What the Research Shows
The evidence behind between-session monitoring—and what it means for your practice
What We Track
—and Why
Each metric is backed by peer-reviewed research connecting it to mental health patterns
Heart Rate Variability
A 2024 meta-analysis of HRV biofeedback for PTSD found moderate-to-large effects on symptom reduction—and remarkably, only 5.8% of participants dropped out, compared to 16–36% in traditional treatments. HRV reflects how someone regulates stress.
Sleep Patterns
Large-scale studies using smartphone and wearable data consistently find that changes in sleep duration, timing, and quality correlate with depression and anxiety patterns—often appearing before patients report feeling worse.
Movement & Routine
Research shows that reduced mobility and disrupted daily routines correlate with depression patterns. A study of over 10,000 participants found location patterns helped identify depression and anxiety indicators.
Self-Reported Check-ins
Brief daily mood and craving assessments achieve compliance rates above 89% in research populations. When paired with passive data, they provide context that sensors alone can't capture.
How We
Use This Research
Translating peer-reviewed findings into practical tools for therapists
Combining multiple signals
Research shows single metrics miss too much. Personalized baselines work better than group averages—so we track HRV, sleep, activity, and mood together, building a picture unique to each patient.
Learning what's normal for each person
A 2024 study in NPJ Digital Medicine found that personalized models outperform one-size-fits-all approaches. We establish each patient's baseline before highlighting meaningful changes.
Focusing on sustained changes
Meta-analyses show that routine monitoring reduces dropout and surfaces patterns earlier—but only when the system distinguishes noise from real shifts. We look for patterns over days, not hours.
Keeping you in the loop
A Danish study found therapists miss 5–10% of client changes. Our briefings surface what you might otherwise notice too late—without replacing your judgment.
Data Security & Standards
Professional-grade infrastructure trusted by EU healthcare institutions
GDPR Compliant
Full compliance with EU data protection regulations. Servers in Frankfurt, Germany.
End-to-end encryption
AES-256 encryption at rest and in transit. Zero-knowledge architecture.
HIPAA-equivalent standards
While not US-based, we meet HIPAA technical safeguards as global best practice.
Regular audits
Quarterly security assessments and penetration testing by independent firms.
Research We Build On
Peer-reviewed studies that inform our approach
Progress feedback reduces dropout by 20%
“De Jong et al. analyzed 58 studies with over 21,000 patients. Routine progress monitoring produced a small but significant effect on engagement—and a 20% reduction in patients leaving treatment early.”
Therapists miss some changes
“Østergård and colleagues at Aalborg University found that even experienced clinicians fail to notice 5–10% of patients who are getting worse—exactly the cases where early data would help most.”
Smartphones can surface symptom patterns
“Stamatis et al. studied over 1,000 participants and found that passively collected phone data—movement, sleep, app usage—correlated with depression and anxiety patterns at both individual and group levels.”
What the Research Also Shows
We believe in being upfront about limitations
Effect sizes in monitoring studies are small (g ≈ 0.15)—meaningful but not transformative on their own
Technology works best for patients struggling, not those already progressing well
Implementation matters as much as the tool—poorly integrated systems accomplish little
No algorithm can capture the full context of a patient's life—your judgment remains essential
Try it with
one patient
See what between-session data looks like in your practice—free for 14 days
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