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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

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...

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Autores principales: Meinert, Edward, Milne-Ives, Madison, Chaudhuri, K Ray, Harding, Tracey, Whipps, John, Whipps, Susan, Carroll, Camille
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555326/
https://www.ncbi.nlm.nih.gov/pubmed/36155396
http://dx.doi.org/10.2196/40317
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author Meinert, Edward
Milne-Ives, Madison
Chaudhuri, K Ray
Harding, Tracey
Whipps, John
Whipps, Susan
Carroll, Camille
author_facet Meinert, Edward
Milne-Ives, Madison
Chaudhuri, K Ray
Harding, Tracey
Whipps, John
Whipps, Susan
Carroll, Camille
author_sort Meinert, Edward
collection PubMed
description 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 activation. Results will be disseminated in academic peer-reviewed journals and in platforms and formats that are accessible to the general public, guided by patient and public collaborators. TRIAL REGISTRATION: ClinicalTrials.gov NCT05414071; https://clinicaltrials.gov/ct2/show/NCT05414071 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40317
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spelling pubmed-95553262022-10-13 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 Meinert, Edward Milne-Ives, Madison Chaudhuri, K Ray Harding, Tracey Whipps, John Whipps, Susan Carroll, Camille JMIR Res Protoc Proposal 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 activation. Results will be disseminated in academic peer-reviewed journals and in platforms and formats that are accessible to the general public, guided by patient and public collaborators. TRIAL REGISTRATION: ClinicalTrials.gov NCT05414071; https://clinicaltrials.gov/ct2/show/NCT05414071 INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40317 JMIR Publications 2022-09-26 /pmc/articles/PMC9555326/ /pubmed/36155396 http://dx.doi.org/10.2196/40317 Text en ©Edward Meinert, Madison Milne-Ives, K Ray Chaudhuri, Tracey Harding, John Whipps, Susan Whipps, Camille Carroll. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 26.09.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Proposal
Meinert, Edward
Milne-Ives, Madison
Chaudhuri, K Ray
Harding, Tracey
Whipps, John
Whipps, Susan
Carroll, Camille
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Proposal
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555326/
https://www.ncbi.nlm.nih.gov/pubmed/36155396
http://dx.doi.org/10.2196/40317
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