Every serious digital health initiative starts with mobile. Whether it's a chronic condition management platform, insurance engagement tool, or preventive wellness program, mobile health apps are now the first point of contact between healthcare providers and patients. But what separates a good app from a great one? Real-time health data and the infrastructure to support it. According to Market.us, the mobile health app market is projected to grow at a CAGR of 11.5%, reaching US$ 187.7 billion by 2032 from US$ 63.2 billion in 2023. This rapid growth is another signal of the urgent need for mobile-first strategies that can scale and adapt to real-time health needs.
From heart rate variability to stress tracking, today’s users expect continuous insights, and regulators, providers, and payors are demanding clinical relevance. To meet this expectation, your app must be designed with a real-time data integration mindset, and that starts with your SDK strategy.
In this post, we outline the key structural, regulatory, and performance aspects that determine whether your mobile app is truly ready for real-time data. Use our checklist as a diagnostic tool to identify strengths, gaps, and opportunities to optimize your tech stack for long-term success.
How to Integrate Real-Time Data in Your Health App
Building a mobile health app goes far beyond UI/UX. It requires robust backend infrastructure, secure data handling, and clinical-grade insights, all delivered in real-time. In order to have a stable-working app, digital health providers should really understand how real-time health apps work. For that check our blog post about turning wearable data into health incentives.
Technical Infrastructure Requirements
Real-time data demands a robust, flexible backend architecture that can ingest, store, and process high-frequency biometric signals at scale. Unlike periodic or batch-uploaded data like daily step counts, real-time data involves continuous, second-by-second streaming of complex metrics such as heart rate variability, glucose trends, and sleep cycles.
Your architecture must support:
- Continuous Ingestion: The ability to capture and buffer data 24/7 without loss, even under poor network conditions or partial sync events.
- Time-Series Storage: Specialized databases optimized for time-stamped records, capable of handling massive datasets while preserving sequence and context. Examples include InfluxDB or AWS Timestream.
- Event-Driven Processing: Stream-processing platforms like Apache Kafka, AWS Firehose, or Google Pub/Sub help transform and route incoming data efficiently, enabling immediate downstream analysis.
- Real-Time Analytics: Deploying stream analytics engines (like Apache Flink or Spark Streaming) allows for near-instant detection of clinical anomalies, behavior deviations, or user-defined thresholds.
- Elastic Scalability: Cloud-native infrastructure must scale both vertically (more compute/memory) and horizontally (more nodes) to accommodate sudden spikes in data or user adoption.
- Resilience and Monitoring: Real-time systems need intelligent monitoring, failover mechanisms, and retry logic to prevent service disruption. Load balancing and distributed caching (e.g., Redis) help maintain uptime and throughput.
Without this structure, your app risks data gaps, delayed insights, and clinical irrelevance. Real-time care delivery starts with real-time data pipelines built to scale, secure, and adapt to your user base.
What Is a Mobile SDK And Why It Matters?
A mobile SDK (software development kit) is a pre-packaged set of development tools, documentation, libraries, and code samples that allows developers to seamlessly integrate advanced functionalities, such as continuous biometric monitoring, real-time data syncing, and secure user authentication, directly into mobile health apps. In the context of digital health, a well-designed SDK serves as the bridge between diverse hardware devices (like wearables and connected sensors) and your app’s backend infrastructure, ensuring data can flow securely, accurately, and in real-time.
For healthcare developers, choosing an SDK-first architecture lays the foundation for scalable, patient-centric solutions. Here's why it matters:
- Accelerated time-to-market: Prebuilt modules reduce engineering effort and simplify feature implementation.
- Built-in compliance: SDKs often come with GDPR and HIPAA-ready data handling logic. Read more about legal frameworks here.
- Continuous biometric tracking: Collect high-frequency metrics such as heart rate variability (HRV), activity levels, sleep cycles, or glucose data without interruptions.
- Future-proofing your platform: As the wearable ecosystem evolves, SDKs simplify the integration of emerging devices and data types.
- Improved reliability and maintainability: Managed SDKs are regularly updated to reflect API changes, privacy laws, and new platform capabilities.
Types of Real-Time Health Data
A successful health app must be designed to handle the diverse and evolving landscape of real-time physiological and behavioral metrics. These data streams include core biomarkers such as:
- Heart Rate and Heart Rate Variability (HRV): Crucial for detecting stress, fatigue, cardiovascular strain, and recovery levels. Read more here.
- Respiratory Rate and Oxygen Saturation: Used for monitoring chronic respiratory conditions and early illness detection.
- Sleep Architecture: Including REM, deep, and light sleep phases to assess recovery quality. Read more here.
- Skin Temperature and Peripheral Body Temperature: Vital for detecting fevers, ovulation cycles, and illness onset.
- Blood Glucose Levels: Especially critical for diabetes management and metabolic health tracking.
- Activity and Movement Patterns: Step count, cadence, intensity minutes, and sedentary behavior.
- Electrodermal Activity and Stress Signals: Used to infer mood, anxiety, or autonomic nervous system balance. Read more here.
- Symptom and Mood Logging (Behavioral Data): Especially useful in digital therapeutics and chronic disease management.
Understanding which of these metrics aligns best with your user’s condition, use case, or clinical objective is the first step in defining your data architecture. To gain a better understanding of different useful metrics, check our passive data blog post! The app must also be able to adapt as new devices and sensor modalities emerge, ensuring future-proof scalability across platforms and populations.
Challenges in Real-Time Data Collection
Nevertheless, real-time health data collection introduces a host of technical and operational challenges that health app developers must navigate carefully.
- Device and OS Fragmentation: Apps must ensure stable connectivity across a fragmented ecosystem of wearable devices and mobile platforms—including Apple Health, Garmin Connect, Fitbit, Samsung Health, and others. Each platform has its own data protocols, sync logic, and access permissions.
- Variable Signal Quality: Sensor accuracy and sampling frequency vary widely across devices, making it difficult to deliver consistent real-time insights without proper data harmonization. Developers must implement intelligent filtering, smoothing, and interpolation mechanisms to ensure usable outputs.
- Battery and Resource Management: Real-time sync can quickly drain the battery and strain the device's CPU/memory. Background sync, especially on iOS, is limited by aggressive power management policies. Efficient scheduling and low-power sync protocols are essential.
- Data Latency and Interruption Handling: Real-world connectivity is never perfect. Apps must anticipate sync dropouts or delays caused by Bluetooth interference, app inactivity, or OS constraints and implement buffered or offline capture mechanisms that resume seamlessly once conditions improve. Check our multiple data integration guide.
- Backend Stability and Throughput: Ingesting continuous streams of data from thousands of users simultaneously requires a backend infrastructure capable of scaling elastically, handling spikes, and preserving data integrity even during outages or retries.
- Security During Transmission: Health data must be encrypted both in transit and at rest. Real-time data often travels through third-party APIs, increasing the surface area for potential vulnerabilities unless robust authentication and secure token exchange are enforced.
Overcoming these challenges is key to ensuring that users, clinicians, and partners can trust the fidelity and availability of the data your app provides.
Ethical and Privacy Concerns
Real-time health data involves highly sensitive user information, ranging from biometric readings to behavioral patterns, making privacy and security non-negotiable. Your health app must implement:
- End-to-End Encryption: All data should be encrypted at the source (on the device), in transit (during API calls), and at rest (in cloud or local storage).
- Role-Based Access Control (RBAC): Limit access based on user role (e.g., patient, provider, admin) to minimize exposure.
- Granular Consent Management: Build user-specific consent flows that allow patients to opt into specific data types or use cases, and revoke access at any time.
- Compliance with Regional Standards: Whether it’s GDPR in Europe, HIPAA in the U.S., or other local health data regulations, your architecture must be audit-ready and include safeguards such as pseudonymization, audit trails, and breach response protocols. We have covered one of the recent changes in data compliance, take a look here!
- Data Minimization and Transparency: Collect only what is necessary, clearly communicate how data is used, and provide intuitive dashboards for users to monitor and control their data.
- Secure SDKs and API Gateways: Ensure that any third-party SDKs or API layers your app uses follow similar security protocols, especially if integrating with wearables or external data sources.
These measures are essential for building not only regulatory compliance but also long-term trust with users, care providers, and data partners alike.
Checklist: Is Your App Ready?
Use the following checklist to evaluate whether your mobile health app is architected for scalable, real-time performance:
🔄 Real-Time Data Integration
- The backend supports continuous real-time data ingestion (not just batch uploads)
- Syncs seamlessly with multiple wearable platforms (Apple Health, Google Fit, Fitbit, Garmin, etc.)
- Detects and reacts to biometric or physiological changes in near real-time
- Supports buffering and offline capture for intermittent connectivity
- Uses time-series databases or appropriate cloud infrastructure (e.g., InfluxDB, AWS Timestream)
🔐 Privacy & Security Compliance
- End-to-end encryption implemented (device, transit, storage)
- Role-based access control (RBAC) for users and admins
- Consent management flows built into the app
- Complies with GDPR, HIPAA, or applicable regional standards
- Pseudonymization and audit trails available
- Third-party SDKs and APIs meet the same security standards
🧰 SDK Mobile Application Readiness
- SDK supports iOS and Android platforms
- Normalizes and harmonizes data from various devices (e.g., Oura, Withings, Garmin)
- Regular SDK updates and documentation are available
- Easy integration into mobile frontend and backend infrastructure
- Compatible with real-time and passive sensor data streams
- Lightweight to avoid battery/performance issues
📈 Performance & Scalability
- Scalable backend infrastructure (cloud-native, elastic resources)
- Can handle high-frequency, high-volume streaming data
- Real-time analytics engine deployed (e.g., Spark Streaming, Apache Flink)
- Load balancing, caching (e.g., Redis), and retry logic implemented
- Monitoring and incident alert systems in place
🧑⚕️ Clinical Relevance & UX Readiness
- Health signals visualized meaningfully for end users
- Alerts or nudges are contextualized (not just raw metrics)
- Includes journaling, symptom tracking, or medication adherence features
- Data presented in a usable format for care teams, coaches, or insurers
- Validated metrics for clinical or insurance use cases
📲 Final Launch Criteria
- MVP tested with real wearables and SDK endpoints
- Performance benchmarked under load
- UX research conducted with target users
- Regional compliance confirmed by legal/ops
- Longitudinal data views supported
- Product roadmap includes additional device or API integrations
How Thryve Makes Real-time Data Collection Easier
We understand that real-time data collection can feel overwhelming, especially for our startup users, who aren’t familiar with all the bumps on the road, and for our bigger clients navigating through complex integration requirements. Thryve accommodates its users when it comes to such complexities through our innovative API, which is specifically designed to integrate real-time data for compliant, innovative, and scalable digital health solutions. It’s not only about the quantity, but also about quality, and with Thryve, you get the best of both worlds. We offer features like:
- Seamless Integration with +500 data sources: Connect to a wide range of devices and medical sensors, including Apple, Fitbit, Garmin, and more, with one standardized API.
- Harmonized Data Models: Harmonize metrics from different sources (activity, sleep, HRV) into a single, actionable format.
- Secure Infrastructure: Ensure GDPR-compliant, encrypted, and privacy-first data management.
- Custom Rules and Triggers: Automate nudges, milestones, and feedback based on individual real-time data.
Now you have all the tools to see if your app is actually ready for real-time data collection, and we are here to make sure that the integration process flows smoothly.
You trust us with your real-time data, and we provide you with real insights!
Book a demo with Thryve!
