Imperial College London
Alternate

EUR ING Dr Edward A Meinert

Faculty of Medicine, School of Public Health

Honorary Senior Lecturer
 
 
 
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Contact

 

e.meinert14

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Meinert:2022:10.2196/40317,
author = {Meinert, E and Milne-Ives, M and Chaudhuri, KR and Harding, T and Whipps, J and Whipps, S and Carroll, C},
doi = {10.2196/40317},
journal = {JMIR Res Protoc},
title = {The Impact of a Digital Artificial Intelligence System on the Monitoring and Self-management of Nonmotor Symptoms in People With Parkinson Disease: Proposal for a Phase 1 Implementation Study.},
url = {http://dx.doi.org/10.2196/40317},
volume = {11},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Nonmotor symptoms of Parkinson disease are a major factor of disease burden but are often underreported in clinical appointments. A digital tool has been developed to support the monitoring and management of nonmotor symptoms. OBJECTIVE: The aim of this study is to establish evidence of the impact of the system on patient confidence, knowledge, and skills for self-management of nonmotor symptoms, symptom burden, and quality of life of people with Parkinson and their care partners. It will also evaluate the usability, acceptability, and potential for adoption of the system for people with Parkinson, care partners, and health care professionals. METHODS: A mixed methods implementation and feasibility study based on the nonadoption, abandonment, scale-up, spread, and sustainability framework will be conducted with 60 person with Parkinson-care partner dyads and their associated health care professionals. Participants will be recruited from outpatient clinics at the University Hospitals Plymouth NHS Trust Parkinson service. The primary outcome, patient activation, will be measured over the 12-month intervention period; secondary outcomes include the system's impact on health and well-being outcomes, safety, usability, acceptability, engagement, and costs. Semistructured interviews with a subset of participants will gather a more in-depth understanding of user perspectives and experiences with the system. Repeated measures analysis of variance will analyze change over time and thematic analysis will be conducted on qualitative data. The study was peer reviewed by the Parkinson's UK Non-Drug Approaches grant board and is pending ethical approval. RESULTS: The study won funding in August 2021; data collection is expected to begin in December 2022. CONCLUSIONS: The study's success criteria will be affirming evidence regarding the system's feasibility, usability and acceptability, no serious safety risks identified, and an observed positive impact on patient acti
AU  - Meinert,E
AU  - Milne-Ives,M
AU  - Chaudhuri,KR
AU  - Harding,T
AU  - Whipps,J
AU  - Whipps,S
AU  - Carroll,C
DO  - 10.2196/40317
PY  - 2022///
SN  - 1929-0748
TI  - The Impact of a Digital Artificial Intelligence System on the Monitoring and Self-management of Nonmotor Symptoms in People With Parkinson Disease: Proposal for a Phase 1 Implementation Study.
T2  - JMIR Res Protoc
UR  - http://dx.doi.org/10.2196/40317
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36155396
VL  - 11
ER  -
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