Omada Insights vs. Industry Beliefs

Myth #1:

Apps and connected devices are sufficient for chronic condition management

Many virtual care companies rely largely or entirely on remote monitoring devices and apps for tracking health and behavior across users and health conditions.

Data  Across Omada programs, we found that:

• Members who engage with their Omada care team and community in the first week of the program are ~94% more likely to achieve their target health outcomes in the Omada program.¹
• Members are more likely to churn—specifically, to stop logging into Omada after the first month if they engage only with connected devices.²
• Members who message their coach or specialist experience two times more weight loss.³

Omada insight:  Apps and devices are not enough. Trusted relationships with healthcare coaches and professionals, especially at the outset of a virtual care program, are crucial for purposeful engagement and sustained outcomes across conditions.

Learning loopback:  As a result of this insight, we strengthened resources for our care teams and propietary care delivery platform, and we offloaded device troubleshooting to our support team so that coaches and specialists could focus on building relationships with members.

Data Analysis of our prediabetes program found that the following factors, ranked in order of importance, predicted weight loss outcomes at four months:⁴

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

Omada insight:  Among Omada members with prediabetes, health education content was the least likely intervention to predict weight loss outcomes. Again, coach and community engagement had the greatest relative effects.

Learning loopback:  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 longterm 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.

Data We conducted prospective studies on protocols for giving food feedback to members after they 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, compared to a control group that received reactive coaching.5 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.

Omada insight:  Proactive, dynamic coaching leads to deeper support and engagement, contributing to more sustained behavior change and better outcomes.

Learning loopback:  We do more proactive coaching across our entire population, and we use artificial intelligence (AI) to assist our coaches and specialists in starting relevant conversations with members. Omada’s care teams now initiate greater than half of messaging behavior on our care platform.

Myth #4:

AI, automated nudges, and gamification are sufficient for generating sustained engagement

Many virtual care companies lean on automated, algorithmic communication with users to encourage behavior change because it’s affordable and scalable.

Hypothesis: We hypothesized that these methods would not be as effective as live care teams at achieving behavior change and improved health outcomes.
 

Myth #5:

Public trust in the U.S. healthcare system is at an all-time low

Over the last four decades, Gallup polling has found that confidence in the medical system has dropped from 80% in 1975 to 36% in 2019.9 Moreover, a 2006 study suggested that distrust in the healthcare system is strongly associated with self-reported fair or poor health.10 Despite low trust, Omada’s competitors in the virtual care space rely on automated nudges and reactive, one-off outreach from clinicians and coaches with whom the member has little or no prior relationship.

Data In a correlational study, we found that members in Omada for Prevention who interacted with devices, completed lessons, and engaged in any tracking or social activity in at least nine out of 16 weeks averaged two times more weight loss than those who didn’t. By contrast, among members in Omada for Diabetes, these measures of engagement were not correlated with their target health outcome, hemoglobin A1c reduction.¹³

Omada insight:  Engagement strategies that work for one condition won’t necessarily work for another—even when health issues are ostensibly similar. In order to get the best outcomes for people with different chronic conditions, we need to give members guidance, support, and care plans that are personalized to their unique health circumstances.

Learning loopback: As a result of this insight, we updated and customized our engagement metrics and care plans for the Omada for Diabetes program to more effectively drive their particular target health outcomes.

• Engagement: We leaned into tracking data from connected blood glucose monitors and continuous glucose monitors, as well as condition-specific surveys to assess medication adherence and diabetes distress.
• Care plans: We added more SMART goals14 that a member can set related to diabetes-specific challenges such as remote monitoring and medication tracking. We also enhanced data sharing with members’ primary care providers due to the importance of care coordination.

Data Based on surveys of our population through October 2020, the Omada Insights Lab found that 70–80% of participants in Omada for Diabetes and Omada for Hypertension have multiple conditions. Among members with diabetes in particular, 85% have elevated body mass index (BMI), 47% have hypertension, and 21% have mild depression symptoms.

Omada insight:  Assessing comorbidities and integrating care across related health conditions is crucial in the effective treatment of chronic disease, especially where chronic issues have similar underlying behavioral causes.

Learning loopback: We started with Omada for Prevention, but this insight prompted our expansion over the last five years to include treatment across four more chronic conditions—diabetes, musculoskeletal health, hypertension, and behavioral health—to allow us to synergize care across conditions to achieve better health outcomes.

Learning loopback (n.): The process of sharing a new insight across our interdisciplinary team of experts in order to apply it to future product design and care delivery; each insight leads to new hypotheses, which leads to further assessment and more insights, in a virtuous circle.

Virtual-first care (n.):

First-line medical care accessed through digital interactions where appropriate, guided by a clinician, and integrated into the wider healthcare ecosystem. Adapted from the Digital Medicine Society (DiMe), https://impact.dimesociety.org/v1c/.