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David H. Perrin

In this essay, I reflect on my life and academic career, detailing my childhood, family background, education, and those who influenced me to study physical education and athletic training. My higher education started with a small college experience that had a transformative impact on my intellectual curiosity, leading to graduate degrees and, ultimately, a career in higher education. I chronicle my academic career trajectory as a non-tenure-track faculty member and clinician, tenured faculty member, department chair, dean, and provost. My personal and professional lives have been undergirded by a commitment to equity, diversity, and inclusion, with examples provided in this essay.

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Nathalie Berninger, Gill ten Hoor, Guy Plasqui, and Rik Crutzen

Purpose : Physical activity (PA) is crucial for health, but there is insufficient evidence about PA patterns and their operationalization. The authors developed two algorithms (SPORTconstant and SPORTlinear) to quantify PA patterns and check whether pattern information yields additional explained variance (compared with a compositional data approach [CoDA]). Methods : To measure PA, 397 (218 females) adolescents with a mean age of 12.4 (SD = 0.6) years wore an ActiGraph on their lower back for 1 week. The SPORT algorithms are based on a running value, each day starting with 0 and minutely adapting depending on the behavior being performed. The authors used linear regression models with a behavior-dependent constant (SPORTconstant) and a function of time-in-bout (SPORTlinear) as predictors and body mass index z scores (BMIz) and fat mass percentages (%FM) as exemplary outcomes. For generalizability, the models were validated using five-fold cross-validation where data were split up in five groups, and each of them was a test data set in one of five iterations. Results : The CoDA and the SPORTconstant models explained low variance in BMIz (2% and 1%) and low to moderate variance in %FM (both 5%). The variance being explained by the SPORTlinear models was 6% (BMIz) and 9% (%FM), which was significantly more than the CoDA models (p < .001) according to likelihood ratio tests. Conclusion : Among this group of adolescents, SPORTlinear explained more variance of BMIz and %FM than CoDA. These results suggest a way to enable research about PA patterns. Future research should apply the SPORTlinear algorithm in other target groups and with other health outcomes.

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Jillian J. Haszard, Tessa Scott, Claire Smith, and Meredith C. Peddie

Short sleep duration is associated with poorer outcomes for adolescents; however, sleep duration is often assessed (either by questionnaire or device) using self-reported bedtime (i.e., the time a person goes to bed). With sedentary activities, such as screen time, being common presleep in-bed behaviors, the use of “bedtime” may introduce error to the estimates of sleep duration. It has been proposed that self-reported “shuteye time” (i.e., the time a person starts trying to go to sleep) is used instead of bedtime. This study aimed to compare the bedtimes and shuteye times of a sample of 15- to 18-year-old female adolescents recruited from 13 high schools across New Zealand. The influence on sleep duration estimates and associations with healthy lifestyle habits was also examined. Sleep data were collected from 136 participants using actigraphy and self-report. On average, 52 min (95% confidence interval [43, 60] min) of sedentary time was misclassified as sleep when bedtime was used instead of shuteye time with actigraph data. Mean bedtimes on weekdays and weekends were 9:56 p.m. (SD = 58 min) and 10:40 p.m. (SD = 77 min), respectively. The relationship between bedtime and shuteye time was not linear—indicating that bedtime cannot be used as a proxy for shuteye time. Earlier shuteye times were more strongly associated with meeting fruit and vegetable intake and sleep and physical activity guidelines than earlier bedtimes. Using bedtime instead of shuteye time to estimate sleep duration may introduce substantial error to estimates of both sleep and sedentary time.

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Thomas L. McKenzie

This essay describes how environmental conditions affected my unexpected evolution from farm life in a rural Canadian community to becoming a physical education specialist and multisport coach and eventually a U.S. kinesiology scholar with a public health focus. I first recount my life on the farm and initial education and then identify the importance of full- and part-time jobs relative to how they helped prepare me for a life in academia. Later, I summarize two main areas of academic work that extended beyond university campuses—the design and implementation of evidence-based physical activity programs and the development of systematic observation tools to assess physical activity and its associated contexts in diverse settings, including schools, parks, and playgrounds. I conclude with a section on people and locations to illustrate the importance of collaborations—essential components for doing field-based work. Without those connections, I would not have had such an extensive and diverse career.

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Olayinka Akinrolie, Sandra C. Webber, Nancy M. Salbach, and Ruth Barclay

The aim of this study was to examine the construct and known-groups validity of the total score of five items adapted from the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire to measure outdoor walking (CHAMPS-OUTDOORS) in older adults. Data from the baseline assessment of the Getting Older Adult OUTdoors (GO-OUT) trial were used. Construct validity of the CHAMPS-OUTDOORS used objective measures of outdoor walking (accelerometry–GPS), Ambulatory Self-Confidence Questionnaire, RAND-36, 6-min walk test, 10-m walk test, and Mini-Balance Evaluation System Test. For known-groups validity, we compared the CHAMPS-OUTDOORS of those who walked < or ≥1.2 m/s. Sixty-five participants had an average age of 76.5 ± 7.8 years. The CHAMPS-OUTDOORS was moderately correlated with total outdoor walking time (r = .33) and outdoor steps (r = .33) per week measured by accelerometry-GPS, and weakly correlated with Mini-Balance Evaluation System Test score (r = .27). The CHAMPS-OUTDOORS did not distinguish known groups based on crosswalk speed (p = .33). The CHAMPS-OUTDOORS may be used to assess outdoor walking in the absence of accelerometry GPS. Further research examining reliability is needed.

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Luciana L.S. Barboza, Larissa Gandarela, Josefa Graziele S. Santana, Ellen Caroline M. Silva, Elondark S. Machado, Roberto Jerônimo S. Silva, Thayse N. Gomes, and Danilo R. Silva

Introduction: The authors’ objective was to identify the minimum number of days required to measure sedentary behavior and physical activity in children during school hours. Methods: Fifty-three children from four classes of the second year of elementary school in a public school in Brazil were selected. Sedentary behavior and physical activity were evaluated using activPAL in the thigh and ActiGraph GT3X on the hip. The devices were used for 4 days during the 4 hr of school. Intraclass correlation coefficient (ICC) and Bland–Altman plots were used for statistical analysis (p < .05). Results: For sedentary/stationary behavior indicators, 1 day showed good agreement with 4 days (sitting time, ICC = .89; bias [limits of agreement 95%, LA95%] = 1.6 [45.1 to −41.9], standing time, ICC = .93; bias [LA95%] 1.1 [30.2 to −28.0], and stationary behavior, ICC = .56; bias [LA95%] = 0.2 [37.2 to −36.7]). However, 2 days were necessary for good agreement, with 4 days for physical activity indicators (walking time, ICC = .91; bias [LA95%] = 1.1 [12.0 to −9.7], light physical activity, ICC = .97; bias [LA95%] = 0.3 [7.6 to −7.0], moderate physical activity, ICC = .93; bias [LA95%] = 0.3 [2.3 to −1.6], and vigorous physical activity, ICC = .93; bias [LA95%] = 0.3 [3.1 to −2.5]). Conclusion: Therefore, 1 evaluation day seems enough to obtain representative data of school sedentary/stationary behavior, while 2 days are necessary for the evaluation of physical activity indicators during school hours.

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Petra Haas, Chih-Hsiang Yang, and Genevieve F. Dunton

Physical activity declines from childhood to adolescence. Affective factors may partially account for this decline. The present study investigated whether within-person changes in children’s enjoyment of physical activity are associated with the age-related decline in physical activity. Children (N = 169, 54% female, 56% Hispanic; 8–12 years old at enrollment) took part in a longitudinal study with six assessment waves across 3 years. At each wave, enjoyment of physical activity was reported, and moderate to vigorous physical activity (MVPA) was measured with an accelerometer across seven consecutive days. MVPA and enjoyment of physical activity both declined across waves. Multilevel analyses revealed that within-person changes in enjoyment moderated the effects of age on within-person changes in MVPA. Enjoyment appeared to be a dynamic factor that buffered against the age-related decline in physical activity in youth. These findings call for health promotion interventions that encourage enjoyable physical activities.

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Hilary Hicks, Alexandra Laffer, Kayla Meyer, and Amber Watts

As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = 3.92%) with the LFE filter (M = 11, SD = 1; M = 4.27%, SD = 3.52%) versus without the LFE filter (all ps < .001). The findings suggest that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should develop separate wrist and waist-worn standard estimates in older adults. Participation in a greater amount of moderate to vigorous intensity physical activity predicted a larger discrepancy in step counts generated with and without the filter (p < .009), suggesting that the LFE filter becomes increasingly inappropriate for use in highly active older individuals.

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Shelby Borowski, Jyoti Savla, and Anisa M. Zvonkovic

Background: Little is known about the link between flexible work arrangements and health behaviors, such as physical activity. This study aimed to explore how self-efficacy and daily barriers to physical activity influence daily levels of physical activity on workdays when university staff members used a flexible work arrangement (flextime or telework). Methods: Full-time university staff employees (N = 61, mean age = 41; 89% female) participated in this daily diary study. Participants completed an initial survey followed by daily surveys over the course of one workweek, resulting in 281 diary days. Results: The most frequently reported barriers to physical activity were as follows: lack of time, feeling tired, and not enough motivation. Multilevel models revealed that as the number of barriers increased, minutes of physical activity significantly decreased. Self-efficacy was not significantly related to daily physical activity. Participants reported fewer minutes of physical activity on flextime workdays compared to days when a flexible work arrangement was not used (ie, traditional workday). Daily use of a flexible work arrangement did not moderate the association between barriers and physical activity. Conclusions: This study illustrated the influence of daily barriers and flextime workdays on physical activity levels, which can inform workplace health programs for university staff.

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Matthew Hobbs, Stuart J.H. Biddle, Andrew P. Kingsnorth, Lukas Marek, Melanie Tomintz, Jesse Wiki, John McCarthy, Malcolm Campbell, and Simon Kingham

Background: This study investigates the association between television (TV) viewing and child adiposity and if parental education and child ethnicity moderate this association. Method: Cross-sectional, pooled (2013/2014–2016/2017) adult and child New Zealand Health Survey were matched resulting in 13,039 children (2–14 y) and parent dyads. Child TV viewing was estimated using self-reported time for each weekday and weekend. The height (in centimeters), weight (in kilograms), and waist circumference of parents and children were measured. Childhood body mass index and obesity were defined using the International Obesity Task Force cutoff values. Effect modification was assessed by interaction and then by stratifying regression analyses by parent education (low, moderate, and high) and child ethnicity (Asian, European/other, Māori, and Pacific). Results: Overall, watching ≥2 hours TV on average per day in the past week, relative to <2 hours TV viewing, was associated with a higher odds of obesity (adjusted odds ratio = 1.291 [1.108–1.538]), higher body mass index z score (b = 0.123 [0.061–0.187]), and higher waist circumference (b = 0.546 [0.001–1.092]). Interactions considering this association by child ethnicity and parent education revealed little evidence of effect modification. Conclusion: While TV viewing was associated with child adiposity, the authors found little support for a moderating role of parental education and child ethnicity.