Xiaoni Zhang

1. Artificial intelligence has transformed human-computer interaction while introducing complex ethical challenges. As organizations deploy AI across business processes, practitioners face increasi...

Background: Although contingency management has shown some efficacy in substance use disorder treatment, digital contingency management (DCM) needs more evidence supporting its value in treating substance misuse. Objective: This study aimed to evaluate the effectiveness of DCM in treating substance use disorder by examining 2 key outcome variables—abstinence and appointment attendance. Methods: A 12-month quasi-experimental design was conducted by enrolling patients into 2 groups using an alternating assignment process: one group receiving treatment-as-usual plus DCM and the other receiving treatment as usual with no contingency management. Propensity score matching was conducted to match groups on covariates. After matching, t tests were conducted to examine the difference between groups on urine abstinence and appointment attendance rates. Results: Two cohorts of propensity-matched patients (66 interventions and 59 controls) were analyzed. Abstinence was significantly higher in the DCM group (mean 0.92, 95% CI 0.88‐0.96) than in the treatment-as-usual group (mean 0.85, 95% CI 0.79‐0.90; P <.01). Appointment attendance also demonstrated significant differences between the groups, with the DCM group achieving a mean rate of 0.69 (95% CI 0.65‐0.74) compared with 0.50 (95% CI 0.45‐0.55) in the treatment-as-usual group ( P <.001). This notable increase highlights the role of DCM in fostering engagement with care, an essential factor for successful treatment outcomes. Conclusions: The results suggest that DCM can be an effective treatment modality for substance use disorder.

BACKGROUND Although contingency management (CM) has shown some efficacy in substance use disorder treatment, digital contingency management (DCM) needs more evidence supporting its value in treating substance misuse. OBJECTIVE This study aims to evaluate the effectiveness of DCM in treating substance use disorder by examining two key outcome variables: abstinence and appointment attendance. METHODS A 12-month quasi-experimental design was conducted by enrolling patients into two groups using an alternating assignment process: one group receiving treatment-as-usual plus DCM and the other receiving treatment-as-usual with no contingency management. Propensity score matching was conducted to match groups on covariates. After matching, t-tests were conducted to examine the difference between groups on urine abstinence and appointment attendance rates. RESULTS Two cohorts of propensity-matched patients (66 interventions and 59 controls) were analyzed. Abstinence was significantly higher in the digital contingency management group (mean = 0.92, 95% CI: 0.88–0.96) than in the treatment-as-usual group (mean = 0.85, 95% CI: 0.79–0.90; P-value < .01). Appointment attendance also demonstrated significant differences between the groups, with the DCM group achieving a mean rate of 0.69 (95% CI: 0.65–0.74) compared to 0.50 (95% CI: 0.45–0.55) in the TAU group (P-value < .001). This notable increase highlights the role of DCM in fostering engagement with care, an essential factor for successful treatment outcomes. CONCLUSIONS The results suggest that DCM can be an effective treatment modality for substance use disorder. CLINICALTRIAL

The popularity of ChatGPT has raised questions surrounding AI’s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing the potential uses of ChatGPT, there is a lack of a systematic approach to identify and analyze the use cases of ChatGPT in health. This study establishes a framework for gathering and identifying tweets (i.e., posts on social media site “X”, formerly known as Twitter) that discuss health use cases of ChatGPT, integrating topic modeling with constructivist grounded theory (CGT) to organize these topics into common categories. Using this framework, nine topics were identified, which were further grouped into four categories: (1) Clinical Workflow, (2) Wellness, (3), Diseases, and (4) Gender Identity. The Clinical Workflow category was the most popular category, and included four topics: (1) Seeking Advice, (2) Clinical Documentation, (3) Medical Diagnosis, and (4) Medical Treatment. Among the identified topics, “Diet and Workout Plans” was the most popular topic. This research highlights the potential of social media to identify the health use cases and potential health applications of an AI-based chatbot such as ChatGPT. The identified topics and categories can be beneficial for researchers, professionals, companies, and policymakers working on health use cases of AI chatbots.

Since the COVID-19 pandemic, the use of mobile health apps has been increasing significantly. Many have started using mobile health apps to manage their health and well-being. People need to develop healthy habits that will increase their job productivity, life expectancy and improve their social relationships. This study tests the Health Belief Model with adults in China in influencing health motivation and in turn improving their health behavior. A mobile and online survey was developed, and 120 usable responses were collected. A structural equation modeling was used to test the relationships between perceived benefits, perceived susceptibility, perceived barriers, health motivation, and health behavior. We found perceived self-efficacy, perceived benefits, and perceived susceptibility predicts health motivation, and health motivation predicts health behavior. We did not find that perceived barrier is a predictor for health motivation. We conclude that to encourage behavioral change, it is important to enhance self-efficacy and address the benefits; in the meantime, educating people on susceptibility is also important.

The clinical decision support systems (CDSs) for diabetes have improved significantly over the years. Multiple factors serve as driving forces for the uptake of CDSs. Newer technologies, initiatives, government mandates, and a competitive environment collectively facilitate advancement in diabetes care. This book chapter summarizes global CDSs development in recent years. Our review of the past few years’ publications on CDSs for diabetes shows that the United States is leading the world in technology development and clinical evidence generation. Developing countries worldwide are catching up in CDSs development and standards of patient care. Though most CDSs and published studies are on diabetes diagnosis, treatment, and management, a small portion of the research is devoted to prediabetes and type I diabetes. Increased efforts worldwide have been devoted to artificial intelligence and machine learning in diabetes care.

An elaborate composite of molecularly imprinted polymer (MIP)-modified gold nanoparticles (AuNPs)@silica dioxide (SiO₂) was designed and prepared for real-time colorimetric determination of glutathione (GSH) in serum. Firstly, the MIPs were synthesized on the surface of SiO₂ utilizing GSH as template molecules. Then, AuNPs were synthesized on the surface of MIPs@SiO₂ to produce a composite of MIPs modified by AuNPs@SiO₂. Compared with plain AuNPs, the composite possessed better peroxidase catalysis activity due to stabilization and protection from hydrophilic SiO₂, which can catalyze H₂O₂ to·OH oxidizing 3,3,5,5-tetramethylbenzidine (TMB) to the colored product. In addition, its selectivity was enhanced by MIP modification with special recognition cavities. With the composite as the sensor, GSH was precisely and sensitively detected in the range 5 ~ 40 μM with a limit of determination of 1.16 μM according to the principle of inhibitive peroxidase catalysis activity by GSH. The proposed colorimetric detection was successfully utilized for selective, convenient, and rapid determination of GSH in serum. It provided a new strategy for drug real-time monitoring and has high potential in clinical drug analysis. Graphical Abstract