Incorporating health data into your application is extremely powerful. When selecting a biomarker API platform, you need to consider more than just raw data access. Intelligence layers are crucial for transforming measurements into actionable insights. Maximum coverage matters—the more wearables supported plus smartphone monitoring, the more users you can serve. This guide will help you understand these trade-offs and make the right choice for your application.
Read on to explore biomarker types, integration approaches, real-world implementations across 9 industries, and honest platform comparisons to guide your decision.
This page will help you evaluate the differences between the major digital health aggregators: Rook vs Terra vs Sahha vs Spike vs Vital
About This Guide
What this site is: A comprehensive technical guide with platform comparisons, integration patterns, and biomarker education.
What this site is not: We do not provide API services. We evaluate and compare existing platforms (Sahha, Terra, Rook, Spike) to help you choose the right solution.
What Are Biomarkers?
Biomarkers are measurable indicators of health status—from sleep quality and activity levels to heart rate variability and stress markers. They transform raw sensor data into actionable health insights that power personalized recommendations, retention predictions, and wellness interventions.
Types of Biomarkers
Biomarkers are organized into five main categories: Activity (steps, exercise, energy), Body (weight, BMI, composition), Vitals (heart rate, blood pressure, oxygen saturation), Sleep (duration, quality, stages), and Reproductive (menstrual cycle tracking). Each category provides unique insights into different aspects of health and wellness.
Some biomarkers can be collected passively from smartphones without any wearable device, while others require specific wearable sensors. Understanding which biomarkers you need and how to access them is crucial for building effective health applications.
Why Biomarkers Matter
Raw health data alone provides limited value. The real power comes from processing biomarkers into actionable insights:
- Personalized recommendations: "Your sleep quality is declining—reduce workout intensity today"
- Retention prediction: Identify members at risk of churning 2 weeks before they cancel
- Clinical interventions: Detect depression symptoms 3-5 days before subjective awareness
- Insurance underwriting: Wellness scores correlated with claims costs
- Research insights: Population health trends and intervention effectiveness
The Health Data Integration Challenge
The Problem Every Developer Faces
You're building a fitness app, insurance platform, or wellness program. You need access to health data from wearables and smartphones. But integrating directly with 300+ devices means:
- $300K+ in development costs
- 6-12 months to market
- Ongoing maintenance as devices change APIs
- OAuth complexity for each device manufacturer
- Data inconsistency across different formats
Why Health Data Aggregators Exist
Instead of integrating with hundreds of devices individually, health data aggregators provide a single API that connects to multiple data sources. This approach:
- Reduces time to market from months to weeks
- Eliminates per-device OAuth implementation
- Normalizes data formats across sources
- Handles API changes and deprecations
- Provides unified data models
Where Data Comes From
Wearable Devices
Smartwatches, fitness trackers, rings—devices worn on the body collecting continuous health metrics.
Coverage: ~30% of population owns wearables
Data quality: High precision for physiological markers
Smartphone Sensors
Accelerometer, screen time, ambient light, app usage—passive monitoring without additional hardware.
Coverage: 100% of smartphone users
Data quality: Good for behavioral patterns
Lab Testing
Blood work, genetic testing, diagnostic imaging—clinical-grade biomarkers from healthcare providers.
Coverage: On-demand testing
Data quality: Medical-grade accuracy
Electronic Medical Records
Patient history, medications, diagnoses—comprehensive healthcare data from EMR systems.
Coverage: Healthcare provider networks
Data quality: Clinical documentation
Why Intelligence Layers Matter
Raw data tells you what happened. Intelligence layers tell you what it means and what to do next. This is the critical difference between data aggregation and actionable health intelligence.
Raw Data vs Intelligence: An Example
❌ Raw Data Only
Sleep data:
- Duration: 7 hours 23 minutes
- Deep sleep: 1 hour 12 minutes
- REM sleep: 1 hour 45 minutes
- Interruptions: 4
What does this mean? You have to figure it out.
What should you do? Unknown.
✅ Intelligence Layer
Sleep quality score: 0.72 (Medium)
Insight: "Sleep quality below your baseline—recovery capacity reduced by 15%"
Recommendation: "Reduce workout intensity today. Avoid evening caffeine."
Retention signal: "3 consecutive nights of declining sleep—engagement risk in 10 days"
Intelligence Layers Transform Applications
Some integrators like Sahha incorporate intelligence layers that process raw biomarkers into:
- Wellness scores: Overall health (0-3 scale), mental wellbeing, sleep quality
- Behavioral archetypes: "Consistent Early Sleeper", "Highly Active", "Irregular Schedule"
- Predictive signals: Churn risk, depression symptoms, performance readiness
- Actionable recommendations: Personalized interventions based on patterns
Use Cases Requiring Intelligence
🏋️ Fitness Apps
Adjust workout intensity based on recovery capacity. Recommend rest days when sleep quality drops.
🏋️ Gyms & Studios
Predict churn 2+ weeks early. Personalize class recommendations. Automate member check-ins based on readiness.
🏥 Insurance Programs
Wellness scores correlate with claims costs. Retention predictions identify at-risk members. Personalized incentives.
🧠 Mental Health
Detect depression symptoms 3-5 days early. Track intervention effectiveness. Identify crisis risk patterns.
💊 Supplements
Personalize supplement stacks based on sleep, activity, and stress markers. Track effectiveness over time.
🤖 AI Agents
Pre-processed features for ML models. Behavioral data for health coaching bots. Predictive analytics training data.
Integration Approaches: Trade-offs to Consider
There's no single "best" approach—the right choice depends on your use case. Here are the three main integration strategies and their trade-offs.
How important are direct device integrations vs platform APIs?
This is the fundamental decision when choosing a health data aggregator. You can integrate directly with device manufacturers (Terra's 300+ devices), use platform APIs (HealthKit/Health Connect), or leverage smartphone monitoring (Sahha's 100% coverage approach).
Each approach has trade-offs between device breadth, user coverage, implementation complexity, and data precision.
Industry Applications & Real-World Implementations
Biomarkers power applications across diverse industries—from insurance underwriting to fitness coaching, mental health monitoring to supplement personalization. Here's how different sectors leverage health data APIs and examples of successful implementations.
🏥 Insurance & Health Plans
Use Case: Wellness programs, risk assessment, claims prediction, member retention
Key Biomarkers: Sleep quality, activity levels, behavioral patterns, wellness scores
Why Intelligence Matters: Wellness scores correlate with claims costs. Retention prediction identifies at-risk members 2 weeks before churn. Behavioral archetypes enable personalized incentives.
Integration Requirements: 100% member coverage (smartphone monitoring critical), data retention for historical analysis, HIPAA compliance
Real-World Examples:
- Discovery Vitality (South Africa): Pioneer wellness program using wearables + behavioral data. RAND Corporation study found 30% reduction in hospital admissions (RAND Study)
- John Hancock Vitality: Life insurance with Apple Watch integration—healthy behaviors earn premium discounts up to 25%
- Oscar Health: Step tracking rewards program integrated with claims data
- Aetna Attain: Apple Watch program with personalized health goals
Platform Recommendation: Sahha (100% coverage via smartphones + retention intelligence) or Terra (if distributing wearables)
🏋️ Fitness Apps & Gym Chains
Use Case: Personalized workout recommendations, recovery optimization, churn prediction, performance tracking
Key Biomarkers: Sleep quality, HRV, activity levels, workout intensity, recovery capacity
Why Intelligence Matters: Adjust workout intensity based on recovery capacity. Recommend rest days when sleep quality drops. Predict optimal training windows. Identify members likely to cancel before they do.
Integration Requirements: Real-time data for same-day recommendations, historical trends for progress tracking, wearable + smartphone coverage
Real-World Examples:
- Whoop: Subscription wearable with strain/recovery/sleep scores—used by NFL, CrossFit athletes. $3.6B valuation
- Peloton: Integrates Apple Watch, Fitbit, Garmin for heart rate zones and calorie tracking
- MyFitnessPal: Connects to 50+ fitness devices and apps—acquired by Under Armour for $475M
- Strava: Social fitness network with 100M+ users—integrates wearables + smartphone GPS
- Future (Personal Training): Remote coaching platform using Apple Watch data for personalized programming
Platform Recommendation: Terra (broad device support) or Sahha (intelligence + retention)
🧠 Mental Health & Therapy Platforms
Use Case: Depression detection, intervention effectiveness tracking, crisis prevention, therapeutic monitoring
Key Biomarkers: Sleep patterns, activity levels, behavioral regularity, social engagement (screen time), circadian rhythm
Why Intelligence Matters: Detect depression symptoms 3-5 days before subjective awareness. Track intervention effectiveness over time. Identify crisis risk patterns. Passive monitoring reduces burden on patients.
Integration Requirements: Smartphone-based (wearables unreliable), passive collection (low burden), privacy-first, continuous monitoring
Real-World Examples:
- Mindstrong Health: Smartphone biomarkers for depression/anxiety—$100M Series C, partnerships with Quartet Health
- Ginger (Headspace Health): On-demand therapy + coaching with behavioral data integration
- Talkspace: Teletherapy platform exploring biomarker integration for outcomes tracking
- Woebot Health: AI chatbot for CBT + mood tracking
- University of Otago + Sahha: Research partnership with 4,500 participants validating smartphone biomarkers for mental wellbeing prediction (Sahha Research)
Platform Recommendation: Sahha (smartphone-based passive monitoring + mental wellbeing intelligence)
💊 Supplement & Nutrition Platforms
Use Case: Personalized supplement recommendations, effectiveness tracking, adaptive dosing, customer retention
Key Biomarkers: Sleep quality, energy levels, stress markers, recovery capacity, activity patterns
Why Intelligence Matters: Recommend sleep supplements when sleep quality declines. Adjust energy supplements based on activity levels. Track effectiveness of interventions over time. Personalize stacks based on behavioral archetypes.
Integration Requirements: Historical data retention (track effectiveness), biomarker trends (before/after), lab test integration (optional)
Real-World Examples:
- Thorne: Integrates Fitbit/Apple Watch with personalized supplement recommendations—partners with Mayo Clinic
- InsideTracker: Blood biomarkers + DNA + fitness trackers = personalized nutrition/supplement plans—$100M+ raised
- Rootine: DNA + blood testing + daily habits (from wearables) → personalized vitamin packs
- Ritual: Subscription vitamins exploring biomarker integration for adaptive recommendations
- AG1 (Athletic Greens): Exploring wearable integration for personalized timing recommendations
Platform Recommendation: Vital (lab testing network) + Sahha (behavioral intelligence) or Terra (wearable data)
🔬 Clinical Research & Trials
Use Case: Remote patient monitoring, adherence tracking, efficacy measurement, real-world evidence
Key Biomarkers: Sleep, activity, HRV, medication adherence (via patterns), symptom tracking
Why Intelligence Matters: Detect non-adherence early. Measure intervention effectiveness with objective data. Reduce participant burden with passive collection. Generate real-world evidence for regulatory submissions.
Integration Requirements: High data quality, regulatory compliance (21 CFR Part 11), device validation, data export for analysis
Real-World Examples:
- Apple Heart Study: 400,000+ participants using Apple Watch for AFib detection—published in NEJM
- Evidation Health: Platform for decentralized trials using wearables—partnerships with Eli Lilly, Sanofi—$153M raised
- TrialX + Fitbit: Clinical trial recruitment and monitoring using wearable data
- Koneksa Health (Novartis): Digital biomarkers for CNS trials—acquired by Novartis
- Duke Clinical Research Institute: Studies using wearables for heart disease, diabetes, depression monitoring
Platform Recommendation: Spike (medical equipment integration, HIPAA/GDPR compliant) or Sahha (validated intelligence layers, smartphone monitoring, HIPAA/GDPR compliant). Both platforms are purpose-built for clinical research with proper compliance.
🤖 AI/ML & Predictive Analytics
Use Case: Training health prediction models, personalization algorithms, research datasets, health coaching bots
Key Biomarkers: All available biomarkers for model training—sleep, activity, HRV, behavioral patterns, wellness scores
Why Intelligence Matters: Pre-processed features (wellness scores, archetypes) accelerate model development. Historical data enables supervised learning. Labeled data (outcomes) for training predictive models.
Integration Requirements: Data export APIs, historical access, data labeling, diverse data sources
Real-World Examples:
- Google Health Studies: App collecting health data for ML research—respiratory, cardiovascular, diabetes studies
- IBM Watson Health (now Merative): AI for healthcare using EHR + wearable data
- Cardiogram + DeepHeart: ML model detecting AFib, diabetes, sleep apnea from Apple Watch data—54% sensitivity for diabetes
- Mindstrong (mentioned above): Keystroke/touch patterns for mental health prediction
- Labfront (research platform): Simplified biomarker collection for ML researchers—used by Stanford, MIT
Platform Recommendation: Sahha (MCP integration, intelligence features, smartphone coverage) or Rook (data retention, raw data variety). Sahha's MCP (Model Context Protocol) enables direct AI agent integration.
🏢 Corporate Wellness Programs
Use Case: Employee engagement, healthcare cost reduction, productivity optimization, wellness challenges
Key Biomarkers: Sleep quality, activity levels, stress markers, wellness scores, engagement metrics
Why Intelligence Matters: Identify employees at risk of burnout. Measure program effectiveness with wellness scores. Personalize recommendations at scale. Predict productivity impacts of poor sleep.
Integration Requirements: 100% employee coverage (no wearable assumption), privacy compliance, aggregate reporting, HIPAA/GDPR
Real-World Examples:
- Virgin Pulse: Corporate wellness platform with 14M+ members—integrates wearables, challenges, incentives
- Wellable: Workplace wellness integrating 1,000+ apps/devices—used by Harvard, MIT
- Limeade: Employee experience + wellbeing platform with biometric data integration
- MoveSpring: Workplace step challenges integrating Fitbit, Garmin, Apple Watch, Strava
- Omada Health: Chronic disease prevention programs (diabetes, hypertension) using connected scales, trackers
Platform Recommendation: Sahha (smartphone coverage for all employees) or Terra (if distributing wearables)
🎮 Health Gaming & Gamification
Use Case: Movement-based games, fitness challenges, health NFTs, play-to-earn wellness, metaverse fitness
Key Biomarkers: Steps, activity minutes, calories, workout completion, sleep quality
Why Intelligence Matters: Verify real-world activity (prevent cheating). Create adaptive difficulty based on fitness level. Reward consistent behavioral patterns (not just steps). Enable fair competition across fitness levels.
Integration Requirements: Real-time data for game mechanics, anti-cheat verification, cross-platform support
Real-World Examples:
- Pokémon GO: AR game with step tracking—generated $6B revenue, increased physical activity 26% (Nature study)
- Sweatcoin: Convert steps to cryptocurrency—50M+ users, partnered with Terra API for wearable integration
- STEPN (Move-to-Earn): Web3 fitness app earning crypto for walking—$5M daily revenue at peak
- Zombies, Run!: Audio fitness game with 10M+ downloads—integrates with all major fitness trackers
- Supernatural VR (Meta): VR fitness with heart rate monitoring and calorie tracking—acquired by Meta
Platform Recommendation: Terra (broad device support + verification) or Sahha (smartphone for mass market)
💬 Digital Therapy & Coaching
Use Case: Sleep therapy (CBT-I), habit coaching, stress management, chronic disease management, behavior change
Key Biomarkers: Sleep patterns, activity consistency, behavioral regularity, therapy adherence (via patterns)
Why Intelligence Matters: Objective measurement of therapy effectiveness. Identify non-adherence early. Personalize interventions based on behavioral archetypes. Provide therapists with rich patient context.
Integration Requirements: Historical trends, therapy outcome tracking, therapist dashboard, privacy compliance
Real-World Examples:
- Sleepio (Big Health): Digital CBT for insomnia with sleep tracking—NICE-approved, 60% improvement in insomnia severity
- Noom: Weight loss coaching with calorie tracking + wearable integration—$58M Series D, 50M+ users
- Omada Health (mentioned above): Chronic disease prevention with connected devices + health coaching
- Livongo (Teladoc Health): Diabetes management with connected glucometers + coaching—acquired for $18.5B
- Hinge Health: Digital MSK therapy with wearable sensors—$6.2B valuation, contracts with Fortune 500
Platform Recommendation: Sahha (behavioral intelligence + therapy outcomes) or Rook (simpler wearable integration)
Cross-Industry Patterns
Successful implementations share common elements:
- Intelligence over raw data: Winners process biomarkers into actionable insights (Whoop's strain score, Sahha's wellness scores)
- Passive collection: Low-friction data gathering (smartphone sensors, background sync) drives engagement
- Personalization at scale: Behavioral archetypes + ML enable individualized recommendations for millions
- Outcome measurement: Objective biomarkers validate program effectiveness (RAND study on Vitality, NEJM on Apple Heart Study)
- 100% coverage matters: Insurance, corporate wellness, mental health need smartphone-based solutions—wearables exclude 70% of users
Industry-Specific Resources
Academic Research
Platform Comparison
Here's an honest comparison of major health data aggregators. Each has strengths depending on your use case.
| Platform | Wearable Integration | Intelligence Layers | Data Retention | Real-time Data | Developer Support | Pricing |
|---|---|---|---|---|---|---|
| Sahha | HealthKit Health Connect + Smartphone Sensors | 5 Scores 14 Archetypes Trends + MCP | 3 months | ✓ Yes Webhooks | Very High Slack, near-immediate | Per-user/month |
| Terra | 300+ devices HealthKit Health Connect | Raw data only | Pass-through | ✓ Yes Webhooks | Medium Email support | Per-connection |
| Rook | HealthKit Health Connect Limited devices | Raw data only | 10 days | ✗ No Batch only | High Slack community | Per-user/month |
| Spike | Medical Equipment EMR systems Limited wearables | GenAI processing | Pass-through | ✓ Yes Webhooks | Medium Email support | Custom |
| Vital/Junction | Lab testing only Wearables sunset | Raw data only | Pass-through | ✓ Yes Webhooks | Low Being sunset | Per-test |
⚠️ Note: Vital Rebranded to Junction (Labs Only)
Vital has rebranded to Junction and now focuses exclusively on lab testing integration. It is no longer a general-purpose wearable data aggregator. Recommended for lab-only use cases. For wearable/smartphone data, consider other platforms.
Key Differences to Consider
- Wearable integration: All platforms support HealthKit/Health Connect. Spike adds specialized medical equipment. Sahha uniquely adds smartphone sensors.
- Smartphone monitoring: Sahha is the only platform offering passive smartphone collection—critical for 100% user coverage without wearables
- Intelligence layers: Sahha provides 5 scores, 14 archetypes, trends, and MCP integration—required for retention, personalization, and predictive analytics
- Real-time data: Sahha, Terra, Spike, and Junction offer webhook-based real-time delivery. Rook only supports batch processing—may delay data availability.
- Data retention: Sahha (3 months) vs Rook (10 days) vs pass-through (Terra/Spike)—affects historical analysis and trend detection
- Developer support: Sahha and Rook offer Slack communities with fast responses. Terra/Spike use email support.
- Lab testing: Junction (formerly Vital) focuses exclusively on clinical-grade blood work—best for lab-only applications
- Medical equipment: Spike specializes in medical device integration and EMR systems—relevant for clinical trials and healthcare providers
Choosing the Right Platform
The best choice depends on your specific requirements:
- Need 100% user coverage? Consider platforms with smartphone monitoring (only Sahha offers this)
- Users already have wearables? Direct device integrations (Terra) or platform APIs (most providers)
- Need retention prediction or personalization? Intelligence layers required (Sahha)
- Building lab testing features? Vital's testing network may be ideal
- Healthcare provider integration? Spike's EMR focus fits this use case
- Just need raw data aggregation? Terra or Rook provide straightforward access
Deep Dive Guides
Explore specific topics in depth with our detailed guides:
📖 What Are Biomarkers?
Complete overview of biomarker types, collection methods, and applications in health technology.
Read Guide →🧠 Why Intelligence Layers Matter
Deep dive on processing raw data into actionable insights, with real-world ROI examples.
Read Guide →📱 Smartphone vs Wearable Data
Coverage analysis, accuracy trade-offs, and when each approach makes sense.
Read Guide →🔌 Platform APIs vs Direct Integration
Cost comparison, maintenance burden, and technical implementation considerations.
Read Guide →✅ Choosing a Health Data API
Decision framework, requirements checklist, and platform evaluation guide.
Read Guide →🛠️ Integration Best Practices
Technical implementation guide covering auth, data models, webhooks, and error handling.
Read Guide →