ArticleNPJ digital medicine2020
mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.
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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
63 citing papers in PubMed, 3 syntheses or guidelines pooled it, 113 citations in OpenAlex.
- Use of Smartphones and Wrist-Worn Devices for Motor Symptoms in Parkinson's Disease: A Systematic Review of Commercially Available Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled it
- Role of zonisamide in advanced Parkinson's disease: a randomized placebo-controlled study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Trial
- Using an unbiased symbolic movement representation to characterize Parkinson's disease states.Scientific reports · 2020Trial
- Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review.Journal of neurology · 2026Article
- Passive sensing of gait and medication-related fluctuations in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies.JMIR aging · 2026Observational
- Association between wearable-derived dyskinesia time burden and longitudinal change in disability in people with Parkinson's disease: a real-world treat-to-target cohort study.Scientific reports · 2026Article
- Continuous observation of Parkinsonian symptoms using symptom diaries & wearable accelerometry.Scientific data · 2026Article
- Digital health technology for Parkinson's disease with comprehensive monitoring and artificial intelligence-enabled haptic biofeedback for bulbar dysfunction.Journal of Parkinson's disease · 2025Article
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.Nature biomedical engineering · 2025Review
- Digital gait biomarkers in Parkinson's disease: susceptibility/risk, progression, response to exercise, and prognosis.NPJ Parkinson's disease · 2025Review
- Early start of opicapone in Parkinson's disease: evidence from a pooled analysis of phase 3 trials for sustained benefit in patients with recent onset of motor fluctuations.Frontiers in neurology · 2025Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data.Scientific reports · 2024Article
- Neurological Examination via Telemedicine: An Updated Review Focusing on Movement Disorders.Medicina (Kaunas, Lithuania) · 2024Review
- Upper limb intention tremor assessment: opportunities and challenges in wearable technology.Journal of neuroengineering and rehabilitation · 2024Review
- A novel machine learning based framework for developing composite digital biomarkers of disease progression.Frontiers in digital health · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.
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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.