Reflections on Building a Digital Health Product

This article was originally published as a guest post in the Rock Health Blog.

Our ability to access increasingly granular and detailed health data is growing rapidly. Digital health innovators, then, must answer the question: is this data simply interesting, or is it also going to positively change behaviors in a persistent way? My hypothesis – with proper design, it can.

I have observed a number of attempts at addressing this specific problem, and I have come to the conclusion that there are a handful of important factors to consider when building a digital health product.

Passive Data

People do not want to interrupt their lives more than they absolutely need to. Data collection needs to be passive.

Companies like Fitbit, Jawbone, Nike, Basis, and Withings have achieved this by allowing users to simply drop a simple form factor device in their pocket or step on a scale when they wake up, and a basic set of data is wirelessly pushed to the cloud. Mobile apps like Moves passively track your activity throughout the day and connect these activities and movements with location-based data, also collected passively.

The next generation of devices will take this idea of passivity even further.  These devices will be embedded in our environment and our bodies. mc10 is pushing the envelope by fabricating flexible electronics that can fit onto a non-rigid form factor, including the body, and Sano Intelligence is developing a biometric sensing skin patch. Eventually these kinds of sensors will be integrated into our lives in a truly passive way our bed, chair, cars, toilet, clothes, and bodies will be transmitting data without any input on our behalf.

This next generation of products will require little or no effort on the part of the user who ends up constantly observing themselves, and this brings increased consciousness and awareness. The fascinating thing here is that through this observation alone, behaviors tend to change.

Intrinsic Motivation

Although behavior change tends to be observable in a statistically significant way when individuals track themselves, these new behaviors generally do not persist over extended periods of time. Keeping individuals motivated to continue making the right kinds of decisions in the short term, that will impact their health in the long term, is critical – it is also difficult. It turns out that building things that keep people engaged in their health long term (years or decades vs a few months) is really hard.

Although extrinsic motivation (giving people rewards, praise, badges, etc) is a powerful tool for achieving short term goals and for helping reinforce the right kinds of behaviors, its overuse can ultimately lead to overjustification, or a decrease in a person’s natural motivation to perform a task or behave in a certain way. A well-understood area of game design, it is often the first thing teams look to when trying to change health related habits and behaviors. Although important, the key to successfully facilitating behavior change is not extrinsic motivation, it is intrinsic motivation, motivation driven by enjoyment of exhibiting a certain behavior in and of itself, rather than relying on any external pressure.

I am not a behavior change expert (here are a few you should follow), however I believe that companies that purely focus on the low hanging fruit of behavior change (extrinsic motivation + ‘gamification’) will not succeed in improving health outcomes in the long term. My recommendation is that, teams should, at a minimum, understand the difference between extrinsic & intrinsic motivation and strive towards the ultimate goal of building a product that is optimized towards the latter.

Stage Dependent Action

So once you have worked out how to make people adopt new behaviors that persist through time, what is it that you should tell them? It is not enough to just passively measure and intrinsically motivate someone to form new habits, if the new habit is not optimal for that individual – if you are simply measuring steps, people will walk more… unfortunately they also may do less weight lifting because this specific metric is not ‘rewarded’.

Although there are some basic things that can be achieved simply by summarizing peoples data in nice charts and graphs and some logic can be driven by algorithms, my belief is that there is an effective intermediate step that relies on, and involves other people. Though we generally disagree on what perfect health is, conditional on understanding an existing state, defining a better state tends to be more realistic – unfortunately computers are currently not great at this.

Companies like Sessions focus on building the technologies to scale this human intervention and claim much higher engagement and effectiveness (80% compliance with plans). Communities built on human interaction like Lift and Fitocracy are examples of  companies where people are positively rewarded for their behavior and learn through the shared knowledge of the community, whether it be expert or otherwise.

At WellnessFX, we connect our members with nutrition and lifestyle coaches who tend to understand specifically what recommendations are going to be most effective for the individual dependent on their stage within this journey (“lets just start by eating less bread and taking some fish oil” vs “I want you to go fully paleo next week”). My hypothesis, the personal relationship our members form with these experts teaches skills that increase self-efficacy, the recommendations themselves are aligned with what is realistic for that individual so they are achievable, and the biometric data visualizations tend to provide the short term extrinsic motivation in the form of a feedback driven goal. As a result, we are able to systematically improve the health outcomes of our member base.

Digital health innovators who are going to prove successful are those who provide realistic and stage dependent recommendations/education that provide incremental improvements to the individuals existing state; reinforce and enhance this individual’s want or desire to change and adopt these new behaviors at an intrinsic level; and provide feedback loops that do not require (much, or) any active management from the users perspective.

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