Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks. Over the past few decades, the increase in demand for video streaming has grown, driven by applications such as teleconference, Internet Protocol Television (IPTV), security systems, Video-on-Demand (VoD), and live video streaming . AccordiAdvanced Video Coding (H.264/MPEG-4 AVC) is one of the well-known encoding algorithms that provide better image quality, improving video compression, and requiring smaller storage capacity when compared to previous encoding standards . The encAs an alternative to reduce packet losses, the Internet Service Providers (ISPs) increase the router’s buffer length in an attempt to better accommodate the traffic. This trend was driven by cheaper memory prices , which iDynamic Adaptive Streaming over HTTP (DASH) has become a de facto standard for VoD, and is widely used for live streaming services . In DASHIP networks were not designed to provide suitable Quality of Service (QoS) for real-time video. End-to-end latency and packet discard in the router’s queue impairs the quality of live videos. In DASH VoD system, the client buffer stores many segments as possible, mitigating effects of delay, jitter, and packet retransmission. However, large buffers should be avoided in live streaming, once a few seconds of buffer delay are not admitted. Packets of live videos have a deadline either on the server or on the client, becoming useless after a few seconds. Thus, the bufferbloat phenomenon results in video quality reduction for live DASH and AQM can be used to prevent congestion of router’s queues and improve the quality of video as perceived by the user .AQM is the proactive packet marking/dropping algorithm designed to cooperate with feedback mechanisms of transport protocols, providing fairness between flows , preventSeveral AQM methods are reported in the literature, but few of them explore how rate-adaptive video traffic interacts with the AQMs . Most AQn is the number of weights [In this article, we present as main contribution a new AQM method to improve video quality for live DASH streaming. The proposed AQM uses Long Short-Term Memory (LSTM)  with Neu weights , allowin weights . The quaThe remainder of this article is organized as follows: Modern video distribution platforms across the Internet have adopted DASH as the primary video delivery technique . In ordeDASH client is responsible for initiating and managing the connection with the server, and dynamically requests the segments that best adapt to network load condition and the current status of playback buffer. Upon initiating a session, the client requests the Media Presentation Description (MPD) file to the server. MPD informs the available segments, the video resolution, the bitrates, timing, and type (live or VoD). As soon as this file is received and analyzed, the client requests the segments. As the video goes on, DASH dynamically requests segments that best match to the current network load.The client’s buffer of DASH operating VoD services should be enough to store 20 to 30 s of video  to ensurSince DASH has become a commercial standard, it attains industry recognition as a streaming solution that enables interoperability between content servers and clients of different vendors . The disThe router’s queue is a place where network congestion arises. Hence, the AQM algorithm gets more accurate and faster information about congestion than traffic sources . MoreoveRED  was one rameters . RED doetive RED  is an alARED dynamic adjust g et al.  and afteg et al. . In Floycenarios .Adapting Karmeshu  suggestetarget and interval. In conformance with RFC 8289 [target are 5% to 10% of the connection Round Trip Time (RTT). Because most unbloated RTTs in open terrestrial-based Internet have a ceiling of 100 milliseconds [interval and target are set as 100 and 5 milliseconds, respectively. At each interval, CoDel computes the delay of all packets dequeued for forwarding. If the minimum queue delay is lower than the target, or the buffer contains fewer than MTU worth of bytes, packets are not dropped. If minimum delay is greater than the target, CoDel enters in drop mode, and a single packet is discarded. Then, the next interval is set in accordance with the inverse square root of the number of successive intervals in which CoDel is in drop mode. Thus, default sequence of the interval in drop mode is given by interval goes below the target, CoDel exits the drop mode, no packets are discarded, and interval returns to its default value. According to the authors, CoDel became attractive to queue management because itn segments: is easy and efficient to implement, in addition to being parameterless [Bufferbloat is the undesirable latency caused by the excessively large and frequently full buffers in network routers. Large buffers have been inserted all over the Internet without sufficient thought or testing . This phRFC 8289 , ideal viseconds , defaulteterless .PIE  is a metEmerging AQM schemes, such as PIE and CoDel, are being progressively deployed either at the ISP end or home gateway to prevent bufferbloat ,36. HoweAbbas, Manzoor, and Masroor  present The ANNs are widely used for video traffic prediction because of their ability to learn complex patterns and estimate linear and non-linear functions . Real-tif is the activation function .An ANN consists of interconnected units called neurons . Each neThe way neurons are arranged creates the ANN architecture. Usually, the neurons are organized in three layers: (a) an input layer, (b) one or more hidden layers, and (c) an output layer. The most popular ANN architectures are the Feed Forward Neural Network (FFNN) and the Recurrent Neural Network (RNN). In the FFNN, information flows only from the input layer toward the output. By contrast, RNN contain one or more feedback layers connected to the hidden layer.A supervised learning algorithm analyzes the training data to adjust the weights of the ANN that best adapt to training cases. Thereby, ANN learns the correlated pattern between input and output data set ,40. WhenProposed by Hochreiter and Schmidhuber , the LSTLSTM memory block comprises four main units: the memory cell, the forget gate, the input gate, and the output gate. The memory cell ,,8), w isThe performance evaluation was done by integrating “real” DASH server and client into a NS-3 simulation. DASH server and client were implemented using virtualization. The network topology was implemented as recommended by International Telecommunication Union (ITU) Recommendation G.1050 . ITU-T GH parameter is typically adjusted between Hurst parameter of Background traffic sources were implemented using the On–Off application class of NS-3. The duration of On and Off states is modeled by random variables. During the Off period, no traffic is generated and in the On state, a Constant Bit Rate (CBR) is produced. Modeling On and Off states, respectively, with the Pareto and Exponential probability distributions, enables the simulation of video traffic with self-similar characteristics ,50. The H=3−α2 . The H peisslein  the VBR Several simulations were performed by varying (i) the AQM method used and (ii) number of background traffic sources. The former were varied from 0 to 14 flows, using maximum transfer unit of 1500 bytes and generating a traffic rate of 500 kbps—as they are TCP sources the traffic will adapt according to network conditions. For each video and background traffic intensity, in addition to the proposed method and Droptail, the following AQMs were tested: RED, ARED, CoDel, and PIE. The RED’s Droptail uses a 3 Gigabytes queue to simulate a bufferbloat problem, then the queue has enough size memory to store all incoming packets without tail drops.We use six full-HD  verThe BBB is the longest video sequence used in the tests, with 1440 frames and 40 s, presenting very low motion intensity and medium detail, those two characteristics lead to a good compression ratio. The SF video uses a fixed camera to capture a bee in the foreground and a flower in the background, with low motion intensity and high detail. RH and PA videos display, respectively, medium and high motion intensity, and medium detail. PA shows people passing by to a very close camera while RH records the vehicle traffic in a rush hour in the city of Munich. RB uses a fixed camera to capture water movements, presenting high motion intensity and detail. We included a Touchdown Pass (TP) video to test the sports genre. TP illustrates fast-moving players on an American football field and consists in 570 frames, 19 s, raw video format of YUV422, and 30 fps.The encoding was done offline using the FFmpeg tool . Live seive GOPs . Segmentive GOPs  where itThe evaluation of the proposed method was performed by estimating the quality of the received DASH video through the SSIM. SSIM is an objective method developed by Wang and Bovik  to estimMost quality assessment metrics rely on quantifying the difference in the value of each pixel between the sample and reference images, computing the mean square error (MSE). In the opposite way, the SSIM calculates the Structural Similarity between two given images exploring the luminance, contrast and structural information of both .x and y are two image signals and considering one of then to have a perfect image, the SSIM can be computed as:x and y, and x and y are very similar while 0 means that x and y are very different [Suppose that ifferent .In addition, we evaluated the number and duration of interruptions in the received video. An interruption occurs when the video playback is temporally stalled as a consequence of buffer starvation due network congestion . As longTo forecast the real-time queue delay, we use a standard three-layer LSTM neural networks . To traiAs the proposed method discards packets according to a predicted queue delay, we evaluated the impact of delay in average SSIM to set values to w was set to 0.8, concentrating the most of packets discard between those thresholds. Above 240 milliseconds, MOS goes down to 1, and attains the worst value (bad). Hence, MOS is a popular subjective metric often used to rate user’s Quality of Experience, ranking the video from 5 (excellent) to 1 (bad) . Table 3Variable We also evaluate the average SSIM for all videos to allow a better understanding of video quality degradation. As illustrated in As shown in Overall, the proposed method presents better average PSNR than competing AQMs. The use of proposed method results in shorter and less frequent interruptions in video playback. With more than 10 traffic sources, Droptail shows the worst performance for both. Indeed, the bufferbloat phenomenon increases packet latency, which causes segment expiration and often freezes video playback.The current generation of DASH client player requires large buffers to store a significant number of segments to avoid video freezing in VoD systems. However, considering the live video streaming, the use of large buffers is not allowed, because large buffers may result in higher delay because of the buffering time, mainly in situations of network congestion. Choosing video segments with better quality increases network congestion, which paradoxically worsens the user-perceived quality. If the network conditions become noticeably bad even a low-quality segment cannot be downloaded in time. This could lead to an expiration of in-flight segments or in the server. This, in turn, can freeze the video for a couple seconds, impairing in the user-perceived quality.In this article, we propose as main contribution a new AQM algorithm to support DASH live video streaming. Long Short-Term Memory was used to forecast the queue delay, anticipating the congestion—this is possible because video traffic is highly auto-correlated and can be predicted. The performance evaluation was done combining computer simulation and real video streaming. We also evaluate the performance of several AQM strategies available in the literature to live video streaming, which is another contribution of this article. Results indicate that the proposed method achieves a better average PSNR for real-time video streaming than ARED, CoDel, Droptail, PIE, and RED. Also, the proposed AQM algorithm decreases the number and duration of video interruptions, mainly for congested networks.


Social interaction requires fast and efficient processing of another person’s intentions. In face-to-face interactions, aversive or appetitive actions typically co-occur with emotional expressions, allowing an observer to anticipate action intentions. In the present study, we investigated the influence of facial emotions on the processing of action intentions. Thirty-two participants were presented with video clips showing virtual agents displaying a facial emotion (angry vs. happy) while performing an action (punch vs. fist-bump) directed towards the observer. During each trial, video clips stopped at varying durations of the unfolding action, and participants had to recognize the presented action. Naturally, participants’ recognition accuracy improved with increasing duration of the unfolding actions. Interestingly, while facial emotions did not influence accuracy, there was a significant influence on participants’ action judgements. Participants were more likely to judge a presented action as a punch when agents showed an angry compared to a happy facial emotion. This effect was more pronounced in short video clips, showing only the beginning of an unfolding action, than in long video clips, showing near-complete actions. These results suggest that facial emotions influence anticipatory processing of action intentions allowing for fast and adaptive responses in social interactions. Social interaction requires the understanding of action intentions. In face-to-face situations, it is crucial to capture another person’s intentions as fast and accurately as possible in order to generate an adaptive response. Recognizing action intentions is thus necessary to ensure that interactive behavior is coordinated in space and time . TypicalIn face-to-face settings, multiple sources of information are available that can be used to anticipate an upcoming action. Previously, it has been demonstrated that observers process the preshaping of the hand , gaze-diCrucially, real-life social interactions provide a range of informative cues besides eye-gaze, such as body posture and facial emotions ,24. EmotThe goal of the present study was to investigate whether facial emotions influence the processing of action intentions, and if so, whether this influence is modulated by the amount of information that is available. To this aim, we presented video clips of two virtual agents  showing facial emotions (angry vs. happy) and performing actions (punch vs. fist-bump) directed towards the observer. Facial emotions and actions were chosen to be either appetitive  or aversive . Importantly, video clips differed in their duration, stopping at various stages of completeness of the unfolding action. Consequently, actions were harder to recognize with decreasing length of the video clip. After every video presentation, participants answered a two-alternative forced-choice task where they had to indicate which action they had recognized in the video.We hypothesized to find an effect of facial emotions on action intention processing. In principle, such an effect could work in two different ways. On the one hand, facial emotions might affect the general accuracy in recognizing actions, i.e. correctly recognizing punches as punches and fist bumps as fist bumps. On the other hand, facial emotions might bias participants’ response towards congruent choices , regardless of the actual presented action. We tested the first claim by investigating participants’ ability to discriminate between the two actions, and the latter by investigating proportions of ‘punch’ judgements in fist bump stimuli and punch stimuli respectively. We evaluated both measures as a function of facial emotion and video clip duration. While we expected that recognition accuracy should increase with video duration, we hypothesized that facial emotions would affect action judgements regardless of the actual presented action. In particular, an angry compared to a happy facial emotion should increase the likelihood to judge an action as a punch, for both punch and bump stimuli. Likewise, a happy compared to an angry facial emotion should increase the likelihood to judge an action for a fist bump, for both punch and bump stimuli. This effect should be strongest when actions are hard to recognize (i.e. short video clips) and less prominent when actions are easy to recognize (i.e. long video clips).age = 22.65, SDage = 3.72, range = 19–40 years, 23 female). The sample size allowed to detect medium effect sizes of δ ~ 0.5 with a power of 1-β = 0.80 . Participants gave written informed consent and received credit points as compensation. Experimental procedures were in line with the Declaration of Helsinki, and the study was approved by the ethics board of the University of Regensburg. The study was conducted according to the approved guidelines.Thirty-two healthy students who did not report any mental or neurological diseases participated in the present study  and performing an action (punch vs. fist bump). Virtual agents were shown from a frontal position and the actions were directed towards the viewer. Importantly, we varied the duration of these video clips videos by stopping them at different end-frames while the action was unfolding. More specifically, videos differed with respect to the number of frames that were presented after a fixed initial period. This initial period contained the facial emotion and the onset of the action see .For stimulus creation, a female and male actor performed two distinct actions, a fist bump and a punch, while their body movements were recorded via a OptiTrack motion capturing system (12 cameras: 8 PRIME 13 and 4 PRIME 13W) tracking a total of 55 optical markers  on a full-body motion tracking suit. Movements and skeleton animation were recorded using Motive software (v 2.2) and then preprocessed in Autodesk 3ds Max (v 2019) by reducing the number of keyframes (from 240 to 30 fps) for automatic noise reduction. Finally, animations of both actions were aligned at a reference frame and the ten initial frames of the actions were averaged, resulting in ten similar, albeit not identical, frames for both animations. This procedure was performed separately for the male and the female animations. Finally, we created three different exemplars per action by inducing slight variations with respect to the end position  of each action. All animations were exported into the Unreal Engine .In a next step, two virtual agents  were created using Daz3D (Daz3D Inc.), based on the standard Genesis 8 models. They received black clothes and standard geometry-based hair. The agents were imported into the Unreal Game Engine 4 and placed into a virtual room in front of an elevator door. Morph targets using Daz3D and the processed mocap animation sequences using Unreal action blueprints were created in which the agents first showed facial emotions (angry or happy) and then performed one of the animated actions (fist bump or punch). Facial emotions always remained constant while actions were performed.nd and 3rd column). Thus, the initial sequence had a length of 1267.7 ms. From that point on, i.e. from 1266.7 ms onwards, we varied the number of frames (1–15) that were presented to the participant, resulting in 15 different video clip lengths per condition.These animation sequences were used to export video stimuli with 30 fps. Each video started with an initial sequence where the facial emotional expression of the agent was presented for 32 frames , and the trajectories of both actions diverged as more frames were added to the video clips which showed the unfolding actions. Naturally, both actions were similar at early stages of the unfolding actions and more distinct at late stages of the unfolding actions.Importantly, fist bump and punch actions started from identical positions and body configurations  by 2 (emotion: angry/happy) by 2 (action: punch/fist bump) by 3 (exemplars) by 15 (video clip duration) = 360 video stimuli. Video length differed according to the number of added frames between 1300 ms (1 frame added) and 1766 ms (15 frames added).Participants were seated in front of a 21.5-inch LCD-screen  with a distance of 50 cm. Stimulus presentation was controlled using PsychToolbox  in Matlavideo duration in subsequent statistical analyses . Furthermore, we grouped trials across agent gender and across versions of the same action, resulting in 2 (emotion) by 2 (action) by 3 (video duration) experimental conditions. Each condition contained data from 30 trials per participant.Statistical analysis was performed using R . In ordemotion  and video duration . D-prime values in all conditions were approximately normally distributed, as assessed by the Shapiro-Wilk-Test .In a first step, we analyzed participants’ recognition accuracy for both punch and fist bump actions. Therefore, we calculated d-prime as a discriminability index . The proemotion, action, and video duration. Proportion of punch responses in all conditions were approximately normally distributed, as assessed by the Shapiro-Wilk-Test . For all analyses, assumption of sphericity was tested using Mauchly’s test of sphericity and sphericity violations were corrected according to Greenhouse-Geisser [The next analysis focused on a potential bias in action recognition with respect to facial emotion and the actual performed action for different video durations. For that reason, the proportion of trials where participants judged an action as a ‘punch’ (regardless of whether correct or incorrect) was analyzed using a repeated-measures ANOVA with the within-subject factors -Geisser , and in -Geisser .emotion and video duration  = 581.36, p < .001, ηp2 = 0.95, but no main effect of emotion F = 1.63, p = .211 and no interaction of emotion and video duration, F = 0.64, pGG = .500 (ε = 0.81). As expected, participants were significantly better to discriminate between actions in long compared to medium duration video clips, t(31) = 16.97, p < .001, d = 3.00, as well as in medium compared to short duration video clips, t(31) = 20.84, p < .001, d = 3.68.Individual d-prime values were analyzed using a repeated-measures ANOVA with factors tion see . There wAccording to signal detection theory a d-prime of zero indicates that participants are unable to discriminate between two stimuli . TherefoIn summary, recognition accuracy, i.e. participants’ ability to discriminate between punch and fist bump actions, increased depending on the degree to which an unfolding action was revealed in the video clips. Participants were unsuccessful in correctly discriminating between both actions when only five or less frames of an unfolding action were added in the video clips. Furthermore, facial emotions of the virtual agents did not influence discriminability between actions.emotion, the actual performed action, and the video duration  depending on the facial duration . Particiemotion, action, and video duration, F = 3.25, p = .046, ηp2 = 0.09, as well as significant two-way interactions of emotion by video duration, F = 11.08, p < .001, ηp2 = .26, and of action by video duration, F = 781.80, p < .001, ηp2 = .96. There were also significant main effects of emotion, F = 23.74, p < .001, ηp2 = .43, action, F = 1151.27, p < .001, ηp2 = .97, and video duration, F = 32.65, pGG < .001, ηp2 = .51 (ε = 0.63). In order to resolve the interaction effects involving video duration, step down analyses were conducted for short, medium, and long video clip duration separately.A repeated-measures ANOVA revealed a three-way interaction of emotion by action, F = 6.68, p = .015, ηp2 = 0.18, as well as a main effect of emotion, F = 22.90, p < .001, ηp2 = 0.42, and a main effect of action, F = 10.07, p = .003, ηp2 = 0.26. Post-hoc t-tests revealed that the proportion of actions judged as punch actions was greater when agents displayed an angry facial emotion compared to when agents displayed a happy facial emotion. This pattern was observed, regardless of whether the video stimulus actually showed a punch, t(31) = 3.72, p = .002, d = 0.66, or a fist bump, t(31) = 5.27, p < .001, d = 0.93. Interestingly, when agents displayed an angry facial emotion, participants reported punch actions more frequently for video clips showing fist bump actions compared to video clips showing punch actions, t(31) = 3.93, p = .001, d = 0.69. There was no significant difference between punch and fist bump video clips when agents were displaying a happy facial emotion, t(31) = 1.40, p = .171, d = 0.25.For short video durations there was a significant interaction of emotion was observed with a larger proportion of punch judgements for angry compared to happy emotions, F = 6.51, p = .016, ηp2 = 0.17. Furthermore, there was a main effect of action, F = 504.93, p < .001, ηp2 = .94, as participants reported punch actions more frequently for punch video clips compared to fist bump video clips. There was no interaction of emotion by action, F = 1.28, p = .267, ηp2 = .04.For medium video clip durations, a significant main effect of action, F = 2199.15, p < .001, ηp2 = .99, with punch actions being reported more frequently for punch video clips compared to fist bump video clips. There was only a tendency for the effect of emotion, F = 3.88, p = .058, ηp2 = .11. According to this tendency, punch actions were reported more frequently when agents showed an angry facial emotion compared to a happy facial emotion. Again, there was no interaction of emotion by action, F = 0.02, p = .896, ηp2 < .01.For long video clip durations, we observed a significant main effect of Finally, we compared the effect of emotion, i.e. the difference in punch responses between the angry emotion condition and the happy emotion condition, between different video durations. The emotion effect was significantly greater for short video durations compared to medium video durations, t(31) = 3.08, p = .008, d = 0.54, and long video durations, t(31) = 4.27, p < .001, d = 0.75. There was no significant difference between the emotion effect at medium and long video durations, t(31) = 1.39, p = .175, d = 0.25.In summary, facial emotions influenced participants’ judgements regarding recognized actions. Angry facial emotions increased the proportion of punch judgements compared to happy facial emotions. This effect was most prominent at short and medium video durations and was observed regardless of whether the actual presented action in the video stimulus was a punch or a fist bump. Interestingly, for short video durations, punch judgements were most frequent when an angry agent produced a fist bump action.The present findings show that facial emotions bias the processing of action intentions. Angry compared to happy facial emotions increased participants’ likelihood to judge a presented action as an aversive punch action . Interestingly, while facial emotions biased participants’ choices, they did not affect recognition accuracy across actions. Furthermore, the emotional bias effect was most prominent at short video durations, when participants were not able to discriminate correctly between actions. Importantly, however, the influence of emotion was still present at medium video durations, when participants’ recognition accuracy improved for both actions. This indicates that facial emotions influence the processing of action intentions, even when actions can be recognized by hand trajectory or preshaping. These results highlight the role of facial emotions in action processing.Facial emotions provide contextual information in social encounters, which allow an observer to infer action intentions and to anticipate actions. This is in line with previous findings related to contextual effects on the recognition of action intentions, for example via eye gaze ,17, and Crucially, while the emotional bias effect was most prominent at short video durations, facial emotions still influenced participants’ responses at medium video durations (for long video durations there was only a tendency). This finding suggests that facial emotions are highly salient cues that impact action intention processing even when actions can be easily recognized on the basis of other information like hand trajectory or preshaping. This is in contrast to findings by Ambrosini et al.  who founAn unexpected finding of the present study was that, for short video durations, participants reported more punch actions in videos showing fist bump actions compared to videos showing punch actions when agents displayed an angry facial emotion. There was no difference between punch and fist bump conditions when agents showed a happy facial emotion. Thus, at short video durations, the emotional bias effect was greater for fist bump videos compared to punch videos. This effect, however, should be treated with caution, as fist bump actions and punch actions were not identical at these short video durations. Therefore, the observed difference might be based on subtle variations in the video stimuli which are relevant in combination with the displayed emotion. Importantly, this finding did not obscure the emotional bias effect, as angry expressions increased punch judgments compared to happy expressions for both fist bump and punch actions.nd column), while the end position of the action was lower for the first bump in comparison to the punch action (cf. th column). Thus, the starting position was spatially closer to the end position of the first bump than to the end position of the punch action. This might explain the general bias to report a fist bump action when the unfolding action was stopped after only a few frames, i.e. when the agent’s hand was still at a low position. In line with this interpretation, previous studies have reported a strong influence of hand preshape and arm movement on processing of action intentions [It should also be noted that overall participants reported more fist bump actions than punch actions . This was possibly due to the fact that both actions were initiated from a low starting position, with the agent’s hand in a relaxed neutral position next to the legs  and facial emotions of the agents . Video duration conditions were aggregated on basis of the number of added frames. Short duration relates to 1–5 added frames, medium duration to 6–10 added frames and long duration to 11–15 added frames. Error bars depict the standard error of the mean.(TIF)Click here for additional data file.S2 FigProportion of punch responses as a function of facial emotions , actions , and varying video clip durations . Video duration conditions were aggregated on basis of the number of added frames. Short duration relates to 1–5 added frames, medium duration to 6–10 added frames and long duration to 11–15 added frames. Error bars depict the standard error of the mean.(TIF)Click here for additional data file.


The gradient in prevalences of >40 g/day (men) and >20 g/day (women) was greatly attenuated after additionally adjusting for HED, while that of ≥4 HED days was only slightly attenuated after additionally adjusting for drinking amount. Among women, the gradients, especially in HED measures, seem steeper in 2009–2017 than in 1997–2007. Educational inequality remained after additional adjustment for income and occupation, although it decreased among women. These results can guide preventive interventions and help explain socioeconomic inequalities in alcohol-related harm.Alcohol-related harm decreases as socioeconomic position increases, although sometimes the opposite happens with alcohol intake. The objective was to know the educational gradient in monthly measures of drinking amount and heavy episodic drinking (HED) among people aged 25–64 years in Spain from 1997–2017. Such gradient was characterized with the relative percent change (PC) in drinking measures per year of education from generalized linear regression models after adjusting for age, year, region, marital status and immigration status. Among men, the PCs were significantly positive ( Preventing alcohol use is essential to reduce the global burden of disease. The amount of alcohol intake and drinking patterns throughout life are the main determinants of alcohol-related health harm ,2. KnowiHowever, the paradox can also be qualified. Although the SEP gradient is usually positive for drinking prevalence in both sexes in all countries, this does not happen with all drinking measures. Thus, the SEP gradient in prevalence of heavy average drinking (HAD), defined as exceeding a certain daily or weekly amount of alcohol intake, is usually negative among men in most countries, but positive among women in many countries, especially in low- and middle-income countries ,26,27,28Regarding heavy episodic drinking (HED) the information is scarcer and generally refers to HED prevalence, having found an SEP gradient usually negative among men and negative in some countries and positive in others among women ,20,26,29Exploring the heterogeneity in the SEP gradients by sex in Spain is interesting because it is an upper-middle income country where the incorporation of women into the workforce and into alcohol intake has accelerated throughout the 21st century. Regarding the drinking measures considered, although there is evidence that they can strongly influence the direction and magnitude of the SEP gradients, there is little research explicitly focused on this aspect. Very little is known, for example, about the change in SEP gradients with increasing HED frequency . There is also almost no information on the threshold in average drinking amount from which the positive SEP gradient is reversed among men . On the other hand, there are studies that estimate the inequality in drinking measures among drinkers ,31, withA relevant topic that is not fully clarified is the relative contribution of average drinking amount  and HED to socioeconomic inequalities in alcohol-related harm. Elucidating it is not easy because drinking amount depends in part on the HED frequency  , and botKnowing the temporal evolution of the SEP gradients in drinking measures is relevant in order to design and evaluate preventive interventions for alcohol intake and also to test the theory of diffusion of innovations. According to this theory, the early adopters of some new behaviors  are high-SEP individuals or societies, and then such behaviors move to other socioeconomic positions ,34,35,36It is proposed to test the following research hypotheses on the educational gradient in drinking measures in the Spanish working-age population from 1997–2017: (1) The gradient will vary widely according to sex and the specific drinking measure. The gradient is expected to be positive for low drinking amounts and HED frequencies and to reverse from a certain threshold and end up being clearly negative for high drinking amounts and HED frequencies among men. However, the gradient is expected to be positive for all drinking measures, among women, although its magnitude will decrease as the drinking amount and HED frequency increase. (2) The educational gradient in drinking amount contributes to explain an important part of the gradient in HED frequency measures and vice versa. (3) The gender gap of the educational gradient in the highest-risk drinking behaviors has narrowed in recent years; more specifically, the negative gradient among men has become steeper and the positive gradient among women has become less steep.The specific objectives of this work are to know the educational inequality in various monthly measures of drinking amount and heavy episodic drinking (HED) by sex and period among people aged 25–64 years in Spain from 1997–2017 and the effect of HED frequency on educational inequality in drinking amount and vice versa.Study participants were extracted from ten biennial editions of national household survey on alcohol and drug use in Spain (EDADES) conducted between 1997 and 2017 . This suData on substance use were collected through a self-administered questionnaire at home (paper-and-pen) and other data through face-to-face interview. Regarding alcohol in the last 30 days, people were asked about: (a) frequency (number of days) and quantity (number of standard drinks/day) of intake of major alcoholic beverages , and (b) frequency (number of days) of HED . Regarding SEP, people were asked to state their highest education category achieved (6–16 categories), the occupation of those who were employed during the interview week or had worked before (10–19 categories), and the total monthly household income and number of household members (only for 2007–2017). Likewise, they were asked about age, sex, living region, country of birth and marital status. Details on the survey methodology can be consulted in .Various outcomes on the amount of alcohol intake and HED frequency during the last 30 days were analyzed. Regarding the first, the outcomes were prevalence of any amount , several daily amount interval prevalences, including that of heavy average drinking (HAD), defined as the proportion of participants with an intake of >40 g/day among men and >20 g/day among women and mean amount per participant in g/day (mean daily drinking amount). The amount of alcohol intake for each individual was calculated by adding the alcohol from the six beverage types mentioned above, following the quantity−frequency approach. For each beverage, the daily amount of alcohol in grams was obtained by multiplying the number of days of intake in the last 30 days by the mean number of standard drinks/day and the grams of alcohol in a standard drink of such beverage and dividing the product by 30. Given the scarcity of updated empirical data, the alcoholic content in grams in the standard drink of each beverage was taken from previous studies ,49, specThe main SEP measure was the highest educational level attained, classified into four categories , corresponding, respectively, to levels 0–1, 2, 3–4 and ≥5 of the 2011 International Standard Classification of Education . AlternaSurvey counts were weighted to account for strata imbalance compared to the universe according to age and region. The analysis of inequalities focused on the population aged 25–64 years. The proportion of missing values for the amount of alcohol intake, HED frequency, education, occupation and income in this population was 11.0%, 1.7%, 0.9%, 1.8% and 26.0%, respectively, and only cases with known values for amount of alcohol intake and the SEP indicator were selected. The effective sample size in the bivariate analysis by education level was 67,590 men and 69,662 women and that in the stratified analysis by education, sex, age group and period is shown in ye) by introducing the number of education years as a quantitative variable and transforming its beta regression coefficient into a more easily interpretable PCye [PCye=100 (eβ−1)] (model I).Regarding educational inequality, age-standardized drinking measures by education level were first calculated using the 2013 European Standard Population weights, as well as the statistical significance of their linear trends across education levels. Subsequently, two alternative indicators of relative educational inequality of drinking measures were estimated from negative binomial regression models with robust variance and log link in the framework of generalized linear models, adjusting for simple age, simple calendar year, region (18 categories), marital status (four categories) and immigration status (born in Spain or abroad), all of them entered as dummy variables. The specific indicators were: (a) the ratios of the drinking measures in each education level to university level by introducing the education level as a dummy variable; (b) the educational gradient or relative percent change in drinking measures per year of education . Finally, complementary analyses were carried out, estimating the inequality in the main drinking measures according to occupational class or income level. Specifically, from multivariate models similar to those mentioned above (model I), the ratios were estimated in each occupational or income level up to the highest level, as well as the income gradient or percent change in drinking measures per thousand euros of annual income. All analyses were performed with IBM SPSS Statistics, Version 26.0 . The Carlos III Health Institute review board approved the study proposal. Ethics committee approval was not required.To clarify the association of education with drinking amount and HED frequency, new estimators of educational inequality were obtained by adding to model I the HED frequency in days as a categorical adjustment covariate, when the outcome was a drinking amount measure, and the drinking amount in grams of alcohol/day, when the outcome was an HED frequency measure (model II). The 95% confidence intervals (95% CI) of age-standardized drinking measures and inequality estimators were obtained. All reported P-values are two-sided, with The crude measures of alcohol intake during the last 30 days by sex, period and education level among the population aged 25–64 years are shown in Regarding drinking amount measures, the minimum prevalence of small drinking amounts  in both periods was observed among the lowest-educated women and the maximum prevalence among the highest-educated men, while the minimum HAD prevalence was observed among the lowest-educated women in 2009–2017, and the highest was observed among the lowest-educated men in 1997–2007. Regarding HED, the minimum values of any measure were observed among the lowest-educated women in 1997–2007 and the maximum values among the lowest-educated men in 1997–2007  or the highest-educated men in 2009–2017  . Other gThe age-standardized measures of drinking amount by sex and education level areplotted in ye: 1.3%) and prevalence of daily intake of ≤20 g/day (PCye: 2.9%), while it was significantly negative for prevalence of daily intake of 21–40 g/day (PCye: −1.1%), HAD prevalence (PCye: −6.0%) and mean drinking amount (PCye: −1.8%) (ye was −5.0% (95% CI: −6.0 to −4.0) for prevalence of 41–60 g/day and −7.3% (95% CI: −8.5 to −6.2) for that of >60 g/day (data not shown in tables). After additionally adjusting for HED frequency (model II), the negative PCye in HAD prevalence and mean daily drinking amount were attenuated from −6.0% to −4.6% and from −1.8% to −0.8%, respectively, although for HAD prevalence, the change did not reach statistical significance.The adjusted indicators of educational inequality in drinking amount measures from multivariate regression models are shown in : −1.8%) . Disaggrye ranging from 3.6% to 5.0%. After additionally adjusting for HED frequency (model II), the gradients in prevalence of intake of 11–20 g/day and HAD prevalence were slightly attenuated, especially the second whose PCye went from 4.7% in model I to 3.6% in model II, although the changes did not reach statistical significance. The rest of the measures changed little. Similar results were observed by focusing on ratios of each education level to university education  and positive without reaching statistical significance for prevalence of 2–3 HED days, while it was significantly negative for prevalence of ≥4 HED days (PCye: −3.2%), mean HED days (PCye: −4.0%) and also HED prevalence (any HED episode) (PCye: −0.6%) (ye was 1.7% (95% CI: 0.6 to 2.7) for prevalence of two HED days, −2.0% (95% CI: −3.6 to −0.4) for that of three HED days, 1.4% (95% CI: 0.8 to 2.0) for that of 1–3 HED days, −1.4% (95% CI: −2.3 to −0.5) for that of 4–7 HED days and −5.3% (95% CI: −6.4 to −4.2) for that of ≥8 HED days (data not shown in tables). After additionally adjusting for drinking amount (model II), all educational gradients were strongly attenuated. Thus, for prevalence of ≥4 HED days, the PCye went from −3.2% in model I to −0.7% in model II. Among women, the educational gradient in all HED measures was significantly positive, with a PCye ranging from 4.6% to 6.0%. After additionally adjusting for drinking amount (model II), the PCye in prevalence of ≥4 HED days went from 5.3% in model I to 0.9% in model II, while the PCye in mean HED days went from 4.6% to 0.4%. Similar results were observed by focusing on ratios of each education level to university education  . Consideducation .The positive educational gradients among population women practically disappear when only drinkers in the last 30 days (current drinkers) were considered. In contrast, among current men drinkers, the positive gradient in prevalence of one HED day attenuated and almost disappeared, while the negative gradient in prevalence of ≥4 HED days and mean HED days became steeper .The direction of the educational gradients was consistent in the periods 1997–2007 and 2009–2017 for all drinking measures. However, there are some between-period differences in the magnitude of such gradients. Among women, the positive educational gradients seem steeper in most recent period for all drinking measures, especially for HED measures, although the differences did not reach statistical significance in any case. However, among men, the changes in the educational gradients for the different drinking measures were not consistent, although the magnitude of the negative gradients in high-risk drinking behaviors as prevalence of HAD or ≥4 HED days increased slightly . The impThe results on income and occupational inequality in the main drinking measures from model I are shown in Finally, we return to the educational inequalities in drinking measures. When the results from model I were further adjusted for income level and occupational class among people aged 25–64 who were at any time employed during 2007–2017, the educational gradients changed little among men and decreased in magnitude among women, especially in HED measures, although their direction remained the same .ye) went from −1.1% for a prevalence of 21–40 g alcohol/day to −7.3% for that of >60 g/day, and from −2.0% for a prevalence of two HED days to −5.3% for that of ≥8 HED days. Regarding women, the results only partially support the hypothesis, showing a clearly positive educational gradient for all drinking measures, without decreasing its magnitude when drinking amount or HED frequency increased. In fact, there was little between-measure heterogeneity in the PCye, which ranged from 3.6% to 5.0% for drinking amount measures and from 4.6% to 6.0% for HED measures.The results support the first hypothesis that in the Spanish population aged 25–64 years from 1997–2017, the educational gradient in drinking measures varied widely according to sex, especially among men, according to the measures analyzed. As expected, after adjusting for sociodemographic factors, the educational gradient among men was positive (increasing drinking measure with increasing education level) for low drinking amounts and low HED frequencies, but it inverted after a certain threshold , becoming progressively steeper with increasing drinking amount and HED frequency. Thus, the relative percent change per year of education  has narrowed in recent years. On the contrary, the gap has been persistent over time and even seems to have widened. Thus, from 2009–2017, the inverse educational gradient in such behaviors among men became steeper than in 1997–2007, and the same happened with the positive educational gradient among women, especially for prevalence 4 HED days, although the differences did not reach statistical significance in any case. In Spain, there are no previous data on the evolution of the SEP inequality in the highest-risk drinking behaviors, and the data from abroad show heterogeneous results ,45,46,47As noted above, there has been an attempt to explain the positive SEP gradient in the highest-risk drinking behavior among women, especially HED and its evolution, by the theory of diffusion of innovations ,34,35,36This study is based on a long series of surveys that has allowed a very detailed analysis of some aspects of educational gradients in multiple measures of drinking amount and frequency of heavy episodic drinking, which had been little analyzed and reported previously. The survey methodology regarding analyzed drinking measures remained reasonably consistent over time, and the sample size is considerable. Educational inequality indicators were adjusted for various sociodemographic factors, including age, year, living region, marital status and immigration status. Furthermore, the findings were consistent when other SEP inequality indicators such as income level or occupational class were used. However, the study has some limitations, mainly because self-reported population-based surveys greatly underestimate alcohol use, especially that from heavy drinking, and such underestimation is growing ,54,55. TThis research suggests that to adequately monitor SEP inequalities in alcohol intake, various measures of drinking amount and HED frequency should be routinely obtained among men and women. In order to anticipate future SEP inequalities in alcohol-related harm, it is more relevant to select high-risk drinking behaviors such as prevalence of HAD or high HED frequency among the population, but to guide preventive programs, it may also be convenient to select other measures. Currently in Spain, preventive interventions for alcohol intake should be directed more intensively at low-educated men, but also at high-educated women. It is also possible that some of the more serious negative effects on health in high-educated women have not yet manifested themselves, possibly because the increased prevalence of the highest-risk drinking measures in that group is relatively recent. In any case, the positive educational gradient among women was already very clear in 1997–2007. Future research, using other sources of information, should investigate whether the socioeconomic gradients in drinking measures characterized here in the working-age population are similar in the older population. Furthermore, considering that the broad international debate on the alcohol-harm paradox continues, the magnitude of the disparity between the socioeconomic gradients of drinking measures and those of alcohol-related harm measures  should be estimated among men and women in different periods, including cohort designs with a sufficient sample size.


Arteriovenous malformation (AVM) of the gastrointestinal (GI) tract can cause bleeding. The treatment choice for GI tract AVM is surgical resection of the involved bowel segment with complete resection of the nidus. The AVM formed in the duodenum or pancreatic head could also cause gastrointestinal bleeding, and there are several reports of pancreaticoduodenectomy as its treatment. However, if the area of AVM can be accurately identified during surgery, it may be possible to completely resect the AVM while preserving the organ. We report a case of duodenal AVM in a patient successfully treated with a subtotal stomach-preserving duodenal bulb resection using intraoperative indocyanine green (ICG) angiography technique.An 18-year-old man was diagnosed with duodenal AVM after several examinations for anemia and was referred to our hospital for further treatment. Preoperative imaging studies showed that the inflow vessels of this duodenal AVM were the inferior pyloric artery and the superior duodenal artery, and the AVM was localized to the duodenal bulb. Thereafter, stomach-preserving duodenal bulb resection preceded by ligation of the inflow vessels was performed. During the surgery, ICG angiography clearly demonstrated the area, where the nidus was distributed, and a duodenal bulb resection with complete resection of the AVM was successfully performed. There was no recurrence at the 6-month follow-up.Intraoperative ICG angiography was a useful procedure for precise identification of the AVM of the GI tract. Gastrointestinal (GI) arteriovenous malformation (AVM) sometimes causes GI bleeding , 2, and An 18-year-old man visited the previous hospital with a chief complaint of black stool and lightheadedness. He had a past medical history of surgery for a congenital skin tag on his buttock when he was 5 years and was noted to have congenital dysplasia of the sacrum. When he visited the hospital, severe anemia  was identified. Upper and lower endoscopy revealed erosions of the duodenal mucosa, and capsule endoscopy revealed a small amount of bleeding in the duodenum bulb. Finally, the angiography revealed duodenal AVM. There were no other lesions in the GI tract that could cause bleeding or anemia, and it was considered that a small amount of continuous bleeding from the duodenal AVM was the cause of the anemia. Thereafter, he was transferred to our hospital for further examination and treatment. Upper GI endoscopy revealed mucosal color changes in the duodenal bulb Fig. .Fig. 1PrBased on these findings, a subtotal stomach-preserving duodenal bulb resection preceded by IPA and SDA ligation to reduce bleeding during surgery was planned. In addition, intraoperative ICG angiography was planned to determine the final resection line. We prepared to perform pancreas-sparing duodenectomy if the resection line of the duodenum indicated by ICG angiography was anorectal to the duodenal minor papilla and pancreaticoduodenectomy if the resection line was over the duodenal papilla. Exploratory laparotomy via midline incision revealed an expanded right gastric vein, which was presumed to be the effect of the increased blood flow due to AVM. However, no abnormalities were observed in the duodenum. To determine the extent of resection, 12.5 mg of ICG  was injected, and ICG imaging was performed using an infrared camera system . Intraoperative ICG angiography clearly demonstrated the lesion of the AVM, and this area was completely resected after IPA and SDA ligation, as planned preoperatively Fig. . The cutAVM is characterized by a nidus, which is an abnormal connection between the arteries and veins without capillaries. No definite pathogenetic explanation has been described for this anomaly. The majority of lesions are congenital and are associated with hereditary hemorrhagic telangiectasia, such as Osler–Weber–Rendu syndrome, whereas an acquired case has also been reported. In the present case, he had no nasal bleeding, vasodilation of body surface, or family history, which are all physical features suggestive of a hereditary disease; thus, we consider him to be a solitary AVM , 4. AVM The treatment of choice for GI tract AVM is surgical resection of the involved bowel segment with complete resection of the nidus. Because AVMs located in the duodenum or pancreatic head can cause bleeding from the duodenum, there are several case reports in which pancreaticoduodenectomy was performed as treatment –9. To deAnother option, treatment with the transarterial embolization (TAE), has been reported as a non-surgical treatment for AVMs , 15. TheIntraoperative ICG angiography was useful in identifying the location of the AVM lesion. Using this technique, we could resect the duodenal AVM in the appropriate area.


Background: Longitudinal studies of drinking behavior have reported inconsistent changes in drinking behavior as people age. Thus, this study aims to characterize the changes in drinking behavior among Korean adults and to reveal differences in their demographics, depression, and suicidal thoughts. Methods: This study used the Korea Welfare Panel Study data over nine years (2009 to 2017), analyzing a total of 7506 participants. Growth mixture modeling was applied to classify patterns of change in drinking in these participants. The χ2 test and analysis of variance were used to analyze the differences in demographics, depression, and suicidal thoughts according to patterns of change in drinking. Results: Changes in drinking among Korean adults were categorized into four types: “high-risk retention”, “medium-risk to high-risk”, “high-risk to low-risk”, and “low-risk retention”. Gender, age, education, marital status, living arrangement, living area, and depression differed among these groups. Conclusion: We identified four types of changes in adult drinking behavior in South Korea, which varied in their demographics and depression levels. These results suggest that tailoring interventions to the type of behavioral changes might be more useful than batch interventions. Alcohol use disorder is a major contributor to the global burden of disease, and is known to be associated with about 3.3 million deaths worldwide annually . In SoutWhile longitudinal approaches to drinking research can overcome the limitations of cross-sectional research, cross-sectional studies also have the limitation of reporting inconsistent changes in drinking patterns. For instance, some studies have shown that drinking behavior tends to decrease as people get older ,7, whereAccording to the Korean Survey on Mental Illness, the monthly amount of alcohol being consumed by Korean adults has been on the rise over the past few years . HoweverGiven the inconsistencies in past research, it is necessary to explore changes in drinking behavior using panel data with a larger sample and more sophisticated methods. We used growth mixture modeling (GMM), which identifies distinct subgroups of growth trajectories and allows previously undetected subgroups to be revealed and isolated from the main sample population. These subgroups can then be visualized for analysis by plotting each of their subgroup-specific mean trajectories, classifying changes in drinking behavior as assessed with the Alcohol Use Disorder Identification Test (AUDIT) using data from the Korea Welfare Panel Study (KOWEPS) collected between 2009 and 2017. This combined growth model allowed us to observe different types of changes in drinking behavior over time. In addition, this study explores differences in demographics, depression, and suicidal thoughts according to the type of change in drinking behavior. As drinking behavior is reported to be different based on demographics  and corrThe purposes of this study were as follows: (1) to identify the types of changes in drinking behavior among Korean adults, (2) to analyze the differences in demographics, and (3) to identify the relations between the drinking behavior types with depression levels and suicidal thoughts. We utilized nine years of data from the KOWEPS between 2009 and 2017 to derive an appropriate GMM for Korean adults’ types of drinking behavior changes, and utilized the data of 2017 for demographics, depression levels, and suicidal thoughts. These allowed us to derive the appropriate number of distinct groups according to our GMM, and to interpret them in relation with demographics, depression level, and suicidal thoughts.The KOWEPS covered a panel of 7072 households selected from the 2005 census, and was designed to explore the current annual income, economic activities, and well-being of households and their members. It has been conducted annually since 2006. The panel covers a wide range of areas and is nationally representative. In this study, we included adults over 20 years old, as that is the legal drinking age in Korea. After excluding individuals with missing data, we ultimately analyzed data from 7506 individuals; during the study, 2.0% of the sample population passed away by committing suicide and were excluded in the data accordingly. The KOWEPS uses the AUDIT to measure drinking behavior; the AUDIT was designed by the WHO and comprises 10 self-report questions on harmful drinking behavior, alcohol dependence, and high-risk drinking . AccordiWe employed a condensed 11-item version of the CES-D-11, which was created by Kohout et al.  after thWe evaluated gender, age, education level, marital status, living arrangement , income, and region as our target demographics. Of these, income was defined as the current annual income . For the analysis, we log-transformed the income values to ensure that the normal distribution assumption was upheld. The target region was divided into urban and rural areas.We evaluated suicidal thoughts using a single question with a binary response option: “I have thought about suicide” and “I have not thought about suicide”. We used the KOWEPS in 2017 for this analysis. 2 test and ANOVA with Scheffe’s test were conducted to analyze differences in demographics, depression level, and suicidal thoughts according to the type of change in drinking. We used SPSS 22.0 and M-plus 8.0. First, we described the demographics and main variables via frequency analysis and descriptive statistics. Second, we used GMM, which can isolate previously undetected subgroups from the sample population, to identify types of drinking changes over the 9 years of KOWEPS data. To assess model fit in the GMM, we used the Akaike information criterion , BayesiThe sample consisted of 7506 adults , with an average age of about 69 years in 2017. Of all participants, 151 (2.0%) died by suicide during the study period . 2 = 653.287, p < 0.000; comparative fit index = 0.990; Tucker–Lewis index = 0.991, root mean square error of approximation = 0.045). As a linear model was found to be statistically significant in the LGCM, we tested a linear model in the GMM as well. Looking at the averages and standard deviations of drinking behavior from 2009 to 2017, we found that the data showed an overall decrease . To veriThe results of the model compatibility analysis of the drinking change types revealed that the fourth model had the highest AIC, BIC, and SSBIC, as well as an entropy value closest to 1, and the VLMR test was statistically significant. Consequently, classifying changes in drinking behavior into four types was the best fit for the data; the model fit is shown in All of the differences reported between the four types of drinking behavior change groups for age, education level, marital status (spouse), living arrangement (one-person households), living area, income, and depression were statistically significant. The only differences that did not reach statistical significance were those for past experience with suicidal thoughts.The differences between the four groups were explored by examining the demographic factors. Males were most likely, by far, to be in the high-risk retention group and females in the low-risk retention group. These findings align with those of previous studies showing that males tend to engage in higher-risk drinking behavior and females in lower-risk drinking behavior . As for More educated participants tended to belong to the high-risk retention or medium-risk to high-risk groups, whereas the low-risk retention group had a notable proportion of poorly educated individuals. People with spouses tended to fall into the high-risk retention, medium-risk to high-risk, and high-risk to low-risk groups, whereas those without spouses were more likely to belong to the low-risk retention group. The low-risk retention group had a substantial proportion of people living in single-person households, whereas the high-risk retention group primarily consisted of individuals living with others. These results do not seem to align with previous studies, many of which suggest that low education levels and insufficient family support are associated with behaviors that put people′s health at risk, such as drinking ,21. FurtThe high-risk retention, medium-risk to high-risk, and high-risk to low-risk groups had a higher proportion of participants living in urban areas as compared to the low-risk retention group. These results are similar to those of previous studies in which urban areas were found to have higher rates of alcohol consumption and problem drinking than rural areas ,23. ThisIt was also observed that depression was highest in the low-risk retention group, followed by the medium-risk to high-risk, high-risk to low-risk, and high-risk retention groups. Given that most of the people in the low-risk retention group were female, we suspect that this result relates to the fact that females generally tend to have higher levels of depression . We shouOn the other hand, while past studies have reported a statistically significant relationship between drinking and suicide ,28, we fRegular prolific alcohol consumption is widespread in South Korean society. Drinking in groups is a regular occurrence in casual social settings, as well as events related to work such as work dinners and team-building exercises, which are both quite common and all but mandatory in the Korean workplace. Furthermore, in South Korea, most forms of alcohol are quite affordable, making drinking easily accessible to people of relatively modest means such as students, retired people on fixed incomes, and people with low incomes. As changes in Korean adult drinking behavior show four distinct types which vary in their demographics and depression levels, tailored interventions might be more useful than batch interventions to combat alcohol abuse. Given that people start drinking, and continue to drink, due to many intrinsic and extrinsic motivations, it makes sense for public interventions to have a multi-pronged approach for the greatest chance of success. This suggests that decision-makers should develop policy-driven interventions in more targeted and prudent ways to handle problem drinking, being sensitive to the social pressures and drinking culture that promote alcohol consumption in the first place.As this study was carried out on secondary data, it was hard to identify other factors related to drinking. There are some differences between evaluating alcohol use problems using the addiction-related symptoms (AUDIT) and the amounts/patterns of alcohol use. Therefore, in future research, it would be important to consider both the addiction scale and the alcohol use patterns. The AUDIT has different cutoffs for males and females. There are challenges in assessing severity with the same AUDIT criteria for both males and females. Participants in this study included both those who used alcohol and those who did not use it at all. Subsequent studies should explore potential differences in family history, development processes, and traumatic experiences. While this study analyzed the prevalence of alcohol consumption and the various patterns of alcohol consumption across South Korea, there are several demographics that are not accounted for. For example, although our model examines the trajectory of drinking behavior in great depth, we have not yet seriously considered how other available factors impact drinking. For example, we have further information about living arrangements and income, but we do not have access to data about what brought about these various living situations. To make a more complete model that can take into account the various motivations and living situations of alcohol consumers, it will be necessary to gather more extensive data in the forms of in-person interviews or surveys. This will require a considerable amount of resources, but it would allow us to make the best predictions as to what types of interventions will be the most efficient and effective in relieving the high rate of problem drinking in South Korea.


As a means of enhancing clinical simulation opportunities for adult-gerontology nurse practitioner students, a series of video simulations were created for use for nurse practitioner education. With funding through the Health Resources and Service Administration (HRSA) Advanced Nursing Education Workforce grant and partnering with nurse practitioner clinical educators from Optum Health Care, a video simulation focused on the concept of treating an older veteran within a long-term care facility rather than transferring to the acute care setting was developed. The case Treating in Place: Nurse Practitioner-Led Team Management of a Long-Term Care Patient Video involved a nurse practitioner collaborating with a physician, a registered nurse, a social worker, and a family member. The interactive simulation video was developed using the eLearning authoring tool H5P to create learning experiences for students that can be used either in face-to-face classroom experiences or embedded in learning management systems. H5P is a web-based authoring tool that helps faculty build interactive course content. H5P activities provide instant feedback to students, allowing them to self-assess their understanding of the dynamic video simulation case. A faculty handbook that describes the case scenario with the interactive questions and suggested discussion questions is available. The adult-gerontology primary care nurse practitioner competencies addressed for this case are identified in the faculty handbook. These videos have been widely disseminated and are being included in nurse practitioner curriculum across the country. A QR code with access to direct viewing of the video will be included in the presentation.


Field hockey, a team sport played by both men and women at both recreational and professional levels, requires maintaining a forward flexed posture putting stress on the lumbar spine. Hence, it is necessary to assess the muscles supporting the lumbar spine, especially those surrounding the hip, to inform strengthening exercises for this population.To establish the best body weight rehabilitation exercises shown to produce high muscle activation  for both the gluteus maximus (Gmax) and medius (Gmed) muscles. Four exercises fell into this category.Surface electromyography (sEMG) was used to record the muscle activation of Gmax and Gmed of four body weight rehabilitation exercises in 26 high-performance female field hockey players. The %MVIC activation data of both Gmax and Gmed were analysed using a three-way ANOVA.p = 0.0487). No statistically significant difference was observed for Gmed between the four body weight rehabilitation exercises (p = 0.6285).The single-leg squat generated the highest %MVIC activation of both Gmax (125.65%MVIC) and Gmed (126.30%MVIC). The only statistically significant difference for Gmax was between the single-leg squat and plank with hip extension (The four exercises generated similar %MVIC activation levels. The single-leg squat produced the highest observed %MVIC of Gmax and Gmed in high-performance female field hockey players and is, therefore, recommended.Implementation of the findings could result in benefits during prehabilitation, injury prevention programmes and the later stages of rehabilitation for high-performance female field hockey players. Field hockey is a non-gender specific international team sport played at many levels, ranging from amateur to elite , play a significant role in transferring forces from the lower extremity in the direction of the spine. It decreases the load on the erector spinae during lumbar extension and stabilises the knee in extension Neumann . HoweverWeakness of Gmax and Gmed will result in compensatory movements of the lower back, hip and knee, notably a pelvic drop, excessive hip adduction, femoral internal rotation and an exaggerated knee valgus angle  high-performance field hockey squad and consented to participate. Players with self-reported lower back, lower extremity or pelvic girdle injuries or a history of lower extremity surgery within the 2 years preceding our study were excluded. Two cohorts of 26 participants in total were recruited for the trial. The first cohort of 14 participants was tested during October 2019, while the second cohort of 12 participants was tested in February 2020.The four body weight rehabilitation exercises conducted by Boren et al.  that werThe participant started in a side-lying position with the dominant leg on the bottom. The lowermost leg was flexed to 45° at the hip and knee to assist in stabilising the pelvis against anterior or posterior tilting , with the knee of the non-dominant leg extended and the hip flexed to avoid the heel of the contralateral leg touching the floor during the execution of the exercise. The participant was then asked to flex the dominant leg’s knee for two beats, touch a chair of 47 cm in height with the buttocks and extend back to the upright position for two beats  whilst the first author demonstrated the MVIC testing positions and the four exercises. She prepared and cleaned the skin. Kendall Meditrace Ag/AgCl pre-gelled electrodes  were used to reduce electrode-skin impedance Konrad . The intParticipants adopted the MVIC testing positions for normalisation of the sEMG signal amplitude consisting of a 5 s isometric hold, 3 × repetitions, 2 min’ rest in between. Normalisation of the sEMG signal amplitude is important because of various influences on the sEMG signal, such as individual differences and anatomical and physiological factors  algorithm and smoothed with a time reference. The sEMG bandwidth was 20 Hz – 450 Hz, common-mode rejection ratio (CMRR) >100 decibels (dB) at 60 Hz, input impedance > 100 electrical resistance and conductance (Ohm), baseline noise < 1 potential difference (µV) RMS, a sufficient sEMG signal gain of 400 Volts (V) at a sample frequency of 1000 Hz.p-values for the pairwise differences in peak %MVIC between the four body weight rehabilitation exercises. For each variable analysed, the overall F-test for the four body weight rehabilitation exercises was determined, and also the partial effect size measure for the ANOVA , with ‘participant’, ‘period’ (referring to the randomised order in which exercises were conducted) and ‘exercise’ as categorical variables in the model. Fitting the ‘participant’ effect also controlled for the cohort effect, namely the potential effect of testing the trial participants in two cohorts. Based on this three-way ANOVA model, point estimates for the mean %MVIC for each exercise were reported, as well as point estimates, 95% confidence intervals (CIs) and Our study was approved by the Health Sciences Research Ethics Committee (HSREC) of the Faculty of Health Sciences, UFS, South Africa (Ethics No. UFS-HSD2019/1349/2910).A written informed consent form was provided to and signed by each of the participants that outlined the risks emanating from the procedures administered and the equipment used during our study.2 .Twenty-six high-performance female field hockey players of the UFS completed the four body weight rehabilitation exercises. The mean age of the players was 20.15 years , the mean height was 164 cm , mean body mass was 64.72 kg  and mean body mass index (BMI) was 23.87 kg/mp = 0.2558) or Gmed (p = 0.6285) activation, and the associated effect sizes for ‘exercise’ were negligibly small. Furthermore, none of the pairwise comparisons of the four exercises with regard to Gmax and Gmed showed statistically significant differences, except for the comparison of the single-leg squat with plank hip extension with regard to Gmax (p = 0.0487).p < 0.0001). Whilst significant differences among participants were expected, although it was not the focus of our study, those differences presented why Gmax and Gmed activations were studied in a crossover trial where the large between-participant variability could be accounted for in the statistical analysis. ‘Period’ was statistically significant for Gmax (p = 0.0816) and Gmed (p = 0.0542) at the 10% significance level, again justifying the crossover trial, which allows one to control the period differences in the trial.p-values associated with the effect of ‘exercise’ on Gmax and Gmed.Whilst various body weight rehabilitation exercises are prescribed clinically to strengthen Gmax and Gmed, no evidence is available on the relative efficacy of these exercises in high-performance female field hockey players. The trial reported here selected exercises that generate a ‘very high’ (> 61%MVIC) muscle activation threshold  and Gmed , as shown in Side-plank hip abduction with the dominant leg on top requires that Gmax and Gmed have to overcome the gravity barrier during concentric hip abduction Lee & Jo , which iHip abduction is one of the essential requirements of field hockey. A player has to step to the side (a pure concentric hip abduction movement) to perform basic actions of field hockey, such as passing a ball, receiving a ball, shooting for goal and engaging in a tackle. Therefore, it was not unexpected that the %MVIC findings of Gmax and Gmed in high-performance female field hockey players were very high, considering the basic actions performed in field hockey.p = 0.4807, d = −1.03) and side-plank hip abduction with the dominant leg on top  when compared to the single-leg squat. However, there was a significant difference between the single-leg squat and plank with hip extension . Concerning Gmed, there was no significant difference in activation amongst the side-plank hip abduction with the dominant leg at the bottom , side-plank hip abduction with the dominant leg on top  and plank with hip extension  when compared to the single-leg squat. Boren et al.  and side-plank hip abduction with dominant leg on top  when compared to the plank with hip extension. However, a significant difference was found between the single-leg squat and plank with hip extension . With regards to Gmed, plank with hip extension was found to be the third-best exercise for strengthening this muscle. Furthermore, there was no significant difference in activation amongst the side-plank hip abduction with the dominant leg at the bottom , side-plank hip abduction with the dominant leg on top  and the single-leg squat , compared to the plank with hip extension.Additionally, Macadam and Feser  concludeConsidering the basic actions performed in field hockey, it was not surprising that the %MVIC findings of Gmax and Gmed in this high-performance population were higher than those reported in other studies. As noted, squatting and side-stepping actions in field hockey require muscle activity of both Gmax and Gmed. Furthermore, performing lunge actions to engage in a tackle or to pass the ball, as well as the kick back action when running with the ball, are all examples of actions in field hockey that involve hip extension. Given that hip extension is the primary function of Gmax, the very high %MVIC generated from the plank with hip extension exercise in field hockey players can be attributed to the exceptional demands of field hockey in this position. Therefore, strengthening a player in this position is crucial for optimal performance.It is important to note that compensation, such as lumbar hyperlordosis, can result if the player lacks adequate core stability  or Gmed (p = 0.6285) activation, as shown in A key observation in our study was that the four exercises investigated produced rather similar Gmax (With regards to Gmed, the estimated effect size was zero , suggestOur study was conducted exclusively on high-performance female field hockey players, therefore limiting generalisation to this group as it only provided data on the participants’ dominant side. The use of sEMG is associated with limitations because of various influences on the sEMG signal amplitude such as cross-talk (Bishop et al. It is theorised that when a specific primary muscle responsible for a particular joint movement weakens, the synergistic muscle becomes the new primary muscle responsible for the movement (Bishop et al. To optimise the performance of high-performance female field hockey players, biokineticists and conditioning coaches should select the body weight rehabilitation exercise that elicits the highest %MVIC that will result in Gmax and Gmed strength improvement. Given that our study is the first of its kind to examine the effect of these exercises on high-performance female field hockey players, biokineticists and conditioning coaches can benefit by incorporating the findings into the programme prescription during prehabilitation or the later stages of rehabilitation for this population. The conditioning coach, in particular, can benefit from the findings presented here, given that these exercises can be performed on the playing pitch as part of a warm-up without the need for any equipment.


They are warning signs that loss is imminent. Long working hours are a risk factor for near-misses along with sleep problems, job-related stress, and depressive symptoms. This study aimed to evaluate the indirect effects of long working hours via mediating variables on near-miss occurrences among Japanese healthcare professionals. (2) Methods: 1490 Japanese healthcare professionals’ reports from a web-based survey of workers in October 2018 were analyzed to evaluate total, direct, and indirect effects of long working hours on near-misses. We applied a generalized structural equation model with three mediating variables: sleep problems, job-related stress, and depressive symptoms. (3) Results: The total effect and direct effect of the categories of working hours longer than 41 h per week (h/w) for occurrence of near-misses were not significantly higher than that of 35–40 h/w. However, for indirect effects on occurrence of near-misses that first passed through job-related stress, there were higher reports for each category compared to 35–40 h/w, with odds ratios (OR) and 95% confidence intervals (95% CI) of OR = 1.12, 95% CI  for 41–50 h/w; 1.25,  for 51–60 h/w; and 1.31,  for ≥ 61 h/w. (4) Conclusion: The results suggest that reducing working hours might improve job-related stress, which could reduce near-misses and prevent injuries. Many healthcare professionals interact with and treat patients in a variety of situations daily ,2. OccupPrevious research has suggested that long working hours are one of the factors increasing the risk of near-misses ,10,11. SMeanwhile, several epidemiological studies have shown the negative effects of long working hours on the risks of chronic fatigue, stress, depressive state, anxiety, and sleep quality ,18. ThesHence, this study evaluated the relationship between long working hours and near-misses, including the indirect influence through the medium of the status of healthcare professionals in Japan. In particular, we assessed the direct effect of long working hours related to occurrence of near-misses and the indirect effects of long working hours, taking into consideration the mediators of job-related stress, sleep problem, and depressive symptoms. In our analyses, we applied a structural equation model (SEM), which is often used to address similar issues in such fields as psychology and social epidemiology . In partThis study conducted a web-based cross-sectional survey of workers in October 2018 in collaboration with a research company that has one of the largest research panels in Japan, with over 1.8 million voluntarily registered panelists . To miniWe selected full-time employees (over 35 working h/w as the average working hours during the last six months) who reported being professional, technical experts, or researchers in the medical/health/welfare industry based on the Japan Standard Industrial Classification established by the Japanese Ministry of Internal Affairs and Communications .The outcome measures of the present study were near-misses, which were defined as events that did not result in but had the potential to cause injury in industrial settings during the past six months. In this survey, this definition of a near-miss was presented as “an unplanned event that did not result in injury, illness, or damage but had the potential to do so,” after which the participants were asked whether they had experienced any near- misses in industrial settings with the following question: “Have you experienced any near-misses in a workplace setting during the past six months?”Self-reported working h/w during the past six months were grouped into the following categories: 35–40 h/w, 41–50 h/w, 51–60 h/w, and ≥61 h/w. To represent the mediated variables of long working hours, we evaluated overall sleep quality, depressive symptoms, and job-related stress. We used the Pittsburgh Sleep Quality Index (PSQI) to assess overall sleep quality . The PSQThe items of job-related stress, sleep problems, and depressive symptoms were included as mediating variables in the structural model of near-misses and working hours. A simple model was used to separate the direct effect of working hours, which was the main objective, from the indirect effect of the mediating variables. The effects of long working hours discussed in previous studies suggest that these mediating variables can cause mental or physical fatigue ,16,27. TIn addition, for our sensitivity analysis of model structure, we employed a model that adds a modifier, such as the four-scaled recovery score of fatigue and the presence or absence of night/shift work see . FurtherDemographics and baseline characteristics were summarized. We employed GSEM to estimate the direct and indirect effect of each category of long working hours relative to 35–40 h/w on near-misses. GSEM procedures have been developed to allow some types of variables, including binary data, and recently have been used for mediation analysis . In thisFurthermore, we conducted a subgroup analysis to assess the difference in the response to working hours on near-misses between novice and experienced workers among younger age groups. We categorized healthcare professionals aged under 35 years into two groups: those with ≤3 years or >3 years of current work experience, and applied the model for each.Statistical analysis was performed using STATA version 16  and R software, version 4.0.4 (R Core Team).From the voluntarily registered panelists, 30,000 participants for the survey were randomly selected. Among all job types of participants, 2028 healthcare professionals were identified. We analyzed the reports of 1490 healthcare professionals with over 35 working h/w as the average working hours during the last six months of service at the time of the survey and with responses for the presence or absence of near-misses.The characteristics of healthcare professionals are shown in In addition, the results of the sensitivity analysis for the model structure, which added the recovery score of fatigue and the presence or absence of night/shift work, showed that the direct and indirect effects on near-misses of each working hour category did not change substantially from the main analysis see . HoweverLastly, the results of the subgroup analysis by category of work experience for healthcare professionals are shown in In the present study, we examined the association between long working hours and near-misses among Japanese healthcare professionals using a nationally representative sample of healthcare workers with respect to sex and age. Near-misses were reported in 49% of the participants in this study. Near-misses related to the administration of medicines were reported in about 10 to 20% of the study population , and thePrevious research on the broader medical/health/welfare industry not limited to healthcare professionals showed that the OR for near-misses for the working hour category of 41–50 h/w was 1.20 with statistical significance relative to 35–40 h/w; however, for the ≥61 h/w category, the OR for near-misses was closer to 1.00 . In our Based on our model, decomposing the impact of working hours on near-misses into direct and indirect effects showed that the direct effects of long working hours were not significant. Most of the impact of long working hours on near-misses is the path that first passed through job-related stress. Stressors such as workload, time pressure, and understaffing, which may be related to long working hours, have been shown to influence near-misses , and theIn addition, the variables having an indirect effect through job-related stress may provide information on what to monitor regarding the role of long working hours on near-misses because the greatest influence of long working hours on near-misses is the indirect path that first passes through job-related stress. The Japanese Industrial Safety and Health Law was expanded in 2015 to include a stress check program that mandates annual screenings of high psychosocial stress workplaces for enterprises with 50 or more employees using the BJSQ ,37. ThusInadequate recovery from sleep problems is an important component of the pathway from long working hours to increased fatigue and the risk of health problems ,39. Our Among the workers in a previous study that included healthcare professionals, depression significantly increased the ORs for near-misses . HoweverThis approach of disentangling the indirect and direct effects could provide information on preventive measures other than reducing long working hours, not only for healthcare professionals. Our result suggests that interventions targeting work-related stressors and positive coping strategies , which reduce the negative effects of work stress on job performance , might pOur study has several limitations. First, we relied on self-reported job-related factors, including working hours, for exposure and near-misses as the outcome measure, which might have been influenced by recall bias. Since this is a web-based survey, there is a possibility that the selection of the respondents may be biased. Second, various confounding individual and environmental factors were not available, such as demographics and working conditions among healthcare professionals. Therefore, this study did not examine the effect of reducing working hours on the occurrence of near-misses. Third, the participants’ reports might not represent the actual occurrence of near-misses, because detecting and reporting them could depend on several factors, such as the experience of current work . HoweverThis study evaluated the direct and indirect effect of long working hours on near-misses among Japanese healthcare professionals. Long working hours are associated with the occurrence of near-misses through job-related stress and sleep problems. Therefore, reducing working hours might improve the job-related stress levels of healthcare professionals, which might reduce near-misses and prevent injuries. To reduce the number of near-misses, it may be important to examine the effect of reducing working hours or to examine the effect of intervention on the intermediate variable, job-related stress.