applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts. ©Burkhardt Funk, Shiri Sadeh-Sharvit, Ellen E Fitzsimmons-Craft, Mickey Todd Trockel, Grace E Monterubio, Neha J Goel, Katherine N Balantekin, Dawn M Eichen, Rachael E Flatt, Marie-Laure Firebaugh, Corinna Jacobi, Andrea K Graham, Mark Hoogendoorn, Denise E Wilfley, C Barr Taylor. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 19.02.2020.BACKGROUND Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. OBJECTIVE This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse-related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. METHODS We employed an iterative annotation strategy, with interannotator discussions held and update, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). CONCLUSIONS Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks. ©Karen O’Connor, Abeed Sarker, Jeanmarie Perrone, Graciela Gonzalez Hernandez. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 26.02.2020.BACKGROUND Previous data have validated the benefit of digital health interventions (DHIs) on weight loss in patients following acute coronary syndrome entering cardiac rehabilitation (CR). OBJECTIVE The primary purpose of this study was to test the hypothesis that increased DHI use, as measured by individual log-ins, is associated with improved weight loss. Secondary analyses evaluated the association between log-ins and activity within the platform and exercise, dietary, and medication adherence. METHODS We obtained DHI data including active days, total log-ins, tasks completed, educational modules reviewed, medication adherence, and nonmonetary incentive points earned in patients undergoing standard CR following acute coronary syndrome. Linear regression followed by multivariable models were used to evaluate associations between DHI log-ins and weight loss or dietary adherence. RESULTS Participants (n=61) were 79% male (48/61) with mean age of 61.0 (SD 9.7) years. We found a significant positive association of total log-ins during CR with weight loss (r2=.10, P=.03). Educational modules viewed (r2=.11, P=.009) and tasks completed (r2=.10, P=.01) were positively significantly associated with weight loss, yet total log-ins were not significantly associated with differences in dietary adherence (r2=.05, P=.12) or improvements in minutes of exercise per week (r2=.03, P=.36). CONCLUSIONS These data extend our previous findings and demonstrate increased DHI log-ins portend improved weight loss in patients undergoing CR after acute coronary syndrome. DHI adherence can potentially be monitored and used as a tool to selectively encourage patients to adhere to secondary prevention lifestyle modifications. TRIAL REGISTRATION ClinicalTrials.gov (NCT01883050); https//clinicaltrials.gov/ct2/show/NCT01883050. ©R Jay Widmer, Conor Senecal, Thomas G Allison, Francisco Lopez-Jimenez, Lilach O Lerman, Amir Lerman. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 26.02.2020.BACKGROUND With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. Ruboxistaurin research buy The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. OBJECTIVE Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. METHODS A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the kee three possible trends in the future development of the cloud health care field. ©Dongxiao Gu, Xuejie Yang, Shuyuan Deng, Changyong Liang, Xiaoyu Wang, Jiao Wu, Jingjing Guo. Originally published in the Journal of Medical Internet Research (http//www.jmir.org), 25.02.2020.BACKGROUND HIV remains a significant health issue in the United States and disproportionately affects African Americans. African American women living with HIV (AAWH) experience a particularly high number of barriers when attempting to manage their HIV care, including antiretroviral therapy (ART) adherence. To enable the development and assessment of effective interventions that address these barriers to support ART adherence, there is a critical need to understand more fully the use of objective measures of ART adherence among AAWH, including electronic medication dispensers for real-time surveillance. OBJECTIVE This study aimed to evaluate the use of the Wisepill medication event-monitoring system (MEMS) and compare the objective and subjective measures of ART adherence. METHODS We conducted a 30-day exploratory pilot study of the MEMS among a convenience sample of community-dwelling AAWH (N=14) in rural Florida. AAWH were trained on the use of the MEMS to determine the feasibility of collecting, capturing, and manipulating the MEMS data for an objective measure of ART adherence.