Abstract
Early diagnosis and progression monitoring in neurodegenerative diseases like Parkinson’s (PD) and Alzheimer’s (AD) remain challenging. Continuous, passively collected multimodal data from wearables (e.g., gait, activity, sleep, voice, ECG) yield sensitive “digital biomarkers.” This review systematically evaluates evidence for these digital phenotypes in discriminating pre-symptomatic individuals, stratifying disease subtypes, and objectively quantifying drug efficacy. We contend that integrating digital biomarkers into clinical assessments and therapeutic trials can transform the neurodegenerative disease paradigm.
Proposed Structure
- Introduction: The challenge of late, subjective assessment in neurodegeneration; rise of digital phenotyping.
- Target Diseases & Digital Phenotypes:
- PD: Using smartwatches to monitor tremor, bradykinesia, gait asymmetry.
- AD: Detecting subtle changes in spatial navigation, daily activity patterns, and speech.
- Early Detection & Risk Stratification: Identifying abnormal patterns in pre-symptomatic or prodromal individuals.
- Remote Monitoring & Treatment Assessment: Continuous, objective evaluation of disease fluctuation and treatment response in real-world settings.
- Data Science & Implementation Challenges: Data privacy, algorithmic explainability, lack of standards, validation against clinical endpoints.
- Future Outlook: Advocating for open validation datasets and regulatory science for digital endpoints.
Key References
- Lipsmeier, F., et al. (2022). Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson’s disease clinical trial. npj Digital Medicine, 5(1), 58.
- Byun, S., et al. (2023). Digital speech biomarkers for remote monitoring of frontotemporal dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 15(1), e12410.
- Espay, A. J., et al. (2019). A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies. Movement Disorders, 34(5), 657-663.
