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Astrid C.J. Balemans, Han Houdijk, Gilbert R. Koelewijn, Marjolein Piek, Frank Tubbing, Anne Visser-Meily and Olaf Verschuren

) compare energy expenditure and muscle activity between SB (lying and sitting) between healthy controls, adults with stroke, and adults with CP and (2) investigate responses of light physical activities (standing and walking) within groups. The hypothesis is that individuals with a mild physical disability

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Jieling Chen, Emily Joy Nicklett, Yaping He and Vivian W.Q. Lou

impairment, and a different psychological state, further research is needed to identify the correlates of physical activity among middle-aged and older adults with CKD. This study aims to (a) describe participation in vigorous, moderate, and light physical activity in middle-aged and older adults with CKD in

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Mark A. Tully, Ilona I. McMullan, Nicole E. Blackburn, Jason J. Wilson, Laura Coll-Planas, Manuela Deidda, Paolo Caserotti, Dietrich Rothenbacher and on behalf of the SITLESS group

least one weekend day; to be included, each day had to include at least 600 min (10 hr per day) of wear time, as in previous studies ( Migueles et al., 2017 ). SB was classified as <100 counts per minute; daily light physical activity (LPA) was 100–2,019 counts per minute, and daily moderate

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Claire L. Cleland, Sara Ferguson, Paul McCrorie, Jasper Schipperijn, Geraint Ellis and Ruth F. Hunter

120 Note . LFE = low-frequency extension; LPA = light physical activity; MVPA = moderate-to-vigorous physical activity; NA = not applicable. a Number of hours in a valid day: 10 hr; and number of valid days in a valid week: 5 days (including one weekend day). Each of the processing criteria were

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Judith Godin, Joanna M. Blodgett, Kenneth Rockwood and Olga Theou

considered valid if participants had worn the accelerometer for at least 10 hr/day on four different days. We used the following NHANES cut points: 0–100 counts/min, sedentary time; 100–2,020 counts/min, light physical activity; and >2,020 counts/min, moderate–vigorous physical activity ( Blodgett et

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Zan Gao, Senlin Chen and David F. Stodden

Purpose:

To compare young children’s different intensity physical activity (PA) levels in physical education, recess and exergaming programs.

Methods:

Participants were 140 first and second grade children (73 girls; Meanage= 7.88 years). Beyond the daily 20-minute recess, participants attended 75-minute weekly physical education classes and another 75-minute weekly exergaming classes. Children’s PA levels were assessed by ActiGraph GTX3 accelerometers for 3 sessions in the 3 programs. The outcome variables were percentages of time spent in sedentary, light PA and moderate-to-vigorous PA (MVPA).

Results:

There were significant main effects for program and grade, and an interaction effect for program by grade. Specifically, children’s MVPA in exergaming and recess was higher than in physical education. The 2nd-grade children demonstrated lower sedentary behavior and MVPA than the first-grade children during recess; less light PA in both recess and exergaming than first-grade children; and less sedentary behavior but higher MVPA in exergaming than first-grade children.

Conclusions:

Young children generated higher PA levels in recess and exergaming as compared with physical education. Hence, other school-based PA programs may serve as essential components of a comprehensive school PA program. Implications are provided for educators and health professionals.

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Peng Zhang, Jung Eun Lee, David F. Stodden and Zan Gao

.01 440.41 113.96  Weekend 301.38 101.43 284.07 98.01 541.95 91.91 344.30 153.17 EE, KPD  Weekday 282.14 138.68 321.39 169.22 303.52 192.75 279.48 170.04  Weekend 254.24 154.66 320.85 209.22 251.69 200.94 254.76 193.25 Abbreviations: EE, energy expenditure; KPD, kilocalories per day; LPA, light physical

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Adilson Santos Andrade de Sousa, Marilia A. Correia, Breno Quintella Farah, Glauco Saes, Antônio Eduardo Zerati, Pedro Puech-Leao, Nelson Wolosker, Gabriel G. Cucato and Raphael M. Ritti-Dias

activity data were included only if the patient had accumulated a minimum of 10 hr/day of recording for at least 4 days, including one weekend day. Using previously reported cutoff thresholds for older people ( Copeland & Esliger, 2009 ; i.e., light physical activity as 100–1,041 counts/min and moderate

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Nicholas L. Lerma, Ann M. Swartz, Taylor W. Rowley, Hotaka Maeda and Scott J. Strath

Background:

The ill-health effects of sedentary behavior are becoming well-documented, yet older adults spend 70–80% of waking hours sedentary.

Purpose:

To determine if a portable elliptical device increases energy expenditure (EE) while performing popular seated activities.

Methods:

Twenty older adults (68.1 ± 1.4 years) participated to compare the measured EE between seated rest and three randomized seated pedaling activities: computer use, reading, TV viewing. Each pedaling activity included 5-min of self-selected paced/no resistance (SSP) and externally paced/added resistance pedaling (Paced).

Results:

A significant increase in EE existed during SSP (+1.44 ± 0.12 kcal/min) and Paced (+2.19 ± 0.09 kcal/min) pedaling relative to Seated Rest (p < .001). EE during the Paced activities was significantly greater than all SSP activities (p <.01).

Conclusion:

Extrapolating these results, pedaling at a SSP for an hour while performing seated activities is equivalent to the net EE of walking 1.6 miles. Future home-based effectiveness and feasibility should be explored.

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Anne L. Adolph, Maurice R. Puyau, Firoz A. Vohra, Theresa A. Nicklas, Issa F. Zakeri and Nancy F. Butte

Purpose:

Given the unique physical activity (PA) patterns of preschoolers, wearable electronic devices for quantitative assessment of physical activity require validation in this population. Study objective was to validate uniaxial and triaxial accelerometers in preschoolers.

Methods:

Room calorimetry was performed over 3 hours in 64 preschoolers, wearing Actical, Actiheart, and RT3 accelerometers during play, slow, moderate, and fast translocation. Based on activity energy expenditure (AEE) and accelerometer counts, optimal thresholds for PA levels were determined by piecewise linear regression and discrimination boundary analysis.

Results:

Established HR cutoffs in preschoolers for sedentary/light, light/moderate and moderate/vigorous levels were used to define AEE (0.015, 0.054, 0.076 kcal·kg−1·min−1) and PA ratio (PAR; 1.6, 2.9, 3.6) thresholds, and accelerometer thresholds. True positive predictive rates were 77%, 75%, and 76% for sedentary; 63%, 61%, and 65% for light; 34%, 52%, and 49% for moderate; 46%, 46%, and 49% for vigorous levels. Due to low positive predictive rates, we combined moderate and vigorous PA. Classification accuracy was improved overall and for the combined moderate-to-vigorous PA level (69%, 82%, 79%) for Actical, Actiheart, and RT3, respectively.

Conclusion:

Uniaxial and triaxial accelerometers are acceptable devices with similar classification accuracy for sedentary, light, and moderate-to-vigorous levels of PA in preschoolers.