Background: Health behaviors are of great importance for public health. Previous research shows that health behaviors are clustered and do not occur by chance. The main objective of this study was to investigate and describe the clustering of alcohol consumption, nutrition, physical activity and smoking while also considering the influence of sex, age and education. Methods: Using data from the population-based KORA S4/F4 cohort study, latent class regression analysis was undertaken to identify different clusters of health behavior patterns. The clusters were described according to demographics. Furthermore, the clusters were described regarding health-related quality of life at baseline and at a 7 year follow-up. Results: Based on a sample of 4,238 participants, three distinct classes were identified. One overall healthy class and two heterogeneous classes. Classes varied especially according to sex, indicating a healthier behavior pattern for females. No clear association between healthier classes and age, education or physical and mental health-related quality of life was found. Discussion: This study strengthens the literature on the clustering of health behaviors and additionally describes the identified clusters in association with health-related quality of life. More research on associations between clustering of health behaviors and important clinical outcomes is needed.

Longitudinal evidence on the association between physical activity (PA) or weight and health-related quality of life (HRQL) is sparse and studies describe inconclusive results. The aim of this study was to examine longitudinal associations between change in PA and HRQL as between change in weight and HRQL respectively. Analyses are based on data from the KORA S4 cohort study (1999–2001; n = 4,261, mean age 49.0 ± 13.3 years) and the two follow-up examinations (F4: 2006–2008; FF4: 2013–2014). Information on PA was collected in standardized interviews. Weight was measured objectively. Mental and physical components of HRQL were assessed via the SF-12 questionnaire. First, change in HRQL was regressed on change in PA and weight. Second, hierarchical linear models were fitted, which allowed estimation of between-subject and within-subject effects. Analyses were adjusted for the covariates sex, baseline diseases, and education. A change to a physically more active lifestyle is positively associated with physical and mental HRQL. Although weight gain is associated with impairments in physical HRQL, the data show an inverse relationship between weight gain and mental HRQL. The results were consistent for both the change score analyses and the hierarchical linear models. Our findings stress the importance of interventions on PA/weight. Nonetheless, more research is needed to reveal the causal relationship between PA/weight and HRQL.

Background: Chronic diseases like diabetes mellitus or hypertension are a major public health challenge. Irregular physical activity (PA) is one of the most important modifiable risk factors for chronic conditions and their complications. However, engaging in regular PA is a challenge for many individuals. The literature suggests that a diagnosis of a disease might serve as a promising point in time to change health behavior. This study investigates whether a diagnosis of diabetes or hypertension is associated with changes in PA. Methods: Analyses are based on 4261 participants of the population-based KORA S4 study (1999–2001) and its subsequent 7-and 14-year follow-ups. Information on PA and incident diagnoses of diabetes or hypertension was assessed via standardized interviews. Change in PA was regressed upon diagnosis with diabetes or hypertension, using logistic regression models. Models were stratified into active and inactive individuals at baseline to avoid ceiling and floor effects or regression to the mean. Results: Active participants at baseline showed higher odds (OR = 2.16 [1.20;3.89]) for becoming inactive after a diabetes diagnosis than those without a diabetes diagnosis. No other significant association was observed. Discussion: As PA is important for the management of diabetes or hypertension, ways to increase or maintain PA levels in newly-diagnosed patients are important. Communication strategies might be crucial, and practitioners and health insurance companies could play a key role in raising awareness.

From 2025, Health Technology Developers (HTDs) have to submit EU HTA dossiers. The joint clinical assessment (JCA) aims to streamline HTA processes and access to medicinal products across Europe. Currently, German HTA bodies IQWiG and G-BA actively shape the JCA methodology. Here we examine if German HTA dossier requirements are suitable for the JCA. We compare the number of safety endpoint and subgroup analyses in German dossiers with analyses considered in IQWIG’s benefit assessment and evaluate if these analyses were considered by the G-BA. We further investigated how the number of analyses was affected by the latest change in the German dossier template. With the current template, HTDs report in median 2.6 times more analyses on adverse events (AE) and 1.1 times more subgroup categories than in the previous template. IQWiG does not consider 33% of AE analyses and 73% of the subgroup categories presented by the HTD under the current template. G-BA considered the same AE as IQWiG in 76% of cases. Subgroups were uncommented by G-BA in most cases, independent of the template (previous: 93%, current 85%) and unconsidered in the conclusion on additional benefit (previous: 77%, current 69%). Thus, changes in the dossier template drastically increased HTD workload, but additional analyses seem unconsidered by the HTA bodies. With a broader scope in JCA, this effect could be amplified. To mitigate duplicative efforts and ensure prompt availability of medicinal products as envisioned by the HTAR, we suggest well-chosen and precise dossier requirements, early consultations, and early HTD engagement.