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Validity of the SenseWear Armband to Assess Energy Expenditure During Intermittent Exercise and Recovery in Rugby Union Players

Zanetti, Sara1,2; Pumpa, Kate L.1; Wheeler, Keane W.1; Pyne, David B.1,3

Journal of Strength and Conditioning Research: April 2014 - Volume 28 - Issue 4 - p 1090–1095
doi: 10.1519/JSC.0000000000000243
Original Research
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Zanetti, S, Pumpa, KL, Wheeler, KW, and Pyne, DB. Validity of the SenseWear armband to assess energy expenditure during intermittent exercise and recovery in rugby union players. J Strength Cond Res 28(4): 1090–1095, 2014—Portable wearable devices that assess energy expenditure during intermittent exercise and recovery would be useful in team sports. Fourteen state-level male rugby union players (mean ± SD: age, 22 ± 4 years; body mass, 88.8 ± 11.2 kg; height, 1.81 ± 0.07 m, body fat, 18 ± 6% ) participated in this study. Energy expenditure was measured by the SenseWear Armband (SWA) and validated against indirect calorimetry as the criterion measure during a 42-minute rugby-specific intermittent exercise test and an immediate postexercise 10-minute recovery period. Energy expenditure measurements from indirect calorimetry and the SWA were only moderately correlated during both the exercise test (r = 0.55, ±0.34; mean, ±90% confidence limits) and recovery period (r = 0.58, ±0.33). The SWA estimate of energy expenditure during exercise was unclear, with a mean bias of −1.9% (±5.3%), and during recovery energy expenditure was overestimated, with a mean bias of 17% (±12%) at the mean estimated energy expenditure. Typical error of SWA energy expenditure estimates expressed as a coefficient of variation (±90% confidence interval) was 10% (8–16%) during exercise and 19% (14–30%) during recovery. The SWA did not provide a valid measure of energy expenditure during rugby-specific intermittent exercise or 10-minute postexercise recovery. Further improvements are required in the performance of the SWA before it can be used routinely in intermittent sports and provide worthwhile information in relation to workloads of athletes for sport scientists and coaches.

1National Institute of Sports Studies, Faculty of Health, University of Canberra, Canberra, Australia;

2Department of Sport Science, ACT Academy of Sport, Canberra, Australia; and

3Department of Physiology, Australian Institute of Sport, Canberra, Australia

Address correspondence to Sara Zanetti, sara.zanetti@adelaide.edu.au.

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Introduction

Measurement of energy expenditure is used to quantify the metabolic demands placed on an individual at rest and during periods of activity and exercise (1). In sports, knowledge of the specific energy requirements of exercise provides valuable information regarding the physiological demands of training and competition. This information can be used by sports scientists, sports dietitians, and coaching staff in the development of training programs, nutrition plans, and prescription of recovery. Accurate assessment of energy expenditure is necessary to ensure that an athlete's dietary intake is adequate for their training load and to optimize body composition and performance (27).

A range of methods have been employed to enable objective measurement of energy expenditure in athletes (1,25). Although traditional methods such as stationary indirect calorimetry systems are primarily employed in exercise research, a number of limitations have been reported (1). Exercise activities are restricted predominantly to treadmill and cycle ergometers, and results obtained may not be truly indicative of free-living exercise because assessment must take place within a laboratory setting (1). Alternatively, portable indirect calorimetry systems may be used to assess energy expenditure during outdoor activity with minimal movement restrictions placed on the participant (25).

Motion sensor technology is an innovative, easily accessible, and efficient method to objectively assess the energy expenditure of free-living individuals outside of a laboratory setting (6). Compared with traditional laboratory measures, motion sensor technology enables data to be collected during most exercise activities without restricting exercise performance with large and uncomfortable equipment (6,22). The SenseWear Armband (SWA) device used to measure energy expenditure, detect physical activity, and monitor sleep is gaining popularity (10). The device incorporates a triaxial accelerometer with other sensor technologies to obtain and collate a variety of physiological data, including galvanic skin response, skin temperature, near body ambient temperature, heat flux, and sweat rate (2,22). These parameters are incorporated into a patented algorithm to provide an estimate of energy expenditure (22).

Despite its varied applications, the accuracy of the SWA to assess energy expenditure has been evaluated predominantly in healthy populations at rest (23,26), and during physical activity in children (3,34) and obese adults (32), as well as during exercise in nonathletic populations (10,17,18). Highly active individuals and athletes typically have a higher metabolic rate expending more energy than nonathletes, both at rest and during activity (28). The SWA has been validated in exercise modalities employing constant (5,10) or ramp-based (8,10,17,20,21) protocols; however, there is only 1 published research study evaluating the device in intermittent-based exercise activities, reporting a substantial underestimation of energy expenditure during 30-minute basketball drills (approximately 27%) (38). Accordingly, there is a need to determine the accuracy of armband technology to measure energy expenditure during intermittent exercise, particularly in athletes. Given the high participation rates in intermittent sports such as soccer, rugby, and hockey, there is a significant practical and research interest in simple measurements of energy expenditure during training and competition (4). The aim of this study was to determine the validity of the SWA to measure energy expenditure during intermittent exercise and recovery in athletes.

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Methods

Experimental Approach to the Problem

The participants were required to attend 3 separate testing sessions to complete the study approximately 1 week apart. The first session involved explanation of all testing procedures, obtainment of informed consent, and assessment of body composition by Lunar Prodigy Pro dual-energy x-ray absorptiometry (DXA) scan analyzed with manufacturer software (enCORE v 14.1 software; GE Healthcare, Sydney, Australia). Dual-energy x-ray absorptiometry scanning was completed in accordance with the University of Canberra's DXA scanning protocol and has been validated in previous research (30,31). Participants were scanned after an overnight fast, before any exercise being completed and wore minimal clothing with all jewelry and metal objects removed.

The second session involved familiarization of participants to the rugby intermittent exercise test and both the indirect calorimetry and SWA systems, where the participants completed 1 cycle (6 minutes) of the exercise test while wearing all equipment. For the third testing session, participants were required to abstain from food and caffeinated beverages for 2 hours before testing. During the session, height and body mass were recorded for use in both the indirect calorimetry and SWA systems. A self-selected warm-up including dynamic stretching and repeat sprinting was conducted for 10 minutes before the completion of the full exercise test protocol. On cessation of the exercise, test participants undertook a 10-minute recovery period involving light static stretching.

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Subjects

Fourteen male state-level rugby union players (mean ± SD: age, 22.0 ± 4 years; body mass, 88.8 ± 11.2 kg; height, 1.81 ± 0.07 m; body fat, 18 ± 6%) with a playing experience of 12 ± 5 years and no recent injury or illness that would restrict their current training volunteered to participate in this study. Physical characteristics of participants are described in Table 1. Participants gave written informed consent on being informed of the testing procedures, requirements, and associated risks of the study. All testing procedures were approved by the Human Research Ethics Committee at the University of Canberra.

Table 1

Table 1

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Procedures

Exercise Protocol

The rugby intermittent exercise test protocol employed in this study was designed specifically to replicate the physiological demands of typical rugby match play (9). Physical work of players during competition is usually quantified as a work to rest ratio, and a ratio of 1:2 was used for the participants in this study because this is typical of players of similar age and competition level (7,14). A 4-minute rest period, comprising 1-minute jogging, 2-minute walking, and 1-minute jogging, immediately followed to complete one 6-minute test period. This 6-minute period was completed 7 times for a total of 42 minutes of intermittent exercise. This exercise test was designed to be approximately equivalent to one half of a rugby union match (9). Exercise testing was completed outdoors on a standard size grass rugby pitch to ensure close replication of match-play conditions.

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Indirect Calorimetry

A portable indirect calorimetry system (Cosmed K4b2, Rome, Italy) was used as the criterion method of energy expenditure assessment to compare with the SWA during the exercise test and postexercise recovery period. The validity and reliability of the measurement system have been reported previously (11,29). The Cosmed system was calibrated with gas of known CO2 and O2 concentrations before each testing session as per manufacturer's specifications. Breath-by-breath sampling to give average energy expenditure was recorded and reported in kilocalories per minute. Participants were required to abstain from any caloric intake and caffeinated or alcoholic beverages for 2 hours before the exercise testing session to limit the effects of postprandial thermogenesis (33).

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SenseWear Mini Armband

The most recent version of the device, the SWA Mini, was positioned on the left mid-upper arm over the triceps muscle belly and worn for 15 minutes before the commencement of exercise (22). Before and at the cessation of exercise, a time stamp was applied to identify exercise periods within the data set. Data were downloaded from the SWA into manufacturer software (SenseWear Professional version 7.0; BodyMedia, Pittsburgh, PA, USA). Energy expenditure was calculated through the manufacturer's proprietary algorithm that is not released to external sources. The algorithm incorporates the participants' body mass, height, age, gender, smoking status, and handedness, determined as the dominant hand of the individual, to estimate energy expenditure.

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Statistical Analyses

Mean energy expenditure during the exercise test and postexercise recovery period was estimated by both indirect calorimetry and SWA in kcal·min−1. All data were log transformed to ensure uniformity of estimate error and to minimize bias (16). A Pearson's correlation coefficient was calculated to determine the association between the indirect calorimetry measurement of energy expenditure (criterion method) and SWA estimates (practical method) during the exercise test and postexercise recovery period. To quantify the level of agreement of the SWA, the typical error of the estimate (TEE) was calculated and reported in kcal·min−1, and the standard error in relative terms as the coefficient of variation (CV) was calculated. Estimation of bias of the SWA was calculated as a percentage of the mean difference between measures. A magnitude-based inferences approach was employed to assess the size of effects and correlations. Log-transformed change scores were converted into standardized (Cohen) effect size scores and interpreted using the following criteria: <0.20, trivial; 0.2–0.6, small; 0.6–1.2, moderate; 1.2–2.0, large; >2.0, very large (15). Precision of estimation was indicated with 90% confidence limits.

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Results

Mean energy expenditure values calculated by indirect calorimetry and SWA during the exercise test and postexercise recovery period are presented in Table 1. The mean TEE (90% confidence limits) of the SWA during the exercise test was 1.1 (0.8–1.6) kcal·min−1 with a CV (90% confidence limits) of 10% (8–16%). During the postexercise recovery period, the mean TEE was 0.8 kcal·min−1 (0.6–1.2) with a CV of 19% (14–30%). The difference in estimated energy expenditure between indirect calorimetry and SWA during the exercise test was unclear because of the large degree of uncertainty (−0.2; ±0.6 kcal·min−1, mean; ±90% confidence limits; −0.15; ±0.43; standardized difference; ±90% confidence limits). In contrast, the SWA overestimated energy expenditure during the postexercise recovery period by approximately 17% (4–28%, 0.8; ±0.5 kcal·min−1). Only a modest correlation between energy expenditure estimates was observed during the exercise test (r = 0.55; ±0.34, r value; ±90% confidence limits) (Figure 1). Similarly, only a modest correlation between energy expenditure estimates was apparent during the 10-minute postexercise recovery period (r = 0.58; ±0.33) (Figure 2). The mean bias of the SWA measurement was unclear during the exercise test (−1.9%; ±5.3%, mean; ± 90% confidence limits); however, there was a moderate overestimation of energy expenditure by the SWA (17%; ±12%) during postexercise recovery.

Figure 1

Figure 1

Figure 2

Figure 2

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Discussion

The results of this study indicate that in the current model and algorithm used by the SWA, the device does not accurately estimate energy expenditure during rugby-specific intermittent exercise and immediate postexercise recovery periods. In the present study, the SWA estimate of energy expenditure was unclear during intermittent exercise and overestimated during postexercise recovery of 10 minutes when compared with indirect calorimetry as the criterion measure of energy expenditure. These findings have important implications for using the device to monitor the energy cost of intermittent-based exercise protocols and immediate recovery from this type of exercise in team-sport athletes. Application of sport-specific algorithms to SWA data has been shown to improve energy expenditure estimates (10) and is needed for athlete cohorts before routine practical use can be recommended.

Only 1 other study to date has addressed the validity of the device during intermittent exercise in team-sport athletes (38). Using the same device model, software version, and criterion method (Cosmed) as the present study, energy expenditure was underestimated by 2.7 kcal·min−1 during a 30-minute basketball skills training session in female college basketball athletes (38). Previous validation studies have reported an underestimation of energy expenditure by the device during exercise of varying modality, duration, and intensity. The SWA underestimated energy expenditure ranging from approximately 5 to 43% during moderate intensity exercise protocols of incline treadmill walking (10,17,34), constant load ergometer cycling and stair stepping, (17) and during in-line skating of variable duration and speed (35). Similar results have been reported during high-intensity exercise protocols of treadmill running (8,21) and outdoor running (8) and incremental exercise to exhaustion in both treadmill running and ergometer cycling (20), underestimating energy expenditure by approximately 7–41%. When considered in conjunction with previous findings, the results collectively highlight the device's tendency toward underestimation of energy expenditure across a range of exercise modalities.

The inability of the SWA to accurately measure energy expenditure could be a result of early phase exercise. nderestimation has been reported during the first 10 minutes only of a 45-minute constant load cycling protocol (5) and the first 10 minutes of a 40-minute constant load cycling protocol (10). Substantial measurement error of the SWA is evident and overestimation of energy expenditure reported in validation studies. The SWA has been shown to overestimate energy expenditure measured during 20-minute arm ergometry exercise (17), treadmill walking on a flat 0% gradient at multiple speeds (10,19), and treadmill walking and running at increasing speeds (19). Collectively, these results draw attention to the inconsistency in energy expenditure estimated by the SWA during exercise. Further investigation and refinement of the algorithms used to quantify energy expenditure is needed.

Research addressing the validity of the SWA to assess postexercise recovery energy expenditure is limited to 1 previous study, reporting an overestimation of EE (approximately 37%) during 8-minute seated recovery from a 40-minute stationary cycling protocol of constant load at 60% V[Combining Dot Above]O2max (10). We observed a substantial overestimation of energy expenditure during 10-minute recovery from exercise by 17%, indicating shortcomings of the device to confidently detect the elevation in energy expenditure above resting values observed immediately after exercise (12). This outcome is likely related to metabolic processes associated with excess postexercise oxygen consumption (EPOC) (12). In the context of this study, EPOC is an important consideration as indirect calorimetry estimates of energy expenditure rely in part on V[Combining Dot Above]O2 measurement, whereas SWA estimates do not. A major determinant of the duration of EPOC is the intensity rather than the duration of exercise, where higher intensity exercise elicits a longer EPOC and subsequent energy expenditure (13). Elevated postexercise V[Combining Dot Above]O2 is higher in repeated-sprint efforts of longer distance (30–40 m) with short recovery between each sprint (30 seconds) (36). The extended sprint distances (30 m) and brief recovery time between subsequent sprint efforts (approximately 15 seconds) employed in this study's protocol seem to have subsequently effected V[Combining Dot Above]O2 measurement and energy expenditure estimation from indirect calorimetry. Because the estimation of energy expenditure by the indirect calorimetry method is reliant on V[Combining Dot Above]O2 measurement and the SWA estimates are produced from non-V[Combining Dot Above]O2 measures, it is reasonable to assume that differing estimates of energy expenditure are likely when directly comparing the 2 methods.

An alternative method to indirect calorimetry measurement of energy expenditure is doubly labeled water that has been used in 2 previous SWA validation studies to compare both total and exercise energy expenditure. Two models of the SWA device (Pro3 and Mini) underestimated energy expenditure, particularly at higher physical activity levels over 14 days (18). The original model (Pro) underestimated physical activity energy expenditure over a 10-day period (37). Although doubly labeled water is the criterion standard for the assessment of energy expenditure under free-living conditions, this method is limited in assessing physical activity energy expenditure because it does not provide information specific to exercise type or intensity. Moreover, the predictive equations used to reach this conclusion need improvement because the underlying assumptions may be too simplistic in data interpretation (37).

A limitation of the present study was the use of a straight-line running protocol to simulate rugby-specific intermittent exercise. Although straight-line running protocols are employed in the current national fitness testing protocols of rugby union players (14), players do not run in a straight line continuously throughout a typical match (9). Players can spend a significant amount of match time executing nonlinear evasive maneuvers and contact actions, such as rucks and mauls (approximately 10% of match time) and scrummaging (approximately 5% of the same) (7). These players are of similar age and competition level to the participants of the present study. Therefore, the estimate of energy expenditure in the present study may be underreporting what is actually expended during training and competition by rugby players.

In the present study, the commercially available SWA Mini was used to collect and analyze data with generic patented algorithms. Algorithms specific to the sport and athletic status of participants may improve the measurement error of the SWA; however, no such algorithms are currently available for rugby or repeat-sprint activity. The SWA incorporates all data from physiological sensors and accelerometry equally into the estimation of energy expenditure. However, of the 2 temperature sensors, the galvanic skin sensor's response to changes in exercise intensity is delayed by several minutes (2). In the rugby exercise test protocol employed in this study, each repeat-sprint period lasted for 2 minutes and active recovery period for 4 minutes; these time periods and rapid change in exercise intensity may not have been sufficient to enable heat sensors to detect changes in metabolic heat production.

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Practical Applications

Although overall SWA failed to quantify small differences in energy expenditure estimates, it may provide a general estimation that could be useful in planning nutritional recovery from training and competition of intermittent-based exercise. The daily average energy intake of professional Rugby League players can reach 4,200 kcal·d−1 during competition phase (24). It is therefore important to ensure adequate nutritional prescription based on the metabolic demands of the athlete to assist in their conditioning for competition. When considering the large volume of daily energy consumed and subsequently expended during training and competition, the extent to which the device underestimates energy expenditure during exercise must be considered in relation to the practical significance of the value. Given the high energy demands of the sport, athletes and coaches may still be provided with a practically significant estimate of energy expenditure from the SWA.

The SWA device provides a noninvasive, simple, and relatively inexpensive option for the assessment of energy expenditure in comparison to other motion sensors and technologies currently available. The algorithms used in this version of the SWA software are not accurate enough to confidently detect small changes and differences in energy expenditure in specific sporting activities. Improved accuracy of this and similar devices is needed to accurately quantify changes and differences occurring during intermittent and repeat-sprint type activity and immediate postexercise recovery periods. With further research, precision of measurement and specificity to sports and athletic status the device has the potential to estimate energy expenditure with an acceptable degree of accuracy during sports and other activities that are intermittent in nature.

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Keywords:

energy cost; motion sensor; accuracy; team sport; indirect calorimetry; repeat sprinting

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