To identify and quantify the factors that influence the risky riding behaviors of electric bike riders, we designed an e-bike rider behavior questionnaire (ERBQ) and obtained 573 valid samples through tracking surveys and random surveys. An exploratory factor analysis was then conducted to extract four scales: riding confidence, safety attitude, risk perception, and risky riding behavior. Based on the exploratory factor analysis, a structural equation model (SEM) of electric bike riding behaviors was constructed to explore the intrinsic causal relationships among the variables that affect the risky e-bike riding behavior. The results show that the relationship between riding confidence and risky riding behavior is mediated by risk perception and safety attitudes. Safety attitude was found to be significantly associated with risky riding behaviors. Specifically, herd mentality is most closely related to safety attitudes, which means that those engaged in e-bike traffic management and safety education should pay special attention to riders’ psychological management and education. Risk perception has a direct path to risky riding behaviors. Specifically, stochastic evaluation and concern degree are significantly related to e-bike riders’ risk perception. The findings of this study provide an empirical basis for the creation of safety interventions for e-bike riders in China. E-bikes are electric-assist bikes that can travel longer distances and provide higher mobility and faster speeds to users compared to conventional bicycles. E-bikes have gradually become the most popular commuting tool for urban citizens in developing countries, especially in China, Vietnam, and Thailand . By the Due to the massive increase in injuries and deaths related to e-bike crashes, improving road safety for e-bike riders is important for transportation engineers. Numerous studies have indicated that human risk behavior factors are the core cause of the most traffic crashes. Analogously, the risky riding behavior of e-bike riders is an essential component of traffic safety management research and practice. Essentially, risky riding behavior is often manifested as lacking a safety-cautious attitude, blind riding confidence, a weak awareness of risky riding behavior, and violating traffic rules, all of which can result in high e-bike accident rates and casualties. Taking the violation of traffic rules as an example, riders usually run red lights, and use vehicle lanes and sidewalks. All these illegal behaviors result in the frequent occurrence and higher severity of traffic crashes. Risky riding behaviors are the external manifestations of the subjective psychological characteristics of riders. Most studies have focused on the relationships between factors influencing risky riding behavior including vision, age, maneuvering capability, transcendence, and distraction. The existing studies have not considered risk perception, risk attitude, riding confidence, and their exogenous indicators. Therefore, studying the deep-seated causes of risky e-bike riding behaviors can provide a theoretical basis for the intervention and control of risky behaviors, which has important practical implications.In terms of the methods used to study these deep-seated causes, traditional statistical analysis methods poorly explain the measurement errors of latent variables; thus, some researchers have used structural equation models (SEMs) to estimate latent variables. Although this method has some limitations, such as its inability to clearly reveal the influence of the omitted variables, its difficulty in dealing with nonlinear problems, and its inability to compensate for limitations in study design and method , it perf(1)What are the significant relationships among risky riding behavior, riding confidence, safety attitude, and risk perception, and their secondary exogenous variables?(2)How can strategies be implemented for intervening and preventing the risky riding behavior and understand the psychological mechanism of e-bike riders based on the SEM results?To explore the internal formation mechanism of risky e-bike riding behavior, this paper proposes an SEM using four scales: riding confidence, risk perception, safety attitude, and risky riding behavior. These scales are abstract concepts of social and behavioral sciences. Risk perception, riding confidence, and safety attitude reflect riders’ psychological feelings, which are directly or indirectly related to risky riding behavior. This study involved the collection of data regarding individuals’ riding behaviors on e-bikes and their intentions through questionnaires. Based on the SEM theory, the relationship model between risky riding behavior and the associated intentions was established. This empirical research on the relationships provides theoretical support for risk education and management. We addressed the following research problems:When studying driving behavior, domestic and foreign scholars have mainly focused on motor vehicles. The existing research on motor vehicle driving behaviors was mostly conducted from the perspective of perception, personality, and attitudes. Ulleberg et al.  first stPsychological factors, such as safety attitude and risk perception, are also considered to be significantly related to the safety of two-wheeled riders . Chen etEarly research on the relationship models in the traffic field mainly used traditional statistical analysis methods such as probit, logistic , and netTo ensure that the sampling error meets a certain confidence level, the sample size in the random sampling should be determined. Considering the huge number of e-bikes, it can be regarded as infinite without violating the reasonable conditions. Therefore, the simple random sample size of statistics was calculated using the following equation:We calculated that at least 385 questionnaires were needed. The preliminary questionnaire survey was administered to 60 individuals with e-bike violations in Guilin, a city in Southern China, as well as some preparatory drivers studying at a driving school. The preliminary questionnaire survey was designed to test whether the meaning and description of the questionnaire could be correctly interpreted by the subjects to avoid bias due to misunderstandings. The reliability of the questionnaire survey was assessed with SPSS , and the items with an acceptable level of reliability (Cronbach’s α > 0.7) were retained . Some ofThe random and tracking surveys were applied in the formal questionnaires. For the random surveys, e-bike riders were selected anonymously in Nanning and Guilin, and a total of 434 questionnaires were collected. After excluding those with incomplete and missing answers, a total of 381 valid questionnaires remained. There were 236 valid questionnaires in Nanning city and 145 valid questionnaires in Guilin city. For the tracking surveys, with the assistance of the traffic police, 192 valid questionnaires were collected among the e-bike riders who had experienced traffic crashes. There were 46 crashes without injuries, 134 with minor injuries, and 12 with serious injuries. The effectiveness rate of the questionnaire was 87.7%.A total of 573 valid questionnaires were obtained from the random surveys and tracking surveys, and finally, the questionnaires were recorded uniformly and converted into analyzable data formats for further statistical analysis.The riding confidence scale, which consisted of four questions in total, was based on the research of Wong et al. . The ridTherefore, we added four questions based on Wong’s riding confidence scale. The riding confidence scale was divided into two dimensions to evaluate technical capacity confidence and environmental judgment ability confidence. Each question was measured by a five-point Likert scale. On this scale, 1 through 5 represented totally disagree, disagree, partially agree, agree, and totally agree, respectively. The higher the score, the higher the confidence of the respondents.The risk perception scale was adapted from the study of Rundmo et al. . Risk peRundmo reported the worry and concern scale based on emotion, which contained six traffic crashes, traffic injury worries, and multiple anxiety-inducing items  . Machin’Therefore, we designed the risk perception scale, which was divided into two dimensions to evaluate the degree of concern and the danger perception of crashes. Each question was measured by a five-point Likert scale. The higher the score, the more worried and anxious the rider is about the risk, the higher the degree of danger of the subjectively perceived risk behaviors, and the more serious consequences of the perceived accident.The safety attitude scale was adapted from Rundmo . This scTherefore, we designed the safety attitude scale, which was divided into three dimensions to evaluate e-bike riders’ attitudes regarding personal responsibility, traffic regulations, and herd mentality. Each question was measured by a five-point Likert scale. The higher the score, the more positive the attitude toward safety, the stronger the sense of obligation to abide by the traffic rules, and the lower the preference of herd mentality.Yao compiled an e-bike riding behavior questionnaire (ERBQ) based on the motorcycling behavior questionnaire and cycling behavior questionnaire, combined with China’s national conditions and e-bike characteristics . The ERBTherefore, referring to previous questionnaires on motor vehicles, motorcycles, and bicycles, and combining them with observations on e-bike riding behaviors, we designed an e-bike riding behavior questionnaire (ERBQ). The ERBQ was used to measure violation, negligence and error, aggressive behavior, and leading behavior. Each question was measured by a five-point Likert scale. The higher the score, the higher the frequency of the risky riding behavior.Crash experience includes the frequency of crashes over nearly three years, including crash severity  and other multi-item indicators.Other information included demographic information , social and economic characteristics, level of education, the type of e-bikes, the main purpose of travel, and riding background .The survey respondents all had e-bike experience. Of the 573 respondents, 328 men accounted for 57.2% of the test population, while 245 women accounted for the remaining 42.8%. The respondents ranged in age from 14 to 63 years, with an average age of 29.7 years, and the youth (age less than 35 years old) accounted for 67.5% of the respondents. Notably, there are no mandatory restrictions on the age of e-bike riders in China, so riders under the age of 16 years were found. In terms of marital status, 253 married people accounted for 44.2% of the total, whereas 320 unmarried people accounted for the remaining 55.8%. In terms of education, 244 respondents had earned a college degree or above, accounting for 42.6% of the test population, and 183 people had a high school or secondary school degree, accounting for 31.9% of the test population; the remainder had education at or below the junior high school level.Most of the e-bike riders were not experienced with motor vehicle driving: 196 e-bike riders had some experience with motor vehicle driving, accounting for 34.2% of the test population, and 383 e-bike riders had no motor vehicle driving experience, accounting for 66.8%. Among the respondents, 431 rode light-motor-type e-bikes, a far higher number than those who rode a pedal-type ordinary e-bike, the proportion of which was 24.8%.In terms of riding experience, the average riding experience was 3.8 years, with riding experience ranging from at least 0.5 years and up to 17 years. After a cross-comparison study, some respondent stated that they started riding their e-bikes before reaching 12 years of age. The respondents covered an average distance of 12.2 km per day, and the standard deviation was 10.7 km; the results showed that there was significant variability in the distance measurement, which might be caused by the differences in the main purpose of e-bike usage. The average weekly use of e-bikes was 4.78 days, and the standard deviation was 2.0 days, indicating a high frequency of use. When evaluating the purpose of travel, 69.1% of respondents used them for commuter activities, such as riding to work or school; 23.9% used them for daily routines; and the remaining 6.8% used them for freight and other business activities.Over the past three years, 16.6% of respondents had been punished or warned for violating the rules and regulations. Of the 264 respondents (46.0%) who had experienced traffic crashes, 81 (14.1%) involved property damage, 164 (28.6%) resulted in minor injuries, 19 (3.3%) resulted in severe injuries necessitating medical intervention, and 108 respondents (18.8%) claimed primary responsibility for the crash. A total of 300 questionnaires were randomly selected for exploratory factor analysis (EFA) to explore the underlying structure for variables measured by multiple items. For the EFA process, we adopted the principal axis and varimax rotation approaches. Based on the EFA results, the main factors of riding confidence, risk perception, safety attitude, and risky riding behavior were extracted. Some items were moved or removed for low loading value or cross-loading. For riding confidence, two dimensions were determined, technical capacity and judgment ability, explaining a total of 76% of the total variance, consistent with the preliminary scale design. For risk perception, the “worry and concern” dimension based on emotion in Rundmo’s study was not complete; the factor analysis results showed that it should be divided into two dimensions. Therefore, three dimensions were determined, danger level, concern degree, and stochastic evaluation, explaining a total of 64% of the total variance. For safety attitudes, three dimensions were determined, safety responsibility, traffic regulation, and herd mentality, explaining a total of 71% of the total variance, consistent with the preliminary scale design. For risky riding behavior, four dimensions were determined, negligence and error, rule violation, aggressive behavior, and leading behavior, explaining a total of 68% of the total variance, consistent with the preliminary scale design. All potential variables showed an acceptable level of reliability (Cronbach’s α > 0.7). The factor loading and Cronbach’s α of the final items are shown in The SEM method is composed of a measurement model and a structural model. SEM is a type of verification model that is mainly used to verify the reliability of scientific assumptions regarding the basic structure of a theoretical model. It can simultaneously handle many endogenous and exogenous variables by establishing the relationships between variables based on linear combinations . The meaThe structural model represents the relationship between exogenous latent variables and endogenous latent variables. The formulation of the model is as follows:N = 573) data were used for the correlation analysis and validation analysis. Based on the results of correlation analysis and factor analysis, the SEM with riding confidence, safety attitude, risk perception, and risky riding behavior was constructed. The correlation analysis demonstrated that the riding confidence was significantly correlated with risk perception, safety attitude, and risky riding behavior (p < 0.05); riding confidence was identified as a potential exogenous variable. Finally, according to the relevant analysis results, six hypothetical analysis paths were determined: riding confidence → risky riding behavior, riding confidence → risk perception, riding confidence → safety attitude, risk perception → safety attitude, risk perception → risky riding behavior, and safety attitude → risky riding behavior.However, the initial path relationships of the theoretical model are unclear. Accordingly, we first employed correlation analysis to preliminarily judge and assess the relationships between the factors, which provided the basis for the hypothetical structural equation model paths. Assuming full path relationships between the four scales, and according to the parameter estimation results of the model to adjust the paths of the variable intrinsic relationships, the full sample , the degree of freedom (DF) was 68, and CMIN/DF was 3.378, which met the standard (between 1 and 5). RMSEA was 0.107, higher than the highest standard (0.05). This result showed that the gap between the theoretical model and the saturated model did not meet the standard. The normed fit index (NFI), comparative fit index (CFI), and increasing fit index (IFI) were 0.846, 0.879, and 0.881, respectively, which did not meet the evaluation standard of 0.90. The goodness-of-fit index (GFI) was 0.873, which did not meet the recommended standard of 0.90 [An adaptive analysis of the proposed theoretical model was conducted to verify whether the established path relationships were valid.  of 0.90 . The thep = 0.193), so this path was also deleted. The SEM with path coefficients and load coefficients is shown in The results of the path analysis showed that some paths had no significant relationship in the overall structural equation model. First, the standardization factor load between the initial potential variable “danger level” and the higher-order potential variable “risk perception” was only 0.314 (preferably between 0.4 and 0.96), so the variable “danger level” could be deleted. Second, the test results of the regression coefficients showed that the direct path between “riding confidence” and “risky riding behavior” was nonsignificant in the tested model (CR (Constituent reliability) = 1.301, p = 0.056), DF was 69, and CMIN/DF was 2.250, conforming to the standard. RMSEA was 0.028, which met the standard of 0.05, indicating that the gap between the theoretical model and saturated model met the standard. The coefficient of determination (R2) was used to measure the variation ratio of the dependent variable that can be explained by independent variables to judge the explanatory power of the model. The values of R2 were 0.41, 0.67, and 0.74, respectively, indicating that the explainable levels of independent variables reached a medium or high level. The variables The relationship between the error items was established, the original independent restriction was released, and one pair of parameters was modified each time the parameters were released. After revising the model, the maximum likelihood estimation method was used to test the overall suitability of the model. The NFI, CFI, IFI, and GFI were 0.913, 0.922, 0.925, and 0.948, respectively, all of which met the recommended standard of 0.90. The index results indicated that the revised structural equation model in this paper had an adequate fit for the observable data. The revised structural equation model with path coefficients and load coefficients is shown in After the verification and correction of the model, the paths were tested. The test results are shown in Safety attitude is directly related to risky riding behavior. As seen from the SEM, the overall utility of the variables on the safety attitude scale was, from large to small: herd mentality (0.83), traffic regulations (0.61), and safety responsibility (0.75). The consequence of the factor load coefficient indicated that e-bikers’ herd mentality has the most significant influence on their safety attitudes. This result is different from the study of driving behaviors of motor vehicle drivers, which showed that the association between traffic regulations and safety attitudes is the most significant. Therefore, more attention should be paid to riders’ psychological management and safety education. The results showed that e-bike riders that have a greater sense of responsibility for safety, a greater sense of obligation to obey traffic rules, and a lower preference to herd others are less likely to engage in risky riding behaviors.Risk perception and risky riding behavior have significant direct and indirect path relationships. The questionnaire defined “danger level” based on the respondents’ understanding of the assessment. According to feedback from the respondents, the mean value of the danger level was 4.37 (SD = 1.09), showing a higher level of danger perception of the risk behavior of respondents. However, as the danger level increased, the path relationship between risk perception and risky riding behavior in the whole structural equation model did not significantly change, indicating that danger level is not a good explanation for the risk perception scale. The overall utility of the variables “concern degree” and “stochastic evaluation” were 0.85 and 0.78, respectively, on the risk perception scale, showing higher load values. Accordingly, we concluded that the problem is not that e-bike riders think their riding behavior is dangerous, but that they believe there is a lower probability of being in danger than others in a general sense. This result showed that e-bike riders who worry more about riding and think there is a higher possibility of being in danger are less likely to display risky riding behaviors. E-bike riders who scored high on risk perception tended to have positive attitudes toward traffic safety, and thus tend to have safe riding behaviors.Riding confidence is not significantly associated with risky riding behavior, but it could be indirectly related to riding behavior by using risk perception and safety attitude as intermediaries. From the perspective of the variables of the riding confidence scale, confidence involved two dimensions: judgment ability (0.90) and technical capacity (0.68). The rider’s judgment ability had more influence on riding confidence than technical capacity. According to the questionnaire, “judgment ability” mainly refers to the riders’ confidence in the judgments of their behavior and the environment, which is accumulated through individual riding experience. For e-bike riders, because traffic management restrictions on riding behavior are not mandatory, the ability to judge one’s accumulated behaviors and the environment with self-affirmation can better explain risky riding behavior. This result showed that e-bike riders who score high on risk confidence tend to have anegative attitude toward traffic safety and risk perception, which in turn causes them to be more likely to display risky riding behaviors.The SEM analysis results provide some useful information for improving the strategies to ensure safety. The best way to intervene and improve the riders’ intentions is to avoid risky behaviors.The observed indicator variables of herd mentality, traffic regulations, and safety responsibility in safety attitude had a negative relationship with risky riding behavior, which means that relevant riding safety regulations and education are necessary to prevent risky behavior. Therefore, we recommend a strict enforcement of regulations for e-bikes to decrease traffic accidents. The strong correlation between herd mentality and safety attitude also suggests that herd mentality education and training should be strengthened to improve the awareness of risky behaviors. A rider license penalty point system should be implemented to regulate risky behaviors; for violators, safety knowledge training and examinations should be strictly implemented. By formulating these policies, the public can be shown the importance attached by the government to the safety of e-bikes, and then constantly correct the users’ attitudes toward safe riding.We also found a significant negative relationship between risk perception and risky riding behavior, and the stochastic evaluation was most closely related to risk perception, showing that the riders have the faulty mindset that traffic crashes will not happen to them. However, traffic accidents are highly unpredictable and no chances should be taken. To increase the awareness of risky behavior and the severity of traffic accidents, education and regulations are key, and must be implemented though a step-by-step process. Gradual and incremental approaches should be implemented to avoid risky riding behavior. For example, uniform riding licenses should be initiated at the first stage, strengthened safety awareness and education should be implemented every year, a mandatory helmet policy should then be regulated to reduce head injuries and fatalities resulting from e-bike and bicycle crashes, and e-bikes with speeds higher than 15 km/h should be forbidden in long-term plans. All of these countermeasures will gradually increase public awareness of risky behaviors. In addition, the media should continue to improve the coverage of traffic accidents, including an in-depth analysis of the causes of traffic accidents to improve public awareness of traffic accidents.The path relationship between riding confidence and risky riding behavior showed that overconfidence or even blind confidence in one’s riding ability indirectly leads to risky riding behavior. Because e-bikes riding behavior is not constrained compulsively in traffic management, the tolerance of risky behavior increases the confidence of riders and encourages undisciplined riding habits, which increase risky behavior and violations. In addition to laws and regulations, transportation infrastructure such as speed humps, separation between motor lanes and non-motorized lanes, and a proprietary signal phase at intersections with a large volume of e-bikes should be ensured to improve the riding environment and increase benign riding confidence, so that riders with poor riding skills or timid riders can feel that they are in a safe traffic atmosphere, which will reduce the occurrence of traffic accidents.In this study, a risky riding behavior scale was designed under China’s national conditions. We used an SEM to evaluate the internal potential relationships between influencing factors and risky riding behaviors. Compared with the traditional statistical methods, the analysis result of the SEM was more accurate; the theoretical model constructed in this study had a better fit as indicated by the various fit indices. The result showed that safety attitude, risk perception, and riding confidence are significantly directly or indirectly related to risky riding behavior. The reported findings provide some useful information for e-bike safety and a theoretical basis for the follow-up research on driving behavior. The research results of the inherent formation mechanism and the relationship among risk riding behaviors provide a theoretical basis for subsequent research.However, several limitations should be noted. Self-reported data may have some bias with expectations; however, other studies confirmed that the social expectation bias has little impact on participants’ responses. Nevertheless, further studies could be conducted to improve the current questionnaire design to better capture these potential psychological factors. The survey samples were only obtained from two cities, Guilin and Nanning, which cannot accurately represent the whole of China. However, currently, small- and medium-sized cities, especially in southern cities like Guilin, have a higher proportion of e-bike use. According to the comparison of the survey samples between Nanning and Guilin, the influence of geographical difference was not significant. Given the limitations of the SEM, the method should be improved in further research to obtain more accurate results. Since we only focused on e-bikes, it might make more sense to compare different types of two-wheeled vehicles in terms of psychological factors and risky riding behaviors. The risky driving behavior model may be further optimized, and additional considerations will be included, such as driving forgiveness, ecological driving, safety climate, and other aspects, into the model research.In the future, studies of these relationships need to consider the autonomous vehicles environment ,38,39. C


The fundamental role of emotions in education has been revealed in recent years. The control-value theory of achievement emotions has been postulated as one of the most used theories in this field. Thanks to the Achievement Emotions Questionnaire (AEQ), achievement emotions have been measured in different subjects and countries. The purpose of this research was to adapt and validate this questionnaire to assess achievement emotions in physical education. The sample of participants consisted of 902  secondary education students from various secondary schools in Spain. The psychometric properties of the Achievement Emotions Questionnaire for Physical Education (AEQ-PE) indicate that the scales are reliable and valid, as demonstrated by exploratory and confirmatory factor analysis, temporal stability, internal consistency and regression analysis. Considering the results achieved in the present study, the AEQ-PE opens a range of possibilities for both teachers and researchers. This instrument will help to understand the role of emotions in student learning and their motivation towards physical education. Emotions include sets of coordinated psychological processes, including affective, cognitive, physiological, motivational and expressive components . EmotionFor most students, positive emotions will be beneficial and negative emotions will be detrimental to academic learning . On the As Simonton stated , physicaFew educational theories focus on student emotions in PE, in spite of their importance . In receThis theory allowed the Achievement Emotions Questionnaire (AEQ)  to be deRecently, Simonton  has propThe participants in this study were 902 secondary education students  aged 11 to 17 years  from five educational institutions (public and private) in the province of Huelva (Spain). There were students from the four grades of lower secondary education: 285 first graders, 332 second graders, 224 third graders and 61 fourth graders. The selection of the sample was non-probabilistic, since it was made depending on those secondary schools that agreed to participate in the study. To carry out the factor analyses, the sample was randomly divided into groups of approximately 50% .The AEQ-PE was adapted to the AEQ-PA  as descrThe Spanish version, adapted to PE , of the The study was approved by the Andalusian Ethics Committee of Biomedical Research TD-OCME-2018) and was carried out in accordance with the ethical principles of the American Psychological Association . First, 018 and wOnce the new scale was defined, participation in the study was requested through school administrators and school boards. Since the students were minors, the Ethics Committee required written informed consent and parental authorization for the participation. Before administering the questionnaire to the entire sample, a pilot sample of 45 students completed the questionnaire. None of them expressed comprehension problems with the items. The data were collected during school hours, with the presence of a member of the research group to answer questions . The stu2/df, comparative fit index (CFI), Tucker–Lewis index (TLI), goodness of fit index (GFI), incremental fit index (IFI), root-mean-square error of approximation (RMSEA) and standardized root-mean-square residual (SRMR). Values equal to or greater than 0.90 for CFI, TLI, GFI and IFI; lower than 3 for χ2/df; and lower than 0.06 for RMSEA and SRMR are considered good fit indices [The psychometric properties of the AEQ-PE scale were tested to determine its validity and reliability for physical education. Because of the changes produced by the adaptation of the questionnaire to physical education and the lack of studies with prior exploratory analyses, it was considered necessary to test the instrument by an exploratory analysis. The robust unweighted least squares (RULS) method was used with promax rotation. Multiple criteria were used for factor selection . Secondl indices ,61.On the other hand, temporal stability and internal consistency were studied using Cronbach’s alpha. Finally, a regression analysis was performed in which the BPNs acted as predictors of achievement emotions. The Factor 10.10.01, SPSS 23.0 and AMOS 23.0  packages were used for the analyses.2 (276) = 4987, p < 0.001)) were calculated. These results show the suitability of the data for the analysis. Exploratory factor analysis (EFA) with robust unweighted least squares (RULS) estimation and promax rotation was employed to identify the latent dimensions that underlie the data. Following objective criteria [Before performing the exploratory factor analysis, the Kaiser–Meyer–Olkin test (KMO = 0.93) and Bartlett’s statistic indices  = 491, p < 0.001, χ2/df = 2.072, CFI = 0.942, TLI = 0.932, IFI = 0.942, GFI = 0.915, RMSEA = 0.049, SRMR = 0.044. The standardized factor loadings were statistically significant (p < 0.001) and ranged from 0.47 to 0.83  were introduced as predictors of the achievement emotions. The results  show howThe aim of this study was to adapt and validate the AEQ to the context of Spanish PE classes for secondary school students. To this end, to normalize the scales for practical application , due to Due to changes from the adaptation of the questionnaire to physical education and the lack of exploratory studies, an exploratory analysis was made. The results showed the existence of one factor for each emotion, which was confirmed by confirmatory analysis. Factor loadings were high and fit indices were good in both cases ,60,61. TBased on the fact that the satisfaction of the BPNs generates positive emotions and their frustration generates negative emotions ,22, a reThe results of the present study provide psychometric support for the AEQ-PE. Nevertheless, some limitations need to be considered. First, this study was carried out with a convenience sample of Spanish pre-adolescents in PE; in the future, other similar groups of students should be examined. Second, the level of measurement invariance has not been studied. For that reason, future research should take this into account when considering that the instrument validation is a continuous process. Similarily, it is recommended that future research continues to study the relationship between the BPNs and other motivational variables with emotions, as well as to find strategies to generate positive emotions and avoid negative emotions in PE. These strategies should be in harmony with the satisfaction of the BPNs as proposed by Deci and Ryan ; howeverIn conclusion, the results of this study provide support for the validity and reliability of the Achievement Emotional Questionnaire for Physical Education (AEQ-PE). This instrument has been shown to measure the main emotions of the CVTAE  with sat


This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different additive signal-to-noise ratio (SNR) increasing from 0 dB to 12 dB, and considering different fetal-to-maternal SNR increasing from −30 dB to 0 dB. The FECG detection of the NSITM is evaluated using statistical measures and results show considerable improvement in the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), as compared with other algorithms. The study demonstrated that the NSITM is a feasible algorithm for FECG extraction. The electrocardiogram (ECG) signal, in a non-invasive method, incorporates of the maternal ECG (MECG) signal, the fetal ECG (FECG) signal, and several sources of interference, such as power line interference, baseline wander, motion artifact, fetal brain activity, muscle artifact, as well as noise, such as instrumentation noise ,2,3. FECFECG extraction and enhancement method requires the elimination of the MECG as well as optimal detection of the FECG. The frequencies of both signals are a few Hertz’s and are possibly overlapping. Thus, separating them using the conventional linear filter fails. To address this problem, a large number of FECG extraction algorithms have been proposed over the past few decades. Some of these algorithms were based on the blind source separation (BSS) or blind source extraction (BSE) techniques ,6. In geThe PCA method is a standard statistical technique focused on finding a transformation matrix that transforms the input data to another set of data that are uncorrelated, yielding an estimate of the unknown source signals. The method is highly affected by noise and the extraction is weak if the set of input data are statistically independent . The quaThe work in  was inteThe work in  analyzedBSS-based ACF was presented in  to estimThe comparison between the relative performances of these algorithms is a challenging task due to the absence of a large public database and of also the absence of a defined evaluation methodology. However, it is possible to highlight the strengths and weaknesses of limited algorithms, evaluated on the same database and using the same methodology .N is the number of samples. Thus, designing new null space separating operator with less computational complexity, with no initialization constraint, and fast convergence, is crucial.The NCA was proposed in 2007 by R. B. Chena and Y. NianWub  to solveIn our previous work , we propThis paper is aimed to develop a non-adaptive FECG detection and extraction algorithm, based on using the null space approach in estimating the FECG and MECG signals from the ITM. The algorithm first reduces the effect of noise and interference using pre-processing filters, computes the ITM, then extracts the FECG and MECG signals from the null space of ITM. The algorithm detects also the fetal heart rate and uses it to improve the quality of extracting the FECG signal. A comparison between the proposed algorithms and other similar algorithms will be provided.The rest of this paper is organized as follows. In M is the number of recorded ECG signals, L is the number of the unknown source signals X=AS,G signal ,4. The EThe estimation of As the BSS model shown in (1) is affected by scaling, permutation, and rotation ambiguities , severalIn this section, we discus some widely used approaches, such as PCA and FastICA ,36. MoreDifferent methods were reported in the literature to estimate d on PCA . The PCA sources . Thus, t sources ,38,39.In the FastICA approach, the matrix , if L≤M . The res, if L≤M . Some wo, if L≤M ,10,27,28N in this method. The prediction error is minimised using the standard gradient descent algorithm (GDA). The update equation of the estimated source signals, is given by:In , a BSE bECG data . In addiThe proposed system is based on extracting the FECG and MECG signals from the null space solution of a certain matrix, named idempotent transformation matrix (ITM), and is being referred to us as The pre-processing stage includes three cascaded stages: low pass, high pass, and notch filters. The input and output of the the preprocessing stage are defined by d by f2, . A seconstandard .jth signal N prediction filter . The control logic considers these two factors and activates the MECG removal to extract a clean FECG signal. However, if the MECG peaks do not exist in the raw FECG signal, the control unit will deactivate the MECG removal stage. Thus, the raw FECG signal will be considered as the clean FECG signal.The control logic stage is required to decide about the existence of unwanted MECG peaks at the raw FECG signal, then activate the MECG removal stage when needed. The stage checks the estimated FHR. If the raw FECG signal contains MECG components, then the estimated FHR from (20) will not fall within the expected FHR ranges, since the values of elements of the vector The MECG removal stage removes the MECG components in the raw FECG signal using ACF, whose unit sample response is denoted by 1, FHR2, … FHRP2−1]. Values of 1–5 are found to be appropriate during simulation. The Algorithm 1 The proposed NSITM extraction algorithm.1:InitialsN, L, M, 2:Read the ECG signals 3:Preprocessing by denoising filters .4:Compute 5:Compute 6:Compute 7:Compute 8:Compute MECG peaks locations by ,43.9:Compute MHR by (19).10:Compute raw FECG peaks locations by ,43.11:Compute FHR by (20).12:1, FHR2, …, FHRP2−1].13:if14: Compute 15: Remove MECG component by: Clean FECG = 16:else17: Clean FECG = Raw FECG.18:endif19:Return clean FECG, MECG.From Five different simulations are provided in this section. The first simulation uses real ECG signals from the database for the Identification of Systems (DAISY) . Then, tRecorded real ECG signals, from pregnant women for 10 s, were used from . The sigRecorded real ECG signals, from pregnant women for one minute, were used from the Physionet Challenge 2013 data set ,46. Eachn viewer . It is cTo ensure the stability of extraction performance over time, the simulation is repeated by taking blocks of data samples from 5000–9999, and from 55,000–59,999 which are the last available data samples. The results obtained are very similar to the results using data samples from 0–4999. Results from those simulations are not shown in this paper to limit the number of pages. In general, the proposed NSITM algorithm as well as the other algorithms are effective in extracting both FECG and MECG signals from the abdominal ECG mixture if ACF is used to remove the MECG R peaks from the raw FECG signals shown in To study the extraction performance of the proposed algorithm, the ECG signals (FECG and MECG) must be first modelled then mixed according to (1). The modelling of ECG signals involves the generation of P, QRS, and T waves. This can be accomplished using the synthesized data taken from Physionet/Fetal ECG Synthetic database (FECGSYNDB) ,47. ThisTo evaluate the FECG extraction performance of the previous simulation, we use the similarity performance index (SPI) ,33, the  follows : (26)SIRThe simulation is repeated by first fixing the SNR at 0 dB and then varying simulated pregnancy numbers from 1 to 10. For each step, the SPI, SIR, SAR, and SDR are computed, and their average values are obtained. The simulation is repeated by varying the SNR to 3 dB, 6 dB, 9 dB, then to 12 dB. Results of simulation  are recorded in p, which is computed according to [p) [k, k = 10, 11, 18, 19. All above steps were repeated to extract FECG and MECG sigals when fmSNR is −25 dB, −20 dB, …, 0 dB. In this section, the FECG extraction performance is evaluated by varying the fetal-to-maternal SNR (fmSNR) from −30 dB to 0 dB and extracting the FECG signal . We used the same data from rding to ,49(29)png by p) ,49. Thisng by p) ,54:(30)In this simulation, we used three statistical measures, the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), in order to evaluate the performance of the proposed algorithm in detecting the FECG peaks ,7,29,54.From the results in The proposed NSITM algorithm is effective in extracting the FECG and MECG signals from the ECG mixture. The extraction shows some background noise, using the proposed NSITM and all used methods. This requires further investigation and is probably covered in future work.As the ECG signal from Daisy database is clean, the proposed NSITM is able to extract the FECG signal directly form the raw FECG signal obtained from Null(.) stage in In the first experiment, visual FECG and MECG waves are provided to demonstrate the advantages of our proposed algorithm, based on using real ECG data from Daisy database. From As the data used in this experiment is noisy, the proposed NSITM algorithm and other algorithms used in this experiment, provide raw FECG signals that contain both FECG and MECG signals, as shown in The extracted FECG and MECG signals, using the proposed NSITM, are better than other extracted FECG and MECG signals using PCA, FastICA, and PLP algorithms.This experiment is similar to experiment 1, in visualization the FECG and MECG signals. However, real ECG data were provided from the Physionet database. From The proposed NSITM algorithm is effective in extracting the FECG and MECG signals from the ECG mixture. As there were no MECG components in the raw FECG signals, the ACF will be deactivated by the control logic and the raw FECG signal is considered as the extracted FECG signal, as shown in As illustrated in As illustrated in In this experiment, visual FECG and MECG signals and four extraction performances  are provided to address the effectiveness of our proposed algorithm, based on using synthetic data from Physionet database, considering MHR/FHR acceleration/deceleration plus noise. From At very low fmSNR, −30 dB, the proposed NSITM algorithm and other algorithms show the same low level of qSNR, which is equal to 1.29 dB. This is expected from all BSS algorithms at very low SNR.As the fmSNR increased, the proposed NSITM shows a considerable qSNR improvement as compared with all other algorithms. The maximum qSNR was recorded to be at 9.1 dB when the fmSNR is 0 dB.The next considerable algorithm is the PLP that shows a qSNR value of 8.2 dB at 0 dB fmSNR.The FastICA and PCA performance scores for the third and the fourth places with qSNR of 3.83 dB and 2.12 dB, respectively, at fmSNR = 0 dB.In this experiment, the effect of varying the fmSNR on the qSNR is provided to address the effectiveness of our proposed algorithm in extracting the FECG for different values of fmSNR, and based on using synthetic data from experiment 3. From The proposed NSITM algorithm scores the highest average SE value (99%) as compared with other algorithms. The next highest scores are , using the PLP, FastICA, and PCA algorithms, respectively.The proposed NSITM algorithm scores the highest average ACC value (97%) as compared with other algorithms. The next highest scores are , using the PLP, FastICA, and PCA algorithms, respectively.The proposed NSITM algorithm scores the highest average PPV value (97.9%) as compared with other algorithms. The next highest scores are , using the PLP, FastICA, and PCA algorithms, respectively.In this simulation, three statistical measures  were used to evaluate the performance of the proposed algorithm in detecting the FECG peaks. A real data from experiment 2 were used in this simulation. From In summary, our best results on the Daisy and Physionet  databases were achieved by using the proposed NSITM as compared with other algorithms, for the majority of used data files. This is the best that we can do due to the absence of large publicly available databases with expert references .Furthermore, the FECG morphological evaluation of both the simulated and real data are in its earliest stages and requires further investigations. However, we have achieved some extraction performances based on synthetic databases.Furthermore, our proposed NSITM uses ACF to remove the MECG components from the raw extracted FECG. This may cause the removal of some information from the raw FECG signal if the FECG and MECG R peaks are overlapped. Thus, further investigation may be required to remove the MECG component using new post processing techniques.Furthermore, as the proposed NSITM has less computational complexity as compared with other NCA algorithms. Hence, the algorithm has the potential to be implemented in real time. Further investigation may be required to implement the algorithm for real-time applications that require cooperation with heath care providers and medical doctors.A noninvasive FECG extraction algorithm, referred to as NSITM, has been presented. The design problem has been formulated and an analysis has also been provided. The proposed algorithm computes first the ITM matrix Results of applying the NSITM algorithm to the Physionet/Fetal ECG Synthetic database (FECGSYNDB) have shown the capability of the algorithm in extraction FECG and MECG signals from all eight data signals used in simulation, and for all selected SNR values (available from the Physionet database from 0 dB to 12 dB), with MHR/FHR acceleration/deceleration plus noise being selected as the event type. The average values of the extraction performance metrics . Results on applying the proposed algorithm on the Physionet Challenge 2013 data/set a have shown the highest statistical values of SE, ACC, and PPV, as compared with other algorithms.


Diabetic macroangiopathy is a further complication of diabetes mellitus and is the leading cause of death for diabetic patients. Shenqi compound (SC) is a traditional Chinese medicine formula widely used in the treatment of diabetes and diabetic macroangiopathy. At present, there is only 1 systematic review on SC in the treatment of diabetes. However, no meta-analysis has evaluated the efficacy and safety of SC on diabetic macroangiopathy.χ2 and I2 tests. We establish 3 hypotheses before the subgroup analysis actually starts: disease status at baseline, duration of intervention, type of concomitant medication. We will conduct sensitivity analysis to evaluate the stability of the results, funnel plot analysis, and Egger test to evaluate the publication bias, and assessment for the quality of evidence by the Grading of Recommendations Assessment, Development, and Evaluate system (GRADE).Three English database and four Chinese medical databases will be searched from its inception to February 2020. Then 2 methodological trained researchers will screen the qualified articles by reading the title, abstract, and full texts according to an established inclusion and exclusion criteria. The assessment of risk of bias will be conducted by using the Cochrane collaboration's tool. We will conduct meta-analyses for fasting blood glucose (FBG), postprandial blood glucose (PBG), glycated hemoglobin (HbA1c), and other outcomes. The heterogeneity of data will be evaluated by Cochrane The results will be published at a peer-reviewed journal.In this study, we will systematically evaluate the evidence of SC in the treatment of diabetic macroangiopathy. Our research is supposed to provide evidence-based support for clinical practice. Animal experiments showed that it can improve the condition of DM and the mechanisms may be related to inhibiting adiponectin, reducing inflammation among others.In China, the application of traditional Chinese herbal medicine in DM has a long history and rich experience. SC is a traditional Chinese medicine formula which is widely used in the treatment of T2DM and DM in China. It consists of eight herbal medicine: Radix et Rhizoma Ginseng  of Observation Studies in Epidemiology recommendations for study reporting. The OSF registration number is DOI 10.17605/OSF.IO/TW9MA. This systematic review and meta-analysis protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P) checklist.2.2Population: Adults patients with an established DM diagnose will be included in our research. There is no limitation about region, sex and age of patients.Intervention: Studies that use SC as a major intervention in experimental group will be included. The control group can use any other medicines or placebo. If the authors use combination therapy of SC and other medicines in experimental group, these studies will be excluded.Outcomes: Only those studies that report detailed data about DM-related outcomes will be included in our research. Studies whose outcomes are not directly related to DM will be excluded. Those studies including abstract articles, conference articles, review articles, systematic review, and meta-analysis will be excluded. For conference articles and abstract articles, we will further look for whether there are any original articles that have made further reports on their research.Study design: We will only include prospective randomized controlled trials (RCTs). Observational studies like case–control studies, cohort studies and cross-sectional studies will be excluded. Similarly, retrospective studies will not be included in our research.2.3Three-point major cardiovascular adverse event (MACE) outcome  or 5-point MACE .All cause death.Disease-related risk score such as Framingham risk scale, China-PAR CVD Risk Calculator, risk factors for diabetes related complications.FBG.PBG.HbA1c.Total effective rate, the definition of effectiveness is elaborated in articles.Ankle brachial index.The peak systolic velocity of the anterior tibial artery, posterior tibial artery, and dorsalis pedis artery.The inner diameter of the anterior tibial artery, posterior tibial artery, and dorsalis pedis artery.The end diastolic velocity of the anterior tibial artery, posterior tibial artery, and dorsalis pedis artery.The mean average velocity of the anterior tibial artery, posterior tibial artery, and dorsalis pedis artery.Safety and tolerability.2.4Three English database including PubMed, Embase, Cochrane Library Central Register of Controlled Trials, and 4 Chinese databases including China National Knowledge Infrastructure (CNKI) database, Wanfang Data Knowledge Service Platform, the VIP information resource integration service platform (cqvip), China Biology Medicine Disc (Sino Med) will be searched from their inception to January 2020 with a language limitation of English and Chinese. In addition, Google scholar and Baidu Scholar will be used to find out potential missing papers. There is no time limitation about literatures. The Chinese Clinical Trial Registry (ChiCTR) and ClinicalTrials.gov will be searched to ensure that no clinical studies are missed.To obtain literature as comprehensively as possible, we will retrieve all the literature related to SC and then screen it according to inclusion and exclusion criteria. The search terms used will be as follows: “shenqi fufang,” “shenqi compound,” “SHEN-QI compound,” “shenqi formula,” “shenqi compound formula,” “Shen-Qi Compound formula,” “Shen-Qi formula.” Two authors (ZH and MY) will search and screen all the citations independently. An example of search process is presented in Table 2.5We will import the documents we download from the database into Endnote X8 for Mac (Thomson Reuters) software and then 2 methodological trained researchers will screen the qualified articles by reading the title and abstract according to the inclusion and exclusion criteria. Those documents that meet the criteria will be downloaded for further reading. A final decision will be made through consensus when there are discrepancies between researchers. A flow chart will be drawn to show the process of study selection Fig. .2.6The data of those qualified articles will be extracted into Microsoft Excel according to a predefined form for further analysis. For those studies with incomplete data, we will contact the authors for more detailed information. If we cannot contact the authors or the authors refuse to provide detailed information, then the document will be excluded. For those literature with the same data as another articles, for instance, repeatedly published articles, we will choose the one with more detailed data.For each study, the following information will be extracted: title, the publication country, the first authors of the article, time of publication, baseline information of participants, interventions in experimental group, interventions in control group, time and dose of treatment, course of disease, number of patients in each group, ages and sex of patients, outcomes, safety, and tolerability.2.7The risk of bias of included studies will be assessed by using the Cochrane collaboration's tool. In this tool, the risk of bias of a study is assessed from 7 aspects: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), other bias.2.8χ2 and I2 tests. The statistical heterogeneity is considered substantial when P < .05 and I2 > 50% and the random-effect model will be applied to pool data. If there is no significant heterogeneity (P > .05 and I2 < 50%), then the fixed-effect model will be used to calculate the effect size. If quantitative synthesis is not appropriate due to substantial heterogeneity, then systematic review will be conducted and the results will be presented with tables and figures.Data analysis will be conducted in Review Manager Version 5.3 and stata 14.0 software for Mac. The former is mainly used to create forest plots and conduct subgroup analysis and the latter is used to conducted sensitivity analysis and publication bias. For the binary variable, the effect size will be represented as risk ratio (RR) and 95% confidence interval (CI) and a Der-Simonian-Laird method will be used to calculate them. For continuous variable, the effect size will be represented as mean difference (MD) and 95% CI. The heterogeneity of data will be investigated by Cochrane 2.9If there is substantial heterogeneity between studies, then we will conduct subgroup analysis to explore the heterogeneity. To avoid post hoc analysis, the subgroup analysis will be conducted according to 3 hypotheses: baseline condition, duration of intervention, type of concomitant medication. To further improve the reliability of subgroup analysis, we will evaluate the credibility of our subgroup analysis according to the guidance for credible subgroup analysis. If there are enough studies included, then meta-regression will be conducted to further explore the heterogeneity. Those subgroup effects that occur simultaneously in subgroup analysis and regression analysis will be considered credible.2.10To ensure the stability of the results, we will conduct sensitivity analysis of the results by excluding each of the studies included in the analysis one by one, then re-analyzing the results, and comparing the differences between the re-obtained results and the original results. In this way, we will be able to assess the impact of individual studies on overall outcomes and their robustness.2.11P < .05.The integrity of the studies is an important factor affecting the accuracy of the results and conclusions of meta-analysis. The integrity of the included studies is mainly measured by reporting bias, of which publication bias is the most common. Therefore, this study will identify report bias by publication bias assessment. A funnel plot will be drawn to investigate the publication bias. Funnel plot will be asymmetric when publication bias exists, such as when research with small sample and no statistically significant results are not published. The more obvious the asymmetry of funnel plot is, the more likely there is publication bias.2.12Finally, this study will evaluate the quality of evidence for each outcome through Grading of Recommendations Assessment, Development and Evaluate system (GRADE), the most widely used evidence quality classification standard at present.The factors affecting the quality of evidence include the risk of bias in a single study , the indirectness of evidence, the heterogeneity of research results, the accuracy of effect estimation, and publication bias.2.13There is no patient and public involved in this study.2.14Ethical approval is not needed for this meta-analysis. This study comprehensively evaluates the existing research evidence of SC and can provide evidence-based medical support for clinical workers. The results of our research will be published in a peer-reviewed journal.3DM is the leading cause of death in T2DM patients. The purpose of this study is to summarize the clinical evidence of SC in the treatment of DM.To avoid bias as much as possible, we collect all relevant documents as comprehensively as possible. As to the exploration of heterogeneity, post hoc subgroup analysis should be avoided as much as possible. Therefore, this study presupposes several subgroup hypotheses before the study actually starts. In addition, we will evaluate the reliability of subgroup analysis, which is lacking in most studies at present. Finally, we will use GRADE system to assess the evidence so as to contribute to a better guide for clinical practice.3.1If any modification is required, we will update our protocol to include any changes in the entire research process.Conceptualization: Zhipeng Hu, Maoyi Yang, Chunguang Xie.Data curation: Hong Gao, Xiaoxu Fu.Formal analysis: Zhipeng Hu, Maoyi Yang.Funding acquisition: Chunguang Xie.Investigation: Hongyan Xie.Methodology: Zhipeng Hu, Maoyi Yang, Chunguang Xie.Project administration: Chunguang Xie.Resources: Zhipeng Hu, Chunguang Xie.Software: Zhipeng Hu, Maoyi Yang.Supervision: Hongyan Xie.Writing – original draft: Zhipeng Hu.Writing – review & editing: Chunguang Xie, Ya Liu.Zhipeng Hu orcid: 0000-0003-1524-6452.


Ankylosing spondylitis (AS) mainly affects the axial skeleton and is an important factor leading to chronic lower back pain in young individuals. However, few studies have explored alterations of brain gray matter volume in AS patients. The purpose of the present study was to describe brain gray matter abnormalities associated with AS pain. A total of 61 AS patients and 52 healthy controls (HCs) were included in this study. Using voxel-based morphometrics, we detected abnormal gray matter volume in AS patients. Based on the voxel-wise analysis, the gray matter volume in the left putamen of the AS group was increased significantly compared with that of the HC group. In addition, we found that the gray matter volume of the left putamen was positively correlated with the duration of AS and total back pain scores, whereas it was not significantly correlated with Bath Ankylosing Spondylitis Disease Activity Index scores, C-reactive protein, or erythrocyte sedimentation rate in AS patients. Taken together, our findings improve our understanding of the neural substrates of pain in AS and provide evidence of AS-related neurological impairment. Hence, further investigation of the pathophysiology of the left putamen in AS is warranted. Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease with unknown etiology that mainly affects the axial skeleton and is an important factor leading to chronic lower back pain in young individuals , 2. RangIn addition, imaging studies have linked chronic pain to specific brain structures and networks. Using the functional connectivity (FC) method, Hemington et al.  found thTherefore, in the present study, we used VBM to explore differences in brain gray matter volume between AS patients and healthy controls (HCs). We hypothesized that there are distinctive abnormalities in brain gray matter volume associated with AS pain, and that such brain changes may provide further insight into the neural substrates of AS-related pain.This study was approved by the Ethics Committee of Guangdong Second Provincial General Hospital. All subjects signed informed consent after fully understanding the research content. A total of 52 HCs and 61 AS participants, all of whom were right-handed, were enrolled in our study. AS patients were only recruited if they met the following conditions: (1) diagnosis of active AS in line with modified New York criteria ; (2) nonThe clinical assessment for each AS patient included BASDAI and TBP scores. BASDAI scores were assessed by the same trained rheumatologist. The BASDAI score—which includes spinal pain, joint pain/swelling, fatigue, localized tenderness, and morning stiffness—provides a comprehensive summary of symptom severity, with a scale from 0 to 10 .2; slice thickness = 1.0 mm; and 185 sagittal slices.A 3T Philips Ingenia MR scanner, with a 32-channel NV coil, was used for MRI scans in the Department of Medical Imaging at Guangdong Second Provincial General Hospital. Fast-field echo (FFE) pulse sequences were used to obtain T1-weighted 3D high-resolution brain structural images via the following parameters: repetition time (TR) = 7.9 ms; echo time (TE) = 3.7 ms; flip angle (FA) = 8°; acquisition matrix = 256 × 256; field of view (FOV) = 256 × 256 mmhttp://www.fil.ion.ucl.ac.uk/) and Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) (Based on SPM12 ((DARTEL) , structut tests, and sex composition was computed by Chi square tests between the AS and HC groups via SPSS 20.0. Two-sample t tests were also used to compare the gray matter volumes between AS patients and HCs. An absolute threshold mask of 0.1 was used. According to random Gaussian field theory, significant differences between the two groups of all voxels were estimated by distribution approximation, and the significance level was set as P < 0.05 (corrected for FWE). Partial correlations between gray matter volumes of different brain regions—which showed group differences and clinical results including BASDAI scores, TBP scores, serum CRP, ESR, and the duration of the disease—were analyzed for AS patients. Again, the significance level was set at P < 0.05.Significant differences in age and education were assessed through two-sample P > 0.05). The mean duration of back pain in the AS group was 7.51 years.The demographics and clinical data of the participants in this study are shown in Compared to that in the HC group, throughout the entire brain, VBM analysis revealed that one gray matter volume cluster was significantly increased in the AS group. The location of the cluster in a standard brain is shown in r = 0.301, P = 0.019) and TBP scores   . HoweverUsing VBM, we explored differences in gray matter volume between AS patients and HCs. Our findings showed that AS patients exhibited significantly increased gray matter volume in the left putamen compared with that in the HC group. In addition, the gray matter volume of the left putamen was positively correlated with the duration of AS and TBP scores. These findings suggest that the left putamen may be the most affected brain area that is implicated with chronic pain in AS. The altered gray matter volume in this brain area suggests that hypertrophy of the left putamen may be a potential abnormality in the central nervous system of AS patients.The putamen, pallidum, and caudate nucleus together constitute the striatum, which is a major source of cortical and subcortical input into the basal ganglia . The strAdditionally, we found that the gray matter volume in the left putamen was positively correlated with the duration of AS and TBP scores. This correlation may indicate the longer the duration of the disease, as well as more intense pain. Furthermore, this finding may be related to the larger gray matter volume of the left putamen in AS patients. Moreover, there were no significantly positive or negative correlations between the gray matter volume of the left putamen and BASDAI scores, ESR, or CRP. BASDAI is considered as the reference standard for the evaluation of AS disease activity. And ESR and CRP are acute phase reactants of inflammation . The resThere were several limitations of our present study. First, as a cross-sectional study, the directionality of the relationship between AS and abnormal gray matter of the left putamen remains unclear; future longitudinal studies are needed to resolve this question. Second, because of the cross-sectional group data, we were unable to observe dynamic volumetric changes over the developmental course of AS. Future studies should address these issues through longitudinal assessment of a large sample of AS patients.In conclusion, we used VBM to reveal increased gray matter volume of the left putamen in AS patients. We also found that the duration of AS and TBP scores was correlated with increased gray matter volume in the left putamen. These results may enhance our understanding of the neural substrates of pain in AS and provide evidence of AS-related neurological impairment. Furthermore, combined with previous research, the abnormal volume of the left putamen may be closely related to pain formation and perception in AS. Hence, further investigation of the pathophysiology of the left putamen in AS is warranted.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by Ethics Committee of Guangdong Second Provincial General Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.GJ designed the experiment. KH, PW, and ZL carried out the experiment. ML, TW, SL, and WZ collected and sorted out the data. XM, CL, SF, YY, PL, TL, and JF helped on data management and processing. KH, PW, and ZL wrote the manuscript. 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.


High myopia is a kind of ametropia with diopter more than −6.00D or axial length ≥26 mm. With the change of the modern environment, the incidence rate is increasing year by year. At present, the pathogenesis of high myopia is not clear. Some current studies indicate that it may be related to the environment and genetics. A Chinese patent medicine named Bu Jing Yi Shi Tablets (BJYST) has many functions including anti-oxidation, expansion of blood vessels, anti-inflammatory, immune regulation, inhibition of retinal photoreceptor cell apoptosis, and promotion of retinal repair. A large number of existing studies have shown that this prescription can relieve the clinical manifestations of high myopia and its complications, but its true efficacy and safety are still unclear. To certify this point, a protocol for a systematic review and meta-analysis of BJYST for high myopia will be performed.Articles that have been identified by electronically searching of 9 English and 5 Chinese databases from their inception to December 4, 2020 will be incorporated into the study. This study only adopts Chinese and English. Two researchers will take charge of completing the selection of research, the extraction of data as well as the assessment of research quality independently. The primary outcomes will be an average change in refractive error measured in diopters and an average change in axial length measured in millimeters. Data analysis will be performed via the RevMan 5.3 software, and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) will help to assess the evidence level.The results of this study will be published in a peer-reviewed journal.This study will conclude whether BJYST is safe and effective in treating high myopia on the basis of evidence-based medicine.The Open Science Framework (OSF) registration number is osf.io/dpk5b. In addition, complications of high myopia may cause irreversible vision impairment, such as glaucoma, cataracts, retinal detachment, and macular degeneration, etc.–5 At present, the pathogenesis of high myopia is not clear. Some current studies indicate that it may be related to the environment and genetics. It is worth noting that the occurrence of high myopia seems to be obviously related to race, age, and gender.–9High myopia is a kind of ametropia with diopter more than −6.00D or axial length ≥26 mm. With the change of the modern environment, the incidence rate is increasing year by year.,11 So far, there are many surgical methods, such as posterior scleral reinforcement, combining femtosecond thin- flap and LASIK with the Triple-A profile, and Single-step Transepithelial photorefractive keratectomy, etc.–14 Although studies have shown that surgical intervention can inhibit vision loss and axial elongation, related side-effects may also occur. For example, photophobia, overcorrection, allergic conjunctivitis, and Higher-Order Aberration increased, etc.–15 The symptoms of these adverse reactions would be relieved by treatment in the later stage, but some patients questioned the effect of the treatment and gave up further treatment, eventually, the development of the disease will cause irreversible damage.–5The purpose of treatment for high myopia is to correct refractive errors and control eye axis elongation. The main methods are conservative and surgical treatment. Conservative treatment including the use of atropine, enhancing outdoor activities, reducing near work, and so on, can play a therapeutic role to a certain extent.–21 A large number of existing studies have shown that this prescription can relieve the clinical manifestations of high myopia and its complications, but its true efficacy and safety are still unclear.–27 To certify this point, a protocol for a systematic review and meta-analysis of BJYST for high myopia will be performed.Chinese herbal medicine is one of the most important parts of Traditional Chinese Medicine(TCM), which has been used for thousands of years. Bu Jing Yi Shi Tablets (BJYST) is Chinese patent medicine which derived from the classic prescription Zhujing Wan in “The Six Classics of Ophthalmology” by Chen Dafu, a well-known expert in Chinese ophthalmology. According to recent pharmacological studies, It's herbal ingredients has antioxidant, vasodilator, anti-inflammatory, immune regulation, inhibit retinal photoreceptor cell apoptosis, and promote retinal repair.2To establish a systematic and comprehensive approach for locating the evidence, a review and meta-analysis will be used to whether the BJYST is effective and safe in the treatment of high myopia.33.1The protocol has been registered on OSF platform and the registration number is osf.io/dpk5b. The protocol was written following the statement guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMAP).3.2Inclusion criteria: all randomized controlled trials (RCTs) about BJYST to treat high myopia. The review only adopts the language of English and Chinese.Exclusion criteria: Non-RCTs, quasi-RCTs, case series, reviews, and animal studies.3.3Patients clinically diagnosed with high myopia will be studied. Participants in this study will not have gender, age, ethnicity, job, or education restrictions.3.43.4.1The experimental group will include patients receiving treatment of BJYST. The treatment frequency and duration are not restricted.3.4.2The Control group will include patients receiving control interventions including placebos or drugs that have been proven effective in the treatment of high myopia.3.53.5.1The primary outcomes will be an average change in refractive error measured in diopters and an average change in axial length measured in millimeters.3.5.2Secondary outcomes include Change of the visual field, Change of retinal light sensitivity, Change of contrast sensitivity function, and Change of electroretinograms b-wave (ERGb) amplitude.3.6Electronic searching will focus on databases of the Cochrane Library, MEDLINE, EMbase, AMED, Nature, Science Online, PubMed, WorldSciNet, World Health Organization International Clinical Trials Registry Platform (ICTRP), the Wanfang Database and China Biology Medicine Disc, and China National Knowledge Infrastructure, the Chongqing VIP Chinese Science and Technology Periodical Database, with the temporal from the inception of database to December 4, 2020. For ongoing RCTs, researchers will contact the trial author to obtain the latest clinical data. Besides, studies associated with the review will be identified by evaluating related conference proceedings. The research flowchart is shown in Figure A search strategy of the combination of text words and Medical Subject Headings (MeSH) terms will be adopted. Appendix Table  displays3.73.7.1After the independent information extraction from literature included in the study, two researchers will input the extracted information into a unified statistical table of data. Ineligible studies as well as duplicate records will be first eliminated, followed by a review of the full text of those eligible ones for confirming their compliance to the abovementioned inclusion criteria. If the two researchers cannot come to an agreement, the final judgment will be made by the third researcher.3.7.2The following information will be extracted from each study: the reference ID, the first author, year of publication, age and gender of patients, the number of included cases, randomization, allocation concealment method, blinding method, interventions and control measures, intervention group's sample size, intervention time, measure of outcome, primary outcomes, period of observation, follow-up duration, routine examination of safety, fund source and type. The researchers will contact the author of study in the case of insufficient reported data. If negotiation cannot help to come to agree on the extraction of data, the final judgment will come to a third researcher.3.7.3 Assessments include selective reporting, random sequence generation, incomplete outcome data, allocation concealment, blinding as well as other possible biases. Related standards proposed in the Cochrane Intervention System Assessment Manual will be considered to classify risk of bias into three levels: high risk, low risk, and unclear risk. The discrepancies will be resolved by discussion and the third author will make the final judgment if the two researchers cannot reach an agreement.Two researchers will independently adopt the Cochrane collaboration risk-of-bias assessment for assessing the quality of literature included in the review, together with completing the STRICTA checklist.3.7.4Odds ratio (OR) and risk ratio (RR) with 95% confidence interval (CI) will be used in measuring the treatment effects specific to dichotomous outcomes. For continuous outcomes, mean difference (MD) or standardized mean difference (SMD) with 95% CI will be calculated to measure the treatment effects.3.7.5The study will use the data of patients in RCTs. Where more than one BJYST group is arranged in an RCT, multiple meta-analyses will be separately performed for each treatment group. Data of crossover trials will be obtained from the first sequence. The intervention and control groups will be analyzed by summarizing all controls’ results if there are many control measures.3.7.6The reason for the loss of data missed in the period of data screening and extraction will be identified here. Corresponding author will be contacted for obtaining missing data. With missing data unable to be obtained, available data will be analyzed only and the reason and effect of such exclusion will be explained.3.7.7Cochrane Handbook for Systematic Reviews of Interventions describes that the Higgins’I2 statistic, a heterogeneity x2 test and a visual check of the forest plot can all help to assess the heterogeneity. A fixed-effects model will be used to pool the data with p value over 0.10 and the I2 value less than 50%. A random-effects model will be adopted in other cases. When a set of studies exhibit an obvious heterogeneity, factors leading to the heterogeneity will be discussed, like the characteristics of patients and the variation degree in interventions. The heterogeneity will be evaluated via the subgroup analysis or the sensitivity group if applicable.The meta-analysis will be carried out with the help of a random- or fixed-effects model. 3.7.8P value less than .05 will be considered that the publication bias is significant.The biases of reporting will be assessed via virtue of a funnel plot if the meta-analysis includes over 10 trials. The asymmetry exhibited by the funnel plot will be evaluated via the Egger and Begger tests, and 3.8The RevMan 5 software  will be used for data analysis. The heterogeneity degree will help to confirm whether a random-effects model or a fixed-effects model will be used. The categorical variables will adopt the index of RR or OR and 95% CI. Continuous variables will adopt the index of MD or SMD and 95% CI. Chi-square distribution test and I2 statistic will be used to analysis the heterogeneity of included studies. If quantitative synthesis is not appropriate due to substantial heterogeneity, only qualitative analysis will be performed. The subgroup analysis shall carefully consider each subgroup in certain case. Meta-analysis will not be conducted if no assessment, like subgroup analysis, is able to explain existing meaningful heterogeneity.3.9If necessary subgroup analysis and sensitivity analysis will be performed to explore possible causes of heterogeneity. Subgroup analyses will consider the heterogeneity exhibited by the following aspects: diopter, progress classification, astigmatism, the dosage of BJYST, duration of treatment, frequency of treatment, and measures in control groups. If quantitative synthesis is not applicable, we will proceed to narrative synthesis.3.10For testing, if review conclusions are robust, primary outcomes will receive a sensitivity analysis based on criteria involving the size of sample, the quality of heterogeneity and the statistic model (whether it is a random-effects model or a fixed-effects model).3.11 The assessment includes risk of bias exhibited by studies, the heterogeneity, evidence directness, estimate precision of effect and publication risk of bias. Evidence will be divided into four categories considering the level: high risk, moderate risk, low risk, and very low risk.The evidence quality for obtained results will be assessed via the GRADE method.3.12Meta-analysis is a secondary study of the published data, thus there is no need for obtaining the ethical approval or patients’ informed consent. The results will be published in a peer-reviewed journal.4 Apart from the substantial socioeconomic cost, severe sight-threatening complications associated with high myopia substantially compromise quality of life. There are significant odds ratios (ORs) for myopic maculopathy, retinal detachment, cataracts, and glaucoma, even for low and moderate levels of myopia, and these ORs increase further with higher levels of myopia. Researchers and clinicians have proposed approaches to treat myopia for many years. However, to date, no ideal approach has been identified as efficacious for the prevention and treatment of myopia with sufficient safety and clinical acceptability.Myopia is a relatively prevalent and increasing public health concern, particularly in East Asia, where it has already reached a pandemic level.Chinese herbal medicines may be a reasonable and safe alternative to treat high myopia. Among the abundant Chinese herbal medicines, BJYST has been proven to be effective in the treatment of high myopia. However, its real efficacy and safety are not well understood. Therefore, this study will evaluate the efficacy and safety of BJYST in treating high myopia by searching and analyzing relevant studies comprehensively. In this study, we would draw a scientific conclusion and provide evidence of evidence-based medicine for BJYST in the clinical treatment of high myopia.Conceptualization: Jie Ma, Ya Mo.Data curation: LinZhi Li, Jie Ma.Formal analysis: Jie Ma.Methodology: LinZhi Li, Jie Ma.Resources: LinZhi Li, Jie Ma.Software: LinZhi Li.Supervision: Ya Mo.Writing – original draft: LinZhi Li, Jie Ma.Writing – review & editing: Ya Mo.