Mobile Web App Development for Diabetic Foot Screening Using Inlow’s 60-Second Screen with Automated Risk Classification

Abstract

Diabetic foot complications constitute a major contributor to preventable lower-extremity amputation, yet primary care screening remains inconsistent due to the absence of integrated digital tools implementing validated clinical protocols. This study presents the design, implementation, and system-centric evaluation of Podiatrix, a mobile web application that operationalizes Inlow's 60-Second Diabetic Foot Screen through an automated, condition-based clinical workflow. Unlike existing tools that address isolated screening criteria, Podiatrix implements all seven Inlow criteria within a unified five-step wizard and applies a deterministic hierarchical classification engine that directly mirrors the original Inlow protocol logic rather than relying on fixed score thresholds. The system was evaluated using three complementary methods: black-box testing across 50 simulated clinical scenarios, Nielsen's heuristic usability evaluation conducted by three independent evaluators, and performance load testing using Apache JMeter under concurrent user conditions. Results demonstrated 100% classification accuracy (50/50 scenarios) matching manual Inlow protocol interpretation, an average heuristic severity score of 1.15 out of 4 indicating high usability, and a mean response time of 820 ms with less than 1% error rate under 100 concurrent users. These findings confirm that Podiatrix provides a computationally robust, highly usable, and scalable digital infrastructure that lays the groundwork for future prospective clinical trials in primary care and community health settings.