Search

BackgroundPeople from underserved communities such as those from lower socioeconomic positions or racial and ethnic minority groups are often disproportionately targeted by the tobacco industry, through the relatively high levels of tobacco retail outlets (TROs) located in their neighborhood or protobacco marketing and promotional strategies. It is difficult to capture the smoking behaviors of individuals in actual locations as well as the extent of exposure to tobacco promotional efforts. With the high ownership of smartphones in the United States—when used alongside data sources on TRO locations—apps could potentially improve tobacco control efforts. Health apps could be used to assess individual-level exposure to tobacco marketing, particularly in relation to the locations of TROs as well as locations where they were most likely to smoke. To date, it remains unclear how health apps could be used practically by health promotion organizations to better reach underserved communities in their tobacco control efforts. ObjectiveThis study aimed to demonstrate how smartphone apps could augment existing data on locations of TROs within underserved communities in Massachusetts and Texas to help inform tobacco control efforts. MethodsData for this study were collected from 2 sources: (1) geolocations of TROs from the North American Industry Classification System 2016 and (2) 95 participants (aged 18 to 34 years) from underserved communities who resided in Massachusetts and Texas and took part in an 8-week study using location tracking on their smartphones. We analyzed the data using spatial autocorrelation, optimized hot spot analysis, and fitted power-law distribution to identify the TROs that attracted the most human traffic using mobility data. ResultsParticipants reported encountering protobacco messages mostly from store signs and displays and antitobacco messages predominantly through television. In Massachusetts, clusters of TROs (Dorchester Center and Jamaica Plain) and reported smoking behaviors (Dorchester Center, Roxbury Crossing, Lawrence) were found in economically disadvantaged neighborhoods. Despite the widespread distribution of TROs throughout the communities, participants overwhelmingly visited a relatively small number of TROs in Roxbury and Methuen. In Texas, clusters of TROs (Spring, Jersey Village, Bunker Hill Village, Sugar Land, and Missouri City) were found primarily in Houston, whereas clusters of reported smoking behaviors were concentrated in West University Place, Aldine, Jersey Village, Spring, and Baytown. ConclusionsSmartphone apps could be used to pair geolocation data with self-reported smoking behavior in order to gain a better understanding of how tobacco product marketing and promotion influence smoking behavior within vulnerable communities. Public health officials could take advantage of smartphone data collection capabilities to implement targeted tobacco control efforts in these strategic locations to reach underserved communities in their built environment.

Recent advances in the collection and processing of health data from multiple sources at scale—known as big data—have become appealing across public health domains. However, present discussions often do not thoroughly consider the implications of big data or health informatics in the context of continuing health disparities. The 2 key objectives of this paper were as follows: first, it introduced 2 main problems of health big data in the context of health disparities—data absenteeism (lack of representation from underprivileged groups) and data chauvinism (faith in the size of data without considerations for quality and contexts). Second, this paper suggested that health organizations should strive to go beyond the current fad and seek to understand and coordinate efforts across the surrounding societal-, organizational-, individual-, and data-level contexts in a realistic manner to leverage big data to address health disparities.

The advancement of health apps and wearables has garnered substantial academic attention, particularly in examining why individuals decide to use or not use them. In response to the extensive body of research on this topic, we conducted a scoping review of 61 articles published from 2007 to 2022, aiming to examine the dominant theoretical frameworks and antecedents of health apps and wearables use. The findings demonstrated that the dominant theoretical frameworks within this domain were rooted in the human-computer interaction theories, notably the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Next, our review identified four levels of antecedents: technological, individual, societal, and policy. At the technological level, emphasis was placed on functionality, reliability, and technological infrastructure. Individual antecedents encompassed socio-demographics, personality traits, cognitive responses to benefits and risks, emotional and affective responses, self-efficacy, and digital literacy. Societal antecedents highlighted the role of social networks and social norms, while policy antecedents elaborated on laws, regulations, and guidelines that encouraged health technology adoption. Our discussion illuminated that the evolving trend of theoretical frameworks in health apps and wearables use research, initially rooted in human-computer interaction, is progressively moving toward more comprehensive perspectives. We further underscored the importance of delving into societal and policy antecedents, which often are overshadowed by the more commonly discussed technological and individual factors. In conclusion, we advocated for a multi-stakeholder collaborative network approach, as this would enable communication researchers to understand the use of health apps and wearables more comprehensively. [ABSTRACT FROM AUTHOR]

Trust in scientific actors and institutions in the United States is at an all-time low. At the same time, studies show that people use social media for science information and become increasingly vulnerable to COVID-19-related misinformation. Yet, we do not know whether low levels of trust cause people to turn to social media in the first place. We argue that if people do not trust the scientific information provided by scientists, they are likely to turn to social media to get alternative scientific information (rather than relying on mainstream sources to get pandemic-related information), which in turn can increase susceptibility to COVID-19 misperceptions. Based on two-wave U.S. panel data, we found that distrust in science drives the uses of social media for COVID-19 information, and reliance on social media for COVID-19 information increases susceptibility to COVID-19 misperceptions. [ABSTRACT FROM AUTHOR]

Drawing upon the social amplification of risk (SARF) and the issue-attention cycle framework, we examined the amplification of COVID-19 risk-related tweets through (a) topics: key interests of discussion; (b) temperament: emotions of tweets; (c) topography (i.e., location); and (d) temporality (i.e., over time). We computationally analyzed 1,641,273 tweets, and conducted manual content analysis on a subset of 6,000 tweets to identify how topics, temperament, and topography of COVID-19 tweets were associated with risk amplification – retweet and favorite count – using negative binomial regression. We found 11 dominant COVID-19 topics—health impact, economic impact, reports of lockdowns, report of new cases, the need to stay home, coping with COVID-19, news about President Trump, government support, fight with COVID-19 by nongovernment entities, origins, and preventive measure in our corpus of tweets across the issue-attention cycle. The negative binomial regression results showed that at the pre-problem stage, topics on President Trump, speculation of origins, and initiatives to fight COVID-19 by non-government entities were most likely to be amplified, underscoring the inherent politicization of COVID-19 and erosion of trust in governments from the start of the pandemic. We also found that while tweets with negative emotions were consistently amplified throughout the issue-attention cycle, surprisingly tweets with positive emotions were amplified during the height of the pandemic – this counter-intuitive finding indicated signs of premature and misplaced optimism. Finally, our results showed that the locations of COVID-19 tweet amplification corresponded to the shifting COVID-19 hotspots across different continents across the issue-attention cycle. Theoretical and practical implications of risk amplification on social media are discussed. [ABSTRACT FROM AUTHOR]

While health apps and wearables are often touted to promote individuals' physical and mental health, a fundamental question that remains unanswered is how individuals from low socioeconomic status (SES) groups benefit from using these digital health technologies. This study draws upon two theoretical frameworks – an extended technology acceptance model and communication inequality to answer this research question. A nationally representative sample of 906 Singaporeans was administered, and data were analysed using hierarchical multiple regression. First, the results showed that factors at both the technological- (e.g. perceived usefulness and design aesthetics) and societal-level (e.g. descriptive and injunctive norms) contributed to closing the gap in health apps and wearables use between higher and lower SES groups. Second, using health apps and wearables was positively associated with physical and mental health and they can also bridge gaps in social well-being between the higher and lower SES groups; Third, the use of health apps and wearables was driven by multifaceted factors across technological-, individual-, and societal-levels, with hedonic motivation having the strongest positive association with both intention and actual use. Finally, theoretical and practical implications were discussed. [ABSTRACT FROM AUTHOR]