Every calorie logged. Every macro tracked. A 12-month nutrition record.
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Protein
Non-negotiable. Leucine threshold (2.5g/meal) drives muscle protein synthesis even in deficit.
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Carbs
Timed around
training for glycogen replenishment. Fuels Zone 2 and strength work.
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Fat
Hormone health floor. Essential fatty acid minimum. Not further restricted.
Macros tell a different story than calories alone. Protein is the constraint — hit 180g and the rest falls into place. Miss it, and the deficit eats muscle instead of fat.
Protein adherence is the non-negotiable
180g/day target. Leucine threshold at every meal.
Hit rate = days at or above target out of total logged days.
0%50%80%100%
The leucine threshold for muscle protein synthesis is roughly 30g protein per meal. Getting 180g in two meals doesn’t count the same as spreading it across four.
Breakfast
42g
Target: 30g+
Threshold met
Lunch
38g
Target: 30g+
Threshold met
Dinner
48g
Target: 30g+
Threshold met
Snacks
22g
Target: 30g+
Below threshold
The meals that appear most often in the log. Sorted by frequency, with averaged macro breakdown per serving.
Loading meal data from MacroFactor...
Top protein sources from 30 days of food logs. Each bar shows average daily contribution.
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Weekday (Mon–Fri)
Avg calories—
Avg protein—
Avg carbs—
Avg fat—
Protein hit %—
Weekend (Sat–Sun)
Avg calories—
Avg protein—
Avg carbs—
Avg fat—
Protein hit %—
Are calories higher on training days? Cross-referencing MacroFactor with Strava activity dates.
Training Days
Avg calories—
Avg protein—
Days—
Rest Days
Avg calories—
Avg protein—
Days—
The most recent distinct meals from the food log. Real entries, not curated.
Consistency matters more than perfection. The goal isn’t to hit exact numbers every day — it’s to keep the trend line flat and the protein dots green.
MacroFactor’s adaptive TDEE learns your true energy expenditure from weight trends over 14+ days. As the body adapts to a deficit, TDEE drifts downward — tracking this prevents stalls.
Month 1
2,680
cal/day
baseline
Month 3
2,520
cal/day
↓ 160 cal
Month 5
2,410
cal/day
↓ 270 cal
Current
2,350
cal/day
↓ 330 cal
Real, relatable content. Where does Matthew actually order from?
About this observatory The story behind this page and Matthew's reflections
Food and I have had a complicated relationship since my twenties. The real shift
came around 2017 — relocation, an MBA, a promotion, my mum getting sick —
when eating stopped being about hunger and started being about quieting everything else.
Convenience. Something to fill a feeling. Food that wasn’t even that good.
MacroFactor didn’t fix the psychology. But it made the invisible visible,
and awareness is where the change starts.
I’ve lost 100 lbs before without tracking a single calorie. This isn’t about macros —
it’s about where my head is. When I’m on, this becomes second nature. When I’m off,
even a DoorDash order can break a streak. The data below captures both.
01
Testing: does front-loading 40g+ protein at breakfast predict hitting daily target?
Tracking correlation between breakfast protein load and end-of-day adherence.
Hypothesis · Under Test
MacroFactor data — pending sufficient sample size
02
Testing: does 30g+ fiber intake meaningfully reduce glucose variability?
Correlating daily fiber logs with CGM standard deviation readings.
Hypothesis · Under Test
MacroFactor fiber data cross-referenced with Dexcom Stelo — Glucose Observatory
03
Testing: does
deficit depth predict
training strain decline?
Correlating weekly caloric deficit with Whoop strain output.
Hypothesis · Under Test
Whoop strain data cross-referenced with MacroFactor deficit — pending data
04
The hardest part of tracking nutrition isn’t the math.
It’s the honesty. Logging the bad days is where the real data lives.
N=1 · Reflection
Every meal logged — even the ones I’d rather forget
“The plan is to log everything — including the 3,400-calorie Saturdays. A gap in the data is data too. It usually means something happened worth paying attention to.”
— Matthew
Hypotheses under test (4) Protein timing, deficit sustainability, meal frequency, fiber threshold
H-01
Testing: does front-loading 40g+ protein at breakfast predict hitting daily target?
Correlating breakfast protein load with end-of-day adherence • pending data
H-02
Testing: fiber intake × CGM glucose variability correlation
H-03
Testing: deficit depth × training strain correlation
Correlating weekly caloric deficit with Whoop strain output • pending data
H-04
Testing: meal distribution × MPS response tracking
Comparing 4-meal vs 2-meal protein distribution on recovery markers • pending data
Genomic context & micronutrients PEMT, VDR, FADS1 variants — why population averages don't apply
Genomics testing flagged specific micronutrient sensitivities. These targets are personalized, not RDA defaults. Deficits in these areas have measurable downstream effects.
Choline
PEMT variant — reduced endogenous synthesis. Liver health, methylation.
Gap: 170mg
Vitamin D
VDR polymorphism — higher dose needed. Immune function, bone density.
4,200 IU
Target: 5,000 IU
Supplemented
Omega-3 (EPA+DHA)
FADS1 variant — poor conversion from ALA. Anti-inflammatory, cardiac.
Near target
Folate
MTHFR C677T — reduced methylation. Homocysteine clearance, DNA repair.
Gap: 280mcg
Behavioral triggers Sleep deprivation, travel, stress — when nutrition falls apart
Nutrition failures aren’t random. They’re predictable. Cross-referencing macro misses with
sleep, stress, and travel data reveals the patterns.
Sleep deprivation
Testing: sleep deprivation × nutrition compliance correlation
Tracking whether short sleep nights predict caloric overshoot and protein misses • under test
Travel days
Testing: travel × caloric surplus pattern
Tracking whether travel days predict caloric overshoot, protein shortfall, and glucose variability • under test
High stress
Testing: stress × logging compliance and macro drift
Tracking whether low-HRV days predict unlogged meals and carb overshoot • under test
Macro deep-dives Carbs, fats, fiber — detailed breakdown and targets
Hydration tracking Water intake, energy correlation, and recovery impact
3.2L
daily average
Target: 3.5L+
Energy correlation
Testing: do days above 3.5L predict higher Whoop recovery scores? Tracking hydration × recovery correlation. Under test.
Recovery impact
Testing: does training-day hydration predict next-day HRV improvement? Comparing electrolyte-enhanced vs plain water. Under test.
Hydration is the most undertracked variable in nutrition. It affects glucose readings, recovery scores, and perceived energy — but rarely gets the same attention as macros.
Nutrition protocol The 5-rule system behind the data
Losing weight without losing muscle
The single biggest risk in aggressive weight loss is losing lean mass alongside fat.
Protein adequacy is the primary defense — 1.6–2.2g per kg of lean body mass preserves
muscle during a caloric deficit.
01
180g protein daily — non-negotiable. Leucine threshold (2.5g/meal) drives MPS even in deficit.
02
500–750 cal deficit via adaptive TDEE. Aggressive enough for progress, conservative enough for training quality.
03
Protein-first meal ordering — protein and fat before carbs. Blunts glucose spike. CGM-confirmed.
04
30g+ fiber daily for satiety, gut health, and glucose stability.
05
Every meal logged within 30 minutes. No cheat meals go unrecorded.