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Digital medicine plays an important role in disease assessment, psychological intervention, and relapse management in mental illnesses. Patients with substance use disorders can be easily affected by the environment and negative emotions, inducing addiction and relapse. However, due to social discrimination, stigma, or economic issues, they are unwilling to go to the hospital for treatment, making it difficult for health workers to track their health changes. Additionally, mental health resources in China are insufficient. Digital medicine aims to solve these problems. This article reviews digital medicine in the field of addiction, hoping to provide a reference for the future exploration of more individualized and effective digital medicine. [ABSTRACT FROM AUTHOR]

Background: This review aims to identify whether risky decision-making is increased in substance users, and the impact of substance type, polysubstance use status, abstinence period, and treatment status on risky decision-making. Methods: A literature search with no date restrictions was conducted to identify case-control studies or cross-sectional studies that used behavioral tasks to measure risky decision-making in substance users. A random-effects model was performed. GRADE criteria was used to assess the quality of evidence. Results: 52 studies were enrolled. The result showed that the difference in risky decision-making performance between user groups and control groups was significant (SMD = − 0.590; 95%CI = − 0.849 to − 0.330; p < 0.001; I2 = 93.4%; Pheterogeneity < 0.001). Subgroup analysis showed that users in the subgroups of alcohol (p < 0.001), tobacco (p < 0.01), cocaine (p < 0.001), opioid (p < 0.001), mixed group (p < 0.01), adult users (p < 0.001), small sample size (p < 0.001), large sample size (p < 0.01), low education (p < 0.001), high education (p < 0.001), short-abstinence period (p < 0.001), long-abstinence period (p < 0.001), without current polysubstance dependence (p < 0.001), and with treatment (p < 0.001) had increased risky decision-making when compared to the controls. On the other hand, elderly substance users with short-abstinence period showed increased risky decision-making. Moreover, current treatment status and polysubstance use may not influence the level of decision-making in substance users. Conclusions: The results show that substance use is associated with impaired risky decision-making, indicating that interventions targeting risky decision-making in substance users should be developed for relapse prevention and rehabilitation. [ABSTRACT FROM AUTHOR]

Abstract : Addiction is associated with high impulsivity. Behavioral impulsivity sets the vulnerability in addiction formation, facilitates drug use, and acts as one most important risk factors for relapse. However, the pattern of individual heterogeneity across addictive disorders for precision medicine is still unclear. We performed network-based analysis using the clinical data of trait impulsivity from 1687 subjects with stimulant and heroin use disorders, as well as Internet gaming disorder (IGD). Based on Barratt Impulsivity Scale (BIS) measurements, the trait impulsivity networks and individual differential impulsivity networks (IDINs) were constructed. The three types of addiction respectively accompany different core impulsivity traits. The global network strength in stimulant use disorder was significantly higher than that in other addiction types, and non-planning impulsivity connections in heroin subjects differed from the rest groups. Based on the individual differential impulsivity networks, these subjects were clustered into three types, as deviated from control subjects. The three deviation patterns were related to addiction type, age, education, and addiction duration. These findings indicated different core impulsivity traits across addictions and heterogeneity of individual trait impulsivity patterns, which supports individualized medicine in managing impulsivity. [ABSTRACT FROM AUTHOR]