against polysomnography (PSG). However, the Actiwatch, manufactured by Philips Respironics, has well-established reliability and validity against PSG to measure TIB in free-living adults ( Lee & Suen, 2017 ; Marino et al., 2013 ). Yet, the reliability and validity of Actiwatch to measure waking behaviors
Mary C. Hidde, Kate Lyden, Josiane L. Broussard, Kim L. Henry, Julia L. Sharp, Elizabeth A. Thomas, Corey A. Rynders, and Heather J. Leach
Orjan Ekblom, Gisela Nyberg, Elin Ekblom Bak, Ulf Ekelund, and Claude Marcus
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.
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.
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%.
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.
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
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).
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.
Sofiya Alhassan, John R. Sirard, Laura B. F. Kurdziel, Samantha Merrigan, Cory Greever, and Rebecca M. C. Spencer
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.
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.
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.
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
Junxin Li, Sarah L. Szanton, Miranda V. McPhillips, Nada Lukkahatai, Grace W. Pien, KerCheng Chen, Melissa D. Hladek, Nancy Hodgson, and Nalaka S. Gooneratne
receive emails on a mobile device or a computer and Have you used any apps on a mobile device (e.g., weather, email, calendar, etc.) Physical activity (PA) The PA was assessed objectively using the Actiwatch 2 (Philips Respironics Inc., Murrysville, PA) and subjectively using the Physical Activity Scale
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
Megan J. Huisingh-Scheetz, Li Li, Kristen E. Wroblewski, L. Philip Schumm, Martha K. McClintock, and Jayant M. Pinto
respondents wore an ActiWatch Spectrum® (Philips Respironics, Pittsburgh, PA) on their nondominant wrist continuously for 72 consecutive hours, including during water activities ( Chen et al., 2003 ; Huisingh-Scheetz et al., 2014 ; Philips Respironics, 2013 ). The ActiWatch Spectrum is a validated