Combining crowd-sourcing, census data, and public review forums for real-time, high-resolution food desert estimation.

BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment's health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes. MATERIALS AND METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments. RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected. CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning With an Urban African American Population of Young Adults: User-Centered Design Approach.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are increasingly affecting younger populations, particularly African Americans in the southern United States. Access to preventive and therapeutic services, biological factors, and social determinants of health (ie, structural racism, resource limitation, residential segregation, and discriminatory practices) all combine to exacerbate health inequities and their resultant disparities in morbidity and mortality. These factors manifest early in life and have been shown to impact health trajectories into adulthood. Early detection of and intervention in emerging risk offers the best hope for preventing race-based differences in adult diseases. However, young-adult populations are notoriously difficult to recruit and retain, often because of a lack of knowledge of personal risk and a low level of concern for long-term health outcomes. OBJECTIVE: This study aims to develop a system design for the MOYO mobile platform. Further, we seek to addresses the challenge of primordial prevention in a young, at-risk population (ie, Southern-urban African Americans). METHODS: Urban African Americans, aged 18 to 29 years (n=505), participated in a series of co-design sessions to develop MOYO prototypes (ie, HealthTech Events). During the sessions, participants were orientated to the issues of CVD risk health disparities and then tasked with wireframing prototype screens depicting app features that they considered desirable. All 297 prototype screens were subsequently analyzed using NVivo 12 (QSR International), a qualitative analysis software. Using the grounded theory approach, an open-coding method was applied to a subset of data, approximately 20% (5/25), or 5 complete prototypes, to identify the dominant themes among the prototypes. To ensure intercoder reliability, 2 research team members analyzed the same subset of data. RESULTS: Overall, 9 dominant design requirements emerged from the qualitative analysis: customization, incentive motivation, social engagement, awareness, education, or recommendations, behavior tracking, location services, access to health professionals, data user agreements, and health assessment. This led to the development of a cross-platform app through an agile design process to collect standardized health surveys, narratives, geolocated pollution, weather, food desert exposure data, physical activity, social networks, and physiology through point-of-care devices. A Health Insurance Portability and Accountability Act-compliant cloud infrastructure was developed to collect, process, and review data, as well as generate alerts to allow automated signal processing and machine learning on the data to produce critical alerts. Integration with wearables and electronic health records via fast health care interoperability resources was implemented. CONCLUSIONS: The MOYO mobile platform provides a comprehensive health and exposure monitoring system that allows for a broad range of compliance, from passive background monitoring to active self-reporting. These study findings support the notion that African Americans should be meaningfully involved in designing technologies that are developed to improve CVD outcomes in African American communities.

Pure PBL, Hybrid PBL and Lecturing: which one is more effective in developing cognitive skills of undergraduate students in pediatric nursing course?

BACKGROUND: Nursing education in Iran has conventionally focused on lecture-based strategies. Improvements in teaching and learning over the years have led to an expansion of the pedagogies available to educators. Likewise, there has been a suggestion for a move toward more learner-centered teaching strategies and pedagogies that can result in improvement in learning. This study was undertaken to investigate the effects of Problem-Based Learning in developing cognitive skills in learning Pediatric Nursing among university students. METHODS: In this quasi-experimental, posttest-only nonequivalent control group design, the subjects were undergraduate students who had enrolled in Pediatric Nursing II at Islamic Azad University in Iran. The experiment was conducted over a period of eight weeks, one two-hour session and two two-hour sessions. Two experimental groups, Pure Problem-Based Learning (PPBL) and the Hybrid Problem- Based Learning (HPBL), and one Lecturing or Conventional Teaching and Learning (COTL) group were involved. In the PPBL group, PBL method with guided questions and a tutor, and in the HPBL group, problem-based learning method, some guided questions, minimal lecturing and a tutor were used. The COTL group, however, underwent learning using conventional instruction utilizing full lecture. The three groups were compared on cognitive performances, namely, test performance, mental effort, and instructional efficiency. Two instruments, i.e., Pediatric Nursing Performance Test (PNPT) and Paas Mental Effort Rating Scale (PMER) were used. In addition, the two-Dimensional Instructional Efficiency Index (IEI) formula was utilized. The statistical analyses used were ANOVA, ANCOVA, and mixed between-within subjects ANOVA. RESULTS: Results showed that the PPBL and HPBL instructional methods, in comparison with COTL, enhanced the students' overall and higher-order performances in Pediatric Nursing, and induced higher level of instructional efficiency with less mental effort (p < 0.005). Although there was no significant difference in lower-order performance among the groups during the posttest (p = 0.92), the HPBL group outperformed the COTL group on the delayed posttest (p = 0.028). CONCLUSIONS: It may be concluded that both forms of PBL were effective for learning Pediatric Nursing. Moreover, PBL appears to be useful where there are shortages of instructors for handling teaching purposes.