| 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.
