@article{HurrelmannReinhardtNoacketal., author = {Hurrelmann, Klaus and Reinhardt, C.H. and Noack, M. J. and Wassmer, G. and Klein, K.}, title = {A strategy for encouraging young adult's adoption of a preferred oral hygiene technique}, series = {Oral Health and Preventive Dentistry}, volume = {8}, journal = {Oral Health and Preventive Dentistry}, number = {1}, publisher = {Quintessenz Publ.}, address = {Berlin}, issn = {1602-1622}, doi = {10.3290/j.ohpd.a18805}, pages = {3 -- 8}, abstract = {PURPOSE: Adherence to dental preventive programmes in young adults is low. The aim of the present longitudinal study was to evaluate whether tutoring peers can be a compliance-enhancing tool or not. METHODS: In Part 1, two randomly selected classes (49 female students, mean age 19.8 + or - 2.3 years) were taught adult toothbrushing technique (the modified Bass technique) in a project-like manner. After the course, knowledge was tested using a class test, and compliance was evaluated using anonymous quantitative questionnaires. Compliance was defined as a reported degree of change from the easy-to-learn childhood toothbrushing techniques to the more efficient and challenging Bass technique. In Part 2 of the present longitudinal study, the compliance of these students was re-evaluated after having developed and applied themselves a programme of how to tutor peers in oral health. Re-evaluation of compliance was performed after 3 and 9 months. RESULTS: In Part 1, 28.5\% of the students were compliant after 1 week. Compared with Part 1, the compliance in Part 2 was significantly higher (P u 0.001), both after 3 months (90\%) and after 9 months (82\%). CONCLUSIONS: Tutoring peers can significantly enhance the compliance over a period of 9 months. Tutoring can function as a form of empowerment and can establish a strong sustained health engagement. Tutoring peers in health-related subjects can readily be implemented in schools and might be an additional means of oral health promotion with fewer additional costs.}, language = {en} } @techreport{KaiserKleinKaack, type = {Working Paper}, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn}, title = {Predicting cycling traffic in cities: Is bikesharing data representative of the cycling volume?}, doi = {10.48462/opus4-4942}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:b1570-opus4-49429}, pages = {5}, abstract = {A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations.}, language = {de} } @article{KaiserKleinKaack, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn H.}, title = {From counting stations to city-wide estimates: data-driven bicycle volume extrapolation}, series = {Environmental Data Science}, volume = {4}, journal = {Environmental Data Science}, publisher = {Cambridge University Press (CUP)}, issn = {2634-4602}, doi = {10.1017/eds.2025.5}, abstract = {Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Access to street-level data on bicycle traffic would assist cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate street-level bicycle counts for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, holiday data, and centrality measures. Our analysis reveals that crowdsourced cycling flow data from Strava in the area around the point of interest are most important for the prediction. To provide guidance for future data collection, we analyze how including short-term counts at predicted locations enhances model performance. By incorporating just 10 days of sample counts for each predicted location, we are able to almost halve the error and greatly reduce the variability in performance among predicted locations.}, language = {en} } @techreport{KaiserKleinKaack, type = {Working Paper}, author = {Kaiser, Silke K. and Klein, Nadja and Kaack, Lynn}, title = {From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation}, doi = {10.48550/arXiv.2406.18454}, pages = {30}, abstract = {Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling and provide civil society with evidence to advocate for cyclists' needs. Yet, the data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin. We predict daily and average annual daily street-level bicycle volumes using machine-learning techniques and various public data sources. These include app-based crowdsourced data, infrastructure, bike-sharing, motorized traffic, socioeconomic indicators, weather, and holiday data. Our analysis reveals that the best-performing model is XGBoost, and crowdsourced cycling and infrastructure data are most important for the prediction. We further simulate how collecting short-term counts at predicted locations improves performance. By providing ten days of such sample counts for each predicted location to the model, we are able to halve the error and greatly reduce the variability in performance among predicted locations.}, language = {en} }