cc4143d3ea
Adds a 5-step negative-mood reflection flow with an evidence-examination step, Socratic templated questions that back-reference prior answers, and a deterministic cognitive-distortion detector that routes the perspective- check prompt to a distortion-specific reframe. Includes CBT plan docs, flowchart, stats research notes, and MCP config. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
155 lines
6.9 KiB
Markdown
155 lines
6.9 KiB
Markdown
# Advanced Statistics — Deep Data Research
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## Temporal Pattern Mining
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### Mood Cycles & Seasonality
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- **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?)
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- **Monthly cycles** — mood patterns across the month (beginning vs end, paycheck timing effects)
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- **Seasonal patterns** — spring vs winter mood baselines. Weather data can separate "it's cold" from "it's January" effects
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- **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.
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### Trend Decomposition
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Instead of just "improving/declining/stable", decompose the mood signal into:
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- **Baseline** (long-term average that shifts slowly)
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- **Trend** (is the baseline rising or falling over months?)
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- **Volatility** (are swings getting wider or narrower over time?)
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This gives users a real answer to "am I actually getting better?" that a simple average can't.
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---
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## Cross-Signal Correlations
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### Health × Mood (Per-User Correlation Ranking)
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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:
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- "Sleep is your #1 mood predictor (r=0.72)"
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- "Steps have no significant correlation for you (r=0.08)"
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- "Your HRV and mood are moderately linked (r=0.45)"
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Personalized and genuinely useful — tells each user what to focus on.
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### Weather × Mood (Beyond Averages)
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Instead of just "sunny days = happier":
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- **Temperature sweet spot** — fit a curve to find their optimal temperature range
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- **Weather transitions** — does a sunny day *after* three rainy days hit differently than a sunny day in a sunny streak?
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- **Humidity as a factor** — stored but not analyzed
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### Tags × Health × Mood (Multivariate)
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Cross-signal analysis:
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- "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
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- "Exercise days tagged 'social' average 4.2, exercise days tagged 'solo' average 3.1" — social exercise matters more
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---
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## Behavioral Pattern Analysis
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### Logging Behavior as Signal
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The *act of logging* contains information:
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- **Entry source patterns** — do they use the widget more on bad days? Watch on good days? Could reveal avoidance patterns
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- **Logging time drift** — are they logging later and later? Often correlates with declining mood
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- **Note length vs mood** — do they write more when upset or when happy? `notes?.count` is free data
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- **Reflection completion rate** — do they bail on guided reflections for certain moods? Completing a negative reflection may itself be therapeutic
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### Gap Analysis (Deeper)
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Beyond simple gap tracking:
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- **What predicts a gap?** Look at the 3 days before each gap — was mood declining? Were they on a negative streak?
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- **Recovery patterns** — how long after returning does mood stabilize? Is there a "bounce" effect?
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- **Gap frequency over time** — are they getting more or less consistent? Consistency trend is a health proxy
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---
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## AI-Enriched Analysis
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### Note/Reflection Sentiment Trends
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- **Sentiment trajectory within a reflection** — does the user start negative and end positive (processing) or start positive and end negative (rumination)?
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- **Topic evolution** — what themes are growing vs fading over months? "Work" mentions peaking = potential burnout signal
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- **Gratitude frequency** — entries tagged "gratitude" tracked as a percentage over time. Research shows gratitude journaling improves wellbeing — show them their own trend
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### Predicted Mood
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With enough data (30+ entries), build a simple predictor:
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- Given today's day of week, recent weather, recent sleep, and current streak — what mood is likely?
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- Show as a "forecast" card: "Based on your patterns, Tuesdays after poor sleep tend to be tough — be gentle with yourself"
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- Uses correlations already computed, just applied forward
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---
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## Comparative & Benchmark Insights
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### Personal Bests & Records
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- Longest positive streak ever (and when it was)
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- Best week/month on record
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- Most consistent month (lowest variance)
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- "Your mood this March was your best March in 2 years"
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### Milestone Detection
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- "You've logged 100 days"
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- "Your 30-day average just hit an all-time high"
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- "First month with no 'horrible' days"
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- Motivational and drives retention
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### Before/After Analysis
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If a user starts a new habit (e.g., enables HealthKit, starts guided reflections, starts tagging), compare stats before vs after:
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- "Since you started doing guided reflections 45 days ago, your average mood is up 0.6 points"
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- "Since enabling Health tracking, your logging consistency improved 23%"
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---
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## Feasibility Notes
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All of this runs on data already collected. The compute is lightweight:
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- Correlations are just `zip` + arithmetic on two arrays
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- Cycle detection is grouping by `weekDay` / `Calendar.component(.month)` / hour-of-day
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- Trend decomposition is a sliding window average
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- Predictions are weighted averages of correlated factors
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- No server needed — Foundation Models handles the narrative, Swift handles the math
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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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---
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## Existing Data Points Available
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### Per Entry (MoodEntryModel)
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1. Date logged (`forDate`)
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2. Mood value (5-point scale)
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3. Entry type (10 sources: app, widget, watch, siri, etc.)
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4. Timestamp created
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5. Day of week
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6. Text notes (optional)
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7. Photo ID (optional)
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8. Weather data — condition, temp high/low, humidity, location (optional)
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9. Guided reflection responses (optional)
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10. AI-extracted tags from 16 categories (optional)
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### HealthKit (9 metrics)
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- Steps, exercise minutes, active calories, distance
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- Average heart rate, resting heart rate, HRV
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- Sleep hours, mindful minutes
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### Already Computed (MoodDataSummarizer)
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- Mood distribution (counts, percentages, averages)
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- Day-of-week averages, best/worst day, weekend vs weekday
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- Trend direction and magnitude
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- Streaks (current, longest, positive, negative)
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- Mood stability score and swing count
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- Tag-mood correlations (good-day tags, bad-day tags)
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- Weather-mood averages (by condition, by temp range)
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- Logging gap analysis (pre/post gap averages)
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- Entry source breakdown
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### Already Visualized
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- Year heatmap + donut chart (YearView)
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- AI-generated text insights (InsightsView)
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- Weekly digest card (WeeklyDigestCardView)
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- AI reports with PDF export (ReportsView)
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### NOT Yet Visualized (Gaps)
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- No trend line charts
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- No health correlation charts
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- No tag/theme visualizations
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- No period comparisons
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- No streak visualizations beyond a number
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- No mood stability visualization
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- No logging behavior analysis
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- No predictive features
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