Current Challenges II. : Individualized treatment and Waiting lists
Review
In our previous blog post 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, namely homework adherence and skill transfer, where digital solutions could be a meaningful substitute or complement to analogue tools and increase the effectiveness and quality of treatment.
In Part 2 of our series we will address two other challenges in mental health care which could potentially be solved or at least mitigated by the implementation of digital solutions: Individualisation of treatment and long waiting lists. In addition, we will also give you a short introduction into blended treatment apporaches in behaviour activation and explain, why we focused on this particular area, both with our product and scientific work.
Challenges in psychotherapy
Individualised treatment
In our previous post we emphasised the importance of adjusting homework assignments to patients’ needs and learning and integrating them into their everyday life. The tasks should be as close to relevant situations and behaviours as possible to support treatment progress. Both these factors are also related to the question of individualising content and tasks of treatment so that patients experience these as relevant and personally meaningful.
However, individualising different elements and tasks within a particular treatment approach is just one level of how psychotherapy can be matched to the patient. An even larger challenge might be to find which treatment approach is most likely to lead to the greatest improvements for each individual patient. Different therapeutic methods and techniques are based on different theoretical foundations and assumptions for which it is often hard or even impossible to provide objective evidence. And even though these theories are mostly reconcilable and rarely mutually exclusive, they often lead to very different treatment practices. In addition to this comes, that the effectiveness of psychotherapy - maybe even more so than other forms of health care - depends highly on how patients feel and what they think about their treatment, and whether they believe that the processes involved in the treatment can really lead to improvements.
Thus, the prediction of how to best match treatment method and patient seems to be one of the largest challenges in therapeutic practice and might explain the - compared to other (physiological) illnesses - quite low recovery and remission rate for mental disorders in general. Brakmeier and Herpetz (2019) reported that the rate of non-responders in psychotherapy can be as high as 33-50% and this number further increases to 60-80% when looking at the patients who do not achieve remission and have a relapse after treatment has ended.
One of the reasons why the question of patient-treatment fit is so hard to answer might be the complexity of emotional, cognitive, biological, social and environmental factors that lead to the development and maintenance of mental disorders and problems. With our current knowledge it seems very hard or even impossible to comprehend how these elements act and interact to affect people’s minds in a particular way, resulting in particular patterns of experiences and behaviour and influencing where professionals should best intervene to stop the cascade of causes and consequences. And this is where technology might be able to solve or at least mitigate this challenge. Artificial intelligence has already been successfully used in medicine to diagnose or predict certain illnesses like cancer or cardiovascular diseases. The huge potential in AI is that it is not just about making work processes more effective by automating time intensive tasks, but it is also able to go beyond what humans can do. AI algorithms are thought to be able to see complex relationships and patterns in data which human eyes and minds can’t. Thus it doesn’t seem unreasonable to assume that based on already existing treatment data, AI could predict the treatment progress of different individuals and also what treatment approach they need to overcome their difficulties, based on their own emotional, cognitive, biological, social and environmental characteristics.
Waiting lists
The last challenge that we would like to address in our blog series is the gap between the need for psychotherapy and the availability of mental health care services, which results in long waiting times until patients requiring help can actually receive it. This discrepancy between need and availability is even larger in developing countries or less urbanised areas, where patients with mental health problems might also face stronger stigma and discrimination than in western countries (e.g. Seeman et al, 2016).
Researchers and practitioners have proposed that digital treatment programs (e.g. iCBT) can mitigate this problem by making treatment more accessible, as in most of today’s societies, the majority of people have access to at least a smartphone and internet. However, stand-alone online treatments in a self-help format with minimal therapist support might pose other challenges that need to be taken into consideration. The need for no or only very little in person guidance increases flexibility and availability significantly, but it also decreases accountability and thus might have a negative effect on adherence. Meta analysis found a quite wide range of drop out rate in unguided internet-based treatments, ranging from 2 to 83 % (Melville et al, 2010) including patients with various disorders. However, another meta-analysis looking only at depression - a disorder characterised by a lack of motivation and engagement - found that the percentage of patients completing treatment was significantly higher in face-to-face treatment sessions compared to guided iCBT interventions without any face-to-face therapist contact (84% in face-to-face vs 65.1% in iCBT, van Ballegooijen et al, 2014).
On the other hand, it seems reasonable to expect that blended treatment approaches that combine face-to-face sessions with digital tools might be able to balance flexibility and adherence and thus have the benefits of both modes of delivery (Siemer et al, 2020). To test this assumption, in the last part of our blog series we will talk in more detail about a systematic literature review in which we looked at the effectiveness and adherence in such blended treatment approaches for the module behaviour activation in depression treatment.
Why blended treatment for behaviour activation
There are multiple reasons why we focused on this particular segment - both with our product and the literature review. Firstly, various studies have supported the efficacy of behaviour activation in improving depression symptoms (e.g Stein et al, 2020), and there are more and more studies pointing to positive effects for other disorders as well, such as anxiety and chronic fatigue syndrome (E.g. Malouf et al, 2015; Stein et al, 2019). Thus, it can be used in the treatment of a quite large part of the patient population. Secondly, several studies have also pointed to homework adherence as a target factor to increase the effectiveness of CBT in general and behaviour activation in particular (E.g. Burns & Spangler, 2000; Busch, Uebelacker, Kalibatseva, & Miller, 2010; Kazantzis, Deane, & Ronan, 2000). And based on the benefits and challenges of digital tools described above, it seems reasonable to assume that the integration of digital tools into face-to-face BA treatment to support patient’s homework compliance might be a valuable addition to therapy.
However, there is a lack of studies investigating blended treatment approaches for behaviour activation. Thus we aimed to fill this gap by summarising available evidence on digital devices for improving adherence and cost effectiveness of therapist-led blended BA within CBT.
Conclusion
Digital tools could increase the effectiveness and quality of mental health care. Not just because of their adventages in terms of homework adherence and skill transfer, but also because they could facilitate the individualisation of psychotherapy and shorten waiting lists. However, it is also important to note, that the format in which digital solutions are administered seems to be an important factor. Blended treatment approaches, which combine face-to-face treatment sessions with digital tools might have the potential to balance flexibility and adherence and thus have the benefits of both modes of delivery.
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References
Brakemeier, E. L., & Herpertz, S. C. (2019). Innovative Psychotherapieforschung: auf dem Weg zu einer evidenz-und prozessbasierten individualisierten und modularen Psychotherapie. Der Nervenarzt, 90(11), 1125-1134.
Burns, D. D., & Spangler, D. L. (2000). Does psychotherapy homework lead to improvements in depression in cognitive–behavioral therapy or does improvement lead to increased homework compliance?. Journal of consulting and clinical psychology, 68(1), 46.
Busch, A. M., Uebelacker, L. A., Kalibatseva, Z., & Miller, I. W. (2010). Measuring homework completion in behavioral activation. Behavior modification, 34(4), 310-329.
Kazantzis, N., Deane, F. P., & Ronan, K. R. (2000). Homework assignments in cognitive and behavioral therapy: A meta-analysis. Clinical Psychology: Science and Practice, 7(2), 189.
Malouff, J. M., Thorsteinsson, E. B., Rooke, S. E., Bhullar, N., & Schutte, N. S. (2008). Efficacy of cognitive behavioral therapy for chronic fatigue syndrome: a meta-analysis. Clinical psychology review, 28(5), 736-745.
Melville, K. M., Casey, L. M., & Kavanagh, D. J. (2010). Dropout from Internet‐based treatment for psychological disorders. British Journal of Clinical Psychology, 49(4), 455-471.
Seeman, N., Tang, S., Brown, A. D., & Ing, A. (2016). World survey of mental illness stigma. Journal of affective disorders, 190, 115-121.
Siemer, L., Brusse-Keizer, M. G., Postel, M. G., Allouch, S. B., Sanderman, R., & Pieterse, M. E. (2020). Adherence to blended or face-to-face smoking cessation treatment and predictors of adherence: randomized controlled trial. Journal of medical internet research, 22(7), e17207.
Stein, A. T., Carl, E., Cuijpers, P., Karyotaki, E., & Smits, J. A. (2021). Looking beyond depression: A meta-analysis of the effect of behavioral activation on depression, anxiety, and activation. Psychological Medicine, 51(9), 1491-1504.
Van Ballegooijen, W., Cuijpers, P., Van Straten, A., Karyotaki, E., Andersson, G., Smit, J. H., & Riper, H. (2014). Adherence to Internet-based and face-to-face cognitive behavioural therapy for depression: a meta-analysis. PloS one, 9(7), e100674.