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Larkin L. Strong, Cheryl B. Anderson, Patricia Y. Miranda, Melissa L. Bondy, Renke Zhou, Carol Etzel, Margaret Spitz and Anna V. Wilkinson

Background:

Understanding the factors that contribute to physical activity (PA) in Mexican-origin adolescents is essential to the design of effective efforts to enhance PA participation in this population.

Methods:

Multivariable logistic regression was used to identify sociodemographic and behavioral correlates of self-reported PA in school and community settings in 1154 Mexican-origin adolescents aged 12–17 years in Houston, TX.

Results:

The majority of adolescents were born in the US (74%), approximately half (51%) were overweight or obese, and nearly three-quarters (73%) watched more than 2 hours of weekday television. Similarities and differences by setting and gender were observed in the relationships between sociodemographic and behavioral characteristics and PA. In boys, parental education and attending physical education (PE) were positively associated with PA across multiple PA outcomes. Adolescent linguistic acculturation was inversely associated with participation in community sports, whereas parental linguistic acculturation was positively associated with PA at school. In girls, PA in school and community settings was inversely associated with TV viewing and positively associated with PE participation.

Conclusions:

These findings highlight similarities and differences in correlates of PA among boys and girls, and point toward potential sources of opportunities as well as disparities for PA behaviors in Mexican-origin adolescents.

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Kristen Holm, Holly Wyatt, James Murphy, James Hill and Lorraine Odgen

Background:

This study examined the association between parent and child change in physical activity during a family-based intervention for child weight gain prevention.

Methods:

Daily step counts were recorded for parents and children in 83 families given a goal to increase activity by 2000 steps per day above baseline. Linear mixed effects models were used to predict child change in daily step counts from parental change in step counts.

Results:

Both maternal (P < .0001) and paternal (P < .0001) change in step counts for the current day strongly predicted child change in step counts for that day. On average, a child took an additional 2117.6 steps above baseline on days his or her mother met her goal versus 1175.2 additional steps when the mother did not meet her goal. The respective values were 1598.0 versus 1123.1 steps for fathers. Day of week moderated the maternal effect (P = .0019), with a larger impact on Saturday and Sunday compared with weekdays. A similar but nonsignificant pattern was observed for fathers.

Conclusions:

Encouraging parents to increase physical activity, particularly on weekends, may be a highly effective way to leverage parental involvement in interventions to increase children’s physical activity.

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David E. Conroy, Steriani Elavsky, Amanda L. Hyde and Shawna E. Doerksen

The intention-behavior gap has proven to be a vexing problem for theorists and practitioners interested in physical activity. Intention stability is one factor which moderates this gap. This study articulated and tested contrasting views of intention stability as (a) a dynamic characteristic of people that influences assessment error (and therefore the predictive power of intentions) and (b) the product of a dynamic process that unfolds within people over time. Using an ecological momentary assessment design, young adults (N = 30) rated weekly physical activity intentions for 10 weeks and wore pedometers for the first 4 weeks of the study. Substantial within-person variability existed in intentions over both 4- and 10-week intervals, and this variability was not a function of time exclusively. Multilevel modeling revealed that overall intention strength (across weeks) and weekly deviations in intention strength interacted to predict weekday (but not weekend) physical activity. These findings indicate that the person and context interact to selectively couple or decouple intentions from daily physical activity.

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Dirk Aerenhouts, Jelle Van Cauwenberg, Jacques Remi Poortmans, Ronald Hauspie and Peter Clarys

This study aimed to estimate nitrogen balance and protein requirements in adolescent sprint athletes as a function of growth rate and physical development. Sixty adolescent sprint athletes were followed up biannually over a 2-yr period. Individual growth curves and age at peak height velocity were determined. Skeletal muscle mass (SMM) was estimated based on anthropometric measurements and fat mass was estimated by underwater densitometry. Seven-day diet and physical activity diaries were completed to estimate energy balance and protein intake. Nitrogen analysis of 24-hr urine samples collected on 1 weekday and 1 weekend day allowed calculation of nitrogen balance. Body height, weight, and SMM increased throughout the study period in both genders. Mean protein intakes were between 1.4 and 1.6 g kg−1 day−1 in both genders. A protein intake of 1.46 g kg−1 day−1 in girls and 1.35 g kg−1 day−1 in boys was needed to yield a positive nitrogen balance. This did not differ between participants during and after their growth spurt. None of the growth parameters was significantly related to nitrogen balance. It can be concluded that a mean protein intake around 1.5 g kg−1 day−1 was sufficient to stay in a positive nitrogen balance, even during periods of peak growth. Therefore, protein intake should not be enhanced in peak periods of linear or muscular growth.

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Freddie Bennett, Pat Eisenman, Ron French, Hester Henderson and Barry Shultz

A single-subject multiple baseline design across subjects was used to discern the effect of a token economy on the exercise behavior and cardiorespiratory fitness of individuals with Down syndrome. The subjects were three females ranging in age from 24 to 26 years, with estimated IQs between 32 and 56. The exercise behavior consisted of pedaling a cycle ergometer for 15 min each weekday at 50-60% of peak VO2 for 6 to 8 weeks. Subjects voluntarily pedaled the cycle ergometer during the baseline phase, and after stabilization entered the intervention phase at 5-day intervals. During the intervention phase, tokens that could be exchanged for preferred items were dispensed for a predetermined number of revolutions. Based on the data and calculations using the split-middle technique, it was concluded that a token economy can be used to increase exercise behavior. Resting heart rates decreased 12.2%, and submaximal exercise heart rates, averaged over three work stages, decreased 18.8% over the course of the study. The small sample size, variable subject response, and a malfunctioning gas analyzer call for caution in inferring any possible cardiorespiratory fitness training effect.

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Katherine L. Downing, Jo Salmon, Anna Timperio, Trina Hinkley, Dylan P. Cliff, Anthony D. Okely and Kylie D. Hesketh

school hours) TV/video/DVD time, computer use, and electronic games during the week (ie, Monday to Friday) and on weekends (ie, Saturday and Sunday). Total minutes in each of these activities on weekdays were summed and divided by 5 to give average weekday minutes of recreational screen time. Similarly

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Louise L. Hardy, Ding Ding, Louisa R. Peralta, Seema Mihrshahi and Dafna Merom

spent watching television or videos/DVDs, using a computer for fun, playing computer or video games, and playing on a smartphone or tablet. We reported hours or fraction of an hour based on reported minutes and present the mean time spent sitting and on screen time for daily (all days/7), weekday

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Liezel Hurter, Anna M. Cooper-Ryan, Zoe R. Knowles, Lorna A. Porcellato, Stuart J. Fairclough and Lynne M. Boddy

recently published ( Rowlands, Edwardson, et al., 2018 ) accelerometer metric describing the intensity distribution of physical activity over the 24-hour day. All outcomes were broken down into weekdays, weekend days, and whole week data. Inclusion criteria for raw data analysis were at least 16 hours of

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Simone A. Tomaz, Alessandra Prioreschi, Estelle D. Watson, Joanne A. McVeigh, Dale E. Rae, Rachel A. Jones and Catherine E. Draper

analysis. For the PA analysis, data were excluded if the child did not have 7 hours of valid waking wear time (after excluding naps) on at least 3 weekdays and 1 weekend day of the 7-day period. 27 Nonwear time, where the device had, for example, been removed for a water-based activity, was defined as 20

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Matthew Pearce, David H. Saunders, Peter Allison and Anthony P. Turner

each epoch as structured or unstructured. A summary of how contexts of physical activity were derived is shown in Table  1 . Minutes of time spent and MVPA in each context were summed by participant and day. Based on individual means across days of measurement, weekday values were calculated for