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  • Author: Zachary C. Pope x
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Zachary C. Pope, Beth A. Lewis and Zan Gao

Background:

The Transtheoretical Model (TTM) has been widely used to understand individuals’ physical activity (PA) correlates and behavior. However, the theory’s application among children in exergaming remains unknown.

Purpose:

Investigate the effects of an exergaming program on children’s TTM-based PA correlates and PA levels.

Methods:

At pretest and posttest, 212 upper elementary children (mean age = 11.17 years) from the greater Mountain West Region were administered measures regarding stages of change (SOC) for PA behavior, decisional balance for PA behaviors, PA self-efficacy, and self-reported PA levels. Following the pretest, a weekly 30-minute, 18-week Dance Dance Revolution (DDR) program was implemented. Children were classified into 3 SOC groups: progressive children (ie, progressed to a higher SOC stage); stable children (ie, remained at the same SOC stage); and regressive children (ie, regressed to a lower SOC stage).

Results:

Progressive children had greater increased PA levels than regressive children (P < .01) from pretest to posttest. Similarly, progressive children had greater increased self-efficacy (P < .05) and decision balance (P < .05) than regressive children.

Conclusions:

The findings indicate that progressive children had more improvements on self-efficacy, decisional balance, and PA levels than regressive children over time. Implications of findings are discussed.

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Zachary C. Pope, Nan Zeng, Xianxiong Li, Wenfeng Liu and Zan Gao

Background: This study examined the accuracy of Microsoft Band (MB), Fitbit Surge HR (FS), TomTom Cardio Watch (TT), and Apple Watch (AW) for energy expenditure (EE) estimation at rest and at different physical activity (PA) intensities. Method: During summer 2016, 25 college students (13 females; M age = 23.52 ± 1.04 years) completed four separate 10-minute exercise sessions: rest (i.e., seated quietly), light PA (LPA; 3.0-mph walking), moderate PA (MPA; 5.0-mph jogging), and vigorous PA (VPA; 7.0-mph running) on a treadmill. Indirect calorimetry served as the criterion EE measure. The AW and TT were placed on the right wrist and the FS and MB on the left—serving as comparison devices. Data were analyzed in late 2017. Results: Pearson correlation coefficients revealed only three significant relationships (r = 0.43–0.57) between smartwatches’ EE estimates and indirect calorimetry: rest-TT; LPA-MB; and MPA-AW. Mean absolute percentage error (MAPE) values indicated the MB (35.4%) and AW (42.3%) possessed the lowest error across all sessions, with MAPE across all smartwatches lowest during the LPA (33.7%) and VPA (24.6%) sessions. During equivalence testing, no smartwatch’s 90% CI fell within the equivalence region designated by indirect calorimetry. However, the greatest overlap between smartwatches’ 90% CIs and indirect calorimetry’s equivalency region was observed during the LPA and VPA sessions. Finally, EE estimate variation attributable to the use of different manufacturer’s devices was greatest at rest (53.7 ± 12.6%), but incrementally decreased as PA intensity increased. Conclusions: MB and AW appear most accurate for EE estimation. However, smartwatch manufacturers may consider concentrating most on improving EE estimate accuracy during MPA.