Data-Driven Behavior Change

Data-Driven Behavior Change: From Awareness > Understanding > Action, by Eli Myers and Camille Rocca

How did Omada incorporate continuous glucose monitoring (CGM) into their product? The goal is to incorporate this data as part of personal health data.

What is personal health data?

  • Behavioral data: physical activity, mood, sleep, etc
  • Biometric data: body weight, glucose, blood pressure, heart rate, etc
  • Clinical data: validated surveys, progress notes, claims, etc

Opportunity

Most people have a large personal health data ecosystem might include tracking sleep, prescriptions, steps, glucose, etc through multiple different apps. What’s not clear is what the user can control. The data is structured differently, the areas don’t talk to each other. The person doesn’t know what to compare.

The problem they were trying to solve: show the person the relationship between the things they’re doing, and what they can control.

Framework

Data collection today looks at:

  • Awareness: viewing a data source (graphs, maps, etc). This is very common.
  • Understanding: looking at 2 or more sources beside one another to see how they affect one another. There’s less of this.
  • Action: this is the hardest thing. What simple prompt can you offer based on data to help someone take action?

This is how they grounded their work. Integrating CGM data into the product to try to map it to other data for understanding, and unlock options for action.

What’s important is to surface the right opportunities in the moment. There are often difficult truths. When you take the action you’re changing behaviors – but we need to guide you to the right action. One you’re capable of, which will move you toward your goals. We want to make a recommendation and support you moving through that.

Whether that action helps impact change or not is also useful information. We need to use that information to update the care plan, so that we’re not showing the same “do this” over and over without impact.

Principles

  • Make it personal: connect health monitoring to personal health goals and motivations. Data isn’t useful if it’s not personal.
  • Make it focused: encourage monitoring of the most important health data. Not everything!!
  • Build trust: Members need to understand how the tools work, where the data is coming from, and how it’s used. If they don’t trust that, they won’t trust the opportunities they’re given.
  • Connect the dots: help the member understand why the data matters.
  • Educate in the moment: provide the right content at the right time.
  • Prompt for reflection: provide multiple paths to reflecting. Checking in with coaches, or doing self-reflection, flagging what they want to remember.
  • Guide with autonomy: provide actionable next steps, based on data. They are given opportunities to repeat a goal, set a new goal, or take a break. This allows for choice.
  • Context drives care: allows people to evolve their care plan. The plan flexes with the changes the member makes.
  • Celebrate effort, not just outcomes: it’s not just about seeing a dip in the scale. We need to focus on the effort people make.

Application

The application of this is by making sure people are recognized for their investment. Knowledge is powerful, and we need to use data to show what people can do. We’re helping people make connections – it can be a game changer.

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