Session Physiology is the body’s record of a behavioral-health session: continuous heart rate and vocal markers, time-aligned to what was said, delivered after the session as structured evidence.It does not diagnose. It shows the clinician where self-report and physiology diverge.
Self-report tells you what a client chooses to share. Session physiology shows how the nervous system responded, moment by moment.When words say “fine” and the body disagrees, that is the say/body gap.
Mechanism
What the field already knows about these signals.
Each stream has its own literature. Below: what SeroState uses in deployment, then what published work supports.
Heart rate
What we use
Dense continuous heart rate from an Apple Watch active session, turned into an arousal signal on the session timeline. HRV only when sampling permits. No raw PPG claimed.
What the literature shows
A 2023 meta-analysis in npj Digital Medicine pooled wearable-AI depression studies at 0.70–0.89 accuracy across tasks (Abd-Alrazaq et al., 2023)[1]. Reduced HRV correlates with major depression (Koch et al., 2019)[2]. Wrist sensors can separate stress from baseline affect (Schmidt et al., 2018)[5]. Field evidence, not a SeroState score.
Voice
What we use
Vocal markers from session audio, time-aligned to the same timeline as heart rate and transcript.
What the literature shows
Speech carries measurable markers of psychiatric state. Marmar et al. (2019) found speech-based PTSD markers in US veterans[3]; Schultebraucks et al. (2021) combined voice features for PTSD and depression assessment[4]. The DAIC-WOZ corpus links clinical interviews to distress in voice[6]. Again: the field, not our product.
Fusion
Why combine them
One stream alone is noisy. A heart-rate spike can be caffeine; a flat voice can be fatigue. Fused and time-aligned to the transcript, the question narrows: did the body react when the words said everything was fine?
Published field accuracies use different populations, sensors, and ground truth. They do not transfer one-to-one to any product, including ours.
We make no accuracy claims for SeroState until pre-registered work is published.
Consumer wearables lose signal (motion, fit, skin contact). Every QOAX record carries a signal-quality score from heart-rate sample continuity over the session.
Physiological arousal is not a diagnosis. An elevated trace tells a clinician where to look, not what to conclude.
SeroState is an intelligence layer, not an FDA-cleared device. Clinicians see the evidence behind every flag and can dismiss it.
Privacy
Evidence in the record, not data in our vault.
Sessions are consented and de-identified. Processing is stateless by default: partners hold PHI, we do not store it, and partner patient data is never used for model training. Output is FHIR R4 with flags, confidence, and signal quality. Full security posture →
References
Verified citations.
Abd-Alrazaq A, et al. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine. 2023. doi:10.1038/s41746-023-00828-5
Koch C, et al. A meta-analysis of heart rate variability in major depression. Psychological Medicine. 2019. doi:10.1017/S0033291719001351
Marmar CR, et al. Speech-based markers for posttraumatic stress disorder in US veterans. Depression & Anxiety. 2019. doi:10.1002/da.22890
Schultebraucks K, et al. Computer vision and voice analysis for diagnostic assessment of PTSD, depression, and neurocognition. Biological Psychiatry. 2021. doi:10.1016/j.biopsych.2021.02.067
Schmidt P, et al. Introducing WESAD, a multimodal dataset for wearable stress and affect detection. Proc. ICMI. 2018. doi:10.1145/3242969.3242985
Gratch J, et al. The Distress Analysis Interview Corpus of human and computer interviews. Proc. LREC. 2014. doi:10.63317/3o7bccg9xequ
Every citation above resolves on Crossref. Papers we could not verify with matching metadata are not listed, whatever their reputation.
Questions about the science?
Researchers and platform teams can ask about methodology, study design,and the prospective trial.
