Health Dashboard

Health Dashboard

Data Sources

Daily Health Check

Step 1: Sleep (Oura)

Pull last night's data:

• Sleep score and contributors (total sleep, efficiency, latency, timing)
• Sleep stages: deep, REM, light, awake — duration and percentages
• HRV: overnight average, trend vs 14-day baseline
• Resting heart rate: value and trend
• Body temperature deviation

Step 2: Readiness (Oura)

• Readiness score and contributors
• Recovery index
• Activity balance (not too much, not too little)
• Flag if readiness < 70 — recommend lighter activity day

Step 3: Activity (Apple Health)

Query via SQL (DuckDB):

• Steps: today vs 7-day average
• Active energy burned
• Exercise minutes
• Stand hours (if tracked)
• Resting heart rate (if tracked by Apple Watch)

Step 4: Workout Status (wger + Fitbod + Peloton)

• Pull wger first:
  • get_workout_sessions
  • get_workout_log
  • get_routines
• If wger is empty or stale, parse the latest Fitbod export for weight training only:
  • fitbod_parse_csv for last lifting date, exercise list, sets/reps/load, and recent strength volume
  • fitbod_preview_mapping or fitbod_list_exercise_aliases only when you need confidence for exercise mapping or muscle-group rollups
  • fitbod_import_csv with dry_run=true is allowed when you need a date-bounded workout slice, coverage verification, or an unresolved-exercise queue without writing to wger
  • Only treat muscle-group rollups as reliable when trailing-365d weighted mapping coverage is at least 90%
  • Never persist a Fitbod import during normal dashboard/check-in runs unless the user explicitly wants an import or repair workflow
  • Exclude Fitbod cardio rows from reporting when a matching Peloton workout exists with the same local start time and duration; treat those rows as synced duplicates
  • Prefer session-level lifting detail like Pull Up 3 x 11 or Dumbbell Bicep Curl 3 x 8 @ 13.6 kg
• If recent Peloton workouts exist:
  • peloton_get_workouts
  • peloton_get_workout_detail for the latest or most notable session to capture class title, instructor, duration, difficulty, tracked metrics, and muscle emphasis
  • peloton_get_performance_graph when interval structure, pace targets, or compliance adds signal
  • Treat Peloton as the cardio source of truth
• Use Apple Health workout summaries as fallback coverage when direct sources are unavailable
• Report:
  • Last workout: date, type, duration
  • Days since last workout — flag if > 3 days
  • This week's workout count vs target
  • Notable direct-source workout detail when it changes the interpretation

Step 5: Nutrition (wger)

• Yesterday's intake: total calories, protein, carbs, fat
• Protein target check: aim for 1g per lb bodyweight (or user's target)

Weekly Health Review

Aggregate 7 days of data:

## Weekly Health Review — [Date Range]

### Sleep
- Avg score: X/100 | Avg duration: Xh Xm
- Avg HRV: X ms (trend: ↑/↓/→)
- Best night: [date] | Worst: [date]
- Consistency: bedtime variation ± X min

### Activity
- Avg daily steps: X (target: Y)
- Total active energy: X kcal
- Exercise days: X/7

### Workouts
- Sessions completed: X
- Muscle groups covered: [list]
- Progressive overload: [any PRs or weight increases]
- Peloton highlights: [class mix, instructor, difficulty, interval structure]

### Nutrition
- Avg daily calories: X
- Avg protein: Xg (target: Yg)
- Avg macro split: P%/C%/F%

### Recovery
- Avg readiness: X/100
- Rest days taken: X
- HRV trend: [improving/declining/stable]

### Recommendations
1. [Specific, actionable recommendation based on data]
2. ...

Trend Analysis

For longer-term analysis (30d, 90d):

• Sleep quality trend (are scores improving?)
• HRV baseline trend (proxy for fitness/recovery)
• Weight trend (if tracked)
• Workout frequency and volume progression
• Correlation: sleep quality vs next-day readiness vs workout performance

Guidelines

• All metrics from MCP tool calls — never estimate or fabricate
• Prefer direct Fitbod CSV for strength detail and Peloton MCP for cardio detail; use Apple Health workout summaries as fallback coverage
• If Fitbod mapping coverage is below 90% weighted coverage on the trailing 365-day window, report exercise-level detail and explicitly mark muscle-group rollups as partial
• Avoid persisting fitbod_import_csv during standard dashboard/review flows unless the task is explicitly an import or sync workflow
• health-graph is authoritative for genome/clinical availability and recommendation context
• If genome-informed guidance is included in a dashboard or weekly summary, make it answer "so what?" directly:
  • Lead with the plain-English takeaway for the subject
  • State whether anything needs to happen now; if not, say that explicitly
  • State what future decision this could affect, if any
  • Avoid unexplained jargon such as metabolizer labels, star alleles, or rsids unless translated immediately
  • For Tier 3 and Tier 4 items, state clearly that they are watchlist or research context and should not drive treatment or behavior changes on their own
• paperless is document-only in this workflow; do not use it as a proxy for genomic data availability
• Flag anomalies: HRV drop > 15% from baseline, sleep score < 60, readiness < 60
• Recovery-first: recommend rest when readiness is low
• When reviewing medications or supplements, use medical.search_drugs for drug interaction context and medical.get_drug_details for dosing reference
• Not medical advice: flag concerning trends but recommend doctor consultation