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.

Clinical indications for medicinal cannabis include chronic conditions; thus users (MCUs) are at an increased risk of morbidity and mortality resulting from SARS-CoV-2 infection (COVID-19). The stu...

Purpose: In the United States, over 2 million cases of COVID-19 cases have been identified and more than 100,000 lives have been lost. While COVID-19 related disparities among those with chronic conditions have been observed, research regarding the uptake of COVID-related preventive behaviors is scarce. Methods: We utilized data from a sample of 2190 U.S. adults from the COVID-19 Impact Survey to examine associations between the presence of underlying chronic health conditions and COVID-19-related preventive behaviors (e.g., use of face masks, hand washing, social distancing, etc.). We used multivariable logistic regression models to model associations between COVID-19 preventive behaviors across demographic and health characteristics. Results: Adults with cardiometabolic disease were more likely to report staying home because they felt unwell, compared with individuals without cardiometabolic disease. Individuals with underlying respiratory conditions were more likely to work from home, compared with individuals without a respiratory condition. Adults with immune conditions were twice more likely to report wearing a face mask when compared with individuals without immune conditions. Conclusion: This study provides U.S. national prevalence estimates and differences in adherence to COVID-19 preventive behaviors among those with and without the presence of underlying chronic health conditions. The prevalence of key preventive measures was high in the overall sample. Yet, engagement in COVID-19-related preventive behaviors varied significantly across chronic disease conditions. Messages around continued maintenance of the behaviors should be reinforced. Study implications suggest a need for more targeted messaging and resources available for individuals with certain underlying chronic conditions.

Background: Adults with chronic conditions are disproportionately burdened by COVID-19 morbidity and mortality. Although COVID-19 mobile health (mHealth) apps have emerged, research on attitudes toward using COVID-19 mHealth tools among those with chronic conditions is scarce. Objective: This study aimed to examine attitudes toward COVID-19, identify determinants of COVID-19 mHealth tool use across demographic and health-related characteristics, and evaluate associations between chronic health conditions and attitudes toward using COVID-19 mHealth tools (eg, mHealth or web-based methods for tracking COVID-19 exposures, symptoms, and recommendations). Methods: We used nationally representative data from the COVID-19 Impact Survey collected from April to June 2020 (n=10,760). Primary exposure was a history of chronic conditions, which were defined as self-reported diagnoses of cardiometabolic, respiratory, immune-related, and mental health conditions and overweight/obesity. Primary outcomes were attitudes toward COVID-19 mHealth tools, including the likelihood of using (1) a mobile phone app to track COVID-19 symptoms and receive recommendations; (2) a website to track COVID-19 symptoms, track location, and receive recommendations; and (3) an app using location data to track potential COVID-19 exposure. Outcome response options for COVID-19 mHealth tool use were extremely/very likely, moderately likely, or not too likely/not likely at all. Multinomial logistic regression was used to compare the likelihood of COVID-19 mHealth tool use between people with different chronic health conditions, with not too likely/not likely at all responses used as the reference category for each outcome. We evaluated the determinants of each COVID-19 mHealth intervention using Poisson regression. Results: Of the 10,760 respondents, 21.8% of respondents were extremely/very likely to use a mobile phone app or a website to track their COVID-19 symptoms and receive recommendations. Additionally, 24.1% of respondents were extremely/very likely to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19. After adjusting for age, race/ethnicity, sex, socioeconomic status, and residence, adults with mental health conditions were the most likely to report being extremely/very or moderately likely to use each mHealth intervention compared to those without such conditions. Adults with respiratory-related chronic diseases were extremely/very (conditional odds ratio 1.16, 95% CI 1.00-1.35) and moderately likely (conditional odds ratio 1.23, 95% CI 1.04-1.45) to use a mobile phone app to track their location and receive push notifications about whether they have been exposed to COVID-19. Conclusions: Our study demonstrates that attitudes toward using COVID-19 mHealth tools vary widely across modalities (eg, web-based method vs app) and chronic health conditions. These findings may inform the adoption of long-term engagement with COVID-19 apps, which is crucial for determining their potential in reducing disparities in COVID-19 morbidity and mortality among individuals with chronic health conditions.

Background: The COVID-19 pandemic has disrupted the continuity of care of U.S. adults living with chronic diseases, including immunocompromised adults. Disruption in care may be a barrier to identifying COVID-19 associated sequelae, such as mental health symptoms, among the immunocompromised. Our objectives were (1) to evaluate COVID-19-related preventive behaviors, with a focus on canceling doctor’s appointments as a proxy for continuity of care, and (2) to compare COVID-19-related mental health symptoms among the immunocompromised with the general population. Methods: We used nationally-representative data of 10,760 U.S. adults from the publicly-available COVID-19 Household Impact Survey. We defined immunocompromised as adults with a self-reported diagnosis of “a compromised immune system” (n=854,7.6%). We compared adherence to self-reported COVID-19 preventive behaviors among immunocompromised adults compared to others using χ2-tests. We focused on continuity of care and estimated determinants of canceling doctor’s appointments among the immunocompromised using multivariable Poisson regression to estimate adjusted prevalence ratios (aPR) with 95% confidence intervals (95% CI). We evaluated associations of mental health symptoms with being immunocompromised using multinomial logistic regression. Results: Immunocompromised adults were more likely to adhere to recommended COVID-19 preventive behaviors including washing or sanitizing hands (96.3% vs. 89.8%,χ2<0.001), maintaining social distance (91.9% vs. 83.7%,χ2<0.001), and canceling a doctor’s appointment (47.1% vs. 29.7%,χ2<0.001). Hispanic adults (aPR:1.47,95% CI:1.12-1.92) and immunocompromised women (aPR:1.25,95% CI:1.00-1.56) were more likely to cancel doctor’s appointments compared to NH-White adults and men, respectively. Immunocompromised adults reported higher odds of feeling nervous/anxious/on edge (cOR:1.89,95% CI: 1.44-2.51), depressed (cOR: 2.81,95% CI: 2.17-3.64), lonely (cOR: 2.28,95% CI: 1.74-2.98), and hopeless (cOR: 2.86,95% CI: 2.21-3.69) 3-7 days in the last week. Conclusion: Immunocompromised adults were more likely to cancel their doctor’s appointments and report COVID19-related mental health symptoms. The continuity of care of immunocompromised adults should be prioritized via alternative interventions, such as telehealth.

Background: The development and use of mobile health (mHealth) apps for asthma management have risen dramatically over the past two decades. Asthma apps vary widely in their content and features; however, prior research has rarely examined preferences of users of publicly available apps. Objective: The goals of this study were to provide a descriptive overview of asthma mobile apps that are publicly available and to assess the usability of asthma apps currently available on the market to identify content and features of apps associated with positive and negative user ratings. Methods: Reviews were collected on June 23, 2020, and included publicly posted reviews until June 21, 2020. To characterize features associated with high or low app ratings, we first dichotomized the average user rating of the asthma app into 2 categories: a high average rating and a low average rating. Asthma apps with average ratings of 4 and above were categorized as having a high average rating. Asthma apps with average ratings of less than 4 were categorized as having a low average rating. For the sentiment analysis, we modeled both 2-word (bi-gram) and 3-word (tri-gram) phrases which commonly appeared across highly rated and lowly rated apps. Results: Of the 10 apps that met the inclusion criteria, a total of 373 reviews were examined across all apps. Among apps reviewed, 53.4% (199/373) received high ratings (average ratings of 4 or 5) and 47.2% (176/373) received low ratings (average ratings of 3 or less). The number of ratings across all apps ranged from 188 (AsthmaMD) to 10 (My Asthma App); 30% (3/10) of apps were available on both Android and iOS. From the sentiment analysis, key features of asthma management that were common among highly rated apps included the tracking of peak flow readings (n=48), asthma symptom monitoring (n=11), and action plans (n=10). Key features related to functionality that were common among highly rated apps included ease of use (n=5). Users most commonly reported loss of data (n=14) and crashing of app (n=12) as functionality issues among poorly rated asthma apps. Conclusions: Our study results demonstrate that asthma app quality, maintenance, and updates vary widely across apps and platforms. These findings may call into question the long-term engagement with asthma apps, a crucial factor for determining their potential to improve asthma self-management and asthma clinical outcomes.