Examining Patterns of Information Exchange and Social Support in a Web-Based Health Community: Exponential Random Graph Models

How Do Team-Level and Individual-Level Linguistic Styles Affect Patients' Emotional Well-Being-Evidence from Online Doctor Teams

BACKGROUND

In the post-epidemic era, online medical care is developing rapidly, and online doctor teams are attracting attention as a high-quality online medical service model that can provide more social support for patients.

METHODS

Using online doctor teams on the Haodf.com platform as the research subject, this study investigates the key factors in the process of doctor-patient communication, which affects patients' emotional well-being. We also explore the different roles played by doctors as leaders and non-leaders in doctor-patient communication. From the perspective of language style, we select representative factors in the process of doctor-patient communication, namely the richness of health vocabulary, the expression of emotions, and the use of health-related terms (including perceptual words and biological words). We extract both team-level and individual-level linguistic communication styles through textual and sentiment analysis methods and empirically analyze their effects on patients' emotional well-being using multiple linear regression models.

RESULTS

The results show that the expression of positive emotions by the team and attention to patients' perceptions and biological conditions benefit patients' emotional well-being. Leaders should focus on the emotional expression, whereas non-leaders should focus on the use of perceptual and biological words.

CONCLUSIONS

This study expands the application of linguistic styles in the medical field and provides a practical basis for improving patients' emotional well-being.

Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis

BACKGROUND

In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care delivery. The importance of integrating natural language processing (NLP) methods to extract patient-reported opinions has been gradually acknowledged over the past years. One form of NLP is sentiment analysis, which extracts and analyses information by detecting feelings (thoughts, emotions, attitudes, etc) behind words. Sentiment analysis has become particularly popular following the rise of digital interactions. However, NLP and sentiment analysis in the context of intrafamilial communication for genetic cancer risk is still unexplored. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk.

OBJECTIVE

The study examined the role of sentiment in predicting openness of intrafamilial communication about genetic cancer risk associated with hereditary breast and ovarian cancer (HBOC) syndrome.

METHODS

We used narratives derived from 53 in-depth interviews with individuals from families that harbor pathogenic variants associated with HBOC: first, to quantify openness of communication about cancer risk, and second, to examine the role of sentiment in predicting openness of communication. The interviews were conducted between 2019 and 2021 in Switzerland and South Korea using the same interview guide. We used NLP to extract and quantify textual features to construct a handcrafted lexicon about interpersonal communication of genetic testing results and cancer risk associated with HBOC. Moreover, we examined the role of sentiment in predicting openness of communication using a stepwise linear regression model. To test model accuracy, we used a split-validation set. We measured the performance of the training and testing model using area under the curve, sensitivity, specificity, and root mean square error.

RESULTS

Higher "openness of communication" scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of "openness of communication" (area under the curve=0.72) in the context of genetic cancer risk associated with HBOC.

CONCLUSIONS

Our study showed that NLP can facilitate assessment of openness of communication in individuals carrying a pathogenic variant associated with HBOC. Findings provided promising evidence that various features from narratives such as sentiment and fear are important predictors of interpersonal communication and self-disclosure in this context. Our approach is promising and can be expanded in the field of personalized medicine and technology-mediated communication.

U-shaped association between online information exchange and app usage frequency: a large-scale survey of China 's online young and middle-aged people with pre diabetes and diabetes

Background

China has the world's largest diabetic population, and the cost of caring for all these people every day is substantial. Online information exchange and app usage frequency have been demonstrated to play a significant influence in the management of blood glucose and enhancement of diabetes-related quality of life. However, the association between online information exchange and app usage frequency among actual online populations remains unclear and deserves additional study. Therefore, we evaluated the factors affecting the frequency of app usage in the online glucose management population, with a particular emphasis on the connection between online information exchange and app use frequency, contributing to the expansion of the research of diabetes management models.

Method

This cross-sectional study was conducted by disseminating questionnaires in blood glucose management-related forums and WeChat groups and included 1586 online users concerned about blood glucose management. Information exchange and app usage frequency were considered as independent and dependent variables, respectively. We performed stratified and single factor analysis, multiple equation regression analysis, smooth curve fitting, and threshold effect and saturation effect analysis. R (version 4.1.3, http://www.Rproject.org) and EmpowerStats were used for data analysis.

Result

After adjusting for other covariates, information exchange was independently and positively associated with app use frequency (β = 8.6, 95% CI: 6.5 to 11.2, p < 0.001). Through interaction analysis, the most significant interaction factors influencing the relationship between information exchange and app usage frequency were identified as health insurance status, whether living with parents, glycated hemoglobin status in the previous month, and self-monitoring of blood glucose (SMBG). The association between information exchange and app usage frequency is U-shaped, with information exchange inflection points of 3.0 and 4.2. Information exchange and app usage frequency are negatively correlated when the average information exchange score is less than 3.0, and for every point increase in the average information exchange score, the likelihood of the app high usage frequency group compared to the app low usage frequency group decreases by 70%. The relationship between information exchange and app usage frequency is strongest when it is greater than or equal to 3.0 and less than or equal to 4.2. The probability of the app high usage frequency group occurring compared to the app low usage frequency group rises 17.3 times for every 1 point increase in the average information exchange score. The probability of the app high usage frequency group occurring in comparison to the app low usage frequency group increased by 1.8 times for every 1 point rise in information exchange when the average information exchange score was higher than 4.2.

Conclusion

Age, body mass index, married, living with parents, hemoglobin level, SMBG, and information exchange were positively connected with app usage frequency in our study of online blood glucose management population. The link between information exchange and app use frequency was significantly U-shaped. The app usage frequency changed the most with the rise in information exchange when the information exchange score was greater than or equal to 3.0 and less than or equal to 4.2. Therefore, we ought to offer effort to concentrate on and increase the health-related behaviors and activities of those in this score interval.

Exploring the User Interaction Network in an Anxiety Disorder Online Community: An Exponential Random Graph Model with Topical and Emotional Effects

The increasing number of people with anxiety disorders presents challenges when gathering health information. Users in anxiety disorder online communities (ADOCs) share and obtain a variety of health information, such as treatment experience, drug efficacy, and emotional support. This interaction alleviates the difficulties involved in obtaining health information. Users engage in community interaction via posts, comments, and replies, which promotes the development of an online community as well as the wellbeing of community users, and research concerning the formation mechanism of the user interaction network in ADOCs could be beneficial to users. Taking the Anxiety Disorder Post Bar as the research object, this study constructed an ADOC user interaction network based on users' posts, comments, and personal information data. With the help of exponential random graph models (ERGMs), we studied the effects of the network structure, user attributes, topics, and emotional intensity on user interaction networks. We found that there was significant reciprocity in the user interaction network in ADOCs. In terms of user attributes, gender homogeneity had no impact on the formation of the user interaction network. Experienced users in the community had obvious advantages, and experienced users could obtain replies more easily from other members. In terms of topics, pathology popularization showed obvious homogeneity, and symptoms of generalized anxiety disorder showed obvious heterogeneity. In terms of emotional intensity, users with polarized emotions were more likely to receive replies from users with positive emotions. The probability of interaction between two users with negative emotions was small, and users with opposite emotional polarity tended to interact, especially when the interaction was initiated by users with positive emotions.