Digitalisation and The Design of Homework Compliance Software

As the world is increasingly shifting to using software as a solution for a range of previously analogue processes, the global healthcare market is no different. The past years iCBT, machine learning diagnostics and various other solutions have gained regulatory approval and are used in real clinical settings. Is there evidence supporting using software as a tool for designing more efficient homework solutions for Cognitive Behavioral Therapy?


A study by Perez-Jover et al (2019) looked at the effects of mobile apps for increasing adherence to prescribed medication, by analysing in total 11 studies comparing traditional pill-taking methods - and overall found statistically significant improvements in adherence, with patients noting the ease of use, notification-reminders, psychoeducation on habit formation and information and tracking of behaviors, as helpful functions. The authors concluded that there was a potential for improving medical adherence through mobile applications.





One study conducted by Ly et al ( 2014) investigated differences in outcome of patients with depression when comparing a behavioral activation application versus a mindfulness-based approach app. Results showed significant improvements in both groups, where the BA application was superior for patients with severe depression. There were some limits to this study - a power analysis was lacking and there was no inactive control group - limiting the studied effects of the applications themselves. Still, authors concluded that a BA application could be a viable alternative to face-face interventions, and added the strength of modules-based applications such as these instead of multicomponent applications, as especially therapeutically suitable.


Zhao et al (2016) performed a literature review of 23 studies of mobile applications promoting health behavior change. Most of the studies were RCT:s except one, most had a small sample and furthermore, most were conducted at high-income countries. There was evidence of some publication bias in total, but not for all studies. Studies included subjects such as alcohol addiction, mental health, weight loss, medication management and lifestyle improvement. The authors tried to review which key functionalities were shown to be most effective for promoting health behavior change for patients, as well as whether digitalisation of standardised treatment could promote statistically significant improvements. In total, 17 of the reported studies reported statistically significant results in favor of the mobile applications. Behavior change theories were reported in 21 studies, and authors concluded that those that did involve an explicit theory performed better than those that did not. Among positive features, personalized feedback, a simple interface, expert-advice and real-time feedback were among the most well received functions.


Kreidler & Tang (2017) has proposed some guidelines for developing mobile application solutions for homework in Cognitive Behavioral Therapy - inspired by Tompkins (2002) guidelines for using homework in therapy. They propose six overarching guidelines - congruency to therapy, fostering learning, guiding therapy, building connections, emphasizing completion and population specificity. Regarding congruency to therapy, the authors emphasize the importance of the application being relevant to the specific modules currently worked with, and enabling integration into the ongoing therapeutic process. Fostering learning means the application is able to adjust accordingly to user progress - following the progression by the patient in an orderly manner. Guiding therapy means the application's data should be used in order to guide the therapists work - making sure relevant information is used in order to progress to the next stage of therapy. Building connections emphasizes the patient's ability to use the application to enhance the therapeutic alliance and/ or helping to connect with others - possibly through online communication in communities or with the therapist. Population specificity means the functionality, in terms of the user interface and graphical design, need to be tailored to the context in which it is used - taking into account racial, demographic, religious or any other variables which might impact upon the adoption of the technology.



But how can those guidelines actually inform the design of the application? Hentati et al (2021) published an RCT-study comparing an optimized user interface versus or a basic user interface. An optimized UI is built from enhanced UX-principles - taking into account functions such as step-wise layering of information to reduce the information needed for every decision, reminder systems and sequencing of information based on user input - all with the intention of developing simple and intuitive UI. 397 participants were randomly assigned to either an optimized UI web application, or a standard UI web application for problem-solving training within standard Cognitive Behavioral Therapy. Results showed no difference in self-report measures of usability and engagement, but actual behavioral engagement was significantly in support of the optimized UX intervention, measured as a larger generation of problem solutions. The authors conclude that there are some limitations to the study - the intervention was slightly shorter than the real analogue euqivalent, and altghough participants were recruited to represent a broad population - most indicated clinical levels of depressive symptoms. Still - the authors concluded that optimized UI could increase the efficiency of healthcare tools.



Overall, there are broad indications that software solutions mimicking analogue clinical tools, could lead to increased adherence to clinical efficiency in a healthcare setting.


References:


Pérez-Jover V, Sala-González M, Guilabert M, Mira JJ

Mobile Apps for Increasing Treatment Adherence: Systematic Review

J Med Internet Res 2019;21(6):e12505

doi: 10.2196/12505


Ly, K. H., Trüschel, A., Jarl, L., Magnusson, S., Windahl, T., Johansson, R., Carlbring, P., & Andersson, G. (2014). Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: a randomised controlled trial. BMJ open, 4(1), e003440. https://doi.org/10.1136/bmjopen-2013-003440


Zhao J, Freeman B, Li M

Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review

J Med Internet Res 2016;18(11):e287

URL: https://www.jmir.org/2016/11/e287

DOI: 10.2196/jmir.5692


Amira Hentati, Erik Forsell, Brjánn Ljótsson, Viktor Kaldo, Nils Lindefors, Martin Kraepelien, (2021). The effect of user interface on treatment engagement in a self-guided digital problem-solving intervention: A randomized controlled trial. Internet Interventions. Volume 26,100448,ISSN 2214-7829. https://doi.org/10.1016/j.invent.2021.100448.


Tang W, Kreindler D

Supporting Homework Compliance in Cognitive Behavioural Therapy: Essential Features of Mobile Apps

JMIR Ment Health 2017;4(2):e20



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