Search results

Injury incidence and risk factors

Objectives To report (1) the injury incidence in recreational runners in preparation for a 8-km or 16-km running event and (2) which factors were associated withan increased injury risk. Methods Prospective cohort study in Amsterdam, the Netherlands. Participants (n=5327) received a baseline survey to determine event distance (8 km or 16 km), main sport, running experience, previous injuries, recent overuse injuries and personal characteristics. Three days after the race, they received a follow-up survey to determine duration of training period, running distance per week, training hours, injuries during preparation and use oftechnology. Univariate and multivariate regression models were applied to examine potential risk factors for injuries. Results 1304 (24.5%) participants completed both surveys. After excluding participants with current health problems, no signed informed consent, missing or incorrect data, we included 706 (13.3%) participants. In total, 142 participants (20.1%) reported an injury during preparation for the event. Univariate analyses (OR: 1.7, 95% CI 1.1 to 2.4) and multivariate analyses (OR: 1.7, 95% CI 1.1 to 2.5) showed that injury history was a significant risk factor for running injuries (Nagelkerke R-square=0.06). Conclusion An injury incidence for recreational runners in preparation for a running event was 20%. A previous injury was the only significant risk factor for runningrelated injuries.

App preferences and experiences of recreational runners, cyclists and walkers

From problem to solution

The aim of this paper is to design and test a smartphone application which supports personalized running experiences for less experienced runners. As a result of a multidisciplinary three-step design approach Inspirun was developed. Inspirun is a personalized running-application for Android smartphones that aims to fill the gap between running on your own (static) schedule, and having a personal trainer that accommodates the schedule to your needs and profile. With the use of GPS and Bluetooth heart rate monitor support, a user's progress gets tracked. The application adjusts the training schedule after each training session, motivating the runner without a real life coach. Results from three user studies are promising; participants were very satisfied with the personalized approach, both in the profiling and de adaptation of their training scheme.

Runner’s perceptions of reasons to quit running

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

How do runners experience personalization of their training scheme

Among runners, there is a high drop-out rate due to injuries and loss of motivation. These runners often lack personalized guidance and support. While there is much potential for sports apps to act as (e-)coaches to help these runners to avoid injuries, set goals, and maintain good intentions, most available running apps primarily focus on persuasive design features like monitoring, they offer few or no features that support personalized guidance (e.g., personalized training schemes). Therefore, we give a detailed description of the working mechanism of Inspirun e-Coach app and on how this app uses a personalized coaching approach with automatic adaptation of training schemes based on biofeedback and GPS-data. We also share insights into how end-users experience this working mechanism. The primary conclusion of this study is that the working mechanism (if provided with accurate data) automatically adapts training sessions to the runners’ physical workload and stimulates runners’ goal perception, motivation, and experienced personalization. With this mechanism, we attempted to make optimal use of the potential of wearable technology to support the large group of novice or less experienced runners and that by providing insight in our working mechanisms, it can be applied in other technologies, wearables, and types of sports.

Who uses running apps and sports watches?

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.