ReviewJournal of Parkinson's disease2018
Management of Parkinson's Disease 20 Years from Now: Towards Digital Health Pathways.
Review in Journal of Parkinson's disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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
42 citing papers in PubMed.
- Trial
- Stakeholder perceptions of components of a Parkinson disease care management intervention, care coordination for health promotion and activities in Parkinson's disease (CHAPS).BMC neurology · 2020Trial
- Local and global microstructural and functional thalamomotor connectivity alterations in Parkinson's disease following motor learning.NPJ Parkinson's disease · 2026Article
- At-home movement state classification using totally implantable cortical-basal ganglia neural interface.Science advances · 2026Article
- WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson's Disease and Age-Matched Controls.Scientific data · 2026Article
- Engagement with the national electronic health records by people with Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- Neural correlates of Parkinson's improvements after combined digital-levodopa therapy: a pilot study.Brain communications · 2026Article
- Home-Based Augmented Reality Exercise For People With Parkinson Disease: Qualitative Acceptability Study.JMIR rehabilitation and assistive technologies · 2025Article
- Consensus on the clinical utility of digital mobility outcomes for personalized clinical decision support in parkinson's disease.Neurological research and practice · 2025Article
- Empowering Capabilities of People With Chronic Conditions Supported by Digital Health Technologies: Scoping Review.Journal of medical Internet research · 2025Article
- Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.Scientific reports · 2025Article
- A multidimensional gender analysis of health technology self-efficacy among people with Parkinson's disease.Journal of neurology · 2024Article
- Self-Management Systems for Patients and Clinicians in Parkinson Care: Protocol for an Integrated Scoping Review, Product Search, and Evaluation.JMIR research protocols · 2024Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Article
- Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- Patient perspectives on the use of digital medical devices and health data for AI-driven personalised medicine in Parkinson's Disease.Frontiers in neurology · 2024Article
- Clinical and device-based predictors of improved experience of activities of daily living after a multidisciplinary inpatient treatment for people with Parkinson's disease: a cohort study.Therapeutic advances in neurological disorders · 2024Article
- Building digital patient pathways for the management and treatment of multiple sclerosis.Frontiers in immunology · 2024Review
- Personalized Integrated Care Promoting Quality of Life for Older People: Protocol for a Multicenter Randomized Controlled Trial.JMIR research protocols · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current best medical treatment for patients with Parkinson's disease (PD) involves a medical professional who applies state-of-the-art knowledge of diagnostics and treatment- as derived from cohort studies and clinical trials- to the healthcare process of individual patients. Thus, the much-needed personalization of medicine depends on the abilities, experience and intuition of medical professionals to adjust group-based knowledge to individual decision making. Within 20 years from now, such personal clinical decisions will be largely supported by digital means, also defining a new ecosystem of healthcare often referred to as "digital medicine". We expect that the next phase of digitalization will include new "digital health pathways": data-driven personalized decision support that is based on a combination of multimodal data sources, including evidence-based medical knowledge (e.g., clinical guidelines), personal disease profiles (including genetic determinants of disease progression and treatment response), insights into individual disease trajectories (thereby defining subgroups of patients) and individual patients' needs. Here, we illustrate the potential of this development by sketching the contours of a digitally supported care pathway for gait disability and falls. Such digital health pathways will support the introduction of personalized medicine for PD patients, allowing patients to benefit optimally from individually tailored treatments. This should result in a better quality of life for patients and lower costs for society.
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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.