Strategies to Minimize Selection Bias in Digital Population-Based Studies in Sport and Health Sciences: Methodological and Empirical Insights from the COMO Study; [Strategien zur Minimierung von Selektionsverzerrungen in digitalen bevölkerungsbasiert

Objectives: Population-based surveys increasingly face challenges of declining response rates and selective participation, particularly since the COVID-19 pandemic. This article presents the COMO study’s design and sampling approach and discusses strategies to minimize selection bias in digital health surveys, focusing on participation enhancement, response rates, and sample composition from the first survey wave of the COMO Study. Methods: The COMO study is a nationwide, prospective panel survey with three annual online waves (2023–2025). In the first wave (COMO1), a probability-based sample of 35,157 families with children aged 4–17 was recruited using a two-stage register-based sampling procedure. Participation was encouraged via a multimodal communication strategy including postal invitations, email reminders, non-monetary incentives, and social media outreach. Weighting procedures were developed based on design weights and calibration to national microcensus benchmarks. Results: A total of 6,097 families submitted at least one complete questionnaire, corresponding to a 17.3% overall participation. The final analytical sample with full parent and child data comprised 5,240 families (15.0%). Response behavior varied by age, gender, and parental education as one proxy of socio economic status (SES), with adolescents, boys, and lower-educational households underrepresented. Reminder strategies boosted participation, particularly in early stages. A weighting procedures was developed to corrected key demographic imbalances. Qualitative analyses of 50 inquiries revealed technical, communicative, and emotional barriers, including privacy concerns and pandemic-related distress. Conclusions: The COMO study demonstrates that targeted reminders, inclusive materials, and post-survey weighting can reduce—but not fully eliminate—selection bias. Findings underscore the need for participatory, equity-oriented , incentives designs to improve representativeness in health research, especially in underserved populations.