2022 International journal of dent…

Oral symptoms and oral hygiene behaviours among patients with type 2 diabetes at a hospital in Japan: A cross-sectional study.

, ,

International journal of dental hygiene Vol. 20 (4) : 708-714 • Nov 2022

OBJECTIVES: Effective daily oral hygiene behaviour will prevent periodontal disease. This study aimed to examine the oral symptoms, oral hygiene behaviours and factors contributing to oral hygiene behaviours among patients with type 2 diabetes in a hospital in Japan. METHODS: A cross-sectional survey was conducted with 198 patients with type 2 diabetes. Oral symptoms were assessed using a dichotomous scale based on clinical guidelines. Oral hygiene behaviours were assessed based on the frequency of daily tooth brushing and the use of interdental cleaning aids. Chi-square tests and logistic regression analyses were also performed. RESULTS: Overall, 71.2% of the participants had oral symptoms. Of the participants, 80.3% performed twice daily tooth brushing and 61.1% did not perform interdental cleaning on a daily basis. Logistic regression analysis revealed that tooth brushing behaviour was negatively associated with male patients (odds ratio [OR] = 0.45, 95% confidence interval [CI]: 0.25-0.80), difficulty with mastication (OR = 0.63, CI: 0.43-0.92) and tooth loss (OR = 0.68, CI: 0.46-1.00) and positively associated with periodontal disease (OR = 1.73, CI: 1.10-2.72). There were no significant variables related to the use of interdental cleaning aids. CONCLUSIONS: Patients need to learn about the necessity for regular oral hygiene behaviours and the appropriate way. In particular, support for male patients, those who have difficulty with mastication, those who have experienced natural tooth loss, and those who have not been diagnosed with periodontal disease are needed.

No clinical trial protocols linked to this paper

Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.
PICO Elements

No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.

Paper Details
MeSH Terms
Associated Data

No associated datasets or code repositories found for this paper.

Related Papers

Related paper suggestions will be available in future updates.