Our Research
How Moodery detects episodes — the methodology, signals, strategies, and clinical research behind every confidence score.
Multi-Strategy Ensemble Detection
The Problem
Single detection algorithms are fragile. They over-weight certain signals, miss atypical presentations like irritable hypomania or high-functioning depression, and give binary yes/no answers without transparency into what triggered the detection.
Our Approach
Four independent detection strategies analyze the same daily data, each emphasizing different biomarkers. An episode is confirmed when multiple strategies agree — like getting a second opinion built into the algorithm.
Daily Data
(Energy, Drive, Sleep, HRV, Activity, ...)
Balanced
Physiological
Self-Report
Sleep-Circadian
Consensus Engine
2 of 4 detect hypomania → Confirmed
Daily Data
(Energy, Drive, Sleep, HRV, Activity, ...)
Balanced
Physiological
Self-Report
Sleep-Circadian
Consensus Engine
2 of 4 detect hypomania → Confirmed
DSM-5 Aligned
Episode duration thresholds, both-pole requirements, and pattern classification follow diagnostic criteria.
Research-Backed
Signal importance drawn from peer-reviewed studies on digital biomarkers for mood episodes.
Transparent
Every threshold and weight is visible and adjustable in-app. No black boxes.
11 Biomarkers, Two Sources
Moodery combines what you report with what your body says. When signals are missing, the algorithm normalizes available weights — it adapts rather than breaks.
User Input
Core 1–5 scale. Sustained low energy is the most self-reportable depression marker; sustained highs correlate with hypomania onset.
Motivation independent of energy. The mismatch between energy and drive reveals transitional states invisible to single-scale trackers.
Composite of mood valence, social battery, arousal, and emotionality. Captures subjective experience that physiology can miss.
Catches high-functioning depression where activity and heart rate appear normal.
1–5 scale tracking irritation intensity. Key for irritable hypomania — negative mood with elevated energy — which gets misclassified without this signal.
Optional flag for impulsive actions (spending, substance use, sexual impulsivity). Hypomania-only signal — contributes zero to depressive scoring.
Apple Watch
Deviation from your personal baseline. Asymmetric thresholds: 15% decrease for hypomania (even mild undersleep matters), 25% increase for depression (20% oversleep is normal variation).
Sleep variability predicts hypomania onset with 0.87 accuracy.
Heart rate variability (SDNN) measures autonomic nervous system balance. Drops indicate dysregulation. Differentiated using resting HR: elevated RHR + low HRV suggests sympathetic activation (hypomania); normal RHR + low HRV suggests parasympathetic withdrawal (depression).
Deviation from personal baseline. Elevated RHR reflects sympathetic nervous system activation, common in both mania and anxiety.
Top single predictor of hypomania with AUROC = 0.852.
Step count deviation from baseline. Both extremes matter: hyperactivity in hypomania, withdrawal in depression.
REM-to-deep sleep ratio compared to baseline. Depression increases REM percentage and reduces deep sleep — an established finding in sleep medicine.
Altered sleep architecture is a DSM-5 specifier for mood episodes.
7-day standard deviation of sleep onset times. Circadian disruption is a prodromal marker — it often fires before episodes fully manifest.
Identified as an early warning signal for mood episodes in recent research.
Four Detection Strategies
Each strategy runs independently on the same daily data but weighs signals differently. This isn't redundancy — each strategy is designed to catch what others might miss.
Balanced
All signals contribute moderately — good all-rounder
Best at catching: General-purpose detection when all data sources are available
Depressive Weights
Hypomanic Weights
Physiological
Emphasizes passive markers — best with consistent Apple Watch data
Best at catching: Catching episodes the user doesn’t self-report — denial, alexithymia, or anosognosia
Depressive Weights
Hypomanic Weights
Self-Report
Emphasizes your subjective experience — catches high-functioning depression
Best at catching: Detecting episodes with normal physiology but altered inner experience
Depressive Weights
Hypomanic Weights
Sleep-Circadian
Based on research showing sleep patterns are the strongest episode predictors
Best at catching: Early warning and prodromal detection — circadian disruption often precedes episodes by days
Depressive Weights
Hypomanic Weights
All weight tables are visible and adjustable in Settings → Algorithm. Every table sums to exactly 1.00.
The Consensus Engine
A single algorithm can be wrong. Multiple independent algorithms agreeing is much harder to fool — the same principle behind ensemble methods in machine learning and second opinions in medicine.
01
Run
All 4 strategies analyze your daily scores independently, each producing its own episode candidates with confidence scores.
02
Compare
The engine finds overlapping time windows where multiple strategies detected the same episode type.
03
Confirm
Episodes where 2+ strategies agree become confirmed. Single-strategy detections remain visible but marked as lower confidence.
Example: 10-Day Detection Window
Reduces False Positives
A sick day might fool the Physiological strategy — elevated resting heart rate, poor sleep. But Self-Report and Balanced won't fire if your energy and mood are explained by illness, not depression.
Reduces False Negatives
High-functioning depression might not show in physiology — normal steps, normal heart rate. But the Self-Report strategy catches the low drive and negative mood. If Sleep-Circadian also fires from disrupted sleep onset, that's 2 of 4 — confirmed.
How It Works In Practice
Three real-world scenarios showing how different strategies respond to the same data — and why a multi-strategy approach catches what single algorithms miss.
The Sick Week
Four days of flu. Low energy, poor sleep, elevated resting heart rate — a naive single-algorithm detector would flag this as a depressive episode.
Consensus: 0 of 4 — No episode. The illness confound tag prevented misclassification.
Without confound awareness, this would be a false positive depressive episode.
Irritable Hypomania
Energy through the roof, sleeping 4 hours and feeling fine, but mood is irritable and dark — not euphoric. Traditional trackers see negative mood and call it depression.
Consensus: 4 of 4 — Hypomania confirmed (0.77 confidence)
The irritability signal prevents misclassification. Without it, the negative mood would pull toward a depressive reading.
High-Functioning Depression
Going through the motions — hitting the gym, showing up to work — but internally empty. Sleep duration is normal but quality is poor. Heart rate and steps look fine.
Consensus: 2 of 4 — Depressive episode confirmed (0.63 confidence)
Self-Report catches what physiology misses. The sleep stage signal adds the second vote despite normal sleep duration.
What We Filter Out
Certain conditions mimic mood episodes. Moodery applies confound modifiers to prevent misclassification.
Illness
Composite scores reduced by 50%. Illness confound flag added to episode factors.
Travel
Sleep and activity signal weights reduced by 50% for that day. Energy and self-report remain full.
Medication Changes
Confidence reduced by 30%. Medication adjustment flag added to factors.
Menstrual Cycle
Depressive confidence reduced by 20%. Hormonal cycle confound flag added.
Research & Sources
Sleep Variability as a Predictor of Hypomania Onset
medRxiv, 2025
Sleep variability predicts hypomanic episode onset with 0.87 accuracy in a longitudinal study of bipolar spectrum participants.
Resting Heart Rate as a Digital Biomarker for Hypomania
JMIR Medical Informatics, 2025
Resting heart rate emerged as the top single predictor of hypomania with AUROC = 0.852, outperforming activity and sleep metrics alone.
Circadian Disruption as a Prodromal Marker for Mood Episodes
PMC, 2025
Circadian rhythm disruption precedes mood episode onset by 3–7 days, suggesting utility as an early warning signal in digital monitoring.
Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5)
American Psychiatric Association
Defines diagnostic criteria for cyclothymia (2+ years of sub-threshold episodes without 2-month symptom-free periods), bipolar II (4+ day hypomania, 2+ week depression), and rapid cycling (4+ episodes per year).
Heart Rate Variability and Autonomic Dysregulation in Bipolar Disorder
Psychophysiology literature review
Reduced HRV correlates with mood episode severity across both poles. Combined with resting heart rate, HRV direction helps differentiate depressive from hypomanic autonomic profiles.
Moodery is a personal tracking tool, not a diagnostic instrument. Detected patterns are statistical estimates based on your data and should not replace professional clinical assessment. Always consult a qualified healthcare professional for diagnostic and treatment decisions.