scholarly journals Agreement between the Apple Series 1, LifeTrak Core C200, and Fitbit Charge HR with Indirect Calorimetry for Assessing Treadmill Energy Expenditure

2019 ◽  
Vol 16 (20) ◽  
pp. 3812
Author(s):  
Peng Zhang ◽  
Ryan Donald Burns ◽  
You Fu ◽  
Steven Godin ◽  
Wonwoo Byun
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Mean Difference ◽  
Treadmill Running ◽  
Equivalence Testing ◽  
Activity Monitors ◽  
Individual Level ◽  
Statistical Equivalence ◽  
Absolute Percent Error

The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean age = 20.1 (1.7) years; 13 females, 17 males). Participants completed six structured 10-minute exercise sessions on a treadmill with speeds ranging from 53.6 m·min−1 to 187.7 m·min−1. Indirect calorimetry was used as the criterion. Participants wore the Apple Watch, LifeTrak, and Fitbit activity monitors on their wrists. Group-level agreement was examined using equivalence testing, relative agreement was examined using Spearman’s rho, and individual-level agreement was examined using Mean Absolute Percent Error (MAPE) and Bland-Altman Plots. Activity monitor agreement with indirect calorimetry was supported using the Apple Watch at 160.9 m·min−1 (Mean difference = −2.7 kcals, 90% C.I.: −8.3 kcals, 2.8 kcals; MAPE = 11.9%; rs = 0.64) and 187.7 m·min−1 (Mean difference = 3.7 kcals, 90% C.I.: −2.2 kcals, 9.7 kcals; MAPE = 10.7%; rs = 0.72) and the Fitbit at 187.7 m·min−1 (Mean difference = −0.2 kcals, 90% C.I.: −8.8 kcals, 8.5 kcals; MAPE = 20.1%; rs = 0.44). No evidence for statistical equivalence was seen for the LifeTrak at any speed. Bland-Altman Plot Limits of Agreement were narrower for the Apple Series 1 Watch compared to other monitors, especially at slower treadmill speeds. The results support the utility of the Apple Series 1 Watch and Fitbit Charge HR for assessing energy expenditure during specific treadmill running speeds in young adults.

scholarly journals Use of consumer monitors for estimating energy expenditure in youth

2020 ◽  
Vol 45 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Samuel R. LaMunion ◽  
Andrew L. Blythe ◽  
Paul R. Hibbing ◽  
Andrew S. Kaplan ◽  
Brandon J. Clendenin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Oxygen Consumption ◽  
Energy Expenditure ◽  
Left Wrist ◽  
Activity Monitors ◽  
Individual Level ◽  
Mean Absolute Percent Error ◽  
Physical Activity Monitors ◽  
The Right ◽  
Absolute Percent Error

The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b2(criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield’s equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b2) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories (p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 – right hip) to 49.9% (Mymo Tracker).Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 – right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

scholarly journals Comparison Of Energy Expenditure Of Overground And Motorized Treadmill Running In Healthy Chinese Young Adults

2019 ◽  
Vol 51 (Supplement) ◽  
pp. 840-841
Author(s):  
Shuo Li ◽  
Jingjing Xue ◽  
Zihong He ◽  
Shuai Jiang ◽  
Xu’nan Tan ◽  
...  
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Treadmill Running ◽  
Healthy Chinese ◽  
Chinese Young Adults

scholarly journals Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251975
Author(s):  
Yang Bai ◽  
Connie Tompkins ◽  
Nancy Gell ◽  
Dakota Dione ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Pearson Correlation ◽  
Living Condition ◽  
Equivalence Testing ◽  
Free Living ◽  
Activity Monitors ◽  
Step Counts ◽  
Comprehensive Comparison ◽  
Rate Correlation ◽  
Absolute Percent Error

Objectives The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. Methods Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. Results The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. Conclusion All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

Validity of resting energy expenditure estimated by an activity monitor compared to indirect calorimetry

2009 ◽  
Vol 102 (1) ◽  
pp. 155-159 ◽  
Author(s):  
Jocilyn E. Dellava ◽  
Daniel J. Hoffman
Keyword(s):  
Energy Expenditure ◽  
Body Fat ◽  
Indirect Calorimetry ◽  
Resting Energy Expenditure ◽  
Bioelectrical Impedance ◽  
Activity Monitor ◽  
Prediction Equation ◽  
Activity Monitors ◽  
Percentage Of Body Fat ◽  
Resting Energy

The use of activity monitors (triaxial accelerometers) to estimate total energy expenditure in kilocalories is dependent on the estimation of resting energy expenditure (REE). However, the REE estimated by activity monitors has not been validated against more precise techniques, such as indirect calorimetry (IC). Therefore, the objective of the present study was to compare REE estimated by the Actical activity monitor (ActMon) to that measured by IC and standard prediction equations of REE. Fifty healthy adults between 18 and 43 years of age were measured for weight and percentage of body fat using a digital scale and bioelectrical impedance. The REE estimated by the ActMon was only 129 kJ/d higher, but not statistically different (P>0·05), than the REE measured with IC. Using multiple linear regression, there was a positive relationship for men, but not for women, between fat mass (kg) and percentage of body fat and the difference in REE estimated by the ActMon compared to IC (P < 0·001). Therefore, in the cohort studied, the use of an activity monitor to estimate REE is valid when compared to IC, but not to a standard prediction equation of REE.

Validation of the Kenz Lifecorder EX and ActiGraph GT1M accelerometers for walking and running in adults

2008 ◽  
Vol 33 (6) ◽  
pp. 1155-1164 ◽  
Author(s):  
Mark G. Abel ◽  
James C. Hannon ◽  
Katie Sell ◽  
Tia Lillie ◽  
Geri Conlin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Walking Speed ◽  
Treadmill Walking ◽  
Activity Monitors ◽  
Step Counts ◽  
Equation Analysis ◽  
Walking Speeds ◽  
Activity Energy

Accelerometer-based activity monitors are commonly used by researchers and clinicians to assess physical activity. Recently, the Kenz Lifecorder EX (KL) and ActiGraph GT1M (AG) accelerometers have been made commercially available, but there is limited research on the validity of these devices. Therefore, we sought to validate step count, activity energy expenditure (EE), and total EE output from the KL and AG during treadmill walking and running. Ten male and 10 female participants performed 10 min treadmill walking and running trials, at speeds of 54, 80, 107, 134, 161, and 188 m·min–1. Step counts were hand tallied by 2 observers, and indirect calorimetry was used to validate the accelerometers’ estimates of EE. AG total EE was calculated using the Freedson equation. Analysis of variance (ANOVA) and Pearson’s correlations were used to analyze the data. At the slowest walking speed, the AG and KL counted 64% ± 15% and 92% ± 6% of the observed steps, respectively. At all other treadmill speeds, both activity monitors undercounted, compared with observed steps, by ≤3%. The KL underestimated activity EE at faster running speeds (p < 0.01), overestimated total EE at some walking speeds, and underestimated total EE at some running speeds (p < 0.01). The Freedson equation inaccurately measured total EE at most walking and running speeds. The KL and the AG are moderately priced accelerometers that provide researchers and clinicians with accurate estimates of step counts and activity EE at most walking and running speeds.

scholarly journals Measurement of Resting Energy Expenditure with Indirect Calorimetry and Predictive Equations in Healthy Young Adults: A Cross-sectional Study

2020 ◽  
Author(s):  
harsha soni ◽  
Sudhanshu Kacker ◽  
Neha Saboo ◽  
Karampreet Buttar ◽  
. jitender
Keyword(s):  
Young Adults ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Resting Energy Expenditure ◽  
Cross Sectional Study ◽  
Energy Requirements ◽  
Predictive Equation ◽  
Sectional Study ◽  
Cross Sectional ◽  
Predictive Equations

Introduction: Resting Energy Expenditure (REE) is the main determinant of energy requirements. An inaccurate estimation of REE can lead to the over or under-prediction of energy requirements. Indirect calorimetry is considered as the gold standard for the assessment of REE. The most of the predictive equations which are formed, are from the studies conducted on Caucasian people while on Asian population these studies are very limited. Aim: To compare the REE measured by indirect calorimetry and predictive equation in healthy young adults. Materials and Methods: A cross-sectional study was done on 100 healthy young adult participants from November 2018 to May 2019, of age group 18 to 25 years to measure REE using indirect calorimetry and predictive equations (Harris-Benedict’s, Schofield, FAO/WHO/UNU and Mifflin-St. Jeor equations). Statistical analysis was carried out using SPSS version 16.0. Unpaired student t-test for comparison of data and Bland Altman test to check for validity of predictive equations were applied. Results: The mean value of REE using Indirect calorimetry was 1994.20±577.33 and that of using four Harris-Benedict’s, Schofield, FAO/WHO/UNU and Mifflin-St.Jeor equations were 1638.15±335.64 kcal/day, 1636.21±359.85 kcal/day, 1636.93±367.59 kcal/day and 1582.41±251.29 kcal/day, respectively. Thus, the highest mean difference between values of REE obtained using predictive equation and indirect calorimetry was 411.79±326.04 kcal/day with respect to Mifflin-St.Jeorand’s and the lowest mean difference was 356.05±241.69 kcal/day with respect to Herris Benedict’s equation. Conclusion: Predictive equations underestimated the REE of young adults when compared with that measured by indirect calorimetry.

Heart Rate Equivalency of the Fitbit Charge HR During Continuous Aerobic Exercise

2018 ◽  
Vol 1 (3) ◽  
pp. 122-129
Author(s):  
Joseph M. Stock ◽  
Ryan T. Pohlig ◽  
Matthew J. Botieri ◽  
David G. Edwards ◽  
Gregory M. Dominick
Keyword(s):  
Heart Rate ◽  
Aerobic Exercise ◽  
Exercise Bout ◽  
Total Activity ◽  
Equivalence Testing ◽  
Warm Up ◽  
Activity Monitors ◽  
Continuous Exercise ◽  
Exercise Onset ◽  
Absolute Percent Error

Purpose: Consumer-grade wrist-worn activity monitors frequently include photoplethysmography (PPG) sensors for estimating heart rate (HR). The Fitbit Charge HR is marketed specifically for tracking fitness; therefore, HR accuracy is critical, especially during exercise. This study examined HR equivalency of the Fitbit Charge HR during continuous aerobic exercise. Method: Participants (N = 19) concurrently wore a Polar H1 and Fitbit Charge HR during a measurement visit that included seated rest (5 minutes), warm-up (5 minutes), continuous treadmill exercise (30 minutes), and cool-down (5–10 minutes). Mean HR differences were examined by protocol phase, total activity (i.e., warm-up, exercise, and cool-down combined), and the first, middle, and last 5 minutes of continuous exercise. Mean absolute percent error (MAPE), Bland-Altman plots, and 95% equivalence testing explained overall and individual HR agreement between devices. Results: The Fitbit Charge HR significantly underestimated HR for all measurement phases (all p ≤ .01) except cool-down (p > .33). HR agreement was notably weaker during warm-up (r = 0.66, d = 0.57) and differences were greatest for the first 5 minutes compared to the middle and end of exercise (6.94±2.16 beats per minute [bpm] vs. 1.76±0.59 bpm, and 1.74±0.58 bpm), F = 4.87, p = .04). Mean exercise HRs were equivalent between devices (±2.69 bpm, 95% CI: 1.41–3.97 bpm); MAPE was 1.96%. Conclusion: The Fitbit Charge HR is relatively accurate for measuring HR during continuous aerobic exercise. Whereas the accuracy of PPG-based HR appears limited at exercise onset, agreement improves throughout the exercise bout and HR differences are negligible.

Accuracy of Multisensor Activity Monitors in Normal Versus High BMI African American Children

2011 ◽  
Vol 8 (8) ◽  
pp. 1124-1134 ◽  
Author(s):  
Daniel Arvidsson ◽  
Mark Fitch ◽  
Mark L. Hudes ◽  
Sharon E. Fleming
Keyword(s):  
African American ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Normal Weight ◽  
The Other ◽  
African American Children ◽  
Overweight Children ◽  
Activity Monitors ◽  
American Children ◽  
High Bmi

Background:Overweight children show different movement patterns during walking than normal-weight children, suggesting the accuracy of multisensory activity monitors may differ in these groups.Methods:Eleven normal and 15 high BMI African American children walked at 2, 4, 5, and 6 km/h on a treadmill wearing the Intelligent Device for Energy Expenditure and Activity (IDEEA) and SenseWear (SW). Accuracy was determined using indirect calorimetry and manually counted steps as references.Results:For IDEEA, no significant differences in accuracy were observed between BMI groups for energy expenditure (EE), but differences were significant by speed (+15% at 2 km/h to −10% at 6 km/h). For SW, EE accuracy was significantly different for high (+21%) versus normal BMI girls (−13%) at 2 km/h. For high BMI girls, EE was overestimated at low speed and underestimated at higher speeds. Underestimations in steps did not differ by BMI group at 4 to 6 km/h, but were significantly larger at 2 km/h than at the other speeds for all groups with IDEEA, and for normal BMI children with SW.Conclusions:Similar accuracies during walking may be expected in normal and overweight children using IDEEA and SW. Both monitors showed small errors for steps provided speed exceeded 2 km/h.

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