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>
This commit is contained in:
lucianoandClaude Opus 4.7 committed 2026-04-29 22:44:16 +02:00
1 parent feb54c1f9a
commit 9401497f03
15 files changed
+734 -16

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+36 -3
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@@ -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({
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,
+68 -1
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@@ -1,7 +1,9 @@
import {
ACTIVITY_MULTIPLIERS,
calculateBmr,
calculateBmrKatchMcArdle,
calculateTdee,
estimateBodyFatPercentageUSNavy,
profileInputSchema,
type ActivityLevel,
type Gender,
@@ -23,12 +25,18 @@ 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({
const bodyFatPct = profile.bodyFatPct ? Number(profile.bodyFatPct) : null;
const bmr = bodyFatPct
? calculateBmrKatchMcArdle(weightCurrentKg, bodyFatPct)
: calculateBmr({
weightKg: weightCurrentKg,
heightCm: profile.heightCm,
ageYears: 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 };
});
};
+29
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@@ -177,10 +177,32 @@
"steps": {
"goal": "L'obiettivo",
"profile": "Anagrafica e attività",
"conditions": "Condizioni mediche",
"preferences": "Esclusioni e cucine",
"notifications": "Promemoria",
"done": "Pronti a partire"
},
"conditionsHeading": "Qualcosa di te che dovremmo sapere?",
"conditionsHint": "Indica le condizioni che ti riguardano. Servono ad adattare i calcoli e a segnalarti i casi in cui meglio consultare un medico prima di proseguire.",
"conditionsAdaptive": "Condizioni adattabili",
"conditionsExcluding": "Condizioni che richiedono il medico",
"conditionsExcludingHint": "Se selezioni una di queste l'app non genera piani autonomamente: vai dal tuo medico.",
"conditionsSaveError": "Non è stato possibile salvare. Riprova fra poco.",
"blockedEyebrow": "Importante",
"blockedMessage": "KetoPath non è uno strumento adatto a questa condizione. Ti consigliamo di rivolgerti al tuo medico curante prima di continuare. Puoi comunque proseguire l'esplorazione, ma il piano non verrà generato automaticamente.",
"conditionLabel": {
"THYROID_HYPO": "Ipotiroidismo",
"DIABETES_T2": "Diabete tipo 2",
"IBS": "Intestino irritabile",
"DYSLIPIDEMIA": "Colesterolo / trigliceridi alti",
"HYPERTENSION": "Ipertensione",
"KIDNEY_ISSUES": "Problemi renali",
"LIVER_ISSUES": "Problemi epatici",
"PREGNANCY": "Gravidanza",
"BREASTFEEDING": "Allattamento",
"DIABETES_T1": "Diabete tipo 1",
"EATING_DISORDER": "Disturbi alimentari"
},
"goalHeading": "Perché sei qui?",
"goalHint": "Scegli il motivo principale: ci aiuterà a parlarti nel modo giusto. Potrai sempre cambiarlo.",
"goals": {
@@ -430,6 +452,13 @@
"weightCurrentKg": "Attuale",
"weightGoalKg": "Obiettivo",
"activityLevel": "Livello di attività",
"measurementsTitle": "Circonferenze (cm) — opzionali",
"measurementsHint": "Se inserisci collo, vita e (per le donne) fianchi, calcoliamo la tua composizione corporea con la formula US Navy e usiamo Katch-McArdle invece di Mifflin: BMR più accurato.",
"neckCm": "Collo",
"waistCm": "Vita",
"hipsCm": "Fianchi",
"targetWeeklyLossKg": "Velocità di calo (kg/settimana)",
"targetWeeklyLossKgHint": "Suggerito: 0.4–0.7 kg/settimana. Oltre 1% del peso corporeo è considerato troppo aggressivo.",
"selectPlaceholder": "Seleziona…",
"save": "Salva profilo",
"saving": "Salvataggio…",
@@ -1,9 +1,12 @@
'use client';
import {
EXCLUDING_CONDITIONS,
MEDICAL_CONDITIONS,
type CookingTimeLevel,
type CuisineTag,
type ExclusionGroup,
type MedicalCondition,
type PreferencesPatch,
} from '@ketopath/shared';
import Link from 'next/link';
@@ -20,11 +23,12 @@ import { Button } from '@/components/ui/button';
import { pushSupported, subscribe, getCurrentSubscription } from '@/lib/notifications/push-client';
import { regeneratePlan } from '../plan/actions';
import { saveConditions } from '../profile/actions';
import { savePreferences, type PreferencesView } from '../profile/preferences-actions';
import { ProfileForm as ProfileFormComponent } from '../profile/profile-form';
import type { ProfileForm } from '../profile/profile-form';
const STEPS = ['goal', 'profile', 'preferences', 'notifications', 'done'] as const;
const STEPS = ['goal', 'profile', 'conditions', 'preferences', 'notifications', 'done'] as const;
type StepKey = (typeof STEPS)[number];
const GOALS = ['LOSE', 'MAINTAIN', 'ENERGY'] as const;
@@ -59,7 +63,7 @@ export function OnboardingFlow({ initialProfile, initialPreferences }: Onboardin
i < stepIndex ? 'text-oliva' : i === stepIndex ? 'text-pomodoro' : 'text-ink-dim'
}`}
>
{['I', 'II', 'III', 'IV', 'V'][i]}
{['I', 'II', 'III', 'IV', 'V', 'VI'][i]}
</span>
<span
className={`font-display text-lg leading-tight ${
@@ -79,10 +83,14 @@ export function OnboardingFlow({ initialProfile, initialPreferences }: Onboardin
) : null}
{step === 'profile' ? (
<StepProfile
initial={initialProfile}
goal={goal}
onSaved={() => setStep('preferences')}
<StepProfile initial={initialProfile} goal={goal} onSaved={() => setStep('conditions')} />
) : null}
{step === 'conditions' ? (
<StepConditions
initial={(initialProfile?.medicalConditions as string[] | undefined) ?? []}
onContinue={() => setStep('preferences')}
onBack={() => setStep('profile')}
/>
) : null}
@@ -92,7 +100,7 @@ export function OnboardingFlow({ initialProfile, initialPreferences }: Onboardin
initialPreferences ? toFormValues(initialPreferences) : DEFAULT_PREFERENCES_VALUES
}
onSaved={() => setStep('notifications')}
onBack={() => setStep('profile')}
onBack={() => setStep('conditions')}
/>
) : null}
@@ -117,7 +125,7 @@ function StepGoal({
return (
<div className="space-y-8">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 1, total: 5 })}</p>
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 1, total: 6 })}</p>
<h2 className="font-display text-ink text-3xl font-medium leading-tight">
{t('goalHeading')}
</h2>
@@ -182,7 +190,7 @@ function StepProfile({
return (
<div className="space-y-8">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 2, total: 5 })}</p>
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 2, total: 6 })}</p>
<h2 className="font-display text-ink text-3xl font-medium leading-tight">
{goal ? t(`profileHeadingGoal.${goal}`) : t('profileHeading')}
</h2>
@@ -195,6 +203,145 @@ function StepProfile({
);
}
function StepConditions({
initial,
onContinue,
onBack,
}: {
initial: string[];
onContinue: () => void;
onBack: () => void;
}) {
const t = useTranslations('Onboarding');
const [selected, setSelected] = useState<string[]>(initial);
const [pending, setPending] = useState(false);
const [error, setError] = useState<string | null>(null);
function toggle(c: MedicalCondition): void {
setSelected((prev) => (prev.includes(c) ? prev.filter((x) => x !== c) : [...prev, c]));
}
const blocked = selected.some((c) => EXCLUDING_CONDITIONS.has(c as MedicalCondition));
async function handleNext(): Promise<void> {
setError(null);
setPending(true);
const result = await saveConditions(selected);
setPending(false);
if (!result.ok) {
setError(t('conditionsSaveError'));
return;
}
onContinue();
}
const adaptive = MEDICAL_CONDITIONS.filter(
(c) => !EXCLUDING_CONDITIONS.has(c as MedicalCondition),
);
return (
<div className="space-y-8">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 3, total: 6 })}</p>
<h2 className="font-display text-ink text-3xl font-medium leading-tight">
{t('conditionsHeading')}
</h2>
<p className="font-display text-ink-soft max-w-xl text-base italic leading-snug">
{t('conditionsHint')}
</p>
</header>
<fieldset className="space-y-3">
<legend className="editorial-eyebrow">{t('conditionsAdaptive')}</legend>
<div className="flex flex-wrap gap-2">
{adaptive.map((c) => (
<ConditionChip
key={c}
label={t(`conditionLabel.${c}`)}
active={selected.includes(c)}
onToggle={() => toggle(c as MedicalCondition)}
/>
))}
</div>
</fieldset>
<fieldset className="space-y-3">
<legend className="editorial-eyebrow">{t('conditionsExcluding')}</legend>
<p className="font-display text-ink-soft text-sm italic leading-snug">
{t('conditionsExcludingHint')}
</p>
<div className="flex flex-wrap gap-2">
{Array.from(EXCLUDING_CONDITIONS).map((c) => (
<ConditionChip
key={c}
label={t(`conditionLabel.${c}`)}
active={selected.includes(c)}
onToggle={() => toggle(c)}
danger
/>
))}
</div>
</fieldset>
{blocked ? (
<div className="border-pomodoro/40 bg-pomodoro/5 border-2 border-dashed p-5">
<p className="editorial-eyebrow">{t('blockedEyebrow')}</p>
<p className="font-display text-ink mt-3 text-base leading-snug">{t('blockedMessage')}</p>
</div>
) : null}
{error ? (
<p className="font-display text-pomodoro text-base italic" role="alert">
{error}
</p>
) : null}
<div className="flex flex-wrap items-center gap-4">
<Button type="button" size="lg" onClick={handleNext} disabled={pending}>
{pending ? t('working') : t('next')}
</Button>
<button
type="button"
onClick={onBack}
className="text-ink-soft hover:text-ink font-mono text-[11px] uppercase tracking-widest"
>
{t('back')}
</button>
</div>
</div>
);
}
function ConditionChip({
label,
active,
onToggle,
danger,
}: {
label: string;
active: boolean;
onToggle: () => void;
danger?: boolean;
}) {
const palette = active
? danger
? 'bg-pomodoro text-carta-light border-pomodoro'
: 'bg-ink text-carta-light border-ink'
: danger
? 'border-pomodoro/40 hover:border-pomodoro text-pomodoro hover:bg-pomodoro/5 bg-transparent'
: 'border-ink/30 hover:border-ink text-ink hover:bg-ink/5 bg-transparent';
return (
<button
type="button"
aria-pressed={active}
onClick={onToggle}
className={`font-display rounded-full border px-4 py-2 text-sm leading-tight transition-colors ${palette}`}
>
{label}
</button>
);
}
function StepPreferences({
initial,
onSaved,
@@ -215,7 +362,7 @@ function StepPreferences({
return (
<div className="space-y-8">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 3, total: 5 })}</p>
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 4, total: 6 })}</p>
<h2 className="font-display text-ink text-3xl font-medium leading-tight">
{t('preferencesHeading')}
</h2>
@@ -265,7 +412,7 @@ function StepNotifications({ onContinue }: { onContinue: () => void }) {
return (
<div className="space-y-8">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 4, total: 5 })}</p>
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 5, total: 6 })}</p>
<h2 className="font-display text-ink text-3xl font-medium leading-tight">
{t('notificationsHeading')}
</h2>
@@ -339,7 +486,7 @@ function StepDone({ goal }: { goal: Goal | null }) {
return (
<div className="space-y-10">
<header className="space-y-2">
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 5, total: 5 })}</p>
<p className="editorial-eyebrow">{t('stepEyebrow', { n: 6, total: 6 })}</p>
<h2 className="font-display text-ink text-4xl font-medium leading-tight">
{goal ? t(`doneHeadingGoal.${goal}`) : t('doneHeading')}
</h2>
@@ -41,3 +41,19 @@ export async function fetchProfile(): Promise<unknown | null> {
const data = (await res.json()) as { profile: unknown };
return data.profile;
}
export type SaveConditionsResult = { ok: true } | { ok: false; error: string };
export async function saveConditions(conditions: readonly string[]): Promise<SaveConditionsResult> {
const cookie = headers().get('cookie') ?? '';
const res = await fetch(`${API_URL}/me/profile/conditions`, {
method: 'PATCH',
headers: { 'Content-Type': 'application/json', cookie },
body: JSON.stringify({ conditions }),
cache: 'no-store',
});
if (!res.ok) {
return { ok: false, error: `api_error_${res.status}` };
}
return { ok: true };
}
@@ -190,6 +190,65 @@ export function ProfileForm({
)}
/>
<div className="space-y-6">
<div className="space-y-2">
<p className="editorial-eyebrow">{t('measurementsTitle')}</p>
<p className="font-display text-ink-soft text-sm italic leading-snug">
{t('measurementsHint')}
</p>
</div>
<div className="grid grid-cols-3 gap-x-6 gap-y-8">
{(['neckCm', 'waistCm', 'hipsCm'] as const).map((name) => (
<FormField
key={name}
control={form.control}
name={name}
render={({ field }) => (
<FormItem className="space-y-3">
<FormLabel>{t(name)}</FormLabel>
<FormControl>
<Input
type="number"
step="0.5"
inputMode="decimal"
{...field}
value={(field.value as number | undefined) ?? ''}
/>
</FormControl>
<FormMessage />
</FormItem>
)}
/>
))}
</div>
</div>
<FormField
control={form.control}
name="targetWeeklyLossKg"
render={({ field }) => (
<FormItem className="space-y-3">
<FormLabel>{t('targetWeeklyLossKg')}</FormLabel>
<FormControl>
<Input
type="number"
step="0.1"
min={0.1}
max={1.5}
inputMode="decimal"
placeholder="0.5"
{...field}
value={(field.value as number | undefined) ?? ''}
/>
</FormControl>
<p className="font-display text-ink-soft text-sm italic leading-snug">
{t('targetWeeklyLossKgHint')}
</p>
<FormMessage />
</FormItem>
)}
/>
{serverError ? (
<p role="alert" className="font-display text-pomodoro text-base italic">
{serverError}
@@ -0,0 +1,4 @@
-- AlterTable
ALTER TABLE "profiles" ADD COLUMN "body_fat_pct" TEXT,
ADD COLUMN "medical_conditions" TEXT,
ADD COLUMN "target_weekly_loss_kg" DOUBLE PRECISION;
+12
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@@ -169,6 +169,18 @@ model Profile {
activityLevel ActivityLevel @map("activity_level")
/// @encrypted
targetDate String? @map("target_date")
// PRD §5.1 — velocità desiderata di calo peso (kg/settimana). Determina il
// deficit calorico in modo dinamico al posto del valore hard-coded.
targetWeeklyLossKg Float? @map("target_weekly_loss_kg")
// PRD §14.3 — condizioni mediche dichiarate dall'utente. Filtrano ricette,
// modulano deficit/macros, e bloccano l'app se "escludenti" (gravidanza,
// allattamento, diabete tipo 1, disturbi alimentari). Cifrate at-rest come
// dato sanitario (art. 9 GDPR — vedi ADR 0002).
/// @encrypted
medicalConditions String? @map("medical_conditions")
// PRD §5.1 — % grasso corporeo stimata (US Navy) per BMR Katch-McArdle.
/// @encrypted
bodyFatPct String? @map("body_fat_pct")
currentPhase Phase @default(INTENSIVE) @map("current_phase")
createdAt DateTime @default(now()) @map("created_at")
updatedAt DateTime @updatedAt @map("updated_at")
+3
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@@ -1,6 +1,9 @@
export * from './medical/conditions.js';
export * from './notifications/schema.js';
export * from './preferences/schema.js';
export * from './nutrition/bmr.js';
export * from './nutrition/body-composition.js';
export * from './nutrition/deficit.js';
export * from './nutrition/tdee.js';
export * from './nutrition/types.js';
export * from './planner/macros.js';
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// PRD §14.3 — Condizioni mediche dichiarate dall'utente.
//
// Le condizioni "escludenti" bloccano l'uso autonomo dell'app: l'utente viene
// invitato a consultare un medico prima di proseguire. Le altre influenzano
// solo i parametri di calcolo.
export const MEDICAL_CONDITIONS = [
// Adattabili
'THYROID_HYPO', // ipotiroidismo: riduce BMR ~10%
'DIABETES_T2', // diabete tipo 2: riduce velocità di chetoadattamento
'IBS', // intestino irritabile: filtra alcuni alimenti irritanti
'DYSLIPIDEMIA', // colesterolo/trigliceridi: monitorare profilo lipidico
'HYPERTENSION', // ipertensione: attenzione al sodio
'KIDNEY_ISSUES', // problemi renali: riduce target proteico
'LIVER_ISSUES', // problemi epatici: monitorare proteine + alcol
// Escludenti — bloccano l'app
'PREGNANCY',
'BREASTFEEDING',
'DIABETES_T1',
'EATING_DISORDER',
] as const;
export type MedicalCondition = (typeof MEDICAL_CONDITIONS)[number];
export const EXCLUDING_CONDITIONS: ReadonlySet<MedicalCondition> = new Set([
'PREGNANCY',
'BREASTFEEDING',
'DIABETES_T1',
'EATING_DISORDER',
]);
export function hasExcludingCondition(conditions: readonly string[]): boolean {
return conditions.some((c) => EXCLUDING_CONDITIONS.has(c as MedicalCondition));
}
/**
* Aggiusta il BMR per condizioni che lo influenzano.
* - Ipotiroidismo non controllato: BMR scende ~10-15%. Usiamo -10% prudente.
*/
export function bmrAdjustmentForConditions(conditions: readonly string[]): number {
let factor = 1;
if (conditions.includes('THYROID_HYPO')) factor *= 0.9;
return factor;
}
/**
* Limite di proteine raccomandato (g/kg di peso corporeo) in base a condizioni.
* - Reni compromessi → tetto 0.8 g/kg
* - Fegato compromesso → tetto 1.0 g/kg
* - Default keto → 1.5-1.8 g/kg (gestito altrove)
*/
export function proteinCeilingGPerKg(conditions: readonly string[]): number | null {
if (conditions.includes('KIDNEY_ISSUES')) return 0.8;
if (conditions.includes('LIVER_ISSUES')) return 1.0;
return null;
}
@@ -0,0 +1,76 @@
import { describe, expect, it } from 'vitest';
import { calculateBmrKatchMcArdle, estimateBodyFatPercentageUSNavy } from './body-composition.js';
describe('estimateBodyFatPercentageUSNavy', () => {
it('uomo medio (180cm, vita 90, collo 38) ≈ 18-22% BF', () => {
const bf = estimateBodyFatPercentageUSNavy({
gender: 'MALE',
heightCm: 180,
neckCm: 38,
waistCm: 90,
});
expect(bf).toBeGreaterThan(15);
expect(bf).toBeLessThan(25);
});
it('donna media (165cm, vita 75, fianchi 100, collo 33) ≈ 25-32% BF', () => {
const bf = estimateBodyFatPercentageUSNavy({
gender: 'FEMALE',
heightCm: 165,
neckCm: 33,
waistCm: 75,
hipsCm: 100,
});
expect(bf).toBeGreaterThan(20);
expect(bf).toBeLessThan(35);
});
it('uomo atletico (180cm, vita 80, collo 40) → BF basso (< 15%)', () => {
const bf = estimateBodyFatPercentageUSNavy({
gender: 'MALE',
heightCm: 180,
neckCm: 40,
waistCm: 80,
});
expect(bf).toBeLessThan(15);
});
it('FEMALE senza hipsCm lancia errore', () => {
expect(() =>
estimateBodyFatPercentageUSNavy({
gender: 'FEMALE',
heightCm: 165,
neckCm: 33,
waistCm: 75,
}),
).toThrow();
});
it('clamp BF% in [3, 60]', () => {
const bf = estimateBodyFatPercentageUSNavy({
gender: 'MALE',
heightCm: 180,
neckCm: 41,
waistCm: 75, // input molto magro: senza clamp uscirebbe negativo
});
expect(bf).toBeGreaterThanOrEqual(3);
});
});
describe('calculateBmrKatchMcArdle', () => {
it('80kg con 20% BF → BMR ~1750', () => {
const bmr = calculateBmrKatchMcArdle(80, 20);
// FFM = 64, BMR = 370 + 21.6*64 = 1752.4
expect(Math.round(bmr)).toBe(1752);
});
it('80kg con 30% BF → BMR < lo stesso peso al 20%', () => {
expect(calculateBmrKatchMcArdle(80, 30)).toBeLessThan(calculateBmrKatchMcArdle(80, 20));
});
it('input negativi → errore', () => {
expect(() => calculateBmrKatchMcArdle(-1, 20)).toThrow();
expect(() => calculateBmrKatchMcArdle(80, 100)).toThrow();
});
});
@@ -0,0 +1,64 @@
// PRD §5.1 — Composizione corporea e BMR Katch-McArdle.
//
// US Navy formula per la stima del % grasso corporeo da circonferenze.
// Più accurata di Mifflin per persone con composizione fuori-norma (atleti,
// over-fat). Una volta nota la BF%, BMR viene calcolato sulla massa magra
// (FFM) che è il driver metabolico reale.
import type { Gender } from './types.js';
export interface UsNavyInput {
gender: Gender;
heightCm: number;
neckCm: number;
waistCm: number;
hipsCm?: number; // richiesto per FEMALE / OTHER
}
/**
* Stima la % grasso corporeo con la formula US Navy (1981).
* Range tipico: 8-30% uomini, 18-40% donne. Errore ±3-4%.
*
* Per Gender 'OTHER' usiamo la formula femminile (più conservativa) se
* `hipsCm` è fornita, altrimenti quella maschile.
*/
export function estimateBodyFatPercentageUSNavy(input: UsNavyInput): number {
const { gender, heightCm, neckCm, waistCm, hipsCm } = input;
if (heightCm <= 0 || neckCm <= 0 || waistCm <= 0) {
throw new RangeError('heightCm, neckCm, waistCm must be positive');
}
// Formule US Navy in unità metriche (Hodgdon-Beckett 1984, conversione SI).
const useFemale = gender === 'FEMALE' || (gender === 'OTHER' && hipsCm != null);
if (useFemale) {
if (hipsCm == null || hipsCm <= 0) {
throw new RangeError('hipsCm is required for FEMALE');
}
// BF% = 495 / (1.29579 − 0.35004 × log10(waist + hips − neck) + 0.22100 × log10(height)) − 450
const denom =
1.29579 - 0.35004 * Math.log10(waistCm + hipsCm - neckCm) + 0.221 * Math.log10(heightCm);
return clampBodyFat(495 / denom - 450);
}
// Maschile: BF% = 495 / (1.0324 − 0.19077 × log10(waist − neck) + 0.15456 × log10(height)) − 450
const denom = 1.0324 - 0.19077 * Math.log10(waistCm - neckCm) + 0.15456 * Math.log10(heightCm);
return clampBodyFat(495 / denom - 450);
}
function clampBodyFat(v: number): number {
if (Number.isNaN(v)) throw new Error('invalid body fat estimate');
return Math.max(3, Math.min(60, Number(v.toFixed(1))));
}
/**
* BMR con la formula Katch-McArdle, basata sulla FFM.
* Più accurata di Mifflin quando la BF% è nota.
*
* BMR = 370 + 21.6 × FFM_kg
*/
export function calculateBmrKatchMcArdle(weightKg: number, bodyFatPct: number): number {
if (weightKg <= 0 || bodyFatPct < 0 || bodyFatPct >= 100) {
throw new RangeError('weightKg must be positive, bodyFatPct in [0, 100)');
}
const ffm = weightKg * (1 - bodyFatPct / 100);
return 370 + 21.6 * ffm;
}
@@ -0,0 +1,70 @@
import { describe, expect, it } from 'vitest';
import { computeDailyKcalTarget } from './deficit.js';
describe('computeDailyKcalTarget', () => {
it('mantenimento (fase 3) → kcal == TDEE, niente calo', () => {
const r = computeDailyKcalTarget({
tdee: 2200,
weightCurrentKg: 80,
phase: 3,
});
expect(r.kcalTarget).toBe(2200);
expect(r.effectiveWeeklyLossKg).toBe(0);
expect(r.cappedToSafe).toBe(false);
});
it('transizione (fase 2) → ~ -200 kcal', () => {
const r = computeDailyKcalTarget({
tdee: 2200,
weightCurrentKg: 80,
phase: 2,
});
expect(r.kcalTarget).toBe(2000);
});
it('intensiva 0.5 kg/sett → deficit ~550 kcal', () => {
const r = computeDailyKcalTarget({
tdee: 2200,
weightCurrentKg: 80,
targetWeeklyLossKg: 0.5,
phase: 1,
});
expect(r.kcalTarget).toBe(2200 - Math.round((0.5 * 7700) / 7));
expect(r.cappedToSafe).toBe(false);
});
it('cap 1% peso/settimana: chiede 1.2 kg su 80kg → effettivi 0.8 kg', () => {
const r = computeDailyKcalTarget({
tdee: 2200,
weightCurrentKg: 80,
targetWeeklyLossKg: 1.2,
phase: 1,
});
expect(r.cappedToSafe).toBe(true);
expect(r.effectiveWeeklyLossKg).toBeLessThanOrEqual(0.85);
});
it('cap 30% TDEE: persona piccola e impaziente non scende sotto soglia', () => {
// 50 kg, TDEE 1500, vuole 1 kg/sett → richiederebbe deficit 1100/giorno
// ma 30% di 1500 = 450 → cap.
const r = computeDailyKcalTarget({
tdee: 1500,
weightCurrentKg: 50,
targetWeeklyLossKg: 1,
phase: 1,
});
expect(r.cappedToSafe).toBe(true);
expect(r.kcalTarget).toBeGreaterThanOrEqual(Math.round(1500 * 0.7));
});
it('default 0.5 kg/sett quando manca targetWeeklyLossKg', () => {
const r = computeDailyKcalTarget({
tdee: 2200,
weightCurrentKg: 80,
phase: 1,
});
expect(r.kcalTarget).toBeLessThan(2200);
expect(r.kcalTarget).toBeGreaterThan(2200 - 700);
});
});
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// PRD §5.1 — Calcolo dinamico del deficit calorico.
//
// 1 kg di tessuto adiposo ≈ 7700 kcal (regola classica). Quindi un calo di
// `r` kg/settimana richiede un deficit medio di r × 7700 / 7 kcal/giorno.
//
// Limiti di sicurezza:
// - max deficit ≤ 30% del TDEE (linee guida Schoenfeld 2014)
// - max calo ≤ 1% peso corporeo / settimana (raccomandazione Helms 2014)
// Se l'utente chiede oltre, capiamo il deficit e segnaliamo.
const KCAL_PER_KG_FAT = 7700;
const SAFE_DEFICIT_PCT = 0.3; // 30% TDEE
const SAFE_LOSS_PCT_BODYWEIGHT = 0.01; // 1% peso/settimana
export interface DeficitInput {
tdee: number; // kcal/giorno
weightCurrentKg: number;
/**
* Velocità desiderata di calo peso, kg/settimana. Se omessa, usiamo
* il fallback per fase passato dal chiamante (default 0.5 kg/wk per LOSE).
*/
targetWeeklyLossKg?: number | null;
/** 1=intensiva, 2=transizione, 3=mantenimento. */
phase: 1 | 2 | 3;
}
export interface DeficitResult {
/** kcal/giorno target. */
kcalTarget: number;
/** kg/settimana effettivamente raggiungibili col target. */
effectiveWeeklyLossKg: number;
/** True se il calo richiesto era oltre i limiti di sicurezza. */
cappedToSafe: boolean;
}
export function computeDailyKcalTarget(input: DeficitInput): DeficitResult {
const { tdee, weightCurrentKg, targetWeeklyLossKg, phase } = input;
// Fase 3 (mantenimento) → eat at TDEE.
if (phase === 3) {
return { kcalTarget: Math.round(tdee), effectiveWeeklyLossKg: 0, cappedToSafe: false };
}
// Fase 2 (transizione) → reverse dieting morbido.
if (phase === 2) {
return {
kcalTarget: Math.round(tdee - 200),
effectiveWeeklyLossKg: Math.max(0, 200 / (KCAL_PER_KG_FAT / 7)),
cappedToSafe: false,
};
}
// Fase 1 (intensiva): deficit derivato da targetWeeklyLossKg, con caps.
const requested = targetWeeklyLossKg ?? 0.5;
const safeMaxByWeight = weightCurrentKg * SAFE_LOSS_PCT_BODYWEIGHT;
const cappedRate = Math.min(requested, safeMaxByWeight);
let dailyDeficit = (cappedRate * KCAL_PER_KG_FAT) / 7;
const maxDeficitByTdee = tdee * SAFE_DEFICIT_PCT;
const finalDeficit = Math.min(dailyDeficit, maxDeficitByTdee);
dailyDeficit = finalDeficit;
const cappedToSafe =
requested > safeMaxByWeight || (cappedRate * KCAL_PER_KG_FAT) / 7 > maxDeficitByTdee;
return {
kcalTarget: Math.round(tdee - dailyDeficit),
effectiveWeeklyLossKg: Number(((dailyDeficit * 7) / KCAL_PER_KG_FAT).toFixed(2)),
cappedToSafe,
};
}
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import { z } from 'zod';
import { MEDICAL_CONDITIONS } from '../medical/conditions.js';
export const GENDERS = ['MALE', 'FEMALE', 'OTHER'] as const;
export const ACTIVITY_LEVELS = ['SEDENTARY', 'LIGHT', 'MODERATE', 'INTENSE'] as const;
@@ -12,6 +14,15 @@ export const profileInputSchema = z.object({
weightGoalKg: z.coerce.number().min(35).max(300),
activityLevel: z.enum(ACTIVITY_LEVELS),
targetDate: z.coerce.date().optional(),
// Velocità di calo desiderata (kg/sett.). Cap 0.25-1.5 per UI; il backend
// applica anche un cap di sicurezza dinamico in base al peso.
targetWeeklyLossKg: z.coerce.number().min(0.1).max(1.5).optional(),
// Condizioni mediche dichiarate. Le "escludenti" bloccano la generazione piano.
medicalConditions: z.array(z.enum(MEDICAL_CONDITIONS)).max(20).optional(),
// Circonferenza collo: serve alla formula US Navy per stimare la BF%.
neckCm: z.coerce.number().min(20).max(60).optional(),
waistCm: z.coerce.number().min(40).max(200).optional(),
hipsCm: z.coerce.number().min(40).max(200).optional(),
});
export type ProfileInput = z.input<typeof profileInputSchema>;