Physical environmental factors related to walking and cycling in older adults: the Belgian aging studies

<p>Abstract</p> <p>Background</p> <p>Socio-ecological models emphasize the relationship between the physical environment and physical activity (PA). However, knowledge about this relationship in older adults is limited. Therefore, the present study aims to investigate the relationship between area of residence (urban, semi-urban or rural) and older adults' walking and cycling for transportation and recreation. Additionally, relationships between several physical environmental factors and walking and cycling and possible moderating effects of area of residence, age and gender were studied.</p> <p>Methods</p> <p>Data from 48,879 Flemish older adults collected in 2004-2010 through peer research were analyzed. Walking, cycling and environmental perceptions were assessed using self-administered questionnaires. The Study Service of the Flemish Government provided objective data on municipal characteristics. Multilevel logistic regression analyses were applied.</p> <p>Results</p> <p>Urban participants were more likely to walk daily for transportation compared to rural (OR = 1.43; 95% CI = 1.22, 1.67) and semi-urban participants (OR = 1.32; 95% CI = 1.13, 1.54). Urban participants were less likely to cycle daily for transportation compared to semi-urban participants (OR = 0.72; 95% CI = 0.56, 0.92). Area of residence was unrelated to weekly recreational walking/cycling. Perceived short distances to services (ORs ranging from 1.04 to 1.19) and satisfaction with public transport (ORs ranging from 1.07 to 1.13) were significantly positively related to all walking/cycling behaviors. Feelings of unsafety was negatively related to walking for transportation (OR = 0.93, 95% CI = 0.91, 0.95) and recreational walking/cycling (OR = 0.95, 95% CI = 0.92, 0.97). In females, it was also negatively related to cycling for transportation (OR = 0.94, 95% CI = 0.90, 0.98).</p> <p>Conclusions</p> <p>Urban residents were more likely to walk for transportation daily compared to semi-urban and rural residents. Daily cycling for transportation was less prevalent among urban compared to semi-urban residents. Access to destinations appeared to be important for promoting both walking and cycling for transportation and recreation across all demographic subgroups. Additionaly, feelings of unsafety were associated with lower rates of walking for transportation and walking/cycling for recreation in all subgroups and cycling for transportation in females. No clear patterns emerged for other environmental factors.</p

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.status: publishe

Tables

This section contains all tables of the chapter as xlsx-files (including the tables of the appendix). The code used to create these tables can be found at https://lucid-bardeen-01fda1.netlify.app/

Figures

This section contains all the figures (except Figure 4.1) of the chapter. Code that was used in creating the figures can be found at https://lucid-bardeen-01fda1.netlify.app/

Patient-level effectiveness prediction modeling for glioblastoma using classification trees

Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide.This work was supported by the Vlerick Business School Academic Research Fund. The funding agreement ensured the authors' independence in designing the study, interpreting the data, and publishing the report