Measuring the success of Exercise is Medicine programs through participant feedback and health outcomes

Measuring the impact of Exercise is Medicine (EIM) programs isn’t just about counting who signs up. It’s about understanding whether real people feel better, move more, and manage health conditions more effectively. If you’re involved in an EIM Level 2 setup, you want a measurement approach that’s practical, meaningful, and honest about what’s working and what isn’t. Here’s how to think about measuring success in a way that sticks.

Why measurement matters (even when it feels quiet)

Let me ask you this: if a program looks busy but doesn’t improve anyone’s health, is it truly successful? Exactly. Numbers and stories should echo each other. Participant feedback gives you the human side—the experience, satisfaction, and perceived benefits. Health outcomes show the tangible effect—the fitness gains, the better numbers on labs and vitals, and the daily life improvements that people notice in real time. Together, they offer a complete picture. That blend is what makes measurement not just a checkbox, but a compass for better design.

Two pillars you can rely on

1. Participant feedback

• What to collect: satisfaction with sessions, perceived ease of integrating movement into daily life, motivation changes, barriers faced, and overall sense of well-being.

• How to collect: short surveys after sessions, periodic in-depth interviews, and quick check-ins via apps or paper forms. Net Promoter Score can be useful to gauge willingness to recommend the program.

• Why it matters: feedback tells you whether the program respects participants’ time, preferences, and realities. It highlights practical tweaks—like better scheduling, clearer coaching, or lighter-touch reminders—that can boost ongoing engagement.

2. Health outcomes

• What to collect: objective measures of fitness (e.g., VO2 max, endurance tests), body composition where appropriate, blood pressure, lipid levels, glucose control (like HbA1c for diabetes risk), weight, waist circumference, activity levels (steps, active minutes), and quality of life indicators.

• How to collect: baseline and follow-up tests at set intervals; use of standardized protocols; integration with electronic health records when possible; wearable data or mobile app logs for ongoing activity tracking.

• Why it matters: these data tell you whether the program moves the needle on health. They also help you connect the dots between what participants do (or don’t do) and the health outcomes that matter to clinicians and payers.

A practical way to structure measurement

• Start with a simple, realistic plan: pick a small set of balanced measures that cover both pillars. For example, a 6-month plan might track participant satisfaction, adherence to sessions, minutes of moderate-to-vigorous activity per week, and a few key health indicators (blood pressure, a fitness test, and a patient-reported health status).

• Use SMART goals for each measure: Specific, Measurable, Achievable, Relevant, Time-bound. “Increase average weekly active minutes from 90 to 150 by month 6” is clearer and more actionable than a vague target.

• Create a lightweight cadence: data collection should feel routine, not a burden. After the first month, you’ll know what’s realistic to pull without overloading staff or participants.

What to measure, exactly (a starter kit)

• Participation and engagement

• Enrollment numbers (for reach, not the sole measure)

• Retention rate (how many stay for the full program)

• Session attendance consistency

• Adherence to recommended activity outside sessions

• Experience and satisfaction

• Post-session quick surveys (1–5 scale)

• Overall satisfaction and perceived value

• Barriers to participation (time, location, cost, transportation)

• Likelihood to continue moving or to refer a friend

• Health and functional outcomes

• Cardiorespiratory fitness (a simple submaximal test or VO2 max if feasible)

• Blood pressure and resting heart rate

• Weight and waist measurements

• Blood glucose/A1c or lipid profiles when relevant

• Self-reported energy levels, sleep, and mood

• Functional measures (short walk tests, balance, strength tasks)

• Quality of life and everyday impact

• Short form health surveys (like SF-12 or similar) or simpler QoL questions

• Ability to perform daily activities without fatigue

Bringing data together: the story behind the numbers

Numbers without context are easy to misread. The magic comes from connecting feedback with outcomes. Here are a few ways to blend them:

• Correlation with intent: do participants who rate the program highly also show bigger improvements in fitness or health markers? If yes, you’re seeing alignment between experience and impact.

• Time-based threads: note when health gains appear relative to engagement milestones. Do people who hit 80% attendance for eight weeks tend to show early improvements? This helps you time coaching nudges.

• Segmented insights: different groups may respond in unique ways. Some people with preexisting conditions might show pronounced health benefits, while others gain more confidence and self-efficacy. Look for patterns, not just averages.

Methods and tools you can lean on

• Surveys and PROMs (patient-reported outcome measures): keep them short but meaningful. A few well-chosen questions can reveal a lot about satisfaction and perceived impact.

• Objective tests and clinical data: use standardized protocols. If you can, get data from medical providers with consent to create a more complete picture.

• Activity tracking: wearables or phone apps can fill in the blanks between sessions. You don’t need perfect data—just consistent data to show trends.

• Data dashboards: a simple dashboard helps teams see the pulse of the program at a glance—what is improving, what isn’t, and where to adjust.

A lightweight framework that helps keep things honest

• RE-AIM is a popular lens in health programs:

• Reach: who’s joining and who isn’t

• Effectiveness (impact): changes in health outcomes and quality of life

• Adoption: who delivers the program and how consistently

• Implementation: how well the program is delivered

• Maintenance: whether gains persist over time

• PDCA (Plan-Do-Check-Act) cycle can keep measurement practical:

• Plan the data you’ll collect

• Do a small pilot

• Check what the data tell you

• Act to refine the program and measurement plan

Common missteps to avoid (and how to sidestep them)

• Focusing only on enrollments: you’ll miss whether people are actually benefiting. Enrollments matter, but outcomes matter more.

• Gathering data you can’t use: if you collect data that no one analyzes, it’s a wasted effort. Tie every metric to a concrete decision you’ll make.

• Overloading participants with surveys: keep feedback requests short and purposeful. A few quick questions after sessions can be enough.

• Ignoring privacy: protect personal data and be transparent about how it’s used. Consent and data security aren’t optional.

• Skimping on follow-up: improvements that show up after several months can be just as important as initial gains. Build in long-term check-ins.

Real-world flavor: what success could look like

Imagine a local community health program that serves adults with sedentary routines. After six months, participants report they feel more energized and capable of everyday tasks. They say they appreciate shorter, more practical workout options that fit into a busy day. Clinically, a subset shows modest reductions in blood pressure and better blood sugar control. The program also notes higher attendance in sessions for people who live farther away, thanks to a new shuttle service and staggered class times.

What’s the takeaway? Measured well, the program proves it’s helping people move more, feel better, and manage health risks more effectively. That’s success that lasts, not just a crowd that shows up.

Putting measurement into action: quick-start tips

• Start small: pick 3–4 core metrics for the first cycle. Add more later as you get comfortable.

• Involve stakeholders early: coaches, clinicians, participants, and admins should help choose the most relevant measures.

• Use simple tools: forms, basic clinical data, and a straightforward dashboard. You don’t need a fancy system to begin with.

• Build feedback loops: share results with participants and staff. Seeing progress or recognizing hurdles keeps motivation high.

• Plan for the long game: maintenance matters. Schedule periodic follow-ups to track whether health gains stick.

A word on why this approach fits EIM’s spirit

EIM is about weaving movement into everyday life and health care. That means measuring isn’t a one-off audit; it’s a continuous conversation between participants, coaches, and clinicians. When you evaluate both how people feel about the program and how their health changes, you honor the full promise of EIM: movement that sticks, supported by clear, honest data that guides better choices for individuals and communities alike.

In closing, here’s the practical takeaway: the most reliable gauge of success combines two voices—the personal, lived experience of participants and the clinical or objective health signals you can measure. Lean into that dual perspective. Use a few well-chosen metrics, collect data with a light touch, and keep the lines of communication open. Do that, and you’ll not only show value—you’ll foster what matters most: healthier lives through regular movement.