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Analysing Apple Watch data end to end

Source: vignettes/apple-watch.Rmd
apple-watch.Rmd
library(PhysioDevices)
#> Loading required package: PhysioCore

This vignette takes an Apple Health export (Health app → profile → Export All Health Data → export.xml, plus any ECG .csv files) all the way through to per-modality analysis, using PhysioDevices for ingestion and PhysioWearable / PhysioECG for the analysis.

1. Ingest the export 

ah <- readAppleHealth("apple_health_export/export.xml", tz = "Asia/Tokyo")
ah
appleHealthTypes(ah)          # what modalities are present, with counts and spans

readAppleHealth() streams the file, so multi-hundred-MB exports are fine. Every <Record> becomes a row (type, source, unit, start/end, value); <Workout> elements become ah$workouts.

2. Heart rate and heart-rate variability

The watch already stores HRV as HeartRateVariabilitySDNN; heart rate is HeartRate. For deeper HRV from the raw beats, use the single-lead ECG (below).

hr  <- appleHealthSeries(ah, "HeartRate")                 # bpm, irregular
hrv <- appleHealthSeries(ah, "HeartRateVariabilitySDNN")  # ms (SDNN)
summary(hr$value_num)
summary(hrv$value_num)

# as a PhysioExperiment (irregular; timestamps in metadata) for the ecosystem
pe_hr <- appleHealthExperiment(ah, "HeartRate")

3. Blood oxygen (SpO2) 

ox <- appleHealthSeries(ah, "OxygenSaturation")           # fraction 0-1
PhysioWearable::spo2Metrics(ox$value_num, time = ox$start)

spo2Metrics() reports mean, nadir, time-below-90% (T90/CT90) and an oxygen desaturation index (ODI). Note the Apple Watch takes periodic spot readings, not a continuous trace, so ODI/T90 are approximate unless you feed continuous overnight oximetry.

4. Sleep

The watch scores sleep stages itself; summarizeAppleSleep() turns them into per-night clinical metrics (TST, efficiency, WASO, latency, stage minutes).

stages <- appleHealthSeries(ah, "SleepAnalysis")          # AsleepCore/Deep/REM, Awake, InBed
PhysioWearable::summarizeAppleSleep(stages)

If you instead have raw activity counts (from another wearable), score sleep/wake with the Cole-Kripke algorithm and summarise it:

sw <- PhysioWearable::coleKripke(counts)                  # counts = per-minute activity
PhysioWearable::summarizeSleep(sw)

5. Activity 

steps  <- appleHealthSeries(ah, "StepCount")
energy <- appleHealthSeries(ah, "ActiveEnergyBurned")
tapply(steps$value_num, as.Date(steps$start), sum)        # steps per day
ah$workouts                                               # per-workout summaries

For epoch-level free-living metrics (ENMO, intensity gradient, fragmentation, HAR → ICF), export raw accelerometry from a Core-Motion app and use PhysioWearable::readAccelCSV() → summarizeFreeLiving() (Apple Health itself stores only derived activity, not raw acceleration).

6. ECG

Apple Watch ECG recordings export as one CSV each; readAppleECG() reads the single-lead trace into a PhysioExperiment that feeds PhysioECG:

ecg <- readAppleECG("ecg_2023-05-01.csv")
samplingRate(ecg)                                         # ~512 Hz
# baseline-correct + Pan-Tompkins R-peaks, then HRV/rhythm, via PhysioECG
proc <- PhysioECG::ecgProcess(ecg)
PhysioECG::ecgAnalyze(proc)                               # time/freq/geometric HRV + rhythm

Scope and limitations

  • Raw accelerometry and ECG voltage are not in export.xml — the ECG is a separate CSV (readAppleECG()); raw motion needs a Core-Motion capture app.
  • SpO2 is periodic spot data → ODI/T90 are approximate.
  • Sleep stages come from Apple’s own on-device scoring; coleKripke() is for raw-count actigraphy, not a re-scoring of Apple’s stages.
  • Timestamps carry the export’s timezone offset; set tz to your local zone.