Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Research Protocols

Date Submitted: Jun 15, 2022
Open Peer Review Period: Jun 15, 2022 - Jun 23, 2022
Date Accepted: Jun 30, 2022
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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 E, Milne-Ives M, Chaudhuri KR, Harding T, Whipps J, Whipps S, Carroll C

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

JMIR Res Protoc 2022;11(9):e40317

DOI: 10.2196/40317

PMID: 36155396

PMCID: 9555326

The impact of a digital artificial intelligence system on the monitoring and self-management of non-motor symptoms in People with Parkinson’s: Proposal for a Phase 1 implementation study

  • Edward Meinert; 
  • Madison Milne-Ives; 
  • Kallol Ray Chaudhuri; 
  • Tracey Harding; 
  • John Whipps; 
  • Susan Whipps; 
  • Camille Carroll

ABSTRACT

Background:

Non-motor symptoms of Parkinson’s 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 NMS.

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 NMS, symptom burden, and quality of life of people with Parkinson’s (PwP) and their care partners (CPs). It will also evaluate the usability, acceptability, and potential for adoption of the system for PwP, CPs, and healthcare professionals (HCPs).

Methods:

A mixed-methods implementation and feasibility study based on the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability framework will be conducted with 60 PwP-CP dyads and their associated HCPs. Participants will be recruited from outpatient clinics at the University Hospitals Plymouth NHS Trust’s Parkinson’s 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. Semi-structured interviews with a subset of participants will gather a more in-depth understanding of users' perspectives and experiences with the system. Repeated measures ANOVA will analyse change over time and thematic analysis will be conducted on qualitative data. The was peer-reviewed by the Parkinson’s UK Non-Drug Approaches grant board, and is pending HRA and REC ethical approval (IRAS reference number: 311333).

Results:

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.

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. Clinical Trial: ClinicalTrials.gov (NCT05414071)


 Citation

Please cite as:

Meinert E, Milne-Ives M, Chaudhuri KR, Harding T, Whipps J, Whipps S, Carroll C

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

JMIR Res Protoc 2022;11(9):e40317

DOI: 10.2196/40317

PMID: 36155396

PMCID: 9555326

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.