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Mareesa O’Dwyer, Stuart J. Fairclough, Nicola Diane Ridgers, Zoe Rebecca Knowles, Lawrence Foweather and Gareth Stratton

Background:

Identifying periods of the day which are susceptible to varying levels of physical activity (PA) may help identify key times to intervene and potentially change preschool children’s PA behaviors. This study assessed variability of objectively measured moderate-to-vigorous physical activity (MVPA) during weekdays and weekend days among preschool children.

Methods:

One hundred and eighty-eight children (aged 3 to 5 years; 53.2% boys) from a northwest English city wore uni-axial accelerometers for 7 consecutive days.

Results:

Higher levels of MVPA were recorded in boys, particularly those who attended preschool for a half day. Children who attended preschool for a full day engaged in 11.1 minutes less MVPA than children who attended for a half day. After-school hours were characterized by a decrease in activity for all groups. Patterns of activity during the weekend were smoother with less variability.

Conclusion:

This study identified discrete segments of the week, specifically afterschool and during the weekend, when preschoolers engage in low levels of PA. Higher levels of MVPA among children who attended preschool for less time each day suggests that the structured preschool environment is related to decreased activity. Consequently, there is a need for interventions in young children to focus on school and home environments.

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Katja Borodulin, Anja Kärki, Tiina Laatikainen, Markku Peltonen and Riitta Luoto

Background:

Daily sitting time may be a risk factor for incident cardiovascular disease (CVD); however, this has not yet been extensively studied. Our aim was to study the association of total sitting time with the risk of CVD.

Methods:

Participants (n = 4516, free of CVD at baseline) from the National FINRISK 2002 Study were followed for fatal and nonfatal CVD using national registers. Participants underwent a health examination and completed questionnaires, including total daily sitting time.

Results:

During a mean follow-up of 8.6 years, 183 incident CVD cases occurred. Sitting on a typical weekday, at baseline, was statistically significantly associated with fatal and nonfatal incident CVD. The hazard ratios (with 95% confidence intervals, CI) for the total amount of sitting were 1.05 (95% CI, 1.00–1.10) in the age and gender adjusted model and 1.06 (95% CI, 1.01–1.11) in the fully adjusted model, including age, gender, employment status, education, BMI, smoking status, leisure time physical activity, use of vegetables and fruit, alcohol use, blood pressure or its medication, and cholesterol or its medication.

Conclusions:

Our findings suggest that total amount of daily sitting is a risk factor for incident CVD. More research is needed to understand the etiology of sedentary behavior and CVD.

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Peter Collins, Yahya Al-Nakeeb and Mark Lyons

Background:

Active school commuting is widely regarded as a key opportunity for youth to participate in physical activity (PA). However, the accurate measurement of the commute home from school and its contribution to total free-living moderateto- vigorous PA (MVPA) is relatively unexplored.

Methods:

Seventy-five adolescents (38 males, 37 females) wore an integrated GPS and heart rate device during after-school hours for 4 consecutive weekdays.

Results:

Active commuters were significantly more active (11.72 minutes MVPA) than passive commuters (3.5 minutes MVPA) during their commute home from school (P = .001). The commute home of walkers and cyclists on average contributed 35% of their total free-living PA. However, there was no significant difference in the overall free-living PA levels of passive and active commuters (P > .05). A total 92.7% of the youth living within 1.5 miles of the school actively commuted, compared with 16.7% of the youth who lived further away. Socioeconomic differences in commuting patterns were also evident.

Conclusions:

The findings highlighted the significant proportion of total free-living PA that was attributed to active commuting home from school. The study demonstrates the usefulness of utilizing GPS and heart rate data to accurately track young people’s after-school PA. Demographic influences and implications for future research are discussed.

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Amanda Louise Lewis and Frank F. Eves

Background/Objective:

While point-of-choice prompts consistently increase stair climbing, experimental comparisons of message content are rare. Here, the effects of 2 messages differing in complexity about the health outcomes obtainable from stair climbing were compared.

Methods:

In a UK train station with 2 independent platforms exited by identical 39-step staircases and adjacent escalators, observers recorded travelers ascent method and gender from 8:00 A.M. to 10:00 A.M. on 2 weekdays during February/March 2008 (n = 48,697). Baseline observations (2-weeks) preceded a 3-week poster phase. Two posters (594 × 841mm) that differed in the complexity of the message were positioned at the point-of-choice between ascent methods, with 1 placed on each side of the station simultaneously. Logistic regression analysis was conducted in April 2010.

Results:

Omnibus analysis contained main effects of the intervention (OR = 1.07, CI = 1.02–1.13, P = .01) and pedestrian traffic volume (OR = 5.42, CI = 3.05–9.62, P < .001). Similar effects occurred for complex (OR = 1.10, CI = 1.02–1.18, P = .01) and simple messages (OR = 1.07, CI = 1.01–1.13, P = .02) when analyses controlled for the influence of pedestrian traffic volume. There was reduced efficacy for the complex message during busier periods (OR = 0.36, CI = 0.20–0.66, P = .001), whereas the simple message was immune to these effects of traffic volume.

Conclusions:

Pedestrian traffic flow in stations can influence message effectiveness. Simple messages appear more suitable for busy sites.

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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

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Heontae Kim and Minsoo Kang

investigator, participants were instructed on how to record their sedentary behavior through the SBR and assess their sedentary behavior time for 4 consecutive days, including 2 weekdays and 2 weekend days, for reliable data collection (reliability coefficient = 0.81). 37 During this period, each participant