Sahha Insights API

Product Introduction

1. Sahha Insights API is a health analytics tool that provides trend detection and comparative analysis of user health data through an application programming interface. It processes behavioral and biometric data to generate actionable insights about sleep patterns, activity levels, and other health-related metrics. The API integrates with digital platforms to deliver contextualized health intelligence for improving user engagement.
2. The core value lies in transforming raw health data into interpretable trends and benchmarks, enabling developers to build applications that proactively respond to changes in user behavior. It bridges the gap between passive data collection and actionable health interventions by quantifying directional changes in biomarkers and behavioral factors over time.

Main Features

1. The API detects directional trends in health metrics such as sleep duration, stress levels, or activity consistency using time-series analysis. It calculates percentage changes, rate of improvement/decline, and statistical significance over customizable periods (e.g., weeks or months).
2. Comparative benchmarking contextualizes individual user data against global averages, demographic cohorts (e.g., females aged 35–40), or personalized baselines. This feature uses percentile ranking to classify metrics as typical, high, or low relative to chosen reference groups.
3. Automated insight generation translates numerical changes into narrative summaries, such as "Your sleep debt increased by 20 minutes" or "Sleep score improved 4.2% versus last month." These outputs are formatted for direct integration into user-facing dashboards or notifications.

Problems Solved

1. It addresses the limitation of surface-level engagement metrics (clicks/views) by providing health-specific behavioral analysis tied to measurable outcomes. Many platforms struggle to correlate user activity with meaningful health improvements.
2. The API targets digital health platforms, wellness apps, and telehealth services requiring granular, evidence-based user insights without building complex analytics infrastructure.
3. Typical use cases include triggering personalized interventions when sleep patterns deteriorate, creating progress reports comparing users against age-matched cohorts, or identifying at-risk users based on biomarker trends exceeding predefined thresholds.

Unique Advantages

1. Unlike generic analytics tools, Sahha specializes in health-specific trend detection using proprietary algorithms validated against clinical research studies referenced in its Research Index.
2. The API dynamically updates benchmarks using aggregated anonymized data from its global user base, ensuring comparisons reflect current population-level health patterns.
3. Competitive differentiation comes from combining multi-source biomarker analysis (e.g., sleep, activity, heart rate variability) with contextual narrative generation, a feature absent in most health data platforms.

Frequently Asked Questions (FAQ)

1. How does Sahha Insights API handle data privacy? All data is processed in compliance with HIPAA and GDPR, with end-to-end encryption and optional on-premises deployment for sensitive health information. User-level data is never shared across platforms.
2. What infrastructure is required for integration? The API requires standard HTTPS endpoints and OAuth 2.0 authentication, with client libraries available for Python, JavaScript, and Swift. No specialized hardware or local data storage is needed.
3. Can benchmarks be customized for specific regions or conditions? Yes, the comparison engine supports custom cohort creation using filters for age, gender, geographic location, and pre-existing health conditions documented in user profiles.
4. How frequently are trends updated? The system processes data in near real-time, with trend recalculations occurring every 24 hours by default and configurable to 1-hour intervals for premium-tier subscribers.
5. Does the API require wearable device integration? No, Sahha analyzes data from both device-connected sources (Apple Health, Fitbit) and manual user inputs, applying normalization algorithms to ensure cross-source consistency.