Video review processes for evaluation and coaching are often incorporated into medical education as a means to accurately capture physician-patient interactions. Compared to direct observation they offer the advantage of overcoming many logistical challenges. However, the suitability and viability of using video-based peer consultations for professional development requires further investigation. This study aims to explore the acceptability and feasibility of video-based peer feedback to support professional development and quality improvement in patient care.Five rheumatologists each provided four videos of patient consultations. Peers evaluated the videos using five-point scales, providing annotations in the video recordings, and offering recommendations. The rheumatologists reviewed the videos of their own four patient interactions along with the feedback. They were asked to document if they would make practice changes based on the feedback. Focus groups were conducted and analysed to explore the effectiveness of video-based peer feedback in assisting physicians to improve clinical practice.Participants felt the video-based feedback provided accurate and detailed information in a more convenient, less intrusive manner than direct observation. Observations made through video review enabled participants to evaluate more detailed information than a chart review alone. Participants believed that reviewing recorded consultations allowed them to reflect on their practice and gain insight into alternative communication methods.Video-based peer feedback and self-review of clinical performance is an acceptable and pragmatic approach to support professional development and improve clinical care among peer clinicians. Further investigation into the effectiveness of this approach is needed. Physician practice evaluation to support maintenance of competence is a complex process that can include multiple components, from charting to patient encounters. Direct observation of clinical encounters between physicians and their patients can be a valuable technique to evaluate clinical performance and communication skills. It may also enhance physicians’ self-reflection and contribute to professional development and quality improvement.Implementing direct observation techniques, however, can be challenging. Direct observation requires the simultaneous availability and participation of an evaluator, the physician, and willing patients. Also, the presence of an outside observer can unintentionally alter the dynamic of physician-patient interactions, as both physicians and patients can be conscious of the presence of an outside observer. Consequently, physicians or patients might act artificially, preventing the observer from examining a more natural interchange between the physician and his/her patients. Direct observation is also labour-intensive for the examiner, requiring travel to different locations to carry out the assessment. Such logistical issues often make direct observation infeasible, particularly for physicians practicing in non-urban locations.Video-based feedback can conceivably contribute to continuing professional development by retaining the benefits of direct observation while simultaneously decreasing many of the above-mentioned challenges. Video-based feedback – whereby the encounter of a learner with a patient is recorded and sent to a reviewer for comments and evaluation – has been applied extensively to medical and non-medical education with the objective of improving individual practical skills like communication or surgical techniques –7. VideoHowever, video-based peer feedback also has potential challenges: a) physicians may not engage with the feedback as readily in a video format that is deprived of interactive dialogues with the reviewers; b) the person being observed is not able to probe deeply into the reviewers’ critique in a manner that could help them better understand the issues and improve upon them; c) asynchronous assessment delays feedback, thereby reducing its timeliness and potentially its effectiveness; and, d) video recording might still be seen as intrusive because patients or physicians might not like to be recorded .Video-based feedback has been researched previously at our institution to evaluate its effectiveness in teaching medical students. In this context, studies found that video annotated feedback helped students identify specific areas of strength and weakness and increased the overall acceptance of scores , 9. AddiFor practicing clinicians, continuing professional development is vital to ensure quality and up-to-date practice in patient care. The capacity for video-based review to support the evaluation of clinical performance and provide feedback, to yield opportunities for self-reflection on clinical approaches, and to allow consideration of alternative approaches to clinical care, holds great potential for improving continuing professional development. However, there is limited understanding of whether video-based feedback would be used by physicians in practice as a method for continuing professional development and quality improvement .To investigate the utility of video-based feedback among peer clinicians as a feasible and acceptable method to support performance improvement, we conducted a qualitative study, named Remote Assessment via Video Evaluation (RAVVE), using a participatory action research model. The overall objective was to explore hypothesized benefits and drawbacks of video-based consultations and whether a group of clinician peers would find this a helpful approach for their continuing professional development and quality improvement.This study used a mixed methods approach, based on the principles of participatory action research . Such reEthics approval was obtained from the university’s Behavioural Research Ethics Board prior to the study. As per ethics requirements, all participating physicians provided signed consent to participate.The clinicians and researchers participated in a focus group to contemplate contextual constraints and generate consensus on how a video-based peer feedback process could be conducted inconspicuously and efficiently while also deciding what tools would be most useful to evaluate each recording.Each physician recruited a convenience sample of four patients to participate. On the day of a medical appointment, the physician office assistant or a co-investigator presented, explained, and reviewed the study protocol with the patient. The patient provided written informed consent to allow video recording of their clinical consultation and the sharing of the patient’s chart among co-investigators. Although selected through convenience sampling, enough patients were recruited to provide the variety and content required to adequately allow for critique of the video assessment method.All five rheumatologists recorded and provided four videos of patient consultations, ranging from 10 to 20 min, along with consultation notes for the recorded visit and prior visits relevant to the current visit. This number allowed for sufficient diversity of observation while also being deemed feasible for the physician co-investigators.To conduct the video review, each reviewing physician received access to two videos of patients from each of the other four physicians. As the initial step, for each peer, the reviewing physician reviewed one video along with the provided consultation notes, completed a questionnaire (described below) to evaluate the performance observed, annotated the video using a software program located on a secure server, and provided a narrative outlining exemplary practices and recommendations for change. Two weeks later, this process was repeated for the second patient video received from each peer physician.All videos were shared with physicians and researchers using a password protected and private web platform that enabled the research team to control access and deliver specific materials only to those who needed access. The video platform and software allowed feedback from the reviewers to be embedded directly into the video at corresponding time stamps  from the university’s behavioural ethics review board prior to its conduct.Five rheumatologists  and 20 patients participated in the study. All but one of the 20 video recordings were reviewed twice by peer physicians. When asked to describe the type of relationship maintained with the physician being reviewed, the physicians most often identified themselves as a “peer”. In only two cases did the physician regard the individual being reviewed as a “consultant”. As previously stated, all five were actively involved in education and known to have an interest in the area of technology enabled pedagogy, thereby allowing them to act as key informants in a participatory action research process.For the patient consultation recordings, the clinicians decided to engage patients that were returning for clinical follow-up rather than new patients cases because: a) physician-patient rapport had already been established, b) such patients would likely be less anxious, c) more of these patients could be seen in less time, and d) the physical exposure of patients during examination was likely to be more limited at follow-up.As described in the Methods, a commonly used multi-source feedback questionnaire was presented to stimulate discussion regarding what could potentially be evaluated with video-based review and consensus-building was used to generate a modified questionnaire. After discussion of the context and likely patient scenarios it was concluded that 17 items  and the physician’s ability to manage complex psychosocial problems (item 12).α = 0.94), regardless of which round of ratings were considered . Therefore, all subsequent analyses were done on the average score assigned to each physician to avoid problems of missing data. The average score across observed performances was 4.52 with a range from 3.60 to 5.00 . Physicians rated their own performances slightly higher  than did peer physician reviewers ; F1,58 = 4.81, p < .05.The internal consistency of the ratings provided to physician performances were very high  relative to reviewing their colleagues’ performance ; t (57) = 2.95, p = .769.A negative correlation of see Fig. . With thAs illustrated in Table Phase 3 of the study investigated participants’ perceptions of the overall project, insights into how video-based peer feedback could improve their clinical practices and professional development, and recommendations. The themes that were identified through content analysis included: 1) equipment feasibility and software, 2) advantages of the peer-review video feedback process, and 3) disadvantages of the peer-review feedback process. The themes are described in further detail below with illustrative quotes.The clinicians involved in this study believed video-based review to be less intrusive and more convenient than direct observation. One participant expressed: “I would find [an observing reviewer to be] intrusive … totally unnatural if there was somebody just sitting there and watching you”. While another participant added: “I don’t think patients would appreciate that either … having another person watching”. Incorporating the video recording into their office for the sake of submitting to a feedback process was quite feasible because the camera was small, inconspicuous, convenient, and easy to operate. Moreover, clinicians reported that patients were receptive to having the consultation recorded. Many favoured the video recording over an in-person reviewer because “it was not obtrusive to patients” and “you forget the camera is there”.Regarding the software used for recording and review of annotated feedback, clinicians found the interface for reviewing peer videos to be helpful. Some of the features found to be particularly useful were the ability to fast forward through the video, to highlight specific instances of certain behaviours, and to incorporate annotations. However, some clinicians found the program to be challenging to use in the beginning and it took time to learn how to use the software properly as some of the features were not intuitive.“I think this is so much more valuable than a just a chart review. I think that the fact that it is in combination … the last consult note, the current consult note and the video, I think is much more information to learn from … even for one patient. Watch one patient interaction with two consult letters and an interaction are much more powerful than doing 10 charts.”The recorded interactions allowed participants to gain insight into other communication methods and styles by seeing how their peers communicate with their patients and highlighting any specific areas from the consultation that were exemplary or areas that could be improved. In this way, they argued that the act of reviewing others might have been as (or even more) valuable to their own improvement than the opportunity to hear reviews about their own performance. Overall, participants found the video feedback to be complementary to chart reviews.One major benefit of the video-based feedback process frequently mentioned was the provision of accurate and detailed information regarding the patient-clinician interaction that a chart review alone would not be able to achieve . One person expressed the benefits of video review relative to chart review in this way:“Having to assess someone else is an important piece to this because I think that they gain something from looking at their own. We also gain something from actually observing somebody else and commenting on somebody else. I think that that process alone helps us improve because we all know around this table that teaching helps us improve our ability to provide clinical care.”Participants had the ability to highlight and obtain consensus on specific behaviours. With annotations at exact points on the video, coupled with feedback from different reviewers of the same video, many respondents found that hearing the consensus of reviewers regarding certain behaviours was validating and reinforcing. From the reviewers’ perspective, participants indicated they found it beneficial to observe and comment on their colleagues’ performance.Through this process, many found that providing feedback was a good mentoring opportunity, and that this type of mentoring would improve their ability to provide better clinical care. One participant explained:“The main and most powerful element in this whole thing is getting to see yourself. You don’t know how you look like or how you act and you just keep doing the same thing you’ve been doing your whole life … And just being able to actually look at that video aside from all the comments and everything else, you just sort of pick up … oh, you know I can do that differently … that works well … That [video review] is super valuable and that every doctor should have it... it’s powerful”.Another participant thought that observing his own performance allowed him to identify his own communication style:“I actually think that evaluating myself is more valuable because … we all have a different style … the medical content I think everybody is fine, but the communication … everyone has their own style. And I think it’s nice to see how your style is because it’s different than what you think it is.”Participants felt that, in general, there were few opportunities to view themselves in their daily practice. The ability to view their own performances via video recording was seen as a valuable opportunity to help them reflect. As one participant said:“I think that when I watch my own videos, independent of what people commented on, I would have commented on the same thing about myself that everybody else said about me. So, I actually don’t know how much extra value there was. Maybe we were just a group of people that were very nice and didn’t say what they really thought … It’s a small group, so … and you know each other and work together”.Another disadvantage to the video recording was that patients may have been uncomfortable discussing negative psychosocial issues and thus may have avoided mentioning these issues during the recorded encounter. Lastly, the time required to review the peer videos and provide feedback may have been too long due to having too many videos to review, and annotating various relevant sections being rather time-intensive.Several drawbacks of the feedback process were brought up at the focus group that can be used to further improve the implementation of video-based feedback processes. Participants indicated that it might have been useful to have a follow-up with the reviewers, once all the feedback was reviewed, to discuss how to reconcile discrepancies between reviewers. Some participants believed the feedback received might have been more beneficial if they had not known each other (and been familiar with others’ practice patterns) prior to the study. Since the reviewers knew each other and had previous positive interactions, some individuals thought some of the comments might have been slightly biased and overly positive. As one participant expressed:We aimed to explore the application of video-based feedback as a way to support continuing professional development and performance improvement among peer clinicians. Using a participatory action research approach we sought to understand how actively practicing physicians might consider using video-based feedback effectively, pre-experience, and what issues they perceived, post-experience, to guide the iterative improvement of this intervention. Overall, our results illustrated that peer clinicians reviewing and annotating video performances using a multi-source feedback questionnaire was a feasible and acceptable approach to support professional development. The study was able to outline advantages of video-based feedback as a convenient way to provide comments to others, observe accurate and detailed information, and self-reflect on one’s own practices. At the same time it highlighted the importance of remaining conscious of time requirements for the review process .Participants believed the process provided an opportunity to learn from patient consultations by allowing peers to mentor one another. Their perspectives suggested that the ability to provide and receive feedback on specific behaviours and obtain an overall consensus from the group had a meaningful impact on their practice. Despite the advantages of having peer reviewers with previously established rapport, the participants suggested that having peers who were familiar with each other would not be objective enough as an approach to peer assessment in other contexts. Therefore, we would recommend that the review process incorporate a mixture of reviewers with varying levels of familiarity. That said, peer review in the form we conducted may not be suited to a formal evaluation process such as a high stakes revalidation process conducted by a licensing body and, as such, we consider the benefits of inducing feedback and reflection to be more central to the benefits of video-based review than the objective assignment of ratings.Our findings also highlight the importance of designing a process with which physicians will engage as the process of reviewing videos was commented upon as time intensive for participants and the software for annotation found to be challenging for some. Disengagement of participants due to technical difficulties might compromise the quality of feedback produced. Our study did not specifically examine this issue, but one would surmise that using this video method would be less time intensive than if reviewers were required to be physically present to observe the interactions between those being reviewed and their patients.Several limitations of our study design should be taken into account. First, the participating rheumatologists were selected purposefully as those who could serve as key informants given their educational interests and activity. As such, they are not likely to be representative of all physicians, which will limit the generalizability of our findings. This trade-off was considered worth making, given the purpose of the study, as it allowed for robust participatory inquiry, self-reflection and critique from an informed pedagogical perspective.Similarly, the patient interactions reviewed for this study were limited to follow-up visits rather than new patient encounters. The physicians already having background knowledge of the particular patients enrolled may have contributed to the relatively high ratings observed and, in turn, to the positive feelings regarding the value of the feedback processes. Video-based feedback for new visits would be different in that the physician would be assessed for a variety of other competencies including basic history-taking skills, establishing rapport, information gathering and initial discussion of the differential diagnosis and plan. Whether or not such benefits are greater than the potential disadvantages that led our physician participants to want to focus on follow-up visits remains to be seen.Third, our study was not designed to offer a definitive answer as to whether or not planned changes were implemented. Participants claimed the opportunity to review their own videos offered a helpful trigger for self-reflection and motivated intention to change. That, coupled with congruous advice from reviewers, could form the powerful alliance required to cause actual change in clinical behaviour, but we cannot say that took place with certainty.Fourth, the asynchronous nature of our pilot is also worth commenting upon, as participants expressed interest in having follow-up discussions with reviewers to go over certain suggestions. While our study did not examine this possibility, adding an interactive component where the reviewers and those being reviewed can interact after the video reviews, in person, via telephone, or by desktop videoconferencing, may further enrich the experience for those being reviewed.Overall, video-based peer consultations offer a feasible and acceptable way to support professional development and quality improvement among a group of clinical peers. The findings further suggest video recordings of patient encounters to be useful tools to help physicians self-reflect on their own clinical practice. This particular approach was able to illustrate the benefit of providing a non-intrusive method for clinicians to view their own patient consultations while gaining feedback from peers, which ultimately acts as a valuable tool for clinical practice improvement. Allowing clinicians to reflect asynchronously on their performance reduces time and logistical constraints that may lessen the capacity to engage in direct observation of peers, but participants still saw value in creating opportunity to obtain clarification regarding the feedback received. At a minimum, video-based feedback creates a stimulus for reflection that might enable physicians themselves to follow up on issues about which they would value more dialogue. Further evaluating this model beyond pilot testing for its efficacy to promote self-reflection and openness to coaching would be worthwhile.


Mobile video applications are becoming increasingly prevalent and enriching the way people learn and are entertained. However, on mobile terminals with inherently limited resources, mobile video streaming services consume too much energy and bandwidth, which is an urgent problem to solve. At present, research on cost-effective mobile video streaming typically focuses on the management of data transmission. Among such studies, some new approaches consider the user’s behavior to further optimize data transmission. However, these studies have not adequately discussed the specific impact of the physical environment on user behavior. Therefore, this paper takes into account the environment-aware watching state and proposes a cost-effective mobile video streaming scheme to reduce power consumption and mobile data usage. First, the watching state is predicted by machine learning based on user behavior and the physical environment during a given time window. Second, based on the resulting prediction, a downloading algorithm is introduced based on the user equipment (UE) running mode in the LTE system and the VLC player. Finally, according to the corresponding experimental results obtained in a real-world environment, the proposed approach, compared to its benchmarks, effectively reduces the data usage  and power consumption (about 19% when there are screen touches) of mobile devices. In recent years, an increasing number of video applications involving entertainment, news, instructional videos, etc. have been developed for mobile users. In order to achieve high-quality media content streaming on the Internet, researchers have produced studies regarding the delivery level, among which HTTP adaptive streaming (HAS) occupies a dominant position, as its flexible service model allows users to increase or decrease video quality as needed during playback . For exaTherefore, setting our sights on the data download level, this paper focuses on data download strategies to provide cost-effective video streaming. Several studies have already examined cost-effective mobile video streaming, focusing on providing bandwidth or energy saving schemes ,6,7,8,9.The proposed scheme does not concentrate on deploying a specific delivery system for video streaming. It provides a cost-effective data download strategy through environment-aware watching state prediction and provides a generalized strategy that can be used for many other video delivery technologies, extending the domain of the traditional data download algorithms for mobile videos.The proposed data download algorithm considers both the physical environment and the operation behavior of a mobile user. Machine learning is adopted to reveal the influence of the user’s physical environment on the user’s operation behavior. In this manner, the data download algorithm can provide differentiated download strategies according to the watching state of the user.Therefore, in this paper, we propose a cost-effective mobile video streaming scheme by considering both the physical environment and the operation behavior of a mobile user. First, a prediction model of the user watching state is developed utilizing machine learning methods, revealing the influence of the user’s physical environment on the user’s operation behavior. An actual watch record from an online education platform is used for the case study in the experiment. Second, a downloading algorithm is proposed to provide differentiated download strategies according to stable and unstable user watching states. When the state is stable, the video streaming strategy tries to save energy through data transmission batching. When the state is unstable, the video streaming strategy seeks to minimize the download of unused data via a conservative download method. Third, we conduct verification experiments to verify the advancement of the algorithm, and the experimental results show that the proposed scheme, compared to existing methods, can effectively reduce power consumption and mobile data usage. Finally, to implement our algorithm, a mobile video streaming prototype based on the VLC player is developed on the Android platform. The main contributions of this paper are summarized as follows.The rest of this paper is organized as follows. In To reduce bandwidth and computing resource consumption on mobile devices, work has been done from several perspectives. First, dynamically adjusting the size of the buffer is one solution. Exploring the impact of the mobile cache threshold on video streams, Wu et al.  proposedSecond, solutions from other perspectives can be applied for some specific tasks. Video codecs can be optimized to reduce power consumption during encoding, decoding, and display . For mobThird, novel and user-friendly changes can be made considering the influence of user behavior. To reduce the resource consumption of mobile devices, Hu W. et al.  analyzedIn the above research on mining and predicting user behavior to achieve power conservation for mobile video applications, the sequence of historical operations that occur when a user watches a video is used as the basis for prediction and further saves energy in mobile devices, but the influence of the physical environment on user behavior is not taken into account, and the consideration of factors affecting user behavior is not comprehensive or accurate enough, resulting in low prediction accuracy. Therefore, in this study, the influence of the user’s environment is considered, and the scope of user behavior prediction is expanded to the prediction of the user’s viewing status rather than specific operations.stable, where the user continues watching the video within the prescribed time interval T, and (2) unstable, where skip/quit operations occur within the prescribed time interval T. The reason why we divided the user watching state into only two categories is that, since the criterion for this classification is stability, besides stable and unstable, there should be at most one intermediate state. However, the main purpose of classifying the user watching state is to determine which downloading strategy out of two should be applied under certain circumstances, and further achieve cost effectiveness. The two alternative strategies achieve minimum data waste and minimum power consumption respectively, which are two representative characteristics of cost effectiveness. Therefore, in order to address these two strategies one by one, only two user watching states need to be defined.During video playback, users perform various operations such as starting playback, pausing, skipping, and quitting. These operations may result in downloading unused data. For example, a portion of the video data may have been downloaded into RAM only to be skipped by the user. In practice, a user’s operation behavior is affected by various factors, so it is difficult to obtain accurate predictions. Therefore, the task of predicting the user’s operation behavior is transformed into that of predicting the watching state in this study, and such states can be divided into two categories: (1) In this study, we analyze the behavior of a user watching an educational courseware video, and three factors are considered to predict the user’s watching behavior: (1) courseware video information, (2) physical environment, and (3) historical watching behavior. These three factors are analyzed to obtain effective features, and machine learning is used to model and predict the user’s next watching state. We collected video content information, sensor data, and learning operation logs from an online educational platform from 1 September 2016–31 December 2017 (486 days), including data for 3905 students and 380 courses (6164 courseware videos). In total, 57,207 samples were obtained through the video player to train the model, and the dataset is made available to potentially further benefit the community since it may be useful for other environment-aware studies . Note thThe data for a courseware video include course ID, courseware ID, and duration. Duration is recorded because, in addition to video content, it affects the user’s watching time. Statistics for the learning time obtained from learning operation logs of the online education dataset are shown in To determine the impact of the physical environment on users’ behavior, it is necessary to assess the users’ current physical environment. Therefore, this study determines whether the current environment is noisy or quiet based on the sensors in the mobile client. Guided by a previous study , accelerAs a user watches a video, sensor data are collected at a frequency of 1 Hz and saved as a CSV file with a unique identifier. The acceleration sensor’s output includes values in three directions  in all operation sequences, selected the top 50 most frequent substrings, and assigned semantics to them, as shown in ith operation sequence sample j was calculated by the formula pth pattern in semantic j, j, and pth pattern in semantic j. There is no need for exactly four operations since for shorter videos, by the time the user performs enough operations, the video may already be nearly over. We only need to know how many times the four-digit patterns occurred during the time window. If none of those patterns occurred, there were other variables in the input without the weighted behavior patterns.study of , we counWe selected the seven most commonly-used machine learning algorithms, namely ridge, LASSO, elastic net, ExtraTrees, random forest, gradient boosting, and XGBoost, for modeling and prediction of the watching state. The 10-fold cross-validation method was used to prevent overfitting. The evaluation indexes of the results were modeling time, mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination Since the LTE technology dominates the current mobile telecom infrastructure, downloading was considered to use the LTE network as the platform for video transmission. In the LTE system, the user equipment (UE) has two running modes that are switched by the RRC by changing the state of the wireless interface. These modes are LTE-ACTIVEand LTE-IDLE, and during the switch from LTE-ACTIVE mode to LTE-IDLE mode, there is an intermediate high-power state of duration T. When the user is in the stable state, we can fully utilize the client buffer to download enough data in the LTE-ACTIVE mode and then switch to LTE-IDLE and remain in that state as long as possible. When the user is in the unstable state, we can download a group of pictures (GoP) that the user will watch, minimizing the download of unused data. A GoP contains a fixed number of I, P, and B frames and is the basic unit of video transmission, with a duration of To both save power and reduce the waste of mobile data, the idea of our algorithm is to make a decision based on the user’s watching state during time window bitrate streaming [ON-OFF streaming [At present, there are two typical cost-effective mobile video streaming scheduling strategies. (1) treaming  is a strtreaming  is a strT, the data size is v is the video playback rate. For bitrate streaming, since the UE is always in the LTE-ACTIVE mode, the total energy consumed during time window T is calculated by Equation ,,CalculatEquation . By lettEquation , we can Equation  and 9)..CalculatDetermining the mode switching time of ON-OFF streaming in the stable state. We used the lower bound p is the probability of skipping/quitting.rding to , the lowIf the predicted result of the watching state is the unstable state, we switch the strategy to bitrate streaming to minimize the download of unused data. Algorithm 1 describes the downloading algorithm, where the function Algorithm 1: Cost-effective mobile video streaming downloading algorithm with environment-aware watching state prediction.The proposed algorithm in this section can switch the download strategy between bitrate streaming and ON-OFF streaming and determine the mode switching time of ON-OFF streaming. However, it relies heavily on the prediction result of the user watching state in After introducing the above video data downloading algorithm in stream_output and user_interfaces modules. The default download strategy for VLC is bitrate streaming [stream_output module and added a strategy switch interface to the user_interfaces, which was controlled by the predicted watching state. Afterwards, we compiled and generated libvlc.aar and embedded it into our application. ON-OFF streaming was used in both our strategy and the energy-aware method, where the default buffer size is 10 MB in VLC. We used MediaPlayer.Event to intercept the user’s click operation and SensorManager provided by Android to collect sensor data at the frequency of 1 Hz. The data were stored in memory, and the features were generated locally and sent to the predictive model.The proposed strategy was developed in an Android application that uses the VLC player by modifying the treaming ; we addetreaming  and our In the experiment, an Android cell phone was used as our client, and third-party applications were used to measure throughput and power consumption; power consumption was measured in mAh, and voltage was assumed to be 3.7 V during the experiment.Bitrate streaming [treaming : DownloaEnergy-aware [gy-aware : Switch Our method: Consider the impact of the physical environment on user behavior by predicting the user’s watching state within a defined window. If it is unstable, use bitrate streaming; otherwise, use ON-OFF streaming to minimize energy consumption and unused data.We analyzed the performance of our solution by comparing it with existing algorithms. We used three different algorithms to record the total energy consumption and the unused data under different conditions. The comparison algorithms were:The downloading experiments were performed on the dataset mentioned in For the three algorithms, when the operations on a single video were performed in the same environment, the results were as shown in Without skipping: No operations or screen touches occurred during the entire video watching process. With skipping: We took a sample of the operation sequence of the courseware from the test dataset; the operation sequence was represented as a series of operation(time). The test sample was 1(29 October 2016 16:22:16) 4(22:21) 4(22:59) 4(23:01) 4(23:04) 4(23:05) 4(23:07) 4(23:39) 4(23:40) 4(23:42) 4(23:43) 4(23:47) 4(23:49) 4(23:53) 4(23:54) 4(23:56) 4(23:57) 4(24:00) 4(24:01) 4(24:39) 4(24:40) 1(36:32) 3(40:02) 1(40:42) 2(42:06). There was no further screen touching during the playback process, and this sequence of operations during playback was analyzed under various algorithms. The results are shown in With skipping and screen touching: We used the previous sequence of operations and added a screen touch between every two operations. The experimental results are shown in The experiment showed that, when there were many screen touches during the video watching period, our strategy obtained the lowest energy consumption and the mobile data usage. When there were few user operations, the energy savings of our approach would be reduced, but the data usage remained less than that of the benchmarks.For two videos with different durations (“Java language features” and “thread scheduling”), experiments were performed in the same environment (quiet) using the three algorithms. The operation trace of “thread scheduling” was 1(8 March 2017 15:45:34) 4(15:47:22) 4(15:49:43) 2(15:52:35), and the new results are shown in Without skipping: For all three methods, when the frame rate and resolution were constant, the power consumption increased dramatically with the increase in video duration, as can be seen from With skipping: Comparing the energy-aware algorithm and our algorithm with bitrate streaming in With skipping and screen touching: As the proportion of screen touches and skip operations increased, the power consumption of the energy-aware algorithm increased relative to that in the with skipping case, while our method showed little increase.For “Java language features”, experiments were performed in quiet and noisy environments using the three algorithms. For the noisy environment, the user operation trace from the with skipping and with skipping and screen touching; thus, they saved less power than in a quiet environment, as shown in Energy-aware and our method were in touch screen and unstable states for a long time under both In the scenario of a single video and multiple environments, since different physical environments were ultimately reflected in skip times during the viewing process, the result was the same as before; i.e., the more skips there were, the lower the energy savings.In this paper, we proposed a mobile video streaming scheme to reduce energy consumption and bandwidth usage in an LTE network. A prediction model of the user watching state was developed using machine learning, considering the influence of the user’s physical environment on user behavior. Afterwards, a download algorithm was introduced based on the UE running mode in the LTE system. The download strategies were adjusted according to the user watching state. When the state was stable, the UE running mode was LTE-IDLE for as long as possible to save energy, and the ON-OFF streaming strategy was used. When the state was unstable, the skip/quit operations occurred. The bitrate streaming strategy was used to minimize the download of unused data. The scheme was implemented in the VLC player on the Android platform, and the performance of the scheme was compared with that of the existing methods under three scenarios of video watching. The experimental results showed that our method was effective and performed best when the user touched the screen frequently.Considering the physical environment, this paper studied on-demand downloading of online video data, mainly concerning MP4 file transfer under the HTTP/1.1 protocol, in which data are actively requested according to user’s needs. Recently, delivery-level research on HAS over HTTP/2 has received increasing attention. Switching between the ON-OFF streaming strategy and the bitrate streaming strategy is also suitable for HTTP/2-based adaptive streaming. The switching strategy can be applied to the dynamic server push strategy to determine the push number of HTTP/2-based adaptive streaming. Therefore, future research will focus on how to provide cost-effective HTTP/2-based adaptive streaming through the environment-aware watching state prediction. In the meantime, detailed video information affecting user’s viewing state prediction, such as the way of video production, the difficulty of video courseware, and the chroma characteristics of the video, was not taken into account in our research. It can be analyzed and added in the follow-up study to achieve more accurate user watching state prediction.


To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 single videos) recorded using 6 indoor cameras deployed on multiple spaces. The video sets represent more than 1 h of video footage, include 77 people tracks and captured different human actions such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting. Moreover, each video has been manually and automatically annotated to include people detection and tracking meta-information. The automatic people detection annotations were obtained by using different complexity and robustness detectors, from machine learning to state-of-art deep Convolutional Neural Network (CNN) models. Concerning the contextual information, the Industry Foundation Classes (IFC) file that represents the environment's Building Information Modeling (BIM) data is also provided. The BIM/IFC file describes the complete structure of the environment, it's topology and the elements contained in it. To our knowledge, the WiseNET dataset is the first to provide a set of videos along with the complete information of the environment. The WiseNET dataset is publicly available at  The network was deployed on the third floor of the ance see . The sma1. The video sets were recorded using from 5 to 6 cameras simultaneously. The features of the 11 sets are described I3M-IFC) and a camera-calibration file for each camera node. From the IFC file, different data could be extracted, such as the building's topology and 2D/3D view, as depicted in 2. The dataset includes the IFC file of the I3M building , as well as people tracking manual annotations (PT-MAN). Furthermore, the use of automatic people annotation aims not only to propose an alternative to the time-consuming manual annotation but also to evaluate the complexity of each video (in terms of difficulty to detect people) using state-of-art people detectors. Therefore, 22.1Eleven video sets were recorded at different times. A description of each video set is presented on 2.2The contextual information of the WiseNET network is composed of two parts, the information of the environment where the network was deployed and the information concerning the camera nodes.The information about the environment is contained in the I3M-IFC file. IFC is a data representation standard, developed by the buildingSMART,The I3M-IFC file was obtained from the company in charge of the construction of the I3M.Remark (ID of elements of interest). The dataset includes a file containing the ID of each element of interest, used for extracting (from the I3M-IFC file) the data presented in camera-calibration files contain the position of the camera nodes in the environment and the information about the objects of interest observed by them.The •devide ID: identification of the camera node.•is Hosted By: space where the camera node is located.•resolution: camera's resolution.○width: camera's field of view (FOV) width.○height: camera's FOV height.•regions Of Interest: information about the regions of interest (ROIs) observed by the camera.○region Of Interest (in singular): identification of the ROI○xywh: position of the ROI in the camera's FOV, where  are the coordinates of the ROI's top-left point, and  are the le width and height respectively.○represents: real object represented by the ROI.These files are structured in a JSON formatSmart Camera 4 is located at the space s2, it has a resolution of d2, d5 and d6.An example of a camera-calibration file is presented in semantic-labelling and its goal is to relate real objects or important space-regions, with their projections in the camera view. In the WiseNET dataset only doors were considered as ROIs due to their importance in a building environment, e.g., they connect two spaces and people have to pass through them to enter/exit a space.Listing 1Camera-calibration file for the Smart Camera 4.Moreover, the selection of ROIs in the camera image is known as 2.31.On each video set, a unique ID should be associated to each person. For example, the person “Mario” should have the same ID in the five videos composing the set 1. Moreover, if “Mario” appears in another set he might be assigned a different ID.2.The Bbox should be created by selecting its top-left and bottom-right corners.3.If only a person's limb is visible, then no Bbox should be drawn.4.If a person is partially occluded, then the Bbox should enclose only the visible parts.5.If a person torso is no visible, e.g., only its head is visible, then no Bbox should be drawn.6.If a person is not visible for the human eye—because is totally occluded by an object, is outside the cameras FoV or the scene is too dark—then no Bbox should be drawn. Even if the person's position could be deduced from previous frames.7.A person is considered around a ROI if: (1) the center of its Bbox is inside the ROI and (2) if the Bbox is at the same level than the ROI, i.e., if the lowest point of the Bbox and the ROI are around the same height.The people detection manual annotations (PD-MAN) were obtained by manually enclosing a bounding box (Bbox) around each person that appears in a video frame, assigning them a unique identifier (ID) and stating if they are around a ROI. The process was performed by using a software developed in PythonRemark (Person around ROI). This information not only relates a person with an element of the environment, but also can be used to determine if a person is entering/leaving a space or to help people re-identification between multiple cameras, as done in Ref. [ in Ref. .Manual annotation is a time-consuming task; therefore, it was only performed on every fifth frame starting from frame 0. However, the information was propagated to the missing frames, e.g., the annotation in frame 0 was propagated into frames 1, 2, 3 and 4, the same for the annotation in frame 5 and so on.The PD-MAN annotation can be used to evaluate people detection algorithms, as well as people re-identification—by considering the unique ID.•The HOG_SVM detector is based on HOG feature descriptors and Support Vector Machine (SVM) in order to detect people . We used•SSD_512 is a one-stage detector that extracts the feature map of the complete image, then applies a sequence of multi-scale convolutional layers and anchor boxes in order to classify the different regions of the feature map . We used•YOLOv3_608 uses a single neural network that predicts bounding boxes and class probabilities directly from full images. We used the pre-trained model—configuration and weights—provided by the authors.nce time , which aThe use of automatic people annotation aims not only to propose an alternative to the time-consuming manual annotation but also to evaluate the complexity of each video (in terms of difficulty to detect people) using state-of-art people detectors. The people detection automatic annotations (PD-AUT) were obtained by passing each video frame through a set of pre-trained people detector models. We used the well-known people detector Histogram of Oriented Gradients (referred as HOG_SVM), as well as two state-of-the-art CNN-based object detector models: Single Shot Detector (referred as SSD_512) and the You-Only-Look-Once version 3 (referred as YOLOv3_608).For the object detectors—SSD and YOLO—we only focus on the person class, i.e., the rest of objects were simply ignored. The process was performed by using a software developed in Python using OpenCV library—the code is provided but not supported. The choice of detectors differs in complexity and robustness, which we consider an interesting factor for evaluating the limitations of systems. Moreover, due to the automatic nature of the annotations, the rules presented for the PD-MAN cannot be considered, only the rule stating if a detection is around a ROI is considered (rule 7).The PD-AUT where obtained for all the frames.•video: name of the video file from which the meta-data was obtained.•resolution: video resolution.•frames: set of frames with BBoxes. The frames with no BBoxes are not considered.○frameNumber: frame number.○deviceID: ID of the smart camera observing the scene.○inXSDDateTime: time stamp obtained from the smart camera.○detections: set of BBoxes (detections) made in the same frame.⁃class: detection's class name. In our case “person”.⁃imageAlgorithm: Algorithm used for detecting. For example, “YOLO” or “SSD”. If it is a manual annotation, then the value is “groundtruth”.⁃xywh: detection's top-left point , width (w) and height (h).⁃id: person's ID.⁃regionOfInterest: ROI's ID. If the detection is not around a ROI then the value is “null”.⁃visualDescriptors: array of features describing the detection.The resulting meta-data from the PD-MAN and PD-AUT annotations were stored using a JSON structure based on the logic that a video has a set of frames and some of those frames present a set of Bboxes (detections). A typical annotated frame is shown on any type of visual features can be used to describe visually the detection. For all the PD annotations in the dataset we decided to use a localize 2D Hue-Saturation (HS) histogram as visual descriptor Notice that  see Eq.  in ordernot from the complete detection region, i.e., the localization of the histogram plays the role of background subtractor. The t-shirt region was defined by the following equations: Moreover, to avoid the inclusion of the background  in the visual descriptor, the histogram was computed only from a t-shirt region Remark . The manual and automatic annotations are not dependent of our choice of visual descriptor and background subtraction. We are providing the video frames and the detections bounding boxes; therefore, the user can feel free to only use the provided bounding boxes and extract any desired visual features.Listing 2Extract of a meta-data stored in a JSON structure associated to the typical annotated frame displayed in 2.4•set: video set number from which the PT-MAN was obtained.•tracks: set of people tracks. A track relates a person with a set of spaces at some periods of time. A track is divided into a set of tracklets.○id: person's ID.○tracklets: set of tracking segments of a person.⁃location: tracklet's space location.⁃start: tracklet's starting time.⁃end: tracklet's end time.People tracking manual annotations (PT-MAN) consisted in manually stating the space location of each person at all times, during a complete video set. This was done by considering the people's ID and the time they enter and leave each space. For each video sequence, the tracking information is given in the form of a space-time graph along with its meta-data stored in a JSON file. The space-time graph is an intuitive way of presenting the location and the changes of spaces of all people during a period of time. The tracking meta-data is stored in a JSON file, where the fields corresponds to:person 1 moved between spaces 1, 2 and 3, while person 2 stayed at space 2 during the whole recording time. The meta-data file can be used for evaluating the tracking algorithms, for example by using the multi-target multi-camera metrics proposed by Ref. [Listing 3Extract of the tracking meta-data stored in a JSON structure related to the space-time graph depicted in  by Ref. .Listing 2.5The experimental evaluation is proposed to validate the usability and quality of the video sets. For this, we used three automatic people detectors—HOG_SVM, SSD_512 and YOLOv3_608— and we evaluate their performance with respect to the PD-MAN annotations, which were consider as ground truth. The following results and the associated analysis aim to demonstrate that each video represent a challenge for people detection and therefore can be used to benchmark the multi-view people tracking.Precision × Recall curves (PR-Curves) from which the Average Precision (AP) can be obtained by computing the area under the curve. These metrics were proposed by the Pascal VOC challenge [The metrics used for the evaluation were the hallenge . We use AP is a numerical metric, which simplifies the comparison of different detectors. It is important to notice that the results presented depend on the quality of the ground truth, which was done by multiple humans, thus is prompt to subjectivity and errors. Moreover, there is some discrepancy between PD-MAN rules and the automatic annotations especially when the person torso is not visible (rule 5), which occurs when the person is much closed to the camera. Furthermore, to obtain the PD-MAN annotations is a time-consuming task. Therefore, for all those reasons we recommend the users of the database to use (if possible) the automatic detections instead of the manual, especially the YOLOv3_608 detections.Remark (Camera 6). Even though camera 6 did not record any person in the sets, it recorded shadows of people walking around space s2 (see e s2 see . Thus, w


A significant problem in eyewitness identification occurs when witnesses view a suspect in one venue such as a mugshot and then later in a lineup where the suspect is the only previously viewed person. Prior research has documented that the witness may select the suspect from the lineup due either to misplaced familiarity from seeing the mugshot or to their prior commitment from identifying the suspect from the mugshot. Two experiments attempted to minimize these biases by using repeated identical lineups, such that both targets and fillers were repeated, to determine if such a procedure could be useful. Across two experiments, we also varied the delay between seeing the event and the first lineup, as well as the delay between lineups. Despite the use of identical lineups, we continued to observe the effects of commitment and misplaced familiarity, so our procedure did not remove these problems. In addition, we also found that both repeated lineups and increasing delays can influence people’s tendency to choose and their willingness to maintain their decisions, regardless of accuracy. Most importantly, however, despite the negative effects of repeated lineups and the relatively long delays used in our experiments, we obtained strong relations between confidence and accuracy when using confidence-accuracy characteristic plots. High confidence responses were associated with high accuracy.The online version of this article (10.1186/s41235-019-0168-1) contains supplementary material, which is available to authorized users. Eyewitness misidentification in the criminal justice system is a major problem. One contributing factor is the use of repeated identification procedures, whereby witnesses’ memories are tested at two separate occasions  with the suspect as the only person to appear both times. Experimental studies have shown that these procedures introduce a bias so that the suspect, even if innocent, is more likely to be selected in the second identification. The suspect may seem familiar not from having committed the crime, but merely from having been seen previously. Another problem is that witnesses tend to stay committed to their initial identification decision even if it is wrong (the commitment effect).We developed a procedure to potentially overcome these problems by using identical lineups on two occasions. Because both the suspect and the fillers were the same in the two cases, differences in familiarity should be nullified . However, our procedure still showed effects of misplaced familiarity and commitment. The positive news from the study is that on both identifications and across various delays, witnesses’ high confidence identifications were highly accurate. Thus, the confidence-accuracy relation seemed resistant to both repetition and to retention interval. These results add to our growing understanding of eyewitnesses’ lineup identification behaviors, as well as further documenting that, on both a first and second test that are not biased, high confidence indicates high accuracy.Erroneous eyewitness identification played a role in over 70% of DNA exoneration cases. One contributor to these misidentification cases involved repeated identification procedures. Witnesses may initially see a mugshot (or a show-up) followed by a lineup, or they may see consecutive lineups (e.g. a photo lineup followed by a live lineup) – and typically the suspect is the only common face between the two. Goldstein  reportedThe common denominator of these cases is that the suspect was the only repeated member in these repeated lineup procedures (single-target-repeated lineups), and studies have shown that such procedures inadvertently bias witnesses toward choosing the suspect. Prior research has focused primarily on repeated identification procedures involving a single repeated member (a guilty or innocent suspect) across multiple identifications , as well as the subsequent delay between the first and second ID.The third issue of concern in the present research is measuring the influence of repeated identifications on the confidence-accuracy (CA) relationship as measured by confidence-accuracy characteristic (CAC) plots. Despite problems with repeated identifications, will high confidence responses still indicate high accuracy, as found in virtually all studies in eyewitness memory when assessed by CAC plots on a single unbiased test . The commitment effect has been observed in the misinformation literature  are seeking should, theoretically, be eliminated. Although repeating identical lineups would not necessarily remove the commitment effect, it should yield a better measure of the commitment effect because it would be potentially uncontaminated by the other two issues.One possible positive use of repeated identical lineups is that performance (identifying the suspect) may improve. Improvement in recall or recognition between two tests is called hypermnesia, and it has been reported in free recall tasks quite regularly  between witnessing an event and being examined on it is a critical variable in all memory research, and eyewitness identification is no exception. However, in repeated identification procedures, there are two relevant delays: the delay between the crime and the initial lineup  and the delay between two repeated identifications (we will refer to this as the subsequent delay).Numerous researchers have examined how the initial delay affects eyewitness identification, but the effects vary across studies. A longer delay has been shown to decrease the number of correct identifications in some studies  What are the effects of repeated identical lineups and delay on the tendency to choose from a lineup and on identification accuracy? 2) Given an unbiased subsequent lineup , are witnesses still committed to their initial decision? 3) Does confidence inflate with repeated lineups? Or does it produce a confidence carryover effect? 4) More importantly, does the confidence-accuracy relationship remain intact despite the effects of repeated lineups and varying delays between them? Because prior studies have shown that both repeated lineups and delay can harm identification accuracy, the primary issue addressed in the current is experiments is whether the combination of these variables significantly impairs the confidence-accuracy relationship.We report two experiments that investigated the joint effects of repeated  lineups and retention interval on eyewitness identification accuracy and the confidence-accuracy relation. The use of repeated lineups was designed to reduce the problems of selective familiarity of the suspect and the demand characteristic of having only one suspect repeated. Such lineups may be difficult but not impossible to carry out in police departments . We also varied the delays between the event and the first lineup , as well as between the two lineups (subsequent delay), to determine effects on identification performance and the CA relationship (measured by CAC plots). Manipulating both the initial interval and the subsequent interval permits us, in certain conditions, to examine the effect of repeated testing without a confound of delay . In short, repeated lineups and delay tend to co-occur in the real world, and prior research has often only manipulated one of these two variables but not both.We predicted a greater tendency to choose from the second lineup than the first lineup, both because all faces were repeated . That is, we expected to observe nonchooser-to-chooser shifts based on familiarity of the faces, but familiarity would not accrue only to the suspect in target-present lineups, as it has in previous research. On the other hand, we should observe a greater tendency to choose after a short-initial delay than a long-initial delay because people may feel less confident in their ability to make an identification after a long delay due to forgetting. Identification accuracy (the number of guilty suspect IDs) should decrease with an increase in retention interval, and we expect more identification errors in the subsequent lineup than the initial lineup due to misattributions of familiarity accruing to all lineup members. There is no evidence from prior repeated lineups studies about whether the CA relationship will remain intact in a repeated lineup; however, based on prior literature, confidence inflation and confidence carryover effects will probably exist and could potentially harm the CA relationship. The present study also compared confidence judgments and response time in making judgments as indicators of accuracy and found confidence judgments were more predictive of accuracy than response times . In Experiment 1, the subsequent delay was always three days. In Experiment 2, the subsequent delay was either one day (a short subsequent delay) or five days (a longer subsequent delay).For Experiment 1, 787 participants were recruited via Amazon Mechanical Turk (MTurk), but only 591 participants completed all the sessions. The remaining 196 participants consisted of those who had technical difficulties, completed the follow-up session later than the deadline, or dropped out of the study; their data were not included in the analyses. The number of participants in between-subject conditions ranged from 141 to 154  after seeing the crime video. Together, the two tasks took approximately 10 min. The purposes were to provide a distractor task and, if needed, to provide co-variates for later analyses. However, we did not use them for this latter purpose. In Experiment 1, participants in the 10 min initial delay condition (i.e. the 10 min-3 days condition) saw lineup 1 after the filler tasks. Those in the 3 days–3 days condition did not see lineup 1 until three days later. Similarly, in Experiment 2, participants in the 10 min-1 day and the 10 min-5 days condition saw lineup 1 after the filler tasks. Those in the 5 days-1 day and the 5 days–5 days condition did not see lineup 1 until five days later.All sessions took place online. Email reminders were sent for each subsequent session along with a link to the online experiment and participant login information. Once they received the email, participants in Experiment 1 were given 36 h to complete the follow-up session and those in Experiment 2 were given 48 h to complete the follow-up session. The longer time window in Experiment 2 was used to minimize the number of late responses.The present study used the video and lineup materials from Mickes, Flowe, and Wixted . The vidParticipants received either a target-present or target-absent lineup consisting of six people. The only difference between the two lineups was that the target in the target-present lineup was replaced by a filler in the target-absent lineup. The same lineup was used twice, but the position of the photos was randomized on both occasions. The lineup photos from Mickes et al.  were digThe eyewitness task materials for Experiment 2 were the same as those used in Experiment 1 except another filler in the target-absent lineup was randomly selected to replace one filler in the target-present lineup. In other words, Experiments 1 and 2 had four overlapping fillers in the target-present lineup condition. This was to see whether using another target-replacement filler would produce different frequencies in the overall filler selections.fruit and the vegetable categories were used for the list  and the lexical decision task (another 5 min). In the free recall task, they saw a total of 30 words in a random order for two seconds each, with a 500 ms interstimulus interval. After the last word, participants were asked to recall as many words as possible, in any order, for four minutes. Then the screen automatically advanced to the lexical decision task in which participants were given instructions to decide whether each upcoming letter string represented a word or nonword. There were 16 practice trials followed by 80 experimental trials. Each trial consisted of the following sequence of events: 1) a fixation point (+) presented at the center of the screen for 400 ms, 2) a blank screen for 200 ms. 3) appearance of the stimulus at the fixation point until the participant made a response. Participants were instructed to press the “M” key for words and the “Z” key for nonwords. If they made a correct response, a blank screen was presented for 1600 ms before the fixation point appeared again. For incorrect responses, the word “Incorrect” was shown slightly below the fixation point for 1600 ms. These tasks took at least ten minutes to complete, but the exact duration depended on the participant’s pace during the lexical decision task.In Experiment 1, after the lexical decision task, participants saw lineup 1 in the 10 min-3 days delay condition or were asked to wait for a follow-up email in the next few days (the 3 days–3 days delay condition). Prior to seeing the lineup, participants were given instructions and were told that the suspect may or may not be present in the lineup. They made their identification decision by clicking on the face of the target, and then they clicked the “next” button to submit their choice. If participants judged that the target was not in the lineup, they could reject the lineup via the “not present” button. They were presented with either a target-present or target-absent lineup, and the same lineup was given in lineup 2 . The position of the lineup members was randomized in both lineups. Response timing for responding started when participants saw the lineup and ended when they clicked the “Next” button after selecting one of the faces or the “not present” button. Participants were not given any instructions about how fast they should make a decision. After making their identification decision, they were asked to make a confidence judgment using a 0 to 100 scale . Participants rated their confidence by moving a slider from 0, where the cursor started, to a point on the scale between that value and 100. As they moved the slider, the corresponding numerical value was displayed above the slider. Both lineups used the same procedure, and no feedback was provided about whether participants made the correct choice. At the start of their follow-up session for Lineup 2, they were asked to identify the suspect from the video and were again told that the suspect may or may not be in the lineup. After making their identification, they were asked to provide a confidence judgment using the same scale. At the end of Lineup 2, participants were thanked for their participation and debriefed.The general lineup identification process was similar for Experiment 2, but unlike in Experiment 1, choosers in Experiment 2 did not have to click on a lineup member’s photo and the “Next” button to submit their response. That is, Experiment 1 participants were free to change their decisions before they submitted their final response. In Experiment 2, they were informed that only their first response would be recorded. This was to equate the response time between choosers and nonchoosers, because nonchoosers in both experiments only had to click on the “Not Present” button to submit their response. The confidence judgment section of the experiment was identical in both experiments.The results that answer our basic questions are rather numerous and complex. We have divided them into sections that address the issues we intended to address, and we discuss the implications of the results after they are presented.Using an identical lineup procedure that made all faces somewhat familiar on the second ID resulted in an increased tendency to choose during the second lineup, just as occurs in other situations. The choosing rate  is shown in Fig. p-values (Holm’s method) were reported for these multiple comparisons. Unless otherwise indicated, alpha = 0.05.To evaluate our hypotheses, we used the lm4 package in R . In order to observe an increased tendency to choose in the subsequent lineup, most choosers must remain choosers and some nonchoosers must make a positive identification in the second lineup, suggesting that (as discussed below) the commitment and misplaced familiarity effects are likely playing a role.As predicted, there was a main effect of repeated lineups in both Experiment 1, z = 3.03, p < .01. Participants were generally less inclined to choose after a long-initial delay (5-day delay) than a short-initial delay (10-min delay). This outcome indicates that participants were less confident in their ability to make a positive identification when the initial test was delayed. This interpretation is reflected our Welsch t-test of the higher average Lineup 1 confidence ratings of choosers in the short versus long initial delay in Experiment 1 (61.0% vs 47.3%), t (346.46) = 5.18, p < .001, and Experiment 2 (61.4% vs. 40.3%), t (585.74) = 9.94, p < .001. Moreover, because there was a main effect of repeated lineups but no interaction between repeated lineups and delay, repeated lineups seem to make participants more inclined to choose regardless of delay. Therefore, even though longer retention intervals made people less inclined to choose and lowered their confidence in their positive identifications, repeated lineups still make witnesses more inclined to choose from a subsequent lineup even with a relatively long delay.Although the main effect of delay did not reach significance in Experiment 1, it was significant in Experiment 2, z = − 2.78, p < .05, and Experiment 2, z = − 4.77, p < .001. However, there was also a significant interaction between delay and lineup type in Experiment 2, z = − 3.37, p < .01. Follow-up tests showed that choosing rate was greater in TP lineup than TA lineup at the 10-min delay (84.5% vs. 68.3%), z = − 5.39, p < .001, but their choosing rates were no different at a long 5-day delay (72.2% vs. 68.4%). After 5 days, participants were just as likely to choose from a TP as a TA lineup, which seems surprising because retention intervals longer than 5 days are common outside the laboratory. All other effects were not significant, ps > .05. The lack of an interaction between repeated lineups and lineup type indicated that the choosing rate for the second lineup increased at about the same level in both TP and TA lineups.The presence of the guilty suspect in the TP lineup did contribute to the higher TP choosing rate relative to TA choosing rate, although the effect seemed surprisingly small. The main effect of lineup type reached significance in both Experiment 1, Together, these findings showed that both the effects of retention interval and repeated lineups influence a witness’s tendency to choose from a lineup. Participants were less inclined to choose after a long-initial delay than after a short-initial delay and even when they did choose, they were less confident. However, this effect was qualified by the presence of the guilty suspect in the TP lineup. Participants were more inclined to choose from a TP lineup than a TA lineup after a short-initial delay, but they were equally likely to choose from either lineup type after a long-initial delay (Experiment 2). Critically, regardless of delay or the lineup type, repeated lineups increased the tendency to choose. Just as in experiments in which only the suspect is repeated between two lineups, repeating all the faces in two lineups leads to an increase in choosing. We turn next to accuracy.Although participants were more likely to choose someone from a second lineup than a first lineup, they were no more accurate in picking the suspect. Accuracy is represented by the guilty suspect ID rate, which is the number of guilty suspect IDs divided by the total number of target-present lineups. Because the correct decision for a target-present lineup is a guilty suspect identification, all other possible responses in a target-present lineup  are incorrect. The top panels of Figs. z = 3.97, p < .001, and Experiment 2, z = 6.85, p < .001, but guilty suspect ID rate did not increase (or decrease) with repeated lineups, ps > .05 in either experiment. In other words, repeated identical lineups had no effect on overall accuracy. No hypmermnesia – improved recognition with repeated testing as obtained by Bergstein and Erdelyi . Therefore, the effects of delay were reflected in choosing rate, guilty suspect ID rate and the corresponding level of confidence when selecting guilty suspects.As predicted, the guilty suspect ID rate decreased as a function of delay in Experiment 1,  Erdelyi  in a difAlthough every lineup member was repeated in the second lineup, most identification decisions were carried over from the initial identification to the subsequent identification. The percentage of same and different decisions across Lineups 1 and 2 for Experiments 1 and 2 are shown in Table 2[df = 1] = 53.01, p < .001, and Experiment 2 (60.5% were consistent decisions), χ2[df = 1] = 38.76, p < .001. Further analysis revealed that this pattern was driven primarily by choosers rather than those who rejected the lineup (nonchoosers). A 2 (choosers vs. nonchoosers) × 2 (consistent vs. inconsistent) chi-squared test confirmed this in both Experiment 1, χ2[df = 1] = 11.98, p < .001, and Experiment 2, χ2[df = 1] = 35.66, p < .001. Therefore, choosers were more likely to repeat their decisions compared to nonchoosers. This finding is consistent with choosing rate results reported in the previous section, because most choosers repeated their decisions and some nonchoosers became choosers in the subsequent lineup identification. Thus, we observed an increase in choosing rate in the subsequent lineup.Collapsing across all delay conditions, our chi-square goodness of fit test indicated a greater proportion of repeated decisions (consistent decisions) than inconsistent decisions in Experiment 1 (65.0% were consistent decisions), χ2[df = 1] = 45.5, p < .001, and Experiment 2 (67.6% were consistent choosers), χ2[df = 1] = 40.8, p < .001. A similar pattern occurred with choosers in TA lineups  in both Experiment 1 (66.3% were consistent choosers), χ2[df = 1] = 17.23, p < .001, and Experiment 2 (67.2% were consistent choosers), χ2[df = 1] = 31.2, p < .001. Thus, those who erroneously picked someone in a TA lineup on a first occasion carried over the error to the second lineup nearly two-thirds of the time, confirming a commitment effect. Although identical lineups might be expected to eliminate the bias observed in situations in which a single target is repeated, we still observed a commitment effect for choosers even when all faces were familiar in the second lineups.In an additional analysis, we examined whether TP and TA choosers repeated their decisions in the subsequent lineup to a similar extent. Predictably, our one-proportion z-test showed that most TP choosers remained consistent in Experiment 1 (74.7% were consistent choosers), χRepeated lineups could affect the CA relationship in two ways: confidence inflation and confidence carryover effect. We examined whether Lineup 2 identification decisions were made with more confidence than Lineup 1 decisions, especially for repeated decisions (consistent decisions). If Lineup 2 confidence is greater than Lineup 1 confidence, then there is a confidence inflation effect. If Lineups 1 and 2 are similar in confidence, then this outcome may indicate a confidence carryover effect.t (590) = 4.78, p < .001, and Experiment 2, t (882) = 4.49, p < .001. Therefore, witnesses were overall more confident in Lineup 1 than Lineup 2. Next, we examined confidence ratings for consistent choosers and consistent nonchoosers .Aggregating all responses, Lineup 1 identification responses were generally made with more confidence than Lineup 2 responses in Experiment 1, 01) of 8.09 and 4, respectively, indicating moderate support for the null hypothesis. However, they were more confident than choosers who initially rejected but chose someone at the second lineup (no-to-suspect shift). In Experiment 1, our independent t-test indicated that witnesses who made no-to-suspect shifts in Lineup 2 were less confident than consistent choosers in Lineup 1(44.4% vs. 56.0%), t (350) = 3.84, p < .001, and at Lineup 2 (44.4% vs. 57.3%), t (350) = 4.14, p < .001. In Experiment 2, our independent t-test revealed that witnesses who made no-to-suspect shifts in Lineup 2 were again less confident than consistent choosers at Lineup 1 (43.0% vs. 54.5%), t (554) = 4.41, p < .001. Due to unequal variance in Lineup 2, we conducted a Welsch t-test and obtained the same pattern of results for our comparison of no-to-suspect shifts and consistent choosers at Lineup 2 (43.0% vs. 53.0%), t (309) = 4.02, p < .001. Therefore, although there was no overall confidence inflation for consistent choosers, they were more confident than those who initially rejected but chose someone only in the second lineup.No inflation in confidence ratings occurred for choosers who repeated their initial decisions in the second lineup . For example, collapsing across delay conditions for Lineup 1, average confidence from consistent guilty suspect IDs in TP lineups was greater than consistent incorrect chooser IDs in TA lineups . The same pattern held for Lineup 2 (66.0% vs. 51.5% and 65.8% vs. 50.4%). Thus, consistent correct choosers (those who made guilty suspect IDs) were generally more confident than those who made consistent incorrect IDs in TA lineups.t (136) = 4.33, p < .001, and Experiment 2 (57.0% to 48.1%), t (132) = 4.62, p < .001. Therefore, without post-ID feedback on the first lineup, a second identical lineup did not cause confidence inflation for consistent choosers, but it made consistent nonchoosers less confident about their decisions. One possible interpretation is that the familiarity of all members of the lineup on the second occasion made it more difficult for participants to reject the lineup and thus, even when they did so, they expressed less confidence in their decision.On the other hand, our paired t-test indicated that nonchoosers who repeated their decisions in the second lineup actually became significantly less confident in both Experiment 1 (62.4% to 53.1%), r (245) = .77, p < .001, and for consistent nonchoosers, r (135) = .63, p < .001. In Experiment 2, we similarly found a high correlation for consistent choosers, r (399) = .82, p < .001, and for consistent nonchoosers, r (131) = .69, p < .001. Together with the average confidence results, consistent choosers and nonchoosers did not show an overall confidence inflation effect; however, their initial confidence tended to be correlated their subsequent identification confidence, suggesting the presence of a confidence carryover effect.Although there was no confidence inflation, Lineup 1 confidence was positively correlated with Lineup 2 confidence. In Experiment 1, we obtained a high correlation for both consistent choosers, Because most participants repeated their initial decisions and their initial confidence was correlated with their subsequent identification confidence, we examined whether the level of confidence expressed at Lineup 1 was predictive of repeated decisions at Lineup 2. Said differently, were witnesses who were highly confident in their first decision (correct or incorrect) more likely to provide the same decision on the second lineup than those who were less confident in their initial decisions? To evaluate this hypothesis, we categorized participants by whether they made a consistent or inconsistent decision across the two lineups. Then using the level of confidence participants expressed during Lineup 1, we calculated the proportion of these participants who repeated their decisions in Lineup 2. Specifically, we were interested in the proportion of consistent and inconsistent decisions at a high confidence cutoff (90% or above). Table 2[df = 1] = 14.9, p < .001. In addition. we decomposed overall results into data for choosers  × 2 (consistent vs. inconsistent responses) chi-square test showed that the proportion of consistent responses above 90% confidence was greater than the proportion of consistent responses below the cutoff , χIn short, participants expressing high confidence were more likely to make the same choice a second time than were those expressing lower confidence on the first lineup. Thus, confidence is not only associated with identification accuracy, but initial confidence can be a predictor of the commitment effect . Putting this outcome together with our previous findings, consistent witnesses generally maintained similar levels of confidence across repeated lineups, but their initial confidence predicted the likelihood that they would provide the same decision (right or wrong) on a subsequent identification. We also considered the effects of subsequent delay on consistent decisions; while the results were in the predicted direction with less consistent decisions after long delays, most of the analyses did not reach significance  lineups and even with long delays. Repeated lineups did not harm the confidence-accuracy relationship in our experiments, but the procedure did not improve it either.Figures We observed a positive relationship between confidence and accuracy in both experiments Figs.  and 7 evConsistent with prior literature  has shown that the procedure biases selection toward the suspect, the only face repeated on the two occasions. We tried to overcome this problem by using identical lineups so that all faces were repeated, albeit in a different random order. Despite this change in procedure, our results were similar to those of prior studies that used a single repeated target . He identified several causes of confidence inflation over time, such as confirmatory feedback from the police and the fact that people are often repeatedly tested. Yet in our experiments, repeated testing did not lead to confidence inflation. Our experiments are the first to use identical repeated lineups, but our results are similar to Steblay et al. . Our lineup, due to the changes we made in shirt color, was quite difficult. On the positive side, we show that even with a difficult lineup, the confidence-accuracy relation is strong: High confidence still indicates high accuracy. Thus, in this sense, the difficulty of our single lineup makes the strong CA relationship more convincing.Another issue is that our experiments did not randomly select lineup members from a pool of faces (a filler pool), which limits the generalization of our results across different arrays of fillers. However, some of our findings, such as the commitment effect, were found in studies that had used materials different from ours. Nevertheless, we of course recommend that the effects reported in the present study be replicated using other sets of eyewitness materials.Steblay and Dysart  had recoAlthough repeated lineups did not harm the confidence-accuracy relationship, we do not argue for the general use of repeated lineups, because they have other costs. Repeated lineups led to an increased tendency to choose a suspect, regardless of delay. With only a single lineup, the greater the retention interval between witnessing the crime and seeing the lineup, the less likely witnesses are to choose a suspect and the lower their overall confidence when they do choose. However, when a lineup was repeated, people were willing to choose and this choosing sometimes occurred on the second lineup for witnesses who had declined to choose on the first lineup. Critically, repeated lineups did not improve the suspect ID rate or the confidence-accuracy relationship.We agree with the overall recommendations from Steblay and Dysart  that repResearch on eyewitness identification often focuses on accurate performance, which is of course critical. Our research examined other behaviors that often receive little attention, such as the witnesses’ tendency to choose (whether right or wrong). Our experiments showed that both the length of initial delay and repeated lineups can affect the rate of choosing. People were more likely to choose after a short delay than after a long delay on a first test, and they were more likely to correctly identify the suspects and were more confident when doing so on the initial test. In addition, those who made a positive identification with high confidence were more likely to repeat their decisions and maintain their level of confidence in a subsequent lineup. Overall, witnesses were likely to choose on a second lineup than on a first lineup.Our results add to the growing body of evidence that confidence of the witness is a powerful indicator of accuracy; the novel feature of our research is that we showed a strong confidence-accuracy relation on a second lineup as well as a first lineup


In China, alcohol consumption has increased significantly in recent decades. Little evidence exists, however, about temporal trends in levels and patterns of alcohol consumption and associated factors in adult populations.In 2004–08, the China Kadoorie Biobank recruited ~ 512,000 adults  from 10  geographically diverse regions across China, with ~ 25,000 randomly selected participants resurveyed in 2013–14. The self-reported prevalence and patterns  of alcohol drinking at baseline and resurvey were compared and related to socio-demographic, health and other factors.At baseline, 33% of men drank alcohol at least weekly , compared to only 2% of women. In men, current regular drinking was more common in urban (38%) than in rural (29%) areas at baseline. Among men, the proportion of current regular drinkers slightly decreased at resurvey (33% baseline vs. 29% resurvey), while the proportion of ex-regular drinkers slightly increased (4% vs. 6%), particularly among older men, with more than half of ex-regular drinkers stopping for health reasons. Among current regular drinkers, the proportion engaging in heavy episodic drinking  increased (30% baseline vs. 35% resurvey) in both rural (29% vs. 33%) and urban (31% vs. 36%) areas, particularly among younger men born in the 1970s (41% vs. 47%). Alcohol intake involved primarily spirits, at both baseline and resurvey. Those engaging in heavy drinking episodes tended to have multiple other health-related risk factors .Among Chinese men, the proportion of drinkers engaging in harmful drinking behaviours increased in the past decade, particularly among younger men. Harmful drinking patterns tended to cluster with other unhealthy lifestyles and health-related risk factors.The online version of this article (10.1186/s12889-019-6502-1) contains supplementary material, which is available to authorized users. Globally, alcohol is the seventh leading risk factor for poor health, accounting for 4.2% of total disability-adjusted life years and 5.2% of deaths in 2016 . Also, sWhile several cross-sectional studies in China reported on drinking patterns during 1994–2007 –15, and Longitudinal studies conducted in Western populations have shown that alcohol drinking varies with age throughout the life-course –22. HoweUsing data from the China Kadoorie Biobank (CKB) prospective cohort study, we aim to assess: 1) the temporal trends in the prevalence and patterns of alcohol drinking between the baseline survey in 2004–8 and a resurvey in 2013–14, and 2) the socio-demographic, health and other factors associated with changes in drinking prevalence and patterns over this time period.The China Kadoorie Biobank (CKB) is a nationwide prospective blood-based cohort study involving 0.5 million participants recruited from 10 geographically defined regions across China, established to investigate genetic and non-genetic causes of many common chronic diseases in the Chinese population. Details of the CKB study design and survey methods have been described previously , 25. BriDetailed information on questionnaire assessment of alcohol consumption in the CKB study has been described previously . In briev/v) typically seen in China [The amount of pure alcohol consumed in grams per session was calculated according to the beverage type and amount drunk on the last time they drank alcohol : regular smoking, lack of daily fresh fruit intake, low physical activity, and hypertension  and from participants attending the resurvey . The crude prevalence of alcohol drinking and mean consumption were calculated at each survey. Among subgroups defined by socio-demographic factors and the risk factor index, the prevalence of alcohol drinking was directly standardized to the age and region structure of the study population. As drinking patterns were assessed in weekly drinkers, the relevant variables were standardized according to the age and region structure of weekly drinkers. Due to the small number of female weekly drinkers (nbaseline = 6248 and nresurvey = 292), analyses of drinking patterns by subgroups were conducted in male weekly drinkers only .As alcohol drinking prevalence and behaviour differed significantly between men and women in CKB, all analyses were conducted separately in men and women. Temporal trends in alcohol drinking were assessed using cross-sectional data from all participants recruited at baseline (n = 9569). A similar standardization approach was applied as above to obtain the prevalence of each status change category standardised to the age and region structure of the longitudinal subset. The statistical association between factors and change in drinking status was tested using logistic regression among baseline non-drinkers and multinomial logistic regression among baseline drinkers. The reasons for stopping weekly drinking were assessed among previous weekly drinkers (i.e. ex-weekly and reduced-intake drinkers) in the resurvey (n = 1166). Crude percentages of each of the reasons reported were calculated overall, and by socio-demographics and health factors. Among men who were current weekly drinkers at both baseline and resurvey, a dependent sample t-test was used to test for the statistical significance of change in alcohol consumption at individual level over time. For a quality check of the self-reported alcohol data, associations of blood pressure with alcohol consumption were assessed using linear regression adjusted for age, study region, education, income, smoking category, physical activity and month of recruitment. Data from the CKB Data Release 10 was used in this study. All analyses were performed in SAS version 9.4.Longitudinal analyses of changes in drinking status were conducted among men who attended both surveys  was broadly representative of the baseline study population in terms of the distribution of baseline characteristics of sex, birth cohorts, area, education, and health and lifestyle factors , 41% were men and 56% were from rural areas. The subset of participants resurveyed , whereas a third of men and women drank occasionally (Table p = 0.004) , as was occasional drinking (38% vs. 26%)  was correlated with alcohol drinking, with weekly drinking prevalence, drinking frequency, mean consumption and heavy episodic drinking prevalence all increasing with the number of risk factors in men at both surveys Fig. a-d. WhenAmong 9569 men who attended both surveys, during 2004–8 and 2013–14, 40% of them continually drank and 18% did not drank alcohol on both occasions, 23% stopped drinking and 4% started drinking since baseline  showed a prevalence of past-year ever drinking of 59% in men, which is comparable to that in our resurvey, but did not report on drinking frequency or other drinking patterns [The findings on drinking prevalence in our study are broadly consistent with previous nationwide cross-sectional surveys at particular time point in China during 2002–2011 , 12, 13.patterns . With depatterns , as suggpatterns . This ispatterns  and in opatterns , and a rpatterns .Previous studies in China conducted in the 2000s reported that harmful drinking patterns peaked between the ages of 30–60 years in men , 15. TheAs in the present study, previous studies in England and Hong Kong also reported the clustering of heavy drinking with smoking, low fruit and vegetables intake and low exercise , 34, in One important step in alcohol policy development is to understand the triggers for drinking cessation in Chinese populations. Our findings that existing illness was the most reported reason for stopping weekly drinking are generally consistent with a study of 14,000 participants in China in the 1990s . HoweverAlthough not designed to be nationally representative, the diverse geographical regions, large sample size in CKB study and rich data on alcohol drinking patterns covered present a good source of data to assess recent alcohol consumption trends for Chinese adults born in 1920s–1970s. Furthermore, to our knowledge, it is the first study to explore longitudinal changes in alcohol drinking among Chinese adults. However, although the expected associations between alcohol intake and blood pressure observed in this study have provided support for the data quality, potential reporting bias of the self-reported alcohol intake data may still exist. Though older participants were slightly less likely to participate in the resurvey, the resurvey response rates were highly consistent across baseline alcohol drinking groups, suggesting that our findings on temporal trends of alcohol drinking were unlikely to be substantially biased by differential resurvey response rates. However, our results may still be partially influenced by the “sick-quitter” effect, i.e. older drinkers had quit alcohol drinking between the study period and the remaining younger heavy drinkers in the weekly drinker group drove up the mean consumption at resurvey. Furthermore, the measure of alcohol use based on “last time drinking” may have limitations subject to time variation –45. NeveIn summary, among men who drank regularly, the proportion engaging in harmful drinking behaviours has increased in China over the past decade, particularly among younger men, and heavy intake was associated with having other unhealthy lifestyles and health-related risk factors. As alcohol is a modifiable risk factor with reversible health hazards after stopping drinking in the long term, encouraging heavy drinkers to stop drinking before illness develops could have significant benefits to public health in China over the next decades.Additional file 1:Supplementary tables and figures. (DOCX 118 kb)