Digital biomarkers derived from wearable and portable devices in prodromal Parkinson’s disease and at-risk populations: a scoping review
- 1Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, Guangdong, China
- 2School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- 3Shenzhen Clinical College of Integrated Traditional Chinese and Western Medicine, Affiliated to Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China
Abstract
Background: Prodromal Parkinson’s disease (pPD) represents a pre-diagnostic stage during which subtle and heterogeneous changes may be difficult to detect through conventional clinical assessments. Digital biomarkers derived from wearable or portable devices offer opportunities for objective and longitudinal assessment. This scoping review aimed to map the current evidence on digital biomarkers in individuals with formally defined pPD or populations at increased risk of Parkinson’s disease (PD), focusing on measurement approaches, analytical methods, types of evidence, and methodological gaps.
Methods: A systematic search of PubMed, Web of Science, Embase, IEEE Xplore, Scopus, and ACM Digital Library was conducted from database inception to August 31, 2026. Studies investigating digital biomarkers derived from wearable or portable devices in individuals with formally defined pPD or populations at increased risk of PD were included. Study characteristics, digital biomarker domains, measurement and analytical approaches, and types of evidence were synthesized descriptively.
Results: Forty-three studies were included, primarily involving individuals with iRBD or other high-risk states associated with α-synucleinopathies, population-based or broader prodromal-risk cohorts, genetic high-risk populations, and individuals with idiopathic hyposmia/anosmia. Digital biomarkers encompassed motor, speech, sleep/activity, and multidomain features, with motor-related measures, particularly gait and mobility, being the most extensively investigated. Machine-learning approaches were applied across several domains, whereas genuinely multidomain studies remained limited. Most studies provided cross-sectional evidence of group differences or discriminative performance, whereas prospective evidence regarding future PD diagnosis or phenoconversion was less extensive. Considerable heterogeneity was observed in measurement protocols, analytical approaches, and validation strategies.
Conclusion: Wearable- and portable-device-derived digital biomarkers represent promising candidate tools for characterizing and monitoring individuals across the prodromal PD risk spectrum. However, evidence supporting clinical prediction of phenoconversion remains limited by methodological heterogeneity and a lack of sufficient longitudinal and external validation. Future research should prioritize standardized assessment, large, multicenter longitudinal cohorts, independent external validation, and integration with established clinical and biological markers.
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BibTeX
@article{yu2026digital,
title = {{Digital biomarkers derived from wearable and portable devices in prodromal Parkinson’s disease and at-risk populations: a scoping review}},
author = {Ting Yu and Qian Wang and Mingxia Pan and Yuanfang Xiong and Shiya Hu and Yiping Deng and Lingyu Kuang and Hanjiao Liu},
journal = {Frontiers in Aging Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnagi.2026.1850991},
url = {https://doi.org/10.3389/fnagi.2026.1850991},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Digital biomarkers derived from wearable and portable devices in prodromal Parkinson’s disease and at-risk populations: a scoping review
AU - Ting Yu
AU - Qian Wang
AU - Mingxia Pan
AU - Yuanfang Xiong
AU - Shiya Hu
AU - Yiping Deng
AU - Lingyu Kuang
AU - Hanjiao Liu
PY - 2026
DA - 2026/10/12
JO - Frontiers in Aging Neuroscience
DO - 10.3389/fnagi.2026.1850991
UR - https://doi.org/10.3389/fnagi.2026.1850991
AB - Background: Prodromal Parkinson’s disease (pPD) represents a pre-diagnostic stage during which subtle and heterogeneous changes may be difficult to detect through conventional clinical assessments. Digital biomarkers derived from wearable or portable devices offer opportunities for objective and longitudinal assessment. This scoping review aimed to map the current evidence on digital biomarkers in individuals with formally defined pPD or populations at increased risk of Parkinson’s disease (PD), focusing on measurement approaches, analytical methods, types of evidence, and methodological gaps. Methods: A systematic search of PubMed, Web of Science, Embase, IEEE Xplore, Scopus, and ACM Digital Library was conducted from database inception to August 31, 2026. Studies investigating digital biomarkers derived from wearable or portable devices in individuals with formally defined pPD or populations at increased risk of PD were included. Study characteristics, digital biomarker domains, measurement and analytical approaches, and types of evidence were synthesized descriptively. Results: Forty-three studies were included, primarily involving individuals with iRBD or other high-risk states associated with α-synucleinopathies, population-based or broader prodromal-risk cohorts, genetic high-risk populations, and individuals with idiopathic hyposmia/anosmia. Digital biomarkers encompassed motor, speech, sleep/activity, and multidomain features, with motor-related measures, particularly gait and mobility, being the most extensively investigated. Machine-learning approaches were applied across several domains, whereas genuinely multidomain studies remained limited. Most studies provided cross-sectional evidence of group differences or discriminative performance, whereas prospective evidence regarding future PD diagnosis or phenoconversion was less extensive. Considerable heterogeneity was observed in measurement protocols, analytical approaches, and validation strategies. Conclusion: Wearable- and portable-device-derived digital biomarkers represent promising candidate tools for characterizing and monitoring individuals across the prodromal PD risk spectrum. However, evidence supporting clinical prediction of phenoconversion remains limited by methodological heterogeneity and a lack of sufficient longitudinal and external validation. Future research should prioritize standardized assessment, large, multicenter longitudinal cohorts, independent external validation, and integration with established clinical and biological markers.
SN - 1663-4365
ER -