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.
Zachary C. Pope, Nan Zeng, Xianxiong Li, Wenfeng Liu and Zan Gao
Connor Burk, Jesse Perry, Sam Lis, Steve Dischiavi and Chris Bleakley
sham or inactive controls. However, the current evidence base is limited due to the high risk of selection and measurement bias, and many of the observed effects may be too small to be clinically important. Acknowledgment The authors have no conflicts of interest to disclose. References 1. Tidball JG
Terence Dwyer, James F. Sallis, Leigh Blizzard, Ross Lazarus and Kimberlie Dean
The objective of this study was to examine the association of scholastic performance with physical activity and fitness of children. To do so, school ratings of scholastic ability on a five-point scale for a nationally representative sample of 7,961 Australian schoolchildren aged 7–15 years were compared with physical activity and fitness measurements. Consistently across age and sex groups, the ratings were significantly correlated with questionnaire measures of physical activity and with performance on the 1.6-kilometer run, sit-ups and push-ups challenges, 50-meter sprint, and standing long jump. There were no significant associations for physical work capacity at a heart rate of 170 (PWC170). The results are concordant with the hypothesis that physical activity enhances academic performance, but the cross-sectional nature of the observations limits causal inference, and the disparity for PWC170 gives reason to question whether the associations were due to measurement bias or residual confounding.
Timothy K. Behrens and Mary K. Dinger
The purpose of this study was to compare steps·d-1 between an accelerometer and pedometer in 2 free-living samples.
Data from 2 separate studies were used for this secondary analysis (Sample 1: N = 99, Male: n = 28, 20.9 ± 1.4 yrs, BMI = 27.2 ± 5.0 kg·m-2, Female: n = 71, 20.9 ± 1.7 yrs, BMI = 22.7 ± 3.0 kg·m-2; Sample 2: N = 74, Male: n = 27, 38.0 ± 9.5 yrs, BMI = 25.7 ± 4.5 kg·m-2, Female: n = 47, 38.7 ± 10.1 yrs, BMI = 24.6 ± 4.0 kg·m-2). Both studies used identical procedures and analytical strategies.
The mean difference in steps·d-1 for the week was 1643.4 steps·d-1 in Study 1 and 2199.4 steps·d-1 in Study 2. There were strong correlations between accelerometer- and pedometer-determined steps·d-1 in Study 1 (r = .85, P < .01) and Study 2 (r = 0.87, P < .01). Bland-Altman plots indicated agreement without bias between steps recorded from the devices in Study 1 (r = −0.14, P < .17) and Study 2 (r = −0.09, P < .40). Correlations examining the difference between accelerometer–pedometer steps·d-1 and MVPA resulted in small, inverse correlations (range: r = −0.03 to −0.28).
These results indicate agreement between accelerometer- and pedometer-determined steps·d-1; however, measurement bias may still exist because of known sensitivity thresholds between devices.
Emma K. Zadow, Cecilia M. Kitic, Sam S.X. Wu and James W. Fell
divided by trial 2 vs the average) (GraphPad Prism 5, version 5.03, La Jolla, CA, USA). In accordance with previous research, 5 relative measurement biases of <1.5%, 1.5% to 2.5%, and >2.5% were deemed highly reliable, moderately reliable, and inaccurate, respectively. Using an Excel spreadsheet for
Matheus Lima Oliveira, Isabela Christina Ferreira, Kariny Realino Ferreira, Gabriela Silveira-Nunes, Michelle Almeida Barbosa and Alexandre Carvalho Barbosa
. SEM was also calculated to provide an estimate of measurement error. Linear regression estimated the coefficient of correlation ( R ) and the adjusted coefficient of determination ( R 2 ). Bland–Altman method estimated the measurement bias, with lower and upper limits of agreement between results. All
Jonathan Miller, Mark Pereira, Julian Wolfson, Melissa Laska, Toben Nelson and Dianne Neumark-Sztainer
-reported in this study, measurement bias may exist. However, a validation substudy at EAT-III showed that this self-report measure did not systematically overestimate or underestimate MVPA when compared with accelerometer measures. 29 This finding was consistent even at high reported levels of MVPA
Rachel Cole, Mohammad Javad Koohsari, Alison Carver, Neville Owen and Takemi Sugiyama
less susceptible to measurement bias and recall errors ( Manaugh & El-Geneidy, 2011 ; Merom, Van Der Ploeg, Corpuz, & Bauman, 2010 ). Another strength is that we focused on walking trips that started or ended at home, which improves the correspondence between where behaviors occur and where
Murat Tomruk, Melda Soysal Tomruk, Emrullah Alkan and Nihal Gelecek
decreased the possibility of measurement bias. There were some limitations to this study. It should be noted that this study was conducted in healthy pain-free individuals, so the findings cannot be generalized to patients with ankle instability or ankle sprain. It should also be kept in mind that long
Bouwien Smits-Engelsman, Wendy Aertssen and Emmanuel Bonney
agreement between the 2 measurement occasions, we calculated LOA (95% interval) by using the mean and the SD of the differences between 2 measurements. Bland–Altman plots were made to visualize the measurement bias and the LOA. To investigate systematic bias, a paired Student t test was conducted to test