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Most research on emotion regulation has focused on understanding individual emotion regulation strategies. Preliminary research, however, suggests that people often use several strategies to regulate their emotions in a given emotional scenario (polyregulation). The present research examined who uses polyregulation, when polyregulation is used, and how effective polyregulation is when it is used. College students (N= 128; 65.6% female; 54.7% White) completed an in-person lab visit followed by a 2-week ecological momentary assessment protocol with six randomly timed survey prompts per day for up 2 weeks. At baseline, participants completed measures assessing past-week depression symptoms, social anxiety-related traits, and trait emotion dysregulation. During each randomly timed prompt, participants reported up to eight strategies used to change their thoughts or feelings, negative and positive affect, motivation to change emotions, their social context, and how well they felt they were managing their emotions. In pre-registered analyses examining the 1,423 survey responses collected, polyregulation was more likely when participants were feeling more intensely negative and when their motivation to change their emotions was stronger. Neither sex, psychopathology-related symptoms and traits, social context, nor subjective effectiveness was associated with polyregulation, and state affect did not moderate these associations. This study helps address a key gap in the literature by assessing emotion polyregulation in daily life.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol can lead to a range of physical health ailments including the progression of tumor growth. The ability to monitor cortisol levels over time can therefore be used to facilitate decision-making during cancer treatment. However, collecting serum or saliva samples to monitor cortisol in situ is inconvenient, costly, and impractical. In this paper, we propose a general predictive modeling process that uses passively sensed actigraphy data to predict underlying salivary cortisol levels using graph representation learning. We compare machine learning models with handcrafted feature engineering and with graph representation learning, which includes Graph2Vec, FeatherGraph, GeoScattering and NetLSD. Our preliminary results generated from data from 10 newly diagnosed pancreatic cancer patients demonstrate that machine learning models with graph representation learning can outperform the handcrafted feature engineering to predict salivary cortisol levels.

Theory-driven gamification principles are developed by examining the intersection of existing frameworks. The principles address problems with the current understanding of gamification within internet interventions.Gamification is a popular method used to add entertaining and appealing dimensions to nongaming activities. Researchers of technology-based behavioral and mental-health-focused interventions have shown considerable interest in gamification to enhance engagement and adherence. There have been a number of gamification frameworks proposed, each with differences in focus but with overlapping similarities. A review of these frameworks highlight critical issues in gamification—lack of clear definitions, standards, and a need for an overarching model for applying gamification, rather than simply describing gamification. These issues leave researchers challenged to apply gamification to its full potential. This paper explores gamification as a construct and endeavors to define its core features. A useful way of evaluating the potential utility of gamification features in the context of an intervention is by distinguishing between exogenous applications of gamification(layering game mechanics externally upon a system) and endogenous application of gamification(developing mechanics intrinsic to the given experience). By then comparing and contrasting six gamification frameworks, components are identified that lay at the intersection and a theoretical model is proposed. A theory-driven set of gamification principles, organized into four categories, is developed and presented. Of particular interest is the utilization of this model as it relates to behavioral and mental-health-focused Internet-based interventions. To demonstrate the potential of this gamification framework, the generated principles are overlaid onto the established Model for Internet Interventions, extending it, and providing a more concrete foundation for researchers of Internet interventions. The presented model will assist researchers and developers who are interested in applying gamification to Internet interventions.

Mental health problems are a leading cause of disease burden and disability worldwide. Social anxiety and depression are highly prevalent among college students. The current methods for detecting symptoms are based on client self-report via questionnaires and interviews in traditional clinical settings, but self-report is subject to recall bias and visiting a clinic requires a high level of motivation. Assessment methods that use both actively and passively collected data hold promise for detecting and monitoring social anxiety and depression symptoms as individuals go about their daily lives. The present study, named DemonicSalmon, investigates how social anxiety and depression symptoms manifest in the daily life of 72 students over a two-week study period. Results show a number of significant correlations between the automatic objective sensor data from smartphones and indicators of mental health. The collected data enhances understanding of how students’ social anxiety, depression and affect levels are associated with their mobility, activity levels, and communication patterns. The DemonicSalmon dataset has been made publicly available on the web to enhance collaborations in this important area of research.

Despite the proliferation of mobile interventions that are delivered through smartphones, and their potential impact on promoting mental health in chronic health populations, little research has examined how to leverage the advanced technological capabilities of smartphones to also monitor response to app-based interventions. Current methods used to evaluate response to digital interventions rely on static and retrospective self-report measures to infer crucial behavioral and affective patterns, which provide little contextual information and limits generalizability. Importantly, this approach can also increase burden among chronic health populations that are already experiencing a heavy treatment load. Using data collected from 7 recently diagnosed breast cancer patients undergoing active cancer treatment (average time from initial diagnosis to study enrollment is 13 days), we propose a framework for integrating self-report surveys and fine-grained mobile sensing data. Preliminary results demonstrate the feasibility and utility of this framework to reduce burden and improve detection of mood in response to an app-based intervention among chronic health populations.