Depression is a common mental disorder in which patients often experience feelings of sadness, fatigue, loss of interest, and pleasure. Exercise is a widely used intervention for managing depression, but the specific molecular mechanisms underlying its antidepressant effect are unclear. In this narrative review, we aim to synthesize current knowledge on the molecular, neural, and physiological mechanisms through which exercise exerts its antidepressant effect and discuss the various exercise interventions used for managing depression. We conducted a narrative review of the literature on the topic of exercise and depression. Our review suggests that exercise impacts peripheral tryptophan metabolism, central inflammation, and brain-derived neurotrophic factors through the peroxisome proliferator-activated receptor γ activating factor 1α (PGC-1α) in skeletal muscles. The uncarboxylated osteocalcin facilitates “bone-brain crosstalk”, and exercise corrects atypical expression of brain-gut peptides, modulates cytokine production and neurotransmitter release, and regulates inflammatory pathways and microRNA expression. Aerobic exercise is recommended at frequencies of 3 to 5 times per week with medium to high intensity. Here we highlight the significant potential of exercise therapy in managing depression, supported by the molecular, neural, and physiological mechanisms underlying its antidepressant effect. Understanding the molecular pathways and neural mechanisms involved in exercise’s antidepressant effect opens new avenues for developing novel therapies for managing depression.

We proved the hypothesis that intermittent exercise would have a better effect on arterial stiffness by shortening the duration of intervals and increasing the number of bouts. Twenty healthy male college students (20.4 ± 0.4 years) were randomly assigned to a quiet control (CON), 30 min continuous exercise (CE), long-interval intermittent exercise with long intervals (IELL), long-interval intermittent exercise with short intervals (IELS), and short-interval intermittent exercise with short intervals (IESS). The intensity was set to 45% of the heart rate reserve. The brachial-ankle pulse wave (baPWV) was measured at baseline (BL), 0 min post-exercise, 20 min post-exercise, 40 min post-exercise, and 60 min post-exercise. BaPWV changes (⊿baPWV) from the BL in the same tests were used for the analysis. ⊿baPWV did not change significantly in the CON. ⊿baPWV decreased significantly at 0, 20, and 40 min in all exercise tests. ⊿baPWV decreased significantly at 60 min in IELS and IESS. At 60 min, the ⊿baPWV of IELS and IESS was still significantly lower than that of CON and CE, and the ⊿baPWV of IESS was still significantly lower than that of IELS. Hence, shortening the intervals of intermittent exercise and increasing the number of repetitions may enhance the effect of improving arterial stiffness.

With the development of the times, traditional fitness running data collection methods have shown a series of problems, such as low data accuracy, slow collection speed, and insufficient search ability of modeling algorithms. These problems have led to a sharp rise in the difficulty of building a more accurate fitness running data model. Therefore, this study proposed a set of methods for fitness running data collection and evolution modeling based on digital information technology. First, it filters out the impulse noise during the transmission of the three-dimensional acceleration signal caused by the accidental state change of the technology, by using the data collection method for fitness running based on the digital information platform, relying on a multisensor and median filter algorithm. Second, it filters out the gravitational component of this platform based on the orientation sensor. Then, according to the results of the first step, an algorithm is constructed for the model of fitness running data evolution. The purpose is to expand the search range so that the fitness running data model with better performance can be obtained more effectively. The experimental results show that the method based on digital information technology designed in this study has an overwhelming advantage over the traditional acquisition method, the constructed model is more accurate, and the efficiency of data acquisition is increased by 88%. Therefore, with the aid of digital information technology, the design of the data model can reduce the probability of mold repair and the time consumption of mold trials and reduce the design cost.