❓ Frequently Asked Questions

Biomarkers are measurable indicators of health status. They include:

• Activity biomarkers: Steps, floors climbed, active hours, calories burned
• Body biomarkers: Weight, BMI, body fat percentage, lean mass
• Vitals biomarkers: Heart rate, blood pressure, oxygen saturation, VO2 max
• Sleep biomarkers: Duration, quality, stages (light/deep/REM), regularity
• Reproductive biomarkers: Menstrual cycle tracking, fertile window, phase tracking

Biomarkers transform raw sensor data into actionable health insights.

The comprehensive biomarker table includes 54 distinct biomarkers across five categories:

• Activity: 10 biomarkers
• Body: 8 biomarkers
• Vitals: 13 biomarkers
• Sleep: 13 biomarkers
• Reproductive: 10 biomarkers

View the complete biomarker table for details on each metric.

Biomarkers are raw measurements (e.g., "7,500 steps", "7 hours sleep"). Intelligence layers transform biomarkers into actionable insights:

• Scores: Normalized 0-1 values (Wellbeing, Activity, Sleep, Mental Wellbeing, Readiness)
• Archetypes: Behavioral labels ("Highly Active", "Consistent Early Riser")
• Trends: 4-week rolling analysis (increasing/decreasing/stable)
• Comparisons: Context vs global/demographic/baseline averages

Intelligence layers enable personalization, churn prediction, and automated interventions. Learn more about intelligence layers and scores.

No. Many biomarkers can be collected from smartphones without any wearable device:

Using smartphone data alone provides ~40% coverage, while adding wearables increases coverage to 100%.

Smartphone-based sleep tracking (using accelerometer and usage patterns) can accurately detect:

• Sleep duration: ~85-90% accuracy vs wearables
• Sleep start/end times: Within 15-30 minutes typically
• Sleep regularity: High accuracy for pattern detection

Smartphones cannot accurately detect:

• Sleep stages (light/deep/REM) - requires heart rate + motion sensors
• Sleep interruptions/awakenings - needs continuous monitoring
• Sleep efficiency - requires wearable sensors

For basic sleep insights, smartphones work well. For clinical-grade sleep analysis, wearables are recommended.

Major health data API platforms support 300+ wearable devices through platform integrations:

• iOS HealthKit: Apple Watch, compatible third-party devices
• Android Health Connect: Samsung Galaxy Watch, Fitbit, Garmin, Oura, Whoop, and hundreds more
• Direct integrations: Specific device APIs (Fitbit API, Garmin API, etc.)

Using HealthKit + Health Connect provides maximum coverage without needing individual device integrations.

The five core health scores (range 0.0-1.0) are:

1. Wellbeing (0-1): Holistic measure combining 13 physical, mental, and behavioral factors
2. Activity (0-1): Daily physical activity levels and intensity (6 factors)
3. Sleep (0-1): Sleep quality, duration, regularity, and stages (7 factors)
4. Mental Wellbeing (0-1): Mental wellness through behavioral patterns (6 factors)
5. Readiness (0-1): Daily readiness and recovery capacity (8 factors)

All scores can be calculated from smartphone data alone (no wearable required). See the complete scores breakdown.

Behavioral archetypes are labels that classify users based on patterns. The 14 archetypes include:

• Activity archetypes: "Sedentary" to "Highly Active", "Rare Exerciser" to "Frequent Exerciser"
• Sleep archetypes: "Consistent Early Riser", "Highly Irregular Sleeper", "Chronic Short Sleeper"
• Wellness archetypes: "Poor Mental Wellness" to "Optimal Mental Wellness"
• Exercise type archetypes: "Cardio Oriented", "Strength Oriented", "Mind-Body Oriented"

Archetypes enable user segmentation, personalized content, and churn prediction. Example: "Highly Irregular Sleeper" with "Poor Mental Wellness" = high churn risk.

Trends analyze the last 4 complete weeks of data on a rolling basis. For each metric, the system:

1. Computes weekly averages
2. Compares to previous weeks
3. Classifies directional movement: increasing, decreasing, or stable
4. Calculates percent change between weeks

Why trends matter:

• Detect declining sleep quality 2-3 weeks before self-reporting
• Trigger interventions when wellbeing decreases for 3+ consecutive weeks
• Celebrate progress: "Your activity is increasing by 15% this month!"
• Alert clinicians to downward mental wellbeing trends

Trends are available for 5 scores + 17 factors. Learn more about trend analysis.

Comparisons provide three reference points to contextualize metrics:

1. Global: Comparison against global population averages
2. Demographic: Comparison against people with similar age/gender/characteristics
3. Baseline: Comparison against individual's own 30-day historical average

Example use cases:

• "Your activity is in the top 15% of your age group" (demographic)
• "You're sleeping 20% better than your 30-day baseline" (baseline)
• "Your VO2 max is average for global standards" (global)

Comparisons enable social motivation, personalized goal-setting, and progress celebration.

There are three primary ways to access health data:

Best approach: Platform API + smartphone monitoring for maximum coverage.

Integration timelines vary by approach:

• Platform API: 2-4 weeks (single API integration)
• Direct device OAuth: 6-12 months (per device, multiply by number of devices)
• Smartphone monitoring: 1-2 weeks (SDK integration)

For reference: Building direct integrations with 10 wearable devices = 5-10 years of engineering time vs 2-4 weeks with a platform API.

Platform API pricing typically follows these models:

• Per active user per month: $0.50 - $3.00 depending on volume and features
• Tiered pricing: Discounts at 1K, 10K, 100K+ users
• Feature-based: Basic biomarkers cheaper, intelligence layers cost more
• Free tiers: Most platforms offer free dev/testing with limited users

Cost comparison: Platform API ($1-2/user/month) vs direct integration ($500K-$2M engineering + maintenance).

For most applications, platform APIs are 10-100x more cost-effective than building direct integrations.

Platform selection depends on your needs:

• Choose Sahha if: You need intelligence layers (scores, archetypes, trends), mental health insights, or smartphone-only monitoring
• Choose Terra if: You need maximum wearable coverage and raw data access
• Choose Vital if: You need clinical-grade data quality and compliance features
• Choose Rook if: You need regional support (Latin America) and budget pricing

Key decision factors:

1. Intelligence layers: Do you need scores/archetypes/trends or just raw biomarkers?
2. Device coverage: Do users have wearables or should you support smartphones?
3. Use case: Fitness, mental health, insurance, or clinical research?
4. Budget: Premium features or cost-optimized solution?

See the detailed platform comparison on the homepage.

You need intelligence layers if you want to:

• Personalize user experiences ("Adjust workout intensity based on Readiness score")
• Predict churn ("Declining wellbeing trends = intervention needed")
• Automate interventions ("Alert when mental wellbeing drops below 0.5")
• Segment users ("Show yoga to mind-body oriented users")
• Track progress ("Your sleep improved 15% this month!")

You don't need intelligence layers if you:

• Only want to display raw metrics ("You walked 7,500 steps today")
• Have ML/data science team to build custom models
• Need basic activity tracking without insights

For most applications, intelligence layers dramatically reduce engineering complexity and time-to-market.

Health data APIs typically provide data via:

• REST API: Poll for data on-demand (GET /biomarkers, GET /scores)
• Webhooks: Real-time push notifications when new data arrives
• SDK: Native iOS/Android libraries for smartphone data collection
• Batch exports: Daily/weekly CSV or JSON exports for analytics

Recommended architecture: Webhooks for real-time updates + REST API for historical data retrieval.

Data refresh rates vary by source:

• Wearable devices: Every 15 minutes to hourly (when synced to phone)
• Smartphone sensors: Continuous or every 5-15 minutes
• Sleep data: Once per day (morning after sleep session)
• Scores/archetypes: Daily or weekly updates
• Trends: Weekly (rolling 4-week calculation)

Most platforms support near-real-time delivery via webhooks within minutes of device sync.

Health data APIs typically use:

• OAuth 2.0: For user authorization and consent flow
• API keys: For server-to-server authentication
• JWT tokens: For stateless authentication
• Webhook signatures: HMAC-SHA256 for webhook verification

User flow: User authorizes via OAuth → receives token → your backend uses token to fetch data via API.

Health data APIs are used across 9+ industries:

• Insurance & Health Plans: Risk assessment, premium discounts, wellness programs
• Fitness & Gyms: Personalized workouts, churn prediction, progress tracking
• Mental Health: Depression screening, symptom monitoring, intervention triggers
• Supplements & Nutrition: Personalized recommendations, effectiveness tracking
• Clinical Research: Remote monitoring, patient recruitment, real-world data
• AI & ML: Training data, pattern recognition, predictive modeling
• Corporate Wellness: Employee health programs, productivity insights
• Health Gaming: Gamification, challenges, social motivation
• Digital Therapy: CBT monitoring, medication adherence, symptom tracking

See detailed industry use cases on the homepage.

Declining health metrics correlate strongly with membership cancellation. Churn prediction models use:

• Declining trends: 3+ weeks of decreasing activity/sleep scores
• Low scores: Wellbeing below 0.4 for extended periods
• Negative archetypes: "Highly Irregular Sleeper" + "Poor Mental Wellness"
• Reduced engagement: Fewer active hours, lower activity levels

Example model: Users showing declining sleep quality + reduced activity + poor mental wellness archetype have 3.2x higher churn risk in next 30 days.

Early detection enables proactive interventions 2-3 weeks before cancellation.

Reputable health data API platforms implement:

• Encryption: Data encrypted in transit (TLS 1.3) and at rest (AES-256)
• Compliance: HIPAA, GDPR, SOC 2 Type II certifications
• User consent: Explicit OAuth authorization required
• Data minimization: Only collect necessary biomarkers
• Anonymization: De-identification for research/analytics
• Access controls: Role-based permissions and audit logs

Key principle: Users own their data and can revoke access at any time.

Health data is subject to multiple regulations:

• HIPAA (US): Applies to covered entities (healthcare providers, insurers) handling PHI
• GDPR (EU): Applies to all health data of EU residents
• CCPA (California): Applies to health data of California residents
• FDA (US): Medical device regulations for diagnostic/therapeutic claims

Important: Consumer wellness apps (non-diagnostic) typically don't require HIPAA compliance, but must follow GDPR/CCPA. Consult legal counsel for your specific use case.

Yes. GDPR and CCPA require data deletion upon request. Health data platforms must provide:

• Revoke access: Users can disconnect device/app authorization anytime
• Delete data: API endpoints for data deletion (DELETE /user)
• Export data: User data portability (download all data as JSON/CSV)
• Retention policies: Automatic deletion after X days of inactivity

Your application must implement similar user data controls to comply with privacy regulations.