Objective: Our objective is to investigate potential digital biomarkers of Parkinson’s Disease (PD) for early diagnosis and treatment.

Background: Parkinson’s disease presents difficulty with early diagnosis and predicting progression because of the complexity with which it affects multiple functions of the body. It is well known that cognitive, sensory, and motor changes can precede clinical manifestations by years or decades [1]. Assessing digital biomarkers of gait asymmetry and sleep disturbances for early detection of PD is poorly understood. With the rise of mobile applications allowing for the data collection on biomarkers with mobile devices, this proves to be a promising field to improve the diagnosis and prognosis for Parkinson’s patients.

Method: A literature review was conducted to identify sleep-related and gait-related digital biomarkers based on their strong association with predicting PD and the feasibility of collecting the biomarker data using mobile and wearable devices.

Results: Negative emotions in dreams proved to predict cognitive and motor decline in early PD [2]. Sleep disturbances such as more nighttime awakenings and sleep fragmentation as well as atonia during rapid eye movement (REM) sleep are other biomarkers in predicting PD [1,3,4]. With the loss of muscle tone, periodic limb movement disorder and dream enactment can occur [3, 5]. REM sleep behavior disorder, excessive daytime sleepiness, and atonia were potential preclinical markers for PD development, prognosis, and severity [6,7]. Also, individuals with PD commonly struggle with impaired walking ability and gait variability, specifically temporal and spatial asymmetries of the lower extremities [8]. Abnormal gait in PD, such as small shuffling steps, is well known and associated with hypokinesia, defects in posture and equilibrium, as well as reduced stride length and swing/stance time ratio [9,10]. These gait-related biomarkers can be monitored through the iPhone’s Mobility metrics in the Health application, providing a non-intrusive way for assessing walking quality between age groups [11].

Conclusion: The biomarkers discussed in this paper are variables that can be collected easily with mobile and wearable devices, can potentially provide insight to early PD diagnoses, track the risk of PD, and improve our understanding of the disease. Thus, further research is required to clearly elucidate the relationships between these biomarkers and Parkinson’s disease.

References: [1] Kourtis, L. C., Regele, O. B., Wright, J. M., & Jones, G. B. (2019). Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. NPJ digital medicine, 2(1), 1-9.
[2] Bugalho, P., Ladeira, F., Barbosa, R., Marto, J. P., Borbinha, C., Salavisa, M., … & Meira, B. (2021). Do dreams tell the future? Dream content as a predictor of cognitive deterioration in Parkinson’s disease. Journal of Sleep Research, 30(3), e13163.
[3] Li, M., Wang, L., Liu, J. H., & Zhan, S. Q. (2018). Relationships between rapid eye movement sleep behavior disorder and neurodegenerative diseases: clinical assessments, biomarkers, and treatment. Chinese medical journal, 131(08), 966-973.
[4] McCarter, S. J., St Louis, E. K., & Boeve, B. F. (2012). REM sleep behavior disorder and REM sleep without atonia as an early manifestation of degenerative neurological disease. Current neurology and neuroscience reports, 12(2), 182-192.
[5] Postuma, R. B., Gagnon, J. F., & Montplaisir, J. Y. (2012). REM sleep behavior disorder: from dreams to neurodegeneration. Neurobiology of disease, 46(3), 553-558.
[6] Zhou, L., Zhu, L., & Liu, J. (2018). From Rapid Eye Movement Sleep Behavior Disorder to Parkinson’s Disease: Possible Predictive Markers of Conversion. ACS Chemical Neuroscience, 10(2), 824-827.
[7] Chaudhuri, K. R., Tolosa, E., Schapira, A. H., & Poewe, W. (Eds.). (2014). Non-motor symptoms of Parkinson’s disease. OUP Oxford
[8] Fling BW, Curtze C, Horak FB. Gait Asymmetry in People With Parkinson’s Disease Is Linked to Reduced Integrity of Callosal Sensorimotor Regions. Front Neurol. 2018;9:215.
[9] Knutsson E. An analysis of Parkinsonian gait. Brain. 1972;95(3):475-86.
[10] Lewek MD, Poole R, Johnson J, Halawa O, Huang X. Arm swing magnitude and asymmetry during gait in the early stages of Parkinson’s disease. Gait Posture. 2010;31(2):256-60.
[11] Apple Inc. Measuring Walking Quality Through iPhone Mobility Metrics. May 2022. Available at: https://www.apple.com/healthcare/docs/site/Measuring_Walking_Quality_Through_iPhone_Mobility_Metrics.pdf. Accessed. March 3, 2023.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/investigating-digital-biomarkers-in-parkinsons-disease/