Philip W. Scruggs

Quantifying Moderate to Vigorous Physical Activity in High School Physical Education: A Pedometer Steps/Minute Standard

The primary objective of this research was to quantify the recommended minimum level (i.e., 50% of the class time) of moderate to vigorous physical activity within high school physical education via pedometry steps/min. A secondary objective was to explore the influence of lesson duration (i.e., traditional vs. block schedules) on quantifying moderate to vigorous physical activity via steps/min. Data (N = 218) were collected from 27 physical education classes taught by 10 certified physical educators in 6 schools. Physical activity measures were steps/min, percent of time engaged in moderate to vigorous physical activity (%MVPA), and time engaged in moderate to vigorous physical activity (MVPAtime, min.). Steps/min and %MVPA were strongly associated for all data and traditional and block schedules (r ≥ .94, p ≤ .01). Diagnostic statistics suggest that steps/min accurately discriminates between achievement and non-achievement of the 50% moderate to vigorous physical activity criterion. A steps/min interval of 82 to 83 was the most accurate indicator of the 50% criterion for all data. Steps/min and MVPAtime were strongly associated for traditional and block schedules independently (r ≥ .93, p ≤ .01) but not for all data combined (r ≥ .64, p ≤ .01). In conclusion, steps/min accurately quantifies the 50% moderate to vigorous physical activity criterion in high school physical education, and it is recommended as a physical education physical activity surveillance measure.

Tri-Axial Accelerometry and Heart Rate Telemetry: Relation and Agreement with Behavioral Observation in Elementary Physical Education

The relation and agreement of tri-axial accelerometry and heart rate telemetry in measuring moderate to vigorous physical activity were examined in association to behavioral observation during 1st- and 2nd-grade physical education. In Study 1, physical activity measures of heart rate and behavioral observation were collected on 346 participants ages 6-9 years (M = 7.27, SD = 0.58). In Study 2, participants (N = 125) were 6-9 (M = 6.98, SD = 0.72) years old and had their physical activity measured via tri-axial accelerometry and behavioral observation. Tri-axial accelerometry and observation (r = .77 to .79, p ≤ .01) demonstrated a stronger relation than heart rate and observation (r = .42 to .49, p ≤ .01). Bland-Altman plots (Bland & Altman, 1986) revealed a marginally better agreement between tri-axial accelerometry and observation than between heart rate and observation, but both accelerometry and heart rate telemetry significantly overestimated physical activity time. In conclusion, neither accelerometry nor heart rate, following the instrumentation data collection protocol in this study, would be an acceptable alternative for behavioral observation.

TLC for Better PE: Girls and Elementary Physical Education

ach year, on each of some 180 school days, teachers have apE proximately six hours (or 37% of a child’s waking day) to influence the knowledge, skills, and attitudes of the children under their supervision. Because physical education is the favorite subject of a large percentage of elementary school children, physical educators are in the enviable position of working with highly motivated students. One would assume that this enjoyment of physical education would lead to a love of physical activity in general. Wliy is it, then, that by the time girls reach adolescence, a high percentage of them dislike physical activity and physical education (Luke & Sinclair, 1991) and have become sedentary (U. S. Department of Health and HumanServices [USDHHS], 1996)? The answer is likely associated with girls’ perceived efficacy in relation to physical activity and physical education. Many terms are used in the literature to define the old notion of “competence motivation” (White, 1959), such as “perceived efficacy,” “perceived ability,” “perceived competence,” “self-efficacy,” and “self-concept of ability.” For the purposes of this article, perceived efficacy is defined as a child’s confidence in his or her ability to successfully complete a task. Harter’s (1990) work on perceived 2fficacy has shown us that children d l persist in an activity if they are :onfident of their ability to succeed. Zonversely, they will disengage from 1 task if they lack this confidence. I‘herefore, it is extremely important that elementary physical educators purposely structure physical education tasks so that girls can approach them Nith the confidence necessary to succeed. With increased perceived efficacy toward physical education, there is a higher probability that girls will learn useful motor skills and continue a pattern of physical activity outside of school. There are three main factors that influence the perceived efficacy (PE) of girls regarding elementary physical education: the teacher (T) , the learning environment (L) , and the curriculum (C) . When these Factors are combined in a pedagogically sound manner, girls will be more likely to respond to TLC by exhibiting better PE.

Physical Activity Measurement Device Agreement: Pedometer Steps/Minute and Physical Activity Time

The purpose of this study was to examine agreement between the Walk4Life DUO pedometer (W4L; Walk4Life, Plainfield, Illinois, USA) and two criterion instruments in the measurement of physical activity. Participants (N = 189, M = 16.74 years, SD = .99) in high school physical education concurrently wore the DUO (i.e., comparison instrument) and Yamax SW651 pedometers (Yamax Corp., Japan; i.e., criterion instrument), and were video recorded for off-site systematic behavioral observation (i.e., criterion instrument). Physical activity measures were steps/minute (i.e., DUO and SW651) and physical activity time (minutes; i.e., DUO and behavioral observation). Relative agreement between physical activity measures was strong (r ≥ .97, p ≤ .05). However, overall absolute agreement suggests that the DUO pedometer underestimates steps/minute (mean difference score [Mdiff ] = 3.13, SD = 4.73, t = 9.07, p = .0001), overestimates physical activity time (Mdiff = −10.76, SD = 6.46, t = −17.22, p = .0001), and does not produce clinically acceptable steps/minute (mean absolute percent error [MAPE ] = 10.82%, SD = 9.82) and physical activity time (MAPE = 76.13%, SD = 37.04) outcomes. In conclusion, it is recommended that researchers and practitioners cautiously interpret DUO steps/minutes and not employ the DUO physical activity time function during physical activity programs, such as high school physical education, to estimate physical activity time.

Pedometer steps/min in physical education: does the pedometer matter?

OBJECTIVES The investigation sought to replicate previous Yamax physical education steps/min findings by quantifying physical activity via pedometry albeit with the Walk4Life (W4L) pedometer. Specifically, the objective was to determine steps/min cut point intervals for the 33% and 50% physical activity (i.e., percent of lesson time engaged in physical activity [%PA]) physical education guidelines via the W4L pedometer. DESIGN Field-based criterion-referenced validation. METHODS Data were collected from 75 lessons on 411 fifth- through twelfth-grade (M(age)=13.83±2.17 y) participants who had concurrently measured pedometer and behavioural observation data. The W4L and Yamax pedometer outcome measure was steps/min, and observation measure was %PA. Pearson r correlation and diagnostic (i.e., sensitivity, specificity, and receiver-operating characteristic [ROC] curve) tests were conducted. RESULTS (a) Steps/min and %PA demonstrated a strong relationship (W4L, r=0.96, p=0.0001; Yamax, r=0.96, p=0.0001), (b) W4L pedometer steps/min accurately discriminated (ROC area under curve ≥ 98%) between achievement or non-achievement of %PA guidelines, (c) the W4L steps/min cut point intervals for the 33%PA guideline (55.0-59.5) were significantly lower than those found for the Yamax pedometer (60.8-65.0), and (d) a borderline overlap was found between W4L (75.7-79.5) and Yamax (79.1-85.8) steps/min cut point intervals for the 50%PA guideline. CONCLUSIONS W4L steps/min demonstrated a strong relationship with %PA, and outstanding accuracy for physical education physical activity guideline discrimination; however, steps/min values indicative of physical education physical activity guideline achievement is pedometer brand dependent, and should be considered for steps/min implementation and surveillance.

Developing a Statewide Childhood Body Mass Index Surveillance Program.

BACKGROUND Several states have implemented childhood obesity surveillance programs supported by legislation. Representatives from Idaho wished to develop a model for childhood obesity surveillance without the support of state legislation, and subsequently report predictors of overweight and obesity in the state. METHODS A coalition comprised of the Idaho State Department of Education and 4 universities identified a randomized cluster sample of schools. After obtaining school administrator consent, measurement teams traveled to each school to measure height and weight of students. Sex and race/ethnicity data were also collected. RESULTS The collaboration between the universities resulted in a sample of 6735 students from 48 schools and 36 communities. Overall, 29.2% of the youth in the sample were classified as overweight or obese, ranging from 24.0% for grade 1 to 33.8% for grade 5. The prevalence of overweight and obesity across schools was highly variable (31.2 ± 7.58%). Hierarchical logistic regression indicated that sex, age, race/ethnicity, socioeconomic status, and region were all significant predictors of overweight and obesity, whereas school was not. CONCLUSIONS This coalition enabled the state of Idaho to successfully estimate the prevalence of overweight and obesity on a representative sample of children from all regions of the state, and subsequently identify populations at greatest risk.

Cultivating Leadership, Pedagogy and Programming for CSPAP and Healthy, Active Lifestyles at the University of Idaho.

This article highlights processes for infusing comprehensive school physical activity programming (CSPAP) into the physical education teacher education (PETE) program at the University of Idaho (UI). The PETE program uses a modified leadership framework to target learning outcomes and activities pertinent to CSPAP. Student CSPAP knowledge and practice is strengthened and put into practice by taking three Department of Movement Science core courses focused on individual and community healthy active lifestyles (HAL). The two community service-learning HAL courses focus on assessment, programming pedagogy and marketing of HAL interventions. Knowledge, skills, dispositions, roles and opportunities are progressively sequenced for preservice students to develop effective Physical Activity Leader (PAL) role knowledge and experience, while meeting state physical education teacher standards. Through PAL training, workshops and teaching opportunities, faculty integrate public health prevention models, behavioral change theories, and motivational strategies in school, after-school and community-based settings. Research activities include leadership skill growth, the link between CSPAP assessment and advocacy, Idaho physical education teacher demand and CSPAP understanding, and the relationship between administrator support and technology use to support CSPAP.

Data Imputation Improves Sedentary Behavior and Physical Activity Estimates in Low Wear Time Accelerometer Data: 3670 Board #117 June 3 8

Missing accelerometer data from low participant wear time underestimates sedentary behavior (SB) and physical activity (PA) measurements. Yet, it remains unclear if imputing data for low participant wear time improves SB and PA estimates. PURPOSE: To determine if a data imputation technique improves SB and PA estimates in accelerometer data with low participant wear time. METHODS: One-hundred participants wore an accelerometer at the hip for ≥22.0 hours/day, at least 4 days including 1 weekend day, to capture habitual SB, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) levels. After removing sleep time (RAW; 15.9±3.5 hours/day), random 60-minute blocks of data were removed from the RAW data set until participants had a unique data set with wear time adherence at 10 hours/day. A minute-by-minute, mean data imputation technique was used to impute estimates of SB, LPA, and MVPA in place of the missing data for the 10-hour adherence level. A series of paired t-tests with a Bonferroni correction (alpha level=0.006) compared the estimates of SB, LPA, and MVPA to the RAW data set at the 10-hour adherence level. Similarly, imputed estimates of SB, LPA, and MVPA were compared to the RAW data set at the 10-hour adherence level. RESULTS: SB, LPA, and MVPA were underestimated by 163.7 (95% confidence intervals [CI]: 156.0, 171.5; p<0.0001), 138.4 (CI: 129.1, 147.9; p<0.0001), and 27.2 (CI: 24.3, 30.1; p<0.0001) minutes/day at 10-hours of wear compared to the RAW data set, respectively. When utilizing the data imputation technique at the 10-hour adherence level, SB and MVPA were underestimated by 16.8 (CI: 8.7, 24.9; p<0.0001) and 17.1 (CI: 14.5, 19.6; p<0.0001) minutes/day compared to the RAW data set, respectively. LPA at the 10-hour adherence level was overestimated by 33.9 (CI: 25.9, 41.9; p<0.0001) minutes/day compared to the RAW data set after utilizing the data imputation technique. CONCLUSION: A minute-by-minute, mean data imputation technique improved SB, LPA, and MVPA estimates in accelerometer data with low wear time adherence. Future studies should examine the impact of data imputation techniques on accelerometer data with low participant wear time.