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Milou van Heuckelum,1,2 Cornelia HM van den Ende,1,3 Sandra van Dulmen,4– 6 Bart JF van den Bemt2,7,8 1Department of Rheumatology, Sint Maartenskliniek, Nijmegen, the Netherlands; 2Department of Pharmacy, Sint Maartenskliniek, Nijmegen, the Netherlands; 3Department of Rheumatology, Radboud University Medical Center, Nijmegen, the Netherlands; 4Department of Primary and Community Care, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands; 5Nivel (Netherlands Institute for Health Services Research), Utrecht, the Netherlands; 6Faculty of Health and Social Sciences, University of South-Eastern Norway, Drammen, Norway; 7Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands; 8Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre+, Maastricht, the NetherlandsCorrespondence: Milou van HeuckelumDepartment of Rheumatology, Sint Maartenskliniek, PO Box 9011, Nijmegen, 9500 GM, the NetherlandsTel +31 24 352 8123Email m.vanheuckelum@maartenskliniek.nlBackground: Non-adherence to medication (range 30– 107%) is a major issue in patients with rheumatoid arthritis (RA). Previous research has shown that electronic monitoring feedback (EMF) might be an effective strategy to improve medication adherence in chronic conditions. Therefore, this study investigated the effectiveness of electronic monitoring feedback in patients with early RA to improve medication adherence and clinical outcomes compared to usual care.Methods: An open-label randomized clinical trial was performed to compare EMF with standard care during a 12-month follow-up period on two sites of the Sint Maartenskliniek (Nijmegen and Boxmeer) in the Netherlands. Patients were eligible if they: (1) had a (working) diagnosis of early RA, (2) were currently using methotrexate, (3) were aged ≥ 18 years, and (4) had a life expectancy of ≥ 12 months. Primary outcome was the difference in p

Milou van Heuckelum,1,2 Elke GE Mathijssen,2 Marcia Vervloet,3 Annelies Boonen,4,5 Renske CF Hebing,6 Annelieke Pasma,7 Harald E Vonkeman,8,9 Mark H Wenink,2,10 Bart JF van den Bemt,1,11–12 Liset van Dijk3,131Department of Pharmacy, Sint Maartenskliniek, Nijmegen, the Netherlands; 2Department of Rheumatology, Sint Maartenskliniek, Nijmegen, the Netherlands; 3Nivel (Netherlands Institute for Health Services Research), Utrecht, the Netherlands; 4Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands; 5Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Centre+, Maastricht, the Netherlands; 6Department of Rheumatology, Reade, Amsterdam, the Netherlands; 7Department of Rheumatology, Erasmus Medical Centre, Rotterdam, the Netherlands; 8Department of Rheumatology and Clinical Immunology, Medisch Spectrum Twente, Enschede, the Netherlands; 9Department of Psychology, Health and Technology, Arthritis Center Twente, University of Twente, Enschede, the Netherlands; 10Department of Rheumatology, Radboud University Medical Center, Nijmegen, the Netherlands; 11Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands; 12Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre+, Maastricht, the Netherlands; 13Department of PharmacoTherapy, Epidemiology & Economics (PTEE), Groningen Research Institute of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, the NetherlandsBackground: Although patients have different treatment preferences, these individual preferences could often be grouped in subgroups with shared preferences. Knowledge of these subgroups as well as factors associated with subgroup membership supports health care professionals in the understanding of what matters to patients in clinical decision-making.Objectives: To identify subgroups of patients

Elke GE Mathijssen,1 Milou van Heuckelum,2 Liset van Dijk,3 Marcia Vervloet,3 Simone MT Zonnenberg,1 Johanna E Vriezekolk,1 Bart JF van den Bemt2 1Department of Rheumatology, Sint Maartenskliniek, Nijmegen, the Netherlands; 2Department of Rheumatology and Pharmacy, Sint Maartenskliniek, Nijmegen, the Netherlands; 3Netherlands Institute for Health Services Research (NIVEL), Utrecht, the Netherlands Objective: To comprehensively describe the identification, refinement, and selection of attributes and levels for a discrete choice experiment (DCE) on preferences of patients with rheumatoid arthritis (RA) regarding disease-modifying antirheumatic drugs (DMARDs). Methods: A mixed-methods approach, consisting of three consecutive steps: a literature review, expert recommendations, and focus groups. Attributes and levels were identified by a scoping review and compiled into a list that was evaluated on its relevance by an expert panel. The list that resulted thereafter was used to inform three focus groups, including 23 patients with RA. New attributes and levels could be identified during the focus groups. Also, a ranking exercise was performed. The patients individually ranked the attributes (ie, the ones on the list and newly identified attributes) by relevance. The patients’ individual rankings were summed to derive a ranking at group level and make an a priori selection of the most relevant attributes. The group discussions were transcribed for qualitative analysis. Results: Nineteen attributes, each specified by two to seven levels, were identified by the scoping review. The expert recommendations resulted in the removal of one attribute. Furthermore, two new attributes and levels were identified and two attributes were split into two. One new attribute was identified during the focus groups. The results of the ranking exercise and qualitative analysis led to the refinement and selection of the following attributes: route of administration, frequency of admi