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Smartphone-based App to Assess Diabetic Peripheral Neuropathy.
Rachel A G Adenekan1, Adeyinka E Adenekan2, Kenneth K Leung3
1Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.
Journal of Diabetes Science and Technology
|April 2, 2026
Summary
A smartphone app measuring vibration perception thresholds (SVPTs) shows promise for early diabetic peripheral neuropathy (DPN) detection. This accessible tool aids in identifying individuals at risk for DPN complications before they become clinically apparent.
Area of Science:
- Neurology
- Biomedical Engineering
- Digital Health
Background:
- Diabetic peripheral neuropathy (DPN) affects 50% of diabetes patients, increasing amputation risk.
- Current screening methods for DPN are inconsistent and often miss early nerve damage.
- A smartphone application for measuring vibration perception thresholds (SVPTs) offers a potential solution for precise DPN monitoring.
Purpose of the Study:
- To assess the clinical relevance and precision of smartphone-based SVPT measurements for large-fiber sensory deficits in diabetic patients.
- To evaluate a novel, patient-operated smartphone application for DPN screening.
Main Methods:
- 71 patients with pre-diabetes or diabetes had their SVPTs measured using a smartphone app.
- SVPT efficacy was compared to traditional tuning fork exams (RSTF).
- Multivariable linear regression analyzed correlations between SVPTs, RSTF scores, 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); diabetes duration negatively associated (β = -0.098, p = 0.003).
Conclusions:
- A patient-operated smartphone application for large-fiber sensory monitoring is clinically relevant for DPN risk assessment.
- This novel platform offers a precise, reliable, and accessible method for early DPN complication identification.
- The technology can identify at-risk individuals before overt clinical manifestation of DPN.
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