Smartphone-based App to Assess Diabetic Peripheral Neuropathy

Area of Science:

• Neurology
• Biomedical Engineering
• Diabetes Research

Background:

• Diabetic peripheral neuropathy (DPN) affects 50% of diabetes patients, increasing amputation risk.
• Current screening methods are inconsistent and often miss early nerve damage.
• A smartphone application for measuring vibration perception thresholds (SVPTs) offers a precise and accessible monitoring solution.

Purpose of the Study:

• To assess the clinical relevance and precision of smartphone-based SVPTs for detecting large-fiber sensory deficits in diabetic patients.
• To compare the efficacy of SVPT measurements with traditional tuning fork exams.

Main Methods:

• Seventy-one patients with pre-diabetes or diabetes had their SVPTs measured using a smartphone app.
• SVPT measurements were compared with Rydel-Seiffer tuning fork (RSTF) scores.
• Multivariable linear regression analyzed correlations between SVPTs, RSTF scores, and clinical markers like HbA1c, age, and diabetes duration.

Main Results:

• SVPTs showed a moderate correlation with RSTF scores (Rs = -0.43, p = 0.0019).
• In adults aged 50-69, SVPTs significantly correlated with clinical markers (F(4, 29) = 4.76, p = 0.00447).
• Age and HbA1c interaction positively associated with SVPTs (β = 0.118, p = 0.001), while diabetes duration negatively associated (β = -0.098, p = 0.003).

Conclusions:

• A novel, patient-operated smartphone application provides clinically relevant monitoring for large-fiber sensory deficits.
• This platform offers a precise, reliable, and accessible method for identifying individuals at risk of DPN complications.
• Early detection via SVPTs can help prevent severe DPN-related outcomes, such as amputations.