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>

Shared/Services/MoodDataSummarizer.swift

@@ -47,15 +47,8 @@ struct MoodDataSummary {
     let hasAllMoodTypes: Bool
     let missingMoodTypes: [String]
 
-    // Health correlations (optional)
-    let healthCorrelations: [HealthCorrelation]
-}
-
-/// Health correlation data for AI insights
-struct HealthCorrelation {
-    let metric: String
-    let insight: String
-    let correlation: String  // "positive", "negative", or "none"
+    // Health data for AI analysis (optional)
+    let healthAverages: HealthService.HealthAverages?
 }
 
 /// Transforms raw MoodEntryModel data into AI-optimized summaries
@@ -69,7 +62,7 @@ class MoodDataSummarizer {
 
     // MARK: - Main Summarization
 
-    func summarize(entries: [MoodEntryModel], periodName: String, healthCorrelations: [HealthCorrelation] = []) -> MoodDataSummary {
+    func summarize(entries: [MoodEntryModel], periodName: String, healthAverages: HealthService.HealthAverages? = nil) -> MoodDataSummary {
         let validEntries = entries.filter { ![.missing, .placeholder].contains($0.mood) }
 
         guard !validEntries.isEmpty else {
@@ -114,7 +107,7 @@ class MoodDataSummarizer {
             last7DaysMoods: recentContext.moods,
             hasAllMoodTypes: moodTypes.hasAll,
             missingMoodTypes: moodTypes.missing,
-            healthCorrelations: healthCorrelations
+            healthAverages: healthAverages
         )
     }
 
@@ -391,7 +384,7 @@ class MoodDataSummarizer {
             last7DaysMoods: [],
             hasAllMoodTypes: false,
             missingMoodTypes: ["great", "good", "average", "bad", "horrible"],
-            healthCorrelations: []
+            healthAverages: nil
         )
     }
 
@@ -423,12 +416,57 @@ class MoodDataSummarizer {
         // Stability
         lines.append("Stability: \(String(format: "%.0f", summary.moodStabilityScore * 100))%, Mood swings: \(summary.moodSwingCount)")
 
-        // Health correlations (if available)
-        if !summary.healthCorrelations.isEmpty {
-            lines.append("Health correlations:")
-            for correlation in summary.healthCorrelations {
-                lines.append("- \(correlation.metric): \(correlation.insight)")
+        // Health data for AI analysis (if available)
+        if let health = summary.healthAverages, health.hasData {
+            lines.append("")
+            lines.append("Apple Health data (\(health.daysWithHealthData) days with data):")
+
+            // Activity metrics
+            var activityMetrics: [String] = []
+            if let steps = health.avgSteps {
+                activityMetrics.append("Steps: \(steps.formatted())/day")
             }
+            if let exercise = health.avgExerciseMinutes {
+                activityMetrics.append("Exercise: \(exercise) min/day")
+            }
+            if let calories = health.avgActiveCalories {
+                activityMetrics.append("Active cal: \(calories)/day")
+            }
+            if let distance = health.avgDistanceKm {
+                activityMetrics.append("Distance: \(String(format: "%.1f", distance)) km/day")
+            }
+            if !activityMetrics.isEmpty {
+                lines.append("Activity: \(activityMetrics.joined(separator: ", "))")
+            }
+
+            // Heart metrics
+            var heartMetrics: [String] = []
+            if let hr = health.avgHeartRate {
+                heartMetrics.append("Avg HR: \(Int(hr)) bpm")
+            }
+            if let restingHR = health.avgRestingHeartRate {
+                heartMetrics.append("Resting HR: \(Int(restingHR)) bpm")
+            }
+            if let hrv = health.avgHRV {
+                heartMetrics.append("HRV: \(Int(hrv)) ms")
+            }
+            if !heartMetrics.isEmpty {
+                lines.append("Heart: \(heartMetrics.joined(separator: ", "))")
+            }
+
+            // Recovery metrics
+            var recoveryMetrics: [String] = []
+            if let sleep = health.avgSleepHours {
+                recoveryMetrics.append("Sleep: \(String(format: "%.1f", sleep)) hrs/night")
+            }
+            if let mindful = health.avgMindfulMinutes {
+                recoveryMetrics.append("Mindfulness: \(mindful) min/day")
+            }
+            if !recoveryMetrics.isEmpty {
+                lines.append("Recovery: \(recoveryMetrics.joined(separator: ", "))")
+            }
+
+            lines.append("Analyze how these health metrics may correlate with mood patterns.")
         }
 
         return lines.joined(separator: "\n")