Associations Between Characteristics of Adolescent and Young Adult Cancer Survivors and Their Use of Different Social Media Platforms: An Observational Study of Social Media Accounts.

Background: The growing number of adolescent and young adult (AYA) cancer survivors and their unmet needs demand innovative communication and care strategies. This study uses social media data to examine how survivors' demographic and clinical characteristics relate to their social media use. Methods: Data from 300 AYA cancer survivors on six social media sites (YouTube, Instagram, Facebook, TikTok, Reddit, X/formerly Twitter) were collected between August 2022 and March 2023 and analyzed using descriptive statistics and statistical tests (chi-square, Fisher's exact, Welch, Games-Howell post-hoc, logistic regression). Results: Significant differences were observed across platforms for mean current age (p < 0.001) and age at diagnosis (p < 0.001). We also found significant associations between social media type used and current age, age at diagnosis, years since diagnosis, and the timing of social media account creation. AYAs who created their social media account post-diagnosis were less likely to use YouTube (p = 0.003) and more likely to use Facebook (p = 0.009). Treatment completion was associated with increased use of platform X (p = 0.004). Non-White individuals in our sample were less likely to use Facebook (p = 0.008). Significant associations were found between observed sex and platform usage (p < 0.001), with males more likely to use Reddit (p < 0.001) and X (p < 0.001). Conclusions: Significant associations were found between demographic and clinical attributes of AYA cancer survivors and the type of social media they used, suggesting that AYA-specific social media-based interventions should be tailored to subgroup characteristics (e.g., social media type, developmental stage based on age at diagnosis and current age, sex).

Using Patient-Reported Health Data From Social Media to Identify Diverse Lupus Patients and Assess Their Symptom and Medication Expressions: A Feasibility Study.

OBJECTIVE: Patient communities use social media for peer support and information seeking. This study assessed the feasibility of using public patient-generated health data from the social network Twitter to identify diverse lupus patients and gather their perspectives about disease symptoms and medications. METHODS: We extracted public lupus-related Twitter messages (n = 47,715 tweets) in English posted by users (n = 8,446) in the US between September 1, 2017 and October 31, 2018. We analyzed the data to describe lupus patients and the expressed themes (symptoms and medications). Two independent coders analyzed the data; Cohen's kappa coefficient was used to ensure interrater reliability. Differences in symptom and medication expressions were analyzed using 2-tailed Z tests and a combination of 1-way analysis of variance tests and unpaired t-tests. RESULTS: We found that lupus patients on Twitter are diverse in gender and race: approximately one-third (34.64%, 62 of 179) were persons of color (POCs), and 85.47% were female. The expressed disease symptoms and medications varied significantly by gender and race. Most of our findings correlated with documented clinical observations, e.g., expressions of general pain (8.39%, 709 of 8,446), flares (6.05%, 511 of 8,446), and fatigue (4.18%, 353 of 8,446). However, our data also revealed less well-known patient observations, e.g., possible racial disparities within ocular manifestations of lupus. CONCLUSION: Our results indicate that social media surveillance can provide valuable data of clinical relevance from the perspective of lupus patients. The medical community has the opportunity to harness this information to inform the patient-centered care within underrepresented patient groups, such as POCs.

Increasing Uptake of Depression Screening and Treatment Guidelines in Cardiac Patients: A Behavioral and Implementation Science Approach to Developing a Theory-Informed, Multilevel Implementation Strategy.

BACKGROUND: Depression leads to poor health outcomes in patients with coronary heart disease (CHD). Despite guidelines recommending screening and treatment of depressed patients with CHD, few patients receive optimal care. We applied behavioral and implementation science methods to (1) identify generalizable, multilevel barriers to depression screening and treatment in patients with CHD and (2) develop a theory-informed, multilevel implementation strategy for promoting guideline adoption. METHODS: We conducted a narrative review of barriers to depression screening and treatment in patients with CHD (ie, medications, exercise, cardiac rehabilitation, or therapy) comprising data from 748 study participants. Informed by the behavior change wheel framework and Expert Recommendations for Implementing Change, we defined multilevel target behaviors, characterized determinants (capability, opportunity, motivation), and mapped barriers to feasible, acceptable, and equitable intervention functions and behavior change techniques to develop a multilevel implementation strategy, targeting health care systems/providers and patients. RESULTS: We identified implementation barriers at the system/provider level (eg, Capability: knowledge; Opportunity: workflow integration; Motivation: ownership) and patient level (eg, Capability: knowledge; Opportunity: mobility; Motivation: symptom denial). Acceptable, feasible, and equitable intervention functions included education, persuasion, environmental restructuring, and enablement. Expert Recommendations for Implementing Change strategies included learning collaborative, audit, feedback, and educational materials. The final multicomponent strategy (iHeart DepCare) for promoting depression screening/treatment included problem-solving meetings with clinic staff (system); educational/motivational videos, electronic health record reminders/decisional support (provider); and a shared decision-making (electronic shared decision-making) tool with several functions for patients, for example, patient activation, patient treatment selection support. CONCLUSIONS: We applied implementation and behavioral science methods to identify implementation barriers and to develop a multilevel implementation strategy for increasing uptake of depression screening and treatment in patients with CHD as a use case. The multilevel implementation strategy will be evaluated in a future hybrid II effectiveness-implementation trial.

Levelling the playing field of RMD research across Europe to address patients' needs: the emerging EULAR Research Centre.

Herein, we describe the Research Centre launched by the European Alliance of Associations for Rheumatology (EULAR) in 2020. The Centre aims to facilitate collaborative research on rheumatic and musculoskeletal diseases (RMD) across Europe. RMDs disable millions of people in Europe and worldwide. Despite progress with improved therapeutics and strategic interventions in several RMDs, there are no cures, and their collective impact remains substantial. Access to RMD-related care, policies prioritizing RMDs, and related research, education, training, and funding differ significantly across European countries. Building a new equipoise in opportunity and capacity across Europe will facilitate optimal understanding of those different factors that influence the epidemiology, pathogenesis, treatment, and outcomes in RMDs. The EULAR Research Centre aims to address the significant barriers to accelerating RMD research across Europe. It provides an RMD research roadmap of unmet needs, expert services, infrastructures, networks, research resources, training, education, and mentoring. It will place RMD research in the ideal position to benefit from forthcoming remarkable advances in digital, biological, and social science anticipated in the coming decades.

Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study.

BACKGROUND: Failure to find and attract clinical trial participants remains a persistent barrier to clinical research. Researchers increasingly complement recruitment methods with social media-based methods. We hypothesized that user-generated data from cancer survivors and their family members and friends on the social network Twitter could be used to identify, engage, and recruit cancer survivors for cancer trials. OBJECTIVE: This pilot study aims to examine the feasibility of using user-reported health data from cancer survivors and family members and friends on Twitter in Los Angeles (LA) County to enhance clinical trial recruitment. We focus on 6 cancer conditions (breast cancer, colon cancer, kidney cancer, lymphoma, lung cancer, and prostate cancer). METHODS: The social media intervention involved monitoring cancer-specific posts about the 6 cancer conditions by Twitter users in LA County to identify cancer survivors and their family members and friends and contacting eligible Twitter users with information about open cancer trials at the University of Southern California (USC) Norris Comprehensive Cancer Center. We reviewed both retrospective and prospective data published by Twitter users in LA County between July 28, 2017, and November 29, 2018. The study enrolled 124 open clinical trials at USC Norris. We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. RESULTS: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. CONCLUSIONS: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561.

General Practice and Digital Methods to Recruit Stroke Survivors to a Clinical Mobility Study: Comparative Analysis.

BACKGROUND: Participant recruitment remains a barrier to conducting clinical research. The disabling nature of a stroke, which often includes functional and cognitive impairments, and the acute stage of illness at which patients are appropriate for many trials make recruiting patients particularly complex and challenging. In addition, people aged 65 years and older, which includes most stroke survivors, have been identified as a group that is difficult to reach and is commonly underrepresented in health research, particularly clinical trials. Digital media may provide effective tools to support enrollment efforts of stroke survivors in clinical trials. OBJECTIVE: The objective of this study was to compare the effectiveness of general practice (traditional) and digital (online) methods of recruiting stroke survivors to a clinical mobility study. METHODS: Recruitment for a clinical mobility study began in July 2018. Eligible study participants included individuals 18 years and older who had a single stroke and were currently ambulatory in the community. General recruiting practice included calling individuals listed in a stroke registry, contacting local physical therapists, and placing study flyers throughout a university campus. Between May 21, 2019, and June 26, 2019, the study was also promoted digitally using the social network Facebook and the search engine marketing tool Google AdWords. The recruitment advertisements (ads) included a link to the study page to which users who clicked were referred. Primary outcomes of interest for both general practice and digital methods included recruitment speed (enrollment rate) and sample characteristics. The data were analyzed using the Lilliefors test, the Welch two-sample t test, and the Mann-Whitney test. Significance was set at P=.05. All statistical analyses were performed in MATLAB 2019b. RESULTS: Our results indicate that digital recruitment methods can address recruitment challenges regarding stroke survivors. Digital recruitment methods allowed us to enroll study participants at a faster rate (1.8 participants/week) compared to using general practice methods (0.57 participants/week). Our findings also demonstrate that digital and general recruitment practices can achieve an equivalent level of sample representativeness. The characteristics of the enrolled stroke survivors did not differ significantly by age (P=.95) or clinical scores (P=.22; P=.82). Comparing the cost-effectiveness of Facebook and Google, we found that the use of Facebook resulted in a lower cost per click and cost per enrollee per ad. CONCLUSIONS: Digital recruitment can be used to expedite participant recruitment of stroke survivors compared to more traditional recruitment practices, while also achieving equivalent sample representativeness. Both general practice and digital recruitment methods will be important to the successful recruitment of stroke survivors. Future studies could focus on testing the effectiveness of additional general practice and digital media approaches and include robust cost-effectiveness analyses. Examining the effectiveness of different messaging and visual approaches tailored to culturally diverse and underrepresented target subgroups could provide further data to move toward evidence-based recruitment strategies.

Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study.

BACKGROUND: Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the United States. Social media provides a platform for patients to find rheumatologists and peers and build awareness of the condition. Researchers have suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their health care. OBJECTIVE: This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the United States between September 1, 2017 and October 31, 2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their health care. METHODS: This is a mixed methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We used Symplur Signals, a health care social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (eg, gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their health care. RESULTS: This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. The institutional review board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to "lupus" from users in the United States published in English between September 1, 2017 and October 31, 2018. We included 40,885 posts in the analysis. Data analysis was completed in Fall 2020. CONCLUSIONS: The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and implementing related health education interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15716.

Correction: General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis.

[This corrects the article DOI: 10.2196/24429.].

The impact of the COVID-19 pandemic on people with rheumatic and musculoskeletal diseases: insights from patient-generated data on social media.

OBJECTIVES: During the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD). METHODS: A convenience sample of Twitter messages in English posted by people with RMDs was extracted between 1 March and 12 July 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (ankylosing spondylitis, RA, PsA, lupus/SLE and gout). RESULTS: The analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (i) lack of understanding of SARS-CoV-2/COVID-19; (ii) critical changes in health behaviour; (iii) challenges in healthcare practice and communication with healthcare professionals; (iv) difficulties with access to medical care; (v) negative impact on physical and mental health, coping strategies; (vi) issues around work participation; (vii) negative effects of the media; and (viii) awareness-raising. CONCLUSION: The findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided 'early signals' of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication and inform clinical practice decisions.

Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis.

BACKGROUND: Psoriasis is an autoimmune disease estimated to affect more than 6 million adults in the United States. It poses a significant public health problem and contributes to rising health care costs, affecting people's quality of life and ability to work. Previous research showed that nontreatment and undertreatment of patients with psoriasis remain a significant problem. Perspectives of patients toward seeking psoriasis treatment are understudied. Social media offers a new data source of user-generated content. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. OBJECTIVE: The objective of this study is to conduct a content analysis of Twitter posts (in English) published by users in the United States between February 1, 2016, and October 31, 2018, to examine perspectives that potentially influence the treatment decision among patients with psoriasis. METHODS: User-generated Twitter posts that include keywords related to psoriasis will be analyzed using text classifiers to identify themes related to the research questions. We will use Symplur Signals, a health care social media analytics platform, to access the Twitter data. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among people with psoriasis. RESULTS: This study is supported by the National Center for Advancing Translational Science through a Clinical and Translational Science Award award. Study approval was obtained from the institutional review board at the University of Southern California. Data extraction and cleaning are complete. For the time period from February 1, 2016, to October 31, 2018, we obtained 95,040 Twitter posts containing terms related to "psoriasis" from users in the United States published in English. After removing duplicates, retweets, and non-English tweets, we found that 75.51% (52,301/69,264) of the psoriasis-related posts were sent by commercial or bot-like accounts, while 16,963 posts were noncommercial and will be included in the analysis to assess the patient perspective. Analysis was completed in Summer 2020. CONCLUSIONS: This protocol paper provides a detailed description of a social media research project including the process of data extraction, cleaning, and analysis. It is our goal to contribute to the development of more transparent social media research efforts. Our findings will shed light on whether Twitter provides a promising data source for garnering patient perspective data about psoriasis treatment decisions. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of psoriasis and treatment options among patients and implementing related health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13731.

General Audience Engagement With Antismoking Public Health Messages Across Multiple Social Media Sites: Comparative Analysis.

BACKGROUND: Public health organizations have begun to use social media to increase awareness of health harm and positively improve health behavior. Little is known about effective strategies to disseminate health education messages digitally and ultimately achieve optimal audience engagement. OBJECTIVE: This study aims to assess the difference in audience engagement with identical antismoking health messages on three social media sites (Twitter, Facebook, and Instagram) and with a referring link to a tobacco prevention website cited in these messages. We hypothesized that health messages might not receive the same user engagement on these media, although these messages were identical and distributed at the same time. METHODS: We measured the effect of health promotion messages on the risk of smoking among users of three social media sites (Twitter, Facebook, and Instagram) and disseminated 1275 health messages between April 19 and July 12, 2017 (85 days). The identical messages were distributed at the same time and as organic (unpaid) and advertised (paid) messages, each including a link to an educational website with more information about the topic. Outcome measures included message engagement (ie, the click-through rate [CTR] of the social media messages) and educational website engagement (ie, the CTR on the educational website [wCTR]). To analyze the data and model relationships, we used mixed effects negative binomial regression, z-statistic, and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: Comparisons between social media sites showed that CTRs for identical antitobacco health messages differed significantly across social media (P<.001 for all). Instagram showed the statistically significant highest overall mean message engagement (CTR=0.0037; 95% CI 0.0032-0.0042), followed by Facebook (CTR=0.0026; 95% CI 0.0022-0.0030) and Twitter (CTR=0.0015; 95% CI 0.0013-0.0017). Facebook showed the highest as well as the lowest CTR for any individual message. However, the message CTR is not indicative of user engagement with the educational website content. Pairwise comparisons of the social media sites differed with respect to the wCTR (P<.001 for all). Messages on Twitter showed the lowest CTR, but they resulted in the highest level of website engagement (wCTR=0.6308; 95% CI 0.5640-0.6975), followed by Facebook (wCTR=0.2213; 95% CI 0.1932-0.2495) and Instagram (wCTR=0.0334; 95% CI 0.0230-0.0438). We found a statistically significant higher CTR for organic (unpaid) messages (CTR=0.0074; 95% CI 0.0047-0.0100) compared with paid advertisements (CTR=0.0022; 95% CI 0.0017-0.0027; P<.001 and P<.001, respectively). CONCLUSIONS: Our study provides evidence-based insights to guide the design of health promotion efforts on social media. Future studies should examine the platform-specific impact of psycholinguistic message variations on user engagement, include newer sites such as Snapchat and TikTok, and study the correlation between web-based behavior and real-world health behavior change. The need is urgent in light of increased health-related marketing and misinformation on social media.

Fall-related functional impairments in patients with neurological gait disorder.

Falls are common in patients with neurological disorders and are a primary cause of injuries. Nonetheless, fall-associated gait characteristics are poorly understood in these patients. Objective, quantitative gait analysis is an important tool to identify the principal fall-related motor characteristics and to advance fall prevention in patients with neurological disorders. Fall incidence was assessed in 60 subjects with different neurological disorders. Patients underwent a comprehensive set of functional assessments including instrumented gait analysis, computerized postural assessments and clinical walking tests. Determinants of falls were assessed by binary logistic regression analysis and receiver operator characteristics (ROC). The best single determinant of fallers was a step length reduction at slow walking speed reaching an accuracy of 67.2% (ROC AUC: 0.669; p = 0.027). The combination of 4 spatio-temporal gait parameters including step length and parameters of variability and asymmetry were able to classify fallers and non-fallers with an accuracy of 81.0% (ROC AUC: 0.882; p < 0.001). These findings suggest significant differences in specific spatio-temporal gait parameters between fallers and non-fallers among neurological patients. Fall-related impairments were mainly identified for spatio-temporal gait characteristics, suggesting that instrumented, objective gait analysis is an important tool to estimate patients' fall risk. Our results highlight pivotal fall-related walking deficits that might be targeted by future rehabilitative interventions that aim at attenuating falls.

Public Response to a Social Media Tobacco Prevention Campaign: Content Analysis.

BACKGROUND: Prior research suggests that social media-based public health campaigns are often targeted by countercampaigns. OBJECTIVE: Using reactance theory as the theoretical framework, this research characterizes the nature of public response to tobacco prevention messages disseminated via a social media-based campaign. We also examine whether agreement with the prevention messages is associated with comment tone and nature of the contribution to the overall discussion. METHODS: User comments to tobacco prevention messages, posted between April 19, 2017 and July 12, 2017, were extracted from Twitter, Facebook, and Instagram. Two coders categorized comments in terms of tone, agreement with message, nature of contribution, mentions of government agency and regulation, promotional or spam comments, and format of comment. Chi-square analyses tested associations between agreement with the message and tone of the public response and the nature of contributions to the discussions. RESULTS: Of the 1242 comments received (Twitter: n=1004; Facebook: n=176; Instagram: n=62), many comments used a negative tone (42.75%) and disagreed with the health messages (39.77%), while the majority made healthy contributions to the discussions (84.38%). Only 0.56% of messages mentioned government agencies, and only 0.48% of the comments were antiregulation. Comments employing a positive tone (84.13%) or making healthy contributions (69.11%) were more likely to agree with the campaign messages (P=0.01). Comments employing a negative tone (71.25%) or making toxic contributions (36.26%) generally disagreed with the messages (P=0.01). CONCLUSIONS: The majority of user comments in response to a tobacco prevention campaign made healthy contributions. Our findings encourage the use of social media to promote dialogue about controversial health topics such as smoking. However, toxicity was characteristic of comments that disagreed with the health messages. Managing negative and toxic comments on social media is a crucial issue for social media-based tobacco prevention campaigns to consider.

Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis.

BACKGROUND: Systemic lupus erythematosus (SLE) is the most common form of lupus. It is a chronic autoimmune disease that predominantly affects women of reproductive age, impacting contraception, fertility, and pregnancy. Although clinic-based studies have contributed to an increased understanding of reproductive health care needs of patients with SLE, misinformation abounds and perspectives on reproductive health issues among patients with lupus remain poorly understood. Social networks such as Twitter may serve as a data source for exploring how lupus patients communicate about their health issues, thus adding a dimension to enrich our understanding of communication regarding reproductive health in this unique patient population. OBJECTIVE: The objective of this study is to conduct a content analysis of Twitter data published by users in English in the United States from September 1, 2017, to October 31, 2018, in order to examine people's perspectives on reproductive health among patients with lupus. METHODS: This study will analyze user-generated posts that include keywords related to lupus and reproductive health from Twitter. To access public Twitter user data, we will use Symplur Signals, a health care social media analytics platform. Text classifiers will be used to identify topics in posts. Posts will be classified manually into the a priori and emergent categories. Based on the information available in a user's Twitter profile (ie, username, description, and profile image), we will further attempt to characterize the user who generated the post. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among patients with lupus. RESULTS: This study has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. The Institutional Review Board at the University of Southern California approved the study (HS-18-00912). Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to "lupus" from users in the United States, published in English between September 1, 2017, and October 31, 2018. We will include 40,885 posts in the analysis, which will be completed in fall 2020. This study was supported by funds from the has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. CONCLUSIONS: The findings from this study will provide pilot data on the use of Twitter among patients with lupus. Our findings will shed light on whether Twitter is a promising data source for learning about reproductive health issues expressed among patients with lupus. The data will also help to determine whether Twitter can serve as a potential outreach platform for raising awareness of lupus and reproductive health and for implementing relevant health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15623.

Corrigendum to "The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials" [Contemp. Clin. Trials Commun. 16 (2019) 100443].

[This corrects the article DOI: 10.1016/j.conctc.2019.100443.].

Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study.

BACKGROUND: Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users' perspectives in the development of ethical norms and regulations. OBJECTIVE: This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. METHODS: We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. RESULTS: While nearly half the respondents-on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)-indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents' general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents' Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people's concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. CONCLUSIONS: The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines.

The significant cost of systematic reviews and meta-analyses: A call for greater involvement of machine learning to assess the promise of clinical trials.

BACKGROUND: More than 90% of clinical-trial compounds fail to demonstrate sufficient efficacy and safety. To help alleviate this issue, systematic literature review and meta-analysis (SLR), which synthesize current evidence for a research question, can be applied to preclinical evidence to identify the most promising therapeutics. However, these methods remain time-consuming and labor-intensive. Here, we introduce an economic formula to estimate the expense of SLR for academic institutions and pharmaceutical companies. METHODS: We estimate the manual effort involved in SLR by quantifying the amount of labor required and the total associated labor cost. We begin with an empirical estimation and derive a formula that quantifies and describes the cost. RESULTS: The formula estimated that each SLR costs approximately $141,194.80. We found that on average, the ten largest pharmaceutical companies publish 118.71 and the ten major academic institutions publish 132.16 SLRs per year. On average, the total cost of all SLRs per year to each academic institution amounts to $18,660,304.77 and for each pharmaceutical company is $16,761,234.71. DISCUSSION: It appears that SLR is an important, but costly mechanisms to assess the totality of evidence. CONCLUSIONS: With the increase in the number of publications, the significant time and cost of SLR may pose a barrier to their consistent application to assess the promise of clinical trials thoroughly. We call on investigators and developers to develop automated solutions to help with the assessment of preclinical evidence particularly. The formula we introduce provides a cost baseline against which the efficiency of automation can be measured.

A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research.

BACKGROUND: Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population. Nearly 70% of US adults use some social media. However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations. OBJECTIVE: The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments. METHODS: The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary. The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI. To test the tool's utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter. We conducted a functional correctness analysis of the generated social media messages to assess the algorithm's ability to produce the expected output for each input. We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error. The percent correct was calculated to determine the probability with which the tool generates accurate messages. RESULTS: The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017). It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques. CONCLUSIONS: A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction. The tool has the potential to support social media-enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public health organizations with a tool to increase their output of health education messages and manage user comments. We call on readers to use and develop the tool and to contribute to evidence-based communication methods in the digital age.