How I lost fat and built muscle at the same bodyweight — and why simple, consistent habits beat chasing the perfect diet, workout, or supplement.
| Metric | Start | End | Change |
|---|---|---|---|
| Bodyweight | 164.6 lb | 163.8 lb | −0.8 |
| Body fat | 14.3% | 13.1% | −1.2 pts |
| Muscle mass | 81.3% | 82.4% | +1.1 pts |
| Fat mass* | 23.5 lb | 21.5 lb | −2.1 lb |
| Lean mass* | 141.1 lb | 142.3 lb | +1.3 lb |
*Directional estimates, not lab-grade.
The experiment set out to answer one question: can a natural lifter with a physically active lifestyle build muscle while staying lean?
Yes. Body recomposition—losing fat while building muscle at the same time—is one of the most difficult goals in fitness. The challenge is physiological: fat loss usually requires an energy deficit, while muscle growth typically benefits from an energy surplus. Recomposition asks the body to perform both opposing tasks simultaneously.
For beginners or individuals carrying significant body fat, this is often achievable because they have more metabolic flexibility and untapped potential for adaptation. For someone who is already lean and resistance-trained, however, that margin for improvement becomes extremely small.
That is precisely the condition tested in this experiment.
Sixteen months of continuous tracking — January 2025 to April 2026, roughly 485 days — from four independent sources:
• Nutrition — daily FatSecret food logs (~485 days), aggregated monthly: calories, protein, carbs, fat.
• Body composition — 16 monthly VitaFit bioimpedance readings.
• Activity — Apple Health, stored as average daily steps per month (~9.1 million steps in total over the 16 months).
• Training — a logged resistance program across four quarterly phases (~4–5 sessions/week).
The one gap in this dataset is sleep tracking. I averaged about eight hours a night, but without a daily sleep log, that figure stays as context rather than analyzable data. It's also important to set expectations on resolution: this is a monthly analysis built on sixteen data points, not daily measurements.
The starting point, January 2025: 164.6 lb, 14.3% body fat, BMI 23.2 — about 141 lb of lean mass, from an experienced lifter (3–5 years training) already active at ~13,000 steps in the slowest month. Lean, trained, and active before the experiment even began — so no easy beginner gains were waiting.
The obvious approach is to line up inputs against outcomes and look for the relationship. I ran those correlations — and they're a lesson in why single-subject, monthly data demands humility. Each chart below answers a specific question.
No. At the monthly level, calories and bodyweight showed almost no relationship — the correlation was just r = +0.05, statistically negligible. The months I ate the most simply weren't the months I weighed the most: intake stayed clustered around 3,100–3,250 kcal/day while my weight drifted across an 8 lb range for reasons that had little to do with calories.
This doesn't mean calories don't matter — they clearly do. It means that within a narrow calorie range, and with high, variable daily activity, calories alone couldn't explain month-to-month changes in weight. Bodyweight during recomposition is shaped by many things at once — activity, training, hydration, glycogen, and shifting body composition — and monthly averages smooth out much of the day-to-day signal. The honest takeaway: weight is a multivariate, adaptive process, not something a single number can predict — and high, consistent activity gave me real dietary flexibility.
Surprisingly, the opposite of what you'd expect. There was a moderate positive correlation (r = +0.45): the months with the most steps tended to have slightly higher body fat, not lower. The fitted line genuinely tilts the "wrong" way.
This is not evidence that walking adds fat — correlation isn't causation. It's a textbook case of step count being a poor stand-in for energy balance on its own. Several things plausibly explain it: my highest-step months (summer) were also my highest-calorie months; more walking can quietly raise appetite and reduce other movement, blunting the deficit you'd expect; and stretches of higher body fat may simply have prompted me to move more. Body fat is shaped by intake, training, protein, sleep, hydration, and hormones all at once — so a single behavior, read in isolation on one person's monthly data, will mislead far more often than it informs.
Consistency beats optimization. The habits I held for 16 months mattered more than any variable I fine-tuned.
High activity buys flexibility. A large daily step base made calories almost irrelevant to weight — room to eat and still stay lean.
Recomposition is slow but measurable. The change was small and real — visible only because I measured the right things over a long enough window.
Single-subject data needs humility. Correlations from one body mislead; trends over time tell the truth.
Each variable earns its place. Steps create a large, flexible energy budget. Resistance training is the signal to build and keep muscle. Protein protects that muscle. Maintenance calories give the slow trade time to happen without adding fat. Sleep underwrites recovery.