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

Implicit and explicit COVID‐19‐vaccine harmfulness/helpfulness associations predict vaccine beliefs, intentions, and behaviors

We investigated the role of implicit and explicit associations between harm and COVID-19 vaccines using a large sample (N = 4668) of online volunteers. The participants completed a brief implicit association test and explicit measures to evaluate the extent to which they associated COVID-19 vaccines with concepts of harmfulness or helpfulness. We examined the relationship between these harmfulness/helpfulness COVID-19 vaccine associations and vaccination status, intentions, beliefs, and behavior. We found that stronger implicit and explicit associations that COVID-19 vaccines are helpful relate to vaccination status and beliefs about the COVID-19 vaccine. That is, stronger pro-helpful COVID-19 vaccine associations, both implicitly and explicitly, related to greater intentions to be vaccinated, more positive beliefs about the vaccine, and greater vaccine uptake

The Role of Uncertainty, Worry, and Control in Well-Being: Evidence From the COVID-19 Outbreak and Pandemic in U.S. and China

Uncertainty about the future often leads to worries about what the future will bring, which can have negative consequences for health and well-being. However, if worry can act as a motivator to promote efforts to prevent undesirable future outcomes, those negative consequences of worry may be mitigated. In this article, we apply a novel model of uncertainty, worry, and perceived control to predict psychological and physical well-being among four samples collected in China (Study 1; during the early COVID-19 outbreak in China) and the United States (Studies 2–4, during 4 weeks in May 2020, 4 weeks in November 2020, and crosssectionally between April and November 2020). Grounded in the feeling-is-for-doing approach to emotions, we hypothesized (and found) that uncertainty about one’s COVID-19 risk would predict greater worry about the virus and one’s risk of contracting it, and that greater worry would in turn predict poorer well-being. We also hypothesized, and found somewhat mixed evidence, that perceptions of control over 1’s COVID-19 risk moderated the relationship between worry and well-being such that worry was related to diminished well-being when people felt they lacked control over their risk for contracting the virus. This study is one of the first to demonstrate an indirect path from uncertainty to well-being via worry and to demonstrate the role of control in moderating whether uncertainty and worry manifest in poor well-being

Aging and symptoms of anxiety and depression: Structural invariance of the tripartite model

Negative affect measures were evaluated in a cross-sectional community sample of adults aged 18–93 (N = 335) to examine the structure of neuroticism, anxiety, and depressive symptoms in young, middle, and older adult cohorts. Structural equation modeling was used to contrast 3 nested models: a 1-factor general distress model; a 2-factor high negative–low positive affect model; and a 3-factor “tripartite model” reflecting a higher order Negative Affect factor that is common to depression and anxiety problems and 2 lower order factors, Low Positive Affect (mostly specific to depression) and Arousal (specific to anxiety/panic). As expected, the tripartite model fit best for all age groups. Further, multigroup analyses indicated age invariance for the tripartite model, suggesting the model can be effectively applied with older populations

Implicit associations for fear-relevant stimuli among individuals with snake and spider fears

This study investigated an implicit measure of cognitive processing, the Implicit Association Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1988), as a measure of fear-related automatic associations. Sixty-seven students with snake or spider fears completed 4 IAT tasks in which they classified pictures of snakes and spiders along with descriptive words indicating valence, fear, danger, or disgust. Results indicated that all 4 tasks discriminated between fear groups in terms of their implicit associations, and fear-specific effects were significant even after controlling for the impact of valence evaluation. Findings are discussed in terms of applications of the IAT methodology to examine cognitive processing and schemata in anxiety and potential uses for assessing anxiety disorders