スマートフォンを用いた糖尿病性末梢神経障害評価アプリ

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 thresh-old (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.

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.

SVPTs moderately correlated with RSTF scores ( Rs = -0.43, p = 0.0019). Among adults aged 50 to 69, SVPTs correlated significantly with clinical markers ( F (4, 29) = 4.76, p = 0.00447, Multiple R ² = 0.396, Adjusted R ² = 0.313, ɛ = 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).

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.