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Why we need digital tools in psychotherapy - Part 3

Blended behaviour activation - Findings from a systematic literature review


Review

In our previous blog posts we have talked about current trends in mental health care and digitalization and how these trends could complement each other by providing opportunities to include digital tools into psychotherapy. We have also described two current challenges in psychotherapy, where digital solutions could be a meaningful substitute or complement to analogue tools and increase the effectiveness and quality of treatment; namely (1) Homework adherence, (2) Skill transfer, (3) Individualisation of treatment and (4) Waiting lists.

At the end of Part 2 we suggested that blended treatment approaches might be able to balance flexibility and adherence in treatment and with this combine the benefits of internet based and face-to-face treatment approaches. We also described why we at Zeeds focused on creating a digital tool for administering homework exercises in behaviour activation (BA) in a blended treatment format. To provide a thorough theoretical background for our claims and product, in this post we would like to summarise the results of a systematic literature review in which we looked at effectiveness and adherence in blended treatment approaches for behaviour activation.



Included studies

We conducted a literature search in scientific databases such as Psychinfo and PubMed, looking for studies that were randomised controlled trials, published between 2000 and 2022, focused on digital or digitally supported interventions with any level of BA-involvement and had some form of therapist support during the intervention. Our main outcomes were depression, adverse events, adherence, cost effectiveness and usability. In addition, we also screened the studies based on their quality using a comprehensive scale developed by Uman et al, 2010.

Our first search resulted in over 7000 potentially relevant studies. After going through our inclusion, exclusion and quality criteria, we included 17 studies in our final analysis.


Study characteristics

The total sample size of our 17 studies was n=2268, with all patients showing depressive symptomatology. Control conditions were mostly waitlist control (n=8) or TAU (n=5). There was a quite high variance in treatment length, amount and format of therapist involvement and amount of behavior activation elements in the treatment protocol.

Intervention length ranged from 6 to 20, with most of the studies being between 10 to 12 (n=8) or 6 to 8 weeks (n=5). Therapist involvement also varied significantly: number of patients per therapist ranged from 1.7 to 49, with an average of 11.7, with the therapist time per patient being between 24 and 840 minutes. One explanation for this wide range is the different formats patients interacted with their therapists. Some interventions were mostly online-based and only included chat or email conversations with professionals, others used regular phone calls with therapists while some interventions were based on face to face therapy sessions, where the digital tools were only a complement to this treatment. And lastly, the amount of elements consistent with the BA methodology also varied. All studies included at least one module which was consistent with behaviour activation. The module coverage of behaviour activation in the treatments ranged from 16 to 50 percent. So none of the interventions was solely based on behaviour activation.


Results

Comparing outcome measures for depression, between intervention and control group gave a moderate to large effect size in all but 3 studies, ranging from 0.39 to 2.07. The significant treatment effects remained for the majority of the studies also at follow-up (3, 6 or 12 months). Attrition or drop out rate was defined as the non completing of the condition intervention. Aggregating findings of the included studies we found a drop out rate of 17%, with a minimum of 0 and a maximum of 39%.

Only about half of the studies (n=9) included some usability or satisfaction indicator. All the interventions in these studies were rated and satisfactory by the participants, and showed ratings either higher than control or statistically non-significantly different. And even less, only about one third of studies explicitly reported adverse events (n=5). However, based on these results, there was no indication that the number of adverse events would be higher in the blended interventions than in the control conditions.

And lastly only two studies measured cost effectiveness of blended interventions. One study showed that blended solutions might be more cost effective for healthcare providers than standard CBT. While another study found both higher life quality (Quality adjusted life years scores) but also higher costs in the blended treatment condition compared to waitlist control. However, these increased costs were considered to be cost-effective in relation to the sum citizens would be willing to pay for increased life quality. However, these results have to be interpreted with caution, as the populations in the studies were either patients in specialised mental healthcare facilities or had an average age of 61. Thus the generalizability of the findings to other populations might be limited.


Summary

Overall, our results support our hypothesis that therapist-led blended treatment BA interventions can lead to the alleviation of depressive symptoms and have acceptable rates of adverse events, usability ratings, cost effectiveness and adherence.

Our results regarding the clinical effects of blended BA interventions on depressive symptoms are in line with previous findings on standardised BA (Stein et al, 2020) and internet CBT interventions (Stavropulos et al, 2019), showing medium to large effects. In addition, adherence to blended treatment solutions seems to be higher than to traditional CBT for depression. Previous studies have found an average drop out rate of 26% for patients with all types of mental problems. When looking only at depression, this number increases even further to 36%. (Fernandez et al, 2015). In contrast, our systematic literature review showed that the average dropout rate to blended treatment solutions was around 17%, about half of that in analog therapies. And lastly, blended treatment solutions also seem to be beneficial from an economic perspective. They can be more cost effective for healthcare providers compared to traditional CBT and their costs also fall below patients’ willingness to pay-threshold. Thus, blended treatment solutions for BA have the potential to reduce the costs of mental health care, while keeping the same quality of treatment.

However, one major limitation of our review was that none of the interventions was exclusively behaviour activation. Thus, it seems that there are no empirically supported blended interventions out there that focus solely on BA. This is the case, even though BA alone has been proven to be effective for various disorders, and there are multiple evidence-based behaviour activation manuals that are used in psychotherapeutic practice, in an analog, paper-based format (eg. BATDR). We at Zeeds would like to fill this gap by providing a digital tool including exercises for almost all evidence based paper-form assignments in BA, that can be used in face-to-face treatment for depression.


Conclusion

Blended behavior activation treatment has the potential to increase the effectiveness and the quality of depression treatment. It is effective both from a clinical and a cost persepctive, and seems to have lower drop out rates than traditional treatment approaches. However, we did not find any empirically supported blended interventions out there that focus solely on behaviour activation. We at Zeeds would like to fill this gap by providing a digital platform for administering homework in face-to-face behaviour activation treatment for depression.



If you would like to know more details about our systematic literature review follow this link to fill out the form and we are going to mail the whole text version to you.


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References

Fernandez, E., Salem, D., Swift, J. K., & Ramtahal, N. (2015). Meta-analysis of dropout from cognitive behavioral therapy: Magnitude, timing, and moderators. Journal of Consulting and Clinical Psychology, 83(6), 1108.

Seeman, N., Tang, S., Brown, A. D., & Ing, A. (2016). World survey of mental illness stigma. Journal of affective disorders, 190, 115-121.

Stavropoulos, V., Cokorilo, S., Kambouropoulos, A., Collard, J., & Gomez, R. (2019). Cognitive behavioral therapy online for adult depression: a 10 year systematic literature review. Current Psychiatry Research and Reviews Formerly: Current Psychiatry Reviews, 15(3), 152-170.

Stein, A., Carl, E., Cuijpers, P., Karyotaki, E., & Smits, J. (2021). Looking beyond depression: A meta-analysis of the effect of behavioral activation on depression, anxiety, and activation. Psychological Medicine, 51(9), 1491-1504. doi:10.1017/S0033291720000239

Uman, L. S., Chambers, C. T., McGrath, P. J., Kisely, S., Matthews, D., & Hayton, K. (2010). Assessing the quality of randomized controlled trials examining psychological interventions for pediatric procedural pain: recommendations for quality improvement. Journal of pediatric psychology, 35(7), 693-703.



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