Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation

Delivering treatment recommendations via pervasive electronic devices such as mobile phones has the potential to be a viable and scalable treatment medium for long-term health behavior management. But active experimentation of treatment options can be time-consuming, expensive and altogether unethical in some cases. There is a growing interest in methodological approaches that allow an experimenter to learn and evaluate the usefulness of a new treatment strategy before deployment. We present the first development of a treatment recommender system for emotion regulation using real-world historical mobile digital data from n = 114 high socially anxious participants to test the usefulness of new emotion regulation strategies. We explore a number of offline contextual bandits estimators for learning and propose a general framework for learning algorithms. Our experimentation shows that the proposed doubly robust offline learning algorithms performed significantly better than baseline approaches, suggesting that this type of recommender algorithm could improve emotion regulation. Given that emotion regulation is impaired across many mental illnesses and such a recommender algorithm could be scaled up easily, this approach holds potential to increase access to treatment for many people. We also share some insights that allow us to translate contextual bandit models to this complex real-world data, including which contextual features appear to be most important for predicting emotion regulation strategy effectiveness.Comment: Accepted at RecSys 202

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

A community based intervention program to enhance neighborhood cohesion: The Learning Families Project in Hong Kong

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Contemp Clin Trials

BackgroundLow-wage workers suffer disproportionately high rates of chronic disease and are important targets for workplace health and safety interventions. Child care centers offer an ideal opportunity to reach some of the lowest paid workers, but these settings have been ignored in workplace intervention studies.MethodsCaring and Reaching for Health (CARE) is a cluster-randomized controlled trial evaluating efficacy of a multi-level, workplace-based intervention set in child care centers that promotes physical activity and other health behaviors among staff. Centers are randomized (1:1) into the Healthy Lifestyles (intervention) or the Healthy Finances (attention control) program. Healthy Lifestyles is delivered over six months including a kick-off event and three 8-week health campaigns (magazines, goal setting, behavior monitoring, tailored feedback, prompts, center displays, director coaching). The primary outcome is minutes of moderate and vigorous physical activity (MVPA); secondary outcomes are health behaviors (diet, smoking, sleep, stress), physical assessments (body mass index (BMI), waist circumference, blood pressure, fitness), and workplace supports for health and safety.ResultsIn total, 56 centers and 553 participants have been recruited and randomized. Participants are predominately female (96.7%) and either Non-Hispanic African American (51.6%) or Non-Hispanic White (36.7%). Most participants (63.4%) are obese. They accumulate 17.4 ( \ub1 14.2) minutes/day of MVPA and consume 1.3 ( \ub1 1.4) and 1.3 ( \ub1 0.8) servings/day of fruits and vegetables, respectively. Also, 14.2% are smokers; they report 6.4 ( \ub1 1.4) hours/night of sleep; and 34.9% are high risk for depression.ConclusionsBaseline data demonstrate several serious health risks, confirming the importance of workplace interventions in child care.P30 DK056350/DK/NIDDK NIH HHS/United StatesR01 HL119568/HL/NHLBI NIH HHS/United StatesU48DP005017/ACL/ACL HHS/United StatesU48 DP005017/DP/NCCDPHP CDC HHS/United States2018-05-10T00:00:00Z29501740PMC5944351896

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Overcoming Roadblocks on the Way to Work: Bridges to Work Field Report

While many low-income, inner-city job seekers are isolated from economic opportunities in the suburbs, transportation alone is unlikely to improve their employment prospects, according to the authors of this report. Based on the lessons of P/PV's $17 million five-city Bridges to Work demonstration, the report indicates that while transportation was certainly critical, much of the sites' success depended more on their ability to recruit, prepare and support job seekers, the essential components of any workforce development program