Telehealth has rapidly expanded since COVID-19. Veterans Health Administration (VHA), the largest integrated health care system in the United States, was well-positioned to incorporate telehealth across specialties due to existing policies and infrastructure.The objective of this study is to investigate predictors of occupational therapy (OT) practitioners’ adoption of video telehealth.This study presents data from a convenience sample of VHA occupational therapy (OT) practitioners administered pre-pandemic, in fall 2019. Survey development was guided by the Promoting Action on Research Implementation in Health Services framework, and gathered clinician attitudes, experiences, and perspectives about video telehealth to deliver OT services. Items included telehealth usage, perceived effectiveness of specific OT interventions, and perceptions about evidence. Our outcome variable denoted practitioners’ level of adoption of video telehealth: telehealth users (adopters), non-users who want to use telehealth , and non-users who do not want to use telehealth (non-adopters). In multiple multinomial logistic regressions, we tested whether level of adoption was associated with years of VHA work experience and perceived strength of evidence.Of approximately 1455 eligible practitioners, 305 VHA occupational therapy practitioners participated in the survey (21% response rate). One hundred and twenty-five (41%) reported using video telehealth, whereas 180 (59%) reported not using video telehealth. Among non-users, 107 (59%) indicated willingness to adopt telehealth whereas 73 (41%) were not willing. More VHA work experience predicted higher odds of being an adopter than a potential adopter; perceptions of stronger evidence regarding video telehealth predicted higher odds of being a potential adopter than a non-adopter.Clinician beliefs and years of experience exerted an influence on clinicians’ use or willingness to use video telehealth. Efforts to enhance adoption of video telehealth should address clinicians’ beliefs regarding the innovative nature of and organizational resources necessary to foster utilization. Video telehealth is a synchronous, live appointment in which patient and clinician are in different locations. Most video telehealth at VHA has historically been between medical center locations , enabled by a telehealth infrastructure which includes facility telehealth staff and equipment across VHA’s broad network of medical centers and community-based outpatient clinics 2. In 2018, prior to the pandemic and ensuing public health emergency, VHA set ambitious benchmarks to expand video telehealth into the homes of veterans to increase access to care through the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks Act of 2018 (MISSION Act) 3. The MISSION Act, which aimed to strengthen VA health care by creating more care options, broadened telehealth services, beginning with primary care and expanding to include all clinical services, such as specialty care. Occupational therapy (OT), which enables individuals across the lifespan to participate in valued daily activities 4, is a specialty care service at VHA. Most VHA OT practitioners are situated at urban medical centers. Thus, in-home video telehealth has the potential to expand the reach of OT services to address the health and wellness of veterans, a population which is aging and has complex care needs 5.Veterans Health Administration (VHA), the largest integrated health care system in the United States 6, particularly for underserved groups, and a regulatory environment which comprises a patchwork of state-level guidelines 7. Individual and system-level factors also influence implementation of telehealth. At the level of the clinician, negative beliefs, such as perceiving telehealth as a threat to one’s role, can influence implementation 8, and a lack of education and training in telehealth may also limit implementation 10. Regarding system-level factors, organizational support for telehealth is a critical factor and can be an effective tool of recruitment and retention of high-quality clinical staff 11. Such investment could be burdensome for smaller facilities 12, underscoring the importance of examples from larger health care systems such as VHA to highlight organizational efficiencies and gaps in implementation.There are several barriers to broadscale implementation of video telehealth in the United States, including gaps in accessible technology 13. PARIHS concepts explicitly guided development of a survey from which these data are derived.The objective of this study is to investigate factors influencing integration of video telehealth at the largest integrated health care system in the United States. In so doing, this study aims to offer guidance for implementation with identified factors to optimize integration of video telehealth. This study is informed by the Promoting Action on Research Implementation in Health Services (PARIHS), which recognizes that diverse contextual factors, including clinician perspectives, inform the systematic integration of interventions into practice 1.114. PARIHS was developed to support the systematic integration of research findings and other evidence-based practice (EBP) innovations into clinical care to enhance the quality and efficacy of health services 13. As a widely utilized framework, PARIHS posits that successful implementation of EBP is a function of three central and interrelated elements: evidence, context, and facilitation. The core element of evidence encompasses multiple sources of information related to the innovation, including published research or guidelines, clinical experience, and patient experience. Socially constructed, evidence includes stakeholder perceptions about the nature and quality of various evidence sources and personal beliefs. Research refers to results of studies utilizing quantitative, qualitative, or mixed methods designs, and published guidelines or recommendations. Clinical experience refers to stakeholders’ experience with the EBP, and respective attitudes, beliefs, history with and motivation toward change. Clinical experience, according to PARIHS, is not necessarily based on available objective evidence but may be tacit or highly subjective. Patient experience includes patient lived experiences, needs, and preferences; it may include exposure to the EBP or related care, personal knowledge, and preferences, which like clinical experience, may not reflect objective research evidence alone. For an overview of PARIHS elements, see The Promoting Action on Research Implementation in Health Services (PARIHS) is a theoretical framework that guides implementation of evidence-based clinical interventions Successful Implementation Tool , an instrument for use by researchers to assist them with operationalizing concepts of the PARIHS framework for implementation studies 18, to inform development of survey questions related to clinician perceptions of the evidence base for video telehealth and to investigate the relationship between beliefs and implementation.To identify factors influencing implementation of video telehealth at VHA, we utilized PARIHS elements, specifically the evidence element and related sub-elements, research, clinical experience, and patient experience, to develop a survey. Specifically, we utilized the Tool See , a guide2This study presents data from a national survey of VHA occupational therapy (OT) practitioners about their use of and beliefs towards video telehealth. We examine (1) adoption of video telehealth to deliver OT services, (2) variation in use of video telehealth by clinician demographics, and (3) awareness of evidence  regarding use of video telehealth to deliver OT services. Increased knowledge of contextual factors potentially influencing clinicians’ adoption of video telehealth may enhance broadscale integration of video telehealth across health care systems.2.1Development of the 23-item survey was guided by PARIHS elements, particularly clinician attitudes, experience, and perspectives. The survey was developed in a multi-pronged, iterative and collaborative process involving a literature review and input from five VHA subject matter experts with expertise in telehealth, occupational therapy, and geriatrics, including a geriatrics-trained occupational therapist, a behavioral neurologist with extensive experience in geriatrics and telehealth, and VHA’s national Occupational Therapy Discipline Lead. Survey respondents were asked whether they agreed to participate (1 item) and if so, whether they used video telehealth (1 item). If not, respondents were asked whether they would like to use it (1 item). Respondents were then presented a table with a list of thirteen occupational therapy interventions and asked to rate on a four-point Likert scale the perceived effectiveness of (a) in-person service delivery (1 item) of each intervention, and, for those answering yes to using video telehealth, perceived effectiveness of (b) video telehealth delivery (1 item). This question reflected the broad range of occupational therapy interventions delivered at VHA, such as home safety evaluations and supporting participation in instrumental activities of daily living such as medication management. All respondents, regardless of use of video telehealth, rated their comfort level  with video telehealth to deliver each of the thirteen OT interventions (1 item). For a complete list of OT interventions, see 18, per above. The statements were modified to focus on VHA’s expansion of video telehealth, and asked respondents to rate on a four-point Likert scale agreement with statements related to the evidence for video telehealth. In alignment with PARIHS, statements broadly conceptualized evidence into three main domains: research, clinical experience, and patient experience. Finally, the survey included demographic questions (7 items), including clinician facility, role, years of practice at VHA, years of practice as an occupational therapy practitioner, education, ethnicity, sex, and race. For a complete list of evidence statements and survey items, see The survey also included ten evidence statements (10 items) adapted from the Organizational Readiness for Change Assessment (ORCA) 19. VHA occupational therapy practitioners on VHA’s OT email listservs received an email including the study description and an anonymous URL link to the online survey only available on the VA intranet or via posting to VHA’s online forum for OTs. The link was kept open for four weeks, with three reminder emails and online forum posts sent prior to the survey closing. Emails specified that participation was voluntary, anonymous, and confidential. Complete study details are reported elsewhere 20.The survey was administered in September and October of 2019 via Research Electronic Data Capture (REDCap), a secure, web-based application designed to support data capture for research studies 2.2This study includes a convenience sample of occupational therapy (OT) practitioners  employed at Veterans Health Administration (VHA).2.3Rurality geo-coding, developed by the Veterans Health Administration Office of Rural Health to estimate the percentage rurality of the catchment area served by each VA Medical Center (VAMC), was applied to respondents’ primary VAMC.2.421. Group 1 included respondents not currently using video telehealth who indicated not wanting to use it, a group we term “non-adopters.” Group 2 included respondents not currently using video telehealth but who indicated wanting to use it, a group we term “potential adopters.” Group 3 includes respondents currently using video telehealth, a group we termed “adopters.”Using data from two survey items, use of video telehealth and desire to use video telehealth, we created three groups delinated by stage of adoption of video telehealth, as informed by the Diffusion of Innovation model 2.5VHA tenure, or years of organizational experience, was an ordinal variable in years . Perceived strength of evidence supporting video telehealth for older adults was measured by summing responses  to 10 statements on evidence, for a total possible range of 10 to 50. Higher scores represent greater endorsement that video telehealth is evidence-based.2.6st, 2nd, 3rd, and 4th quartile, with higher quartiles indicating greater rurality. Ethnicity was specified as a categorical variable . Practitioners’ highest level of education was an ordinal variable . Perceived effectiveness of in-person delivery with older Veterans was a categorical variable ; comfort with using video telehealth with Veterans of all ages was a categorical variable .Each model controlled for education, ethnicity, facility’s rurality level, comfort with video telehealth for older adults, and perceived effectiveness of in-person delivery. The level of rurality of respondents’ primary VAMC was a continous variable which was measured in the following categories: 12.7--were specific to one of the 13 OT interventions. For example, the regression we term “ADL model” had these variables: tenure, perceived strength of evidence, comfort with ADL intervention delivered over video telehealth, perceived effectiveness of ADL intervention delivered in-person, and other control variables. This analytic approach was designed to provide insights into whether perceptions about specific OT interventions influenced adoption of video telehealth, i.e., whether respondents perceived that certain OT interventions translated better than others to video telehealth . All analyses were performed using Stata. Analysis for research purposes was approved by the VA Bedford Health Care System Institutional Review Board (IRB).We performed descriptive, bivariate, and multivariate analyses. We examined descriptive differences between the groups. We then assessed bivariate correlations among all our variables. Using multinomial logistic regressions, we modeled the outcome  on tenure and perceived strength of evidence and all control variables. We performed 13 multinomial logistic regressions. In each regression, two control variables-- comfort with telehealth and perceived effectiveness of in-person delivery322.The study sample represents a 20% response rate, with 305 of approximately 1455 eligible VHA occupational therapy practitioners participating in the survey. More than half  reported not using video telehealth and were asked whether they would use it. Of those, under half  indicated not wanting to use video telehealth. The remaining  indicated willingness to adopt telehealth. In predicting non-adopter status, evidence beliefs were consistently a significant variable in all 13 intervention models. Greater perceived strength of evidence was significantly associated with lower likelihood of being a non-adopter compared to potential adopters. For example, in the Leisure intervention model, one additional unit of strength of evidence was associated with 15% lower odds of being a non-adopter. VHA tenure was significant in three models  but was not significant in 10 of 13 of the remaining intervention models predicting non-adopters. For example, for Education and Work, more years of VHA practice experience was associated with 37% lower odds of being a non-adopter.When predicting adopters , in nine of 13 models, VHA tenure was significant in predicting adopter status. (Strength of evidence beliefs was not a significant variable predicting odds of being an adopter.) For example, in the Instrumental Activities of Daily Living (IADL) intervention model, more years of VHA experience were associated with 42% higher odds of being an adopter.425. Comprehensive evaluation results in development of a plan of care which often includes multiple sessions over time to accomplish the clients’ identified goals. The higher the level of impairment, adhering to the recommended plan of care may present a burden to many clients and their families. Thus, some clients may not continue treatment, due to difficulty getting to the medical center. This burden can be mitigated by video telehealth. Given the importance of video telehealth to address access issues, it is important to understand potential barriers and facilitators to intentionally promote expanded utilization.Occupational therapy (OT) practitioners work with clients across the lifespan (womb to tomb) with a variety of challenges that interfere with their ability to successfully participate in day-to-day functional activities. OT practitioners are an integral part of the health care team, providing a comprehensive evaluation that assesses multiple domains, including how physical, cognitive, sensory, emotional, and social functioning affect what clients need and want to do on a daily basis This study examined findings related to use of video telehealth from a convenience sample of occupational therapy practitioners at the largest integrated health care system in the United States. At the time of the survey , most respondents were not utilizing video telehealth. We were able to gather insights from the majority of those not using video about their potential use of video. By exploring perspectives of VHA OT practitioners at a time when their utilization of video telehealth was generally low, this study offers a lens into the factors driving early adoption and potential adoption of video telehealth removed from the exigent nature of the global COVID pandemic, which limited access to traditional forms of care and thereby forced utilization of telehealth strategies by clinicians who may not otherwise have employed the approach. As such, this study offers a critical window into potential ways to ensure sustained utilization of video telehealth  in tandem with in-person options as the health care context continues to evolve in response to the pandemic.Regarding predicting adoption status of VHA occupational therapy practitioner survey respondents, evidence beliefs did not predict adoption compared to potential adoption of video telehealth. Thus, OTs’ willingness to use video telehealth before it was mandated by the MISSION Act and before the COVID pandemic was not driven by beliefs about the strength of the evidence. Data from our related study highlighted facilitators to early adoption of video telehealth to be practitioners’ positive beliefs about video telehealth versus organizational factors like supervisor support. This suggests that intrinsic motivational factors may contribute more to the decision to use video telehealth than extrinsic factors like organizational pressures. Individual decisions to adopt a novel intervention appear to be complex and require further investigation.26. Thus, the relationship may be both bottom up (driven by clinician factors) and top down . This is a reminder that telehealth programs are influenced by individual-level factors and a broader organizational context 27. Further studies should explicate the interplay of these factors using qualitative means.We also found that more years of VHA practice experience increases the likelihood of OT practitioners adopting telehealth as a service delivery option. More years of practice may imply greater job knowledge, e.g., confidence in skills or increased flexibility in applying skills in new ways, or increased job security. This suggests that the longer one is at an organization, there may be more opportunities for innovative practice. It might also reflect an alignment between the individual employee and the organization’s strategic priorities and values. For example, organizational factors at VHA that encourage innovation include its infrastructure of clinical research, including funded centers of excellence and a stable of clinician scientists 28.Regarding the relationship between perceived strength of evidence and adoption of video telehealth, greater belief in the evidence was associated with a lower likelihood of saying ‘no’ when most OT practitioners not using video telehealth were asked, “would you use it?” As a reminder, survey evidence statements, as modified from the ORCA, broadly conceptualized evidence to include results from scientific studies , clinical practice experience of self and other clinicians, beliefs about the extent to which video telehealth occupational therapy service delivery aligns with OT practice more generally, and perceived patient beliefs about video telehealth  reportedly using virtual visits, compared to less than 5% prior to the pandemic33. Similarly, a survey by the World Federation of Occupational Therapists during COVID found an increase in telehealth OT service delivery across the world, with reported benefits including positive employee morale and safety34.This also highlights the need for organizations to continually monitor and assess implementation of video telehealth by gathering the perspectives of clinician users to ensure that it continues to be perceived as beneficial. COVID restrictions on traditional care resulted in a rapid uptake of video telehealth, with clinicians quickly pivoting to integrate telehealth often in the absence of training or established infrastructure. Occupational therapy has seen a rapid growth in telehealth since the pandemic, with OT practitioners using telehealth to provide a range of services, including home health OTs using telehealth to treat homebound clients37. There were also population-specific barriers for older adults and individuals from rural areas. Barriers to older adult engagement with video telehealth include factors such as lower technical literacy, decreased hearing and sensory processing, and cognitive impairment40. Regarding rural clients, limitations in resources such as broadband, staffing, and needed technology limit telehealth 42, in addition to client cultural factors, including resistance to transitioning traditional forms of treatment to telehealth 43. It is not surprising, then, that clients receiving telerehabilitation may be younger and reside in urban settings 44. Lastly, while the importance of caregivers to facilitate telerehabilitation is frequently discussed as a facilitator to telehealth, particularly in regards to older adults and those with cognitive impairment 47, caregiver willingness to participate in telehealth and their support needs are not well-understood 48. These are areas in need of further study.However, there were challenges to implementation of telehealth which add meaningful context to long-term implementation. Challenges were rife, including difficulty with the technology  as well as with the virtual visit workflow, e.g., patients waiting in virtual waiting rooms55, there is a need to develop and disseminate best practices in adapting complex clinical services to telehealth. Challenges unique to OT include the need for adequate visualization of the client and the environment for assessment and to ensure client safety, especially when clinical care requires moving throughout the home, and the need to compensate for the lack of hands-on care. Gaps in available training about how to adapt OT clinical services to telehealth was a primary reported barrier of OT practitioners who utilized telehealth in response to COVID restrictions 56. Further research is also needed identify the best match between specific client capacities, the goals of therapeutic intervention, and technology. Further, more outcome studies specific to OT  are critical to advance current knowledge 57.In the current context, the re-opening of health care settings has also prompted a need to identify the ideal mix of telehealth and traditional care going forward; that is, for whom and for what types of care telehealth is suggested. Given the complexity of services which occupational therapy practitioners provide, which include home safety evaluations, training in use of adaptive equipment, provision and evaluation of wheelchairs, group treatment for clients with mental health and other diagnoses, cognitive and low vision rehabilitation, caregiver education and training, and interventions to support sleep, driving, and community mobility To the extent that practitioner beliefs drive implementation of video telehealth, it is necessary that health care settings ensure use of telehealth aligns with individual clinician groups’ standards of practice and core beliefs. Concurrently, there is a need for more published evidence related to OT delivery via telehealth in the areas outlined above to support continued expansion of telehealth opportunities and optimize quality of service delivery. Rapid uptake of telehealth OT in response to COVID has resulted in increased research evidence which may in turn promote further uptake.5There were several limitations to this study. This study included a convenience cohort sample of Veterans Health Administration (VHA) employees, which limits generalizability of findings. While the sample characteristics were similar to occupational therapy practitioners as described by the American Occupational Therapy Association, Veterans Health Administration is a setting which has unique characteristics such as established telehealth infrastructure. As such, findings may not be applicable to non-VA settings. We also did not gather practitioner age, which would have enhanced our understanding of the impact of VHA tenure. Non-respondent bias may limit generalizability, as respondents with strong beliefs may have been more likely to participate in the survey. We also did not include the opportunity to follow up with respondents.6Overall, this study found that clinicians beliefs and years of practice experience exerted an influence on clinicians’ decision about whether to use video telehealth. Clinicians with stronger beliefs in the evidence and more years of VHA experience were more disposed to opt for video telehealth as a health care delivery option. This aligns with prior work indicating the primacy of clinician attitudes in predicting utilization of health care innovations. Continual assessment of clinician attitudes, and recognition of the importance in workforce training strategies may be indicated to ensure successful, sustained integration of telehealth. More studies are needed to explore the relationship between person-level factors such as age and organizational factors such as availability of technology on implementation of video telehealth.Video telehealth is clearly here to stay, given that forced utilization of telehealth during the COVID pandemic demonstrated its viability as a strategy to address access challenges for diverse populations. Veterans Health Administration, which was well-positioned to integrate telehealth given its existing infrastructure and policy initiatives, is an ideal setting from which to draw lessons learned regarding sustained integration of telehealth. As health care systems move towards developing best practices in telehealth, the perspectives of stakeholders including front-line clinicians must be understood. Given the importance of clinician attitudes in willingness to use of video telehealth, it is critical that health care systems explicate how both objective and subjective data influence that decision-making process. Understanding how beliefs inform utilization will ensure availability of telehealth as a high-quality option for patient care.Appendix A. Survey


RFID) system (grid of antennas underneath the pen) and video tracking for the determination of location and movement of 3 color-marked broilers at d 18. Furthermore, a systems comparison of derived behavioral metrics such as space usage, locomotion activity and apparent feeding and drinking behavior was made. Color-marked broilers simplified the computer vision task for YOLOv5 to detect, track, and identify the animals. Animal locations derived from the RFID-system and based on video were largely in agreement. Most location differences (77.5%) were within the mean radius of the antennas’ enclosing circle , and 95.3% of the differences were within a one antenna difference . Animal movement was not always registered by the RFID-system whereas video was sensitive to detection noise and the animal's behavior . The method used to determine location and the systems’ sensitivities to movement led to differences in behavioral metrics. Behavioral metrics derived from video are likely more accurate than RFID-system derived behavioral metrics. However, at present, only the RFID-system can provide individual identification for non-color marked broilers. A combination of verifiable and detailed video with the unique identification of RFID could make it possible to identify, describe, and quantify a wide range of individual broiler behaviors.Phenotypes on individual animals are required for breeding programs to be able to select for traits. However, phenotyping individual animals can be difficult and time-consuming, especially for traits related to health, welfare, and performance. Individual broiler behavior could serve as a proxy for these traits when recorded automatically and reliably on many animals. Sensors could record individual broiler behavior, yet different sensors can differ in their assessment. In this study a comparison was made between a passive radio frequency identification ( Breeding programmes require phenotypes on individual animals to be able to select for traits. However, not every novel trait for the breeding goal is easy to measure. The assessment of traits related to health, welfare, and performance can be time-consuming, difficult, or both. Alternatively, individual broiler behavior could serve as a proxy for an individual's health, welfare, and performance . FurtherIMUs; RFID; UWB; Several sensors for recording individual broiler behavior, both wearable and remote, have been introduced over the years. Wearable sensors include, for example, accelerometers , inertiaBoth wearable and remote sensors could provide individual broiler behavioral data. The sensors may be used to identify animals, determine their location, detect activity, provide detailed behavioral data, or to provide a combination of these features. In turn, behavioral metrics could be derived from these features . In broilers, locomotion activity has been indicated as a potential proxy for an individual's health, welfare, and performance  and may The objective of this study is to compare RFID and video for the determination of location and movement in broilers. We show the potential of RFID and video to assess individual behavioral metrics, such as space usage, locomotion activity and apparent feeding and drinking behavior, derived from both systems under the same circumstances. To show the potential of video, and forgo previously mentioned challenges regarding detecting, identifying, and tracking, we use color-marked broilers to simplify the computer vision task.The experimental data used in this study have previously been described in the research of A passive RFID-system  was used to track individual broilers. Each broiler was fitted with a high-frequency RFID-tag  around its leg, attached using a rubber band and a piece of tape . The RFIAn RGB-camera placed above the pen recorded the birds from 10:44 am to 12:59 pm at 18 d of age. The 2-h period was recorded in a series of 20 videos of approximately 7 minutes each. The videos were recorded in full-HD (1920 × 1080 px) at 25 frames per second with a Zavio B6210 2MP camera . Video was used for human annotation of ‘ground-truth’ to train the computer vision detection model described below.Five-hundred frames were randomly selected from all extracted video frames for ‘ground-truth’ annotation of the 3 color-marked birds. The selected frames were randomly divided between 2 human annotators (annotator A and B). The Computer Vision Annotation Tool CVAT;  was usedIOU), calculated by dividing the area of overlap (intersection) with the area of the union of the respective bounding boxes IOU and 0.95 (IQR = 0.07) for annotator A and B, respectively, and the inter-annotator reliability was 0.92 (IQR = 0.08), expressed in median IOU.The quality of annotation and agreement between annotations were considered sufficient to continue model training. Intra-annotator reliabilities were 0.95  were calculated as the Euclidean distance in meters between center points of the recorded antennas between consecutive locations in time .(2)d=∥x−Where x and y are consecutive locations over time.COCO) dataset , false positives (FP) and false negatives (FN) between predictions and ground-truth annotations. Whether a prediction was a TP, FP or FN depended on the confidence score, classification, and IOU with the ground-truth annotation. A prediction was a TP if the confidence score was greater than or equal to the confidence threshold, had a correct classification, and the IOU was greater than or equal to the IOU-threshold. A FP was a prediction with a confidence score greater than or equal to the confidence threshold, but with either an IOU greater or equal to the IOU-threshold but with an incorrect classification (wrong class), or an IOU less than the IOU-threshold . A FN was a broiler that was not matched with a prediction, that is, when there was no prediction of the right class and with the IOU greater or equal to the IOU-threshold.Different metrics were used to evaluate YOLOv5s’ ability to detect color-marked broilers on video. The different evaluation metrics detailed below were based on the number of true positives AP=1NWhere mAP; The mean average precision mAP;  is the Aith class and C is the total number of classes (C = 3).Where Precision and recall are reported for the confidence threshold that maximized F1  and an IAll metrics have a value between 0 and 1 in which 1 is perfect.A comparison between the RFID-system derived and video-based animal locations was made to assess the localization agreement between both systems. This comparison was performed using two different approaches, one based on Euclidean distances and the other on total time spent per antenna differences. These two approaches help to deduce the difference between both systems in determining animal's location and movement over time.In the first approach, differences in RFID-system derived and video-based animal locations were assessed with the Euclidean distance. The Euclidean distances between corresponding locations in time from both systems were calculated using In the second approach, differences in RFID-system derived and video-based animal locations were assessed with regards to total time spent per antenna in seconds. To make a more direct comparison between RFID-system derived and video-based animal locations, video locations were made to mimic the RFID-system. The center point coordinates of the detected bounding boxes by YOLOv5s were translated to RFID-antennas . In case the center point of the bounding box was outside the predefined antenna grid, which happened when the birds were close to the wall of the pen, the detection was considered missing. Thus, the birds were assumed to have remained at the last detected location.Behavioral metrics were assessed to show the potential of an RFID-system and video to assess individual behavioral metrics. In this study, the behavioral metrics space usage, locomotion activity and apparent feeding and drinking behavior were measured and compared between both systems. The defective antenna had a minimal effect on locomotion activity and other behavioral metrics Appendix  and A-2.For both systems, space usage was defined as the total time spent per unique antenna in seconds. In this case, video-as-RFID was used to generate an antenna grid based heatmap. Furthermore, the number of unique visited antennas and the total number of switches between antennas was calculated. Space usage has overlap with the localization agreement because similar locations will result in similar approximations of space usage.Locomotion activity, expressed as distance moved was calculated for the RFID-system as the sum of the Euclidean distances between the center points of the recorded antennas of the RFID-grid in meters. For the evaluation of the camera-based system, it was calculated as the sum of the converted Euclidean distances between the center point coordinates of the bounding boxes detected by YOLOv5s in meters (pixel-based). The pixel-based total distance moved was calculated for 3 different minimum movement thresholds  and drinker (antenna D1). Feeding and drinking behavior were recorded as the total number of visits and the total duration of those visits in seconds. Feeding and drinking behavior were apparent because an animal's presence on a feeder or drinker associated RFID-antenna does not equate actual feeding or drinking behavior.The apparent feeding behavior on video was determined as pixel-based and as video-as-RFID. To determine pixel-based apparent feeding behavior, the presence of the animal's bounding box center point within a circular sector  occurred within the mean radius of the antennas’ enclosing circle , and 95.3% of the differences were within a one antenna difference . It is important to note that these differences can originate from both systems and relate to differences in location determination and movement detection.The location reference point to determine the location of the animal differed between the two systems. The RFID-system derived animal location was based on the position of the RFID-tag attached to one of the broilers’ legs, whereas the video-based location was based on the detected bounding box's center point. As a result, the location reference point of the two systems could be located at different antennas in some cases . In otheThe presence on or movement over an antenna for the broilers was not always registered by the RFID-system. On video, movement to antennas non-adjacent to the previously recorded antenna was observed only for the light blue color-marked broiler on video. Consecutive RFID-readings or detections on video-as-RFID could comprise non-adjacent antennas, and differences larger than two antennas could be observed, as well.The presence on or movement over an antenna for the light blue color-marked broiler was not always registered on video. In this study, the detection on video could switch between the light blue and dark blue color-marked broilers. Two consecutive nonoverlapping detections indicated a possible switch between the animals, which was corrected by assuming that the animal remained at its last known location. Three consecutive nonoverlapping detections could be potential movement, which was not corrected. This correction strategy worked for short switches, but not for longer switches. The light blue color-marked broiler was still observed to move to nonadjacent antennas, which may be attributed to non-corrected detection switches between the light blue and dark blue color-marked broiler. In this study, filtering and thresholding were kept to a minimum with the assumption that the animal remained at its last known location in case of missing detections on video or missing reads by the RFID-system. The reduction of video detections from 25 frames per seconds to 1 frame per second inadvertently applied a filter that reduced the number of switches between objects. Unreduced detections showed switches between some color-marked birds , as well as the black color-marked broiler and the black top of the drinker. The occurrence of these switches highlights the importance of a good detection model, especially when moving to higher frame rates. The animal's behavior, changes in posture while lying down, and detection noise all influenced where the detected bounding box's center was located, and consequently, video is sensitive to movement of the bounding box's center point which might not necessarily be movement of the animal as such .Figure ADifferences between the systems in the recorded values of the behavioral metrics stem from the way that location is determined and the sensitivity of the system to changes in location. The systems’ agreement on animal location indicates that both systems should report similar approximations of space usage and apparent feeding and drinking behavior. However, the ability of a system to detect movement is critical, especially when concerned with locomotion activity. A system's method of location determination as well as its sensitivity to changes in location are related and will thus be discussed in parallel when discussing the behavioral metrics.The RFID-system and video both showed a similar general area pattern in which the animal spent the most time. However, a general area pattern does not suffice for the determination of apparent feeding and drinking behavior, where the time spent and number of visits to a specific area are important. The definition of that specific area plays an important role. In this study, the feeder areas were defined by the RFID-antenna or a circular sector, and the drinker by the RFID-antenna. An animal's presence on a feeder or drinker associated RFID-antenna does not equate actual feeding or drinking behavior, which is why we define it as apparent feeding and drinking behavior. Apparent feeding and drinking behavior could be used as a proxy for actual feeding or drinking behavior. Actual feeding behavior is more uncertain than drinking behavior given the ratio of the RFID-antenna area to the area in which the animal can reach the feed.Broiler movement on or off an antenna was not always registered by the RFID-system, and with the assumption that the animal remained at its last known location this could result in missed visits and overestimated or underestimated time-spent on antennas . The moiLikewise, differences in locomotion activity between the two systems could be explained by similar mechanisms. The RFID-system did not always register broiler movement on or off an antenna which could result in movement being missed, whereas video-based animal locations were sensitive to small movements of the detected bounding box's center point. Moreover, movement could only be registered between antennas for the RFID-system, whereas video could register movement within and between antennas. The effect of the small movements of the bounding box's center point was reduced with a minimum movement threshold. However, there was still a difference in locomotion activity within-broilers between RFID and video pixel-based. This locomotion activity difference is potentially movement within antennas but revealed a likely overestimation of movement by RFID for the black color-marked broiler .Overall, behavioral metrics from video are most probably closer to the true behavioral metrics than those from the RFID-system. The RFID-system and video gave similar indications of space usage, and similar animal rankings for locomotion activity. However, video provides more detail than the RFID-system and had more pronounced differences in locomotion activity. In this experiment, video seemed better suited than the RFID-system for determining apparent feeding and drinking behavior than the RFID-system.Broiler location, movement, and behavioral metrics are likely best derived from video. The RFID-system gives good approximations of broiler location, movement, and behavioral metrics, except for apparent feeding and drinking behavior, but has limitations to the level of detail it can provide. Nevertheless, at present, only the RFID-system can provide individual identification for non-color marked broilers. Potentially, a sensor-fusion approach can combine the strengths of video and an RFID-system  in a comThe details in a video make it possible to identify, describe, and quantify a wide range of behaviors. The potentially wide range of recorded behaviors of specific broilers and its conspecifics offers the opportunity to place the behavior into context. Individual broiler behavioral data presents opportunities to improve the precision of breeding, feeding, and management . Often nIn this study a comparison was made between RFID and video for the determination of location and movement in broilers. The potential of both systems to assess individual behavioral metrics, such as space usage, locomotion activity and apparent feeding and drinking behavior, was shown. Animal locations derived from the RFID-system and based on video were largely in agreement. Most animal location differences (77.5%) were within the mean radius of the antennas’ enclosing circle , and 95.3% of the differences were within a one antenna difference . Animal movement was not always registered by the RFID-system whereas video was sensitive to detection noise and the animal's behavior . The method used to determine location and the systems’ sensitivities to movement led to differences between the obtained behavioral metrics. The RFID-system gives good approximations of broiler location, movement and behavioral metrics, with the exception of apparent feeding and drinking behavior, but has limitations in the level of detail it can provide. Broiler location, movement, and behavioral metrics are likely best derived from video. Currently, only the RFID-system can provide individual identification for non-color marked broilers. Potentially, a combination of verifiable and detailed video with the unique identification of RFID will make it possible to identify, describe, and quantify a wide range of individual broiler behaviors. Individual broiler behavioral data presents opportunities to improve the precision of breeding, feeding, and management.


Atrial fibrillation (AF) is the most common arrhythmia. Hybrid convergent ablation (HCA) is an emerging procedure for treating longstanding AF with promising results. HCA consists of a subxiphoid, surgical ablation followed by completion endocardial ablation. This meta-analysis of randomized control trials (RCT’s) and propensity score-matched studies aims to examine the efficacy and safety of HCA compared to endocardial catheter ablation (ECA) alone on patients with AF.This review was written in accordance with preferred reporting items for systematic reviews and meta-analyses recommendations and guidance. The primary outcome for the analysis was freedom from AF (FFAF) at final follow up. Secondary outcomes were mortality and significant complications such as tamponade, sternotomy, esophageal injury, atrio-esophageal fistulae post procedurally.2 = 0). Major post-operative complications were observed in significantly more patients in the HCA group, with an OR of 5.14 . There was only one death reported in the HCA cohorts, with no deaths in the ECA cohort.Four studies where included, with a total of 233 patients undergoing HCA and 189 patients undergoing ECA only. Pooled analysis demonstrated that HCA cohorts had significantly higher rates of FFAF than ECA cohorts, with an OR of 2.78 (95% CI 1.82–4.24, P < 0.01, IHCA is associated with a significantly higher FFAF than ECA, however, it is associated with increased post-procedural complications. There was only one death in the HCA cohort. Large RCT’s comparing the HCA and ECA techniques may further validate these results.The online version contains supplementary material available at 10.1186/s13019-022-01930-7. Atrial fibrillation (AF) is the most common cardiac arrythmia, affecting approximately 33 million people worldwide . Only 3%The success rate of a surgical approach is superior to that of ECA in persistent AF . The advThe primary aim of this study is to assess the freedom from AF in a HCA cohort compared to an ECA cohort alone. The secondary aim of this study is to assess the incidence of significant complications such as tamponade, sternotomy, esophageal injury, atrio-esophageal fistulae post procedurally and post procedural mortality. In order to account for bias associated with retrospective studies, we included randomized control trials (RCT) and propensity score-matched (PSM) analyses only.This review was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) recommendations and guidance . Four elStudies were eligible for inclusion for this systematic review if (1) they compared HCA procedures to ECA only, (2) they reported freedom from AF, (3) were a RCT or PSM analysis (4), and were published in English. HCA was defined as a subxiphoid ablation followed by a catheter-based ablation either in the same sitting or staged sitting. Studies were excluded if patients underwent concurrent cardiac surgery, as well as conference abstracts, case reports, editorials, reviews and expert opinions. Article identification and inclusion were performed independently by two authors (AE and ARWS) and discussed until consensus was reached. A third author (CDF) resolved any conflicts. Study quality was subsequently assessed utilising the Delphi Criteria .The primary outcome for the analysis was freedom from AF (FFAF) at final follow-up. This was defined as per the study methodology (see above). Secondary outcomes were significant complications post-procedurally. These include, but were not limited to, death, tamponade, emergency sternotomy, pericardial hernia, phrenic nerve palsy, stroke and esophageal injury. Baseline variables including age, gender, pre-procedural duration of AF, left ventricular ejection fraction (LVEF), left atrial (LA) size were aggregated. An ad-hoc analysis was conducted and extraction was performed with analysis of all texts, figures, tables, and supplementary data. This was performed independently by two authors (AE and ARWS) until consensus was reached for final data.2 test statistic. Low heterogeneity was denoted by I2 < 50%, moderate heterogeneity by I2 50–74%, and high heterogeneity by I2 ≥ 75%. Publication bias was further assessed with an influential study analysis using the leave one out method. P vales less than 0.05 were deemed significant. Kaplan–Meier survival curves were digitized where presented and an algorithmic computational tool was utilized to derive individual patient data as outlined by Guyot et al. [For baseline variables, nominal data was recorded as the number of events (n) and expressed as a percentage. Continuous variables were expressed as mean and standard deviation, or median and interquartile ranges. Baseline patient data were aggregated. Medians and interquartile ranges were first converted to mean and standard deviation utilizing the method outlined by Hozo et al. . Signifit et al. . Event aOur search produced a total of 812 articles, of which 349 duplicates were removed. 463 abstracts were screened and 18 studies were selected for full text review. Four studies were selected for inclusion –17. The All four studies utilized a subxiphoid transdiaphragmatic approach in the HCA arm. In terms of lesion sets, all four studies conducted parallel/linear lesions on the posterior left atrial wall, followed by pulmonary vein isolation in the catheter arm. All four studies also utilised additional endocardial ablation lines, and utilised electroanatomic mapping to guide lesion sets. Three studies –16 definA total of 233 patients underwent HCA and 189 patients underwent ECA for AF. The mean age for the HCA and ECA cohorts were comparable, at 63.7 years and 63.4 years respectively (P = 0.77). There were more males in the HCA cohort (185 vs. 127 P = 0.045). There were no differences in BMI between the cohorts, with a mean BMI of 33.3 for the HCA group and 33.7 for the ECA group (P = 1.00). The mean preoperative EF was 54.9% for the HCA group and 54.6% for the ECA group (P = 0.97). Left atrial size were also comparable, with a mean diameter of 45.9 mm in the HCA group and 46.1 mm in the ECA group (P = 0.70) (Table 2 = 0). All four studies reported a significantly greater freedom from AF in the HCA cohort. Three studies reported freedom from AF off anti-arrhythmic drug therapy (AAD) with 50% and 26% in the HCA and ECA cohorts, respectively. The OR was 2.75    was 2.78    curves, however, only three studies provided numbers at risk and therefore only these were included in the aggregate survival analysis. Actuarial survival (FFAF) at intervals of 6, 12, 18, 24 and 30 months were 87%, 85%, 83%, 83% and 83% for the HCA cohort, compared to 70%, 61%, 57%, 50%, 50% for the ECA cohort  for the primary outcome of interest.This systematic review and meta-analysis of RCT’s and PSM studies aims to compare the efficacy of HCA to ECA. The inclusion of unmatched retrospective analyses into meta-analysis adds a significant degree of heterogeneity. Firstly, these studies have inherent patient related bias. Their retrospective design means inability to control for baseline variables that impact procedural efficacy, principally, number of previous ablations, LA dimensions, BMI, duration of AF and use of AAD –22. SecoThe FFAF in the HCA cohort was significantly higher than the ECA cohort and is higher than the FFAF in previously published meta-analyses. Mhanna et al. reported a FFAF of 70% with an OR of 1.48  favoring HCA . The maiThree studies reported FFAF off AAD. Subgroup analysis suggests that FFAF was 50% in the HCA cohort compared to 26% in the ECA cohort. Accordingly, reintervention rates were also low with HCA cohorts less likely to require subsequent DCCV. Some studies also suggest that HCA cohorts are less likely to require further ECA in the future, however this data was not reported by all included studies . Our stuThree studies performed the HCA in the same sitting, and one study performed a staged HCA. Varzaly et al. performed a meta-analysis of rhythm maintenance following hybrid ablation, and found no significant differences between a staged or sequential approach . AdvantaIndication for AF in three studies was persistent AF, and one study performed convergent procedures on patients with paroxysmal AF. The recurrence rate for AF is usually dependent on the duration of AF, as the pathological mechanisms for persistent AF and long-standing persistent AF are more complex than paroxysmal AF . The appComplications were more common in HCA cohort, with 9.4% of patients the HCA cohort and 1.6% in the ECA cohort reporting a post-procedural adverse event. Overall, mortality was rare, reported in only one patient (0.005%) in the HCA cohort secondary to a gastrointestinal bleed. Varzaly et al. reported a similar overall complication rate across 22 studies of 6.5%, with a low mortality rate of 0.2% reflecting the overall safety of the procedure . OverallCessation of oral anticoagulation therapy (OAC) post procedurally is an important outcome as the bleeding risk on OAC is not negligible. Themisocclatis et al. observed a 2% risk of major haemorrhage in patients on warfarin following catheter ablation . The cesThere is also a paucity of evidence exploring the efficacy of convergent ablation in high-risk patients such as those with a high BMI and a previous history of cardiac surgery. Obese patients face greater risks of complications from ablation procedures due to their comorbidities pThere were limitations to the present meta-analysis. A small number of studies, with small patient numbers, were included in the meta-analysis as a result of the inclusion of RCT’s and PSM studies only. Secondly, rhythm monitoring post-procedurally varied, with one study utilising an internal loop recorder and three utilising Holter monitoring and ECG’s. The definition of FFAF also varied between the studies, with three studies defining recurrence as 30 s of AF outside the blanking period and one study defining recurrence as an AF burden > 1% of the time. Again, this impacts the FFAF and adds systematic bias to the review. Finally, there was a paucity if individual patient data (IPD) to aggregate the KM curves past the one-year mark. Both Jan et al. and Kress et al. reported small patient numbers past the one year so deriving conclusions on AF free survival past the one-year mark is tenuous.Future RCT’s, with larger patient cohorts and including LAA exclusion will further consolidate this data. As HCA is an emerging technology, trials with subgroup analysis assessing the advantage LAA exclusion or the vein of Marshall ablation are warranted. Also, pulsed field ablation as a part of convergent ablation is a novel technology with high efficacy, and future trials assessing this in larger patient populations will be beneficial .HCA is associated with a significantly higher FFAF than ECA alone and a higher likelihood of rhythm maintenance off AAD. HCA, however, is also associated with more post-procedural complications than ECA alone. The overall risk of mortality remains low. Future RCT’s with larger patient cohorts comparing the HCA and ECA may further validate these results.Additional file 1: Supplementary Figure 1. PRISMA flow-chart summarizing the search strategy for relevant publications. Supplemental Table 1. Operative Characteristics. Supplemental Table 2a. Postoperative complications (HCA). Supplemental Table 2b. Postoperative complications (ECA).


Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a deep summarization network with auxiliary summarization losses to address this problem. We introduce an unsupervised auxiliary summarization loss module with LSTM and a swish activation function to capture the long-term dependencies for video summarization, which can be easily integrated with various networks. The proposed model is an unsupervised framework for deep reinforcement learning that does not depend on any labels or user interactions. Additionally, we implement a reward function ( According to YouTube, there were approximately 5.5 billion daily video views in the first quarter of 2022. The massive amount of video information available causes people to spend significant time browsing and understanding redundant videos. Therefore, it is important to determine how to find relevant videos quickly among the endless video supply. Video retrieval techniques can help people find videos related to keywords, whereas video summarization techniques can extract representative information directly from a video. Video summarization techniques can generate concise summaries for videos that convey the important components of a complete video. Generating video summaries typically involves using a limited number of images or video clips to represent the main content of an original video sequence, which preserves the authenticity of the video information and saves a significant amount of space.Research on video summarization techniques has been conducted for decades and good results have been achieved by traditional methods based on sparse modeling representative selection (SMRS) , DC progHowever, deep-learning-based methods rely on labels, whereas reinforcement learning does not rely on labels and allows models to explore and select features in an unsupervised manner. Reinforcement learning updates model parameters using reward functions and gradient descent techniques. In recent years, various researchers have combined deep learning with reinforcement learning to apply deep reinforcement learning (DRL) methods to the task of video summarization. Zhou et al.  proposedHowever, the performance of a deep reinforcement learning model degrades as video length increases because of the long-term dependency problem. In video summarization tasks, connections between adjacent frames are often established using recursive neural networks, but for tens of thousands of frames of video information, it is difficult to retain information from distant frames, which may lead to the degradation of model performance. This problem is called the long-term dependency problem. This problem limits the performance of existing approaches that focus on semantic objects, actions, emotions, and diversity. Even BiLSTM-based methods are not immune to the problems caused by long-term dependency. Large kernel sizes and deep networks give convolutional neural networks (CNNs) the ability to alleviate the long-term dependency problem. However, RNNs have a completely different structure compared to CNNs and we cannot directly employ the CNN structure in an RNN. To solve this problem, Trinh et al.  introducIn this paper, to address the long-term dependency problem in the video summarization task, we equate video summarization to a sequential decision process based on inspiration from Trinh et al. . We propAn unsupervised auxiliary summarization loss module is proposed to reconstruct random segments of a video by connecting to the LSTM network in parallel at randomly determined anchor points during model training. This loss is used to adjust the parameters of the model by calculating the degree of video feature loss during training. Experimental results demonstrate that the proposed unsupervised auxiliary summary loss can accurately capture the long-term dependencies in videos, significantly improving the performance and generalization ability of the LSTM network.Furthermore, we explored several reward functions in reinforcement learning for the video summary model. The diversity-representation reward was proposed by Zhou et al. . The divIn summary, the main contributions of this paper are threefold.We propose a DRL framework with an unsupervised summary loss for video summarization called AuDSN, which solves the long-term dependency problem of reinforcement-learning-based key frame extraction algorithms. Compared to RNN/ LSTM-based video summarization methods, our proposed video summarization framework can capture the long-term dependencies between video frames more effectively;We employ an unsupervised auxiliary summarization loss for video summarization, which assists in tuning network parameters by calculating the percentage differences between original and selected features. Additionally, the unsupervised auxiliary summarization loss does not increase the parameters of the model and can be easily integrated into other video summarization models;We propose a novel reward function called dispersion reward  to extrfunction  and the To enhance the ability of LSTM to capture long-term dependencies, we propose a form of unsupervised auxiliary summarization loss that applies to video summarization. This loss not only enhances the memory capabilities of LSTM but can also be easily migrated to other models. It is experimentally demonstrated that this auxiliary summarization loss can help the summarization network establish longer-lasting dependencies and improve the performance of video summarization. During the training progress, we randomly sample multiple anchor locations in the LSTM and insert an unsupervised auxiliary summarization loss function at each location.An unsupervised auxiliary summarization loss function first reconstructs memories from the past by sampling the subsequence after its anchor point, copying the hidden state of a subsequence of length n after its anchor point, and inserting the first input of this subsequence into the decoder network. The rest of the subsequence features are reconstructed using the decoder network, as shown in i-th sampling segment, and the overall auxiliary summarization loss is calculated by summing all subsequence auxiliary summarization losses for a segment. We define the subsequence auxiliary summarization loss as follows:l denotes the feature dimension , Y denotes the original features of the subsequence, and C denotes the key summary features of the subsequence after decoder selection. The auxiliary summarization loss of each subsequence is obtained by computing the infinite norm error between the vectorized Y and C features. This auxiliary summarization loss represents the percentage of the variance between the original features Y and key summary features C of the subsequence selected by the decoder.The introduction of unsupervised auxiliary summarization loss in the decoder network results in the need for some additional hyperparameters, namely the sampling interval Tuning hyperparameters is a very expensive process, so we set the lengths of all selected subsequences In reinforcement learning, the goal of an agent is represented as a special signal called a reward, which is transmitted to the agent through the environment. At each moment, the reward is a single scalar value. Informally, the goal of an agent is to maximize the total reward it receives. This means that what needs to be maximized is not the current reward, but the long-term cumulative reward.An agent always learns how to maximize its gain and the amount of gain is determined by the reward function. If we want an agent to perform a particular task, then we must provide rewards in such a manner that the agent maximizes its gain while completing the task. Therefore, it is critical that we design the reward function in a way that truly achieves our goal. In video summarization, the key frames are selected from the video, and the selected frames should be diverse, representative, and uniform. Gygli et al.  mentioneThe diversity reward function calculates the degree of diversity of a video summary by evaluating the dissimilarity between selected key frames. Specifically, it calculates the variability between selected video frames in the computational feature space to evaluate the diversity of a video summary quantitatively. The diversity reward function is defined as follows:Therefore, the reward obtained by the agent is higher when the selected frames are more diverse . To ignore the similarity between two temporally distant frames, we set deration .y is the set of selected frames, as described above.The representativeness reward is used to measure how well selected key frames represent an original video . EvaluatWhen using representativeness rewards, the degree of representativeness is defined as a k-medoids problem. To ensure that the selected video frames are uniform, we propose a dispersion reward function to complement the representativeness reward function to achieve better selection results. The dispersion reward function uses more intuitive criteria. As shown in w, w, and c denotes the number of categories classified. The intra-class departure matrix is generated by calculating the distance from each sample point to the cluster center. The intra-class distance criteria function, which produces an intra-class departure matrix c denotes the number of classes to be classified, m is the mean vector of all samples to be classified, w, The inter-class distance criterion function, which produces an inter-class deviation matrix The dispersion reward based on the intra-class distance criteria function and inter-class distance criteria function is defined as follows:R is computed using Equation   method sTo compare AuDSN to existing methods, we used a GoogleNet  model prIn In SUP  is a supSUP  uses CheSUP  is a verSN model . CompareIt is noteworthy that most of the previous classical baseline models are tens or even hundreds of megabytes in size. These models require a large number of parameters, large memory, high computing power, and a long time for training. The size of our proposed model is only 2.7 MB, which is much smaller than most previous models. In An extremely small number of parameters allows AuDSN to save significant training time and computational cost, which is important given the exponential growth in the number of online videos in recent years. AuDSN can significantly reduce the computational pressure on video websites so that they can handle larger batches of videos simultaneously. It is worth noting that our lightweight model also makes it possible to deploy video summarization on edge devices and mobile devices. When AuDSN is successfully deployed on mobile devices, it can improve the experience of mobile users and reduce the pressure on servers. Deploying AuDSN to edge devices can enrich the information processing capabilities of such devices and may give rise to novel application scenarios.In this section, we present qualitative comparison results for an example video called “playing ball” from the SumMe dataset. The frame labels of the video are presented in In general, our proposed models produce high-quality video summaries. The basic AuDSN model typically selects the high-quality parts of the summaries. AuDSN-SD using swish as an activation function with the combination of As shown in To allow our model to achieve better results, we evaluated the proposed AuDSN model by assigning different weights to the three reward functions. For the TVSum and SumMe datasets, we present several results based on different weights in In In this paper, we proposed AuDSN, which is a deep reinforcement network model with unsupervised auxiliary summarization loss. We introduced unsupervised auxiliary summarization loss in the decoder and explored a novel reward function with a dispersion reward. Experimental results on two datasets (SumMe and TVSum) demonstrated that introducing unsupervised auxiliary summarization loss can improve the long-term dependency-capturing ability for a deep summarization network. Additionally, the swish activation function and dispersion reward function can help a deep summarization network construct more coherent, diverse, and representative video summaries. Furthermore, AuDSN is a very lightweight model with a size of only 2.7 MB, presenting the opportunity of deploying it on low-computing-power edge devices. In future work, we will also try to incorporate multi-information, such as sound features, into the model and explore multi-information versions of the deep summarization network framework. Furthermore, we will try to incorporate the proposed video summarization approach into modern media tools to make practical use of the proposed summarization algorithms.


Parallel to the tremendous growth and expansion of video technology, it is easy and enjoyable for students to create a video as a learning activity. However, most previous studies primarily focused on declarative knowledge learning  rather than motor skill learning. The current study aimed to investigate whether creating and sharing a video with classmates would be more effective than merely creating a video and self-exercise to learn a motor skill in terms of intrinsic motivation, perseverance in learning, learning satisfaction, and roller-skating skill. Partially consistent with our hypothesis, we found that creating and sharing a video with classmates increased students’ intrinsic motivation, perseverance in motor tasks, and learning satisfaction, but not roller-skating skill, followed by merely creating a video and then self-exercise. The findings have an important implication for motor skills learning: during teaching motor skills, teachers can use encourage students to create and share a video with classmates as a homework activity to increase students’ intrinsic motivation, perseverance in motor tasks, and learning satisfaction. Technology has developed significantly, and its impacts are inevitable on education in multiple disciplines  rather than motor skill learning. There are some differences between declarative knowledge and motor skill learning . Specifiexercise . Physicaexercise . Creatinexercise .Researchers have proposed several potential mechanisms for the effects of creating video. First, the exercise effect postulates that exercise has beneficiary effects on motor skill consolidation . ExercisSecond, the self-assessment effect builds on empirical studies and suggests that creating a video enables students to self-assess related to their learning and performance by viewing a created video . ConsequThird, the social facilitation effect in motor tasks argues that the mere presence of others increases students’ arousal level, intrinsic motivation, and elicits the perception of evaluation by others, and thus influences their performance in motor tasks . IntrinsAlthough accumulative studies confirmed the benefits of creating a video on students’ intrinsic motivation, engagement, and learning performance , it is yThe present study tested whether creating and sharing a video with classmates or not would improve students’ motor skill learning  compared to the self-exercise condition. To control time on task, students created videos or did self-exercise in a physical class. Merely creating a video and self-exercise were used as control conditions, which also encourages exercise and self-assessment by viewing the created video but lacks others presence. Furthermore, compared to creating and sharing a video with classmates, self-exercise lacks self-assessment and others presence. We examined effects on intrinsic motivation, perseverance in motor tasks, learning satisfaction, and roller-skating skill. Based on previous studies and theories, we proposed the following hypotheses.H1: Students who create and share a video with classmates will show higher intrinsic motivation, followed by those who merely create a video and, finally, those who do self-exercise.H2: Students who create and share a video with classmates will show higher perseverance in motor tasks, followed by those who merely create a video and, finally, those who do self-exercise.H3: Students who create and share a video with classmates will show better motor skills, followed by those who merely create a video and, finally, those who do self-exercise.SD = 1.28, Range: 18–22 years) from six classes in one Chinese university participated in the study. None of their majors are physics or physics related. We adopted a between-subjects design and thus randomly assigned two classes of students one of three conditions to learn roller-skating. In the creating and sharing a video with classmates condition, there were 53 students ; In the merely creating a video condition, there were 53 students ; In the self-exercise condition, there were 54 students . All the students were informed of the learning tasks and volunteered to participate in the study.A total of 160 students , in which we used the scores on the physical fitness tests as covariates in our models. All analyses were carried out on SPSS 26.SD) of all variables in the current study.F = 5.27, p = 0.006, pη2 = 0.06. Post hoc comparison (LSD) showed that participants’ time in the creating and sharing a video condition was significantly shorter than those in the self-exercise condition .We first tested the differences in physical fitness tests among the three conditions. The results of one-way ANOVAs showed significant differences in squatting against the wall, F = 4.36, p = 0.014, pη2 = 0.05. Post hoc comparison showed that participants’ time in the creating and sharing a video condition was significantly shorter than those in the self-exercise condition .There were significant differences in standing on one leg with eyes closed, F = 2.41, p = 0.094, pη2 = 0.03. Post hoc comparison showed that participants’ distance in the creating and sharing a video condition was significantly longer than those in the merely creating a video condition ;There were marginally significant differences in sitting and bending forward, F = 5.39, p = 0.005, pη2 = 0.06. Post hoc comparison showed that participants’ time in the creating and sharing a video condition was significantly longer than those in the self-exercise condition .There were significant differences in 50 m running, F = 5.34, p = 0.006, pη2 = 0.06. Post hoc comparison showed that participants’ distance in the creating and sharing a video condition was significantly shorter than those in the merely creating a video condition , and was significantly shorter than those in the self-exercise condition ;There were significant differences in standing long jump, F = 5.59, p = 0.004, pη2 = 0.07, and post hoc comparison showed that participants’ number in the creating and sharing a video condition was significantly more than those in the merely creating a video condition , and was significantly more than those in the self-exercise condition .There were significant differences in sit-ups, In sum, the above results indicated that participants in the three conditions were different in physical fitness. Therefore, we conducted ANCOVAs to test the effects of creating video on motor skill learning, the scores on the physical fitness tests as covariates.F = 0.59, p = 0.556, pη2 = 0.01; F = 0.63, p = 0.536, pη2 = 0.01; F  = 0.50, p = 0.607, pη2 = 0.01.For the interest, competence, and pressure dimensions, there was no significant difference between the three conditions, F = 3.15, p = 0.046, pη2 = 0.04. The post hoc tests showed that participants in the creating and sharing a video condition reported a significantly higher score than those in the merely creating a video condition  and a higher score, in a marginally significant level, than those in the self-exercise condition . See For the value dimension, there were significant differences between the three conditions, F = 6.42, p = 0.002, pη2 = 0.08. The post hoc tests showed that participants in the creating and sharing a video condition reported a significantly higher score than those in the merely creating a video condition  and the self-exercise condition . See The ANCOVA showed that there were significant differences in the score of the perseverance in motor tasks questionnaire between the three conditions, F = 2.60, p = 0.078, p2η = 0.03. The post hoc test showed that participants in the creating and sharing a video condition reported a significantly higher score than those in the self-exercise condition . See For the teaching dimension, there were differences in a marginally significant level between the three conditions, F  = 2.65, p = 0.074, pη2 = 0.03. The post hoc test showed that participants in the creating and sharing a video condition reported a significantly higher score than those in the merely creating a video condition , and a higher score, in a marginally significant level, than those in the self-exercise condition . See For the teacher-student interaction dimension, there were a difference in a marginally significant level between the three conditions, F = 2.23, p = 0.111, pη2 = 0.03; F = 1.95, p = 0.146, pη2 = 0.03.For the course content and the learning environment and equipment dimensions, there was no significant difference between the three conditions, F = 0.68, p = 0.508, pη2 = 0.01.We calculated the average time of three roller-skating tests as the final scores. The result showed no significant difference between the three conditions, The current study aimed to investigate whether creating and sharing a video with classmates would be more effective than merely creating a video and self-exercise to learn a motor skill in terms of intrinsic motivation, perseverance in learning, learning satisfaction, and roller-skating skill. Partially consistent with our hypothesis, we found that creating and sharing a video with classmates increased students’ intrinsic motivation, perseverance in motor tasks, and learning satisfaction, but not roller-skating skill, followed by merely creating a video and then self-exercise. The results broadened our understanding of the effects of creating a video on motor skills learning.In line with our first and second hypotheses that creating a video improved intrinsic motivation and perseverance in motor tasks compared with self-exercise, we confirmed the benefits of creating a video on students’ intrinsic motivation  and perseverance in motor tasks compared to self-exercise. The results were consistent with previous studies, showing creating a video increases students’ intrinsic motivation  and learning satisfaction . PreviouIn contrast to our third hypothesis that creating and sharing a video with classmates facilitated roller-skating skills most, we did not observe differences in the roller-skating skills among the three conditions. The results suggested that self-exercise was enough to lead to good performance in the roller-skating test. This study has the following limitations. First, this study did not test the long-term effect of creating and sharing a video with classmates. We observed that creating and sharing a video increased students’ intrinsic motivation and perseverance in motor tasks. Intrinsic motivation is a powerful “engine” of learning . IntrinsAlthough there were some limitations, the present study advances our understanding of the various effects of two types of creating video compared to self-exercise. With the rapid development of video technology, it is easy for students to create a video as an enjoyable activity . The preIn conclusion, the main finding of the present study was that students who created and shared a video with classmates reported higher intrinsic motivation, perseverance in motor tasks, and learning satisfaction, but not the roller-skating skill, followed by those who merely creating a video and then those who self-exercise. With the easy availability of video production software, the findings have an important implication for motor skills learning: during teaching motor skills, teachers can use encourage students to create and share a video with classmates as a homework activity to increase students’ intrinsic motivation, perseverance in motor tasks, and learning satisfaction.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics Committee of Zhejiang Gongshang University. The participants provided their written informed consent to participate in this study.QX: conceptualization, methodology, writing – original draft, and writing-reviewing and editing. LK: investigation, data curation, visualization, and methodology. ZZ: supervision and conceptualization. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.