View clinical trials related to Risk-taking.
Filter by:The persistent political conflicts and COVID-19 pandemic have led to elevated chronic stress levels in Hong Kong, with far-reaching and profound negative impacts on the citizen's mental health. An important pathway via which chronic stress negatively impacts health is through promoting high-risk behaviours, such as addiction, suicide, and antisocial acts. Therefore, testing means to break the association between chronic stress and high-risk behaviour is essential to reducing the adverse consequences of stress and promoting stress resilience. The transcranial direct current stimulation (tDCS) may be a viable method for reducing risky tendency in high-stress individuals, through modulating brain functions and plasticity. Although single-session tDCS has been shown to reliably reduce risky decision making and behaviours acutely, its efficacy over extended periods of time has not been demonstrated, particularly among non-clinical samples. Being able to show that tDCS could lead to long-lasting reduction of risky tendency is necessary for promoting the wide application of this method in therapeutic settings. In this project, we aim to conduct a randomised control trial to systematically and comprehensively test whether 10 sessions of tDCS on either the dorsolateral prefrontal cortex or the orbitofrontal cortex would lead to reduction in risky tendency not only immediately after treatment, but also at 1 month and 3 months after treatment. Participants will be healthy male and female adults (21-40 years old) under relatively high levels of chronic stress, as selected from an online survey prior to the study. Participants will be randomly allocated to one of 3 treatment groups: DLPFC tDCS, OFC tDCS, and sham control. At baseline, participants will complete several risk-taking assessments, including an established computerised task that measures both risk taking and a cognitive bias that was shown to increase irrational risky tendency (illusion of control), an established questionnaire that measures risky decision making in real-life scenarios, and a scale measuring past engagement in common risky activities. Participants will also complete various personality and mood questionnaires, along with assessments on important cognitive abilities. We hypothesized that both DLPFC and OFC tDCS would reduce risk taking across the 3 timepoints, but the effect of DLPFC tDCS would be mediated by reduction in cognitive bias, whereas that of OFC tDCS would be mediated by increase in inhibition functions. These hypotheses will be tested by linear mixed models and mediation analyses. Additional exploratory analyses also test whether the tDCS effect would be moderated by relevant personality factors such as impulsivity.
As most adolescents visit a healthcare provider once a year, health behavior change interventions linked to clinic-based health information technologies hold significant promise for improving healthcare quality and subsequent behavioral health outcomes for adolescents (Baird, 2014, Harris, 2017). Recognizing the potential to leverage recent advances in machine learning and interactive narrative environments, the investigators are now well positioned to design health behavior change systems that extend the reach of clinicians to realize significant impacts on behavior change for adolescent preventive health. The proposed project centers on the design, development, and evaluation of a clinically-integrated health behavior change system for adolescents. CHANGEGRADIENTS will introduce an innovative reinforcement learning-based feedback loop in which adolescent patients interact with personalized behavior change interactive narratives that are dynamically personalized and realized in a rich narrative-centered virtual environment. CHANGEGRADIENTS will iteratively improve its behavior change models using policy gradient methods for Reinforcement Learning (RL) designed to optimize adolescents' achieved behavior change outcomes. This in turn will enable CHANGEGRADIENTS to generate more effective behavior change narratives, which will then lead to further improved behavior change outcomes. With a focus on risky behaviors and an emphasis on alcohol use, adolescents will interact with CHANGEGRADIENTS to develop an experiential understanding of the dynamics and consequences of their alcohol use decisions. The proposed project holds significant transformative potential for (1) producing theoretical and practical advances in how to realize significant impacts on adolescent health behavior change through novel interactive narrative technologies integrated with policy-based reinforcement learning, (2) devising sample-efficient policy gradient methods for RL that produce personalized behavior change experiences by integrating theoretically based models of health behavior change with data-driven models of interactive narrative generation, and (3) promoting new models for integrating personalized health behavior change technologies into clinical care that extend the effective reach of clinicians.