Background: Coronary heart disease (CHD) is the leading cause of death and disability among American women. The prevalence of CHD is expected to increase by more than 40% by 2035. In 2015, the estimated cost of caring for patients with CHD was US $182 billion in the United States; hospitalizations accounted for more than half of the costs. Compared with men, women with CHD or those who have undergone coronary revascularization have up to 30% more rehospitalizations within 30 days and up to 1 year. Center-based cardiac rehabilitation is the gold standard of care after an acute coronary event, but few women attend these valuable programs. Effective home-based interventions for improving cardiovascular health among women with CHD are vital for addressing this gap in care. Objective: The ubiquity of mobile phones has made mobile health (mHealth) behavioral interventions a viable option to improve healthy behaviors of both women and men with CHD. First, this study aimed to examine the usability of a prototypic mHealth intervention designed specifically for women with CHD (herein referred to as HerBeat). Second, we examined the influence of HerBeat on selected health behaviors (self-efficacy for diet, exercise, and managing chronic illness) and psychological (perceived stress and depressive symptoms) characteristics of the participants. Methods: Using a single-group, pretest, posttest design, 10 women participated in the 12-week usability study. Participants were provided a smartphone and a smartwatch on which the HerBeat app was installed. Using a web portal dashboard, a health coach monitored participants’ ecological momentary assessment data, their behavioral data, and their heart rate and step count. Participants then completed a 12-week follow-up assessment. Results: All 10 women (age: mean 64.4 years, SD 6.3 years) completed the study. The usability and acceptability of HerBeat were good, with a mean system usability score of 83.60 (SD 16.3). The participants demonstrated statistically significant improvements in waist circumference (P=.048), weight (P=.02), and BMI (P=.01). Furthermore, depressive symptoms, measured with the Patient Health Questionnaire-9, significantly improved from baseline (P=.04). Conclusions: The mHealth prototype was feasible and usable for women with CHD. Participants provided data that were useful for further development of HerBeat. The mHealth intervention is expected to help women with CHD self-manage their health behaviors. A randomized controlled trial is needed to further verify the findings.

The article evaluates the feasibility of extending agile principles to larger, dynamic, and possibly distributed software development projects by uncovering the theoretical basis for agile values and principles for achieving agility. The extant literature focuses mainly on one theory – complex adaptive systems – to support agile methods, although recent research indicates that the control theory and the adaptive structuration theory are also applicable. This article proposes that at least three other theories exist that are highly relevant: transaction cost economics, social exchange theory, and expectancy theory. By employing these theories, a rigorous analysis of the Agile Manifesto is conducted. Certain agile values and principles find theoretical support and can be applied to enhance agility dynamic projects regardless of size; some agile principles find no theoretical support while others find limited support. Based on the analysis and the ensuing discussion, the authors propose a framework with five dimensions of agility: process, design, people, outcomes, and adaptation.

Real-time bidding (RTB) for digital advertising is becoming the norm for improving advertisers’ campaigns. Unlike traditional advertising practices, in the process of RTB, the advertisement slots of a mobile application or a website are mapped to a particular advertiser through a real-time auction. The auction is triggered and is held for a few milliseconds after an application is launched. As one of the key components of the RTB ecosystem, the demand-side platform gives the advertisers a full pledge window to bid for available impressions. Because of the fast-growing market of mobile applications and websites, the selection of the most pertinent target audience for a particular advertiser is not a simple human-mediated process. The real-time programmatic approach has become popular instead. To address the complexity and dynamic nature of the RTB process, we propose an auto pricing strategy (APS) approach to determine the applications to bid for and their respective bid prices from the advertising agencies’ perspective. We apply the APS to actual RTB data and demonstrate how it outperforms the existing RTB approaches with a higher conversion rate for a lower target spend. A video abstract is available at https://doi.org/10.1287/ijoc.2018.0812 .

Background: While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective: Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods: We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis–based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results: Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. Conclusions: It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.