Test-Driven Development (TDD) for Health: Optimising My Body Like My Code

As a software developer, Test-Driven Development (TDD) is something I try to do. I (sometimes) write tests before writing code, ensuring each function does what it’s supposed to before it goes live. But recently, I had a thought: why shouldn’t we apply the same approach to health?

For years, traditional health advice has been one-size-fits-all—"eat more of this," "avoid that," "exercise in this way." But just like generic coding practices don’t always apply to every system, broad health recommendations don't necessarily work for everyone. That’s where a TDD approach to health comes in.

Defining Health ‘Tests’

Instead of blindly following trends, I set objective health tests using tools like:
✅ MediChecks for 60+ blood biomarkers
✅ Zoe for metabolic response tracking
✅ Garmin for heart rate variability and fitness metrics
✅ Body composition tracker at my local gym

These are my unit tests for health—measuring key indicators before making any changes.

Iterating Based on Data

Just like in software, once I define the tests, I adjust my inputs—diet, exercise, and lifestyle—and measure the impact.

Example: Omega-3 vs. B12

One of the biggest adjustments I made was increasing my Omega-3 intake while reducing B12. Why? Because my biomarker tests showed my Omega-3 index was lower than optimal, while my B12 levels were already high. Without testing, I wouldn’t have known I needed to tweak anything.

This kind of data-driven approach ensures that every health choice is backed by evidence rather than guesswork.

The Unexpected Benefit: Caring More

One of the most surprising outcomes? I care more about my health now than ever before.

Just like monitoring performance metrics in software development makes you more mindful of optimisations, tracking health data makes it impossible to ignore. Seeing real-time progress (or regression) gives instant feedback, making me more intentional about my choices.

When your biomarkers are in front of you, you can’t just hope you’re healthy—you know.

From "XYZ is Good for You" to "Is XYZ Good for Me?"

Health advice is often given in absolutes:
💬 “Turmeric is anti-inflammatory.”
💬 “Intermittent fasting boosts longevity.”
💬 “Strength training is essential.”

But now, instead of asking, "Is this healthy?" I ask, "Is this healthy for me?"

This shift is game-changing. I no longer follow generalised health trends—I follow what my own data tells me.

Why This Is the Best Health Paradigm Yet

To me, this approach is the closest thing to a ‘perfect’ health paradigm.

✅ It’s individualised—it accounts for my unique biology, not just population averages.
✅ It’s iterative—I can tweak, measure, and optimise over time.
✅ It’s proactive—I don’t wait for problems to arise; I prevent them.

Most health approaches rely on generic advice and trial-and-error. TDD for health takes an "N=1" approach, treating every individual as their own personal case study.

Final Thoughts: A Developer’s Approach to Health

I’d encourage anyone—especially data-minded people—to think about TDD for health. Define your own tests, track your metrics, and iterate accordingly.

After all, we optimise our code for performance—why not optimise our bodies in the same way?

Of course, TDD for health has its limitations. For example, women's menstrual cycles can influence snapshot metrics at different times of the month, making it trickier to interpret certain biomarkers consistently. Additionally, science doesn’t know everything. While data helps inform decisions, there are still unknowns and evolving research that mean we must balance testing with a degree of flexibility.

This is why I still stick to the fundamentals: a whole, organic, nutritionally dense, and diverse diet. Regardless of what individual tests suggest, a solid baseline of good nutrition is a non-negotiable foundation for long-term health.