Countries all over the world implemented lockdowns to counteract COVID-19. These lockdowns heavily limited people’s exercise possibilities. At the same time, experts advocated to remain physically active to prevent future health problems. Based on an online survey, this study examines adults’ exercise levels and patterns during the COVID-19 lockdown in Belgium. Ordinal logistic regression analyses of 13,515 valid and population-weighted responses indicate a general increase in exercise frequencies, as well as in sedentary behavior. Except for people aged 55+, previously low active adults self-reported to exercise more during the lockdown. Among the people who were already high active before COVID-19, those above 55 years old, those with low education, those used to exercise with friends or in a sport club, and those who were not using online tools to exercise, self-reported to exercise less during the lockdown. Having less time, sitting more, and missing the familiar way and competitive element of exercising were the main reasons for a self-reported exercise reduction. Given the health risks associated with physical inactivity, results imply that governments should consider how those who were not reached can be encouraged to exercise during a lockdown. After all, additional COVID-19 lockdowns might be implemented in the future.

This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types—in terms of their attitudes, interests, and opinions (AIOs) with regard to running—and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed the questionnaire. Principal component analysis and cluster analysis were used to identify the different running types, and crosstabs obtained insights into the use of technology between different typologies. Based on the AIOs, four distinct runner types were identified: casual individual, social competitive, individual competitive, and devoted runners. Subsequently, we related the types to their use of sports watches and apps. Our results show a difference in the kinds of technology used by different runner types. Differentiation between types of runners can be useful for health professionals, policymakers involved in public health, engineers, and trainers or coaches to adapt their services to specific segments, in order to make use of the full potential of running-related systems to support runners to stay active and injury-free and contribute to a healthy lifestyle.

The United Nations (UN) considers sports as an important enabler of sustainable development. The popular and fast-growing Participatory Sports Event (PSE) sector can play an important role in this regard, however, research that measures and reports sustainability in PSEs is scarce. Therefore, the aim of this paper was to construct and validate a research instrument based on the UN’s sustainable development goals, and to examine sustainability in PSEs. To this end, an online survey was administered among a representative sample of 303 PSE organisers, located in Flanders, Belgium. A confirmatory factor analysis affirmed the social, economic and environmental dimensions of the instrument and provided evidence for its validity and reliability. The results reveal significant discrepancies between the three dimensions, with a noticeable lower score for environmental sustainability compared to social and economic sustainability. Furthermore, challenges are highlighted in the field of the civil society sector and in walking sports events. The findings also indicate that large-sized events are more likely to be sustainable. The current study can act as a foundation for future research on sustainability in PSEs and can assist PSE organisers and policymakers to increase the sustainability-related performance of the sector.

Based on the multi-dimensional framework of Damanpour and Schneider [2006. “Phases of the Adoption of Innovation in Organizations: Effects of Environment, Organization and Top Managers 1.” British Journal of Management 17 (3): 215–236], the purpose of this study is to identify the managerial, organizational and environmental determinants of social innovation, depicted in terms of the amount of special initiatives initiated for specific target groups. while considering the initiative's type (i.e. administrative versus technical). A lognormal double hurdle model, on a sample of approximately 5000 voluntary sports clubs (VSCs) from 9 different European countries, indicates significant effects for each level. In particular, positive managerial attitudes and public support through both financial and infrastructural resources are important predictors for a VSC to adopt special initiatives towards a target group. Furthermore, differences between determinants of administrative and technical initiatives are found. These results call for measures in terms of resource support from sports policy-makers at European, national and regional levels, along with the need for VSCs to meet the demands from neglected target groups.

The impact of the COVID-19 crisis and its related measures on how people practice and experience leisure continues to be significant. In the current study, a survey measuring sport participation during the third week of the first lockdown, that started on the 18^th of March 2020 and has been gradually loosened as of the 18^th of April 2020, was carried out among 13,515 Flemish citizens. Through a canonical correlation analysis, four clusters of COVID-19-sports-participants are distinguished, i.e. people who experience restrictions when it comes to (i) time and sports infrastructure, (ii) closed sports clubs and cancelled event activities, (iii) time and fear/sickness, and (iv) those who experience no impact. Logistic regression results indicate that participants with online sports experience have a (more) positive effect on their sports participation behaviour, while less missing and having to adapt their previous sports behaviour. The opposite is found for sports club members, while former sports event participation has a positive effect on sports frequency, but a negative effect on their motivation. The results of both analyses give insight into the role that different policy instruments (i.e. sports clubs, events, type of sports) play in keeping different clusters of sports participants motivated to stay sports-active.

Purpose: First, the income elasticities are calculated for different levels of income, for both the decision to spend money on sports and the amount of money that is spent. Second, the study researches whether different operationalisations of income (i.e. family versus personal) result in different elasticity values. Third, the effect of sports-specific and non-sports leisure variables on sports participation is investigated.Design/methodology/approach: A representative dataset of 3,775 adults is used containing a wide variety of leisure characteristics, gathered by means of a face-to-face survey. By means of a Tobit regression model both the determining factors of sports expenditure and the income elasticities are analysed.Findings: For lower income individuals, a rise in income has a relatively bigger influence on the probability to spend money on sports participation, than is the case for higher income individuals. A positive relationship is found with sex (male), education, number of minutes and disciplines of sports and membership of a socio-cultural organisation, while age, watching TV and attending cultural events have a negative effect.Social implications: The study provides evidence that income-based segmentation of sports participants could turn out to be an efficient policy tool. By lowering the monetary-burden for lower incomes, it can be expected that participation rates can be raised efficiently.Originality/value: For the first time the relationship between income and expenditure is explored for different levels of income and for two operationalisations of income. Moreover, the inclusion of non-sports leisure variables allows investigating relationships between sports consumption and other leisure activities.