Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 4:27:e63105.
doi: 10.2196/63105.

Modernizing the Staging of Parkinson Disease Using Digital Health Technology

John Michael Templeton  1   2 , Christian Poellabauer  3 , Sandra Schneider  4 , Morteza Rahimi  3 , Taofeek Braimoh  1 , Fhaheem Tadamarry  1 , Jason Margolesky  5 , Shanna Burke  6 , Zeina Al Masry  7
Affiliations

Modernizing the Staging of Parkinson Disease Using Digital Health Technology

John Michael Templeton et al. J Med Internet Res. .

Abstract

Due to the complicated nature of Parkinson disease (PD), a number of subjective considerations (eg, staging schemes, clinical assessment tools, or questionnaires) on how best to assess clinical deficits and monitor clinical progression have been published; however, none of these considerations include a comprehensive, objective assessment of all functional areas of neurocognition affected by PD (eg, motor, memory, speech, language, executive function, autonomic function, sensory function, behavior, and sleep). This paper highlights the increasing use of digital health technology (eg, smartphones, tablets, and wearable devices) for the classification, staging, and monitoring of PD. Furthermore, this Viewpoint proposes a foundation for a new staging schema that builds from multiple clinically implemented scales (eg, Hoehn and Yahr Scale and Berg Balance Scale) for ease and homogeneity, while also implementing digital health technology to expand current staging protocols. This proposed staging system foundation aims to provide an objective, symptom-specific assessment of all functional areas of neurocognition via inherent device capabilities (eg, device sensors and human-device interactions). As individuals with PD may manifest different symptoms at different times across the spectrum of neurocognition, the modernization of assessments that include objective, symptom-specific monitoring is imperative for providing personalized medicine and maintaining individual quality of life.

Keywords: AI; Parkinson disease; artificial intelligence; digital health; disease classification; neurocognition; personalized medicine; wearables.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Timeline of both the history of the classification of Parkinson disease (PD) and the history of digital health technology. ADL: activity of daily living; IoT: Internet of Things; mHealth: mobile health; PDQ-39: Parkinson’s Disease Questionnaire-39.
Figure 2
Figure 2
Chronological depiction of publications on digital health technology for the assessment of both motor and nonmotor symptoms of Parkinson disease (PD), highlighting the emergence of “digital health” from 2015 to the present.
Figure 3
Figure 3
A proposed foundation for a staging schema that builds on the structure of clinically implemented scales (eg, Hoehn and Yahoo Scale, Movement Disorder Society–Unified Parkinsons Disease Rating Scale and Berg Balance Scale) that emphasize motor functionality—fine motor, gross motor, balance, and reflexes—while also integrating all other functional areas of neurocognition related to Parkinson disease (PD; eg, memory, speech, language, executive function, autonomic function, behavior, sleep, and sensory function). This schema would allow for a nuanced, weighted approach to tracking PD progression while also enabling greater insights into the possible subtyping of PD through the use of digitized assessment tools for the collection of objective data. PRO: patient-reported outcome.
Figure 4
Figure 4
Benefits of integrating digital health technology into disease classification, staging, and prediction. ALS: amyotrophic lateral sclerosis; DBS: deep brain stimulation; MDS-UPDRS: Movement Disorder Society–Unified Parkinson’s Disease Rating Scale; PD: Parkinson disease; PDQ-39: Parkinson’s Disease Questionnaire-39; PRO: patient-reported outcome.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Dorsey ER, Bloem BR. The Parkinson pandemic-a call to action. JAMA Neurol. 2018 Jan 01;75(1):9–10. doi: 10.1001/jamaneurol.2017.3299. https://jamanetwork.com/journals/jamaneurology/fullarticle/2661302 2661302 - DOI - PubMed
    1. Templeton JM, Poellabauer C, Schneider S. Enhancement of neurocognitive assessments using smartphone capabilities: systematic review. JMIR Mhealth Uhealth. 2020 Jun 24;8(6):e15517. doi: 10.2196/15517. https://mhealth.jmir.org/2020/6/e15517/ v8i6e15517 - DOI - PMC - PubMed
    1. Blake-Krebs B, Herman L. When Parkinson's Strike Early: Voices Choices Resources and Treatment. New York, NY: Hunter House Inc; 2001.
    1. Laganas C, Iakovakis D, Hadjidimitriou S, Charisis V, Dias SB, Bostantzopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Trivedi D, Chaudhuri KR, Hadjileontiadis LJ. Parkinson’s disease detection based on running speech data from phone calls. IEEE Trans Biomed Eng. 2022 May;69(5):1573–1584. doi: 10.1109/TBME.2021.3116935. - DOI - PubMed
    1. Yang W, Hamilton JL, Kopil C, Beck JC, Tanner CM, Albin RL, Ray Dorsey E, Dahodwala N, Cintina I, Hogan P, Thompson T. Current and projected future economic burden of Parkinson's disease in the U.S. NPJ Parkinsons Dis. 2020 Jul 09;6(1):15. doi: 10.1038/s41531-020-0117-1. https://doi.org/10.1038/s41531-020-0117-1 117 - DOI - DOI - PMC - PubMed