ReviewFrontiers in neurology2022
Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.
Review in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- The neurologist of 2050: Rethinking the role in movement disorders in a technologically transformed society.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- The future digital outpatient clinic for movement disorders: from technological innovation to ethical and systemic implementation-a position paper.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Digital Technologies for Symptom Monitoring in Parkinson Disease.Current neurology and neuroscience reports · 2026Review
- Evolution of disease modifying therapy clinical trial design and therapeutic endpoints for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Home-Based Augmented Reality Exercise For People With Parkinson Disease: Qualitative Acceptability Study.JMIR rehabilitation and assistive technologies · 2025Article
- Stakeholder Perspectives on Trustworthy AI for Parkinson Disease Management Using a Cocreation Approach: Qualitative Exploratory Study.Journal of medical Internet research · 2025Article
- Objective monitoring of motor symptom severity and their progression in Parkinson's disease using a digital gait device.Scientific reports · 2025Article
- Recommendations for Successful Development and Implementation of Digital Health Technology Tools.Journal of medical Internet research · 2025Article
- Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.Journal of medical Internet research · 2025Article
- Predictive modeling to uncover Parkinson's disease characteristics that delay diagnosis.NPJ Parkinson's disease · 2025Article
- Explaining facial action units' correlation with hypomimia and clinical scores in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Facial expression analysis to uncover the relationship between sialorrhea and hypomimia in Parkinson's disease.Frontiers in neurology · 2025Article
- Personalized progression modelling and prediction in Parkinson's disease with a novel multi-modal graph approach.NPJ Parkinson's disease · 2024Article
- Predicting age of onset and progression of disease in late-onset genetic neurodegenerative diseases: An ethics review and research agenda.European journal of human genetics : EJHG · 2024Review
- Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.NPJ digital medicine · 2024Article
- Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis.NPJ Parkinson's disease · 2024Article
- Mapping the ethical landscape of digital biomarkers: A scoping review.PLOS digital health · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.