Changing Patient and Public Beliefs About Antimicrobials and Antimicrobial Resistance (AMR) Using a Brief Digital Intervention

Abstract

Background: A key driver of antimicrobial resistance (AMR) is patient demand for unnecessary antibiotics, which is driven by patients’ beliefs about antibiotics and AMR. Few interventions have targeted beliefs to reduce inappropriate demand.Objective: To examine whether a brief, online algorithm-based intervention can change beliefs that may lead to inappropriate antibiotic demand (i.e. perceptions of antibiotic necessity and lack of concern about antibiotic harm).Design: Pre- and post-intervention study.Participants: Participants were 18 years or older, and residing in the United Kingdom, who self-selected to participate via Amazon mTurk, an online survey plaform, and via research networks.Intervention: Participants were presented with a hypothetical situation of cold and flu symptoms, then exposed to the intervention. The online intervention comprised: 1) a profiling tool identifying individual beliefs (antibiotic necessity, concerns, and knowledge) driving inappropriate antibiotic demand; 2) messages designed to change beliefs and knowledge (i.e. reduce antibiotic necessity, and increase antibiotic concerns and knowledge), and 3) an algorithm linking specific messages to specific beliefs and knowledge.Main measures: The profiling tool was repeated immediately after the intervention and compared with baseline scores to assess change in beliefs. A paired samples t-test was used to determine intervention effect.Key Results: A total of 100 respondents completed the study. A significant change in beliefs relating to inappropriate demand was observed after the intervention, with a reduction in beliefs about antibiotic necessity (t = 7.254; p < 0.0001), an increase in antibiotic concerns (t = −7.214; p < 0.0001), and increases in antibiotic and AMR knowledge (t = −4.651; p < 0.0001).Conclusion: This study is the first to demonstrate that patient beliefs about antibiotics and AMR associated with inappropriate demand can be changed by a brief, tailored online intervention. This has implications for the design of future interventions to reduce unnecessary antimicrobial use.

Influencing attitudes towards antimicrobial use and resistance in companion animals—the impact on pet owners of a short animation in a randomized controlled trial

“…Interventions founded on psychology and health behaviour change principles have been shown successful in prompting behaviour change. 32 The HBM model has previously been used in veterinary research assessing owner compliance with diet recommendations, 33 demonstrating the adaptability of this tool to veterinary medicine. This study represents the first time the HBM has been applied to antibiotic use in the companion animal setting.…”

“… 43 Similar to other behaviour change interventions, the animation was designed to discourage requests for antimicrobials by emphasizing the threat and consequences of AMR. 32 , 44 Scores for pet owners in the animation group indicated that they would be less likely to expect or request antimicrobials if their pet had diarrhoea (statements 8 and 19), would be prepared to wait longer before considering antimicrobial use (statement 16) and would also be more likely to consider delayed prescription of antimicrobials (statement 10). There was also a demonstration of greater awareness that requesting antimicrobials could increase unnecessary use (statement 12).…”

Knowledge, attitude and practicing behaviour regarding antimicrobial use and awareness of antimicrobial resistance among clinicians

“…34,35 The participants read online scientific journals (78.2%) as a major information source on antibiotic therapy and resistance. 36…”

Online Fake News Opinion Spread and Belief Change: A Systematic Review

“…Explanation personalization was also addressed in this dimension. The authors of [77] showed that a personalized online algorithm-based intervention can change beliefs that may lead to inappropriate antibiotic demand by patients. Conversely, results obtained in [75] show that personalization enhances user experience, but the so-called "filter bubbles" favor the emergence of opinion polarization and radicalization through confirmation bias.…”