SEFAS is launching a new study in collaboration with Neuro-SysMed and the Helgetun living lab. ActiveAgeing will investigate the current possibilities for enhanced activity and quality of life in healthy elderly and people with Parkinson’s disease. Using innovative home-based sensors the ActiveAgeing project will produce big data to develop AI-based algorithms that can describe and predict function and activity in elderly people. ActiveAgeing also aims to determine how smart housing and health systems can be used to promote healthy ageing and empower individuals to stay active in health and disease.
Background: A considerable and increasing number (150 of 100 000) of people are affected by Parkinson’s disease, and about 50% develop dementia within 10 years from diagnosis. This has detrimental consequences for the person’s independence, activities and social relationships, ability to continue living at home safely, and quality of life (QoL). Disease progression, symptomatology, treatment response and prognosis show high variability, but little is known about what characterizes subgroups of individuals with Parkinson’s disease. Recent developments in technology have great potential to increase the knowledge-base and support people with Parkinson’s disease.
Digital phenotyping is understood as the moment-to-moment quantitative description of a person in their own environment, obtained by automatically aggregated data collected by devices such as smartphones, activity monitors, video surveillance, etc., and aims to measure human behaviour and function in health and disease. Data streams include sensor measurements, activity logs and user-generated content, and may include all types of sensor readable variables such as heart rate, respiratory rate, body temperature, activity and rest, brain waves, etc. In combination with artificial intelligence (AI) methods to analyze big and complex data outputs, digital phenotyping holds vast potential to allow precise description of healthy ageing as well as to show how neurological disease presents and develops in the individual – for instance by analyzing the progression of characteristic motor symptoms in Parkinson’s disease.
Objective: In ActiveAgeing, we will monitor disease patterns and etiology including symptom development (e.g. motor, cognitive and psychiatric symptoms) and treatment response by utilizing wearable and sensor technology, such as Wi-Fi signals, image sensors, smartphones or smartwatches, integrated in assistive devices to increase safety and independence for people living at home. The aim is to improve classification of Parkinson’s disease, thereby facilitating individually targeted patient-centered treatment and ActiveAgeing.
To succeed we aim to utilize multiple data sources and advanced data analysis to describe trajectories of healthy ageing and neurological disease in elderly people, identifying factors that promote sustained health and function into an active old age as well as factors that may increase the risk of functional decline and should be targeted for prevention. Thus, ActiveAgeing aims to unite state-of-the-art developments within the following areas: assistive living technology; sensor technology; data analysis using AI techniques; clinical assessment; biomarkers; medical imaging; genetic or molecular profiling (omics).
Participants and contributors: The innovative multidisciplinary ActiveAgeing project connects GC Rieber Fondene, University of Bergen (UiB), Haukeland University Hospital, Western Norway University of Applied Sciences, industry partners, and international collaborators with elderly people and those who are affected by neurological disease. Partners include among others: Helgetun living lab, a senior housing project aiming to promote mental, social and physical activity, creativity and healthy ageing, consisting of 31 apartments in rural surroundings near Bergen; Neuro-SysMed, a national center of excellence for the treatment of neurodegenerative disorders which leads several ongoing clinical trials on Parkinson’s disease; the AI group, a group of researchers at UiB with a shared interest in medical artificial intelligence led by Eli Renate Grüner, Director of Research, Helse Bergen HF and Helse Vest RHF and Assoc. Prof. Dept of Physics and Technology, UiB.
Methods: People with Parkinson’s disease will be recruited to ActiveAgeing via the ongoing STRAT-PARK cohort study led by Neuro-SysMed. STRAT-PARK aims to investigate the heterogeneity of people with Parkinson’s disease and identify clinically meaningful disease subgroups. Participants receive lifelong regular follow-up including clinical testing, omics-based tests, biopsies from e.g. muscle and intestine, biomarkers and medical imaging.
ActiveAgeing will include 80-100 home-dwelling people with Parkinson’s disease enrolled in STRAT-PARK and living in Bergen and surrounding area. Participants will receive a multi-component technical intervention at home to conduct digital phenotyping, support safe living and aid communication with informal and formal caregivers. The intervention will include the development and feasibility testing of sensing technology to monitor treatment response and motor, cognitive, behavioural and psychological symptoms related to Parkinson’s disease. During the project period it is a goal to recruit patients with MS from ongoing Neuro-SysMed studies following the methodology developed for the subgroup of ActiveAgeing participants with Parkinson’s disease.
In parallel, ActiveAgeing will explore added values from the optimized Helgetun living lab as a control environment to describe elderly wishes and activities following the question “What matters to you”? Combined with qualitative interviews, we will implement the same technology in the homes of the healthy elderly people recruited from the Helgetun living lab. Helgetun will serve as a model to investigate how healthy ageing is affected by a positive living environment. By digital phenotyping of elderly people from Helgetun living lab, trajectories for healthy ageing will be modelled, and key ingredients for ActiveAgeing can be determined.
Conclusion: ActiveAgeing investigates the added value of digital phenotyping and technological support towards meaningful endpoints such as high-precision individualized treatment, maintenance of function and social relationships, and QoL. Additionally, combining safety data from the intervention in the STRAT-PARK and Helgetun subgroups, ActiveAgeing will yield robust evidence for the cost-effectiveness and socioeconomic impacts of the intervention. Introducing wearable and sensing technology to facilitate digital phenotyping and AI-based predictions are novel and groundbreaking strategies to improve the QoL, support independent living and increase safety for people with and without Parkinson’s disease and dementia, as well as to support their informal caregivers (family members or others) and health care providers, and moving health and care systems towards more targeted delivery of higher quality services at a sustainable cost. Quantitative and qualitative measures will be applied.
Responsibility: ActiveAgeing is coordinated by the Centre for Elderly and Nursing Home Medicine (SEFAS), and employees will be included in the multidisciplinary work environment at the Department of Global Public Health and Primary Care (IGS) in the new Alrek Health Cluster, in close proximity and collaboration with Neuro-SysMed. Candidates will be integrated in an active and experienced research network including national and international expert partners, expected to yield high-impact results eligible for publication in esteemed peer-reviewed journals, and with emphasis on facilitating career building beyond the scope of the Ph.D. or postdoctoral periods.