Background
The study of how the digital environment impacts on the development of the therapeutic alliance was established in 2018 as one of the top ten research priorities for mental digital healthcare by the James Lind Alliance, an initiative of the National Institute of Health Research of the United Kingdom.Reference Hollis, Sampson, Simons, Davies, Churchill and Betton1 Defined by Bordin,Reference Bordin2 therapeutic alliance encompasses the collaborative relationship and adjustment between patient and therapist. It is composed of three components: (a) agreement on intervention aims; (b) agreement on tasks carried out during the psychotherapeutic process; and (c) the bond formed by the affective ambience. Research studies have consistently shown that the strength of therapeutic alliance not only predicts in-person psychotherapy outcomes,Reference Flückiger, Del Re, Wampold, Symonds and Horvath3 including cancer patients,Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4 but also improves other key factors, such as treatment adherence.5 However, its role and significance in new, upcoming psychosocial care approaches are not yet well studied. This is particularly true in the context of digital and stepped care interventions,Reference Hollis, Sampson, Simons, Davies, Churchill and Betton1,Reference Bower and Golbody6 where the dynamics of patient–therapist interactions may differ significantly from in-person settings.
Stepped care interventions involve tailoring interventions based on patients’ clinical complexity, with increasing intensity as needed. Several research teams have explored them to address emotional distress among diverse cancer populations, with varying effectiveness: some studies have shown significant reductions in distress,Reference Arving, Assmus, Thormodsen, Berntsen and Nordin7 while others found this effect in patients with higher baseline distress.Reference Krebber, Jansen, Witte, Cuijpers, de Bree and Becker-Commissaris8 In contrast, some researchers reported no significant changes in this outcome after the intervention.Reference Schuurhuizen, Braamse, Beekman, Cuijpers, Van Der Linden and Hoogendoorn9 Another element that may impact therapeutic alliance and that is being used to improve access to psychosocial care in cancer is the use of eHealth technologies.Reference Lohmiller, Schäffeler, Sütterlin, Zipfel and Stengel10 However, studies comparing digital interventions with conventional ones have produced mixed results; some report similar effect sizes between digital and in-person interventions,Reference Uemoto, Yamanaka, Kataoka, Wada, Aoyama and Kizawa11 whereas others observed smaller effects in digital interventions.Reference Gitonga, Desmond, Duda and Maguire12 Furthermore, the integration of eHealth into stepped interventions is a relatively recent development in healthcare, with limited empirical evidence on the effectiveness of fully digital stepped interventions in cancer populations.Reference Hauffman, Alfonsson, Bill-Axelson, Bergkvist, Forslund and Mattsson13 For example, Hauffman et alReference Hauffman, Alfonsson, Bill-Axelson, Bergkvist, Forslund and Mattsson13 evaluated a digital stepped psychosocial intervention in different cancer populations, resulting in heterogeneous psychological outcomes: reductions in depressive symptoms were observed, but no effects were found on anxiety, post-traumatic stress, or quality of life.
Disparities in the effectiveness of digital interventions may be potentially explained due to the lack of theoretical and empirical frameworks that define how different action mechanisms correlate with effectiveness outcomes in digital settings.Reference McAlpine, Joubert, Martin-Sanchez, Merolli and Drummond14 Those inconsistent results on their effectiveness highlight the importance of studying therapeutic process factors,Reference Gitonga, Desmond, Duda and Maguire12 particularly those that differ the most from in-person interventions, such as therapeutic alliance.Reference Seuling, Fendel, Spille, Göritz and Schmidt15,Reference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16 By understanding how therapeutic alliance works in digital settings, we can better address the factors contributing to inconsistent effectiveness and improve the design of eHealth interventions.
Current knowledge about therapeutic alliance in digital settings
Over the past decade, there has been increasing interest in exploring therapeutic alliance across various digital intervention formats.Reference Hollis, Sampson, Simons, Davies, Churchill and Betton1 According to several studies, patients report high levels of therapeutic alliance, regardless of patient sociodemographic characteristics (e.g. age, gender, education level), diagnosis, communication type (i.e. synchronous or asynchronous communication) or intervention format (e.g. video-consultations, text messages).Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Lohmiller, Schäffeler, Sütterlin, Zipfel and Stengel10,Reference Cataldo, Chang, Mendoza and Buchanan17,Reference Stoeten, de Haan, Postel, Brusse-Keizer and ter Huurne18 However, recent studies involving cancer patients have found both text messages (i.e. asynchronous communication)Reference Compen, Bisseling, Schellekens, Jansen and Van der Lee19 and video consultationsReference van der Lee and Schellekens20 to hinder the development of a fluid dialogue, negatively affecting therapeutic alliance. In the same line, therapists tend to report lower levels of therapeutic alliance compared with patients, with some expressing concerns about the negative impact of digital tools on their own therapeutic alliance.Reference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16–Reference Stoeten, de Haan, Postel, Brusse-Keizer and ter Huurne18,Reference Simpson and Reid21
Other authors had focused on how different intervention formats may influence digital therapeutic alliance diversely. For instance, video consultations, characterised by synchronous communication of verbal and non-verbal information, are considered the most similar to in-person interventions, and may establish stronger therapeutic alliance compared with other digital interventions.Reference Seuling, Fendel, Spille, Göritz and Schmidt15,Reference Cataldo, Chang, Mendoza and Buchanan17,Reference Norwood, Moghaddam, Malins and Sabin-Farrell22
Moreover, previous studies suggested that the proficiency or ease of use of eHealth tools can moderate therapeutic alliance perception of both patients and therapists.Reference Lopez, Schwenk, Schneck, Griffin and Mishkind23,Reference Doukani, Free, Michelson, Araya, Montero-Marin and Smith24 High usability can enhance user experience and satisfaction, fostering seamless interactions, greater involvement in the therapeutic process, and ultimately leading to a higher therapeutic alliance.Reference Cataldo, Chang, Mendoza and Buchanan17,Reference Doukani, Free, Michelson, Araya, Montero-Marin and Smith24,Reference Goldberg, Baldwin, Riordan, Torous, Dahl and Davidson25 In turn, patients’ age could be related to tool useReference Medina, Flix-Valle, Rodríguez-Ortega, Hernández-Ribas, Lleras de Frutos and Ochoa-Arnedo26 affecting those variables.
Although digital interventions generally show high levels of therapeutic alliance, research in this area remains limited and results are not entirely conclusive, particularly in oncology patients or from therapists’ perspective.Reference Compen, Bisseling, Schellekens, Jansen and Van der Lee19,Reference Lopez, Schwenk, Schneck, Griffin and Mishkind23,Reference Nissen, Zachariae, O'Connor, Kaldo, Jørgensen and Højris27 Additionally, there is a lack of evidence on therapeutic alliance in digital stepped interventions, where therapists only interact with patients on detecting psychosocial needs and may need to adapt communication approaches based on the level of care required.
Rationale, aims and hypotheses
This study presents further analysis of a broader multicentre randomised controlled trial (RCT) which assessed the effectiveness of ICOnnecta't, a stepped digital psychosocial intervention designed to prevent emotional distress, to promote adaptation in breast cancer patients, and to facilitate communication between patients and healthcare providers.Reference Ochoa-Arnedo, Medina, Flix-Valle and Anastasiadou28 A recent preliminary study has shown its feasibility.Reference Medina, Flix-Valle, Rodríguez-Ortega, Hernández-Ribas, Lleras de Frutos and Ochoa-Arnedo26 In this secondary analysis we aim to: (a) compare the development of therapeutic alliance between ICOnnecta't and psychosocial treatment as usual (PTAU) from the perspectives of breast cancer patients and their therapists; (b) analyse the level of agreement between patients’ and therapists’ therapeutic alliance ratings for both treatment conditions; (c) explore potential variables associated with therapeutic alliance during ICOnnecta't intervention, in particular age, platform usability and satisfaction, and type and amount of patient–therapist communication. We hypothesised that: (a) there will not be significant differences in the development of therapeutic alliance between ICOnnecta't and PTAU from patients’ and therapists’ perspectives; (b) therapists will report lower levels of therapeutic alliance compared with patients in both interventions; (c) younger age, high usability and satisfaction, greater communication and video consultations will be positively associated with therapeutic alliance scores for both patients and therapists.
Method
Design
This is a multicentre RCT with two parallel groups, ICOnnecta't versus PTAU, with a 1:1 allocation. The study design contains two treatment conditions and four assessments (2 × 4 factors) during a 12-month intervention period. Extensive methodological and intervention protocols were previously published.Reference Medina, Flix-Valle, Rodríguez-Ortega, Hernández-Ribas, Lleras de Frutos and Ochoa-Arnedo26,Reference Ochoa-Arnedo, Medina, Flix-Valle and Anastasiadou28
All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and research committee and with the Helsinki Declaration of 1975, as revised in 2013. The protocol was approved by the Clinical Research Ethics Committee of the participant institutions on 7 November 2019 (PR289/19). It was submitted to ClinicalTrials.gov on 24 April 2020 (NCT04372459).
Participants
Participants were recruited from two public health centres located in the province of Barcelona (Spain), namely a specialised cancer institute (Institut Català d'Oncologia L'Hospitalet) and a general hospital's oncology service (Hospital de la Santa Creu i Sant Pau). Inclusion criteria were: (a) adult women (≥18 years); (b) diagnosed with a first episode of breast cancer within the previous 8 weeks; (c) who had a mobile phone with internet access and user-level skills; and (d) were fluent in Spanish (both reading and writing). Patients with major depressive disorder, psychosis, substance abuse, autolytic ideation or cognitive impairments (e.g. neurological disorders) were excluded and referred to more specialised care.
Group conditions and therapists
Experimental group ICOnnecta't
ICOnnecta't is a stepped digital intervention comprising four care levels staggered by psychosocial complexity (see Fig. 1): (a) psychosocial screening and monitoring through a mobile app; (b) guided psychoeducation through a Moodle Campus integrated within the app;Reference Ciria-Suarez, Costas, Flix-Valle, Serra-Blasco, Medina and Ochoa-Arnedo29 (c) supervised peer-support community app; and (d) group psychotherapy treatment through multi-video consultation based on the Positive Group Psychotherapy Program in Cancer.Reference Lleras de Frutos, Medina, Vives, Casellas-Grau, Marzo and Borràs30 By systematically monitoring the emotional distress in the first level, the patient's psychosocial status can be measured to decide if they need to continue to a more intensive and complex level of care.
Stepped digital psychosocial intervention ICOnnecta't: intervention levels and stepped protocol. Adapted from a previous ICOnnecta't protocol with permission from the authors and the publisher.Reference Serra-Blasco, Souto-Sampera, Medina, Flix-Valle, Ciria-Suarez and Arizu-Onassis31
Throughout the entire intervention period, the patient is consistently accompanied by the same therapist. The app enables asynchronous communication via text messages during the 12-month intervention period. Additionally, the progression from one level to another is often preceded by a synchronous video consultation; however, there are patients who reject video consultations and prefer to do the entire stepped intervention through messages. These interactions and accompaniment are intended to provide ongoing support throughout the patient's cancer journey.
Control group psychosocial treatment as usual (PTAU)
Participants in the control group received a standard in-person psychosocial treatment for cancer patients to prevent emotional distress and facilitate the illness adaptation during the first year after diagnosis. To homogenise criteria, patients received eight individual 45–60-min sessions during the 12-month intervention period, focusing on emotional support and psychoeducation.Reference Ochoa-Arnedo, Medina, Flix-Valle and Anastasiadou28
Therapists
The study involved six postgraduate psychologists with specific psycho-oncology training (i.e. master's degree in psycho-oncology and health or clinical psychology) and previous experience in psycho-oncological interventions. Four of them participated in both group conditions, and two exclusively in the experimental group. Several training and supervision sessions were conducted throughout the implementation of the study to ensure adherence to the intervention protocol.
Procedure
Participants were recruited from the breast cancer units of the participating hospitals from 21 June 2021 to 30 June 2022. Eligible patients were invited to participate. Patients who expressed interest were scheduled to meet with a psychologist of the research team to discuss the study details, confirm eligibility (i.e. inclusion and exclusion criteria), provide and sign informed consent, and randomly assign them to one intervention group. The randomisation was conducted by an independent researcher using a list of randomly generated numbers via IBM SPSS.27.32 The psychologist in charge of the recruitment interview was responsible for communicating the assigned treatment.
The app was downloaded onto experimental group patients’ smartphones, and they were provided with both oral, written and video instructions on how to use it. In contrast, a first in-person visit was arranged between the control group patients and a psychologist of the team. Treatment was administered for 12 months, and assessments were conducted using online instruments administered by QualtricsXM33 at study baseline (T1), and at 3 (T2), 6 (T3) and 12 (T4) months after the inclusion.
Measures
Primary outcome
Therapeutic alliance
The working alliance inventory short form (WAI-S) is a self-report questionnaire designed to measure the therapeutic alliance between therapist and patient.Reference Andrade-González and Fernández-Liria34 Both the patient (WAI-S-P) and therapist (WAI-S-T) versions consist of 12 items, with four items measuring each of the three components of the therapeutic alliance described by Bordin:Reference Bordin2 (a) agreement regarding intervention’ goals; (b) agreement on the tasks; and (c) the affective bond between therapist and patient. Although there are several instruments to measure therapeutic alliance with good psychometric properties, this one was designed to assess the therapeutic alliance components across various therapy modalities.Reference Martin, Garske and Davis35 That is why it has been widely used in digital interventions with cancer patients.Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Nissen, Zachariae, O'Connor, Kaldo, Jørgensen and Højris27 The validity of this instrument has been established in the Spanish population,Reference Andrade-González and Fernández-Liria34 showing excellent reliability for both forms, WAI-S-P (α = 0.93) and WAI-S-T (α = 0.94). The subscales also demonstrated high reliability. Total scores range from 12 to 84, while subscales range from 4 to 28, with higher scores indicating stronger therapeutic alliance. Consistent with previous research,Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Nissen, Zachariae, O'Connor, Kaldo, Jørgensen and Højris27 the present study utilised the same instrument in both treatment conditions, without modifying any item, to ensure comparability of therapeutic alliance measures. This approach was adopted due to the lack of consensus and specific scales for evaluating digital therapeutic alliance.Reference Henson, Wisniewski, Hollis, Keshavan and Torous36 Therapeutic alliance assessments were conducted on patients and therapists from both treatment conditions at T2, T3 and T4.
Secondary outcomes
Usability
The system usability scale (SUS) was used to assess the ease to use the digital platform among patients and therapists at T3 for the experimental group. It is a 10-item questionnaire designed to measure the perceived usability of a system or product.Reference Brooke, Jordan, Thomas, Weerdmeester and McClelland37 The SUS has been widely used and validated in various domains, including healthcare and technology.Reference Inal, Wake, Guribye and Nordgreen38 It has shown good reliability (α = 0.70–0.97). The scores range from 0 to 100, with higher scores indicating better usability. Researchers have suggested that a score above 68 is above average, while a score above 80 is excellent.Reference Bangor, Kortum and Miller39
Satisfaction
Satisfaction with the app was assessed at T3 of experimental group with a 0–10 visual analogue scale (VAS) (i.e. How satisfied are you with the ICOnnecta't app, where 0 is completely unsatisfied and 10 is completely satisfied?). Therapists scored a similar VAS about the satisfaction with the professional ICOnnecta't platform. The literature does not provide a clear cut-off, so we considered scores ≥5 as indicating some level of satisfaction, while scores ≥8 were considered high.Reference Medina, Flix-Valle, Rodríguez-Ortega, Hernández-Ribas, Lleras de Frutos and Ochoa-Arnedo26
Communication type
Experimental group patients’ communications were categorised based on the intervention format employed: (a) unanswered asynchronous communication, for patients who received and read the therapist text messages but never answered; (b) asynchronous communication, for patients who interacted with therapist through text messages; and (c) mixed communication, for patients who interacted through text messages and video consultations.
Communication amount
The number of text messages exchanged between therapist and patient, and the number of video consultations conducted were added together to analyse the interaction quantity between both.
Data analysis
Categorical variables were presented as the number of cases and percentages. Continuous variables were presented as means and s.d. or medians and interquartile range, depending on whether the distribution was normal or non-normal. Normality was assessed through visual inspection of quantile–quantile plots, histograms and s.d. from normality plots.
Multiple imputation by chained equations was applied to account for missing data in measures of therapeutic alliance. The assumption that unobserved values were missing at random was deemed to be appropriate because we could not find any pattern among the missing values.Reference Jolani, Debray, Koffijberg, van Buuren and Moons40 Fifty iterations of imputation were performed.
Logistic regressions were conducted to compare attrition between arms, assessing both adherence (i.e. participants who did not drop out of treatment) and retention (i.e. participants who completed assessments at all follow-ups). Then, modified intention-to-treat analyses were conducted, excluding 15 participants without any observation in WAI-S-P or WAI-S-T.
Moving on the study objectives, to analyse the growth curve of the therapeutic alliance over the treatment period and compare it between arms separately for patients and therapists (i.e. aim 1), we calculated linear mixed-effect models (LMMs) with patient clustering between T2 and T4 of the WAI-S-P and WAI-S-T total and subscales scores.
Second, to study the agreement between patients and therapists on therapeutic alliance scores in each assessment time point and treatment group (i.e. aim 2), t-test and Cohen's d for paired data were calculated. Cohen's d values less than 0.50 indicate small effect; between 0.50 and 0.80 indicate medium effect; and above 0.80 are considered large.Reference Cohen41 The intraclass correlation coefficient (ICC) was also estimated, because it provides a single measure of agreement that captures both the correlation and the level of conformity between ratings. ICC values lower than 0.70 show weak concordance.Reference Koo and Li42
Finally, to explore factors associated with both WAI-S-P and WAI-S-T at T4 (i.e. the end of intervention, when the therapeutic alliance is considered established) in the experimental group (i.e. aim 3), independent univariate linear regressions were estimated for each variable. Then, a multivariate model was performed using forward and backward steps. In backward elimination, variables with the highest P values above the significance level are removed sequentially, refitting the model each time until all remaining predictors are statistically significant. In forward selection, starting with a minimal model with the factors selected in backward elimination, variables with the smallest P values below the significance level are added one by one, repeating the process until no additional variables meet the significance criterion.
For all outcomes, 95% confidence intervals (CI) were calculated. The significance threshold was set at a two-sided alpha value of 0.05, unless otherwise indicated. All statistical analyses were conducted with R software, version 4.3.3.43
Results
Participants characteristics
Out of the 383 referred patients, 184 agreed to participate in the study. This indicated an acceptance rate of 48.04%. Demographic and clinical baseline characteristics are summarised in Table 1.
Demographic and clinical characteristics of intervention groups (N = 184)
Analyses indicated no statistically significant differences in attrition between arms for participant adherence to the intervention (coefficient [95% CI] = 0.654 [0.787–4.697], P = 0.151) or retention at T1 (coefficient [95% CI] = −0.143 [0.311–2.417], P = 0.785), at T2 (coefficient [95% CI] = 0.511 [0.849–3.271], P = 0.137), at T3 (coefficient [95% CI] = 0.423 [0.792–2.945], P = 0.207), and at T4 (coefficient [95% CI] = 0.168 [0.631–2.218], P = 0.600). Fifteen patients were excluded from the final analysis due to the absence of WAI-S-P or WAI-S-T observations, leaving a final sample of 169 participants. Details of the participant flowchart are provided in Fig. 2.
Participants’ flowchart. PTAU, psychosocial treatment as usual; WAI-S-P, working alliance inventory short form, patient version; WAI-S-T, working alliance inventory short form, therapist version.
Regarding therapists’ characteristics, out of the six therapists, four were women. The median age was 32.33 years (s.d. = 9.89, range 26–52); and the median years of psycho-oncologist experience were 8.33 years (s.d. = 9.85, range 2–28).
Aim 1: development of therapeutic alliance
The initial LMM for both WAI-S-P and WAI-S-T and their subscales included the intercept, as well as time and group condition, as fixed effects. These data, along with the mean scores, are detailed in Table 2.
Total and subscales therapeutic alliance scores for patients (WAI-S-P) and therapists (WAI-S-T) at T2, T3 and T4, and intention-to-treat repeated measures analyses between ICOnnecta't (n = 95) and PTAU (n = 74)
T2, 3 months since the inclusion; T3, 6 months since the inclusion; T4, 12 months since the inclusion and end-treatment; PTAU, psychosocial treatment as usual; WAI-S-P, working alliance inventory short form, patient version; WAI-S-T, working alliance inventory short form, therapist version.
* P < 0.05.
The analysis for WAI-S-P indicated statistically significant differences between interventions in total therapeutic alliance and its subscales, favouring PTAU. Regarding time, there were significant improvements for total, goals and tasks scores from T2 to T3, but not to T4, suggesting that therapeutic alliance increased in the first stages of treatment and then remained stable for both arms. In contrast, the bond subscale showed significant increase only from T2 to T4 because it registered a decrease at the end for both treatments.
The analysis for WAI-S-T also revealed significant differences between interventions in total scale and subscales, with higher scores in PTAU. Over time, there were significant improvements in total, goals, and tasks scores in both groups, whereas bond scores remained stable.
Later, the interaction between time and group was added in a subsequent model. This interaction was not significant for any of the WAI-S-P models, indicating that the development of therapeutic alliance and its components did not differ between ICOnnecta't and PTAU. However, it was significant for total WAI-S-T (coefficient [95% CI] = −2.91 [−5.53 to −0.29], P = 0.030), suggesting that therapeutic alliance varied between interventions, with greater development in PTAU.
Finally, an additional model was built adjusted for patients’ age and therapists’ experience. No significant changes were observed either in WAI-S-P (age: coefficient [95% CI] = 0.07 [−0.15 to 0.29], P = 0.551; experience: coefficient [95% CI] = −0.11 [−0.32 to 0.10], P = 0.307) or WAI-S-T (age: coefficient [95% CI] = −0.03 [−0.14 to 0.08], P = 0.581; experience: coefficient [95% CI] = −0.09 [−0.20 to −0.01], P = 0.079).
Aim 2: agreement between patients and therapists
Table 3 presents therapeutic alliance scores comparisons between patients and therapists. At T2 and T3 no significant differences were found for ICOnnecta't or PTAU, although significant differences were observed at T4 in both groups, with therapists reporting stronger therapeutic alliance. However, only one significant and positive ICC was found in therapeutic alliance scores of ICOnnecta't group at T3, although weak (for WAI-S subscales details, see Supplementary Table 1 available at https://doi.org/10.1192/bjo.2024.844).
Level of agreement between patients (WAI-S-P total) and therapists (WAI-S-T total) at T2, T3 and T4, for ICOnnecta't (n = 95) and PTAU (n = 74) groups
ICC, intraclass correlation coefficient; PTAU, psychosocial treatment as usual; WAI-S-P, working alliance inventory short form, patient version; WAI-S-T, working alliance inventory short form, therapist version; T2, 3 months since the inclusion; T3, 6 months since the inclusion; T4, 12 months since the inclusion and end-treatment.
* P < 0.05.
Aim 3: variables associated with therapeutic alliance in ICOnnecta't intervention
Variables description
Usability and satisfaction
We had valid data on these instruments from 64 participants. The ICOnnecta't app received a mean patient satisfaction score of 7.64 (s.d. = 1.88). Among these participants, 35.94% (n = 23) expressed satisfaction with the platform, while 59.38% (n = 38) reported being very satisfied. The usability assessment, measured by the SUS, resulted in a mean score of 74.14 (s.d. = 16.14). The 59.38% (n = 38) of participants found that the platform was usable, while 39.06% (n = 25) considered it very usable. Regarding the professional platform, therapists’ mean satisfaction level was 7.67 (s.d. = 0.82), and the usability was 85.42 (s.d. = 5.79).
Communication type
Ten patients (10.53%) did not establish any communication with their therapists; 15 (15.79%) received and read the therapist's messages but never answered (i.e. unanswered asynchronous communication); 39 patients (41.05%) interacted with their therapist just through messages (i.e. asynchronous communication); and 31 patients (32.63%) interacted through both messages and video-consultations (i.e. mixed communication).
Communication amount
Patients sent a mean of 7.71 messages (s.d. = 11.01, range 0–59), while they received 13.3 from their therapist (s.d. = 12.69, range 0–76). A mean of 1.08 video consultations were conducted per patient (s.d. = 2.68, range 0–16). The mean communication amount (i.e. sum of messages exchange and video consultations conducted) was 22.26 (s.d. = 25.58, range 0–151).
Association analysis
In the univariate regression model for patients, satisfaction (coefficient [95% CI] = 5.2 [2.9–7.5], P < 0.001) and usability (coefficient [95% CI] = 0.57 [0.29–0.85], P < 0.001) significantly explained the variance in WAI-S-P at T4. These two variables were the only ones selected in the multivariate model (R2 adjusted = 0.287), indicating significant association with WAI-S-P (satisfaction: coefficient [95% CI] = 3.70 [1.15–6.26], P = 0.005; usability: coefficient [95% CI] = 0.37 [0.57–0.67], P = 0.017). Patients’ age, communication type, and communication amount were not significant in explaining therapeutic alliance scores.
Finally, in the univariate regression model for therapists, satisfaction (coefficient [95% CI] = −7.1 [−10 to −3.8], P < 0.001), mixed communication (coefficient [95% CI] = 12 [1.9–22], P = 0.022) and communication amount (coefficient [95% CI] = 0.11 [0.02–19], P = 0.014) significantly explained the variance in WAI-S-T at T4. In the multivariate model (R2 adjusted = 0.421), satisfaction (coefficient [95% CI] = −5.98 [−8.91 to −3.05], P < 0.001) indicated significant association with WAI-S-T at T4. The other variables were not significant in explaining therapeutic alliance scores (for statistical details, see Supplementary Table 2).
Discussion
The present research extends the knowledge about the development of therapeutic alliance in a digital setting, and particularly in a stepped intervention. To our knowledge, this is the first RCT to compare therapeutic alliance, in both patients and therapists, between a stepped digital intervention and in-person conventional intervention for cancer patients. This research contributes to expanding the literature on the nature of therapeutic alliance in digital contexts while focusing on variables that may be directly associated with the development of a strong therapeutic alliance.
Aim 1: development of therapeutic alliance
From the patients’ perspective, current results differ from our first hypothesis and previous research in finding lower therapeutic alliance in the digital intervention compared with the conventional one.Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Lopez, Schwenk, Schneck, Griffin and Mishkind23,Reference Berger44,Reference van Lotringen, Jeken, Westerhof, ten Klooster, Kelders and Noordzij45 Two recent studies with cancer patients indicated that both asynchronous communication via text messagesReference Compen, Bisseling, Schellekens, Jansen and Van der Lee19 and synchronous communication via video consultationsReference van der Lee and Schellekens20 could be perceived by patients as a barrier, making it difficult to establish a fluid dialogue with the therapist, which could explain our results. However, it is important to emphasise that ICOnnecta't patients reported remarkably strong therapeutic alliance, even surpassing the levels reported in previous studies with an oncological population.Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Nissen, Zachariae, O'Connor, Kaldo, Jørgensen and Højris27 Additionally, the therapeutic alliance developed within the first 6 months of the intervention and then remained stable until its completion, showing the same pattern in both groups. These findings indicate that breast cancer patients could establish and maintain a strong therapeutic alliance throughout the stepped digital intervention.
A similar pattern emerged from the therapist's perspective, who established high therapeutic alliance through the ICOnnecta't intervention, even though it was significantly lower than that in PTAU over time. Three reasons have been proposed from eHealth reviews to explain this phenomenon. First, therapists might feel insecure about establishing an emotional bond in a digital setting,Reference Lopez, Schwenk, Schneck, Griffin and Mishkind23 because they must adapt their behaviour to convey warmth, compassion, and mutual trust to compensate the absence of physical therapeutic contact, and they often lack training in this area.Reference Simpson and Reid21 Second, therapists may feel that they are unable to assess the patient accurately and under equivalent circumstances as they would in conventional settings, leading them to perceive their care as superficial.Reference Lopez, Schwenk, Schneck, Griffin and Mishkind23 Third, the negative attitudes and expectations that professionals feel towards eHealth could have a negative impact on the perception of the therapeutic alliance established with patients.Reference Berger44 Nevertheless, our results challenge all these hypotheses: bond scores were higher than the other therapeutic alliance components, and their progression throughout the intervention period was comparable to that of conventional intervention. Moreover, our therapists expressed high satisfaction with the professional eHealth platform, which allowed them systematically to assess and monitor the psychological status of patients. Accordingly, a qualitative study concluded that technologies enabling the understanding of patients’ needs and facilitating connection by a different communication method enhance personalisation and prevent the dehumanisation of the intervention therapist’ experience, favouring therapeutic alliance and the affective bond.Reference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16 Overall, we can explain the high therapeutic alliance established by therapists in the stepped digital intervention, although future deeper explorations are needed to understand the differences compared with conventional interventions.
Aim 2: agreement between patients and therapists
The second hypothesis was partially supported in this objective. The literature comparing therapeutic alliance between patients and therapists generally reports lower therapeutic alliance in therapists.Reference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16–Reference Stoeten, de Haan, Postel, Brusse-Keizer and ter Huurne18,Reference Simpson and Reid21 However, our results showed that while there were no differences during the interventions, by the end, therapists reported higher therapeutic alliance than patients. Furthermore, as previous authors have found,Reference Stoeten, de Haan, Postel, Brusse-Keizer and ter Huurne18 there was no agreement between them in the perception of this relationship at any time during the interventions, even if a weak agreement was observed at 6 months in ICOnnecta't. The fact that therapists may have higher expectations regarding interventions and therapeutic alliance could be an explanation for why this occurs in both groups.Reference Wang, Chung, Stuart-Maver, Schreier, Galligan and Davis46 It is important to address this phenomenon in future studies because the disagreement in the perception of therapeutic alliance could impact the therapeutic process and adversely affect intervention outcomes.Reference Nienhuis, Owen, Valentine, Winkeljohn, Halford and Parazak47
Aim 3: variables associated with therapeutic alliance in ICOnnecta't intervention
The primary challenge of the present study was to identify unique factors of the stepped digital intervention that could influence the development of therapeutic alliance. In the ICOnnecta't intervention, as a stepped model, the interaction between patient and therapist only occurs when a patient actively reports her psychological status, and psychosocial needs are detected. Furthermore, therapists adapt their communication approach and sometimes combine different formats of care delivery depending on patients’ needs and symptoms’ severity. In line with the third hypothesis, our results indicated that the usability and satisfaction with the ICOnnecta't app were associated with the therapeutic alliance in patients, while satisfaction with the monitoring professional platform was associated with therapeutic alliance in therapists. In line with recent studies, digital tools seem to be key factors in the patient-therapist relationship,Reference Doukani, Free, Michelson, Araya, Montero-Marin and Smith24,Reference Goldberg, Baldwin, Riordan, Torous, Dahl and Davidson25 because technological mediation may influence both the quality and perception of verbal and non-verbal communication directly impacting the therapeutic alliance.Reference Cataldo, Chang, Mendoza and Buchanan17 Doukani et alReference Doukani, Free, Michelson, Araya, Montero-Marin and Smith24 went one step further proposing a new conceptual framework of the Bordin's therapeutic alliance model in the context of a low-intensity digital intervention for depression. This model adds a fourth therapeutic alliance component named usability heuristics, which involves the digital tool elements that promote active engagement: ease of use, accessibility, interactivity, aesthetic appeal and self-directed. According to Cataldo et al,Reference Cataldo, Chang, Mendoza and Buchanan17 incorporating tools and platforms as an element in therapeutic alliance development could improve the quality of clinical studies by facilitating its design and analysis. To do this, various adaptations of the WAI have been proposed, although all of them were designed for self-guided interventions.Reference Goldberg, Baldwin, Riordan, Torous, Dahl and Davidson25 This approach assumes that the patient establishes a relationship with the technology as there is no therapist involved providing the intervention, so they could not be used in our ICOnnecta't intervention described herein.
Regarding the other potentially associated variables, it is worth noting that neither the type (text messages, video consultation) nor the amount of communication was related to therapeutic alliance in both patients and therapists, contrary to the third hypothesis. This implies that they could develop a strong therapeutic alliance regardless of the quantity of interactions or the intervention format employed, even if there was minimal or no communication. In accordance with our findings, Richards et alReference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16 suggested that text messages offer patients a genuine sense of care, interest and therapeutic process control as they could contact their therapist when they decided, and with the frequency they feel comfortable with. Moreover, video consultations give patients control over personal space and a sense of shared responsibility over the communication tool employed.Reference Norwood, Moghaddam, Malins and Sabin-Farrell22 These features likely enhance the feeling of connection, presence and empowerment, even if they almost never actually interact.Reference Richards, Simpson, Bastiampillai, Pietrabissa and Castelnuovo16,Reference Norwood, Moghaddam, Malins and Sabin-Farrell22 Besides, interaction through these communication formats did not appear to have a differential impact on therapeutic alliance in our study. Given its similarity to conventional settings, video consultations should establish stronger therapeutic alliance compared with other digital interventions.Reference Cataldo, Chang, Mendoza and Buchanan17 However, text messages could promote the online disinhibition effect, which would explain our results. This occurs when messages encourage emotional expression by converting thoughts and feelings into words to compensate for the absence of non-verbal cues which would facilitate the establishment of a stronger bond.Reference van der Lee and Schellekens20,Reference van Lotringen, Jeken, Westerhof, ten Klooster, Kelders and Noordzij45 Therefore, the lack of association found in our study between therapeutic alliance and the type and amount of communication, combined with the literature discussed here, may suggest that the sense of control, flexibility and availability provided by digital communication methods could support the development of a strong therapeutic alliance, regardless of the format or frequency of interactions. Concerning this, the lower therapeutic alliance observed in ICOnnecta't compared with PTAU could be partially attributed to the limitations of the WAI in capturing and assessing all the added values that digital communication methods can bring to the therapeutic relationship (i.e. increased flexibility, a greater sense of control for patients, and enhanced accessibility to therapists), underestimating the therapeutic alliance developed.Reference Norwood, Moghaddam, Malins and Sabin-Farrell22
Limitations
The present study has limitations that should be considered to interpret the results with caution. First, the sample size for this RCT was estimated considering the hospital anxiety and depression scale (HADS), not the WAI-S, as it was the main outcome to assess the effectiveness of ICOnnecta't.Reference Ochoa-Arnedo, Medina, Flix-Valle and Anastasiadou28 Moreover, there appears to be an uneven sample allocation between intervention groups. Randomisation was conducted by an independent researcher using a list of randomly generated numbers, and the enrolment period had to conclude with this random disparity. Although no significant difference in acceptance, attrition and retention was found between groups, the control group had excluded more final data from the analysis, which increases the sense of sample disparity. Second, the study focused on recently diagnosed breast cancer women. This limits the generalisability of the results to other genders, cancer diagnoses and cancer stages, who may have different therapeutic alliance needs and eHealth approach requirements. Third, as two therapists did not participate in PTAU, the LMM could not be adjusted by the therapist to control for potential differences. Fourth, the type and amount of communication did not seem to have a differential impact on therapeutic alliance in our study. Future research with larger sample sizes could better explore whether these variables are truly associated with therapeutic alliance or not. Finally, it is likely that there are additional variables influencing therapeutic alliance that were not considered in this study, such as the rate of app use,Reference Medina, Flix-Valle, Rodríguez-Ortega, Hernández-Ribas, Lleras de Frutos and Ochoa-Arnedo26 treatment adherence,5 attrition with the intervention,Reference Bisseling, Cillessen, Spinhoven, Schellekens, Compen and van der Lee4,Reference Stoeten, de Haan, Postel, Brusse-Keizer and ter Huurne18 or other usability heuristic variables (e.g. accessibility, interactivity, aesthetic appeal).Reference Doukani, Free, Michelson, Araya, Montero-Marin and Smith24 Future research could delve deeper into these aspects to provide a more comprehensive understanding of factors that impact therapeutic alliance in digital interventions and how they may be considered in the design of those interventions.
Conclusions
This study enhances our understanding of the development of the digital therapeutic alliance, particularly within a stepped intervention. Our findings expose the potential of these interventions to establish and maintain a strong therapeutic alliance, both in patients and therapists. However, there was no agreement in the perception of therapeutic alliance between them that could potentially affect the therapeutic process. Notably, neither the type nor amount of communication impacted the development of patients’ therapeutic alliance, suggesting that sense of control, flexibility and availability of digital communication could foster the sense of care, interest and affective connection. Furthermore, therapists expressed high satisfaction with the digital platform, suggesting that stepped tools could effectively support the management of the therapeutic process. Finally, this study reveals the contribution of usability and satisfaction with the digital tools in the level of therapeutic alliance. It seems crucial to incorporate and evaluate digital tools and platforms as integral components of the therapeutic alliance, while considering usability heuristics and digital empowerment as therapeutic process factors in digital interventions. A more comprehensive understanding of these factors will enhance the interventions’ design to facilitate the therapeutic process and improve their effectiveness on mental health outcomes.
Supplementary material
Supplementary material is available online at https://doi.org/10.1192/bjo.2024.844
Data availability
The data are available on request from the corresponding author. The data are not publicly available due to privacy of research participants.
Acknowledgements
The authors thank all women for their participation in the study, and CERCA Program for institutional support. The authors also thank the biostatics service of IDIBELL for providing data analysis guidance, especially to Arnau Lagarda Tous and Judith Peñafiel Muñoz.
Author contributions
A.F.-V.: funding acquisition; conceptualisation; project management; methodology; carried out the experiment; data curation; writing original draft. J.C.M.: conceptualisation; methodology; formal analysis; oversight original draft. A.S.-S.: carried out the experiment; data curation; oversight original draft. A.A.-O.: carried out the experiment; oversight original draft. E.J.-L.: carried out the experiment. M.S.-B.: oversight original draft. L.C.-S.: oversight original draft. G.F.: supervision. C.O.-A.: leadership; funding acquisition; supervision. All authors: writing review and editing; approve the final version of the manuscript.
Funding
The study has been supported by the Secretaria d'Universitats i Recerca of the Generalitat de Catalunya and the European Social Fund under the FI grant no. 2020 FIB00288. This work has also supported by the Carlos III Health Institute under the FIS grants no. PI15/01278 and no. PI19/01880 (co-financed by the European Regional Development Fund (ERDF) under the initiative ‘A way to build Europe’). Additionally, the research team has received financial backing from the Emergent Agència d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya (AGAUR), Research Group: Psycho-oncology and Digital Health (no. 2021 SGR 01003).
Declaration of interest
None.
Transparency declaration
The manuscript is an honest, accurate, and transparent account of the study being reported; no important aspects of the study have been omitted; and any discrepancies from the study as planned have been explained.
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