A content analysis of twitter backlash to Georgia's abortion ban.

OBJECTIVE: Georgia's House Bill 481: The Living Infants Fairness and Equality Act (LIFE Act), which prohibits abortion after cardiac activity is detected, was signed into law on May 7, 2019. The bill's proponents framed the ban to protect the "unborn." However, less is known about the framing used by abortion rights supporters to oppose the bill in the context of social media. The aim of this study is to systematically examine the content of Twitter to investigate how users organically engaged in activism in response to the passage of HB 481. We contend that understanding this opposition is critical for activists, healthcare providers, and policymakers interested in mobilizing public support for maintaining legal access to abortion care. METHODS: A coding team of 6 researchers conducted a combined a priori and iterative thematic content analysis of 583 #HB481 tweets to systematically examine how social media users framed their responses to HB 481. Tweets were coded for full group consensus. RESULTS: Opposition to HB 481 was framed around three major themes: mobilizing political action, emphasizing a collective gender identity, and general rejection of the bill. Fourteen subthemes are contained in these major themes, which reflect the nuanced and multifaceted ways abortion rights supporters presented their opposition to the bill. CONCLUSION: Twitter users invoked emotional reactions and drew on their experiences to promote a collective identity that called attention to systemic gender oppression and urged widespread mobilization to oppose HB 481.

Performance of Atrial Fibrillation Detection in a New Single-Chamber ICD.

BACKGROUND: Patients with implantable cardioverter defibrillators (ICDs) often have a history of atrial fibrillation (AF) or will develop AF after device implant. Optimal management of ICD patients includes early diagnosis of AF and monitoring of AF burden. We evaluated the performance of an algorithm for monitoring AF in single-chamber ICDs. METHODS: The RR interval variability-based detection algorithm determines RR variability by creating a Lorenz plot of the change in RR intervals for the most recent interval pair versus the previous interval pair. A new plot is created every 2 minutes and the AF evidence score of the plot is computed. Patient RR interval data from several Holter databases were pooled to test the performance of the AF detection algorithm. RESULTS: In total, 187 recordings from 186 patients were used to assess the performance of the AF detection algorithm integrated into a single-chamber ICD by comparing the ICD detection results to the Holter annotated truth. Thirty-five of 186 patients had a total of 94 AF episodes in their Holter recordings lasting a total of 250.5 hours (mean episode duration 7.2 hours). The generalized estimating equations-adjusted estimate of episode sensitivity was 94.8% with 95% lower confidence limit of 87.2%. Gross duration sensitivity was 95.0% for AF episodes of at least 6 minutes duration with gross duration specificity of 99.6%. CONCLUSION: This RR interval-based AF detection algorithm performs well with high sensitivity and specificity. Integration of this algorithm into single-chamber ICDs will help monitor and detect AF, thus facilitating optimal therapy to prevent AF-related sequelae.