Decoding Death and Dying in people with Dementia by Digital thanotyping
The 5-D project aims to enhance end-of-life care for dementia patients by using sensors and algorithms to accurately predict the point of no return in dying, improving symptom management and understanding.
Projectdetails
Introduction
How can we recognize that a person with dementia is at the end of life? When we are dying, our physical, mental, and social abilities are gradually declining. No reliable method of predicting perceived dying currently exists, although the technology is available (sensors, algorithms).
Project Aim
The aim of Decoding Death and Dying in Dementia by Digital Thanotyping (5-D) is to provide methods and tools to diagnose and describe dying to an unprecedented level of accuracy and robustness. This will be achieved within a timespan larger than is possible now, focusing on the case of dying people with dementia as one of the most vulnerable and difficult to study groups.
5-D combines clinical assessment tools with wearable sensing technology to monitor:
- a) pain and distressing symptoms,
- b) behavioral and psychological symptoms in dementia (BPSD),
- c) oral changes,
- to decode the point of no return as the beginning of perceived dying.
Research Hypothesis
To obtain this outcome in nursing home patients with dementia, I will test the main hypothesis: from monitoring the evolution of thanotype components over time and their interdependencies, the prediction of the point of no return is possible.
Objectives
The objectives of 5-D are:
- Collect data using sensors and validated assessment scales.
- Develop estimation methods for BPSD from sensor measurements.
- Develop digital tools to capture the expression of pain.
- Determine the relationship between breathing and oral symptoms.
- Develop models for symptom interdependencies at the end of life and the point of no return.
- Perform human-in-the-loop validation of developed tools, models, and algorithms.
Significance
The ground-breaking interdisciplinary novelty of 5-D endeavors to enhance our understanding of end-of-life underlying pain and symptoms in people with dementia. Advancing our theoretical knowledge to uncover how, when, and why perceived dying can be identified opens the doors for transferable research across several scientific fields.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.000.000 |
Totale projectbegroting | € 2.000.000 |
Tijdlijn
Startdatum | 1-1-2024 |
Einddatum | 31-12-2028 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITETET I BERGENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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This project investigates the complex end-of-life trajectories of older adults with chronic diseases using mixed methods to identify commonalities and individual differences, aiming to enhance understanding and improve care.
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This project aims to identify Alzheimer's disease subtypes through CSF proteomics to develop tailored treatments and theragnostic tools linked to cognitive decline and genetic factors.
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Minimally invasive endoscopes for neuronal activity monImaging-assisted single-cell specific activity monitoring and optogenetic stimulation of deep brain structures in motile and awaken animal models
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Wearable Dementia Analytics: bevordering autonoom leven dementiepatiënten
WH ontwikkelt slimme sensoren in kleding voor dementiepatiënten die data verzamelen en omzetten in een dynamisch behandelplan, voor persoonlijke zorg en kostenbesparing.
Human Sensivity
Het project richt zich op het ontwikkelen van een digitale aanpak om alle zintuigen van mensen met dementie gelijktijdig te prikkelen, ter verbetering van hun welzijn en vermindering van zorgdruk.
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Win from within
Het project richt zich op het ontwikkelen van een schaalbaar digitaal leefstijlplatform met AI-technologieën om metabole gezondheid te verbeteren en chronische ziekten te voorkomen bij ouderen.
Haalbaarheidsonderzoek Cognitief Open Data Model voor vroege fase geheugenverlies
Dit project onderzoekt de haalbaarheid van een Cognitief Open Data Model voor vroegtijdig geheugenverlies om voorspellingen te doen en onderliggende ziekten eerder vast te stellen.