Journal of medical Internet research
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J. Med. Internet Res. · Feb 2021
Using Tweets to Understand How COVID-19-Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study.
The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. ⋯ As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is "unhealthy" that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading.
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J. Med. Internet Res. · Feb 2021
Mobile Remote Monitoring of Intimate Partner Violence Among Pregnant Patients During the COVID-19 Shelter-In-Place Order: Quality Improvement Pilot Study.
Intimate partner violence (IPV) is one of the leading causes of pregnancy-related death. Prenatal health care providers can offer critical screening and support to pregnant people who experience IPV. During the COVID-19 shelter-in-place order, mobile apps may offer such people the opportunity to continue receiving screening and support services. ⋯ App-based screening for IPV is feasible during times when in-person access to health care providers is limited. Our results suggest that the incidence of IPV slightly increased during the shelter-in-place order. App-based screening may also address the needs of those who are unwilling or unable to share their IPV experiences with their health care provider.
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J. Med. Internet Res. · Feb 2021
Exposure to COVID-19-Related Information and its Association With Mental Health Problems in Thailand: Nationwide, Cross-sectional Survey Study.
The COVID-19 pandemic has had a negative impact on both the physical and mental health of individuals worldwide. Evidence regarding the association between mental health problems and information exposure among Thai citizens during the COVID-19 outbreak is limited. ⋯ These findings suggest that social media is the main source of COVID-19-related information. Moreover, people who are exposed to information for 3 or more hours per day are more likely to develop psychological problems, including depression, anxiety, and insomnia. Longitudinal studies investigating the long-term effects of COVID-19-related information exposure on mental health are warranted.
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J. Med. Internet Res. · Feb 2021
ReviewThe Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.
There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities-foreign-born, immigrant, refugee, and culturally marginalized-are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. ⋯ At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.
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J. Med. Internet Res. · Feb 2021
Observational StudyA Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.
Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. ⋯ The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.