ActiveAgeing – the DIGI.PARK branch

PD is characterized by disturbances in motor behaviour, including tremors, slowness, stiffness, and several other problems due to a degeneration of neural pathways for which there are no biomarkers, making diagnosis and research challenging. The symptoms are difficult to measure over time due to subjective and low-resolution assessment methods. Current tools for assessing clinical phenotypes and severity of Parkinson’s disease (PD) are based on observation while the patient performs a series of tasks. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is considered the gold standard for assessing the efficacy of clinical trials testing symptomatic and neuroprotective agents. These tools are, however, limited by a lack of objectivity, low sensitivity and reproducibility, and vast variations depending on the time of the examination, time of last received dose of dopaminergic treatment, etc. One approach to circumvent these limitations and establish more objective measures of severity is that of digital phenotyping via the use of wearable sensor devices. 

The aim of the DIGI.PARK study is to explore the use of wearable sensors for symptom tracking in home-dwelling people with Parkinson’s disease. 

This branch of ActiveAgeing is an observational study comprised of two phases. In the first phase, we investigated the use of wearable sensor technology for research and clinical work in Parkinson’s disease. A 2-week data collection was conducted in the participants’ homes on 15 participants with Parkinson’s and 15 participants without Parkinson’s from the innovative living environment Helgetun. We employed clinical assessment tools (cognitive assessment, Parkinsonian symptomology, sleep disturbances), two smartwatches (Fitbit Sense and Empatica E4), and a smart ring (Oura). 

The second study is based on the results of the first study, as the data collection procedure is refined according to the first study's data analysis. The second study involves data collection from persons with Parkinson’s disease and their spouses, to compare the crossover effects of the disease. Both studies include the design of specific Parkinson’s disease digital biomarkers for symptom tracking.

Part of a two-sided study 

The ActiveAgeing study consists of two branches – the DIGI.PARK branch and the Helgetun branch. The Helgetun branch is exploring how living in an innovative, community-based environment can affect the lives of older adults, using a qualitative approach. See separate description on the Helgetun branch here.

Team 

DIGI.PARK is a collaborative initiative between the Centre of Excellence Neuro-SysMed and SEFAS. Our team comprises Haakon Reithe, PhD student, Shakil Salim Rajan, PhD student,  Dr. Monica Patrascu, systems engineer, Dr. Juan Carlos Torrado Vidal, computer engineer, Dr. Brice Marty, electrical engineer and neuroscientist, Elise Førsund, PhD student in the Helgetun branch, Professor Bettina S. Husebø, palliative care physician and leader of SEFAS, and Professor Charalampos Tzoulis, neurologist and Neuro-SysMed Director. 

Current status 

The first study was initiated in the spring of 2021, and all data were collected in 2021/2022. The comparative cross-correlation analysis of the three wearable devices is finalized, and a digital biomarker for tremor quantification and one for physical activity response are designed. The second study is being designed and is planned to start in the first half of 2026. 

Reithe had his second paper accepted for publication, where we designed and tested an algorithm that quantifies tremor in ranges of 3 to 12 Hz. The algorithm was developed together with Monica Patrascu and Brice Marty, and in the paper, we test the algorithm on participants who exhibit unilateral tremors by examining the differences between the most tremor-affected hand with the least affected hand. The algorithm output gives us a tremor index (TI). Comparing the most affected with the least tremor-affected side, we find a separation in the distributions of the TIs. Moreover, we find that tremor severity and type correlated with the number of frequencies, showing a separation between the hands. This indicates that the TI may have utility for tracking tremor in free living conditions, but needs to be validated in larger samples, such as in a second study of DIGI.PARK. 

For the third paper, we propose three types of biomarkers for tracking medication response, two tracking the response to medication in particular, and one biomarker tracking ON/OFF fluctuations. For the two former biomarkers, we are making use of the TI to track the response to medication by comparing the TI’s leading up to medication intake and the TI’s following, giving us an evolution of tremor and its response to medication. For the latter, we calculated thresholds for “OFF” medication states, calculating the amount of OFF over the recorded period. This article is to be published in the first months of 2026. In this work, we find that there is variation between participants in their response to medication, with some showing clear responses while others remain unchanged. For the ON/OFF, we find that those who report ON/OFF according to standard clinical measurements show comparable amounts of OFF. The proposed biomarkers highlight the potential of the TI but need to be further tested and optimized to be properly validated. 

Shakil Rajan Salim is our newest PhD candidate to the DIGI.PARK team. Rajan is a pharmacist with a master’s in international community health from the University of Oslo. He was accepted into UiB’s PhD program in December and is currently working on his first article using data from the first DIGI.PARK study. His PhD thesis will be based on the second DIGI.PARK study. The second study will have a higher number of participants, i.e., 110, utilizing the Axivity ax6 wearable device to collect sensor data. The aim of his thesis will be to evaluate medication response in participants with PD by analysing changes in Axivity-derived data of motor symptoms in relation to dosing intervals and dosage of antiparkinsonian medications. The compensatory behaviour of the non-dominant hand in people with PD through comparison with movement data from control (people without PD) will also be evaluated in his thesis. 

Impact 

This project investigates how technology can assist society in addressing the challenges posed by an increasingly aging population with PD. This provides important knowledge for planning of future health care, which is transferable to other diseases that share symptomology with PD, such as tremor and motor dysfunction. 

See previous article from the startup of the project.