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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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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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Andressa Silva, Fernanda V. Narciso, Igor Soalheiro, Fernanda Viegas, Luísa S.N. Freitas, Adriano Lima, Bruno A. Leite, Haroldo C. Aleixo, Rob Duffield and Marco T. de Mello

and importance of the research. The procedures were started after direction by the team. Methodology Actigraphy In the present study, the players’ sleep behavior was monitored using an Actiwatch 2 wrist activity monitor actigraph (Philips Respironics, Andover, MA), which continuously measured the

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David R. Bassett, Patty S. Freedson and Dinesh John

MVPA ( Loprinzi et al., 2012 ). Moreover, the emergence of new research-grade wearable devices (ActivPAL, Actiwatch, GeneActiv, Axivity, etc.) and new placement sites (wrist, ankle, etc.) has led to even more diversification in the methods and metrics that characterize various aspects of human physical

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Benita J. Lalor, Shona L. Halson, Jacqueline Tran, Justin G. Kemp and Stuart J. Cormack

was scored as “sleep.” Scoring was conducted using Philips Respironics’ Actiwatch algorithm with sensitivity set at medium. 6 The following information was collected from the activity monitors: bedtime (hh:mm), wake-up time (hh:mm), sleep-onset latency (min), sleep duration (h), wake time (min

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Sara Knaeps, Stijn De Baere, Jan Bourgois, Evelien Mertens, Ruben Charlier and Johan Lefevre

.1080/02640414.2016.1140220 17. Shin M , Swan P , Chow CM . The validity of Actiwatch2 and SenseWear Armband compared against polysomnography at different ambient temperature conditions . Sleep Sci . 2015 ; 8 ( 1 ): 9 – 15 . PubMed doi:10.1016/j.slsci.2015.02.003 26483937 10.1016/j.slsci.2015.02.003 18. De Baere S