A Study on Oral Health-Related Quality of Life and Its Influencing Factors in Elderly Diabetic Patients

Objective:To investigate the oral health related quality of life in elderly diabetic patients and analyze its influencing factors.Methods:Convenience sampling was used to select 190 elderly patients with diabetes under the Department of Endocrinology and Department of Integrated Traditional Chinese and Western Medicine in the Affiliated Hospital of Hebei University as the research subjects.The Chinese version of the Oral Health Influence Scale(OHIP-14)was used to conduct a questionnaire survey,and univariate analysis and multiple stepwise regression analysis were used to analyze the influencing factors of oral health related quality of life in elderly diabetic patients.Results:The elderly diabetic patients'oral health related quality of life score was 34.48±3.23,which is in the middle-lower range.The findings of multivariate stepwise regression analysis revealed that the course of disease,regular visits to the dentist,sleep quality,oral health knowledge,and oral health attitude together explained 58.9%of the total variance in elderly diabetic patients in terms of their oral health related quality of life(p<0.05).Conclusion:The oral health related quality of life of elderly diabetic patients is generally low,and is affected by the duration of diabetes,sleep quality,and oral health knowledge,attitude,and behavior(regular visits to the dentist).Improving patientsJ attention to oral health problems by improving sleep as well as their own oral health knowledge,attitude,and behavior is an effective way to enhance oral health related quality of life.

Discrete-frequency Convergence of Iterative Learning Control for Linear Time-invariant systems with Higher-order Relative Degree

In this paper, a discrete-frequency technique is developed for analyzing sufficiency and necessity of monotone convergence of a proportional higher-order-derivative iterative learning control scheme for a class of linear time-invariant systems with higher-order relative degree. The technique composes of two steps. The first step is to expand the iterative control signals, its driven outputs and the relevant signals as complex-form Fourier series and then to deduce the properties of the Fourier coefficients. The second step is to analyze the sufficiency and necessity of monotone convergence of the proposed proportional higher-order-derivative iterative learning control scheme by assessing the tracking errors in the forms of Paserval s energy modes. Numerical simulations are illustrated to exhibit the validity and the effectiveness.