Integrating a Smart Sensor Chip and AI Predictive Analytics Into the Sehhaty App to Enhance Diabetes Management in Saudi Arabia

TL;DR

This paper proposes enhancing Saudi Arabia's Sehhaty app with a smart sensor chip and AI to improve diabetes care through real-time monitoring and predictive analytics.

Contribution

A novel integration of smart sensor chips and AI predictive analytics into the Sehhaty app for proactive diabetes management.

Findings

The proposed system enables continuous glucose and vital sign monitoring via a wearable smart sensor chip.

AI-based predictive analytics can forecast glycemic trends to support personalized diabetes care.

The design addresses usability, privacy, and accessibility to overcome barriers to mHealth adoption.

Abstract

Diabetes is a major public health challenge in the Kingdom of Saudi Arabia. A substantial proportion of adults are affected, placing significant pressure on the healthcare system. Although digital health initiatives have expanded in recent years, patients continue to encounter barriers to adopting mobile health (mHealth) technologies, including technical limitations, usability concerns, and privacy issues.

This article proposes a comprehensive digital health solution for diabetes management that enhances the national health application Sehhaty by integrating two complementary technologies: a smart sensor chip (SSC) for continuous physiological monitoring and AI-based predictive analytics (AIPA) for forecasting glycemic trends. The aim is to strengthen proactive and personalized diabetes care.

An Agile development framework consisting of two sprints is proposed. The first sprint integrates a wearable SSC into the Sehhaty ecosystem to transmit real-time glucose and vital sign data through secure wireless communication. The second sprint develops AIPA using machine-learning models to analyze data patterns and predict glycemic fluctuations. System requirements were identified through stakeholder engagement, and the architecture includes a secure cloud backend, structured data flow, and user-centered interface design.

The integration of SSC and AIPA into Sehhaty could enhance diabetes management by enabling continuous monitoring, personalized alerts, and earlier intervention. The system design prioritizes reliability, user-centered usability, and data privacy safeguards to address common barriers to digital health adoption. Consideration of perceived usefulness, ease of use, trust, and accessibility informed the development strategy.

The proposed SSC-AIPA framework has the potential to transform Sehhaty into an advanced diabetes management platform that supports early detection, predictive insights, and individualized care. Future work will include prototyping, usability evaluation, and clinical validation.

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