Introduction
Psychological interventions are increasingly delivered over the internet and evidence suggests comparable effects of online v. traditional face-to-face therapy for a range of clinical problems (Hedman-Lagerlöf et al., Reference Hedman-Lagerlöf, Carlbring, Svärdman, Riper, Cuijpers and Andersson2023; Titov et al., Reference Titov, Hadjistavropoulos, Nielssen, Mohr, Andersson and Dear2019). Most online treatments include cognitive-behavioral interventions and there is support for better outcomes in therapist-guided online treatments, compared to unguided (Andersson, Titov, Dear, Rozental, & Carlbring, Reference Andersson, Titov, Dear, Rozental and Carlbring2019; Baumeister, Reichler, Munzinger, & Lin, Reference Baumeister, Reichler, Munzinger and Lin2014). Online treatments can either be synchronous (i.e. the therapist and the patient are present at the same time in therapy sessions, e.g. videoconference) or asynchronous (i.e. learning materials are provided in a digital format and the therapist and patient interacts via asynchronous messages). The focus of the present study is on the latter form of online treatment.
In any clinical intervention, there are factors other than the active ingredient that may shape the clinical outcome. These are oftentimes referred to as non-specific treatment factors or common factors (Cuijpers, Reijnders, & Huibers, Reference Cuijpers, Reijnders and Huibers2019), and are part of the placebo response (Finniss & Benedetti, Reference Finniss and Benedetti2005). For example, patients' treatment expectations (patients' prognostic beliefs about how they will respond to treatment (Constantino, Visla, Coyne, & Boswell, Reference Constantino, Visla, Coyne and Boswell2018)) have been shown to shape symptoms and to be a robust predictor in face-to-face psychotherapy research (Constantino, Arnkoff, Glass, Ametrano, & Smith, Reference Constantino, Arnkoff, Glass, Ametrano and Smith2011). This has also been suggested for online treatments (El Alaoui et al., Reference El Alaoui, Ljotsson, Hedman, Kaldo, Andersson, Ruck and Lindefors2015; El Alaoui et al., Reference El Alaoui, Ljotsson, Hedman, Svanborg, Kaldo and Lindefors2016). However, there has been no formal comparison of the effect of treatment expectations between studies in which patients have been randomized to either internet or face-to-face treatment. It is therefore unclear if expectations play the same role in symptom reduction in online therapeutic settings as seen in face-to-face treatment.
There are many obvious differences between online and face-to-face treatment contexts such as the clinical environment (i.e. being at the hospital v. being at home) and the clinician-patient interaction (i.e. physical meeting v. reading and writing messages). Online interventions often involve asynchronous communication and lack of access to non-verbal behaviors. This apparent difference between modalities has raised concerns if online interventions can deliver the same important ingredients involved in therapy (Rochlen, Zack, & Speyer, Reference Rochlen, Zack and Speyer2004; Wells, Mitchell, Finkelhor, & Becker-Blease, Reference Wells, Mitchell, Finkelhor and Becker-Blease2007). In addition, large patient surveys have found low acceptability for online treatments (Handley, Perkins, Kay-Lambkin, Lewin, & Kelly, Reference Handley, Perkins, Kay-Lambkin, Lewin and Kelly2015; Mohr et al., Reference Mohr, Siddique, Ho, Duffecy, Jin and Fokuo2010) and high dropout rates (Waller & Gilbody, Reference Waller and Gilbody2009). This might, however, be more common for certain subgroups of patients (Karyotaki et al., Reference Karyotaki, Kleiboer, Smit, Turner, Pastor, Andersson and Cuijpers2015). Finally, lay people and health professionals show a preference for face-to-face treatment (Gun, Titov, & Andrews, Reference Gun, Titov and Andrews2011).
One of the most used measurements for treatment expectation in clinical trials is the Credibility and expectancy questionnaire by Devilly and Borkovec (Devilly & Borkovec, Reference Devilly and Borkovec2000) as it is in the public domain and easily adapted to different treatments and populations (Constantino et al., Reference Constantino, Visla, Coyne and Boswell2018). It consists of six questions where three items are suggested to load on the credibility factor and the other half on the expectancy factor, although the factors strongly correlate (Haanstra et al., Reference Haanstra, Tilbury, Kamper, Tordoir, Vliet Vlieland, Nelissen and Ostelo2015). Credibility is defined as ‘how believable, convincing and logical the treatment is’ and expectancy as ‘improvements that clients believe will be achieved’ (Devilly & Borkovec, Reference Devilly and Borkovec2000). The scale has been used to investigate treatment expectations, by using the expectancy factor separately but also together by using a sum score of the scale (Constantino et al., Reference Constantino, Visla, Coyne and Boswell2018).
In the present study, we performed an Individual Participant Data Meta-Analysis (IPDMA) based on data from existing clinical trials in which patients had been randomized to face-to-face or therapist-guided online behavioral therapy with asynchronous communication. The aim was to determine if patients' treatment expectations predict clinical outcomes at study end, and differential effects of expectations in online v. face-to-face treatment. Three research questions were investigated: (1) Are there differences in patients' treatment expectations of online v. face-to-face treatment? (2) Can treatment expectations predict the clinical outcomes at study end? (3) Does type of treatment modality moderate the effect of treatment expectations on clinical outcomes? We hypothesized that higher treatment expectations would predict better treatment outcome in both treatment modalities but with a larger effect in face-to-face treatment compared to online treatment.
Method
Study sample and procedures
The study was performed according to the PRISMA guidelines and Cochrane Collaboration recommendations. Each study included in the IPDMA was approved by its own local ethical review board, and an additional ethical approval was granted before onset of the present IPDMA (Swedish Ethical Review Authority # 2021-03833). The study and its research questions were preregistered in PROSPERO (CRD42021245299) and the protocol was followed without deviations.
Searches of MEDLINE (Ovid) and PsycINFO (Ovid) were last conducted 2 February 2021 in collaboration with staff at the Karolinska Institutet University Library. The study selection process is shown in a PRISMA flow diagram (see online eAppendix in the Supplement). We searched for randomized clinical trials (RCTs) involving internet-delivered and face-to-face psychological interventions for all types of diagnoses and outcome measures.
Inclusion criteria were: comparison between therapist-guided Internet-delivered therapy with asynchronous communication and face-to-face therapy using a randomized controlled design; interventions aimed at treatment of psychiatric or somatic conditions (e.g. not merely prevention or psychoeducation); comparison of treatments that were similar in content in both treatment conditions; Internet-delivered behavioral therapy where the Internet treatment was the main component and not a secondary complement to other interventions; outcome data from an adult patient sample; outcomes in terms of assessment of symptoms of the target problem. Exclusion criteria were: other types of studies than randomized controlled studies, articles in non-English. The search strategy and preregistered methods can be found in the online eAppendix in the Supplement.
Two pairs of reviewers (M.P, M.J, E.M, V.V) independently screened the titles and abstracts of all studies to identify those that potentially met the inclusion criteria. Any disagreement within the pair was resolved through discussion with a third researcher. The full texts of the potentially eligible studies were retrieved and, independently, assessed for eligibility by two researchers (M.P and M.J). Two independent reviewers (E.M, V.V) extracted data to describe the included studies (see Table 1).
Study and patient characteristics
a Main outcome used in the analysis.
ICBT, internet-based cognitive behavioral therapy; ITT, Intention to treat; INT, Internet-based treatment; FTF, face-to-face treatment; SIAS, Social Interaction Anxiety Scale; SPS, Social Phobia Scale; HAI, 18-item Health Anxiety Inventory; MADRS-S, Montgomery Åsberg Depression Rating Scale; GAD-7, Generalized Anxiety Disorder 7-item scale; MPI, Multidimensional Pain Inventory; LSAS, Liebowitz Social Anxiety Scale; THI, The Tinnitus Handicap Inventory; Mini-TQ, Mini-Tinnitus Questionnaire; TRQ, Tinnitus Reaction Questionnaire.
Methodological quality and risk of bias
Methodological quality and risk of bias of the included studies were independently assessed by two researchers (E.M, V.V) using the Psychotherapy Outcome Study Methodology Rating Form (POMRF) and the Jadad Scale. POMRF is a 22-item assessment of methodological quality (e.g. representativeness of sample, use of blind evaluators) (Ost, Reference Ost2008). The Jadad scale independently assesses the methodological quality of RCTs and takes the following factors into account: randomization, blinding and withdrawal/dropouts. The maximum score of 5 is representative of a good quality study (Jadad et al., Reference Jadad, Moore, Carroll, Jenkinson, Reynolds, Gavaghan and McQuay1996).
Data collection
Authors of studies that met the inclusion criteria were contacted according to the standardized schedule described by Veroniki and colleagues (Veroniki et al., Reference Veroniki, Ashoor, Le, Rios, Stewart, Clarke and Tricco2019), where authors were contacted by email and then sent reminders up to four times. In addition, a letter was sent if there was no answer. Credibility and Expectancy Questionnaire (CEQ) ratings and pre- and post-treatment primary outcome data were requested as well as basic clinical characteristics and demographics.
Statistical analysis
All included studies used different primary outcomes, and the CEQ sum score was calculated. Two studies used a six item CEQ, three studies used a five-item questionnaire, and one study used a three item questionnaire. For the CEQ sum score, the pre-treatment outcome score and the post-treatment outcome score, harmonization was performed by first subtracting the minimum value within each study and then dividing by the maximum value within each study. After this normalization, all aforementioned variables received a range of 0–1, with 0 standing for the lowest score observed in the study, and 1 standing for the highest score observed in the study.
A one-stage method was employed (Tierney, Stewart, & Clarke, Reference Tierney, Stewart, Clarke, Higgins, Thomas, Chandler, Li., Page and Welch2023). For our primary analysis, a linear mixed model was used (as assuming normality of residuals in a mixed model is less restrictive and reasonably robust to deviations from normality). We modeled the effect of the CEQ sum on treatment outcomes with the individual participant at level 1, and study at level 2. The post-treatment outcome score was regressed on the CEQ sum, the treatment condition (online v. face-to-face), and the pre-treatment outcome score. The model included a study-level random CEQ slope but no random intercept due to data limitations. Test of overall difference on CEQ sum between online v. face-to-face was tested using a linear mixed model with treatment condition as independent variable. p values for the mixed model were obtained using the Satterthwaite method (for more information about the model see online eAppendix in the Supplement). Sensitivity analyses were conducted to assess whether country of the included studies or disorder were related to outcome.
The primary analysis was performed on complete case basis. For completeness, we also provide results from an imputed model that includes all participants (see online eAppendix in the Supplement). Imputation of the missing variables post-treatment and CEQ sum was performed using multiple imputation with 20 imputed datasets, the factors age, sex, education, and pre-treatment outcome were included as well as a categorical variable for Study to take into account between-study variation. In addition, we specified the imputation to be computed by treatment group.
All analyses were performed in R version 4.1.2. All tests were two-sided and p values below 0.05 were considered significant.
Results
Study selection
We identified 7045 unique records of which 62 full-text articles were retrieved. Twenty-six studies met the inclusion criteria, and the corresponding authors were approached and asked whether the CEQ had been administered and if they would be willing to share individual participant data. Thirteen studies did not administer the CEQ and three research groups were not able to send data, citing limited resources (Carlbring et al., Reference Carlbring, Nilsson-Ihrfelt, Waara, Kollenstam, Buhrman, Kaldo and Andersson2005; Zerwas et al., Reference Zerwas, Watson, Hofmeier, Levine, Hamer, Crosby and Bulik2017) or unavailability of data (Kiropoulos et al., Reference Kiropoulos, Klein, Austin, Gilson, Pier, Mitchell and Ciechomski2008). Authors of four studies did not respond to any emails or mail. Six studies were able to provide such data and were thus included in our analyses (Andrews, Davies, & Titov, Reference Andrews, Davies and Titov2011; Axelsson et al., Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020; Boersma et al., Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019; Hedman et al., Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011; Jasper et al., Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014; Kaldo et al., Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008).
Study characteristics
Study characteristics are listed in Table 1. The following diagnoses were included (clinical trials, k): social anxiety (k = 2), tinnitus (k = 2), pathological health anxiety (somatic symptom disorder or illness anxiety disorder) (k = 1), chronic pain with comorbid depression and anxiety (k = 1). Four studies diagnosed the participants at the beginning of the trial (Axelsson et al., Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020; Boersma et al., Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019; Jasper et al., Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014; Kaldo et al., Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008) and in two studies participants were diagnosed before referral (Andrews et al., Reference Andrews, Davies and Titov2011; Hedman et al., Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011). Participants were recruited from routine care referral (Andrews et al., Reference Andrews, Davies and Titov2011), self-referral (Boersma et al., Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019) or a combination of the two (Axelsson et al., Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020; Hedman et al., Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011; Jasper et al., Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014; Kaldo et al., Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008). Four studies were carried out in Sweden (Axelsson et al., Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020; Boersma et al., Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019; Hedman et al., Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011; Kaldo et al., Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008), one study in Australia (Andrews et al., Reference Andrews, Davies and Titov2011) and one in Germany (Jasper et al., Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014). The CEQ was administered before randomization (Jasper et al., Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014; Kaldo et al., Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008), after randomization (Andrews et al., Reference Andrews, Davies and Titov2011) at week two (Axelsson et al., Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020; Hedman et al., Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011) and mid treatment (Boersma et al., Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019).
Participant characteristics
Among the 491 participants, the mean (s.d.) age was 41 (13) years and 274 (56%) were female. Across the included studies, 20% of subjects (124/491) were removed due to missing data for CEQ and/or the outcome measure (19% in the internet treatment and 21% in the face-to-face groups). The following number of subjects was removed for each study: 10 (29%) from Andrews et al. Reference Andrews, Davies and Titov2011, 14 (7%) from Axelsson et al. Reference Axelsson, Andersson, Ljotsson, Bjorkander, Hedman-Lagerlof and Hedman-Lagerlof2020, 53 (46%) from Boersma et al. Reference Boersma, Sodermark, Hesser, Flink, Gerdle and Linton2019, 28 (22%) from Hedman et al. Reference Hedman, Andersson, Ljotsson, Andersson, Ruck, Mortberg and Lindefors2011, 8 (10%) from Jasper et al. Reference Jasper, Weise, Conrad, Andersson, Hiller and Kleinstaeuber2014, and 11 (22%) from Kaldo et al. Reference Kaldo, Levin, Widarsson, Buhrman, Larsen and Andersson2008.
Results of the IPDMA
The overall CEQ ratings indicated no significant difference in treatment expectations among patients randomized to online or face-to-face treatment [β (s.e.), −0.01 (0.038); p = 0.81], as the median (IQR) was similar for online treatment 0.72 (0.56; 0.81) and face-to-face 0.73 (0.61–0.83), see Table 2. Treatment expectations (the CEQ sum) was predictive of the post-treatment clinical outcome across groups [β (s.e.), −0.27 (0.057); p < 0.01], where higher expectations were associated with greater reductions of symptoms. When testing the interaction between CEQ sum and treatment group on clinical outcomes there was no significant difference between online v. face-to-face treatments [β (s.e.), 0.01 (0.08); p = 0.93], see Fig. 1. The statistical significance was stable after sensitivity analyses controlling for studies' country of origin and disorder.
Treatment expectations for online v. face-to-face treatment
Median values for the CEQ score for each treatment type (TRT) in each study (Internet = 1; face-to-face = 0).
Visualization of the association between treatment expectations and clinical improvement for each study. A significant association between expectations (CEQ score) and clinical outcomes was seen in all studies, demonstrating that higher expectations are associated with a decrease in symptoms from pre to post treatment. However, there was no significant interaction between CEQ scores and treatment type (Blue = face to face; Red = internet). The standard deviation random effect for treatment expectation was 0.08, meaning that 95% of studies were expected to have a CEQ sum effect between −0.27±0.16.
Risk of bias
Out of a possible total of 44 points on the POMRF scale, the mean score for the reviewed studies was 24.3 (s.d. = 5.9), median 24 (IQR = 3.7) with a range of 14 to 31. Out of a possible 5 points on the Jadad scale the mean score was 3 (s.d. = 0), median 3 (IQR = 0). Total scores for each study can be found in the online eAppendix in the Supplement.
Discussion
Internet-based treatments have been available for almost 20 years and are becoming more common for delivering evidence-based psychological treatment. While the efficacy of online treatment programs is now well-established (Hedman-Lagerlöf et al., Reference Hedman-Lagerlöf, Carlbring, Svärdman, Riper, Cuijpers and Andersson2023) the relative importance of expectancy effects in the online v. face-to-face setting is largely unknown. The purpose of this study was to investigate the differential effects of treatment expectations in online v. face-to-face psychological treatments.
Based on research from face-to-face studies, treatment expectations are closely linked to the patient-clinician relationship (Connolly Gibbons et al., Reference Connolly Gibbons, Crits-Christoph, de la Cruz, Barber, Siqueland and Gladis2003; Visla, Constantino, Newkirk, Ogrodniczuk, & Sochting, Reference Visla, Constantino, Newkirk, Ogrodniczuk and Sochting2018) where positive clinical encounters have been associated with better treatment outcome (Fluckiger, Del Re, Wampold, Symonds, & Horvath, Reference Fluckiger, Del Re, Wampold, Symonds and Horvath2012) higher placebo responses (Kaptchuk et al., Reference Kaptchuk, Kelley, Conboy, Davis, Kerr, Jacobson and Lembo2008) and self-estimated ability to return to work, among sick-listed patients (Lynoe, Wessel, Olsson, Alexanderson, & Helgesson, Reference Lynoe, Wessel, Olsson, Alexanderson and Helgesson2011). Despite fundamental differences in treatment contexts and patient-clinician interactions, the present study found equivalent expectations in online and face-to-face treatment and no significant or clinically meaningful difference in the effect of expectations on the treatment outcome between the two modalities.
With regards to research question number one, the comparable expectation ratings between face-to-face and online treatment may be an indication of a high acceptability of these interventions among patients. This is in contrast with previous surveys showing low acceptability for online interventions in the general population (Handley et al., Reference Handley, Perkins, Kay-Lambkin, Lewin and Kelly2015; Mohr et al., Reference Mohr, Siddique, Ho, Duffecy, Jin and Fokuo2010) and that Internet-based cognitive behavioral therapy is heavily underutilized (Carper, McHugh, & Barlow, Reference Carper, McHugh and Barlow2013; Hennemann, Beutel, & Zwerenz, Reference Hennemann, Beutel and Zwerenz2017) [although showing comparable efficacy with face-to-face (Carlbring, Andersson, Cuijpers, Riper, & Hedman-Lagerlof, Reference Carlbring, Andersson, Cuijpers, Riper and Hedman-Lagerlof2018)]. The present analysis suggests that once patients are randomized to online therapy, they hold a more positive view of the treatment. This is in line with previous reports of acceptability in various randomized controlled trials, showing high acceptability for the internet treatment (Hedman, Ljótsson, & Lindefors, Reference Hedman, Ljótsson and Lindefors2012; van Ballegooijen et al., Reference van Ballegooijen, Cuijpers, van Straten, Karyotaki, Andersson, Smit and Riper2014). This underscores the importance of acceptability-facilitating interventions for online treatments (Molloy, Ellis, Su, & Anderson, Reference Molloy, Ellis, Su and Anderson2021), as the online intervention seems to be appreciated once the patients commence treatment.
There is also a possibility that individuals who choose to participate in a randomized trial that includes online treatment have a more positive view of this type of treatment from the beginning. Results should be interpreted while taking this possible bias into consideration. On the other hand, a large proportion of the patients were recruited through the regular health care system, which might mean a more naturalistic setting, and is a strength in the current material. Also, it is still important to compare differences in expectancies among the group of help-seeking patients that could accept both face-to-face and internet-based treatment. The overall preference for different forms of treatment modalities in a target population, for example everyone with a certain diagnosis, should be further explored in epidemiological studies.
With regards to research question number two, we found that expectation ratings predicted clinical outcome at post-treatment, which is consistent with the previous literature (Constantino et al., Reference Constantino, Arnkoff, Glass, Ametrano and Smith2011, Reference Constantino, Visla, Coyne and Boswell2018), Patients' expectation ratings have shown relatively robust correlations with treatment outcome across different diagnoses and treatments (Greenberg, Constantino, & Bruce, Reference Greenberg, Constantino and Bruce2006; Noble, Douglas, & Newman, Reference Noble, Douglas and Newman2001) even though a recent review suggested that constructs other than expectation may be more predictive of treatment outcomes among patients with chronic conditions who have treatment failures in their medical history (Kaptchuk, Hemond, & Miller, Reference Kaptchuk, Hemond and Miller2020). Future studies could perhaps benefit from adding other constructs, such as hope or motivation, as predictors in psychological interventions for chronic conditions.
With regards to research question number three, there was no interaction between online v. face-to-face treatments regarding the predictive role of expectations. This suggests that treatment expectations have a similar impact on therapeutic outcomes in online and face-to-face treatment. This adds to previous research that has shown similar effect of patient clinician relationship on treatment outcome in online and face-to-face treatments (Fluckiger, Del Re, Wampold, & Horvath, Reference Fluckiger, Del Re, Wampold and Horvath2018).
It is important to note that all online studies in the present study were guided, i.e., included therapist contact (asynchronous). Most online treatments involve a clinician who guides the client through the program, provides feedback and general support. Guided treatment programs tend to be more effective than self-guided (Baumeister et al., Reference Baumeister, Reichler, Munzinger and Lin2014; Spek et al., Reference Spek, Cuijpers, Nyklicek, Riper, Keyzer and Pop2007). It is generally believed that the relationship between a patient and clinician (the so-called therapeutic alliance) is central for expectancy effects (Jensen & Kelley, Reference Jensen and Kelley2016) and equivalent expectations might indicate that patients do develop a positive relationship with their online therapist. However, it is also possible that the whole online program is rated and not just to the interactions between the client and the therapist (Andersson et al., Reference Andersson, Titov, Dear, Rozental and Carlbring2019). It would be of interest to investigate if online treatment without guidance also induces equally high expectations.
Online settings seem to evoke similar expectations as face-to-face treatment. An early indication of this was demonstrated in a recent double-blind experiment (Ponten, Ljotsson, & Jensen, Reference Ponten, Ljotsson and Jensen2019) where we tested the novel hypothesis that expectations about pain relief, and placebo analgesia, can be induced via online patient-clinician communication. With no face-to-face communication prior to pain testing, participants displayed significant placebo responses with an effect size comparable to ordinary face-to-face experiments. Our placebo experiment also indicated that participants' perception of the online communication (how positive it felt) transferred to the perceived alliance during the subsequent face-to-face pain testing. This suggests that online and face-to-face communication may be interchangeable in some clinical contexts, as the online interaction was highly associated with the perception of face-to-face interaction, although not tested in a clinical context. The present IPDMA confirms that online expectations are relevant in a clinical context, demonstrated in a large number of patients, with several different clinical problems.
The role of expectations in online treatment settings has been poorly investigated. To our knowledge, this was the first systematic review and IPDMA to consider the association between expectation ratings and treatment outcomes in online v. face-to-face settings. To include as many studies as possible, different diagnoses and treatment outcomes were combined in this IPDMA, which is both a strength (large dataset) and limitation (heterogeneity). One limitation that may have introduced heterogeneity in our data was that the expectation measure (CEQ) was administered at different time-points between the studies, however exploratory analysis shows that the timepoint when CEQ was administered had no impact on the main results. If the CEQ is administered early, we suspect that scores are more likely to mirror widespread presumptions about the treatment format, and if the CEQ is administered at a later stage, scores are more likely to be a function of actual experiences with the treatment including its effects. In addition, the selected studies used different versions of the CEQ (two studies used a six-item CEQ, three studies used a five-item CEQ, one study used a three-item CEQ). The bias this could introduce was reduced by normalizing the scores. We only included studies that randomized participants between internet and face-to-face treatment. This limited the number of studies that met our inclusion criteria but had the advantage of ensuring that patients had not selectively chosen internet or face-to-face treatment. Finally, we chose to perform our analyses on cases with complete data, which reduced the number of participants, however we provide the analyses performed on imputed data in the online supplementary material and the results are comparable. Importantly, the number of participants with complete data was comparable between internet and face-to-face and has not led to any systematic bias.
In conclusion, we found no difference in how expectation ratings predict treatment response in guided Internet-delivered v. face-to-face behavioral therapy. Treatment expectation thus seems to play just as an important role in online as in face-to-face treatment. Our results have clinical implications as they imply that therapists need to attend to the patient-clinician relationship and foster positive expectations to the same extent in online as face-to-face treatment. This may be done by repeatedly assessing patients' treatment expectations and deliberately verify and validate the patient's belief in order to shape expectancy. The efficacy of digital healthcare interventions would be greatly aided by the further investigation of the placebo components of online therapeutic relationships and interventions, for example by using expectation ratings in predictive models in clinical practice (Forsell et al., Reference Forsell, Jernelov, Blom, Kraepelien, Svanborg, Andersson and Kaldo2019).
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291723003033.
Acknowledgements
The authors would like to thank Emma-Lotta Säätela from Karolinska Institutet University Library for assistance and guidance in the study search part of the project, Gustav Nilsonne for the valuable contribution to the ethical application. Thank you Gavin Andrews with the help of Ian Li and Ashlee Grierson who contributed individual patient data.
Funding statement
Dr Jensen received Pro Futura funding from Riksbankens Jubileumsfond, the funder had no role in the scientific work.
Competing interest
None.
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