The current article reports on the second large-scale prevalence study on transgressive behavior in sport in the Netherlands, and is a follow up of an earlier, comparable prevalence study in 2015. Using a dedicated and customized online questionnaire, approximately 4000 adults who met the inclusion criteria (18 to 50 years old and have played sports in an organized context during childhood in the Netherlands) were surveyed with respect to their experiences of childhood psychological, physical, and sexual transgressive behavior while playing sports. The survey showed that 71.7% experienced some form of transgressive behavior as a child, in which 48.6% of these events also made an impact (in other words, was significant at the time it took place). The degree of impact the event made is also related to the severity of the event. Severe emotional transgression events occurred in 22% of the youth athletes, severe physical assault events in 12.7%, and severe sexual assault events occurred in 6.9% of the youth athletes. Disabled athletes, and those competing at national and international levels, report more experiences of transgressive behavior in sport. The results are consistent with former research and indicate the need for structural attention to create a safe sports climate.

The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predictive ability on new data. This prevents us from systematically comparing studies, hindering the evaluation and accumulation of knowledge. In this paper, we present two datasets which can be used to study the predictability of fertility outcomes in the Netherlands. One dataset is based on the LISS panel, a longitudinal survey which includes thousands of variables on a wide range of topics, including individual preferences and values. The other is based on the Dutch register data which lacks attitudinal data but includes detailed information about the life courses of millions of Dutch residents. We provide information about the datasets and the samples, and describe the fertility outcome of interest. We also introduce the fertility prediction data challenge PreFer which is based on these datasets and will start in Spring 2024. We outline the ways in which measuring the predictability of fertility outcomes using these datasets and combining their strengths in the data challenge can advance our understanding of fertility behaviour and computational social science. We further provide details for participants on how to take part in the data challenge.

Background: Accurate measurement of physical activity (PA) is key to identifying determinants of health and developing appropriate interventions. Self-reports of PA (eg, in surveys or diary studies) often suffer from measurement error. Providing study participants with wearable devices that passively track PA reduces reactivity and recall error but participants’ noncompliance and high device costs are problematic. Many older adults now have smartphones that track PA. Based on legal requirements, data controllers (eg, health apps) must provide users with access to their data, and individuals can request and donate these data for research. This user-centric approach provides researchers with access to individual-level data, and it gives users control over what data are shared. Objective: We conduct a first test of the data donation approach for PA data among older adults. We study (1) how willing and successful older adults are to donate their PA data from different smartphone apps, (2) what drives donation of PA data at the different stages of participation, and (3) what biases arise from selective data donation. Methods: To answer our research questions, we use cross-sectional observational data from a probability-based online panel of the Dutch general population. A total of 2086 members of the Longitudinal Internet Studies for the Social Sciences panel aged 50 years and older completed a web survey in 2024. All iPhone and Android smartphone owners were asked to download passively collected PA data from their devices (Apple Health, Google Location History, or Samsung Health) and donate them via the Port platform. Results: Out of the 2086 survey participants, 1889 (91%) reported owning an iPhone or Android phone compatible for data donation, 606 (29%) reported willingness to donate PA data, 354 (17%) started the data donation, and 256 (12%) successfully provided a data package. Gender, age, educational attainment, monthly personal net income, smartphone usage behavior, privacy- and trust-related attitudes, and type of health app from which the data were requested correlated with behavior at the different stages of study participation. Self-reported reasons for nonwillingness to donate related mainly to expected technical issues, privacy concerns, and perceived usefulness. Compared with the entire sample, data donors reported better health, fewer health-related limitations, fewer difficulties performing tasks, and more PA. Conclusions: Our study shows that data donation from smartphones as part of a probability-based web survey of older adults is a feasible alternative for the measurement of PA, especially for iPhone owners younger than 70 years. Limitations relate to nonparticipation which correlates strongly with characteristics of smartphone ownership and comfort with device use. Substantive bias in health and PA outcomes persists for those who donated in comparison with all survey respondents.

Despite the increasing popularity and importance of mobile instant messenger services (MIMSs) such as WhatsApp, Telegram and Signal, the vast majority of the research on social media is focused on the “traditional” social media platforms such as Twitter and Facebook, mostly due to the difficulty of accessing MIMSs data. Consequently, we lack knowledge about even the most basic topological features of the societal-scale instant messenger network. To overcome this knowledge gap, we employ the innovative approach of data donation by respondents of a high-quality probability-based panel in the Netherlands (the LISS panel) to collect user data while preserving their privacy. Focusing on WhatsApp as the most popular MIMS, this study collects the first measurement of MIMS usage on a national probability sample. We find that the degree distribution of contacts is best approximated by a log-normal distribution, while the distribution of group membership is best approximated by the exponential distribution. At the individual level, we find that predictors of degree derived from the literature on extended networks mostly replicate.

Background : Recent advancements in technology have led to the integration of devices into physical activity research. A key challenge in using devices in population-based surveillance is ensuring representativeness. This study investigates factors associated with the willingness to wear a thigh-worn accelerometer and with personal activity tracker ownership. Insights gained will enhance understanding of the representativeness of device-based surveillance methods. Methods : Around 3,800 participants in a Dutch probability-based online panel were asked to fill out questionnaires about physical activity and sedentary behavior at two time points with 9 months in between. In addition, they were asked about health-related variables. Sociodemographics of the panel members were known. Multiple logistic regression analyses were used to examine factors associated with willingness to wear an activPAL and with ownership of a personal activity tracker. Results : Approximately 2,700 participants completed both questionnaires, approximately half were willing to wear an accelerometer. Willingness was higher among women, individuals aged 35–44, those with higher education, personal activity tracker owners, and those meeting PA guidelines. In contrast, first-generation western migrants and individuals with chronic diseases were less willing. Approximately 20% of participants owned a personal activity tracker, with ownership associated with being younger, more highly educated, healthier, meeting PA guidelines more often, and having overweight or obesity. Conclusion : Device-based PA surveillance holds promise, but differences in who is willing to wear accelerometers or who owns personal activity trackers can lead to biased estimates. Ensuring representativeness is essential for accurate public health monitoring.