Shigellosis is one of the main diarrhea diseases in developing countries. However, the transmissibility of shigellosis remains unclear.b) and from reservoir to person (bw), and the effective reproduction number (Reff) were estimated. To simulate the contribution of b and bw during the transmission, we performed a “knock-out” simulation in four scenarios: A) b = 0 and bw = 0; B) b = 0; C) bw = 0; D) control (no intervention).We used the dataset of shigellosis cases reported between January 2005 and December 2017, from Hubei Province, China. A mathematical model was developed based on the natural history and the transmission mechanism of the disease. By fitting the data using the model, transmission relative rate from person to person . The mean values of b and bw were 0.0898  and 1.1264 × 10− 9 (95% CI: 4.1123 × 10− 10–1.8416 × 10− 9), respectively. The “knock-out” simulation showed that the number of cases simulated by scenario A was almost the same as scenario B, and scenario C was almost the same as scenario D. The mean value of Reff of shigellosis was 1.19 (95% CI: 1.13–1.25) and decreased slightly with a Linear model until it decreased to an epidemic threshold of 0.99 (95% CI: 0.65–1.34) in 2029.A total of 130,770 shigellosis cases were reported in Hubei province, among which 13 cases were dead. The median annual incidence was 19.96 per 100,000 persons  with a decreased trend (trend The incidence of shigellosis is still in high level. The transmissibility of the disease is low in Hubei Province. The transmission would be interrupted in the year of 2029. Shigella spp which are Gram-negative bacteria, is a fecal-oral transmission disease with a symptom of severe colitis and induced by a very low infectious dose : 2.3 days). Each year had a similar distribution of DID  and 2 days (30.87%).Of all the cases, male cases were account for 56.57% 73,981/130770) which was significantly higher than that of female cases  and the highest cumulative incidence during the past 13 years. The second highest number of cases was reported in Yichang City (8.66%) and Jingzhou City 8.24%). However, the rank of reported incidence was very different. Xiantao City and Yichang City had the second and third highest incidence, respectively. Shennongjia, which is a forest region, had the least reported cases (0.05%) and the lowest reported incidence .The mean reported incidence of shigellosis was 58.53 per 100,000 in urban areas  and 14.10 per 100,000 in rural areas  . Calculated by the model, the mean value of b was 0.0898 (95% CI: 0.0851–0.0946) and bw was 1.1264 × 10− 9 (95% CI: 4.1123 × 10− 10–1.8416 × 10− 9) from 2005 to 2017 in the province. The difference between the two parameters was as much as more than seven orders of magnitude in each year (Table b = 0 and bw = 0) was almost the same as that simulated by scenario B (b = 0), and that the number of cases simulated by scenario C (bw = 0) was the almost same as that simulated by scenario D (control), which means that parameter bw had almost no contribution to the transmission.The results of curve fitting Fig.  showed tReff of the disease was calculated with mean value of 1.19 (95% CI: 1.13–1.25) using Model 2  in the year of 2005. It reached a peak value of 1.39 (95% CI: 1.14–1.65) in 2006. Although it had a lowest value in 2010, it decreased slightly yearly  in the year of 2028 and be down to an epidemic threshold of 0.99 (95% CI: 0.65–1.34) in 2029. Based on the Quadratic model, Reff would reach a low value of 1.00 (95% CI: 0.00–2.93) in the year of 2031 and be down to an epidemic threshold of 0.999 (95% CI: 0.00–3.12) in 2032. However, the Cubic model predicted an increasing trend after the year 2018.The effective reproduction number l 2 Fig. . The meaReff trend of Shigellosis in China. Located in the lake region, Hubei Province has a high burden of shigellosis and provides a large data for the modelling study. Our model developed in Hubei Province could also be used to study the transmission of the shigellosis in other places. Therefore, our study has an important significance to provide a reference for deeply understanding the transmission characteristics of the disease.To our knowledge, this is the first study to estimate the long-term transmissibility and forecast the In our study, the SEIARW model was employed to fit the epidemic curves of the reported data, the results of Chi-square test showed a high good-of-fitness of our model to the reported data, suggesting that the model is suitable for this study and can be used to estimate the transmissibility of the disease.In this study, we found that the incidence of the disease was still in a high level although with a decreasing trend. This finding is similar to the epidemiological characteristic in many areas in China –11, 18.Shigella spp than females; 2) individuals who were infants or old were more likely to be infected by the pathogen. These findings are similar to the epidemiological characteristics of shigellosis [Our findings of sex and age distribution showed that: 1) male individuals were more likely to be infected by gellosis , 32, 33.Our findings of area distribution showed that the incidence of the disease had a high heterogeneity in different sub-regions of Hubei Province. Shigellosis had a high transmission in large cities such as Wuhan City  and Yichang City (which has a population more than 400 million) or cities near Wuhan City such as Xiantao City. We also found that the incidence of shigellosis in urban areas was higher than that in rural areas. It might due to the higher population density in urban areas. However, the transmissibility remains unclear in urban and rural areas and further research is needed to explore the force of the transmission in different areas.DID of Shigellosis cases.About a half of the cases were diagnosed in 1.7 days from their illness onset date. However, this also meant that about 50% of the cases were diagnosed after 1.7 days from their illness onset date. There is a room to improve the surveillance system’s ability of diagnosing the disease immediately and to shorten the bw had almost no contribution during the transmission which meant that the transmission route of reservoir to person was interrupted in the area. This is mainly attributed to the government’s progress of improving sanitation drinking water and lavatories, food safety, and people’s health behaviors like drinking boiled water. Therefore, the remained major transmission route is person to person which will be the main public health focus to control the disease. This result also provided a mathematical evidence of the epidemiological characteristics of shigellosis that the pathogen was transmitted primarily through person-to-person [The “knock-out” simulation showed that parameter o-person . TherefoReff of Shigellosis was 1.19, meaning that the expected number of secondary infections that resulted from introducing a single infected individual into an otherwise susceptible population was only 1.19 on average. This revealed that the transmissibility of shigellosis is lower than many other infectious diseases such as influenza, Ebola virus disease, and Norovirus infection [Our results showed that the mean value of nfection , 15, 34.Reff would down to an epidemic threshold (Reff = 1.00) in 2029. This was an interesting result which meant that the disease would probably be eliminated after the year of 2029. But this result was based on the assumption that the transmission condition would remain stable in the future years. Therefore, this trend might be different in other provinces. Hubei Province is located in a lake region with many cities suffered from floods frequently. The transmissibility of Shigellosis would be increased after the flood and provides a potential transmission route of water to person. On the other hand, the year of the transmission interrupted might be ahead of schedule since the disease control strategy would be strengthened in the future years, especially with the progress of “Health China” action launched by the Chinese government. More researches are needed to simulate the trend of transmissibility of the shigellosis with the interventions considered.In addition, the transmissibility of Shigellosis decreased slightly from 2005 to 2017. Although the Linear, Quadratic, and Cubic models had the best goodness of fit, the 95%CIs of Quadratic model and Cubic model were not stable after the year 2019  which might affect the spread of the disease were not considered in the model.Secondly, due to the transmission heterogeneity, the features and trends of the transmissibility of the disease might be different in different areas in Hubei Province.Thirdly, our study only focused in Hubei Province, the characteristics and the trends of transmissibility of shigellosis might be different in other provinces. The applicability of the mathematical models remains unclear to different datasets of reported shigellosis. However, Hubei Province has a high burden of shigellosis and provides us a large data for the modelling. The ordinary differential equation model has a strong applicability. It could be used in different populations including school or community with small-scale outbreaks , 19, andDID in different scenarios.In addition, we did not simulate the sensitivity and specificity improvement of the surveillance system by the model. More researches are needed to perform the simulation of shorten Shigella spp. Most of the cases were diagnosed in 3 days from their illness onset date. The transmission route of reservoir to person had no contribution to the disease transmission in this area. The transmission of the disease would probably be interrupted in the year of 2029.The disease burden of Shigellosis is still in a high level in Hubei Province, China. Individuals who were children, new-born, elders, and living in urban areas were more likely to be infected by


Capillary malformation-arteriovenous malformation (CM-AVM) is an autosomal dominant inherited rare type of vascular malformation encountered in a neonate and first described in 2003. It has been reported in association with heterozygous mutations in the RASA1 gene, which encodes the protein RASp21. In 2010, a German doctor proposed rhodoid nevus as a name for this type of capillary malformation; in ancient Greek, rhodoides means “rose-like” or “rose-colored.” Accordingly, CM-AVM could also be called “rhodoid nevus syndrome.” We report this case as its very challenging diagnosis with its further differentials and its association with thrombocytopenia. The development of the dermal vessels requires penetration of capillary vessels, which is induced by vascular endothelial growth factor (VEGF) that is secreted by keratinocyte in the avascular epidermis. Any defect in this cutaneous vascular development reveals malformed vessels that vary in location, size, blood flow, and clinical severity. Although CMs are common, CM-AVM is a newly recognized autosomal dominant disorder that has been reported in association with heterozygous mutations in the RASA1 gene, which encodes the RASp21, a protein involved in growth factor signaling for cell proliferation, migration, and survival .A three-day old female patient in the nursery ward was consulted for assessment of multiple, red skin lesions on the chest and left shoulder. The baby was born full-term and by a spontaneous vaginal delivery. Soon after delivery the patient noticed to have low platelet count for which a pediatric hematologist was consulted, and the patient diagnosed with autoimmune thrombocytopenia, treated with platelet transfusion and intravenous immunoglobulin (IVIG). Two of the patient's siblings were also born with similar skin lesions and diagnosed with autoimmune thrombocytopenia Figure . Her graIn 2003, a distinct autosomal dominant trait characterized by multiple, small capillary malformations of round or oval shape was described by Miikka Vikkula's team from Brussels. They found that this phenotype that had previously been mapped to chromosome 5q was caused by RASA1 mutations. Because the skin lesions were sometimes associated with a fast-flow arteriovenous malformation of the Parkes Weber type, they chose the name “capillary malformation-arteriovenous malformation (CM-AVM)” . In 2010The capillary malformation of rhodoid nevus could be sometimes associated with arteriovenous malformation for which we can call it rhodoid nevus syndrome . RegardiThe rhodoid nevus, formerly CM-AVM, is a distinct AD trait characterized by multiple, small capillary malformations of round or oval shape, caused by RASA1 mutations, sometimes associated with a fast-flow AVM. Further investigations are warranted for the rhodiod nevus association with thrombocytopenia.


There has been a constant increase in the number of published surgical videos with preference for open-access sources, but the proportion of videos undergoing peer-review prior to publication has markedly decreased, raising questions over quality of the educational content presented. The aim of this study was the development and validation of a standard framework for the appraisal of surgical videos submitted for presentation and publication, the LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) video assessment tool.An international committee identified items for inclusion in the LAP-VEGaS video assessment tool and finalised the marking score utilising Delphi methodology. The tool was finally validated by anonymous evaluation of selected videos by a group of validators not involved in the tool development.9 items were included in the LAP-VEGaS video assessment tool, with every item scoring from 0 (item not presented in the video) to 2 (item extensively presented in the video), with a total marking score ranging from 0 to 18. The LAP-VEGaS video assessment tool resulted highly accurate in identifying and selecting videos for acceptance for conference presentation and publication, with high level of internal consistency and generalisability.We propose that peer review in adherence to the LAP-VEGaS video assessment tool could enhance the overall quality of published video outputs.The online version of this article (10.1007/s00464-020-07517-4) contains supplementary material, which is available to authorized users. Minimally invasive surgery platforms facilitate the production of audio–visual educational materials with the video recording of the procedure providing viewers with crucial information concerning the anatomy and the different steps and challenges of the surgical procedure from the operating surgeon’s point of view. Surgical trainers consider online videos as a useful teaching aid  that maxThe aim of this study was the development and validation of a standard framework for the appraisal of surgical videos submitted for presentation and publication, the LAP-VEGaS video assessment tool.https://www.agreetrust.org/agree-ii).An international consensus committee was established and tasked with the development of an assessment tool for surgical videos submitted for conference presentation or journal publication. Committee members were selected based on the previously published research on minimally invasive surgery training delivery  and evalThe first phase of the study consisted in identifying the items for inclusion in the LAP-VEGaS video assessment tool. The steering committee was responsible for the selection of the different topics to be discussed and items were finalised after discussion through e-mails, teleconferences, and face-to-face meetings with semi-structured interviews. The discussion focused on skill domains that are important for competency assessment and on the structure of a useful video assessment marking sheet, taking into account the need for a readily applicable and easy to use marking tool, preferring items assessing the required standards for acceptance of a video for publication or conference presentation. Items for inclusion were identified from the previously published LAP-VEGaS guidelines  . The Delphi method is a widely accepted technique for reaching a consensus amongst a panel of experts [These items were revised by all members of the committee and based on the results of the discussion; the steering committee prepared a Delphi questionnaire, which committee members voted upon during phase II of the study utilising an electronic survey tool  curves analysis was used. The Area under the ROC curve was used as an estimator of the test concurrent validity, with values superior to 0.9 indicating high validity . The YouInternal test consistency (i.e. across-items consistency) was estimated by Cronbach’s Alpha and using the Spearman–Brown Prophecy Coefficient, to make this analysis independent from the numbers of items. Each item’s impact on the whole tool reliability was measured as changes in Cronbach’s alpha following item deletion. r > 80 indicating good reliability).Inter-observer reliability was estimated by the analysis of the Intra-class Correlation Coefficient (ICC) and Cronbach’s alpha. ICC was estimated along with its 95% confidence interval, based on the mean rating and using a one-way random model, since each video was rated by a different set of observers. Intra-observer reliability, which estimated the test consistency over the time, was analysed by the test and re-test technique and the Pearson’s r correlation coefficient was used  coefficient was estimated according to a two-facet nested design  with thep values ≤ 0.05 were considered as statistically significant. All statistical analysis was performed using IBM SPSS Statistics for Windows, version 25.0 .Phase I terminated with the steering committee preparing a Delphi questionnaire of 14 statements (Appendix 2).All 15 committee members completed both the first and the second round of the Delphi questionnaire with results presented in Table K = 0.87, p < 0.001). The distribution of scores for each of the 9 items of the assessment tool is presented in Fig. The newly developed LAP-VEGaS video assessment tool was used for assessment of 102 free access videos, which were evaluated by at least 2 reviewers and 2 validators. There was an excellent agreement amongst different reviewers in the decision to recommend the video for conference presentation and journal publication . No item exclusion was found to significantly improve the test reliability .p < 0.001) and when comparing scores between experts and validators .Strong inter-observer reliability was found amongst the different reviewers , Fig. p < 0.001). The Area under the ROC curve demonstrated that a total score of 11 or higher at the LAP-VEGaS video assessment tool correlated with recommended acceptance for publication or podium presentation, with a sensitivity of 94% and specificity of 73%, whilst a score of 12 or higher had a sensitivity of 84% and a specificity of 84%.The LAP-VEGaS video assessment tool resulted highly accurate in identifying and selecting videos for acceptance for conference presentation and publication  lists reporting guidelines which have been developed, mainly driven by the insufficient quality of published reports [We present the LAP-VEGaS video assessment tool, which has been developed and validated through consensus of surgeons across different specialties to provide a framework for peer review of minimally invasive surgery videos submitted for presentation and publication. Peer review of submitted videos aims to improve the quality and educational content of the video outputs and the LAP-VEGaS video assessment tool aims to facilitate and standardise this process. Interestingly, there is currently no standard accreditation or regulation for medical videos as training resources . The HON reports . Some of reports , STROBE  reports  and PRIS reports . The pre reports  and the Reporting guidelines facilitate good research and their use is indirectly influencing the quality of future research, as being open about the study shortcomings when reporting one study can influence the conduct of the next study. Constructive criticism based on the LAP-VEGaS video assessment tool could ensure the credibility of the resource and the safety of the procedure presented, with an expected resultant improvement in the quality of the educational videos available on the World Wide Web.The LAP-VEGaS video assessment tool provides a basic framework that standardises and facilitates video content evaluation when peer-reviewing videos submitted for publication or presentation, despite recognising that the cognitive load of the procedure presented is only one of several key elements in video-based learning in surgery . TeamworIt is important to acknowledge that there are minimal data available in the published literature to base this consensus video assessment tool development and validation on high-quality evidence. Nevertheless, the Delphi process with pre-set objectives is an accepted methodology to reduce the risk of individual opinions prevailing, and the selected co-authors of these practice guidelines have previously reported on the topic of surgical videos’ availability, quality , contentMoreover, the LAP-VEGaS video assessment tool may generate widespread availability of videos demonstrating an uncomplicated procedure , resultiSupplementary file1 (DOCX 18 kb)Supplementary file2 (DOCX 19 kb)Below is the link to the electronic supplementary material.


Physical therapists (PT) and clinicians must be skilled in identifying gait features through observation to assess motor deficits in patients and intervene appropriately. Inconsistent results in the literature have led researchers to question how clinical experience influences PT’s gait perception and to seek the key kinematic features that should be trained to enhance PT’s skill. Thus, this study investigated (1) what are the informative kinematic features that allow gait-deviation perception in amputee gait and (2) whether there are differences in observational gait skills between PT and individuals with less clinical experience (PT students [PTS] and Novices). We introduced a new method that combines biological motion and principal component analysis to gradually mesh amputee and typical walking patterns. Our analysis showed that on average the accuracy rate in identifying gait deviations between PT and PTS was similar and better than Novices. Also, we found that PT’s experience was demonstrated by their better perception of gait asymmetry. The extracted principal components demonstrated that the major gait deviation of amputees was the medial–lateral body sway and spatial gait asymmetry. This is observed even when much of the information such as the face, clothes, or hairstyle is obscured. One explanation for such perceptual ability is that there is critical information that is distributed, and therefore perceivable, through movement; information that is kinematically invariant3.Perception of intentions, emotions and actions through the movement of others is an important skill that individuals use to aid our own survival and the wellbeing of all in a social group. It is impressive that by just observing someone’s walking pattern, we can recognize the emotional status, gender, and health condition of the walker and, even, identify a known individual4. Such visual assessment has been widely used in rehabilitation because of rapidity, simplicity and economy. Observational gait analysis (OGA) can provide an overall assessment of an individual’s gait pattern and helps to determine gross abnormalities that may exist5. Since accuracy on OGA requires practice, this skill is a focus of PT education. Thus, it is expected that through training and experience, PT learn to attend to the most useful information allowing them to judge gait accurately.This ability to analyze gait patterns is clinically relevant as, through observation, physical therapists (PT) and other clinicians can assess motor deficits in patients6, reasonably accurate7, to accurate9. Studies using slow-motion10 or freeze-frame videotapes11 found limited inter- and intra-rater reliability . Given the lack of a standardized training or assessment protocol to ensure the accuracy of OGA10, including the inconsistent results in the literature, we question whether clinicians exhibit better perception of gait patterns than untrained individuals.Several studies have investigated this skill by asking PT and other clinicians to judge gait impairment by watching patients walking in situ or through videotapes. The results show that PT and other clinicians’ judgments vary from inaccurate13, condition of patients10, and the use of an analysis form15. Hughes and Bell11 attributed the low rate in identifying gait asymmetry to the change of viewing angle as participants walked toward and away from the camera. Eastlack, Arvidson, Snyder-Mackler, Danoff and McGarvey10 suspected that structural deformities and abnormalities in the gait pattern  could mislead the rater in judging step length. Importantly, the lack of control in the experimental design also limits the possibility of understanding the specific gait features that PT use to judge. This is of relevance; if gait features perceived by PT are found to be useful, these should be goals for intervention, else, we understand why clinical observation is inaccurate. Whereas many studies recognize the inconsistency in the literature, there has been no attempt to accommodate these inconsistencies or discern the perceivable gait features that clinicians attend to. Therefore, we have no firm stance on the question of whether clinicians are accurate and reliable on gait judgement and which gait features would base accurate judgements.The explanation for the disparate results could be due to the methods used for evaluating OGA, such as the methods of displaying gait17 and rating through OGA15. Biomechanical analyses are helpful to identify potential parameters of the movement that differentiate the gait pattern between patients and unimpaired walkers. However, the variables identified in these studies  cannot be directly regarded as the PT perceived variables in their visual gait examination, since the found differences might be too small to be perceived6. OGA is well suited to provide clues about gait perception given it is directly based on judgements13. Nevertheless, OGA is performed in a context with a possible range of confounding information. Redundant information such as a grimace of pain, body figure  or stereotype of certain gait dysfunction  might confound experimenters on the features that raters might be attending.There are two common paradigms employed in finding gait features: biomechanical analyses18 showed that a video of a small number of dots moving, representing the major joints of a walker. The motions of the dots contain information of the global shape and motion. In addition, the relative motion among all local individual dots results in a vivid perception of a human walking. This point-light display is designed to separate biological motion information from other sources of information that are normally intermingled. Many studies have since used this technique to investigate the critical information that is available throughout the movement3. Such information is kinematically invariant, not affected by the underlying structural difference in walkers . It has been demonstrated that point-light displays contain enough information for recognition of gender20, emotional status21, and the identity of the walker—perceivers can even determine whether the pattern refers to themselves23. The phenomena of biological motion perception can be explained by the ability of the human visual system to perceptually organize the individual moving dots into the coherent shape of an articulated human body. Hence, we expect that it can serve as a tool to reveal the capacity of PT to judge gait abnormality and to infer the critical gait features that PT use to form their judgment.A method to guarantee that individuals can only attend to kinematic features is to isolate the biological motion. JohanssonWe acknowledge that OGA also encompass the interpretation of the observed movement pattern linking it to a cause (diagnosis), and proposing effective interventions to improve the pattern (treatment). However, PTs capacity to differentiate the pattern from normality is the 1st step of OGA. Thus, a valid diagnosis has to be based on an accurate perception of the observed gait. We therefore focused this paper on the initial part of OGA—perception of amputee gait deviations.In this study, we investigated (1) if clinical experience influences how PT perceive gait, and (2) the kinematic features PT employ to differentiate amputee gait patterns from typical walking. In order to achieve our goals, we utilized the principal components  of amputees and a control individual walking pattern to develop the biological motion videos.Principal component analysis (PCA) is a technique for reducing the dimensionality of data set, increasing interpretability but at the same time minimizing information loss. The method finds, through the covariance matrix, the relation between variables and it extracts them as uncorrelated variables from the original data set. From this new set of variables , we can utilize the ones that capture a high percentage of variance from the original data set, preserving a great amount of the original structure. This method have been applied in studying coordination of human walking, identifying normal gait features in healthy young adults24,25 and gait disorders . Here, we used PCA to extract the spatial–temporal features of amputees and, synthesizing it in varying scale with a typical walking, we evaluated PT, PTS and Novice raters in their ability to differentiate from most to least abnormal patterns of gait.The joint trajectories of three amputee walkers  and a control walker were utilized to generate three sets of testing videos. These videos were built to present a range of scaled gait abnormalities. Each set of testing videos contained six biological motion videos generated from one of the amputees, and the biological motion within the six videos linearly scaled from 0 to 100% (in steps of 20%) from the control walker to the amputee walking pattern . The level of gait abnormality within the 6 videos was varied from most abnormal (the amputee gait) to most normal  for raters to sequence. The 3 amputees were treated as independent variables/conditions in this study. We selected one K3 and two K4 amputees in order to have two levels of gait abnormality within the generated videos—large differences between K3 and K4 and small differences within the two K4 amputees. This was meant to better identify experience differences between the groups.Then, we tested the accuracy in judging gait of three group of raters . For each trial, one set of videos were simultaneously presented to the rater to rank the videos from 1 to 6 based on their perceived gait abnormality . The three sets of videos were tested and presented for 10 times in a random order . We analyzed each rater’s (1) accuracy rate and (2) absolute error in judging each Walker’s videos to estimate the participants abilities in judging walking patterns  and walkers (p = 0.001), and no significant interaction between walkers and groups (p = 0.541). The pairwise comparison between groups showed that the Novice group was less accurate than PT (p = 0.016) and PTS (p = 0.027) with no significant differences between PT and PTS. Comparison between walkers showed that all groups rated Walker A video set more accurately than Walker B (p = 0.001) and C (p = 0.002) video sets. Also, Walker C video set was rated more accurately than Walker B video set (p = 0.040).Figure p’s < 0.045). However, this trend was not significant for PT on Walker C’s videos and for Novices on Walker A’s videos.Figure p = 0.002; Groups: p = 0.063; Interaction: p = 0.899). The pairwise comparison indicated that Walker B’s video set was more misjudged compared with Walker A’s video set (p = 0.002) with no other differences between videos.To assess the magnitude of misjudgment, the absolute error between viewers’ judgment and correct answers was calculated. We found that most misjudgments occurred between similar videos  and Walker B’s video set was the one with more misjudgments. Figure Table Figure Table Figure 29. Athletes are sensitive to the opponent’s serving posture to anticipate ball landing location30; musicians are better at discriminating pitch and musical style/genre32. Hence, we expected that PT, given their training and experiences, would show better performance than PTS and Novices in rating gait abnormality. We found that PT and PTS were not significantly different from each other and that both were more accurate than the Novice group . As the PT group has more practical experience with observing and assessing gait symmetry, they likely have learned to promptly attend to this particular feature. This is one potential reason why the PT group may have performed better at discriminating between different levels of gait impairment.Gait asymmetry is associated with many pathologies , has been used in clinical gait assessment, and is largely discussed in the literatureWe believe that the greatest outcome from PTs experience is the ability to infer the appropriate causes and plan interventions based on the observed specific pattern. It might be possible that the main differences between PT and PTS are this later part of OGA: the diagnosis. Hence, we might be able to see larger differences between PT and PTS if they were asked to perform the diagnosis. A future study can consider this to fully test our proposed method.37 or Steven’s power law38. These were demonstrated on the nonlinear relation between discrimination of and actual increase in magnitude of force, brightness, and temperature. Our study demonstrated a similar nonlinear relation. This is of interest as it identifies a region on the spectrum of gait “quality” that requires further practice to be differentiated. Then, this region, closer to normal walking (representing more subtle gait deviations), should be emphasized during PT education.Furthermore, we also found that the relation between actual change of gait impairment and the perceived change is nonlinear Fig. . The gai39. Some of these studies reported lower explained variance in the first 4 PCs25 when analyzing healthy individuals compared to our results. This could be due to the normalization process that minimizes the variability of the input variables. Such procedure was applied given the intent to compare different tasks 24 or include different types of input variables 26. Nevertheless, the explained variance in our study (~ 90%) is similar to others that applied the same PCA procedure39.In this study, PCA-based movement analysis, as opposed to classical gait analysis, quantified differences in the coordination/covariation between variables. The original marker positions represent directly measured information without any pre-selection of variables or definition of arbitrary axes to define the motion of the joint. The marker positions facilitate the creation of stick figures to visualize the differences in the movement patterns between amputee and control walker Fig.  and 5 anPCA quantified the major dissimilar features between amputee and control walkers that allow individuals to perceive and differentiate walker patterns. The quantified features represent the magnitude of medial–lateral body sway, degree of gait asymmetry, and percentage of gait deviation.40. Since PT and PTS have been taught these compensatory mechanisms in gait, they might attend to these features to evaluate gait.We found that the largest spatial deviation is on the medial–lateral body sway for all amputee walkers Table . Also, aBy identifying similar PCs between amputee and control walker, we could quantify the amputee’s gait deviation in percentage. This percentage provides a general idea about the qualitative difference in gait and seems to align with the accuracy rate—the lower percentage of gait similarity  resulted in a higher accuracy rate (> 67%). We expected Walker C’s pattern to be the hardest to differentiate due to the high gait similarity . However, we found that the Walker B’s video set was the one with largest misjudgment (absolute error > 1.38) and lower accuracy. Even though Walker B is more dissimilar than the control walker than Walker C, when considering the overall pattern of his motion, gait asymmetry might play a stronger role in OGA.20 or differentiate movement patterns41. Even though biological motion presents the motion of joints center, it is not that individuals are based on the motion of the joint centers per se in biological motion. The point-light display elicits perception of the kinematic features that inform what is happening  and features of the walker . Thus, the relation to the clinical routine lies in that these kinematic features are also present in the real life situation and can be based on the motion of other parts of the body. We used the joint center motions to analyze the data but, here we were concerned with the kinematic features that could be extracted from their motion. Thus, although the usage of point-light display seems far removed from the clinical settings, it preserves its main features.Biological motion has been used to find kinematic information that is beneficial to identify genderThe present method, based on biological motion, can be a useful tool for training and evaluating individual’s capability of observation gait analysis. The method allows the viewer to direct attention to the kinematic information distributed from the motion of major joints, and hence the training/evaluation is purely on viewer’s perception of motion instead of other confounding sources of information. The biological motion model is also customizable, allowing manipulation of walking speed, body structure and different levels of gait impairment. Point-light displays motion also simplify the information for visual attention. Thus, a teacher can control/emphasize the kinematic information that they would like students to attend to. We believe this has large potential to improve the training of OGA, especially in the early stage of learning.43 and this has been a formidable challenge. In the clinics, clinicians must manually and heuristically tune the control parameters of robotic prostheses for individual amputees based on observed gait patterns in order to provide personalized gait assistance. This procedure continues until the gait pattern “looks good”, which is burdensome, inaccurate, and time consuming. One of our approaches was to design a cyber expert system that mimicked the decision-making process of clinicians42. However, one challenge for this concept and application was the difficulty in knowing the tuning goal of clinicians. The results and methods of this study might help fill this knowledge gap. We showed that clinical professionals seem to attend to gait asymmetry and mediolateral body sway. These features could facilitate the fine-tuning of gait and could be used as the optimization goal for automated tuning machines in the future44.Understanding the method that PT use to identify kinematics features could then be incorporated into machine-learning based gait diagnosis, the design of automatic powered prosthesis tuning, or other new technologies in smart health applications. Our research group has been interested in the design of automatic tuning methods for robotic prostheses for lower limb amputeesThis study has a few limitations arising from its novelty. First, we presented the biological motion only in the frontal plane. Considering we did not have prior knowledge on this matter, we chose the frontal plane based on our pilot results that demonstrated a higher performance for Novices. We then considered this plane to be more “informative” and/or easier to result in differentiation between rater groups when judging videos in a short period of time. Nevertheless, this was not problematic as we distinguished experience levels reasonably well. Future studies could investigate another viewing plane to see whether our results are generalizable. Second, our analyses  were based on the motion of markers rather than joint angles—which can be another source of features perceived by our raters. Additionally, we made arbitrary choices related to video playing time and the size of the viewing figure. This was based on the requirement to display the videos with a comfortable view and to complete the data collection within PT/PTS’s break hours. It could be that with longer time for gait judgement, PT might demonstrate a better performance as perception of some features might take longer periods of time—a matter for future studies. Finally, it is possible that the gait demonstrated by the three amputee walkers in this study might not be generalizable to the overall amputee population. However, the large ML body sway and gait asymmetry demonstrated by the three amputee walkers are common features of amputee gait. Moreover, the three walkers were sufficiently different to highlight gait features and differentiate the raters’ experience level.We recruited three males with unilateral transfemoral amputation (age: 51.3 ± 18.7 years) so that we could create the biological motion videos based on their gait patterns. All amputee participants wore a prosthesis daily for at least 8 or more hours, were able to walk independently without assistance (i.e. K-level 3 or 4) and had previous experience with treadmill walking. All of them reported regularly participation in sports or activities besides walking. The details of the amputees are provided in Table Three groups of raters participated in this study. The PT group consisted of 10 licensed physical therapists  who had 18.8 ± 13.2 years of experience in treating and performing OGA in clinic. Five PT were currently working with amputees. The PTS group consisted of 10 physical therapy students  who had successfully completed coursework in observational gait analysis and had fieldwork experience working in clinical settings. The Novice group consisted of 10 inexperienced raters . These novice raters had no training in gait analysis/clinical experience.All participants (amputees and raters) signed an informed written consent form to participate in our protocol—All procedures performed in study was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill. We conducted our study in accordance with the relevant guidelines and regulations.We recorded gait kinematics from the 3 amputee walkers to elaborate the biological motion videos. In the experimental visit, the amputee walkers were asked to walk on a treadmill at a self-selected comfortable speed. For familiarization with the context, participants completed at least 2 min of walking before we started to record the 40 steps (20 full-gait cycles). Participants were not notified when the recording started. We employed the Vicon full body plug-in gait model  for the kinematics data collection. VICON motion capture system equipped with 12 high-speed cameras was used for tracking the three-dimensional trajectories of the markers at 100 Hz sample rate .20 from 25 able-bodied men and 25 able-bodied women. This model was derived using PCA and a sine function by having a classifier distinguishing male and female features in walking. It can generate average walking pattern by means of weighing the gender scale. The male structure (weighting as 1) was used for the Control walker to fit the gender of the amputee walkers.In order to formulate the biological motion videos, we first computed the location of virtual markers positioned at the major joints or segments of the body . The locations of these virtual joint markers in the biological motion video matched those used for the control walker. For the control walker (nonimpaired gait), the 15 joint marker trajectories were reconstructed using a human walking model made by Trojet. Equation 1 describes the model of the control walker:j’s joints position in 3D (45-dimensions). The joints positions are the average posture j and The biological motion videos presented a range of scaled gait abnormalities to test the accuracy of raters. We built three models using PCA, each of which was derived based on the gait pattern of one amputee walker and the control walker. Each model described the 3D motion of the 15 joints and was used to synthesize six biological motion videos that scaled gait abnormality by varying the features from the original amputee gait (0%) to the control walker (100%) in steps of 20%. Thus, the level of gait abnormality within the 6 videos was varied from most abnormal (the amputee gait) to most normal  for raters to sequence. To synthesize each biological motion video, first, the joint trajectories of amputee walkers were low-pass filtered using a fourth order zero-lag Butterworth filter set at 6 Hz. We time-normalized the number of data samples within each gait cycle for the joint trajectory data of each amputee walker and the control walker. Then, PCA was performed to extract the eigenvalues, eigenvectors and scores for each amputee walkers:j and control walker by weighting To synthesize the gait features ranged from control walker to amputee walker, we combined Amputee PCA model and Control walker model with a weighting term:We generated the six biological motion videos from each model }Bjk Eq. . The sixAll biological motion videos were presented in 2D frontal view  with gait frequency at 1 Hz. We chose this view given that Novice participants, in a pilot test, preferred and performed better on the frontal view. In all biological motion videos, the joint positions of the synthesized walker appeared as yellow dots on a black background. The videos were generated using MATLAB . A sample testing video set generated from amputee Walk A is presented in Supplementary Movie A.The video sets were shown at raters’ preferred distance and height and displayed on a DELL 27-in. LED color monitor with 1920X1080 resolution at 60 Hz. For each trial, six biological motion videos generated from one of the amputees simultaneously arranged into 2 row and 3 columns in a random order were presented for 23 s (20 gait cycles). The goal for the raters were to rank the videos from 1 to 6 based on their perceived gait abnormality . Raters received no feedback on their answers. After raters completed their answer, the next set of videos was displayed. All three videos sets were tested and presented for 10 times in a random order . The total testing time was about 40 min. A practice trial  was presented for raters to familiarize the task.To estimate the participants abilities to judge walking patterns, we calculated each rater’s (1) accuracy rate and (2) absolute error in judging each Walker’s videos. The accuracy rate evaluates the rater’s ability to differentiate abnormal gait and the absolute error measures the magnitude of misjudgment. We expect that the experience level will determine the accuracy rate and absolute error .To investigate what are the key kinematics features that raters perceived to identify gait deviations, we quantified the gait pattern difference between amputee walkers and the control walker based on the extracted patterns from PCA.20 used for the control walking model. We reported the variance accounted for (VAF) by each PCs and reconstructed joint position variance to compare the overall PC differences between amputee and control walker.To synthesize the point-light walkers, we utilized the first four dominant Principal Components (PC) of the PCA analysis matching the number Trojei and PCj. If the absolute value of ij overlap, meaning that position of joints motion in both PCi and PCj covary in the same manner and demonstrated similar movement pattern. If the value is 0, the covariation of joint motion in PCi and PCj are orthogonal to each other and are not related.Given the task constraints , we expected that amputees shared similarities with the control walker. This would be evident in the PCs structure. Similarly, some PCs would reveal differences referring to the functional limitations of amputee’s motor impairment. To differentiate similar and dissimilar PCs, the normalized dot product was calculated between all possible PC pairs across amputees and control walker.We calculated the main eigenvector on the reconstructed trajectories of shoulder, elbow, wrist, hip, knee and ankles on both right and left limbs, and measured the angle between eigenvector and horizontal axis. For the limbs on the right and left side, the angle was defined on the 1st and 3rd quadrant, respectively. The difference between angles of the paired contralateral joints was used to determine the degree of gait symmetry. Values closer to zero refer to symmetrical pattern.groups and walkers (3*3) to examine if experience leads to a better judgment of walkers; and in terms of rater groups and video (3*6) was to test if experience leads to better sensitivity to different gait patterns. A permutation test was applied based on Manly’s approach45 for factorial 2-way ANOVA design. By using a permutation test, we can bypass assumptions of continuity, normality, and homoscedasticity still taking advantage of the full sample information46 providing that the judgement rates and absolute errors are not continuous numbers. The permutation multiple comparison was utilized based on one-sample t-test for group difference and paired t-test for walker/videos. T-max method was used for adjusting the p-values of each variable for multiple comparison47. The significant level was set at α = 0.05.We performed permutation tests comparing accuracy rate and absolute error in terms of rater Supplementary Video Control_PC1.Supplementary Video Control_PC2.Supplementary Video Control_PC3.Supplementary Video Control_PC4.Movie A - testing trial.Movie B - similar & dissimilar PCs.Supplementary Video Walker A_PC1.Supplementary Video Walker A_PC2.Supplementary Video Walker A_PC3.Supplementary Video Walker A_PC4.Supplementary Video Walker B_PC1.Supplementary Video Walker B_PC2.Supplementary Video Walker B_PC3.Supplementary Video Walker B_PC4.Supplementary Video Walker C_PC1.Supplementary Video Walker C_PC2.Supplementary Video Walker C_PC3.Supplementary Video Walker C_PC4.


Social media has arisen to be a new and important channel for information users for seeking and creating user-generated content. For health consumers, social media has long been regarded and employed as an important source to find health-related information and emotional support. This study investigated the characteristics of diabetes-related videos posted on YouTube, one of the most popular video-based social media platforms, and explored the factors influencing users’ preference towards the investigated videos.A mixed research method including coding and negative binomial regression test was applied. Coding was utilized to identify the status of the diabetes-related video clips and the factors related to users’ attitude to them. Negative binomial regression approach was employed to detect significant relationships among the factors and users’ attitude.The researchers selected eight factors  to represent the characteristics of the diabetes-related video clips. Eleven subjects were identified by examining the diabetes-related videos and three subjects, Treatment, Sign & Symptom, and Social & Culture, appeared the most frequently. Media type, presentation setting, post period, presenter role, and presenters’ gender affect the users’ positive attitude significantly. Post period, presenter role, and the Sign & Symptom subject and the Nutrient subject have significant influence on the users’ negative attitude.Treatment, Sign & Symptom, and Social & Culture are the most popular subjects of the investigated video clips. The users are less likely to show their attitude to old videos. They prefer journalists and patients on videos but dislike male presenters compared with other presenters, and show more negative attitude towards the videos about nutrients. The findings of this study can be used to enhance the content creation of diabetes-related video clips for video contributors, the design and organization of the diabetes-related content for multimedia-based social media Website designers, and the information seeking and communication among health information users. Social media has arisen to be a new and important channel for information users for seeking and creating user-generated content. As a powerful tool, social media provides patients and health professionals with a new channel to communicate and collaborate regarding health issues . BecauseWhile a picture has long been considered to be worth more than a thousand words, multimedia-related information technology such as information visualization and graphical interfaces had not obtained an expanding role until the ability of computers to display resolution increased greatly in recent decades . With maAs firmly established social media Website, YouTube has obtained popularity among health consumers and providers both in the United States and Europe, and approximately 100 million people have been reported to take some kinds of social interaction on YouTube . TherefoAccording to the definition claimed by the 2014 National Diabetes Statistics Report , “DiabetPrevious literature has been concentrating on a great number of video-based social media research on YouTube including statistical results of Epilepsy-related resources , organ dThe findings of this study would benefit contributors, users, and designers of a video-based social media like YouTube from three aspects: (1) understanding the characteristics of the diabetes-related user-generated content on YouTube; (2) discovering content themes among diabetes-related video clips on YouTube; and (3) providing useful suggestions to video creators, who are also named YouTubers, and information seekers of diabetes-related information based on users’ preference.Users’ attitude has two sides: positive and negative. This study explores the influencing factors for both positive and negative sides of users’ attitude. Five research questions are stated: (1) what are the characteristics of diabetes-related information on YouTube? (2) What are the factors influencing users’ positive attitude towards the diabetes-related videos on YouTube? (3) What are the associations between these factors and users’ positive attitude? (4) What are the factors influencing users’ negative attitude towards the diabetes-related videos on YouTube? (5) What are the associations between these factors and users’ negative attitude? In the research questions, characteristics mean the specific features of the collection of the investigated diabetes-related videos and factors are the attributes of a video that potentially affect the users’ attitude.A mixed research method takes advantages of both quantitative and qualitative methods to solve a complicated problem in a study. Both coding and statistical methods were applied in this study. The qualitative method was used to reveal hidden characteristics and theme pattern of the investigated YouTube videos while the quantitative method was employed to investigate the factors influencing users’ attitude towards those videos.In this study YouTube was selected as a data source because it is one of the largest and most popular video sharing platforms in the world . The vidThe researchers used diabetes-related search terms to retrieve relevant video clips. The search terms were diabetes, diabetic, type 1 DM (diabetes mellitus), and type 2 DM. For each search term, the videos on top 50 pages were examined. Noticed that there were dead links and private videos that the researchers could not access. In addition, there were duplicate videos returned on different pages or retrieved by different search terms. The duplicate and inaccessible ones were excluded from the raw data set.After the videos on YouTube were retrieved, each video and its related information were manually examined and the irrelevant videos were excluded by the researchers. A video’s page usually contains a video clip, video clip’s title, the number of views, the number of likes, the number of dislikes, video creator, publish time, a brief introduction of the video, the number of subscriptions, comments of the video, and some recommended videos in the “Up next” section.The information of the relevant videos was collected, but not every relevant video met the requirements for data analysis. Users’ rate information (the number of likes and the number of dislikes) of a video represents users’ attitude towards the video. The number of likes reflects users’ positive attitude while the number of dislikes shows their negative attitude. Since this study aims to explore users’ attitude, the videos without users’ rate information were excluded.In the attribute extraction process, useful attributes were identified and extracted from the investigated videos. The attributes included: the number of views, post period, video duration, presenter’s gender, presentation setting, presenter role, media type, subjects of a video, and rating data (the number of likes and the number of dislikes). Some of these attributes were found directly from the Web pages of the videos, such as the number of views, post period, video duration, and rating data (the number of likes and the number of dislikes); some were obtained after examining the videos, such as presenter’s gender, presentation setting, presenter role, and media type; and subjects of a video were identified and assigned after the researchers conducted content analysis for the videos.The number of views is a unique attribute which has a close connection with the number of likes or dislikes because a user can like or dislike a video only after s/he views it. Both the number of likes and the number of dislikes reflect users’ attitude to a YouTube video. The remaining attributes  were the possible factors influencing the users’ attitude.Subject analysis can give an overall picture of the content coverage of the diabetes-related videos on YouTube. The original subject categories of the relevant videos came from a schema in Zhang and Zhao’s paper . It was Living with diabetes – a mum and son’s story was related to both medication and treatment of diabetes. In other words, a video could be assigned to more than one subject category.After the titles, descriptions, and transcripts of the videos and the videos themselves were examined, subject analysis was conducted. The videos related to the subjects in Table Regression modeling analysis helps the researchers explore the identified factors’ influence on the users’ attitude because this method can effectively deal with multiple factors on different data types. Negative binomial regression is a generalization of Poisson regression. It can be applied to a study when: (1) the values of the dependent variable are non-negative integers; (2) the data of the dependent variable are over-dispersed; and (3) the independent variables are in different data scales , 19.In this study both the positive attitude and the negative attitude were the dependent variables. The positive attitude was measured by the number of likes of a video and the negative attitude was measured by the number of dislikes of a video. Both the number of likes and the number of dislikes were non-negative integers. Overdispersion was found in the number of likes data and the number of dislikes data obtained, respectively. The mean of the number of likes was 218.029 and the variance was 917,496. The mean of the number of dislikes was 6.636 and the variance was 390.486. Each variance was much larger than the corresponding mean. In this situation, the Poisson regression model was not applicable, while the negative binomial regression model, was utilized to solve the overdispersion problem.Post period, video duration, presenter’s gender, presentation setting, presenter role, media type, and subjects of a video were the independent variables for each dependent variable. Among these independent variables, post period and video duration were numeric, while the presenter’s gender, presentation setting, presenter role, media type, and subjects of a video were categorical. Each subject was regarded as a two-level categorical independent variable. If a video was related to a specific subject, its corresponding value for the variable was 1; if not, then the value was zero. The independent variables were on different data scales. As a result, the negative binomial regression model could be utilized.The negative binomial regression model was frequently used in geometric design, medicine, and health science fields , but onlThe model used in this study is:Y is the dependent variable (positive attitude or negative attitude in this study), i represents a specific video and Yi stands for the number of likes or dislikes of video i. xji  is the jth independent variable that describes an attribute of video i. βj  represents the coefficient of the jth independent variable (xji) of video i. xi0 equals to one for all i and β0 is the intercept. ti is an exposure variable that represents the number of times that the events happen. In this study, the number of times was measured by the number of views of a video. Therefore, the exposure variable is the number of views of a video. In this model exp is the natural exponential function and exp(x) = ex. The significance level for the regression tests was set to 0.05. It was employed as a criterion for hypothesis testing. Table i j = 1, , …, k isAfter examining all the YouTube videos retrieved, we obtained 206 qualified diabetes-related videos. Table Y-axes represents the number of videos while the X-axes of Fig. Figures X-axis of Fig. The The results of the subject analysis show that there were 11 subject categories extracted from the 206 videos, which were Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Affect, Social & Culture, Lifestyle, and Nutrition. Compared with the original subject categories, no new category was discovered in this study, while one original subject category, Education and Info Resource, was removed because no relevant video was retrieved in this category. Table Figure 1), Sign & Symptom (Subject2), Diagnosis & Test (Subject3), Organ & Body Part (Subject4), Complication & Related Disease (Subject5), Medication (Subject6), Treatment (Subject7), Affect (Subject8), Social & Culture (Subject9), Lifestyle (Subject10), and Nutrient (Subject11).The factors’ influences on the positive attitude and the negative attitude to diabetes-related video clips were examined by a series of negative binomial regression tests. The positive attitude was measured by the number of likes and the negative attitude was measured by the number of dislikes. The factors included post period, video duration, presenter’s gender, presentation setting, presenter role, media type, Cause & pathophysiology , zero was the base level. Zero represented that a video was not related to a specific subject, while one represented that it was relevant to the subject.P-value for each independent variable, respectively. The standard errors measure the accuracy of the coefficients. Together with the coefficients, they were used to calculate the corresponding Z-values and P-values. If the P-value is smaller than 0.05, then the corresponding variable has significant influence on the dependent variable. From Tables P-values smaller than 0.05 are marked with “*”. If the rows of the base levels had no value, then the corresponding cells were filled with hyphen symbols.Tables The first negative binomial regression test examined the influences of all the factors on the positive attitude. Table To further test these factors’ influences, the factors which had no significant influence in the first model were removed from the regression model. Therefore, only the influences of the media type, presentation setting, post period, presenter role, and presenters’ gender were retested in the second test. Table P-value = 0.003 < 0.05). The number of likes of a video belonging to the other media type was about 53.8% of the number of likes of a video which was public service announcement = e(−0.620) =0.538). The number of likes of the TV news, TV documentary, interview, or user-generated video was not significantly different from the public service announcement. The results show that the YouTube users preferred the diabetes-related TV news, TV documentary, interview, user-generated video, and public service announcement to the other related video clips.Table P-value = 0.021 < 0.05). Since exp.(− 0.825) = 0.438, the outdoor setting videos’ numbers of likes were 43.8% of the home setting videos. It means that the YouTube users liked home setting much more than outdoor. The other settings had no significant difference from the Home setting.The results demonstrate that the presentation setting factor affected the users’ positive attitude significantly. The diabetes-related videos whose presentation setting was outdoor had significantly less numbers of likes than the videos whose presentation settings was home . Since the coefficient was − 0.001 and exp.(− 0.001) = 0.999, the value of the post period factor increased one unit, then the value of the positive attitude decreased 0.1%. The results mean that the post period of a video extended 1 d, its number of likes decreased 0.1%. Therefore, older diabetes-related videos usually had less numbers of likes than newer ones.The post period also had significant effect on the positive attitude  or patient , the number of likes was significantly larger than a video whose presenter was medical professional. The number of likes of a video whose presenter was journalist was nearly three times of a video whose presenter was medical professional (exp (1.081) = 2.948). The number of likes of a video whose presenter was patient was two times larger than a video whose presenter was medical professional (exp (0.801) = 2.228). However, there was no significant difference between the videos whose presenters were reporters, or parents and the videos whose presenters were medical professionals. The findings illustrate that journalists and patients in the diabetes-related videos received more positive attitude than the other roles.The presenter role affected the users’ positive attitude significantly according to the results. When the presenter of a video was journalist . The number of likes of a video whose presenter was male was 41.7% of a video whose presenter was female (exp(− 0.875) = 0.417). There was no significant difference between the videos whose presenters included females, the videos whose presenters’ gender could not be identified, and the videos without human. Therefore, the videos whose presenters were male obtained less positive attitude than the other videos. Figure Regarding the presenters’ gender, the results show that this factor affected the positive attitude significantly. Compared with the videos whose presenters were female, the videos whose presenters were male have significantly lower numbers of likes . The coefficient is − 0.001 and exp.(− 0.001) = 0.999, so when the value of the post period factor increased one unit, the value of the negative attitude decreased 0.1%. The results mean that the post period of a video extended 1 d, its number of dislikes decreased 0.1%. In other words, older diabetes-related videos usually had less numbers of dislikes than newer ones.The results in Table P-value = 0.048 < 0.05). The coefficient of the Journalist level was − 1.412 and exp.(− 1.412) = 0.244, which means that the number of dislikes of a video having journalist as its presenter was less than one-fourth of the number of dislikes of a video having medical professional as its presenter. When the presenter of a video was not a reporter, journalist, medical professional, parent, or patient, its number of dislikes was significantly lower than a video whose presenter was medical professional . The coefficient of the Others level was 0.588 and exp.(− 0.588) = 0.555, which means that the number of dislikes of a video having other role as its presenter was about half of the number of dislikes of a video having medical professional as its presenter. These findings demonstrate that the videos having reporters, medical professionals, parents, and patients as their presenters received more negative feedback than the videos having journalists and other roles as their presenters.For the presenter role, the results present that this factor had significant effect on the negative attitude. When the presenter of a video was journalist, its number of dislikes was significantly lower than a video whose presenter was medical professional . The number of dislikes of a video related to the Sign & Symptom subject was 48.6% of the number of dislikes of a video irrelated to the subject  = 0.486). The Nutrient subject had significant positive effect on the negative attitude . The number of dislikes of a video related to the Nutrient subject was 1.846 times of the number of dislikes of a video irrelated to the subject  = 1.846). Therefore, the users were less likely to have negative attitude on the videos about the signs and symptoms of diabetes, but they were more likely to have negative attitude to the videos about nutrients. Figure Regarding the subjects, the Sign & Symptom subject and the Nutrient subject both affected the negative attitude significantly. The Sign & Symptom subject had significant negative effect on the negative attitude  the characteristics  of the investigated diabetes-related video clips obtained from YouTube were examined; (2) several factors that would probably impact users’ preferences of both positive and negative sides to these video-clips were identified. The factors included post period, presenters’ genders, roles, presentation settings, and subjects. Various levels were recognized for each of the factors to further explore the relationships between them and users’ feedback; (3) there were significant relationships between some of the factors and the two sides of users’ preferences. For the positive attitude, significant relationships were detected in the post period, media type, presentation setting, presenter role, and presenter’s gender while for the negative attitude, significant relationships were identified in the post period, presenter role, and two subjects (Nutrient and Sign & Symptom).The analyses were conducted based on the collected records which contained users’ preference information. Records without users’ preference information were excluded in the data analyses. Users who did not make contributions to preference of records definitely had their preference on the viewed clips. Unfortunately, since the preference data were not kept, they were not taken into consideration in this study, which is a limitation of this study. Future research directions include, but are not limited to, keyword clustering analysis which offers insight into the diabetes-related content on YouTube from a quite different perspective, and studies on other health-related topics such as arthritis and asthma on social media.