M = 20,9; SD = 4,5), and males clearly dominated (95%). Furthermore, the mean weakly playing time was 20.03 hours (SD = 15.8). Training occupied 38.85% (7.75 h) of the playing time on average. On the one hand, the results reveal game-specific competence and training structures in the five esports selected for the study . On the other hand, the factor structure of competencies closely resembles the esports performance model. As a conclusion, esports training methods should always consider the specific competence profile of the respective esports game.Esports as the competitive play of digital games has gained considerable popularity. However, a comprehensive framework for esport training is still missing. In this paper, a performance model integrating insights from game research and sport science is developed. Based on this model, an online questionnaire was designed and applied to investigate training in different esports regarding relevant competencies and training areas. Overall, 1,835 esports players voluntarily participated in the study. Age ranged from 13 to 47 years ( While in 2015 the esports audience was estimated at 235 million, in 2018 it already reached 395 million. In the next few years, a continued growth in popularity is expected . HoweverEsports can be differentiated into specific genres such as Multiplayer Online Battlefield Arena (MOBA), First Person Shooters (FPS), Real Time Strategy (RTS) and sport simulations . In everConsidering the lack of theory and empirical studies, this paper aims to create a basic framework for future training research in esports and to provide a perspective on esports performance that integrates sports science and  game science. It adds insights into the structure of esports performance as well as current training methods of esports athletes and identifies the most important components in representative esports disciplines and genres. The paper is structured as follows: First, we analyse the performance models in game and sports science and propose an integrative framework of esports performance. Second, we describe an empirical study addressing the structure of performance and training in esports. Finally, we discuss the results and draw conclusions for further research.In this section, performance models in game and sport science are introduced and analysed regarding common components. In a second step, a unified framework for esports is proposed integrating the insights from the game and sport domain.In today´s game science, models of game competence are often used to describe certain competencies or skills relevant to digital games. These competence models can also be used as a basis to identify appropriate methods to improve player performance in video games and esports. In game science, there are several models, concepts and theories as well as single studies addressing competencies of video game players.Comprehensive models of game competence are provided by Kraam-Aulenbach ,8 and WiEmpirical studies exist that confirm the effect of digital games on competencies claimed by the above-mentioned models. For example, Li et al.  reveal tIn sports, various performance models exist. In particular, generic models of human performance play an important role in training science, since this discipline of sport science aims at systematically and sustainably influencing sport performance by training interventions. A prototypical model is illustrated in Coordination/skillsConditionCognitive-tactical skillsPsychic abilitiesSocial abilitiesDisposition, constitution, age, gender, and genesSensorimotor coordination primarily regards features of information processing when performing movements. This concept comprises two important sub-components, i.e., specific sensori-motor skills such as running, jumping or throwing techniques which are typical for particular sports, and generic coordinative abilities, i.e., skill-independent competencies such as balance, spatial orientation, and sensory discrimination as well as precision and speed.Regarding sensori-motor skills, different types are distinguished depending on the situational demands on skill execution. Regarding situations, three types can be distinguished: static situations, and situations changing in an expected or unexpected way. When responding to these different situations, the respective execution must be kept constant or has to be adapted.Regarding generic coordinative abilities, numerous abilities are proposed that influence the control, adaptation and learning of motor skills: Motor imagery, motor memory, balance, anticipation/reaction, spatial orientation, rhythm, dexterity, agility, open- and closed-loop control, eye-hand and eye-foot coordination .Another approach to sensori-motor coordination distinguishes between informational demands of the tasks regarding sensory and motor processing as well as various pressure conditions influencing the control of movements ; see Fig.The condition category includes four components regarding energetics of human movements: Endurance, strength, speed, and flexibility ,19.Endurance denotes the ability to perform physical activities of certain duration without loss in performance and to recover fast from physical load (strain). Different categories of endurance are distinguished with respect to metabolism , proportion of activated muscle mass , type of muscle work  and duration .Strength is defined as the ability to produce as much force as possible in order to overcome a resistance (concentric or positive work), hold a joint position (isometric or static work) or give way to supramaximal load (eccentric or negative work). Beyond type of muscle action maximum force is distinguished from power and force endurance.Speed denotes the ability of the sensori-motor system to react to certain events and to perform movements as fast as possible. Speed abilities can be distinguished into cyclic and discrete actions as well as reactions. Speed can be specific or elementary.Flexibility is defined as the ability of the sensori-motor system to perform movements with the required, i.e. optimal, range of motion (ROM). Flexibility is distinguished into active versus passive as well as static versus dynamic ROM.In many sports, cognitive-tactical skills are required. For example, in sport games it is important to perceive and judge the current situation as early and fast as possible in order to take and implement the adequate decisions. Tactical abilities are distinguished into individual, group and team tactics. Tactical abilities heavily depend on perception, decision making and creativity as well as executive functions such as working memory, attention, and multitasking –22.When it comes to psychic abilities, beyond cognitive factors mentioned in the previous section, further factors are considered by training science, such as motivation, emotion and volition as well as personality . For example, achievement motivation, i.e. motivation to meet challenging standards, emotional stability and modes of control  have a In addition, personality features may be associated with successful performance. There is substantial scientific evidence that “personality traits relate to long-term athletic success, interpersonal relationships, and athletes’ psychological states before, during, and after competitions” , for exaWhen playing in teams and against teams, social abilities such as communication, cooperation and collaboration are important factors influencing performance. Many social abilities are influenced by personality .Finally, the sixth group of factors indirectly influence performance in sports: disposition, body constitution, age, gender, and genes. Due to the fact, that these factors cannot be directly influenced by training, they will not be addressed in this paper.When comparing the competence model of games (section Models of game competence) with the performance model of sports (section Models of sport performance), distinctive similarities are recognizable. Based on the game and sports views, an integrative model of esports performance was derived . In the Just like in sports, tactical and cognitive abilities affect the performance in esports. Strategy games, for example, require the abilities of strategic thinking and decision making based on knowledge induced from experience and rules deduced from theories or models . Gebel eTo perform at the highest level in esports, the players must meet all requirements as effectively as possible. Therefore, tactical-cognitive abilities are essential components of esports performance. Since esports games are often team-based, not only single-player but also team tactics play an important role in many esports games. As a consequence, training in esports clearly concentrates on this factor .Regarding esports, both specific and generic components play an important role. A game is controlled by specific sensori-motor actions (skills) on interfaces and sensors such as hand-mouse, finger-keyboard or hand-joystick interactions as well as body-camera or body-force-platform interactions . Input dOne of the most important generic coordinative competencies which is deemed relevant to esports is spatial abilities. Players have to perceive their own position as well as the position of other players, avatars, or manipulanda. The construct of spatial abilities includes several components such as the perception of static or dynamic objects in different spatial reference systems . SeveralConsidering the informational demands in esports in relation to the model proposed by Neumaier , the visRegarding pressure conditions, the following dimensions are distinguished in the Neumaier model: time pressure, precision pressure, situational pressure, complexity pressure and stress-strain pressure. In Again, the assumption is plausible, that coordinative and sensori-motor abilities are two of the key factors for performance in esports and therefore should be an integral part of training.Due to particular competition-induced pressure, esports players have to be emotionally stable and flexible while acting fast, accurately and precisely. Among others, emotional stability and flexible actions depend on the players’ personality traits. An important characteristic for success in esports is overcoming barriers of optimal performance :Ineffective attentional controlNegative consequences of mistakesGoing on tilt and being harassedLimited ability to regulate emotionsDwelling on past performancesTrouble performing under pressureConfidence issuesInability to repeat flow experienceInadequate physical and mental preparationJust like professional sports athletes, esports players are exposed to stress situations. Dealing with disappointment, performance pressure and fear of failure is therefore part of esports as well.To succeed in esports, achievement-motivated acting is crucial. Lee and Schoenstedt  concludeThe ability to communicate, collaborate and cooperate within a team is an important component of successful performance in esports . BesidesAs mentioned above, the condition category includes four components regarding energetics of human movements: Endurance, strength, speed, and flexibility.In esports, on the one hand fast movements are required to control the game and on the other hand competitions can take many hours. Therefore, local anaerobic-alactacide endurance in fingers, hands and arms is required as well as global aerobic endurance.Strength abilities do not seem to play an important role in esports. However, maximal strength is an important determinant of speed. Therefore, this specific subtype of strength may contribute indirectly to game performance. In addition, to control posture, force endurance of the trunk muscles is required. Reactions as well as cyclic and discrete actions play a substantial role in esports. Fast reactions and fast movements constitute the success in many game situations.In esports, flexibility does not seem to be a factor limiting game performance in general. However, due to the repetitive execution of movements within spatial constraints and prolonged postures, e.g., in a sitting condition , specifiRegarding conditioning factors in eSport, global aerobic endurance, local anaerobic-alactacide endurance as well as selected speed abilities seem to be most important. Players have to move quickly according to particular events in the game. On the other hand, strength and flexibility may not be as important. Interestingly, athletes engaged in esports agree that physical fitness has a positive influence on esports performance . As a coJust like in sports, many constitutional factors contributing to performance, such as dispositions, constitution, age, and gender, cannot be directly influenced by training and will therefore not be addressed in this paper. However, health state can be supported by adequate behaviour, e.g., nutrition, sleep, relaxation, and physical activity.Media competencies are a fundamental prerequisite for practising esports. To play games the players must be able to handle the relevant media . Basic technical skills must be present . The player has to modify the game or the devices used . Knowledge of menu structures, how to deal with communication options  and orientation in the game environment can also be important in esports . AnotherIn this section, the integrated model of esports performance proposed in the previous section will be used to analyse the performance requirements and training behaviour of esports players in various esports titles. We will introduce related work, the methods used in our study and will present the results as well as discuss them.https://esportsresearch.net/literature-on-esports/  was performed. By this strategy, 45 articles were added. After the full-text assessment of 89 articles, 43 articles were excluded because of lack of eligibility. Overall, 46 articles were used in this research.Relevant studies were located via systematic search shown in The great majority of existing studies addresses selected components of esports performance . With reRegarding the training in esports, some studies exist that provide insight into the structure of training. In a survey of 1,319 esports players, Adamus  examinedThe existing studies show a great heterogeneity. This may be on the one hand due to method-related issues  and on the other hand to the heterogeneity of the esports domain. To investigate training, the differentiation between playing time and training time is highly relevant. The training time stands for the deliberate effort of systematically improving esports-related skills and abilities, while the playing time may focus primarily on fun, team experience or outcomes.Furthermore, esports game titles show a great variety. As stated in the beginning, esports can comprise different genres and different game modes. Every genre as well as different esports within the same genre have their specific goals, rules and requirements in different settings and different control schemes . Game moIn https://counterstrike.fandom.com/wiki/Competitive; last check on 10.12.2019).Counter Strike (CS) is the most popular esport in the action or first-person shooter genre; the game is played in teams (5 vs 5). The principle of the game follows an attacking-defending paradigm: One team has to lay a bomb and protect it until it explodes, while the other team tries to prevent bomb laying and neutralize the bomb. The avatars are controlled from a first-person perspective and are equipped with various weapons, which may be used to eliminate enemies. Competitive games are mostly played in a best of 30 rounds mode, with a round duration of approximately 2 minutes. CS is played on the PC with mouse and keyboard (Starcraft II (SCII) is a renowned single player (1 vs 1) real time strategy (RTS) esport. To win a game in Starcraft II, the players have to build and control different units from bird’s-eye view and destroy the enemies’ base. The game duration is not fixed (average: 10–15 minutes) but can vary considerably. Starcraft II is played exclusively on PC with mouse and keyboard.https://www.leagueofgraphs.com/rankings/game-durations/na; last check on 10.12.2019). LoL is also played exclusively on PC with mouse and keyboard.League of Legends (LoL) is currently the most popular esport worldwide . LoL as https://www.rocketleagueesports.com/rules/; last check on 10.12.2019). The game can be played with keyboard and mouse or a gamepad, however, the use of a gamepad is more common.Rocket League (RL) is currently the most popular team esport in the sports genre. In competitive gaming, RL is mainly played in teams (3 vs 3) and is in principle a vehicular soccer game. The players control their cars from a first-person perspective and have to score goals. The specificity of the game is that the controlled cars are not only able to fly, but also to destroy opponent cars. The controls in RL are challenging, as the avatar (car) can be rotated around the longitudinal, as well as transverse and vertical axis by means of hand and finger movements. RL is played on PC as well as on consoles . The game duration is exactly 5 minutes, plus overtime added in case of a draw . The most used control device is the gamepad.FIFA is a soccer simulation and the most popular single player (1 vs 1) game in the sports genre. The game can be termed a “virtual simulation” of soccer. While FIFA can be played on PC, it is more commonly played on Consoles (Xbox One and Playstation 4) in esports tournaments. The game duration in competitive FIFA is 12 minutes . In SCII, LoL and FIFA, the player has an overview from a bird's eye view. For this reason, the spatial orientation in these esports might not play as big of a role as in CS and RL.Strategic thinking is expected to be more important in tactics and strategy esports games . However, tactics can be decisive in any esport and are therefore important in FIFA and RL as well.The action speed is considered important in all studied esports. Nevertheless, it can be expected that skills related to action speed will be considered particularly important by SCII players, as frequent and extremely fast movements are mandatory .The endurance to perform over a longer period of time is important in every esport. Tournaments are often performed on one single day. The players have to maintain top level of their performance all day long during the tournament. However, in some esports endurance is particularly important. In SCII and LoL, games can have a much longer duration. The players have to maintain their concentration over a longer period of time. In CS, RL and FIFA the individual games are shorter, which gives the players the opportunity to recover.Reasonably, teamwork may be more important in multiplayer disciplines  as compared to single-player disciplines .To reach a great number of esports players, an online survey method was applied in this study. For the creation and implementation of the survey, the platform “sosci-survey” was used. Closed single- and multiple-choice questions as well as scale and valued measure questions were developed, while some questions were a mixture of open and closed questions . In totahttps://www.alexa.com/siteinfo/reddit.com; last check on 10.12.2019) and is very popular in the esports community. Every relevant esports game has its own subforum. Only the FIFA subforum did not allow to publish the survey. Therefore, public Facebook groups and the communication platform “discord” were used to recruit FIFA players , an approval by the Ethics Committee was not required, as the study did not involve medical treatment nor exposure of the participants to physical or psychological risk. This position was confirmed informally by two members of the Ethics committee.According to the guidelines of the German Research Association , anonymity was preserved.For the data analysis, IBM SPSS Version 25 was used. The statistical tests are specified in Table 11Overall, 1,835 participants completed the survey. N = 1,749; 95%). Furthermore, 67 (4%) females and 11 (1%) diverse took part in the survey. The skill level of the participants was rated high on average, i.e., mainly experienced players took part in the survey  per week was reported by the participants. The relative training time was 38.85% of the total playing time. Therefore, the respondents exercise for 7.78 hours per week.Regarding fitness level, most players (46%) estimated their fitness level as intermediate, whereas 33% rated their fitness level high or very high and 21% rated it low or very low .The players get information for their training methods from different sources . Very feRegarding competencies, game-dependent profiles were identified. Confidence, accuracy, spatial orientation and hand-eye coordination were considered more important in RL and CS than in the other three e-sports. The competence of dealing with pressure was of paramount importance for CS and SCII. Analytical thinking, strategic thinking, decision-making and personal attitude were considered less important in FIFA than in SCII, LoL, RL, and CS. In terms of motivation and response time, a similar assessment of significance was found for all e-sports studied, while teamwork and acceptance of team feedback were rated much more important by LoL, RL and CS than by SCII and FIFA players. The handling of technical problems, the adaptation of game settings and physical strength showed a greater significance in FIFA compared to the other e-sports examined. The importance of endurance, speed and agility was rated highest by the SCII and FIFA players.A two-factor ANOVA revealed significant main effects of e-sports disciplines and competence as well as a significant interaction .Mann-Whitney U follow-up tests  Tables sIn The training areas of reaction time and stamina were rated similarly important by all studied esports. Training of speed of single movements was considered most relevant in RL and FIFA, while training of repetitive movements was considered most critical in SCII and FIFA. Both, technique training and movement accuracy training were rated most important by RL players. Strategy training was considered most important by SCII, LoL and CS players. The importance of physical fitness training was rated highest by FIFA players.A two-factor ANOVA revealed a significant main effect of training area as well as a significant interaction of esport and training area/field .Mann-Whitney U follow-up tests see  Tables sIn Regarding the training areas, specific profiles were identified. Speed of single movements, as well as technique and movement accuracy are most often trained by RL players, while FIFA players are least likely to practice these areas. Stamina and physical fitness training are performed more often by SCII, LoL and CS players than by RL and FIFA players. Repetitive movements are most often trained by SCII and RL players, while strategy training is most frequently addressed by SCII and LoL players. The training frequency of reaction time was similar in all esports.A two-factor variance analysis revealed significant main effects of training area and esport as well as a significant interaction .Mann-Whitney U follow-up tests see  Tables sSignificant positive correlations between showing and improving skills and training effort were revealed. A significant, but weak negative correlation was found between enjoyment and training effort. No correlations were discovered between social connectness and social satisfaction and training effort .In sum, H4 could not be fully confirmed. Rather selected aspects of motivation are related to training effort.A significant, but weak correlation of skill level and training engagement was found .N = 1835; r = -0.056, 2p<0.05).A significant, but weak negative correlation between the playing time and the subjectively assessed fitness level was found  revealed a six-factor solution explaining 62% of variance . All 19 The six components can be interpreted as follows:Component 1: physical competencies (condition)Component 2: sensori-motor or coordinative competenciesComponent 3: strategic-cognitive competenciesComponent 4: psychic competenciesComponent 5: social competenciesComponent 6: media-related competenciesRegarding components of training that were estimated important by the esports athletes, factor analysis  revealed a three-factor solution with a clear contribution of the 8 variables explaining 60.5% variance :Component 1: speedComponent 2: strategy and psychophysical fitnessComponent 3: sensori-motor controlRegarding actual training of the esports athletes, a factor analysis  revealed a two-factor solution explaining 54.5% variance :Component 1: speed and sensori-motor controlComponent 2: strategy and psychophysical fitnessTherefore, a distinction between speed of actions and accurate sensori-motor control is not present anymore: Speed and accuracy are collapsed in actual training.In the following, the results are discussed and an outlook on further research is given. The results of the hypotheses and research questions are summarized in Only a small proportion of the forum members was recruited for the study , while the participants gender was dominated by males (95%). The results are conforming to the study of Adamus , where tPlayers spend an average of 20.03 (SD = 15.8) hours a week playing, but only 38.85% (7.78 h/wk) of the playing time is used for training. In the study of Adamus , playersSince there was an imbalance between the participants of the different esports and a dominant representation of CS (50%) and RL (33%) compared to the other esports , a different degree of representativity can be expected for every esport involved in this study.The results of H1 revealed that the esports addressed in this study differ in the estimated importance of competencies. At least one significant difference between the esports was found for every competence.Before the survey, assumptions were made regarding the importance of individual competencies. Differences in the esports were expected in the competencies of accuracy, spatial orientation, strategic and analytical thinking, action speed, endurance and team related competencies such as teamwork and acceptance of critical team feedback .In the upper part (coloured) of In terms of accuracy, the predicted profile was consistent with the one found in the study. As expected, accuracy was significantly more important in the esports RL and CS than in the other esports. This is mainly due to the game mechanics in the respective games. In CS, the accuracy of aiming is one of the basic mechanics . In RL, With regard to spatial orientation, the expected great importance in RL was confirmed. The control in RL is particularly challenging because the avatar (car) can fly and be controlled around all three axes. An additional difficulty in RL is the "ball cam". The "Ball cam" is an indispensable camera setting for the players, that always aligns the third person view of the player towards the ball. The associated fast changes of direction render the orientation much more difficult. These complex controls pose high requirements on the orientation. As expected for CS, spatial orientation fell into the group of "somewhat important" competencies. In CS, the avatar is controlled from the first person's perspective, which also poses challenges for orientation due to the limited field of vision but does not have as much freedom of movement and orientation requirements as the controls in RL. In SCII, LoL and FIFA the importance of spatial orientation was underestimated. However, estimations (between 3.31 and 3.67) are clearly below CS (3.99).In the category of strategic and analytical thinking, the results only matched the predicted profile for analytical thinking. Strategic thinking had a greater importance in RL than expected. It is surprising that the estimated importance of strategic thinking in RL falls into the same category as in SCII, LoL and CS, since SCII and LoL are classified as strategy games. For CS, a tactical shooter, strategy is also a key element. RL, on the other hand, is a very fast game in which strategic thinking seems to be more important than expected.For action speed, only two predictions were confirmed, i.e., RL and FIFA. In the remaining three games , action speed was overestimated. Since the fast execution of movements in SCII, LoL and CS is necessary in certain situations, this result is quite surprising.With the exception of FIFA, endurance was overestimated. The lower importance of endurance, contrary to expectations, can be explained by the fact that esports players of all skill levels were included in the survey and therefore a regular tournament participation cannot be assumed for every player. Since tournaments in esports often take place on one single day, stamina most likely plays a greater role for regular tournament participants than hobby players.In terms of teamwork and acceptance of team feedback, the predicted difference between team and individual esports was confirmed but overestimated. In LoL, RL and CS, the two competencies were rated more important than in the other two esports. It is surprising that acceptance of team feedback was estimated as “somewhat important” in FIFA, since this game is a single player esport.Regarding confidence, personal attitudes, dealing with pressure, motivation and decision making, despite statistical differences, a great importance is documented for all games. Considering the means, the most important competence was decision making. Being able to make right decisions in fractions of a second is critical in all esports.Reaction time represents the competence where the fewest differences between esports were discovered. The only significant difference was found between RL (4.26) and CS (3.97). CS was the only esport that did not rate reaction time as "important or very important". The significant difference in the importance of reaction time between RL and CS is very surprising, as quick reactions to the opponent or game action should play a similarly important role in both esports. Why this difference exists is therefore not clear and requires further investigation.The eye-hand coordination was rated as equally important for all esports, with the exception of FIFA (3.98).With one exception, media-related competencies were estimated as “somewhat important” for all esports. Only SCII players rated the ability to cope with technical issues as "rather unimportant”.Physical strength was rated as less important in all esports, with small differences between the cluster of SCII, LoL and RL compared to FIFA and CS. Obviously, physical strength plays a less important role in the esports.Agility was rated differently by the esports players as well. SCII and FIFA players rated this competence on average as "somewhat important" while the other three esports rated it on average as "rather unimportant". The differences between the esports can be explained by the fact that the mobility of the fingers could play a greater role in SCII and FIFA, since more keys are involved and a more frequent hitting with the fingers is necessary, than in the other esports. To confirm this thesis, however, further research will have to be carried out.This study revealed esports-specific differences regarding the estimated importance of training areas . SignifiThe training of the speed of single movements was rated as important/very important by RL and FIFA players, while the other three esports rated it as somewhat important. The significant difference found between RL (4.05) and CS (3.74) was surprising, because acting faster than the opponent can be viewed as a win condition in many situations in both esports.The training of repetitive movements was rated as somewhat important for all esports studied. Nevertheless, significant differences between the esports were discovered. The biggest significant difference was found between SCII (3.75) and CS (3.04). The difference can be explained by the fact, that repetitive movements are particular important in SCII, while there are not many repetitive movements in CS.The training of the technical skills was rated as important/very important by every esport studied. The biggest significant difference was found between RL (4.55) and CS (4.18), FIFA (4.17) and LoL (4.20). In RL skills training is significantly more important because of the complicated and versatile controls of the avatar. In FIFA, LoL and CS the same skills are also required but are not as important and therefore not as high of a priority in training.LoL players rated movement accuracy training as somewhat important, while the other esports rated it as important or very important. Even though the rating of SCII, RL, FIFA and CS is placed in the same category, significant differences between the esports were found. The most surprising significant difference was found between RL (4.49)and CS (4.23), as movement accuracy is considered crucial in CS  and in tStrategy training was rated as important/very important by every studied esport as well. Significant differences between the esports were still found. The most noticeable differences were found between esports that are from or related to the strategy genre  and the sports simulations (RL and FIFA). Due to the fact that the game principle of the first mentioned esports is much more strategy-oriented than in the two sports simulations, the results are not surprising. Nevertheless, as the results show, tactics training is important in every esports studied.Stamina training was rated as somewhat important by every esport studied and only few significant differences  were found. While RL players rated stamina training the lowest, SCII players rated it the highest in comparison to the other esports. This can be explained by the fact that SCII players have to perform many fast and repetitive moves over a longer period of time.With the exception of FIFA, physical fitness training was rated as „rather unimportant” by every esport studied. The biggest significant difference was found between FIFA (3.07) and RL (2.16). Why this difference exists, is not clear. It is remarkable that physical fitness was the least important training area for every esport studied.Differences in training frequency of the training areas were discovered . AnalogoSpeed of single movements are trained occasionally in SCII, RL and CS players, while LoL and FIFA players practice this area rarely. The most surprising significant difference in the training frequency of speed of single movements was found between RL (3.63) and CS (3.17). Although individual movements are particularly important in RL, the speed of individual movements, such as fast and precise mouse movements, also play an important part in CS, since fast aiming is a crucial part of the game. Therefore, the results are surprising.Repetitive movements are occasionally trained in SCII and RL, while LoL, FIFA and CS players train them rarely. Regarding significant differences, results conform to the results of H2, as the greatest significant difference was discovered between SCII (3.31) and CS (2.78). Frequent training of repetitive movements is important in SCII, while they are not necessarily a win condition in CS and therefore probably not the main objective of training.Technique or skill is trained often/very often by RL players, while the other studied esports players train it occasionally. The biggest significant difference was found between RL (4.35) and FIFA (3.26) players. Technique or skill is the most important and most frequently trained area in RL.Movement accuracy is trained often or very often by RL players, while the other esports players train this area occasionally. The predominant significant difference of RL (4.03) to CS (3.61) is particularly contraintuitive, because the movement accuracy is one of the most important and central traits in CS.Strategy training is performed often or very often by SCII players. LoL, RL, FIFA and CS players occasionally engage in strategy training. The significant difference in strategy training between SCII (4.05) and CS (3.39) is surprising, since in both esports the players are very dependent on the right strategy or tactics to win the game. There is no logical explanation for the results.Stamina is trained rarely by all esports studied. The significant differences in stamina training can be explained by the different game durations of the studied esports (see Selection of esports games for the study).Physical fitness is trained rarely in all esports studied as well. Significant differences were found between SCII and RL as well as RL and CS. There is no clear explanation why the frequency of physical fitness training between the esports should differ.The correlations found in this study were either negligible or weak. It is reasonable that intrinsic motivation, i.e., improving skills, shows the strongest correlation.A significant positive but weak correlation was found between skill level and training engagement. This may be due to the fact that training engagement comprises only about 39% of the total playing time (see RQ2).A negligible negative correlation was found. The difference to the results found by Froboese et al.  may be dSignificant weak positive correlations were found between the skill level and the total playing time as well as the playing hours until a break, while the correlation between skill level and the break length was negligible. These results are consistent with the well-known spacing effect in learning which claims a superiority of learning with breaks ,51. In pOnly weak correlations were found between the subjective skill level and the use of a professional trainer and scientific methods as a source of information for training methods used.It is particularly noteworthy that relatively few players use professional trainers (2%). This can probably be explained by the fact that professional trainers have to be paid. Another noticeable aspect is the low proportion of players who use scientific sources for their training methods (11%). This may indicate a need for transformable scientific information on correct and effective training methods in esports.Significant correlations were also found between the skill level and the other players as a source of information. This is the dominating source of information for the players (84%). Other players' information is free of charge and can be obtained through easy communication. In addition, this type of information retrieval requires little effort. Whereas top-level and low-level players use this source less often (75% to 77%), players ranging from advanced beginners to experts rely on this source more often (85% to 90%). This could be due to the fact that beginners are new and may not yet know any players, while the highest tier players do not consider other players as a valuable source of information.Another significant correlation was found between the skill level and players who are not looking for any information. In total, 17% of the players said they were not looking for any information at all. There was a tendency for beginners (28%) and top players (22%) to look less for information. This could be due to the fact that these beginners try to find their way without any external information at the beginning and deal with the game themselves before searching for training methods, while a substantial portion of highest tier players might think they do not need any additional information.For all training areas, significant correlations were found, with explained variance ranging from 11% to 40%. The weakest correlations were found in the training of physical fitness. This can be explained by the fact that physical fitness plays a subordinate role for performance in esports. Nevertheless, the majority of players (57%) are engaged in physical training. This result corresponds to the study of Froboese et al. , who fouThe strongest correlations were found for the training of reaction time (explained variance: 26%) and repetitive movements (explained variance: 40%). These training areas are estimated as “somewhat important”. There is no clear reason for this result. It is surprising that high-estimated areas show a lower correlation between estimated and actual practice.The exploratory factor analysis revealed a six-factor solution of the 19 variables, that was interpreted as follows:Component 1: physical competencies (Conditioning)Component 2: sensori-motor or coordinative competenciesComponent 3: strategic-cognitive competenciesComponent 4: psychic competenciesComponent 5: social competenciesComponent 6: media-related competenciesThe components are congruent with the integrative model of esports performance (reference) developed in the first part of the paper.For the estimated importance of 8 training areas, a three-factor solution was found. The factors were interpreted as follows:Component 1: speed of actionsComponent 2: strategy and psychophysical fitnessComponent 3: sensori-motor controlThe components closely resemble the first three factors found in the estimated competence structure. The difference is that strategy and physical factors form a common component in contrast to the estimated competence structure. This could be explained by the fact that strategic actions can also depend on psychophysical endurance or fitness.For the training engagement of 8 training areas, a two-factor solution was found. The factors were interpreted as follows:Component 1: speed and sensori-motor controlComponent 2: strategy and psychophysical fitnessFor training engagement, the factor structure shows a lower dimensionality. The components of speed of actions and sensori-motor control found in RQ 5 are collapsed. Sensori-motor control and speed are closely related in esports, because fast movements often have to be executed very accurately and in the right order (e.g. mouse or finger movements). With regard to strategic action, a certain psychophysical endurance or fitness is always required in order to make the right decisions in the process of a game.This paper provides valuable insights into the training of esports players. In the first step, a performance model of esports based on game research and sports science was developed. In the second part, an online survey with 1,835 participants was performed. The survey revealed, that there are indeed differences between e-sports with regard to the importance and training of certain competencies and training areas. Among other things, insights were gained into the playing time, actual training time and break behaviour of esports players. In addition, factor structures of competencies, critical training components and actual training engagement were determined, which confirmed the performance model of esports developed in the first part of the paper. It was shown that many performance factors of sports can also be decisive in esports. Limitations of the work are due to the fact that it was not possible to ensure an even ratio of the players of the individual esports.All in all, the work showed that esports deserve attention from the scientific community. Especially in the time of digitalization, a thorough understanding of esports training is important to develop and implement effective training as well as to discover possible deficits regarding health-related physical training. This study can be considered as a basis for future esports-specific training studies. Of course, future studies should address the specific structure of the different esports in more detail.S1 Data(SAV)Click here for additional data file.S1 Filehttps://doi.org/10.6084/m9.figshare.12423842.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Tablew(DOCX)Click here for additional data file.


Sarcopenia is a common condition in older individuals, especially in the elderly with type 2 diabetes mellitus (T2DM). The aim of the present study was to examine the risk factors for sarcopenia in elderly individuals with T2DM and the effects of metformin.  A total of 1732 elderly with T2DM were recruited to this cross-sectional observational study, and we analyzed the data using logistic regression analyses. Skeletal muscle mass, grip strength, and usual gait speed were measured to diagnose sarcopenia according to the criteria of the Asian Working Group for Sarcopenia, combined with expert consensus on sarcopenia in China. P < 0.05). We observed that sarcopenia increased with age and decreased with increasing BMI and educational level (P < 0.05). Participants who took metformin alone or combined with other drugs exhibited a lower risk for sarcopenia than those who took no medication .  The overall prevalence of sarcopenia was 10.37% of the participants. In the multivariate analysis, sex, age, educational level, and BMI were risk factors for sarcopenia, with women more likely to develop sarcopenia relative to men (OR = 2.539, 95% CI = 1.475–4.371;  We showed that being female and at an older age, lower educational level, and lower BMI were risk factors for sarcopenia in elderly T2DM and that metformin acted as a protective agent against sarcopenia in these patients. One of the most pressing problems facing mankind today is the aging of the general population, particularly in terms of quality-of-life issues. As a result of population aging, sarcopenia has also become a worldwide social issue . SarcopeChina now leads the world in terms of the size of its aging population. Since 1980, the number of people over 60 has grown by an average of 3.2% annually, and currently, the proportion of those 60 years of age and older in China is 17.3% of the total population. In 2017, China became the only country in the world to have more than 200 million elderly . In a stDiabetes mellitus (DM), a chronic metabolic disease, has reached epidemic status; and it now poses one of the major threats to human health in the 21st century. In 2017, the International Diabetes Federation (IDF) estimated that 425 million individuals worldwide were suffering from DM, and it is expected that the number will rise to 629 million by 2045 . ApproxiChiCTR-ERC-17011100).Metformin lowers blood glucose levels by sensitizing the liver to the effects of insulin, thus suppressing hepatic glucose output. According to updated guidelines, metformin is considered a first-line treatment, including treatment of the elderly . It has We used the United Nations (UN) guideline of 60+ years to define the elderly population , and theData were collected during a single visit to the clinic by endocrinologists and trained nurses from a face-to-face interview that entailed a standard questionnaire, as reported previously . Data on2). Waist circumference (WC) was measured as follows. The feet were separated by 30–40 cm (approximately the width of the shoulders), and a soft ruler was used to measure the circumference of the body at the midpoint of the connecting line between the anterior superior iliac crest and the 12th costal margin. Hip circumference (HC) was measured around the widest portion of the pelvis. Both WC and HC were determined using the same measuring tape, and the soft ruler was applied to the skin without compression. We use a standardized automatic electronic sphygmomanometer  to measure blood pressure (BP) three times at 3 min intervals followed by at least 5 min of rest. The mean of the three BP measurements was then calculated and used for all analyses. The participants were seated with their arms supported at the level of their hearts throughout these measurements. Blood samples were taken before breakfast in the morning and after an 8–12-hour overnight fast. Fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), total protein (TP), albumin (Alb), and hemoglobin (HB) were measured in all of the subjects.Physical examinations included weight, height, waist circumference, hip circumference, and blood pressure. Body weight was measured using digital scales to the nearest 0.1 kg, and height was assessed to the nearest 0.1 cm using a wall-mounted stadiometer; both measurements were procured while the participants were fasting and wearing light clothes and were without socks or shoes. We then calculated the body mass index , magnetic resonance imaging (MRI), computed tomography (CT), and bioimpedance analysis (BIA) are recommended by AWGS for measuring muscle mass. However, these sophisticated medical instruments are rarely available in communities of northeastern China, and they are too expensive to be used for screening sarcopenia. Therefore, we decided to apply weight and body fat measures for estimating muscle mass because of the low cost and ease of application. We used a V. Body HBF-701 —which is also a muscle rate analyzer—for the evaluation of body composition, we obtained the data for body weight (kg), body fat mass rate (%), body muscle rate (%), and appendicular muscle mass (%). Muscle mass was defined as the weight multiplied by the whole-body muscle rate. To calculate the cutoff values for muscle mass, we selected a reference group of young participants aged 18–40 years who had completed the measures . Low muscle mass was defined as muscle mass values less than double the standard deviation of the normal population. In our study, the cutoff value used to define low muscle mass was 16.30 kg for males and 11.88 kg for females.Handgrip strength (HS) was measured to the nearest 0.1 kg using a handheld dynamometer based on strain-gauge sensors . The measurement for each subject was performed on both arms and repeated three times, and the highest value for either hand was then used for the analyses . CombineAccording to the AWGS2014  and expeAs our study and data processing commenced prior to AWGS2019 , our diat-test for continuous variables. Multivariate logistic regression analyses were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the effects of several factors on the presence of sarcopenia. Our statistical analyses were performed using SAS Version 9.4  and two-sided tests. A P value < 0.05 was considered to be significant.Epidate was used to build the database, and we performed statistical analyses on the sarcopenic and nonsarcopenic groups. We used multiple imputation—based on the Markov chain Monte Carlo method in the SAS MI procedure—to account for missing data. Continuous variables are presented as means ± 1 standard deviation (SD), and categorical are presented as frequencies (%). The statistical significance of the differences between groups was assessed using a chi-squared test for categorical variables and a two-sample In the present study, a total of 1732 participants with T2DM were screened: 654 men and 1078 women. Of the total, 305 participants were excluded for either incomplete data or for using GLP-1 agonists and/or DPP4 inhibitors. We thus ultimately analyzed 1427 participants, of which 535 were men and 892 were women .P < 0.001): in men, it was 5.79%, while in women, it was 13.11%. When the prevalence of sarcopenia was evaluated by age group, it significantly increased with age—from 6.51% in the 60- to 69-year-old group to 27.67% for those >80 years of age (P < 0.001). As BMI and educational level increased, the incidence of sarcopenia decreased commensurately (P < 0.001). Although sarcopenia was associated with alcohol intake, we showed that its incidence was relatively low in persons with alcohol intake. With the increase in TC, the prevalence of sarcopenia increased, whereas the relationship between TG and sarcopenia was inversely proportional. As for hypoglycemic drug use, the prevalence of sarcopenia among those who did not take any hypoglycemic drugs was 17.12%, whereas the prevalence of the metformin-only group and the metformin combined group was 8.1% and 6.67%, respectively (P < 0.001). There were no significant differences between nonsarcopenic and sarcopenic groups when further subdivided for smoking, exercise, HB, presence of hypertension, TP, Alb, HDL-C, LDL-C, FBG, or HbA1c , except for usual gait speed in men (P < 0.05).P < 0.05), whereas alcohol use, TC, and TG were not associated with the presence of sarcopenia.The results of the multivariate logistic regression analyses are detailed in To the best of our knowledge, this is the first study aimed at detecting the risk factors for sarcopenia in elderly T2DM patients and also at detecting the effects of metformin on the condition according to the AWGS criteria, combined with expert consensus on sarcopenia in China.P = 0.035) [There are notable differences in the reported prevalence of sarcopenia, which may be affected by the study and reference populations , and eve= 0.035) . From ouThe prevalence of sarcopenia increases with age , and ourSarcopenia is also related to sex of the individual, but its impact on the prevalence of sarcopenia differs among studies. Some studies showed a higher prevalence among men , 34, 39,Although the prevalence of obesity in combination with sarcopenia is increasing in older adults , the autWe also studied the relationship between educational level and sarcopenia and found that the higher the educational level, the lower the prevalence of sarcopenia. This is consistent with some studies , 44. HowMetformin, as a first-line hypoglycemic drug, also plays an important role in improving sarcopenia –23, 47. Several strengths and limitations should be taken into account when considering the results of this study. This study has a number of strengths. We are the first to establish the risk factors for sarcopenia in elderly patients with T2DM and also the first to evaluate the effects of metformin on this condition—combining the AWGS criteria with expert consensus on sarcopenia in China. Second, this study was conducted on a relatively large sample of well-characterized, urban-dwelling elderly men and women living in a defined geographic area. Third, both sarcopenic and nonsarcopenic patients were recruited by the same research assistant from the same population and from the same source to reduce confounding bias. Fourth, the diagnostic guidelines that we used for sarcopenia were according to the AWGS criteria in combination with expert consensus on sarcopenia in China, which are more suitable for the Chinese population. Despite extensive efforts to curb them, there were also a number of limitations to our study. First, this study used a cross-sectional design, so it was not possible to determine causal relationships. Second, all of the participants in the present study were physically active; we did not include participants who demonstrated a history of stroke, carpal tunnel syndrome, and severe hip or knee osteoarthritis or were unable to perform a handgrip strength test or the 4-meter walking test. Because of this, our results might in fact underestimate the prevalence of sarcopenia. Third, although DEXA, magnetic resonance imaging, computed tomography, or BIA are recommended for measuring muscle mass by the AWGS, we used a muscle-rate analyzer for the evaluation of body composition. Fourth, although one aim of our study was to investigate the effects of metformin on sarcopenia, other hypoglycemic drugs in our study included sulfonylureas/glinides, alpha-glucosidase inhibitors, and insulin, and these were not specifically subcategorized, even though they might also have elicited protective effects against sarcopenia.In summary, we examined the prevalence of sarcopenia and determined several associated factors in an elderly population with T2DM. We also assessed the effect of metformin on the sarcopenic condition using the AWGS definition, combined with expert consensus on sarcopenia in China. We found that being female, older age, lower educational level, and lower BMI were risk factors for sarcopenia in elderly diabetic patients and that metformin acted as a protective factor against sarcopenia in our patients. Our study also showed that other hypoglycemic drugs  might exert a protective effect on sarcopenia.


NO is the earliest discovered gas signal molecule which is produced by normal healthy endothelial cells, and it has many functions, such as maintaining cardiovascular homeostasis, regulating vasodilation, inhibiting intimal hyperplasia and preventing atherosclerosis in the blood system. Insufficient NO release is often observed in the pathological environment, for instance atherosclerosis. It was discovered that NO could be released from the human endogenous NO donor by many compounds, and these methods can be used for the treatment of certain diseases in the blood system. In this work, a series of copper-loaded polydopamine (PDA) coatings were produced through self-polymerization time for 24, 48 and 72 h. The chemical composition and structure, coating thickness and hydrophilicity of the different copper-loaded PDA coatings surfaces were characterized by phenol hydroxyl quantitative, X-ray photoelectron spectroscopy, ellipsometry atomic force microscopy and water contact angles. The results indicate that the thickness and the surface phenolic hydroxyl density of the PDA coatings increased with the polymerization time.This copper-loaded coating has glutathione peroxidase-like activity, and it has the capability of catalyzing NO releasing from GSNO. The surface of the coating showed desirable hemocompatibility, the adhesion and activation of platelets were inhibited on the copper-loaded coatings. At the same time, the formation of the thrombosis was also suppressed. These copper-loaded PDA coatings could provide a promising platform for the development of blood contact materials.  Science Magazine [NO was generally considered to be one of the atmospheric pollutants, and it was regarded as one toxic and harmful gas before 1987. NO was not proved to be endothelium-derived derivative factor until it was discovered in the vasculature by Robert Furchgott, Louis Ignarro and Ferid Murad , wound rMagazine , and numMagazine .l-arginine in vascular endothelial cells, and it has pleiotropic effects including maintaining cardiovascular homeostasis, regulating vasodilation, promoting ECs growth, maintaining ECs integrity, inhibiting platelet and leukocyte adhesion, aggregation, inhibiting SMCs proliferation and preventing atherosclerosis [NO is produced by endothelial nitric oxide synthase (eNOS) catalyzed oxidation of clerosis . The adeclerosis . Due to clerosis , 16, bioclerosis , vasculaclerosis , 18, etcclerosis . For theclerosis  enclosesclerosis , 21, 22.clerosis , such asclerosis . The NO clerosis . The proclerosis .in situ. Copper is a trace element required by the human body and it is widely distributed in the body [A variety of substances have been reported that they are able to catalyze endogenous NO donor releasing NO, such as diselenodipropionic acid, cystamine and selenocyst-amine . They hathe body , which pthe body , 35. Sinthe body , 36. TheThis study proposes a strategy for building NO catalyzing releasing coatings via loading copper ions into a PDA coatings. Dopamine has been shown to be coated on many substrate surfaces . It has 2) was purchased from Aladdin Reagent. Dopamine and Tris base were supplied from Sigma-Aldrich. All other chemical agents were analytical grade and used without further treatment.Copper chloride  and an aqueous solution of 2 mg/ml copper chloride (DI water) were advance prepared. Then, the substrates, including 316L stainless steel (SS), silicon (Si) wafer or titanium (Ti), were dipped in dopamine solution, and then washed by DI water after immersed 24, 48 and 72 h, respectively. The samples were named PDA24, PDA48 and PDA72. Then, the samples were immersed in an aqueous solution of 2 mg/ml CuCl2+ to Cu+ under alkaline conditions, and the Cu+ can react with bicinchoninic acid (BCA) forming colored complex. The complex is purple and exhibits strong absorbance at 562 nm [The thickness of the different samples was measured by an ellipsometer  at a fixed angle of incidence (60°) and wavelength (658 nm). All coating thickness data were obtained by the Cauchy mode. The surface topography and roughness of samples were measured by an atomic force microscope . In this experiment, AFM was used to detect the surface roughness of PDA24, PDA48, PDA72, PDA24 Cu3, PDA48 Cu3 and PDA72 Cu3 coatings which were deposited on the silicon wafers. The contact angle apparatus  was used to evaluate the change of the hydrophilicity and hydrophobicity of the surfaces the samples. All tests were performed using DI water at room temperature. In order to investigate the copper ion carrying ability of the different PDA coatings, the surface density of phenolic hydroxyl groups was tested. The Micro-BCA method was used to evaluate the surface phenolic hydroxyl groups density of each 316 L SS. The phenolic hydroxyl group can reduce Cut 562 nm . The X-r2 (g).A chemiluminescence NO analyzer (NOA)  was used to monitor the real time release of NO. Here, copper-loaded PDA coatings and copper-free samples were prepared on Ti foil (0.2 × 1 cm). The samples were immersed into a test solution containing SNAP (10 μM) and glutathione . NO produced by membrane catalysis was delivered to the NO analyzer through a stream of N2+to form Cu+. Then BCA alkaline solution could react with Cu+ and form colored complex which have the maximum absorption at 562 nm. Then, a standard curve was prepared at 562 nm using a soluble copper salt. The absorbance of the purple complex at 562 nm on different samples was examined, and the copper ion release rate was calculated by comparing the standard curve.Micro-BCA can be used to detect the phenolic hydroxyl groups on the surface of the material. Based on this principle, the copper content on the surface also can be measured using the Micro-BCA assay. Briefly, GSH solution could react with Cuin vivo whole blood dynamic circulation system was established. Briefly, the system consisted of a New Zealand White Rabbit and one pipeline, the end of which connected the rabbit’s carotid artery and jugular vein. A sample of the copper-loaded coating prepared on the 316L stainless steel foil was placed in the pipeline before the start of the experiment. After circulating the blood for 1 h, the foil was taken out and washed with PBS for three times. Then the surface of the foil was capture by using a digital camera. The foil was fixed with glutaraldehyde aqueous (2.5%) for 4 h. The sample was then dehydrated, de-alcoholate and gold sputtered. The morphology of coating was observed using SEM .In order to evaluate the hemocompatibility of copper-loaded coating, a semi-P values were regarded as statistically significant when the probability value of less than 0.05.All quantitative analysis experiments were repeated at least three times independently for statistical significance. The results of data were expressed as mean 2 solution, the thickness of the copper-loaded PDA coatings is not significantly different from the thickness of the unloaded copper.The coatings were prepared on Si wafer and the film thickness of the PDA coatings was measured by an ellipsometer, before and after copper ion loaded. The PDA coatings were deposited on the silicon wafers surfaces. As shown in Surface topography and roughness calculations were performed on several process coatings deposited on the surface of Si wafers by AFM. As shown in The results of Micro-BCA assay for detecting phenolic hydroxyl density on each 316L SS surface are shown in There would be mutual penetration between different layers, resulting in a growing density of functional groups on the surface of coatings. For the copper-loaded samples compared with copper-free samples, the density of phenolic groups was significantly reduced. In other words, the loading of copper consumes a part of phenolic groups. Comparing the three groups of samples, the surface phenolic hydroxyl density of the sample decreased by nearly 50% after loading copper ions.The water contact angle (WCA) of samples was shown in 2+. Because if there is Cu2+ on the surface, a satellite peak appears between 938 and 945 eV [XPS test was performed to observe the chemical structure and composition of the copper-loaded and copper-free coatings on 316L SS. Since the elemental composition of the PDA24, PDA48, PDA72 have no significant differences, so the PDA48 sample was chosen as the PDA coating before copper loaded in this test. As shown in d 945 eV .The NO release rate was measured by chemiluminescence method using NOA280i instruments. As shown in in vivo whole blood dynamic circulation system was established, which consisted of New Zealand Whiter Rabbits and one pipe, as shown in The anticoagulant effect of blood contact materials is critical for thrombosis. Therefore, the anticoagulant behavior of the copper-loaded coating in the blood was further investigated. For blood compatibility assessment, a semi-+. This process can release NO. This study proposes a surface modification strategy based on PDA coating-mediated copper immobilization to address the complications associated with implanted blood contact materials. The method was simple and convenient, and had no shape limitations. The modified surface was capable of catalyzing RSNO decomposition and NO release. In the PDA coating, copper ions were deeply buried inside the coating structure, and as a result, the coating can continuously generate NO, thereby ensuring antithrombotic for a long period of time. At the same time, the NO release rate was a good inhibitor of thrombus formation. Overall, our work provides a surface modification method and a promising coating for blood contact materials.The core problem of long-term implanted blood contact materials is how to achieve anticoagulant effects based on the functionality of the material. NO, which can inhibit platelet adhesion and activation, is a signal molecule in the body. The abundant RSNO in the body can be catalytically decomposed by CuThis work was financially supported by the National Key Research and Development Program of China (2017YFB0702504), the National Natural Science Foundation of China (NSFC Project 81801853), the Postdoctoral Science Foundation of China (2018M633400) and the Sichuan Science and Technology Program (19GJHZ0058). Conflict of interest statement. None declared.


Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China. Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses in Europe and Asia , 2. ChinSeveral studies have been performed to establish models to analyze the HFRS epidemic in several provinces of China –7. This www.chinacdc.cn). The province-level data (from 2004 to 2018) on annual HFRS cases were from the China Public Health Statistical Yearbook, from 2005 to 2019. There was no HFRS data from Macao, Taiwan, and Hong Kong. In mainland China, HFRS is a notifiable infectious disease of class B, and all confirmed HFRS cases should be reported to CCDC in 48 h through the National Notifiable Infectious Diseases Reporting Information System , from January 2004 to December 2019 (www.geodata.gov.cn). According to the average annual incidence of HFRS, all provinces were divided into five categories: very high endemic areas with incidences >5.0/100,000 persons; high endemic areas with incidences between 1.0 and 5.0/100,000 persons; medium endemic areas with incidences between 0.5 and 1.0/100,000 persons; low endemic areas with incidences between 0.01 and 0.5/100,000 persons; very low endemic areas with incidences between 0 and 0.01/100,000 persons, and No data areas; respectively. On the maps, these five types of categories were color-coded. The annual province-level HFRS incidence was analyzed separately and then mapped using ArcGIS software .To analyze the spatiotemporal distribution of the HFRS epidemic, a map of China at provincial administrative regions and 1:1,000,000 scale was from the National Natural Resources and Geospatial Basic Information Database  by the operation of dilation and translation, ψ (x) ∈ L2(R) is called a mother wavelet function that satisfies the admissibility condition:ψx).where x) in L2(R) can be represented by:Grossmann and Morlet  proved ta, b) given by:Where  is the continuous wavelet transform of f (x) (Where of f (x) .scalogram or wavelet periodogram) is defined as:In analogy with the terminology used in the Fourier case , the  is obtained from:The WPS may be averaged over time for comparison with classical spectral methods. When the average is taken over all times, the The artificial neural network (ANN) is a computing system made up of several simple and highly interconnected processing elements, those mimic neurons and process information through their dynamic state responses to external input . ANN is 1, X2, …, Xk is the input parameters of WNN, Y1, Y2, …, Ym is the prediction output of the WNN, and the ωij and the ωjk are the weights of the WNN. The input signal sequence is Xi , the formula for calculating the output of the hidden layer is:h(j) is the output value of j in the hidden layer; ωij is the connection weights of the input layer and the hidden layer; aj is the expansion factor of the wavelet base function hj; bj is the translation factor of hj; hj is the wavelet basis function; l is the number of hidden layer nodes. In this study, we used the Morlet mother wavelet function as the wavelet base function , relative mean squared error (RMSE), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE) are calculated, whose formulae are as follows :actual(t) denotes the tth actual value, and forecast(t) denotes the tth predicted value. The forecast(max) and forecast(min) denote the maximum and minimum predicted value respectively.Where A total of 199,092 HFRS cases were reported from 2004 to 2018 in mainland China. The annual province-level incidence of HFRS ranged from 0 to 13.05 per 100,000 persons. Analyses of the WPS unfolded the cyclical fluctuation of the HFRS epidemic in mainland China. Furthermore, for ease of interpretation, we converted frequencies into period cycles. GWPS  presenteThe ANN is not only a mathematical model but also a supervised learning algorithm, that distributes parallel information processing based on the behavior characteristics of animal neural network. WNN is one kind of ANN consists of many simple connected processors called neurons or units, and linked by directed connections. The dataset of monthly HFRS incidence was split into a training period (2004/1–2017/12), and a validation period (2018/1–2019/12), the latter was used to test the predictive ability of the WNN model. First, we initialized the weights of network and wavelet function parameters, then set the weight function-learning rate to 0.1, the wavelet-based parameters learning rate to 0.01, the maximum error to 0.001. To compare the accuracy of WNN models with different structures, we calculated the MSE, RMSE, NRMSE, and MAPE for each WNN structure, then select the fittest model with a minimum value of MSE  . FinallyHFRS is an infectious disease caused by hantaviruses, with relatively high prevalence and mortality, which has brought a severe threat to human health during the past decades. Hantaviruses are transmitted to humans mainly via inhalation of virus-contaminated excreta and secreta, contaminated food, and rodent bites . TherefoThe GIS-based spatiotemporal analyses have been widely used to analyze the changing patterns of infectious diseases in the field of surveillance and infectious disease management , 25. HerTime-dimension analysis of the incidence of infectious disease could help to explain the observed epidemic status, subsequently establish a quality control system, and reallocate public health resources. As above mentioned, long-term changes in climate, environment, rodent abundance, and social features of human populations have generated non-stationarities in numerous epidemics. To overcome the problems of analyzing non-stationary time-series, wavelet analysis was applied to characterize them and estimate dependencies among non-stationary signals. Wavelet analysis is particularly attractive in view of epidemiological and environmental time-series and the relationships between them . The appTraditional time-series models are designed to reveal the linear functional relationship between the current incidence and historical incidence of infectious diseases and establish linear regression models to predict their incidence in the future , 29. TheOur study has limitations. First, the factors of climate, environment, rodent density, and urbanization level, which might have important impacts on HFRS epidemic, were not included in this study due to data unavailable. Second, besides the WNN, there are other non-linear approaches that could predict the incidence of infectious diseases, such as the back-propagation neuron network (BPNN), and support vector machine (SVM). Therefore, the accuracy of WNN prediction was also compared with BPNN and SVM using parameters of MSE, RMSE, NRMSE, and MAPE . All parThis study explored the spatiotemporal features of HFRS from 2004 to 2019 in mainland China using GIS, the cyclical fluctuation of HFRS was described by the WPS, and a WNN model was constructed to monitor and predict the trends of HFRS outbreaks. Our results could provide valuable tools for the hygiene authorities to design and implement effective measures for the control and prevention of HFRS in China.www.chinacdc.cn and the China Public Health Statistical Yearbook.Publicly available datasets were analyzed in this study. This data can be found at: The datasets for this study can be found in the L-XZ and LS: conception or design of the work, acquisition, analysis, or interpretation of data for the work, and drafting the work or revising. All authors: contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.