Literature DB >> 34151710

Using a novel smartphone application for capturing of patient-reported outcome measures among patients with inflammatory arthritis:A randomized, crossover, agreement study.

L Uhrenholt1,2,3,4, R Christensen3,5, L Dreyer1,2, A Schlemmer1, E-M Hauge6,7, N S Krogh8, M K Abildtoft8, P C Taylor9, S Kristensen1,2.   

Abstract

Objectives: In Denmark, patients with inflammatory arthritis (IA) have completed patient-reported outcome measures (PROMs) via touchscreens in the outpatient clinic since 2006. However, current technology makes it possible for patients to use their own smartphone via an application (app) developed for the Danish Rheumatology Database (DANBIO). This study aims to evaluate the agreement of PROMs between the DANBIO app and outpatient touchscreen in patients with IA.Method: Patients with IA (rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis) were enrolled in a randomized, crossover, agreement study. Participants answered PROMs through the two device types in a randomized order. Differences in PROM scores with 95% confidence intervals (CIs) were evaluated for similarity according to prespecified equivalence margins.
Results: The touchscreen invitation was accepted by 138 patients. Sixty patients (20 with each diagnosis) were included. The difference in Health Assessment Questionnaire Disability Index between the two device types was -0.007 (95% CI -0.043 to 0.030); thus, equivalence was demonstrated. In addition, all other PROMs obtained with the two device types were equivalent, except for the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), which was within the limits of minimally clinically important difference (MCID). In total, 78.3% preferred the DANBIO app.
Conclusion: In patients with IA, equivalence was demonstrated between two device types for all PROMs except BASDAI; however, BASDAI was within the limits of the MCID. Implementation of the DANBIO app is expected to optimize outpatient visits, thereby improving healthcare for the individual patient and society.

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Year:  2021        PMID: 34151710     DOI: 10.1080/03009742.2021.1907925

Source DB:  PubMed          Journal:  Scand J Rheumatol        ISSN: 0300-9742            Impact factor:   3.641


  2 in total

1.  Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.

Authors:  Nicola Luigi Bragazzi; Charlie Bridgewood; Abdulla Watad; Giovanni Damiani; Jude Dzevela Kong; Dennis McGonagle
Journal:  Front Immunol       Date:  2022-03-10       Impact factor: 7.561

Review 2.  Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).

Authors:  Diederik De Cock; Elena Myasoedova; Daniel Aletaha; Paul Studenic
Journal:  Ther Adv Musculoskelet Dis       Date:  2022-06-30       Impact factor: 3.625

  2 in total

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