ATHLETICA | Science & Methodology

Nutrition Science

FORMULAS &
CITATIONS

Your daily calorie and macro targets are computed from these formulas, not generated by AI. Logged meals, the meal health score and the body scan are AI estimates: how they work and their sources are in sections 16 and 17.

Calorie Target

TDEE & DAILY CALORIE GOAL

Your daily calorie target starts from your Basal Metabolic Rate (BMR), the energy your body needs at rest, calculated with the Mifflin-St Jeor equation. It is multiplied by an activity factor based on your training minutes per week (sessions × session length), then adjusted for your goal. For fat loss the deficit is moderate and never goes below a safe floor. Each coach can tune these values from their Sport Science panel, but only inside the safe limits shown below, which the engine enforces.

Formulas Used

BMR, maleBMR = (10 × weight_kg) + (6.25 × height_cm) − (5 × age) + 5

BMR, femaleBMR = (10 × weight_kg) + (6.25 × height_cm) − (5 × age) − 161

TDEETDEE = BMR × Activity Factor

Minutes/wksessions per week × session length

< 60 min/wkFactor = 1.375 (lightly active) · coach range 1.2–1.45

60–179 min/wkFactor = 1.55 (moderately active) · coach range 1.2–1.6

180–299 min/wkFactor = 1.725 (very active) · coach range 1.4–1.8

≥ 300 min/wkFactor = 1.9 (extremely active) · coach range 1.55–1.95

CutTarget = TDEE − deficit, 300 kcal/day by default (coach can set 100–500)

FloorTarget ≥ 70% of TDEE and ≥ 1,200 kcal (women) / 1,500 kcal (men), never above TDEE

BulkTarget = TDEE + surplus, surplus ≤ 300 kcal/day (coach can set 100–350)

MaintainTarget = TDEE

Upper limit4,500 kcal/day

Mifflin et al. (1990): Mifflin-St Jeor equation, used for the BMR formulas above

A new predictive equation for resting energy expenditure in healthy individuals

American Journal of Clinical Nutrition, 51(2), 241-247

doi.org/10.1093/ajcn/51.2.241 ↗

Frankenfield, Roth-Yousey & Compher (2005)

Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review

Journal of the American Dietetic Association, 105(5), 775-789

doi.org/10.1016/j.jada.2005.02.005 ↗

Hall et al. (2011)

Quantification of the effect of energy imbalance on bodyweight

The Lancet, 378(9793)

doi.org/10.1016/S0140-6736(11)60812-X ↗

ACSM Guidelines for Exercise Testing and Prescription, 11th Ed. (2022)

Activity multiplier classifications for TDEE

American College of Sports Medicine

acsm.org ↗

Protein

PROTEIN TARGET

Protein is calculated from bodyweight, at 1.8 g per kg by default. Coaches can set it between 1.4 and 2.2 g/kg, the evidence-based range for people who do resistance training. Higher intakes help preserve muscle during a calorie deficit.

Formula Used

ProteinProtein (g) = weight_kg × 1.8 (coach range 1.4–2.2)

Safety range60 – 220 g/day

Helms et al. (2014)

Dietary protein during caloric restriction in resistance trained lean athletes

International Journal of Sport Nutrition and Exercise Metabolism, 24(2)

doi.org/10.1123/ijsnem.2013-0054 ↗

EFSA Panel on Dietetic Products (2012)

Scientific Opinion on Dietary Reference Values for protein

EFSA Journal, 10(2)

doi.org/10.2903/j.efsa.2012.2557 ↗

Jäger et al. (2017), ISSN

International Society of Sports Nutrition Position Stand: protein and exercise

Journal of the International Society of Sports Nutrition, 14, 20

doi.org/10.1186/s12970-017-0177-8 ↗

Fat

FAT TARGET

Fat is set at 25% of total daily calorie target — within the 20–35% range recommended for active adults by ACSM and USDA.

Formula Used

FatFat (g) = (Target kcal × 0.25) ÷ 9

Atwater factorFat = 9 kcal/g

Safety range40 – 100 g/day

Thomas et al. (2016) — ACSM/AND/DC Joint Position Statement

Nutrition and Athletic Performance

Medicine & Science in Sports & Exercise, 48(3)

doi.org/10.1249/MSS.0000000000000852 ↗

Carbohydrates

CARBOHYDRATE TARGET

Carbohydrates fill the remaining calories after protein and fat are allocated. This prioritises protein and fat targets while keeping carbs flexible and aligned to energy needs.

Formula Used

CarbsCarbs (g) = (Target kcal − Protein kcal − Fat kcal) ÷ 4

Protein kcalProtein (g) × 4

Fat kcalFat (g) × 9

Atwater factorsProtein = 4 kcal/g · Fat = 9 kcal/g · Carbs = 4 kcal/g

Safety range50 – 400 g/day

USDA Dietary Guidelines for Americans 2020–2025

Recommends 45–65% of calories from carbohydrate for healthy adults

U.S. Department of Agriculture & U.S. Dept. of Health and Human Services

dietaryguidelines.gov ↗

Safety Rule

48-HOUR MUSCLE RECOVERY LOCK

No muscle group trained in the previous 48 hours is assigned as a primary mover. Enforced in code before the AI is called — cannot be overridden by any instruction or conversation.

Rule Logic

BLOCKED ifhours_since_last_session < 48 OR muscle ∈ lastMusclesHit

ELIGIBLE ifhours_since_last_session ≥ 48 AND muscle ∉ lastMusclesHit

Garber et al. (2011): ACSM Position Stand

Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults

Medicine & Science in Sports & Exercise, 43(7), 1334-1359 · At least 48 h between resistance sessions for the same muscle group

doi.org/10.1249/MSS.0b013e318213fefb ↗

MacDougall et al. (1995)

The time course for elevated muscle protein synthesis following heavy resistance exercise

Canadian Journal of Applied Physiology, 20(4), 480-486

doi.org/10.1139/h95-038 ↗

Progression Rule

RPE-DRIVEN PROGRESSIVE OVERLOAD

After every session, the student rates their exertion (RPE 1–10). The engine computes the next load target from this — not the AI. The AI receives it as a directive it cannot deviate from.

Load Targets by RPE

RPE ≤ 6.0Next load = last load × 1.05 [or +3 reps if bodyweight]

RPE ≤ 7.5Next load = last load + 2.5 kg [or +1 rep if bodyweight]

RPE ≤ 8.5Next load = last load, reps + 1

RPE > 8.5Next load = last load, reps unchanged [consolidate]

1RM estimate1RM ≈ load × (1 + reps ÷ 30) (Epley formula)

Zourdos et al. (2016)

Novel Resistance Training-Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve

Journal of Strength and Conditioning Research, 30(1)

doi.org/10.1519/JSC.0000000000001049 ↗

Fatigue Rule

FATIGUE SCORE & AUTOMATIC DELOAD

Systemic fatigue accumulates from session RPE, volume and density. When it exceeds a threshold, a deload is automatically mandated — 40% volume reduction, no load increase. The AI cannot cancel it.

Fatigue & Deload Logic

AccumulationfatigueScore += session_RPE × volume_factor × density_factor

RangefatigueScore clamped to 1.0 – 10.0

IntermediateDeload mandated if fatigueScore > 7.5

AdvancedDeload mandated if fatigueScore > 8.0

Deload volumenormal volume × 0.60 (40% reduction)

Meeusen et al. (2013) — ECSS/ACSM Joint Consensus Statement

Prevention, diagnosis and treatment of the overtraining syndrome

European Journal of Sport Science, 13(1)

doi.org/10.1080/17461391.2012.730061 ↗

Volume Rule

WEEKLY VOLUME — MEV & MAV

Athletica tracks weekly sets per muscle group and enforces a floor (Minimum Effective Volume) and a ceiling (Maximum Adaptive Volume). Below MEV the muscle is under-stimulated. Above MAV the body cannot adapt positively — additional volume becomes junk volume that increases injury risk.

Volume Tracking & Enforcement

AccumulationweeklyMuscleSets[muscle] += sets_assigned_this_session

Below MEVMuscle flagged as priority → engine increases assignment

MEV ≤ sets ≤ MAVNormal zone — standard assignment

Above MAVMuscle blocked from additional sets this week

Major musclesMEV ≈ 6–10 sets/wk · MAV ≈ 16–22 sets/wk

Minor musclesMEV ≈ 4–6 sets/wk · MAV ≈ 12–16 sets/wk

ResetweeklyMuscleSets = 0 at start of each training week

Schoenfeld, Ogborn & Krieger (2017)

Dose-response relationship between weekly resistance training volume and increases in muscle mass: a systematic review and meta-analysis

Journal of Sports Sciences, 35(11), 1073-1082

doi.org/10.1080/02640414.2016.1210197 ↗

Israetel et al. (2019) — Renaissance Periodization

Scientific Principles of Strength Training — MEV / MAV / MRV framework

Renaissance Periodization

rpstrength.com ↗

Structure Rule

SPLIT ROTATION

Today's target muscles are determined by a structured training split that rotates automatically after each session. The split is assigned by the engine — the AI receives it as a directive and cannot reorder or skip days.

Split Assignment Logic

Day indexsplitDayIndex = (splitDayIndex + 1) mod split.length after each session

3 days/wkFull Body × 3

3–4 days/wkUpper / Lower

4 days/wkPush / Pull / Legs / Core

Fat loss goalFat Loss Circuit — metabolic conditioning format

Mobility goalMobility Flow — low intensity, controlled movement

AI receives"Today is a Push day" — as unbreakable directive

Schoenfeld et al. (2015)

Influence of Resistance Training Frequency on Muscular Adaptations in Well-Trained Men

Journal of Strength and Conditioning Research, 29(7)

doi.org/10.1519/JSC.0000000000000970 ↗

Periodisation Rule

8-WEEK PERIODISATION BLUEPRINT

A complete 8-week macro training plan is generated at program start and stored as a structured blueprint. Each week has a defined block type, volume percentage, intensity percentage and priority muscles. The engine reads this blueprint every session and injects the current week's directives. The AI cannot jump weeks, skip deload weeks or operate outside the current block type.

Blueprint Structure per Week

Week blueprint{ block_type, volume_pct, intensity_pct, priority_muscles, coach_note }

Block sequenceFoundation → Hypertrophy → Strength → Power → Deload

Foundationvolume_pct = 60% · intensity_pct = 55%

Hypertrophyvolume_pct = 85% · intensity_pct = 70%

Strengthvolume_pct = 75% · intensity_pct = 85%

Powervolume_pct = 65% · intensity_pct = 95%

Deloadvolume_pct = 40% · intensity_pct = 60% · no load increase

Sets this sessionmax_sets = base_sets × (volume_pct ÷ 100)

Rhea & Alderman (2004)

A meta-analysis of periodized versus nonperiodized strength and power training programs

Research Quarterly for Exercise and Sport, 75(4)

doi.org/10.1080/02701367.2004.10609174 ↗

ACSM Position Stand (2009)

Progression Models in Resistance Training for Healthy Adults

Medicine & Science in Sports & Exercise, 41(3)

doi.org/10.1249/MSS.0b013e3181915670 ↗

Onboarding Rule

GOAL FEASIBILITY ENGINE

When a student declares a weight-loss goal, Athletica runs a pure mathematical assessment of what is achievable with their stated plan and presents three honest options. This is computed deterministically — not generated by AI. The student sees the maths, not just the answer.

Feasibility Computation

Weekly lossweekly_fat_loss_kg = (daily_deficit × 7) ÷ 7,700

Weeks to goalweeks_to_goal = fat_to_lose_kg ÷ weekly_fat_loss_kg

Option ASolve for sessions/week needed to hit goal in target weeks

Option BProject actual result with current plan and timeline

Option CSolve for weeks needed at current commitment level

Energy density7,700 kcal/kg adipose tissue (EFSA reference value)

Hall (2008)

What is the Required Energy Deficit per unit Weight Loss?

International Journal of Obesity, 32(3)

doi.org/10.1038/sj.ijo.0803720 ↗

Jensen et al. (2013) — AHA/ACC/TOS Clinical Guideline

Guideline for the Management of Overweight and Obesity in Adults

Circulation, 129(25 Suppl 2) · Establishes 0.5–1.0 kg/week as safe and sustainable

doi.org/10.1161/01.cir.0000437739.71477.ee ↗

Session Metric

CALORIE EXPENDITURE ESTIMATION

Session calorie burn is estimated from exercise-specific MET-derived coefficients scaled to the student's actual body weight. Each exercise in the library carries three coefficients computed at creation time. These are estimates — individual metabolic rates vary.

Per-Exercise Calorie Estimation

Rep-basedkcal = caloriesPerRep × reps_completed × (bodyweight_kg ÷ 75)

Timedkcal = caloriesPerSecond × duration_s × (bodyweight_kg ÷ 75)

Distancekcal = caloriesPerMetre × distance_m × (bodyweight_kg ÷ 75)

Session totalsession_kcal = Σ (kcal per exercise across all sets)

Reference body75 kg (WHO/EFSA standard reference)

Ainsworth et al. (2011)

Compendium of Physical Activities: a second update of codes and MET values

Medicine & Science in Sports & Exercise, 43(8) · The standard reference for exercise energy expenditure

doi.org/10.1249/MSS.0b013e31821ece12 ↗

Daily Adaptation

READINESS ASSESSMENT

Before each session, students report sleep quality, soreness and energy on a 1–5 scale. These are assembled into a ReadinessSnapshot that influences session intensity. Subjective readiness metrics are validated by the literature as reliable training load management tools — this is how Athletica adapts to the student's actual day, not just their program week.

Readiness Computation

Inputssleep (1–5) · soreness (1–5) · energy (1–5)

Averagereadiness_avg = (sleep + soreness + energy) ÷ 3

avg ≥ 4.0High readiness → standard or increased session volume

3.0 ≤ avg < 4.0Moderate readiness → standard session

avg < 3.0Low readiness → volume reduced · AI notified via SessionConstraints

DefaultModerate assumed if readiness not reported

Saw et al. (2016)

Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review

British Journal of Sports Medicine, 50(5), 281-291 · Subjective well-being measures track training load better than common objective markers

doi.org/10.1136/bjsports-2015-094758 ↗

Nutrition Tab

ACTIVE BURN ESTIMATE

The weekly active burn shown on the Nutrition tab estimates the calories spent in training from your weekly training minutes, your body weight and the average energy cost of a mixed strength and circuit session. It is an estimate: individual metabolic rates vary.

How it is calculated

kcal/minkcal/min = MET × 3.5 × weight_kg ÷ 200 (ACSM metabolic equation)

Session MET5.5 (between resistance training, 8-15 reps = 3.5 and vigorous circuit training = 8.0)

Active burnkcal/week = days/week × minutes/session × kcal/min

Example75 kg adult ≈ 7.2 kcal/min

Ainsworth et al. (2011)

2011 Compendium of Physical Activities: a second update of codes and MET values

Medicine & Science in Sports & Exercise, 43(8), 1575-1581 · Codes 02054 (3.5 METs) and 02040 (8.0 METs)

doi.org/10.1249/MSS.0b013e31821ece12 ↗

ACSM Guidelines for Exercise Testing and Prescription, 11th Ed. (2022)

Metabolic calculations: converting METs to energy expenditure

American College of Sports Medicine

acsm.org ↗

Nutrition Tab

PROTEIN TOP-UP

When the planned meals of the day fall short of your protein target, the gap is shown with an equivalent in protein servings. A supplement is a convenient option, not a requirement: protein-rich foods such as Greek yogurt, eggs or cottage cheese cover the same gap.

How it is calculated

Gapgap_g = protein_target_g − protein_in_planned_meals_g

Shown ifgap_g > 10 g and goal = muscle gain

Servingsservings = ceil(gap_g ÷ 25) (1 scoop of whey ≈ 25 g protein)

Jäger et al. (2017): ISSN Position Stand

International Society of Sports Nutrition Position Stand: protein and exercise

Journal of the International Society of Sports Nutrition, 14, 20 · Protein doses of 20-40 g and whole foods vs supplements

doi.org/10.1186/s12970-017-0177-8 ↗

Food Scanner · AI estimate

MEAL SCANNER & HEALTH SCORE

When you log a meal with a photo or a description, Athletica AI recognises each food and estimates its weight. Calories and macros come from standard food composition reference values, and calories are checked with the Atwater factors. Every weight can be corrected by the user. The health score from 1 to 10 follows the World Health Organization healthy diet guidance and the NOVA classification of ultra-processed foods. It is general information, not medical or dietary advice.

How it is calculated

Per foodkcal, protein, carbs, fat for the estimated weight, from USDA FoodData Central / ANSES Ciqual values

Consistencykcal ≈ 4 × protein_g + 4 × carbs_g + 9 × fat_g (Atwater factors)

Health score upprotein, fibre, fruit, vegetables, legumes, whole grains

Health score downadded sugars, salt, saturated or trans fat, deep frying, ultra-processed foods (NOVA group 4)

Accuracyphoto estimates can be off by 20-30%: the user can edit every weight

USDA Agricultural Research Service

FoodData Central

U.S. Department of Agriculture · Food composition database

fdc.nal.usda.gov ↗

ANSES

Ciqual French food composition table

French Agency for Food, Environmental and Occupational Health & Safety

ciqual.anses.fr ↗

FAO (2003)

Food energy: methods of analysis and conversion factors

FAO Food and Nutrition Paper 77 · Atwater factors

fao.org ↗

World Health Organization

Healthy diet, fact sheet

WHO

who.int ↗

Monteiro et al. (2019)

Ultra-processed foods: what they are and how to identify them

Public Health Nutrition, 22(5), 936-941 · NOVA classification

doi.org/10.1017/S1368980018003762 ↗

Body Scan · AI estimate

BODY SCAN ESTIMATE

Athletica AI looks at your photo and gives a visual estimate of body composition. It is not a measurement: methods such as DXA or bioimpedance are needed for that, and the app always shows this disclaimer next to the figures. The estimated body fat is compared with the healthy ranges published for adults and is used only to personalise training.

How it is calculated

Inputone photo, analysed by Athletica AI

Outputestimated body fat % and muscle mass %, shown as estimates

Used forprogram emphasis

Not used forany diagnosis or medical decision

Gallagher et al. (2000)

Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index

American Journal of Clinical Nutrition, 72(3), 694-701

doi.org/10.1093/ajcn/72.3.694 ↗

Onboarding

BODY MASS INDEX (BMI)

During onboarding the app shows your BMI with its World Health Organization category for adults. The same thresholds apply to men and women. BMI is only a screening measure: it does not tell muscle from fat, so muscular and athletic people often score above 25. It is one input among others and never a diagnosis.

How it is calculated

BMIBMI = weight_kg ÷ height_m²

< 18.5Underweight

18.5 - 24.9Healthy weight

25 - 29.9Overweight (pre-obesity)

≥ 30Obesity

World Health Organization

Obesity and overweight, fact sheet

WHO · Adult definitions: overweight BMI ≥ 25, obesity BMI ≥ 30

who.int ↗

WHO Technical Report Series 894 (2000)

Obesity: preventing and managing the global epidemic. Report of a WHO consultation

World Health Organization · Full adult BMI classification

pubmed.ncbi.nlm.nih.gov ↗

COMPLETE REFERENCE LIST

All citations alphabetically. Click any link to access the source.

ACSM (2009). Position stand: Progression models in resistance training for healthy adults. Med Sci Sports Exerc, 41(3), 687-708. doi.org ↗

ACSM (2022). Guidelines for Exercise Testing and Prescription, 11th Ed. acsm.org ↗

Ainsworth et al. (2011). 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc, 43(8), 1575-1581. doi.org ↗

ANSES. Ciqual French food composition table. ciqual.anses.fr ↗

EFSA (2012). Scientific Opinion on Dietary Reference Values for protein. EFSA Journal, 10(2), 2557. doi.org ↗

FAO (2003). Food energy: methods of analysis and conversion factors. FAO Food and Nutrition Paper 77. fao.org ↗

Frankenfield, Roth-Yousey & Compher (2005). Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc, 105(5), 775-789. doi.org ↗

Gallagher et al. (2000). Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr, 72(3), 694-701. doi.org ↗

Garber et al. (2011). Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults (ACSM). Med Sci Sports Exerc, 43(7), 1334-1359. doi.org ↗

10.

Hall (2008). What is the required energy deficit per unit weight loss? Int J Obes, 32(3), 573-576. doi.org ↗

11.

Hall et al. (2011). Quantification of the effect of energy imbalance on bodyweight. The Lancet, 378(9793), 826-837. doi.org ↗

12.

Helms et al. (2014). A systematic review of dietary protein during caloric restriction in resistance trained lean athletes: a case for higher intakes. IJSNEM, 24(2), 127-138. doi.org ↗

13.

Israetel et al. (2019). Scientific Principles of Strength Training. Renaissance Periodization. rpstrength.com ↗

14.

Jäger et al. (2017). International Society of Sports Nutrition Position Stand: protein and exercise. J Int Soc Sports Nutr, 14, 20. doi.org ↗

15.

Jensen et al. (2014). 2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults. Circulation, 129(25 Suppl 2). doi.org ↗

16.

MacDougall et al. (1995). The time course for elevated muscle protein synthesis following heavy resistance exercise. Can J Appl Physiol, 20(4), 480-486. doi.org ↗

17.

Meeusen et al. (2013). Prevention, diagnosis and treatment of the overtraining syndrome (ECSS/ACSM). Eur J Sport Sci, 13(1), 1-24. doi.org ↗

18.

Mifflin et al. (1990). A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr, 51(2), 241-247. doi.org ↗

19.

Monteiro et al. (2019). Ultra-processed foods: what they are and how to identify them. Public Health Nutr, 22(5), 936-941. doi.org ↗

20.

Rhea & Alderman (2004). A meta-analysis of periodized versus nonperiodized strength and power training programs. Res Q Exerc Sport, 75(4), 413-422. doi.org ↗

21.

Saw, Main & Gastin (2016). Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med, 50(5), 281-291. doi.org ↗

22.

Schoenfeld et al. (2015). Influence of Resistance Training Frequency on Muscular Adaptations in Well-Trained Men. J Strength Cond Res, 29(7), 1821-1829. doi.org ↗

23.

Schoenfeld, Ogborn & Krieger (2017). Dose-response relationship between weekly resistance training volume and increases in muscle mass: a systematic review and meta-analysis. J Sports Sci, 35(11), 1073-1082. doi.org ↗

24.

Thomas, Erdman & Burke (2016). ACSM/AND/DC Joint Position Statement: Nutrition and Athletic Performance. Med Sci Sports Exerc, 48(3), 543-568. doi.org ↗

25.

USDA & HHS (2020). Dietary Guidelines for Americans 2020-2025. dietaryguidelines.gov ↗

26.

USDA ARS. FoodData Central. fdc.nal.usda.gov ↗

27.

World Health Organization (2000). Obesity: preventing and managing the global epidemic. WHO Technical Report Series 894. pubmed.ncbi.nlm.nih.gov ↗

28.

World Health Organization. Healthy diet, fact sheet. who.int ↗

29.

World Health Organization. Obesity and overweight, fact sheet. who.int ↗

30.

Zourdos et al. (2016). Novel Resistance Training-Specific Rating of Perceived Exertion Scale Measuring Repetitions in Reserve. J Strength Cond Res, 30(1), 267-275. doi.org ↗

THE SCIENCE
BEHIND ATHLETICA

THE ATHLETICA
BRAIN

FREEDOM INSIDE AN
UNBREAKABLE CAGE.

FORMULAS &
CITATIONS

TDEE & DAILY CALORIE GOAL

PROTEIN TARGET

FAT TARGET

CARBOHYDRATE TARGET

ENGINE RULES &
SPORT SCIENCE

48-HOUR MUSCLE RECOVERY LOCK

RPE-DRIVEN PROGRESSIVE OVERLOAD

FATIGUE SCORE & AUTOMATIC DELOAD

WEEKLY VOLUME — MEV & MAV

SPLIT ROTATION

8-WEEK PERIODISATION BLUEPRINT

GOAL FEASIBILITY ENGINE

CALORIE EXPENDITURE ESTIMATION

READINESS ASSESSMENT

ACTIVE BURN ESTIMATE

PROTEIN TOP-UP

MEAL SCANNER & HEALTH SCORE

BODY SCAN ESTIMATE

BODY MASS INDEX (BMI)

COMPLETE REFERENCE LIST

THE SCIENCEBEHIND ATHLETICA

THE ATHLETICABRAIN

FREEDOM INSIDE ANUNBREAKABLE CAGE.

FORMULAS &CITATIONS

TDEE & DAILY CALORIE GOAL

PROTEIN TARGET

FAT TARGET

CARBOHYDRATE TARGET

ENGINE RULES &SPORT SCIENCE

48-HOUR MUSCLE RECOVERY LOCK

RPE-DRIVEN PROGRESSIVE OVERLOAD

FATIGUE SCORE & AUTOMATIC DELOAD

WEEKLY VOLUME — MEV & MAV

SPLIT ROTATION

8-WEEK PERIODISATION BLUEPRINT

GOAL FEASIBILITY ENGINE

CALORIE EXPENDITURE ESTIMATION

READINESS ASSESSMENT

ACTIVE BURN ESTIMATE

PROTEIN TOP-UP

MEAL SCANNER & HEALTH SCORE

BODY SCAN ESTIMATE

BODY MASS INDEX (BMI)

COMPLETE REFERENCE LIST

THE SCIENCE
BEHIND ATHLETICA

THE ATHLETICA
BRAIN

FREEDOM INSIDE AN
UNBREAKABLE CAGE.

FORMULAS &
CITATIONS

ENGINE RULES &
SPORT SCIENCE