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.
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.
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.
