Observational studyNature biotechnology2022
Remote smartphone monitoring of Parkinson's disease and individual response to therapy.
Observational study in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.
What it found
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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
69 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Real-world gait study of Parkinson's disease using wearable sensors: a systematic review.Journal of neuroengineering and rehabilitation · 2026Pooled it
- Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Smartphone-Based Multimodal Digital Biomarker Integration for Parkinson's Disease Screening and Diagnostic Support.Neurology and therapy · 2026Article
- Explicit Mechanistic Causal Analyses or Interventional Trials Are Required for Objective, Clinical, Voice-Based Parkinson Disease Characterization.Journal of medical Internet research · 2026Article
- Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review.Journal of neurology · 2026Article
- NeuroSift for Task-Aware Quality Assurance of Multimedia Data in Remote Parkinson Disease Assessment: Machine Learning Model Development and Validation Study.Journal of medical Internet research · 2026Article
- How to advance the pharmacological management of cognitive impairment in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Scalable biomarkers of Parkinson's disease: insights from mobile EEG in Peru.Scientific reports · 2026Article
- Automatic and explainable assessment for Parkinson's disease by video-based human motion understanding.Journal of neuroengineering and rehabilitation · 2026Article
- Associations Between Smartphone-Based Finger Tapping and Cognitive Performance in Older Adults: Observational Study.Journal of medical Internet research · 2026Observational
- Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.Movement disorders clinical practice · 2026Review
- Dynamic Assessment of Fine Motor Control and Vocalization in Parkinson Disease Through a Smartphone App: Cross-Sectional Study of Time-Severity Interaction Effects.JMIR mHealth and uHealth · 2025Article
- Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores inJournal of Parkinson's disease · 2025Article
- Remote real time digital monitoring fills a critical gap in the management of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Review
- Exploratory digital outcome measures of motor sign progression in Parkinson's disease patients treated with prasinezumab.NPJ digital medicine · 2025Article
- Article
- Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.Biomedical engineering letters · 2025Article
- Diverse preferences, different solutions: Exploring remote monitoring preferences in Parkinson's disease through a discrete choice experiment.Journal of Parkinson's disease · 2025Article
- Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
- AssociatedDataset
Authors and funding
22 authors at 6 institutions in 3 countries.
Funding
Abstract
Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10
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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.