Improving outcomes in mHealth apps through behavior change
Most mobile health (mHealth) apps are built around the core mission of improving their user’s health outcomes. Nutrition apps aspire to help users monitor their diet by giving them deep insights into every mouthful they eat, meditation apps aspire to help users combat anxiety and stress, and fitness apps aspire to get their users to be more active. However, as I’ve mentioned in my previous post, health goals are often time intensive to achieve and require discipline and practice on a day-to-day basis to get results. Furthermore, the more an app can get it’s users to do something (i.e. log that meal, finish that workout), the higher the likelihood of improving that health outcome (i.e. losing weight, managing stress) — if done right! But as the creator or PM of an app, how does one determine what features to introduce to bring about a behavior change and ultimately improve the user’s health outcome (sooner, more effectively)?
Prior to delving into target users and feature sets, it’s important to understand the various aspects of user behaviors, particularly as they help in driving an intervention or an outcome. A popular framework to understand user behaviors that drive a change is the COM-B model, which spans across 3 major constructs of the ‘behavior wheel’, Capability, Opportunity & Motivation.
The capability construct address psychological measures such as self-monitoring (i.e. providing visibility into sleep or exercise metrics over time) and stress-management (i.e. providing informational reading on coping with health concerns like diabetes or anxiety) as well as physical measures (i.e. access to workout videos or tutorials) to improve behavioral outcomes through an app.
Motivation is a rather significant construct when considering ‘adherence’ or behavior stickiness. Motivations can be extrinsic or intrinsic and the level of enjoyment (and therefore adherence) gets higher as the underlying intrinsic user motivations are catered to. Extrinsic motivations span four subcategories, which can be easily explained in the context of exercising.
External Regulation is when the extrinsic motivation is derived from external factors like rewards, points, freebies, etc. So in the context of an mHealth app — gamifying the user experience by awarding the user points for completing a set of exercise is one way to motivate users who seek external regulations.
Introjected Regulation is when the extrinsic motivation is derived from an external cause that is slightly more internalized — for example, deriving the motivation to exercise because of guilt, or wanting the approval or acceptance of others. For users who seek introjected regulations, a feature to team-up and workout with a friend in-app may be one way to extrinsically motivate the user to exercise more.
Note that for both external and introjected regulations, the level of enjoyment derived from performing the activity (in this case, exercise) is actually very low, and the enjoyment is derived mostly from external and introjected factors.
Identified Regulation is when the user is motivated to perform a certain activity to meet a personal goal, although the activity in itself may still not be enjoyable. For instance, a user may be motivated to exercise more to lose weight and improve their physique, but not necessarily to be more healthy. An example of a feature that could work for such users is to upload progress photos over time for a ‘before-after’ comparison.
Integrated Regulation is when the extrinsic motivation to do an activity is fully internalized, i.e the motivation behind performing a certain activity is to realize a clinical outcome, and the user comes to an agreement with the behavior. For example, the user may realize that exercise is important for overall health & mood, even if it isn’t necessarily enjoyable. In this case a feature providing periodic correlation updates between exercise & overall mood in-app may be motivating for the user to enforce the behavior of exercising more.
Intrinsic motivations are when the motivation to perform the activity purely comes from enjoyment of performing the said activity. This is when behavior adherence is the highest and a feature catering to an intrinsic motivation is most likely to have the highest engagement.
The opportunity construct includes social enforcement such as peer pressure (i.e. competition with friends or other users of app) and physical enforcement such as cues-to-action (i.e. push notifications & reminders). These constructs are widely used across most apps, mHealth included, and are proven methods to drive user behaviors in app.
While these behavior constructs have been explained here in the context of a health outcome, thinking through this framework is often helpful in user research while identifying the target demographic for a feature and validating the constructs that drive user behaviors to ultimately design the right solution for the right problem.
Additional Reading & References:
C. Lister, J. West, B. Cannon, T. Sax, D. Brodegard, LaughModel Health Communication Research Group, Department of Health Science, Brigham Young University, Provo, UT, United States:
Self-Determination & Organismic Integration Theory (OIT):
