Early Detection of Diabetic Neuropathy Based on Health Belief Model: a Scoping Review

Overview

Journal Front Endocrinol (Lausanne)

Specialty Endocrinology

Date 2024 May 9

PMID 38721145

Authors

Okti Sri Purwanti

Nursalam Nursalam

Moses Glorino Rumambo Pandin

Affiliations

Soon will be listed here.

Abstract

Introduction: Uncontrolled blood sugar levels may result in complications, namely diabetic neuropathy. Diabetic neuropathy is a nerve disorder that causes symptoms of numbness, foot deformity, dry skin, and thickening of the feet. The severity of diabetic neuropathy carries the risk of developing diabetic ulcers and amputation. Early detection of diabetic neuropathy can prevent the risk of diabetic ulcers. The purpose: to identify early detection of diabetic neuropathy based on the health belief model.

Method: This research searched for articles in 6 databases via Scopus, Ebsco, Pubmed, Sage journal, Science Direct, and SpringerLink with the keywords "screening Neuropathy" AND "Detection Neuropathy" AND "Scoring Neuropathy" AND "Diabetic" published in 2019-2023. In this study, articles were identified based on PICO analysis. Researchers used rayyan.AI in the literature selection process and PRISMA Flow-Chart 2020 to record the article filtering process. To identify the risk of bias, researchers used the JBI checklist for diagnostic test accuracy.

Results: This research identified articles through PRISMA Flow-Chart 2020, obtaining 20 articles that discussed early detection of diabetic neuropathy.

Conclusion: This review reports on the importance of early detection of neuropathy for diagnosing neuropathy and determining appropriate management. Neuropathy patients who receive appropriate treatment can prevent the occurrence of diabetic ulcers. The most frequently used neuropathy instruments are the vibration perception threshold (VPT) and questionnaire Michigan Neuropathy Screening Instrument (MNSI). Health workers can combine neuropathy instruments to accurately diagnose neuropathy.

Citing Articles

Integrating bioinformatics and multiple machine learning to identify mitophagy-related targets for the diagnosis and treatment of diabetic foot ulcers: evidence from transcriptome analysis and drug docking.

Guo H, Xiao K, Zheng Y, Zong J Front Mol Biosci. 2024; 11:1420136.

PMID: 39044840 PMC: 11263085. DOI: 10.3389/fmolb.2024.1420136.

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