Translation, Adaptation and Validation of the European Portuguese Version of the NMS-Quest for Parkinson’s Disease

Pedro, Tiago; Moreira, Fradique; Silva, Alexandra

doi:10.20344/amp.13160

Cited by 2 publications

References 25 publications

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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

Meinert¹,

Milne‐Ives²,

Harding³

et al. 2022

JMIR Res Protoc

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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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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

Meinert¹,

Milne‐Ives²,

Harding³

et al. 2022

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

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 (Preprint)

Meinert¹,

Milne‐Ives²,

Harding³

et al. 2022

Preprint

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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. CLINICALTRIAL ClinicalTrials.gov (NCT05414071)

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Translation, Adaptation and Validation of the European Portuguese Version of the NMS-Quest for Parkinson’s Disease

Cited by 2 publications

References 25 publications

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

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

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 (Preprint)

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