Pass raw health metrics to AI instead of hardcoded correlations
- Replace HealthService.analyzeCorrelations() with computeHealthAverages() - Remove hardcoded threshold-based correlation analysis (8k steps, 7hrs sleep, etc.) - Pass raw averages (steps, exercise, sleep, HRV, HR, mindfulness, calories) to AI - Let Apple Intelligence find nuanced multi-variable patterns naturally - Update MoodDataSummarizer to format raw health data for AI prompts - Simplifies code by ~200 lines while improving insight quality 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Trey t
@@ -30,14 +30,35 @@ class HealthService: ObservableObject {
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// MARK: - Data Types
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// Core activity metrics
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private let stepCountType = HKQuantityType.quantityType(forIdentifier: .stepCount)!
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private let exerciseTimeType = HKQuantityType.quantityType(forIdentifier: .appleExerciseTime)!
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private let activeEnergyType = HKQuantityType.quantityType(forIdentifier: .activeEnergyBurned)!
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private let distanceType = HKQuantityType.quantityType(forIdentifier: .distanceWalkingRunning)!
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// Heart & stress indicators
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private let heartRateType = HKQuantityType.quantityType(forIdentifier: .heartRate)!
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private let restingHeartRateType = HKQuantityType.quantityType(forIdentifier: .restingHeartRate)!
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private let hrvType = HKQuantityType.quantityType(forIdentifier: .heartRateVariabilitySDNN)!
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// Sleep & recovery
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private let sleepAnalysisType = HKCategoryType.categoryType(forIdentifier: .sleepAnalysis)!
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// Mindfulness
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private let mindfulSessionType = HKCategoryType.categoryType(forIdentifier: .mindfulSession)!
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// State of Mind
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private let stateOfMindType = HKSampleType.stateOfMindType()
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// Workouts
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private let workoutType = HKWorkoutType.workoutType()
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private var readTypes: Set<HKObjectType> {
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[stepCountType, exerciseTimeType, heartRateType, sleepAnalysisType, stateOfMindType]
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[
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stepCountType, exerciseTimeType, activeEnergyType, distanceType,
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heartRateType, restingHeartRateType, hrvType,
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sleepAnalysisType, mindfulSessionType, stateOfMindType, workoutType
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]
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}
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// MARK: - Initialization
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@@ -71,13 +92,25 @@ class HealthService: ObservableObject {
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struct DailyHealthData {
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let date: Date
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// Activity metrics
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let steps: Int?
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let exerciseMinutes: Int?
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let activeCalories: Int?
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let distanceKm: Double?
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// Heart metrics
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let averageHeartRate: Double?
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let restingHeartRate: Double?
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let hrv: Double? // Heart Rate Variability in ms
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// Recovery metrics
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let sleepHours: Double?
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let mindfulMinutes: Int?
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var hasData: Bool {
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steps != nil || exerciseMinutes != nil || averageHeartRate != nil || sleepHours != nil
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steps != nil || exerciseMinutes != nil || averageHeartRate != nil ||
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sleepHours != nil || hrv != nil || mindfulMinutes != nil
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}
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}
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@@ -86,17 +119,28 @@ class HealthService: ObservableObject {
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let startOfDay = calendar.startOfDay(for: date)
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let endOfDay = calendar.date(byAdding: .day, value: 1, to: startOfDay)!
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// Fetch all metrics concurrently
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async let steps = fetchSteps(start: startOfDay, end: endOfDay)
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async let exercise = fetchExerciseMinutes(start: startOfDay, end: endOfDay)
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async let activeCalories = fetchActiveCalories(start: startOfDay, end: endOfDay)
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async let distance = fetchDistance(start: startOfDay, end: endOfDay)
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async let heartRate = fetchAverageHeartRate(start: startOfDay, end: endOfDay)
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async let restingHR = fetchRestingHeartRate(start: startOfDay, end: endOfDay)
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async let hrv = fetchHRV(start: startOfDay, end: endOfDay)
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async let sleep = fetchSleepHours(for: date)
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async let mindful = fetchMindfulMinutes(start: startOfDay, end: endOfDay)
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return await DailyHealthData(
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date: date,
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steps: steps,
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exerciseMinutes: exercise,
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activeCalories: activeCalories,
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distanceKm: distance,
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averageHeartRate: heartRate,
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sleepHours: sleep
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restingHeartRate: restingHR,
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hrv: hrv,
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sleepHours: sleep,
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mindfulMinutes: mindful
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)
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}
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@@ -231,6 +275,153 @@ class HealthService: ObservableObject {
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}
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}
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// MARK: - Active Calories
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private func fetchActiveCalories(start: Date, end: Date) async -> Int? {
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guard isAuthorized else { return nil }
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let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)
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return await withCheckedContinuation { continuation in
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let query = HKStatisticsQuery(
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quantityType: activeEnergyType,
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quantitySamplePredicate: predicate,
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options: .cumulativeSum
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) { _, result, error in
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guard error == nil,
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let sum = result?.sumQuantity() else {
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continuation.resume(returning: nil)
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return
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}
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let calories = Int(sum.doubleValue(for: .kilocalorie()))
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continuation.resume(returning: calories)
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}
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healthStore.execute(query)
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}
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}
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// MARK: - Distance
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private func fetchDistance(start: Date, end: Date) async -> Double? {
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guard isAuthorized else { return nil }
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let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)
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return await withCheckedContinuation { continuation in
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let query = HKStatisticsQuery(
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quantityType: distanceType,
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quantitySamplePredicate: predicate,
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options: .cumulativeSum
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) { _, result, error in
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guard error == nil,
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let sum = result?.sumQuantity() else {
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continuation.resume(returning: nil)
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return
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}
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let km = sum.doubleValue(for: .meterUnit(with: .kilo))
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continuation.resume(returning: km)
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}
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healthStore.execute(query)
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}
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}
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// MARK: - Resting Heart Rate
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private func fetchRestingHeartRate(start: Date, end: Date) async -> Double? {
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guard isAuthorized else { return nil }
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let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)
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return await withCheckedContinuation { continuation in
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let query = HKStatisticsQuery(
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quantityType: restingHeartRateType,
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quantitySamplePredicate: predicate,
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options: .discreteAverage
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) { _, result, error in
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guard error == nil,
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let avg = result?.averageQuantity() else {
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continuation.resume(returning: nil)
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return
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}
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let bpm = avg.doubleValue(for: HKUnit.count().unitDivided(by: .minute()))
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continuation.resume(returning: bpm)
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}
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healthStore.execute(query)
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}
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}
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// MARK: - Heart Rate Variability (HRV)
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private func fetchHRV(start: Date, end: Date) async -> Double? {
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guard isAuthorized else { return nil }
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let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)
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return await withCheckedContinuation { continuation in
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let query = HKStatisticsQuery(
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quantityType: hrvType,
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quantitySamplePredicate: predicate,
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options: .discreteAverage
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) { _, result, error in
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guard error == nil,
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let avg = result?.averageQuantity() else {
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continuation.resume(returning: nil)
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return
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}
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// HRV is measured in milliseconds
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let ms = avg.doubleValue(for: .secondUnit(with: .milli))
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continuation.resume(returning: ms)
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}
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healthStore.execute(query)
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}
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}
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// MARK: - Mindful Minutes
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private func fetchMindfulMinutes(start: Date, end: Date) async -> Int? {
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guard isAuthorized else { return nil }
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let predicate = HKQuery.predicateForSamples(withStart: start, end: end, options: .strictStartDate)
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return await withCheckedContinuation { continuation in
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let query = HKSampleQuery(
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sampleType: mindfulSessionType,
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predicate: predicate,
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limit: HKObjectQueryNoLimit,
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sortDescriptors: nil
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) { _, samples, error in
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guard error == nil,
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let mindfulSamples = samples as? [HKCategorySample] else {
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continuation.resume(returning: nil)
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return
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}
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// Sum up all mindful session durations
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var totalSeconds: TimeInterval = 0
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for sample in mindfulSamples {
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totalSeconds += sample.endDate.timeIntervalSince(sample.startDate)
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}
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if totalSeconds > 0 {
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let minutes = Int(totalSeconds / 60)
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continuation.resume(returning: minutes)
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} else {
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continuation.resume(returning: nil)
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}
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}
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healthStore.execute(query)
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}
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}
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// MARK: - Batch Fetch for Insights
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func fetchHealthData(for entries: [MoodEntryModel]) async -> [Date: DailyHealthData] {
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@@ -259,121 +450,78 @@ class HealthService: ObservableObject {
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return results
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}
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// MARK: - Correlation Analysis
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// MARK: - Health Averages for AI Analysis
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struct HealthMoodCorrelation {
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let metric: String
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let correlation: String // "positive", "negative", or "none"
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let insight: String
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let averageWithHighMetric: Double
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let averageWithLowMetric: Double
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/// Aggregated health metrics for a period - passed to AI for correlation analysis
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struct HealthAverages {
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// Activity
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let avgSteps: Int?
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let avgExerciseMinutes: Int?
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let avgActiveCalories: Int?
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let avgDistanceKm: Double?
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// Heart health
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let avgHeartRate: Double?
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let avgRestingHeartRate: Double?
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let avgHRV: Double? // milliseconds
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// Recovery
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let avgSleepHours: Double?
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let avgMindfulMinutes: Int?
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// Sample sizes (for AI context)
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let daysWithHealthData: Int
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let totalMoodEntries: Int
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var hasData: Bool {
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daysWithHealthData > 0
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}
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}
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func analyzeCorrelations(entries: [MoodEntryModel], healthData: [Date: DailyHealthData]) -> [HealthMoodCorrelation] {
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var correlations: [HealthMoodCorrelation] = []
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/// Compute aggregate health averages for a set of mood entries
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/// The AI will analyze these alongside mood data to find correlations
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func computeHealthAverages(entries: [MoodEntryModel], healthData: [Date: DailyHealthData]) -> HealthAverages {
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let calendar = Calendar.current
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// Prepare data pairs
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var stepsAndMoods: [(steps: Int, mood: Int)] = []
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var exerciseAndMoods: [(minutes: Int, mood: Int)] = []
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var sleepAndMoods: [(hours: Double, mood: Int)] = []
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var heartRateAndMoods: [(bpm: Double, mood: Int)] = []
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var steps: [Int] = []
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var exercise: [Int] = []
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var calories: [Int] = []
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var distance: [Double] = []
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var heartRate: [Double] = []
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var restingHR: [Double] = []
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var hrv: [Double] = []
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var sleep: [Double] = []
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var mindful: [Int] = []
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for entry in entries {
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let date = calendar.startOfDay(for: entry.forDate)
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guard let health = healthData[date] else { continue }
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let moodValue = entry.moodValue + 1 // Use 1-5 scale
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if let steps = health.steps {
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stepsAndMoods.append((steps, moodValue))
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}
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if let exercise = health.exerciseMinutes {
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exerciseAndMoods.append((exercise, moodValue))
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}
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if let sleep = health.sleepHours {
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sleepAndMoods.append((sleep, moodValue))
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}
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if let hr = health.averageHeartRate {
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heartRateAndMoods.append((hr, moodValue))
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}
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if let s = health.steps { steps.append(s) }
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if let e = health.exerciseMinutes { exercise.append(e) }
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if let c = health.activeCalories { calories.append(c) }
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if let d = health.distanceKm { distance.append(d) }
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if let h = health.averageHeartRate { heartRate.append(h) }
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if let r = health.restingHeartRate { restingHR.append(r) }
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if let v = health.hrv { hrv.append(v) }
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if let sl = health.sleepHours { sleep.append(sl) }
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if let m = health.mindfulMinutes { mindful.append(m) }
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}
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// Analyze steps correlation
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if stepsAndMoods.count >= 5 {
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let threshold = 8000
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let highSteps = stepsAndMoods.filter { $0.steps >= threshold }
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let lowSteps = stepsAndMoods.filter { $0.steps < threshold }
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let daysWithData = Set(healthData.keys).count
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if !highSteps.isEmpty && !lowSteps.isEmpty {
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let avgHigh = Double(highSteps.map { $0.mood }.reduce(0, +)) / Double(highSteps.count)
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let avgLow = Double(lowSteps.map { $0.mood }.reduce(0, +)) / Double(lowSteps.count)
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let diff = avgHigh - avgLow
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if abs(diff) >= 0.3 {
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correlations.append(HealthMoodCorrelation(
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metric: "Steps",
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correlation: diff > 0 ? "positive" : "negative",
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insight: diff > 0
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? "Your mood averages \(String(format: "%.1f", diff)) points higher on days with 8k+ steps"
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: "Interestingly, your mood is slightly lower on high-step days",
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averageWithHighMetric: avgHigh,
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averageWithLowMetric: avgLow
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))
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}
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}
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}
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// Analyze sleep correlation
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if sleepAndMoods.count >= 5 {
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let threshold = 7.0
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let goodSleep = sleepAndMoods.filter { $0.hours >= threshold }
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let poorSleep = sleepAndMoods.filter { $0.hours < threshold }
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if !goodSleep.isEmpty && !poorSleep.isEmpty {
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let avgGood = Double(goodSleep.map { $0.mood }.reduce(0, +)) / Double(goodSleep.count)
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let avgPoor = Double(poorSleep.map { $0.mood }.reduce(0, +)) / Double(poorSleep.count)
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let diff = avgGood - avgPoor
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if abs(diff) >= 0.3 {
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correlations.append(HealthMoodCorrelation(
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metric: "Sleep",
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correlation: diff > 0 ? "positive" : "negative",
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insight: diff > 0
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? "7+ hours of sleep correlates with \(String(format: "%.1f", diff)) point higher mood"
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: "Sleep duration doesn't seem to strongly affect your mood",
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averageWithHighMetric: avgGood,
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averageWithLowMetric: avgPoor
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))
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}
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}
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}
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// Analyze exercise correlation
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if exerciseAndMoods.count >= 5 {
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let threshold = 30
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let active = exerciseAndMoods.filter { $0.minutes >= threshold }
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let inactive = exerciseAndMoods.filter { $0.minutes < threshold }
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if !active.isEmpty && !inactive.isEmpty {
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let avgActive = Double(active.map { $0.mood }.reduce(0, +)) / Double(active.count)
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let avgInactive = Double(inactive.map { $0.mood }.reduce(0, +)) / Double(inactive.count)
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let diff = avgActive - avgInactive
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if abs(diff) >= 0.3 {
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correlations.append(HealthMoodCorrelation(
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metric: "Exercise",
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correlation: diff > 0 ? "positive" : "negative",
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insight: diff > 0
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? "30+ minutes of exercise correlates with \(String(format: "%.1f", diff)) point mood boost"
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: "Exercise doesn't show a strong mood correlation for you",
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averageWithHighMetric: avgActive,
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averageWithLowMetric: avgInactive
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))
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}
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}
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}
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return correlations
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return HealthAverages(
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avgSteps: steps.isEmpty ? nil : steps.reduce(0, +) / steps.count,
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avgExerciseMinutes: exercise.isEmpty ? nil : exercise.reduce(0, +) / exercise.count,
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avgActiveCalories: calories.isEmpty ? nil : calories.reduce(0, +) / calories.count,
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avgDistanceKm: distance.isEmpty ? nil : distance.reduce(0, +) / Double(distance.count),
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avgHeartRate: heartRate.isEmpty ? nil : heartRate.reduce(0, +) / Double(heartRate.count),
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avgRestingHeartRate: restingHR.isEmpty ? nil : restingHR.reduce(0, +) / Double(restingHR.count),
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avgHRV: hrv.isEmpty ? nil : hrv.reduce(0, +) / Double(hrv.count),
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avgSleepHours: sleep.isEmpty ? nil : sleep.reduce(0, +) / Double(sleep.count),
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avgMindfulMinutes: mindful.isEmpty ? nil : mindful.reduce(0, +) / mindful.count,
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daysWithHealthData: daysWithData,
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totalMoodEntries: entries.count
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)
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}
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}
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Block a user