentary time and MVPA. On day 8, participants completed the International Physical Activity Questionnaire (IPAQ) to subjectively assess sitting time and MVPA. Fitness was assessed via a maximal treadmill test, and participants were classified as unfit if the result was in the bottom tertile of the study population by sex or fit if in the upper 2 tertiles.

Results:

Overall, estimates of MVPA between the IPAQ and SWA were not significantly different (IPAQ minus SWA, 67.4 ± 919.1 MVPA min/wk, P = .29). However, unfit participants overestimated MVPA using the IPAQ by 37.3% (P = .02), but fit participants did not (P = .99). This between-group difference was due to overestimation, using the IPAQ, of moderate activity by 93.8 min/wk among the unfit individuals, but underestimation of moderate activity among the fit participants by 149.4 min/wk.

Conclusion:

Subjective measures of MVPA using the IPAQ varied by fitness category; unfit participants overestimated their MVPA and fit participants accurately estimated their MVPA.

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Sarah M. Nusser, Nicholas K. Beyler, Gregory J. Welk, Alicia L. Carriquiry, Wayne A. Fuller and Benjamin M.N. King

Background:

Physical activity recall instruments provide an inexpensive method of collecting physical activity patterns on a sample of individuals, but they are subject to systematic and random measurement error. Statistical models can be used to estimate measurement error in activity recalls and provide more accurate estimates of usual activity parameters for a population.

Methods:

We develop a measurement error model for a short-term activity recall that describes the relationship between the recall and an individual’s usual activity over a long period of time. The model includes terms for systematic and random measurement errors. To estimate model parameters, the design should include replicate observations of a concurrent activity recall and an objective monitor measurement on a subsample of respondents.

Results:

We illustrate the approach with preliminary data from the Iowa Physical Activity Measurement Study. In this dataset, recalls tend to overestimate actual activity, and measurement errors greatly increase the variance of recalls relative to the person-to-person variation in usual activity. Statistical adjustments are used to remove bias and extraneous variation in estimating the usual activity distribution.

Conclusions:

Modeling measurement error in recall data can be used to provide more accurate estimates of long-term activity behavior.