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COVID-19 has adversely impacted the health behaviors of billions of people across the globe, modifying their former trends in health and lifestyle. In this paper, we compare the psychosocial language markers associated with diet, physical activity, substance use, and smoking before and after the onset of COVID-19 pandemic. We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.e., subreddits) that belong to four broader groups—diet, physical activity, substance use, and smoking. We identified that before the COVID-19 pandemic, posts involved sharing information about vacation, international travel, work, family, consumption of illicit substances, vaping, and alcohol, whereas during the pandemic, posts contained emotional content associated with quarantine, withdrawal symptoms, anxiety, attempts to quit smoking, cravings, weight loss, and physical fitness. Prevalent topic analysis showed that the pandemic was associated with discussions about nutrition, physical fitness, and outdoor activities such as backpacking and biking, suggesting users’ focus shifted toward their physical health during the pandemic. Starting from the week of March 23, 2020, when several stay-at-home policies were enacted, users wrote more about coping with stress and anxiety, alcohol misuse and abuse, and harm-reduction strategies like switching from hard liquor to beer/wine after people were socially isolated. In addition, posts related to use of substances such as benzodiazepines (valium, xanax, clonazepam), nootropics (kratom, phenibut), and opioids peaked around March 23, 2020, followed by a decline. Of note, unlike the general decline observed, the volume of posts related to alternatives to heroin (e.g., fentanyl) increased during the COVID-19 pandemic. Posts about quitting smoking gained momentum after late March 2020, and there was a sharp decline in posts about craving to smoke. This study highlights the significance of studying social media discussions on platforms like Reddit which are a rich ecological source of human experiences and provide insights to inform targeted messaging and mitigation strategies, and further complement ongoing traditional primary data collection methods.

Abstract Objective The objective of this study was to investigate the differences in patient‐reported experiences related to emergency department (ED) care using a post‐discharge text messaging survey. Methods This was a prospective cohort study of patients discharged from the ED using an automated text messaging platform to assess patient experience and impact of race on ED care. The study was conducted for 7 weeks between August 6 and September 24, 2021. Participants included adults (aged ≥18 years) discharged from 2 urban, academic EDs with an active mobile phone number in the electronic health record. The primary outcome of interest was patient‐reported impact of race on overall rating of ED care. Secondary outcomes included overall satisfaction with care and perceived impact of race on components of care, including respect, communication, and quality of care. A 6‐point Likert scale was used, and chi‐square and Wilcoxon rank sum tests were used to analyze responses. Results A total of 590 (14%) discharged patients consented, and 462 patients completed the entire survey; the mean age was 43 years (SD 17.3); 67% were women, and 60.0% were Black. Black patients reported a higher overall rating of ED care (median 5 [3, 5]; P = 0.013). Proportionately, when compared with White patients, more Black patients reported that race negatively impacted the rating of care (10.8% vs 1.4%; P = 0.002). More than a quarter of Black patients (27.4%) reported race highly impacting being treated with respect (P = 0.024), and 22.4% reported a high impact on quality of service (P = 0.003) when compared with White patients. Conclusion Health systems lack methods that specifically identify patient experiences of racism. We demonstrate the feasibility of using text messaging to collect patient‐reported experiences of racism. For a significant number of Black patients, race negatively impacted their care, including communication, quality, and respect.

Abstract An understanding of healthcare super-utilizers’ online behaviors could better identify experiences to inform interventions. In this retrospective case-control study, we analyzed patients’ social media posts to better understand their day-to-day behaviors and emotions expressed online. Patients included those receiving care in an urban academic emergency department who consented to share access to their historical Facebook posts and electronic health records. Super-utilizers were defined as patients with more than six visits to the Emergency Department (ED) in a year. We compared posts by super-utilizers with a matched group using propensity scoring based on age, gender and Charlson comorbidity index. Super-utilizers were more likely to post about confusion and negativity (D = .65, 95% CI-[.38, .95]), self-reflection (D = .63 [.35, .91]), avoidance (D = .62 [.34, .90]), swearing (D = .52 [.24, .79]), sleep (D = .60 [.32, .88]), seeking help and attention (D = .61 [.33, .89]), psychosomatic symptoms, (D = .49 [.22, .77]), self-agency (D = .56 [.29, .85]), anger (D = .51, [.24, .79]), stress (D = .46, [.19, .73]), and lonely expressions (D = .44, [.17, .71]). Insights from this study can potentially supplement offline community care services with online social support interventions considering the high engagement of super-utilizers on social media.

Background Google and Apple’s Exposure Notifications System (ENS) was developed early in the COVID-19 pandemic to complement existing contact tracing efforts while protecting user privacy. An analysis by the Associated Press released in December 2020 estimated approximately 1 in 14 people had downloaded apps in states one was available. In this study, we assessed the motivation and experience of individuals who downloaded ENS apps from the Google Play and Apple App Stores. Methods We collected review text, star rating, and date of rating for all the reviews on ENS apps in the Google Play and Apple App stores. We extracted the relative frequency of single words and phrases from reviews and created an open vocabulary language, with themes categorized by the research team, to study the salient themes around reviews with high (3–5 stars), neutral (3 stars), and negative (1–2 stars) ratings using logistic regression. Results Of 7622 reviews obtained from 26 states between 04/07/2020 to 03/31/2021, 6364 were from Google Play Store, and 1258 were from Apple App Store. We obtained reviews for a total of 38 apps, with 25 apps from the Google Play Store and 13 apps from the Apple Play Store. 78% of the reviews are either 1 star or 5 stars. Positive reviews were driven by ease of use, support for the state government in creating the app, and encouragement for others to download, as well as engage in other COVID-19 precautions. Negative and neutral reviews focused on issues with app functionality (i.e., installation and tracking errors). Conclusions Uptake was the largest barrier to success for ENS apps, but states can use insight from app store reviews to better position themselves if they choose to develop further public health apps.

Background Online platforms are used to manage aspects of our lives including health outside clinical settings. Little is known about the effectiveness of using online platforms to manage hypertension. We assessed effects of tweeting/retweeting cardiovascular health content by individuals with poorly controlled hypertension on systolic blood pressure (SBP) and patient activation. Methods and Results We conducted this 2‐arm randomized controlled trial. Eligibility included diagnosis of hypertension; SBP >140 mm Hg; and an existing Twitter account or willingness to create one to follow study Twitter account. Intervention arm was asked to tweet/retweet health content 2×/week using a specific hashtag for study duration (6 months). The main measures include primary outcome change in SBP; secondary outcome point change in Patient Activation Measure (PAM). We remotely recruited and enrolled 611 participants, mean age 52 (SD, 11.7). Mean baseline SBP for the intervention group was 155.8 and for control was 155.6. At 6 months, mean SBP for intervention group was 137.6 and for control was 135.7. Mean change in SBP from baseline to 6 months for the intervention group was −18.5 and for control was −19.8 (P=0.48). Mean PAM at baseline for the intervention group was 70.3 for control was 72.7. At 6 months, mean PAM scores were 71.1 (intervention) and 75.6 (control). Mean change in PAM score for the intervention group was 0.0 and for control was 3.3 (P=0.12). Conclusions Recruiting and engaging patients and collecting outcome measures remotely are feasible using Twitter. Encouraging patients with poorly controlled hypertension to tweet or retweet health content on Twitter did not improve SBP or PAM score at 6 months. Registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT02622256.

Abstract Background Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients’ perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use. Methods Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types’ health relatedness. Factor analysis was used to identify the structure in patients’ perceptions of willingness to share different digital data, and their health relatedness. Results Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know. Conclusion Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

Background Current cardiac arrest guidelines have limited high‐quality scientific evidence to support recommendations for care. The quality of scientific evidence on which guidelines are based may correlate with improved patient outcomes and meaningful survival. We sought to develop a prioritized list of knowledge gaps in resuscitation to assist researchers, policy makers, and funding agencies in their decision‐making process. Methods and Results A 4‐stage modified Delphi method was used with a panel of cardiac arrest experts. Experts addressed the prompt: “What are the top 3 gaps in knowledge involving cardiac arrest care that should be research priorities for National Institutes of Health/American Heart Association funding to have the greatest impact on public health?” Knowledge gaps were identified in the initial round, rated in a second round, and rank ordered in the third round, and they underwent final review and consensus (final round). The outcome was 10 knowledge gaps, with prioritization of the top 3 gaps. A total of 61 gaps, with 19 distinct themes, were identified by participants. The 10 knowledge gaps most likely to affect public health identified by the expert panel included, in order, the following: telecommunicator cardiopulmonary resuscitation, hemodynamic monitoring for goal‐directed resuscitation, reasons why bystanders fail to respond, optimization of postarrest care, out‐of‐hospital cardiac arrest identification and response, individualizing resuscitation strategies, predicting patients at risk, tools for neuroprognostication, optimal airway management, and optimizing educational strategies. Conclusions Ten priorities for cardiac arrest research were identified, but consensus was not reached on the prioritized top 3. Future research should address these gaps to potentially improve resuscitation guideline evidence quality.

BackgroundIn‐hospital cardiac arrest (IHCA) is a major public health problem with significant mortality. A better understanding of where IHCA occurs in hospitals (intensive care unit [ICU] versus monitored ward [telemetry] versus unmonitored ward) could inform strategies for reducing preventable deaths. Methods and ResultsThis is a retrospective study of adult IHCA events in the Get with the Guidelines—Resuscitation database from January 2003 to September 2010. Unadjusted analyses were used to characterize patient, arrest, and hospital‐level characteristics by hospital location of arrest (ICU versus inpatient ward). IHCA event rates and outcomes were plotted over time by arrest location. Among 85 201 IHCA events at 445 hospitals, 59% (50 514) occurred in the ICU compared to 41% (34 687) on the inpatient wards. Compared to ward patients, ICU patients were younger (64±16 years versus 69±14; P

Background Healthcare worker (HCW) anxiety and depression worsened during the pandemic, prompting the expansion of digital mental health platforms as potential solutions offering online assessments, access to resources and counselling. The use of these digital engagement tools may reflect tendencies and trends for the mental health needs of HCWs.Objectives This retrospective, cross-sectional study investigated the association between the use of an online mental health platform within a large academic health system and measures of that system’s COVID-19 burden during the first 3 years of the pandemic.Methods The study investigated the use of Cobalt, an online mental health platform, comprising deidentified mental health assessments and utilisation metrics. Cobalt, serves as an online mental health resource broadly available to health system employees, offering online evidence-based tools, coaching, therapy options and asynchronous content (podcasts, articles, videos and more). The analyses use validated mental health assessments (Generalised Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and post-traumatic stress disorder (PTSD)) alongside publicly available COVID-19 data. Statistical analyses employed univariate linear regression with Stata SE Statistical Software.Results Between March 2020 and March 2023, 43 308 independent user sessions were created on Cobalt, a majority being anonymous sessions (72%, n=31 151). Mental health assessments, including PHQ-4, PHQ-9, GAD-7 and primary care-PTSD, totalled 9462 over the time period. Risk for self-harm was noted in 17.1% of PHQ-9 assessments. Additionally, 4418 appointments were scheduled with mental health counsellors and clinicians. No significant associations were identified between COVID-19 case burden and Cobalt utilisation or assessment scores.Conclusion Cobalt emerged as an important access point for assessing the collective mental health of the workforce, witnessing increased engagement over time. Notably, the study indicates the nuanced nature of HCW assessments of anxiety, depression and PTSD, with mental health scores reflecting moderate decreases in depression and anxiety but signalling potential increases in PTSD. Tailored resources are imperative, acknowledging varied mental health needs within the healthcare workforce. Ultimately, this investigation lays the groundwork for continued exploration of the impact and effectiveness of digital platforms in supporting HCW mental health.

BackgroundBurnout and the mental health burden of the COVID-19 pandemic have disproportionately impacted health care workers. The links between state policies, federal regulations, COVID-19 case counts, strains on health care systems, and the mental health of health care workers continue to evolve. The language used by state and federal legislators in public-facing venues such as social media is important, as it impacts public opinion and behavior, and it also reflects current policy-leader opinions and planned legislation. ObjectiveThe objective of this study was to examine legislators’ social media content on Twitter and Facebook throughout the COVID-19 pandemic to thematically characterize policy makers’ attitudes and perspectives related to mental health and burnout in the health care workforce. MethodsLegislators’ social media posts about mental health and burnout in the health care workforce were collected from January 2020 to November 2021 using Quorum, a digital database of policy-related documents. The total number of relevant social media posts per state legislator per calendar month was calculated and compared with COVID-19 case volume. Differences between themes expressed in Democratic and Republican posts were estimated using the Pearson chi-square test. Words within social media posts most associated with each political party were determined. Machine-learning was used to evaluate naturally occurring themes in the burnout- and mental health–related social media posts. ResultsA total of 4165 social media posts (1400 tweets and 2765 Facebook posts) were generated by 2047 unique state and federal legislators and 38 government entities. The majority of posts (n=2319, 55.68%) were generated by Democrats, followed by Republicans (n=1600, 40.34%). Among both parties, the volume of burnout-related posts was greatest during the initial COVID-19 surge. However, there was significant variation in the themes expressed by the 2 major political parties. Themes most correlated with Democratic posts were (1) frontline care and burnout, (2) vaccines, (3) COVID-19 outbreaks, and (4) mental health services. Themes most correlated with Republican social media posts were (1) legislation, (2) call for local action, (3) government support, and (4) health care worker testing and mental health. ConclusionsState and federal legislators use social media to share opinions and thoughts on key topics, including burnout and mental health strain among health care workers. Variations in the volume of posts indicated that a focus on burnout and the mental health of the health care workforce existed early in the pandemic but has waned. Significant differences emerged in the content posted by the 2 major US political parties, underscoring how each prioritized different aspects of the crisis.

COVID-19 has adversely impacted the health behaviors of billions of people across the globe, modifying their former trends in health and lifestyle. In this paper, we compare the psychosocial language markers associated with diet, physical activity, substance use, and smoking before and after the onset of COVID-19 pandemic. We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.e., subreddits) that belong to four broader groups-diet, physical activity, substance use, and smoking. We identified that before the COVID-19 pandemic, posts involved sharing information about vacation, international travel, work, family, consumption of illicit substances, vaping, and alcohol, whereas during the pandemic, posts contained emotional content associated with quarantine, withdrawal symptoms, anxiety, attempts to quit smoking, cravings, weight loss, and physical fitness. Prevalent topic analysis showed that the pandemic was associated with discussions about nutrition, physical fitness, and outdoor activities such as backpacking and biking, suggesting users' focus shifted toward their physical health during the pandemic. Starting from the week of March 23, 2020, when several stay-at-home policies were enacted, users wrote more about coping with stress and anxiety, alcohol misuse and abuse, and harm-reduction strategies like switching from hard liquor to beer/wine after people were socially isolated. In addition, posts related to use of substances such as benzodiazepines (valium, xanax, clonazepam), nootropics (kratom, phenibut), and opioids peaked around March 23, 2020, followed by a decline. Of note, unlike the general decline observed, the volume of posts related to alternatives to heroin (e.g., fentanyl) increased during the COVID-19 pandemic. Posts about quitting smoking gained momentum after late March 2020, and there was a sharp decline in posts about craving to smoke. This study highlights the significance of studying social media discussions on platforms like Reddit which are a rich ecological source of human experiences and provide insights to inform targeted messaging and mitigation strategies, and further complement ongoing traditional primary data collection methods.

BackgroundIncorporating insights from social media into the patient-provider encounter is increasingly being explored in health care settings. Less is known about the utility of these data in mental health therapy. ObjectiveThis study aims to prospectively investigate and characterize how social media and digital data are used in mental health therapy from both the patient and mental health therapist perspective. MethodsPatients enrolled in mental health therapy and mental health therapists were interviewed using a semistructured interview guide. All interviews were transcribed and coded using a deductive framework analysis. Themes and subthemes were identified. Participants completed a sociodemographic survey, while mental health therapists also completed a behavioral norms and elicitation survey. ResultsSeventeen participants, that is, 8 (48%) mental health therapists and 9 (52%) patients were interviewed. Overall, participants identified 4 themes and 9 subthemes. Themes were current data collection practices, social media and digital data in therapy, advantages of social media and digital data in therapy, and disadvantages of social media and digital data in therapy. Most subthemes were related to the advantages and disadvantages of incorporating digital data in mental health therapy. Advantage subthemes included convenience, objective, builds rapport, and user-friendliness while disadvantage subthemes were nonreflective, ethically ambiguous, and nongeneralizable. The mental health therapists' behavioral norms and elicitation survey found that injunctive and descriptive normative beliefs mapped onto 2 advantage subthemes: convenience and objectivity. ConclusionsThis qualitative pilot study established the advantages and disadvantages of social media and digital data use in mental health therapy. Patients and therapists highlighted similar concerns and uses. This study indicated that overall, both patients and therapists are interested in and are comfortable to use and discuss social media and digital data in mental health therapy.

BackgroundThe quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. ObjectiveThe aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. MethodsWe identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. ResultsA total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P

BackgroundThe COVID-19 pandemic has placed strains on communities. During this public health crisis, health systems have created remote methods of monitoring symptom progression and delivering care virtually. ObjectiveUsing an SMS text message-based system, we sought to build and test a remote model to explore community needs, connect individuals to curated resources, and facilitate community health worker intervention when needed during the pandemic. The primary aims of this pilot study were to establish the feasibility (ie, engagement with the text line) and acceptability (ie, participant ratings of resources and service) of delivering automated well-being resources via smartphone technology. MethodsEligible patients (aged 18 years or older, having a cell phone with SMS text messaging capability, and recently visited the emergency department) were identified using the electronic health record. The patients were consented to enroll and begin receiving COVID-19–related information and links to community resources. We collected open-ended and close-ended resource and mood ratings. We calculated the frequencies and conducted a thematic review of the open-ended responses. ResultsIn 7 weeks, 356 participants were enrolled; 13,917 messages were exchanged including 333 resource ratings (mean 4) and 673 well-being scores (mean 6.8). We received and coded 386 open-ended responses, most of which elaborated upon their self-reported mood score (29%). Overall, 77% (n=274) of our participants rated the platform as a service they would highly recommend to a family member or friend. ConclusionsThis approach is designed to broaden the reach of health systems, tailor to community needs in real time, and connect at-risk individuals with robust community health support.

We sought to evaluate whether there was variability in language used on social media across different time points of pregnancy (before, during, and after pregnancy, as well as by trimester and parity). Consenting patients shared access to their individual Facebook posts and electronic medical records. Random forest models trained on Facebook posts could differentiate first trimester of pregnancy from 3 months before pregnancy (F1 score = .63) and from a random 3-month time period (F1 score = .64). Posts during pregnancy were more likely to include themes about family (β = .22), food craving (β = .14), and date/times (β = .13), while posts 3 months prior to pregnancy included themes about social life (β = .30), sleep (β = .31), and curse words (β = .27), and 3 months post-pregnancy included themes of gratitude (β = .17), health appointments (β = .21), and religiosity (β = .18). Users who were pregnant for the first time were more likely to post about lack of sleep (β = .15), activities of daily living (β = .09), and communication (β = .08) compared with those who were pregnant after having a child who posted about others’ birthdays (β = .16) and life events (.12). A better understanding about social media timelines can provide insight into lifestyle choices that are specific to pregnancy.

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.