Development and Evaluation of the Canteen Connect Online Health Community: Using a Participatory Design Approach in Meeting the Needs of Young People Impacted by Cancer

Adolescent and young adults (AYAs) impacted by their own or familial cancer require information and peer support throughout the cancer journey to ameliorate feelings of isolation. Online Health Communities (OHC) provide social networks, support, and health-related content to people united by a shared health experience. Using a participatory design (PD) process, Canteen developed Canteen Connect (CC), an OHC for AYAs impacted by cancer. This manuscript outlines the process used to develop CC: (1) A mixed-methods implementation evaluation of Version I of CC (CCv.1); (2) Qualitative workshops utilizing strengths-based approaches of PD and appreciative inquiry to inform the development of CC Version 2 (CCv.2); quantitative implementation evaluation to assess the appropriateness, acceptability, and effectiveness of CCv.2. Through several iterations designed and tested in collaboration with AYAs, CCv.2 had improvements in the user experience, such as the ability to send a private message to other users and the site becoming mobile responsive. Results from the evaluation showed CCv.2 was appropriate for connecting with other AYAs. Most AYAs reported satisfaction with CCv.2 and a positive impact on their feelings of sadness, worry, and/or anxiety. CCv.2 fills an important service provision gap in providing an appropriate and acceptable OHC for AYAs impacted by cancer, with initial promising psychological outcomes.

Sentiment Analysis in Portuguese Texts from Online Health Community Forums: Data, Model and Evaluation

This study introduces novel data and models for the task of Sentiment Analysis in Portuguese texts about Diabetes Mellitus. The corpus contains 1290 posts retrieved from online health community forums in Portuguese and annotated by two annotators according to 3 sentiment categories (e.g. Positive, Neutral and Negative). Evaluation of traditional (Support Vector Machine, Decision Tree, Random Forest and Logistic Regression classifiers) and state-ofthe-art (BERT-based models) machine learning classifiers for the task showed the advantage in performance of the latter models as expected. Data and models are available to the community upon request.

q-Rung orthopair fuzzy TOPSIS method and the application to information service quality evaluation in online health community

Since the information quality in the online health community is very important for users to obtain valuable health information, information quality evaluation is a necessary research that involves a multi-attribute decision-making (MADM) problem. However, few researches have been done to address both the construction of evaluation criteria and the expression and processing of fuzzy information, especially in online health community. This manuscript proposes a novel evaluation framework of information service quality combined principal component analysis (PCA) method with the TOPSIS method under q-rung orthopair fuzzy set (q-ROFS) environment. An accurate evaluation criteria system is optimized by the PCA method, and the q-ROF TOPSIS method is proposed to process larger space of fuzzy evaluation information and overcome information loss and information distortion, in which a new distance measure between q-ROFSs is defined and an entropy weight model is initiated to determine the unknown weight of attribute. Moreover, a numerical example is performed to prove the practicability and superiority of the method through comparative analysis, which gives clear results of information quality evaluation of four online health communities. This research ends with fuzzy decision-making theory and application, and provides references for standardizing and improving the information quality of online health communities.

Adverse effects of COVID-19 vaccination among cancer patients: Results from an Internet-based survey.

2621 Background: The rapid development of safe and effective vaccines against SARS-CoV-2 may stem the global COVID-19 pandemic. However, since individuals with cancer were under-represented during clinical vaccine trials, experience with COVID-19 vaccines among cancer patients is limited. Methods: An internet-based survey was conducted January 15 - February 10, 2021 among members of the Inspire online health community. The 63-item survey was emailed to members of the Inspire community who had opted-in for research. Results: Out of 19,152 respondents, 4895 (25%) self-reported a cancer diagnosis. Of these, 1337 (27%) were receiving active therapy. Cancer respondents were 66% female, 77% white, 44% college educated, with a median age range 55-65 years. 88% had solid tumors and 12% hematologic malignancies. 241 (5%) had prior COVID-19 and 148 (3%) thought they had had it but were not tested. Among cancer patients with COVID-19 approximately 30% reported ongoing late symptoms. At the time of survey, 1335 (27%) cancer patients had received a COVID-19 vaccine (Moderna 51% Pfizer-BioNTech 46%, Astra-Zeneca 3%, Other/unknown >1%). Following the first injection, 63% had local adverse events (AEs): injection site pain (51%), swelling (8%), redness (6%), and itching (4%). 34% reported systemic AEs including myalgia (32%), fatigue (18%), headache (12%), joint pain (5%), and chills (5%). 199 (15%) had received the second (booster) vaccination. 76% reported local AEs including pain (69%), swelling (14%), itching (8%), and redness (7%). 67% reported systemic AEs including fatigue (49%), myalgia (30%), headache (29%), chills (23%), fever (16%), joint pain (15%), and nausea (12%). AEs were comparable to the clinical trial results obtained from the general population (fda.gov/media/144245/download & 144434/download). Conclusions: In this internet-based survey drawn from the Inspire online health community 1335 cancer patients reported receiving COVID-19 vaccinations. By self-report the vaccines were well tolerated with AEs patterns mimicking clinical trial results conducted in the general population. These safety results should be reassuring to cancer patients although attention to COVID-19 vaccine efficacy is required (and will be studied during follow-up surveys).[Table: see text]

Health Privacy Information Self-Disclosure in Online Health Community

The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care, which has alleviated the above problem. The survival and sustainable development of the online health community requires users to continuously disclose their health and privacy. Therefore, it is a great practical significance to find out the factors and mechanisms that promote users' self-disclosure in the online health community. From the perspective of individual and situation interaction, this study constructed influencing factors model of health privacy information self-disclosure. Finally, we collected 264 valid samples from the online health community through online and offline questionnaire surveys and then use the SPSS20.0 and AMOS21.0 to conduct exploratory factor analysis, confirmatory factor analysis, scale reliability and validity analysis, and structural equation model analysis. The main findings are as follows: trust in websites and trust in doctors reduce the privacy concern. The privacy trade-off will not occur when trust is enough to offset the privacy concerns caused by personalized services, reciprocity norms, and other factors. Second, reciprocity norms are inevitably compulsive, which will increase privacy concerns. However, based on voluntariness, reciprocity norms can enhance user trust. Third, service quality caused by personalized services not only enhance the social rewards of users but also eliminate the privacy concern. Fourth, users' health privacy attention and information sensitivity are too high to decrease the influence of user' privacy concerns on personal health privacy information disclosure. The conclusions of this paper will help us to supplement privacy calculus theory and the application scope of the attention-based view. The proposed strategy of this article can be used to stimulate the information contribution behavior of users and improve the medical service capabilities in online health community.