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Validation of the SenseWear Armband in different ambient temperatures a

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Karen Van Hoye , Filip Boen & Johan Lefevre

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Physical Activity, Sports and Health, Kinesiology, Leuven, Belgium Published online: 24 Dec 2014.

Click for updates To cite this article: Karen Van Hoye, Filip Boen & Johan Lefevre (2015) Validation of the SenseWear Armband in different ambient temperatures, Journal of Sports Sciences, 33:10, 1007-1018, DOI: 10.1080/02640414.2014.981846 To link to this article: http://dx.doi.org/10.1080/02640414.2014.981846

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Journal of Sports Sciences, 2015 Vol. 33, No. 10, 1007–1018, http://dx.doi.org/10.1080/02640414.2014.981846

Validation of the SenseWear Armband in different ambient temperatures

KAREN VAN HOYE, FILIP BOEN & JOHAN LEFEVRE Physical Activity, Sports and Health, Kinesiology, Leuven, Belgium

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(Accepted 25 October 2014)

Abstract This study examines the validity of the SenseWear Armband in different temperatures using the old (SenseWear v2.2) and newest version of the algorithm (SenseWear v5.2) against indirect calorimetry (IC). Thirty-nine male and female students (21.1 ± 1.41 years) completed an exercise trial in 19°C, 26°C and 33°C consisting of 5 min standing followed by alternating walking/running at 35% and 65% of their maximal oxygen uptake. The accuracy of the algorithms was evaluated by comparing estimated energy expenditure (EE) to IC using a mixed-model design. No difference was reported in EE between the different temperatures for IC. Both algorithms estimated EE significantly higher when exercising at high intensity in 33°C compared to 19°C. Compared to IC, SenseWear v2.2 accurately estimated EE during standing and light intensity exercise but underestimated EE when exercising in a hot environment and at high intensity. SenseWear v5.2 showed a difference when exercising at high intensity in thermoneutral and warm conditions. The new algorithm improved EE estimation in hot environments and at high intensity compared to the old version. However, given the inherent inaccuracy of the EE estimates of SenseWear, greater weight should be given to direct monitor outputs rather than the ability of a monitor to estimate EE precisely. Keywords: activity monitor, assessment, physical activity

Introduction Several sport events take place in unfavourable environmental conditions, and this can impose an additional stress on participants (Maughan & Shirreffs, 2004). When the air temperature exceeds 25°C and the work rate is reasonably high, coaches should be aware of the potential negative effects on athletes (Rushall & Pycke, 1990). It has previously been suggested that fatigue in hot circumstances is related to a diminished drive to exercise. In a study with eight male cyclists, participants rode to exhaustion in four different temperatures (approximately 4°C, 11°C, 21°C and 31°C). Time to exhaustion was nearly 42 min shorter (44%) in the warmest environment relative to the study optimum (11°C). A relationship was found between temperature and exercise capacity with the best exercise able to be performed in the moderate temperature ranges and with the lowest capacity on either end of the temperature extremes (Galloway & Maughan, 1997). Tatterson, Hahn, Martin, and Febbraio (2000) examined the effect of heat stress on exercise performance by performing cycling time trials in an

environmental chamber set at either 32°C or 23°C. Power output was 6.5% lower and mean skin temperature and sweat rate were higher in the hot temperature condition compared with 23°C. The authors concluded that heat stress was associated with a reduced power output during self-paced exercise in highly trained men. Another cycling study examined the influence of environmental heat stress (35°C) on 4-km cycling time trial performance (Altareki, Drust, Atkinson, Cable, & Gregson, 2009). Mean performance time was reduced in 35° C compared to 13°C (95% CI of difference = 4.0 to 10.6 s; P < 0.01). This was consistent with a decline in mean power output throughout the duration of exercise. Mean skin temperature and mean body temperature were elevated at rest and throughout the duration of exercise in the hot temperature condition. According to the authors, the decrease in work rate in the heat is mediated through feedback arising from changes in heat storage, which serve to regulate the subsequent exercise intensity in attempt to preserve thermal homeostasis. A more recent study determined the effect of skin temperature on

Correspondence: Karen Van Hoye, Physical Activity, Sports and Health, Kinesiology, Tervuursevest 101, Leuven, 3001 Belgium. E-mail: [email protected] © 2014 Taylor & Francis

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power output during a 7.5-km cycling time trial (Levels, De Koning, Foster, & Daanen, 2012). The trials were performed at 15°C, with heat stress and with or without precooling. Heat stress was applied by infrared heaters positioned in front of the cycle ergometer. Because applying heat stress did not result in a decrease in power output, the study concluded that skin temperature does not affect the selection and modulation of exercise intensity in a 7.5-km cycling time trial. Several studies investigated the effect of ambient temperature variations on the oxygen consumption during exercise (Cheuvront, Kenefick, Montain, & Sawka, 2010; Hargreaves, 2008). In a hot environment, it takes more cardiovascular effort to cool the body, which is enabled by pumping blood to the skin to promote sweating (Brooks, Fahey, & Baldwin, 2005). In thermoneutral conditions, the skin receives 5–10% of the resting cardiac output, whereas in conditions of heat stress, skin blood flow reaches 50–70% of the resting cardiac output. According to Cheuvront et al. (2010), the increase in cardiac output is inadequate to meet the demands of increased blood flow to the skin for thermoregulation while maintaining active skeletal muscle blood flow. It is suggested that a reduction in active muscle blood flow leads to a lower oxygen delivery, suppressed muscle aerobic energy turnover and a greater reliance of the exercising muscles on anaerobic metabolism (Gonzalez-Alonso, 2012). By measuring the energy expenditure (EE) expended during hot climatic conditions, athletes and coaches could recognise its implication for exercise performance. Assessing EE has been proven to be a challenging task. This can be partially attributed to the multifaceted nature of movement (e.g., type, duration, frequency and intensity) and to the limitations of self-report measures (Keim, Blanton, & Kretsch, 2004; Vanhees et al., 2005), which are prone to error (Melanson & Freedson, 1996). Open-circuit indirect calorimetry (IC) is commonly used as criterion method when assessing EE in a laboratory setting and is considered a valid measure of short-term expenditure (Pinheiro Volp, Esteves De Oliveira, Duarte Moreira, Esteves, & Bressan, 2011). This method is based on the assumed relationship between oxygen uptake and the caloric cost of substrate oxidation (Berntsen et al., 2010). Recent technological advancements such as the SenseWear® Pro3 armband (BodyMedia Inc., Pittsburgh, PA, USA) provide a synchronous measurement of acceleration and other physiological responses to estimate EE. This may overcome the limitations of other assessment devices, for example, pedometers and heart rate monitors, and potentially allow for the accurate assessment of EE during all types of physical activity, including non-weight-

bearing activities (e.g., cycling, stair stepping and resistance exercise) and upper body movement. Researchers have validated the armband by comparing EE estimates to EE measurement from gold standard laboratory equipment (Berntsen et al., 2010). The armband appears to be sensitive to changes in EE (Dwyer, Alison, McKeough, Elkins, & Bye, 2009; Hill, Dolmage, Woon, Goldstein, & Brooks, 2010); however, this device may overestimate the energy cost for certain activities such as walking uphill (Machač, Procházka, Radvanský, & Slabý, 2013) and underestimate the EE of running activities (Drenowatz & Eisenmann, 2011; Koehler et al., 2011). The validity of the armband has been investigated in different populations including adults (Berntsen et al., 2010; Fruin & Rankin, 2004; Malavolti et al., 2007), children (Arvidsson, Slinde, Larsson, & Hulthen, 2007; Calabró, Welk, & Eisenmann, 2009; Dorminy, Choi, Akohoue, Chen, & Buchowski, 2008) and patients affected by different diseases (Cereda et al., 2007; Dwyer et al., 2009; Hill, et al., 2010; Papazoglou et al., 2006; Van Remoortel et al., 2012). In two studies with individuals with chronic obstructive disease, agreement between the armband and the metabolic cart was found to be fair (Dwyer et al., 2009) or good (Hill, et al., 2010) but use of a walker increased error variability and reduced agreement. The armband was also considered a valid tool to estimate EE during daily activities in people with rheumatoid arthritis (Tierney, Fraser, Purtill, & Kennedy, 2013). However, in this population, attention should be paid to its tendency to underestimate EE, particularly during slow walking. In cardiac patients, correlations between the metabolic cart EE and the armband (version 2.2) ranged from 0.67 to 0.90 for arm and rowing ergometry, treadmill and stepper (Cole, LeMura, Klinger, Strohecker, & McConnell, 2004). Correlations between the metabolic cart EE estimates and the armband improved with the use of cardiac-specific equations. The armband estimates EE by using algorithms integrating information from different sensors including a near-body ambient temperature sensor, a skin temperature sensor and a heat flux sensor. The developers of the armband state that the accuracy of the armband may depend on these algorithms that are population- or activity-specific, thereby creating possible measurement error (Andre, 2007). The algorithms are constantly being improved as multiple studies continue and results are analysed. A newer version of the SenseWear algorithm v5.2 was recently developed. Surprisingly, no studies were found that have tested the validity of the armband in different ambient temperature conditions relative to a reference method. Measuring the EE of people exercising in

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Validation of Armband in ambient temperatures various temperatures at different intensities can be interesting to objectively and unobtrusively determine the exercise load in warm and hot conditions. Therefore, the present study has three main aims. First, our study wants to assess the EE by means of the gold standard of IC in three different ambient temperatures (19°C, 26°C and 33°C) while standing and exercising at 35% and 65% of the individual VO2peak. We hypothesise that exercising at high intensity in a hot environment will result in a higher EE compared to exercising at the same intensity in a thermoneutral environment. Second, the EE estimates of the old and new SenseWear algorithm will be assessed in those three different ambient temperatures at the different intensities. It is our hypothesis that both SenseWear algorithms will estimate EE higher when exercising in the high ambient temperature conditions compared to the thermoneutral condition because of the heat-related sensors and the higher sweat rates. Finally, the validity of both SenseWear algorithms will be discussed by comparing EE estimates to EE measurements of IC. We hypothesise that both algorithms will be valid in estimating EE while standing still and at light intensity. Because the algorithms are being continuously improved by the developers of the SenseWear Armband, it is our hypothesis that the new algorithm will measure EE more accurate at higher exercise intensities compared to the old SenseWear algorithm. Methods Participants A sample of 21 male and 18 female bachelor students from the Faculty of Kinesiology and Rehabilitation Sciences at the KU Leuven (Belgium) volunteered for this study after providing informed consent. The age of the group of students investigated varied from 20.2 to 26.1 years of age, with a mean of 21.1 ± 1.4 years. All participants were recreationally active and nonacclimatised to the heat and volunteered for this study after providing informed consent. This study was previously approved by the Medical Ethics Committee of the KU Leuven. Experimental protocol All participants performed an incremental maximal treadmill test with an initial speed of 1.50 m · s−1 (5.4 km · h−1) to determine the individual VO2peak. After 5 min, all participants had a running speed of 2.00 m · s−1 (7.2 km · h−1). From this point on, every 3 min running speed was increased by 0.50 m · s−1 (1.8 km · h−1) until individual exhaustion. During the maximal exercise test, EE was measured

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simultaneously using IC and the SenseWear Armband. The criterion to terminate the test was the inability to continue due to fatigue and exceeding a respiratory exchange ratio of 1.0. Participants performed the laboratory validation trials on three separate days in three different ambient temperatures. A counter-balanced design was used to randomly assign the order of these trials. The ambient dry bulb temperature was manipulated, fan airflow was absent and a relative humidity of ± 50% was maintained for all exercise tests using a climatic chamber. The ambient temperature was 19 ± 1.6°C for the lowest temperature condition, 26.4 ± 1.2°C for the warm temperature condition and 33.3 ± 1.4°C for the hot temperature condition. The experimental trials are referred to as 19°C, 26°C and 33°C. Heart rate was measured by means of a Polar ® FT7 heart rate monitor to assess the maximum heart rate during each trial. During the intermittent treadmill test, each participant performed six 5-min treadmill blocks with changing intensities (35% vs. 65% of the individual VO2peak). Exercise intensity can be expressed as either an absolute measure, for example, metabolic equivalent of task (MET), or as a relative measure such as a percentage of maximal oxygen consumption (Haskell et al., 2007). A position statement on physical activity and exercise intensity terminology proposes five categories (sedentary, light, moderate, vigorous and high intensity), which reflect clusters of activities that place similar relative physiological stress on the exercising individual. Thirty-five percent of the individual VO2peak can be considered as “light intensity” whereas a relative intensity of 65% of the individual VO2peak reflects “vigorous intensity” (Norton, Norton, & Sadgrove, 2010). Prior to testing, some anthropometric measurements were performed. Participants’ stature was measured without shoes using a calibrated stadiometer to the nearest 0.1 cm . Body mass was assessed in light clothing using a physician’s scale to the nearest 0.1 kg. Individual data about the participants such as age, height, weight and gender were programmed into the armband. All participants wore the armband on the right arm over the triceps muscle, as recommended by the manufacturer. Upon entering the laboratory, the armband was placed on the participants’ arm and worn while in a stationary position for a period of 10 min to allow the armband to equilibrate in a resting, steady-state condition. The Flemish Physical Activity Computerized Questionnaire (Matton et al., 2007) was filled in to assess the individual’s physical activity level and to get detailed information on the different dimensions of physical activity and sedentary behaviour over an average week. A person’s physical activity level is defined as the total energy expended over 24 h divided by the basal metabolic rate over 24 h.

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Reference measure

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IC was used as criterion measurement of EE. A Cortex Metabolic Measurement system (Metalyzer 3B, Cortex Biophysik, Leipzig, Germany) was used for measurements of VO2. A gas calibration of the O2 and CO2 analyser, volume calibration of the volume transducer and calibration of the pressure analyser were performed before all tests according to manufacturer’s instructions. Expired gases were collected via a breathing mask on a breath-by-breath basis, and these values were averaged at 1-min intervals. Data were analysed using Metasoft v1.1 (Cortex Biophysic, Leipzig, Germany). Activity monitor The SenseWear Armband (Body Media Inc., Pittsburgh, PA, USA) is an advanced multisensor device including several sensors that can identify the physiological changes caused by exercise that are not only associated with movement. These include heat exchange, skin temperature and sweat, allowing the algorithm to accurately estimate EE (Jones et al., 2011; Wilson & Haglund, 2003). A heat flux sensor measures the amount of heat being dissipated by the body by measuring the heat loss along a thermally conductive path between the skin and a vent on the side of the armband. The rate of change in near-body ambient temperature can be used to assess the presence of sleeves or other thermal barriers and variations in temperature conditions. The armband also measures galvanic skin response (the conductivity of the wearer’s skin), which varies due to physical and emotional stimuli. A two-axis accelerometer tracks the movement of the upper arm and provides information about body position. The armband takes into consideration gender, age, height and weight of the participant and uses proprietary algorithms developed by the manufacturer to estimate EE (Papazoglou et al., 2006). Data processing During all activity sessions, data were stored in the armband and these raw data were downloaded at the end of each activity trial into the SenseWear® Software (Version 6.1, algorithm version 2.2). The data were also processed using a new proprietary algorithm (version 5.2). Estimates of total EE were computed for each minute and were expressed in kcal · min−1. Data from IC and the armband were imported into Microsoft Excel 2007 (Microsoft® software, Microsoft Corporation, Redmond, WA, USA) and synchronised minute by minute for further analysis. Due to the time it takes for an individual to reach a steady-state condition in

oxygen uptake and for the IC measurement to reflect actual EE, the first 2 min of each 5-min measurement were excluded from the analysis. The average VO2 and respiratory quotient (RQ) for each minute were used to compute a caloric value (kcal · min−1) as described by McArdle, Katch, and Katch (1997): EE (kcal · min−1) = VO2 (l · min−1)*caloric equivalent per litre of O2 at the given RQ. An RQ of 0.82, derived from the metabolism of a mixture of 40% carbohydrate and 60% fat, is assumed during activities ranging from complete bed rest to aerobic exercise. Therefore, the caloric equivalent of 4.82 kcal · l−1 O2 for EE is applied in the formula. Using 4.82 kcal, the maximum error possible in estimating EE from steady-state oxygen uptake equals about 4% (McArdle, Katch, & Katch, 2006). Prior to our analyses, EE was divided into three intensity categories: standing still, EE at 35% of the individual VO2peak (mean of the 1st, 2nd and 3rd stage at 35%) and EE at 65% of the individual VO2peak (mean of the 1st, 2nd and 3rd stage at 65%). Statistical analysis To answer our first and second research question, 3 × 3 analysis of variances (ANOVAs) were performed with temperature (19°C, 26°C and 33°C) and workload (standing still, 35% VO2peak and 65% VO2 peak) as random effects. For our third and last hypotheses, the accuracy of both SenseWear algorithms (SenseWear v2.2 and SenseWear v5.2) was evaluated against the matched data from the IC using mixed-model analysis for repeated measures designs. The model used the method (armband or IC) as fixed effect and temperature (19°C, 26°C and 33°C) and workload (standing still, 35% VO2peak and 65% VO2 peak) as random effects. Least-square means and standard error were estimated within the model and overall effects were examined with standard F-tests. The method effect provided an indication of agreement between the armband and the criterion method, the temperature effect enables direct evaluation of the three temperatures and the workload effect reveals whether agreement varied across the three intensity levels. Emphasis was placed on possible two- and three-way interactions; post hoc analyses using Tukey–Kramer comparisons were conducted to examine differences in EE agreement for specific comparisons. Confidence intervals defining the limits of agreement (LoA) between IC and SWA were set at 1.96 s from the mean difference between the two methods (Bland & Altman, 1986). To examine the strength of the linear relation of EE measured by IC and estimated by the armband, intraclass correlation coefficients were calculated.

Validation of Armband in ambient temperatures

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The accuracy of the old and new versions of the SenseWear algorithm was evaluated by calculating the mean absolute percentage error (mean [(|true measured value – estimated value|/true measured value) × 100)] between measured (IC) and estimated (armband) EE values. This calculation reflects the true error in estimation and was executed at the three workloads for the different ambient temperatures. If not stated otherwise, values are given as mean ± standard deviation. Statistical analyses were performed in SAS 9.2 (SAS Institute, Cary, NC, USA). The significance of values is indicated in Tables I and II as *** for P < 0.001, ** for P < 0.01 and * for P < 0.05.

Results Physical characteristics, exercise responses and the physical activity level for the 39 students are presented in Table I. All participants completed the three treadmill trials (19°C, 26°C and 33°C). The mean walking speed when exercising at 35% of the individual VO2peak was 6.5 ± 1.3 km · h−1. Exercising at 65% of the individual VO2peak equalled a mean running speed of 10.3 ± 1.9 km · h−1. When exercising in 33°C, the participants reached a significantly lower VO2peak compared to the 19°C and 26°C condition and had a significantly shorter time to exhaustion with no significant difference in maximal heart rate. The students had a high fitness level when comparing their VO2peak values to agerelated VO2peak norms (Shvartz & Reibold, 1990). A physical activity level of 1.79 METs was

Table I. Participants’ characteristics and exercise test results (mean ± s; n = 39). Variable Age (years) Height (cm) Weight (kg) BMI (kg · m−2) Speed at 35% of VO2peak (km · h−1) Speed at 65% of VO2peak (km · h−1) Time to exhaustion 19°C (min) Time to exhaustion 26°C (min) Time to exhaustion 33°C (min) VO2peak 19°C (ml · kg−1 · min−1) VO2peak 26°C (ml·kg−1 · min−1) VO2peak 33°C (ml·kg−1 · min−1) HRpeak 19°C (bpm) HRpeak 26°C (bpm) HRpeak 33°C (bpm) Physical activity level (METs)

Value 21.1 176.3 68.2 21.8 6.5 10.3 40 40 38 58 55 51 201 203 200 1.79

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.4 9.8 9.9 1.9 1.3 1.9 2*** 2*** 3 11** 10* 10 13 14 15 0.32

Notes: VO2peak, maximal oxygen uptake; HRpeak, maximal heart rate; METs, metabolic unit; ***, P < 0.001; **, P < 0.01; *, P < 0.05 significantly different from 33°C.

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registered, which equals the activity level of an athletic population (Carlsohn et al., 2011). Figure 1 illustrates the EE of IC, SenseWear v2.2 and SenseWear v5.2 measured in different ambient temperatures and for different exercise intensities, for example, when standing still (A), at 35% of the individual VO2peak (B) and at 65% of the individual VO2peak (C). The asterisks in the figures show a significant difference with the gold standard of IC. When standing still (Figure 1(A)), no significant differences were reported for the old and new SenseWear algorithm compared to the IC for the different ambient temperatures. A significant underestimation was reported for SenseWear v2.2 when exercising at the 35% workload (Figure 1(B)) in the highest temperature condition while no significant difference with IC was found for the new SenseWear algorithm. When exercising at 65% of the individual VO2peak (Figure 1(C)), the old SenseWear algorithm underestimated EE in each temperature condition. The new SenseWear algorithm underestimated EE in the 19°C condition and 26°C condition while no significant difference was reported for the highest temperature condition. Table II contains the average EE values for IC, the old version of the SenseWear algorithm (SenseWear v2.2) and the new version of the SenseWear algorithm (SenseWear v5.2) measured at three different intensity modes (standing still, 35% workload and 65% workload). For each intensity mode, EE estimates are presented for the three different ambient temperatures (19°C, 26°C and 33°C). Our first research question considered the mean EE estimates of standing still and exercising in different ambient temperatures measured by the criterion method of IC. Our analysis revealed no significant differences in EE between the three ambient temperatures for each of the three exercise modes (P = 0.994). As to be expected, a significant main effect for workload was observed with significantly higher EE estimates for the 65% workload compared to the 35% workload (P < 0.001). A comparison of EE estimates of the old and new SenseWear algorithms in the three different ambient temperatures gives an answer to our second research question. The superscript letters a and b, shown in Table II, indicate a significant difference between the temperature conditions within each SenseWear algorithm. For the old SenseWear algorithm, the mixed-model analysis reported a significant temperature × workload interaction effect (P < 0.001). When exercising at the 35% workload, a significantly higher EE was estimated in 19°C compared to the 33°C condition (mean value 0.75, S = 0.22 kcal · min−1; P = 0.026). In contrast, when exercising at the 65% workload, our analysis revealed a lower EE in 19°C compared to the hot ambient temperature

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Figure 1. Energy expenditure (EE) of the IC, SenseWear v2.2 and SenseWear v5.2 measured in different ambient temperatures and when standing still (A), at 35% of the individual VO2peak (B) and at 65% of the individual VO2peak (C). IC standing, indirect calorimetry while standing still; v2.2 standing, SenseWear Pro3 Armband software version 2.2 while standing still; v5.2 standing, SenseWear Pro3 Armband software version 5.2 while standing still; IC 35 VO2, indirect calorimetry while exercising at 35% of the individual VO2peak; v2.2 35 VO2, SenseWear Pro3 Armband software version 2.2 while exercising at 35% of the individual VO2peak; v5.2 35 VO2, SenseWear Pro3 Armband software version 5.2 while exercising at 35% of the individual VO2peak; IC 65 VO2, indirect calorimetry while exercising at 65% of the individual VO2peak; v2.2 65 VO2, SenseWear Pro3 Armband software version 2.2 while exercising at 65% of the individual VO2peak; v5.2 65 VO2, SenseWear Pro3 Armband software version 5.2 while exercising at 65% of the individual VO2peak; ***, P < 0.001; *, P < 0.05.

condition (mean value −0.82, S = 0.22 kcal · min−1; P = 0.0003). As to be expected, a significant main effect for workload was observed with a higher EE estimate at the 65% workload compared to the 35% workload (P < 0.001). A significant temperature × workload interaction effect (P < 0.001) was also found for the new SenseWear algorithm. No significant difference in EE estimates was found for exercising at 35% of the individual VO2peak between the three ambient

temperatures. In contrast, when exercising at the 65% workload, EE was estimated significantly higher in the 33°C condition compared to the 19°C condition (mean value 1.80, S = 0.27 kcal · min−1; P < 0.001) and the 26°C condition (mean value 0.88, S = 0.27 kcal · min−1; P = 0.038). Our analyses also showed a higher EE estimate at 26°C compared to the 19°C condition (mean value 0.92, s = 0.27 kcal · min−1; P = 0.026). A main effect for temperature was observed for the new SenseWear algorithm showing a significant higher EE

Notes: EE, energy expenditure; IC, indirect calorimetry; ICC, intraclass correlation coefficient; SenseWear v2.2, SenseWear Pro3 Armband software version 2.2; SenseWear v5.2, SenseWear Pro3 Armband software version 5.2; Ta, ambient temperature; a, significantly different from 33°C; b, significantly different from 26°C; *, P < 0.05; ***, P < 0.001.

0.70*** 0.76*** 0.79*** −2.18 ± 1.99*** −1.14 ± 1.68* −0.60 ± 1.66 0.68*** 0.70*** 0.70*** −3.02 ± 1.94*** −2.25 ± 1.80*** −2.41 ± 1.85*** 19 26 33 65% VO2peak

12.66 ± 2.97 12.53 ± 2.76 12.87 ± 2.93

9.64 ± 1.66a 10.28 ± 1.75 10.47 ± 1.66

10.48 ± 2.06a,b 11.39 ± 1.97a 12.27 ± 2.15

0.90*** 0.80*** 0.77*** −0.67 ± 1.03 −0.29 ± 1.50 −0.38 ± 1.65 0.82*** 0.81*** 0.78*** −0.81 ± 1.24 −0.97 ± 1.29 −1.83 ± 1.40*** 19 26 33 35% VO2peak

8.54 ± 2.36 8.40 ± 2.25 8.81 ± 2.22

7.73 ± 1.69a 7.43 ± 1.86 6.98 ± 2.02

7.87 ± 2.36 8.11 ± 2.42 8.43 ± 2.59

0.58*** 0.60*** 0.39** 0.37 ± 0.52 0.23 ± 0.55 0.01 ± 0.60 2.32 ± 0.62 2.20 ± 0.54 2.22 ± 0.47 0.47*** 0.60*** 0.34* 0.19 ± 0.71 0.10 ± 0.66 −0.42 ± 0.63 2.14 ± 0.83 2.07 ± 0.78 1.79 ± 0.48 19 26 33 Standing

1.95 ± 0.51 1.97 ± 0.68 2.21 ± 0.61

EE estimated (kcal · min−1) Mean bias (kcal · min−1) Agreement (ICC) EE estimated (kcal · min−1) Mean bias (kcal · min−1) Agreement (ICC) EE measured (kcal · min−1) Ta (°C) Intensity

SenseWear v5.2 – IC SenseWear v5.2 SenseWear v2.2 – IC SenseWear v2.2 IC

Table II. Energy expenditure measured by indirect calorimetry (IC) and estimated by SenseWear v2.2 and SenseWear v5.2 (mean ± s; n = 39), for different intensity modes and different ambient temperatures.

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estimate in 26°C and 33°C compared to 19°C (P = 0.03 and P < 0.001, respectively) and a higher EE estimate in 33°C compared to 26°C (P = 0.01). As to be expected, participant’s EE was estimated significantly higher at the 65% workload compared to the 35% workload, illustrating a main effect of workload (P < 0.001). To assess the accuracy of both SenseWear algorithms, two separate repeated measures ANOVAs were performed: one analysis compared the EE estimates of the old SenseWear algorithm with IC for each workload and within each workload for each temperature condition, and another analysis performed the same comparisons for the new SenseWear algorithm. Mixed-model analysis reported a significant temperature × method × workload interaction effect (P = 0.0258) for the old SenseWear algorithm. At 35% of the individual VO2peak, an underestimation of SenseWear v2.2 was observed only for the highest temperature condition (mean value −1.83, S = 1.40 kcal · min−1; 95% LoA = 0.92; −4.58 kcal · min−1; P < 0.001) while when exercising at the 65% workload, SenseWear v2.2 underestimated EE at every temperature condition (mean value −3.02, s = 1.40 kcal · min−1 at 19°C; 95% LoA = −6.82; 0.79 kcal · min−1, mean value −2.25, s = 1.80 kcal · min−1 at 26°C; 95% LoA = −5.77; 1.27 kcal · min−1 and mean value −2.41, s = 1.85 kcal · min−1 at 33°C; 95% LoA = −6.03; 1.21 kcal · min−1, P < 0.001). A significant temperature × method × workload interaction effect was also found for the new SenseWear algorithm (P = 0.0137). No significant differences in EE between IC and the new SenseWear algorithm were observed when exercising at the 35% workload for the three ambient temperatures. In contrast, when exercising at 65% of the individual VO2peak, SenseWear v5.2 significantly underestimated EE in the 19°C condition (mean value −2.18, s = 1.99 kcal · min−1; 95% LoA = −6.08; 1.72 kcal · min−1 and the 26°C condition (mean value −1.14, s = 1.68 kcal · min−1; 95% LoA = −4.42; 2.15 kcal · min−1). However, no significant difference was observed in the highest temperature condition. Table II additionally presents intraclass correlation coefficients to examine the strength of the linear relation between EE measured by IC and EE estimated by the armband. All correlations were significant (P < 0.05). The correlation of SenseWear v2.2 and IC for standing still in the different ambient temperatures was weak to moderate. At the 35% workload, correlation values exceeded the generally accepted threshold for a strong linear relationship of 0.70. A strong relationship was also found at 65% of the individual VO2peak for SenseWear v2.2 and IC. Comparable correlation coefficients were seen between the newest algorithm of SenseWear and IC. In line with the results of the older SenseWear

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Figure 2. Mean absolute percent error of both SenseWear Armband algorithms for different intensities and ambient temperatures. *, significant difference at P < 0.001 between SenseWear v2.2 and SenseWear v5.2 algorithms.

algorithm, lower correlations were presented for SenseWear v5.2 for standing still in the three temperature conditions. Figure 2 shows the mean absolute percentage error of both SenseWear algorithms for the different ambient temperatures for each of the three workloads ranging from 13% to 30% for the old SenseWear algorithm and from 9% to 30% for the newest algorithm. The mean absolute percentage error in EE estimates for both algorithms was largest for standing still and smallest for exercising at the 35% workload. When comparing both algorithms, the mean absolute percentage error was significantly lower with the newest SenseWear algorithm compared to the old algorithm in the hot temperature condition for the 35% workload (mean difference −7.9, s = 12.6%). A significantly smaller error score in EE estimate was found when exercising at high intensity (65% workload) for the newest SenseWear algorithm compared to old algorithm, and this improvement in EE estimation was seen for all ambient temperatures (mean difference −5.2, s = 6.3% in 19°C; mean difference −6.2, s = 7.7% in 26°C and mean difference −8.7, s = 8.6% in 33°C). Discussion This study investigated the EE estimates of two SenseWear algorithms in three temperature conditions (19°C, 26°C and 33°C) when standing still and at two different exercise intensities (35% and 65% of VO2peak). Before the question of accuracy of the SenseWear Armband could be answered, an analysis had to be performed on the influence of different ambient temperatures on oxygen consumption measured by the criterion method of IC. A different

measurement of oxygen consumption would lead to a different estimation of EE. Previous research implied that metabolic events in the muscle and muscle metabolism might be affected by high temperature during exercise (Febbraio, 2001). In general, the literature examining metabolic responses to exercise and heat stress has demonstrated a shift towards increased carbohydrate use and decreased fat use. In the current study, no significant differences were found in oxygen consumption when exercising at vigorous intensity in a hot environment compared to a thermoneutral environment. An explanation for not finding any differences in oxygen consumption can be found in the limitation of the method of IC in estimating EE during higher intensity exercise. During high-intensity exercise (of short duration), energy is supplied from the breakdown of phosphocreatine and the anaerobic glycolysis. During this type of exercise, oxygen consumption measured by IC underestimates the total EE because the anaerobic system is involved (Scott, 2005). Although research states that the anaerobic EE needs to be large to make a significant contribution to total EE, this can possibly explain why no significant differences were found. Previous research has already shown that the old SenseWear algorithm accurately predicts EE while lying down (Bertoli et al., 2008; Fruin & Rankin, 2004). However, results on the accuracy of this algorithm during standing still or data on the accuracy of the SenseWear Armband in different ambient temperatures at this intensity mode are lacking. Our study found no significant difference in EE estimates of SenseWear v2.2 between the three ambient temperatures for standing still and showed evidence for a valid estimation of EE during standing still in a thermoneutral, warm and hot environment.

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Validation of Armband in ambient temperatures Several studies investigated the validity of the SenseWear Armband during light and moderate intensity (Fruin & Rankin, 2004; Jakicic et al., 2004). Our results show a valid estimation of the old SenseWear algorithm when exercising at light intensity in thermoneutral and warm ambient temperatures. However, the old algorithm caused problems in estimating EE in the hot temperature condition. No other study examined the accuracy of the armband when affecting the near-body ambient temperature sensor. Because the armband estimates EE by gathering information of several heat-related sensors, we expected a higher estimation of EE at a higher ambient temperature. In contrast to our hypothesis, EE estimates of SenseWear were lower in a hot environment compared to IC. Recently, a new version of the SenseWear algorithm was developed. One research goal of the current study was to analyse the performance of the newer algorithm (SenseWear v5.2) by comparing the EE estimates in three different ambient temperatures and by validating the EE estimates with those of IC. Our study found a significant higher EE estimate when exercising at high intensity in 33°C compared to the 19°C and 26°C temperature conditions. A significant higher EE estimate was also reported for the 26°C condition compared to the lowest temperature condition. When validating the EE estimates to the gold standard of IC, different results were found for the old and new SenseWear algorithms for exercising at light intensity in the different ambient temperatures. In contrast with the results of the old algorithm, no significant difference was reported for the new algorithm when exercising at the 35% workload. The assumption can be made that an improvement has been made in the new algorithm for exercising at this workload in hot temperature conditions. Previous research illustrated the underestimation of the old SenseWear algorithm at high intensities. Koehler et al. (2011) found a significant underestimation of the SenseWear Armband compared to IC for all running speeds between 2.8 and 4.8 m · s−1. The mean difference between the SenseWear and IC values was −4.5 kcal · min−1 (95% LoA: −11.4 to 2.4 kcal · min−1) and increased significantly with increasing speeds. Drenowatz and Eisenmann (2011) stated that the armband does not accurately estimate EE at intensities above 10 METs or a running speed of 2.7 m · s−1. The current study confirmed the underestimation of the SenseWear v2.2 at higher intensities. The difference in EE estimates of SenseWear v2.2 was lower than the values reported in the study of Koehler et al. (95% LoA: −6.23 to 1.12 kcal · min−1). This indicates that, according to our study, the true difference between the IC and the armband would lie between −6.24 kcal · min−1 and

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1.12 kcal · min−1. Translated into practice, when an individual is exercising at high intensity for 1 h, the armband would underestimate the EE by 375 kcal or overestimate the EE by 67 kcal. Our study is the first to study the accuracy of the newest SenseWear algorithm at high intensity. Our results showed that compared to IC, the newest SenseWear algorithm still significantly underestimated EE in 19°C and 26°C. However, no significant difference was reported for the highest temperature condition. This research made it possible to directly compare the validity of two SenseWear algorithms at three different exercise intensities and three ambient temperatures. A significant improvement was found for the newest algorithm in assessing EE while exercising in a hot environment and at high intensity. Considering the hot temperature condition, the new SenseWear algorithm improved its mean absolute percentage error by 8% when exercising at light intensity and by 9% when exercising at high intensity. When looking at the high-intensity mode, SenseWear v5.2 improved its EE estimation for all ambient temperatures (−5% in 19°C, −6% in 26°C and −9% in 33°C). The armband could not improve its EE estimation by using the newest algorithm when standing still for all ambient temperatures or when exercising at light intensity in a thermoneutral or warm environment. However, for light intensity, the smallest error scores were reported. Because the algorithms are proprietary, it is not possible to speculate why algorithm v5.2 outperforms algorithm v2.2. Only a few studies evaluated the validity of different SenseWear algorithms compared to a criterion method. A study of Mackey et al. (2011) evaluated the accuracy of two versions of the armband (SenseWear v6.1 and SenseWear v5.1) in free-living older adults. The authors used the method of the doubly labelled water as a criterion method for total and active EE. No difference in total EE was reported from doubly labelled water measurements versus EE estimates of both algorithms. For active EE, mean values from SenseWear v6.1 underestimated EE by 18.5% compared to criterion values. The authors concluded that acceptable levels of agreement were observed between the armband and criterion measurements of total and active EE in older adults. A previous study by Smith, Lanningham-Foster, Welk, and Campbell (2012) evaluated the validity of the two algorithms of a recently developed SenseWear Mini Armband monitor to estimate EE. A group of pregnant woman completed a series of activities of daily living (typing, laundry, sweeping, treadmill walking: 2.0, 2.5, 3.0 mph, 3% incline) while EE was estimated by the Mini Armband and measured by IC. The results are in line with the current study and also revealed

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a significantly smaller error for the new SenseWear v5.2 algorithm (0.57 ± 0.06 kcal · min−1) compared to the old SenseWear v2.2 algorithm (0.78 ± 0.06 kcal · min−1). Due to the different types of activities, the different population and the use of the SenseWear Pro3 Armband instead of the SenseWear mini Armband, a comparison of the results of both studies remains difficult. Our study has its strengths and limitations. A major strength of this study is the comparison of EE estimates of the SenseWear Armband to a gold standard technique. This study is the first to investigate the accuracy of the armband in a thermoneutral, warm and hot environment using a controlled laboratory setting. A second strength is the validation of the newest SenseWear algorithm. A unique aspect of the SenseWear Armband is that the proprietary algorithms applied by the software to estimate EE are continuously being improved by the manufacturer in an effort to reduce error. Several studies have demonstrated that the enhancements improve the accuracy of the assessment (Calabró, Welk, & Eisenmann, 2009; Calabro et al., 2009; Jakicic et al., 2004; Smith, Foster, & Campbell, 2011). Our research group has contributed to this work by collecting data needed to “train” the algorithms. This research is not without limitations. An acknowledged methodological limitation of this study that may have affected the results was the incremental, discontinuous protocol of 5-min stages that may have been insufficient to determine steadystate VO2 versus workload relationships. The assessment of EE occurred in a laboratory environment and the protocol of walking and running at 35% and 65% of the individual VO2peak differed from activities of daily living. Future research should validate the new SenseWear algorithm in a real-life condition. Another limitation concerns our study sample. Participants were a homogeneous population of young, fit students which are not representative for the average population. The lack of control for participants’ hydration status is another limitation of this study. Limitations of the monitors themselves include the cost of the device and the proprietary nature of the algorithms, which do not allow for independent investigators to work with the algorithms. Given the inherent inaccuracy of the EE estimates of the armband (especially during high intensity exercises), perhaps greater weight should be given to direct monitor outputs (steps, activity counts, minutes of activity) and their relation to activity EE, rather than the ability of a monitor to estimate EE precisely. It is very unlikely that an activity monitor will be able to capture accurately all the factors affecting EE (i.e. movement efficiency, resting metabolism, distribution of fat-free mass and

fat mass). Hence, the lack of accuracy against EE does not render activity monitors invalid tools to assess physical activity over time (for which precision is more important) or to capture the physical activity level of an individual (for which validity, represented by the correlation with true EE, is more important than absolute accuracy). The acceptable correlations between VO2 and EE estimates of the armband are therefore encouraging for the use of this device to assess physical activity in an adult population. In conclusion, no significant differences in EE measured by IC were found between the three temperature conditions for any of the intensity modes, possibly relating to the anaerobic energy component present while exercising in the heat. Considering the validity of the SenseWear Armband in different temperatures, a significant difference was reported for both algorithms when exercising at high intensity between the highest and lowest ambient temperatures. Moreover, a significant higher EE estimate was found for exercising at high intensity in 26°C compared to 19°C for the new SenseWear algorithm. Since the underestimation of EE by the armband at high-intensity activities had been reported to be a problem, the main purpose of our study was to examine the validity of the armband at different ambient temperatures and different exercise intensities. A newer version of the SenseWear algorithm was recently developed trying to improve the accuracy of the monitor in assessing EE. To date, no research has ever investigated if exercising in different ambient temperatures would result in different EE values objectively estimated by the armband. The present study wanted to compare both SenseWear algorithms in estimating EE at different exercise intensities in several temperature conditions. When comparing the mean absolute percentage error of the two algorithms, the newer version (SenseWear v5.2) had less error compared to the older version when exercising in a hot temperature condition and at high intensity. However, measuring EE at higher intensities remained challenging since the newer version of SenseWear algorithm could not overcome problems in estimation EE resulting in a significant underestimation of EE except at the highest temperature condition. Acknowledgement The corresponding author wishes to thank all the students that participated in this study. Funding There has been no external funding for the project. BodyMedia, Inc., analysed energy expenditure values for the version 5.2 algorithm but did not

Validation of Armband in ambient temperatures provide any funding for the current study. The corresponding author wishes to thank the Department of Kinesiology for providing an internal funding grant making this study possible. Disclaimer The use of commercial names in this manuscript is solely for informational purposes and does not represent any endorsement.

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