Multimodal Real-World Data for Cardiometabolic Health

GLP-1 treatment behavior in the real world

Why patients start, stop, and switch GLP-1 therapies in the real world

Understand how cost, side effects, behavior, and treatment experience shape real-world GLP-1 use, beyond what is visible in claims data.

GLP-1 adherence and discontinuation are not well understood
GLP-1 therapies are transforming weight management and cardiometabolic care, but real-world outcomes remain difficult to explain. Life sciences teams can track prescriptions and persistence, but lack visibility into what actually drives patient behavior over time.

Claims and clinical data show what happened. They don’t explain:

Why patients discontinue GLP-1 therapies after initiation
How side effects, cost, and daily experience impact adherence
What drives switching between therapies or dosing strategies
Why eligible patients never start treatment

Questions life sciences teams are asking about GLP-1 therapies

Teams across research, medical, and commercial functions are trying to understand what drives behavior and ultimately outcomes

Why do patients discontinue GLP-1 therapies in the real world?
What are the most common reasons for GLP-1 drop-off?
How do side effects impact adherence and persistence?
What factors influence switching between GLP-1 treatments?
Why do eligible patients choose not to start therapy?
How does real-world patient behavior differ from clinical trial assumptions?

A different way to understand GLP-1 treatment behavior

Evidation connects directly with individuals over time, capturing continuous, multimodal real-world data across behavior, experience, and outcomes.

This enables:

Longitudinal tracking within the same individuals
Real-time insight into adherence, discontinuation, and decision-making
Recontact at key moments such as initiation, switching, or drop-off
A more complete understanding of treatment experience beyond clinical settings

Data to understand what drives GLP-1 treatment
Through continuous, direct engagement with individuals, Evidation captures what patients experience, decide, and do over time, not just what is recorded in claims or clinical visits.

Who patients are

Age, sex, gender identity, race and ethnicity
Income, education, employment, and household structure
Insurance type, out-of-pocket costs, affordability perception
Access barriers including transportation, food, and care access

What patients want

Weight goals and satisfaction with current health and progress
Motivations including health, appearance, energy, and activity
Beliefs about drivers of weight: behavioral, biological, environmental

What patients do

Diet, activity, sleep, stress, and substance use patterns
Engagement with apps, programs, and support communities
Medication history across GLP-1 and alternative treatments

Why eligible patients do not start GLP-1 therapies

Cost and insurance barriers
Side effect concerns
Injection aversion or discomfort with treatment format
Lack of perceived need or efficacy
Preference for non-medication approaches

What shapes individual treatment decisions

Injectable versus oral therapies, branded versus compounded
Dosing patterns including titration and microdosing behaviors
Access channels including HCP, telehealth, and direct-to-consumer models

Treatment preferences and decision drivers

Preference for flexible dosing versus convenience
Tradeoffs between simplicity and control
Injection aversion versus daily pill preference
Drivers of microdosing including side effect management and cost

Why patients drop off GLP-1 therapies

Cost and affordability pressure over time
Side effects, including type, severity, and persistence
Complexity, burden, and lack of personalization
Adherence friction: forgetfulness, discomfort, confusion

Real-world outcomes and experience

Weight trajectory: starting weight, current weight, total loss
Physical and functional changes, including muscle perception
Satisfaction, goal attainment, expected duration of use
Quality of life across physical, emotional, social domains