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Robert J. Brychta, Vaka Rögnvaldsdóttir, Sigríður L. Guðmundsdóttir, Rúna Stefánsdóttir, Soffia M. Hrafnkelsdóttir, Sunna Gestsdóttir, Sigurbjörn A. Arngrímsson, Kong Y. Chen and Erlingur Jóhannsson

Adolescent sleep patterns are often measured with self-report ( Lewandowski, Toliver-Sokol, & Palermo, 2011 ) or actigraphy ( Galland et al., 2018 ). Although self-report is easier to administer, lower in cost, and requires less technical expertise than actigraphy, it is often affected by social

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Jacopo A. Vitale, Giuseppe Banfi, Andrea Galbiati, Luigi Ferini-Strambi and Antonio La Torre

present study was to evaluate actigraphy-based sleep behavior and perceived recovery before and after a night game in top-level volleyball athletes. We hypothesized that we would detect lower sleep quality and perceived recovery both in the night immediately precompetition and postcompetition compared

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Barbara Resnick, Elizabeth Galik, Marie Boltz, William Hawkes, Michelle Shardell, Denise Orwig and Jay Magaziner

The purpose of this study was to characterize physical activity (PA) based on survey and ActiGraphy data from older adults at 2 mo post–hip fracture and consider the factors that influence PA among these individuals. The sample included participants from a current Baltimore hip study, the BHS-7. Measurement of PA was based on the Yale PA Survey (YPAS) and 48 hr of ActiGraphy. The sample included the first 200 individuals enrolled in the study, with analyses including 117 individuals (59%) who completed the YPAS and wore the ActiGraph for 48 hr. Half the participants were male, with an overall mean age of 81.3 yr (SD = 7.9). Findings indicate that at 2 mo post–hip fracture participants were engaged in very limited levels of PA. Age and comorbidities were the only variables to be significantly associated with PA outcomes.

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Nathan W. Pitchford, Sam J. Robertson, Charli Sargent, Justin Cordy, David J. Bishop and Jonathan D. Bartlett

Purpose:

To assess the effects of a change in training environment on the sleep characteristics of elite Australian Rules football (AF) players.

Methods:

In an observational crossover trial, 19 elite AF players had time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) assessed using wristwatch activity devices and subjective sleep diaries across 8-d home and camp periods. Repeated-measures ANOVA determined mean differences in sleep, training load (session rating of perceived exertion [RPE]), and environment. Pearson product–moment correlations, controlling for repeated observations on individuals, were used to assess the relationship between changes in sleep characteristics at home and camp. Cohen effect sizes (d) were calculated using individual means.

Results:

On camp TIB (+34 min) and WASO (+26 min) increased compared with home. However, TST was similar between home and camp, significantly reducing camp SE (–5.82%). Individually, there were strong negative correlations for TIB and WASO (r = -.75 and r = -.72, respectively) and a moderate negative correlation for SE (r = -.46) between home and relative changes on camp. Camp increased the relationship between individual s-RPE variation and TST variation compared with home (increased load r = -.367 vs .051, reduced load r = .319 vs –.033, camp vs home respectively).

Conclusions:

Camp compromised sleep quality due to significantly increased TIB without increased TST. Individually, AF players with higher home SE experienced greater reductions in SE on camp. Together, this emphasizes the importance of individualized interventions for elite team-sport athletes when traveling and/or changing environments.

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Ryan S. Falck, Glenn J. Landry, Keith Brazendale and Teresa Liu-Ambrose

Evidence suggests sleep and physical activity (PA) are associated with each other and dementia risk. Thus, identifying reliable methods to quantify sleep and PA concurrently in older adults is important. The MotionWatch 8© (MW8) wrist-worn actigraph provides reliable estimates of sleep quality via 14 days of measurement; however, the number of days needed to monitor PA by MW8 for reliable estimates is unknown. Thus, we investigated the number of days of MW8 wear required to assess PA in older adults. Ninety-five adults aged > 55 years wore MW8 for ≥ 14 days. Spearman-Brown analyses indicated the number of monitoring days needed for an ICC = 0.95 was 6–7 days for sedentary activity, 9–10 days for light activity, and 7–8 days for moderate-to-vigorous PA. These results indicate 14 days of MW8 monitoring provides reliable estimates for both sleep and PA. Thus, MW8 is ideal for future investigations requiring concurrent measures of both sleep quality and PA.

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Dana L. Wolff-Hughes, Eugene C. Fitzhugh, David R. Bassett and James R. Churilla

Background:

Accelerometer-derived total activity count is a measure of total physical activity (PA) volume. The purpose of this study was to develop age- and gender-specific percentiles for daily total activity counts (TAC), minutes of moderate-to-vigorous physical activity (MVPA), and minutes of light physical activity (LPA) in U.S. adults.

Methods:

Waist-worn accelerometer data from the 2003-2006 National Health and Nutrition Examination Survey were used for this analysis. The sample included adults >20 years with >10 hours accelerometer wear time on >4 days (N = 6093). MVPA and LPA were defined as the number of 1-minute epochs with counts >2020 and 100 to 2019, respectively. TAC represented the activity counts acquired daily. TAC, MVPA, and LPA were averaged across valid days to produce a daily mean.

Results:

Males in the 50th percentile accumulated 288 140 TAC/day, with 357 and 22 minutes/day spent in LPA and MVPA, respectively. The median for females was 235 741 TAC/day, with 349 and 12 minutes/day spent in LPA and MVPA, respectively.

Conclusions:

Population-referenced TAC percentiles reflect the total volume of PA, expressed relative to other adults. This is a different approach to accelerometer data reduction that complements the current method of looking at time spent in intensity subcategories.

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Allison Naber, Whitney Lucas Molitor, Andy Farriell, Kara Honius and Brooke Poppe

( Koltyn, 2002 ; Mathesom et al., 2013 ). One method that may be beneficial in altering lifestyle habits and health behaviors is through the utilization of wearable technology. When measuring activity levels, this technology is referred to as actigraphy, which is used to objectively measure physical

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Catherine R. Marinac, Mirja Quante, Sara Mariani, Jia Weng, Susan Redline, Elizabeth M. Cespedes Feliciano, J. Aaron Hipp, Daniel Wang, Emily R. Kaplan, Peter James and Jonathan A. Mitchell

within an individual. We, therefore, tested if the timing of meals, light exposure, physical activity, and sleep were associated with body mass index (BMI) in a sample of healthy adults who recorded the timing of behaviors over multiple days using a novel smartphone application and actigraphy. We first

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Benjamin G. Serpell, Barry G. Horgan, Carmen M.E. Colomer, Byron Field, Shona L. Halson and Christian J. Cook

Approval to conduct this research was granted by the University of Canberra and Australian Institute of Sport Human Research Ethics Committees. All participants voluntarily gave informed consent to participate. Sleep Monitoring Throughout the monitoring period, all participants wore a wrist actigraphy

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Emma L. Sweeney, Daniel J. Peart, Irene Kyza, Thomas Harkes, Jason G. Ellis and Ian H. Walshe

to keep a consistent bed and wake time. Wrist actigraphy (GeneActiv; Activinsights Ltd, United Kingdom) was used in conjunction with time-stamped text messages to ensure compliance. Participants sent hourly messages to the researcher between 2300 and 0300 hr in SR and SRE conditions. Participants