Online Decision Aids for Knee Osteoarthritis and Low Back Pain: An Environmental Scan and Evaluation

Michael Anthony Fajardo, Bandar Durayb, Haoxi Zhong, Lyndal Trevena, Adrian Traeger and Carissa Bonner
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Michael Anthony Fajardo: School of Public Health, University of Sydney, Sydney, NSW, Australia
Bandar Durayb: School of Public Health, University of Sydney, Sydney, NSW, Australia
Haoxi Zhong: School of Public Health, University of Sydney, Sydney, NSW, Australia
Lyndal Trevena: School of Public Health, University of Sydney, Sydney, NSW, Australia
Adrian Traeger: School of Public Health, University of Sydney, Sydney, NSW, Australia
Carissa Bonner: School of Public Health, University of Sydney, Sydney, NSW, Australia

Medical Decision Making, 2019, vol. 39, issue 4, 328-335

Abstract: Background . Musculoskeletal conditions are leading causes of disability. Management options are plentiful, but the current evidence base suggests many are ineffective or unproven. Online decision aids can help support patients make informed health care choices. However, there are little data on the quality of online decision aids for common musculoskeletal conditions such as knee or low back pain. Purpose . To identify all publicly available online decision aids for knee osteoarthritis and low back pain and evaluate them against the International Patient Decision Aids Standards Inventory (IPDASi). Data Sources . Google Australia. Study selection. Two reviewers independently screened websites for inclusion and assessed the quality of included online decision aids between April and May 2018. Included online decision aids were free, provided information about knee osteoarthritis or low back pain, and written in English. Online decision aids that required payment, targeted health professionals, addressed rheumatoid arthritis, or addressed a screening decision were excluded. Data Extraction . IPDASi Version 4. Data Synthesis . Twenty-five online decision aids were identified: 15 knee osteoarthritis and 10 low back pain. Only 3 online decision aids (12%) provided a “wait-and-see†option. Nineteen (75%) met IPDASi criteria to be considered a decision aid and 3 (12%) met IPDASi criteria to state that the online decision aid was unbiased. Limitations . Dynamic nature of Google searches may not be replicable easily. Conclusions . Few good-quality online decision aids are available for people with knee osteoarthritis or low back pain. Most online decision aids failed to explicitly provide a wait-and-see option, suggesting a bias toward intervention. These online decision aids would benefit from explicitly highlighting a wait-and-see option to support informed choice.

Keywords: decision aid; evaluation; low back pain; knee osteoarthritis (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:39:y:2019:i:4:p:328-335

DOI: 10.1177/0272989X19844720

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