Accuracy of the SenseWear Armband during Short Bouts of Exercise

A goal of mobile monitoring is to approximate metabolic energy expenditure (EE) during activities of daily living and exercise. Many physical activity monitors are inaccurate with respect to estimated EE and differentiating between activities that occur over short intervals. The objective of our study was to assess the validity of the SenseWear Armband (SWA) compared to indirect calorimetry (IC) during short intervals of walking and running. Twenty young, fit participants walked (preferred speed) and ran (75%, 85%, and 95% of predicted VO2max run speeds) on a treadmill. EE estimates from IC, SWA, and prediction equations that used the SWA, speed, and heart rate were examined during each 4 min interval and across the whole protocol (Total). The level of significance was p < 0.05. The SWA overestimated EE relative to IC by 1.62 kcal·min-1 while walking and 1.05 kcal·min-1 while running at 75%. However, it underestimated EE at the 85% (0.05 kcal·min-1) and 95% (0.92 kcal·min-1) speeds, but not significantly, and overestimated total EE by 28.29 kcal. Except for walking, our results suggest that the SWA displayed a good level of agreement (ICC = 0.76 to 0.84) with IC measures. Activity-specific algorithms using SWA, speed, and heart rate improved EE estimates, based on the standard error of the estimates, but perhaps not enough to justify extra sensors. The SWA may enable EE estimation of locomotion outside the laboratory, including those with short bouts of high intensity activity, but continued development of the SWA, or devices like it, is needed to enable accurate monitoring.

The Validity and Reliability of Wearable Microtechnology for Intermittent Team Sports: A Systematic Review

BACKGROUND

Technology has long been used to track player movements in team sports, with initial tracking via manual coding of video footage. Since then, wearable microtechnology in the form of global and local positioning systems has provided a less labour-intensive way of monitoring movements. As such, there has been a proliferation in research pertaining to these devices.

OBJECTIVE

A systematic review of studies that investigate the validity and/or reliability of wearable microtechnology to quantify movement and specific actions common to intermittent team sports.

METHODS

A systematic search of CINAHL, MEDLINE, and SPORTDiscus was performed; studies included must have been (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of wearable microtechnology to quantify movements or specific actions common to intermittent team sports.

RESULTS

A total of 384 studies were retrieved and 187 were duplicates. The titles and abstracts of 197 studies were screened and the full texts of 88 manuscripts were assessed. A total of 62 studies met the inclusion criteria. Additional 10 studies, identified via reference list assessment, were included. Therefore, a total of 72 studies were included in this review.

CONCLUSION

There are many studies investigating the validity and reliability of wearable microtechnology to track movement and detect sport-specific actions. It is evident that for the majority of metrics, validity and reliability are multi-factorial, in that it is dependent upon a wide variety of factors including wearable technology brand and model, sampling rate, type of movement performed (e.g., straight line, change of direction) and intensity of movement (e.g., walk, sprint). Practitioners should be mindful of the accuracy and repeatability of the devices they are using when making decisions on player training loads.

Validity and Reliability of Physiological Data in Applied Settings Measured by Wearable Technology: A Rapid Systematic Review

The purpose of this review was to evaluate the current state of the literature and to identify the types of study designs, wearable devices, statistical tests, and exercise modes used in validation and reliability studies conducted in applied settings/outdoor environments. This was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We identified nine articles that fit our inclusion criteria, eight of which tested for validity and one tested for reliability. The studies tested 28 different devices with exercise modalities of running, walking, cycling, and hiking. While there were no universally common analytical techniques used to measure accuracy or validity, correlative measures were used in 88% of studies, mean absolute percentage error (MAPE) in 75%, and Bland–Altman plots in 63%. Intra-class correlation was used to determine reliability. There were not any universally common thresholds to determine validity, however, of the studies that used MAPE and correlation, there were only five devices that had a MAPE of < 10% and a correlation value of > 0.7. Overall, the current review establishes the need for greater testing in applied settings when validating wearables. Researchers should seek to incorporate multiple intensities, populations, and modalities into their study designs while utilizing appropriate analytical techniques to measure and determine validity and reliability.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes' performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes' performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

Validity of sports watches when estimating energy expenditure during running

Background

The aim of this study was to assess the accuracy of three different sport watches in estimating energy expenditure during aerobic and anaerobic running.

Methods

Twenty 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.

Results

The 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.

Conclusions

To 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.

Monitoring Energy Expenditure Using a Multi-Sensor Device-Applications and Limitations of the SenseWear Armband in Athletic Populations

In order to monitor their energy requirements, athletes may desire to assess energy expenditure (EE) during training and competition. Recent technological advances and increased customer interest have created a market for wearable devices that measure physiological variables and bodily movement over prolonged time periods and convert this information into EE data. This mini-review provides an overview of the applicability of the SenseWear armband (SWA), which combines accelerometry with measurements of heat production and skin conductivity, to measure total daily energy expenditure (TDEE) and its components such as exercise energy expenditure (ExEE) in athletic populations. While the SWA has been shown to provide valid estimates of EE in the general population, validation studies in athletic populations indicate a tendency toward underestimation of ExEE particularly during high-intensity exercise (>10 METs) with an increasing underestimation as exercise intensity increases. Although limited information is available on the accuracy of the SWA during resistance exercise, high-intensity interval exercise, or mixed exercise forms, there seems to be a similar trend of underestimating high levels of ExEE. The SWA, however, is capable of detecting movement patterns and metabolic measurements even at high exercise intensities, suggesting that underestimation may result from limitations in the proprietary algorithms. In addition, the SWA has been used in the assessment of sleep quantity and quality as well as non-exercise activity thermogenesis. Overall, the SWA provides viable information and remains to be used in various clinical and athletic settings, despite the termination of its commercial sale.

Validity of the ActiGraph GT3X+ and BodyMedia SenseWear Armband to estimate energy expenditure during physical activity and sport

OBJECTIVES

The purpose of this study was to assess the validity of the ActiGraph GT3X+ (GT3X+) and the BodyMedia SenseWear Armband (SWA) to estimate energy expenditure (EE) during physical activity and field sport movements.

DESIGN

Criterion validity.

METHODS

Twenty-six active adults completed a single 90min session involving alternating intervals of exercise (5min) and recovery (10min). Exercise involved walking (4km/h), jogging (8km/h), running (12km/h) or a sport-simulated circuit (three intervals). Participants wore two triaxial accelerometers (GT3X+ and SWA) and a portable gas analyser (MetaMax 3B), used as the criterion measure.

RESULTS

Total EE was significantly underestimated (p<0.01) by the GT3X+ (mean bias±SD: -374.5±132.84kJ; % difference=-29.3%) and SWA (-244.3±148.0kJ; -18.2%). Overestimations were made by both accelerometers during the walk (GT3X+: 27.4±30.8kJ; SWA: 32.1±15.4kJ) and jog (38.0±30.0kJ; 34.5±31.6kJ). Underestimations were evident during the run (-41.2±25.1kJ; -43.8±33.5kJ) and circuit (C1: GTX+: -127.2±41.6kJ; SWA: -86.1±40.2kJ). Error of estimation increased in magnitude as the intensity of exercise increased (GT3X+: 40.8-143.0kJ; SWA: 35.5-102.0kJ).

CONCLUSIONS

The ActiGraph GT3X+ and BodyMedia SWA do not provide valid EE estimates across a range of exercise modalities and intensities when compared to a criterion measure. Poor accuracy and large precision errors, particularly during high intensity and intermittent movement patterns, suggest these devices have limitations and should be used cautiously in the field.

MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy

Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

Inertial sensors to estimate the energy expenditure of team-sport athletes

OBJECTIVES

To quantify the energy expenditure of Australian Football training and matches and the total daily energy expenditure of Australian Football players using tri-axial accelerometers.

DESIGN

Cross sectional observation study.

METHODS

An algorithm was developed for the MiniMax 4.0 (Catapult Innovations, Scoresby Australia) using measured oxygen uptake and accelerometer data to estimate energy expenditure of 18 Australian Football players during training and matches. The algorithm was used to validate a metabolic power calculation used by Catapult Innovations (Scoresby Australia) in their proprietary GPS software. The SenseWear™ (Model MF-SW, Bodymedia, Pittsburgh, PA) armband was used to determine non-exercise activity thermogenesis and was worn for 7 days leading into a match. Training, match and non-exercise activity thermogenesis data was summed for total daily energy expenditure.

RESULTS

Energy expenditure for field training was estimated to be 2719±666kJ and for matches to be 5745±1468kJ. The estimated energy expenditure in the current study showed a large correlation (r=0.57, 90% CI 0.06-0.84) with the metabolic power calculation. The mean total daily energy expenditure for an in-season main training day was approximately 18,504kJ and match day approximately 19,160kJ with non-exercise activity thermogenesis contributing approximately 85% and 69% on training and match days, respectively.

CONCLUSIONS

The MiniMax 4.0 and SenseWear™ armband accelerometers provide a practical, non-invasive and an effective method to successfully measure training and match energy expenditure, and non-exercise activity thermogenesis in field sport athletes. Taking methodological limitations into consideration, measuring energy expenditure allows for individualised nutrition programming to enhance performance and achieve body composition goals.