Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions

Increasing evidence has shown that theory-based health behavior change interventions are more effective than non-theory-based ones. However, only a few segments of relevant studies were theory-based, especially the studies conducted by non-psychology researchers. On the other hand, many mobile health interventions, even those based on the behavioral theories, may still fail in the absence of a user-centered design process. The gap between behavioral theories and user-centered design increases the difficulty of designing and implementing mobile health interventions. To bridge this gap, we propose a holistic approach to designing theory-based mobile health interventions built on the existing theories and frameworks of three categories: (1) behavioral theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior, and the Health Action Process Approach), (2) the technological models and frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design and Behavior Change Support System, and the Just-in-Time Adaptive Interventions), and (3) the user-centered systematic approaches (e.g., the CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic approach provides researchers a lens to see the whole picture for developing mobile health interventions

Depression in Low-Income Adolescents: Guidelines for School-Based Depression Intervention Programs

Adolescent depression is growing in interest to clinicians. In addition to the estimated 2 million cases of adolescent major depressive episodes each year, depressive symptoms in youth have become indicators of mental health complications later in life. Studies indicate that being low-income is a risk factor for depression and that socioeconomically disadvantaged teenagers are more than twice as likely to develop mental illnesses. Only an estimated 1 in 4 children with mental illnesses receive adequate help and 80% of these resources come through schools. Thus, this study focuses on establishing the importance of depression intervention programs in low-income high schools and designing novel guidelines for effective protocols. A compilation of expert opinion on depression screening, education, and treatment, as well as analysis of previously implemented school screening and awareness programs, are examined in order to understand key strategies. The results of this study finds that a multi-layered approach with screening, universal education, and interventions for those identified as being high-risk is most effective in addressing the mental health needs of low-income adolescents. To ensure feasibility and efficacy, screening should be conducted with a modified PHQ-a test and followed-up by timely clinical interviews by school psychologists. All students should receive universal depression education curriculum consisting of principles such as: depression literacy, asset theory, and promotion of help-seeking behaviors. Extending universal education to teachers would also be beneficial in promoting mental health communication and positive classroom environments. It is vital that those screening positive for depression or suicidality receive protocols geared towards high-risk youths, such as group Cognitive-Behavioral Therapy and facilitated mental health center referrals based on individual severity. Effectively addressing depression in school systems requires integration of mental health promotion, depression prevention, and psychotherapy—by taking this multidimensional approach, public health officials and school administrations can ensure that adequate resources are directed to those most in need

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability

A Time Like No Other: Charting the Course of the Next Revolution - A Summary of the Boston Indicators Report 2004-2006

Summarizes findings from the Boston Indicators Project, a long-term research study of the city's economic, social, and technical progress across ten sectors

Discussion quality diffuses in the digital public square

Studies of online social influence have demonstrated that friends have important effects on many types of behavior in a wide variety of settings. However, we know much less about how influence works among relative strangers in digital public squares, despite important conversations happening in such spaces. We present the results of a study on large public Facebook pages where we randomly used two different methods--most recent and social feedback--to order comments on posts. We find that the social feedback condition results in higher quality viewed comments and response comments. After measuring the average quality of comments written by users before the study, we find that social feedback has a positive effect on response quality for both low and high quality commenters. We draw on a theoretical framework of social norms to explain this empirical result. In order to examine the influence mechanism further, we measure the similarity between comments viewed and written during the study, finding that similarity increases for the highest quality contributors under the social feedback condition. This suggests that, in addition to norms, some individuals may respond with increased relevance to high-quality comments.Comment: 10 pages, 6 figures, 2 table