The Role of Smartwatch Data in Monitoring Non-Motor Symptoms of Parkinson’s Disease

Accurate and continuous monitoring of symptoms can enable better care and timely intervention. Wearables could facilitate this but are underexplored for monitoring of non-motor symptoms.
Like

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Parkinson's disease (PD) is widely recognized as a motor disorder, but it encompasses a variety of non-motor symptoms that significantly impact patients' quality of life. These non-motor symptoms, including cognitive impairment, autonomic dysfunction like sleep disturbances and digestive issues, and psychiatric issues, often precede the motor symptoms and continue to affect patients throughout the disease course. Accurate and continuous monitoring of these is needed to improve our understanding of the disease course and ultimately facilitate better care.

The potential of smartwatches

Following a diagnosis of Parkinson's disease, routine care involves a visit to the clinic every 6 months where the main symptoms and their impact on daily life will be assessed via questionnaires and clinical tests. Those visits only provide limited information on the actual status of the patient in real-life and disregard day-to-day variability. Wearables like smartwatches can address these limitations by passively collecting data in real-world settings and have shown promising capabilities in measuring the motor aspects of the disease. The non-motor symptoms remain understudied.

Comparing digital measures to clinical assessments

The study analyzed data from 149 participants from the Parkinson's Progression Markers Initiative (PPMI) diagnosed with Parkinson’s disease, using the Verily Study Watch to collect digital outcome measures such as physical activity, sleep metrics, and vital signs​. Weekly averages of the digital outcome measures around the clinic visit (Figure 1) were compared with clinical assessments of non-motor symptoms to evaluate their correlation and potential utility of smartwatch data in clinical settings.

Figure 1: Computation of digital weekly averages
Using the continuously collected digital sensor data, digital weekly averages spanning 3.5 days around the clinic visit were computed excluding the visit day itself.

Key Findings

  1. Digitally Measured Physical Activity and Sleep are Associated with Non-Motor Symptoms: Associations were found for clinical assessments of cognition, autonomic functioning, and impairment in daily life. No association were identified for psychiatric symptoms.

  2. Digital Timeseries Capture Clinical Progression: Long-term digital timeseries data reflects progression of medication, motor symptoms, autonomic functioning, and impairment in daily living.
  3. Limited Predictive Performance: Despite found associations, predicting clinical scores based on digital weekly averages on an individual level did not outperform baseline models for most. More tailored sensors and digital measures are required to capture the nuanced aspects of non-motor symptoms more effectively.

    Figure 2: Correlation of digital weekly averages and clinical scores
    For each pair of clinical score and digital weekly average, the correlation coefficient is displayed on a divergent color map (red: positive relation, blue: negative relation) with asterisks indicating statistically significant findings.

Challenges and Future Directions

The study highlighted several challenges and areas for improvement in using smartwatch data for monitoring Parkinson's disease:

  • Sensor Limitations: The sensors used in the study primarily captured movement-based data, which is limited in assessing non-motor symptoms. Future research should explore the use of more specific sensors, such as those tracking bathroom usage for urinary symptoms or phone usage patterns for depressive symptoms​.
  • Individual Variability: Non-motor symptoms vary widely among PD patients, necessitating personalized approaches to digital monitoring. Developing algorithms that can adapt to individual patient profiles and provide personalized insights could improve the predictive power of smartwatch data​.

Conclusion

Smartwatch data holds significant potential for enhancing the monitoring of non-motor symptoms in Parkinson’s disease. The ability to collect continuous, real-life data offers a valuable complement to traditional clinical assessments, helping to capture the full spectrum of a patient’s condition.  Digital health technologies can play a crucial role in improving the management and quality of life for individuals with Parkinson's disease.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Parkinson's disease
Life Sciences > Health Sciences > Clinical Medicine > Neurology > Neurological Disorders > Neurodegenerative diseases > Parkinson's disease
Biomarkers
Life Sciences > Health Sciences > Clinical Medicine > Diagnosis > Biomarkers
Health Care
Life Sciences > Health Sciences > Health Care
Medical and Health Technologies
Life Sciences > Health Sciences > Clinical Medicine > Medical and Health Technologies

Related Collections

With collections, you can get published faster and increase your visibility.

Clinical research in neurological disorders

The editors at Nature Communications, Communications Medicine, npj Parkinson’s disease and Scientific Reports invite original research articles on the clinical aspects of neurological disorders and neurodegenerative diseases.

Publishing Model: Open Access

Deadline: Jun 30, 2024

Invasive and non-invasive neuromodulation in movement disorders

This Collection highlights cutting-edge advances in research on neuromodulation therapies for Parkinson’s Disease and related movement disorders.

Publishing Model: Open Access

Deadline: Jun 10, 2024