eBehandling - Health and Work Utility, Use and User Experience of a New Digital Transdiagnostic Work-Focused Intervention for Patients in Specialist Mental Health Care
Phase: NA
Conditions: Anxiety, Depression
Sponsor: Solli Distriktspsykiatriske Senter

 
 
 
eBehandling  
Health and Work Utility, Use and User Experience of a New 
Digital Transdiagnostic Work-Focused Intervention for 
Patients in Specialist Mental Health Care 
 
 
 
 
NCT ID not yet assigned 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Date: 22nd of June 2024 

 
 
 
eBehandling – Health and Work 
Utility, use and user experience of a new digital transdiagnostic work-focused 
intervention for patients in specialised mental health care 
 
Protocol 
Research group 
Research fellow and planned PhD candidate Henning Monsen, special occupational therapist, 
Solli District Psychiatric Centre 
Project leader Pål William Wallace, PhD, Solli District Psychiatric Centre 
Main Supervisor Jon Vøllestad, PhD, Specialist in Clinical Psychology, Associate Professor, 
Department of Clinical Psychology, UiB 
Co-supervisor Helge Molde, Professor, Specialist in Clinical Psychology, Department of 
Clinical Psychology, UiB 
Head of research Kristine Sirevåg, Solli District Psychiatric Centre 
Content author, Peter Prescott, Specialist in Clinical Psychology 
Content supervisor, Torkil Berge, Specialist in Clinical Psychology, professional development 
advisor at Vinderen DPS  
Executive Director, Professor Tine Norgreen, Specialist in Clinical Psychology, Research 
Centre on Digital Healthcare, Norway 
Executive Director MindSpot®, Professor Nick Titov, Specialist in Clinical Psychology, 
School of Psychological Sciences - Faculty of Medicine, Health and Human Sciences,      
Macquarie University, Australia 
 
 
 
 
 

 
 
 
Content 
Background and status of knowledge 
            Treatment for people with common mental disorders on sick leave 
            Internet-delivered treatment for people on sick leave 
            Work-focused therapy in a digital format 
            Summary of knowledge 
Knowledge gap 
Study aims 
Research questions 
Hypothesis of main effects 
Hypothesis of moderating and mediating effects and predictors of outcome  
Methods and procedures 
            Design 
            Sample 
            Measures 
                          Demographic variables 
                          Questionnaires 
                          System data on user behaviour 
                          Qualitative in-depth interviews 
            Intervention 
            Statistical analysis 
                          Quantitative analysis 
                          Qualitative analysis 
            Power analysis 
            Procedure 
Project management 
            Academic collaborators 
            Clinical collaborators 
Ethical considerations 
The therapist’s level of competence            
 
Monitoring suicidal risk or behaviour 
            Transdiagnostic treatment for specific or comorbid diagnosis 
            In the case of participant drop-out 
            Data and privacy protection 
            Financing sources 
            Conflicts of interest 
Publications and communication of the results 
References 
 
 
 

 
 
 
 
Background and status of knowledge 
“Twelve billion working days are lost every year to depression and anxiety alone”.  
                                                                                                                                (WHO 2024) 
Mental health problems and disorders affect one in every five amongst the working- 
age adults in the OECD countries (Kessler et al 2005). And as many as two thirds of new 
disability pensions are due to poor mental health (OECD 2016). This constitutes a major 
public health challenge that also has large implications for work participation and 
productivity. People with mental health conditions are more often unemployed, more often in 
need of sick-leave and are absent for longer durations (OECD 2020). People with mental 
disorders report a 30% lower employment rate than those with no mental disorders (OECD 
2021). Of employees reporting mental disorders, half had days away from work, compared to 
under a third of people without mental ill-health. As many as two thirds of new disability 
pensions are due to poor mental health (OECD 2016). Mental health issues are also the largest 
cause of disability pensioning for adults from 25 - 39 years (Brage & Thune, 2020).  Mental 
disorders as a cause of sick-leave and disability pension have increased in the OECD 
countries and Norway. Major depression and anxiety are the most prevalent of the mental 
disorders and are therefore named common mental disorders (CMD). CMD have become a 
major cause of work absenteeism, with 30% of all sick leave in Norway being due to CMDs 
in 2024, which is an increase from 20% in 2019 (NAV 2024; Gundersen & Medalen, 2019). 
  
The option for paid sick-leave is an important tool and an effective way to stop 
deterioration when used correctly. However, long-term sick leave can also have adverse 
consequences (Folkehelseinstituttet, 2015). This seems especially relevant for people with 
mental disorders due to the adverse effects of absenteeism on mental health and negative 

 
 
 
effects on workplace relationships due to the stigma associated with mental disorders (OECD 
2016; Gundersen & Medalen).  
While mental disorders are a major cause of prolonged sick leave (NAV 2020), return 
to work (RTW) has been shown to be positively associated with improved health, especially 
mental health (Folkehelseinstituttet 2015).  Early interventions addressing psychological 
aspects and vocational challenges related to work have shown a positive effect on RTW 
(OECD 2014). Therefore, finding ways to increase availability and reduce barriers to 
evidence-based treatments seems a viable route to reducing mental health’s impact on sick 
leave and absenteeism. It may be needed to give people on sick-leave not only treatment for 
their mental health symptoms, but also interventions that facilitate a planned return to work as 
an integrated part of the treatment process.  
Treatment for people with common mental disorders on sick leave  
  
Although a reduction in symptoms of CMD seems positive for RTW the results are 
not straight forward. Findings from Cullen et al. (2012) show that improvement in symptoms 
from ordinary psychotherapy has little impact on sick-leave. The same results have been 
found for internet-based cognitive therapy (ICBT) by Udd-Granat et al (2023). One of the 
challenges in specialised mental health care is that the work focus has been an additive or 
extended part of the treatment, not integrated in the treatment itself. This is not in line with the 
recommendations for work-focused therapy (Slater et al., 2023) and might be the cause of the 
low impact on sick leave.  
There thus seems to be a need for a focus on RTW within therapy.  Several studies 
have been conducted on psychological treatments that specifically target return to work for 
people with CMDs. Systematic reviews have shown that work-focused therapy (W-CBT) has 
a small, but significant effect over treatment as usual on return to work (Mikkelsen & 
Rosholm 2018; Slater et al. 2023). Mikkelsen & Rosholm (2018) finds that 20% more 

 
 
 
workers in the intervention groups had returned to work, compared to control groups. There is 
also evidence that some of the components in work-focused therapy are more important than 
others, namely: work related goal setting, work related psychoeducation, work-related 
behavioural activation with a gradual return to work plan, work related problem solving, 
work-related cognitive therapy and homework. (Mikkelsen & Rosholm 2018; Slater et al. 
2023). Work-focused therapy, incorporating several of these efficacious components, has 
been studied in a Norwegian setting with good results on RTW (Reme et al. 2015; Gjengedal, 
et al. 2020).  
  
Internet-delivered treatment for people on sick leave  
 
 Another challenge for traditional psychotherapy is that access to treatment for CMD 
is lower than the demand (OECD 2016) and the treatment available varies greatly based on 
country of residence and if one is situated in an urban or rural area (Barbato, et al. 2016). 
Internet-delivered Cognitive Behavioural Therapy (ICBT) has the potential to increase 
accessibility to evidence-based treatment through increased availability and flexibility, while 
lowering cost and reducing need for travel for the patient. It has also been estimated that 
ICBT is cost-effective over traditional CBT mainly due to reduction in waiting time, which 
reduces time spent in an untreated state that may otherwise worsen the course of the illness 
(Baumann, et al., 2020; OECD 2016; Standal et al. 2021). Internet-delivered treatments have 
shown their effectiveness across several mental disorders, both with diagnosis-specific and 
transdiagnostic protocols (Etzelmueller et al. 2020, Taylor et al. 2021, Hedman-Lagerlöf et al. 
2023). However, the impact of traditional symptom-focused treatment on sick-leave is 
suboptimal and in line with findings for traditional psychotherapy (Cullen et al. 2012; 
Cederberg et al. 2022; Udd-Granat et al. 2023). There is therefore a need for treatment 

 
 
 
programs with an explicit focus on RTW using the efficacious components of work-focused 
therapy. 
 
A further consideration is how to accommodate for the heterogeneity when the 
inclusion criteria is sick-leave. A transdiagnostic approach can be better suited for 
heterogeneous patient groups, as it targets common vulnerability factors rather than diagnosis-
specific symptom clusters (Dalgleish et al. 2020). 
Work-focused therapy in a digital format  
  
We identified three studies explicitly using W-CBT via an iCBT platform addressing 
work related stressors, for people on sick leave or in danger of absenteeism. These have used 
self-referrals and participants are recruited from a non-clinical setting, with mild to moderate 
symptoms. Two of these studies have focused on mental stress and its reduction to decrease or 
prevent absenteeism (Asplund et al. 2019, Persson et al., 2023). One also addressed the 
treatment of depression (Geraedts et al. 2014). Of the trials identified Persson et al. (2023) 
found a significant effect on work ability and short-term sickness absence using W-iCBT for 
stress. However, to the best of our knowledge no studies have investigated the effects of W-
CBT in a digital format aimed at CMD where the population is in an out-patient clinic setting. 
Nor have we found a study using a transdiagnostic approach to W-iCBT.   
Summary of knowledge  
 
The evidence that W-CBT delivered face-to-face improves return to work over CBT or 
treatment as usual on CMD seems strong (Mikkelsen & Rosholm 2018; Slater et al., 2023). 
There is growing evidence that CMDs can be effectively targeted using transdiagnostic 
models both face-to face (Sakiris & Berle, 2019; Schaeuffele et al., 2021) and via ICBT 
(Dear, Staples et al. 2015, Titov, Dear et al. 2015, González-Robles, Díaz-García et al. 2020, 
Schaeuffele, Homeyer et al. 2020). It also seems that the perceived level of self-efficacy 
towards return to work can be a good predictor for reduction in work absenteeism (Gjengedal 

 
 
 
et al. 2021). The length of absenteeism predicts an increased risk of not returning to work 
(OECD, 2003; Blank et al., 2008; Roelen et al., 2012; OECD, 2021). There is some evidence 
that baseline treatment credibility is a predictor for both dropout, adherence and outcome in 
ICBT (El Alaoui et al., 2015; Alfonsson et al., 2016) and that treatment adherence predicts 
outcome (El Alaoui et al., 2015; Hedman et al. 2015).  
 Knowledge gap 
Current treatment is often targeting specific disorders without explicitly addressing 
RTW challenges and the coping strategies needed for RTW in the treatment packages. There 
is a need for studies that examine the potential contribution of interventions that integrate a 
systematic focus on work-related issues in a comprehensive therapeutic framework.  
To the best of our knowledge there has been little or no research on a transdiagnostic 
cognitive behavioural therapy approach to work-focused therapy, nor a digital version of 
work-focused therapy for CMD targeting out-patients. The individual evidence of 
transdiagnostic ICBT and face-to-face W-CBT is compelling. An effective combination of 
these would be an important development towards increased availability of work-focused 
therapy. A small feasibility study on the program “eBehandling - Health and Work” 
performed by the authors have shown that the combination seems feasible, but there is a need 
for a larger scale study to determine the utility and use of the program for wider 
implementation. The potential contribution of this study is to explore the utility and feasibility 
of a broadly applicable treatment program for emotional disorders, that is built around RTW. 
There is also a need for more knowledge about the potential negative effects, as these have 
earlier not been adequately assessed in treatment research. 
The single arm trial design aims to obtain preliminary results on treatment effects on 
RTW and clinical symptoms, as well as data on use and utility of the ICBT program as 
experienced by the patients. It is also the case that relatively little is known about what works 

 
 
 
and for whom in the field of work-focused ICBT. This study aims to address this knowledge 
gap by exploring how common clinical and demographic factors impact treatment response.    
Study aims  
  
The main aim of the current study is to investigate the utility and use of a digital 
transdiagnostic work-focused CBT treatment program for patients with common mental 
health disorders on sick leave or work assessment allowance. This includes the user 
experience of participation and experienced utility of the treatment program. Secondary aims 
are related to mediators and moderators of treatment efficacy and the participants' experience 
with the program.  
Research questions 
1. To what extent does the treatment program impact Return to Work, self-reported 
symptoms, and everyday functioning?  
2. To what extent do clinical, work-related and demographic factors affect treatment 
adherence and treatment response? 
3. To what extent do adherence and changes in work-related self-efficacy predict 
outcomes? 
4. What type and frequency of negative effects does participants report? 
5. How do participants describe their experience of use and utility of the program, as 
well as its challenges and benefits? 
 
 
 
 

 
 
 
 
Hypotheses of main effects 
 H1a. We expect a large main effect of treatment on total percentage sick leave from pre- to 
post treatment, with gains maintained at follow up.  
H1b. We expect a large main effect of treatment on the number of participants with full return 
to work from pre- to post treatment, with gains maintained at follow up. 
H2. We expect a large main effect of treatment on reduction on symptoms of depression and 
anxiety. 
H3. We expect a positive main effect of treatment on Return to Work Self-Efficacy, from pre 
to post treatment, and sustained through follow up. 
Hypotheses of moderating and mediating effects and predictors of outcome 
H4. We expect a negative relationship between duration of baseline work absenteeism and 
degree of return to work at post. Baseline work absenteeism will moderate the relationship 
between treatment and return to work. 
H5a. We expect treatment credibility at baseline to be predictive of treatment adherence. 
H5b. We expect that treatment adherence will be a mediator between treatment credibility 
and treatment outcome. 
H6. We expect changes on the Return to Work Self-Efficacy Scale to predict the degree of 
absenteeism post treatment. 
H7. We expect change on the Return to Work Self-Efficacy Scale to be a mediator between 
baseline absenteeism and return to work post-treatment. 

 
 
 
H8. We expect higher depression scores (PHQ-9/CORE-OM score) to be a moderator 
between treatment credibility and treatment outcome and adherence. 
H9. We expect that fewer than 10% of participants experience negative effects caused by 
participation, as measured by the Negative Effects Questionnaire (NEQ). 
 
Methods and procedures 
Design 
  
This study is a non-randomized, single arm, multi-center mixed methods clinical trial 
in a naturalistic setting (see Figure 1 for an overview of the study design). Participants are 
recruited in a real life setting in Norwegian specialised mental health care. After recruitment 
participants fill out questionnaires pre, interim and post treatment, in addition to follow up at 
6 and 12 months after completing treatment. Interim measures are collected after six weeks. 
After the treatment intervention, some participants are invited to an in-depth qualitative 
interview. 
 
Figure 1. Study design. 
 

 
 
 
 
Sample 
  
Inclusion criteria for the sample are 1) being an outpatient in specialised mental health 
care service, 2) above 18 years old, 3) full or graded sick leave, or on work assessment 
allowance 4) primary diagnosis of F30 – F40 (ICD-10), 5) currently employed. 
  
Exclusion criteria are 1) ongoing psychotic disorder and/or mania, 2) ongoing 
substance abuse, incl. sedative medications, 3) ongoing episode of major depression, 4) 
organic brain disease, 5) severe reading and writing difficulties. 
Measures 
Demographic variables. Self-reported data will be collected on gender, age, level of 
education, length and degree of sick leave, length of mental health problems and marital 
status. Primary and secondary F-diagnosis (ICD-10) will be reported by the eTherapist.  
Questionnaires. Primary outcome measures: The reduction in the degree of sick leave is one 
of our primary outcomes and will be measured through self-report. The first question is: “are 
you currently on sick-leave?”. The second question is activated if there is a positive answer to 
the first: ” To what degree are you currently on sick-leave?”. The answer is given on a 0-100 
scale with 5 point increments.     
The Return to Work Self-Efficacy Scale (RTWSE-11; (Lagerveld et al. 2010, Gjengedal et al. 
2021) is an 11-item questionnaire measuring an individual’s confidence in their ability to 
work and perform work tasks. Items are scored on a scale from 1 (“Totally disagree”) to 6 
(“Totally agree”). Items 2 and 6 are reversed. Total scores of 3.7 or below are associated with 
less probability of returning to work, while scores of 4.6 and above are associated with 
increased probability of returning to work (Gjengedal et al. 2021).  

 
 
 
Secondary outcome measures: The Generalized Anxiety Disorder-7 (GAD-7; (Spitzer et al. 
2006) is a measure of worry and anxiety and consists of seven items scored from 0 (“Not at 
all”) to 3 (“Nearly every day”) and one additional item measuring how the symptoms affect 
functioning. The Patient Health Questionnaire-9 (PHQ-9; (Kroenke, Spitzer & Williams 
2001) measures depression severity. The measure consists of nine items scored from 0 (“Not 
at all”) to 3 (“Nearly every day”). The Work and Social Adjustment Scale (WSAS; (Mundt et 
al. 2002) measures impairment in functioning. The measure consists of five items scored from 
0 (not at all impaired) to 8 (very severely impaired). CORE-OM (Evans et al. 2002) is a 34-
item self-report, measuring well-being, symptoms, elements of risk, and function on a five-
point scale ranging from “not at all” to “most, or all the time”. CORE-10 (Barkham et al. 
2013) is a ten item self-report measuring symptoms, well-being, functioning and risk on a 
five-point scale ranging from “not at all” to “most, or all the time”. The Negative Effects 
Questionnaire (NEQ) (Rozental et al. 2016, Rozental et al. 2019) is a 20-item questionnaire 
evaluating adverse or unwanted events during treatment. Participants report whether a specific 
event occurred during treatment (“Yes”/”No”) and then indicate how negatively they were 
affected by it on a scale ranging from 0-4. An adapted version of the Credibility Rating Scale 
(Borkovec & Nau 1972) will be used to assess treatment credibility. The scale consists of five 
items where participants rate their credibility/expectancy-for-improvement on a 10-point 
scale.    
System data on user behaviour. Data on use and user behaviour will be extracted from the 
system and combined with data from the self-report questionnaires collected. Information 
about the actual program use will be generated by the e-health platform Youwell. The data 
includes the extent to which participants complete the treatment, where and when they drop 
out, and how long they need to complete individual parts. Further we will look into whether 
there are areas in the treatment that require more time than others, which elements are used 

 
 
 
most and least frequently, and how the therapist guidance is used. Further we also will analyse 
how long each session lasts and what system and platform they use. Each eBehandling 
location will also maintain local statistics on the number of assessment interviews, 
participants included and excluded to assess acceptance in the patient population and hospital 
setting. This data collection will be recorded by on-site super users. Efforts will be taken to 
ensure that this registration is implemented in the Youwell platform. 
Qualitative in-depth interviews. Two months after completion of the program, a subset of 
participants will be invited to an in-depth interview (See Appendix A for the complete 
Interview Guide). We will use purposive sampling to select participants who represent a 
diverse range of demographics (age, gender) and outcomes (treatment responders and non-
responders). Care will be taken to include participants from different treatment sites. We aim 
to recruit a total of 20 participants for the qualitative interviews, likely ensuring richly-
textured information on a broad array of participant experiences.  
The interview has a semi-structured design with six questions and related probes (See 
Appendix A for the complete Interview Guide). The interviews will be conducted by research 
fellow Henning Monsen. The interviews will focus on the participants’ experience of the 
feasibility of the intervention, including aspects of the treatment viewed as helpful and less 
helpful. All interviews will be audio recorded and transcribed verbatim.  
 
Intervention 
  
The treatment intervention is a digital transdiagnostic work-focused cognitive 
behaviour therapy program. The program consists of seven modules (See Table 1 for an 
overview, and Appendix B for an in-depth review of the modules) consisting of 
psychoeducational texts, imagery and models, in addition to therapist-tailored tasks for the 

 
 
 
participants. The treatment model is based on traditional CBT principles and adjusted to target 
transdiagnostic maintaining mechanisms in anxiety and depression such as frequent negative 
emotions and aversive reactions to these that leads to various forms of avoidant coping that 
serve to reinforce the negative emotional cycle.  
Table 1. 
The modules of the digital program eBehandling – Health and Work 
Module 1 
Introduction to the program  
Module 2 
Where can I get help? How can I support myself?  
1Module 3 
Self-help through constructive and supporting thoughts 
Module 4 
Helpful actions 
Module 5 
Working with/through the body 
Module 6 
Emotions 
Module 7 
Put it all together and relapse prevention 
  
Treatment will be given by trained eTherapists with specific training in the eBehandling - 
health and work protocol. They will be given a two-day certified course in work-focused 
therapy delivered by the National Centre of Competence at Vinderen DPS. Further they will 
be given a one-day introduction on the use of W-CBT within a digital format by the authors of 
the program. Further supervision will be facilitated by the research fellow. Adherence and 
fidelity will be measured through reading eTherapist-participant correspondence and will be 
rated by the main author of the treatment protocol (Peter Prescott) and the main author of the 
Norwegian translation of W-CBT, Torkil Berge. 
 
 
 

 
 
 
Statistical analyses 
Quantitative analyses. In order to identify factors associated with treatment success and 
outcomes, hierarchical linear mixed modelling analyses (HLM, “multilevel”) will be 
conducted. In longitudinal HLM, measurement occasions (time) are nested within 
participants. Fixed effects are average (main) effects, and do not vary across subjects. 
Random effects vary across subjects and/or measurement occasions (e.g., a random intercept 
model vs a random intercept, random slope model). In these models, data may be missing at 
random and at different occasions for different subjects (Tasca & Gallop 2009). The models 
will be checked if they fulfil the model assumptions of normality of residuals, linearity and 
homoscedasticity. The dependent variables are sick leave, work related self-efficacy (as 
measured by RTWSE-11), depression severity (PHQ-9), anxiety (GAD-7), well-being, 
symptoms, risk and function (CORE-OM) and impairment in functioning (WSAS). The 
predictors are: Treatment (time), duration of sick-leave prior to treatment, treatment 
credibility, work related self-efficacy, depression severity and adherence. Comparisons are 
modelled through main effect and interaction effect analysis (e.g. Time x “Baseline work 
absenteeism” interaction). Predictors will be centred using grand mean centering. T-values 
and degrees of freedom will be estimated using “Sattertwaite” correction (Luke 2017). The 
effects sizes will be converted from t-values and degrees of freedom to Cohen’s d using the 
R-package “effectsize”. Estimating indirect effects (mediation), we will apply “causal 
mediation analysis” (CMA), that is a method to split a total effect of an independent predictor 
(X) into a direct (C) and indirect effect (M). The indirect effect is transmitted through a 
mediator to the outcome. CMA will be conducted under the assumption of sequential 
ignorability. The X will be measured at baseline, the M at intermediate treatment, and the 
outcome(s) at post-treatment (Zhang et al. 2016). See Figure 2 below for a tentative model. 

 
 
 
 
Figure 2. Model of mediation. 
Corrections for multiple analyses will be done by using Jaccard`s correction (Jaccard, 1998) 
to decrease risk of Type I error when applying several statistical tests.  
Qualitative analyses. Transcribed interviews will be analysed with the software NVivo 14 
using reflexive thematic analysis (Braun & Clarke 2006, Binder, Holgersen & Moltu 2012, 
Braun & Clarke 2019). By comparing the individual accounts, we want to identify both 
patterns of commonalities and differences in participants' experiences and formulate these as 
themes. This phenomenological and interpretive qualitative approach can serve to explore 
experiences of self in contexts, interpretations of events, narrative structuring and meaning 
making processes closely, providing important descriptions of patient experiences that may 
contribute to detect challenges with the treatment protocol and improve the future delivery of 
the intervention. We have not set a prior hypothesis for the qualitative analysis, as we wish to 
explore the data inductively and to formulate themes as richly as possible in accordance with 

 
 
 
participants’ responses. Care will be taken to stay close to the wording and narratives of 
participants, grounding our findings in their experiences rather than our preconceived notions 
as researchers.  
Power analyses 
  
In a multilevel model, the fixed effects are comparable to ordinary repeated t-tests, and 
thus, using this as a proxy model with a moderate effect size (d = 0.6), power = 0.8, alpha = 
0.05, tails = "two.sided", we need 25 subjects for detecting a single main effect Murayama, 
Usami and Sakaki (2022); see Summary-statistics-based power analysis (shinyapps.io). But, 
as stated by Van Voorhis and Morgan (2007): “if the circumstances allow, a researcher would 
have better power to detect a small effect size with approximately 30 participants per 
variable” (page 48). Following this rule of thumb, we plan for 30 participants per predictor 
(unique main effects), thus a total sample size of 300 participants. Calculating a drop-out rate 
of 30% gives a total sample size of N = 390. Calculating power for indirect effects – or 
mediation – we used the following online shiny app Monte Carlo Power Analysis for Indirect 
Effects (shinyapps.io), published by Schoemann, Boulton and Short (2023). Using (El Alaoui 
et al. 2015) as a proxy study for the direction and strength of association between our 
variables, they found that both treatment credibility and treatment adherence were 
significantly predictors of faster change and lower post treatment scores in a routine 
psychiatric care based ICBT for social anxiety disorder. In the same study, a high level of 
treatment credibility was associated with higher treatment adherence. Hence, defining X = 
treatment credibility, M = treatment adherence and Y = treatment outcomes, we set the 
correlation of X-M (a-path) = 0.3 (SD = 1), X-Y (c-path) = 0.30 (SD = 1) and M-Y (b-path) = 
0.30  (SD = 1) , power = 0.8, alpha= 0.05, setting random seed to “0512”, using 5000 
replications with 20000 Monte Carlo draws per replication, we need N = 155 to have a power 
of 0.80, 95% CI [0.79-0.82]. 

 
 
 
Procedure 
 
Inclusion will be open for 18 months, planned from September 2024. Participants will 
be recruited from outpatient specialised mental health care facilities in the Western Norway 
Regional Health Authority and South-Eastern Norway Regional Health Authority. After 
recruitment, each participant will undergo an assessment for eligibility where inclusion and 
exclusion criteria are evaluated. If eligible, participants are asked to sign a written informed 
consent to participation before enrolment in the study. Participants will receive access to the 
program after consent has been given, using level four secure login, for example BankID or 
Buypass ID. Participants receive a short tutorial of the program by their eTherapist. When 
logged in the first time, and as a mandatory task before given access to the program, 
participants are asked to revise their consent in regards to their unidentifiable data being used 
in research. Participants can at any time during treatment withdraw their consent. In this case 
they will be able to continue treatment, but their user data will not be exported for research 
purposes. Demographic variables and questionnaires will be given as a mandatory task before 
the first module and are registered directly in the program. The program is set to take 12 
weeks to finish. 
  
The main form of contact between participant and eTherapist will be via asynchronous 
messages. If asked for by the participant, or evaluated as necessary by the eTherapist there 
will be contact via phone or face-to-face. If a participant fails to log in for more than seven 
days the eTherapist will make contact via phone or SMS giving them an appointment to 
evaluate further progress. If a patient drops out of the program the eTherapists are to use their 
local procedures to how this person is taken care of, see also section for Ethical 
Considerations.  
  
After 12 weeks the participants will be given a concluding session face-to-face where 
the next step is decided: End treatment, further treatment within the specialist mental health 

 
 
 
care services or referred to a different treatment option. Regardless of this the participants will 
have access to the treatment program, without therapist contact, for six months after 
completion.  
  
Participants will be asked to take part in a voluntary interview with a research fellow 
in the project. After six and 12 months, participants will be notified via SMS asking them to 
fill out self-report questionnaires. They will be given two reminders via SMS if they do not 
respond. If they do not fill in the forms after the two reminders they will be regarded as lost to 
follow up.     
 
 

 
 
 
Project management 
The project has academic and clinical collaborators. For a description of 
implementation of the project, see the complete Project Directive for Implementation in 
Appendix C. 
Academic collaborators 
University of Bergen. Main supervisor for the PhD candidate is associate professor Jon 
Vøllestad and co-supervisor is Professor Helge Molde. They both have extensive experience 
with conducting clinical trials and supervision. 
Centre for Research-based Innovation (SFI) ForHelse. Professor Tine Nordgreen, 
Specialist in Clinical Psychology, is head of SFI ForHelse and leads the work package related 
to treatment efficacy. She has an important role in the development and implementation of 
digital treatment interventions nationally. In this project she represents the SFI which will 
provide expert know-how on implementation of digital treatment programs. In addition, she 
will personally contribute to the design of the study and in interpreting the results as a co-
author. 
Diakonhjemmet Hospital dept. Vinderen. Specialist in Clinical Psychology Torkil Berge 
and Head of Department of Research and Innovation Ragne Gjengedal, PhD, Specialist in 
Clinical Psychology, are collaborators at Diakonhjemmet hospital. They both have long 
experience in conducting studies and special competence on work-focused therapy. 
Department Vinderen has a national responsibility for the dissemination of work-focused 
therapy and has authored the translated manual for work-focused therapy into Norwegian 
(Berge et al., 2019). They will continue to contribute with counselling and guidance on the 
development of W-CBT and in the education and guidance of eTherapists delivering the 
program.    

 
 
 
Solli DPS. PhD and analyst Pål W. Wallace is project leader. Head of Research and 
psychologist Kristine Sirevåg is an academic collaborator involved in study design and 
evaluation of results. Research fellow Henning Monsen is a planned PhD candidate in the 
study and is key to the implementation of the project as the daily manager. 
Macquarie University, Sidney, Australia / Mindspot Australia. Professor Nick Titov, 
Specialist in Clinical Psychology, is the Executive Director of the Australian national 
MindSpot Clinic, Australia's version of eBehandling. Nick will contribute to the design of the 
study and the evaluation of the results. 
User representatives. Trine Halhjem and Sigve Moss-Knutsen are participating in the 
evaluation of the program and the study procedure from a user perspective, including alpha-
testing and reviewing. Astrid Gytri represents the user perspective in the steering committee.   
 
Clinical collaborators 
Solli DPS. Director Frédéric Larsen is responsible for ensuring patient recruitment from Solli 
DPS.  
Vestre Viken Hospital Trust. Monika Strand is a research consultant in the department of 
research and development for Vestre Viken Hospital Trust and will help coordinate the 
research participation at the hospital and its clinics. Nils Lagerström Breines is the head of 
eBehandling and will be responsible for the patient recruitment. 
Østfold Hospital. Tommy A. Jørgensen is the Project Lead for Technology Supported 
Treatment at the Psychiatric clinic at Østfold Hospital and will ensure that the hospital 
recruits and treats patients.  

 
 
 
Akershus University Hospital. Tale Øi leads the Clinic for Work focused Therapy at 
Groruddalen District Psychiatric Hospital and will ensure that the hospital recruits and treats 
patients.  
Innlandet Hospital Trust. Pål Andreassen is the department head of Lillehammer District 
Psychiatric Hospital and will ensure that the hospital recruits and treats patients.  
Diakonhjemmet Hospital. Torkil Berge is a Subject development advisor 
(Fagutviklingsrådgiver) at the Hospital and will recruit and treat patients.  
Bjørgvin DPS. Fredrik Hiis Bergh is the Clinic Director of Bjørgvin District Psychiatric 
Hospital will ensure that the hospital recruits and treats patients.  
Vestfold Hospital Trust. Kenneth Kvisle is the Section leader at Vestfold District Psychiatric 
Hospital which focuses on work-focused therapy and will ensure that the hospital recruits and 
treats patients.  
Fonna Hospital Trust. Arne Thomas Lundquist leads the general psychiatric polyclinic at  
Haugaland District Psychiatric Hospital and will ensure that the hospital recruits and treats 
patients.  
 
 
 

 
 
 
Ethical considerations 
The therapist’s level of competence 
The therapists giving the treatment program are clinical psychologists or have at least 
a bachelor degree in health (Nurses, social workers and occupational therapists). In addition 
all therapists are trained in eTreatment in general, this program specifically and are given 
weekly or bi-weekly guidance on eTherapy within their workplace and by the project group.   
Monitoring suicidal risk or behaviour 
  
Participants will each week answer question 10 (suicidal ideations) of the 
Montgomery and Åsberg Depression Rating Scale (MADRS; Malt, 2016; Montgomery & 
Asberg 1979) and the total CORE-10. Both of these questionnaires have a cut-off for risk 
which, if surpassed, leads to an automatic text message and e-mail to the participants' 
eTherapist and local program supervisor. The eTherapist, or the program supervisor contacts 
the participant and follows their local procedure on assessing suicidal risk and take 
precautionary measures as deemed necessary. This will include a face-to-face evaluation and 
assessment of the level of care needed. The process and conclusion will be documented in the 
participants electronic journal (EPJ).  
Transdiagnostic treatment for specific or comorbid diagnosis 
There is compelling evidence that transdiagnostic treatment protocols are effective in 
influencing the underlying mechanisms of CMDs through the reduction of fear and avoidance 
of-, and increasing acceptance of emotions, physical sensations and establishing alternative, 
more adaptive behaviours (González-Robles et al. 2020, Schaeuffele et al. 2021). This is 
found in both face-to-face therapy and ICBT. As we have developed the treatment in the 
current study on these established principles we see no imminent increased risk of reduced 
effect of the treatment on CMDs in this study. 

 
 
 
In the case of participant drop out  
  
If a patient drops out of treatment, or together with their eTherapist concludes that the 
treatment program of this study is not suitable, they will be given access to other, suitable 
treatment either via “eBehandling” or face-to-face treatment at their local District Psychiatric 
Center. (DPS). 
Data and privacy protection 
  
The Youwell platform and concerns of health information have been processed and 
approved for clinical use via the Western Norway Regional Health Authority based on a risk 
and vulnerability analysis and Data Protection Impact Assessment (DPIA, See Appendix D 
for the DPIA). The process in regard to research within a clinical setting is started within 
Helse Vest and is scheduled to be finished before July 2024. Regarding the specific 
considerations concerning data extraction this has been evaluated together with a data 
protection officer (PVO) using a DPIA. Data will be extracted by the PhD candidate using a 
researcher's access. This grants access to all the reports on self-evaluation and program usage, 
bar free text answers. It does not give access to the personal profiles of the participants, nor 
their activities in the program. The export is further limited by default to exclude any who 
have withdrawn their consent during treatment. Data will be encrypted and saved on secure 
storage area from Helse Vest IKT and on safe storage (SAFE) at UiB.  
Financing sources 
  
The study is currently funded by Solli DPS and previously assigned strategic funding 
from the Western Norwegian Regional Health Authority. We will apply for funding from the 
Western Norwegian Regional Health Authority.  
Conflicts of interest 
  
No conflicts of interest are stated by the research project group. 

 
 
 
Publications and communication of results 
 
The results will be published in high impact, international open access peer review 
journals. The titles of the planned papers are: 
Paper I: The use and utility of a new digital transdiagnostic work-focused cognitive 
behaviour therapy program for patients on sick leave in specialised mental health care. 
Paper II: Mediators and moderators for treatment utility of a digital work-focused therapy 
program. 
Paper III: Participants experience with digital work-focused therapy – a qualitative study. 
 
The results will be presented at international and national conferences. The PhD 
candidate in the study will contribute with popular science articles for user groups and the 
general population. Speeches and lectures will be given to relevant groups such as health care 
practitioners in specialised mental health care, municipal health care practitioners and relevant 
user organisations.  
 
 
 
 
 
 
 
 

 
 
 
References  
 
Alfonsson, S., et al. (2016). "Motivation and Treatment Credibility Predicts Dropout, Treatment 
Adherence, and Clinical Outcomes in an Internet-Based Cognitive Behavioral Relaxation Program: A 
Randomized Controlled Trial." J Med Internet Res 18(3): e52. 
Asplund, R. P., et al. (2019). "Experiences of internet-delivered and work-focused cognitive 
behavioral therapy for stress: A qualitative study." Internet Interventions 18: 100282. 
Attridge, M. (2020). "Internet-Based Cognitive-Behavioral Therapy for Employees With Anxiety, 
Depression, Social Phobia, or Insomnia: Clinical and Work Outcomes." Sage Open 10(1): 
2158244020914398. 
Barbato, A., et al (2016) “Access to Mental Health Care in Europe” EU Compass Consortium 
Barkham, M., et al. (2013). "The CORE-10: A short measure of psychological distress for routine use in 
the psychological therapies." Counselling and Psychotherapy Research 13(1): 3-13. 
Baumann, M., et al. (2020). "Cost-Utility of Internet-Based Cognitive Behavioral Therapy in Unipolar 
Depression: A Markov Model Simulation." Appl Health Econ Health Policy 18(4): 567-578. 
Binder, P.-E., et al. (2012). "Staying close and reflexive: An explorative and reflexive approach to 
qualitative research on psychotherapy." Nordic Psychology 64(2): 103-117. 
Blank, L., et al. (2008). "A Systematic Review of the Factors which Predict Return to Work for People 
Suffering Episodes of Poor Mental Health." Journal of Occupational Rehabilitation 18(1): 27-34. 
Borkovec, T. D. and S. D. Nau (1972). "Credibility of analogue therapy rationales." Journal of behavior 
therapy and experimental psychiatry 3(4): 257-260. 
Braun, V. and V. Clarke (2006). "Using thematic analysis in psychology." Qualitative Research in 
Psychology 3(2): 77-101. 
Braun, V. and V. Clarke (2019). "Reflecting on reflexive thematic analysis." Qualitative Research in 
Sport, Exercise and Health 11: 589-597. 
Cederberg, M., et al. (2022). "Effects of a Person-Centered eHealth Intervention for Patients on Sick 
Leave Due to Common Mental Disorders (PROMISE Study): Open Randomized Controlled Trial." JMIR 
Ment Health 9(3): e30966. 
 Cullen KL, Irvin E, Collie A, et al. Effectiveness of workplace interventions in return-to-work for 
musculoskeletal, pain-related and mental health conditions: an update of the evidence and messages 
for practitioners. J Occup Rehabil 2018;28:1–15 
Dalgleish, T., Black, M., Johnston, D. & Bevan, A. (2020). (Dalgleish, Black et al. 2020, Etzelmueller, Vis 
et al. 2020). Journal of Consulting and Clinical Psychology, 88(3), 179–195. 
https://doi.org/10.1037/ccp0000482 
Dear, B. F., et al. (2015). "Transdiagnostic versus disorder-specific and clinician-guided versus self-
guided internet-delivered treatment for generalized anxiety disorder and comorbid disorders: A 
randomized controlled trial." Journal of Anxiety Disorders 36: 63-77. 
El Alaoui, S., et al. (2015). "Predictors of Symptomatic Change and Adherence in Internet-Based 
Cognitive Behaviour Therapy for Social Anxiety Disorder in Routine Psychiatric Care." PLoS ONE 10(4): 
e0124258. 

 
 
 
Etzelmueller, A., et al. (2020). "Effects of Internet-Based Cognitive Behavioral Therapy in Routine 
Care for Adults in Treatment for Depression and Anxiety: Systematic Review and Meta-Analysis." J 
Med Internet Res 22(8): e18100. 
Evans, C., et al. (2002). "Towards a standardised brief outcome measure: psychometric properties 
and utility of the CORE-OM." Br J Psychiatry 180: 51-60. 
Folkehelseinstituttet (2015, 05.05.2015). "Sammenheng mellom arbeid og god helse." Retrieved 
21.05.2021, 2021, from https://www.fhi.no/publ/2015/sammenheng-mellom-arbeid-og-god-helse/. 
Geraedts, A. S., et al. (2014). "Long-term results of a web-based guided self-help intervention for 
employees with depressive symptoms: randomized controlled trial." J Med Internet Res 16(7): e168. 
Gjengedal, R. G. H., et al. (2021). "The Return-to-Work Self-efficacy Questionnaire (RTW-SE): A 
Validation Study of Predictive Abilities and Cut-off Values for Patients on Sick Leave Due to Anxiety or 
Depression." Journal of Occupational Rehabilitation 31(3): 664-673. 
Gjengedal, R. G. H., et al. (2020). "Work-focused therapy for common mental disorders: A naturalistic 
study comparing an intervention group with a waitlist control group." Work 66: 657-667. 
González-Robles, A., et al. (2020). "Effectiveness of a Transdiagnostic Guided Internet-Delivered 
Protocol for Emotional Disorders Versus Treatment as Usual in Specialized Care: Randomized 
Controlled Trial." J Med Internet Res 22(7): e18220. 
Gundersen, U. E. and H. G. Medalen (2019). Langtidspsykefravær - Hva øker sjansen for tilbakeføring 
til arbeid? Psykologisk institutt. Norway, University of Oslo. Master. 
Hedman-Lagerlöf, E., et al. (2023). "Therapist-supported Internet-based cognitive behaviour therapy 
yields similar effects as face-to-face therapy for psychiatric and somatic disorders: an updated 
systematic review and meta-analysis." World Psychiatry 22(2): 305-314. 
Hedman, E., et al. (2015). "Predictors in Internet-delivered cognitive behavior therapy and behavioral 
stress management for severe health anxiety." Behaviour Research and Therapy 64: 49-55. 
Jaccard, J. (1998). Interaction effects in factorial analysis of variance. Sage. 
Kroenke, K., et al. (2001). "The PHQ-9: validity of a brief depression severity measure." J Gen Intern 
Med 16(9): 606-613. 
Lagerveld, S. E., et al. (2010). "Return to work among employees with mental health problems: 
Development and validation of a self-efficacy questionnaire." Work & Stress 24(4): 359-375. 
Luke, S. G. (2017). "Evaluating significance in linear mixed-effects models in R." Behavior Research 
Methods 49: 1494–1502. 
Malt, U. (2016, 15.06.2016). "MADRS-S selvutfylling." Retrieved 06.04, 2024, from 
https://www.helsebiblioteket.no/innhold/lenker/psykisk-helse/skaringsverktoy/madrs-s-
selvutfylling. 
Mikkelsen, M. B. and M. Rosholm (2018). "Systematic review and meta-analysis of interventions 
aimed at enhancing return to work for sick-listed workers with common mental disorders, stress-
related disorders, somatoform disorders and personality disorders." Occup Environ Med 75(9): 675-
686. 
Montgomery S. A. and Asberg M. (1979). "A new depression scale designed to be sensitive to 
change." British Journal of Psychiatry 134(4): 382–389. 
Mundt, J. C., et al. (2002). "The Work and Social Adjustment Scale: a simple measure of impairment 
in functioning." Br J Psychiatry 180: 461-464. 

 
 
 
 
 
Murayama, K., et al. (2022). "Summary-statistics-based power analysis: A new and practical method 
to determine sample size for mixed-effects modeling." Psychological Methods 27(6): 1014-1038. 
NAV (2020). Diagnoser uføretrygd. Norway, Arbeid og Velferdsforvaltinga. 
NAV (2024, 04.03.2024). "Sykefraværsstatistikk - Kvartalstatistikk." Retrieved 04.04, 2024, from 
https://www.nav.no/no/nav-og-samfunn/statistikk/sykefravar-statistikk/sykefravar. 
OECD (2003). Transforming disability into ability: policies to promote work and income security for 
disabled people. Paris, Organisation for Economic Co-operation and Development: 1-219. 
OECD (2014). Mental Health and Work: Switzerland. 
OECD (2016). Mental Health and work: Fit mind, Fit job: from evidence to practice in mental health 
and work. Paris, OECD. 
OECD (2021). Fitter Minds, Fitter Jobs : From Awareness to Change in Integrated Mental Health, Skills 
and Work Policies, OECD. 
Persson Asplund, R., et al. (2023). "Work-Focused Versus Generic Internet-Based Interventions for 
Employees With Stress-Related Disorders: Randomized Controlled Trial." J Med Internet Res 25: 
e34446. 
Reme, S. E., et al. (2015). "Work-focused cognitive–behavioural therapy and individual job support to 
increase work participation in common mental disorders: a randomised controlled multicentre trial." 
Occup Environ Med 72(10): 745-752. 
Roelen, C. A., et al. (2012). "Employees sick-listed with mental disorders: who returns to work and 
when?" J Occup Rehabil 22(3): 409-417. 
Rozental, A., et al. (2016). "Negative Effects of Psychological Treatments: An Exploratory Factor 
Analysis of the Negative Effects Questionnaire for Monitoring and Reporting Adverse and Unwanted 
Events." PLoS ONE 11(6): e0157503. 
Rozental, A., et al. (2019). "The Negative Effects Questionnaire: psychometric properties of an 
instrument for assessing negative effects in psychological treatments." Behavioural and Cognitive 
Psychotherapy 47(5): 559-572. 
Sakiris, N. and D. Berle (2019). "A systematic review and meta-analysis of the Unified Protocol as a 
transdiagnostic emotion regulation based intervention." Clinical Psychology Review 72: 101751. 
Schaeuffele, C., et al. (2020) The Unified Protocol as an Internet-based Intervention for Emotional 
Disorders: Randomized Controlled Trial.   
Schaeuffele, C., et al. (2021). "CBT at the Crossroads: The Rise of Transdiagnostic Treatments." 
International Journal of Cognitive Therapy 14(1): 86-113. 
Schoemann, A. M., et al. (2023). "Monte Carlo Power Analysis for Indirect Effects."  1.0. Retrieved 
06.04.2024, 2024, from https://schoemanna.shinyapps.io/mc_power_med/. 
Slater, D., et al. (2023). "Defining work-focused cognitive behavioural therapy (W-CBT) and whether 
it is effective at facilitating return to work for people experiencing mental health conditions: A 
systematic review and narrative synthesis." Health Psychol Open 10(2): 20551029231217840. 
Spitzer, R. L., et al. (2006). "A brief measure for assessing generalized anxiety disorder: the GAD-7." 
Arch Intern Med 166(10): 1092-1097. 

 
 
 
Standal, M. I., et al. (2021). "Health, Work, and Family Strain - Psychosocial Experiences at the Early 
Stages of Long-Term Sickness Absence." Front Psychol 12: 596073. 
Tasca, G. A. and R. Gallop (2009). "Multilevel modelling of longitudinal data for psychotherapy 
researchers: 1 The basics." Psychotherapy Research 19(4-5): 429–437. 
Taylor, C. B., et al. (2021). "Current state of scientific evidence on Internet-based interventions for 
the treatment of depression, anxiety, eating disorders and substance abuse: an overview of 
systematic reviews and meta-analyses." Eur J Public Health 31(31 Suppl 1): i3-i10. 
Titov, N., et al. (2015). "Disorder-specific versus transdiagnostic and clinician-guided versus self-
guided treatment for major depressive disorder and comorbid anxiety disorders: A randomized 
controlled trial." J Anxiety Disord 35: 88-102. 
Udd-Granat, L., et al. (2023). "Internet-delivered cognitive behavioral therapy (iCBT) for common 
mental disorders and subsequent sickness absence: a systematic review and meta-analysis." Scand J 
Public Health 51(1): 137-147. 
Van Voorhis, C. R. and B. L. Morgan (2007). "Understanding Power and Rules of Thumb   
for Determining Sample Sizes." Tutorials in Quantitative Methods for Psychology 3(2): 43-50. 
WHO (2024). "Mental health at work." Retrieved 19.03.2024, 2024, from 
https://www.who.int/teams/mental-health-and-substance-use/promotion-prevention/mental-
health-in-the-workplace. 
Zhang, Z., et al. (2016). "Causal mediation analysis in the context of clinical research." Annals of 
Translational Medicine 4(21): 425. 
 
 
 
 
 
 
 
 
 
 
Dalgleish, T., et al. (2020). "Transdiagnostic approaches to mental health problems: Current status 
and future directions." J Consult Clin Psychol 88(3): 179-195. 
 
Despite a longstanding and widespread influence of the diagnostic approach to mental ill 
health, there is an emerging and growing consensus that such psychiatric nosologies may no 

 
 
 
longer be fit for purpose in research and clinical practice. In their place, there is gathering 
support for a "transdiagnostic" approach that cuts across traditional diagnostic boundaries 
or, more radically, sets them aside altogether, to provide novel insights into how we might 
understand mental health difficulties. Removing the distinctions between proposed 
psychiatric taxa at the level of classification opens up new ways of classifying mental health 
problems, suggests alternative conceptualizations of the processes implicated in mental 
health, and provides a platform for novel ways of thinking about onset, maintenance, and 
clinical treatment and recovery from experiences of disabling mental distress. In this 
Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology, we 
provide a narrative review of the transdiagnostic literature in order to situate the Special 
Section articles in context. We begin with a brief history of the diagnostic approach and 
outline several challenges it currently faces that arguably limit its applicability in current 
mental health science and practice. We then review several recent transdiagnostic 
approaches to classification, biopsychosocial processes, and clinical interventions, 
highlighting promising novel developments. Finally, we present some key challenges facing 
transdiagnostic science and make suggestions for a way forward. (PsycINFO Database Record 
(c) 2020 APA, all rights reserved). 
 
Etzelmueller, A., et al. (2020). "Effects of Internet-Based Cognitive Behavioral Therapy in Routine 
Care for Adults in Treatment for Depression and Anxiety: Systematic Review and Meta-Analysis." J 
Med Internet Res 22(8): e18100. 
 
BACKGROUND: Although there is evidence for the efficacy of internet-based cognitive 
behavioral therapy (iCBT), the generalizability of results to routine care is limited. OBJECTIVE: 
This study systematically reviews effectiveness studies of guided iCBT interventions for the 
treatment of depression or anxiety. METHODS: The acceptability (uptake, participants' 
characteristics, adherence, and satisfaction), effectiveness, and negative effects 
(deterioration) of nonrandomized pre-post designs conducted under routine care conditions 
were synthesized using systematic review and meta-analytic approaches. RESULTS: A total of 
19 studies including 30 groups were included in the analysis. Despite high heterogeneity, 
individual effect sizes of investigated studies indicate clinically relevant changes, with effect 
sizes ranging from Hedges' g=0.42-1.88, with a pooled effect of 1.78 for depression and 0.94 
for anxiety studies. Uptake, participants' characteristics, adherence, and satisfaction indicate 
a moderate to high acceptability of the interventions. The average deterioration across 
studies was 2.9%. CONCLUSIONS: This study provides evidence supporting the acceptability 
and effectiveness of guided iCBT for the treatment of depression and anxiety in routine care. 
Given the high heterogeneity between interventions and contexts, health care providers 
should select interventions that have been proven in randomized controlled clinical trials. 
The successful application of iCBT may be an effective way of increasing health care in 
multiple contexts.