Can an Apple Watch Really Detect Pregnancy? The AI Breakthrough Explained!

How Apple Watch AI Detects Pregnancy & Other Health Conditions
Unlike traditional health sensors that track heart rate or blood oxygen, Apple’s Wearable Behaviour Model (WBM) studies long-term patterns like:
Sleep quality & duration
Activity levels & mobility changes
Heart rate variability
Daily gait (walking patterns)
Trained on 2.5 billion hours of data from 160,000+ participants in the Apple Heart and Movement Study (AHMS), the AI model uses Mamba-2 architecture—a next-gen system optimized for detecting gradual health shifts.

Key Findings:
92% accuracy in pregnancy detection by combining behavioural data with biometrics (like PPG heart-rate tracking).
Outperformed traditional sensor-only models in 57 health prediction tasks, including infections and injury recovery.
Behavioural trends (e.g., reduced mobility, sleep changes) were more reliable than short-term heart rate spikes.

Q&A
Q: What is the accuracy of Apple Watch AI in detecting pregnancy?
A: 92% (as per the Wearable Behaviour Model study).
Q: Which architecture does the Apple Watch AI use for health predictions?
A: Mamba-2 (for time-series behavioural data analysis).
Q: How many participants were involved in the Apple Heart and Movement Study?
A: Over 160,000.
Q: Name two behavioural metrics the Apple Watch AI analyzes for health predictions.
A: Sleep patterns and mobility changes.
Q: What is the key advantage of WBM over traditional sensor-based models?
A: It detects long-term health trends, not just immediate biometric spikes.

Final Thoughts
While the Apple Watch isn’t a medical device, this study showcases how AI can revolutionize preventive healthcare. For exam aspirants, staying updated on such innovations ensures an edge in general awareness and tech-based questions.