Literature DB >> 22994378

Elderly patients' experiences using adaptive conjoint analysis software as a decision aid for osteoarthritis of the knee.

Donna Rochon1, Jan M Eberth, Liana Fraenkel, Robert J Volk, Simon N Whitney.   

Abstract

BACKGROUND: Decision making in knee osteoarthritis, with many treatment options, challenges patients and physicians alike. Unfortunately, physicians cannot describe in detail each treatment's benefits and risks. One promising adjunct to decision making in osteoarthritis is adaptive conjoint analysis (ACA).
OBJECTIVE: To obtain insight into the experiences of elderly patients who use adaptive conjoint analysis to explore treatment options for their osteoarthritis.
DESIGN: Participants, all 65 and older, completed an ACA decision aid exploring their preferences with regard to the underlying attributes of osteoarthritis interventions. We used focus groups to obtain insight into their experiences using this software.
RESULTS: Content analysis distributed our participants' concerns into five areas. The predicted preferred treatment usually agreed with the individual's preference, but our participants experienced difficulty in four other domains: the choices presented by the software were sometimes confusing, the treatments presented were not the treatments of most interest, the researchers' claims about treatment characteristics were unpersuasive and cumulative overload sometimes developed.
CONCLUSION: Adaptive conjoint analysis presented special challenges to our elderly participants; we believe that their relatively low level of computer comfort was a significant contributor to these problems. We suggest that other researchers choose the software's treatments and present the treatment attributes with care. The next and equally vital step is to educate participants about what to expect, including the limitations in choice and apparent arbitrariness of the trade-offs presented by the software. Providing participants with a sample ACA task before undertaking the study task may further improve participant understanding and engagement.
© 2012 John Wiley & Sons Ltd.

Entities:  

Keywords:  aged; anti-inflammatory agents; arthralgia/therapy; attitude to computers; computer literacy; computer-assisted; decision making; feasibility studies; knee; non-steroidal; osteoarthritis; patient education; patient participation; patient preference; peptic ulcer/chemically induced; physician-patient relations; task performance and analysis; user-computer interface

Mesh:

Year:  2012        PMID: 22994378      PMCID: PMC3883995          DOI: 10.1111/j.1369-7625.2012.00811.x

Source DB:  PubMed          Journal:  Health Expect        ISSN: 1369-6513            Impact factor:   3.377


  31 in total

1.  An experiment on simplifying conjoint analysis designs for measuring preferences.

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Journal:  Health Econ       Date:  2003-12       Impact factor: 3.046

2.  Using conjoint analysis to assess depression treatment preferences among low-income Latinos.

Authors:  Megan Dwight-Johnson; Isabel T Lagomasino; Eugene Aisenberg; Joel Hay
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4.  Eliciting stated health preferences: an application to willingness to pay for longevity.

Authors:  F R Johnson; W H Desvousges; M C Ruby; D Stieb; P De Civita
Journal:  Med Decis Making       Date:  1998 Apr-Jun       Impact factor: 2.583

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Authors:  Dawn Stacey; Carol L Bennett; Michael J Barry; Nananda F Col; Karen B Eden; Margaret Holmes-Rovner; Hilary Llewellyn-Thomas; Anne Lyddiatt; France Légaré; Richard Thomson
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7.  Practitioner review: computerized assessment of neuropsychological function in children: clinical and research applications of the Cambridge Neuropsychological Testing Automated Battery (CANTAB).

Authors:  Monica Luciana
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8.  Informed choice and the widespread use of antiinflammatory drugs.

Authors:  Liana Fraenkel; Dick R Wittink; John Concato; Terri Fried
Journal:  Arthritis Rheum       Date:  2004-04-15

9.  If You Want Patients with Knee Osteoarthritis (OA) to Exercise: Tell them about NSAIDS.

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10.  Adaptive Conjoint Analysis as individual preference assessment tool: feasibility through the internet and reliability of preferences.

Authors:  Arwen H Pieterse; Frank Berkers; Monique C M Baas-Thijssen; Corrie A M Marijnen; Anne M Stiggelbout
Journal:  Patient Educ Couns       Date:  2009-07-05
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2.  Patients' preferences for osteoarthritis treatment: the value of stated-preference studies.

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Review 3.  Discrete choice experiments in health economics: a review of the literature.

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Journal:  Pharmacoeconomics       Date:  2014-09       Impact factor: 4.981

Review 4.  Methods to Assess Patient Preferences in Old Age Pharmacotherapy - A Systematic Review.

Authors:  Annette Eidam; Anja Roth; André Lacroix; Sabine Goisser; Hanna M Seidling; Walter E Haefeli; Jürgen M Bauer
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  4 in total

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