Understand your
body better.
Evidence-based guides on fitness, nutrition, and understanding health metrics.
✦ AI-powered — understands natural language
The Relative Age Effect: Why Birth Month Shapes School and Sport
Why 10,000 Steps on a Hill Burns Far More Than 10,000 Steps on Flat Ground: Gradient and Calorie Reality
Walking uphill burns substantially more calories per step than flat walking — but most step-to-calorie calculators don't account for this. A 70 kg person walking brisk on flat ground burns ~315 kcal/hour; the same person hiking uphill at 10% grade burns ~525 kcal/hour for the same steps. Here's the physics of vertical work, how MET values capture gradient, why GPS devices estimate terrain calories better than accelerometers alone, and the descending asymmetry (downhill costs fewer calories but isn't free).
Hyponatraemia and Endurance Sports: When Plain Water Isn't Enough — And Drinking Too Much Becomes the Risk
"Drink more water" advice has a limit — exercise-associated hyponatraemia, caused by drinking more fluid than you lose during prolonged exercise, has caused documented fatalities in endurance events. Here's why thirst-based drinking is now generally preferred over fixed schedules, who's at higher risk, and what role sodium intake actually plays.
How Step Counters Actually Work — and Why Your Phone and Smartwatch Never Quite Agree
A smartphone in your pocket and a fitness tracker on your wrist can disagree by 10-20% on the same walk — and neither is necessarily "wrong." Here's how accelerometer-based step counting actually works, why wrist-worn and hip-worn devices have different error patterns, what validation studies show about accuracy, and why trend-tracking matters more than absolute numbers.
Your Predicted Period Date Is an Average — Here's the Range Hiding Behind It
A period tracker says "your next period: March 15" — a single date, derived from averaging your past cycles. But "regular" cycles still vary day-to-day within a typical range, and factors like travel, stress, or illness can shift any individual cycle. Here's why treating the predicted date as the center of a range — rather than a precise point — provides more useful information for planning travel, events, and supplies.
Shift Work and Sleep: Why Night Shifts Fight Your Circadian Rhythm and What Helps
Night shift workers fight their own circadian rhythm — bright daylight signals "daytime" to the brain exactly when they need to sleep. Here's why standard sleep hygiene isn't enough for shift workers, the strategic light exposure techniques (including sunglasses on the commute home), rotating shift challenges, and napping strategies for night shifts.
Planning Leave Around Your Due Date: Why "Count Back From the Due Date" Is Just the Starting Point
"My due date is March 15th" doesn't directly answer "when should I start my leave" — leave planning means working backward from an estimated date, applying jurisdiction-specific policies, and building in a buffer for the very real possibility of early arrival. Here's why most leave frameworks let leave begin automatically upon birth regardless of the planned date, why work-handover targets should sit several weeks before the planned leave-start date, and why partner/co-parent leave planning is inherently date-range-based rather than fixed.
Period Tracking and Contraception: Why Predictions Mean Different Things Depending on Your Method
A period tracker calculates predictions based on natural cycle mechanics — but hormonal contraception works precisely by overriding that cycle, meaning "fertile window" predictions can mean something completely different (or nothing at all) depending on the method. Here's how combined pills, progestin-only methods, hormonal IUDs, and copper IUDs each affect what a tracker can meaningfully predict.
Body Fat Percentage Tracking: Why Your Daily Reading Swings 3% and What to Actually Pay Attention To
A body-fat reading that swings 2-3 percentage points overnight isn't your body composition changing that fast — actual fat changes occur over weeks, while day-to-day swings are almost entirely hydration, meal timing, and measurement-method noise. Here's why a consistent measurement protocol matters more than any single reading's accuracy, why trend lines over weeks reveal the real signal, and why switching measurement methods means starting a new trend, not continuing the old one.
Why "Exact Age in Years, Months, and Days" Has More Than One Right Answer — and What Happens If You're Born on Feb 29
"33 years, 9 months, 21 days" sounds precise — but because months have different lengths, the algorithm used to calculate the "months and days" portion involves a genuine implementation choice that can produce different results for the same two dates. Here's how the year/month/day "borrowing" algorithm works, why February 29th birthdays create a recurring ambiguity with real legal implications, and why "months" as a unit is inherently approximate.
"Ideal Weight" Gives You One Number — Health Guidance Gives You a Range, and the Difference Matters
"Ideal weight" formulas all output a single number — but health guidance (BMI ranges) is expressed as ranges, not single targets, and the gap between "a single target" and "a healthy range" matters for how people relate to these numbers. Here's why single-number framing can create "missed the target" feelings even within a healthy range, why it can encourage precision-seeking behaviors that body-fat-tracking articles already cautioned against, and why "healthy weight range" is generally the more useful framing.
DEXA, BIA, Calipers, and Hydrostatic Weighing: How Body Fat Measurement Methods Compare
DEXA, hydrostatic weighing, BIA scales, and skinfold calipers can give different body fat percentages for the same person on the same day. Here's what each method actually measures, why BIA scales are so sensitive to hydration, and how to choose between accuracy and practicality for tracking your own body composition.
Muscle, Metabolism, and Aging: Why Sarcopenia — Not "Slowing Down" — Explains Metabolic Change
A landmark study tracking 6,400 people found metabolism is remarkably stable from age 20 to 60 — what actually changes is muscle mass. Here's the Pontzer 2021 findings, how sarcopenia drives the "skinny fat" phenomenon, why protein needs increase with age, and why resistance training is the primary evidence-based intervention.
