Validation of Diabetes Knowledge Questionnaire (DKQ) in the Taiwanese Population - Concurrent Validity with Diabetes-Specific Quality of Life Questionnaire Module.

Purpose: Diabetes knowledge is important for people with type 2 diabetes mellitus (DM) to improve their health. Therefore, it is important to validate an instrument for assessing diabetes knowledge. The present study aimed to validate the 24-item Diabetes Knowledge Questionnaire (DKQ). Patients and Methods: The 24-item DKQ and Diabetes-specific Quality of Life Module (DMQoL) were administered to 425 patients (mean±SD age=58.4±11.6) with type 2 DM. Results: The 24-item DKQ was first examined for its factor structure using exploratory factor analysis (EFA). Items with low factors loadings were removed and 18 items were retained to make a DKQ-18. In DKQ-18, five factors were identified, which were named as diabetes etiology and symptoms (F1), intermediate nursing (F2), complications (F3), diet and treatment (F4), and elementary nursing (F5). The DKQ-18 had satisfactory internal consistency (Cronbach's α= 0.732 and McDonald's ω=0.748), good known-group validity (participants with a higher level of education showed better score in DKQ-18; participants with HbA1c ≤7 had better score in DKQ-18 compared to group of HbA1c level >8.5), acceptable test-retest reliability (r=0.69), adequate responsiveness (DKQ-18 can detect knowledge change), and concurrent validity with DMQoL. Conclusion: The DKQ-18 is a valid measure for assessing diabetes knowledge. The DKQ-18 could evaluate participants' diabetes knowledge and improve their diabetes knowledge and self-care through a diabetes team and serve as a tool to evaluate the knowledge of participants with type 2 DM.

Evaluation of the elderly health examination app based on the comprehensive evaluation method of AHP-fuzzy theory.

This research hopes to provide scientific research support for the optimization and development of the elderly health check application by studying the evaluation method of the elderly health check APP, so that the elderly users can easily enjoy the intelligent health check service. First, use the in-depth interview method and the affinity graph analysis method to extract the evaluation elements, and then use the comprehensive evaluation method that combines the analytic hierarchy process and the fuzzy theory to evaluate the health check APP for the elderly. The results show that in the evaluation research of the elderly health examination APP, the operation learning and information processing of the software are the most important. For the elderly, a health examination APP that can be used quickly and has clear and accurate information processing functions is the most satisfactory. When designing the elderly health check application program, the physical and mental factors of the elderly should be considered, taking the elderly as the center, and designing a health check application suitable for the elderly according to the characteristics of the elderly.

Optical health analysis of visual comfort for bright screen display based on back propagation neural network.

BACKGROUND: The visual comfort of liquid crystal display (LCD) is the subjective evaluation of the user. It is a multi-dimensional and multi-factor problem, which is affected by the luminous characteristics of the LCD screen, the physiological factors of the user, and some other environmental factors. METHODS: Based on the theory of visual comfort under the guidance of ergonomics, this paper adopts a combination of objective measurement and subjective evaluation to obtain objective data such as blink frequency and pupil size changes, and subjective evaluation data on screen parameters. Correlation analysis was used to screen subjective and objective data, and an LCD visual comfort evaluation using the back propagation (BP) neural network was constructed with the aim of a concise evaluation of the LCD's own light-emitting characteristics, user's physiological factors, and environmental factors. RESULTS: After testing, the model can successfully predict the optimal visual level of the screen. After training, the relative error between the predicted value of visual comfort and the actual evaluation value is mostly within 10%. Based on this model, the display brightness and color temperature control system combined with the ambient light sensor can automatically adjust the brightness of the screen and the temperature of color parameters in correlation to user's gender, age, and ambient light changes to achieve the effect of improving visual comfort. Setting and user parameter adjustment provide a new method. The maximum adjustment error of the system after testing is 5.378%. CONCLUSION: Our proposed technique can serve as a useful analysis platform for understanding and evaluating the visual comfort of the bright LCD screen at home or in the workplace, and enhancing optical health of humans.