The Engagement Trap: Why More Interaction Isn’t Always Better

Imagine you’ve just designed a meditation app. You measure success by how often users open the app and how long they stay engaged. The numbers look great—users are logging in daily, spending an average of 10 minutes per session. But after six months, you realize something’s off: despite high engagement, few users report lasting improvements in stress levels or mindfulness. The app is sticky, but it’s not creating meaningful behavior change.

This is the engagement trap: designing for interaction instead of behavioral outcomes. Many products optimize for engagement, assuming that if people use something frequently, they must be benefiting from it. The more check-ins, the higher the retention rate, the more notifications clicked, the more “success” the team can measure. But behavior change doesn’t work like that. If the goal is to improve lives, we must design for behavior change first, with engagement as a tool to support that goal. Engagement is a means, not an end. 

Engagement vs. Behavior Change: What’s the Difference?

So what really is the difference between designing for engagement and designing for behavior change?

• Designing for engagement aims to keep users interacting with a product. Success is often measured by metrics like time spent, session frequency, and retention rates.

• Designing for behavior change focuses on helping users achieve meaningful, lasting changes in their behavior outside the product. Success is measured by real-world outcomes (e.g., whether they actually adopt a new behavior in their daily lives) not just in-app activity.

Why This Distinction Matters

There are three important reasons why focusing on engagement alone is a problem:

1. Engagement doesn’t guarantee behavior change: Designing only for engagement can keep people busy without actually progressing. They can spend hours on a fitness app without exercising more or scroll through financial management content without saving money. 

2. Over-focusing on engagement can backfire: Over-optimizing for interaction alone can lead to compulsive usage, or unintended consequences like stress from excessive tracking. What’s more, users may become reliant on the app itself, rather than gaining autonomy and confidence in their behavior outside the digital environment.

3. Behavior change requires different strategies: Effective design is based on behavioral science principles, such as habit formation, motivation, and ability. It requires an understanding of motivation, barriers, and evidence-based techniques rooted in human psychology.

Again, a focus on engagement isn’t bad necessarily—it’s indeed very useful! But it becomes problematic when it gets mistaken for the goal. And if engagement metrics themselves become the goal, they cease to be a good measure. Because when we treat engagement like the goal, we end up optimizing for the wrong things.

The Difference Between Little ‘e’ and Big ‘E’

A useful framework [1] to bridge engagement and behavior change is distinguishing between Little e and Big E:

• Little e (engagement with a product feature or behavioral intervention): These are small, frequent interactions within the app, like quick check-ins, logging a meal, or notification-driven interactions. They bring users back and guide them toward valuable features.

• Big E (engagement with the target behavior): These involve the user applying something they learned or practiced in-app into their real-life routines and decisions that ultimately lead to real-world impact, like eating healthier, exercising, practicing mindfulness.

Most digital products are designed to maximize Little e—nudging users to interact, tap, scroll, and track. But just because someone is engaging with a product doesn’t mean they’re engaging in the behavior it’s supposed to support.

The critical point is this: one doesn’t go without the other. Little e supports Big E. When aligned with the right behaviors, engagement features are a tool to enable the behavioral outcomes users care about. But simply assuming that more engagement will automatically lead to behavior change is a flawed approach. Instead, we must test whether engagement is genuinely supporting the behaviors we want to reinforce.

Building Engagement That Drives Real Change

An effective way to balance Little e and Big E is through creating engagement frameworks. These frameworks clearly help you connect every feature or product decision onto the real-world behaviors they’re intended to drive. Here are some steps to get you started: 

1. Start With the Key Behavior (Not the Feature)

Before designing any new feature, consider the behavior you’re trying to improve:

• What’s the behavior you’re designing for? (Big E)

• How will this in-app feature support that? (Little e)?

For example, Duolingo (language learning app) famously uses streaks (Little e) to build consistent language learning (Big E). However, their challenge remains ensuring streaks translate into genuine language proficiency rather than mere check-ins to keep up the streak.

2. Map Engagement Metrics to Real-World Impact

Ideally, every Little e interaction has a connection to a Big E behavioral outcome. If a feature doesn't clearly support real-world behavior change, maybe it’s time to reconsider its value.

For example, Calm (meditation app) ties its ‘daily calm’ feature to measurable outcomes like reduced stress or improved sleep quality—outcomes they validate regularly through user surveys. This way, the feature remains aligned with the user benefit, rather than superficial interactions.

3. Reinforce the Behavior (not Just Engagement)

It’s easy to reward users for showing up, but the real value comes from reinforcing meaningful progress. Instead of just incentivizing app interactions, tie rewards to real-world actions. When rewards align with real progress, users stay engaged for the right reasons, not to just keep their streak alive.

Noom (weight loss app) encourages users not just to log meals but to reflect on their eating patterns to reinforce lasting mindfulness and behavior change over passive tracking.

4. Redefine Measures of Success

In addition to tracking KPIs like session length and daily active users, develop metrics that better reflect real-world impact. Once you’ve clearly identified the desired behavioral outcome, you can proxies for it: indirect indicators for whether increased app use translates to real-world outcomes.

If you want to improve sleep quality, you can use proxies like engagement with sleep content, reduced late-night screen time, and self-reported sleep improvements. By triangulating data from, in this case, in-app behavior, self-assessment, and device usage, you can build a stronger case for behavior change.

5. Validate Impact on Outcomes Over Time

Tracking proxies is a good start, but ultimately, we want to know whether engagement translates to sustained behavior change over time. There are multiple ways to do this, and the right approach depends on your specific product and target behavior, but the key is to measure whether users are actually reaching (and maintaining!) their intended goals.

For example, Fitbit (fitness app and wearable) tracks step counts but also analyzes long-term trends in activity levels and health markers to assess fitness improvements over time.

Final Thought: Designing for Impact over Clicks

Designing for engagement (Little e) makes sense and is important, but when we focus too much on it without considering real impact, we risk shooting ourselves in the foot. After all, we’re in the business of building products that help people improve their behavior. If your mission is to genuinely help users lead better, healthier lives, you need to look beyond clicks, screen time, or conversion rates. 

Designing for behavior change asks for deeper reflection, strategic rigor, and a willingness to measure success differently. It challenges us to create products that serve users rather than exploit their attention. At the end of the day, success isn’t about how much time people spend in your app—it’s about how much their lives improve because of it. 

So next time you celebrate hitting those engagement KPIs, ask yourself: Have we truly made our users' lives better or did we just keep them busy?

This article was originally published at Nuance Behavior.

References

Cole-Lewis, H., Ezeanochie, N., & Turgiss, J. (2019). Understanding health behavior technology engagement: Pathway to measuring digital behavior change interventions. JMIR formative research, 3(4), e14052.