Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences最新文献

Reliable objective measures of a person's intoxication and impairment from alcohol consumption are not readily available to the public. Wearable biosensors have the potential to provide a ubiquitous on-demand tool to deliver this kind of objective assessment in real world settings. This study evaluated the feasibility of assessing ethanol intoxication in N=28 healthy participants in a police academy's intoxication lab using wrist-worn biosensors to continuously measure heart rate, skin temperature, electrodermal activity, and accelerometry. Participants consumed ad hoc standard alcoholic drinks in a controlled setting and had regular breath alcohol content assessments and underwent standard field sobriety testing. The analysis showed statistically significant changes in each physiologic parameter between the sober and intoxicated periods. An XGBoost model was applied to this data producing machine learning algorithms to identify impairment with an accuracy as high as 0.80. These results demonstrate that it is feasible to assess ethanol intoxication using wrist-worn biosensors.

Craving, or the subjective, strong desire to use a substance, is a central factor in addiction, and part of the diagnostic criteria for substance use disorders (SUDs). Cravings can also occur for other triggers such as food, and cravings for food and drugs have been found to activate distinct neural pathways in the brain. Recently, physiologic signals from wearable devices have been applied to digitally detect cravings in patients with SUDs. But to date, no studies have explored digital detection of cravings by subtype. We collected continuous physiologic sensor data from N = 12 participants with opioid use disorder (OUD), treated with extended-release buprenorphine (BUP-XR). Data were analyzed to assess whether sensor signals carried differential information that could distinguish between food-, drug- and mixed-craving types. Accelerometer, heart rate and heart rate variability features significantly differed between drug, food and mixed trigger cravings. Cross validated models trained with these features distinguished each type of craving with area under ROC curve ranging from 75%-80%. These findings support the ability of wearable sensor-based digital biomarkers to distinguish craving subtypes in individuals with OUD.

Marginalized populations experience high prevalence of chronic health conditions - many of which require optimal medication adherence to avoid significant consequences for mortality and morbidity. Yet, marginalization and its complex sequelae create barriers for adherence and access to care, creating a cycle of health inequity. Individuals are not fully benefiting from evidence-based behavioral adherence interventions, like "Life-steps", potentially due to lack of embedded experiential learning, which is key for individuals experiencing complex barriers to medication adherence. Leveraging interactive artificial intelligence technologies through immersive virtual reality is a promising avenue to bolster behavioral adherence interventions. We present our current proof-of-concept work for "Life-steps VR", an integration of such technologies into the empiric behavioral medication adherence intervention. We then discuss our next steps for further refinement and testing of the technology with consideration for equity, democratization, and accessibility of health technologies. We conclude with a discussion of future potential iterations of Life-steps VR.

Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial participants. Visual analytics allows integrative examination of temporal event patterns of patient experience, thus generating insights for better care decisions. Towards this end, we introduce TrialView, an information system that combines graph artificial intelligence (AI) and visual analytics to enhance the dissemination of trial data. TrialView offers four distinct yet interconnected views: Individual, Cohort, Progression, and Statistics, enabling an interactive exploration of individual and group-level data. The TrialView system is a general-purpose analytical tool for a broad class of clinical trials. The system is powered by graph AI, knowledge-guided clustering, explanatory modeling, and graph-based agglomeration algorithms. We demonstrate the system's effectiveness in analyzing temporal event data through a case study.

For marginalized populations with ongoing HIV epidemics, alternative methods are needed for understanding the complexities of HIV risk and delivering prevention interventions. Due to lack of engagement in ambulatory care, such groups have high utilization of drop-in care. Therefore, emergency departments represent a location with those at highest risk for HIV and in highest need of novel prevention methods. Digital phenotyping via data collected from smartphones and other wearable sensors could provide the innovative vehicle for examining complex HIV risk and assist in delivering personalized prevention interventions. However, there is paucity in exploring if such methods are an option. This study aimed to fill this gap via a cross-sectional psychosocial assessment with a sample of N=85 emergency department patients with HIV risk. Findings demonstrate that although potentially feasible, acceptability of digital phenotyping is questionable. Technology-assisted HIV prevention needs to be designed with the target community and address key ethical considerations.

Various social media communities can lead conversations in entirely divergent directions, shaping the nature of information shared on these platforms. Deliberate disinformation and manipulated messages, disseminated both within and beyond these communities, hold the potential to reshape public opinion on a broader scale. A constructive analysis that delves into the disparities between these opposing groups could prove invaluable in discerning the pathways through which information flows. Our research examines the temporal dynamics of social media groups, assessing their behavior through metrics such as time dependent post and retweets. Using functional data analysis, we investigate Tweets related to incidents like the Skripal/Novichok case and the Bucha Crimes. Our goal is to quantify the disparities between these communities and uncover the strategies employed by each group to promote specific campaigns. Our preliminary findings shed new light on the mechanics of information dissemination, offering insights that may inform decisions about optimal response times.

Once-daily oral pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, but its efficacy is dependent on adherence, which can be challenging for men who have sex with men (MSM) with substance use. Digital pill systems (DPS) represent a novel tool for directly measuring adherence through ingestible radiofrequency sensors that confirm ingestions in real-time. We examined operational challenges across two studies involving DPS to measure PrEP adherence. While most participants successfully operated the system, a number of technological and sociobehavioral challenges requiring intervention were identified across both studies. Technological issues were both system- and participant-related, and were primarily addressed with technical updates and participant re-education, while sociobehavioral issues, including health and housing changes and issues with technology access, warranted innovative solutions. Future research leveraging DPS technology should develop robust supportive infrastructure and mitigation procedures to promptly identify and resolve operational issues to optimize the potential benefits of DPS use.

Individuals with a history of incarceration face many barriers to accessing resources to meet their basic needs when returning to community settings. Digital health tools have potential to reduce health inequities by facilitating connections to health and social services, and peer support. This study aimed to employ a user-centered design approach to create a digital Personal Health Library (PerHL) for previously incarcerated individuals. The design process included in-depth interviews followed by rapid analysis, interpretation sessions, and user experience/user interface (UX/UI) design of a high-fidelity prototype. Semi-structured interviews were conducted with individuals with a history of incarceration (n=20) to understand their experience rejoining their communities. Findings highlight the need for an app that allows users to easily access resources for employment, housing, healthcare and medical needs, formal and informal support, and legal counsel.