Butterfly Effect

Hospital / clinical sleep unit

A research collaboration model for longitudinal sleep, symptoms, and physiological interpretation.

Best suited for hospitals, sleep units, and clinical research groups with longitudinal wearable or PSG-linked sleep data and repeated symptom measurements.

Why this institution is a strong fit

Hospitals are the strongest fit when they can provide longitudinal sleep-linked symptom data, medication/context variables, and ideally a small PSG-anchored subset. The highest-value collaboration is one that helps test nightly transport and clinical interpretation boundaries, not just another single-cohort benchmark.

Longitudinal symptomsClinical contextPSG subset highly valuable

What is already defensible

Night stress
R² 0.238
Night D+1 stress
R² 0.346
Event stress portable
R² 0.316
Main gap
Night transport

Institutional fit

Clinical datasets can close the biggest scientific gap in the project

The main missing piece is not another model. It is longitudinal, clinically interpretable data that can test cross-cohort nightly generalization under real conditions.

What we bring

  • A working nightly interpretation stack with held-out validation and two routes that now clear promotion gates
  • Route governance, calibration, and explicit deployment boundaries
  • Harmonization logic across open cohorts and support datasets
  • A pilot-ready research pipeline from ingestion to interpretable output

What you contribute

  • Longitudinal sleep-linked symptom data
  • Medication and clinical context variables
  • Wearable sleep plus HR/HRV or cardiovascular signals
  • Optional PSG subset with EEG, EOG, EMG for gold-standard anchoring

Expected outcome

  • Feasibility of nightly transport under clinically relevant conditions
  • Subgroup robustness by medication, symptom burden, or diagnosis
  • Alignment between wearable interpretation and richer physiology
  • A realistic path to prospective validation

Data package

What a hospital dataset should contain

The project does not require full PSG on every subject to start, but a small gold-standard subset would materially strengthen the scientific case.

Minimum required

  • Nightly sleep summaries over weeks or months
  • Nocturnal HR / HRV or IBI-derived autonomic signal
  • Movement / actigraphy / fragmentation
  • Repeated stress, fatigue, anxiety, depression, or pain outcomes
  • Age, sex, medication, alcohol/caffeine, exercise, acute events

High-value expansion

  • PPG / IBI raw or semi-raw signals
  • Respiration, SpO2, temperature, and daily activity context
  • PSG subset with EEG, EOG, EMG
  • Clinician-validated symptom labels or structured endpoints
Key point: A hospital collaboration is most valuable when it combines longitudinal symptoms with richer physiology, not when it only contributes another sleep summary table.

Pilot model

Recommended pilot for a hospital or sleep unit

Start with one cohort, one outcome family, one sharply defined question.

Recommended first pilot

  • One retrospective cohort with longitudinal nightly data
  • Primary endpoint family: stress, anxiety, depression, or pain
  • Matched clinical context variables and medication data
  • Optional PSG subset for physiological validation

Outputs to the institution

  • Harmonization report and data map
  • Benchmarking against the open baseline
  • Route/family feasibility analysis with subgroup breakdowns
  • Decision on prospective continuation or protocol refinement