3 Ways Healthcare Gets Behavioral Change Wrong

An integral component of changing health outcomes is changing behaviors. How does the current slate of virtual health options out there address behavior change when it comes to treating patients with chronic conditions? It turns out most virtual health providers are buying into certain industry beliefs which can lead to misguided approaches to encouraging sustainable behavior change in patients.

Industry Belief #1: Education is sufficient for behavior change.


Many virtual care solutions depend on self-serve educational content to motivate behavior change among people with chronic conditions. But does data back up this dependency? The team behind Omada’s Insights Lab—our internal data corps—took a look at whether or not individuals with chronic conditions need help finding intrinsic motivation to transform information into action.

Their analysis of our prevention program found that the following factors, ranked in order of importance, predicted weight loss outcomes at four months: 

  1. Engagement with coaches
  2. Engagement with peers
  3. In-app meal tracking
  4. Physical activity
  5. Remote monitoring via connected devices
  6. Interaction with health lessons

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That’s right. Health education content was the least likely intervention to predict weight loss outcomes. In response to this insight, we removed lesson completion from our milestone-based pricing model in favor of coach and community interaction, along with other, more diverse interactions that align incentives with purposeful engagement and long-term health outcomes. Value-based care arrangements have always been at our core, and we apply insights and innovations beyond our products to our pricing models as well.

Industry Belief #2: Reactive health coaching is sufficient for sustained behavior change and outcomes.


Virtual care providers typically offer health coaching only for acute needs. Coaches are assigned based on availability, not on prior relationship with or knowledge of the member who needs support.

We decided to explore the impact of this type of reactive health coaching on engagement and outcomes, as compared to Omada’s proactive health coaching style. We analyzed the impact of when our coaches gave food feedback relative to when members track their meals in the app. We found that timely, proactive feedback from Omada coaches led to a 10–15% increase in meal tracking retention, which directly caused 0.5% more weight loss in four months. 

This type of proactive feedback included personalized guidance on possible meal substitutions, recipe recommendations, callouts if the member included photos, and probing questions if the coach needed clarification on a meal log. Armed with the knowledge that proactive, dynamic coaching leads to more sustained behavior change and better outcomes, we offer proactive coaching across our entire population. Our care teams now initiate more than half of messaging behavior on our care platform.

Industry Belief #3: AI, automated nudges, and gamification are sufficient for generating sustained engagement. 


Most virtual care companies lean on automated, algorithmic communication with users to encourage behavior change because it’s affordable and scalable. We figured that these methods would not be as effective as live care teams at achieving behavior change and improved health outcomes.

To find out, we ran a series of internal analyses comparing automated nudges and coach-led encouragement. Here’s what we found:

  • Automated nudges resulted in a 30% increase in meal tracking, but showed no corresponding change in health outcomes, specifically, weight loss at four months.
  • Members who received coach-led encouragement had 10% more weigh-ins than members who received the same feedback via automated nudges.
  • Coaches are 10% better at re-engaging disengaged members than nudges.

Do automated nudge tactics increase short-term engagement? Yes. But they don’t seem to improve short- or long-term health outcomes for people with chronic conditions. Instead, members who interact with live care teams are more likely to purposefully engage with providers and experience sustained behavior change and health improvements.

To read our Insights Lab’s full white paper click here.

1. Omada internal analysis, member population data 1/2016-1/2020. Based on a feature importance analysis, a machine learning technique that outputs relative scores to explain which variables contribute most to the prediction of a target outcome.
2. Omada internal analysis, member population data 1/2017–1/2019, on our prediabetes program. Reactive coaching was defined as coaches primarily responding to member-initiated messages or providing no response to members. Proactive coaching was defined by contacting members proactively based on behavioral cues.
3. Omada internal analysis, member population data 1/2017–1/2020. Nudges for meal tracking included push notification reminders, group post reminders, and in-app modals to celebrate streaks and milestones, Nudges for weigh-ins included push notification reminders, badges for weighing in consistently, and push notifications to celebrate when a member weighed in.
4. Omada internal analysis, member population data 6/2017–3/2019. Nudges for meal tracking included push notification reminders, group post reminders, and in-app modals to celebrate streaks and milestones, Nudges for weigh-ins included push notification reminders, badges for weighing in consistently, and push notifications to celebrate when a member weighed in.
5. Omada internal analysis, member population data 6/2017–3/2019.