Factors and Design Features Influencing the Continued Use of Wearable Devices

The initial healthy uptake of wearable devices is not necessarily accompanied by sustained or continued use. Accordingly, this study investigates the factors influencing the continuous use of wearable devices with a particular emphasis on design features. We complemented the expectation-confirmation model (ECM) theoretical foundation with various design features such as trust, readability, dialogue support, personalization, device battery, appeal, and social support. The study employs a simultaneous mixed method research design denoted as QUANT + qual. The quantitative analysis leverages partial least squares structural equation modeling (PLS-SEM) using survey data collected from wearable device users. The qualitative analysis complements the quantitative focus of the research by providing insights into the results obtained from the quantitative analysis. We found that subjects tend to use wearables daily (60%) or several times a week (33%), and 91% plan to use them even more. Subjects indicated multiple usages for wearables. Most subjects were using wearables for healthcare and wellness (61%) or sports and fitness (54%) and had smartwatches wearable type (74%). The model explains 24.1% (p < 0.01) of the variance of continued intention to use. As a theoretical contribution, the findings support using the ECM as a theoretical foundation for explaining the continued use of wearables. Partial least squares (PLS) and qualitative data analysis highlight the relative importance that wearable users place on perceived usefulness. Most notable are tracking functions and design features such as device battery, integration with other apps/devices, dialogue support, and appeal.

A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

BACKGROUND

Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.

OBJECTIVE

This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.

METHODS

We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series.

RESULTS

The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days.

CONCLUSIONS

These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.