In field studies, measurements are often collected over extended periods, during which subtle shifts in data quality or instrument performance can occur. Recognizing and quantifying such measurement heterogeneities over time is essential to ensure the validity of study results and to intervene at an early stage if possible. However, the performance of available statistical approaches for...
Longitudinal observational studies and clinical trials routinely collect extensive phenotypic data under changing organisational, technical, and environmental conditions. Variations in examiners, devices, protocols, or ambient factors can introduce consequential forms of measurement heterogeneity and measurement error over time. Although these sources of bias are well recognised, systematic...
Background: Tattoos and permanent make-up (PMU) gain increasing popularity, yet their potential systemic health implications remain poorly understood.
Methods: To investigate associations between tattoos/PMU and chronic disease outcomes, we analyzed data from the LIFE-Adult Study, a population-based cohort of 10,000 adults recruited in Leipzig, Germany (2011–2014). A dedicated...
High-quality data are essential for reliable epidemic surveillance. Traditional systems relying on passive case reporting that may lead to unreliable prevalence estimates depending on the specific disease. Using the example of the COVID-19 pandemic, we show that once prevalence exceeds moderate levels, conventional reporting becomes biased and unstable. Beyond this point, drawing additional...
Device-based assessment of physical behaviour (PB) is essential in health and behavioural research, to evaluate the impact of interventions, and to examine diverse health outcomes. Accelerometers are widely used but converting tri-axial signals to PBs remains challenging. Machine learning (ML) is a promising approach for classifying PBs from accelerometer data. However, the performance of ML...
