Background With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

Background Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

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Background A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders’ willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36–1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89–7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37–2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16–2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03–3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing.

BACKGROUND Wearable healthcare devices offer tremendous promise to improve the health and well-being of individuals. Despite growing popularity of wearable healthcare devices, we have limited understanding about the actual use of these devices by US adults, and the key factors affecting the use. OBJECTIVE 1. To examine the usage patterns of wearable healthcare devices (use of wearable healthcare devices, frequency of their use and willingness to share health data from wearable with a provider) 2. To assess the associations between a set of predictors that pertain to personal demographics (age, gender, race, education, marital status and household income), individual's health (general health, presence of chronic conditions, weight perceptions and frequency of provider visits), technology self-efficacy and attitude towards exercise, with use of wearable healthcare device. METHODS We use data from National Cancer Institute’s Health Information National Trends Survey–5, Cycle 3 (HINTS), collected from January to April 2019 to examine our research questions. Multivariate logistic regression model is used to assess the associations between predictor variables and wearable use. RESULTS About 30% US adults use wearable healthcare devices. Among the users, nearly half (47.33%) use the devices every day with a majority (82.38%) willing to share the health data from wearables with their care providers. Women (16.25%), Whites (19.74%), adults aged between 18-50 (19.52%), those with some level of college education or college graduates (25.6%), and annual household incomes above $75K (17.66%) were most likely to report using wearable healthcare devices. We found that use of wearables declines with age: adults aged over 50 are less likely to use wearables as compared to those who are aged between 18-34 (Odds ratio OR values between 0.46 to 0.57. Women (OR = 1.26; 95% CI 0.96 -1.65), Whites (OR = 1.65; 95% CI 0.97 - 2.79), college graduates (OR = 1.05; 95% CI 0.31-3.51), and those annual household incomes above $75K (OR = 2.6; 95% CI 1.39 - 4.86 ) are more likely to use wearables. US adults who felt healthier (OR = 1.17; 95% CI 0.98 - 1.39), overweight (OR = 1.16; 95% CI = 1.06-1.27), enjoyed exercise (OR = 1.23; 95% CI 1.06 - 1.43) and those with higher levels of technology self-efficacy (OR = 1.33; 95% CI 1.21 - 1.46) are more likely to adopt and use wearables for tracking or monitoring their health . CONCLUSIONS The potential of healthcare wearable devices is under realized with less than one-third of US adults actively using it. With only young, healthier, wealthier, educated, techno-literate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers and healthcare policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

Abstract-The current research paper proposes two models for the determinants of E-banking Loyalty for large business customers. The results demonstrated that five main quality dimensions were identified for the e-banking portals: assurance, reliability, convenience and quality monitoring by the financial director of the company. The results also confirm that e-banking quality has a strong impact on e-banking loyalty via the mediating effect of e-trust and switching costs have strong a impact on e-banking loyalty. Further our findings also suggest that interpersonal relationships generated between the bank manager and the decision maker of the company have a strong impact on the perceptions of service quality in online banking, and will also affect positively E-Banking Loyalty.

Purpose -The purpose of this paper is to provide a conceptual overview of the process of unsolicited commercial e-mail (UCE), propose a typology of UCE, and delineate key stakeholders of UCE, their roles and potential responses through a stakeholder analysis. Design/methodology/approach -Based on the extant literature, this paper provides a conceptualization of the UCE process, delineating specific types of UCE. It uses stakeholder analysis to identify key members in the UCE process and the potential roles to be played by them in combating UCE. Findings -This paper proposes a four-way typology of the UCE process, identifies key stakeholders, and also mechanisms for tackling UCE.Research limitations/applications -Given the limited empirical research on this topic, this paper is exploratory in nature, integrating concepts from marketing and e-commerce research streams. It will provide a solid conceptual foundation for future empirical research on UCE. Practical implications -This paper will be a useful resource for customers, Internet merchants, policy makers, direct marketing associations, agencies and consumer awareness groups that are working on Internet security, privacy and anti-spam issues. Originality/value -This paper addresses an important and timely issue, filling an important gap in current research on e-mail marketing. It provides conceptual foundations on UCE, and deploys stakeholder analysis to suggest useful guidelines for practice.