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The SDK is built to help you show that your app changes the body, not only how people say they feel. This page collects what has worked in the NEUROFIT app and in the validation study.

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Pre/post around a session

The cleanest single-session design is a reading immediately before and immediately after your intervention (a breathing exercise, a meditation, a coaching session).
  • Same posture, same place. Seated, feet on the floor, phone resting on a table or the lap, both times.
  • Same finger and pressure. The guidance handles this, but tell users to use the same fingertip.
  • Let the body settle first. A minute of quiet sitting before the pre reading avoids measuring the walk to the chair.
  • Keep both readings at 60 s. The accuracy figures apply to 60-second readings.
  • Use only clean and usable readings. A withheld reading is a retry, not a data point.
  • Store VitalsResult whole, including flags, diagnostics, engineVersion and sdkVersion, so you can audit and re-analyse later.
For a trend, the reading conditions matter more than the count.
  • Morning, before caffeine, seated. A consistent time of day removes most day-to-day noise.
  • Weekly averages beat daily values. HRV (RMSSD) varies day to day in healthy people; a 7-day rolling mean is a steadier signal for a trend line.
  • Compare people with themselves. Absolute HRV differs widely between people (age, fitness, genetics). A change from a person’s own baseline is meaningful; a comparison to a population table usually is not.
  • Record context your app already knows (sleep, training, illness) alongside readings, so a dip has an explanation rather than becoming a worry.

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Is a change real?

Every measurement has noise. A pre/post difference smaller than the measurement’s own variation is not evidence of a change. The SDK ships a helper that applies per-metric thresholds derived from the validation study’s 95% limits of agreement:
These thresholds are provisional. They come from agreement with a chest strap, which is a lower bound on the variation you will see between two readings taken minutes apart. A repeatability analysis (same person, back to back readings) is under way and will replace the constants in a future release. Until then, treat “beyond typical variation” as a strong hint, not a verdict, and lean on group-level statistics for claims.
For a group claim, pre-register the metric (HRV (RMSSD) is the usual primary), the reading protocol, and the analysis, and report the withheld rate along with the results.

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Wording for users

Calm and specific wording keeps people engaged with a metric that moves around.
  • Prefer “your HRV (RMSSD) was 42 ms this morning, in your usual range” to “your HRV is low”.
  • Show change over a week or a month before change over a day.
  • Explain a withheld reading as a signal-quality issue with the fingertip or lighting, not as a health signal.
  • Never present the metrics as diagnosis. The SDK is not a medical device.

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Why a finger scan, not a face scan

RMSSD is the HRV number WHOOP, Oura, Polar and Fitbit report, and the hardest one to measure with a camera. It is built from tiny beat-to-beat changes, so any error in timing a beat adds straight to it and makes HRV read high. That is a physics problem for face scans. On a face, the pulse is a faint colour change in reflected room light, easily swamped by shifts in lighting and small head movements. A fingertip pressed over the flash is lit by a bright, steady light right against the skin, giving a pulse about 6 times larger, or over 30 times the signal power. The SDK’s average HRV (RMSSD) error is 3.4 ms, while the best published face-scan result is 10.5 ms.
Facial pulse amplitude: Wang et al., Biomedical Optics Express, 2017 (15 participants, medians read from their Figure 2a), compared with the 30 NEUROFIT study participants using the same method. Face-video RMSSD: Bioengineering, 2023 (UBFC-rPPG dataset). Different studies, cameras and references, so not a head-to-head comparison.
A finger scan also has practical advantages for outcomes work: the user controls the conditions (no dependence on room lighting), the reading is the same indoors and outdoors, and the torch-lit fingertip is what the validation study measured.