Social Disparities in Online Health-Related Activities and Social Support: Findings from Health Information National Trends Survey

2021 ◽  
pp. 1-12
Author(s):  
Soeun Yang ◽  
Chul-joo Lee ◽  
Jiyen Beak
Keyword(s):  
Social Support ◽  
Health Information ◽  
Social Disparities ◽  
Health Related ◽  
National Trends

Prevalence and Correlates of US Adult Public Opinion on Restricting Junk Food Advertising to Children on Social Media: 2020 Health Information National Trends Survey

2021 ◽  
pp. 1-13
Author(s):  
Ariella R. Korn ◽  
Kelly D. Blake ◽  
Heather D’Angelo ◽  
Jill Reedy ◽  
April Oh
Keyword(s):  
Social Media ◽  
Health Information ◽  
Normal Weight ◽  
Food Advertising ◽  
Junk Food ◽  
Cross Sectional ◽  
Health Related ◽  
Advertising To Children ◽  
National Trends ◽  
Advertising Restrictions

Abstract Objective: To describe US adults’ levels of support, neutrality, and opposition to restricting junk food advertising to children on social media and explore associations with sociodemographic and health-related characteristics. Design: In 2020-2021, we used cross-sectional data from the National Cancer Institute’s 2020 Health Information National Trends Survey to estimate the prevalence of opinions toward advertising restrictions, and correlates of neutrality and opposition using weighted multivariable logistic regression. Setting: United States. Participants: Adults aged 18+ years. Results: Among the analytic sample (n=2852), 54% of adults were neutral or opposed to junk food advertising restrictions on social media. The odds of being neutral or opposed were higher among Non-Hispanic Black adults (vs non-Hispanic White; OR: 2.03 (95% CI: 1.26, 3.26)); those completing some college (OR: 1.68 (95% CI: 1.20, 2.34)) or high school or less (OR: 2.62 (95% CI: 1.74, 3.96)) (vs those with a college degree); those who were overweight (vs normal weight; OR: 1.42 (95% CI: 1.05, 1.93)); and those reporting a moderate (OR: 1.45 (95% CI: 1.13, 1.88)) or conservative (OR: 1.71 (95% CI: 1.24, 2.35)) political viewpoint (vs liberal). Having strong (vs weaker) weight and diet-related cancer beliefs was associated with 53% lower odds of being neutral or opposed to advertising restrictions (OR: 0.47 (95% CI: 0.36, 0.61)). Conclusions: This study identified subgroups of US adults for whom targeted communication strategies may increase support for policies to improve children’s food environment.

Is Social Support From Family Associated With PSA Testing? An Exploratory Analysis Using the Health Information National Trends Survey (HINTS) 2005

2008 ◽  
Vol 4 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Kamilah B. Thomas ◽  
Sean L. Simpson ◽  
Will L. Tarver ◽  
Clement K. Gwede
Keyword(s):  
Prostate Cancer ◽  
Social Support ◽  
Health Information ◽  
Specific Antigen ◽  
White Men ◽  
The United States ◽  
Social Contexts ◽  
Psa Testing ◽  
Instrumental Social Support ◽  
National Trends

African American and White men have the highest rates of prostate cancer in the United States. Families represent important social contexts within which illness occurs.The purpose of this study is to explore whether prostate-specific antigen (PSA) testing is associated with instrumental and informational social support from family members among a sample of Black and White men aged 45 and older. Data from the 2005 Health Information National Trends Survey were analyzed using logistic regression. The dependent variable was having a PSA test within the past year or less. The independent variables consisted of selected demographic and family informational and instrumental social support variables. The statistically significant variables included age and having a family member with cancer. Additional studies to elucidate the mechanisms of social support from family for prostate cancer are needed.

scholarly journals Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data (Preprint)

2019 ◽  
Author(s):  
Camella J Rising ◽  
Roxanne E Jensen ◽  
Richard P Moser ◽  
April Oh
Keyword(s):  
Health Behaviors ◽  
Health Information ◽  
Mobile Health ◽  
Technology Use ◽  
Older Age ◽  
Health Apps ◽  
Behavioral Tracking ◽  
The Us ◽  
Health Related ◽  
National Trends

BACKGROUND Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences. OBJECTIVE The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns. METHODS We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals. RESULTS Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33). CONCLUSIONS Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.

scholarly journals Social and Demographic Patterns of Health-Related Internet Use Among Adults in the United States: A Secondary Data Analysis of the Health Information National Trends Survey

2020 ◽  
Vol 17 (18) ◽  
pp. 6856 ◽  
Author(s):  
Rose Calixte ◽  
Argelis Rivera ◽  
Olutobi Oridota ◽  
William Beauchamp ◽  
Marlene Camacho-Rivera
Keyword(s):  
Social Media ◽  
Health Information ◽  
Information Seeking ◽  
Internet Use ◽  
Secondary Data ◽  
The United States ◽  
The Internet ◽  
Health Related ◽  
Demographic Patterns ◽  
National Trends

National surveys of U.S. adults have observed significant increases in health-related internet use (HRIU), but there are documented disparities. The study aims to identify social and demographic patterns of health-related internet use among U.S. adults. Using data from the Health Information National Trends Survey (HINTS) 4 cycle 3 and HINTS 5 cycle 1, we examined HRIU across healthcare, health information seeking, and participation on social media. Primary predictors were gender, race/ethnicity, age, education, income, and nativity with adjustments for smoking and survey year. We used multivariable logistic regression with survey weights to identify independent predictors of HRIU. Of the 4817 respondents, 43% had used the internet to find a doctor; 80% had looked online for health information. Only 20% had used social media for a health issue; 7% participated in an online health support group. In multivariable models, older and low SES participants were significantly less likely to use the internet to look for a provider, use the internet to look for health information for themselves or someone else, and less likely to use social media for health issues. Use of the internet for health-related purposes is vast but varies significantly by demographics and intended use.

scholarly journals Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data

10.2196/16299 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16299 ◽  
Author(s):  
Camella J Rising ◽  
Roxanne E Jensen ◽  
Richard P Moser ◽  
April Oh
Keyword(s):  
Health Behaviors ◽  
Health Information ◽  
Mobile Health ◽  
Technology Use ◽  
Older Age ◽  
Health Apps ◽  
Behavioral Tracking ◽  
The Us ◽  
Health Related ◽  
National Trends

Background Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences. Objective The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns. Methods We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals. Results Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33). Conclusions Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.

Perceived Level of Interest in Health Research among Those Who View Health-Related YouTube Videos: A Secondary Data Analysis from the Health Information National Trends Survey (Preprint)

2019 ◽  
Author(s):  
Joyce Balls-Berry ◽  
Emily Valentin-Méndez ◽  
Ian Marigi ◽  
Liaa Ferede ◽  
Numra Bajwa ◽  
...  
Keyword(s):  
Health Information ◽  
Patient Engagement ◽  
Primary Source ◽  
Secondary Data ◽  
Sociodemographic Characteristics ◽  
Or Education ◽  
Health Related ◽  
Youtube Videos ◽  
Patient Engagement In Research ◽  
National Trends

BACKGROUND More and more, people are using internet resources, such as YouTube, as a primary source of health-related information. While evidence exists of how this behavior affects the patient-physician relationship and the clinician perspective, it is still uncertain how it affects patient engagement in research. OBJECTIVE The aims of this study were to (1) determine if an association exists between watching health-related YouTube videos and being interested in patient engagement in research and (2) explore if any associations exist between sociodemographic characteristics, health-related YouTube use, and interest in patient engagement in research. METHODS We analyzed data from the 2013 Health Information National Trends Survey (n = 3039). Our independent variable of interest was whether individuals had watched health-related videos in the las 12 months; our dependent variable of interest was whether respondents were interested in patient engagement in research. Analysis included bivariate analyses and multivariate logistic regression modeling between sociodemographic characteristics, YouTube viewing, and being interested in patient engagement in research. RESULTS Interest in patient engagement in research was significantly associated with watching a health-related video on YouTube, after adjustment for relevant covariates. Individuals who watched a health-related video on YouTube, had a 2.11-fold increased odds ratio of being interested in patient engagement in research, compared to those who did not watch health-related videos (OR = 2.11, 95% CI = 1.40, 3.18, P <.001). We did not find any statistically significant associations between being interested in patient engagement in research and gender, age, race/ethnicity, or education. CONCLUSIONS YouTube has the potential to be used as a tool to increase interest in patient engagement in research. Future studies could use YouTube to evaluate its effectivity promoting participation in research of underrepresented communities.

scholarly journals Cancer Caregiving and Health Information Technology: An Analysis of the Health Information National Trends (HINTS) Survey

2021 ◽  
Author(s):  
Victoria R. Nelson ◽  
Katharine M. Mitchell ◽  
Bree E Holtz
Keyword(s):  
Health Information ◽  
Technology Use ◽  
Sociodemographic Factors ◽  
Relationship Type ◽  
Technological Support ◽  
Caregiver Health ◽  
Significant Difference ◽  
Health Related ◽  
National Trends ◽  
General Technology

Abstract Purpose: In this paper, we explore how health technology use impacts informal caregivers’ health and how sociodemographic factors are related, using the Health Information National Trends Survey (HINTS). Methods: Data for this study were obtained from the National Cancer Institutes’ Health Information and National Trends Survey (HINTS 5, Cycle 2, 2018). Participants for the current study were chosen based on their response to one question related to their caregiving status. The sample size was 483 respondents. Variables of interest included caregiver relationship type, general technology use, portal use, and overall health status. Results: The results indicate that there was not a significant difference of caregiving role on portal usage, [F(5,99) = .975, p = .44, η2 = .049], and technology use [F(7, 462)=2.625, p=.01]. This demonstrates that those caregiving for a child are more likely to use technology for health related issues. There was not a significant effect of portal use on caregiver health. However, there was a significant effect of technology use on overall health (t = 2.074, p=.04). There was also a significant effect of demographics on general technology use [F(7, 434)= 14.858, p < .001]. Demonstrating as education and income increases, technology use also increases, and as age increases technology use decreases. Conclusion: This study affirmed that demographic inequalities can negatively impact technology and portal use, which could reduce the burden on caregivers. Therefore, it is important to work to engage cancer patients and their caregivers with technological support and resources.

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