Fasting, Ketones, and Glucose Tracking

ISEF Category: Biomedical and Health Sciences

Subcategory: Nutrition and Natural Products  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: Full Year

The Hook

Your body can shift fuel sources overnight. That switch can show up as darker ketone strips, lower fasting glucose, or a different resting HRV pattern. A 16:8 fasting plan gives you a simple way to test whether those signals move together. The data can come from your own routine, which makes the project feel real.

What Is It?

Intermittent fasting with a 16:8 pattern means you eat during an 8-hour window and fast for the other 16. That timing can change how your body uses fuel. When food is scarce, your liver can make ketones, which are small fuel molecules made from fat. A ketone strip is like a color meter for that fuel shift.

Fasting glucose is your blood sugar after a night without food. Resting HRV, or heart rate variability, is the tiny beat-to-beat change in your pulse, and many wearables use it as a rough sign of recovery and stress. Your project asks whether these three signals move together when meal timing changes.

Why This Is a Good Topic

This is a strong science fair topic because you can measure it at home, track it day after day, and compare each person with their own baseline. It connects to sleep, metabolism, and meal timing, which people care about in real life. You can also learn real analysis skills, like cleaning repeated-measure data, checking adherence, and fitting a mixed-effects model.

Research Questions

• How does a 16:8 eating window change morning ketone strip readings over repeated days?
• What is the effect of a 16:8 fasting schedule on fasting glucose compared with each person's baseline?
• Does resting HRV differ on fasting days versus non-fasting days?
• To what extent do ketone strip readings track with fasting glucose within the same person?
• Which participant traits, such as sleep duration or starting body mass, predict larger changes in ketones?
• How does adherence to the eating window affect day-to-day variation in morning ketones and glucose?

Basic Materials

• Urine ketone strips.
• Home glucose meter with enough test strips and lancets.
• Finger-prick alcohol swabs and bandages.
• Wearable or chest strap that records resting HRV.
• Smartphone with camera for strip photos.
• Spreadsheet or paper log for daily entries.
• Kitchen scale for tracking body weight.
• Informed-consent forms and a daily symptom checklist.

Advanced Materials

• Continuous glucose monitor.
• Blood beta-hydroxybutyrate meter.
• ECG chest strap or 3-lead ECG system.
• Automated blood pressure cuff.
• REDCap or another secure data capture system.
• R or Python for mixed-effects modeling.
• Tablet or phone with a locked lighting setup for strip photos.
• University-approved consent and privacy workflow.

Software & Tools

• R: Fits mixed-effects models, checks assumptions, and makes repeated-measure plots.
• Python: Cleans daily logs, merges wearable files, and flags missing data.
• jamovi: Lets you run mixed-effects analyses with a point-and-click interface.
• Google Sheets: Tracks daily entries and helps you spot adherence gaps.
• REDCap: Collects repeated survey and measurement data in a secure form.

Experiment Steps

1. Define your primary outcome, then decide whether ketones, glucose, or HRV gets the main analysis.
2. Set one fixed morning measurement routine so every day uses the same order and context.
3. Build a codebook for fasting days, eating-window slips, missing entries, and wearable glitches.
4. Plan your comparison groups and covariates before you touch the final data set.
5. Choose the mixed-effects model structure, then map the plots that will test the pattern you expect.

Common Pitfalls

• Reading ketone strips under changing light, which makes the color call drift across days.
• Mixing up true fasting mornings with days after late snacks, which weakens the exposure definition.
• Comparing raw glucose values across people without adjusting for each person's baseline, which hides the within-person signal.
• Treating one low-quality HRV reading as a real shift, which can happen when a wearable loses signal during sleep.
• Averaging all repeated days into one number, which removes the structure that mixed-effects models are built to analyze.

What Makes This Competitive

A stronger version of this project does more than compare averages. You can preregister one main outcome, track adherence carefully, and separate within-person change from between-person differences. If you add covariates like sleep, meal timing, or activity, you can test whether fasting itself explains the signal or just rides along with other habits. Careful model choice and clean repeated measures matter more here than a flashy graph.

Project Variations

• Compare 16:8 fasting with a later eating window to see whether morning ketones shift more.
• Swap urine ketone strips for blood ketone meters and test whether the two measures tell the same story.
• Add sleep duration or step count as a covariate to see whether recovery or activity explains part of the pattern.

Learn More

• PubMed: Search review articles on intermittent fasting, ketone metabolism, fasting glucose, and heart rate variability.
• NCBI Bookshelf: Read free physiology chapters on energy balance, liver metabolism, and endocrine control.
• NIH MedlinePlus: Find patient-friendly background on glucose, ketones, and home health measurements.
• Nutrients: Search open-access review articles on time-restricted eating and cardiometabolic markers.
• Frontiers in Physiology: Look for open-access papers on heart rate variability, stress, and metabolic state.