AbstractA nocebo effect is the experience of unpleasant symptoms in response to an inert exposure. The effect may explain many of the side effects that are attributed to medications. Preventing nocebo effects should reduce costs to health services and improve patients’ quality of life.
The mechanisms underlying nocebo effects may include misattribution of symptoms, learning from past experiences, conditioning or social observation, and conscious expectation. In a systematic review of 89 studies testing predictors of symptom reporting following an inert exposure, I found considerable evidence supporting the role of expectations.
Using this evidence, I developed and piloted an intervention to reduce expectations by reframing the side effect information in patient information leaflets (PILs), reporting the chance of remaining side effect free rather than the risk of developing side effects. In a randomised controlled trial (RCT) of 203 healthy volunteers, this reframing significantly reduced the likelihood of participants attributing symptoms to a sham medicine.
To determine how the English population interpret the current side effect wording in PILs, I carried out a cross-sectional survey of a nationally representative sample of 1003 people. This showed that current wording results in unreasonably high side effect expectations, adding justification for the wording to be altered.
As part of the systematic review and RCT, I also examined associations between baseline characteristics and symptom reporting. The results showed little support for the importance of demographic or personality characteristics. However, medicine-related beliefs are important risk factors for nocebo effects, and could be targeted in future interventions.
Overall, my works suggests that the way we currently communicate about side effect risks is ineffective, that reframing risk may be beneficial, and that assessing medicine-related beliefs may allow us to identify patients at risk of experiencing nocebo effects. Future work is needed to test these findings in a patient sample.
|Date of Award||2018|
|Supervisor||John Weinman (Supervisor)|