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Integrating Behaviour Change Theories with the Adaptive Decision-Making Framework

By CBC Digi-Hub Blog, on 25 May 2021

Written by Dr Chao Zhang (Department of Psychology, Utrecht University)

For someone new to behaviour change research, the large number of behaviour change theories is both a blessing and a curse. Yes, you have many options in terms of selecting a theory as the basis of your intervention, but you may also get lost in the ocean of theories. When I started my PhD project, I certainly struggled to decide which theory to use as the backbone of my thesis. This was partly because of my working environment. In the Human-Technology Interaction group at a technical university, my supervisors generally had a very pragmatic approach and were not committed to any particular theory, so I had all the freedom to explore. It was also due to my personality and intellectual style. I am not a person who easily commits myself to a particular idea, but prefer to have an overview on things and to connect pieces of ideas together. So the adaptive decision-making framework started as a literature review of behaviour change theories in my first year as a PhD student. With the encouragements from fellow PhDs and senior researchers, who found my review to be really useful, it is now a published paper in the Journal of Medical Internet Research.

Why a new framework for behaviour change theory integration?

While I was researching the literature, my urge to connect theories was accompanied by a growing discontent with many popular theories in the field. I was influenced by several articles criticising traditional behaviour change theories, but no one inspired me more than the 2011 article by William Riley and colleagues. They almost bluntly asked the question in their title: “…are our theories up to the task [of informing digital interventions]?.

One criticism they raised was the mismatch between the temporal granularities of behaviour representations used in traditional theories and digital interventions. Many of the popular theories used today were invented in the 70s or 80s, when there were no technologies available for observing or altering behaviours as they happen in people’s daily lives. For example, the famous “stage model” describes behaviour change as going through 5 stages that usually last for weeks or months, so interventions based on this theory are tailored to these coarse stages. In contrast, an e-health app on your phone can adapt its intervention strategy in almost real-time, as long as there is a need for an adaptation, e.g., to match the momentary context of a user or to tailor intervention messages to the user’s personal progress on a specific day. To inform the design of such apps, we need theoretical frameworks that match to the temporal granularity of lifestyle behaviours and digital technologies.

Another problem with traditional theories is that many of them are variable theories that describe statistical relationships between variables rather than process theories that explain the underlying mechanisms or cognitive processes of behaviour change. Variable theories are often represented visually by nodes of behaviour and behavioural determinants and the links between them that denote statistical or causal relationships. For example, based on the links connecting attitude, intention and behaviour in the Theory of Planned Behaviour, if you know the attitude of a person towards physical exercise at a certain time, you can most likely predict their exercise behaviours in the following period with some level of certainty. However, besides its predictive value, the theory does not tell you much about how to change attitude or intention in order to change behaviour, or what the cognitive processes behind the statistical relationships are. Since I was trained partly in applied cognitive psychology, I was aware of many other psychological theories that focus on processes and mechanisms, such as habit formation, reinforcement learning and decision-making. Models from these areas have certainly been applied to behaviour change interventions, but they are still not at the central stage of the field. Therefore, I was motivated to integrate such process theories into a new framework.

What is the “adaptive decision-making” framework?

When I say “behaviour change” in this blog, I almost exclusively refer to lifestyle behaviour change, not just any behaviour change. This distinction is crucial for understanding the “adaptive decision-making” framework in our paper. Some interventions may target single-time health decisions, such as a decision to receive a vaccination or not. These decisions are very consequential and usually require a lot of thinking from the decision-makers. But changing the behaviour requires nudging people to choose differently only once. However, lifestyle behaviours are about routines, habits and how people behave repeatedly, e.g. on a daily or hourly basis. For example, eating one meal of high-fat food won’t do any harm, but continuously following a high-fat diet increases one’s risk of obesity. Accordingly, lifestyle interventions should target the repeated meal choices in a person’s diet, not just a single meal choice. The characteristics of lifestyle behaviours naturally determined how behaviour change processes are represented in our framework. Instead of “stages” or a diagram of interlinked variables, behaviour change is represented as a series of repeated and interrelated decisions (see Figure 1).

Figure 1. A two-level representation of lifestyle behaviour (change). Reproduced with permission from Zhang et al. (2021).


To give an example of what repeated decision-making means, let’s suppose you just started a new job and you eat your lunch everyday at the canteen of your organisation. Each day, you are exposed to different lunch options and you make your choice based on several considerations, including taste, nutritional aspects, and price. Over time, you explore different foods at the canteen by trying them out, and eventually you end up with alternating between a few of your favourite options based on your personal goals at the time. Your lunch behaviour remains habitual for a while, until you change your personal goal(s). For instance, you may become aware of your health risks after a medical test, and after some deliberation you decide to follow a low-fat diet. You take this goal into consideration when making your lunch choices and gradually you may change your lunch habit.

By considering daily decisions as the building blocks of behaviour change, the temporal granularity of the theoretical framework is matched to that of digital interventions. Even if a person tends to behave in a certain way for a certain time period, the framework allows individual decisions to vary, and at each time a decision is made, it may be affected by a different intervention (or no intervention). This representation of how lifestyle behaviours unfold over time also makes it clear what are the different explananda in a behaviour change process. First, we need to explain how individual daily decisions are made and what factors influence them. These decisions do not occur in isolation, so we also need to explain how a decision and its associated outcomes influence a person’s subsequent decisions. Finally, the adaptive decision-making framework distinguishes reflection-level processes from action-level processes. In addition to the individual daily decisions, occasionally there are also reflective moments when people set up new goals or reconsider their goals based on self-monitoring of daily behaviours. We also need to know how these reflection-level processes work. The two-level representation allowed me to integrate a wide range of theoretical ideas in psychology into a single framework, including decision-making, self-control, reinforcement learning, habit, implementation intentions, and goal-setting (see Figure 2). You can read more about how the framework was developed and its component parts in the published article.

Figure 2. A full representation of the adaptive decision-making framework. Reproduced with permission from Zhang et al. (2021).


How can the framework be used?

When I was struggling with revising the paper, my supervisor encouraged me by saying that many researchers would thank me for the paper because I did an important and difficult job for them. Indeed, I think the framework can help many researchers, especially those who just step into behaviour change research, to navigate through the literature more efficiently. Before discussing the framework, our paper also includes a review of important individual theories of behaviour change. There are of course other excellent introductory materials that give a more comprehensive overview, such as the ABC of Behaviour Change Theories, but our paper can be read as a supplement to facilitate a deeper understanding of the connections among the individual theories and one’s own ideas for theory integration.

Several other practical uses of the framework are discussed in the paper, including, for example, the identification of intervention techniques based on cognitive constructs and processes in the framework or combining multiple intervention techniques. Here I want to emphasise one specific use, i.e., the development of computational models of behaviour change based on the framework. For my own PhD work, the framework is the starting point for building computational models for the specific processes in the framework. Many people have argued for the advantages of computational models over verbal theories in behaviour change research (e.g., see this article by Spruijt-Metz and colleagues). A particular practical advantage is that computational models are essentially computer programs, which means they can be readily implemented in digital intervention systems for adaptive interventions. This is the core idea behind the psychological computing approach to digital lifestyle interventions proposed in my PhD thesis. For interested readers, this preprint describes our recent work on modelling habit formation and use of the model for better behaviour prediction in behaviour change trials. I would be very grateful if our framework inspires more research in this direction.


  1. I use “I” as the pronoun most of the time in this blog post because I want to tell my personal story behind the paper. However, the paper would not be possible without the contribution and support of my co-authors and amazing supervisors – dr. Daniel Lakens and prof. dr. Wijnand A. IJsselsteijn.
  2. I also want to express my appreciation for the PRIME theory by Robert West and colleagues. The PRIME theory is the closest to our work among all theoretical frameworks in the literature. Even though I worked out most of the framework before discovering the PRIME theory, I was certainly assured after reading it that I was going in the right direction.


Chao Zhang is currently a post-doc researcher in the Department of Psychology at Utrecht University and he is a coordinator of the HUMAN-AI alliance program. He obtained his PhD from the Human-Technology Interaction Group at Eindhoven University of Technology. He has broad interests in topics such as habit formation, behaviour change, cognitive modelling and human-centered artificial intelligence. He is keen on applying theory-based computational models to digital behaviour change interventions.

Email: chao.zhang87@gmail.com

Webpage: https://www.uu.nl/medewerkers/CZhang3

Twitter: @forzazhang

Environmental sustainability in digital health

By CBC Digi-Hub Blog, on 2 April 2021

This blog features an interview with Dr Guillaume Chevance, following his talk about environmental sustainability in digital health delivered as part of a newly formed discussion group on ethical considerations in digital health research, led by UCL researchers Dr Olga Perski, Dr Melissa Oldham and Dr Claire Garnett. You can watch Guillaume’s talk here. If you are a researcher interested in joining the discussion group, please e-mail olga.perski@ucl.ac.uk.

Can you tell me a bit about your research background?

I have a Master’s degree in Sport Sciences, with a focus on rehabilitation and physical activity. After having developed and implemented physical activity interventions for people living with chronic health conditions, I became interested in behavioural science. I therefore did another Master’s degree in Human Movement Sciences with a major in Health Psychology. I then did a PhD at the intersection of Health Psychology and Rehabilitation; the topic of my thesis was to understand the role of specific motivational factors in the regulation of physical activity in adults living with chronic conditions and participating in rehabilitation programs. Following my PhD, I did a post-doc focused on eHealth at the University of California, San Diego.

How did you come become interested in digital health and environmental sustainability?

Thanks to my mother, ecology has always been a frequently discussed topic at home. Similar to most human activities, I knew (or had the feeling, to be more precise) that too much digitalisation in the healthcare sector would not be sustainable. An interview with a French engineer, Philippe Bihouix, further sparked my interest in this topic and after reading a couple of his books, I learnt more about the issue of digital health and environmental sustainability. In 2019, a think tank called the Shift Project wrote a report called “Lean ICT: toward digital sobriety” and their conclusion was pretty straightforward:

“…the current trend of digital consumption in the world is unsustainable in terms of the supply of energy and materials that is required…”

Approximately at the same time, a colleague and good friend of mine, Paquito Bernard from the Université du Quebec à Montréal, wrote a piece for the health psychology community titled “Health Psychology at the age of the Anthropocene”. All these texts combined motivated me to try to disseminate the results from the Shift Project’s report to the eHealth community, summarised in a commentary titled “Digital health at the age of the Anthropocene”.

What’s the Anthropocene?

The Anthropocene is an unofficial geologic epoch, coined by biologist Eugene Stormer and chemist Paul Crutzen in 2000. It is used to describe the most recent period in the Earth’s history when human activity started to have a significant impact on the planet’s climate and ecosystems. Crutzen suggested that the start date of the Anthropocene should be placed near the end of the 18th century, about the same time as the start of the industrial revolution. The Anthropocene is also related to the concept of “The Great Acceleration”, which refers to the increasing human pressures on the Earth’s ecosystems (notably post-World War II) (see Figure 1). To me, the Anthropocene is a kind of a last call to avoid generalised forms of environmental and societal collapses in the next decades.

Figure 1. Illustration of “The Great Acceleration” across different sectors and domains. Reproduced from Steffen et al. 2015.

What are, in your opinion, the most important digital health related threats to the environment?

Technically, and according to the Shift Project and other experts, the two main threats are related to (i) the rapidly growing energy consumption of the information and communication technology sector and (ii) issues of soil pollution and resources scarcity related to the rare metals that are required to build the infrastructures supporting this sector. Specifically, it seems that the explosion of video streaming and consumption of digital devices with short life spans are the main drivers of these threats. The growing electricity consumption is incompatible with the Paris Agreement and the soil pollution issue is related to both the extraction of the metals required for information and communication technologies and the lack of recycling when those digital products reach their end of life. Instead, digital devices are usually exported to low- and middle-income countries as e-waste.

Beyond environmental threats, there is also proven cases of child labour in the mines where the metals required for our digital infrastructures are extracted. Further, most rare metals are produced in conflict zones or areas controlled by monopolistic entities, which causes environmental problems and creates fragility in supply chains. These issues, coupled with inherent planetary resource limits, raise questions about our capacity to continue to access and build health devices in the future.

In my opinion, and more philosophically speaking, we who live in high-income countries are stuck in a “growth mentality”. We are always looking for more and accelerating everything. Instead, I believe it’s time to slow down so as not to further accelerate climate change and mitigate against some of its already observable implications and to leave space to communities in low- and middle- income countries to develop their digital infrastructures and reduce inequalities in this domain.

What suggestions do you have for how to overcome these environmental challenges?

The key thing for me is that we have to slow down our digital consumption. I believe that the healthcare sector, because of its laudable goals, might deserve prioritisation over other sectors (e.g., entertainment; advertising), but the size of the problem is so important that, even for health, environmental consequences should be accounted for when scaling up e- and mHealth technologies. Low-tech options requiring fewer environmental resources (e.g., simpler telemedicine solutions, well-designed text message programmes or phone-based interventions) should be prioritised over high-tech solutions promising a new digital health era made up of artificial intelligence and big data and we need to get better at recycling digital devices (see, for example, this brilliant initiative called RecycleHealth). I am not arguing against scientific and technological progress, but rather that high-tech solutions should be kept for very specific problems of high societal relevance and not scaled-up to everything and for everyone, as is often argued these days. You can read more about suggested mitigation strategies in our commentary.


Dr Guillaume Chevance is Associate Research Professor and Head of the eHealth Group at the Barcelona Institute for Global Health (ISGlobal).

Using behavioural science to increase engagement with online learning: Reflecting on a term of online delivery

By CBC Digi-Hub Blog, on 15 December 2020

Written by Dr. Danielle D’Lima, Senior Teaching Fellow for the MSc in Behaviour Change at University College London.

Due to the global pandemic, I have spent a lot of time thinking about converting the face-to-face versions of our core modules to online versions. To accommodate learners from across the globe and make best use of online pedagogy, I took the approach of dividing the content into a mixture of asynchronous and synchronous online learning activities.

Asynchronous activities consist of essential readings, watching short pre-recorded mini-lectures and contributing to online discussion boards. These can be completed in students’ own time and pace, and are followed by a two-hour synchronous seminar in which students are supported to complete small group tasks in break-out rooms and receive some additional content developed by us based on the ‘data’ accumulated from their discussion board contributions.

This blog post offers some reflections from my experience of a term of online delivery of behaviour change teaching and considers how behavioural science itself can support us in further increasing student engagement with online learning in the future.

What did I do?

Over the summer of 2020, I worked on translating the teaching materials for online delivery, including chunking the lecture content to create a series of pre-recorded mini-lectures. To chunk the lecture content, I began by reviewing all of the lectures that I usually give on each module and separating the content into ‘meaningful’ sections. A meaningful section equates to a chunk of learning that stands alone but is clearly connected to what comes before and after it.

I went through several iterations of chunking in this way and recording, as I found that recording a section sometimes made me think about chunking it in a different way, and this had implications for the overall organisation of the material. The final pre-recorded mini-lectures ranged in length according to the content and purpose. For example, some took three minutes to introduce a task for the discussion board or prepare students for the upcoming synchronous session, right up to twenty minutes to cover key content for the week’s learning objectives. I was excited to undertake this exercise as I was drawn to the idea of categorising the content and reassessing it against the learning objectives. I then continued to make small iterations week by week to ensure that I could adapt and develop the mini-lecture chunks based on my experience of teaching the new MSc cohort, and the feedback I received from students across the term. For example, for Week 2, I added a short mini-lecture chunk which contained some reflections on Week 1 and additional examples to support further learning.

How has it gone so far?

The experience of Term 1 (October-December 2020) has been really positive. I have gained so much as an educator that I would not have got from delivering the content face-to-face. Despite regularly reflecting on the individual lectures that I give and how to improve them, it is sometimes difficult to see the relationships between smaller chunks of information (and the learning activities that they are nested in) after having delivered the entire content.

This approach has also helped me to address some of the specific teaching challenges that come from having a very mixed student audience from different disciplinary backgrounds. Our course covers multiple theories and models of behaviour change. Some students have relatively advanced experience and understanding of certain theories and models (e.g. those that have come to the MSc directly from a psychology undergraduate degree). However, we also have many students (around half) on our course who come from other disciplinary backgrounds (e.g. economics, arts, law, politics, history, business, and health) and therefore come to the module with no or very little prior knowledge of these theories and models.

Through the process of chunking the lecture content, I organically began to identify components that were of particular relevance to different subgroups of students (i.e. those with more or less prior knowledge or experience). I was therefore able to highlight this in my audio by clearly signposting who it was most relevant to. I could also tailor the learning where appropriate by creating and highlighting particular recordings that go into more detail on some of the theories and models. This has hopefully helped students to better understand the position that they are sitting in within the cohort, and how they can ensure that they take what they need from the pre-recorded mini-lecture chunks; it gives them autonomy to engage with the material in a way that best suits their learning needs.

I have noted that this tailored approach also has a positive impact on the synchronous sessions that I am running. For example, students are able to join the session with the right level of information to engage fully in the interactive group tasks, and I am able to join the session with prior information on how students have engaged with the asynchronous activities and the extent to which they have met the learning objectives. By having access to the ‘data’ on the discussion board in advance of each synchronous session, I have been able to proactively tailor the session in a way that I would not when delivering the lecture and seminar as part of the same face-to-face session. This offers an additional safety net to ensure that the learning objectives are successfully met for all students.

Where next?

After receiving positive and encouraging feedback from our students, I plan to take the same approach to online teaching in Term 2. However, the next module, “Behaviour Change Intervention Development and Evaluation”, will present slightly different challenges. For example, it is a highly interactive module that relies on students having the time and space to practice key skills for intervention design. I anticipate that chunking the material will help me to identify exactly which components of the material are essential for setting students up to practice and develop the skills – either independently or as part of synchronous group work – interspersed with formative feedback.

Summary of techniques used to increase engagement with online learning:

  • mixture of asynchronous and synchronous activities
  • chunking of lectures
  • feedback
  • signposting
  • tailoring
  • autonomy

How can behavioural science support us in further increasing student engagement with online learning?

As behavioural scientists, we have a unique advantage in understanding what students might need in order to engage with online learning. Engagement, after all, is a behaviour or set of behaviours and we know that in order to enact a behaviour people require capability, opportunity and motivation!

I include below some early reflections on what capability, opportunity and motivation might look like in the context of engagement with online learning and how we, as educators, could go about better supporting our students. These ideas are not intended to be exhaustive but instead food for thought about how we can better apply our skills as behavioural scientists when designing and delivering online education.


Students require the necessary knowledge and skills to engage with online platforms and complete the required learning activities. Clear communication to students regarding what they need to do and how they should go about doing it is essential. For example, this may involve sending a weekly alert to students explaining exactly what is expected of them in advance of the next synchronous session. Allowing students to practice interacting with the online learning environment can also go some way in supporting development of the necessary skills.


With numerous learning activities being allocated to students from multiple modules, there is a risk of them becoming overwhelmed. Students require sufficient time and resources to complete the learning activities (e.g. engage with the materials in advance of live sessions) and this needs to be incorporated into the planning of the activities (e.g. ensuring materials are available with sufficient time). Students also need reliable online learning platforms that are well designed and easy to navigate.


It is important that students understand how learning activities were designed and why they are worth engaging with. By clearly communicating the rationale to students, where appropriate, educators can help them to identify the potential outcomes of their efforts. It is also important that students are rewarded for their efforts. For example, by providing informative responses to student discussion board posts and using key themes from the discussion board activity to develop teaching content.

Of course, educators themselves also need the capability, opportunity and motivation to demonstrate the behaviours required for successful online teaching (e.g. pre-recording a lecture, creating learning activities, communicating to students, etc.). But I will save that for another blog!


Dr Danielle D’Lima is the Senior Teaching Fellow for the MSc Behaviour Change at the Centre for Behaviour Change, University College London. Her role includes designing and delivering teaching and training as well as overseeing research projects on implementation science and health professional behaviour change. She also has an evolving interest in the application of behaviour change science to teaching and training.

SMARTFAMILY – mobile health behaviour change in the family setting

By CBC Digi-Hub Blog, on 30 November 2020

Written by Dr. Kathrin Wunsch and Janis Fiedler on behalf of the SMARTFAMILY project

Everyone knows that a lack of physical activity, too much sedentary time (e.g., extended screen time and nonactive media usage), and an unhealthy diet are serious concerns in modern societies as they accelerate the development of non-communicable diseases, which are causing millions of deaths every year. Research has repeatedly shown that human beings do not sufficiently engage in physical activity and frequently make unhealthy food choices throughout their entire lifespan. Therefore, it is of great interest to public health policymakers to reverse this trend at the earliest possible life stage. Longitudinal studies have shown that behavioural patterns in childhood and adolescence are vital because of their influence on physical activity patterns in adulthood.

As health behaviours in childhood and adolescence are to a large degree formed by parental behaviours, it is fundamental to target whole families. Research shows that supportive interactions within the family setting and shared values about health behaviour affect children’s physical activity engagement and eating behaviour. This has two major advantages: 1) the provision of early interventions can enable children and adolescents to adapt a healthy lifestyle, which they will likely transfer into (older) adulthood, and 2) by targeting both adults and children, there’s the added benefit of parental support, which is an important correlate of healthy behaviour in youth, and adults are also capable of behaviour change towards an active lifestyle.

As so many people today have access to the internet (4.5 billion active internet users in 2020 worldwide) and mobile Health (mHealth) interventions are popular – especially in young people –  we decided to use mHealth technology to reach as many families as possible and to provide a cost-effective behaviour change intervention. Specifically, smartphone-based apps offer a great promise for enhancing physical activity and healthy eating as well as for making health care more accessible and scalable, more cost-effective, more equitable, and offer multiple opportunities for new, sophisticated developments.

But what are the key facets to include when developing such an intervention? To answer this question, we conducted an umbrella review of mHealth interventions targeting physical activity and healthy eating. Overall, findings suggested that the majority (59%) of e- and mHealth interventions were effective and used a theoretical foundation. In addition, we identified behaviour change techniques that were potential moderators of intervention effectiveness. However, most studies did not assess the impact of embedding interventions into social contexts (e.g. involving family members, peers or co-workers in the intervention). We did not see any mention of ‘ecological momentary interventions’ or ‘just-in-time adaptive interventions’ – i.e. interventions delivered at the right moment in time for each individual – in this umbrella review. With the possibility to tailor and to continually adapt mHealth interventions to each person’s unique needs, as well as to deliver support at the most promising moment in time, these parameters were identified by many study authors as being important to incorporate in new app designs.

With SMARTFAMILY, we wanted to fill these research gaps. As we identified mHealth to be a promising approach in our review and due to the known influence of parental support on early child and adolescent physical activity, we created an app that included all of these elements. Our app was developed based on social-cognitive and self-determination theory and included several behaviour change techniques. Importantly, we embedded the intervention in the social setting (i.e., the family) and enabled family members to set collective (instead of competitive) goals for physical activity and healthy eating. Our hypothesis is that by targeting multiple behaviours in multiple people, who can support each other to achieve goals, it should be easier to implement and maintain behaviour change. Moreover, we included gamification features into the app to enhance user engagement along with a just-in-time adaptive intervention approach and ecological momentary assessment features.

SMARTFAMILY is an app for the whole family (see Figure below), which incorporates the following features:

– an interactive goal-setting coach which provides useful facts to improve health literacy and supports goal setting within families

– device-based measured physical activity of participants with immediate feedback on goal achievement, in addition to the option to manually enter physical activity where the accelerometer has not been worn (e.g. during swimming)

– self-reported meals, common meals and common physical activity as well as physical activity where the sensor has not been worn (e.g. swimming)

– triggered and app-based ecological momentary assessment for physical activity and healthy eating; real-time measurement of behavioural and affective correlates of physical activity and fruit and vegetable intake, including current mood, stress, and exhaustion

– push notifications about inactivity sent by the coach when the participant is inactive for at least 60 minutes during waking hours (i.e. when the system detects that neither <2 sensor values at >2MET nor 100 steps has been achieved) – i.e. a just-in-time adaptive intervention; notifications are inhibited the rest of the day if the participant reaches at least 60 minutes of moderate-to-vigorous physical activity on the respective day

– a sleep-mode set by participants before going to bed and after waking up, providing the time frame during which the app was used and a resting time estimation

– gamified goal achievement, where participants gather stars for every 10% of progress towards the weekly physical activity and healthy eating goal. The goal setting coach motivates the family to achieve their goals by prompting a “this far to go” summary every morning after waking up. In addition, the families are advised about a promising goal for the following week depending on their previous goal achievement by the coach.


Overall, we aim to close identified research gaps by evaluating an mHealth app which is theory-based, incorporates several behaviour change techniques, takes the social context into account and uses sophisticated new approaches like gamification, just-in-time adaptive interventions and ecological momentary assessments in a cluster randomized controlled trial design. We expect that our research will shed light on the important factors for health behaviour change in families and help to design more efficient interventions for (early) primary prevention in the future.

More information about SMARTFAMILY (@SMARTFAMILY9):

SMARTFAMILY is supported by the German Federal Ministry of Education and Research Grant FKZ 01EL1820C within the SMARTACT consortium (PI Prof. Dr. Britta Renner), with Prof. Dr. Alexander Woll, Karlsruhe Institute of Technology, Germany, as PI of SMARTFAMILY. Besides the PI and the authors, Tobias Eckert as well as our student staff needs to be acknowledged as being part of our project.





Dr. Kathrin Wunsch (@KathrinWunsch) is a post-doctoral researcher and Project Lead (@SMARTFAMILY9) at the Karlsruhe Institute of Technology. Her research focuses on influences of physical activity and multiple health behaviours on psychological and physiological health throughout the lifespan as well as on the development and implementation of mobile health interventions.



Janis Fiedler (@FieJanis) is a doctoral student at the Karlsruhe Institute of Technology. His research focuses on mobile behaviour change interventions to enhance physical activity and healthy eating as well as different aspects of exercise physiology. In general, he is very interested in anything related to primary prevention, physical activity and nutrition.



How can we use behavioural science to increase the uptake of the NHS COVID-19 track and trace app in England and Wales?

By CBC Digi-Hub Blog, on 13 October 2020

Felix Naughton and Dorothy Szinay discuss how the uptake of contact tracing apps during the ongoing COVID-19 pandemic could be increased by drawing on what we know about factors that influence people’s uptake of health apps more generally.

From the 24th September 2020, the NHS COVID-19 track and trace app was made available for use by individuals living in England and Wales.

From a technological point of view, it could be argued that the timing of the COVID-19 pandemic is quite fortunate: smartphone ownership is widespread in the UK and globally, and can act as a powerful tool for containing the spread of the disease. In the UK, around 80% of people currently own a smartphone. Using the phone’s Bluetooth functionality, contact tracing apps (such as the NHS COVID-19 track and trace app) can estimate when and for how long people running the app on their phones spend time together in close proximity. Then, if any of those people test positive for COVID-19, the app can alert those who have spent sufficient time with the infected individual, asking them to self-isolate to reduce the risk of onward transmission.

On paper, the COVID-19 app sounds like a critical tool in the fight against the virus. However, a key factor influencing the impact of a contact tracing app is its adoption rate in the population, i.e., the proportion of people that select to download and run the app on their phones. Contrary to an incorrect assumption that 60% of the population needs to use the app for it to suppress the virus, benefit can still be gained at low levels of adoption. However, the benefit is much greater at higher levels of uptake and ongoing use. As uptake (i.e., downloading and installing an app) is primarily a behavioural issue rather than a technological one, we should draw on behavioural science to better understand factors that influence app uptake and devise strategies to improve it.

Recently, we reviewed all the studies we could identify that investigated psychological and behavioural factors that might influence the uptake of and engagement with health and wellbeing apps. We further conducted an interview study to deepen our understanding. Several important factors were identified. Below, we revisit these factors and consider the implications for the NHS COVID-19 app.

A key factor is app literacy. When individuals have less skill and confidence in their use of apps, they are less likely to install and use them. Although the setup of the NHS COVID-19 app is relatively straightforward, people may still expect it to be complicated to use, which can put them off installing it. The complexity in how the app senses other app users and anonymously alerts them could translate into concerns that using the app will require advanced app skills and competence. It is therefore important that the most vulnerable subgroups in society, including the elderly, receive help to install the app.

Social influence has a big impact on the uptake of apps in general and will likely be an important factor in the uptake of the NHS COVID-19 app. Social influence includes things like other people’s ratings and reviews on app stores, and recommendations from loved ones, friends, health practitioners, and even celebrities. The team that developed the app reports having conducted research with Black and minority ethnic (BAME) communities to find out optimal ways to increase app uptake. One suggestion was to involve influencers within the BAME community in the dissemination of the app. Social influence, however, can be a double-edged sword. Negative reviews on app stores have the potential to undo positive influence. Furthermore, sceptical views or concerns among family and friends could damage uptake intentions. Another type of positive social influence is the use of a credible source: having the app developed by a trusted organisation and endorsed by the NHS and influential scientists and public health organisations, should help increase uptake. Participants in our interview study suggested that the promotion of health apps through a newsletter sent by GPs or family physicians would encourage them to use health and wellbeing apps.

Another factor is the availability of apps. While not having access to an app is an obvious barrier, it is an important one. In the case of the NHS COVID-19 app, while it is availabile in the two major app stores (i.e., for Android and iPhone users), it is not available to the small minority with other types of smartphones. Even among those who can use the compatible app stores, we estimate that 4% of Android users and 25% of iOS users have a phone running an operating system version that is too low to run the COVID-19 app (i.e., below Android version 6 and iOS version 13.5). We know that smartphone ownership is lower among those who are older, and we also anticipate that a larger proportion of older adults own smartphones with older operating systems compared with younger people.

Having adequate user guidance is a factor that can affect both the uptake of and engagement with health and wellbeing apps. For example, understanding QR codes is a requirement for checking into venues using the NHS COVID-19 app. Anecdotal user reports during the piloting phase of the app in Newham, London, suggested that many people didn’t understand how to use QR codes. It is therefore important to provide easy to understand instructions for how to use track and trace apps.

Another perhaps obvious but no less crucial factor is app awareness. Although we live in a world where information can travel fast, advertisements and media reports won’t reach everyone. When it comes to the NHS COVID-19 app, there has been an intensive mass media campaign to raise awareness, which means that many people would have heard about the app. However, the reliance on digital platforms for spreading information about the app might have excluded key groups who typically have low engagement in the online world. As people tend to forget information rapidly, it’s important that efforts to raise app awareness are sustained over time.

The perceived utility of the app was another key factor identified in our review. This includes providing a clear description of what the app does, what it can offer to the user and how it can help the user achieve their goals. One challenge with the NHS COVID-19 app is that its use generally benefits others rather than oneself. Some negative reviews on the app stores have highlighted this. In terms of detailing what the app does, the description clearly describes the features included. In our interview study, we found that highlighting the benefits of the app early on may prompt uptake. For instance, it might be useful to have the message ‘Protect your loved ones. Please download the app’ appear at the start of the app description as opposed to at the end.

Data protection is also an important factor. While the privacy policy for the app is well described, it is not that easy to get to. It is also very important to explain in non-technical language how the anonymity of the users is assured, which is likely to be a barrier to uptake and reduce trust. This has not been helped by the government’s refusal to make the findings of the pilot study public, which has led the Health Foundation to call for greater transparency.

Emotions may also play a role in app uptake. Our review found that curiosity, such as people seeing promotions of the app and feeling curious about trying it, may prompt them to download it. Anxiety was found in our interview study as another reason why individuals may want to use a health app, with the perceived threat of COVID-19 to one’s health or the health of others is a good example of this.

In sum, to maximise the uptake of the NHS COVID-19 track and trace app, evidence from behavioural science suggests the following:

  • provide practical support either for enhancing app literacy skills or by providing user guidance
  • ensure sustained promotion of the app through multiple channels simultanouesly (i.e., social and digital media, celebrities and influencers, primary care)
  • facilitate the experience of relevant emotions when people hear about the app
  • highlight the benefits of the app
  • ensure transparency about data protection
  • explain how anonymity is maintained using non-technical language


Felix (@FelixNaughton) is a Health Psychologist and a Senior Lecturer in Health Psychology within the School of Health Sciences, University of East Anglia. He has a key interest in the development and evaluation of mobile phone interventions to promote and support health behaviour change (mHealth), particularly those promoting smoking cessation. He is also the co-lead of the COVID-19 Health Behaviour and Wellbeing Daily Tracker study https://www.uea.ac.uk/groups-and-centres/addiction-research-group/c19-wellbeing-study

Dorothy (@DorothySzinay) is a final year PhD candidate at the at the School of Health Sciences, University of East Anglia. Her research focuses on developing ways to increase the uptake of and engagement with health and wellbeing smartphone apps.