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Related Concept Videos

Parkinson's Disease: Treatment01:24

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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study

Gent Ymeri1,2, Dario Salvi3,2, Carl Magnus Olsson3,2

  • 1Department of Computer Science and Media Technology (DVMT), Malmö University, Malmö, Sweden gent.ymeri@mau.se.

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This study explores using smartphones and wearables for remote Parkinson's disease (PD) symptom monitoring. Objective, frequent assessments at home can improve patient care and capture disease variability.

Keywords:
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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Digital Health

Background:

  • Clinical Parkinson's disease (PD) assessment faces reliability challenges and infrequent monitoring (6-month intervals).
  • Frequent symptom variability in PD is difficult to capture with traditional clinical visits.
  • Objective, remote monitoring using technology can overcome these limitations.

Purpose of the Study:

  • To assess the feasibility, compliance, and user experience of remote Parkinson's disease symptom measurement.
  • To investigate the use of smartphone and wearable sensors for passive and active data collection in PD patients' homes.
  • To explore the potential of digital health tools for frequent and objective PD symptom assessment.

Main Methods:

  • A feasibility study involving PD patients using the Mobistudy smartphone app for weekly activity tests (e.g., TUG, tremor, finger tapping) over 2 months.
  • Continuous actigraphy data collection using a GENEActiv wrist device for 28 days.
  • Integration of questionnaires (e.g., NMSQuest, PDQ-8, sleep scales) and clinical assessments (MDS-UPDRS) for comprehensive data.
  • Application of signal processing and machine learning to analyze sensor data and correlate with clinical outcomes.

Main Results:

  • Data collection is ongoing, with participants actively engaged in weekly remote assessments.
  • User experience and technology acceptance are being evaluated through dedicated questionnaires.
  • Correlation analysis between sensor-derived metrics and clinical PD severity scores (MDS-UPDRS) is planned.

Conclusions:

  • Smartphone and wearable technology show promise for frequent, remote, and objective assessment of Parkinson's disease symptoms.
  • This approach can potentially improve the management of PD by capturing disease fluctuations.
  • Further analysis will determine the long-term feasibility and clinical utility of this digital health strategy.