feat: composizione corporea, deficit dinamico, condizioni mediche (PRD §5.1)

Tre leve per migliorare l'accuratezza del piano oltre Mifflin-St Jeor.

1. Composizione corporea (US Navy + Katch-McArdle)
   - Profile: neckCm/waistCm/hipsCm opzionali in input
   - bodyFatPct calcolato lato server con la formula US Navy metrica
     (Hodgdon-Beckett 1984) e cifrato at-rest come dato sanitario
   - BMR usa Katch-McArdle (FFM-based) quando bodyFatPct è noto, Mifflin
     altrimenti — più accurato del 5-10% sui casi fuori-norma
   - Test: estimateBodyFatPercentageUSNavy + calculateBmrKatchMcArdle

2. Deficit dinamico
   - Profile.targetWeeklyLossKg (kg/settimana, opzionale)
   - computeDailyKcalTarget calcola il deficit da kg/sett ×7700/7 con due cap
     di sicurezza: 30% TDEE (Schoenfeld 2014) e 1% peso/sett (Helms 2014)
   - plan.routes lo usa al posto del -500/-200 hard-coded
   - Test: 6 casi (mantenimento, transizione, intensiva, cap peso, cap TDEE,
     default mancante)

3. Condizioni mediche strutturate
   - Profile.medicalConditions (array JSON cifrato)
   - 11 condizioni: 7 adattabili (tiroide → BMR -10%, diabete II, IBS,
     dislipidemia, ipertensione, reni → cap proteine 0.8 g/kg, fegato →
     1.0 g/kg) + 4 escludenti (gravidanza, allattamento, diabete I, disturbi
     alimentari)
   - PATCH /me/profile/conditions per salvataggio mirato dall'onboarding
   - hasExcludingCondition: plan.routes restituisce 409 medical_block se
     attiva una condizione incompatibile

Onboarding allargato a 6 step: aggiunto "Condizioni mediche" tra Profile
e Preferences. Step labels e numeri rinumerati.

Schema migration 20260429203451_add_profile_health_fields.

44/44 unit test verdi.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

apps/api/src/modules/plan/plan.routes.ts

@@ -1,6 +1,10 @@
 import {
+  bmrAdjustmentForConditions,
   calculateBmr,
+  calculateBmrKatchMcArdle,
   calculateTdee,
+  computeDailyKcalTarget,
+  hasExcludingCondition,
   macrosForPhase,
   matchMeals,
   protocolPlanForDay,
@@ -28,6 +32,16 @@ function phaseToInt(p: string): 1 | 2 | 3 {
   return 3;
 }
 
+function parseConditions(raw: string | null): string[] {
+  if (!raw) return [];
+  try {
+    const v = JSON.parse(raw) as unknown;
+    return Array.isArray(v) ? v.filter((x): x is string => typeof x === 'string') : [];
+  } catch {
+    return [];
+  }
+}
+
 export const planRoutes: FastifyPluginAsync = async (fastify) => {
   fastify.post('/me/meal-plans', { preHandler: requireAuth() }, async (request, reply) => {
     const userId = request.user!.id;
@@ -75,17 +89,36 @@ export const planRoutes: FastifyPluginAsync = async (fastify) => {
       };
     });
 
+    // Condizioni escludenti: PRD §14.3 — blocchiamo la generazione e
+    // rimandiamo l'utente al medico (lato UI mostriamo un disclaimer).
+    const conditions = parseConditions(profile.medicalConditions);
+    if (hasExcludingCondition(conditions)) {
+      return reply.code(409).send({ error: 'medical_block', conditions });
+    }
+
     const weightCurrentKg = Number(profile.weightCurrentKg);
-    const bmr = calculateBmr({
-      weightKg: weightCurrentKg,
-      heightCm: profile.heightCm,
-      ageYears: profile.age,
-      gender: profile.gender,
-    });
+    const bodyFatPct = profile.bodyFatPct ? Number(profile.bodyFatPct) : null;
+    // BMR: Katch-McArdle (FFM-based) se BF% noto, altrimenti Mifflin.
+    let bmr = bodyFatPct
+      ? calculateBmrKatchMcArdle(weightCurrentKg, bodyFatPct)
+      : calculateBmr({
+          weightKg: weightCurrentKg,
+          heightCm: profile.heightCm,
+          ageYears: profile.age,
+          gender: profile.gender,
+        });
+    // Aggiusto BMR per condizioni che lo influenzano (es. ipotiroidismo -10%).
+    bmr *= bmrAdjustmentForConditions(conditions);
     const tdee = calculateTdee(bmr, profile.activityLevel);
     const phaseInt = phaseToInt(profile.currentPhase);
-    // Deficit base solo in fase 1; in fase 2/3 si avvicina a TDEE.
-    const baseKcal = Math.round(phaseInt === 1 ? tdee - 500 : phaseInt === 2 ? tdee - 200 : tdee);
+
+    // Deficit dinamico da targetWeeklyLossKg (con caps di sicurezza).
+    const { kcalTarget: baseKcal } = computeDailyKcalTarget({
+      tdee,
+      weightCurrentKg,
+      targetWeeklyLossKg: profile.targetWeeklyLossKg,
+      phase: phaseInt,
+    });
     const baseDailyTarget = macrosForPhase({
       kcalTarget: baseKcal,
       weightKg: weightCurrentKg,

apps/api/src/modules/profile/profile.routes.ts

@@ -1,7 +1,9 @@
 import {
   ACTIVITY_MULTIPLIERS,
   calculateBmr,
+  calculateBmrKatchMcArdle,
   calculateTdee,
+  estimateBodyFatPercentageUSNavy,
   profileInputSchema,
   type ActivityLevel,
   type Gender,
@@ -23,17 +25,23 @@ function serialize(
     weightGoalKg: string;
     activityLevel: ActivityLevel;
     targetDate: string | null;
+    targetWeeklyLossKg: number | null;
+    medicalConditions: string | null;
+    bodyFatPct: string | null;
     currentPhase: string;
   } | null,
 ) {
   if (!profile) return null;
   const weightCurrentKg = Number(profile.weightCurrentKg);
-  const bmr = calculateBmr({
-    weightKg: weightCurrentKg,
-    heightCm: profile.heightCm,
-    ageYears: profile.age,
-    gender: profile.gender,
-  });
+  const bodyFatPct = profile.bodyFatPct ? Number(profile.bodyFatPct) : null;
+  const bmr = bodyFatPct
+    ? calculateBmrKatchMcArdle(weightCurrentKg, bodyFatPct)
+    : calculateBmr({
+        weightKg: weightCurrentKg,
+        heightCm: profile.heightCm,
+        ageYears: profile.age,
+        gender: profile.gender,
+      });
   const tdee = calculateTdee(bmr, profile.activityLevel);
   return {
     age: profile.age,
@@ -44,15 +52,29 @@ function serialize(
     weightGoalKg: Number(profile.weightGoalKg),
     activityLevel: profile.activityLevel,
     targetDate: profile.targetDate,
+    targetWeeklyLossKg: profile.targetWeeklyLossKg,
+    medicalConditions: parseConditions(profile.medicalConditions),
+    bodyFatPct,
     currentPhase: profile.currentPhase,
     derived: {
       bmr: Math.round(bmr),
       tdee: Math.round(tdee),
       activityMultiplier: ACTIVITY_MULTIPLIERS[profile.activityLevel],
+      bmrFormula: bodyFatPct ? 'katch_mcardle' : 'mifflin_st_jeor',
     },
   };
 }
 
+function parseConditions(raw: string | null): string[] {
+  if (!raw) return [];
+  try {
+    const v = JSON.parse(raw) as unknown;
+    return Array.isArray(v) ? v.filter((x): x is string => typeof x === 'string') : [];
+  } catch {
+    return [];
+  }
+}
+
 export const profileRoutes: FastifyPluginAsync = async (fastify) => {
   fastify.get('/me/profile', { preHandler: requireAuth() }, async (request, reply) => {
     const profile = await fastify.prisma.profile.findUnique({
@@ -72,6 +94,25 @@ export const profileRoutes: FastifyPluginAsync = async (fastify) => {
 
     const targetDateStr = data.targetDate ? data.targetDate.toISOString().slice(0, 10) : null;
 
+    // Stima %BF se le misure essenziali sono tutte presenti.
+    let bodyFatPct: string | null = null;
+    if (data.neckCm && data.waistCm) {
+      try {
+        const pct = estimateBodyFatPercentageUSNavy({
+          gender: data.gender,
+          heightCm: data.heightCm,
+          neckCm: data.neckCm,
+          waistCm: data.waistCm,
+          ...(data.hipsCm != null ? { hipsCm: data.hipsCm } : {}),
+        });
+        bodyFatPct = String(pct);
+      } catch {
+        bodyFatPct = null; // input fuori range → nessuna stima
+      }
+    }
+
+    const conditionsJson = data.medicalConditions ? JSON.stringify(data.medicalConditions) : null;
+
     const profile = await fastify.prisma.profile.upsert({
       where: { userId },
       create: {
@@ -84,6 +125,9 @@ export const profileRoutes: FastifyPluginAsync = async (fastify) => {
         weightGoalKg: String(data.weightGoalKg),
         activityLevel: data.activityLevel,
         targetDate: targetDateStr,
+        targetWeeklyLossKg: data.targetWeeklyLossKg ?? null,
+        medicalConditions: conditionsJson,
+        bodyFatPct,
       },
       update: {
         age: data.age,
@@ -94,9 +138,32 @@ export const profileRoutes: FastifyPluginAsync = async (fastify) => {
         weightGoalKg: String(data.weightGoalKg),
         activityLevel: data.activityLevel,
         targetDate: targetDateStr,
+        targetWeeklyLossKg: data.targetWeeklyLossKg ?? null,
+        medicalConditions: conditionsJson,
+        // Se l'utente ricalcola con misure nuove, sovrascriviamo; altrimenti
+        // lasciamo il valore precedente.
+        ...(bodyFatPct != null ? { bodyFatPct } : {}),
       },
     });
 
     return { profile: serialize(profile) };
   });
+
+  // PATCH dedicato per le condizioni mediche: l'onboarding lo usa per salvare
+  // solo questo delta senza richiedere tutti i campi obbligatori del PUT.
+  fastify.patch('/me/profile/conditions', { preHandler: requireAuth() }, async (request, reply) => {
+    const body = request.body as { conditions?: unknown };
+    if (!Array.isArray(body.conditions)) {
+      return reply.code(400).send({ error: 'invalid_body' });
+    }
+    const conditions = body.conditions.filter((c): c is string => typeof c === 'string');
+    const userId = request.user!.id;
+    const existing = await fastify.prisma.profile.findUnique({ where: { userId } });
+    if (!existing) return reply.code(404).send({ error: 'profile_not_found' });
+    await fastify.prisma.profile.update({
+      where: { userId },
+      data: { medicalConditions: JSON.stringify(conditions) },
+    });
+    return { conditions };
+  });
 };