RESUMO
BACKGROUND: Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment. OBJECTIVE: A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response. METHODS: The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk. RESULTS: Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022. CONCLUSIONS: This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30710.
RESUMO
Bio-logging tags are an important tool for the study of cetaceans, but superficial tags inevitably increase hydrodynamic loading. Substantial forces can be generated by tags on fast-swimming animals, potentially affecting behavior and energetics or promoting early tag removal. Streamlined forms have been used to reduce loading, but these designs can accelerate flow over the top of the tag. This non-axisymmetric flow results in large lift forces (normal to the animal) that become the dominant force component at high speeds. In order to reduce lift and minimize total hydrodynamic loading this work presents a new tag design (Model A) that incorporates a hydrodynamic body, a channel to reduce fluid speed differences above and below the housing and wing to redirect flow to counter lift. Additionally, three derivatives of the Model A design were used to examine the contribution of individual flow control features to overall performance. Hydrodynamic loadings of four models were compared using computational fluid dynamics (CFD). The Model A design eliminated all lift force and generated up to ~30 N of downward force in simulated 6 m/s aligned flow. The simulations were validated using particle image velocimetry (PIV) to experimentally characterize the flow around the tag design. The results of these experiments confirm the trends predicted by the simulations and demonstrate the potential benefit of flow control elements for the reduction of tag induced forces on the animal.