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Purpose@#This study was conducted to evaluate the validity of the Gene-Health application in terms of estimating energy and macronutrients. @*Methods@#The subjects were 98 health adults participating in a weight-control intervention study. They recorded their diets in the Gene-Health application, took photographs before and after every meal on the same day, and uploaded them to the Gene-Health application. The amounts of foods and drinks consumed were estimated based on the photographs by trained experts, and the nutrient intakes were calculated using the CAN-Pro 5.0 program, which was named ‘Photo Estimation’. The energy and macronutrients estimated from the Gene-Health application were compared with those from a Photo Estimation. The mean differences in energy and macronutrient intakes between the two methods were compared using paired t-test. @*Results@#The mean energy intakes of Gene-Health and Photo Estimation were 1,937.0 kcal and 1,928.3 kcal, respectively. There were no significant differences in intakes of energy, carbohydrate, fat, and energy from fat (%) between two methods. The protein intake and energy from protein (%) of the Gene-Health were higher than those from the Photo Estimation. The energy from carbohydrate (%) for the Photo Estimation was higher than that of the Gene-Health. The Pearson correlation coefficients, weighted Kappa coefficients, and adjacent agreements for energy and macronutrient intakes between the two methods ranged from 0.382 to 0.607, 0.588 to 0.649, and 79.6% to 86.7%, respectively. @*Conclusion@#The Gene-Health application shows acceptable validity as a dietary intake assessment tool for energy and macronutrients. Further studies with female subjects and various age groups will be needed.
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BACKGROUND/OBJECTIVES: Obesity is a serious concern worldwide, for which the restaurant industry holds partial responsibility. This study was conducted to estimate restaurant consumers' intention to select healthy menu items and to examine the relationships among behavioral beliefs, past behaviors, attitudes and behavioral intentions, which are known to be major determinants of consumer behaviors. SUBJECTS/METHODS: An online, self-administered survey was distributed for data collection. The study sample consisted of customers who reported having visited casual dining restaurants in the last three months at the time of the survey. Structural equation modeling was used to verify the fit of the proposed research model. RESULTS: Structural equation modeling revealed that the proposed model supports the sequential, mediated (indirect) relationships among behavioral beliefs, past behaviors, attitudes and behavioral intentions toward healthy menu selection. CONCLUSION: This study contributes to the available literature regarding obesity by adding past behaviors, one of the most influential variables involved in prediction of future behaviors of consumers, to the TPB model, enabling a better understanding of restaurant consumers' rational decision process regarding healthy menu choices. The results of this study provide practical implications for restaurant practitioners and government agencies regarding ways to promote healthy menus.
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Comportamento do Consumidor , Coleta de Dados , Órgãos Governamentais , Intenção , Obesidade , RestaurantesRESUMO
PURPOSE: This study was performed to investigate the effectiveness of gastric cancer (GC) screening methods in a community-based prospective cohort of the Korean Multi-center Cancer Cohort (KMCC) with over a 10-year follow-up. MATERIALS AND METHODS: A total 10,909 and 4,773 subjects from the KMCC with information on gastroendoscopy (GE) and upper gastrointestinal series (UGIS) were included in this study. Cox proportional hazard model adjusted for age, sex, Helicobacter pylori infection, cigarette smoking, and alcohol drinking was used to estimate the hazard ratios (HRs) and 95% confidence interval (CI). RESULTS: The GE screened subjects had almost half the risk of GC-specific death than that of unscreened subjects (HR, 0.58; 95% CI, 0.36 to 0.94). Among the GC patients, GE screenees had a 2.24-fold higher survival rate than that of the non-screenees (95% CI, 1.61 to 3.11). In particular, GE screenees who underwent two or more screening episodes had a higher survival rate than that of the non-screenees (HR, 13.11; 95% CI, 7.38 to 23.30). The effectiveness of GE screening on reduced GC mortality and increased survival rate of GC patients was better in elderly subjects (≥ 65 years old) (HR, 0.47; 95% CI, 0.24 to 0.95 and HR, 8.84; 95% CI, 3.63 to 21.57, respectively) than that in younger subjects (< 65 years old) (HR, 0.66; 95% CI, 0.34 to 1.29 and HR, 1.83; 95% CI, 1.24 to 2.68, respectively). In contrast, UGIS screening had no significant relation to GC mortality and survival. CONCLUSION: The findings of this study suggest that a decreased GC-specific mortality and improved survival rate in GC patients can be achieved through GE screening.