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.

Precision medicine may revolutionise healthcare by tailoring interventions to patients’ specific genetic, biological, and behavioural markers. Targeted therapies can lead to better health outcomes, such as increased life expectancy and remission rates, notably in cancer (Cutler, 2020). Researchers are now attempting to extend precision medicine approaches to other conditions such as chronic pain (Reimer et al., 2021). Further, advancements in artificial intelligence may soon broaden the use of personalisation for drug dosing (Rybak et al., 2020) and treatment selection (Ahmed et al., 2020). However, the greater effectiveness of tailored interventions may be due to more than just their pharmacological ingredients: contextual factors, such as the treatment setting and patient beliefs, may also directly contribute to better outcomes. The influence of contextual factors in precision medicine remains relatively unexplored, despite experts highlighting their potential influence on intervention outcomes (Haga et al., 2009). Thus, isolating the role of the contextual factors involved in the personalisation process could help control for them in precision medicine research and possibly optimise them in clinical practice.

Research in placebo science has shown that the contextual factors surrounding the intervention, such as verbal suggestions, patient expectations, social cues, and observational learning increase its effectiveness and reduce the associated side effects (Bernstein et al., 2020; Colloca and Barsky, 2020; Olson et al., 2021a). Some of these contextual factors can both modulate the effectiveness of inactive treatments in lab settings as well as increase the placebo effect of the real treatments in clinical settings (Blasini et al., 2018). Additionally, these effects are present for both subjective symptoms (e.g. pain, depression) and physiological ones (e.g. immune response, motor function) (Benedetti et al., 2005). Precision medicine may already benefit from greater patient expectations given the public’s high hopes for the field (Collins and Varmus, 2015), increased trust, and possible preference to be seen as different from others (i.e., high need for uniqueness), which may all increase placebo effects.

The public generally believes that personalised interventions are fully unique, so much so that the field rebranded from ‘personalised’ to ‘precision’ medicine in an effort to dispel this exaggerated view (Juengst et al., 2016). Despite the field’s more modest focus on targeting patient subgroups, tailored interventions use information about individual genetics and biology—elements most people believe to define their individual essence (Gelman, 2003). If an intervention was tailored to something so unique, the treatment would indeed be more likely to work, in turn possibly raising patients’ expectations. This appeal may be particularly strong in the broader context of rising individualism (Santos et al., 2017) and may speak to patients’ desire to be seen as distinct individuals. Although no studies to our knowledge have directly explored the influence of genetic information on perceived treatment effectiveness, receiving sham genetic feedback itself can affect behaviour and physiology, suggesting the potential for placebo effects in precision therapies. For example, simply learning about one’s increased genetic risk for obesity may lead to lower self-efficacy, reduced perceived control over related behaviours (Beauchamp et al., 2011; Dar-Nimrod et al., 2014), and worse cardiorespiratory capacity (Turnwald et al., 2019); learning one has a protective genetic makeup may cause the opposite results (Turnwald et al., 2019), regardless of the actual genes involved. Providing genetic and physiological feedback and then using it to tailor a treatment may similarly influence outcomes for precision therapies.

Beyond the appeal to individuality, the personalisation process may implicitly suggest stronger perceived treatment effectiveness by harnessing factors well-known to increase the placebo effect (Olson et al., 2021a). Pharmacological tailoring is an intricate process and often requires biomarker tests that are sometimes invasive (Corcoran, 2020), take longer to process (Rieder et al., 2005), or use advanced technology (Rybak et al., 2020). Indeed, studies in placebo science show that treatments that are more elaborate, invasive (de Craen et al., 2000; Hróbjartsson and Gøtzsche, 2010; Meissner et al., 2013), or use complex technology may cause larger improvements (Kaptchuk et al., 2000; Kaptchuk et al., 2008). Given the complexity of the procedure, tailoring treatments requires more physician attention and the involvement of practitioners specifically trained in therapeutic communication, such as genetic counsellors (Austin et al., 2014; Kohut et al., 2019). Practitioners may also inadvertently suggest greater effectiveness of the treatment by explaining it in more detail. Similarly, placebo studies show that providing enhanced information about a treatment can increase the effects of already potent drugs like opioids (Amanzio et al., 2001; Benedetti et al., 2003), and positive communication strategies may reduce the side effects of sham pills (Barnes et al., 2019; Colloca and Finniss, 2012). More broadly, a warm and empathetic encounter can improve outcomes for active and inactive treatments in clinical settings (Blasini et al., 2018).

Despite the similarities between the ideal contextual factors for strong placebo effects and the typical contextual factors involved in precision medicine, the influence of the personalisation process on perceived treatment effectiveness is largely unknown. Thus, we tested whether believing that a treatment is tailored to one’s physiology and genetics may improve its perceived efficacy. We predicted that participants using the machine presented as personalised would report greater placebo effects than those in a control group.

To isolate the role of the placebo effects of personalisation while avoiding the ethical issues involved in deceiving severely ill patients—the typical participants in precision clinical trials—we tested healthy adults. We developed an elaborate procedure to plausibly simulate treatment personalisation and then tested it in a feasibility study (N₁=17) before confirming the findings in a pre-registered double-blind experiment (N₂=85). The procedure was based on studies of complex placebo interventions (Olson et al., 2021a; Olson and Raz, 2021c) and simulated both the nature of the tests (i.e., genetic, physiological) and the medical context (i.e., room setting, location) of treatment tailoring. We also measured several personality traits that could potentially interact with the placebo effects of personalisation. Recent studies suggest that traits such as interoceptive awareness (attention to one’s physical sensations) and openness to experience predict the magnitude of placebo response (Vachon-Presseau et al., 2018); we additionally expected that other traits such as need for uniqueness (the desire to be seen as different from others) may moderate the specific placebo effects of personalisation.

With interest in precision medicine and personalisation on the rise (ANA, 2019; Joshua, 2019), understanding how contextual factors influence the perceived effectiveness of targeted treatments can impact research and delivery. In a feasibility study and a pre-registered double-blind experiment, we found that completing a sham biological personalisation process led to greater placebo analgesia. In the feasibility study, participants experienced double the reduction in pain intensity when receiving treatment from a machine presented as personalised; we found similar but smaller effects in the confirmatory study. Thus, participants that received a machine framed as personalised to their genetics perceived it to be more effective in reducing their pain. Our findings provide some of the first evidence for this novel placebo effect and suggest its further study in clinical contexts, echoing experts in the field (Haga et al., 2009). The results also support the need for more consistent use of blinding, inactive control groups, and randomisation, especially for pivotal trials determining FDA approval of precision drugs. Indeed, only half of the FDA-approved precision treatments in recent years were based on double- or single-blinded pivotal trials, and only 38% of all pivotal trials used a placebo comparator (Pregelj et al., 2018). Although precision treatments are often developed for difficult-to-study diseases, their potential to elicit stronger placebo effects calls for more robust research designs.

Better control over placebo effects in precision medicine may become especially important given the future trend of the field to use increasingly complex technologies such as brain scanning and artificial intelligence (Ahmed et al., 2020; da Silva Castanheira et al., 2021) for more extensive personalisation. Depending on the disease, targeted treatments may soon be adjusted to dozens of genetic, neural, and physiological biomarkers instead of only a few genetic markers. Such personalisation may magnify the focus on individuality, boost treatment complexity, and increase patient–practitioner interaction—likely increasing the placebo effects in the process. Preferentially using blind, randomised, and placebo-controlled trial designs can help successfully isolate the active treatment effects in such a context.

Curiously, we found that the placebo effects of personalisation may also potentially be ‘personal’: some participants may benefit from them more based on their personality traits. Participants high in need for uniqueness—the desire to be seen as different from others—responded strongest to the sham personalised machine, yet less so to the one in the control group. Other personality traits including attentiveness to bodily sensations (emotion awareness, attention regulation, and noticing physical sensations) as well as openness to experience also moderated the effect, in line with recent findings and the general hope for eventual personalisation of the treatment context (Enck et al., 2013; Geers et al., 2006; Vachon-Presseau et al., 2018). Indeed, some of the same traits (Vachon-Presseau et al., 2018) and general attention to symptoms (Geers et al., 2006) predicted increased placebo effects in other studies; our findings tentatively suggest that these traits may also amplify the specific placebo effects due to personalisation. Future studies may explore which complex personality profiles benefit the most from these placebo effects and through which mechanisms.

Several methodological strengths increased the validity of our results. We used a two-step approach of first testing the effectiveness of the deceptive procedure in a feasibility study and then confirming our findings in a pre-registered experiment; the results are thus more likely to replicate than a single study. Using the elaborate deception procedure may have also helped reduce participant suspicion (Olson and Raz, 2021c) and increase the reliability of their pain ratings. Only 12% of the participants suspected the placebo effect and none guessed the purpose of the experiment in the confirmatory study, despite many participants having graduate training in biology, genetics, or psychology. This is in line with previous studies on complex deception using intentionally elaborate placebos (Olson et al., 2016; Olson et al., 2020; Olson et al., 2023).

Finally, we imitated parts of the personalisation process such as the medical setting (Figure 1), therapeutic interactions (e.g., the explanation of the genetic results), and the level of complex testing (i.e., multiple tests) to somewhat increase generalisability. Together, these elements strengthened our conclusion that contextual factors may potentially play a role in increasing the placebo effect of precision treatments.

The main limitations of the study are its focus on healthy participants, the use of an inactive treatment, a sample with imbalanced genders, and the focus on subjective outcomes. Together, these factors restricted the generalisation of our findings to clinical settings. Our effect was also small; the 11% reduction in pain intensity and 16% reduction in unpleasantness reached the lower threshold of minimal clinical significance of pain reduction (10 to 20%) suggested by guidelines (Dworkin et al., 2009). Nevertheless, testing placebo effects with experimental pain may have led to a conservative estimate of the placebo effect and may not map directly onto the clinical experience of chronic pain. Patients differ from healthy participants on many characteristics, including their motivation to get better (National Cancer Institute, 2021), the mechanisms through which they experience placebo effects (short- or long-term; Vase et al., 2005), and the methods of assessing pain ratings (immediate versus retrospective). Our effect sizes were similar to that of paracetamol (Jürgens et al., 2014) and morphine (Koppert et al., 1999) on thermal pain, suggesting the potential for clinical significance if tested in patients. Future studies could build on our proof-of-concept findings and explore whether these placebo effects apply to clinical populations who receive real personalised treatments focused on more objective measures. These additional investigations will help determine the clinical significance of placebo effects due to personalisation for active treatments.

Finding a mixed relationship between expectations and the tailoring process also limited our understanding of the mechanism underlying our effects. Post-conditioning expectations predicted a greater reduction in pain ratings, suggesting that conditioning is crucial for inducing placebo effects in pain, as has been demonstrated in previous studies (Colloca et al., 2020). However, there were no expectancy differences between the groups: both were moderate after the conditioning. The mechanism behind the placebo effect of personalisation may thus rely on an interaction with additional elements that need to be explored, such as increases in mood from receiving a personalised treatment. It is also possible that the more complex mechanism is responsible for the general lack of placebo effects in the control group, but not in the experimental group.

All data is freely available at Open Science Framework (https://osf.io/6j7z5/).

1. 1. Sandra DA
   2. Olson JA
   3. Langer EJ
   4. Roy M

   (2023) Open Science Framework

   ID 6j7z5. Pain ratings with and without two types of a placebo machine.

   https://osf.io/6j7z5/

1. 1. Ahmed Z
   2. Mohamed K
   3. Zeeshan S
   4. Dong XQ

   (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

   Database 1:baaa010.

   https://doi.org/10.1093/database/baaa010

   • Google Scholar
2. 1. Amanzio M
   2. Pollo A
   3. Maggi G
   4. Benedetti F

   (2001) Response variability to analgesics: A role for non-specific activation of endogenous opioids

   Pain 90:205–215.

   https://doi.org/10.1016/S0304-3959(00)00486-3

   • PubMed
   • Google Scholar
3. Report

   1. ANA

   (2019) ANA 2019 marketing word of the year: Personalization

   Association of National Advertisers.

   https://www.ana.net/miccontent/show?id=ii-2019-ana-word-of-year

   • Google Scholar
4. 1. Austin J
   2. Semaka A
   3. Hadjipavlou G

   (2014) Conceptualizing genetic counseling as psychotherapy in the era of genomic medicine

   Journal of Genetic Counseling 23:903–909.

   https://doi.org/10.1007/s10897-014-9728-1

   • PubMed
   • Google Scholar
5. 1. Barnes K
   2. Faasse K
   3. Geers AL
   4. Helfer SG
   5. Sharpe L
   6. Colloca L
   7. Colagiuri B

   (2019) Can positive framing reduce nocebo side effects? Current evidence and recommendation for future research

   Frontiers in Pharmacology 10:167.

   https://doi.org/10.3389/fphar.2019.00167

   • PubMed
   • Google Scholar
6. 1. Beauchamp MR
   2. Rhodes RE
   3. Kreutzer C
   4. Rupert JL

   (2011) Experiential versus genetic accounts of inactivity: Implications for inactive individuals’ self-efficacy beliefs and intentions to exercise

   Behavioral Medicine 37:8–14.

   https://doi.org/10.1080/08964289.2010.540263

   • Google Scholar
7. 1. Benedetti F
   2. Maggi G
   3. Lopiano L
   4. Lanotte M
   5. Rainero I
   6. Vighetti S
   7. Pollo A

   (2003) Open versus hidden medical treatments: The patient’s knowledge about a therapy affects the therapy outcome

   Prevention & Treatment 6:61a.

   https://doi.org/10.1037/1522-3736.6.1.61a

   • Google Scholar
8. 1. Benedetti F
   2. Mayberg HS
   3. Wager TD
   4. Stohler CS
   5. Zubieta JK

   (2005) Neurobiological mechanisms of the placebo effect

   The Journal of Neuroscience 25:10390–10402.

   https://doi.org/10.1523/JNEUROSCI.3458-05.2005

   • PubMed
   • Google Scholar
9. 1. Bernstein MH
   2. Locher C
   3. Kube T
   4. Buergler S
   5. Stewart-Ferrer S
   6. Blease C

   (2020) Putting the ‘art’ into the ‘art of medicine’: The under-explored role of artifacts in placebo studies

   Frontiers in Psychology 11:1354.

   https://doi.org/10.3389/fpsyg.2020.01354

   • PubMed
   • Google Scholar
10. Book

    1. Blasini M
    2. Peiris N
    3. Wright T
    4. Colloca L

    (2018) The role of patient–practitioner relationships in placebo and nocebo phenomena

    In: Blasini M, editors. International Review of Neurobiology. Academic Press Inc. pp. 211–231.

    https://doi.org/10.1016/bs.irn.2018.07.033

    • Google Scholar
11. 1. Collins FS
    2. Varmus H

    (2015) A new initiative on precision medicine

    The New England Journal of Medicine 372:793–795.

    https://doi.org/10.1056/NEJMp1500523

    • PubMed
    • Google Scholar
12. 1. Colloca L
    2. Finniss D

    (2012) Nocebo effects, patient-clinician communication, and therapeutic outcomes

    JAMA 307:567–568.

    https://doi.org/10.1001/jama.2012.115

    • PubMed
    • Google Scholar
13. 1. Colloca L
    2. Akintola T
    3. Haycock NR
    4. Blasini M
    5. Thomas S
    6. Phillips J
    7. Corsi N
    8. Schenk LA
    9. Wang Y

    (2020) Prior therapeutic experiences, not expectation ratings, predict placebo effects: an experimental study in chronic pain and healthy participants

    Psychotherapy and Psychosomatics 89:371–378.

    https://doi.org/10.1159/000507400

    • Google Scholar
14. 1. Colloca L
    2. Barsky AJ

    (2020) Placebo and nocebo effects

    The New England Journal of Medicine 382:554–561.

    https://doi.org/10.1056/NEJMra1907805

    • PubMed
    • Google Scholar
15. 1. Corcoran RB

    (2020) Liquid biopsy versus tumor biopsy for clinical-trial recruitment

    Nature Medicine 26:1815–1816.

    https://doi.org/10.1038/s41591-020-01169-6

    • Google Scholar
16. 1. Cutler DM

    (2020) Early returns from the era of precision medicine

    JAMA 323:109.

    https://doi.org/10.1001/jama.2019.20659

    • Google Scholar
17. 1. Dar-Nimrod I
    2. Cheung BY
    3. Ruby MB
    4. Heine SJ

    (2014) Can merely learning about obesity genes affect eating behavior?

    Appetite 81:269–276.

    https://doi.org/10.1016/j.appet.2014.06.109

    • PubMed
    • Google Scholar
18. 1. da Silva Castanheira J
    2. Orozco Perez HD
    3. Misic B
    4. Baillet S

    (2021) Brief segments of neurophysiological activity enable individual differentiation

    Nature Communications 12:5713.

    https://doi.org/10.1038/s41467-021-25895-8

    • PubMed
    • Google Scholar
19. 1. de Craen AJM
    2. Tijssen JGP
    3. de Gans J
    4. Kleijnen J

    (2000) Placebo effect in the acute treatment of migraine: Subcutaneous placebos are better than oral placebos

    Journal of Neurology 247:183–188.

    https://doi.org/10.1007/s004150050560

    • Google Scholar
20. 1. Dworkin RH
    2. Turk DC
    3. McDermott MP
    4. Peirce-Sandner S
    5. Burke LB
    6. Cowan P
    7. Farrar JT
    8. Hertz S
    9. Raja SN
    10. Rappaport BA
    11. Rauschkolb C
    12. Sampaio C

    (2009) Interpreting the clinical importance of group differences in chronic pain clinical trials: IMMPACT recommendations

    PAIN 146:238–244.

    https://doi.org/10.1016/j.pain.2009.08.019

    • PubMed
    • Google Scholar
21. 1. Enck P
    2. Bingel U
    3. Schedlowski M
    4. Rief W

    (2013) The placebo response in medicine: minimize, maximize or personalize?

    Nature Reviews Drug Discovery 12:191–204.

    https://doi.org/10.1038/nrd3923

    • Google Scholar
22. 1. Geers AL
    2. Helfer SG
    3. Weiland PE
    4. Kosbab K

    (2006) Expectations and placebo response: A laboratory investigation into the role of somatic focus

    Journal of Behavioral Medicine 29:171–178.

    https://doi.org/10.1007/s10865-005-9040-5

    • PubMed
    • Google Scholar
23. 1. Gelman SA

    (2003)

    The essential child: Origins of essentialism in everyday thought

    The essential child, The essential child: Origins of essentialism in everyday thought, Oxford University Press, 10.1093/acprof:oso/9780195154061.001.0001.

    • Google Scholar
24. 1. Guest NS
    2. Horne J
    3. Vanderhout SM
    4. El-Sohemy A

    (2019) Sport nutrigenomics: personalized nutrition for athletic performance

    Frontiers in Nutrition 6:8.

    https://doi.org/10.3389/fnut.2019.00008

    • PubMed
    • Google Scholar
25. 1. Haga SB
    2. Warner LR
    3. O’Daniel J

    (2009) The potential of a placebo/nocebo effect in pharmacogenetics

    Public Health Genomics 12:158–162.

    https://doi.org/10.1159/000189628

    • PubMed
    • Google Scholar
26. 1. Hróbjartsson A
    2. Gøtzsche PC

    (2010) Placebo interventions for all clinical conditions

    The Cochrane Database of Systematic Reviews 2010:CD003974.

    https://doi.org/10.1002/14651858.CD003974.pub3

    • PubMed
    • Google Scholar
27. 1. Janssens A
    2. Gwinn M
    3. Bradley LA
    4. Oostra BA
    5. van Duijn CM
    6. Khoury MJ

    (2008) A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions

    American Journal of Human Genetics 82:593–599.

    https://doi.org/10.1016/j.ajhg.2007.12.020

    • PubMed
    • Google Scholar
28. 1. John OP
    2. Donahue EM
    3. Kentle RL

    (1991) Big five inventory (BFI)

    APA PsycNet Direct 1:t07550-000.

    https://doi.org/10.1037/t07550-000

    • Google Scholar
29. Book

    1. John O
    2. Srivastava S

    (1999)

    The big-five trait taxonomy: History, measurement, and theoretical perspectives

    University of California Press.

    • Google Scholar
30. 1. Joshua CD

    (2019) The "All of Us" research program

    New England Journal of Medicine 381:668–676.

    https://doi.org/10.1056/NEJMsr1809937

    • Google Scholar
31. 1. Juengst E
    2. McGowan ML
    3. Fishman JR
    4. Settersten RA

    (2016) From "personalized" to "precision" medicine: The ethical and social implications of rhetorical reform in genomic medicine

    The Hastings Center Report 46:21–33.

    https://doi.org/10.1002/hast.614

    • PubMed
    • Google Scholar
32. 1. Jürgens TP
    2. Sawatzki A
    3. Henrich F
    4. Magerl W
    5. May A

    (2014) An improved model of heat-induced hyperalgesia—repetitive phasic heat pain causing primary hyperalgesia to heat and secondary hyperalgesia to pinprick and light touch

    PLOS ONE 9:e99507.

    https://doi.org/10.1371/journal.pone.0099507

    • PubMed
    • Google Scholar
33. 1. Kaptchuk TJ
    2. Goldman P
    3. Stone DA
    4. Stason WB

    (2000) Do medical devices have enhanced placebo effects?

    Journal of Clinical Epidemiology 53:786–792.

    https://doi.org/10.1016/s0895-4356(00)00206-7

    • PubMed
    • Google Scholar
34. 1. Kaptchuk TJ
    2. Kelley JM
    3. Conboy LA
    4. Davis RB
    5. Kerr CE
    6. Jacobson EE
    7. Kirsch I
    8. Schyner RN
    9. Nam BH
    10. Nguyen LT
    11. Park M
    12. Rivers AL
    13. McManus C
    14. Kokkotou E
    15. Drossman DA
    16. Goldman P
    17. Lembo AJ

    (2008) Components of placebo effect: Randomised controlled trial in patients with irritable bowel syndrome

    BMJ (Clinical Research Ed.) 336:999–1003.

    https://doi.org/10.1136/bmj.39524.439618.25

    • PubMed
    • Google Scholar
35. 1. Kohut K
    2. Limb S
    3. Crawford G

    (2019) The changing role of the genetic counsellor in the genomics era

    Current Genetic Medicine Reports 7:75–84.

    https://doi.org/10.1007/s40142-019-00163-w

    • Google Scholar
36. 1. Koppert W
    2. Likar R
    3. Geisslinger G
    4. Zeck S
    5. Schmelz M
    6. Sittl R

    (1999) Peripheral antihyperalgesic effect of morphine to heat, but not mechanical, stimulation in healthy volunteers after ultraviolet-B irradiation

    Anesthesia & Analgesia 88:117–122.

    https://doi.org/10.1213/00000539-199901000-00022

    • Google Scholar
37. 1. McNeil DW
    2. Rainwater AJ

    (1998) Development of the fear of pain questionnaire - III

    Journal of Behavioral Medicine 21:389–410.

    https://doi.org/10.1023/a:1018782831217

    • PubMed
    • Google Scholar
38. 1. Mehling WE
    2. Price C
    3. Daubenmier JJ
    4. Acree M
    5. Bartmess E
    6. Stewart A

    (2012) The multidimensional assessment of Interoceptive awareness (MAIA)

    PLOS ONE 7:e48230.

    https://doi.org/10.1371/journal.pone.0048230

    • PubMed
    • Google Scholar
39. 1. Meissner K
    2. Fässler M
    3. Rücker G
    4. Kleijnen J
    5. Hróbjartsson A
    6. Schneider A
    7. Antes G
    8. Linde K

    (2013) Differential effectiveness of placebo treatments: A systematic review of migraine prophylaxis

    JAMA Internal Medicine 173:1941–1951.

    https://doi.org/10.1001/jamainternmed.2013.10391

    • PubMed
    • Google Scholar
40. Website

    1. National Cancer Institute

    (2021) Targeted cancer therapies fact sheet

    Accessed July 1, 2021.

    https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/targeted-therapies-fact-sheet
41. 1. Nichols AL
    2. Edlund JE

    (2015) Practicing what we preach (and sometimes study): Methodological issues in experimental laboratory research

    Review of General Psychology 19:191–202.

    https://doi.org/10.1037/gpr0000027

    • Google Scholar
42. 1. Olson JA
    2. Landry M
    3. Appourchaux K
    4. Raz A

    (2016) Simulated thought insertion: Influencing the sense of agency using deception and magic

    Consciousness and Cognition 43:11–26.

    https://doi.org/10.1016/j.concog.2016.04.010

    • PubMed
    • Google Scholar
43. 1. Olson JA
    2. Suissa-Rocheleau L
    3. Lifshitz M
    4. Raz A
    5. Veissière SPL

    (2020) Tripping on nothing: Placebo psychedelics and contextual factors

    Psychopharmacology 237:1371–1382.

    https://doi.org/10.1007/s00213-020-05464-5

    • Google Scholar
44. 1. Olson JA
    2. Lifshitz M
    3. Raz A
    4. Veissière SPL

    (2021a) Super placebos: A feasibility study combining contextual factors to promote placebo effects

    Frontiers in Psychiatry 12:644825.

    https://doi.org/10.3389/fpsyt.2021.644825

    • PubMed
    • Google Scholar
45. 1. Olson JA
    2. Nahas J
    3. Chmoulevitch D
    4. Cropper SJ
    5. Webb ME

    (2021b) Naming unrelated words predicts creativity

    PNAS 118:e2022340118.

    https://doi.org/10.1073/pnas.2022340118

    • PubMed
    • Google Scholar
46. 1. Olson JA
    2. Raz A

    (2021c) Applying insights from magic to improve deception in research: The Swiss cheese model

    Journal of Experimental Social Psychology 92:104053.

    https://doi.org/10.1016/j.jesp.2020.104053

    • Google Scholar
47. 1. Olson JA
    2. Cyr M
    3. Artenie DZ
    4. Strandberg T
    5. Hall L
    6. Tompkins ML
    7. Raz A
    8. Johansson P

    (2023) Emulating future Neurotechnology using magic

    Consciousness and Cognition 107:103450.

    https://doi.org/10.1016/j.concog.2022.103450

    • PubMed
    • Google Scholar
48. 1. Pregelj L
    2. Hwang TJ
    3. Hine DC
    4. Siegel EB
    5. Barnard RT
    6. Darrow JJ
    7. Kesselheim AS

    (2018) Precision medicines have faster approvals based on fewer and smaller trials than other medicines

    Health Affairs 37:724–731.

    https://doi.org/10.1377/hlthaff.2017.1580

    • Google Scholar
49. 1. Reimer M
    2. Sachau J
    3. Forstenpointner J
    4. Baron R

    (2021) Bedside testing for precision pain medicine

    Current Opinion in Supportive and Palliative Care 15:116–124.

    https://doi.org/10.1097/SPC.0000000000000556

    • PubMed
    • Google Scholar
50. 1. Rieder MJ
    2. Reiner AP
    3. Gage BF
    4. Nickerson DA
    5. Eby CS
    6. McLeod HL
    7. Blough DK
    8. Thummel KE
    9. Veenstra DL
    10. Rettie AE

    (2005) Effect of Vkorc1 haplotypes on transcriptional regulation and warfarin dose

    The New England Journal of Medicine 352:2285–2293.

    https://doi.org/10.1056/NEJMoa044503

    • PubMed
    • Google Scholar
51. 1. Rief W
    2. Barsky AJ
    3. Glombiewski JA
    4. Nestoriuc Y
    5. Glaesmer H
    6. Braehler E

    (2011) Assessing general side effects in clinical trials: Reference data from the general population

    Pharmacoepidemiology and Drug Safety 20:405–415.

    https://doi.org/10.1002/pds.2067

    • PubMed
    • Google Scholar
52. 1. Rybak MJ
    2. Le J
    3. Lodise TP
    4. Levine DP
    5. Bradley JS
    6. Liu C
    7. Mueller BA
    8. Pai MP
    9. Wong-Beringer A
    10. Rotschafer JC
    11. Rodvold KA
    12. Maples HD
    13. Lomaestro BM

    (2020) Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: A revised consensus guideline and review by the American society of health-system pharmacists, the infectious diseases society of America, the pediatric infectious diseases society, and the society of infectious diseases pharmacists

    American Journal of Health-System Pharmacy 77:835–864.

    https://doi.org/10.1093/ajhp/zxaa036

    • PubMed
    • Google Scholar
53. 1. Sabatello M
    2. Insel BJ
    3. Corbeil T
    4. Link BG
    5. Appelbaum PS

    (2021) The double helix at school: Behavioral genetics, disability, and precision education

    Social Science & Medicine 278:113924.

    https://doi.org/10.1016/j.socscimed.2021.113924

    • PubMed
    • Google Scholar
54. 1. Santos HC
    2. Varnum MEW
    3. Grossmann I

    (2017) Global increases in individualism

    Psychological Science 28:1228–1239.

    https://doi.org/10.1177/0956797617700622

    • PubMed
    • Google Scholar
55. 1. Sellami M
    2. Elrayess MA
    3. Puce L
    4. Bragazzi NL

    (2021) Molecular big data in sports sciences: State-of-art and future prospects of OMICS-based sports sciences

    Frontiers in Molecular Biosciences 8:815410.

    https://doi.org/10.3389/fmolb.2021.815410

    • PubMed
    • Google Scholar
56. 1. Snyder CR
    2. Fromkin HL

    (1977) Abnormality as a positive characteristic: The development and validation of a scale measuring need for uniqueness

    Journal of Abnormal Psychology 86:518–527.

    https://doi.org/10.1037/0021-843X.86.5.518

    • PubMed
    • Google Scholar
57. 1. Sullivan MJL
    2. Bishop SR
    3. Pivik J

    (1995) The pain catastrophizing scale: Development and validation

    Psychological Assessment 7:524–532.

    https://doi.org/10.1037/1040-3590.7.4.524

    • Google Scholar
58. 1. Tabry V
    2. Vogel TA
    3. Lussier M
    4. Brouillard P
    5. Buhle J
    6. Rainville P
    7. Bherer L
    8. Roy M

    (2020) Inter-individual predictors of pain inhibition during performance of a competing cognitive task

    Scientific Reports 10:21785.

    https://doi.org/10.1038/s41598-020-78653-z

    • PubMed
    • Google Scholar
59. 1. Turnwald BP
    2. Goyer JP
    3. Boles DZ
    4. Silder A
    5. Delp SL
    6. Crum AJ

    (2019) Learning one’s genetic risk changes physiology independent of actual genetic risk

    Nature Human Behaviour 3:48–56.

    https://doi.org/10.1038/s41562-018-0483-4

    • PubMed
    • Google Scholar
60. 1. Vachon-Presseau E
    2. Berger SE
    3. Abdullah TB
    4. Huang L
    5. Cecchi GA
    6. Griffith JW
    7. Schnitzer TJ
    8. Apkarian AV

    (2018) Brain and psychological determinants of placebo pill response in chronic pain patients

    Nature Communications 9:3397.

    https://doi.org/10.1038/s41467-018-05859-1

    • PubMed
    • Google Scholar
61. 1. Vase L
    2. Robinson ME
    3. Verne NG
    4. Price DD

    (2005) Increased placebo analgesia over time in irritable bowel syndrome (IBS) patients is associated with desire and expectation but not endogenous opioid mechanisms

    Pain 115:338–347.

    https://doi.org/10.1016/j.pain.2005.03.014

    • PubMed
    • Google Scholar
62. 1. Wager TD
    2. Rilling JK
    3. Smith EE
    4. Sokolik A
    5. Casey KL
    6. Davidson RJ
    7. Kosslyn SM
    8. Rose RM
    9. Cohen JD

    (2004) Placebo-induced changes in fMRI in the anticipation and experience of pain

    Science 303:1162–1167.

    https://doi.org/10.1126/science.1093065

    • PubMed
    • Google Scholar
63. 1. Wager TD

    (2005) Expectations and anxiety as mediators of placebo effects in pain

    Pain 115:225–226.

    https://doi.org/10.1016/j.pain.2005.03.018

    • PubMed
    • Google Scholar
64. 1. Wampold BE
    2. Minami T
    3. Tierney SC
    4. Baskin TW
    5. Bhati KS

    (2005) The placebo is powerful: Estimating placebo effects in medicine and psychotherapy from randomized clinical trials

    Journal of Clinical Psychology 61:835–854.

    https://doi.org/10.1002/jclp.20129

    • PubMed
    • Google Scholar

Author details

1. Dasha A Sandra

   Integrated Program in Neuroscience, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   dasha.sandra@mail.mcgill.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9930-2807

2. Jay A Olson

   Department of Psychology, Harvard University, Cambridge, United States

   Present address

   University of Toronto Mississauga, Mississauga, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1161-5209

3. Ellen J Langer

   Department of Psychology, Harvard University, Cambridge, United States

   Contribution

   Conceptualization, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Mathieu Roy

   Department of Psychology, McGill University, Montreal, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3335-445X

• 1,584

  views

• 183

  downloads

• 3

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.