IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i4p3364-d1068509.html
   My bibliography  Save this article

The Role of Web-Based Adaptive Choice-Based Conjoint Analysis Technology in Eliciting Patients’ Preferences for Osteoarthritis Treatment

Author

Listed:
  • Basem Al-Omari

    (Department of Epidemiology and Population Health, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
    Faculty of Health and Life Sciences, The University of Northumbria, Benton, Newcastle upon Tyne NE7 7XA, UK)

  • Joviana Farhat

    (Department of Epidemiology and Population Health, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Mujahed Shraim

    (Department of Public Health, College of Health Sciences, Qatar University, QU Health, Doha P.O. Box 2713, Qatar)

Abstract

Objective: To assess the feasibility of using adaptive choice-based conjoint (ACBC) analysis to elicit patients’ preferences for pharmacological treatment of osteoarthritis (OA), patients’ satisfaction with completing the ACBC questionnaire, and factors associated with questionnaire completion time. Methods: Adult patients aged 18 years and older with a medical diagnosis of OA, experiencing joint pain in the past 12 months, and living in the Northeast of England participated in the study. The participants completed a web-based ACBC questionnaire about their preferences regarding pharmaceutical treatment for OA using a touchscreen laptop independently, and accordingly, the questionnaire completion time was measured. Moreover, the participants completed a pen-and-paper feedback form about their experience in completing the ACBC questionnaire. Results: Twenty participants aged 40 years and older, 65% females, 75% had knee OA, and suffering from OA for more than 5 years participated in the study. About 60% of participants reported completing a computerized questionnaire in the past. About 85% of participants believed that the ACBC task helped them in making decisions regarding their OA medications, and 95% agreed or strongly agreed that they would be happy to complete a similar ACBC questionnaire in the future. The average questionnaire completion time was 16 min (range 10–24 min). The main factors associated with longer questionnaire completion time were older age, never using a computer in the past, and no previous experience in completing a questionnaire. Conclusions: The ACBC analysis is a feasible and efficient method to elicit patients’ preferences for pharmacological treatment of OA, which could be used in clinical settings to facilitate shared decision-making and patient-centered care. The ACBC questionnaire completion consumes a significantly longer time for elderly participants, who never used a computer, and never completed any questionnaire previously. Therefore, the contribution of patients and public involvement (PPI) group in the development of the ACBC questionnaire could facilitate participants’ understanding and satisfaction with the task. Future research including patients with different chronic conditions may provide more useful information about the efficiency of ACBC analysis in eliciting patients’ preferences for osteoarthritis treatment.

Suggested Citation

  • Basem Al-Omari & Joviana Farhat & Mujahed Shraim, 2023. "The Role of Web-Based Adaptive Choice-Based Conjoint Analysis Technology in Eliciting Patients’ Preferences for Osteoarthritis Treatment," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3364-:d:1068509
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/4/3364/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/4/3364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
    3. Vithala R. Rao, 2014. "Applied Conjoint Analysis," Springer Books, Springer, edition 127, number 978-3-540-87753-0, December.
    4. Edward J. D. Webb & David Meads & Ieva Eskyte & Natalie King & Naila Dracup & Jeremy Chataway & Helen L. Ford & Joachim Marti & Sue H. Pavitt & Klaus Schmierer & Ana Manzano, 2018. "A Systematic Review of Discrete-Choice Experiments and Conjoint Analysis Studies in People with Multiple Sclerosis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(4), pages 391-402, August.
    5. Charles Cunningham & Ken Deal & Yvonne Chen, 2010. "Adaptive Choice-Based Conjoint Analysis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 257-273, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Schlereth & Bernd Skiera, 2017. "Two New Features in Discrete Choice Experiments to Improve Willingness-to-Pay Estimation That Result in SDR and SADR: Separated (Adaptive) Dual Response," Management Science, INFORMS, vol. 63(3), pages 829-842, March.
    2. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    3. Mengelkamp, Esther & Schönland, Thomas & Huber, Julian & Weinhardt, Christof, 2019. "The value of local electricity - A choice experiment among German residential customers," Energy Policy, Elsevier, vol. 130(C), pages 294-303.
    4. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    5. Will, Christian & Lehmann, Nico & Baumgartner, Nora & Feurer, Sven & Jochem, Patrick & Fichtner, Wolf, 2022. "Consumer understanding and evaluation of carbon-neutral electric vehicle charging services," Applied Energy, Elsevier, vol. 313(C).
    6. Bodo Herzog, 2018. "Valuation of Digital Platforms: Experimental Evidence for Google and Facebook," IJFS, MDPI, vol. 6(4), pages 1-13, October.
    7. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    8. Lukas Kornher & Martin Schellhorn & Saskia Vetter, 2019. "Disgusting or Innovative-Consumer Willingness to Pay for Insect Based Burger Patties in Germany," Sustainability, MDPI, vol. 11(7), pages 1-20, March.
    9. Christian P Theurer & Andranik Tumasjan & Isabell M Welpe, 2018. "Contextual work design and employee innovative work behavior: When does autonomy matter?," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-35, October.
    10. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    11. Danaf, Mazen & Atasoy, Bilge & de Azevedo, Carlos Lima & Ding-Mastera, Jing & Abou-Zeid, Maya & Cox, Nathaniel & Zhao, Fang & Ben-Akiva, Moshe, 2019. "Context-aware stated preferences with smartphone-based travel surveys," Journal of choice modelling, Elsevier, vol. 31(C), pages 35-50.
    12. Xiang Wu & Bin Hu & Jie Xiong, 2020. "Understanding Heterogeneous Consumer Preferences in Chinese Milk Markets: A Latent Class Approach," Post-Print hal-02489646, HAL.
    13. Isihara, Paul & Shi, Chaojun & Ward, Jonathan & O'Malley, Leo & Laney, Skyler & Diedrichs, Danilo & Flores, Gabriel, 2020. "Identifying most typical and most ideal attribute levels in small populations of expert decision makers: Studying the Go/No Go decision of disaster relief organizations," Journal of choice modelling, Elsevier, vol. 35(C).
    14. Poulissen, Davey & de Grip, Andries & Fouarge, Didier & Künn, Annemarie, 2021. "Employers’ willingness to invest in the training of temporary workers: a discrete choice experiment," Research Memorandum 010, Maastricht University, Graduate School of Business and Economics (GSBE).
    15. Frank Ebbers & Jan Zibuschka & Christian Zimmermann & Oliver Hinz, 2021. "User preferences for privacy features in digital assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 411-426, June.
    16. Knoblauch, Theresa A.K. & Trutnevyte, Evelina & Stauffacher, Michael, 2019. "Siting deep geothermal energy: Acceptance of various risk and benefit scenarios in a Swiss-German cross-national study," Energy Policy, Elsevier, vol. 128(C), pages 807-816.
    17. Scholz, Michael & Pfeiffer, Jella & Rothlauf, Franz, 2017. "Using PageRank for non-personalized default rankings in dynamic markets," European Journal of Operational Research, Elsevier, vol. 260(1), pages 388-401.
    18. Sunghi An & Daisik Nam & R. Jayakrishnan & Soongbong Lee & Michael G. McNally, 2021. "A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    19. Carsten Herbes & Johannes Dahlin & Peter Kurz, 2020. "Consumer Willingness To Pay for Proenvironmental Attributes of Biogas Digestate-Based Potting Soil," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    20. Paolo Mariani & Andrea Marletta & Mauro Mussini & Mariangela Zenga, 2021. "Professional Profiles and Job Requirements in Labour Demand: An Analysis of the Italian Information Technology Sector," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 651-667, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3364-:d:1068509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.