health-data-analysis

name	health-data-analysis
description	Biotrackr health data schema, metric extraction patterns, and analysis techniques for Fitbit activity data

Health Data Analysis

Data Schema

Biotrackr health data arrives as a JSON object with an items array. Each item represents one day:

{
  "items": [
    {
      "date": "2026-04-05",
      "activity": {
        "activities": [
          {
            "name": "Strength training",
            "calories": 665,
            "duration": 5271000,
            "steps": 4972,
            "startTime": "06:57",
            "distance": 2.52
          }
        ],
        "summary": {
          "steps": 15732,
          "caloriesOut": 4049,
          "activityCalories": 2341,
          "fairlyActiveMinutes": 47,
          "veryActiveMinutes": 78,
          "lightlyActiveMinutes": 234,
          "sedentaryMinutes": 567,
          "distances": [{"activity": "total", "distance": 12.02}],
          "floors": 238,
          "restingHeartRate": 51
        }
      }
    }
  ],
  "totalCount": 7,
  "note": "Optional note about missing data"
}

Metric Extraction

• Steps: item["activity"]["summary"]["steps"]
• Calories (total): item["activity"]["summary"]["caloriesOut"]
• Activity calories: item["activity"]["summary"]["activityCalories"]
• Active minutes: fairlyActiveMinutes + veryActiveMinutes (combine both fields)
• Distance: Extract from distances array where activity == "total": next(d["distance"] for d in distances if d["activity"] == "total")
• Floors: item["activity"]["summary"]["floors"]
• Resting heart rate: item["activity"]["summary"]["restingHeartRate"]

Duration Conversion

Activity durations in the data are in milliseconds. Convert to minutes by dividing by 60000:

duration_minutes = round(activity["duration"] / 60000)

Goal Definitions

Standard daily goals for achievement tracking:

Goal	Target	Field
Steps	≥ 10,000	steps
Distance	≥ 8.0 km	total distance
Active Minutes	≥ 30 min	fairlyActiveMinutes + veryActiveMinutes
Calories	≥ 2,500 kcal	caloriesOut

Goal achievement per day = count of how many of the 4 goals were met.

Standout Day Identification

Identify these standout categories across the reporting period:

• Highest steps day: max(days, key=lambda d: d["steps"])
• Highest calories day: max(days, key=lambda d: d["caloriesOut"])
• Most active minutes day: max(days, key=lambda d: d["activeMinutes"])
• Best goal achievement day: max(days, key=lambda d: d["goals_met"])
• Longest single session: Find the activity with the maximum duration across all days

Weekly Aggregates

Calculate these summary statistics:

• Averages: steps, calories, active minutes, distance, floors, resting heart rate (divide totals by number of days with data)
• Totals: steps, distance, floors, calories, active minutes
• Heart rate range: min and max resting heart rate with corresponding days
• Trend: Compare first day to last day for resting heart rate direction

Handling Missing Days

• Not all 7 days in a week may have data (the note field indicates gaps).
• Calculate averages using actual day count (len(items)), not 7.
• Clearly label missing days in output tables and narratives.