Using Social Media Data to Understand Consumers' Information Needs and Emotions Regarding Cancer: Ontology-Based Data Analysis Study.

BACKGROUND: Analysis of posts on social media is effective in investigating health information needs for disease management and identifying people's emotional status related to disease. An ontology is needed for semantic analysis of social media data. OBJECTIVE: This study was performed to develop a cancer ontology with terminology containing consumer terms and to analyze social media data to identify health information needs and emotions related to cancer. METHODS: A cancer ontology was developed using social media data, collected with a crawler, from online communities and blogs between January 1, 2014 and June 30, 2017 in South Korea. The relative frequencies of posts containing ontology concepts were counted and compared by cancer type. RESULTS: The ontology had 9 superclasses, 213 class concepts, and 4061 synonyms. Ontology-driven natural language processing was performed on the text from 754,744 cancer-related posts. Colon, breast, stomach, cervical, lung, liver, pancreatic, and prostate cancer; brain tumors; and leukemia appeared most in these posts. At the superclass level, risk factor was the most frequent, followed by emotions, symptoms, treatments, and dealing with cancer. CONCLUSIONS: Information needs and emotions differed according to cancer type. The observations of this study could be used to provide tailored information to consumers according to cancer type and care process. Attention should be paid to provision of cancer-related information to not only patients but also their families and the general public seeking information on cancer.

A three-year autoregressive cross-lagged panel analysis on nicotine dependence and average smoking.

OBJECTIVES: Previous studies have been limited to the use of cross sectional data to identify the relationships between nicotine dependence and smoking. Therefore, it is difficult to determine a causal direction between the two variables. The purposes of this study were to 1) test whether nicotine dependence or average smoking was a more influential factor in smoking cessation; and 2) propose effective ways to quit smoking as determined by the causal relations identified. METHODS: This study used a panel dataset from the central computerized management systems of community-based smoking cessation programs in Korea. Data were stored from July 16, 2005 to July 15, 2008. 711,862 smokers were registered and re-registered for the programs during the period. 860 of those who were retained in the programs for three years were finally included in the dataset. To measure nicotine dependence, this study used a revised Fagerström Test for Nicotine Dependence. To examine the relationship between nicotine dependence and average smoking, an autoregressive cross-lagged model was explored in the study. RESULTS: The results indicate that 1) nicotine dependence and average smoking were stable over time; 2) the impact of nicotine dependence on average smoking was significant and vice versa; and 3) the impact of average smoking on nicotine dependence is greater than the impact of nicotine dependence on average smoking. CONCLUSIONS: These results support the existing data obtained from previous research. Collectively, reducing the amount of smoking in order to decrease nicotine dependence is important for evidence-based policy making for smoking cessation.

U-health service for managing chronic disease: a case study on managing metabolic syndrome in a health center in South Korea.

OBJECTIVES: We were to analyze the effect of managing metabolic syndrome using a u-health service in a health center. METHODS: We collected biometric data from 316 subjects living in a county (gun) in South Korea before and after the introduction of uhealth services in 2010. Analysis was done by contingency table using SPSS and latent growth model using AMOS. RESULTS: We found that regional u-health services affected instance of metabolic syndrome. Further, biometrics and health behavior improved. After six months of u-health services, the number of subjects with three or more factors for metabolic syndrome decreased by 62.5%; 63.3% of regular drinkers stopped drinking; 83.3% of subjects who rarely exercised began to exercise twice a week or more; and 60.9% of smokers stopped smoking. CONCLUSIONS: U-health services can change health behavior and biometrics to manage metabolic syndrome in rural areas. The usefulness of u-health services is discussed.