Energy expenditure estimates of the Caltrac accelerometer for running, race walking, and stepping (original) (raw)
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Calorimetric validation of the Caltrac accelerometer during level walking
Physical Therapy
The primary purpose of this study was to compare the Caltrac ® accelerometer output with measured energy expenditure (Ee). Twenty-five volunteers (10 men, 15 women) walked on a level motor-driven treadmill at four different speeds (54, 81, 104, and 130 m . min -1 ) with the Caltrac ® device affixed to the waistline. Each of the four experimental trials lasted eight minutes, and the testing was completed within an hour. During the test, oxygen consumption (Vo 2 ) (in L . min -1 and in mL . kg -1 . min -1 ) and nonprotein respiratory exchange ratio were monitored by the Beckman Horizon metabolic cart. The accelerometer output at the end of each exercise bout was also monitored and subsequently divided by 8 to convert the readings to counts . min -1 . The mean Vo 2 (L . min -1 ) at steady state (ie, 6th-8th minutes of exercise) was converted to a caloric value. We obtained a moderate correlation coefficient (r) of. 76 between the accelerometer output and the Vo 2 (mL . kg -1 . min -1 ) and a high correlation coefficient of .92 between the Ee and the accelerometer readings. The Caltrac ® accelerometer output (counts . min -1 ) was significantly higher (p < .01) than the Ee (kcal . min -1 ) at the four walking speeds. The difference between the accelerometer output and the Ee ranged from 133% to 52.9%. The data were further analyzed with linear, polynomial, multiple, and stepwise regression models. The results of the analyses revealed that the Caltrac ® accelerometer output is a valid predictor of Ee during level walking when the appropriate regression equation is used to adjust the values. Because the accelerometer device tends to overestimate Ee, the raw accelerometer readings should be applied with caution. [Balogun J A, Martin DA, Clendenin MA Calorimetric validation of the Caltrac ® accelerometer during level walking. Phys Ther 69: [501][502][503][504][505][506][507][508][509] 1989] Over the last 20 years, there has been a considerable interest in the assess ment of physical activity levels and how activity levels relate to cardiovas cular fitness and health. 1-3 A variety of methods have been used in measur ing physical activity levels including self-reports by questionnaires and interviews, direct observation of phys ical activities, monitoring of heart rate by telemetry, calorimetric measure ment of oxygen uptake (Vo 2 ), isotope ratio mass spectrometry, and the use of motion activity sensors. Of all of these methods, the motion sensors are reasonably nonobstructive, have the advantage of being objective, and are the most cost-effective for clinical use.
European journal of applied …, 2010
Combining accelerometry with heart rate monitoring has been suggested to improve energy estimates, however, it remains unclear whether the single, currently existing commercially available device combining these data streams (Actiheart) provides improved energy estimates compared to simpler and less expensive accelerometry-only devices. The purpose of this study was to compare the validity of the heart rate (HR), accelerometry (ACC), and combined ACC/HR estimates of the Actiheart to the ACC estimates of the Actical during low and moderate intensity activities. Twenty-seven participants (mean age 26.3 ± 7.3) wore an Actical, Actiheart and indirect calorimeter (K4b 2 ) while performing card playing, sweeping, lifting weights, walking and jogging activities. All estimates tended to underestimate energy, sometimes by substantial amounts. Viewed across all activities studied, there was no significant difference in the ability of the waist-mounted Actical and torso-mounted Actiheart (ACC, HR, ACC/HR) estimates to predict energy expenditure. However, the Actiheart provided significantly better estimates than the Actical for the activities in which acceleration of the pelvis is not closely related to energy expenditure (card playing, sweeping, lifting weights) and the Actical provided significantly better estimates for level walking and level jogging. Similar to a previous study, the ACC component of the Actiheart was found to be the weakest predictor of energy suggesting it may be responsible for the failure of the combined ACC/HR estimate to equal or better the estimates derived solely from a waist mounted ACC device.
Minute-By-Minute Heart Rate Monitoring to Estimate Energy Expenditure During Cycling and Running
Medicine & Science in Sports & Exercise, 2001
The main purpose o f this study was to examine the accuracy o f the HRM in estimating EE and substrate utilization using the subjects' primary mode o f training (cycling), and an alternate mode (running). A secondary aim was to stress the accuracy o f the linear regression equations by incorporating two different exercise intensities during the experimental phase o f testing. Eleven trained male subjects (peak VO2 run=6I.17±13.30, cycle=61.82±11.91) completed protocols on non-consecutive days using the cycle ergometer and treadmill for the development o f linear regression equations. Expired gases were analyzed during all testing using a Parvo Medics metabolic system. HR was recorded each minute using a chest strap monitor. Subjects later performed a total o f 40 minutes o f exercise (20-min cycling, 20-min running). For each mode, a lower intensity was used for minutes 1-10, and a higher intensity was used for minutes 11-20. This yielded three sets o f linear regression equations, based on HR. VO2 and HR:VC02, for each individual and for each mode. The equations were based on the first ten minutes (LI), the last ten minutes (HI), and the entire twenty minutes (OA) o f exercise. HR values during each exercise bout were integrated into the regression equations to estimate energy expenditure (kcals/min) and substrate utilization (g/min fat, and g/min CHO). Actual values were obtained by indirect calorimetry. Apriori planned comparisons were performed to examine differences between estimated and actual values. Based on the overall equation, estimated values o f VO2 (L/min) were significantly different (p<0.05) for the run (est.=2.78±0.36; act.=3.02±0.33), not for the cycle (est.=2.40±0.16; act.=2.47±0.22). Estimated values o f EE (kcal/min) were significantly different (p<0.05) for the run (est.=13.46±l .69; act.=14.±1.59), not the cycle (est.=l 1.62+0.75; act.=l 1.95+1.09). Estimated values o f carbohydrate utilization (g/min CHO) were only significantly different (p<0.05) for the run (est.= l.73+0.44; act.=2.09+0.43), not the cycle (est.=1.34+0.40; act.=1.52+0.48). Estimated values o f fat utilization (g/min fat) were not significantly different for the run (est. =0.61+0.20; act. =0.60+0.15) or cycle (est.=0.52+0.14; act.=0.48+0.10). These data suggest that individual regression equations offer promising potential for predicting energy expenditure and substrate utilization from HR data, particularly when utilizing a mode o f exercise that an individual is accustomed to performing. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
International Journal of Behavioral Nutrition and Physical Activity, 2011
Background: Objective measurement of physical activity remains an important challenge. For wearable monitors such as accelerometer-based physical activity monitors, more accurate methods are needed to convert activity counts into energy expenditure (EE). Purpose: The purpose of this study was to examine the accuracy of the refined Crouter 2-Regression Model (C2RM) for estimating EE during the transition from rest to walking and walking to rest. A secondary purpose was to determine the extent of overestimation in minute-by-minute EE between the refined C2RM and the 2006 C2RM. Methods: Thirty volunteers (age, 28 ± 7.7 yrs) performed 15 minutes of seated rest, 8 minutes of over-ground walking, and 8 minutes of seated rest. An ActiGraph GT1M accelerometer and Cosmed K4b 2 portable metabolic system were worn during all activities. Participants were randomly assigned to start the walking bout at 0, 20, or 40 s into the minute (according to the ActiGraph clock). Acceleration data were analyzed by two methods: 2006 Crouter model and a new refined model. Results: The 2006 Crouter 2-Regression model over-predicted measured kcal kg-1 hr-1 during the first and last transitional minutes of the 20-s and 40-s walking conditions (P < 0.001). It also over-predicted the average EE for a walking bout (4.0 ± 0.5 kcal kg-1 hr-1), compared to both the measured kcal kg-1 hr-1 (3.6 ± 0.7 kcal kg-1 hr-1) and the refined Crouter model (3.5 ± 0.5 kcal kg-1 hr-1) (P < 0.05). Conclusion: The 2006 Crouter 2-regression model over-predicts EE at the beginning and end of walking bouts, due to high variability in accelerometer counts during the transitional minutes. The new refined model eliminates this problem and results in a more accurate prediction of EE during walking.
A Comparison of Various Activity Trackers for Estimating Energy Expenditure During Exercise
Medicine and Science in Sports and Exercise, 2014
Percent differences between portable device and Douglas Bag (DB) VO 2 measurements are reported as overestimated (+), underestimated (-) or not available (n/a). An asterisk (*) denotes a significant difference between DB and portable device (P<0.05). TEEM values were compared to a SensorMedics 2900 metabolic cart. Cart Device Sampling Method Rest Moderate Intensity High Intensity KM MPS n/a n/a n/a Oxylog MPS n/a n/a n/a TEEM MPS +1.4%-3.9% n/a KB1-C MPS +82%* <1.5%-5.9% Fitmate MPS-1.7% n/a-3.3%* K2 MPS n/a-4.6%-4.1%* VO2000 MPS-45%* +9.0%* +6.1%* K4b 2 BxB-14.1% +4.6%* +0.3% Oxycon BxB n/a +1.3%*-1.4% MM3B BxB +7.4%* n/a +11.8%* CONCLUSION: There are differences between and within portable indirect calorimeters in VO 2 validity at rest, moderate, and high intensities. Future improvements in technology could lead to more precise sampling techniques to ensure accurate VO 2 measurements across all intensities.
International Journal of Sport Nutrition and Exercise Metabolism, 2010
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors’ purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner’s training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and crite...
EMG, Heart Rate, and Accelerometer as Estimators of Energy Expenditure in Locomotion
Medicine & Science in Sports & Exercise, 2014
Precise measures of energy expenditure (EE) during everyday activities are needed. This study assessed the validity of novel shorts measuring EMG and compared this method with HR and accelerometry (ACC) when estimating EE. Methods: Fifty-four volunteers (39.4 T 13.9 yr) performed a maximal treadmill test (3-min loads) including walking with different speeds uphill, downhill, and on level ground and one running load. The data were categorized into all, low, and level loads. EE was measured by indirect calorimetry, whereas HR, ACC, and EMG were measured continuously. EMG from quadriceps (Q) and hamstrings (H) was measured using shorts with textile electrodes. Validity of the methods used to estimate EE was compared using Pearson correlations, regression coefficients, linear mixed models providing Akaike information criteria, and root mean squared error (RMSE) from cross-validation at the individual and population levels. Results: At all loads, correlations with EE were as follows: EMG(QH), 0.94 T 0.03; EMG(Q), 0.
Journal of Physical Activity and Health, 2011
Background:The purposes of this study were to determine the accuracy and reliability of step counts and energy expenditure as estimated by a pedometer during treadmill walking and to clarify the relationship between step counts and current physical activity recommendations.Methods:One hundred males (n = 50) and females (n = 50) walked at stride frequencies (SF) of 80, 90, 100, 110, and 120 steps/min, during which time step counts and energy expenditure were estimated with a Walk4Life Elite pedometer.Results:The pedometer accurately measured step counts at SFs of 100, 110, and 120 steps/min, but not 80 and 90 steps/min. Compared with energy expenditure as measured by a metabolic cart, the pedometer significantly underestimated energy expenditure at 80 steps/min and significantly overestimated measured energy expenditure at 90, 100, 110, and 120 steps/ min.Conclusions:The pedometers’ inability to accurately estimate energy expenditure cannot be attributed to stride length entered into...
Comparison of Energy Expenditure during Walking between Female Athletes and Non-Athletes
Asian Social Science, 2013
This study aimed to determine, if there is a difference in energy expenditure during walking among athletes and non-athletes at two different speeds of walking. Ninety five healthy female students (47 athletes and 48 non-athletes) with a mean age of 22.4 (±1.6) years purposively participated in this study. Medical and sport participation history of the subjects was acquired through a questionnaire. Two experimental tests including anthropometric measurements,V O , and walking tests on treadmill at speeds of 3.00 and 3.5 mph were conducted. Results showed no difference in weight, height, body mass index, and leg length between both groups. The non-athletes expended a greater amount of energy than athletes (3.78±.1 and 2.95±.6 kcal.min-1 , respectively) at both speeds of 3.00mph and 3.5mph (4.89±1 and 3.94±.7 kcal.min-1). Based on energy requirements for walking at similar weights and speeds by ACSM's guideline, the female athletes walked at a slow, moderate and brisk pace. Most of the female non-athletes walked at a moderate, brisk and very brisk pace. This study revealed that regular exercise could improve walking efficiency, and the energy expenditure of walking would play an important role in the information processing for total energy requirement that progressively affects weight management and health.
Comparison of Activity Monitors to Estimate Energy Cost of Treadmill Exercise
Medicine & Science in Sports & Exercise, 2004
Purpose: To evaluate the validity of five physical activity monitors available for research: the CSA, the TriTrac-R3D, the RT3, the SenseWear Armband, and the BioTrainer-Pro. Methods: A total of 10 healthy men and 11 healthy women performed 10 min of treadmill walking at 54, 80, and 107 m•min Ϫ1 and treadmill running at 134, 161, 188, and 214 m•min Ϫ1. The CSA, TriTrac-R3D, RT3, and BioTrainer-Pro accelerometers were placed side by side bilaterally at the waist in the axillary position, and the SenseWear Armband monitors were placed bilaterally on the posterior portion of each arm in the mid-humeral position. Simultaneous measurements of body motion and indirect calorimetry were continuously recorded during all exercise. Data were analyzed with repeated measures ANOVA and pairwise Bonferroni-adjusted estimated marginal means. Results: There was no significant difference in the mean energy expenditure (EE) recorded bilaterally by any of the monitors (P Ͼ 0.05) at any treadmill speed. The SenseWear Armband, the TriTrac-R3D, and the RT3 had significant increases in mean EE across all walking and running speeds (P Ͻ 0.05). Below 161 m•min Ϫ1 , the mean EE recorded by the BioTrainer-Pro and the CSA increased significantly (P Ͻ 0.001); however, there was no significant difference (P Ͼ 0.10) in mean EE recorded by either monitor for speeds above 161 m•min Ϫ1. In general, all monitors overestimated EE at most treadmill speeds when compared with indirect calorimetry (P Ͻ 0.001), except for the CSA which underestimated EE at the lowest and highest speeds. Conclusion: The CSA was the best estimate of total EE at walking and jogging speeds, the TriTrac-R3D was the best estimate of total EE at running speeds, and the SenseWear Armband was the best estimate of total EE at most speeds.