Completed Master on Wearable sensing-driven assessment of REM sleep behavior disorder in Parkinson’s Disease
Congratulations to Lisa Aaslestad at SEFAS and IGS with her completed Master degree - and with the best assessment (A)! Her thesis is called "Bridging Gaps: Wearable sensing-driven assessment of REM sleep behavior disorder in Parkinson’s Disease. Results from the DIGI.PARK study", and in her project, Lisa has looked at sleep behaviour disorders in Parkinson's disease and how the use of wearable sensor technology can enhance the assessment of such probable disorders in Parkinson's.
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The Master dissertation defense took place June 10, 2024. Supervisors have been Line Iden Berge (main supervisor), Monica Patrascu, and Haakon Reithe.
Rapid Eye Movement Sleep Behaviour Disorder (RBD) is a prevalent non-motor symptoms in Parkinson's Disease (PD), often assessed using self-reported questionnaires prone to recall bias and low compliance. Consequently, there is critical need for more objective and reliable assessment methods. This study aims to explore the use of wearable sensor technology to enhance the assessment of probable RBD (pRBD) in PD.
Fourteen participants with PD wore a multi-sensor wearable wrist device for 14 nights, capturing nocturnal movements and heart rate variability (HRV). They also completed the REM Sleep Behavior Disorder Questionnaire (RBDSQ). We identified sleep disturbances in sensor data through visual inspection and movement classification and scored the detected movements using the Cole-Kripke sleep-scoring algorithm. We integrated this scoring procedure with the traditional scale into the enhanced D-RBDSQ assessment tool, which we analyzed for internal consistency using Cronbach’s alpha coefficient.
There were significant discrepancies between self-reported RBD symptoms in RBDSQ items and objective data from the wearable device. Accelerometry data showed a higher frequency of nocturnal movements in patients with pRBD, which was not fully captured in the RBDSQ scores. Cronbach's alpha indicated high consistency (α = 0.87) for the developed D-RBDSQ scale.
Supplementing self-reported questionnaires with wearable sensor data offers a more objective method for assessing pRBD in people with PD. This approach could improve symptom assessment accuracy by reducing the subjective biases inherent in self-reported data and highlight underreported or unrecognized symptoms.
We look forward to seing more of Lisa in her future career!