PhD Defense: Marit Dagny Kristine Jenssen

Marit Dagny Kristine Jenssen will defend her PhD thesis for the PhD degree in Science at the UiT The Arctic University of Norway, April 10th, 12:15 PM

The title of the thesis is "Exploring Patient Generated Health Data in Fibromyalgia Statistical and Machine Learning Approaches to Chronic Pain and Physical Activity"

The trial lecture title will be announced soon.

Summary of the thesis

In Norway, chronic musculoskeletal pain is a leading cause of disability benefits and sick leave. Fibromyalgia is a common chronic pain condition characterized by widespread pain, chronic fatigue, poor sleep, and depression. Despite extensive research, no efficient treatment has been developed. Several studies suggest that physical activity can help manage chronic pain, but interventions should be tailored to the individual.

This dissertation combines machine learning, time series analysis, and clinical trial methodology to advance the understanding and treatment of fibromyalgia. The work is organized around four research questions addressing the use of machine learning in chronic pain research, the predictability of pain from wearable data, the replicability of activity-based pain reduction, and the design of a factorial clinical trial.

The scoping review (Paper I) identifies opportunities for using machine learning to improve chronic pain management, finding that while diagnostic classification is well-studied, treatment and self-management applications remain underexplored. The case study (Paper II) applies SiZer, a statistical method for detecting significant changes, to analyze pain and activity patterns in seven fibromyalgia patients using Fitbit data. Results showed that three patients achieved pain reduction following physiotherapist-guided activity modifications.

Building on this foundation, a time series analysis investigates whether daily pain can be predicted from wearable-derived activity and sleep data. Both classical methods (ARIMA) and modern machine learning approaches (XGBoost, foundation models) were evaluated. The analysis reveals that pain exhibits strong temporal autocorrelation, and activity and sleep features provide only modest improvements for some patients. Predicting pain change direction showed more consistent gains over baseline than regression on exact levels, though overall prediction performance was modest.

Finally, the insights from Papers I and II informed the design of a randomized controlled trial (Paper III). The protocol applies a 2 × 2 factorial design to evaluate the effects of group exercise and somatic tracking—a psychological i ii intervention—on fibromyalgia outcomes.

Together, these contributions demonstrate the potential of combining wearable technology, advanced analytics, and clinical expertise to develop more personalized approaches to chronic pain management.

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