Reflect/stats.md

Advanced Statistics — Deep Data Research

Temporal Pattern Mining

Mood Cycles & Seasonality

• Weekly cycles — not just "best/worst day" but the actual shape of the week (do they dip mid-week and recover Friday, or crash on Sunday night?)
• Monthly cycles — mood patterns across the month (beginning vs end, paycheck timing effects)
• Seasonal patterns — spring vs winter mood baselines. Weather data can separate "it's cold" from "it's January" effects
• Time-of-day patterns — timestamp (when they logged) vs forDate. Late-night loggers vs morning loggers may show different patterns. Logging time itself could correlate with mood.

Trend Decomposition

Instead of just "improving/declining/stable", decompose the mood signal into:

• Baseline (long-term average that shifts slowly)
• Trend (is the baseline rising or falling over months?)
• Volatility (are swings getting wider or narrower over time?)

This gives users a real answer to "am I actually getting better?" that a simple average can't.

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Cross-Signal Correlations

Health × Mood (Per-User Correlation Ranking)

9 health metrics available. Instead of showing all, rank which health signals matter most for THIS specific user. Compute per-user Pearson correlation between each health metric and mood:

• "Sleep is your #1 mood predictor (r=0.72)"
• "Steps have no significant correlation for you (r=0.08)"
• "Your HRV and mood are moderately linked (r=0.45)"

Personalized and genuinely useful — tells each user what to focus on.

Weather × Mood (Beyond Averages)

Instead of just "sunny days = happier":

• Temperature sweet spot — fit a curve to find their optimal temperature range
• Weather transitions — does a sunny day after three rainy days hit differently than a sunny day in a sunny streak?
• Humidity as a factor — stored but not analyzed

Tags × Health × Mood (Multivariate)

Cross-signal analysis:

• "On days tagged 'work' + sleep < 6hrs, your mood averages 1.8. On 'work' + sleep > 7hrs, it's 3.4" — sleep is a buffer against work stress
• "Exercise days tagged 'social' average 4.2, exercise days tagged 'solo' average 3.1" — social exercise matters more

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Behavioral Pattern Analysis

Logging Behavior as Signal

The act of logging contains information:

• Entry source patterns — do they use the widget more on bad days? Watch on good days? Could reveal avoidance patterns
• Logging time drift — are they logging later and later? Often correlates with declining mood
• Note length vs mood — do they write more when upset or when happy? notes?.count is free data
• Reflection completion rate — do they bail on guided reflections for certain moods? Completing a negative reflection may itself be therapeutic

Gap Analysis (Deeper)

Beyond simple gap tracking:

• What predicts a gap? Look at the 3 days before each gap — was mood declining? Were they on a negative streak?
• Recovery patterns — how long after returning does mood stabilize? Is there a "bounce" effect?
• Gap frequency over time — are they getting more or less consistent? Consistency trend is a health proxy

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AI-Enriched Analysis

Note/Reflection Sentiment Trends

• Sentiment trajectory within a reflection — does the user start negative and end positive (processing) or start positive and end negative (rumination)?
• Topic evolution — what themes are growing vs fading over months? "Work" mentions peaking = potential burnout signal
• Gratitude frequency — entries tagged "gratitude" tracked as a percentage over time. Research shows gratitude journaling improves wellbeing — show them their own trend

Predicted Mood

With enough data (30+ entries), build a simple predictor:

• Given today's day of week, recent weather, recent sleep, and current streak — what mood is likely?
• Show as a "forecast" card: "Based on your patterns, Tuesdays after poor sleep tend to be tough — be gentle with yourself"
• Uses correlations already computed, just applied forward

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Comparative & Benchmark Insights

Personal Bests & Records

• Longest positive streak ever (and when it was)
• Best week/month on record
• Most consistent month (lowest variance)
• "Your mood this March was your best March in 2 years"

Milestone Detection

• "You've logged 100 days"
• "Your 30-day average just hit an all-time high"
• "First month with no 'horrible' days"
• Motivational and drives retention

Before/After Analysis

If a user starts a new habit (e.g., enables HealthKit, starts guided reflections, starts tagging), compare stats before vs after:

• "Since you started doing guided reflections 45 days ago, your average mood is up 0.6 points"
• "Since enabling Health tracking, your logging consistency improved 23%"

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Feasibility Notes

All of this runs on data already collected. The compute is lightweight:

• Correlations are just zip + arithmetic on two arrays
• Cycle detection is grouping by weekDay / Calendar.component(.month) / hour-of-day
• Trend decomposition is a sliding window average
• Predictions are weighted averages of correlated factors
• No server needed — Foundation Models handles the narrative, Swift handles the math

The heavy lift is visualization (Swift Charts) and narrative framing (using Foundation Models to turn "r=0.72 for sleep" into "Sleep is your superpower — on nights you get 7+ hours, your mood jumps by a full point").

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Existing Data Points Available

Per Entry (MoodEntryModel)

1. Date logged (forDate)
2. Mood value (5-point scale)
3. Entry type (10 sources: app, widget, watch, siri, etc.)
4. Timestamp created
5. Day of week
6. Text notes (optional)
7. Photo ID (optional)
8. Weather data — condition, temp high/low, humidity, location (optional)
9. Guided reflection responses (optional)
10. AI-extracted tags from 16 categories (optional)

HealthKit (9 metrics)

• Steps, exercise minutes, active calories, distance
• Average heart rate, resting heart rate, HRV
• Sleep hours, mindful minutes

Already Computed (MoodDataSummarizer)

• Mood distribution (counts, percentages, averages)
• Day-of-week averages, best/worst day, weekend vs weekday
• Trend direction and magnitude
• Streaks (current, longest, positive, negative)
• Mood stability score and swing count
• Tag-mood correlations (good-day tags, bad-day tags)
• Weather-mood averages (by condition, by temp range)
• Logging gap analysis (pre/post gap averages)
• Entry source breakdown

Already Visualized

• Year heatmap + donut chart (YearView)
• AI-generated text insights (InsightsView)
• Weekly digest card (WeeklyDigestCardView)
• AI reports with PDF export (ReportsView)

NOT Yet Visualized (Gaps)

• No trend line charts
• No health correlation charts
• No tag/theme visualizations
• No period comparisons
• No streak visualizations beyond a number
• No mood stability visualization
• No logging behavior analysis
• No predictive features