Fighting “zombie” health apps through K-Factor virality and other mobile gaming techniques – The application of commercial gaming techniques to create more effective mHealth solutions

• Main Author: Sorcha Moore
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-10-01
• Series: Frontiers in Public Health
• Subjects: Behaviour Change; mHealth; Mobile app; Mobile analytics; app engagement
• Online Access: http://journal.frontiersin.org/Journal/10.3389/conf.FPUBH.2016.01.00048/full

Background Mobile today is recognised as being one of the highest value channels in the marketing mix . The potential for mobile applications to influence user behaviour and establish behavioural habits at scale is evidenced in apps such as Cow Clicker and Candy Crush attracting massive active audiences at their peak, and high levels of viral growth and user activity. mHealth has enormous potential to effect positive behavioural change for health. However with an estimated 80% of overall apps in the app stored being reported as “Zombie” apps , and a reported 80% of health apps being removed after just one use it is evident that sustained impact via mobile health app solutions is difficult to achieve. During the development of the Knowsley Health Hub, a mobile app designed to instigate healthy lifestyle changes in communities, we looked at what can we learn from the success of mobile games to enable us to best utilize the mobile channel as a tool to deliver on this goal. This presentation shows how we examined commercial gaming techniques to create a framework for the delivery of a healthy behaviour intervention programme via the mobile medium and devise a method of evaluation of success. Description After establishing overall behaviour change intervention goals and targets for the project facilitated by traditional behaviour change models, we looked at how this could best be delivered via the mobile medium. In order to begin designing a system for success, it is necessary to understand what a measurement of success might look like beyond simply tracking downloads. We undertook a review of a number of mobile gaming analytics tools such as App Annie, Flurry, Localytics and Apsalar amongst others. For our purposes mobile app analytics could be simply broken into segments of Aquisition, Retention and Conversion with metrics specific to each segment. Each metric indicates a Key Performance Indicator (KPI) as a measurement of a user interaction per segment enabling identification and measurement of potential success or failure points along the user journey. Using this as a basis, we identified the following framework as a guide in the design of the app. Acquisition: “Zombie” apps are widely regarded as those apps which have no or few downloads. To deliver a successful programme of healthy behaviour change to a community it is necessary to establish an audience of users in that medium. Acquisition focuses on gathering those users to create traction. • Optimization of the Click Through Rate (CTR): It is necessary to both capture an audience’s attention to alert them of the availability of an app, and subsequently persuade the audience of a sufficient potential benefit in use to cross the barrier of effort to download it. It is essential to identify effective distribution channels to both grab the attention of users, as well as provide sufficient persuasion to break through the barriers of belief particular to health in building trust and credibility. • The 3x3 Churn and Burn Rule: As a general rule, potential for a user to “Churn” (to discontinue use of the app after a few uses) is widely recognised as a downside characteristic of the mobile app medium. The 3x3 Rule indicated that statistically this is reduced in apps by obtaining multiple (3 or more) user interactions with the app in the first 3 days after download. • K-Factor/Virality: For mobile and social apps, K-Factor describes optimization of acquisition by utilizing social interaction as peer referral building exponential growth, often by encouraging users to introduce others to the game via sharing or invitations. When used in a behaviour change context this has an added benefit of both building peer community support, as well as building trust and credibility within its intended audience. This viral effect can be measured via the Virality coefficient, or “K-factor”. Retention: DAU and MAU: Daily Average Users and Monthly Average Users are commonly used to measuring app ‘success’. These measures commonly reflect engagement more effectively than download numbers by accounting for actual use. • Gamification is the utilization of game mechanics to encourage users to continue use. Gamification is employed to increase engagement through leveraging people's natural desires for socializing, learning, mastery, competition, achievement, status, self-expression, altruism, and closure. • Gamification is also employed to increase “Stickiness” - providing reasons for users to increase their session length or continually return to the application thereby increasing user engagement. Some of the techniques used within mobile games to promote this activity include virtual or real “rewards” schedules, time limited “Challenges”, or “Appointment Dynamics” . • Other common techniques to include Gaming Capitals within the user journey include badges/medals, trophies, feature ‘unlocking’ and other accolades that indicate the user’s progress and accomplishments . Conversion: Typically apps focus on Monetization as the ultimate Conversion goal. However in the context of this health app, Conversion refers to the adoption of healthy behaviours and assimilation of educational content. In order to track this, it is necessary to formulate in app events which can record such behaviour change such as user tracking and recording the action to indicate adoption of the desired behavioural. Some examples include recording of exercise or activity, self tracking of dietary change, and self assessment of education modules. In addition, the challenge to the app designer is to devise elements in the UX that reward self-quantification of the user as progress is made along the path of change. Conclusions By engaging certain commercial techniques of mobile apps, we can deliver more effective digital health programmes through the ability to identify our goals and methods of measurement. At present, the Knowsley app is still being developed, and using this approach and framework we are better able to identify goals, plan measurement of performance of the programme and continually improve via user feedback. With Health and Fitness apps representing just 2.9% of the Apple app store alone (sourced via Statista in September 2015) there is untapped potential to effect positive behaviour change for health at significant scale.