Cardiovascular disease management, especially after an acute event, requires consideration of cardiovascular risk factors, which are generally lifestyle habits. Current implementation of recommended care is suboptimal, due in part to the difficulty of implementing and maintaining health behaviour changes. mHealth apps offer new approaches to support behavioural changes, in particular with the use of gamification strategies. These strategies have the potential to help maintain engagement over time. Reviews of current health apps that use gamification strategies show low integration of theoretical behavioural models in app design, and low use of gamification for cardiovascular self-management apps in general. We propose the integration of gamification strategies in a validated behaviour-change model, based on a patient survey and focus group. This model can be used to design a future smartphone app to support self-management of cardiovascular disease.
Key words: cardiovascular disease, mHealth, gamification, long-term engagement
Cardiovascular disease (CVD) is a common, chronic disease that is now the first cause of mortality and morbidity in Switzerland . Nearly half of deaths due to CVD involve coronary heart disease. It is one of the most important drivers of healthcare costs . CVD is strongly associated with unhealthy lifestyle habits, such as physical inactivity, smoking, unhealthy diet, stress, or non-adherence to recommended treatments. Improving cardiovascular risk factors is a key process in the prevention and treatment of CVD, as they can affect long-term outcomes .
Implementing health behaviour changes is a difficult process with often insufficient support or guidance, as the majority of self-management in chronic diseases takes place away from the healthcare providers . After changing behaviour, patients face the additional challenge of maintaining this new approach over time. A multi-centric study reported increasing medication discontinuation over time, with nearly a third of the patients stopping therapy at just one month after a myocardial infarction, with subsequent higher mortality . Likewise, Schwarzer et al.’s health access process approach (HAPA) behaviour-change model bears several interesting concepts (figure 1) . It has been studied for physical exercise adherence after cardiac rehabilitation in a population similar to the one we are targeting. A motivational phase (characterized by preintenders and intenders) takes into consideration prior knowledge and beliefs (risk perception) and expected outcomes. Actions are planned and then carried out in a volition phase (by actors). The concepts of self-efficacy, adapted to intention, early and late stages of the process, and barriers, carry influence throughout the whole model.
Smartphones are changing our daily lives in many ways, and are beginning to transform the delivery of healthcare . The high adoption rate of these devices, even among older individuals or those with lower socio-economic status, provides a unique opportunity to support behaviour changes across the population level [8, 9]. A wide range of possibilities for support is starting to be explored, such as timely prompts, parsed messages delivered according to current activity or place, and customized messages adapted to individual preferences.
A common challenge for behaviour change and smartphone app use is maintaining long-term engagement. Gamification, or the use of game elements and strategies in non-gaming contexts, shows promise as a new approach to help bolster engagement and motivation [10–15]. Reviews on gamification in mhealth apps report low use of theoretical models, both for game elements and the use of health-behaviour-theory constructs . Further research is needed with these new approaches.
The aim of this paper is to integrate gamification strategies into a validated health-behaviour model. This model will then be the basis for the design of an interactive smartphone application for patients with coronary heart disease. We will present the resulting mobile app in our future work.
This study uses three approaches to integrate gamification strategies in the health-behaviour model: an overview of gamification strategies, a patient survey, and a focus-group session.
We established a list of strategies and features for gaming that are reported in the literature, focusing on mHealth studies and reviews. We explored how to integrate these gamification strategies in the HAPA behaviour-change model.
We created a written survey to collect basic demographic data, smartphone, internet and social-media use, and motivation and barrier for CVD self-management, as well as tracked health parameters. We conducted this anonymous survey among individuals attending the cardiac rehabilitation centre for our hospital, recruiting participants during group sessions from May to November 2016. The inclusion criteria were age over 18 years, recent myocardial infarction, and ability to respond to the survey in French. This survey was first tested on non-medical collaborators to ensure comprehension. We analysed the survey with descriptive statistics. Based on the results of the survey, we were able to select and assign gamification strategies that were adapted to areas of the HAPA model.
We recruited adult participants currently enrolled in the cardiac rehabilitation programme for a focus-group session. This meeting took place in February 2017. We explored their needs for support tools, then presented our concept of a gamified app to support and encourage CVD self-management over time. Notes were taken during the session for subsequent analysis.
We present a list of gamification strategies and elements in table 1, based on recent systematic reviews of this topic [11–14, 16]. We chose to present elements that have been assessed for effectiveness in mhealth apps, rather than simply list all existing gaming strategies.
|Table 1: List of gamification strategies reported in mHealth apps.|
|Game element or gamification|
|Points||Quantify users’ participation and performance.|
|Rewards, badges||Incentives to mark a completed goal. Rewards can be of different types, such as virtual rewards or financial incentives.|
|Levels or progress bar||Provide an overview of progress.|
|Challenges and quests||Ways to guide progression towards a more distal goal. These challenges can help the goal-setting process.|
|Trigger||Cue for action that can be related to time constraints.|
|Feedback||Returning information to users about actions, performance, etc. This can be through presentation of results, such as a progress bar for a chosen goal.|
|Leaderboards||Help users visualize how their performance relates to those of other users.|
|Social connection||Can provide social support to help engage users in ways other than simply performance. For example, this is a “like” in Facebook.|
|Story, narrative||A context within the app to create an alternate reality, and used to guide the user, for example to perform certain tasks before being able to access the next part of the story.|
|Avatar||Used to represent the user in the context of the application.|
We recruited 37 participants in total for this survey. The surveys were conducted during group sessions that included participants at all durations of the six-week programme. Participant characteristics are presented in table 2, and are representative of the general participants in the programme. Educational attainment shows a large proportion who completed compulsory schooling, most of whom may have completed vocational training rather than secondary school (a common practice in Switzerland).
The summary of the survey results is presented in table 3. Our cardiac rehabilitation programme is an outpatient initiative, which allowed us to assess the perceptions of hospital discharge after myocardial infarction. The top concern expressed by patients was the recurrence of a myocardial event. They also felt unprepared to tackle CVD self-management, particularly for dietary changes. Physical activity and patient education are the goals of the cardiac rehabilitation programme, but patients at different stages of the programme still felt that they lacked practical advice for self-management.
Just over half the participants of the survey owned a smartphone or tablet in this sample. Close to 90% accessed the internet on a desktop computer, whereas smartphone owners also used their phone for internet access. Nearly half of the participants were social-media users (45.9%). Patients were monitoring several health parameters, in particular weight and blood pressure. The majority of the patients read the results on a measurement device, and did not use an app to track their data. Using pen and paper or simply not collecting the data were barriers to understanding associations in CVD management and outcomes, such as changes in medication and their effect on blood pressure.
|Table 2: Survey participants.|
|Participant characteristics (n=37)||n (%)|
|<40 yo||1 (2.7%)|
|40–55 yo||10 (27.0%)|
|56–70 yo||18 (48.6%)|
|>70 yo||8 (21.6%)|
|Highest completed education|
|Compulsory school||15 (41.7%)|
|Secondary school||8 (22.2%)|
|Table 3: Survey results.|
|Health management (n=37)||n (%)|
|Top three motivators|
|The desire to become healthy again||30 (81.1%)|
|My family||21 (56.8%)|
|Fear of a new infarction or even death||15 (40.5%)|
|Three biggest issues after hospital discharge|
|Coping with anxiety of recurrent event||14 (37.8%)|
|Adopting a health diet||10 (27.0%)|
|Smoking cessation||8 (21.6%)|
|Goals during cardiac rehabilitation programme|
|Dealing with stress||13 (35.1%)|
|Better understanding of the disease||10 (27.0%)|
|Tracked health parameters|
|Blood pressure||22 (59.5%)|
|Blood glucose||3 (8.1%)|
We recruited five cardiac rehabilitation participants for a focus group session, where we discussed in more depth the possibilities of mobile apps and gamification strategies for CVD self-management. Four participants were men and one was a woman. The median age was 50 years old, with a range of 32 to 62. All five had smartphones, but two only used basic phone functionalities. Two other participants used apps regularly to monitor their health. The notion of parsed information, with gamification strategies to help maintain motivation, was well accepted by the group. They explained the difficulties in daily tracking for certain behaviours, such as diet. Dietary changes were a common difficulty for the participants. Although the general principles were understood, they felt that they lacked practical information to put strategies into practice.
The survey findings gave us better insight into participants’ motivations and barriers. This allowed us to select gamification strategies that could address these barriers, and which we integrated into the mechanisms of the HAPA model. We selected gamification strategies that could help bolster different elements in the HAPA model, and present our modified HAPA model in figure 2. For example, realizing that participants needed more practical facts to implement actions, a gamified strategy is to propose tasks such as quests or challenges. A quest can also be a self-evaluation questionnaire, or a quiz to assess levels of knowledge about various aspects of the CVD. These in turn can help participants have more realistic outcome expectations or risk perception. This process also emphasized the need for support in goal setting, with a list of goals for participants to choose from. We added the notion of individualized goal setting, since ideally the proposed list should propose goals that are appropriate (i.e., avoid proposing smoking cession to a non-smoker). Rewards can be of different types in this model: for example, the rewards in the outcome expectancies or risk perception could be “fun facts” related to CVD or nutrition, or new discoveries in science. These can help stimulate curiosity about CVD self-management. Feedback plays an important role throughout the model, and can also be visualized in different ways in an app. Graphic representations may be useful for changes in outcomes like weight, whereas progress bars can indicate how close one is to reaching a goal. Feedback can also be from social connections. Social connections could also include sharing results with healthcare providers, who can then in turn provide more timely, focused feedback to the user. The notion of triggers is particularly interesting with a smartphone app, since it can provide timely and context-appropriate cues for behaviour. In the case of HeartSteps, an app to stimulate physical activity, the app relies on time of day, current weather, and individual preferences to send cues about types of activities that could be carried out at a given time .
Not all the listed gamification strategies were integrated in the model, such as the use of avatars. The notion of “story” was included in the self-efficacy box to indicate that it can be used to provide a context to allow an overview of progression.
This paper aimed to propose a modified HAPA health behaviour model, which integrates different gamification strategies. A survey of patient perceptions helped us focus on certain aspects of CVD management. Patients reported wanting to improve their understanding of CVD and its management, and felt unprepared, with a lack of practical advice. The proposed model provides several interesting ideas for future app development, based on gamification strategies in particular, which were discussed in its preliminary stages with participants of the focus group. Participants all recognized the difficulties of behaviour changes, and after their acute event were particularly keen to be successful in improving their lifestyles. Using gamification strategies to bolster motivation and engagement was readily accepted, even though our participants were not gamers. Gamification strategies (such as reward systems for loyalty) are now so widely used that they are easy to understand and to accept for self-care.
The perceived lack of practical advice from the survey results and focus group discussion could be due to individualization. One of the many challenges for lifestyle changes is the need for individualization due to personal preferences, cultural background, prior habits, and different health states. During group sessions, general notions are discussed, and participants need to adapt those notions to their individual situation. Mobile apps provide a unique opportunity for individualization. They can adapt suggestions to user data, to their context and even to their current activities. Using individualized goal-setting suggestions can help build confidence in the app, but this also means that app content needs to be well tested and validated.
Mobile apps can be helpful in several ways, such as making information easily accessible at all times, tracking results or having reminders at certain times of day. Another approach, which is enhanced with gamification strategies, is to parse the information in order to generate a series of baby steps towards the final goal. This approach improves self-efficacy by making the intermediate goals more easily reachable. Here again, feedback, especially in terms of progression, can help to maintain motivation.
Based on the results of our survey, proposing a mobile app is feasible for this targeted population as over half the participants already own a smartphone or tablet. Although few were current users of health apps, this population already monitors several health parameters. Mobile apps can provide more depth in understanding the disease by graphic visualization of the data, or by presentation of associations between these monitored parameters, for example.
In conclusion, our modified HAPA model proposes integrated gamification strategies that are selected and adapted to cardiac rehabilitation patients’ needs and barriers. We plan to develop an app that is based on the proposed model to help guide and support patients in their daily self-management of CVD. Our survey underlined the patients’ needs in many different aspects of CVD, which can be addressed through the range of gamification strategies in our model. We hope to present our app and its impact on patient care in the near future.
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a Medical directorate, University Hospitals of Geneva, Geneva, Switzerland
b Division of Cardiology, Department of Medical Specialties, Geneva University Hospital, University of Geneva, Geneva, Switzerland
c Division of medical information sciences, University Hospitals of Geneva, Geneva, Switzerland
No potential conflict of interest relevant to this article was reported.
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