A Personalized Approach to Effects of Affective Bias Modification on Symptom Change and Rumination
Phase: NA
Conditions: Major Depressive Disorder
Sponsor: University of Oslo

 
Official title of study:  
A Personalized Approach to Effects of Affective Bias Modification on Symptom Change and Rumination  
NCT04137367  
Date: September 23rd 2019 
 
 
 

 
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1. A personalized approach to effects of affective bias modification on symptom change and 
rumination. A randomized controlled trial.  
2. Introduction.  
A main objective of this project is to investigate whether transdiagnostic rumination mediates the 
effects of affective bias modification on depressive symptoms. By combining general mechanisms 
research with a personalized symptom network approach, we will be in the forefront of 
understanding how a drug-free treatment option works and for whom it works best. 
There is a growing consensus that the traditional approach to mental disorders in psychopathology 
research is constraining new scientific discoveries. Up until recently, research has been guided by 
the idea that a specific constellation of psychiatric symptoms reflects a specific psychiatric disorder, 
and that a specific pathogenic pathway causes this. However, findings based on this approach have 
not aligned with findings from clinical neuroscience and genetics, is poor in predicting treatment 
response, and does not seem to capture the underlying mechanisms of dysfunction (Insel et al., 
2010). The problem is particularly disturbing for depression research, as depression is a very 
heterogenic disorder (Goldberg, 2011) with numerous plausible etiological and maintaining 
pathways (Charney & Manji, 2004; Wittenborn, Rahmandad, Rick, & Hosseinichimeh, 2016)and 
high levels of comorbidity (e.g., anxiety and alcohol use disorder; (Brown, Campbell, Lehman, 
Grisham, & Mancill, 2001; Kessler et al., 1997). 
Rumination. There is growing evidence that rumination, a maladaptive form of self-reflection, 
might constitute a transdiagnostic factor. A particularly maladaptive sub-type of rumination, 
brooding, reflects the degree to which individuals passively focus on symptoms of distress and the 
meaning of those symptoms. Brooding is a vulnerability factor to Major Depressive Disorder 
(MDD) and it contributes to the development and maintenance of the disorder (Abela & Hankin, 
2011; Sarin, Abela, & Auerbach, 2005). In bipolar disorder (BP), compared to MDD, ruminative 
responses are even more present (Kim, Yu, Lee, & Kim, 2012). Rumination predicts prospective 
changes in anxiety (Segerstrøm, Stanton, Alden, & Shortridge, 2003), as well as alcohol use in out-
patients with a diagnosis of alcohol abuse, independent of depression and initial level of alcohol use 
(Caselli et al., 2010). Furthermore, brooding mediates the prospective relation between anxiety and 
depression (Grant et al., 2013). These associations suggest that rumination may play an important 
role across psychopathologies and contribute to the high rates of comorbidity among those disorders 
(Caselli et al., 2010; McLaughlin & Nolen-Hoeksema, 2011). 
Affective bias. Affective biases, i.e. preferential attention to threatening information have been 
documented in subjects with clinical and subclinical levels of anxiety (Bar-Haim, Lamy, Pergamin, 
Bakermans-Kranenburg, & Van Ijzendoorn, 2007; Cisler & Koster, 2010). It has also been 
demonstrated that clinically depressed subjects, previously depressed subjects and never-depressed 
individuals at high risk because of family history, all display a bias towards sad faces (Gotlib, 
Krasnoperova, Yue, & Joormann, 2004; Joormann & Gotlib, 2007; Joormann, Talbot, & Gotlib, 
2007). Studies indicate that the negative affective biases may constitute causative, vulnerability and 
maintenance factors for affective disorders, like depression and anxiety (Chan, Goodwin, & 
Harmer, 2007; MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002). However, although 
there have been quite a high number of studies examining affective biases in depression or anxiety 
individually, surprisingly few studies have examined affective biases among individuals with 
comorbid depression and anxiety (Gibb, McGeary, & Beevers, 2016).  
Linking rumination, attentional control, symptoms and affective bias. Self-reports of rumination 
predict an affective bias towards negative stimuli, even when depressive symptoms are statistically 
controlled for (Donaldson, Lam, & Mathews, 2007). Negative biases are particularly strong in 
subjects with heightened disposition to engage in ruminative brooding (Joormann, 2006). 

 
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Rumination can be understood as dysregulated attentional focus on depression symptoms. Both trait 
and experimentally induced state rumination have been linked to impairment in executive control 
functions, in particular cognitive inhibition and switching (Joormann & Gotlib, 2010). This 
indicates that information-processing impairments might make it difficult for ruminators to 
disengage from negative content (Koster, De Lissnyder, Derakshan, & De Raedt, 2011).  
The aforementioned findings have motivated an increase in 
interest in using knowledge about the mechanisms underlying 
the vulnerability and maintenance of affective disorders for 
therapeutic purposes (Harmer, Duman, & Cowen, 2017; 
Koster & Hoorelbeke, 2015). Given that one of the central 
mechanism in affective disorders is a selective attentional 
focus towards disorder-relevant stimuli, then reducing this 
focus could potentially alleviate the disorder. Computerized 
tasks that aim to induce changes to the bias in question have 
been developed (Hertel & Mathews, 2011). These procedures appears successful in reducing 
affective biases observed in depression, and might also lead to reductions in depression symptoms 
(Hallion & Ruscio, 2011).  
 
The symptom network approach. Given the above challenges, there has been a call for multi-level 
etiological models involving multiple causal processes at the micro and macro level, within and 
outside the individual (Kendler, Zachar, & Craver, 2011). The network approach offers a promising 
conceptual framework to explore the complex interplay between important psychopathological 
processes at the dimensional level (Hofmann, Curtiss, & McNally, 2016). By network analysis one 
can determine which symptoms are the most central (i.e., influential) based on the amount of 
influence that flows from one symptom to another (Borgatti, 2005). See figure 2. Networks with 
many strong connections between symptoms (dense networks) are more likely pathogenic than 
networks characterized by weaker connections (Borsboom, 2017). Simulation studies show that in 
dense networks, only a minimal of worsening in a central symptom may trigger a downstream 
cascade of symptoms. This might lead to a “vicious cycle” of negative cognition and symptoms 
(Wichers, 2014), with a depressed state as the result (Cramer et al., 2016). 
Using intensive time series data on symptoms in combination with cutting-edge statistics, such as 
graphical vector autoregressive modelling, we can capture the within- and between-person temporal 
dynamics of individual symptom networks (Wild et al., 2010). Relevant psychological, 
physiological and behavioral variables can be gathered relatively easy 
by the use of smartphones (Miller, 2012). Analyzing such time-series 
data can reveal dynamic processes within each individual (Trull & 
Ebner-Priemer, 2009). In this manner, symptoms and other variables 
can be tracked over time to optimally model the continuous dynamics 
of the interplay between processes during improvement and worsening 
of a disorder, and before and after interventions. This approach can 
provide a more fine-grained examination of the dynamic interplay 
between depression-related processes at the individual level, and 
possibly help identify symptom profiles for which ABM might be 
effective. Thus, the network approach provides a potential framework 
for personalized assessment and treatment (Fried et al., 2017), and 
guide clinicians in choosing treatments that are tailor-made to address each patient’s problem more 
effectively (Fisher, Reeves, Lawyer, Medaglia, & Rubel, 2017). 
Recently, researchers have advocated for the inclusion of non-symptom variables that are assumed 
to be plausible causal candidates in the etiology or maintenance of disorders (Jones, Heeren, & 
McNally, 2017). Rumination and affective bias are relevant variables that can be included in 
network analyses of depression. To date, only a few studies have included such variables (Heeren & 
Figure 2. Example of a depression 
symptom network 
Figure 1. The relation between depression, 
attentional control, affective bias and rumination. 
Adapted from LeMoult & Gotlib (2018). 

 
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McNally, 2016; Hoorelbeke, Marchetti, De Schryver, & Koster, 2016). Whether rumination 
mediates the relation between affective bias and depressive symptoms is still not accounted for. For 
whom, and under which circumstances these interventions have reliable effects on depression 
symptoms is also under debate (Cristea, Kok, & Cuijpers, 2017; Grafton et al., 2017). The 
intervention of this clinical trial is a computerized training program, called affective bias 
modification (ABM), known to produce a generalized affective bias change (Browning, Holmes, 
Charles, Cowen, & Harmer, 2012). A main aim of this project is to investigate how the effects of 
this ABM intervention on depressive symptoms are mediated by transdiagnostic rumination and 
how characteristics of the symptom network moderate these effects. 
2.1. Needs description. Depression affects more than 300 million people worldwide and is an 
increasing global health issue (Bender & Farvolden, 2008), however, treatment efficacy is still 
unsatisfying (Cuijpers, van Straten, Bohlmeijer, Hollon, & Andersson, 2010). Forty percent of 
patients do not, or only partially, respond to treatment and less than one-third are completely 
recovered after treatment (Andrews, Issakidis, Sanderson, Corry, & Lapsley, 2004). Furthermore, 
MDD with co-morbid anxiety predicts poor outcome with a higher percentage of treatment 
resistance than either disorder occurring alone (Coplan, Aaronson, Panthangi, & Kim, 2015), 
requiring new treatment options. While all available treatments of depression, psychologically as 
well as pharmacological, seem to be about equally effective, little is known about who benefits 
from which treatment (Cuijpers & Christensen, 2017). By combining an intervention based on 
transdiagnostic neuroscientific knowledge with Ecological momentary assessment (EMA) of 
affective symptoms, this study might contribute to close this knowledge gap. 
Whether ABM directly changes rumination in transdiagnostic clinical patients and has not been 
systematically investigated previously. If successful, ABM could improve the outcome for patients 
where negative affective bias is central, for example as a supplement to treatment-as-usual. If 
effective, ABM expands the possibilities to choose non-pharmacological treatment options, which 
is important to many patients. The ABM intervention is highly scalable and is an economic way to 
alleviate symptoms, and can be done in the patients homes (Holmes et al., 2018). Combining EMA 
and personalized statistical approaches may provide a low-cost assessment method aiding clinicians 
in identifying the processes that are most likely maintaining symptoms. This assessment method 
can easily be modified to capture other relevant pathological dynamics (e.g., resilience, relational 
problems, other symptoms). Identifying these processes more precisely, will have the potential to 
make interventions more focused on the patient’s problem, and lead to better treatment efficacy.  
 
3. Hypotheses, aims and objectives. 
Primary hypothesis: Subjects who are in the active ABM group 
will exhibit less tendency for stress related (state) rumination 
compared to those in the placebo group (2 weeks). Active vs 
placebo ABM will decrease depressive symptoms (6 months) 
and this effect will be mediated by the change in state 
rumination. Densely connected symptom network and high 
strength centrality of rumination at baseline will moderate the 
effect of ABM. See figure 3.  
Secondary hypotheses: After the intervention, patients with 
changes in affective bias after ABM develop a less densely connected symptom network and have 
reduced strength centrality of rumination. Reduced network density and reduced strength centrality 
of rumination after ABM leads to reduction in depressive symptoms at 6 months. 
4. Project methodology  
Design and participants. The Affective Bias Modification Task (ABM) will be applied in a 
randomized controlled, double blind clinical trial with 6 months follow-up. Personalized networks 
Figure 3. Model of the primary hypothesis 

 
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are generated from prospective assessment of depression-related processes at baseline and follow-
ups, allowing us to investigate for whom this procedure is effective. Patients (N = 150) will be 
recruited from an out-patient clinic at Diakonhjemmet Hospital, and randomized into one of three 
conditions: active, sham and assessment only. Patients aged 18-65 with depression (major 
depressive disorder) or bipolar disorder 2, with or without comorbid anxiety and/or alcohol use 
disorder will be included. Exclusion criteria are a history of mania, psychosis, or a neurological 
disorder.   
4.1. Project arrangements, method selection and analyses. 
Negative affective bias. In the dot-probe task (MacLeod et al., 
2002) paired stimuli (e.g. a negative and a positive face) are 
presented, followed by one or two probes (dots) appearing in the 
spatial location of one of the stimuli. See figure 4. Participants are 
then required to press one of two buttons as quickly as possible to 
indicate the number of dots in the probe. Thus, if the participant 
systematically orients towards a negative emotional stimulus (i.e. 
has a negative affective bias), they respond faster to probes 
presented at the spatial location of the negative stimulus compared 
to probes at the location of the positive stimulus. Stimuli 
presentation time is 50% 500 ms and 50 % 1000 ms (evenly distributed throughout the task). In 
total, the dot probe task will comprise 90 trials of paired images of faces of different valences.  
The Facial Expression Recognition Task (FERT; (Harmer, O’Sullivan, et al., 2009) is another 
measure of negative affective bias and is included to make sure that biases measured with the dot 
probe task transfers to an independent measure. In this task, pictures of faces are gradually morphed 
between neutral, and positive and negative facial expressions. The detection threshold for correctly 
identifying the facial expression is the variable of interest.  
Affective bias modification. The task is almost identical to the dot probe task. The differences are 
that the probe appears at the location of the most positive stimuli of each pair in 87 % of trials 
(encouraging a positive affective bias). In the control condition, there is no contingency between 
facial expressions shown and the probe location. Participants will do ABM in their homes (approx. 
5 min.) twice a day for two weeks (28 sessions) using laptop computers provided by us. Our 
experience with previous ABM studies indicate good adherence by participants using this regime. 
EMA of affective symptoms. The estimation of the each participant’s symptom network is based on 
data from three separate two-weeks of EMA (baseline, follow-up and six months), conducted by 
means of the PsyMateTM-app on their smartphones. The app pings four times per day (in the 
morning, middle of the day, afternoon, evening), at slightly irregular intervals, and instructs the 
participant to report on mood and symptoms guided by Positive and Negative Affective Schedule 
(PANAS; (Thompson, 2007)) and DSM-5 criteria of MDD, respectively, and the extent they have 
been engaged in brooding rumination. In total, the participant is pinged 56 times per two-week 
period and each self-report takes approximately 1 minute to complete.  
Clinical and cognitive assessment. Diagnostic assessment will be made in accordance with the 
MINI International Neuropsychiatric Interview PLUS 5.0.0 (Sheehan et al., 2006). Depression 
symptoms will be assessed using Beck Depression Inventory – II (BDI-II; (Beck, Steer, & Brown, 
1996)). Anxiety symptoms will be assessed using the Beck Anxiety Inventory (BAI; (Beck, 
Epstein, Brown, & Steer, 1988)). Trait rumination will be assessed by means of the Rumination 
Response Scale (RRS; (Treynor, Gonzalez, & Nolen-Hoeksema, 2003)). State rumination will be 
assessed by means of the Brief State Rumination Inventory (BSRI; Marchetti, Mor, Chiorri, & 
Koster, 2018). General attentional control will be assessed by the Attention Network Test (ANT; 
Fan, McCandliss, Fossella, Flombaum, & Posner, 2005).  
 
Figure 4. Dot probe 

 
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Figure 5. Design, procedures and schedule. ABM = Attention Bias Modification. AUDIT = Alcohol use disorder 
identification test. BAI = Beck Anxiety Inventory. BDI-II = Beck Depression Inventory - Second Edition. DUDIT = 
Drug use disorder identification test. EMA = Ecological Momentary Assessment. FERT= Facial expression recognition 
task. MINI = MINI International Neuropsychiatric Interview PLUS 5.0.0. RSS = Ruminative response scale. 
Negative mood induction paradigm. Drawing on diathesis-stress models of affective disorder 
(Disner, Beevers, Haigh, & Beck, 2011), we will include a negative mood-induction procedure prior 
to assessment of state rumination and stress reactivity (Westermann, Stahl, & Hesse, 1996). A 
standardized mood induction script, personalized by stressful autobiographical events (Sinha & 
Tuit, 2012), will be recorded for each patient. Upon listening to the script, they are asked to imagine 
themselves being in the situation. Imagery vividness scale (Sinha & Tuit, 2012) is used to assess 
how clear they manage to imagine themselves in the situation. Afterwards, subjects will be left 
alone for five minutes to trigger state rumination. A shortened version of the Profile of Mood States 
(POMS; 37 items) will be administered before and after the mood induction as a manipulation 
check (Shacham, 1983). Our research group have previously shown that POMS reflects changes in 
mood states over short intervals in healthy controls (Walderhaug et al., 2007). Stress reactivity is 
operationalized as reduced HRV (measured via the Polar RS800 heart rate monitoring system) and 
rising cortisol levels (measured via Salivette® saliva sampling). A meta-analysis of cortisol 
response after stress induction has shown that peak cortisol is reached 38 minutes after stress 
induction onset (Goodman, Janson, & Wolf, 2017). Between post-induction and recovery, the 
participants will be encouraged to watch emotionally neutral videos (selected National Geographic 
films). See figure 6 for procedure and time schedule. 
 
Figure 6. Negative mood induction procedure. BSRI = Brief state ruminative inventory. HRV = Heart rate variability. 
POMS = Profile of mood states.  
Statistical power. Calculation of statistical strength regarding detecting an effect of ABM is based 
on an assumption that 20 % in the placebo group will report 3 points improvement on BDI-II at 
follow-up compared to 50 % in the active ABM group. The National Institute for Health and Care 
Excellence (NICE, 2009) , suggests a difference of ⩾3 BDI-II points is a clinically significant 
treatment effect for depression. By assuming 15 % drop-out at follow-up, 50 patients in each 
condition (total n= 100) will be required when 1 –β= .80 and α= .05. The literature provides no 
clear guidelines as to how many data points that is necessary to reliably estimate a patient’s 
Screening
• M.I.N.I.
• EMA-measures 
(2 weeks)
Baseline
• Demographics
• Interview-
stressful event
• Questionnaires:
• BDI-II
• BAI
• AUDIT
• DUDIT
• RRS
• Computer tasks
• Dot Probe Task
• ANT
• FERT
• Negative mood 
induction
Intervention
• ABM (2 weeks)
• Training 
vs control vs 
nothing
Follow-up
• Questionnaires:
• BDI-II
• BAI
• Computer tasks:
• Dot probe task
• ANT
• FERT
• Negative mood 
induction
• EMA-measures (2 
weeks)
6 months
• Questionnaires:
• BDI-II
• BAI
• AUDIT
• DUDIT
• RRS
• EMA-measures 
(2 weeks)

 
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symptom network, but simulation studies suggest adequate performance for estimating an eight-
node network using 50 observations (Epskamp et al., 2018). Given multiple regression analysis 
with up to six predictors and a small effect size (<.10), β= .80 and α= .05, the estimated required 
sample size is 81. When examining the effect of ABM, we are only interested in effect sizes of 
clinical relevance (i.e. < 0.25). Given an ANOVA repeated measures analysis with effect size = 
0.25, β= .80 and α= .05, the estimated required sample size to detect a difference in changes in the 
network metrics between the conditions is 34.  
Mediation and moderation analysis.  The mediation analysis, investigating whether the relation 
between ABM and symptoms change is mediated by rumination, will be performed according to the 
procedure established by Baron and Kenny (1986). The moderated mediation analysis, investigating 
whether ABM changes depressive symptoms as a function of symptom network, will be performed 
according to recommendations made by Muller, Judd, and Yzerbyt (2005). 
Feasibility. Labs for conducting the research are available at the Department of psychology, 
University of Oslo. The biobank “Genes in cognition and Emotion” REK SørØst 2011/2593 will be 
used to store salvia samples. Twenty-five laptop computers for conducting the ABM-intervention 
and Polar watches for recording HRV are already acquired. Neurocognitive labs are available at the 
Department of Psychology. All questionnaires are translated into Norwegian by means of back 
translation procedures. The negative mood induction procedure will be piloted before 
commencement. The project is currently under evaluation by REK SørØst 2019/330. During the last 
decade, through our longstanding collaboration with Diakonhjemmet Hospital and other nearby 
clinics, we have managed to recruit hundreds of patients in different stages of their depressive 
illness to take part in clinical studies. The TSD (in Norwegian, Service for Sensitive Data) service 
will be used for storing and post-processing sensitive-data in compliance with the Norwegian 
“Personal Data Act” and “Health Reaserch Act”. TSD is developed and maintained by USIT at the 
University of Oslo and supports research activities run at Norwegian public institutions.  
4.2. Participants, organization and collaboration. The PI Professor Nils Inge Landrø (NIL) is an 
expert on the neuroscience of affective disorders. He has a part time position (20%) as research 
advisor at Diakonhjemmet Hospital. Postdoc Ragnhild Bø (RB) is currently involved in parts of this 
project through funding received from Extrastiftelsen. She has experience with research in 
cognition, emotion and alcohol use. Our research group, The Clinical Neuroscience Research Group 
(CNRG), have extensive experience with the ABM method and we have recently concluded a big 
randomized, double blind, placebo controlled, pre-registered study (ClinicalTrials.gov Identifier 
02658682). In this study, formerly depressed persons (N = 321) were included with the main aim of 
reducing residual depression symptoms. Based on this study, five articles are accepted, one 
including network analysis, (Hilland, Landrø, Harmer, Browning, et al., 2018; Hilland, Landrø, 
Harmer, Maglanoc, & Jonassen, 2018; Jonassen et al., 2019; Kraft et al., 2019; Kraft, Jonassen, 
Ulset, Stiles, & Landrø, 2018). 
The project builds on an established collaboration between CNRG at Department of Psychology, 
University of Oslo and Department of Psychiatry, Diakonhjemmet Hospital. Kåre Osnes, MD, PhD, 
specialist in psychiatry, Diakonhjemmet Hospital, is an expert in diagnostics and methodology, 
including statistics. Rune Jonassen, PhD and Associate Professor OsloMet, has experience with the 
ABM-procedure and biological underpinnings of depression. Professor Jutta Joormann, Department 
of Psychology, Yale University is an expert in state and trait rumination and attentional bias. 
Professor Alexander Heeren, Université Catholique de Louvain, is an expert on network analysis 
within the field of depression and anxiety. Professor Catherine J Harmer, University Department of 
Psychiatry, University of Oxford. She has also a Professor II position at Department of Psychology, 
University of Oslo and is affiliated to CNRG. She is an expert on experimental psychopathology, 
the ABM- procedure and implications of attentional bias change. NIL is the PI of the project. RB 
will be in charge of the day-to-day administration of the project, and coordinate team efforts. 
Collaborators will be involved if unexpected situations occur, if procedures needs to be adapted, 
when analyzing and interpreting data, and during writing of articles.  

 
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4.3. Budget. Please see the e-application for details.  
4.4. Plan for activities, visibility and dissemination. This research program will be of 
considerable interest for a broad range of scientists and clinicians. The results will be published in 
in high-ranked peer-review journals in the fields of psychiatry, clinical and abnormal psychology 
and neuroscience. Main findings will be summarized and published in national journals, like The 
Journal of the Norwegian Medical Association and The Journal of the Norwegian Psychological 
Association. The results will also be relevant for people with affective disorders, family and friends, 
stakeholders and the general public. Thus, the main results and their implications will be 
disseminated to patients and the public through popular talks and newspapers, like Aftenposten and 
Dagbladet. We have an ongoing collaboration with Mental Helse, a nationwide volunteer 
organization promoting mental health. We will write blog posts about this ongoing project and main 
results, which will be published on their website. We will also publish summary of the main 
findings on forebygging.no, a national knowledgebase for promoting health, supported by the 
Norwegian Directorate of Health. This will be done in collaboration with the user representative. 
The researchers will be involved in more "popular" forms of dissemination, by actively 
participating at "Forskningsdagene" led by the Norwegian Research Council. We will also present 
the results of the project at events, such as Pecha Kucha or TedX. 
4.5. Plan for implementation. There is a pressing need to improve treatment and thus clinical trials 
should focus not only on efficacy, but also on identification of the underlying mechanisms through 
which treatment operate and within a transdiagnostic frame. Biased attention towards negative 
stimuli is such a mechanism, cutting across diagnostic boundaries and is also related to 
dysfunctional rumination and stress reactivity. These mechanisms can be targeted separately 
applying a simple behavioral procedure, The Attention Bias Modification Task; ABM. Should this 
project be successful, ABM can easily by implemented into the clinics. Most people have their own 
computer, and may perform the ABM-task at home if instructed to do so by their therapists. 
Opportunities for online downloading of ABM software are then required. The app used for 
extracting the patient symptom networks are readily available, and may guide treatment 
recommendation following the results of the current project. Today, treatments such as cognitive 
behavioral therapy and antidepressant medication might change affective biases (Bowler et al., 
2012; Harmer, Goodwin, & Cowen, 2009); however, by implementing ABM we could more readily 
target these biases. By combining ABM with today’s treatment options, we could potentially 
increase treatment efficacy and shorten treatment duration. Furthermore, the symptom network 
approach allows treatment to be personalized and adapted to each patient’s specific symptom 
profile. This is in contrast to the current "one size fits all"-approach. Hence, this research has the 
potential for improving treatment outcomes for persons with depression and comorbid disorder, and 
thus release resources in the health services. Therefore, we will work actively to ensure that 
computerized cognitive interventions, such as ABM, combined with the symptom network 
approach, will be considered supplement to treatment-as-usual in national clinical guidelines for 
these disorders. In collaboration with DigPsyk, an interest organization promoting the use of 
technology in treatment of psychological problems and that is part of the Norwegian Psychological 
Association; we will disseminate the methodologies employed in this project. The use of 
technology in treatment for psychological problems is gradually paving its way into the clinics; 
however, there is still some way to go before technology is a go-to method for treating 
psychological problems. Strong studies, like this, which is thoroughly disseminated to both users 
and clinicians, in combination with a low-cost, easy-to-use platform implemented at patient homes, 
makes it readily feasible as a treatment. 
5. User involvement  
In clinics, drug free treatment options are sought after. Our project is rooted in the user organization 
Mental Helse. One person from this organization will help us plan the project and adapt it according 
to patient needs, ensuring that patients are given adequate information and that the working load on 

 
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behalf of participants is adequate. This will help us recruit patients and prevent drop-out, which is 
among the major risks associated with this project. This person will also help disseminate the 
results of the current project, in particular in fora outside of academia. By interviewing a focus 
group of research participants, we will gather their opinion on the ABM-procedure and the EMA. 
By adapting the procedures according to their experiences and suggestions, we will ensure that they 
are acceptable to patients. We will also interview therapists as to what they need in order to 
recommend this type of treatment to their patients. Should the project be successful, this will allow 
us more readily to implement these procedures in clinics, who then could offer personalized drug-
free treatment options as supplement to treatment as usual. 
6. Ethical consideration.  
The project is currently under review by the Regional Committee for medical and health research 
ethics (REK SørØst 2019/33). In particular, the potential negative consequences following the 
negative mood induction procedure will need to be justified, even when this procedure is commonly 
used. We will provide appropriate help should participants experience subsequent problems 
associated with it. Except from this, there is no reason to believe that the project will lead to 
undesirable effects, except from the time spent on behalf of the individual participants. Salvia 
samples will be stored in the biobank “Genes in cognition and Emotion” (REK SørØst 2011/2593) 
and used solely in relation to this project. 
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Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55-71. doi:10.1016/j.socnet.2004.11.008 
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Bowler, J. O., Mackintosh, B., Dunn, B. D., Mathews, A., Dalgleish, T., & Hoppitt, L. (2012). A comparison of cognitive bias 
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Brown, T. A., Campbell, L. A., Lehman, C. L., Grisham, J. R., & Mancill, R. B. (2001). Current and lifetime comorbidity of the DSM-IV 
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