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Randomized Controlled Trial
. 2023 Jul 5:12:e84691.
doi: 10.7554/eLife.84691.

Presenting a sham treatment as personalised increases the placebo effect in a randomised controlled trial

Dasha A Sandra  1 , Jay A Olson  2 , Ellen J Langer  2 , Mathieu Roy  3
Affiliations
Randomized Controlled Trial

Presenting a sham treatment as personalised increases the placebo effect in a randomised controlled trial

Dasha A Sandra et al. Elife. .

Abstract

Background: Tailoring interventions to patient subgroups can improve intervention outcomes for various conditions. However, it is unclear how much of this improvement is due to the pharmacological personalisation versus the non-specific effects of the contextual factors involved in the tailoring process, such as the therapeutic interaction. Here, we tested whether presenting a (placebo) analgesia machine as personalised would improve its effectiveness.

Methods: We recruited 102 adults in two samples (N1=17, N2=85) to receive painful heat stimulations on their forearm. During half of the stimulations, a machine purportedly delivered an electric current to reduce their pain. The participants were either told that the machine was personalised to their genetics and physiology, or that it was effective in reducing pain generally.

Results: Participants told that the machine was personalised reported more relief in pain intensity than the control group in both the feasibility study (standardised β=-0.50 [-1.08, 0.08]) and the pre-registered double-blind confirmatory study (β=-0.20 [-0.36, -0.04]). We found similar effects on pain unpleasantness, and several personality traits moderated the results.

Conclusions: We present some of the first evidence that framing a sham treatment as personalised increases its effectiveness. Our findings could potentially improve the methodology of precision medicine research and inform practice.

Funding: This study was funded by the Social Science and Humanities Research Council (93188) and Genome Québec (95747).

Keywords: contextual factors; expectation; human; medicine; neuroscience; pain; personalised medicine; placebo; precision medicine.

Plain language summary

Precision treatments are therapies that are tailored to a patient’s individual biology with the aim of making them more effective. Some cancer drugs, for example, work better for people with specific genes, leading to improved outcomes when compared to their ‘generic’ versions. However, it is unclear how much of this increased effectiveness is due to tailoring the drug’s chemical components versus the contextual factors involved in the personalisation process. Contextual factors like patient beliefs can boost a treatment’s outcomes via the ‘placebo effect’ – making the intervention work better simply because the patient believes it to. Personalised treatments typically combine more of these factors by being more expensive, elaborate, and invasive – potentially boosting the placebo effect. Sandra et al. tested whether simply describing a placebo machine – which has no therapeutic value – as personalised would increase its effectiveness at reducing pain for healthy volunteers. Study participants completed several sham physiological and genetic tests. Those in the experimental group were told that their test results helped tailor the machine to increase its effectiveness at reducing pain whereas those in the control group were told that the tests screened for study eligibility. All volunteers were then exposed to a series of painful stimuli and used the machine to reduce the pain for half of the exposures. Participants that believed the machine was personalised reported greater pain relief. Those with a stronger desire to be seen as different from others – based on the results of a personality questionnaire – experienced the largest benefits, but only when told that the machine was personalised. This is the first study to show that simply believing a sham treatment is personalised can increase its effectiveness in healthy volunteers. If these results are also seen in clinical settings, it would suggest that at least some of the benefit of personalised medicine could be due to the contextual factors surrounding the tailoring process. Future work could inform doctors of how to harness the placebo effect to benefit patients undergoing precision treatments.

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Conflict of interest statement

DS, JO, EL, MR No competing interests declared

Figures

Figure 1.
Figure 1.. Participants completed sham medical tests and then rated pain stimulations in a room with various medical equipment.
Figure 2.
Figure 2.. On half of the stimulations, participants used a complex placebo machine with dials, vibration, and flashing lights to help reduce pain.
This machine was presented as either personalised to their test results or as generally effective. The machine’s design (over a dozen of switches and dials) allowed us to simulate complex personalisation to the participants’ profile.
Figure 3.
Figure 3.. Procedure for the confirmatory study.
We first asked participants to complete personality questionnaires and calibrated heat stimulations to their individual pain perception. Participants then completed sham medical tests (i.e., genetics, skin conductance) before being randomised to receive the placebo machine described as personalised to their sham test results or not (control). A research assistant blind to the experimental condition then led participants through a pain rating task that was similar to the calibration. On half of the heat stimulations, participants used the machine (turned on) to counteract the heat pain (on the other half, the machine was turned off). In the conditioning phase, we simulated machine effectiveness by covertly reducing the intensity of pain stimulations when the machine was turned on. For the testing phase, we kept the temperature stable and quantified the placebo effect as the difference between the trials with the machine off and on.
Figure 4.
Figure 4.. Participants in the personalised group reported nearly twice the reduction in pain intensity (A) and unpleasantness (B; N=17).
The placebo effect was calculated as ratings with the machine off – machine on. Black dots show means, coloured dots show individual raw scores, violin widths show frequency, and error bars show 95% confidence intervals.
Figure 5.
Figure 5.. Individual pain score changes with the placebo machine turned on or off for pain intensity (A) and unpleasantness (B).
Large coloured dots show means, small coloured dots show individual scores, and error bars show 95% confidence intervals.
Figure 6.
Figure 6.. Participants in the personalised group reported higher placebo effects than those in the control group for pain intensity (A) and unpleasantness (B; N=85).
The panels show changes calculated as ratings with the machine off – machine on. Black dots show means, coloured dots show individual raw scores, violin widths show frequency, and error bars show 95% confidence intervals.
Figure 7.
Figure 7.. Individual pain score changes with the placebo machine turned on or off for pain intensity (A) and unpleasantness (B).
Large coloured dots show means, small coloured dots show individual scores, and error bars show 95% confidence intervals.
Figure 8.
Figure 8.. Exploratory predictors of placebo effects on pain intensity (N=85).
Participants high in Need for uniqueness (A), Attention regulation (B), Emotion awareness (C), and Noticing (D) showed stronger placebo effects with a sham-personalised machine than those in the control group. Shaded regions denote 95% confidence intervals and correlations are between the trait and the pain ratings in each group.
Figure 9.
Figure 9.. Expectations as a predictor of placebo effects with groups combined (N=84).
Dots show individual scores and shaded regions denote 95% confidence intervals.
Appendix 1—figure 1.
Appendix 1—figure 1.. The differences in pain intensity and unpleasantness during the conditioning phase of the confirmatory study.
Dots show means and error bars show 95% confidence intervals.
Appendix 1—figure 2.
Appendix 1—figure 2.. Personality traits that significantly moderated the placebo effects of personalisation (N=85).
Shaded regions show 95% confidence intervals, equations represent proportion of variance explained by each group.
Appendix 1—figure 3.
Appendix 1—figure 3.. Correlations between all personality traits measured as potential predictors of placebo effects of personalisation.
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