Editorial: Wearable Sensor Technology for Monitoring Training Load and Health in the Athletic Population
- 1Integrative and Experimental Exercise Science & Training, University of Würzburg, Würzburg, Germany
- 2Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- 3Department of Health Sciences, Mid Sweden University, Östersund, Sweden
- 4School of Kinesiology, University of British Columbia, Vancouver, BC, Canada
- 5Department of Physiology and Pharmacology, Biomedicum C5, Karolinska Institutet, Stockholm, Sweden
Editorial on the Research Topic
Wearable Sensor Technology for Monitoring Training Load and Health in the Athletic Population
Various measures of the internal and external loads on athletes, as well as parameters related to their health are now being provided to a greater and greater extent by wearable sensors (wearables) (Düking et al., 2018a,b,c). These devices, including sensors and software embedded in e.g., textiles, watches and patches located on or in proximity to the body, collect, transmit, and analyse a range of physiological and biomechanical data designed to improve performance, recovery, and/or other aspects of health (Düking et al., 2018a). However, it is still unclear to what extent wearables are actually useful for monitoring load in connection with different sports and settings.
In 2017, we launched a special coverage of the Research Topic “Wearable Sensor Technology for Monitoring Training Load and Health in the Athletic Population” in Frontiers in Physiology with the following aims:
(i) to identify and critically evaluate promising wearable technology designed to monitor training load and health in athletic populations;
(ii) to develop novel approaches to data analysis based on advanced modeling, time series, machine learning, data mining, etc.;
(iii) to encourage the use of (best-practice) models for monitoring training load and health in athletes; and
(iv) to indicate directions for future development in this area.
One hundred thirteen authors have now published 28 articles in Frontiers in Physiology on this Research Topic, including 18 original articles based on field and laboratory data, four (mini) reviews, three opinion papers, one perspective and one technology report. Table 1 summarizes the main features of all of these studies. With more than 148,000 views (as of November 2019), this Research Topic is among those published in the Physiology section of Frontiers in Physiology that have received most interest. To achieve the aims described above, we have grouped these articles in the table on the basis of the specific sport involved or evaluation of new technologies without consideration of any specific population, describing only those articles we consider to be of primary importance in the field.
Table 1. Summary of all studies within the Research Topic including type of article, athletes involved, sensors employed and main outcome.
Evaluation of the Quality of New Technology
Peake et al. critically reviewed consumer-grade wearables, mobile applications, and equipment designed to provide biofeedback to physically active individuals. While acknowledging that wearable technology has much to offer, these investigators concluded that only 5% of the technologies they reviewed have been formally validated and that manufacturers should invest in studies on the effectiveness of their products.
Wahl et al. showed that under different sporting conditions, the majority of 11 wrist-worn wearables demonstrated acceptable validity with respect to counting steps, whereas the distance covered and energy expenditure could not be assessed validly.
Reviewing the relevant literature, Koehler and Drenowatz concluded that while the SenseWear armband can estimate energy expenditure validly in the general population, it tends to underestimate this parameter during high-intensity exercise (>10 METs).
Wearables in Connection with Winter Sports
Wearables are often utilized to assess parameters associated with different skiing disciplines. Employing a global navigation satellite system, Karlsson et al. found that cross-country skiers repeatedly perform at intensities that exceed their maximal aerobic power, with more pronounced oxygen deficits during uphill skiing than on flat terrain.
Gilgien et al. applied a differential global navigation satellite system (dGNSS) to evaluate the physical demands and safety associated with different skiing disciplines. The physical demands made by giant slalom, super-G and downhill skiing differ substantially. Furthermore, these researchers concluded that to increase safety, skiing speed can best be reduced by enhancing the friction between the skis and snow and in the case of giant slalom and super-G, whereas for downhill skiing an elevation in air drag force might be equally effective.
Using five accelerometers and a global navigation satellite system, Supej et al. found that low-frequency whole-body vibrations during alpine skiing enhance the risk for pain in the lower back, particularly in combination with large ground reaction forces. They concluded that the number of runs involving such vibrations (e.g., during side-skidding) should be reduced, especially in the case of younger skiers.
Spörri et al. evaluated vibrations acting on different body segments during giant slalom and slalom skiing with 6 wearable inertial measurement units. Power distribution over frequency (PSD) was largest with frequencies of <30 Hz in the case of the shank, with vibrations being attenuated by the knee and hip joints. PSD values were pronounced at frequencies between 4 and 10 Hz, increasing the risk of overuse back injuries in alpine skiers.
Applying 11 inertial measurement units, Fasel et al. could assess the kinematics of the relative center of mass and positions of joint centers of alpine skiers with sufficient accuracy and precision, while the ankle joints were only just within the acceptable range of accuracy and precision.
Wearables in Connection with Team Sports
In their original article, Fuss et al. employed a pressure-sensitive sensor matrix incorporated into a soccer shoe to identify a “sweet spot” on the foot that maximizes the chances of hitting the goal with a direct curved free kick of 58–86°. This sensor may allow soccer players to analyse their foot-to-ball impact and improve their technique.
In connection with team sports, tracking technologies, such as global positioning (GPS), local positioning (LPS), and vision-based (VBS) systems, allow activity profiles to be monitored. Analysis of these profiles may be influenced by the relative amount of time spent in different velocity or acceleration zones and Sweeting et al. emphasize in their review article that there is presently no generally accepted definition of a sprint or acceleration, not even within a given team sport, which complicates comparison of different studies.
With respect to training load, Weaving et al. argue that no single parameter is likely to capture the complexity of this parameter and, moreover, practitioners can be overwhelmed by the amount of data they receive. A multivariate approach employing selected orthogonal composite variables may be helpful in providing sufficient data without “flooding.”
For quantifying aspects of external loading in connection with indoor team sports, Roell et al. found a wearable inertial unit designed to measure average and peak acceleration to be acceptably valid in all three orthogonal axes.
In their case study, Pettersen et al. demonstrate that wearable radio-based positioning systems can provide insights into the performance of individual soccer players and their teams.
Wearables in Connection with Running and Cycling
Belbasis and Fuss found that a pressure-sensitive sensor located inside compression garments provided data on the activity on five thigh muscles during cycling comparable to that obtained by electromyography (EMG). Arguably, this smart compression garment monitors mechanical muscle activity (i.e., the pressure exerted by the contracting muscle on the sensor), whereas EMG measures neural activity and may therefore be more suitable for biomechanical modeling.
In the case of runners, Falbriard et al. showed that temporal parameters, involving ground contact, flight, step, and swing times can be estimated accurately, but that the results obtained are dependent on the speed.
Wouda et al. found that estimation of the peak vertical ground reaction force, as well as maximal knee flexion-extension angles during stance in runners by three inertial measurement systems in combination with artificial neural networks did not differ significantly from the reference values.
The 28 articles on this Research Topic have clearly improved our knowledge concerning the use of wearables for monitoring training load and health in athletes involved in a different sports. Novel technologies have been introduced and technologies already existing evaluated. New approaches to monitoring and analyzing (training) load in connection with different sports have been described. Nonetheless, much remains to be determined concerning the usage of wearables by athletic populations.
While some findings involve physiological parameters, e.g., those of Nicolò et al., most of the wearable technology investigated provides biomechanical data. Therefore, we encourage future studies on physiological parameters in this area of research. Since future monitoring frameworks (Düking et al., 2018a) may provide instant feedback concerning internal load to coaches and athletes, such research is certainly warranted. Appropriate combination of physiological and psychological data with biomechanical data will be a future challenge in connection with providing relevant and seamless feedback to the athlete.
Currently, on the basis of the articles included here, it remains unclear whether monitoring with wearables is actually beneficial for controlling the load and improving the health of athletes. To date, no publication has addressed these questions directly.
Future advancements in smart technology will involve devices designed to share and interact with their users, as well as with other smart devices. Wearables should, however, be convenient and usable without hindering the athlete with cumbersome sensors. Optimal integration of sensors into equipment (e.g., ski boots, garments) will require the involvement of manufacturers of sporting equipment.
Today, 3 years after we launched “Wearable Sensor Technology for Monitoring Training Load and Health in Athletes” as a Research Topic, interest remains quite high, as indicated, among other things, by global fitness trends (Thompson, 2019). We look forward to the novel insights arising from future research in this growing field.
BS, PD, KA, and H-CH wrote and edited the manuscript.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Düking, P., Achtzehn, S., Holmberg, H. C., and Sperlich, B. (2018a). Integrated framework of load monitoring by a combination of smartphone applications, wearables and point-of-care testing provides feedback that allows individual responsive adjustments to activities of daily living. Sensors 18:E1632. doi: 10.3390/s18051632
Düking, P., Fuss, F. K., Holmberg, H. C., and Sperlich, B. (2018b). Recommendations for assessment of the reliability, sensitivity, and validity of data provided by wearable sensors designed for monitoring physical activity. JMIR Mhealth Uhealth 6:e102. doi: 10.2196/mhealth.9341
Düking, P., Stammel, C., Sperlich, B., Sutehall, S., Muniz-Pardos, B., Lima, G., et al. (2018c). Necessary steps to accelerate the integration of wearable sensors into recreation and competitive sports. Curr. Sports Med. Rep. 17, 178–182. doi: 10.1249/JSR.0000000000000495
Keywords: wearables, data analysis, personalized medicine, monitoring, sensor, biofeedback, innovation, digital health
Citation: Sperlich B, Aminian K, Düking P and Holmberg H-C (2020) Editorial: Wearable Sensor Technology for Monitoring Training Load and Health in the Athletic Population. Front. Physiol. 10:1520. doi: 10.3389/fphys.2019.01520
Received: 23 May 2019; Accepted: 03 December 2019;
Published: 08 January 2020.
Edited by:Matt Brughelli, Auckland University of Technology, New Zealand
Reviewed by:Grant Abt, University of Hull, United Kingdom
Monoem Haddad, Qatar University, Qatar
Pantelis Theodoros Nikolaidis, University of West Attica, Greece
Copyright © 2020 Sperlich, Aminian, Düking and Holmberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Billy Sperlich, email@example.com