Underestimated Energy Expenditure Research Articles

Move less, spend more: the metabolic demands of short walking bouts.

The metabolic cost of steady-state walking is well known; however, across legged animals, most walking bouts are too short to reach steady state. Here, we investigate how bout duration affects the metabolic cost of human walking with varying mechanical power, metabolic intensity and duration. Ten participants walked for 10- to 240-s bouts on a stair climber at 0.20, 0.25 and 0.36 m s-1 and on a treadmill at 1.39 m s-1. Oxygen uptake was time-integrated and divided by bout duration to get bout average uptake (V̇O2(b)). Fitting of oxygen uptake kinetics allowed calculating non-metabolic oxygen exchange during phase-I transient and, hence, non-steady-state metabolic cost (C met(b)) and efficiency. For 240-s bouts, such variables were also calculated at steady state. Across all conditions, shorter bouts had higher V̇O2(b) and C met(b), with proportionally greater non-metabolic oxygen exchange. As the bout duration increased, V̇O2(b), C met(b) and efficiency approached steady-state values. Our findings show that the time-averaged oxygen uptake and metabolic cost are greater for shorter than longer bouts: 30-s bouts consume 20-60% more oxygen than steady-state extrapolations. This is partially explained by the proportionally greater non-metabolic oxygen uptake and leads to lower efficiency for shorter bouts. Inferring metabolic cost from steady state substantially underestimates energy expenditure for short bouts.

Validity of Apple Watch 6 and Polar A370 for monitoring energy expenditure while resting or performing light to vigorous physical activity.

The purpose of this study was to assess the validity of energy expenditure measurements taken by the Apple Watch 6 and Polar A370 during resting, light- to vigorous-intensity running on the treadmill and ground, and post-exercise of each speed. Randomized cross-over trial. This study included 11 male adults (22.5 ± 1.8 years old). Participants were asked to wear the Apple Watch 6 and the Polar A370 simultaneously to measure energy expenditure under various intensities of physical activities on the treadmill and ground, which were then compared with results from a gas metabolism analysis system. Monitoring energy expenditure for both treadmill and ground, the Apple Watch 6 (range: 13.40-50.34 %) had a higher mean absolute percent error than the Polar A370 (range: 12.74-27.70 %) in resting and running state, while the mean absolute percent error of the Apple Watch 6 (range: 9.71-32.81 %) is smaller than that of the Polar A370 (15.79-43.51 %) in post-exercise. The Apple Watch 6 tends to overestimate energy expenditure, with a mean percent error ranging from -6.61 % to 53.24 %, while the Polar A370 tends to underestimate energy expenditure, with a mean percent error ranging from -3.51 % to 11.33 %. No estimated energy expenditure of both devices fell in the equivalence testing zone. For young adult males, the Apple Watch 6 and Polar A370 provide similar levels of accuracy in monitoring energy expenditure during treadmill and ground running exercise. However, both devices are still inadequate in this regard.

Validity of the iPhone M7 Motion Coprocessor to Estimate Physical Activity During Structured and Free-Living Activities in Healthy Adults

Modern smartphones such as the iPhone contain an integrated accelerometer, which can be used to measure body movement and estimate the volume and intensity of physical activity. Objectives: The primary objective was to assess the validity of the iPhone to measure step count and energy expenditure during laboratory-based physical activities. A further objective was to compare free-living estimates of physical activity between the iPhone and the ActiGraph GT3X+ accelerometer. Methods: Twenty healthy adults wore the iPhone 5S and GT3X+ in a waist-mounted pouch during bouts of treadmill walking, jogging, and other physical activities in the laboratory. Step counts were manually counted, and energy expenditure was measured using indirect calorimetry. During two weeks of free-living, participants (n = 17) continuously wore a GT3X+ attached to their waist and were provided with an iPhone 5S to use as they would their own phone. Results: During treadmill walking, iPhone (703 ± 97 steps) and GT3X+ (675 ± 133 steps) provided accurate measurements of step count compared with the criterion method (700 ± 98 steps). Compared with indirect calorimetry (8 ± 3 kcal·min−1), the iPhone (5 ± 1 kcal·min−1) underestimated energy expenditure with poor agreement. During free-living, the iPhone (7,990 ± 4,673 steps·day−1) recorded a significantly lower (p < .05) daily step count compared with the GT3X+ (9,085 ± 4,647 steps·day−1). Conclusions: The iPhone accurately estimated step count during controlled laboratory walking but recorded a significantly lower volume of physical activity compared with the GT3X+ during free-living.

Energy expenditure in a Syme’s amputee triathlete

Energy expenditure prediction equations developed for able-bodied populations may be inaccurate for para-athletes, a population characterized by different types of impairments. Sufficient data do not exist currently to generate or validate energy expenditure predictive equations for para-athletes to establish dietary recommendations to cover the demands of sport. The purpose of this observational study was to assess energy expenditure and substrate usage from a trained para-athlete at rest and while performing the athlete’s specific mode of exercise. One para-athlete recruited from the Challenged Athletes Foundation (female; age 32) participated in the study. The participant was a left leg Syme’s amputee triathlete. Resting metabolic rate was measured for the triathlete. Energy expenditure was also measured at moderate (64-76% of age-predicted HRmax) and vigorous intensity exercise (77-95% of age-predicted HRmax) on a stationary cycle ergometer on a separate day. The triathlete’s energy expenditure during exercise was higher than that predicted by the metabolic equivalent of task (MET) matched by exercise type at a moderate (measured: 7.0 kcal/min, predicted: 5.0 kcal/min) and vigorous intensity (measured: 9.3 kcal/min, predicted: 7.0 kcal/min). The Revised Harris-Benedict and the Mifflin-St Jeor equations underestimated resting metabolic rate in the triathlete (measured: 2,194 kcal/day, predicted: 1,348 ± 39 kcal/day). This study indicates that equations developed for able-bodied populations underestimate energy expenditure in a Syme’s amputee when compared to the measured energy expenditure values assessed by indirect calorimetry at rest and during a workout on both a stationary cycle ergometer and while running on the track.

Quantification of energy expenditure during daily living activities after stroke by multi-sensor

ABSTRACTObjective: To explore the validity of energy expenditure estimates using the SenseWear Armband during a sequence of four daily living activities in patients post-stroke.Method: Patients with stroke who were able to walk during 6 min without human assistance were asked to wear the SenseWear Armband on the non-paretic arm while performing transfers, a manual task, walking, and walking up and down stairs. The energy expenditure estimated using the SenseWear Armband was compared to the energy expenditure calculated from oxygen consumption, measured by a portable indirect calorimeter (Metamax 3B). The mean of energy expenditure was pooled for each task. Accuracy was explored by mean bias (MB) of Bland–Altman analysis and root mean square error (RMSE), agreement by 95% of limits of agreement (95%LoA) and coefficient of correlation (r).Results: Thirty-eight participants (65.7 ± 13.5 years) were included. The SenseWear Armband globally underestimated energy expenditure, MB = 9.77 kcal for the whole sequence. RMSE were large, accounting for 15% to 41% of the measured energy expenditure. Agreement was low with r < 0.70 and 95%LoA from 42% to 93% of the measured energy expenditure.Conclusions: This study reported a global underestimation and a low level of agreement of the energy expenditure estimated by SenseWear Armband in four daily living activities in patients after stroke.Abbreviations: EE: Energy Expenditure; NIHSS: National Institute of Health Stroke Score

Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015-2017

Wearable physical activity trackers are a popular and useful method to collect biometric information at rest and during exercise. The purpose of this systematic review was to summarize recent findings of wearable devices for biometric information related to steps, heart rate, and caloric expenditure for several devices that hold a large portion of the market share. Searches were conducted in both PubMed and SPORTdiscus. Filters included: humans, within the last 5 years, English, full-text, and adult 19+ years. Manuscripts were retained if they included an exercise component of 5-min or greater and had 20 or more participants. A total of 10 articles were retained for this review. Overall, wearable devices tend to underestimate energy expenditure compared to criterion laboratory measures, however at higher intensities of activity energy expenditure is underestimated. All wrist and forearm devices had a tendency to underestimate heart rate, and this error was generally greater at higher exercise intensities and those that included greater arm movement. Heart rate measurement was also typically better at rest and while exercising on a cycle ergometer compared to exercise on a treadmill or elliptical machine. Step count was underestimated at slower walking speeds and in free-living conditions, but improved accuracy at faster speeds. The majority of the studies reviewed in the present manuscript employed different methods to assess validity and reliability of wearable technology, making it difficult to compare devices. Standardized protocols would provide guidance for researchers to evaluate research-grade devices as well as commercial devices used by the lay public.

Validity of sports watches when estimating energy expenditure during running

BackgroundThe aim of this study was to assess the accuracy of three different sport watches in estimating energy expenditure during aerobic and anaerobic running.MethodsTwenty trained subjects ran at different intensities while wearing three commercial sport watches (Suunto Ambit2, Garmin Forerunner920XT, and Polar V800). Indirect calorimetry was used as the criterion measure for assessing energy expenditure. Different formulas were applied to compute energy expenditure from the gas exchange values for aerobic and anaerobic running.ResultsThe accuracy of the energy expenditure estimations was intensity-dependent for all tested watches. During aerobic running (4–11 km/h), mean absolute percentage error values of −25.16% to +38.09% were observed, with the Polar V800 performing most accurately (stage 1: −12.20%, stage 2: −3.61%, and stage 3: −4.29%). The Garmin Forerunner920XT significantly underestimated energy expenditure during the slowest stage (stage 1: −25.16%), whereas, the Suunto Ambit2 significantly overestimated energy expenditure during the two slowest stages (stage 1: 38.09%, stage 2: 36.29%). During anaerobic running (14–17 km/h), all three watches significantly underestimated energy expenditure by −21.62% to −49.30%. Therefore, the error in estimating energy expenditure systematically increased as the anaerobic running speed increased.ConclusionsTo estimate energy expenditure during aerobic running, the Polar V800 is recommended. By contrast, the other two watches either significantly overestimated or underestimated energy expenditure during most running intensities. The energy expenditure estimations generated during anaerobic exercises revealed large measurement errors in all tested sport watches. Therefore, the algorithms for estimating energy expenditure during intense activities must be improved before they can be used to monitor energy expenditure during high-intensity physical activities.

Validation of SenseWear Armband in children, adolescents, and adults.

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi-structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist-worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%-32%) and for basketball and biking (19%-35%), compared to walking and running (8%-20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%-74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research.

Energy expenditure, metabolic power and high speed activity during linear and multi-directional running.

The purpose of the study was to compare measures of energy expenditure derived from indirect calorimetry and micro-technology, as well as high power and high speed activity during linear and multi-directional running. Repeated measures. Twelve university standard team sport players completed a linear and multi-directional running condition. Estimated energy expenditure, as well as time at high speed (>14.4kmh-1) and high power (>20Wkg-1) were quantified using a 10Hz micro-technology device and compared with energy expenditure derived from indirect calorimetry. Measured energy expenditure was higher during the multi-directional condition (9.0±2.0 cf. 5.9±1.4kcalmin-1), whereas estimated energy expenditure was higher during the linear condition (8.7±2.1 cf. 6.5±1.5kcalmin-1). Whilst measures of energy expenditure were strongly related (r>0.89, p<0.001), metabolic power underestimated energy expenditure by 52% (95% LoA: 20-93%) and 34% (95% LoA: 12-59%) during the multi-directional and linear condition, respectively. Time at high power was 41% (95% LoA: 4-92%) greater than time at high speed during the multi-directional condition, whereas time at high power was 5% (95% LoA: -17-9%) lower than time at high speed during the linear condition. Estimated energy expenditure and time at high metabolic power can reflect changes in internal load. However, micro-technology cannot be used to determine the energy cost of intermittent running.

The Unsuitability of Energy Expenditure Derived From Microtechnology for Assessing Internal Load in Collision-Based Activities.

This aim of this study was to examine the validity of energy expenditure derived from microtechnology when measured during a repeated-effort rugby protocol. Sixteen male rugby players completed a repeated-effort protocol comprising 3 sets of 6 collisions during which movement activity and energy expenditure (EEGPS) were measured using microtechnology. In addition, energy expenditure was estimated from open-circuit spirometry (EEVO2). While related (r = .63, 90%CI .08-.89), there was a systematic underestimation of energy expenditure during the protocol (-5.94 ± 0.67 kcal/min) for EEGPS (7.2 ± 1.0 kcal/min) compared with EEVO2 (13.2 ± 2.3 kcal/min). High-speed-running distance (r = .50, 95%CI -.66 to .84) was related to EEVO2, while PlayerLoad was not (r = .37, 95%CI -.81 to .68). While metabolic power might provide a different measure of external load than other typically used microtechnology metrics (eg, high-speed running, PlayerLoad), it underestimates energy expenditure during intermittent team sports that involve collisions.

Increased Energy Cost of Mobility in Chronic Stroke.

The purpose of this study was to compare the energy cost of completing mobility-related activities in chronic stroke to the estimated energy cost found in the compendium of physical activities, a resource that estimates and classifies energy cost of various human physical activities. Men (n=18) and women (n=10) with chronic hemiparetic gait (stroke latency: 4 ± 2 years, age: 60.4 ± 1.6 years, BMI: 31.5 ± 1.1 kg/m2) participated in the study. Portable energy cost monitoring (COSMED K4b2) was performed during five mobility activities of varying intensity to determine metabolic equivalents (METs, or oxygen consumption in multiples of resting level) for each activity. The METs achieved during the five activities were compared to the following compendium MET values for: 1) floor sweeping; 2) stepping in place; 3) over-ground walking; 4) lower speed treadmill walking (1.0 mph at 4% incline); and 5) higher speed treadmill walking (2.0 mph at 4% incline). Measurements were obtained for 10 min at rest and 5 minutes during each of the five activities. The energy cost of rest was only 85% of Compendium METS, while mobility-related activities were ~1.25-1.50 fold greater when measured in stroke vs. Compendium METS for all measures (P's<0.05), except floor sweeping, which was similar between groups. These data indicate that MET levels provided in the compendium are not applicable to chronic stroke survivors as they overestimate energy expenditure at rest and underestimate energy expenditure during physical activity, indicating poor efficiency in movement, thus elevating the oxygen cost of completing general daily activities.

Criterion validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF) for use in patients with rheumatoid arthritis: comparison with the SenseWear Armband

ObjectivesThe International Physical Activity Questionnaire Short Form (IPAQ-SF) is a self-report questionnaire commonly used in patients with rheumatoid arthritis (RA) to measure physical activity. However, despite its frequent use in patients with RA, its validity has not been ascertained in this population. The aim of this study was to examine the criterion validity of energy expenditure from physical activity recorded with the IPAQ-SF in patients with RA compared with the objective criterion measure, the SenseWear Armband (SWA) which has been validated previously in this population. DesignCross-sectional criterion validation study. SettingRegional hospital outpatient setting. ParticipantsTwenty-two patients with RA attending outpatient rheumatology clinics. InterventionsSubjects wore an SWA for 7 full consecutive days and completed the IPAQ-SF. Main outcome measuresEnergy expenditure from physical activity recorded by the SWA and the IPAQ-SF. ResultsEnergy expenditure from physical activity recorded by the IPAQ-SF and the SWA showed a small, non-significant correlation (r=0.407, P=0.60). The IPAQ-SF underestimated energy expenditure from physical activity by 41% compared with the SWA. This was corroborated using Bland and Altman plots, as the IPAQ-SF was found to overestimate energy expenditure from physical activity in nine of the 22 individuals, and underestimate energy expenditure from physical activity in the remaining 13 individuals. ConclusionThe IPAQ-SF has limited use as an accurate and absolute measure for estimating energy expenditure from physical activity in patients with RA.

Accuracy of energy expenditure estimation by activity monitors differs with ethnicity.

The aim of this project is to explore the accuracy of 2 activity monitors (SenseWear Armband & Actical) to estimate energy expenditure during rest and light to moderate intensity exercises in 2 ethnic groups. 18 Caucasian and 20 Black adults (age: 26.8±5.2 years; body mass index: 23.9±3.0 kg/m(2)) wore the 2 devices simultaneously during 3 standardised activities: 30-min rest, 45-min of treadmill at 40% of their V˙O2peak and 45-min of stationary cycling at 50% of their V˙O2peak. Energy estimated with the 2 devices was compared to indirect calorimetry measurements. Both devices overestimated energy expenditure during rest (SenseWear: 36% in Black vs. 16% in Caucasian; Actical: 26% vs. 11%, p<0.01 between groups) and treadmill (SenseWear: 50% vs. 25%; Actical: 67% vs. 32%, p<0.01 between groups). Both devices significantly underestimated energy expenditure during stationary cycling (SenseWear: 24% vs. 26%; Actical: 58% vs. 70%, p=NS between groups). Equations used to estimate energy expenditure from accelerometer data is less precise among Black adults than Caucasian adults. Ethnic-specific formulas are probably required.

Comparison of FitBit® Ultra to ActiGraph™ GT1M for Assessment of Physical Activity in Young Adults During Treadmill Walking

Introduction: Accelerometers are used to quantify energy expenditure in field research. The ActiGraph™ GT1M (ActiGraph™) is a commonly used accelerometer for research. The FitBit ® Ultra (FitBit ® ) is a low-cost alternative to the ActiGraph™; however, there is limited research on the validity of this device. Purpose: The pilot study compares the FitBit ® against the ActiGraph™ and metabolic cart for measurement of energy expenditure and step counts during treadmill walking. Methods: Thirty-two (25 female) adults, mean age 22±2 years, performed two thirty-minute phases of walking (slow and brisk) on a treadmill while concurrently wearing the FitBit ® and the ActiGraph™. Energy expenditure estimates were compared against energy expenditure measured by a metabolic cart. The Pearson's correlation and t-tests determine the linear association and similarity between the accelerometers. Results: Energy expenditure estimate is moderately correlated between the two accelerometers during slow walking (r=0.584, p=0.011) and strongly correlated during brisk walking (r=0.910, p<0.001). Step count is strongly correlated between the accelerometers during slow (r=0.974, p<0.001) and brisk (r=0.996, p<0.001) walking. The FitBit ® significantly underestimated energy expenditure during brisk walking compared to metabolic cart data. There is no difference between the slow and brisk phases' step counts using either accelerometer. Conclusion: The results of this pilot study suggest that the FitBit ® and the ActiGraph™ can be used interchangeably to measure steps, but not to measure kilocalories. Furthermore, the FitBit ® underestimates energy expenditure, compared to a metabolic cart, as exercise intensity increases. This limits its ability to accurately measure energy expenditure in active populations.

A quantification of the treadmill 6-min walk test using the MyWellness Key™ accelerometer

BackgroundAccelerometers have been suggested to provide additional information during the 6-min walk test which may be useful in evaluating changes in functional exercise capacity. The aim of the study was to identify whether the additional information measured by the MyWellness Key™ (MWK) accelerometer during a treadmill 6-min walk test (t-6MWT) was related to currently used outcome measures. MethodsFifteen participants (9 males, 6 females) performed a self-paced t-6MWT. Respiratory gas analysis and walking distance were measured whilst wearing the MWK. ResultsA significant correlation was established between activity counts and 6-min walk distance (6MWD) (r = −0.847, p < 0.001) yet not 6-min walk work (6MWW) (r = −0.337, p = 0.220). Energy expenditure estimated by the MWK was strongly correlated to 6MWW (r = 0.938, p < 0.001) but not 6MWD (r = 0.477, p = 0.072). The MWK significantly underestimated energy expenditure (36.73, CI = 33.9–39.7 kcal) compared to gas analysis (54.35, CI = 46.2–61.4 kcal) demonstrating poor agreement between the two analyses (Bias = −17.61 kcal, Limits of agreement = −37.4, +2.2 kcal). Measurement of time spent undertaking light, moderate, and vigorous physical activity was not significantly different (p > 0.05) between the MWK and gas analysis. ConclusionEstimated energy expenditure provided by the MWK was strongly correlated to 6MWW; however, MWK underestimated energy expenditure as measured by gas analysis. The MWK may provide outcome data that supplement those currently provided by the 6MWD for functional capacity assessment during the t-6MWT.

Validity of the Pedusa PE-771 Pedometer

The purpose of this study was to examine the accuracy of a new, commerically-developed pedometer, Pedusa PE-771. Thirteen participants completed various ambulatory-based activity trials while wearing up to six pedemeters, with observed steps assessed using a tally counter. The four main experiements included 1) participants wearing the pedometers during a 20 step-test, 2) an assessment of walking speed accuracy of the pedometer, 3) and an assessment of the accuracy of the pedometer over different surfaces (e.g., grass, concrete), and 4) an assessment of the pedometer’s propriatory energy expenditure algorithm. During these trials, pedometers were worn on the waist, in shorts with snug pockets, and in shorts with loose pockets. For the 20 step-test, and across all conditions (i.e., pedometers worn on the belt, in snug pocket and in loose pocket), each pedometer demonstrated acceptable classification accuracy, with the error-rate range being 0.01% to 4.5%. When the pedometers were worn on the belt, the accuracy of each pedometer was exact at speeds ≥ 3 mph. However, at 2 and 2.5 mph, there was some degree of under- and over-estimation. These findings were identical to when the pedometers were worn in snug or loose pockets. The Pedusa PE-771 pedometer demonstrated accuracy (i.e., 1% error rate) when placed in snug pockets during outdoor self-selected walking speed over concrete, which was not the case when the pedometer was worn in loose pockets. Lastly, the Pedusa PE-771 pedometer overestimated and underestimated energy expenditure, respectively, during walking and jogging.

Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry

Epidemic levels of inactivity are associated with chronic diseases and rising healthcare costs. To address this, accelerometers have been used to track levels of activity. The Fitbit and Fitbit Ultra are some of the newest commercially available accelerometers. The purpose of this study was to determine the reliability and validity of the Fitbit and Fitbit Ultra. Twenty-three subjects were fitted with two Fitbit and Fitbit Ultra accelerometers, two industry-standard accelerometers and an indirect calorimetry device. Subjects participated in 6-min bouts of treadmill walking, jogging and stair stepping. Results indicate the Fitbit and Fitbit Ultra are reliable and valid for activity monitoring (step counts) and determining energy expenditure while walking and jogging without an incline. The Fitbit and standard accelerometers under-estimated energy expenditure compared to indirect calorimetry for inclined activities. These data suggest the Fitbit and Fitbit Ultra are reliable and valid for monitoring over-ground energy expenditure.

Necessidades nutricionais do doente crítico

Given the inaccessibility of indirect calorimetry, intensive care units generally use predictive equations or recommendations that are established by international societies to determine energy expenditure. The aim of the present study was to compare the energy expenditure of critically ill patients, as determined using indirect calorimetry, to the values obtained using the Harris-Benedict equation. A retrospective observational study was conducted at the Intensive Care Unit 1 of the Centro Hospitalar do Porto. The energy requirements of hospitalized critically ill patients as determined using indirect calorimetry were assessed between January 2003 and April 2012. The accuracy (± 10% difference between the measured and estimated values), the mean differences and the limits of agreement were determined for the studied equations. Eighty-five patients were assessed using 288 indirect calorimetry measurements. The following energy requirement values were obtained for the different methods: 1,753.98±391.13 kcal/day (24.48 ± 5.95 kcal/kg/day) for indirect calorimetry and 1,504.11 ± 266.99 kcal/day (20.72±2.43 kcal/kg/day) for the Harris-Benedict equation. The equation had a precision of 31.76% with a mean difference of -259.86 kcal/day and limits of agreement between -858.84 and 339.12 kcal/day. Sex (p=0.023), temperature (p=0.009) and body mass index (p<0.001) were found to significantly affect energy expenditure. The Harris-Benedict equation is inaccurate and tends to underestimate energy expenditure. In addition, the Harris-Benedict equation is associated with significant differences between the predicted and true energy expenditure at an individual level.

Evaluation of two portable sensors for energy expenditure assessment during high-intensity running

The correct assessment of individual energy requirements of athletes remains a challenge. Available literature indicates that accelerometry-based sensors underestimated energy expenditure in athletes, especially during high-intensity exercise. The goal of our study was to evaluate two commercially available devices (Actiheart; SenseWear Pro3) during high-intensity running and to identify potential sources of error. Twenty-nine male endurance- and strength-trained athletes participated in an incremental running exercise, which started at 2.8 m · s−1. Running speed was increased by 0.4 m · s−1 every 5 minutes until 4.8 m · s−1 or individual exhaustion were reached. Energy expenditure was measured with indirect calorimetry (IC) and both sensors were worn according to the manufacturers' instructions. Twelve participants also conducted a step test in order to individually recalibrate the Actiheart data. The Actiheart monitor underestimated energy expenditure (EE) for all speeds by 1.1 to 8.3 kcal · min−1 and showed a weak correlation with IC (r=0.61). Following individual recalibration there were no significant differences to IC but validity correlation did not improve (r=0.56). Heart rate was assessed correctly with the Actiheart when compared to a Polar 610 recorder. Activity counts increased only between 2.8 and 3.6 m · s−1 but plateaued thereafter. SenseWear significantly underestimated EE for all speeds by 1.0 to 9.5 kcal min−1 and was moderately correlated with IC (r=0.66). Longitudinal and transversal acceleration increased significantly between 2.8 and 4.4 m · s−1 and were correlated with running speed (0.63 < r<0.69). Our results show that both devices underestimate EE during high-intensity running. For Actiheart, individual calibration is recommended in order to obtain sufficiently accurate results. For SenseWear, acceleration data could be used to improve EE prediction during high-intensity running. Further research is necessary to validate these portable devices in other groups, in field settings and during other exercise types.

Accuracy of the SenseWear Armband™ during Ergocycling

The present study aims to show the accuracy of a portable motion sensor, the SenseWear Armband, for the estimation of energy expenditure vs. energy expenditure measured by indirect calorimetry during ergocycling. 31 healthy adults (52% women; age: 26.7±6.3 years; Body Mass Index: 23.9±3.3 kg/m2) completed a 45-min ergocycling session at 50% of their VO2(peak). Despite a significant underestimation of 18.7±13.2 kcal during the first 10 min of the activity (T=5.06; p<0.001), we observed an overall good agreement between energy expenditure estimated by the SenseWear Armband during ergocycling and indirect calorimetry (260.3±80.1 vs. 287.8±97.1 kcal, respectively) (T=-2.148; p=0.04) and a significant intra-class correlation (r=0.81; p<0.001). The results of the present study indicate that the SenseWear Armband underestimated energy expenditure during a 45-min ergocycling session at a 50% VO2(peak) intensity, mainly during the first 10 min. Underestimation at the onset of the activity warrants further research.