Evidence map›Paper›PMID 41757168›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Smartphone-based App to Assess Diabetic Peripheral Neuropathy.

Rachel A G Adenekan, Adeyinka E Adenekan, Kenneth K Leung, Srikanth Muppidi, Sarada Sakamuri, Marilyn Tan, Sandra A Tsai, Modupe Osikomaiya, Allison M Okamura, Cara M Nunez and 2 more

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Rachel A G AdenekanDepartment of Mechanical Engineering, Stanford University, Stanford, California, USA.ORCID 0000-0003-1058-6837
Adeyinka E AdenekanIndependent Researcher, Abeokuta, Ogun State, Nigeria.
Kenneth K LeungDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.ORCID 0000-0001-7186-0831
Srikanth MuppidiDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.
Sarada SakamuriDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA.
Marilyn TanDivision of Endocrinology, Gerontology and Metabolism, Stanford Health Care, Stanford, California, USA.ORCID 0000-0002-6651-4691
Sandra A TsaiPrimary Care Population Health, Stanford Health Care, Stanford, California, USA.
Modupe OsikomaiyaSchool of Medicine, Stanford University, Stanford, California, USA.
Allison M OkamuraDepartment of Mechanical Engineering, Stanford University, Stanford, California, USA.ORCID 0000-0002-6912-1666
Cara M NunezSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA.ORCID 0000-0003-3132-393X
Sun H KimDivision of Endocrinology, Gerontology and Metabolism, Stanford Health Care, Stanford, California, USA.ORCID 0000-0003-0895-7491
Kyle T YoshidaDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles, California, USA.ORCID 0000-0001-6579-8752

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
NIDDK NIH HHS P30 DK116074
6 · The paper itself

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, ands 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. 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: SVPTs moderately correlated with RSTF scores ( 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.

Indexed as

diabetes foot examdiabetic peripheral neuropathydigital healthearly detectionremote patient monitoringself-monitoring

Identifiers

PMID41757168
PMCPMC12934888

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.