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Aashirwad Mahajan, Satish Mahajan, and Swanand Tilekar

The primary objective of this pilot randomized controlled trial was to study the feasibility (recruitment and retention rates) for interval training and sleep hygiene (SH) in adults aged above 60 years. Thirteen out of 46 screened individuals from a home for older adults in Shirdi (Maharashtra, India) were randomly assigned by permuted block randomization to either an interval training with SH group (n = 6) or an SH alone group (n = 7). The authors measured sleep with the S+ sleep monitor manufactured by ResMed (USA) Pittsburgh Sleep Quality Index and quality of life with Short Form-12 health survey version 2. Interval training consisted of 8 weeks of stationary cycling, whereas SH consisted of lecture and handouts. Recruitment was 38.2%, retention was >80% for both the interventions, and there was one loss to follow-up in SH. Interval training and SH were feasible for older adults and supported a full-scale randomized controlled trial.

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Lyndel Hewitt, Anthony D. Okely, Rebecca M. Stanley, Marjika Batterham, and Dylan P. Cliff

Background: Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants. Methods: The analysis was conducted on accelerometer data from 32 healthy infants (4–25 wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3 min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland–Altman plots. Results: Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and −0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838–0.842) and specificity (0.948, 95% confidence interval, 0.944–0.948). Conclusions: Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than −0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.

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Victor E. Ezeugwu, Piush J. Mandhane, Nevin Hammam, Jeffrey R. Brook, Sukhpreet K. Tamana, Stephen Hunter, Joyce Chikuma, Diana L. Lefebvre, Meghan B. Azad, Theo J. Moraes, Padmaja Subbarao, Allan B. Becker, Stuart E. Turvey, Andrei Rosu, Malcolm R. Sears, and Valerie Carson

Background: Movement behaviors (physical activity, sedentary time, and sleep) established in early childhood track into adulthood and interact to influence health outcomes. This study examined the associations between neighborhood characteristics and weather with movement behaviors in preschoolers. Methods: A subset of Canadian Healthy Infant Longitudinal Development birth cohort (n = 385, 50.6% boys) with valid movement behaviors data were enrolled at age 3 years and followed through to age 5 years. Objective measures of neighborhood characteristics were derived by ArcGIS software, and weather variables were derived from the Government of Canada weather website. Random forest and linear mixed models were used to examine predictors of movement behaviors. Cross-sectional analyses were stratified by age and season (winter and nonwinter). Results: Neighborhood safety, temperature, green space, and roads were important neighborhood characteristics for movement behaviors in 3- and 5-year-olds. An increase in temperature was associated with greater light physical activity longitudinally from age 3 to 5 years and also in the winter at age 5 years in stratified analysis. A higher percentage of expressways was associated with less nonwinter moderate to vigorous physical activity at age 3 years. Conclusions: Future initiatives to promote healthy movement behaviors in the early years should consider age differences, neighborhood characteristics, and season.

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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.