Evidence map›Paper›PMID 39697442›Full record

ArticleFrontiers in neurology2024

Patient perspectives on the use of digital medical devices and health data for AI-driven personalised medicine in Parkinson's Disease.

Ivana Paccoud, Mayca Marín Valero, Laura Carrasco Marín, Noémi Bontridder, Alzhraa Ibrahim, Jüergen Winkler, Messaline Fomo, Stefano Sapienza, Fouad Khoury, Jean-Christophe Corvol and 2 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Clinical utility of digital technologies in Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2026
    Review
  7. Article
  8. Article
  9. Observational
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ivana PaccoudDepartment of Precision Medicine, Luxembourg Institute of Health, Strassen, Luxembourg.
Mayca Marín ValeroAsociación Parkinson Madrid (APM), Madrid, Spain.
Laura Carrasco MarínAsociación Parkinson Madrid (APM), Madrid, Spain.
Noémi BontridderResearch Centre in Information, Law and Society, Namur Digital Institute, University of Namur, Namur, Belgium.
Alzhraa IbrahimDepartment of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Jüergen WinklerCentre for Rare Diseases Erlangen (ZSEER), University Hospital Erlangen, Erlangen, Germany.
Messaline FomoDepartment of Precision Medicine, Luxembourg Institute of Health, Strassen, Luxembourg.
Stefano SapienzaDepartment of Precision Medicine, Luxembourg Institute of Health, Strassen, Luxembourg.
Fouad KhourySorbonne University, Paris Brain Institute - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Pitié-Salpêtrière Hospital, Paris, France.
Jean-Christophe CorvolSorbonne University, Paris Brain Institute - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Pitié-Salpêtrière Hospital, Paris, France.
Holger FröhlichDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany.
Jochen KluckenDepartment of Precision Medicine, Luxembourg Institute of Health, Strassen, Luxembourg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Parkinson's Disease (PD) affects around 8.5 million people currently with numbers expected to rise to 12 million by 2040. PD is characterized by fluctuating motor and non-motor symptoms demanding accurate monitoring. Recent advancements in digital medical devices (DMDs) like wearables and AI offer promise in addressing these needs. However, the successful implementation of DMDs in healthcare relies on patients' willingness to adopt and engage with these digital tools. Methods: To understand patient perspectives in individuals with PD, a cross-sectional study was conducted as part of the EU-wide DIGIPD project across France, Spain, and Germany. Multidisciplinary teams including neurodegenerative clinics and patient organizations conducted surveys focusing on (i) sociodemographic information, (ii) use of DMDs (iii) acceptance of using health data (iv) preferences for the DMDs use. We used descriptive statistics to understand the use of DMDs and patient preferences and logistic regression models to identify predictors of willingness to use DMDs and to share health data through DMDs. Results: In total 333 individuals with PD participated in the study. Findings revealed a high willingness to use DMDs (90.3%) and share personal health data (97.4%,) however this differed across sociodemographic groups and was more notable among older age groups (under 65 = 17.9% vs. over 75 = 39.29%, Conclusion: The study emphasizes the importance of considering patient perspectives for the effective deployment of digital technologies, especially for older and more advanced disease-stage patients who stand to benefit the most.

Indexed as

acceptance of digital medical devicesAdoption of AIParkinson’s Diseasepatient-centerednesspatient preferencespersonalized medicinetrustuse of health data

Identifiers

PMID39697442
PMCPMC11652348

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Registered trials

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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.