Medical treatments typically occur in the context of a social interaction between healthcare providers and patients. Although decades of research have demonstrated that patients’ expectations can dramatically affect treatment outcomes, less is known about the influence of providers’ expectations. Here we systematically manipulated providers’ expectations in a simulated clinical interaction involving administration of thermal pain and found that patients’ subjective experiences of pain were directly modulated by providers’ expectations of treatment success, as reflected in the patients’ subjective ratings, skin conductance responses and facial expression behaviours. The belief manipulation also affected patients’ perceptions of providers’ empathy during the pain procedure and manifested as subtle changes in providers’ facial expression behaviours during the clinical interaction. Importantly, these findings were replicated in two more independent samples. Together, our results provide evidence of a socially transmitted placebo effect, highlighting how healthcare providers’ behaviour and cognitive mindsets can affect clinical interactions.
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The data that support the findings of this study are available at https://github.com/cosanlab/socially_transmitted_placebo_effects/
The code that support the findings of this study is available at https://github.com/cosanlab/socially_transmitted_placebo_effects/
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We thank M. Meyer and E. Templeton for providing comments on earlier drafts of this paper. We thank S. Byrnes for helping us to create the visualization of the facial expression models. We also thank A. Brandt and S. Sadhukha for helping with data collection. This research was supported by a Chiang Ching-Kuo Foundation for International Scholarly Exchange award (no. GS040-A-16 to P.-H.C.), a National Institute of Health grant (no. R01MH076136 to T.D.W.), National Institute of Health grants (nos. R01MH116026 and R56MH080716 to L.J.C.) and a National Science Foundation grant (no. CAREER 1848370 to L.J.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Extended Data Fig. 1 Statistics of all factors from models tested during the Doctor Conditioning phase in Study 1.
Factors highlighted in bold were reported in the result section.
Extended Data Fig. 2 Subjective reports from doctors and patients during the doctor–patient interaction phase in Study 1.
(A) A demonstration of the experimental design. (B) Patients reported experiencing less pain in the thermedol treatment compared to the control treatment based on their maximal pain level from their continuous pain ratings. (C) Doctors’ beliefs formed in the Doctor Conditioning phase were maintained and showed no change after administering each treatment. (D) Patients reported findings the doctors more empathetic in the thermedol treatment compared to the control treatment. All panels include data from 24 dyads. Error bars represent S.E.M.
(A) Coefficients of the pain expression (PE) model. Features are represented by max, min or tmax followed by name of action unit. Higher coefficients contribute to higher pain. (B) PE model out of sample permutation test. To test if our PE model was actually capturing meaningful signal, we evaluated the performance of our model compared to a distribution of models generated from within-subject shuffled pain ratings. We repeated this procedure 5,000 times, and found our original pain model test-set accuracy in a leave-one-subject-out cross-validation of r = .41, calculated as the average across within-subject correlations between the actual z-scored and predicted pain ratings, was at the 99.92 percentile rank (p = .003, two-tailed) suggesting that the pain model was significantly performing better than chance. (C) Permutation test for the prediction of patients’ pain ratings. We repeated a similar shuffling procedure 5,000 times in which we shuffle the pain ratings from the training set from the doctor conditioning phase then testing the model on the patients’ faces during the interaction phase to predict their pain ratings. The accuracy was determined as the average across within-subject correlations between the actual z-scored and predicted pain ratings. The PE model prediction test-set accuracy of r = .24 was at the 99.84 percentile rank (p = .003, two-tailed) suggesting that using the PE model to predict patients’ pain ratings was significantly performing better than chance.
Extended Data Fig. 4 Statistics of all factors from models tested during the Doctor Conditioning phase in Study 2.
Factors highlighted in bold were reported in the result section.
(A) When the two treatments were administered in the original order, patients’ SCRs were significantly weaker for the thermedol than control treatment. (B) When the two treatments were administered in the reverse order, patients’ SCRs between the two treatment were not significantly different. All panels include data from 30 patients across both orders. Error bars represent S.E.M.
Extended Data Fig. 6 Statistics of all factors from models tested during the Doctor Conditioning phase in Study 3.
Factors highlighted in bold were reported in the result section.
Extended Data Fig. 7 Subjective reports of pain within each pain stimulation site from patients in Study 1 and skin conductance responses from patients in Study 3.
(A) Overall pain ratings within each site on average across both conditions indicated strong within-site habituation effect. Trial 0 indicated the practice trial for each site and trials 1 & 2 were the experimental trials. (B) The patients showed stronger SCR to the control (red) than the thermedol treatment (blue) in Study 3. Panel A includes data from 24 patients in Study 1, and panel B includes data from 24 patients in Study 3. Error bars represent S.E.M.
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Chen, P.A., Cheong, J.H., Jolly, E. et al. Socially transmitted placebo effects. Nat Hum Behav (2019) doi:10.1038/s41562-019-0749-5