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OBJECTIVE Intimate partner violence (IPV) is a serious public health concern and impacts the entire family unit, particularly children. We implemented an IPV screening and referral program in an urban pediatric emergency department (ED) and aimed to screen 30% of patient families for IPV by January 1, 2017. METHODS We used a quality improvement initiative using a nonverbal screening card to screen families when the caregiver was the sole adult present and spoke English and/or Spanish, and the patient was medically stable. Interventions included education, culture of screening initiatives, feedback, and process changes to emergency medical record (EMR) documentation. The primary outcome measure was percentage of caregivers screened in the ED over time. Our balancing measure was ED length of stay. RESULTS After process improvement implementations that include requiring IPV screen documentation in the EMR, using Research Electronic Data Capture for referrals, and standardizing and simplifying the screening process, caregiver screening rates increased to 30% and have remained consistently at or above that rate during the 15-month postevaluation phase. This intervention did not impact length of stay in the ED. CONCLUSIONS An innovative multiphase quality improvement approach to screen for IPV using a nonverbal screening card and technology within the EMR was successfully implemented in our pediatric ED. Both IPV screening and documentation rates demonstrated greatest improvement and sustainability after process improvements over other initiatives.

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.

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.

Background: Widespread metaphors contribute to the public’s understanding of health. Prior work has characterized the metaphors used to describe cancer and AIDS. Less is known about the metaphors characterizing cardiovascular disease. Objective: The objective of our study was to characterize the metaphors that Twitter users employ in discussing hypertension and diabetes. Methods: We filtered approximately 10 billion tweets for keywords related to diabetes and hypertension. We coded a random subset of 5000 tweets for the presence of metaphor and the type of metaphor employed. Results: Among the 5000 tweets, we identified 797 (15.9%) about hypertension or diabetes that employed metaphors. When discussing the development of heart disease, Twitter users described the disease as a journey (n=202), as transmittable (n=116), as an object (n=49), or as being person-like (n=15). In discussing the experience of these diseases, some Twitter users employed war metaphors (n=101). Other users described the challenge to control their disease (n=34), the disease as an agent (n=58), or their bodies as machines (n=205). Conclusions: Metaphors are used frequently by Twitter users in their discussion of hypertension and diabetes. These metaphors can help to guide communication between patients and providers to improve public health.

BACKGROUND Widespread metaphors contribute to the public’s understanding of health. Prior work has characterized the metaphors used to describe cancer and AIDS. Less is known about the metaphors characterizing cardiovascular disease. OBJECTIVE The objective of our study was to characterize the metaphors that Twitter users employ in discussing hypertension and diabetes. METHODS We filtered approximately 10 billion tweets for keywords related to diabetes and hypertension. We coded a random subset of 5000 tweets for the presence of metaphor and the type of metaphor employed. RESULTS Among the 5000 tweets, we identified 797 (15.9%) about hypertension or diabetes that employed metaphors. When discussing the development of heart disease, Twitter users described the disease as a journey (n=202), as transmittable (n=116), as an object (n=49), or as being person-like (n=15). In discussing the experience of these diseases, some Twitter users employed war metaphors (n=101). Other users described the challenge to control their disease (n=34), the disease as an agent (n=58), or their bodies as machines (n=205). CONCLUSIONS Metaphors are used frequently by Twitter users in their discussion of hypertension and diabetes. These metaphors can help to guide communication between patients and providers to improve public health.

Importance As society is increasingly becoming more networked, researchers are beginning to explore how social media can be used to study person-to-person communication about health and health care use. Twitter is an online messaging platform used by more than 300 million people who have generated several billion Tweets, yet little work has focused on the potential applications of these data for studying public attitudes and behaviors associated with cardiovascular health. Objective To describe the volume and content of Tweets associated with cardiovascular disease as well as the characteristics of Twitter users. Design, Setting, and Participants We used Twitter to access a random sample of approximately 10 billion English-language Tweets originating from US counties from July 23, 2009, to February 5, 2015, associated with cardiovascular disease. We characterized each Tweet relative to estimated user demographics. A random subset of 2500 Tweets was hand-coded for content and modifiers. Main Outcomes and Measures The volume of Tweets about cardiovascular disease and the content of these Tweets. Results Of 550 338 Tweets associated with cardiovascular disease, the terms diabetes (n = 239 989) and myocardial infarction (n = 269 907) were used more frequently than heart failure (n = 9414). Users who Tweeted about cardiovascular disease were more likely to be older than the general population of Twitter users (mean age, 28.7 vs 25.4 years; P P Conclusions and Relevance Twitter offers promise for studying public communication about cardiovascular disease.

Background. Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. Objectives. To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. Search methods. We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. Selection criteria. We searched for peer-reviewed original research studies that primarily used Twitter for health research. Data collection and analysis. Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. Main results. Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). Conclusions. We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user’s Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.