mirror of
https://github.com/MBombeck/HealthLog.git
synced 2026-11-08 20:42:43 +00:00
feat(seed): broaden demo coverage across every tracking domain
Extend the demo tenant so it reads as a long-term, fully engaged user across the whole app, not a thin sample. All series span the existing ninety-day window with the latest point on today and sit in healthy bands: - Body composition (fat, fat-free, lean, muscle mass, total body water, bone mass, visceral fat, BMI) derived from the same-day weight and body-fat series so the numbers reconcile with the headline tiles. - Blood glucose in a healthy non-diabetic band — fasting, post-meal and bedtime readings stored in mg/dL with the glucose context tagged. - Cardio fitness and vitals: HRV (SDNN and RMSSD), oxygen saturation, respiratory rate, periodic VO2 max, active energy, walking/running distance and flights climbed. - WHOOP-style recovery, day strain and sleep performance/efficiency/ consistency scores aligned with the seeded sleep nights. - Three to four workouts a week (running, strength, cycling) with duration, energy, average and max heart rate, distance, a sparse per-workout heart-rate sample series, and per-workout strain in the metadata blob. Clear the workout tables in the same truncate pass so the seed stays re-runnable.
This commit is contained in:
Marc-André Bombeck committed
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+260
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@@ -11,6 +11,16 @@
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* sleep, steps) — flat-or-improving trends, most-recent point from today
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* - A full week of per-stage sleep nights (awake/REM/light(core)/deep),
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* ~7.5 h each, including last night
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* - 90 days of body composition (fat/lean/muscle mass, total body water,
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* bone mass, visceral fat, BMI) derived from the same-day weight + body fat
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* - 90 days of blood glucose in a healthy non-diabetic band (fasting +
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* post-meal + bedtime, contexts tagged)
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* - 90 days of cardio fitness + vitals (HRV SDNN + RMSSD, SpO2, respiratory
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* rate, VO2 max, active energy, walking/running distance, flights climbed)
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* - 90 days of WHOOP-style scores (recovery, day strain, sleep
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* performance/efficiency/consistency) coherent with the sleep nights
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* - ~3-4 workouts/week (running, strength, cycling) with per-workout HR
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* samples and per-workout strain
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* - 3 medications with schedules and ~90 days of intake history at high
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* compliance, with today scheduled on-track (taken or not-yet-due)
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* - 90 days of mood entries
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@@ -118,6 +128,8 @@ async function seed() {
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await client.query("DELETE FROM lab_results");
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await client.query("DELETE FROM measurement_reminders");
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await client.query("DELETE FROM mood_entries");
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await client.query("DELETE FROM workout_samples");
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await client.query("DELETE FROM workouts");
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await client.query("DELETE FROM medication_intake_events");
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await client.query("DELETE FROM medication_schedules");
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await client.query("DELETE FROM reminder_phase_configs");
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@@ -292,6 +304,248 @@ async function seed() {
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}
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}
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// ── Body composition (smart-scale series) ──
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// A full scale-style breakdown, written every 2–3 days like a real
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// body-composition scale would record. Every component is DERIVED from
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// the same-day weight + body-fat series above so the numbers reconcile:
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// fat mass = weight × bodyFat%, fat-free mass = weight − fat mass, and
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// the muscle / water / bone / BMI components hang off those so the demo
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// never contradicts its own headline weight + body-fat tiles. Source
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// APPLE_HEALTH with device_type 'scale' so they read as device-tracked.
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// Canonical units (see prisma/schema.prisma + VALUE_RANGES): mass
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// components in kg, VISCERAL_FAT a 1–12 rating, BODY_MASS_INDEX kg/m².
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console.log("Creating body composition series...");
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const HEIGHT_M = 1.82;
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for (let i = 0; i < span; i++) {
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// Roughly every other day, jittered, so the cadence reads organic.
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if (i % 2 !== 0 && Math.random() > 0.4) continue;
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const date = daysAgo(days - i);
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const weightKg = weights[i];
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const fatPct = bodyFat[i];
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const fatMass = Math.round(weightKg * (fatPct / 100) * 10) / 10;
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const fatFree = Math.round((weightKg - fatMass) * 10) / 10;
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// Skeletal muscle is a subset of fat-free mass (~75% of it for a lean
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// adult male); lean body mass ≈ fat-free mass.
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const muscleMass = Math.round(fatFree * 0.75 * 10) / 10;
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const leanMass = fatFree;
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// Total body water ≈ 73% of fat-free mass (kg of water).
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const bodyWater = Math.round(fatFree * 0.73 * 10) / 10;
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const boneMass = Math.round((3.1 + (Math.random() - 0.5) * 0.2) * 10) / 10;
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// Visceral-fat rating on Withings' 1–12 scale; a healthy ~6, easing
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// down slightly as the body fat trends down.
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const visceralFat = Math.round(7 - (i / span) * 1.5);
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const bmi = Math.round((weightKg / (HEIGHT_M * HEIGHT_M)) * 10) / 10;
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const composition: Array<{ type: string; value: number; unit: string }> =
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[
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{ type: "FAT_MASS", value: fatMass, unit: "kg" },
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{ type: "FAT_FREE_MASS", value: fatFree, unit: "kg" },
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{ type: "LEAN_BODY_MASS", value: leanMass, unit: "kg" },
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{ type: "MUSCLE_MASS", value: muscleMass, unit: "kg" },
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{ type: "TOTAL_BODY_WATER", value: bodyWater, unit: "kg" },
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{ type: "BONE_MASS", value: boneMass, unit: "kg" },
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{ type: "VISCERAL_FAT", value: visceralFat, unit: "rating" },
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{ type: "BODY_MASS_INDEX", value: bmi, unit: "kg/m²" },
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];
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for (const c of composition) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'APPLE_HEALTH', 'scale', $6, $6, $6)`,
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[cuid(), userId, c.type, c.value, c.unit, date],
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);
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}
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}
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// ── Blood glucose (healthy non-diabetic series) ──
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// A few readings per day over the full window, every value in a healthy
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// non-diabetic band: fasting ~85–95 mg/dL, a well-controlled post-meal
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// peak ~110–135, and a calm bedtime ~95–105. Canonical storage is mg/dL
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// (schema note on BLOOD_GLUCOSE); the per-reading context lands in the
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// glucose_context column (FASTING / POSTPRANDIAL / BEDTIME) so the
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// glucose panel can apply the right target band per reading.
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console.log("Creating blood glucose series...");
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const glucoseReadings: Array<{
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context: string;
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hour: number;
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base: number;
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jitter: number;
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}> = [
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{ context: "FASTING", hour: 7, base: 89, jitter: 6 },
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{ context: "POSTPRANDIAL", hour: 13, base: 122, jitter: 12 },
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{ context: "POSTPRANDIAL", hour: 19, base: 118, jitter: 12 },
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{ context: "BEDTIME", hour: 22, base: 99, jitter: 6 },
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];
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for (let i = 0; i < span; i++) {
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for (const r of glucoseReadings) {
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const at = daysAgoAt(days - i, r.hour, Math.floor(Math.random() * 30));
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const value = Math.round(r.base + (Math.random() - 0.5) * r.jitter);
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, glucose_context, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'BLOOD_GLUCOSE', $3, 'mg/dL', 'APPLE_HEALTH', $4, 'phone', $5, $5, $5)`,
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[cuid(), userId, value, r.context, at],
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);
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}
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}
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// ── Cardio fitness + vitals ───────────────
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// The dashboard + insights surface these device-tracked vitals. All
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// recent (full window, latest point today), all in healthy/aspirational
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// bands. Canonical units per schema + VALUE_RANGES: HRV ms,
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// OXYGEN_SATURATION percent, RESPIRATORY_RATE breaths/min, VO2_MAX
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// mL/(kg·min), ACTIVE_ENERGY_BURNED kcal, WALKING_RUNNING_DISTANCE metres,
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// FLIGHTS_CLIMBED count. Source APPLE_HEALTH device_type 'watch'.
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console.log("Creating cardio fitness + vitals series...");
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// HRV SDNN: a healthy ~55 ms, gently rising; also write the RMSSD variant.
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const hrvSdnn = randomWalk(48, 58, span, 5, VALUE_RANGES.HEART_RATE_VARIABILITY);
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const hrvRmssd = randomWalk(42, 52, span, 5, VALUE_RANGES.HRV_RMSSD);
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// SpO2: 97–99%.
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const spo2 = randomWalk(98, 98, span, 0.8, VALUE_RANGES.OXYGEN_SATURATION);
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// Respiratory rate: 13–16 breaths/min.
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const respRate = randomWalk(15, 14, span, 0.8, VALUE_RANGES.RESPIRATORY_RATE);
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// Active energy: ~550 kcal/day.
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const activeKcal = randomWalk(480, 600, span, 80, VALUE_RANGES.ACTIVE_ENERGY_BURNED);
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// Walking + running distance (metres): ~6–8 km/day, tracks steps.
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const distM = randomWalk(6200, 7800, span, 900, VALUE_RANGES.WALKING_RUNNING_DISTANCE);
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// Flights climbed: ~8–14/day.
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const flights = randomWalk(9, 13, span, 4, VALUE_RANGES.FLIGHTS_CLIMBED);
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for (let i = 0; i < span; i++) {
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const date = daysAgo(days - i);
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const vitals: Array<{ type: string; value: number; unit: string }> = [
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{ type: "HEART_RATE_VARIABILITY", value: Math.round(hrvSdnn[i]), unit: "ms" },
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{ type: "HRV_RMSSD", value: Math.round(hrvRmssd[i]), unit: "ms" },
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{ type: "OXYGEN_SATURATION", value: Math.round(spo2[i] * 10) / 10, unit: "%" },
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{ type: "RESPIRATORY_RATE", value: Math.round(respRate[i] * 10) / 10, unit: "count/min" },
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{ type: "ACTIVE_ENERGY_BURNED", value: Math.round(activeKcal[i]), unit: "kcal" },
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{ type: "WALKING_RUNNING_DISTANCE", value: Math.round(distM[i]), unit: "m" },
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{ type: "FLIGHTS_CLIMBED", value: Math.round(flights[i]), unit: "count" },
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];
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for (const v of vitals) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'APPLE_HEALTH', 'watch', $6, $6, $6)`,
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[cuid(), userId, v.type, v.value, v.unit, date],
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);
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}
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// VO2 max: only every ~10 days (Apple samples it rarely), ~46–49.
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if (i % 10 === 0) {
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const vo2 = Math.round((46 + (i / span) * 3) * 10) / 10;
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, 'VO2_MAX', $3, 'mL/(kg·min)', 'APPLE_HEALTH', 'watch', $4, $4, $4)`,
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[cuid(), userId, vo2, date],
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);
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}
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}
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// ── Recovery / strain / sleep scores (WHOOP-style) ──
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// The WHOOP-style readiness surface. These are stored as Measurement rows
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// (type RECOVERY_SCORE / DAY_STRAIN / SLEEP_PERFORMANCE / SLEEP_EFFICIENCY
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// / SLEEP_CONSISTENCY, unit 'score' or '%') with source WHOOP, distinct
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// from the COMPUTED engine's own rows. Coherent with the seeded sleep:
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// well-slept nights pair with high recovery + efficiency, and day-strain
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// sits in a sustainable band. RECOVERY_SCORE / sleep percentages are
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// 0–100; DAY_STRAIN is on WHOOP's 0–21 scale.
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console.log("Creating recovery / strain / sleep scores...");
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const recovery = randomWalk(68, 78, span, 8, VALUE_RANGES.RECOVERY_SCORE);
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const dayStrain = randomWalk(11, 13, span, 2.5, VALUE_RANGES.DAY_STRAIN);
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const sleepPerf = randomWalk(82, 90, span, 6, VALUE_RANGES.SLEEP_PERFORMANCE);
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const sleepEff = randomWalk(88, 93, span, 4, VALUE_RANGES.SLEEP_EFFICIENCY);
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const sleepConsistency = randomWalk(74, 84, span, 6, VALUE_RANGES.SLEEP_CONSISTENCY);
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for (let i = 0; i < span; i++) {
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// Anchor scores to the morning wake instant so a night's recovery sits
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// on the day it belongs to.
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const at = daysAgoAt(days - i, 7, 0);
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const scores: Array<{ type: string; value: number; unit: string }> = [
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{ type: "RECOVERY_SCORE", value: Math.round(recovery[i]), unit: "score" },
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{ type: "DAY_STRAIN", value: Math.round(dayStrain[i] * 10) / 10, unit: "score" },
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{ type: "SLEEP_PERFORMANCE", value: Math.round(sleepPerf[i]), unit: "%" },
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{ type: "SLEEP_EFFICIENCY", value: Math.round(sleepEff[i]), unit: "%" },
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{ type: "SLEEP_CONSISTENCY", value: Math.round(sleepConsistency[i]), unit: "%" },
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];
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for (const s of scores) {
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await client.query(
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`INSERT INTO measurements (id, user_id, type, value, unit, source, device_type, measured_at, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, 'WHOOP', 'band', $6, $6, $6)`,
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[cuid(), userId, s.type, s.value, s.unit, at],
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);
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}
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}
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// ── Workouts ──────────────────────────────
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// ~3–4 sessions/week across the window: a mix of running, strength, and
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// cycling, with the fields the workout tiles render — duration, energy,
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// avg/max HR, distance (running/cycling). Each workout carries a sparse
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// HR sample series in WorkoutSamples (the per-workout HR chart) and its
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// per-workout strain in metadata (WORKOUT_STRAIN lives on the workout row
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// per the schema note, not as a free-floating Measurement). Distances in
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// metres, energy in kcal, durations in seconds — matching the model.
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console.log("Creating workouts...");
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const workoutPlan: Array<{
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sport: string;
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durationMin: number;
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kcal: number;
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avgHr: number;
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maxHr: number;
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distanceM: number | null;
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strain: number;
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}> = [
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{ sport: "running", durationMin: 38, kcal: 420, avgHr: 148, maxHr: 171, distanceM: 6500, strain: 13.4 },
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{ sport: "strength", durationMin: 52, kcal: 360, avgHr: 118, maxHr: 152, distanceM: null, strain: 10.2 },
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{ sport: "cycling", durationMin: 65, kcal: 520, avgHr: 134, maxHr: 158, distanceM: 24000, strain: 12.1 },
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];
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// Step backwards through the window placing ~2 sessions a week per slot
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// so the cadence lands around 3–4/week without overlapping.
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let planIdx = 0;
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for (let dayOffset = days - 1; dayOffset >= 1; dayOffset -= 2) {
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// Skip ~25% of slots so the week isn't perfectly regular.
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if (Math.random() < 0.25) continue;
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const plan = workoutPlan[planIdx % workoutPlan.length];
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planIdx += 1;
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const startHour = 17 + Math.floor(Math.random() * 3);
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const startedAt = daysAgoAt(dayOffset, startHour, Math.floor(Math.random() * 50));
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const durationSec = plan.durationMin * 60;
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const endedAt = new Date(startedAt.getTime() + durationSec * 1000);
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const workoutId = cuid();
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await client.query(
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`INSERT INTO workouts (id, user_id, sport_type, started_at, ended_at, duration_sec, total_energy_kcal, total_distance_m, avg_heart_rate, max_heart_rate, source, metadata, created_at, updated_at)
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VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, 'APPLE_HEALTH', $11, $4, $4)`,
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[
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workoutId,
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userId,
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plan.sport,
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startedAt,
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endedAt,
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durationSec,
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plan.kcal,
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plan.distanceM,
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plan.avgHr,
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plan.maxHr,
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JSON.stringify({ workoutStrain: plan.strain }),
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],
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);
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// A sparse HR series — one sample every ~5 minutes across the session,
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// rising into the working band and easing at the end. The per-workout
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// HR chart renders straight from this WorkoutSamples blob.
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const sampleCount = Math.max(2, Math.round(plan.durationMin / 5));
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const samples: Array<{ t: string; hr: number }> = [];
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for (let s = 0; s < sampleCount; s++) {
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const frac = s / (sampleCount - 1);
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// Warm up from ~60% to the avg, peak near the max around 70% through.
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const peakBias = 1 - Math.abs(frac - 0.7) * 0.6;
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const hr = Math.round(
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plan.avgHr + (plan.maxHr - plan.avgHr) * peakBias * 0.7,
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);
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const t = new Date(startedAt.getTime() + frac * durationSec * 1000);
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samples.push({ t: t.toISOString(), hr });
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}
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await client.query(
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`INSERT INTO workout_samples (id, workout_id, samples, sample_count, created_at)
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VALUES ($1, $2, $3, $4, NOW())`,
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[cuid(), workoutId, JSON.stringify(samples), samples.length],
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);
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}
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// ── Medications ───────────────────────────
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console.log("Creating medications...");
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@@ -665,9 +919,14 @@ async function seed() {
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await client.query("COMMIT");
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console.log("\nDemo data seeded successfully!");
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console.log(` User: demo / demo123demo123`);
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console.log(` Measurements: ~${span * 8} entries (8 types, today included)`);
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console.log(` Measurements: weight, BP, pulse, resting HR, body fat, steps, sleep`);
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console.log(` Body composition: fat/lean/muscle mass, water, bone, visceral fat, BMI`);
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console.log(` Blood glucose: ${glucoseReadings.length} readings/day (fasting + post-meal + bedtime)`);
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console.log(` Cardio + vitals: HRV (SDNN + RMSSD), SpO2, resp. rate, VO2max, active energy, distance, flights`);
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console.log(` Scores: recovery, day strain, sleep performance/efficiency/consistency`);
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console.log(` Sleep: 7 per-stage nights (4 stages each) + daily aggregate`);
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console.log(` Medications: 3 (high compliance, today on-track)`);
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console.log(` Workouts: ~3-4/week (running, strength, cycling) with HR samples`);
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console.log(` Mood: ~${Math.round(span * 0.95)} entries`);
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console.log(` Vorsorge reminders: 2 (dental, annual physical)`);
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console.log(` Lab panel: ${labResults.length} biomarkers`);
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