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Runners' Perspectives on 'Smart' Wearable Technology and Its Use for Preventing Injury
International Journal of Human-Computer Interaction
Abstract
Understanding the user experience between runners and wearable technology is crucial for designing personalized and effective wearable technology features for injury prevention. Therefore, the overall objective of this study was to understand the attitudes and beliefs for competitive and recreational runners towards wearable technology as well as its potential use for preventing injury. Survey data were drawn from 663 respondents. Competitive runners preferred GPS running watches and were interested in tracking personalized data to optimize running efficiency, whereas recreational runners used mobile phones/apps and wristband activity trackers to increase motivation. All runners believed that basic metrics found in wearable technology were most important for injury prevention; however, more advanced metrics had little importance. This paper illustrates the importance of understanding different user experiences for recreational and competitive runners in relation to wearable technology, and encourages the human-computer interaction research community to identify methods in personalizing complex running-related wearable technology data.
... Other studies have emphasized the added value of self-monitoring in improving athletes' performance (Saw et al., 2015) or the quality of their sleep and recovery (Jakowski, 2022). In the running sphere, real-time self-quantified data enable experienced runners to improve their performance (Clermont et al., 2020), regulate their running pace, moderate their cardiorespiratory parameters during exercise and adjust their training session (e.g., Karahanoglu et al., 2021;Rapp & Tirabeni, 2018). In this respect, Clermont et al. (2020) demonstrate that expert runners are more sensitive to particularly sharp and specific quantitative variables (e.g., ground contact time) than beginners, who make greater use of connected tools to boost their motivation. ...
... In the running sphere, real-time self-quantified data enable experienced runners to improve their performance (Clermont et al., 2020), regulate their running pace, moderate their cardiorespiratory parameters during exercise and adjust their training session (e.g., Karahanoglu et al., 2021;Rapp & Tirabeni, 2018). In this respect, Clermont et al. (2020) demonstrate that expert runners are more sensitive to particularly sharp and specific quantitative variables (e.g., ground contact time) than beginners, who make greater use of connected tools to boost their motivation. What's more, elite athletes are able to put their bodily sensations into dialogue with quantitative datasets to construct doubly numerical and sensory knowledge (Rapp & Tirabeni, 2018): "Data provided by the tool can then 'teach' the athlete that different body sensations can be retraced to the same physiological condition" (p. ...
... Complementarily, we wish to examine the cyclists' relationship with the digital selfquantification device. Indeed, it seems heuristic to investigate in particular the way in which these athletes become attached to or detached from the digitalized device (e.g., Toner et al., 2023), the trust they attribute or not to the data generated by the tool (e.g., Newpert et al., 2019), the relationship they establish between the self-quantified data and their somatic knowledge (e.g., Rapp & Tirabeni, 2018), the way they perceive the self-measurement tool as disruptive (e.g., Little, 2017) or as helping them to perform -and, if applicable, the quantitative variables they take into account during their activity (e.g., Clermont et al., 2020). ...
... Other studies have emphasized the added value of self-monitoring in improving athletes' performance (Saw et al., 2015) or the quality of their sleep and recovery (Jakowski, 2022). In the running sphere, real-time self-quantified data enable experienced runners to improve their performance (Clermont et al., 2020), regulate their running pace, moderate their cardiorespiratory parameters during exercise and adjust their training session (e.g., Karahanoglu et al., 2021;Rapp & Tirabeni, 2018). In this respect, Clermont et al. (2020) demonstrate that expert runners are more sensitive to particularly sharp and specific quantitative variables (e.g., ground contact time) than beginners, who make greater use of connected tools to boost their motivation. ...
... In the running sphere, real-time self-quantified data enable experienced runners to improve their performance (Clermont et al., 2020), regulate their running pace, moderate their cardiorespiratory parameters during exercise and adjust their training session (e.g., Karahanoglu et al., 2021;Rapp & Tirabeni, 2018). In this respect, Clermont et al. (2020) demonstrate that expert runners are more sensitive to particularly sharp and specific quantitative variables (e.g., ground contact time) than beginners, who make greater use of connected tools to boost their motivation. What's more, elite athletes are able to put their bodily sensations into dialogue with quantitative datasets to construct doubly numerical and sensory knowledge (Rapp & Tirabeni, 2018): "Data provided by the tool can then 'teach' the athlete that different body sensations can be retraced to the same physiological condition" (p. ...
... Complementarily, we wish to examine the cyclists' relationship with the digital selfquantification device. Indeed, it seems heuristic to investigate in particular the way in which these athletes become attached to or detached from the digitalized device (e.g., Toner et al., 2023), the trust they attribute or not to the data generated by the tool (e.g., Newpert et al., 2019), the relationship they establish between the self-quantified data and their somatic knowledge (e.g., Rapp & Tirabeni, 2018), the way they perceive the self-measurement tool as disruptive (e.g., Little, 2017) or as helping them to perform -and, if applicable, the quantitative variables they take into account during their activity (e.g., Clermont et al., 2020). ...
Digitalization and self-quantification have permeated the field of high-level sport, particularly professional cycling. The data generated by connected objects and applications are used to improve riders' performance. However, no longitudinal study has documented the dynamics of the psychological determinants of professional cyclists' trajectories in digitization and self-quantification. To this end, the present research elaborates on the theoretical and methodological considerations regarding a longitudinal protocol, within the framework of a mixed research method.
... Alongside the increase in running participation there has also been a rise in runners' use of running-related technology (RRT), such as smartphone applications and GPS watches, to support their running. It has been reported that 90% of recreational runners use RRT (DeJong et al., 2021) and it is common for runners to use more than one RRT (Clermont, Duffett-Leger, Hettinga, & Ferber, 2020;Janssen et al., 2017;Zeng, Cuskelly, & Luo, 2020). RRT allows runners to monitor variables such as running distance, pace, intensity, heart rate and cadence (Nielsen et al., 2019). ...
... Most runners in this study monitored their training and did this via smartphone apps and GPS watches, often using more than one method to monitor training. This is a similar finding to previous research (Clermont et al., 2020;Janssen et al., 2017;Zeng et al., 2020) with one study finding that 8 out of 10 runners used at least one monitoring device and 1 out of 4 runners used both a smartphone app and a GPS watch (Janssen et al., 2017). Further analysis identified that runners with 3 months to 2 years' experience were using more smartphone apps to monitor training. ...
... However, even though novice runners have been identified as being more likely to use smartphone apps and using a larger number of apps, experiences of other sub-groups of runners should not be ignored. Competitive runners who ran more than 4 days a week have been reported to be more likely to use running watches to monitor training while recreational runners (running less than 4 days a week are more likely to use smartphone apps (Clermont et al., 2020). This is reflected in the current study which found that runners who ran more than 30 miles a week were more likely to use a smartphone app to monitor training. ...
... 30 Running wearables typically provide real-time information on GPS-derived metrics such as running distance, duration, and absolute running speed. 12 To mimic the information that runners obtain with wearables typically used in practice, both the intervention and the control groups were provided with real-time updates on their running distance, duration, and absolute running speed. ...
... 64 A second way by which wearables may contribute to lower dropout rates is by improving motivation to exercise; for example, by increasing perceived competence using real-time feedback or simply by tracking running distance and speed so that runners can use this information for goal setting. 12 However, motivation to exercise did not significantly change for either the intervention or the control groups from pre-to poststudy, and the change did not differ between the groups (Appendix Table A3, available online). The absence of change in motivational outcomes may be because the baseline Behavioral Regulation in Exercise Questionnaire-2 (BREQ-2) scores on identified and intrinsic motivation were already relatively high, while scores on extrinsic motivation-related scales were low compared with nonactive runners, 54 allowing only a small potential for change. ...
... In contrast, more advanced runners also prefer more advanced biomechanical metrics. 12 Wearables aimed at recreational runners may, therefore, implement training plans and biomechanical feedback to improve wearable uptake in practice. Furthermore, providing feedback via other devices (eg, a smartwatch) as opposed to a phone would also ensure further uptake, in line with previous research. ...
Background
Running technique and running speed are considered important risk factors for running injuries. Real-time feedback on running technique and running speed by wearables may help reduce injury risk.
Purpose
To investigate whether real-time feedback on spatiotemporal metrics and relative speed by commercially available pressure-sensitive insoles would reduce running injuries and improve running performance compared with no real-time feedback.
Study Design
Randomized controlled trial; Level of evidence, 1.
Methods
A total of 220 recreational runners were randomly assigned into the intervention and control groups. Both groups received pressure-sensitive insoles, but only the intervention group received real-time feedback on spatiotemporal metrics and relative speed. The feedback aimed to reduce loading on the joint/segment estimated to exhibit the highest load. Injury rates were compared between the groups using Cox regressions. Secondary outcomes compared included injury severity, the proportion of runners with multiple injuries, changes in self-reported personal best times and motivation (Behavioral Regulation in Exercise Questionnaire–2), and interest in continuing wearable use after study completion.
Results
A total of 160 participants (73%) were included in analyses of the primary outcome. Intention-to-treat analysis showed no significant difference in injury rate between the groups (Hazard ratio [HR], 1.11; P = .70). This was expected, as 53 of 160 (33%) participants ended up in the unassigned group because they used incorrect wearable settings, nullifying any interventional effects. As-treated analysis showed a significantly lower injury rate among participants receiving real-time feedback (HR, 0.53; P = .03). Similarly, the first-time injury severity was significantly lower (–0.43; P = .042). Per-protocol analysis showed no significant differences in injury rates, but the direction favored the intervention group (HR, 0.67; P = .30). There were no significant differences in the proportion of patients with multiple injuries (HR, 0.82; P = .40) or changes in running performance (3.07%; P = .26) and motivation. Also, ~60% of the participants who completed the study showed interest in continuing wearable use.
Conclusion
Real-time feedback on spatiotemporal metrics and relative speed provided by commercially available instrumented insoles may reduce the rate and severity of injuries in recreational runners. Feedback did not influence running performance and exercise motivation.
Registration
NL8472 (Dutch Trial Register).
... In the past decades, however, the profile of runners has become more heterogenous (e.g., sex, age, motivations to run) [11][12][13]. Several studies have been undertaken to analyze the profiles of runners [14,15], often in the context of event participation [11,12,[16][17][18][19]. Nonetheless, it is assumed that the profile of event runners differs from runners in general [18]. Past research highlighted the need for and utility of attitudinal variables, in addition to demographic characteristics, to distinguish different types of sports participants [17,[19][20][21][22][23]. ...
... In the current technological society, the sports sector was introduced by smart technologies and devices (e.g., online subscriptions to participate in running events, sensors to track your running cadence or length of your steps, use of mobile apps, etc.) [47]. Currently, the majority of sports participants use wearables [14], as research shows that 75 to 90 percent of event runners use applications or sports watches [11,20,48]. Depending on the type of runner, however, other kinds of technology are used. ...
... Depending on the type of runner, however, other kinds of technology are used. Both sociodemographic and sports related variables turned out to be decisive for the technology used [11,14,20,48,49]. A commonly used theory to explain the use of technology is the Unified Theory of Acceptance and Use of Technology (UTAUT), which was developed by Venkatesh et al. [50]. ...
As the two prime examples of sport light, running and walking have become very popular sports activities in the past decades. There are references in the literature of similarities between both sports, however these parallels have never been studied. In addition, the current digitalisation of society can have important influences on the further diversification of profiles. Data of a large-scale population survey among runners and walkers (n = 4913) in Flanders (Belgium) were used to study their sociodemographic, sports related and attitudinal characteristics, and wearable usage. The results showed that walkers are more often female, older, lower educated, and less often use wearables. To predict wearable usage, sports-related and attitudinal characteristics are important among runners but not among walkers. Motivational variables to use wearables are important to predict wearable usage among both runners and walkers. Additionally, whether or not the runner or walker registers the heart rate is the most important predictor. The present study highlights similarities and differences between runners and walkers. By adding attitudinal characteristics and including walkers this article provides new insights to the literature, which can be used by policymakers and professionals in the field of sport, exercise and health, and technology developers to shape their services accordingly.
... Primarily, wearable devices in this market function to collect global positioning system (GPS) data and information on running technique to provide summary reports for assisting running performance [5][6][7]. This is achieved by the tracking of personal running data [8,9], planning of running goals [10], and/ or by increasing a runner's motivation to train [9,11]. However, despite the high incidence of running related injuries (RRIs), recently reported at 40% [12] and 46% [13], and the popular use of wearable devices to manage other illnesses and injuries [14][15][16], there is a dearth of research investigating the perceived usefulness of injury focused wearable technologies in runners. ...
... Identifying runners' perceived facilitators and barriers to the use of wearable technologies is also deemed essential for technology adoption [22]; however, the majority of this research has to date focused on performance insights as motivators to the use of wearable technologies [22][23][24][25][26] rather than on injury. Only one study [8] appears to have examined the barriers and facilitators to the use of running technologies for reducing RRIs. ...
... Previous research investigating runners' usage of wearable technologies in relation to performance and injury has predominantly used questionnaires and surveys as the methodological approach [8,[22][23][24][25]. To further explore runners' perceptions of such topics, a qualitative study would provide more insightful and detailed understanding [27,28]. ...
Purpose
Understanding the perceived efficacy and ease of use of technologies will influence initial adoption and sustained utilization. The objectives of this study were to determine the metrics deemed important by runners for monitoring running-related injury (RRI) risk, and identify the facilitators and barriers to their use of injury focused wearable technologies.
Methods
A qualitative focus group study was undertaken. Nine semi-structured focus groups with male (n = 13) and female (n = 14) recreational runners took place. Focus groups were audio and video recorded, and transcribed verbatim. Transcripts were thematically analysed. A critical friend approach was taken to data coding, and multiple methods of trustworthiness were executed.
Results
Excessive loading and inadequate recovery were deemed the most important risk factors to monitor for RRI risk. Other important factors included training activities, injury status and history, and running technique. The location and method of attachment of a wearable device, the design of a smartphone application, and receiving useful injury-related information will affect recreational runners’ adoption of injury focused technologies.
Conclusions
Overtraining, training-related and individual-related risk factors are essential metrics that need to be monitored for RRI risk. RRI apps should include the metrics deemed important by runners, once there is supporting evidence-based research. The difficulty and/or ease of use of a device, and receiving useful feedback will influence the adoption of injury focused running technologies. There is a clear willingness from recreational runners to adopt injury focused wearable technologies whilst running.
... Over 75% of runners use wearables, and most runners use wearable technology to monitor spatiotemporal parameters, such as distance or speed [247][248][249]. Competitive runners are more likely to use wearables to monitor running form or biomechanics than recreational runners [249]. ...
... Over 75% of runners use wearables, and most runners use wearable technology to monitor spatiotemporal parameters, such as distance or speed [247][248][249]. Competitive runners are more likely to use wearables to monitor running form or biomechanics than recreational runners [249]. Even if runners are not personally using IMUs that monitor their biomechanics, based on the results of survey studies, runners have a large appetite for using and consuming data from wearable technology [247,249]. ...
... Competitive runners are more likely to use wearables to monitor running form or biomechanics than recreational runners [249]. Even if runners are not personally using IMUs that monitor their biomechanics, based on the results of survey studies, runners have a large appetite for using and consuming data from wearable technology [247,249]. Yet the number of participants in the included studies is low. Considering the popularity of running and runners' attitudes towards wearable technology, investigations of real-world running biomechanics should be able to recruit large numbers of participants. ...
Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.
... Why people run has an impact on what technology and its features they use (Clermont et al. 2020;Stragier, Abeele, and Marez 2018). Therefore, the subjectively attributed meanings of running provide a framework for a detailed examination of the reasons for not using technology. ...
... Runners who consider technology most necessary are typically those who are engaged in goal-oriented training (Tainio 2020). Competitive runners are interested in optimizing the efficiency of their running, while recreational runners use technology to increase motivation (Clermont et al. 2020). If someone does not run with measurable objectives, the features related to such goals may seem unnecessary: 'I don't train so goal-oriented that I would need running technology to fine-tune my fitness' (Runner 40). ...
The use of digital sports technology has become more the rule than the exception in digitalized societies. The normativity of technology use is also present in the research literature and there is only a little research on the non-use of technology. We see the non-use of technology as an active and conscious choice, reflecting people's relationship with digital society, with sport, and with themselves. We have limited the research context to recreational running, which, as a popular and highly technologized form of sport, offers a rich environment for research into the non-use of technology. Through an abductive analysis of the qualitative questionnaire data, we identified four partially overlapping themes: 1) Technology, and its use and non-use, are not categorical and binary things; 2) Non-use of technology as freedom; 3) The relationship between technology and the meanings attributed to running ; and 4) The material reasons for non-use of technology.
... The data gathered from wearable devices also have the potential to prevent and detect imbalances or injuries during sports. By finding abnormal movement patterns and unexplainable drops in performance both on the fly and retrospectively, potential injuries can be anticipated and prevented by adjusting the training of an athlete [20,21]. ...
... For this, the measured angular rate data was inverted by multiplying it by minus one. After that, the gyroscope values were integrated backwards from , to , by applying Equation 20. Finally, we converted the obtained quaternion sequence to the Euler angle representation using Equation 22 and concatenated it with the Euler angle orientation sequence obtained from , to , +1 . ...
Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports environments outside of laboratories. One of the first sports disciplines, where many athletes have been using wearable devices, is endurance running. With the rising popularity of smartphones, smartwatches and inertial measurement units (IMUs), many runners started to track their performance and keep a digital training diary. Due to the high number of runners worldwide, which transferred their data of wearables to online fitness platforms, large databases were created, which enable Big Data analysis of running data. This kind of analysis offers the potential to conduct longitudinal sports science studies on a larger number of participants than ever before. In this dissertation, both studies showing how to extract endurance running-related parameters from raw data of foot-mounted IMUs as well as a Big Data study with running data from a fitness platform are presented.
... We found that more recreational runners who participate in parkrun are now using GPS watches (87.6%) compared to previous studies, which varied between 45 and 71% [16,18,30,31]. A similar proportion are using mobile phone applications (56.2%), which previously varied between 19 and 62% [15-17, 30, 31]. ...
... With increasing amounts of cost-free online information relating to running injuries and management, future research could explore its uptake amongst recreational runners, versus more traditional face-to-face methods. The high uptake of monitoring technology in this study shows that this is an important medium to educate and monitor training practices in recreational runners [31,41]. However, basic technology tracking univariate factors eg. ...
Background
Monitoring technology is increasingly accessible to recreational runners. Our aim was to examine patterns of technology use in recreational runners, and its potential association with injury.
Methods
We conducted a cross-sectional questionnaire study in a sample of adult runners. Recruitment took place at three different 5 km parkrun event across Northern Ireland. Demographics, technology use, running behaviour and running-related injury (RRI) history were examined. Regression analyses were performed to determine relationships between variables.
Results
Responses were obtained from 192 of 483 eligible finishers (39.8% response rate). Average age was 45.9 years (SD 10.3), with males (47.1 years SD 9.7) slightly older than females (44.8 years SD 10.8). On average, participants ran 3.0 days per week (SD 1.3), with an average weekly distance of 22.6 km (SD 19.7). Males typically ran further (MD 6.2 km/week; 95% CI 0.4 to 12.0) than females. Monitoring technology was used by 87.4% (153/175); with GPS watches the most common device (87.6% (134/153)). Runners using monitoring technology ran further (MD 14.4 km/week; 95% CI 10.3 to 18.5) and more frequently (MD 1.3 days/week; 95% CI 0.7 to 1.9) than those who did not use monitoring technology. There was no significant difference in average age between runners who used monitoring technology and those who did not (MD 4.0 years; 95% CI −0.7 to 8.7). RRI was reported by 40.6% (71/175) of participants in the previous 12 months. In a univariate analysis, none of the selected predictors (age, number of days run per week, distance run per week, or usage of technology to modify training pattern) ( p > 0.1) were associated with RRI.
Conclusions
This study found a high prevalence of monitoring technology usage among recreational runners. While the incidence of RRI remains high, it is not associated with the usage of monitoring technology. Further prospective research should examine if monitoring technology can reduce RRI incidence among recreational runners in future.
... Clermont discussed the user experience between runners and wearable technology, which was critical to designing personalized and effective wearable technology functions to prevent injuries. By understanding the importance of different user experiences of leisure and competitive runners related to wearable technology, he encouraged the human-computer interaction research community to determine the methods for personalized wearable technology data related to complex running [8]. Seshadri studied the application of wearable technology in the evaluation of biomechanics and physiological parameters of athletes to further improve the practicability of this technology and help athletes in various sports fields return to the competition field [9]. ...
Aerobics is a kind of sports activity, which can not only exercise the body but also cultivate the sentiment and reduce the psychological burden. With the improvement of people’s living standards, aerobics is becoming more and more popular, but in fitness activities, there are often some sports injury accidents, which cause certain harm to human health. Therefore, people must take effective precautions against aerobics. Intelligent wearable sensor device is a new high-tech product developed based on Internet technology. It can not only realize real-time monitoring and diagnosis analysis of human body status information, but also combine with mobile terminals such as mobile phones to apply in the field of health management, and also provide personalized services according to user needs. The data collected by the sensor can be used to judge the human health status and exercise situation and make corresponding decisions, to help patients reduce or eliminate the disease burden. It collects the patient’s body data stores it in the database, and then generates corresponding action commands and corresponding motion tracks or speed control modules according to the results fed back by the sensors. At last, it sends these signals to the cloud server to complete the operation process required by the entire system, to achieve the purpose of real-time measurement, processing user health, and assisting athletes in learning training methods. According to human neuroscience, based on intelligent wearable sensing devices, this paper analyzed the prevention and rehabilitation of aerobics injuries and discussed the factors that lead to injuries in aerobics, injury treatment effects, rehabilitation time, and injury treatment satisfaction. The experimental results show that the patient’s satisfaction with the application of intelligent wearable sensing devices in the prevention of aerobics injury and rehabilitation training has increased by 6.84%. The intelligent wearable sensor device realizes the collection and processing of human health data. Applying big data analysis to user behavior analysis, can provide scientific and effective guidance and suggestions for athletes and improve their physical fitness and competitive level.
... Many contemporary runners use smartphone apps, smartwatches, or trackers to gather performance data. Although the use of fitness trackers for measuring steps and running speed has become common, it is important to recognize that current technology offers much more than those particular applications (Clermont et al., 2020;Šinkovec & Rugelj, 2022). Smartwatches have become an essential accessory for runners at all levels of participation from beginners to professional athletes (Hahm, 2017). ...
This study explored the rise of smart wearables as an emerging area of research in the field of fitness applications by athletes and aims to contribute to the growing body of knowledge on the intersection of sports, technology, and health. Vos-viewer and R-studio were used to conduct the bibliometric analysis of the data extracted from the Scopus database in the usage of fitness applications by the participants in sports and other physical activities. To gain a holistic view of the current research field, clustering by keywords coupling was used to identify the significant research themes and provide suggestions for future research. The cluster labeled “Smart wearables to access the fitness” is ranked 1 based on highest cluster frequency, centrality, and impact. The findings of thematic analysis of keywords suggest some motor themes, basic themes, niche themes, and emerging themes, as the usage of smart wearables is found to be an emerging theme regarding the increasing utilization of fitness applications by athletes, which suggests the need to integrate fitness applications in physical education to promote fitness in children. The findings also provide valuable insights into trends and advancements in the field of fitness applications, highlighting the potential benefits for athletes, trainers, and researchers alike. As smart wearables continue to evolve, they are expected to play an increasingly integral role in optimizing sport performance and fostering a new era of data-driven training methodologies.
... Wearable sensors have been used to measure changes in running patterns over the course of a marathon, offering valuable information about the impact of fatigue on running biomechanics [5]. By enabling tailored data tracking and analysis, this technology can help runners maximize their performance and possibly avoid injuries [6]. ...
Reflecting societal developments, marathons have changed from being endurance tests to become venues for inclusivity and creativity. While COVID-19 has made virtual marathons more popular, advanced footwear such as carbon-plated shoes has sparked discussions about fairness. The future of the marathon depends on striking a balance between technological progress, ethics, and the environment. This essay looks at the current patterns and potential future paths of marathon running. The first section of the essay describes how advanced footwear and wearable technology have enhanced athlete performance and how new technology has changed marathon gear. The article also discusses the impact of the epidemic on virtual and online marathons, indicating that these race formats may become popular in the future. Finally, the environmental impact of the marathon is examined along with sustainable measures to mitigate it. In conclusion, this essay provides insightful viewpoints on future developments in marathon running. Maintaining the appeal of marathons and promoting fairness can be achieved by standardizing footwear technology. Creating interesting online venues for virtual marathons can increase participation. Setting high sustainability goals can help reduce ecological footprints. Marathons can benefit from positive environmental change through promoting information sharing and community involvement, which links them to broader sustainability objectives.
... This also challenges the engineering community to develop more intelligent, real-time, accurate information, making it user-friendly and offering athletes and clinicians actionable insights based on context-specific evaluation frameworks. As noted by Clermont et al [70], personalized and effective wearable technology should be rooted in a thorough understanding of the user's experience, attitudes, and opinions which, if not properly considered, can severely hamper the potential of applications. ...
Background
In recent years, researchers have delved into the relationship between the anatomy and biomechanics of sacroiliac joint (SIJ) pain and dysfunction in endurance runners to elucidate the connection between lower back pain and the SIJ. However, the majority of SIJ pain and dysfunction cases are diagnosed and managed through a traditional athlete-clinician arrangement, where the athlete must attend regular in-person clinical appointments with various allied health professionals. Wearable sensors (wearables) are increasingly serving as a clinical diagnostic tool to monitor an athlete’s day-to-day activities remotely, thus eliminating the necessity for in-person appointments. Nevertheless, the extent to which wearables are used in a remote setting to manage SIJ dysfunction in endurance runners remains uncertain.
Objective
This study aims to conduct a systematic review of the literature to enhance our understanding regarding the use of wearables in both in-person and remote settings for biomechanical-based rehabilitation in SIJ dysfunction among endurance runners. In addressing this issue, the overarching goal was to explore how wearables can contribute to the clinical diagnosis (before, during, and after) of SIJ dysfunction.
Methods
Three online databases, including PubMed, Scopus, and Google Scholar, were searched using various combinations of keywords. Initially, a total of 4097 articles were identified. After removing duplicates and screening articles based on inclusion and exclusion criteria, 45 articles were analyzed. Subsequently, 21 articles were included in this study. The quality of the investigation was assessed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) evidence-based minimum set of items for reporting in systematic reviews.
Results
Among the 21 studies included in this review, more than half of the investigations were literature reviews focusing on wearable sensors in the diagnosis and treatment of SIJ pain, wearable movement sensors for rehabilitation, or a combination of both for SIJ gait analysis in an intelligent health care setting. As many as 4 (19%) studies were case reports, and only 1 study could be classified as fully experimental. One paper was classified as being at the “pre” stage of SIJ dysfunction, while 6 (29%) were identified as being at the “at” stage of classification. Significantly fewer studies attempted to capture or classify actual SIJ injuries, and no study directly addressed the injury recovery stage.
Conclusions
SIJ dysfunction remains underdiagnosed and undertreated in endurance runners. Moreover, there is a lack of clear diagnostic or treatment pathways using wearables remotely, despite the availability of validated technology. Further research of higher quality is recommended to investigate SIJ dysfunction in endurance runners and explore the use of wearables for rehabilitation in remote settings.
... The most common types of existing wearables are typically wrist-worn smartwatches, chest straps, devices mounted on or in the footwear, or, more recently, those located within sports clothing [2]. Analysis of subsequent data can be used to gauge improvements in fitness, help mitigate injury risk [3,4], inform recovery [5], monitor technique [6], or, at a consumer level, simply provide motivation [7]. The typical sensors used to date are inertial measurement units (IMUs comprising accelerometers, gyroscopes, and magnetometers), Global Positioning Systems (GPS), and heart rate (electrocardiography, ECG) and muscle excitation (electromyography, EMG) sensors. ...
E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development.
... IMUs overcome these limitations and can be used in-field, facilitating the collection of large volumes of running biomechanics data under real-world conditions [11]. These devices are much more accessible to the general population than captive systems, with >90% of runners already reporting regularly wearing a tracking device or watch (similar in size and cost to an IMU) to improve their training outcomes or avoid injury [12][13][14][15]. These advantages have led to the use of IMUs to collect data in ways that captive technology cannot. ...
Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU’s location has on running data, a reference IMU was ‘correctly’ placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was ‘misplaced’ 0.05 m away, simulating a ‘worst-case’ misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to −10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.
... Ten healthy male recreational runners volunteered to participate in the present study (age: 22 ± 3 years, height: 181 ± 8 cm, body mass: 79 ± 8 kg) (Peñailillo et al. 2015). Participants were considered "recreational runners" if they ran less than 4 days per week (Kuru 2016;Clermont et al. 2020). Prior to their inclusion in this study, the participants were screened for the following exclusion criteria: smoking, current medication or drug consumption, and presence of apparent cardiovascular, metabolic, neurologic, or musculoskeletal disease. ...
Purpose
To examined the time-course of the early and late phase of the rate of voluntary force development (RVFD) and muscle damage markers after downhill running.
Methods
Ten recreational runners performed a 30-min downhill run at 10 km h⁻¹ and −20% (−11.3°) on a motorized treadmill. At baseline and each day up to 4 days RVFD, knee extensors maximum voluntary isometric force (MVIC), serum creatine kinase (CK) concentration, quadriceps swelling, and soreness were assessed. The early (0–50 ms) and late (100–200 ms) phase of the RVFD, as well as the force developed at 50 and 200 ms, were also determined.
Results
MVIC showed moderate decrements (p < 0.05) and recovered after 4 days (p > 0.05). Force at 50 ms and the early phase were not impaired (p > 0.05). Conversely, force at 200 ms and the late phase showed moderate decrements (p < 0.05) and recovered after 3 and 4 days, respectively (p > 0.05). CK concentration, quadriceps swelling, and soreness increased (p < 0.05) were overall fully resolved after 4 days (p > 0.05).
Conclusion
Downhill running affected the knee extensors RVFD late but not early phase. The RVFD late phase may be used as an additional marker of muscle damage in trail running.
... The most used type of smart devices is smart sport watches. Also, recreational runners use their smart phones and apps as well as wristbands more commonly than competitive runners (Clermont, Duffett-Leger, Hettinga & Ferber, 2020). In the literature, it is also claimed that most runners consider these devices as virtual coaches that assist them during the activity (Scataglini, Cools, Neyrinck & Verwulgen, 2021). ...
Technology has always been a big part of human life. Since the time when westarted using tools, we have ignited a fire which is today called technology.Technology is being used not only in the daily life, but also in the activitiesthat individuals do in their leisure. One of the leisure activities that is popularand attractive to people of all ages is Outdoor Recreation activities. Outdoorrecreation activities provide the participants thrill, risk, and adventure thatthey seek. However, the use of technology in outdoor recreation is a significantaspect as it provides safety to some point, navigation skills, comfort, andmotivation. Therefore, the aim of this chapter is to investigate the technologyuse involved in the outdoor recreation activities. Thus, in this chapter thetechnology involved in outdoor recreational activities is discussed in differentaspects as a motivator (social media and image), as a comfort (developmentin materials used), as a performance auditing device (wearable technologyand tracking apps) and as simulation (virtual reality and augmented reality).
... /10.1145/3577190.3614228 smartwatches, and wearable sensors to monitor and improve their performance and physical health [11,24,32]. However, there is a relatively underexplored research area on the use of real-time feedback technologies to enhance runners' technique during their runs, potentially preventing injuries caused by incorrect running technique or overuse [21,46]. ...
The utilization of drones to assist runners in real-time and post-run remains a promising yet unexplored field within human-drone interaction (HDI). Hence, in my doctoral research, I aim to delve into the concepts and relationships surrounding drones in the context of running, than focusing solely on one specific application. I plan on accomplishing this through a three-stage research plan: 1) investigate the feasibility of drones to support outdoor running research, 2) empathize with runners to assess their preferences and experiences running with drone, and 3) implement and test an interactive running with drone scenario. Each stage has specific objectives and research questions aimed at providing valuable insights into the utilization of drones to support runners. This paper outlines the work conducted during my Ph.D. research along with future plans, with the goal of advancing the knowledge in the field of runner drone interaction.
... smartwatches, and wearable sensors that monitor different aspects of runners' physical health during activity [14,29,49]. These technologies generally enable runners to track their activity levels and obtain bio-mechanical data to understand their running patterns, identify performance changes and detect potential injuries [27]. ...
... Typically, the most interested in technology and data are those for whom running is "serious leisure" (Feng & Agosto, 2019;Kuru, 2016;Stebbins, 2017), which includes goal-oriented training and regular participation in competitions (Qiu et al., 2020;Tainio, 2020). Others may run for health-related reasons (Feng & Agosto, 2019;Le on-Guereño et al., 2021;Mertala & Palsa, 2023) and wish to monitor their activity levels and rest but do not need (or are interested in) as detailed and fine-grained metrics as more "serious" runners (see Clermont et al., 2020). Running can also be approached from a post-sport perspective, in which running is primarily an aesthetic experience instead of fitness (Atkinson, 2010;Feng & Agosto, 2019;Tainio, 2020), which arguably provides different stances toward technology and data (Mertala & Palsa, 2023;Tainio, 2020). ...
Data literacy is typically described in a decontextualized manner, and many data literacy frameworks are detached from the "messy" realities of everyday life. In the present study, we selected a specific context (recreational running), specific data technology (self-tracking devices), and specific viewpoint (accuracy of data and analyses) to construct a substantial theory of (one form of) contextual data literacy. The research question is: How does recreational runners' everyday data literacy appear in relation to the accuracy of measurements and analyses of self-tracking devices? Through an abductive analysis of qualitative survey data (N ¼ 1057), we identified the data literacy actions that runners engaged with when assessing the accuracy of data in relation to their subjective needs, objectives, and life situations. The first-order data literacy actions (comparison and evaluation) captured how runners assessed and analyzed the accuracy of data, and they took place mainly in the immediate context of running. The second-order data literacy actions (accept-ance, adaptation, and optimization) were the result of the runners' reflections on what they sought from running and how they valued data, as well as their broader life situation.
... These authors suggest that future iterations of these devices could be used to provide users with real-time biomechanical feedback that will further reduce the risk of overuse injuries and discontinuation rates amongst runners. This position seems somewhat ambitious, however, given the extensive running-related biomechanical research which has pointed to the complex interrelationship of numerous biomechanical metrics that explain the aetiology of running-related injuries (Clermont et al., 2020). Considering the various shortcomings of these devices, how might we explain the continued allegiance to the predictive capacities of these systems? ...
... Sillä miksi ihminen juoksee, on merkitystä sille, millaisia teknologioita (ja niiden ominaisuuksia) hän käyttää (Stragier ym. 2018;Clermont 2020). Täten juoksulle annetut subjektiiviset merkitykset tarjoavat kehikon myös teknologian käyttämättä jättämisen perusteiden hienosyiselle tarkastelulle. ...
Digitaalisen liikuntateknologian käytöstä on tullut 2020-luvulla enemmän sääntö kuin poikkeus Suomen kaltaisissa digitalisoituneissa yhteiskunnissa. Teknologian käytön normatiivisuus on läsnä myös tutkimuskirjallisuudessa, sillä liikuntateknologian käyttämättä jättämistä tarkasteleva tutkimus on vähäistä ja ihmiskuvaltaan yksipuolista. Tässä artikkelissa olemme valinneet hermeneuttisen eli ymmärtämään pyrkivän näkökulman liikuntateknologian käyttämättä jättämiseen ja näemme sen olevan aktiivinen ja tietoinen valinta, joka heijastaa ihmisen suhdetta digitalisoituneeseen yhteiskuntaan, liikuntaan sekä omaan itseensä. Liikuntalajien suhteen olemme rajanneet tutkimuskontekstiksi juoksun, joka yleisenä ja voimakkaasti teknologisoituneena liikuntamuotona tarjoaa rikkaan ympäristön teknologian käyttämättä jättämisen tutkimukselle. Laadullisen kyselyaineiston abduktiivisen analyysin kautta tunnistimme neljä osin toisiaan leikkaavaa teemaa: 1) Teknologia, sekä sen käyttö ja käyttämättä jättäminen eivät ole kategorista ja binäärisiä asioita; 2) Teknologian käyttämättä jättäminen vapautena; 3) Teknologian tarpeettomuus suhteessa juoksulle annettuihin merkityksiin ja 4) Teknologian käyttämättä jättämisen materiaaliset perusteet. Tutkimuksen tulokset tarjoavat juoksijoille, heidän kanssaan toimiville ammattilaisille, teknologian kehittäjille sekä tutkijoille käsitteellisiä työkaluja liikunnan ja teknologian suhteisuuden tarkastelemiseen.
... Smart wearable users want maximum utility from their devices (Clermont et al., 2020). Utility-based features enhance the perceived usefulness of smart wearables, which results in a positive consumer attitude. ...
... Others may run for health-related reasons (León-Guereño ym. 2021; Mertala & Palsa, under review) and wish to monitor their activity levels and rest but do not need (or are interested in) as detailed and fine-grained metrics as more "serious" runners (see Clermont et al., 2020). Running can also be approached from a post-sport perspective, in which running is primarily an aesthetic experience instead of fitness (Atkinson, 2010;Tainio, 2020), which arguably provides different stances toward technology and data (Mertala & Palsa, under review;Tainio, 2020). ...
Data literacy is typically described in a decontextualized manner, and many data literacy frameworks are detached from the "messy" realities of everyday life. In the present study, we selected a specific context (recreational running), specific data technology (self-tracking devices), and specific viewpoint (accuracy of data and analyses) to construct a substantial theory of (one form of) contextual data literacy. The research question is: How do recreational runners' everyday data literacy appear in relation to the accuracy of measurements and analyses of self-tracking devices? Through a grounded theory-inspired analysis of qualitative survey data (N = 997), we identified the data literacy actions that runners engaged with when assessing the accuracy of data in relation to their subjective needs, objectives, and life situations. The first-order data literacy actions (comparison and evaluation) captured how runners assessed and analyzed the accuracy of data, and they took place mainly in the immediate context of running. The second-order data literacy actions (acceptance, adaptation, and optimization) were the result of the runners' reflections of what they sought from running and how they valued data, as well as their broader life situation. Research highlights • Many data literacy (DL) frameworks are decontextualized and detached from the "messy" realities of everyday life. • A substantial theory of contextualized everyday DL based on qualitative survey data from self-tracking runners is provided. • This paper identified 1st (evaluation, comparison) and 2nd (acceptance, adaptation, optimization) order DL actions (DLAs). • 1st order DLAs describe how runners assess the accuracy of the data, and they take mainly place in the immediate context. • 2nd order DLAs involve the results of runners' profound reflection on data, running, and their life situations.
... First, articles not published in English pose a language bias regarding article selection. Additionally, sensor modality was restricted to wearable accelerometers, gyroscopes, magnetometers or a combination of those (IMU), or pressure insoles, thus excluding GPS or mobile phone applications, which are common amongst runners [195]. Because of the varying definitions and methods of calculation, studies were also excluded if they focused solely on shock, stiffness or neuromuscular load. ...
Background Running gait assessment has traditionally been performed using subjective observation or expensive laboratory-based objective technologies, such as 3D motion capture or force plates. However, recent developments in wearable devices allow for continuous monitoring and analysis of running mechanics in any environment. Objective measurement of running gait is an important (clinical) tool for injury assessment and provides measures that can be used to enhance performance.
Objectives To systematically review available literature investigating how wearable technology is being used for running gait analysis in adults.
Methods A systematic search of literature was conducted in the following scientific databases: PubMed, Scopus, WebofScience, and SportDiscus. Information was extracted from each included article regarding the type of study, participants, protocol, wearable device(s), main outcomes/measures, analysis, and key findings.
Results A total of 131 articles were reviewed: 56 investigated the validity of wearable technology, 22 examined the reliability and 77 focused on applied use. Most studies used inertial measurement units (IMU) (n=62) (i.e., a combination of accelerometers, gyroscopes, and magnetometers in a single unit) or solely accelerometers (n=40), with one using gyroscopes alone and 31 using pressure sensors. On average, studies used one wearable device to examine running gait. Wearable locations were distributed among the shank, shoe and waist. The mean number of participants was 26 (± 27), with an average age of 28.3 (± 7.0) years. Most studies took place indoors (n =93), using a treadmill (n =62), with the main aims seeking to identify running gait outcomes or investigate the effects of injury, fatigue, intrinsic factors (e.g., age, sex, morphology) or footwear on running gait outcomes. Generally, wearables were found to be valid and reliable tools for assessing running gait compared to reference standards.
Conclusions This comprehensive review highlighted that most studies that have examined running gait using wearable sensors have done so with young adult recreational runners, using one IMU sensor, with participants running on a treadmill and reporting outcomes of ground contact time, stride length, stride frequency and tibial acceleration. Future studies are required to obtain consensus regarding terminology, protocols for testing validity and reliability of devices and suitability of gait outcomes.
... Smart wearable users want maximum utility from their devices (Clermont et al., 2020). Utility-based features enhance the perceived usefulness of smart wearables, which results in a positive consumer attitude. ...
... It also challenges the research community to develop more intelligent, real-time, accurate information, making it user-friendly and offering coaches and athletes actionable insights based on context-specific evaluation frameworks and on the ability to identify correct forms and common deviations of specific movements according to an agreed-upon clinical consensus [266]. Indeed, personalised and effective wearable technology should be rooted in a thorough understanding of the user's experience, attitudes, and opinions which, if not properly considered, can severely hamper the potential of applications [267]. ...
Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features—such as research design, scope, experimental settings, and applied context—were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.
... Predominantly, the relevant IS literature on smartwatches examine the factors that contribute to intentions to purchase (Hsiao & Chen, 2018;Wu et al., 2016), adoption (Adapa et al., 2018;Chuah et al., 2016;Dehghani, 2016;Dutot et al., 2019;Krey et al., 2019), use Clermont et al., 2020;Mettler & Wulf, 2019), continuous intentions to use Bölen, 2020;Dehghani, 2018;Hong et al., 2017), and use discontinuance (Nascimento et al., 2018;Wairimu & Sun, 2018;Xiao-Liang et al., 2018). Nevertheless, to better understand where the smartwatch innovation is headed can be remarked in the few articles that question users' intentions to continue or discontinue to use a smartwatch. ...
Smartwatches are wearable devices intended to be smartphone companions that capture health data and ease access to notifications. They have also become personalisable standing as a fashion statement. This combination resulted in staggering adoption rates recently leading to question whether smartwatch users’ choice and use satisfaction emerge from utility features or from its fashion characteristics. This paper proposes and validates a fit theory to investigate the antecedents of adopters’ satisfaction. Besides evaluating fit with identity, the model assesses both perceived and actual task-technology fit of smartwatches. A questionnaire-based quantitative approach is used to collect data from about 300 smartwatch users in Qatar. To test the proposed model, data is analysed using structural equation modeling (SEM) and artificial neural networks (ANN). Furthermore, ANN sensitivity analysis ranks the importance of the fit factors affecting users’ choice during pre- and post-adoption stages. Both task-technology and technology-identity fit factors are quasi-equally important in explaining 62% of satisfaction variance. ANN analysis revealed that post-adoption satisfaction is primarily attributed to smartwatches’ ability to fit with users’ identity and secondarily to its perceived fit with tasks. Nevertheless, pre-adoption choice of smartwatches is mainly guided by their functionality. This paper is the first to propose and validate an integrated task-technology-identity fit model to explain smartwatch utilization and users’ satisfaction. The originality also lies in assessing actual task-technology fit and as perceived by users. Employing two modes of analysis revealed extra insights too.
... Whilst this position is a plausible location for a wearable sensor, 'body worn' sensors have been found to be used by only 5.8% and 4.2% of recreational and competitive runners, respectively. However, wrist-based sensors are used by 71.3% of recreational and 82.4% of competitive runners (Clermont et al., 2020). With the overarching target to provide accurate technique analysis to as many runners as possible, the integration into commonly used wearable technologies is crucial. ...
Greater understanding of differences in technique between runners may allow more beneficial feedback related to improving performance and decreasing injury risk. The purpose of this study was to develop and test a support vector machine classifier, which could automatically differentiate running technique between experienced and novice participants using only wearable sensor data. Three-dimensional linear accelerations and angular velocities were collected from six wearable sensors secured to current common smart device locations. Cross-validation was used to test the classification accuracy of models trained with a variety of combinations of sensor locations, with participants running at different speeds. Average classification accuracies ranged from 71.3% to 98.4% across the sensor combinations and running speeds tested. Models trained with only a single sensor location still showed effective classification. With the models trained with only upper arm data achieving an average accuracy of 96.4% across all tested running speeds. A post-hoc comparison of biomechanical variables between the two subgroups showed significant differences in upper body biomechanics throughout the stride. Both the methodology used to perform the classifications and the biomechanical differences identified could prove useful when aiming to shift a novice runner's technique towards movement patterns more akin to those with greater experience.
... Although the use of inertial sensors is popular among runners, usually in the form of wearables [15][16][17], they still have limitations that need to be resolved. First, the wearable sensors are often located on the legs [7,8,10,13] or the lower back [9,13], which is not very practical for the runner. ...
Instrumented earbuds equipped with accelerometers were developed in response to limitations of currently used running wearables regarding sensor location and feedback delivery. The aim of this study was to assess test–retest reliability, face validity and concurrent validity for cadence and stance time in running. Participants wore an instrumented earbud (new method) while running on a treadmill with embedded force-plates (well-established method). They ran at a range of running speeds and performed several instructed head movements while running at a comfortable speed. Cadence and stance time were derived from raw earbud and force-plate data and compared within and between both methods using t-tests, ICC and Bland–Altman analysis. Test–retest reliability was good-to-excellent for both methods. Face validity was demonstrated for both methods, with cadence and stance time varying with speed in to-be-expected directions. Between-methods agreement for cadence was excellent for all speeds and instructed head movements. For stance time, agreement was good-to-excellent for all conditions, except while running at 13 km/h and shaking the head. Overall, the measurement of cadence and stance time using an accelerometer embedded in a wireless earbud showed good test–retest reliability, face validity and concurrent validity, indicating that instrumented earbuds may provide a promising alternative to currently used wearable systems.
... As an example, running measures (e.g., speed, cadence), which are measurable by commercially available devices, such as smartwatches, may be used when developing algorithms to predict step-specific forces in the patellar and Achilles tendons. Commercially available devices are widely used by runners, as they can be worn unobtrusively during running [13,14]. These devices have made it possible to obtain indirect measures of training load (such as the number of strides, cadence, ground contact time, and vertical oscillation) in an outdoor environment [15,16]. ...
Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.
... The available data, in a broad sense, makes sports technology a helpful and effective companion in improving performance and physical condition [7], enhancing motivation [8] and preventing injuries [9,10]. There has been a recent focus on measuring and improving running technique through technology, as technique is an essential determinant of running performance and running-related injuries [3]. ...
The purpose of this research is to explore the roles that sports trackers and running-related data play in runners’ personal goal achievement. A two-week diary study and semi-structured interviews were conducted with 22 runners to explore how runners engage with their running-related data to set and achieve their running goals. We found that participants pursued and transitioned between different running goals as their needs, abilities, and surrounding environment changed. We also found multiple motivations that shaped the use of sports trackers. We identified two main categories in runners’ motivations for using trackers and data to achieve their goals. These categories were (i) documenting and tracking in running, and (ii) supporting goal-oriented reflections and actions, with various reasons for use while preparing for and during running. This study provides insights into the psychological effects of running-related data and signals practical implications for runners and developers of tracking technology.
... Ultrawide bandwidth technology is a radio frequency signal that operates in a bandwidth equal to or greater than 500 MHz [11]. Most off-the-shelf GPS systems only have a sampling rate of 5 Hz or 10 Hz [4], and over 75% of runners at an official road race event use GPS-based watches [12], which typically have a sampling rate of 1 Hz [13]. If the sampling rate is lower than 10 Hz, precision and accuracy may be reduced, as there may be critical position data points not being measured. ...
One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements.
Running has recently gained popularity, and many runners use wearable technology to enhance their race preparation. This paper provides a brief review of measuring running performance through technology usage. The focus is on the effectiveness of those related technologies in providing accurate data, which at the same time could improve runners’ performance as well as their technique. The finding from this study showed that existing technologies could provide good data (at least for individual reference) since other aspects of running techniques, such as stride length, cadence, and power, were integrated into the technology. Nonetheless, it is hoped that future technology will encourage the development of quick interfaces that could enhance running techniques and training by considering injury-related features in human–computer interaction and running.
Introduction: Wearable technology (WT) has become common place in sport. Increased affordability has allowed WT to reach the wrists and bodies of grassroots and community athletes. While WT is commonly used by sport populations to monitor training load, the use of WT among dancers and dance teachers is unknown. Therefore, the purpose of this study is to explore the perspectives of dancers, dance teachers, and dance parents on using WT in the dance studio environment. Methods: Dancers (aged 14+), dance teachers (aged 18+), and dance parents (with a child <18 years registered in a dance program) were recruited from local dance studios (including those offering vocational programs and/or professional training opportunities), and dance retail stores. Participants provided informed consent/assent and completed a one-time online survey about their attitudes, self-efficacy, motivations, barriers, and current practices of using WT in the studio. Results: Sixty-seven participants (19 dancers, 32 dance teachers, and 16 dance parents) completed the survey. Attitudes toward using WT were similar across all groups (mean score range = 34-38/45). Thirteen dancers (68%), 29 teachers (91%), and 7 dance parents reporting on behalf of their children (47%) were permitted to use WT in the studio. Smartwatches were the most common WT used in the studio by dancers (7/9) and teachers (13/17), while dance parents reported that their children primarily used wristband activity trackers (3/4). Among all groups, the primary reason for using WT was to track personalized training data, with calories, total duration, and heart rate being the most important perceived metrics for improving dancing. Conclusion: Across all groups, attitudes toward WT were modest. Prevalence of WT use in the dance studio varied, with wrist-based gadgets being the most common. As WT research continues in dance populations, it will be important for future studies to consider studio permissions as well as participants’ existing WT use practices.
The major changes that the COVID-19 pandemic has brought into the lives of the student population are also visible in the altered way in which students spend their free time, the growing trend of sedentary habits prompted by an increased time spent in front of the screen, i.e. playing online games. Over the last decade, numerous authors have tried to explore the frequency and time spent playing online games and their impact on the psychophysical condition of people by means of various questionnaires. After the American Psychiatric Association (APA) defned the term IGD (Internet Gaming Disorder) as a gaming disorder in 2013. and included it in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), in 2017 Király et
al.24 constructed their Ten Item Internet Gaming Disorder Test (IGDT-10), a short psychometric instrument that showed in statistical analyses its validity and reliability for the evaluation of IGD as proposed in the DSM-5.
The primary goal of this study was to investigate the association of time spent playing online games with kinesiological activities.
The sample of the subjects in the study consisted of 1000 students of the University of Zagreb (M=480 and F=520). The data collection was carried out online by an anonymous questionnaire comprised of the Ten-item Internet Gaming Disorder Test (IGDT-10)24, International Physical Activity Questionnaire (IPAQ-SF)9 and Previous engagement in kinesiological activities Questionnaire (KINAKT)10.
The SPSS software package (version 26.0, SPSS Inc., Chicago, IL, USA) was used to process the data. For all variables, descriptive parameters expressed through
frequencies and percentages, arithmetic mean and standard deviations were calculated. Univariate and multivariate methods, correlation analysis and multiple linear regression were used. To check differences with regard to sociodemographic indicators, the Kruskal-Wallis H test was used, and to test differences in independent variables with two level (level of kinesiological activity, playing time, year of study), a nonparametric replacement for a t-test (MannWhitney U test) was used.
The results showed that all the values indicating physical activity are negatively related to the time spent playing online games, so it can be concluded that with
more frequent and longer playing of online games, values indicating physical activity decrease. Also, a statistically significant positive correlation was established between the total weekly sitting time and the frequency of playing online games, so it can be concluded that more frequent and extended playing of online games increases the time spent sitting and its frequency, thereby reducing physical activity.
In conclusion, the results suggest that students are becoming less and less involved in any form of kinesiological activities, spending too much time sitting, on the internet or playing online games more frequently, which results in an increased risk of IGD.
Keywords: online games, kinesiological activity, students, sedentary lifestyle
In the medical technology and sports industry, wearable devices, such as exoskeletons and running shoes, are developed to improve mobility and enhance performance. To ensure this, experimental studies are conducted in motion laboratories, where extensive measurements enable biomechanical movement and performance analysis. In a lab environment, however, natural movements can only be captured to a limited extent. Furthermore, recordings are cost- and time-intensive and an isolated consideration of all degrees of freedom in the design is practically impossible. Therefore, this thesis aims to bring human movement studies outside the lab in two ways. In order to enable studies in natural environments, novel methods for biomechanical gait analysis using Inertial Measurement Units (IMUs) are presented [Dor+19a; Dor+20]. In order to perform virtual studies for prediction of human-product interactions, a simulation framework is developed and evaluated for a use case in running shoe design [Dor+19a]. This cumulative thesis is built around three publications. In the first publication [Dor+19a], I showed with my co-authors that we can accurately estimate two-dimensional gait biomechanics from IMU data by solving a single optimal control problem. The advantage of this approach is that a dynamically consistent simulation of a musculoskeletal model is obtained, while sensor noise and drift are suppressed. In the second publication [Dor+20], we estimated two-dimensional gait biomechanics from IMU data using Convolutional Neural Networks (CNNs), which reduced computation time while achieving higher accuracy of kinetics compared to [Dor+19a]. We also showed that the generalization of CNNs can be improved for kinematics by augmenting the training dataset using optimal control simulation of musculoskeletal models. In the third publication [Dor+19b], my co-authors and I found that optimal control simulation of musculoskeletal models can predict differences in energy cost in running between two midsole materials, which were modeled with non-linear regression models using mechanical test data. This highlights the ability for studies in the virtual environment, which can further be used to answer “what if” questions in footwear design [Nit+21]. Additionally, the thesis contains an original literature review and a discussion of the contributed publications in the overarching context of this literature analysis. In conclusion, this thesis shows that combining musculoskeletal optimal control with machine learning is a valuable approach for IMU-based biomechanical analysis of human gait and for predicting human responses. Follow-up work can extend these methods for three-dimensional movement studies [Nit+20], and further explore the combination of physics-based with machine learning models. This work contributes to the improvement of sports and medical products by enabling future analysis of human motion in natural environments and prediction of human-product interactions in a virtual testing environment.
Las tecnologías portátiles forman parte de la cotidianidad incluso en la esfera deportiva. Las mismas se aferran al running como herramientas consolidadas dentro de esta práctica atlética global. En ese sentido, Argentina no es ajena a la tendencia, aunque no se conoce con claridad la aplicabilidad de estos dispositivos en los agentes involucrados.
Es por ello que el objetivo de este estudio fue indagar y analizar el uso de las tecnologías portátiles en corredores y en entrenadores.
La muestra estuvo conformada por 514 corredores y 41 entrenadores de diferentes ciudades de Argentina. Los resultados arrojaron que el 94,6% de corredores y el 90,4% de los entrenadores han utilizado estos dispositivos en el último año, considerándolos, en cierta medida, esenciales para este deporte. Mientras los corredores optan principalmente por los relojes deportivos, los entrenadores utilizan el reloj y las aplicaciones móviles de forma combinada. Por otra parte, el tiempo, la velocidad y la distancia recorrida son las principales variables de interés en ambos grupos.
Se puede concluir que el uso de tecnologías por parte de los corredores estará asociada al autoconocimiento asociado a las variables de rendimiento dentro del deporte, y que los entrenadores las consideran herramientas importantes para mejorar su rol profesional a partir de la posibilidad de recopilar datos de los corredores en el largo plazo.
In this article, we focus on fitness workout scenarios. To examine the current practices, we first conducted a substantial user research, through the content analysis procedure we summarized insights regarding fitness purpose, essential data, psychological state, emotional changes and social habits. Subsequently, we generalized related design opportunities of improving the fitness experience and motivation to enhance the fitness performance and communication. Feedback and data representation have great potential to address the challenges, therefore we first proposed a design process for the feedback mode design of fitness workout, including the contextual information, feedback strategy, and realization ways. The feedback designs are implemented on a wearable augmented feedback system ‘FitSleeve’, focusing on individual and group scenarios separately. In group sessions, FitSleeve can demonstrate participants’ experience level, real-time heart rate zone and feedback regarding the correct movement execution. In individual sessions, FitSleeve can display the training progress and provide continuous encouragement. Finally, we evaluated FitSleeve by adopting the System Usability Scale, User Experience Questionnaire, Intrinsic Motivation Inventory and user subjective interviews. The findings indicate the potential of FitSleeve to improve the user experience and motivation of fitness participants.KeywordsFitnessWearable technologyFeedback
Modern wearable monitors and laboratory equipment allow the recording of high-
frequency data that can be used to quantify human movement. However, currently,
data analysis approaches in these domains remain limited. This paper proposes a new
framework to analyze biomechanical patterns in sport training data recorded across
multiple training sessions using multilevel functional models. We apply the methods
to subsecond-level data of knee location trajectories collected in 19 recreational
runners during a medium-intensity continuous run (MICR) and a high-intensity
interval training (HIIT) session, with multiple steps recorded in each participant-
session. We estimate functional intra-class correlation coefficient to evaluate the
reliability of recorded measurements across multiple sessions of the same training
type. Furthermore, we obtained a vectorial representation of the three hierarchical
levels of the data and visualize them in a low-dimensional space. Finally, we
quantified the differences between genders and between two training types using
functional multilevel regression models that incorporate covariate information. We
provide an overview of the relevant methods and make both data and the R code
for all analyses freely available online on GitHub. Thus, this work can serve as a
helpful reference for practitioners and guide for a broader audience of researchers
interested in modeling repeated functional measures at different resolution levels in
the context of biomechanics and sports science applications.
The purpose of this study was to examine the effect of major intrinsic (functional, expressive, aesthetic, tracking) and extrinsic (brand name, price) attributes of broadly defined wearables on consumers’ attitude and intention of using such products. An online survey was conducted with a convenience sample of 317 college students whose age was 18 years old and over at a large US Midwestern university. Confirmatory factor analysis and structural equation modelling were performed to test the measurement model fit and hypotheses testing. The results revealed the significant influence of tracking attributes on perceived ease of use, perceived usefulness, attitude, and intention of using wearables. This study highlights the importance of considering tracking attributes along with functional, aesthetic, and expressive attributes in predicating consumer behavioural aspects of using wearables. Limitations and managerial and academic implications were also discussed.
Nowadays, the Internet of Things (IoT) and artificial intelligence (AI) is the emerging field in which researchers are still finding new methods and techniques to reduce human efforts. This chapter contains the basic introduction of the AI and IoT systems. The seven-layer architecture of the IoT frameworks is discussed with the functioning of the individual layers. The elementary facility of things and complete operation of the particular layer. In this chapter, we have discussed the relationship between AI and the IoT. There are various real-time applications of IoT with AI. Some of the uses of AI and IoT are also being discussed in the chapter.
In technological era of rapid development in the fields of Internet of Things (IoT) and artificial intelligence (AI), there is a continuous event constituting a new stage in the changing world. AI has enabled devices to act smart and intelligent so that it brings actuation to the life of all by making it a reality. AI has widespread its wings with IoT. A number of data sets and machine learning algorithms are available to test the advancements and evaluate the integration of AI and IoT as Internet of Intelligent Things (IoIT). This chapter on the whole focuses on the major impact of recent impacts of IoT connected and communicating with a close relation with AI and bringing the solution which brings universally acceptable and welfare for all. In this chapter, we have included the random forest regression model of machine learning and evaluation of our model is done with variance score. It checks how they consumers and products communicate with each other and with humans through the Internet and make the store to act smart. This chapter focuses on the areas how the IoT and AI will interact together to make the devices IoIT, that is, intelligent devices to interact with the environment and produce smart results for the devices.
The objective of the article was to present the authors' conceptual model of using lifestyle sports applications.
Proposed model provides classification of lifestyle sports mobile applications types used by companies for brand
promotion purposes and identification of ways in which companies can reach users through applications. The
recognition of benefits that applications can provide to the enterprises and possible benefits associated with the
presence of brands in the application for its user was also provided in the conceptual model. The second objective
was to study the opinions of runners about the presence of brands in sports mobile applications on the basis of
own quantitative research (n = 2434 questionnaires). The research results indicate that presence of commercial
brands in the app is treated by their users as too invasive, therefore this communication tool should be used with
moderate intensity. In spite of the fact that majority of sports applications users are reluctant to see brands in
their apps, a significant proportion of them participate in the activities and challenges proposed by companies.
According to the authors, lifestyle mobile applications could be promising marketing space for vendors, especially
regarding the growing market of sports mobile apps users. However, companies should use more personalized,
innovative and socially responsible approach to application users. Although the article uses the results of
empirical research, it should be treated primarily as a signalling of a new research problem, which is the new
brand communication channel with consumers. Thus, it is descriptive rather than exploratory.
Physical inactivity has become a major public health concern and, consequently, the awareness of striving for a healthy lifestyle has increased. As a result, the popularity of recreational sports, such as running, has increased. Running is known for its low threshold to start and its attractiveness for a heterogeneous group of people. Yet, one can still observe high drop-out rates among (novice) runners. To understand the reasons for drop-out as perceived by runners, we investigate potential reasons to quit running among short distance runners (5 km and 10 km) (n = 898). Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS16). Binary logistic regressions were used to investigate the relation between reasons to quit running and different variables like socio-demographic variables, running habits and attitudes, interests, and opinions (AIOs) on running. Our results indicate that, not only people of different gender and age show significant differences in perceived reasons to quit running, also running habits, (e.g., running context and frequency) and AIOs are related to perceived reasons to quit running too. With insights into these related variables, potential drop-out reasons could help health professionals in understanding and lowering drop-out rates among recreational runners.
Abstract Background Previous studies have suggested that distinct and homogenous sub-groups of gait patterns exist among runners with patellofemoral pain (PFP), based on gait analysis. However, acquisition of 3D kinematic data using optical systems is time consuming and prone to marker placement errors. In contrast, axial segment acceleration data can represent an overall running pattern, being easy to acquire and not influenced by marker placement error. Therefore, the purpose of this study was to determine if pelvic acceleration patterns during running could be used to classify PFP patients into homogeneous sub-groups. A secondary purpose was to analyze lower limb kinematic data to investigate the practical implications of clustering these subjects based on 3D pelvic acceleration data. Methods A hierarchical cluster analysis was used to determine sub-groups of similar running profiles among 110 PFP subjects, separately for males (n = 44) and females (n = 66), using pelvic acceleration data (reduced with principal component analysis) during treadmill running acquired with optical motion capture system. In a secondary analysis, peak joint angles were compared between clusters (α = 0.05) to provide clinical context and deeper understanding of variables that separated clusters. Results The results reveal two distinct running gait sub-groups (C1 and C2) for female subjects and no sub-groups were identified for males. Two pelvic acceleration components were different between clusters (PC1 and PC5; p
Running-related injuries are common and are associated with a high rate of reoccurrence. Errors in applied training loads are often cited as a primary cause of running-related injuries. Clinicians and runners are beginning to utilize wearable technologies to quantify training loads with the hope of reducing the incidence of running-related injuries. Wearable devices can objectively assess training loads and biomechanics in runners, yet guidelines for their use by clinicians and runners are not currently available. This article outlines several applications for the use of wearable devices in the prevention and rehabilitation of running-related injuries. Applications for monitoring of training loads, running biomechanics, running epidemiology, return to running programs and gait retraining are discussed. Best-practices for choosing and use of wearables are described to provide guidelines for clinicians and runners. Finally, future applications are outlined for this rapidly developing field.
Individual and unorganized sports with a health-related focus, such as recreational running, have grown extensively in the last decade. Consistent with this development, there has been an exponential increase in the availability and use of electronic monitoring devices such as smartphone applications (apps) and sports watches. These electronic devices could provide support and monitoring for unorganized runners, who have no access to professional trainers and coaches. The purpose of this paper is to gain insight into the characteristics of event runners who use running-related apps and sports watches. This knowledge is useful from research, design, and marketing perspectives to adequately address unorganized runners’ needs, and to support them in healthy and sustainable running through personalized technology. Data used in this study are drawn from the standardized online Eindhoven Running Survey 2014 (ERS14). In total, 2,172 participants in the Half Marathon Eindhoven 2014 completed the questionnaire (a response rate of 40.0%). Binary logistic regressions were used to analyze the impact of socio-demographic variables, running-related variables, and psychographic characteristics on the use of running-related apps and sports watches. Next, consumer profiles were identified. The results indicate that the use of monitoring devices is affected by socio-demographics as well as sports-related and psychographic variables, and this relationship depends on the type of monitoring device. Therefore, distinctive consumer profiles have been developed to provide a tool for designers and manufacturers of electronic running-related devices to better target (unorganized) runners’ needs through personalized and differentiated approaches. Apps are more likely to be used by younger, less experienced and involved runners. Hence, apps have the potential to target this group of novice, less trained, and unorganized runners. In contrast, sports watches are more likely to be used by a different group of runners, older and more experienced runners with higher involvement. Although apps and sports watches may potentially promote and stimulate sports participation, these electronic devices do require a more differentiated approach to target specific needs of runners. Considerable efforts in terms of personalization and tailoring have to be made to develop the full potential of these electronic devices as drivers for healthy and sustainable sports participation.
The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle “big data”. Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V’s definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called “topological data analysis” and directions for future research are outlined and discussed.
Background
Today, runners use wearable technology such as global positioning system (GPS)–enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors.
Objective
The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially “smart” devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices.
MethodsA pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other.
ResultsA total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P
Wearable fitness trackers have gained a new level of popularity due to their ambient data gathering and analysis. This has signalled a trend toward self-efficacy and increased motivation among users of these devices. For consumers looking to improve their health, fitness trackers offer a way to more readily gain motivation via the personal data-based insights the devices offer. However, the user experience (UX) that accompanies wearables is critical to helping users interpret, understand, gain motivation and act on their data. Despite this, there is little evidence as to specific aspects of fitness tracker user engagement and long-term motivation. We report on a 4-week situated diary study and Healthcare Technology Self-efficacy (HTSE) questionnaire assessment of 34 users of two popular American fitness trackers: JawBone and FitBit. The study results illustrate design implications and requirements for fitness trackers and other self-efficacy mobile healthcare applications.
The modern-day athlete participating in elite sports is exposed to high training loads and increasingly saturated competition calendar. Emerging evidence indicates that inappropriate load management is a significant risk factor for acute illness and the overtraining syndrome. The IOC convened an expert group to review the scientific evidence for the relationship of load—including rapid changes in training and competition load, competition calendar congestion, psychological load and travel—and health outcomes in sport. This paper summarises the results linking load to risk of illness and overtraining in athletes, and provides athletes, coaches and support staff with practical guidelines for appropriate load management to reduce the risk of illness and overtraining in sport. These include guidelines for prescription of training and competition load, as well as for monitoring of training, competition and psychological load, athlete well-being and illness. In the process, urgent research priorities were identified.
Running has been perceived as an easy way of becoming physically active. Over time, amateur runners start to use technology to keep track of their training and be aware of their improvement. In that sense, this article explores runners’ experience with activity tracking technology. After giving a brief review of literature in user experience and running experience, this paper demonstrates the runners’ experience with tracking technology through an empirical study conducted with 30 runners. The paper illustrates the experience of runners with sports tracking technology over time, by discussing the importance of usefulness, interactivity, connectivity, and personalization of information within the experience.
Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18–25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorre-lation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p < .05), decreased the anteroposterior step regularity (p < .05), and increased the anteroposterior sample entropy (p < .05) of trunk accelerometry patterns. Our findings indicate that treadmill running-induced fatigue might reveal itself in a greater contribution of variability in horizontal plane trunk accelerations, with anteroposterior trunk accelerations that are less regular from step-to-step and are less predictable. It appears that trunk accelerometry parameters can be used to detect deviations in dynamic CoM motion induced by treadmill running fatigue, yet it is unknown how robust or generalizable these parameters are to outdoor running environments.
A high number of recreational runners sustain a running-related injury each year. To reduce injury risk, alterations in running form have been suggested. One simple strategy for running stride frequency or length has been commonly advocated.
To characterize how running mechanics change when stride frequency and length are manipulated.
In January 2012, a comprehensive search of PubMed, CINAHL Plus, SPORTDiscus, PEDro, and Cochrane was performed independently by 2 reviewers. A second search of the databases was repeated in June 2012 to ensure that no additional studies met the criteria after the initial search.
Inclusion criteria for studies were an independent variable including manipulation of stride frequency or length at a constant speed with outcome measures of running kinematics or kinetics.
Systematic review.
Level 3.
Two reviewers independently appraised each article using a modified version of the Quality Index, designed for assessing bias of nonrandomized studies.
Ten studies met the criteria for this review. There was consistent evidence that increased stride rate resulted in decreased center of mass vertical excursion, ground reaction force, shock attenuation, and energy absorbed at the hip, knee, and ankle joints. All but 1 study had a limited number of participants, with several methodological differences existing among studies (eg, overground and treadmill running, duration of test conditions). Although speed was held constant during testing, it was individually self-selected or fixed. Most studies used only male participants.
Despite procedural differences among studies, an increased stride rate (reduced stride length) appears to reduce the magnitude of several key biomechanical factors associated with running injuries.
To review articles utilising accelerometers and gyroscopes to measure running gait and assess various methodology utilised when doing so. To identify research- and coaching-orientated parameters which have been previously investigated and offer evidence based recommendations as to future methodology employed when investigating these parameters. Electronic databases were searched using key-related terminology such as accelerometer(s) and gyroscope(s) and/or running gait. Articles returned were then visually inspected and subjected to an inclusion and exclusion criteria after which citations were inspected for further relevance. A total of 38 articles were then included in the review. Accelerometers, gyroscopes plus combined units have been successfully utilised in the generation of research-orientated parameters such as head/tibial acceleration, vertical parameters and angular velocity and also coach-orientated parameters such as stride parameters and gait pattern. Placement of sensors closest to the area of interest along with the use of bi/tri- axial accelerometers appear to provide the most accurate results. Accelerometers and gyroscopes have proven to provide accurate and reliable results in running gait measurement. The temporal and spatial running parameters require sensor placement close to the area of interest and the use of bi/triaxial sensors. Post data analysis is critical for generating valid results.
Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".
The purpose of this study was to present a systematic overview of published reports on the incidence and associated potential risk factors of lower extremity running injuries in long distance runners. An electronic database search was conducted using the PubMed-Medline database. Two observers independently assessed the quality of the studies and a best evidence synthesis was used to summarise the results. The incidence of lower extremity running injuries ranged from 19.4% to 79.3%. The predominant site of these injuries was the knee. There was strong evidence that a long training distance per week in male runners and a history of previous injuries were risk factors for injuries, and that an increase in training distance per week was a protective factor for knee injuries.
The purpose of this study was to classify runners in sex-specific groups as either competitive or recreational based on center of mass (CoM) accelerations. Forty-one runners participated in the study (25 male and 16 female), and were labeled as competitive or recreational based on age, sex, and race performance. Three-dimensional acceleration data were collected during a 5-minute treadmill run, and 24 features were extracted. Support vector machine classification models were used to examine the utility of the features in discriminating between competitive and recreational runners within each sex-specific subgroup. Competitive and recreational runners could be classified with 82.63 % and 80.4 % in the male and female models, respectively. Dominant features in both models were related to regularity and variability, with competitive runners exhibiting more consistent running gait patterns, but the specific features were slightly different in each sex-specific model. Therefore, it is important to separate runners into sex-specific competitive and recreational subgroups for future running biomechanical studies. In conclusion, we have demonstrated the ability to analyze running biomechanics in competitive and recreational runners using only CoM acceleration patterns. A runner, clinician, or coach may use this information to monitor how running patterns change as a result of training.
Background:
The National Center for Injury Prevention and Control, noting flaws in previous running injury research, called for more rigorous prospective designs and comprehensive analyses to define the origin of running injuries.
Purpose:
To determine the risk factors that differentiate recreational runners who remain uninjured from those diagnosed with an overuse running injury during a 2-year observational period.
Study design:
Cohort study; Level of evidence, 2.
Methods:
Inclusion criteria were running a minimum of 5 miles per week and being injury free for at least the past 6 months. Data were collected at baseline on training, medical and injury histories, demographics, anthropometrics, strength, gait biomechanics, and psychosocial variables. Injuries occurring over the 2-year observation period were diagnosed by an orthopaedic surgeon on the basis of predetermined definitions.
Results:
Of the 300 runners who entered the study, 199 (66%) sustained at least 1 injury, including 73% of women and 62% of men. Of the injured runners, 111 (56%) sustained injuries more than once. In bivariate analyses, significant ( P ≤ .05) factors at baseline that predicted injury were as follows: Short Form Health Survey-12 mental component score (lower mental health-related quality of life), Positive and Negative Affect Scale negative affect score (more negative emotions), sex (higher percentage of women were injured), and knee stiffness (greater stiffness was associated with injury); subsequently, knee stiffness was the lone significant predictor of injury (odds ratio = 1.18) in a multivariable analysis. Flexibility, quadriceps angle, arch height, rearfoot motion, strength, footwear, and previous injury were not significant risk factors for injury.
Conclusion:
The results of this study indicate the following: (1) among recreational runners, women sustain injuries at a higher rate than men; (2) greater knee stiffness, more common in runners with higher body weights (≥80 kg), significantly increases the odds of sustaining an overuse running injury; and (3) contrary to several long-held beliefs, flexibility, arch height, quadriceps angle, rearfoot motion, lower extremity strength, weekly mileage, footwear, and previous injury are not significant etiologic factors across all overuse running injuries.
Background:
Quantitative gait analysis is essential for evaluating walking and running patterns for markers of pathology, injury, or other gait characteristics. It is expected that the portability, affordability, and applicability of wearable devices to many different populations will have contributed advancements in understanding the real-world gait patterns of walkers and runners. Therefore, the purpose of this systematic review was to identify how wearable devices are being used for gait analysis in out-of-lab settings.
Methods:
A systematic search was conducted in the following scientific databases: PubMed, Medline, CINAHL, EMBASE, and SportDiscus. Each of the included articles was assessed using a custom quality assessment. Information was extracted from each included article regarding the participants, protocol, sensor(s), and analysis.
Results:
A total of 61 articles were reviewed: 47 involved gait analysis during walking, 13 involved gait analysis during running, and one involved both walking and running. Most studies performed adequately on measures of reporting, and external and internal validity, but did not provide a sufficient description of power. Small, unobtrusive wearable devices have been used in retrospective studies, producing unique measures of gait quality. Walking, but not running, studies have begun to use wearable devices for gait analysis among large numbers of participants in their natural environment.
Conclusions:
Despite the advantages provided by the portability and accessibility of wearable devices, more studies monitoring gait over long periods of time, among large numbers of participants, and in natural walking and running environments are needed to analyze real-world gait patterns, and would facilitate prospective, subject-specific, and subgroup investigations. The development of wearables-specific metrics for gait analysis provide insights regarding the quality of gait that cannot be determined using traditional components of in-lab gait analyses. However, guidelines for the usability of wearable devices and the validity of wearables-based measurements of gait quality need to be established.
Running-related injuries (RRI) may result from accumulated microtrauma caused by combinations of high load magnitudes (vertical ground reaction forces; vGRFs) and numbers (strides). Yet relationships between vGRF and RRI remain unclear – potentially because previous research has largely been constrained to collecting vGRFs in laboratory settings and ignoring relationships between RRI and stride number. In this preliminary proof-of-concept study, we addressed these constraints: Over a 60-day period, each time collegiate athletes (n = 9) ran they wore a hip-mounted activity monitor that collected accelerations throughout the entire run. Accelerations were used to estimate peak vGRF, number of strides, and weighted cumulative loading (sum of peak vGRFs weighted to the 9th power) across the entirety of each run. Runners also reported their post-training pain/fatigue and any RRI that prevented training. Across 419 runs and > 2.1 million strides, injured (n = 3) and uninjured (n = 6) participants did not report significantly different pain/fatigue (p = 0.56) or mean number of strides per run (p = 0.91). Injured participants did, however, have significantly greater peak vGRFs (p = 0.01) and weighted cumulative loading per run (p < 0.01). Results from this small but extensively studied sample of elite runners demonstrate that loading profiles (load magnitude-number combinations) quantified with activity monitors can provide valuable information that may prove essential for: (1) testing hypotheses regarding overuse injury mechanisms, (2) developing injury-prediction models, and (3) designing and adjusting athlete- and loading-specific training programs and feedback.
Accelerometers have been used to classify running patterns, but classification accuracy and computational load depends on signal segmentation and feature extraction. Stride-based segmentation relies on identifying gait events, a step avoided by using window-based segmentation. For each segment, discrete points can be extracted from the accelerometer signal, or advanced features can be computed. Therefore, the purpose of this study was to examine how different segmentation and feature extraction methods influence the accuracy and computational load of classifying running conditions. Forty-four runners ran at their preferred speed and 25% faster than preferred while an accelerometer at the lower back recorded 3D accelerations. Computational load was determined as the accelerometer signal was segmented into single and five strides, and corresponding small and large windows, with discrete points extracted from the single stride segments and advanced features computed from all four segment types. Each feature set was used to classify speed conditions and classification accuracy was recorded. Computational load and classification accuracy were compared across all feature sets using a repeated-measures MANOVA, with follow-up t-tests to compare feature type (discrete vs. advanced), segmentation method (stride- vs. window-based), and segment size (small vs. large), using a Bonferroni-adjusted α = 0.003. The five-stride (97.49 (±4.57)%) and large-window advanced (97.23 (±5.51)%) feature sets produced the greatest classification accuracy, but the large-window advanced feature set had a lower computational load (0.0041 (±0.0002)s) than the stride-based feature sets. Therefore, using a few advanced features and large overlapping window sizes yields the best performance of both classification accuracy and computational load.
This annual survey of worldwide fitness trends is now in its 12th year. New this year is the inclusion of some member organizations of the Coalition for the Registration of Exercise Professionals (CREP). Participating organizations included the American College of Sports Medicine (ACSM), American Council on Exercise (ACE), National Council on Strength and Fitness (NCSF), and The Cooper Institute®.
Apply It: From this article, the reader should understand the following concepts: Tell the difference between a fad and a trend. Apply and use worldwide trends in the commercial, corporate, clinical (including medical fitness), and community health fitness industry. Read expert opinions about identified fitness trends for 2018.
Medial tibial stress syndrome (MTSS) is a common overuse running injury with pathomechanics likely to be exaggerated by fatigue. Wearable accelerometry provides a novel alternative to assess biomechanical parameters continuously while running in more ecologically valid settings. The purpose of this study was to determine the influence of outdoor running fatigue and MTSS on both dynamic loading and dynamic stability derived from trunk and tibial accelerometery. Runners with (n=14) and without (n=16) history of MTSS performed an outdoor fatigue run of 3200m. Accelerometer-based measures averaged per lap included dynamic loading of the trunk and tibia (i.e. axial peak positive acceleration, signal power magnitude, and shock attenuation) as well as dynamic trunk stability (i.e. tri-axial root mean square ratio, step and stride regularity, and sample entropy). Regression coefficients from generalised estimating equations were used to evaluate group by fatigue interactions. No evidence could be found for dynamic loading being higher with fatigue in runners with MTSS history (all measures p>0.05). One significant group by running fatigue interaction effect was detected for dynamic stability. Specifically, in MTSS only, decreases mediolateral sample entropy i.e. loss of complexity was associated with running fatigue (p<0.01). The current results indicate that entire acceleration waveform signals reflecting mediolateral trunk control is related to MTSS history, a compensation that went undetected in the non-fatigued running state. We suggest that a practical outdoor running fatigue protocol that concurrently captures trunk accelerometry-based movement complexity warrants further prospective investigation as an in-situ screening tool for MTSS individuals.
Learning Objectives From this article, the reader should understand the following concepts: • The difference between a fad and a trend. • Worldwide trends in the commercial, corporate, clinical (including medical fitness), and community health fitness industry. • Expert opinions about identified fitness trends for 2017.
Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.
Recent developments in wearable and wireless sensor technology allow for a continuous three dimensional analysis of running mechanics in the sport specific setting. The present study is the first to demonstrate the possibility of analyzing three dimensional (3D) running mechanics continuously, by means of inertial magnetic measurement units, to objectify changes in mechanics over the course of a marathon.
Three well trained male distance runners ran a marathon while equipped with inertial magnetic measurement units on trunk, pelvis, upper legs, lower legs and feet to obtain a 3D view of running mechanics and to asses changes in running mechanics over the course of a marathon. Data were continuously recorded during the entire 42.2 km (26.2 Miles) of the Marathon. Data from the individual sensors were transmitted wirelessly to a receiver, mounted on the handlebar of an accompanying cyclist. Anatomical calibration was performed using both static and dynamic procedures and sensor orientations were thus converted to body segment orientations by means of transformation matrices obtained from the segment calibration. Joint angle (hip, knee and ankle) trajectories as well as center of mass (COM) trajectory and acceleration were derived from the sensor data after segment calibration.
Data were collected and repeated measures one way ANOVA׳s, with Tukey post-hoc test, were used to statistically analyze differences between the defined kinematic parameters (max hip angle, peak knee flexion at mid-stance and at mid-swing, ankle angle at initial contact and COM vertical displacement and acceleration), averaged over 100 strides, between the first and the last stages (8 and 40 km) of the marathon. Significant changes in running mechanics were witnessed between the first and the last stage of the marathon.
This study showed the possibility of performing a 3D kinematic analysis of the running technique, in the sport specific setting, by using inertial magnetic measurement units. For the three runners analyzed, significant changes were observed in running mechanics over the course of a marathon. The present measurement technique therefore allows for more in-depth study of running mechanics outside the laboratory setting.
Originally, running was a sporting activity which was mainly, if not only, practised by competitive athletes in private track and field clubs or through extracurricular school and university programmes (Bale, 2004). Today, running has become an immensely popular pastime pursued in the public sphere by millions of recreational participants worldwide. Up to the 1960s, however, recreational jogging along the street, in a park or in a forest was considered a strange activity. In his analysis of public order, Goffman (1971) described patterned characters of everyday life and, among others, analysed pedestrian traffic systems. At that time, people huffing, puffing, hobbling, plodding and sweating while running in the streets was less evident than it is nowadays. Stokvis (2006) noted that in this context, leisure-time running was rather perceived as a disruption of social codes between pedestrians, and thus marring the existing public order. If people ran in public, this was mainly the case because they were in a hurry. Doing forms of physical exercise in public meant that one was ‘frivolous’, ‘idle’ or even ‘subversive’ (Florida, 2002; Paunonen, 2009). Running in public was seen as a waste of energy and, therefore, people practising leisure-time running activities risked being scoffed and jeered at (Van Bottenburg et al., 2010a). Thus, apart from the club- and school-organised version, recreational running used to be a rather unusual physical activity for the greater part of the twentieth century.
Purpose (1) To quantify current practice at the most elite level of professional club football in Europe with regard to injury prevention strategy; (2) to describe player adherence and coach compliance to the overall injury prevention programme.
Methods A structured online survey was administered to the Head medical officers of 34 elite European teams currently participating in the UEFA Elite Club Injury Study. The survey had 4 sections; (1) risk factors for injury, (2) assessment and monitoring of injury risk, (3) prevention strategies and (4) coach compliance and player adherence to the injury prevention process.
Results 33 (97%) Medical officers of the teams responded. The most important perceived injury risk factor was previous injury. Four of the top 6 risk factors—physical fitness, accumulated fatigue, reduced recovery time between matches and training load—were related to player workload. The top 3 preventative exercises were eccentric, balance/proprioception and core training. Regarding monitoring, the top 3 tools implemented were measurement of workload, subjective wellness and a general medical screen. The subjectively rated level of coach compliance in UEFA teams was perceived as ‘high’, while the player adherence varied from none at all to perfect.
Summary and conclusion Medical officers place importance on workload-related variables as risk factors for injury in elite European football players. A lack of consistently high player adherence may limit the effects of contemporary injury prevention programmes in elite European footballers.
From this article, the reader should understand the following concepts:
• the difference between a fad and a trend
• worldwide trends in the commercial, corporate, clinical (including
medical fitness), and community health fitness industry
• expert opinions about identified fitness trends for 2014
Electronic computers facilitate greatly carrying out factor analysis. Computers will help in solving the communality problem and the question of the number of factors as well as the question of arbitrary factoring and the problem of rotation. "Cloacal short-cuts will not be necessary and the powerful methods of Guttman will be feasible." A library of programs essential for factor analysis is described, and the use of medium sized computers as the IBM 650 deprecated for factor analysis. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This paper analyses the sport expenditures of people who are members of non-profit sports clubs (NÂ =Â 10,013) in Germany. Adult members, active in 21 sports, were asked about their sport expenditure relating to several defined categories. The results show that members spend an average of [euro]1610 per year on their chosen sport. Sport specific analyses reveal big differences in expenditure between sports, ranging from badminton ([euro]338) to equestrian ([euro]7902). According to sport-specific regression analyses, personal income, level of performance, and weekly time of participation are the main predictors of sport-specific expenditures. Compared to other studies, these results show that the financial status of members of non-profit sports clubs is very strong.
To determine, by longitudinal study, whether regular vigorous running activity is associated with accelerated, unchanged, or postponed development of disability with increasing age.
8-year prospective, longitudinal study with yearly assessments.
451 members of a runners' club and 330 community controls who were initially 50 to 72 years old (also characterized as "ever-runners" [n = 534] and "never-runners" [n = 247], respectively).
The dependent variable was disability as assessed by the Health Assessment Questionnaire and separately validated in these participant cohorts. Covariates included age, sex, body mass index, comorbid conditions, education level, smoking history, alcohol intake, mean blood pressure, initial disability level, family history of arthritis, and radiologic evidence of osteoarthritis of the knee in a subsample.
Initially, the runners were leaner, reported joint symptoms less frequently, took fewer medications, had fewer medical problems, and had fewer instances of and less severe disability, suggesting either that the average previous 12 years of running had improved health or that self-selection bias was present. After 8 years of longitudinal study, the differences in initial disability levels (0.026 compared with 0.079; P < 0.001) had steadily increased to 0.071 for runners compared with 0.242 for controls (P < 0.001). The difference was consistent for men and women. The rate of development of disability was several times lower in the runners' club members than in community controls; this difference persisted after adjusting for age, sex, body mass, baseline disability, smoking history, history of arthritis, or other comorbid conditions (slopes of progression of disability for the years 1984 to 1992, after adjusting for covariates: men in the runners' club, 0.004 [SE, 0.002]; community controls, 0.012 [SE, 0.002]; women in the runners' club, 0.009 [SE, 0.005]; community controls, 0.027 [SE, 0.004]; P < 0.002 for both sets of comparisons). In addition to differences in disability, there were significant differences in mortality between the runners' club members (1.49%) and community controls (7.09%) (P < 0.001). These differences remained significant after adjusting for age, sex, body mass, comorbid conditions, education level, smoking history, alcohol intake, and mean blood pressure (P < 0.002, conditional risk ratio for community controls compared with the runners, 4.27; 95% CI, 1.78 to 10.26).
Older persons who engage in vigorous running and other aerobic activities have lower mortality and slower development of disability than do members of the general population. This association is probably related to increased aerobic activity, strength, fitness, and increased organ reserve rather than to an effect of postponed osteoarthritis development.
Forces that are repeatedly applied to the body could lead to positive remodeling of a structure if the forces fall below the tensile limit of the structure and if sufficient time is provided between force applications. On the other hand, an overuse injury could result if there is inadequate rest time between applied forces. Running is one of the most widespread activities during which overuse injuries of the lower extremity occur. The purpose of this article is to review the current state of knowledge related to overuse running injuries, with a particular emphasis on the effect of impact forces. Recent research has suggested that runners who exhibit relatively large and rapid impact forces while running are at an increased risk of developing an overuse injury of the lower extremity. Modifications in training programs could help an injured runner return to running with decreased rehabilitation time, but it would be preferable to be able to advise a runner regarding injury potential before undertaking a running program. One of the goals of future research should be to focus on the prevention or early intervention of running injuries. This goal could be accomplished if some easily administered tests could be found which would predict the level of risk that a runner may encounter at various levels of training intensity, duration, and frequency. The development of such a screening process may assist medical practitioners in identifying runners who are at a high risk of overuse injury.
MaRS market insights wearable tech : Leveraging canadian innovation to improve health
- H Salah
- E Macintosh
- N Rajakulendran
Salah, H., MacIntosh, E., & Rajakulendran, N. (2014). MaRS market
insights wearable tech : Leveraging canadian innovation to improve
health. Retrieved from www.marsdd.com/news-insights/marsreports/
Influence of outdoor running fatigue and medial tibial stress syndrome on accelerometer-based loading and stability. Gait and Posture
- K H Schütte
- S Seerden
- R Venter
- B Vanwanseele
Schütte, K. H., Seerden, S., Venter, R., & Vanwanseele, B. (2018).
Influence of outdoor running fatigue and medial tibial stress syndrome on accelerometer-based loading and stability. Gait and
Posture, 59, 222-228. doi:10.1016/j.gaitpost.2017.10.021
How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury
- T Soligard
- M Schwellnus
- J.-M Alonso
- R Bahr
- B Clarsen
- H P Dijkstra
- L Engebretsen
Soligard, T., Schwellnus, M., Alonso, J.-M., Bahr, R., Clarsen, B.,
Dijkstra, H. P., … Engebretsen, L. (2016). How much is too much?
(Part 1) International Olympic Committee consensus statement on
load in sport and risk of injury. British Journal of Sports Medicine, 50
(17), 1030-1041. doi:10.1136/bjsports-2016-096572
Incidence and determinants of lower extremity running injuries in long distance runners: A systematic review
- B R Van Gent
- D D Siem
- M Van Middelkoop
- T A Van Os
- S S M A Bierma-Zeinstra
- B B W Koes
van Gent, B. R., Siem, D. D., van Middelkoop, M., van Os, T. A., Bierma-Zeinstra, S. S. M. A., & Koes, B. B. W. (2007). Incidence and determinants of lower extremity running injuries in long distance runners:
A systematic review. British Journal of Sports Medicine, 41, 469-480.
doi:10.1136/bjsm.2006.033548