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Development of a Decision Aid to Support Shared Decision-Making on Cannabis Use for Arthritis: Protocol for a
Heba A T Aref1, Yazid N Al Hamarneh2, Tony K L Kiang1
1Faculty of Pharmacy & Pharmaceutical Sciences, University of Alberta, 3-229 Dianne and Irving Kipnes Health Research Academy, 11405 87 Ave. NW, Edmonton, AB, T6W 0B3, Canada, 1 780-492-5078, 1 780-492-1217.
This study is developing a decision aid (DA) to help older adults with arthritis make informed choices about medical cannabis. The DA will support shared decision-making (SDM) by providing evidence-based information on potential benefits and risks.
Area of Science:
- Gerontology
- Pharmacology
- Health Informatics
Background:
- Arthritis pain management often requires exploring alternative therapies beyond conventional treatments.
- Medical cannabis is a potential option for arthritis symptom relief, necessitating informed patient decisions.
- Patients need to weigh the benefits and risks of cannabis use against personal values.
Purpose of the Study:
- To systematically develop a theory-driven, evidence-based, and user-centered decision aid (DA).
- The DA aims to support shared decision-making (SDM) for older adults with arthritis considering medical cannabis.
Main Methods:
- The development process follows the Ottawa Decision Support Framework and International Patient Decision Aids Standards.
- Four phases include scoping reviews, patient and clinician interviews, DA development, and alpha testing.
- Recruitment for patient and clinician interviews is underway in Alberta, Canada.
Main Results:
- Phase 1 completed: two scoping reviews published, identifying common SDM tool frameworks and limited evidence on medical cannabis for arthritis.
- Phases 2 and 3 initiated, with patient and clinician recruitment ongoing.
- An advisory board is assembled, and the DA development is progressing.
Conclusions:
- This protocol details the systematic development of a patient-centered DA for medical cannabis decisions in arthritis.
- The findings will assess the DA's feasibility and inform its refinement for beta testing and future implementation.

