Smartphone-based App to Assess Diabetic Peripheral Neuropathy

Abstract

Background:

Diabetic peripheral neuropathy (DPN) affects approximately 50% of individuals with diabetes and is a risk factor for amputations. Unfortunately, foot exams and screening tools are inconsistent and miss early-stage nerve damage. A smartphone-based application that delivers controlled vibrations, records patient responses, and computes a vibration perception threshold (SVPT) may present an accessible, precise monitoring avenue. This study assesses the clinical relevance and precision of SVPTs for measuring large-fiber sensory deficits in patients with diabetes.

Methods:

We measured SVPTs in 71 patients with pre-diabetes or diabetes and compared their efficacy with tuning fork exams. We analyzed the correlation between SVPT and Rydel-Seiffer tuning fork (RSTF) scores, along with their relationship with clinical DPN markers such as hemoglobin A1c (HbA1c), age, and disease duration using multivariable linear regression.

Results:

The SVPTs moderately correlated with RSTF scores (R_s = −0.43, p = 0.0019). Among adults aged 50 to 69 years, SVPTs correlated significantly with clinical markers [F(4, 29) = 4.76, p = 0.00447, Multiple R² = 0.396, Adjusted R² = 0.313, ϵ = 0.167]. The interaction between age and HbA1c was positively associated with SVPTs (β = 0.118, p = 0.001), while SVPTs were negatively associated with diabetes duration (β = −0.098, p = 0.003).

Conclusions:

We present a clinically relevant, patient-operated smartphone application for large-fiber sensory monitoring, tested on patients with varying DPN risk. This novel platform has the potential to provide a precise, reliable, and accessible avenue for identifying individuals at risk of developing DPN complications, prior to overt clinical manifestation.