European Journal of Integrative Medicine 11 (2017) 31–38Research paper Positive messages may reduce patient pain: A meta-analysis Jeremy Howicka,*, George Lewithb, Alexander Mebiusa, Thomas R. Fanshawea, Felicity Bishoph, Mara van Oschc, Sandra van Dulmenc,d,e, Nick Christelisf, Ted Kaptchukg, Patriek Mistiaenc aNuffield Department of Primary Care Health Sciences,Radcliffe Primary Care Building, Radcliffe Observatory Quarter, University of Oxford, Oxford OX2 6GG, United Kingdom b Primary Medical Care, Aldermoor Health Centre, Aldermoor Close, Southampton SO16 5ST, United Kingdom cNIVEL (Netherlands Institute for Health Services Research), Utrecht, The Netherlands dDepartment of Primary and Community Care, Radboud University Medical Center, Nijmegen, The Netherlands e Faculty of Health Sciences, Buskerud and Vestfold University College, Drammen, Norway fConsultant Victoria Pain Specialists, Senior Adjunct Lecturer Monash University, Melbourne, Australia gBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States h Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom A R T I C L E I N F O Article history: Received 19 November 2016 Received in revised form 14 March 2017 Accepted 15 March 2017 Keywords: Expectations Meta-analysis Positive messages Context factors Pain Healthcare consultations A B S T R A C T Introduction: Current treatments for pain have limited benefits and worrying side effects. Some studies suggest that pain is reduced when clinicians deliver positive messages. However, the effects of positive messages are heterogeneous and have not been subject to meta-analysis. We aimed to estimate the efficacy of positive messages for pain reduction. Methods: We included randomized trials of the effects of positive messages in a subset of the studies included in a recent systematic review of context factors for treating pain. Several electronic databases were searched. Reference lists of relevant studies were also searched. Two authors independently undertook study selection, data extraction, risk of bias assessment, and analyses. Our primary outcome measures were differences in patientor observer-reported pain between groups who were given positive messages and those who were not. Results: Of the 16 randomized trials (1703 patients) that met the inclusion criteria, 12 trials had sufficient data for meta-analysis. The pooled standardized effect size was 0.31 (95% confidence interval [CI] 0.61 to 0.01, p = 0.04, I2 = 82%). The effect size remained positive but not statistically significant after we excluded studies considered to have a high risk of bias (standard effect size 0.17, 95% CI 0.54 to 0.19, P = 0.36, I2 = 84%). Conclusion: Care of patients with chronic or acute pain may be enhanced when clinicians deliver positive messages about possible clinical outcomes. However, we have identified several limitations of the present study that suggest caution when interpreting the results. We recommend further high-quality studies to confirm (or falsify) our result. © 2017 Elsevier GmbH. All rights reserved. Contents lists available at ScienceDirect European Journal of Integrative Medicine journa l home page : www.e l sev ier .com/euj im1. Introduction Chronic and acute pain are common, debilitating, and costly conditions. One hundred million people in the United States alone suffer from chronic pain, and treatment costs exceed an estimated $250 billion per year, which is a considerable burden on the US economy in terms of lost productivity [1]. Reliable estimates of the* Corresponding author. E-mail address: jeremy.howick@phc.ox.ac.uk (J. Howick). http://dx.doi.org/10.1016/j.eujim.2017.03.005 1876-3820/© 2017 Elsevier GmbH. All rights reserved.expenditure for acute pain are not available, but the market for pharmacological treatments for acute pain is believed to be large [2]. Analyses of non-steroidal anti-inflammatory drug treatment of pain suggest that, when taken alone, these drugs have very modest effects [3]. Opioids and intra-articular corticosteroids may have more substantial short-term benefits, but are usually accompanied by more serious adverse effects and therefore an unattractive longterm treatment option [4,5]. The pursuit of safer and more effective pain treatments is fraught with difficulties, but there are promising strategies readily available to enhance our current therapeutic efforts [6]. 32 J. Howick et al. / European Journal of Integrative Medicine 11 (2017) 31–38Increasing evidence from clinical trials suggests that clinicians can enhance pain relief by delivering positive messages that modulate patients' expectations and experience of pain [10–12]. The efficacy of expectation-inducing interventions was tested in one trial in which the patient was impelled to believe that "the medication was a potent painkiller [and] that their pain was going to subside within a few minutes" [13]. In another trial, patients were induced to believe that the medication they were about to receive was a groundbreaking drug that "recently became available in the Netherlands . . . [t]his drug, according to my experience, is very effective and will decrease the pain quickly after taking it" [10]. In both trials, patients who received positive messages experienced less pain than those who did not. These results indicate that expectation-inducing interventions have a considerable effect on the outcome of pain treatments. Specifically, the way practitioners deliver positive messages is a key component of expectation-inducing interventions and is therefore likely to be an important part of multipronged treatment efforts for clinical pain. The main mechanism explaining how positive messages enhance pain is likely to involve the brain's reward system, which could produce a physiological response to pain that prompts the patient's body to produce endogenous opioid analgesia [15]. Pain relief interventions that use positive messages could be mediated through similar mechanisms to drug interventions. There is compelling evidence to support the role of positive messages in reducing anxiety, a condition closely related to pain [16] and evidence to support the role of expectation-inducing interventions in reducing stress and improving well-being in patients suffering from chronic disease [17]. Crucially, enhancing patients' own pain relief mechanisms may be more effective than administering opiate-related drugs. Expectation-inducing methods of pain relief may provide the additional benefit of reducing side effects of drug treatment, such as withdrawal symptoms, addiction, and drug abuse. Understanding brain and nervous system functioning in the production of pain relief using positive messages is an important step in the scientific understanding of core mechanisms and treatments for pain-related conditions, for which there is no cure. An early systematic review and meta-analysis of clinical studies investigated the extent to which positive messages can induce pain relief in patients [18]. However, several new trials have been published since this study was published, including a recent extensive review by Mistiaen et al. [19]. However, Mistiaen et al. chose not to pool the results of expectation-inducing interventions and therefore did not address the magnitude of response to verbal suggestions by generating an effect size. Quantitative synthesis of data is required to generate a pooled estimate and confidence intervals (CIs) for treatment effects of interventions, even in cases where the pooled effect needs to be interpreted with caution owing to moderate heterogeneity (which was judged to be the case by Mistiaen et al. [19]). However, data pooling is important to generate power calculations for future high-quality trials that may resolve heterogeneity issues [20,21]. This allows the possibility of investigating the mechanisms that contribute to variability in study results [22]. Thus, building on the Mistiaen et al. review [19], the present study attempts to provide a quantitative assessment and a meaningful interpretation of the efficacy of positive messages for clinically relevant pain. Another review conducted by Peerdemann et al. investigated the potential benefits of positive messages or imagery or conditioning and found a positive effect. [14] However the Peerdeman study included experimental pain, they included studies with a high risk of bias, and they combined positive messages with other treatments (conditioning and imagery).Hence the aims of the Peerdeman et al. study were different and it does not provide an unbiased estimate of the effects of practitioner positive messages, whereas ours does. 1.1. Aims and objectives We aimed to provide a pooled estimate of the effect of delivering positive messages on patient pain. 2. Methods 2.1. Eligibility criteria We included randomized trials of the effects of positive messages in a subset of the studies included in a recent systematic review of context factors for treating pain [19]. The review did not pool the results and was also potentially confounded by the inclusion of non-randomized studies. The protocol, eligibility criteria, information sources, and study selection were reported previously [19]. Reflecting the variety of communication styles within clinical practice, we included trials comparing the effects of positive messages with neutral or negative control [23]. We also included studies comparing a neutral message (as would be provided as part of standard care) with a negative message, since these were relatively positive. However we excluded these in a sensitivity analysis. Trials were excluded if an interpreter or translator was used to induce the positive message or if the interaction between patients and care providers was not face-toface. 2.2. Search strategy The following electronic databases were searched from their start date to June 2015: CINAHL, Controlled-trials.com, EMBASE, LILACS, OpenGREY, PROQUEST Dissertations, PsycINFO, PubMed, Sociological Abstracts, the Cochrane Central Register of Controlled Trials, Web of Science. Reference lists of relevant included studies were also searched. 2.3. Outcome measures: primary outcome Our primary outcome measures were patients' self-reported pain scores, most commonly patients' perception of pain intensity reported on a visual analogue scale (VAS), and (to a lesser extent) observer-reported outcomes (such as physiological changes or observer-reported perceived pain) [24,25]. We only identified one eligible study with dichotomous outcomes. We used the primary outcome chosen by study authors where possible. If there was more than one primary outcome, or it was unclear, we chose the one most relevant to patients and provided a rationale for our choice. For example, longer-term follow-up (weeks rather than hours) is more likely to be clinically relevant. We contacted authors by email if data required for pooling was not included in published reports. 2.4. Data extraction and management We did additional data extraction as there was insufficient data extracted from the Mistiaen et al. review [19]. Because it was crucial to our analysis we also confirmed the risk of bias assessments by re-conducting the risk of bias. Two authors (from JH, AM, and TF) independently extracted data and assessed the risk of bias in accordance with the Cochrane Handbook guidelines [26]. Discrepancies were resolved by discussion with another author (PM). The extracted data included study design, types of participant, description of intervention and intervention J. Howick et al. / European Journal of Integrative Medicine 11 (2017) 31–38 33components, description of comparison group, completeness of outcome data, outcome measures, country, and funding source. To investigate possible bias, we recorded data on random sequence generation, allocation sequence concealment, blinding of participants and personnel, blinding with respect to outcome assessment, completeness of outcome data, selective outcome reporting, and other sources of bias. 2.5. Impact of bias Studies were deemed to have a high risk of bias if they were scored as having a high or unclear risk of bias for either sequence generation or allocation concealment. This is because empirical studies have shown that these characteristics influence outcomes [26,27]. To assess publication bias, we used a funnel plot and investigated funnel plot asymmetry using the trim-and-fill method [28,29]. 2.6. Synthesis of results For continuous measures, we analyzed data based on the mean, standard deviation, and number of participants. Where data were not reported in a format suitable for pooling, we reported outcomes narratively. Where possible, we made assumptions to allow the data to be included in the analysis, such as assuming a normal distribution, using the median to estimate the mean, and estimating the standard deviation as 0.75 times the inter-quartile range. For studies that did not report the number of individuals randomized to each group, we assumed that an equal proportion of participants were randomized to each group. If the standard deviation at follow-up was not reported, we used the standard deviation at baseline. We used GRABIT software to extract data presented in graphical form (Matlab Central, 2015). Because of the variety of pain measures used, we calculated standardized mean differences (SMDs), using 95% CIs and P-values for the difference between groups. All analyses used a random effects model. 2.7. Assessment of heterogeneity We anticipated heterogeneity in terms of intervention modalities, conditions, outcome measures, patients, and effect sizes, and therefore used a random effects model for the meta-analysis. Where studies were sufficiently similar to allow pooling of data, quantified heterogeneity was assessed using the I2 statistic; an I2 value of 50% or more was interpreted as indicating a substantial level of heterogeneity. Because of the study aims was to quantify the efficacy of positive messages to estimate potential benefits and make comparisons with other treatments, we pooled data despite substantial heterogeneity and noted the limitations of relying on the estimate (Higgins and Green, [26]). 2.8. Subgroup/sensitivity analyses We conducted one pre-planned subgroup analysis that excluded studies with a high risk of bias. This was to establish the least biased estimate possible. We also conducted several exploratory subgroup analyses that were not pre-planned: 1. Exclusion of studies for which we had to make assumptions about the data to obtain poolable data, to test whether our assumptions affected the results. 2. Isolation of studies that measured the effects of expectations on chronic and acute pain, to detect differences in the effects of positive messages on different types of pain. 3. Examination of the effects of positive messages in specific conditions (those that had more than one study investigatingthem), to detect differences in the effects of positive messages on different types of pain. 4. Exclusion of studies that measure pain as part of a composite outcome, to detect differences in the effects of positive messages on different types of pain outcomes. 5. Exclusion of studies that compared neutral vs. negative or positive vs. negative messages, to detect differences in the effects of positive messages in different types of studies. 6. Exclusion of outlier studies, to detect an effect size that was not unduly influenced by an anomalous result. 2.9. Statistical software used Analysis was carried out with the 'meta' package in R. (Schwarzer [31], R Core Team [32]) 3. Results Sixteen studies (1703 patients) within the systematic review met our inclusion criteria [10,12,13,23–41]. These studies included a variety of interventions (see Tables 1 and 2). Twelve of these (1426 patients) had sufficient data for meta-analysis [10,13,23,25,33,34,36,37,39–41]. One study report [13] conveyed results for two trials, which were treated separately. We could not extract suitable data for meta-analysis from four studies [12,24,35,38], mostly because no measure of variability of the primary outcome was reported (and attempts to obtain this data from the authors failed). The risk of bias was judged as high in 10 of the 16 studies included (see Fig. 1) [10,13,24,33–36,38,39]. Pain intensity was measured directly using a VAS in most of the studies, and as part of a composite outcome in two studies [36,37]. 3.1. Continuous outcomes For the continuous outcome measures, the pooled estimate of the SMD was 0.31 (95% CI 0.61 to 0.01, P = 0.04), indicating a beneficial effect of delivering positive messages to patients. Statistical heterogeneity was high (I2 = 82%, P < 0.001) (see Fig. 1). A pre-planned subgroup analysis was conducted excluding studies with a high risk of bias. Based on the six remaining studies eligible for meta-analysis [23,25,33,37,40,41] with a low risk of bias and data suitable for pooling, the pooled estimate of the SMD was positive but not statistically significant: 0.17 (95% CI 0.54 to 0.19, P = 0.36, I2 = 84%). 3.2. Subgroup analysis Excluding studies for which we had to make assumptions about the data to pool results, [10,13,23,33,34,36,41] the effect was 0.15 (95% CI 0.31 to 0.02, P = 0.08, I2 = 0%). Excluding studies that measured pain as part of a composite outcome [36,37], the pooled estimate was 0.32 (95% CI 0.68 to 0.04, P = 0.08, I2 = 85%). Excluding studies that compared neutral versus negative [13,33] or positive versus negative suggestions [41], the pooled estimate was 0.14 (95% CI 0.43 to 0.14, P = 0.33, I2 = 72%). For studies that measured chronic pain, the effect size was 0.10 (95% CI 0.25 to 0.05, p = 0.18, I2 = 0%) and in studies measuring acute pain, it was 0.49 (95% CI 1.04 to 0.07, P = 0.08, I2 = 89%). For studies of a single condition (postoperative pain), the effect was positive but not statistically significant: [13,23] 0.79 (95% CI 2.31 to 0.72, P = 0.30). We conducted an exploratory analysis that excluded outliers in which we repeated the meta-analysis after removing each study separately [42]. This produced point estimates of the effect size that varied from 0.22, 95% CI 0.50 to 0.07 (omitting [13]) to Table 1 Description of included studies. Study Country Participants No. Participants Mean age Male Sex, % Interventions Intervention timing Method for assessing pain intensitya Anderson et al. [24] United States Dental patients needing at least 2 fillings 34 26 42 Positive suggestion After dental filling procedure VASb Benedetti et al. [13] Italy Patient having undergone thoracotomy 42 55 57 Open administration of treatment with positive suggestion 1 h after operation VAS Benedetti et al. [11,43] Italy Patient having undergone thoracotomy 36 55 57 Open administration of treatment with positive suggestion 1 h after operation VAS De Craen et al. [10] The Netherlands Patients with chronic pain attending an outpatient clinic for a routine visit 111 52 32 Open administration of treatment with positive suggestion 1 h after administration of experimental pain VAS Dutt-Gupta et al. [33] Australia Unpremedicated patients requiring placement of an intravenous cannula 101 46 19 Negative suggestion Within 2 min of cannula placement VAS Goodenough et al. [34] Australia Children aged 3–17 years requiring venepuncture 117 10 62 Placebo with positive suggestion Immediately after venepuncture Faces pain scale Kincheloe et al. [35] United States Dental patients requiring injection 128 n/a 46 Positive suggestion Immediately after injection VAS Knipschild and Arntz [12] The Netherlands and Belgium Patients in general practice with pain complaints 77 n/a n/a Positive message Between 7 and 100 days VAS Litt et al. [36] United States Dental patients undergoing molar extraction 34 26 39 Positive message Immediately after oral surgery VAS Little et al. [37] United Kingdom Patients with low back pain 122 42 43 Positive message 1 week after visit to doctor Combined pain/ function score with numerical rating scale Petersen et al. [38] Denmark Patient having undergone thoracotomy 38 62 63 Positive message Immediately after treatment VAS Petersen et al. [39] Denmark Patients with chronic neuropathic pain 36 57 55 Open administration of painkiller Immediately after treatment Mechanical VAS Ronel et al. [25] Germany Patients 18–80 presenting with biomarkernegative chest pain 28 64 83 Positive message 1 min after angiogram VAS SuarezAlmazor et al. [40] United States Age 50+, patients with painful knee osteoarthritis 418 64 36 Positive message 3 months after treatment VAS Varelmann et al. [41] United States Healthy patients at term requesting labor epidural analgesia/nonlaboring parturients presenting for elective cesarean delivery under spinal anesthesia 140 33 0 Positive message Immediately after injection VAS Wang et al. [23] China Abdominal hysterectomy patients, aged 18–65 241 44 0 Positive or neutral/ negative suggestions 6 h after operation VAS a Was pain intensity unless otherwise stated. b Visual analog scale. 34 J. Howick et al. / European Journal of Integrative Medicine 11 (2017) 31–380.39, 95% CI 0.68 to 0.10 (omitting [23]); the corresponding Pvalues varied between 0.13 to 0.009. As expected from Fig. 2, the studies that appeared to have the most influence on the pooled estimate were [13,23,41,43]. However, we found no substantive reason to exclude any of these studies from our primary analysis. 3.3. Assessment of risk of bias within studies When studies with a high risk of bias were excluded, the effect of positive messages remained positive but not statistically significant. The nature of the intervention makes it difficult to blind practitioners, but it is possible to blind patients. Four studies had a low risk of bias for blinding participants [10,23,25,35]. Ten of the included studies had a low risk of bias for blinding outcomeassessors [10,12,23–37,40,41]. Seven studies were ranked as having a high or unclear risk of bias for incomplete outcome data [12,23,35,37–40]. Only one study had a high risk of bias for selective reporting [39]. Other sources of bias included inadequate description of methodology [13] and "floor effects" leading to inability to detect treatment effects [38]. 3.4. Publication bias Visual inspection of the funnel plot indicated the possibility of publication bias, with two smaller studies having the largest effect sizes [13,43]. Investigation of funnel plot asymmetry using the trim-and-fill method [28] led to a small attenuation in the pooled estimate, to 0.22, 95% CI 0.53 to 0.09, P = 0.17, with the method Table 2 Positive message delivered in trial. Study Description of positive message delivered to participants in the intervention group Anderson et al. [24] Patients were told; "a procedure involving music has been effective in suppressing pain in 5000 dental operations." They were then given music and told: "This new procedure is quite simple. We let you listen to music tapes you particularly like over headphones, and most crucial, we let you make changes in the volume any time you feel any particular increase in tension, discomfort, anxiety, or pain. You do this by moving a dial we've located on your chair up and down." Turning the dial actually moderated the volume slightly, and patients were told that variations in volume would help them attend to the music. Benedetti et al. [13] The open administration of morphine was performed at the bedside by a doctor, who told the patients that the medication was a potent painkiller, according to routine clinical practice. In other words, the patients were informed that their pain was going to subside within a few minutes. Benedetti et al. [11,43] The open administration of morphine was performed at the bedside by a doctor, who told the patients that the medication was a potent painkiller, according to routine clinical practice. In other words, the patients were informed that their pain was going to subside within a few minutes. De Craen et al. [10] "This is a medication that recently became available in the Netherlands. This drug, according to my experience, is very effective and will decrease the pain quickly after taking it." Dutt-Gupta et al. [33] "I am going to apply the tourniquet on the arm. As I do this many people find the arm becomes heavy, numb and tingly. This allows the drip to be placed more comfortably." Goodenough et al. [34] "We are trying out a new special cream. I am going to put some cream on your arm that might make it (the needle) hurt less. Kincheloe et al. [35] Patients were instructed that the topical aesthetic (in fact a placebo) would numb them and make the injection a lot less painful. Knipschild and Arntz [12] (After ascertaining there was no serious disease) patients were told: "You will be better within a week or so." Litt et al. [36] Patients were told that their relaxation efficacy was high by the experimenter. The experimenter's positive suggestion was reinforced by (false) biofeedback which appeared to confirm the patients' ability to relax. Little et al. [37] A booklet provided to patients gave positive messages: "you can ease your pain" and "most people do get better within 4 weeks." Petersen et al. [38] Participants were told: "An active medication that has been shown to be effective for some types of pain will be tested." The active medication was given in full view of the patients, and the patients were told: "The agent you have just been given is known to powerfully reduce pain in some patients." Petersen et al. [39] The intervention (placebo or treatment) was administered openly (as opposed to covertly for the control patients). Ronel et al. [25] "Mrs./Mr. XYZ, we are now injecting a drug through the catheter which will widen your coronary vessels. This procedure will improve the blood flow in your heart. This drug is very effective and starts its action immediately. It is possible that you might feel some agreeable warmness or formication after only a few seconds." Suarez-Almazor et al. [40] "I think this will work for you," "I've had a lot of success with treating knee pain," and "Most of my patients get better." Varelmann et al. [41] "We are going to inject the local anesthetic that will numb the area where we are going to do the epidural/spinal anesthesia and you will be comfortable during the procedure." Wang et al. [23] "The PCA pump was great in treating pain, especially for people who like you underwent abdominal surgeries", "You took a correct decision on using a PCA pump for your postoperative pain", and "The PCA pump was very effective in removing the postoperative pain affliction." J. Howick et al. / European Journal of Integrative Medicine 11 (2017) 31–38 35indicating the possibility of a missing study with a large positive effect size (effectively balancing the result of one of Benedetti's studies). Egger's test of funnel plot asymmetry gave a nonsignificant P-value of 0.31 [29]. Although we note the relatively large effect size observed in the two Benedetti studies, considering both these results and the visual inspection of the funnel plot, we did not find compelling evidence of publication bias. However, publication bias is a potential problem with all evidence syntheses [44,45]. 4. Discussion 4.1. Summary of evidence This is the first meta-analysis of randomized trials of positive messages for patient pain. Although the effects of positive messages are generally encouraging, contrary to what has been reported in previous systematic reviews [18,46], they are not unambiguously so. When non-randomized trials are excluded from analysis, the effects are small. Our subgroup analyses (in which we excluded studies with a high risk of bias, investigated chronic and acute pain individually, and investigated postoperative pain individually) showed that the benefits of positive messages persisted but were not always statistically significant. Our meta-analysis is also the first to provide a likely effect size which could be used to make power calculations for future, more definitive (higher quality) randomized trials. Assuming a typical standard deviation of between 1 and 2 units, our review demonstrated a pooled effect size estimate of approximately half of 1 point on a 10-point VAS. This falls short of the 1 to 2point reduction deemed to be clinically relevant [47]. We found no reason to believe that the effects of positive messages are only beneficial in the short term.4.2. Consistency with existing evidence Several systematic reviews of placebos (which can include positive messages) have shown placebo to be effective in treating pain [48–51]. However, the delivery of placebo interventions includes more than positive messages; they also include the effects of a sham intervention [52] and classical conditioning [53]. 4.3. Comparison of positive messages with drug effects Although some drugs have a substantial clinical analgesic effect [54] others do not. Some drug interventions for treating dental pain [55,56], a condition investigated in three of the included studies [24,35,36], knee pain [57], neuropathic pain, and low back pain [58] revealed effect sizes similar to those found in our study. Pharmacological interventions may be more likely than positive messages to have adverse side effects [58,59]. Although our preliminary analysis suggests that they are similar, at this stage, a robust comparison of positive messages with drug effects is not possible. Comparisons between the effects of positive messages with drugs must be treated with caution. This is because recent estimates of analgesic efficacy use the Oxford League Table [60], which is not straightforwardly translatable to the continuous VAS measures typical of studies in this review. Our effect size comparisons are based on older effect estimates of analgesic drugs compared with placebo. 4.4. Strengths and limitations This is the first study that quantifies the efficacy of positive messages for treating pain. There are several limitations to this meta-analysis. The risk of bias was high in many of the studies and Fig. 2. Forest plot of comparison: Effects of positive suggestions vs usual care. Fig.1. Risk of bias summary: review authors' judgments about each risk of bias item for each included study. 36 J. Howick et al. / European Journal of Integrative Medicine 11 (2017) 31–38the effect size estimate was reduced in subgroup analyses that excluded these studies. There was also some evidence of publication bias. The included studies were also heterogeneous, both statistically and in terms of the interventions. Statistical heterogeneity remained in most cases even after subgroup analyses. This was expected owing to the wide range of interventions, patients, settings, and practitioners. Our use of a random effects model partly addressed this problem.Heterogeneity of the control interventions could have influenced the effect in either direction. As the effects of positive messages were estimated by subtracting the effects in the control groups from effects in the treatment groups, Hawthorne effects in the control group might have reduced the effect size. A recent systematic review suggested that "untreated" (neutral) control groups in clinical trials benefit from Hawthorne effects [61]. If so, then the effects of positive messages revealed by our study are an underestimate. Our decision to pool studies with different types of controls reflects clinical practice, in which caregivers have a range of consultation styles. The decision to pool results despite the high heterogeneity was also justified because all but one of the studies [23] revealed results consistent with a positive effect. Reporting bias in the individual studies might have arisen because of the difficulty of blinding caregivers and participants. However, outcomes were assessed by blind observers in two of the included studies [24,25] and the effects of positive messages were statistically significant in both of these. Finally, two dangers may arise when implementing the results of these studies. First, if there is a large discrepancy between the patient's experience of pain and what the doctor tells the patient they will feel, the patient may adjust their beliefs to develop a negative expectation (and a subsequent "nocebo" effect) [62]. Delivering an overly positive message that does not match the patient's experience may also threaten the patient/doctor relationship, as the patient may perceive that the doctor is deceiving them [63]. Hence future trials in this area must consider likely realistic prognoses and ensure that any positive messages delivered by clinicians are realistic and non-deceptive. 4.5. Conclusions and implications Positive messages appear to be moderately effective for treating pain. Patients are likely to benefit from positive messages, either alone or together with other treatments. Limitations to the study, most notably heterogeneity, warrant caution when interpreting the results. However, given the lack of serious harms, the ease of implementation, and the potential benefit to the many patients suffering from pain, clinicians are warranted in implementing the results of this review by delivering (realistic and non-deceptive) positive messages to their patients. 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