Projects

Project Overview My PhD project is about assessing intersectional social inequalities in daily movement behavior – i.e. physical activity (PA), sedentary behavior (SB) and sleep. Intersectionality offers a critical framework for understanding how overlapping social identities, such as gender, race, ethnicity, income, and educational level, shape privilege and disadvantage in daily movement behavior. These intersecting […]

Project Overview My project aims to advance methods for estimating daily physical activity volume and intensity from wearable accelerometry, with a special focus on comparability of volume and intensity outcomes, and their associations with health, when derived from different common epoch summary measures. First Study The intensity gradient (IG) is a measure of the distribution

Project Overview My project is about investigating the association of machine learning derived sleep, sedentary behaviour and physical activity with health outcomes. To reach this objective, I apply epidemiological methods and reasoning on data from large-scale population-based cohorts. First Study Chronic low back pain is a prevalent and costly condition, and regular physical activity may

Project Overview My project aims to improve how 24-hour movement behaviour from accelerometers are communicated and visualised. As these data grow more multidimensional, clear and interpretable visualisations become essential for effective understanding and decision-making. Through quantitative, qualitative, and design-based approaches, I will develop principles, frameworks, and tools that make data visualisations more accessible, accurate, and

Project Overview My project develops a universal taxonomy of 24-hour physical behaviour that is inclusive across cultures and adaptable to evolving technologies. Instead of beginning from device signals alone, I start with how people naturally describe their day, as discrete, time-bounded events with purpose and context, and integrate these narratives with sensor data. I build

Project Overview My project focuses on developing and applying temporal-modelling approaches to accelerometry to extract interpretable dynamical features of human activity and relate them to health outcomes in population datasets (mainly NHANES). Secondments: Leiden University (Netherlands) – Collaboration on causal representation learning for physical activity using foundation models with My Little Moves data from Amsterdam

First Study Physical activity decreases from childhood to adolescence, with girls being less active than boys. The timing of these differences remains unknown. Using accelerometer data from three cross-sectional studies in Norway and Belgium (n=2507, age=3-17years), we assessed sex differences in sedentary behaviour and physical activity levels (light, moderate, vigorous) throughout the day and across

Project Overview My PhD project, “Taxonomy of Movement Behaviours,” focuses on enriching movement data with greater contextual detail. I develop and validate novel methods that integrate data from accelerometers and GPS sensors to infer not just if a person is moving, but also how, where, and why. These techniques can identify transportation modes, characterize behaviours

Project Overview My PhD focuses on evaluating the validity and reliability of wrist-worn accelerometers for measuring 24-hour movement behaviours such as physical activity intensity, activity types, and sleep-wake patterns. These sensors are widely used in research, clinical trials, and public health surveillance. However, there is considerable variation in how validation studies are conducted and reported,