Background: Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact Web 20 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence However, the relationship of the these new metrics to traditional metrics such as citations is not known Objective: (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles Methods: Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later A heuristic to predict the top-cited articles in each issue through tweet metrics was validated Results: A total of 4208 tweets cited 286 distinct JMIR articles The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 4394% of all tweets in a 60-day period) or on the following day (528/3318, 159%), followed by a rapid decay The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from 42 to 72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations A linear multivariate model with time and tweets as significant predictors (P < 001) could explain 27% of the variation of citations Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 075/007 = 1075, 95% confidence interval, 34–336) Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity Conclusions: Tweets can predict highly cited articles within the first 3 days of article publication Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact Social impact measures based on tweets are proposed to complement traditional citation metrics The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time [J Med Internet Res 2011;13(4):e123]

Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

https://faculty.ist.psu.edu/vhonavar/Papers/AJPM.pdf

Mobile health technology evaluation: The mHealth evidence workshop

[Comparison of early invasive and conservative strategies in patients with unstable coronary syndromes treated with the glycoprotein IIb/IIIa inhibitor tirofiban].

Objective This systematic review was conducted to determine user satisfaction and effectiveness of smartphone applications and text messaging interventions to promote weight reduction and physical activity. Methods Studies of smartphone applications and text messaging interventions related to the cardiovascular risk factors of physical inactivity and overweight/obesity published between January 2005 and August 2010 were eligible. Studies related to disease management were excluded. Study characteristics and results were gathered and synthesized. Results A total of 36 citations from CINAHL, EMBASE, MEDLINE, PsycINFO, and PubMed were identified; 7 articles were eligible for inclusion. The most frequent outcome measured in the studies was change in the weight of participants (57%). More than half of the studies (71%) reported statistically significant results in at least 1 outcome of weight loss, physical activity, dietary intake, decreased body mass index, decreased waist circumference, sugar-sweetened beverage intake, screen time, and satisfaction or acceptability outcomes. Conclusions All of the technology interventions that were supported by education or an additional intervention demonstrated a beneficial impact of text messaging or smartphone application for reduction of physical inactivity and/or overweight/obesity. More rigorous trials that determine what parts of the technology or intervention are effective as well as establishment of cost-effectiveness are necessary for further evaluation of smartphone and text messaging interventions.

Mobile phone interventions to increase physical activity and reduce weight: a systematic review.

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(14)60921-1.pdf

ST-segment elevation myocardial infarction in China from 2001 to 2011 (the China PEACE-Retrospective Acute Myocardial Infarction Study): a retrospective analysis of hospital data

Background: Challenges remain in translating the well-established evidence for management of cardiovascular disease (CVD) risk into clinical practice. Although electronic clinical decision support (CDS) systems are known to improve practitioner performance, their development in Australian primary health care settings is limited. Objective: Study aims were to (1) develop a valid CDS tool that assists Australian general practitioners (GPs) in global CVD risk management, and (2) preliminarily evaluate its acceptability to GPs as a point-of-care resource for both general and underserved populations. Methods: CVD risk estimation (based on Framingham algorithms) and risk-based management advice (using recommendations from six Australian guidelines) were programmed into a software package. Tool validation: Data from 137 patients attending a physician’s clinic were analyzed to compare the tool’s risk scores with those obtained from an independently programmed algorithm in a separate statistics package. The tool’s management advice was compared with a physician’s recommendations based on a manual review of the guidelines. Field test: The tool was then tested with 21 GPs from eight general practices and three Aboriginal Medical Services. Customized CDS-based recommendations were generated for 200 routinely attending patients (33% Aboriginal) using information extracted from the health record by a research assistant. GPs reviewed these recommendations during each consultation. Changes in CVD risk factor measurement and management were recorded. In-depth interviews with GPs were conducted. Results: Validation testing: The tool’s risk assessment algorithm correlated very highly with the independently programmed version in the separate statistics package (intraclass correlation coefficient 0.999). For management advice, there were only two cases of disagreement between the tool and the physician. Field test: GPs found 77% (153/200) of patient outputs easy to understand and agreed with screening and prescribing recommendations in 72% and 64% of outputs, respectively; 26% of patients had their CVD risk factor history updated; 73% had at least one CVD risk factor measured or tests ordered. For people assessed at high CVD risk (n = 82), 10% and 9%, respectively, had lipid-lowering and BP-lowering medications commenced or dose adjustments made, while 7% newly commenced anti-platelet medicines. Three key qualitative findings emerged: (1) GPs found the tool enabled a systematic approach to care; (2) the tool greatly influenced CVD risk communication; (3) successful implementation into routine care would require integration with practice software, minimal data entry, regular revision with updated guidelines, and a self-auditing feature. There were no substantive differences in study findings for Aboriginal Medical Services GPs, and the tool was generally considered appropriate for use with Aboriginal patients. Conclusions: A fully-integrated, self-populating and potentially Internet-based CDS tool could contribute to improved global CVD risk management in Australian primary health care. The findings from this study will inform a large-scale trial intervention. [J Med Internet Res 2009;11(4):e51]

An Electronic Clinical Decision Support Tool to Assist Primary Care Providers in Cardiovascular Disease Risk Management: Development and Mixed Methods Evaluation

Objective: To describe the investigation and management of patients admitted to hospitals in China with suspected acute coronary syndromes (ACS) and to identify potential areas for improvement in practice. Design: A multicentre prospective survey of sociodemographic characteristics, medical history, clinical features, in-hospital investigations, treatment practices and major events among patients with suspected ACS. Setting: Large urban public hospitals. Patients: Consecutive patients admitted to in-patient facilities with a diagnosis of suspected acute myocardial infarction (MI) or unstable angina pectoris. Main outcome measures: Myocardial infarction/re-infarction, heart failure, death. Results: Between September 2004 and May 2005, data were collected prospectively from 2973 patients admitted to 51 hospitals in 18 provinces of China. An initial diagnosis of ST elevation MI, non-ST elevation MI and unstable angina was made in 43%, 11% and 46% of patients, respectively. Diagnosis was inconsistent with objective measures in up to 20% of cases. At both tertiary and non-tertiary centres, there was little evidence that clinical risk stratification was used to determine the intensity of investigation and management. The mortality rate during hospitalisation was 5% overall and similar in tertiary and non-tertiary centres, but reported in-hospital re-infarction rates (8%) and heart failure rates (16%) were substantially higher at non-tertiary centres. Conclusion: This study has identified a number of areas in the management of ACS patients, including diagnosis and risk stratification, which deviate from current guidelines. These findings will help inform the introduction of widely used quality improvement initiatives such as clinical pathways.

https://heart.bmj.com/content/heartjnl/94/5/554.full.pdf

Patrick Groenestein

Papers

An Electronic Clinical Decision Support Tool to Assist Primary Care Providers in Cardiovascular Disease Risk Management: Development and Mixed Methods Evaluation

Prospective observational study of acute coronary syndromes in China: practice patterns and outcomes

Late thrombotic occlusion of a left internal mammary artery graft causing ST-elevation myocardial infarction.