Abstract
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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The data that support the findings of this study are available at https://github.com/cosanlab/socially_transmitted_placebo_effects/
Code availability
The code that support the findings of this study is available at https://github.com/cosanlab/socially_transmitted_placebo_effects/
References
Beecher, H. K. The powerful placebo. J. Am. Med. Assoc. 159, 1602–1606 (1955).
Gold, H., Kwit, N. T. & Otto, H. The xanthines (theobromine and aminophylline) in the treatment of cardiac pain. J. Am. Med. Assoc. 108, 2173–2179 (1937).
Rosenzweig, S. Some implicit common factors in diverse methods of psychotherapy. Am. J. Orthopsychiatry 6, 412–415 (1936).
Houston, W. R. The doctor himself as a therapeutic agent. Ann. Intern. Med. 11, 1416 (1938).
Uhlenhuth, E. H., Canter, A., Neustadt, J. O. & Payson, H. E. The symptomatic relief of anxiety with meprobamate, phenobarbital and placebo. Am. J. Psychiatry 115, 905–910 (1959).
Shapiro, A. K. & Shapiro, E. The Powerful Placebo: From Ancient Priest to Modern Physician (JHU Press, 2000).
Ioannidis, J. P. et al. Comparison of evidence of treatment effects in randomized and nonrandomized studies. J. Am. Med. Assoc. 286, 821–830 (2001).
Świder, K. & Bąbel, P. The effect of the sex of a model on nocebo hyperalgesia induced by social observational learning. Pain 154, 1312–1317 (2013).
Vögtle, E., Barke, A. & Kröner-Herwig, B. Nocebo hyperalgesia induced by social observational learning. Pain 154, 1427–1433 (2013).
Colloca, L. & Benedetti, F. Placebo analgesia induced by social observational learning. Pain 144, 28–34 (2009).
Koban, L. & Wager, T. D. Beyond conformity: social influences on pain reports and physiology. Emotion 16, 24–32 (2016).
Yoshida, W., Seymour, B., Koltzenburg, M. & Dolan, R. J. Uncertainty increases pain: evidence for a novel mechanism of pain modulation involving the periaqueductal gray. J. Neurosci. 33, 5638–5646 (2013).
Haaker, J., Yi, J., Petrovic, P. & Olsson, A. Endogenous opioids regulate social threat learning in humans. Nat. Commun. 8, 15495 (2017).
Benedetti, F., Durando, J. & Vighetti, S. Nocebo and placebo modulation of hypobaric hypoxia headache involves the cyclooxygenase-prostaglandins pathway. Pain 155, 921–928 (2014).
Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4, 839–849 (2017).
Ashar, Y. K., Chang, L. J. & Wager, T. D. Brain mechanisms of the placebo effect: an affective appraisal account. Annu. Rev. Clin. Psychol. 13, 73–98 (2017).
Price, D. D., Finniss, D. G. & Benedetti, F. A comprehensive review of the placebo effect: recent advances and current thought. Annu. Rev. Psychol. 59, 565–590 (2008).
Benedetti, F., Mayberg, H. S., Wager, T. D., Stohler, C. S. & Zubieta, J.-K. Neurobiological mechanisms of the placebo effect. J. Neurosci. 25, 10390–10402 (2005).
Benedetti, F. The Patient’s Brain: The Neuroscience Behind the Doctor–Patient Relationship (Oxford Univ. Press, 2011).
Geuter, S., Koban, L. & Wager, T. D. The cognitive neuroscience of placebo effects: concepts, predictions, and physiology. Annu. Rev. Neurosci. 40, 167–188 (2017).
de la Fuente-Fernández, R. et al. Expectation and dopamine release: mechanism of the placebo effect in Parkinson’s disease. Science 293, 1164–1166 (2001).
Jensen, K. B. et al. Sharing pain and relief: neural correlates of physicians during treatment of patients. Mol. Psychiatry 19, 392–398 (2014).
Olanow, C. W. et al. Gene delivery of neurturin to putamen and substantia nigra in Parkinson disease: a double-blind, randomized, controlled trial. Ann. Neurol. 78, 248–257 (2015).
Luborsky, L. et al. The researcher’s own therapy allegiances: a ‘wild card’ in comparisons of treatment efficacy. Clin. Psychol. Sci. Pract. 6, 95–106 (1999).
Walach, H., Sadaghiani, C., Dehm, C. & Bierman, D. The therapeutic effect of clinical trials: understanding placebo response rates in clinical trials—a secondary analysis. BMC Med. Res. Methodol. 5, 26–37 (2005).
Greenberg, R. P., Bornstein, R. F., Zborowski, M. J., Fisher, S. & Greenberg, M. D. A meta-analysis of fluoxetine outcome in the treatment of depression. J. Nerv. Ment. Dis. 182, 547–551 (1994).
Holroyd, K. A., Tkachuk, G., O’Donnell, F. & Cordingley, G. E. Blindness and bias in a trial of antidepressant medication for chronic tension-type headache. Cephalalgia 26, 973–982 (2006).
Margraf, J. et al. How ‘blind’ are double-blind studies? J. Consult. Clin. Psychol. 59, 184–187 (1991).
Morin, C. M. et al. How blind are double-blind placebo-controlled trials of benzodiazepine hypnotics. Sleep 18, 240–245 (1995).
Miller, F. G., Colloca, L. & Kaptchuk, T. J. The placebo effect: illness and interpersonal healing. Perspect. Biol. Med. 52, 518–539 (2009).
Blasini, M., Peiris, N., Wright, T. & Colloca, L. The role of patient–practitioner relationships in placebo and nocebo phenomena. Int. Rev. Neurobiol. 139, 211–231 (2018).
Rosenthal, R. & Rubin, D. B. Interpersonal expectancy effects: the first 345 studies. Behav. Brain Sci. 1, 377–386 (1978).
Rosenthal, R. Interpersonal expectancy effects: a 30-year perspective. Curr. Dir. Psychol. Sci. 3, 176–179 (1994).
Pfungst, O. Clever Hans:(The Horse of Mr. Von Osten.) A Contribution to Experimental Animal and Human Psychology (Holt, Rinehart and Winston, 1911).
Rosenthal, R. & Lawson, R. A longitudinal study of the effects of experimenter bias on the operant learning of laboratory rats. J. Psychiatr. Res. 2, 61–72 (1964).
Rosenthal, R. & Jacobson, L. Pygmalion in the classroom. Urban Rev. 3, 16–20 (1968).
Rosenthal, R. Experimenter Effects in Behavioral Research (Appleton–Century–Crofts, 1966).
Doyen, S., Klein, O., Pichon, C.-L. & Cleeremans, A. Behavioral priming: it’s all in the mind but whose mind? PLoS One 7, e29081 (2012).
Joyce, A. S. & Piper, W. E. Expectancy, the therapeutic alliance, and treatment outcome in short-term individual psychotherapy. J. Psychother. Pract. Res. 7, 236–248 (1998).
Meyer, B. et al. Treatment expectancies, patient alliance and outcome: further analyses from the national institute of mental health treatment of depression collaborative research program. J. Consult. Clin. Psychol. 70, 1051–1055 (2002).
Arnkoff, D. B., Glass, C. R. & Shapiro, S. J. in Psychotherapy Relationships that Work: Therapist Contributions and Responsiveness to Patients (ed. Norcross, J. D.) 325–346 (Oxford Univ. Press, 2002).
Gracely, R. H., Dubner, R., Deeter, W. R. & Wolskee, P. J. Clinicians expectations influence placebo analgesia. Lancet 1, 43–43 (1985).
Schafer, S. M., Colloca, L. & Wager, T. D. Conditioned placebo analgesia persists when subjects know they are receiving a placebo. J. Pain 16, 412–420 (2015).
Voudouris, N. J., Peck, C. L. & Coleman, G. The role of conditioning and verbal expectancy in the placebo response. Pain 43, 121–128 (1990).
Orne, M. T. On the social psychology of the psychological experiment: with particular reference to demand characteristics and their implications. Am. Psychol. 17, 776 (1962).
Cheong, J., Brooks, S. & Chang, L. J. “FaceSync: Open Source Framework for Recording Facial Expressions with Head-Mounted Cameras.” F1000Res. 8, 702 (2019).
Littlewort, G. et al. The computer expression recognition toolbox (CERT). Face Gesture 2011, 298–305 (2011).
iMotions Biometric Research Platform v.6.0 (iMotions A/S, 2016).
Ekman, P. & Friesen, W. V. Measuring facial movement. J. Nonverbal Behav. 1, 56–75 (1976).
Prkachin, K. M. The consistency of facial expressions of pain: a comparison across modalities. Pain 51, 297–306 (1992).
Lucey, P. et al. Automatically detecting pain in video through facial action units. IEEE Trans. Syst. Man Cybern. B 41, 664–674 (2011).
Walach, H., Schimdt, S., Bihr, Y.-M. & Wiesch, S. The effects of a caffeine placebo and experimenter expectation on blood pressure, heart rate, well-being, and cognitive performance. Eur. Psychol. 6, 15 (2001).
Walach, H., Schmidt, S., Dirhold, T. & Nosch, S. The effects of a caffeine placebo and suggestion on blood pressure, heart rate, well-being and cognitive performance. Int. J. Psychophysiol. 43, 247–260 (2002).
Halverson, A. M., Hallahan, M., Hart, A. J. & Rosenthal, R. Reducing the biasing effects of judges’ nonverbal behavior with simplified jury instruction. J. Appl. Psychol. 82, 590 (1997).
Learman, L. A., Avorn, J., Everitt, D. E. & Rosenthal, R. Pygmalion in the nursing home. The effects of caregiver expectations on patient outcomes. J. Am. Geriatr. Soc. 38, 797–803 (1990).
Wu, L. M., Mohamed, N. E., Winkel, G. & Diefenbach, M. A. Patient and spouse illness beliefs and quality of life in prostate cancer patients. Psychol. Health 28, 355–368 (2013).
Wampold, B. E., Mondin, G. W., Moody, M. & Stich, F. A meta-analysis of outcome studies comparing bona fide psychotherapies: empiricially, ‘all must have prizes’. Psychol. Bull. 122, 203–215 (1997).
Wampold, B. E. & Imel, Z. E. The Great Psychotherapy Debate: The Evidence for What Makes Psychotherapy Work (Routledge, 2015).
Weinberger, J. Common factors aren’t so common: the common factors dilemma. Clin. Psychol. Sci. Pract. 2, 45–69 (1995).
Frank, J. D. & Frank, J. B. Persuasion and Healing: A Comparative Study of Psychotherapy (JHU Press, 1993).
Rogers, C. R. The necessary and sufficient conditions of therapeutic personality change. J. Consult. Psychol. 21, 95–103 (1957).
Yahne, C. E. & Miller, W. R. in Integrating Spirituality into Treatment: Resources for Practitioners (ed. Miller, W. R.) 217–233 (American Psychological Association, 1999).
Ackerman, S. J. & Hilsenroth, M. J. A review of therapist characteristics and techniques positively impacting the therapeutic alliance. Clin. Psychol. Rev. 23, 1–33 (2003).
Lester, G. W. & Smith, S. G. Listening and talking to patients. A remedy for malpractice suits? West. J. Med. 158, 268–272 (1993).
Luborsky, L., McLellan, A. T., Woody, G. E., O’Brien, C. P. & Auerbach, A. Therapist success and its determinants. Arch. Gen. Psychiatry 42, 602–611 (1985).
Okiishi, J., Lambert, M. J., Nielsen, S. L. & Ogles, B. M. Waiting for supershrink: an empirical analysis of therapist effects. Clin. Psychol. Psychother. 10, 361–373 (2003).
Wampold, B. E. & Brown, G. S. J. Estimating variability in outcomes attributable to therapists: a naturalistic study of outcomes in managed care. J. Consult. Clin. Psychol. 73, 914–923 (2005).
Martin, D. J., Garske, J. P. & Davis, M. K. Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. J. Consult. Clin. Psychol. 68, 438–450 (2000).
Derksen, F., Bensing, J. & Lagro-Janssen, A. Effectiveness of empathy in general practice: a systematic review. Br. J. Gen. Pract. 63, e76–84 (2013).
Horvath, A. & Luborsky, L. The role of the therapeutic alliance in psychotherapy. J. Consult. Clin. Psychol. 61, 561–573 (1993).
Horvath, A. & Symonds, B. D. Relation between working alliance and outcome in psychotherapy: a meta-analysis. J. Couns. Psychol. 38, 139 (1991).
Rakel, D. et al. Perception of empathy in the therapeutic encounter: effects on the common cold. Patient Educ. Couns. 85, 390–397 (2011).
Necka, E. A. & Atlas, L. Y. The role of social and interpersonal factors in placebo analgesia. Int. Rev. Neurobiol. 138, 161–179 (2018).
Thurstone, L. L. A law of comparative judgment. Psychol. Rev. 34, 273–286 (1927).
Fechner, G. T. Elemente der Psychophysik (Breitkopf and Hartel,1860).
Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979).
Flaten, M. A., Aslaksen, P. M., Lyby, P. S. & Bjørkedal, E. The relation of emotions to placebo responses. Phil. Trans. R. Soc. Lond. B 366, 1818–1827 (2011).
Lyby, P. S., Aslaksen, P. M. & Flaten, M. A. Is fear of pain related to placebo analgesia? J. Psychosom. Res. 68, 369–377 (2010).
Wager, T. D. & Atlas, L. Y. The neuroscience of placebo effects: connecting context, learning and health. Nat. Rev. Neurosci. 16, 403–418 (2015).
Batt-Rawden, S. A., Chisolm, M. S., Anton, B. & Flickinger, T. E. Teaching empathy to medical students: an updated, systematic review. Acad. Med. 88, 1171–1177 (2013).
Sulzer, S. H., Feinstein, N. W. & Wendland, C. L. Assessing empathy development in medical education: a systematic review. Med. Educ. 50, 300–310 (2016).
Fine, V. K. & Therrien, M. E. Empathy in the doctor–patient relationship: skill training for medical students. J. Med. Educ. 52, 752–757 (1977).
Ha, J. F. & Longnecker, N. Doctor–patient communication: a review. Ochsner J. 10, 38–43 (2010).
Vandewauw, I. et al. A TRP channel trio mediates acute noxious heat sensing. Nature 555, 662–666 (2018).
Jepma, M., Jones, M. & Wager, T. D. The dynamics of pain: evidence for simultaneous site-specific habituation and site-nonspecific sensitization in thermal pain. J. Pain 15, 734–746 (2014).
Davis, M. H. Measuring individual differences in empathy: evidence for a multidimensional approach. J. Pers. Soc. Psychol. 44, 113–126 (1983).
McKinney, W. Data structures for statistical computing in Python. Proc. 9th Python Sci. Conf. 445, 51–56 (2010).
Oliphant, T. E. A Guide to NumPy, vol. 1 (Trelgol, 2006).
Waskom, M. et al. Seaborn: Statistical Data Visualization (accessed on 15 May 2017); https://seaborn.pydata.org/
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).
Millman, K. J. & Aivazis, M. Python for scientists and engineers. Comput. Sci. Eng. 13, 9–12 (2011).
Chang, L., Jolly, E., Cheong, J. H., Burnashev, A. & Chen, P.-H. A. cosanlab/nltools: 0.3.11 https://doi.org/10.5281/zenodo.2229813 (Zenodo, 2018).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://www.jstatsoft.org/article/view/v067i01 (2015).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest: Tests for random and fixed effects for linear mixed effect models. R package version 2.0-11 http://CRAN.R-project.org/package=lmerTest (2014).
Lenth, R. V. Least-squares means: the R Package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Jolly, E. Pymer4: connecting R and Python for linear mixed modeling. J. Open Source Softw. 3, 862 (2018).
Werner, P. et al. Automatic pain assessment with facial activity descriptors. IEEE Trans. Affect. Comput. 8, 286–99 (2017).
Lucey, P. et al. in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 94–101 (IEEE, 2010).
Baltrušaitis, T., Robinson, P. & Morency, L. P. in IEEE Winter Conference on Applications of Computer Vision 1–10 (IEEE, 2016).
Acknowledgements
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.
Author information
Authors and Affiliations
Contributions
All authors designed the study. P.-H.C., J.H.C., E.J. and H.E. collected the data. P.-H.C., J.H.C. and L.J.C. analysed the data. P.-H.C., J.H.C., E.J., T.D.W. and L.J.C. wrote the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Primary Handling Editor: Mary Elizabeth Sutherland.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
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.
Extended Data Fig. 3 Stats of the pain expression model.
(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.
Extended Data Fig. 5 Skin conductance responses from patients in study 2.
(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.
Supplementary information
Supplementary Information
Supplementary Tables 1–4.
Rights and permissions
About this article
Cite this article
Chen, PH.A., Cheong, J.H., Jolly, E. et al. Socially transmitted placebo effects. Nat Hum Behav 3, 1295–1305 (2019). https://doi.org/10.1038/s41562-019-0749-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-019-0749-5
This article is cited by
-
A new role for spinal manual therapy and for chiropractic? Part II: strengths and opportunities
Chiropractic & Manual Therapies (2024)
-
Interpersonal physiological and psychological synchrony predict the social transmission of nocebo hyperalgesia between individuals
Communications Psychology (2024)
-
Imaginary pills and open-label placebos can reduce test anxiety by means of placebo mechanisms
Scientific Reports (2023)
-
Synchronized affect in shared experiences strengthens social connection
Communications Biology (2023)
-
Py-Feat: Python Facial Expression Analysis Toolbox
Affective Science (2023)