The effects of nicotine withdrawal on exercise-related physical ability and sports performance in nicotine addicts: a systematic review and meta-analysis.

BACKGROUND: Previous research has established that nicotine withdrawal can ameliorate cardiovascular and pulmonary function in smokers. Nevertheless, the impact on physical fitness and athletic performance remains under-investigated. OBJECTIVE: To evaluating the impacts of nicotine withdrawal on both exercise performance and exercise-associated physical capabilities in nicotine-dependent individuals. STUDY DESIGN: A comprehensive systematic review and meta-analysis. DATA SOURCES: The data was compiled from databases such as PubMed, Scopus, Web of Science, Cochrane Central, and EBSCO. STUDY SELECTION: The selection criteria required studies to elucidate the effects of nicotine withdrawal on exercise performance or exercise-related physical abilities. Moreover, the selected studies needed to provide discernible experimental results. DATA SYNTHESIS AND ANALYSIS: The random effects model was employed in data analysis, utilizing the standardized mean difference (SMD) and the 95% confidence intervals (95% CIs) to estimate participants' exercise performance and physical abilities, referencing the Mean ±SD during baseline and withdrawal states. RESULTS: Out of the selected studies, 10 trials were included, encompassing 13,538 participants aged 18 to 65 years. The findings suggest that nicotine withdrawal could potentially enhance sports performance (SMD = 0.45, 95% CI: 0.03 to 0.88; I^2 = 83%), particularly in terms of aerobic capacity. Short-term nicotine withdrawal (spanning 12 to 24 hours) might lead to a decline in participants' physical abilities in certain aspects like reaction time and sustained attention (SMD = -0.83, 95% CI: -1.91 to 0.25; I^2 = 79%), whereas long-term withdrawal (lasting 48 hours or more) demonstrated an opposing trend (SMD = 0.25, 95% CI: 0.12 to 0.39; I^2 = 81%). Overall, the results show that long-term nicotine withdrawal exhibited some positive impacts on sports performance and exercise-related physical ability, with the withdrawal duration being an indicator of subsequent physical performance. CONCLUSIONS: Mid- to long-term (≥3 months) nicotine withdrawal significantly improved the exercisers' exercise-related physical ability and sports performance. Conversely, short-term (≤24 hours) nicotine withdrawal considerably hampered exercisers' performance and physical cognition. It is suggested that exercises avoid abrupt nicotine cessation prior to competitions, as long-term nicotine withdrawal has been shown to significantly enhance exercise-related physiological capacities and athletic performance. By referring to existing literatures we also found that athletes with existing nicotine addiction may could consume nicotine 15-30 minutes before competition to enhance athletic performance and physical function.PROSPERO registration number CRD42023411381.

[VisConnectome: an independent and graph-theory based software for visualizing the human brain connectome].

As a complex system, the topology of human's brain network has an important effect on further study of brain's structural and functional mechanism. Graph theory, a kind of sophisticated analytic strategies, is widely used for analyzing complex brain networks effectively and comparing difference of topological structure alteration in normal development and pathological condition. For the purpose of using this analysis methodology efficiently, it is necessary to develop graph-based visualization software. Thus, we developed VisConnectome, which displays analysis results of the brain network friendly and intuitively. It provides an original graphical user interface (GUI) including the tool window, tool bar and innovative double slider filter, brain region bar, runs in any Windows operating system and doesn't rely on any platform such as Matlab. When importing the user-defined script file that initializes the brain network, VisConnectome abstracts the brain network to the ball-and-stick model and render it. VisConnectome allows a series of visual operations, such as identifying nodes and connection, modifying properties of nodes and connection such as color and size with the color palette and size double slider, imaging the brain regions, filtering the brain network according to its size property in a specific domain as simplification and blending with the brain surface as a context of the brain network. Through experiment and analysis, we conclude that VisConnectome is an effective visualization software with high speed and quality, which helps researchers to visualize and compare the structural and functional brain networks flexibly.