Background As a global infectious disease, Acquired Immune Deficiency Syndrome (AIDS) poses a serious threat to the safety and health of the society. In recent years, the proportion of drug addicts infected with HIV has been increased, and drug addicts became one of the main carriers of the spread of AIDS, which has attracted worldwide attention. It has been reported that physical activity has positive effects on improving the inhibitory function of drug addicts and reducing their drug craving, but the mechanism of the internal inhibition remains to be further explored. Method The drug addicts in an AIDS treatment center in Chongqing were investigated by means of a Physical Activity Rating Scale (PARS – 3), Internal Inhibition Scale and Drug Craving Scale, and a structural equation model was established. Results (1) There is no gender difference in the internal inhibition and drug craving of drug addicts with AIDS, and there are significant differences across the types and years of drug abuse. (2) The amount of physical activity in drug addicts with AIDS is positively correlated with the intrinsic inhibition, while the internal inhibition is negatively correlated with the drug craving, and the physical exercise amount is negatively correlated with the drug craving. The years of drug abuse were negatively correlated with internal inhibition and positively correlated with drug craving. (3) Internal inhibition plays a partly mediatory role between the physical activity amount and the craving for drugs, and internal inhibition plays a partly mediatory role between the number of years of drug abuse and the craving for drugs, which indicates that internal inhibition has a dual mediating effect. Conclusion Actively participate in physical activity, especially high-intensity physical activity, can effectively enhance the internal inhibition of drug addicts with AIDS and reduce their drug craving. Meanwhile, the difference in the drug-taking years among addicts should be paid attention to, and the physical activity prescription should be formulated according to the actual situation.

Background: Maximal oxygen uptake (VO 2max), a vital physiological indicator, has been widely used in many fields. In recent years, the measurement method of VO 2max has been widely explored in various populations, but few studies have been conducted for women drug abusers. For the importance of VO 2max in the formulation of aerobic exercise intensity for drug users, the present study estimated VO 2max using the step test index combined with heart rate variability in women with drug use disorder. Methods: Forty women methamphetamine (MA) users without cardiovascular disease and dyskinesia participated in a cardiopulmonary exercise test (CPX) and a 3-minute step test. Each of them performed a heart rate variability (HRV) monitoring test after the step test, and VO 2max was estimated by step test index and HRV. Results: (1) The step test index had a significant positive correlation with VO 2max. The standard deviation of normal-to-normal interval (SDNN) had a significant positive correlation with VO 2max and a significant positive correlation with the step test index; (2) the R-square values of the estimated VO 2max by step test index and post-SDNN for overall MA users were 0.29 and 0.22, with an accuracy of 93.19 and 92.85%, respectively; (3) the R-square values of the estimated VO 2max by step test index and post-SDNN in group I were 0.27 and 0.36, respectively, with an accuracy of 94.04 and 93.99%. The R-square value of the estimated VO 2max by step test index in group II was 0.44, with an accuracy of 92.65%, however, post-SDNN cannot adequately estimate the VO 2max in group II; and (4) there was no significant difference in VO 2max obtained by CPX, step test index, or post-SDNN, regardless of overall or grouping variable analysis. Conclusion: The 3-minute step test combined with HRV can estimate the VO 2max of women MA users to a certain extent, but the size and the coverage of the sample size should be further considered. In the future, more methods, such as machine learning or artificial neural networks, should be used.