Search

BackgroundDrug use disorder has high potential for relapse and imposes an enormous burden on public health in China. Since the promulgation of the Anti-drug law in 2008, community-based rehabilitation has become the primary approach to treat drug addiction. However, multiple problems occurred in the implementation process, leading to a low detoxification rate in the community. Mobile health (mHealth) serves as a promising tool to improve the effectiveness and efficiency of community-based rehabilitation. Community-based addiction rehabilitation electronic system (CAREs) is an interactive system for drug users and their assigned social workers. ObjectiveThe study aimed to examine the feasibility and preliminary efficacy of CAREs in community-based rehabilitation from the perspective of drug users and social workers in Shanghai, China. MethodsIn this pilot randomized controlled trial, 40 participants were recruited from the community in Shanghai from January to May 2019. Participants randomized to the intervention group (n=20) received CAREs + community-based rehabilitation, while participants in the control group (n=20) received community-based rehabilitation only for 6 months. CAREs provided education, assessment, and SOS (support) functions for drug users. The assigned social workers provided service and monitored drug use behavior as usual except that the social workers in the intervention group could access the webpage end to obtain drug users’ information and fit their routine workflow into CAREs. The primary outcome was the feasibility of CAREs, reflected in the overall proportion and frequency of CAREs features used in both app and webpage end. The secondary outcomes were the effectiveness of CAREs, including the percentage of drug-positive samples, longest period of abstinence, contact times with social workers, and the change of Addiction Severity Index (ASI) from baseline to the 6-month follow-up. ResultsThe number of participants logged in to the app ranged from 7 to 20 per week, and CAREs had relatively high levels of continued patient use. Drug users preferred assessment and education features in the app end while their social workers showed high levels of use in urine results record and viewing assessment results on the webpage end. After the 6-month intervention, 3.3% (17/520) of samples in the intervention group and 7.5% (39/520) in the control group were drug-positive (F=4.358, P=.04). No significant differences were noted between the control and intervention groups in terms of longest duration of abstinence, number of contact times and ASI composite scores. ConclusionsThe study preliminarily demonstrated that with relatively good feasibility and acceptability, CAREs may improve the effectiveness and efficiency of the community-based rehabilitation, which provided instruction for further improvement of the system. Trial RegistrationClinicalTrials.gov NCT03451344; https://clinicaltrials.gov/ct2/show/NCT03451344 International Registered Report Identifier (IRRID)RR2-10.3389/fpsyt.2018.00556

BackgroundCognitive rehabilitation therapy has been found to improve cognitive deficits and impulse control problems in methamphetamine use disorder (MUD). However, there is limited research regarding this therapy’s feasibility when using mobile-based health technologies in supporting recovery from MUD in China. ObjectiveThe main aim of this study was to test whether 4 weeks of a newly designed computerized cognitive addiction therapy (CCAT) app can improve cognitive impairments, eliminate drug-related attention bias, and attenuate risk decision-making behaviors in participants with MUD. MethodsForty MUD participants were assigned randomly to either the CCAT group (n=20), who received 4 weeks of CCAT plus regular detoxification treatment as usual, or the control group (n=20), who only received the regular detoxification treatment as usual, in drug rehabilitation centers in Shanghai. The CCAT was designed by combine methamphetamine use-related picture stimuli with cognitive training with the aim of improving cognitive function and eliminating drug-related attention bias. The CogState Battery, Delay Discounting Task (DDT), Iowa Gambling Task (IGT), and Balloon Analog Risk Task (BART) were administered face-to-face to all participants before and after CCAT interventions. ResultsForty male patients were recruited. The mean age was 32.70 (SD 5.27) years in the CCAT group and mean 35.05 (SD 8.02) years in the control group. Compared to the control group, CCAT improved working memory in the CCAT group (P=.01). Group×time interactions were observed among DDT, IGT, and BART tasks, with rates of discounting delayed rewards, IGT, and BART scores (P

Objective·To investigate if male alcohol-dependent patients have attention bias to alcohol-related pictures, show the behavioral and eye movement characteristics of attention bias, and explore the correlation between attention bias and sleep, anxiety, depression, impulsion and other psychological factors.Methods·Twenty-nine subjects in the case group (alcohol-dependent patients who were hospitalized in Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, from August 2021 to February 2022) and 29 healthy subjects in the healthy control group participated in the study. The experimental design was a mixed experimental design of three-factor hybrid. The cue types were alcohol-related and emotion pictures. The probe was on the same side or on the opposite side of the alcohol or emotional picture. The characteristics of subjects' attentional bias to alcohol cues were investigated by using the classical dot detection paradigm and eye movement monitoring technique. Groups were inter-group variables, and clue types and relative locations of probe points were intra-group variables. The behavioral and eye movement data were collected while the study subjects viewed pairs of alcohol-related and neutral pictures, or pairs of emotional and neutral pictures in a dot-probe paradigm. Dependent variables included behavioral indicators and eye movement indicators. Pittsburgh Sleep Quality Index (PSQI), 7-tiem Generalized Anxiety Disorder Scale (GAD-7), Patien Health Questionnaire (PHQ-9), and Barratt Impulsiveness Scale (BIS-11) scales were used to assess psychosomatic conditions such as sleep, anxiety, depression, and impulsivity, respectively. Behavioral and eye movement characteristics of attention bias in the alcohol-dependent group were analyzed. In addition, the correlation with behavioral and eye movement characteristics of attention bias and scale scores was analyzed.Results·The behavioral results showed that the reaction time of the alcohol-dependent group was significantly longer than that of the healthy control group (P=0.006). The eye movement results showed that when the type of cue was alcohol-related picture, the alcohol-dependent group had slower attention detection (P=0.031) and longer attention maintenance (P=0.005) on alcohol-related cues. Moreover, the pupil size during the viewing of alcohol-related cues (P=0.001) and emotional cues (P=0.008) were significantly smaller than those of the healthy control group. It was also found that, there was a negative correlation between the correct rate of the alcohol-dependent group and the BIS-11 scale score (r=-0.437, P=0.032), and a positive correlation between reaction time and GAD-7 score (r=0.407, P=0.033). The time of the first visual fixation entry was positively correlated with GAD-7 score (r=0.414, P=0.045), and the PSQI score was positively correlated with PHQ-9 score (r=0.422, P=0.041) in the case group.Conclusion·Alcohol-dependent patients have attention bias towards alcohol-related cues according to the behavioral and eye movement result. Compared with the traditional behavioral regression of attention bias based on correct rate and reaction time results, eye movement provides more direct and multi indicator evidence for the evaluation of attention bias in alcohol-dependent patients.

As the energy carrier of electric vehicles, how to accurately estimate the remaining power (SOC) of the battery is one of the key technologies in the field of electric vehicle design. Effective estimation of SOC can bring accurate continuous mileage information to the driver, theoretically avoid overcharging and discharging the battery, and also protect the driver's driving safety. In the research of SOC estimation method, constructing a suitable battery model is an important means to improve SOC estimation and to improve the prediction accuracy. In order to obtain a higher response accuracy of the model, this paper proposes an electric vehicle SOC model based on the extended Kalman filter algorithm. Based on the actual data of lithium-ion power battery, SOC estimation research is carried out. The research shows that: when the internal temperature of the battery is the same as the ambient temperature, and both are 25 °C, the model is accurate, the terminal voltage difference is small, and the average voltage difference is 9mV respectively; at room temperature, the extended Kalman filter algorithm has a significant effect on the recovery percentage of SOC voltage. The average is over 73%, and the accuracy is high. The extended Kalman algorithm in this paper we use to estimate the SOC current waveform. The simulation results show that the SOC discharge current is 4A, which has high estimation accuracy and strong applicability.