Abstract Details

Title Smartphone-based Remote Digital Monitoring for Parkinson’s Disease and Dementia

Topic Movement Disorders

Presentation(s) S35 - Movement Disorders: Hyperkinetic Movement Disorders (4:42 PM-4:54 PM)

Poster/Presentation
Number 007

Background

PD and dementia are becoming increasingly prevalent and have significant impact on morbidity and mortality. Challenges include medication management, limited clinical resources, and therapy access disparities, particularly in rural, minority, and low-income communities. Innovative solutions to objectively measure disease progression, assess patient risk, and optimize medication management are needed.

Objective Develop an objective, multi-modal digital platform (Neu Health) to remotely measure changes and predict clinical outcomes in patients with Parkinson's disease (PD) and dementia using a smartphone application and interactive dashboard.

Design/Methods A software-based platform was developed that utilizes smartphone sensors to quantify signs and symptoms remotely. Digital assessments included voice, balance, gait, finger tapping dexterity, reaction time, rest and postural tremor, and behavioural cognitive tests. 689 healthy controls and over 600 patients with either idiopathic PD or Alzheimer’s disease completed the assessments. Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and Addenbrookes Cognitive Examination (ACE-III) were also performed. Clinical scores and risk of future disease outcomes were estimated using smartphone features, heuristic analysis, and machine learning models. Key metrics were displayed on a clinician dashboard.

Results Digital motor features captured from the smartphone assessments showed significant correlations with corresponding clinical questions from the MDS-UPDRS (r 0.11 to 0.51, p<0.05). The tests were sensitive to daily fluctuations in PD symptoms and the dashboard could be used to view clinical characteristics and monitor response to medication. Cognitive tests also significantly correlated with ACE-III (r>0.52, p<0.05). Smartphone features could be used to estimate clinical scores and predict future outcomes, including risk of falls, disease progression and cognitive decline, up to 18 months in advance (AUC > 0.7).

Conclusions

Remote smartphone assessments can estimate clinical scores and risk outcomes that are clinically interpretable. A reliable digital platform integrating assessment and predictive capabilities could improve neurodegenerative condition management. Further research should be performed to assess impact on clinical care.

Authors/Disclosures
Kinan Muhammed, MBBS, PhD, MRCP (Neu Health) PRESENTER	Dr. Muhammed has received personal compensation for serving as an employee of Neuhealth Digital Limited. Dr. Muhammed has stock in Neuhealth Digital Limited. Dr. Muhammed has received research support from National Institute of Heath and Care Research. Dr. Muhammed has received intellectual property interests from a discovery or technology relating to health care.
Francis Chmiel	No disclosure on file
Siddharth Arora	No disclosure on file
Katarina Gunter	No disclosure on file
Masud Husain, MD, FAAN (University of Oxford)	Dr. Husain has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Brain. Dr. Husain has stock in NeuHealth. The institution of Dr. Husain has received research support from Wellcome Trust. Dr. Husain has received publishing royalties from a publication relating to health care.
Michele Hu, MD	The institution of Dr. Hu has received personal compensation in the range of $0-$499 for serving as a Consultant for Lundbeck. The institution of Dr. Hu has received personal compensation in the range of $0-$499 for serving as a Consultant for ESCAPE Bio. The institution of Dr. Hu has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Evidera. The institution of Dr. Hu has received research support from Parkinson's UK. The institution of Dr. Hu has received research support from Oxford BRC.