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The digitalisation of medicine opens up new possibilities in prevention, diagnosis and therapy. At the same time, it presents researchers with major challenges: Data from wearables, health apps or mobile sensors are often highly complex, incomplete and individually different. Eleonora Carrozzo, a researcher at Salzburg Research Forschungsgesellschaft, is therefore developing new statistical methods that are specifically tailored to digital health data. The focus of the research work is on cardiovascular care, but research with small case numbers, such as in the case of rare diseases, will also benefit.
Digital technologies such as wearables, health apps or mobile sensors help to collect data from patients, e.g. on heart rate, exercise or blood pressure. They offer great potential, particularly in the prevention and treatment of cardiovascular diseases: through continuous data collection, they enable more individualised, patient-centred care.
However, analysing this data poses enormous challenges for researchers: high data complexity, outliers, missing values or low case numbers, as is the case with rare diseases, for example. Eleonora Carrozzo is therefore focussing on the development of new statistical methods that are specifically designed for such challenging health data.
‘The aim of my work is to arrive at statistically sound and clinically relevant conclusions despite small case numbers, high data complexity or erroneous values,’ says Eleonora Carrozzo from Salzburg Research.
New statistics for new health data
The aim of Carrozzo's research work is to create innovative analysis procedures based on so-called non-parametric methods. These do not make strict assumptions about the distribution of the data - and are therefore particularly suitable for digital health data that is incomplete, highly dimensional, irregular or highly individualised.
Carrozzo's aim is to close existing methodological gaps and support medical professionals in particular: The new tools are intended to help evaluate digital health measures in a well-founded manner and utilise them in a clinically meaningful way.
From research to practice: R software packages for clinical application
A particular focus is on practical implementation: the methods developed will be made available in the form of easy-to-use software packages (R packages). These are intended to be used not only in research, but also in clinical practice or in evaluation studies. Even complex study designs - for example with several groups or very small samples, many measurement time points or high-dimensional data - can be better modelled with the new methods.
This enables well-founded decisions to be made even when conventional statistical methods would fail due to a lack of data.
The research project "Evaluating digital health interventions with complex designs" is funded by the Austrian Science Fund FWF as part of the Elise Richter programme.
About Dr. Anna Eleonora Carrozzo
Anna Eleonora Carrozzo is a postdoctoral researcher at the Salzburg Research Forschungsgesellschaft and the Paris Lodron University of Salzburg in the joint EXDIGIT programme (funded by the State of Salzburg as part of the WISS2030 programme). She previously worked at the Ludwig Boltzmann Institute for Digital Health and Prevention in Salzburg. Anna Eleonora Carrozzo completed her doctorate in Management and Engineering at the University of Padua in 2016. She previously obtained a Master's degree in statistics there. Her research focuses on biostatistics, non-parametric statistics, statistical methods in medical research and data science in the field of digital health.
Anna Eleonora Carrozzo
Salzburg Research Forschungsgesellschaft mbH
eleonora.carrozzo@salzburgresearch.at
From wearables to apps: new statistical methods facilitate the analysis of complex health data.
Copyright: © Salzburg Research/shutterstock
Eleonora Carrozzo
Copyright: © Salzburg Research
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From wearables to apps: new statistical methods facilitate the analysis of complex health data.
Copyright: © Salzburg Research/shutterstock
Eleonora Carrozzo
Copyright: © Salzburg Research
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