Autonomous Sensory Meridian Response (ASMR) describes the experience of tingling sensations in the crown of the head, in response to a range of audio-visual triggers such as whispering, tapping, and hand movements. Public interest in ASMR has risen dramatically and ASMR experiencers watch ASMR videos to promote relaxation and sleep. Unlike ostensibly similar emotional experiences such as “aesthetic chills” from music and awe-inspiring scenarios, the psychological basis of ASMR has not yet been established. We present two studies  that test the emotional and physiological correlates of the ASMR response. Both studies showed that watching ASMR videos increased pleasant affect only in people who experienced ASMR. Study 2 showed that ASMR was associated with reduced heart rate and increased skin conductance levels. Findings indicate that ASMR is a reliable and physiologically-rooted experience that may have therapeutic benefits for mental and physical health. Imagine that you are in a quiet library. Two people behind you start whispering, others are gently typing on keyboards, and someone starts quietly eating an apple. You look up and watch someone delicately turn the pages of a book, carefully scratching some notes with a newly sharpened pencil. For many, these might be frustrating and irritating distractions in a supposedly quiet environment. But for others, these sights and sounds would trigger a feeling known as Autonomous Sensory Meridian Response (ASMR)—a warm, tingling, and pleasant sensation starting at the crown of the head and spreading down the body. The subjective experience of ASMR ‘tingles’  is ofteASMR occurs involuntarily in response to certain external  triggers, including: whispering, soft-speaking, tapping, scratching, slow and expert hand movements and close personal attention . Many peASMR appears to share similarities with more well-established sensory experiences including the “shivers-down-the-spine” that some  people experience during music-listening and profound aesthetic experiences (such as those associated with the emotion of awe). Research on aesthetic chills has assessed the physiological parameters that correspond with these complex emotional experiences, typically by presenting participants with chill-inducing stimuli  and measuring aspects of physiology e.g., , 14–16).–16.14–16Despite the potential parallels between ASMR and aesthetic chills, one point of departure between the two experiences is that ASMR is typically considered to be relaxing and soothing (it is often used as a sleep aid), whereas chills are associated with excitement and physiological arousal , 15, 22.The parallels between aesthetic emotional experiences and the psychological state of ASMR suggest that ASMR is likely to be characterized by a strong emotional response when sensory stimuli produce a distinct emotional profile  in response to ASMR triggers. This response may be one that naturally occurs but is harder to inhibit in individuals with ASMR. However, whether or not ASMR stimuli produce distinct emotional states  and accompanying physiological responses  in ASMR experiencers compared to non-experiencers is an important but unresolved issue. Examining the psychological and physiological effects of ASMR is a particularly timely issue because the growing public recognition of ASMR media  suggestsIn the present research we systematically tested whether watching ASMR videos produce: (i) self-reports of tingling and pleasant affect  and (ii) objective physiological concomitants (changes in heart rate and skin conductance), in people who report having ASMR. Study 1 was a large-scale online experiment where participants watched a subset of three videos  and then reported their affective response and tingling sensations. This study had several strengths for examining ASMR. First, we used immediate reports of several core dimensions of affect rather than retrospective reports that may be systematically biased . Second,Study 2 examined the physiological parameters underlying the psychological state of ASMR. Identification of physiological changes from watching ASMR videos compared to non-ASMR videos would not only provide the much-needed objective evidence to substantiate the experience and distinguish it from aesthetic chills, but would also shed light on whether ASMR might provide physiological, as well as psychological, health benefits. We recruited ASMR experiencers and matched controls and recorded their physiological and affective responses whilst they watched two ASMR videos (one of which was self-selected) and one control non-ASMR video for comparative purposes. Because ASMR is purported to induce relaxation, we expected ASMR videos to be associated with a commensurate physiological response in ASMR experiencers but not in non-experiencers; specifically, reduced heart rate and skin conductance level e.g., , 31). In. In31]).The study employed a 2 x 3 mixed design. The between-subjects variable was ASMR group (ASMR participant vs. non-ASMR participant) and the within-subjects variable was video type (control video vs. ASMR soft-spoken video vs. ASMR sound video). The dependent variables were self-reported changes in affect and frequency of tingling sensations experienced during video watching.Mage = 29.40 years, SD = 10.79, Range = 18–77). We discarded data from 898 participants who did not complete the study, and 173 participants who took longer than 1 hour to complete the study (suggesting that they may not have completed it in one sitting). Of the final sample, 813 (81%) identified as experiencing ASMR. ASMR group was determined by a “yes” vs. “no” response to the question: “Having watched these videos, or just from your everyday life, would you classify yourself as somebody who experiences ASMR?”. Ethical approval was obtained from the University of Sheffield Psychology department ethics committee and was conducted in accordance with principles expressed in the Declaration of Helsinki.The online experiment (described as an investigation into ASMR) was advertised widely on social media , on a dedicated website, and through a University mailing list. We determined the sample size by collecting as many responses as possible within a three-month period (during which the survey was active online). During this time, 2073 participants started the study, but the final sample consisted of 1002 participants  six soft-spoken ASMR videos , (b) six ASMR videos with sound, but no speaking, and, (c) six control (non-ASMR) videos . ASMR videos were taken from YouTube and were selected by the authors, two of whom experience ASMR and watch ASMR videos. We chose to include both spoken and sound-only videos because this distinction is present in ASMR communities for ASMR trigger videos . The content of the videos was typical of the ASMR genre and the videos were selected on the basis that they contained multiple ASMR triggers . For exaHow frequently  did you experience tingling sensations during the video?”) from 1(none of the time) to 7.After watching each video, participants indicated how frequently they experienced tingling sensations during the video . Three items measured high activation unpleasant affect (referred to as ‘stress’) ‘anxious’, ‘nervous’, and ‘tense’ (average α = .88). Three items measured low activation pleasant affect  ‘calm’, ‘relaxed’, and ‘at ease’ (average α = .93). Three items measured low activation unpleasant affect (referred to as ‘sadness’) ‘depressed’, ‘dejected’, and ‘hopeless’ (average α = .85). Two additional, face valid, items measured sexual arousal  and connectedness (‘connected with others’). Participants rated how they felt for each item after watching each video by responding to the question “Please indicate how you feel now compared to before you watched the video” from 1(much less) to 7(much more). Items were individually randomized for each presentation.Twelve items measured the affective outcomes of watching each video. Items were taken from the Multi-affect Indicator  to measuAfter providing informed consent and demographic information, participants watched one of each type of video, randomly chosen from the preselected videos. These videos were presented in a random order. After watching each video, participants rated the frequency of tingles experienced during the video and their affective response to the video. Several additional measures were also taken. Participants provided a written description of their experience of each video, and if they experienced ASMR, answered several questions about their common ASMR triggers and general experiences of ASMR . A number of other exploratory individual difference measures were also taken  to examine differences between ASMR and non-ASMR participants (these are reported in the supporting information).r’s ranged from -.74 to .66). In this study, we were specifically interested in interaction effects between video type and ASMR group which would provide evidence for a response unique to ASMR participants after watching ASMR videos . The effect size d for all between-subjects effects was calculated using the formula for Hedges’ gs [We were interested in whether participants who identify as having ASMR would show differences in several dependent variables  after watching ASMR, but not control, videos. To assess this we conducted a series of analyses with video type  as the within-subjects factor and ASMR group (ASMR participant vs. non-ASMR participant) as the between-subjects factor; ANOVA was used to examine tingle frequency whereas MANOVA was used for all affective responses because these variables were highly correlated  = 58.54, p < .001, η2p = .06. Consistent with the idea that ASMR involves sensations of tingling, ASMR participants reported tingling sensations more frequently than non-ASMR participants to the sound-based ASMR videos  and the spoken-based ASMR videos ; but less so to the control videos .The interaction between ASMR group and video type on tingle frequency was significant, F = 8.06, p < .001, η2p = .01), calmness  = 41.33, p < .001, η2p = .04), stress  = 23.02, p < .001, η2p = .02), sadness  = 18.45, p < .001, η2p = .02), and connectedness  = 3.26, p = .044, η2p = .003). After watching sound-based ASMR videos, ASMR participants compared to non-ASMR participants felt significantly more excited , more calm , less stressed , and less sad . The same pattern was observed for spoken-ASMR videos for the affective responses of excitement , calmness , stress  and sadness . The spoken-ASMR videos also made ASMR participants feel more socially connected compared to non-ASMR participants ; a result that did not occur for the sound-only ASMR videos . As expected, none of the videos induced different levels of sexual arousal in ASMR compared to non-ASMR participants, F = .48, p = .611, η2p < .001.The results of the MANOVA revealed significant interactions between video type and ASMR group for feelings of excitement  These findings are important because they demonstrate that the ASMR response was unique to both ASMR participants and ASMR videos.Crucially, there were no differences between any of the affective responses to the control videos for ASMR and non-ASMR participants  asked ASMR-participants to self-select an ASMR video clip and (ii) showed participants the video clip from Study 1 that produced the most reliable ASMR response. This approach of using self-selected and standardized stimuli has been used in previous research on music-induced chills [After watching a range of ASMR videos, ASMR participants (compared to non-ASMR participants) reported more frequent tingling, increased levels of excitement and calmness, and decreased levels of stress and sadness. Notably, these effects were specific to ASMR videos: there were no significant differences between ASMR and non-ASMR participants in their affective responses to control videos. We should also note that the effect sizes for calmness and stress were medium to large whereas those for excitement and sadness were small to medium. These results provide empirical support for anecdotal claims that ASMR videos promote pleasant affect and reduce negative affect in people who self-identify as having ASMR. In Study 2, we built on these findings to explore whether the experience of ASMR extends beyond self-reported affect to physiology. We sought to: (i) replicate the findings of Study 1 under controlled laboratory conditions and (ii) examine whether there is a reliable physiological response unique to ASMR experiencers when watching ASMR videos. We made a number of changes to the basic design of Study 1 and subsequent analyses. In Study 1, participants reported on their changes in affect from before to after watching each video, a design feature that may have been affected by the order of video presentation . Therefore, in Study 2, participants instead reported on their affect immediately following each video  which allowed us to calculate difference scores for d chills –19, 35. d chills , 35, 36 The study employed a 2 x 3 mixed design. The between-subjects variable was ASMR group (ASMR participant vs. non-ASMR participant) and the within-subjects variable was video type (control video vs. ASMR standard video vs. ASMR self-selected video). The dependent variables were: (i) the frequency of tingles experienced during each of the videos and (ii) changes in affect, heart rate, and skin conductance level for each of the two ASMR videos .Mage = 26.14 years, SD = 8.63, Range = 18–59). Ethical approval was obtained from the University of Sheffield Psychology department ethics committee and was conducted in accordance with principles expressed in the Declaration of Helsinki.One hundred and twelve volunteers were recruited to the study; 56 participants self-identified as ASMR experiencers and 56 non-ASMR experiencers were recruited as control participants who were matched according to age and gender. Participants were recruited through various methods . Sample size was determined by collecting data from as many participants as possible over a six-month period, with the goal of 50 ASMR and 50 non-ASMR participants. Although our sample size was agreed a priori, it was not based on an a priori power analysis. However, it is comparable to (and often exceeds) sample sizes of physiological studies on music-induced chills , 15. TwoVideo clips were each approximately three minutes in length and included three videos: one ‘standard’ ASMR video, one control (non-ASMR) video, and one self-selected ASMR video. The standard ASMR video was selected from the six presented in Study 1 and was chosen because this video had elicited the strongest ASMR response . This ASMR video showed female demonstrating how to fold a towel neatly and patiently in a soft-spoken Russian voice, with delicate hand movements. In the video, the actor speaks directly to the viewers and the camera angle focuses on the female’s hand movements. The control video was also selected from the six presented in Study 1 and was chosen because this video had elicited the least tingles and was considered the most affectively neutral . This control video showed a male chef demonstrating how to make pasta. The control video closely matched the standard ASMR-video on several features: they were both instructional/demonstration videos, the actor speaks directly to the viewers and the camera angle focuses on hand movements). However, unlike the ASMR video, the control video did not contain softly spoken instructions or slow, delicate, hand movements. ASMR participants were asked to self-select a 3-minute video segment from any ASMR video of their choosing that would reliably induce their ASMR How frequently  did you experience tingling sensations during the video?”) from 1(none of the time) to 7.After watching each video, participants indicated how frequently they experienced tingling sensations during the video  to 7(extremely). Items were individually randomized for each presentation.Affective responses to the videos were measured using the same items as in Study 1. The same four subscales indexed excitement (average α = .84), stress (average α = .84), calmness (average α = .73), and sadness (average α = .65). The same two single items as in Study 1 measured connectedness and sexual arousal. Rather than reporting on changes in affect as in Study 1, Study 2 participants rated the extent to which they felt each item “right now”  from 1 from 1(much less intense) to 5(much more intense) .Participants provided informed consent and completed their demographic information, after which the physiological sensors were attached. Participants were left to acclimatize to the experimental situation and were given a short passage of text to read for three minutes. No physiological data was recorded during this period and participants were told that this period was to make sure that they were comfortable with wearing the physiological equipment. Next, a baseline physiological reading was taken for three minutes where participants sat quietly in the room doing nothing, after which participants rated their current affect. Participants then viewed each of the three videos in a counterbalanced order. The order of the three videos was counterbalanced such that the six possible order variations were delivered equally among participants . Physiological responses were recorded during each video and they rated their tingling and affective responses immediately after viewing each video. At the end of the experiment, ASMR participants were asked to rate their ASMR experience in the laboratory situation to daily life (“We sought to determine whether ASMR (compared to non-ASMR) participants showed differences in affect and physiology after watching ASMR videos. We calculated a series of difference scores that reflected affective and physiological changes from the control video in order to: (i) obtain a meaningful index of the effect of the ASMR videos  on participants’ affective and physiological responses and (ii) reduce noise related to individual variation in physiological reactivity. The use of difference scores is preferable to using an ANCOVA with baseline measures as a covariate in situations where between-subjects group allocation is non-random , 41, as r’s ranged from -.59 to .54). We expected to find significant main effects of ASMR group on affective and physiological measures, a finding that would establish that ASMR (compared to non-ASMR participants) showed different changes in affect and physiology from watching the control video to watching the ASMR videos. The effect size d for all between-subject’s effects was calculated using the formula for Hedges’ gs [We used the difference scores as dependent variables in a set of ANOVAs  and a MANOVA .We removed physiological data from two ASMR participants who showed irregularities in their physiological data due to equipment malfunction. We also identified and removed skin conductance data for one ASMR participant who had consistently high skin conductance responses (i.e. greater than three times the interquartile range from the upper quartile of the dataset). All subsequent analyses were conducted with these outliers removed. A series of t-tests indicated that there were no differences between ASMR and non-ASMR participants in their baseline physiology or affect  = 22.06, p < .001, η2p = .17. Replicating the findings in Study 1, ASMR participants reported tingling sensations more frequently than non-ASMR participants to the standard ASMR video  and the self-selected ASMR video ; but less so to the control video .As predicted, the interaction between ASMR group and video type on tingle frequency was significant, F = 3.85, p = .002, η2p = .18. Consistent with Study 1, ASMR participants (compared to non-ASMR participants) showed significantly greater increases in both excitement , and calmness  after watching ASMR videos. However, changes in sadness , stress , connectedness , and sexual arousal  did not differ between ASMR and non-ASMR participants.There was a significant overall main effect of ASMR group on changes in affect from watching ASMR videos, F = 2.13, p = .056, η2p = .11. In general, participants reported greater increases in calmness after watching the standard, compared to self-selected, ASMR video . There were no differences between changes in other affective variables after watching the two different videos: excitement ; sadness ; stress ; connectedness ; sexual arousal . There was no significant interaction between ASMR video type and ASMR group on changes in affect, F = 1.86, p = .095, η2p = .10.There was also a marginally significant main effect of ASMR video type on changes in affect, F = 4.95, p = .028, η2p = .05. ASMR participants showed significantly greater reductions in heart rate after watching both ASMR videos compared to non-ASMR participants . On average, this reduction was 3.41 bpm (d = 0.39) for ASMR participants . Additionally, there was a significant main effect of ASMR group on changes in skin conductance, F = 5.92, p = .017, η2p = .05. ASMR participants showed significantly greater increases in skin conductance after watching both ASMR videos compared to non-ASMR participants . On average, this increase was 0.30 μS (d = 0.17) for ASMR participants .There was a significant main effect of ASMR group on changes in heart rate (beats per minute), F = 0.07, p = .796, η2p = .001; skin conductance level: F = 0.21, p = .650, η2p = .002) showing that overall physiological changes did not differ between the standard and self-selected ASMR videos. Interactions between video type and ASMR group were also non-significant  = 3.33, p = .071, η2p = .03; skin conductance level: F = 0.33, p = .569, η2p = .003).The main effect of video type on changes in heart rate and skin conductance level were non-significant (heart rate: Autonomous Sensory Meridian Response (ASMR) is an anecdotally reported, pleasant, tingling, calming sensation that some people experience in response to specific audio-visual triggers such as whispering and careful hand movements. Compared to other ostensibly similar sensory phenomena such as awe and music-induced chills, ASMR has received relatively little scientific attention. The present research aimed to empirically test whether ASMR is a reliable and physiologically rooted experience, one that might have a distinct physiological profile from aesthetic chills, and potential to benefit physiological and psychological health for those that experience ASMR.In Studies 1 and 2, we found consistent evidence that ASMR videos elicit tingling sensations and promote positive affect . Crucially, these responses occurred only in people who identified as having ASMR and only when these people watched ASMR videos (rather than control non-ASMR videos—with the exception of tingles in Study 1). In Study 2, we showed that ASMR extended beyond self-reported feelings to physiological measures: specifically, reduced heart rate and increased skin conductance level in ASMR participants while watching ASMR videos. The results from both studies—at both a self-report and physiological level—are consistent with the idea that ASMR is a pleasant, calming but also activating experience. Notably, this physiological response profile differs from that of aesthetic chills, which are associated with increased heart rate , 17. TheAlthough we expected ASMR videos to be predominately associated with self-reports and physiological indices of relaxation (reduced heart rate and skin conductance level), we found evidence that ASMR is also an arousing  experience. ASMR videos were associated with increased excitement and skin conductance levels . The faIn addition to the effect of ASMR videos on pleasant affect and physiology, ASMR videos in Study 1 were also associated with increased feelings of connectedness. This suggests that an additional benefit of ASMR may be that of increased connectedness, most likely because of the social and interpersonal context in which ASMR is triggered . One posIt is also worth noting that both studies demonstrated that ASMR is not associated with sexual arousal. Despite most people describing ASMR as a distinctly non-sexual feeling, the idea that ASMR is sexual and that ASMR videos are used for sexual gratification is a common misconception . This mThe current studies have several strengths including the use of non-ASMR control groups, physiological data, and larger sample size compared to previous research on the topic. There are however a number of limitations of the present study that should be borne in mind when considering our results. First, both samples relied on a self-selected sample of participants who identified as experiencing ASMR without independent verification of their ASMR status. Independent and standardized protocol for establishing whether an individual experiences ASMR is a key priority for future research and would help with participant selection in future studies. Like synesthesia  consisteA key limitation in both studies is the possibility that our findings (particularly those related to tingle frequency and affective states) reflect a demand characteristic or expectation effect; that is, ASMR participants experienced changes in affect and physiology because they expected to whereas non-ASMR participant had no such expectations. Although we cannot be sure that expectation did not play a role in our findings, it is worth pointing out that ASMR participants in Study 2 indicated experiencing ASMR less intensely in the laboratory than in daily life. This suggests that the effects of expectation may have been minimal . However, to rule out the potential confounding effects of expectation and familiarity with eliciting stimuli, future research would be required to determine the extent to which expectation and familiarity might account for any effects observed. That said, conducting research on ASMR without participant’s awareness, as with any non-universal phenomenon, is likely to be a difficult if not insurmountable issue.Taken together, our studies provide empirical evidence to support anecdotal claims that ASMR is a tingling, pleasant feeling specific to some individuals, and that it has a distinct physiological profile from the experience of aesthetic chills. For the first time, we have found both self-reported and physiological evidence for the ASMR experience, when it is occurring in real-time. These effects were observed despite the fact that our participants reported experiencing ASMR less intensely in the laboratory compared to in their daily life. As such, the present findings may be an underestimate of the affective and physiological effects of ASMR videos. Nevertheless, our findings support and extend a small (but growing) body of ASMR research showing that ASMR-experiencers find ASMR videos relaxing  and thatHundreds of thousands of people watch ASMR videos and anecdotally report that these videos help them to sleep, relax, and combat stress and anxiety . Our resS1 Table(DOCX)Click here for additional data file.


Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality. The great progress in aviation, new energy and artificial intelligence (AI) technologies has led to the rapid development of unmanned aerial vehicles (UAVs). They are becoming much smaller, lighter and more intelligent and are widely used in both civilian and military fields. UAVs can acquire images and videos in real time by the video sensors they carry. Meanwhile, they compress and encode the multimedia, and finally upload them through the wireless network. This way of real-time shooting and transmission makes UAVs play a great role in civil fields, such as land mapping, pollution monitoring, disaster management, and personal aerial photography ,2,3. In Although UAV videos have brought great convenience, they are a kind of information that may occupy a lot of resources, and the transmission capacity of wireless channel is limited. Current video compression algorithms have been developed to be quite mature, such as H.264/AVC and H.265/HEVC, which can remove the redundancy in video signals more effectively. However, as the video resolution has been greatly improved, the requirement of wireless resource will also drastically increase.The coverage, information acquisition ability and destruction resistance of a single UAV is relatively limited. These problems can be solved well in the UAV cluster. A cluster of UAVs can present information on a much larger scale and from more angles . HoweverWe study the issue of rate allocation when multi-UAVs capture videos and send them back via the wireless channel simultaneously. The total QoE of all videos are considered as the optimization goal and the costs for channel renting and energy consumption have been deducted.Based on the potential game, we build a new distributed resource allocation framework. According to the potential function we propose, the game is proved as a complete potential game and the correlated equilibrium of the game exists and is unique.In order to make all the UAVs in the cluster iteratively update their bandwidth strategy, we adopt a distributed self-learning algorithm, by which the correlated equilibrium could be achieved with a relatively fast convergence rate.The rate ranges and characteristics of some real videos are analysed and these videos are applied in the simulations. From the real-time flow rate and total utility, we find that the algorithm converges rapidly and each UAV can intelligently select and maintain a stable video uplink rate, so that a reasonable allocation of wireless resources could be achieved. The influence of total channel bandwidth and cost factor is also analyzed.The insufficiency of channel resource may deteriorate the quality of video transmission, so we mainly consider the situation of limited channel bandwidth in the paper. The question of how to measure the deterioration, or from another aspect, how to measure the utilities, should be answered first. We need an objective metric. As an application layer service, the ultimate goal of transmitting video is to provide users with better viewing experience. Therefore, Quality of Experience (QoE) is a better satisfaction metric. The total QoE of all the videos could provide a clear indicator to evaluate the allocation effects. In this situation, the transmission rate is the main factor that could influence the QoE value. Therefore, when multi-UAVs in a cluster separately uplink videos through a wireless access point at the same time, how to allocate the limited bandwidth resource among them in order to maximize the total QoE has become an urgent and complex problem. The main contributions of this paper are summarized as follows:The rest of this paper is organized as follows. In There are many related studies on UAV resource allocation. Usually, they are different from three aspects: the goal, the object, and the method. Different allocation goals will bring totally different results. Unlike some methods for improving the physical layer parameters, the resource allocation for video transmission should naturally be set from the application layer. The object determines the main body of the allocation. At this point, the UAVs flying in cluster are not only different from single UAV, but also different from other mobile networks. Allocation methods are the concrete algorithms and steps adopted in the process. Because of the particularity of UAV cluster, a decentralized method may guarantee more robustness of the system.QoE is defined as the degree of delight or annoyance of the user of an application or service . CompareWhen delivering multimedia, QoE will be affected by more specific issues, such as video capture, coding, storage, delivery, decoding, rendering, display, context of use and user factors . These cSome research in wireless transmission has better analyzed and solved the issue of resource allocation by improving QoE ,9,10,11.The communication links are different in multi-UAV networks and they can be summarized into four categories: Air-to-Ground (A2G), Ground-to-Air (G2A), Air-to-Air (A2A), and Ground-to-Ground (G2G) . Among tIn wireless communication systems, there are usually two kinds of channels: control channel and data channel. A typical UAV cluster communication topology is shown in From the view of control channel, there are usually two kinds of ways for the control information transmission. One is achieved by the star topology, which relies on the central node to uniformly schedule resource allocation, as shown by the green dotted line in The other way to realize the allocation control is through the distributed information interaction between UAVs in mesh topology, as shown in the red dotted line in In addition to the links and networking issues, the problems of flying altitude, trajectory and speed, completion time, energy consumption, etc. ,24 also Although the traditional optimization method can better solve the problem of wireless resource allocation, not all problems can easily get the optimal solution. Especially when the optimization object contains multiple elements, the complexity of optimization methods will be further increased. This will bring a huge computing burden and big time delay to the real-time transmission service. In addition, the payloads of small UAVs cannot afford the high complex calculations. As we need to take the cluster of UAVs’ video transmission into consideration, in order to avoid the complexity above, game theory method is chosen as the resource allocation algorithm in this paper. Game Theory is a branch of applied mathematics and the players can interact among themselves to obtain a stable allocation of system resources . In geneIn a cooperative game, the participants reach an agreement to obtain better overall benefits. Cooperative game can be divided into two-person cooperation and multi-person cooperation. The former is often called Nash bargaining problem and the latter is also called coalition game. Ref.  modeled Compared with cooperative games, non-cooperative games pay more attention to the maximization of each player’s own benefit, and their application is more popular. Ref.  modeled Although the studies above have made some achievements, they are improper for the multi-UAV video transmission service. Especially in the application scenario of this paper, UAVs not only need to compete for resources, but also need to consider the overall consequences of video uplink brought by such competition. The nature quality of Potential Game (PG): the motivation of all players to change their strategies can be expressed as a global function , which cIn a word, the rate allocation in the UAV cluster for video transmission is not a simple algorithm for resource divisions. The mechanism should consider to improve the video QoE, take the flying cluster as the object, and adopt a distributed method like game theory. In previous related works, these three aspects have not been fully studied and we will discuss about them further in this paper.The development of various wireless technologies has greatly expanded the application of UAVs. For example, the Massive Multiple-Input Multiple-Output (MIMO) technology used in 5G network breaks the limitations of traditional 2D-MIMO and increases the vertical dimension, which is more suitable for the three-dimensional nature of UAVs. At present, people have completed the UAV flight communication tests based on the 5G network for many times and achieved High-Definition (HD) live videos’ broadcasting. Not only 5G cellular networks, but also many other wireless technologies could provide UAVs with favorable communication environments . WithoutEach UAV is equipped with video sensors and communication payload for filming and sending the encoded video back via the wireless network. In this process, the flying position and speed of the UAV may be different, and the shooting angle and object may also be different, which makes them get different videos. From the perspective of intra-frame difference, if the background of the image is messy or the targets are numerous, then such video is relatively complex in terms of content. From the perspective of inter-frame difference, if the motion rate of subjects is fast or the change range is large, the video content will be more complex. Video scene fragments collected by different UAVs in the cluster are compressed, encoded and finally sent to the UAV Control and Data Center (UCDC). Besides controlling the flight speed and trajectory of the UAV cluster, the UCDC also integrates and edits the encoded fragments obtained by the UAVs, which are eventually used for various commercial purposes. Meanwhile, UCDC needs to lease wireless channels from the NSP for each UAV. Flight control information usually consumes very few resources, so most of the rental fee is related to the actual channel bandwidth occupied by each video stream. It is hoped that UCDC can obtain high-quality video signals, so that they can meet the users’ needs in QoE finally. When the wireless resource is insufficient, the unfairness of uplink channel allocation will lead to severe transmission error or packet loss of some individuals and eventually affect the total watching experience of all gathered videos. Therefore, the limited bandwidth resources need to be reasonably allocated.i-th UAV, which varies from the minimum rate constraint Suppose that As mentioned in On one hand, the video feature-oriented models extract the feature information from the video itself. This is consistent with our method because the analysis in this paper is based on rate ranges and characteristic of each video. On the other hand, the results of the models oriented to video features are closer to real MOS values. Therefore, we start from the model of VQMTQ  and furtSome studies suggest that there is a simple linear mapping between PSNR and MOS ,46 and ier, Ref. ,48 show er, Ref. ,50. Meaner, Ref.  and thisa, b and c are constants and have been discussed in detail in the literature. For  PSNR in  and PSNRIn this paper, two aspects of transmission cost are considered. One is the charge by NSP due to the occupation of channel resources. The charging way is determined by the ratio between the actual video transmission rate and the total uplink channel bandwidth. Thus, Compared with traditional optimization algorithms, game theory is another efficient way to solve resource allocation. The ordinary optimization algorithm is to find the optimal solution by solving the optimization expression, while the method of game theory is to find Nash equilibrium by iterations. Sometimes, there’s no optimal solution in the former one, or the solving will take much time. Thus, this paper adopts the later one, the game theory, to solve the uplink allocation problem in multi-UAV video transmission. Each UAV is regarded as a game player to compete with others.Generally speaking, game methods can be divided into cooperative game and non-cooperative game. In practice, there is no absolute boundary between the two. From the perspective of a single player, each UAV intends to occupy the bandwidth as much as possible, but it is difficult to achieve due to the the limited total bandwidth the operator can provide. In addition, finally, the UCDC needs to improve the overall QoE of all videos. In this process, UAVs have a rational cooperation trend as well as a non-cooperative and selfish competition. Therefore, the Potential Game method can be used to build the model. On this basis, this paper believes that the behavior of game players is not completely independent, and the choice of game strategies depends on a kind of probability information outside the game process. Thus, the players can partially cooperate to change their strategies. Compared with the completely independent and non-cooperative Nash equilibrium, we hope to achieve the correlation equilibrium in this way and further improve the performance of the algorithm.i-th player, and i as i, where × denotes Cartesian product. We hope that, by less information interaction, each player can choose a reasonable strategy without breaking the equilibrium. In this section, we construct a potential function as The game can be expressed as Theorem 1.Although, as a participant in the game, each UAV player can change its video transmission rate strategy without cooperation, the whole game process takes the total utility of UCDC as the goal, which makes each player conduct rational cooperation. Therefore, this game process is a complete potential game.Proof. i-th UAV, we assume that it has another rate strategy to choose, expressed as I is the sum of the single utilities, we can get □For the G is a complete potential game, and the potential function I is a complete potential function. According to Theorem 1, in the process of rate allocation, as the participant of the game, a single UAV’s rate updating will change its own utility, while the changing value is just the same with the total utility’s variation. That means the application scenarios presented in this paper satisfies the definition of potential game. In order to maximize the total QoE utility, UAVs not only have to compete with each other, but also need to cooperate partially.The change of potential function is equal to the change of utility function of each player. According to , the gamTheorem 2.In potential game Proof. From Equation , we can I is continuous and differentiable. Since Obviously, Equation  is a conEquation  identicaEquation  can be sG is a continuously differentiable convex function in the strategy space. According to [G has a correlation equilibrium, and the result is unique. In the process of rate allocation, UAVs compete for bandwidth as game players. When anyone’s single strategy change can no longer bring increase to the total utility function, the system reaches a stable and unique equilibrium state. At this point, the extreme rate strategy i-th UAV. Thus, all the N UAVs will keep their own extreme rate strategies and the stability won’t be broken as long as the transmitting environment and the video characteristics remain unchanged.The above results show that the potential function constructed in the potential game rding to , potentiThe uplink allocation problem has been modeled as a potential game and we need to solve it for the correlated equilibrium. Generally, the process of solving Nash equilibrium is often a process of seeking the optimal solution by continuous iteration. Common iterative algorithms include Gauss–Seidel, Jacobi iterations, etc. The Jacobi iteration method is an earlier and simple algorithm, and its convergence speed is slow. Gauss–Seidel iteration is an improvement of the Jacobi algorithm. Although it has improved the convergence speed compared with the former one, it is still not suitable for real-time transmission service. In this paper, there is both competition and partial cooperation in the UAV cluster. In order to achieve a state of correlated equilibrium with a relatively fast convergence rate, the regret matching algorithm in  is adopt(1) Initialization:Each player can choose the minimum rate as the first strategy from the strategy space (2) Iterative Update Process:Strategy Update:t, and that means When time Strategy Decision:t, the i-th player chooses the strategy i in the strategy space Assuming that at time We conduct some simulations to evaluate the game model and the algorithm we proposed. Suppose, in the scenario shown in When the same video is compressed and encoded in different rates, it will bring users different viewing experiences. In order to validate this effect, we first use FFmpeg codec to encode the CIF video Coastguard into H.265 files. The original video’s format is yuv420p, 352 × 288, and the frame rate is 30 fps. After the encoding, at the time 1 s, 1.5 s and 2 s, the compressed video frames are extracted for comparison, and the results are shown in The simulation is based on Matlab R2017a platform. Ref.  gave somThe encoding rate of each UAV plays a crucial role in this paper. It is both the cause and the result of strategy update. Thus, the rate range of each video in We first analyze the convergence of the algorithm. To analyze the performance of the proposed algorithm, we compare it with some resource allocation algorithms in multi-rate and multi-user scenarios, including AFR, MSPSNR  and AverFrom  in SectiIn this paper, the uplink channel allocation problem of multi-UAV video streaming is discussed. Not only the total video QoE, but also the cost for channel leasing and energy consumption is considered to formulate the utility function. Based on game theory, the distributed model is established, which further enhances the flexibility and robustness of the system. We have proved that the model converges to the correlation equilibria and have solved the model by the distributed self-learning algorithm. Simulation results show that the proposed mechanism can effectively solve the rate allocation problem of the UAV cluster.


We aimed to compare the predictive capacity of malnutrition, sarcopenia, and malnutrition combined with sarcopenia for mortality in nursing home residents. We conducted a prospective study in four nursing homes in China. Nutrition status and sarcopenia were measured according to the new European Society of Clinical Nutrition and Metabolism (ESPEN) definition and SARC-F, respectively. The study population was divided into four groups: malnutrition with sarcopenia (MN+/SA+), malnutrition without sarcopenia (MN+/SA−), sarcopenia without malnutrition (MN−/SA+), and normal nutrition without sarcopenia (MN−/SA−). The participants were followed up for 12 months. We included 329 participants. Thirty-eight participants (11.6%) had MN+/SA+, 38 participants (11.6%) had MN+/SA−, and 93 participants (28.3%) had MN−/SA+. The 1-year mortality was 18.3%, 21.5%, 18.4%, and 47.4% in the MN−/SA−, MN−/SA+, MN+/SA−, and MN+/SA+ groups, respectively. Compared to participants with MN−/SA−, participants with MN+/SA+ were at a significantly higher risk of mortality ; however, MN−/SA+  and MN+/SA−  were not predictors of mortality. In conclusion, the coexistence of malnutrition and sarcopenia is a significant predictor of mortality in a study population of Chinese nursing home residents. The prevalence of malnutrition increases with aging, comorbidities, and levels of care2. Therefore, malnutrition is also considered as a geriatric syndrome3. Malnutrition is prevalent in elderly adults, especially in those living in nursing homes4. According to a recent review, approximately 20% of nursing home residents had malnutrition, but the prevalence ranged from 1.5% to 66.5% due to various definitions of malnutrition4. However, there is currently no data addressing malnutrition in nursing home residents in mainland China.Malnutrition refers to the imbalance between nutrition intake and requirements that ultimately causes the changes in body weight, body composition, and physical function5. To address this issue, the European Society of Clinical Nutrition and Metabolism (ESPEN) published a new consensus statement on the diagnosis of malnutrition in 20155. Since then, many studies have applied the new ESPEN definition of malnutrition in different clinical settings14. However, most of these studies were conducted in hospitals and no study applied the new ESPEN definition of malnutrition in nursing homes.The lack of unique diagnostic criteria of malnutrition hinders the extensive research of it15. Sarcopenia refers to the age-related loss of muscle mass, strength, and performance16. The diagnosis of sarcopenia also lacks unique criteria, but it is device-dependent , time-consuming, and sometimes inaccessible according to current international consensuses of sarcopenia19. A brief screening tool for sarcopenia is therefore needed, especially for nursing home residents. SARC-F, a classical sarcopenia screening tool, has been widely applied in various populations20. However, to our knowledge, SARC-F has not been applied to nursing home residents yet.Sarcopenia is another geriatric syndrome that is highly prevalent in elderly adults, especially in hospitalized and institutionalized elders21. However, whether the new ESPEN definition of malnutrition or SARC-F can predict mortality in nursing home residents remains unclear. In addition, we hypothesized that nursing home residents with coexisting malnutrition and sarcopenia were at a higher risk of mortality compared to those with either malnutrition or sarcopenia alone.Both malnutrition and sarcopenia have been shown to be related to an increased risk of mortality and other adverse health outcomes, such as poor quality of life and functional impairmentsTherefore, the aim of this study is (1) to investigate the prevalence of malnutrition, sarcopenia, and the coexistence of malnutrition and sarcopenia in a study population of Chinese elderly adults living in nursing homes according to the new ESPEN definition of malnutrition and SARC-F; and (2) to compare the predictive capacity of malnutrition, sarcopenia, and malnutrition combined with sarcopenia for mortality in this population.We conducted a prospective observational study in four nursing homes in Chengdu City, China. The study protocol was approved by the Research Ethics Committee of our university. A written informed consent was obtained from all participants . All methods in this study were in accordance with relevant regulations and guidelines.From September to November 2016, we consecutively recruited elderly adults (≥70 years of age) living in the nursing homes. The exclusion criteria were as follows: (1) living in the nursing homes less than two weeks; (2) unable to communicate with the interviewers; and (3) refusing to participate in this study.Trained nurses collected the key baseline data through face-to-face interviews and performed the following anthropometric measurements: calf circumference (CC) and waist circumference (WC). The CC was measured using a flexible tape at its widest point when the subjects placed in the supine position, with their left knee raised and the calf placed at a right angle to the thigh. The WC was measured using a flexible tape at the top of the hip bone on the naked skin at the end of light exhalation with the subject standing. Additionally, body mass index (BMI) was collected from the medical records of the nursing homes.5, the diagnosis of malnutrition includes two steps: First, a validated risk screening tool is suggested to identify individuals at risk of malnutrition. In this study, trained nurses applied the Mini Nutritional Assessment Short-Form (MNA-SF)22 as the screening tool. A total score of MNA-SF ≤ 11 indicated that an individual was at risk of malnutrition22. Second, among the participants who were at risk of malnutrition by the MNA-SF, malnutrition was confirmed according to either of the following criteria: (1) BMI < 18.5 kg/m2; or (2) unintentional weight loss >10% indefinite of time (or >5% over the last 3 months) combined with BMI < 20 kg/m2 if age <70 years or <22 kg/m2 if age ≥70 years5.According to the new ESPEN criteria20. A total score of SARC-F ≥ 4 indicates sarcopenia20.We applied the SARC-F to screen sarcopenia in this study. The SARC-F includes five items: strength, assistance in walking, rising from a chair, climbing stairs, and falls , malnutrition without sarcopenia (MN+/SA−), sarcopenia without malnutrition (MN−/SA+), and normal nutrition without sarcopenia MN−/SA−).23. ADL disability was defined as “need some help from other people” or “cannot do by myself” in any of the six items of PSMS: eating, dressing, grooming, walking, bathing, and toileting23.Trained nurses evaluated the activities of daily living (ADL) of the participants using the Physical Self-Maintenance Scale (PSMS) developed by Lawton and BrodyIn addition, the following data were obtained from the medical records in the nursing homes: age, gender, smoking status, alcohol drinking status, and comorbidities .th month after the baseline investigation. For those who left the nursing homes, we collected the survival status via telephone interviews. Next, the time to death was calculated as the period between the baseline investigation and the date of death.The survival status of the participants was collected via the medical records in the nursing homes at the 12We performed all statistical analyses using SPSS 20.0 . A p value of <0.05 was considered statistically significant. The categorical variables in this study were presented as counts and percentages. The normality of the continuous variables was explored by the Kolmogorov-Smirnov test. The continuous variables were not normally distributed; therefore, they were presented as a median and interquartile range (IQR). For categorical variables, the difference between groups was compared using the Pearson chi-squared test. For continuous variables, the difference between groups was compared using the Kruskal-Wallis H test (for the comparisons across multiple groups) or Mann-Whitney U test (for the comparisons between two groups).We applied univariate Cox proportional hazard models to explore the possible predictors of 1-year all-cause mortality. The results were presented as hazard ratio (HR) and 95% confidence intervals (CIs). We also applied multivariable Cox proportional hazard models with a backward stepwise selection to explore the independent predictors of mortality. Age, gender, and the variables that exhibited a significant association in univariate analysis were considered to enter the initial model of the backward stepwise selection. In these models, the criteria for entry and removal were p < 0.05 and p > 0.10, respectively. In these models, sarcopenia and malnutrition were treated as variables, separately. Furthermore, we treated sarcopenia combined with malnutrition as one variable in a new model. In addition, survival curves were estimated using the Kaplan-Meier method. The difference between the survival curves was compared using the log-rank test.We included 329 participants (105 men and 224 women) in the baseline investigation  were malnourished. There was no significant difference between men and women with respect to malnutrition . The characteristics of participants with or without malnutrition are presented in Supplementary Table 3.1% wereAccording to the SARC-F, 131 participants (39.8%) had sarcopenia. Women were more prone to sarcopenia than men . The characteristics of participants with or without sarcopenia are presented in Supplementary Table Compared to those without malnutrition, participants with malnutrition had significantly higher SARC-F scores  and were more prone to sarcopenia . On the other hand, participants with sarcopenia had significantly lower MNA-SF scores  and were more prone to malnutrition  compared to those without sarcopenia.When considering malnutrition and sarcopenia together, 38 participants (11.6%) suffered from MN+/SA+, 38 participants (11.6%) had MN+/SA−, and 93 participants (28.3%) had MN−/SA+.During the 1-year follow-up, seven participants (2.1%) lost follow-up. A total of 73 participants (22.7%) died during the follow-up. The mortality was 23.5% in men and 22.2% in women (p = 0.752).The 1-year mortality was 32.9% and 19.5% in participants with or without malnutrition, respectively (p = 0.015). Table After adjusted for the potential confounders, malnutrition was also independently associated with an increased risk of 1-year mortality  . The univariate Cox proportional hazard model revealed that sarcopenia was a predictor of 1-year mortality . After adjusted for the potential confounders, sarcopenia was also independently associated with an increased risk of 1-year mortality  ; however, MN−/SA+ and MN+/SA−  were not predictors of mortality  were at significantly higher risk of mortality; however, sarcopenia alone (MN−/SA+) or malnutrition alone (MN+/SA−) did not increase the risk of mortality.5. Therefore, it is important to validate the new ESPEN criteria of malnutrition in elderly adults  because malnutrition occurs primarily in the elderly in developed countries24 and is highly prevalent in nursing homes4. Our study identified 23.1% participants had malnutrition defined by the new ESPEN criteria. A recent review summarized the relevant studies and demonstrated that the prevalence of malnutrition was approximately 20% in nursing homes. This finding was very similar to our results; however, none of these studies applied the new ESPEN criteria4.The aim of the new ESPEN consensus on malnutrition is to “provide a general diagnosis that is relevant for all subjects in all clinical settings”4. Recently, two studies also addressed the association between the new ESPEN definition of malnutrition and mortality in hospitalized patients10. Our study confirmed that the new ESPEN definition of malnutrition can predict mortality in nursing home residents for the first time. Therefore, our study adds new evidence to the application of the new ESPEN definition of malnutrition.The association between malnutrition and mortality in elderly adults has been well studied in various populations29. However, previous studies addressing SARC-F were conducted in community-dwelling elderly adults29. Our study supports the application of SARC-F in nursing home residents. SARC-F-defined sarcopenia can predict 1-year mortality in our study population. A previous study also found SARC-F-defined sarcopenia was associated with an increased risk of 4-year mortality in community-dwelling elderly adults30.As a classic sarcopenia screening tool, SARC-F has been validated in various ethnic populations, such as Chinese, Korean, Japanese, and Spanish31. Our study revealed that both syndromes were prevalent in Chinese nursing home residents as well. Furthermore, as previously suggested, malnutrition and sarcopenia were also related geriatric syndromes in our study population. Because malnutrition and sarcopenia shared some risk factors  and clinical features  and because they usually occur in individuals simultaneously32, Vandewoude and colleagues32 recently coined a term, malnutrition-sarcopenia syndrome (MSS), to describe the coexistence of malnutrition and sarcopenia. However, we did not apply the term of MSS in this study, because it has not been widely accepted yet.Previous studies have shown that malnutrition and sarcopenia are both common in elderly adultsOur study showed that coexisting malnutrition and sarcopenia can predict mortality in nursing home residents, but malnutrition without sarcopenia or sarcopenia without malnutrition cannot. This finding emphasized the importance of screening both malnutrition and sarcopenia simultaneously in the management of nursing home residents. The SARC-F is based on five self-reported questions. The new ESPEN definition of malnutrition is based on self-reported questions and BMI. Therefore, both tools are brief and suitable for nursing home residents. However, this finding needs to be further studies in other ethnic populations.5. But we did not apply the FFMI in this study because the measurement of FFMI needs medical devices  that are generally unavailable in nursing homes. In fact, the new ESPEN consensus statement per se argued that “it was crucial not to mandate FFMI for the diagnosis of malnutrition”5. Second, the sample size of our study was relatively small, especially for those with coexisting malnutrition and sarcopenia. Third, our study was conducted in Chinese nursing home residents, therefore, it should be cautious when generalizing the results to other ethnic populations. Fourth, our study did not address the relationship between sarcopenia and malnutrition with disease-specific mortality. Last, like all cohort study, survival bias prior to entry in the cohort should be considered.Some limitations of our study need to be addressed. First, the second ESPEN diagnostic criterion for malnutrition is based on the combination of unintentional weight loss, and low BMI or low fat-free mass index (FFMI)In conclusion, according to the new ESPEN criteria and SARC-F, malnutrition and sarcopenia are common in our study population and are independent predictors of mortality, respectively. When considering the two syndromes together, the coexistence of malnutrition and sarcopenia is a significant predictor of mortality, but malnutrition without sarcopenia or sarcopenia without malnutrition are not. Therefore, we suggest using brief and validated tools (like the new ESPEN definition and SARC-F) to assess malnutrition and sarcopenia simultaneously in nursing home residents. However, further prospective studies are certainly needed to validate this suggestion.Supplementary Tables


Young adults have a higher prevalence of alcohol consumption and binge drinking than any other age group. They also drink more heavily and experience more negative consequences of drinking. Rates of alcohol abuse and dependence are disproportionately higher among those between the ages of 18 and 29 compared with other age groups. Young adults are also overrepresented among alcohol-related traffic fatalities. Over time, distinct patterns of change in frequent binge drinking occur, and most heavy-drinking young adults appear to “mature out” of abusive drinking patterns as the responsibilities of later adulthood supervene. Drinking patterns are affected by demographic, psychological, behavioral, and social factors as well as minimum drinking age legislation and the cost of alcohol. Motivational programs designed to reduce risks and consequences associated with young-adult drinking may help in reducing alcohol consumption and its consequences. Young adults have a higher prevalenceThis article summarizes results of several studies regarding the prevalence, patterns, and consequences of youthful drinking and provides an overview of risk factors for problem drinking. Many studies of young adults use samples composed exclusively of college students. Therefore, where possible, this review draws from studies that are national in scope and not focused predominantly on college populations.The second survey, the National Household Survey on Drug Abuse (NHSDA) , found sBinge drinking, or discrete episodes of heavy drinking, is common among youthful populations. The rate for binge drinking, defined by the Monitoring the Future study as consumption of five or more drinks on one occasion at least once within the 2-week period preceding the survey, was 34.4 percent 1 to 4 years after graduation from high school. The highest rate, 41 percent, was among 21- to 22-year-olds .less frequently than non-college-bound students (Young adults in college have a higher rate of binge drinking (40 percent) relative to their noncollege peers. This difference may reflect easier access to alcohol and parties among students, as well as non-college students’ earlier adoption of adult roles involving work or marriage. Relative to high school seniors, these rates reflect a 20-percent increase in binge drinking for young adults not in college compared with a 42-percent increase for young adults in college . This trstudents .In the NHSDA, 18- to 25-year-old respondents showed a lower prevalence of alcohol consumption than the 26- to 34-year-old cohort. With regard to patterns of heavier consumption, however, the younger group reported a higher prevalence of heavy drinking or frequent monthly binge drinking. The rates of frequent monthly binge drinking, defined as drinking five or more drinks per day on each of 5 or more days in the past 30 days, are presented in Gender differences are also apparent in binge-drinking rates . Men betEthnic and regional differences in frequent monthly binge-drinking rates also have been documented. White NHSDA respondents most often reported frequent monthly binge drinking, followed by Hispanic and African-American respondents . A patteDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV) . The criteria for these disorders reflect in part the physical, occupational, interpersonal, and psychological consequences associated with the use of alcohol. Criteria for diagnosing alcohol abuse and alcohol dependence are outlined in the (DSM–IV) .Alcohol abuse generally refers to a maladaptive drinking pattern involving recurrent difficulties in one or more of the following areas: failure to fulfill major obligations , alcohol use in physically hazardous situations , legal difficulties caused by drinking , and alcohol-related social consequences  .Criteria for alcohol dependence include tolerance, withdrawal,One-year prevalence for alcohol abuse is the more common of the two alcohol-related disorders  Grant 1. In totaYoung adults are overrepresented among alcohol-related traffic fatalities . AlthougYoung-adult drivers (ages 16 to 24) involved in automobile accidents are more likely to have a lower blood alcohol concentration (BAC) than older drivers . This paMany studies of young adults’ alcohol-consumption patterns have used samples of college students, limiting researchers’ ability to generalize study findings to other young-adult populations. However, because the most detailed information about the nature and prevalence of alcohol-related consequences within this age group has been gathered from college students, some examples of the more comprehensive studies are presented here.Although specific consequences of drinking have not been reported for noncollege young-adult samples, it is likely that many of the same consequences would occur given comparable levels of consumption.Although the pattern of drinking and the amount consumed remain similar from late adolescence through young adulthood, most heavy-drinking young adults appear to “mature out” of abusive drinking patterns as they pass into subsequent stages of adulthood.Another group of respondents who were initially frequent binge drinkers were no longer frequent binge drinkers at the time of the final assessment . This paMost young adults in the study who engaged in binge drinking matured out of this pattern of drinking over time. Some of these respondents decreased their binge drinking gradually, whereas others experienced a brief (“fling”) period of binge drinking between periods of no binge drinking. Some young adults increased the frequency of drinking over time, perhaps responding to the increased opportunities for drinking available to young adults; these subjects may or may not mature out of this pattern over time. Understanding the changes in drinking patterns as these individuals approach mid-adulthood may clarify the processes involved in later onset binge drinking.Marital status, ethnicity, and number of years of education may be predictive of continued binge drinking. Those who had not married by age 23 or 24 were generally more likely to continue or increase episodes of binge drinking. Caucasians tended to be the ethnic group most likely to maintain or increase binge drinking. Men who had more years of education or whose parents had more years of education were more likely to continue or increase their binge-drinking frequency. Men who engaged in frequent reckless driving were less likely to mature out than nonreckless drivers , a findiChanges in marital and employment status also have been associated with changes in consumption. Specifically, Regarding employment status, Given that the highest prevalence of heavy drinking and associated problems occurs during the late teens and early to mid-twenties, young adulthood may be considered a risk period for heavy drinking and related consequences. Demographic risk factors were discussed earlier; other risk factors are discussed below.Of the psychological risk factors, personality characteristics, such as impulsivity and lack of behavioral control, also have been implicated as risk factors for heavy alcohol use and attendant negative consequences. A history of childhood behavior problems has been associated with greater increases in alcohol use and symptoms of alcoholism among youth entering college . Some yoBeliefs about the positive or negative effects of alcohol consumption are known as expectancies. Expectancies relate not only to alcohol’s mood-altering effects but also to broader issues of social behavior and acceptance by one’s peers. Young adults who expect alcohol to have a positive effect have been shown to have heavier patterns of consumption compared with those without such expectancies ; they thSocial risk factors may include norms regarding use and acceptability of alcohol, social and residential environment, and family influences. Residence in a fraternity or sorority and adoption of a party-centered lifestyle are strong predictors of college binge drinking b. In addExamples of environmental factors that may affect consumption rates include minimum legal drinking age (MLDA) and the cost of alcohol. Computer simulation is a research tool that has been used to predict trends in alcohol consumption based on cost. Studies using computer simulation have concluded that raising the cost of alcohol through taxation would significantly decrease binge and frequent drinking among youth . These rA family history of alcoholism has been evaluated as a risk factor for alcohol problems in young adults. Epidemiological studies show that the offspring of alcoholics are three to four times more likely to develop alcohol problems than are the offspring of nonalcoholics, regardless of the environment in which they are raised . ConsequFamily history as a risk factor for alcohol problems may be mediated, in part, by a genetically influenced decreased sensitivity to alcohol . IndepenHowever, a few well-designed studies examining genetic predisposition to alcohol problems among young adults (and college students in particular) have not supported a genetic-vulnerability hypothesis . These sIndividual differences in responsiveness to alcohol’s stress-reducing properties have been posited as a risk factor for alcohol abuse. Subjects in whom alcohol greatly diminishes certain physiological responses associated with stress may be more prone to alcohol abuse than those who exhibit weaker responses .In general, the presence of multiple risk factors may be considered to be additive in evaluating risk status. For example, with all other things equal, a 20-year-old Caucasian man who frequently drank five or more drinks in high school would be viewed as having a higher risk for alcohol abuse or related consequences during young adulthood than a 20-year-old Caucasian man who did not drink excessively in high school. However, changes in roles may serve as “protective factors” leading to discontinuation of heavy use or maintenance of lower risk drinking practices. The addition of adult roles  may be incompatible with and preclude the continuation of a heavy pattern of consumption.Programs designed to reduce risks and consequences associated with young adults’ drinking have been effective in reducing consumption and consequences. The Lifestyle project  and the


Presently, undernutrition still goes undetected in pediatric hospitals despite its association with poor clinical outcomes and increased annual hospital costs, thus affecting both the patient and the health care system. The reported prevalence of undernutrition in pediatric patients seeking care or hospitalized varies considerably, ranging from 2.5 to 51%. This disparity is mostly due to the diversity of the origin of populations studied, methods used to detect and assess nutritional status, as well as the lack of consensus for defining pediatric undernutrition. The prevalence among inpatients is likely to be higher than that observed for the community at large, since malnourished children are likely to have a pre-existent disease or to develop medical complications. Meanwhile, growing evidence indicates that the nutritional status of sick children deteriorates during the course of hospitalization. Moreover, the absence of systematic nutritional screening in this environment may lead to an underestimation of this condition. The present review aims to critically discuss studies documenting the prevalence of malnutrition in pediatric hospitals in developed and in-transition countries and identifying hospital practices that may jeopardize the nutritional status of hospitalized children. Although malnutrition in pediatrics is of concern in low resource settings, this state is also of major worry for hospitalized children in developed as well as in-transition countries ,2,3,4. HPoor nutritional status at admission or worsening of nutritional status during hospitalization is recognized to adversely affect clinical outcomes. Among other systems, it disturbs immune response, thereby causing children to have piteous wound healing with higher risk of infections and complications of their underlying disease ,11,12,13In comparison to adults, children are particularly vulnerable to malnutrition, having a lower caloric reserve and higher nutritional requirements per unit of body weight, to account for growth ,25. WhenThe objectives of the present review are to provide a short historical account, briefly describe the tools developed for nutritional screening and assessment, present an overview on the prevalence of pediatric malnutrition in patients seeking care and/or hospitalized, and document the compliance of the published guidelines in pediatric hospitals in developed and in-transition countries. We have selected examples of countries that are classified as developed economies and economies in transition according to the definition used by World Economic Situation and Prospects and prepared by the Development Policy and analysis Division of the Department of Economic and Social Affairs of the United Nations Secretariat .This review encompasses guidelines and clinical studies published from 1995 to 2018, which address the screening, assessment and management of malnutrition in hospitalized children in industrialized countries. The PubMed database was searched. The following generic search terms were used: malnutrition, pediatric hospital, assessment and prevalence with the following filters: clinical studies, human, publication dates from 1995 to 2018, language: French and English, and aged from birth to 18 years old.The generic term malnutrition encompasses deficient, excessive or imbalanced intake of a variety of nutrients jeopardizing the health status. It can be causal or consequential. The present review essentially addresses acute and chronic undernutrition. In the context of this review, malnutrition is synonym of undernutrition and results from disease-related deprivation or malabsorption of nutrients, leading to altered body composition . It is qAs of today, there is still no consensus on the best definition of pediatric malnutrition. This lack of agreement, accounting partly for the disparity in the reported malnutrition prevalence, impacts on child health outcomes as it precludes adequately identifying children at risk of malnutrition/undernutrition. Assessing the nutritional status in children with moderate, acute malnutrition is particularly problematic as no single indicator can be used alone. Different definitions and classification methods have been used over the years to describe malnutrition based on anthropometric parameters in children. A brief historical account follows.Gomez et al.  in 1956 In 2013, the Pediatric Malnutrition Definitions Working Group (PMDWG), commissioned by the American Society for Parenteral and Enteral Nutrition (ASPEN), defined malnutrition (undernutrition) in their thorough report as “an imbalance between nutrient requirements and intake that results in cumulative deficits of energy, protein or micronutrients that may negatively affect growth, development and other relevant outcomes” . The expIn the following sections, we will describe the different clinical tools developed for assessing malnutrition and verify whether these tools have been applied for children admitted to hospitals in developed and in-transition countries. Finally, we will attempt to examine whether hospital practice related to nutritional support have improved since 1995, when the first nutritional management tools were reported.kids) in 2010, the Pediatric Yorkhill Malnutrition Screening (PYMS) in 2011, the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) in 2012. Moreover, in 2015, new tools were introduced to serve the general pediatric population: The Pediatric Digital Scaled Malnutrition Risk Screening Tool (PeDiSMART) and the Pediatric Nutrition Screening Tool (PNST), as well as the modified STAMP: The Pediatric Malnutrition Screening Tool (PMST) in 2016. They are, however, not yet fully validated [Malnutrition at admission as well as nutritional status deterioration during hospital stay may lead to adverse events in children. However, malnutrition evaluation and classification are part of nutritional assessment, a complex and time-consuming process that must consider features other than growth indicators. Therefore, clinical tools were developed to identify patients who may benefit the most of a full nutritional assessment, which is the goal of malnutrition screening ,37. Sevealidated . These talidated ,38. Tablalidated  in 1995 Five years later, Sermet-Gaudelus et al. in 2000  publishekids” tool standing for Screening Tool for Risk on Nutritional status and Growth. It consisted of 4 elements: subjective clinical evaluation , high-risk disease or expected major surgery, nutritional intake excessive diarrhea, vomiting, reduced food intake, pre-existing dietary intervention, inability to consume adequate quantity of food due to pain) and weight loss/no weight gain each attributed a score of 1 or 2 points with a maximum score of 5. Objective elements were weight for and length for height expressed as WFH and HFA standard deviation-scores. Patients scored 1 to 3 had similar WFH, they were regrouped as moderate risk and those with higher score (4 to 5) were classified as high risk. The length-of-hospital stay as one of the outcomes was directly related to the global score. However, no data was reported on the relation between the nutritional risk score and the readmission rate, an important morbidity index.Hulst et al.  in 2010 Gerasimidis et al.  in 2010,©,  a nutrition assessment tool targeting children above 2 years old and adolescents that consisted of 3 elements, namely: anthropometric factors , dietary factors  and risk related to the clinical condition. Each of these elements was scored. A global score ≥4 meant high risk of malnutrition. In the context of the evaluation environment, STAMP had a sensitivity of 70%, a specificity of 91%, a positive predictive value of 0.548 and a negative predictive value of 0.948. It however displayed a moderate reliability when compared to the full nutritional assessment and but compared well with the PYMS [McCarthy et al.  developethe PYMS .kids. Interestingly, Huysentruyt et al. [kids) for assessing nutritional risk in hospitalized children in developed countries concluded there were insufficient data to select one tool over another, and that the choice should be related to the availability of resources and dietetic staff.PMST, a recent tool available is in fact a minor modification of the STAMP by adding the BMI concept to define obesity (BMI > 98th percentile) or overweight (BMI > 85th percentile) . Finallyt et al.  in a sysOn the other hand, Secker et al. , arguingIn summary, despite the considerable efforts devoted collectively by the health professionals, as of today there is still no reference or a gold standard method for nutritional of hospitalized patients in a pediatric setting. Nevertheless, many national guidelines call for malnutrition risk screening and assessment of patients upon hospital admission and ideally during their stay. However, these tools should be considered for evaluating the nutritional risk and not absolute diagnostic tools.The disparity of the reported prevalence of acute malnutrition in hospitalized children stems from the population studied, clinical settings and tools for defining malnutrition. The sample studies cited in kids criteria and 14.5% were malnourished based according to <−2 SD BMI in a population selected from tertiary care facility pediatric wards. Chourdakis et al. [kids criteria in 1258 children from 14 European general hospital pediatric and surgery wards. The prevalence was 22%, 22% and 10% respectively. Although there was a good agreement between PYMS and STAMP, STRONGkids criteria provided a lower prevalence reflecting again the difference in the outcomes measured. More recently, in a study involving 1994 patients from Italian tertiary care and general pediatric wards and using either indiscriminately BMI or WFH, Lezo et al. [kids criteria. Carvalho et al. [6 children and using the International Classification of Disease (9th Revision) to identify coded diagnoses of pediatric malnutrition based on an etiology-related definition, reported a prevalence of 3.7%, in 2011 compared to 1.9% in 2002, the lowest values reported in the studies listed in kids criteria were considered. They reported intermediate values for BMI (9.5%) and WFA (14.8%). Finally, Sissaoui et al. [Larger-size studies would be expected to provide data with less variation. Six studies that included ±1000 patients recruited in different hospital settings, therefore better representing the “true” prevalence for the respective geographical locations have been reported. Wyrik et al.  from thes et al.  comparedo et al.  reportedo et al.  when usio et al.  in a US i et al.  reportedIn conclusion, these examples amply illustrate the inter- and intra-study difference in the prevalence of malnutrition depending on the variables used, subjective evaluations vs. objective measures, interpretation of the thresholds, and selected severity degrees. Despite this, the severity of the illness, the existence of underlying diseases, and the young age have all been identified as risk factors for malnutrition. Last but not the least, the prevalence estimates also varied according to the population sample. For instance, a higher prevalence of malnutrition would be expected for toddlers and patients below 5 years of age admitted to a PICU, compared to subjects admitted in a general pediatric ward.It is noteworthy that, 46 years ago, Baker et al.  argued tRecognizing the varying prevalence of undernutrition in hospitalized children, the question is whether hospital measures are undertaken to manage this condition. In 1994, Butterworth , in his Malnutrition is multi-factorial. Although the disease state or trauma may deteriorate the nutritional status of children, some hospital practices may exacerbate the condition ,69,70,71Bunting et al.  in a proIn a retrospective chart review of US well-child clinics, Chen et al.  evaluateSimilarly, in a retrospective study, Ramsden et al.  reportedInterestingly, Restier et al.  reportedThe French group PREDIRE  were theAs hospital food service is often a low priority, the budget allocation for food is often limited. When public health budgets cuts are needed in the hospital, food items will often be targeted. Considering that patients consuming <50% of presented hospital meals are more likely to lose weight and have longer LOS, it is pertinent to promote the pleasure of eating and to maximize the overall food intake ,84. LimiOur review emphasizes that child malnutrition in pediatric hospitals persists despite past recommendations and guidelines. The lack of consistency in the type of measures and their cut-off values prevents estimating the true prevalence of malnutrition. As reflected in the review, the assessment of nutritional status in hospitalized children is complex, many factors having to be considered when choosing indicators for documenting malnutrition. Health professionals should consider the strengths and limitations of indices, as some may lead to over- or under-estimation of malnutrition. Unfortunately, the hospital itself may have a potential negative impact in the nutrition of the child. Multiple practices have been acknowledged as unfavorable for the patient such as the absence of nutritional screening and assessment, unnecessary prolonged periods of fasting or poor flexibility with mealtimes. These organizational and logistic barriers undoubtedly result in increased complications, poorer tolerance to treatment and prolonged length of hospital stay for the pediatric inpatient. Future studies need to concentrate on improving nutrition screening or assessment tools by adding for example biological markers in the algorithms. Zhang et al.  have tak


Twater), river flow and eutrophication. Riverine habitats contain many economically and ecologically important fishes. One such group is the migratory salmonids, which are sensitive to warm Twater and low O2 (hypoxia). While several studies have investigated the independent effects of Twater and hypoxia on fish physiology, the combined effects of these stressors is less well known. Furthermore, no study has investigated the effects of Twater and O2 saturation levels within the range currently experienced by a salmonid species. Thus, the aim of this study was to investigate the simultaneous effects of Twater and O2 saturation level on the energetics and kinematics of steady-state swimming in brown trout, Salmo trutta. No effect of O2 saturation level (70 and 100% air saturation) on tail-beat kinematics was detected. Conversely, Twater  did affect tail-beat kinematics, but a trade-off between frequency (ftail) and amplitude  maintained the Strouhal number  within the theoretically most mechanically efficient range. Swimming oxygen consumption rate (U and Twater. The only effect of O2 saturation level was observed at the highest Twater (22°C) and fastest swimming speed (two speeds were used—0.6 and 0.8 m s−1). As the extremes of this study are consistent with current summer conditions in parts of UK waterways, our findings may indicate that S. trutta will be negatively impacted by the increased Twater and reduced O2 levels likely presented by anthropogenic climate change.Climate change is predicted to impact freshwater aquatic environments through changes to water temperature ( How these challenges affect survival and distribution of economically important fresh water and anadromous fishes, including species native to the British Isles, is of particular interest to conservation physiologists, ecologists and consumers alike  is believed to be the main abiotic factor limiting the geographical distribution of S. trutta, which is commonly found in 5–25°C waters , which is often a direct result of anthropogenic pollution and eutrophication of freshwater habitats, as well as an indirect consequence of decreased O2 solubility in water due to increases in Twater  undertake repeat migrations up and down rivers during spring and autumn where they can encounter changes in water flow, temperature and hypoxia. Thus, understanding how swim speed (U in m s−1), Twater and hypoxia affect their swimming efficiency is of utmost importance in order to understand how climate change may affect this species. One way to quantify kinematic efficiency in animals that use oscillatory propulsion (such as tail or wing beats) is to calculate the Strouhal number (St). St is a dimensionless parameter and in fish, and other swimming animals is derived from tail-beat frequency (ftail), amplitude (A) and swimming speed (U), which is representative of vortex shedding frequency, wake width and forward velocity, respectively . COT provides an index of overall swimming efficiency; the lower the COT value, the more efficient the locomotion  or severe  under combined stressors by modulating their individual kinematic parameters (ftail and A). Additionally, it was predicted that energetic costs (Twater and decreases in O2 saturation.The independent effects of S. trutta) with a mean body mass of 236 ± 54 g (range from 172–380 g) and mean body length (BL) of 25.47 ± 1.81 cm (range from 23.0–30.2 cm). Two studies were conducted. The first (study 1) in March of 2014 using 12 fish and the second (study 2) in March of 2015 using 6 fish. In both cases, the fish were collected from Dunsop Trout farm  and kept at the University of Manchester at 10°C (± 1°C) in 500 l recirculating freshwater tanks (8–10 fish per tank) under a daily 12 h:12 h light cycle for at least 4 weeks prior to the start of the experiments. A 10°C acclimation was chosen as it is at the lower end, yet sufficiently above the lower critical range, of the brown trout’s preferred temperature tolerance polygon and allowed for temperature increases of sufficient difference to be tested without approaching the upper incipient lethal temperature  air saturation. Twater values were selected to provide breadth while remaining within the range experienced by wild trout in the UK during the summer months (PO2 ~ 14.6 kPa and [O2] ~ 7.91 mg/l−1 at 10°C ranging to PO2 ~ 14.4 kPa and [O2] ~ 6.16 mg/l−1 at 22°C) was chosen due to O2 dip-probe records from the Ribble River Trust  and because this is the level of routine hypoxia commonly found in S. trutta in fish farms in the UK  and 100% air saturation (normoxia) to acclimatize to the flume. For study 1, the Twater was increased slowly during the 12-h dark period so that it reached 14°C by the following morning. The following morning, the water velocity was increased from 0.4 m s−1 to 0.6 m s−1 (low U) and the trout swam at this speed for 40 min. Then the velocity was increased to 0.8 m s−1 (high U) and trout were once more swam for 40 min. These swimming velocities are at the upper range of swimming speeds for salmonids of this size, particularly swimming over this temperature range , the two-speed swimming protocol was repeated, followed by the 40-min recovery and a second warming period up to 22°C (18°C for study 2) over 2 h. The speed was then once more increased to 0.6 and 0.8 m s−1. At the end of the 40 min at high U, the velocity returned to 0.4 m s−1 and the swim flume was rapidly cooled down to 14°C (10°C for study 2). The trout recover at this Twater overnight while the O2 content was slowly changed to 70% air saturation (hypoxia) using an automated gas mixer  connected to a nitrogen gas cylinder. The following day, the warming/swimming protocol was repeated in the 70% air saturation This time at the end of the warmest Twater 40 min swimming trial, the trout was removed, placed back in its holding tank and closely monitored, while the swim flume was drained, cleaned, refilled and set ready for the next fish. The order of 70% and 100% saturation days was switched at random to account for any potential training effects. Experimental Twater was changed in a consistent order. The results obtained, however, were comparable to previous studies  at 100 frames/second using a Sony HDR-SR8E video recorder . Recordings were only taken when the trout were swimming steadily in the selected water velocity (0.6 or 0.8 m s−1).At all ftail).The video footage was imported into Tracker 4.8 video analysis software (Open Source Physics). Clips of three consecutive tail beats were chosen for frame-by-frame digitization of three body parts through time—head, dorsal fin and tail tip. The coordinates of head and tail tip position at each tail beat were used to calculate tail beat amplitude. The midpoint of the tail excursion was taken as a line drawn through the centre of the head parallel to the flow. The max distance that the tail tip reached perpendicularly from this line was then taken as the peak amplitude. Left and right excursions were then summed to give peak-to-peak tail tip amplitude (A). In addition, the time stamp at each video frame was used to calculate tail-beat frequency in Hz  St = U were made to account for fore–aft positional change between the beginning and end of the video clips.If necessary, adjustments to 2 consumption  was placed in the swim flume to record the O2 concentration in the water. Water oxygenation and the flushing pump were controlled through a DAQ-4 data acquisition and relay system (Loligo Systems). Flushing duration was controlled and The mass specific rate of O2 kg−1 h−1) = [(Vr-Vf) • Δt • Mf), (2)Vr is the respirometer volume, Vf is the fish volume , Δ2 concentration in the closed swim flume, Δt is the time duration (h) and Mf is trout body mass (kg).where −1 kg−1 as described by 2 = 3.24 Cal and 1 Cal = 4.18 J, and, therefore, 1 mg O2 = 13.54 J.The COT was calculated by converting ftail, A, St, Twater, U and high O2 saturation as fixed factors, and individual trout as a random factor in all cases. All interaction terms were included in the initial models, but were removed if not significant by stepwise backwards deletion. The deletion stopped when either no more interaction terms were non-significant or when only the main effects remained in the statistical model. Interaction terms are only presented in the results when having a significant effect on the dependent variable. All data are plotted as means ± SEM, unless otherwise stated. P-values were generated using Type II Wald X2 tests.All data were analysed in R version 3.1.2 . The datn = 6, 18, 18 and 12 at 10, 14, 18 and 22°C, respectively. The n is the same for all combinations of speeds and oxygen saturation levels. Final sample sizes, however, differed slightly in the data analyses due to some fish refusing to swim under some treatments. The reluctance to swim did not appear to be size dependent. Although the smallest fish (BL = 23 cm) refused to swim in 22°C at 0.6 m s−1 under normoxia and in 22°C at 0.8 m s−1 under hypoxia, trout ranging from 25.2 to 25.4 cm also refused to swim under the latter conditions. Furthermore, those trout refusing to swim in other treatment combinations ranged from 25.2 to 26.5 cm BL. The sample sizes for all kinematic parameters  swimming and n = 6, 18, 17 and 11 at 10, 14, 18 and 22°C, respectively, for high U (0.8 m s−1) swimming. For hypoxia, they are n = 6, 18, 17 and 12 at 10, 14, 18 and 22°C for low U swimming and n = 6, 17, 17, 9 at 10, 14, 18 and 22°C, respectively, for high U swimming.The number of trout swum at each temperature were rameters  and 3 fon = 6, 18, 18 and 12 at 10, 14, 18 and 22°C for low U (0.6 m s−1) swimming and n = 6, 18, 17 and 12 at 10, 14, 18 and 22°C, respectively, for high U (0.8 m s−1) swimming. In hypoxia, n = 6, 18, 17 and 11 at 10, 14, 18 and 22°C for low U swimming and n = 6, 17, 17 and 11 at 10, 14, 18 and 22°C, respectively, for high U swimming.For both  and COT  in normoftail  and increased with increasing Twater . In contrast, O2 saturation level had no detectable effect on ftail . A  or O2 level , but decreased with increasing Twater . There was no detectable effect of either Twater  or O2 saturation level  on St. St, however, was lower at the higher U .ftail  was grea.316). A  was not X2 = 349.239, P < 0.001) and increased with increases in Twater . Taken together across all temperatures, X2 = 3.122, P = 0.077). There was, however, a higher incremental increase (interaction between temperature and U) in Twater at the higher speed . A significant interaction between U and O2 saturation level  indicated that the effect of mild hypoxia on ocument}  was greaX2 = 82.946, P < 0.001) and increased with increases in Twater , but it was not affected by oxygen saturation level .COT  was highS. trutta life history. The aim of this study was to determine the combined effects of Twater, O2 saturation level and swimming speed on the tail-beat kinematics and energetics of brown trout, with applicability to climate change. Arguably, two of the findings are particularly pertinent to this aspect; both Twater or O2 saturation level that is consistent with the growing body of work showing that fish maintain St in an optimal (or preferred) range  range . Thus, i to drop   was marginally lower at the high nt study  in ftail2 saturation level had no detectable effect. The 2 production measurements has its limitations. Nevertheless, the values calculated here are within the range of values seen in other athletic fish species at similar U and at similar Twater, such as the European sea bass, Dicentrarchus labrax  aquaculture, fish exposed to cyclic episodes of hypoxia at O2 concentrations of 6 mg/l and lower  show signs of physiological changes such as sub-optimal growth , relates to a decrease in A with Twater, which is supported logically. Specifically, because St remained unaffected by, whilst ftail increased with increases in Twater, clearly A had to decrease with increasing Twater, because St is a product of ftail and A (when U is fixed). Hence, we would argue that the conclusion of a significant effect here is robust. The second P-value (P = 0.049) relates to the interaction between U and O2 saturation level on Twater and U combination in the low O2 saturation level condition. Although this result does not have the logic-based support of the A and Twater result, it, nonetheless, is certainly not an unexpected finding.Multiple statistical tests were conducted within this present study and two ery rate  would be2-limiting stressors for energetically demanding activities such as locomotion (Twater changes or O2 saturation level. However, the energetic cost of locomotion (Twater and with higher U. Mild hypoxia had no detectable effect on swimming performance except at the most extreme combination of conditions experienced. Future studies should expand the range of the conditions tested in this study to further probe the vulnerability of this ionic fish. Indeed, our data suggests that if anthropogenic climate change pushes conditions beyond those used in this study and the trout are unable to either migrate or avoid compromised areas, they will experience detrimental levels of physiological stress.Both warming waters and hypoxia are O