COM-B: Using behavioural psychology in UX

Published in

Bootcamp

4 min readNov 19, 2022

How you can use behavioural psychology to encourage behavioural changes when designing the experience

Psychology fascinates me. It is probably on par with my passion for astronomy. So, when I attended a UXR Conference earlier this year, and heard there was a session titled Integrated Behavioural Science by Christelle Ngnoumen from Headspace, my eyes lit up. The session did not disappoint, and this article is about how I used what I was taught specifically about COM-B, to encourage behavioural changes for users of a gut health app, a menopause app and for users buying a blood test kit.

What is COM-B?

As defined by Social Change UK, COM-B ‘proposes that there are three components to any behaviour (B): Capability ©, Opportunity (O) and Motivation (M). In order to perform a particular behaviour, one must feel they are both psychologically and physically able to do so ©, have the social and physical opportunity for the behaviour (O), and want or need to carry out the behaviour more than other competing behaviours (M). As each of these components interact, interventions must target one or more of these in order to deliver and maintain effective behaviour change.’ [1] It is in essence:

A process of introducing interventions to help people make behavioural changes, based on what is blocking/stopping them from doing so. The blockers are grouped into 1 of 3 categories (Capability, Opportunity or Motivation)

Once you understand the reason behind the resistance to making the behavioural change (COM), you can then introduce the interventions which are allocated to each specific blocker.

As an example from the below table, if it was identified that someone was resistant due to their psychological capacity (C), you could introduce education, training or enablement:

Definitions of the intervening functions are explained well in this article [2] for those unsure of them

Project background

As Christelle talked through COM-B, I knew this was something that could be used by my team and I. We were trying to improve users health by encouraging them to change their behaviour in categories of their life such as diet and 3 others. We had created solutions to user problems, but were aware that not all users would not be receptive to all of them based on their own requirements.

Approach

As a team, we already knew the 4 behavioural changes we were trying to encourage because they originated from our medical expert. What we didn’t know was which of them users would be open to and which they would be more closed of to so we took a 4 step process using COM-B

Step 1: Research

Get quantitative research via a survey. The participants were people who had the specific health issues we were looking (gut health, menopausal health, health test kits), and that they were intestered in improving it. We then asked them out of the 4 solutions we had, which (if any) they had actively looked at doing or started doing.

Step 2: Explore

Once we knew which ones they were and weren’t interested in pursuing, we then asked them to explain why certain ones DID NOT appeal to them. We needed to understand what was blocking them from some of the solutions we offered.

Step 3: Assign

Once we got the responses, we started to synthesis them and see what the common themes were around why participants weren’t interested in certain options. We then assigned these themes into the COM categories

As an example if skepticism was a reason for not wanting to make dietary changes we would assign it against M (motivation)

Step 4 Intervene

Once we had all the themes assigned a component (COM) we discussed as a team what would be the best intervening function.

Again as an example if it was motivational, which option would help to overcome that blocker best for the user that would be manageable for us to based on our resources.

Learns

Overall this approach worked really well. There were key insights and takeaways for the team to action. That said, if I were to do it again, I would create a slightly less subjective conclusion in step 4 for deciding which intervening function was best. Until that moment it was all based on the established model and then it became a discussion in our team.

Another takeaway was that 1 of the 4 solutions we offered in the initial survey had a lot of people currently using or considering it. As a result we had a low response rate on it to look into for why they wouldn’t use it — be wary that you may not get enough insights the first time round.

THE PERSON BEHIND THE WORDS

Its always encouraging to see your work recognised, so if you found this article interesting, useful or just a general good read, please do leave a clap or follow. I hope to write more content soon and this will be the best way to find them!