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Orjan Ekblom, Gisela Nyberg, Elin Ekblom Bak, Ulf Ekelund, and Claude Marcus

Background:

Wrist-worn accelerometers may provide an alternative to hip-worn monitors for assessing physical activity as they are easier to wear and may thus facilitate long-term recordings. The current study aimed at a) assessing the validity of the Actiwatch (wrist-worn) for estimating energy expenditure, b) determining cut-off values for light, moderate, and vigorous activities, c) studying the comparability between the Actiwatch and the Actigraph (hip-worn), and d) assessing reliability.

Methods:

For validity, indirect calorimetry was used as criterion measure. ROC-analyses were applied to identify cut-off values. Comparability was tested by simultaneously wearing of the 2 accelerometers during free-living condition. Reliability was tested in a mechanical shaker.

Results:

All-over correlation between accelerometer output and energy expenditure were found to be 0.80 (P < .001).Based on ROC-analysis, cut-off values for 1.5, 3, and 6 METs were found to be 80, 262, and 406 counts per 15 s, respectively. Energy expenditure estimates differed between the Actiwatch and the Actigraph (P < .05). The intra- and interinstrument coefficient of variation of the Actiwatch ranged between 0.72% and 8.4%.

Conclusion:

The wrist-worn Actiwatch appears to be valid and reliable for estimating energy expenditure and physical activity intensity in children aged 8 to 10 years.

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Stephanie A. Hooker, Laura B. Oswald, Kathryn J. Reid, and Kelly G. Baron

). Foods not available in the database were found on company or restaurant websites. When caloric information was not available, the closest substitute was used. Total daily caloric intake each day was computed and averaged. Sleep Duration and Timing The Actiwatch Spectrum (Philips/Respironics, Inc, Bend

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Louise A. Kelly, John J. Reilly, Sheila C. Fairweather, Sarah Barrie, Stanley Grant, and James Y Paton

The primary aim of this study was to test the validity of two accelerometers, CSA/MTI WAM-7164 and Actiwatch®, against direct observation of physical activity using the Children’s Physical Activity Form (CPAF). CSA/MTI WAM-7164 and Actiwatch accelerometers simultaneously measured activity during structured-play classes in 3- to 4-year olds. Accelerometry output was synchronized to CPAF assessments of physical activity in 78 children. Rank order correlations between accelerometry and direct observation evaluated the ability of the accelerometers to assess total physical activity. Within-child minute-to-minute correlations were calculated between accelerometry output and direct observation. For total physical activity, CSA/MTI output was significantly correlated with CPAF (r = .72, p < .001), but output from the Actiwatch was not (r = .16, p > .05).

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Christina A. Taylor and Joonkoo Yun

This study examined the psychometric properties of the System for Observing Fitness Instruction Time (SOFIT) and the Children’s Activity Rating Scale (CARS) for use with children with mental retardation (MR). Eleven children with MR were videotaped while participating in a university-based community outreach program. Actiwatch accelerometers were used as the criterion measure. Results indicated that SOFIT and CARS both demonstrated adequate levels of generalizability (ϕ= 0.98 and 0.75), but a low concurrent validity coefficient for SOFIT (r = .10) and a moderate level of validity coefficient for CARS (r = .61) were observed. CARS demonstrates stronger validity evidence than SOFIT, but it is important to have sufficient rater training before using CARS for measuring physical activity level of children with MR.

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Sofiya Alhassan, John R. Sirard, Laura B. F. Kurdziel, Samantha Merrigan, Cory Greever, and Rebecca M. C. Spencer

Purpose:

The purpose of this study was to cross-validate previously developed Actiwatch (AW; Ekblom et al. 2012) and AcitGraph (AG; Sirard et al. 2005; AG-P, Pate et al. 2006) cut-point equations to categorize free-living physical activity (PA) of preschoolers using direct observation (DO) as the criterion measure. A secondary aim was to compare output from the AW and the AG from previously developed equations.

Methods:

Participants’ (n = 33; age = 4.4 ± 0.8 yrs; females, n=12) PA was directly observed for three 10-min periods during the preschool-day while wearing the AW (nondominant wrist) and AG (waist). Device specific cut-points were used to reduce the AW-E (Ekblom et al. 2012) and AG (AG-S, Sirard et al. 2005; AG-P, Pate et al. 2006) data into intensity categories. Spearman correlations (rsp) and agreement statistics were used to assess associations between the DO intensity categories and device data. Mixed model regression was used to identify differences in times spent in activity intensity categories.

Results:

There was a significant correlation between AW and AG output across all data (rsp = 0.41, p < .0001) and both were associated with the DO intensity categories (AW: rsp = 0.47, AG: rsp = 0.47; p < .001). At the individual level, all devices demonstrated relatively low sensitivity but higher specificity. At the group level, AW-E and AG-P provided similar estimates of time spent in moderate-to-vigorous PA (MVPA, AW-E: 4.7 ± 4.1, AG-P: 4.4 ± 3.3), compared with DO (5.1 ± 3.5). Conclusion: The AW-E and AG-P estimated times spent in MVPA were similar to DO, but the weak agreement statistics indicate that neither device cut-point equations provided accurate estimates at the individual level.

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Craig Thomas, Helen Jones, Craig Whitworth-Turner, and Julien Louis

actiwatch (Actiwatch 4; Cambridge Neurotechnology Ltd, Cambridge, United Kingdom) was provided and set to an epoch length of 1 minute at a medium sensitivity. 22 On each night, participants were asked to wear the actiwatch on their nondominant wrist at least 30 minutes before they retired to bed and then

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

rating for the previous night. Methodology Actigraph Monitoring All subjects wore a wrist activity monitor, the Actiwatch 2 actigraph (Philips Respironics, Portland, OR), to record their sleep parameters. For logistical reasons, the actigraph monitoring lasted 4 days. A high actigraphic sensitivity

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Michele Lastella, Gregory D. Roach, Grace E. Vincent, Aaron T. Scanlan, Shona L. Halson, and Charli Sargent

diaries and wrist-worn activity monitors (Actiwatch-64; Philips Respironics, Bend, OR). Sleep diaries were used to record the start date/time and end date/time for each sleep period. Players wore an activity monitor on the same wrist throughout the data collection period and removed the monitor when

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Matteo Bonato, Antonio La Torre, Marina Saresella, Ivana Marventano, Giampiero Merati, Giuseppe Banfi, and Jacopo A. Vitale

the ball into the goal. Verbal encouragement was provided by the coaches to maintain motivation. HR was continuously monitored (Polar RS800; Polar Electro Oy). Objective Sleep Variables Sleep parameters were monitored using an Actiwatch 2 ActiGraph (Philips Respironics, Bend, OR). Sleep data were

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Rodrigo Torres-Castro, Luis Vasconcello-Castillo, Roberto Acosta-Dighero, Nicolás Sepúlveda-Cáceres, Marisol Barros-Poblete, Homero Puppo, Roberto Vera-Uribe, Jordi Vilaró, and Mario Herrera-Romero

(Philips Respironics, Murrysville, PA); 28 , 29 , 45 3 used Power Walker (Yamax, Tokyo, Japan); 31 , 47 , 50 2 used SenseWear Armband (BodyMedia Inc, Pittsburgh, PA); 26 , 40 and 2 used RT3 (Stayhealthy, Monrovia, CA). 27 , 36 One study used ActiWatch (Mini-Mitter Company Inc, Bend, Oregon), 39 1