The provision of psychotherapy over distance using technology is a growing market reaching many patients and therefore the risks and benefits need to be known by all psychotherapists whether they themselves practice online or not. This comprehensive review of the main ethical arguments for and against different forms of online psychotherapy aims to enhance discussion of ethical issues in this growing area.A search of three databases  was conducted in August 2019 using a specific search protocol yielding 249 publications.Of 24 ethical arguments in favor of online psychotherapy and 32 against, the top five ethical arguments in favor of online psychotherapy were (1) increased access to psychotherapy and service availability and flexibility; (2) therapy benefits and enhanced communication; (3) advantages related to specific client characteristics (e.g. remote location); (4) convenience, satisfaction, acceptance, and increased demand; and (5) economic advantages. The top five ethical arguments against engagement in online psychotherapy were (1) privacy, confidentiality, and security issues; (2) therapist competence and need for special training; (3) communication issues specific to technology; (4) research gaps; and (5) emergency issues.The findings may be of help to practitioners in deciding whether to engage in online psychotherapy, and in informing patients about risks and benefits, improving ethical guidelines, and stimulating further ethical discussion. The findings are argumentative and qualitative in nature, and further quantitative research is needed. Technological innovation has led to rapid change in many professions, bringing both benefits and challenges. Since the late 1990s, a growing body of research has addressed issues related to online psychotherapy. To begin, that research focused mainly on the usefulness of online psychotherapy before shifting in emphasis to situations in which online psychotherapy might be used, with a view to evaluating the strengths and shortcomings of this approach ; for details, see Full Search Code in To construct the search protocol, the research question was structured in terms of three topics: Publication Selection Criteria in Publication titles and abstracts were screened according to defined selection criteria  included 179 articles, 6 books, 55 book chapters, 2 doctoral theses, 5 book reviews, 1 brief communication, and 1 item of correspondence. Among these, there were 32 literature reviews, 2 systematic reviews , 6 editorials or introductions, 5 book reviews, and 6 books (including 4 practice guides). Additionally, there was one case study, one correspondence, one research digest, one discussion, and one paper on ethical reasoning. In total, 30 of the selected publications were empirical studies, including 14 surveys and 6 website analyses. Six related to specific mental illnesses, one was a comparison of in-person and online psychotherapy, two included practitioner interviews, one was a study protocol for a meta-analysis, and one related to a practitioner discussion forum. Among other notable features of the final sample, 18 included guidelines, and 25 focused on specific countries.The selected publications ranged across disciplines that included psychology (70), psychiatry (33), psychology and psychiatry (2), social work (12), and telemedicine (6). The remaining publications (125) related to psychotherapy in general, with no specific disciplinary focus. In terms of technological modality, these included email (13), telephone (5), videoconferencing (28), text-based methods including email and chat (14), email and text messages via cellphone (3), mobile phones (3), text messages (1), email and social media (2), and videoconferencing and mobile phones (6). The remaining 173 related to communication technologies in general rather than any specific technology or mode of communication.Online psychotherapy can improve and enhance access to health care services and evidence-based care, especially for those living in rural or remote areas and populations that are underserved for other reasons [see ]. Servicin vivo exposure, the therapist could be virtually present [see  [see ]. Multipent [see ]. Howeveent [see ]. Onlineent [see ]. This aent [see ]. Data rent [see ].Online psychotherapy can be especially useful for clients living in geographically remote, rural, or otherwise underserved areas where few or no therapists are available [see ], as welOnline psychotherapy is perceived as convenient and comfortable by patients and therapists alike [see ], not leOnline psychotherapy is reported to be more cost-efficient [see ], with tBecause online psychotherapy can be provided anonymously and one is not seen entering the therapist's office [see ], it canBy reducing or eliminating barriers such as fear of social stigma, online psychotherapy can reach patients who might never have sought traditional in-person therapy [see ]. This mThe therapeutic relationship established in online psychotherapy is commonly perceived as equal to or better than in-person therapy, and an established therapeutic relationship can be enhanced using online communication [see ].Technology-mediated communication can contribute positively to teaching and supervision and facilitates inter-professional and inter-collegial exchange worldwide [see ]. OnlineAny stigma, stigmatization, or perceived stigma associated with seeking mental help services or concerns about being stereotyped can be reduced or eliminated by online psychotherapy. This may in turn help to address barriers to traditional psychotherapy such as concerns about anonymity and privacy [see ].Online psychotherapy empowers the patient because, for example, it is much easier to move to another therapist [see ], givingOnline psychotherapy can be provided from anywhere, without regard to geographical boundaries, state lines, national borders, or time-zones, allowing therapists to reach patients who are, for instance, temporarily abroad [see ].Online psychotherapy can be useful for emergencies and crisis interventions. As compared to traditional in-person therapy, it may provide more immediate access to services, and disclosure of suicidal or homicidal tendencies may be easier online [see ]. In theOnline psychotherapy can offer services that specifically match patients' needs [see ], faciliLevels of adherence, attendance, and compliance as good as or better than in-person treatment can be achieved using online psychotherapy [see ].Online psychotherapy offers unique opportunities for research [see ]; for exFailure to provide online psychotherapy to vulnerable people who need it can be seen as unethical—for example, patients living in rural or remote areas with few or occasional local options [see ].Online psychotherapy can afford the therapist greater freedom [see ], includOnline psychotherapy increases the accountability of both therapist and patient, not least because it is easier to keep records and to make transcripts available to both parties [see ], reduciSecurity issues raised by risky environments or when communicating with potentially dangerous patients can be reduced by online service provision [see ].Offering unprecedented opportunities for access and connecting with patients and other therapists, social media can be a useful therapeutic tool [see ].As the distance provided by technology inhibits physical proximity, online psychotherapy can help to reduce the risk of patient-therapist  intimacy [see ].The informed consent process can be enhanced by online communication—for example, web pages can be revisited , with liThe view that one should not engage in online psychotherapy is legally problematic because it restricts trade and the ethical right to a free market .Among concerns about privacy, confidentiality, security, and safety in online psychotherapy [see ], one reTo provide online psychotherapy, training is needed to ensure appropriate technology-related competences, as well as clinical and therapeutic competences specific to the online setting. In particular, the therapist would require knowledge of ethical approaches and guidelines, as well as specific legal requirements and policies [see ]. In genAmong negative issues, one of the most widely discussed is the absence of non-verbal cues in the therapeutic interaction, especially when using text-based media but also when using telephone or videoconferencing, which may lead to misunderstandings and miscommunication [see ]. If a tMany authors claim that there is insufficient research in support of online psychotherapy or that there are too many knowledge gaps, especially with regard to effectiveness, efficacy, and long-term outcomes and as compared to in-person treatment [see ].Questions also arise as to whether an emergency or crisis situation involving threat to self or others can be detected and addressed where patient and therapist are at different locations [see ]. Other In light of the many differences from in-person therapy , online psychotherapy requires a particular form of informed consent [see ]. HoweveA therapist's lack of technological competence and patient and therapist awareness of their respective skills are important issues in this context, as discomfort or fear of using technology is not uncommon [see ].Regulatory guidelines and standards of practice or care in this area are considered incomplete or absent. Guidance by legal or regulatory bodies is also lacking, especially in terms of global or international regulation of cross-border practice, and the absence of specific ethical guidelines or codes of conduct for online psychotherapy leaves many ethical questions unanswered [see ].Unresolved jurisdiction and few or no specific laws governing licensing, certification, training and education, informed consent, and cross-border practice are problematic issues for online psychotherapy [see ].Many issues arise in relation to online psychotherapy conducted across state or national borders, including legislative, licensing, and cultural differences [see ]. For inOnline psychotherapy may not be suitable for all patients, clinical conditions, psychiatric disorders, and problems; it may sometimes be contraindicated, especially in the case of severe mental disorder, or for patients who are highly dysfunctional and/or pose a threat to themselves or others [see ]. A patiTechnical difficulties and failures are major concerns in this context, possibly leading to frustration and anger, which may be distracting or disturbing [see ].Payment, reimbursement, fee structure, and billing for online psychotherapy raise many questions, such as how interruption or technical failure will be handled [see ]. AnotheMany authors have questioned whether an effective and successful therapeutic alliance can be developed solely through technology [see ] and wheBecause technology often creates a sense of permanent access, this may become a problem, as the therapist cannot and will not guarantee this [see ]. ResponAs it may be difficult to verify the identity of the patient (or the therapist) online, deception or fraud is a possibility—for example, a therapist might inadvertently treat a minor without parental consent [see ].Many therapists have a negative view of online psychotherapy and are clearly concerned or strictly against it, with poor reported satisfaction and acceptance among therapists [see ] and conUnethical, malign, or abusive behavior may be easier online [see ]—for insOnline psychotherapy may make it more difficult to maintain professional boundaries, posing a threat to the professional relationship—for example, an interaction mediated by technology might seem social, conversational, or less formal, and the flexibility of location and time might lead to communication in inappropriate locations or at odd times, as the therapist might be tempted to communicate while on holidays, traveling, or while ill [see ]. As anoOne important open question is whether online psychotherapy is truly comparable to in-person treatment, and whether it can replace traditional in-person therapy. Many authors have expressed doubts in this regard, to the extent that some believe online services may prevent patients from seeking more suitable traditional therapy [see ].Online psychotherapy entails some initial costs for the therapist, which may make access to online psychotherapy services too expensive for some patients [see ]. These Therapists who provide an online service may be more exposed to litigation and increased liability, as for example in cross-border cases [see ].Online psychotherapy may contribute to internet overuse and ultimately internet addiction [see ], potentThe use of social networking sites poses new ethical challenges and invites potentially unethical interactions in the context of online psychotherapy, such as friend requests from patients [see ] or probThere is a danger that online psychotherapy might be conducted for financial gain without due regard to the best interests of the patient [see ].Online psychotherapy may risk loss of therapeutic control [see ]—for exaCompliance and adherence to therapy may be undermined in an online setting, given the ease of dropping out, logging off, hanging up the phone, or terminating the connection [see ].Supervising and teaching online raises a number of specific ethical issues and questions [see ].In online psychotherapy, the patient may experience less control , and theOnline psychotherapy may hamper patient autonomy [see ]—for exaOnline psychotherapy may lead to dehumanization of the therapeutic environment [see ] or of tAn online setting may promote inadvertent discrimination or cultural insensitivity by masking important cues [see ].Online psychotherapy offers many advantages like benefits for the therapeutic process and the therapeutic communication itself, also by being more convenient than traditional settings of psychotherapy. Both many patients and therapists seem to be satisfied with the use of online psychotherapy. Not surprisingly, this kind of psychotherapy is increasingly being used. Online psychotherapy promises to solve economic pressures by being more cost-effective, offering a solution for workforce shortage problems and increasing access to necessary psychotherapy for many different populations suffering from mental health problems who might be difficult to reach such as patients living in rural areas. Reducing barriers to engaging in psychotherapy by reducing stigma, being able to attend online sessions anonymously thus enhancing a sense of privacy, reaching patients worldwide and across borders are other advantages of online psychotherapy. Conducting psychotherapy online gives the possibility to adapt services to specific patients offering more personalized care, enhancing patient control, and empowering the patient resulting in more adherence to and compliance with the treatment itself. The specific setting of online psychotherapy gives the therapist more freedom and offers physical protection, but might also protect the patient from breaches of intimacy boundaries and enhance the accountability of both patient and therapist. Additionally, online psychotherapy offers new opportunities for research, teaching, and supervision, enhancing the informed consent process, offering new opportunities through the use of social media and might give good assistance in emergency situations. Online psychotherapy might even improve and extend the therapeutic relationship.One of the biggest and most discussed disadvantages of using psychotherapy is the risks with regard to privacy, confidentiality, and data security. Online psychotherapy creates new challenges to therapist competences which brings about the need for new forms of training and education, especially technological competences regarding many technical issues that might occur. Technological competence is not only needed by the therapist, but also by the patient. New communication skills are needed and particular attention must be paid to the development of the therapeutic relationship regarding the many boundary issues that might occur. Difficulties herein are put up by new access and availability issues and the loss of therapeutic control. Broader research, new guidelines, and a consideration of legal issues in general are needed especially regarding the practice across borders of nations, new payment and insurance issues, challenges to the informed consent process, dealing with emergency situations, enhancing the identification process of the therapist and the patient, and selecting patients that are suitable for online psychotherapy. The comparability to in-person treatment might be questioned and some fear danger to the image of psychotherapy. Therapists might fear increased liability and litigation. Online, new forms of misuse are possible and charlatans might utilize this kind of psychotherapy to achieve financial gains. Other feared disadvantages of online psychotherapy are the dehumanization and stigmatization of patients, patient dependence, and loss of patients' autonomy. New adherence issues might occur regarding the ease of ending an online session. Online supervision and teaching and the use of social media raise further ethical questions. Extensive costs might be faced by patient and therapists, when using online psychotherapy, for example, to set up the new technologies. Last but not least, the negative influence of technology itself might endanger its users.This review has a number of limitations. First, only articles in English and German were included. However, only fourteen of the articles that were found to meet the search criteria were in a different language and therefore excluded. Among the emerging research topics not included in this review are online training and supervision, social media, avatar, second life, robots and bots, artificial intelligence, computer-mediated (self-help) therapy, psychology-related smartphone apps, internet-based group therapy and telecare, online forums, open chat, therapy for older adults, therapy for children and adolescents, and marriage and family therapy.It is beyond the scope of the present review to offer exhaustive recommendations for clinical practice or how these ethical risks might be resolved in practice, and further systematic research should more fully address this topic. For some recommendations directly deducted from the results of this review, see Counting the frequency of arguments does not clarify their relative importance; to evaluate their true weight, a more quantitative survey of experts' ratings is needed. Without that deeper understanding, the risks and benefits reviewed here remain anecdotal and qualitative, with only limited validity .In future systematic research on efficacy, effectiveness, and efficiency of online psychotherapy is needed and practice guidelines, legal and ethical frameworks need to be developed. Further research in the fast growing field of online psychotherapy seems vital. Some important topics requiring further investigation are summarized in If trained psychotherapists choose not to participate in the new and emerging field of online psychotherapy, it seems likely that charlatans will emerge to meet the ever-growing demand, perhaps driving professional psychotherapists out of the market . For thaJS and MT designed the review and developed the search strategy. JS and JM were involved in search, exclusion, and argument extraction processes. JS and MT wrote and edited the final article, which was reviewed and approved by JM.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.


The Clinical and Translational Science Award (CTSA) Consortium and the National Center for Advancing Translational Science (NCATS) undertook a Common Metrics Initiative to improve research processes across the national CTSA Consortium. This was implemented by Tufts Clinical and Translational Science Institute at the 64 CTSA academic medical centers. Three metrics were collaboratively developed by NCATS staff, CTSA Consortium teams, and outside consultants for Institutional Review Board Review Duration, Careers in Clinical and Translational Research, and Pilot Award Publications and Subsequent Funding. The implementation program included training on the metric operational guidelines, data collection, data reporting system, and performance improvement framework. The implementation team provided small-group coaching and technical assistance. Collaborative learning sessions, driver diagrams, and change packages were used to disseminate best and promising practices. After 14 weeks, 84% of hubs had produced a value for one metric and about half had produced an initial improvement plan. Overall, hubs reported that the implementation activities facilitated their Common Metrics performance improvement process. Experiences implementing the first three metrics can inform future directions of the Common Metrics Initiative and other research groups implementing standardized metrics and performance improvement processes, potentially including other National Institutes of Health institutes and centers. Nationally, CTSA Consortium “hubs,”founded on the concept of continuous improvement and the imperative to translate ‘what we know’ into ‘what we do’.”1 To move toward realizing this vision, the IOM Committee recommended that NCATS undertake an evaluation process to determine the effectiveness and impact of individual CTSA Consortium hubs and the program as a whole.In 2013, a congressionally mandated Institute of Medicine (IOM)  report on the CTSA Consortium envisioned it as an integral part of a learning health care system “2 included a primary objective to excel as a federal science agency by managing for results. Seen as an important piece of this effort, the Common Metrics Initiative was made a high priority for NCATS and the CTSA Program.The report suggested “common metrics” to enhance transparency and accountability related to the activities undertaken by the CTSA Consortium. In response to this recommendation, NCATS embarked on a collaborative process with the CTSA Consortium grantees to develop the Common Metrics Initiative. Subsequently, the NIH research strategic plan for Fiscal Years 2016–2020This article describes the design and implementation of the first three metrics for the CTSA Consortium Common Metrics Initiative, which entailed the use of shared metrics and an improvement framework across the CTSA Consortium, to promote collaborative performance improvement.3Starting in 2015, the Common Metrics Initiative was adopted across the CTSA Consortium and at each hub. Within the mission of promoting clinical and translational research, hub characteristics vary, including size, geographic location, patient populations, students, programmatic and translational science focus, number and complexity of stakeholder collaborations, and length of time they have received NIH funding.S1) was developed, and metric implementation by the remaining hubs used postpilot versions of the metrics and Operational Guidelines.The development of specific metrics was a collaborative process which included NCATS staff, CTSA Consortium leaders and their teams, CTSA Consortium Evaluators , and outside consultants. In 2015, working groups comprised of CTSA Consortium leaders and staff, and NCATS staff met in a series of facilitated webinars. Four groups were formed to reflect key services provided by hubs, and the activities of these workgroups resulted in the selection of three initial metrics for implementation: (1) Median Institutional Review Board (IRB) Review Duration; (2) Careers in Clinical and Translational Research; and (3) Pilot Funding Publications and Subsequent Funding. Each metric, as well as its specifications and key definitions , was developed by a group of topic-specific experts, evaluators, CTSA hub principal investigators (PIs), and NCATS staff. This process included reviewing relevant literature, discussions with other academics and across NIH, and group meetings. After development and a pilot test by four volunteer hubs, a revised Operational Guideline template  performance improvement plan. The Common Metrics are intended to serve as a starting point for a larger effort to understand the data within the context of the local hub environment and to develop strategies to improve performance. Thereby, the Common Metrics results can assist in assessing whether the CTSA Consortium’s resources are being optimally used to develop and deliver services and resources – at individual hubs and Consortium-wide.The Common Metrics Initiative adopted and is currently guided by the Results-Based Accountability (RBA) improvement framework,Between June 2016 and December 2017, an implementation team from the Tufts Clinical and Translational Science Institute (CTSI) Research Process Improvement Program led the roll-out of the first three Common Metrics. Bi-weekly meetings were held with NCATS representatives, the Tufts Implementation Team, an RBA consultant, and the Tufts CTSI PI to address implementation issues and track progress. A Common Metrics Executive Committee comprised of CTSA PIs, CTSA hub program evaluation and administrative leadership staff, NCATS CTSA Program staff, an RBA consultant, and Tufts Implementation Team representatives, met monthly to build buy-in and stakeholder engagement across the CTSA Consortium.The Implementation Team supported 64 CTSA hubs in implementing metric data collection, the use of the RBA improvement framework, and a supporting web-based data reporting and communication system (“Scorecard”). The implementation was not considered human subjects research by the Tufts Health Sciences IRB.Project Champion – ensured everyone at the hub was “on-board” and committed to the project’s success.Project Leader – responsible for overall project planning and execution.RBA Framework Lead – responsible for helping their team learn and implement the RBA framework.Scorecard Software Lead – responsible for helping others at the hub learn the Scorecard software.Metrics Topic Lead – a subject matter expert responsible for overseeing metric data collection. Hubs could elect to have a different metric expert for each Common Metric.Each CTSA hub was asked to form a “core” Common Metrics team covering five roles:Hubs were also instructed to identify additional subject matter experts to assist with data collection and performance improvement for specific metrics .“Onboarding” session: A review of the Common Metrics project activities and timeline and guidance on finalizing the hub’s team.Common Metrics Training: A review of the Operational Guideline for each metric.PI Training: A review of the RBA framework and Scorecard software for CTSA hub PIs.RBA and Scorecard Training: Three sessions for hub team members using a mix of prerecorded lectures, training videos, and live interactive webinars to build knowledge and skills in the RBA framework and in using the Scorecard software.Kickoff session: This session provided the full hub team project timeline updates, examples of completed Scorecards, and guidance on next steps.Hub teams participated in training and subsequent coaching in one of three implementation groups based on hubs’ preferences. Initially, each group received online training on the metrics, software, and performance improvement concepts. The webinar training sessions included:Based on participant feedback, the training was modified after the first and second implementation groups. Interactivity was increased, and CTSA-specific examples were added.After training, each hub selected one of the three Common Metrics for the first phase of data collection and TTC performance improvement activities. Each Implementation Group was divided into smaller coaching groups of four to six hubs. During seven every-other-week small group coaching sessions, a quality improvement consultant from the Tufts Implementation Team facilitated a 1-hour webinar during which hubs discussed their progress, challenges and barriers in collecting metric data according to the Operational Guidelines, and developing TTC plans for performance improvement. Hubs could ask questions about defining terms, data sources, and how to apply the Operational Guidelines in specific situations.It was the expectation that for one Common Metric, hubs would complete data collection, metric computation and data entry to Scorecard, and an initial TTC plan by the conclusion of the their respective Implementation Group’s coaching period. At that point, hub teams were instructed to move on to data collection and TTC planning for the second and third Common Metrics. The Implementation Team remained available for technical support upon request.As hubs began performance improvement, the Implementation Team convened a series of collaborative Learning Sessions to showcase and share emerging best and promising improvement strategies. All hub team members were encouraged to attend these optional 1-hour webinars of didactic presentations and group interaction.5 designed around an aim or a team’s objectives. Drivers, factors that, if present, can help achieve the aim, along with potential strategies for improving performance, were identified through a literature review and initial TTC plans. The change packages included example strategies from hubs for each driver, including a brief rationale.Initial versions of a driver diagram and change package were also developed for each metric. A driver diagram is an evidence-based resource used in improvement efforts,The Implementation Team provided ongoing technical assistance to hubs to help them: (a) interpret and apply the Operational Guidelines; (b) identify and overcome barriers to data collection and performance improvement; (c) complete TTC plans; and (d) locate and use resources developed by the Implementation Team. A website provided a range of initiative-related resources, including answers to frequently asked questions, training materials for RBA, Scorecard, and the individual metrics, and slides and video recordings of the Learning Sessions.Training materials included metric calculation worksheets and example improvement plans. Despite guidance in the Operational Guidelines, hubs had numerous questions and misunderstandings about how to calculate metric score values. They also questioned the level of detail that should be included in an initial TTC plan. Therefore, the Implementation Team developed: (1) worksheets for both the Pilot Publications and the Careers Common Metrics with examples and tools to help calculate each metric score and (2) an example TTC plan for the Careers Metric.Technical assistance also addressed data quality. The Implementation Team performed an iterative metric value checking process and provided feedback to hubs when metric scores were out of anticipated ranges or were obviously inconsistent with the Operational Guidelines.Finally, a set of annotated slides, based on the initial training, was provided to allow core team members to train additional staff at their hub.Hub participation in training and coaching activities was tracked by Implementation Team staff and the webinar log-in software. Attendance was defined as at least one person from the hub logging into the webinar.S2). These assessments also helped identify content for upcoming calls and needs for individual hub support.Hub progress was assessed by whether a hub produced a metric value (yes/no) and the extent to which its TTC plan met criteria for applying the RBA framework. Prior to each coaching webinar, the quality improvement consultants reviewed metric values and TTC plans entered by hub teams into Scorecard. Implementation Team staff took detailed notes during each webinar. After each webinar, progress on TTC planning for each hub was assessed by the quality improvement consultants based on 13 criteria, using a structured tool , all hubs received an online, self-report survey designed in part to assess the implementation activities. Administered in January–February 2018, the survey addressed, in part, the amount and time allocated to implementation program components, measured on a five-point Likert-type scale (“much less than was needed” to “much more than was needed”). An invitation email was sent to one PI per hub, who was instructed to assign one person to complete the survey with input from others at the hub. The survey was completed by 59 of the 60 hubs (98%) receiving it.p-values from overall F-tests.Quantitative data were summarized using frequencies and proportions for categorical variables and using means and standard deviations for continuous and count variables. Means for Implementation Groups were compared using an analysis of variance model and All hubs were assigned to their first or second choice of Implementation Group. The number of hubs per group and their choice of metric on which to focus during the coaching period are depicted in Hubs configured their teams based on their current staffing. At some hubs, the same person assumed more than one role (such as project leader or RBA expert) or two people shared the same role . The resulting teams varied in size (range: 2–11). Some teams had broad membership, whereas in a few, one person, the “Common Metrics person,” conducted virtually all activities. Additionally, teams often lacked participation of additional subject matter expert members with specialized knowledge of metric topics or with influence over relevant operational areas.There was wide participation in the training and coaching and, on average, at least one person from each hub attended most sessions . CompareHub responses to questions about the amount of time allocated for training on RBA and the metric Operational Guidelines and ratings of the didactic training overall are summarized in There were no statistically significant differences in the evaluation of coaching, discussion, and homework by Implementation Group. Hub responses to questions about the amount of time allocated for coaching sessions and the ratings of the amount of discussion and homework are summarized in Hubs progressed at variable rates in all components of the Common Metrics Implementation. Although many hubs had been collecting data prior to metric implementation, for some hubs and some metrics, existing data sources did not align with Operational Guidelines or access to the data was limited. Developing new data sources, revising existing sources, or gaining access to data led to delays in collecting and reporting metric values. By the conclusion of their respective coaching periods, 54 of 64 hubs (84%) had produced a value for the metric they focused on during coaching. However, some of the values were inconsistent with the Operational Guidelines.Many hubs also experienced challenges in completing one or more elements of the RBA framework. Approximately half of hubs met the Implementation Team’s assessment criteria for four RBA components: Story Behind the Curve (56%), Engaging Partners (40%), What Works (53%), and Strategies (50%) . SeveralTo our knowledge, this is the first implementation of performance metrics and a process improvement framework across a national research consortium. In partnership with NCATS, the Tufts Implementation Team developed and delivered a program of activities intended to provide the CTSA Consortium and its hubs with tools to support full adoption of collaborative performance measurement and improvement. Three metrics were developed by NCATS staff and CTSA Consortium leaders and staff. The Tufts Team provided all hubs training, coaching, and technical assistance on metric data collection and performance improvement. Collaborative learning sessions, driver diagrams, and change packages were used to disseminate best and promising practices. The majority of hubs produced a value for a first metric and about half produced an initial performance improvement plan by the conclusion of their respective coaching period. Overall, hubs reported that the implementation activities facilitated their Common Metrics performance improvement process.The results of implementing these first three metrics can inform decision-making on future directions of the Common Metrics Initiative. The lessons learned may also be of interest to other research groups or networks implementing standardized metrics and performance improvement processes, potentially including other NIH institutes and centers.CTSA organizations and their personnel are very diverse. Accordingly, hubs were heterogeneous in their approach to, and engagement with, the Common Metrics Implementation. They varied in their perceptions of metric usefulness at the local and national level. Common Metrics did not always align with hub institutional priorities and were not consistently viewed as useful for local improvement. Getting input from and expanding pilot testing to include insights from broader groups of hub staff and stakeholders could help metrics have greater utility to hubs. Although this initial implementation was generally positively received, having metrics that are closer to hubs’ specific objectives will no doubt further increase support for, and engagement in, shared metrics and performance improvement activities.Hubs also expressed a wide range of organizational and participant needs. Optimally, if resources allow, training and coaching should be customized, providing options to meet the varying needs of adult learners and various levels of prior performance improvement experience. Coaching is fundamental to implementing metrics and performance improvement. Coaching can: (1) promote accountability to meet implementation milestones; (2) help assess implementation progress; (3) identify needs for implementation assistance; and (4) connect participants with peers for better identification of barriers and facilitators. Therefore, small group coaching was useful when implementing each new metric. Additional optional coaching could be provided after initial metric implementation.The initial training aimed to provide hub team members basic proficiency in RBA, which was new to the majority. Over time, as participants become more proficient in creating performance improvement plans, additional training on more advanced performance improvement concepts could be provided.6 Active involvement of the CTSA PI is essential. Some teams may also need mechanisms to promote accountability for meeting milestones.Metric implementation could also benefit from greater emphasis on including metric-specific team members and addressing factors that facilitate effective improvement teams. Including more partners and active subject matter experts beyond the core team would help teams gain a deeper understanding of the underlying causes of existing performance, address data issues, and identify, select, and implement effective metric-specific improvement strategies. Team functioning could be heightened by identifying one team member who “owns” the project and including a local champion on the team.To fully realize the potential of the Common Metrics Initiative, or similar initiatives, ongoing attention should be given to metric data quality and completeness. This is particularly true to ensure data comparability for cross-consortium comparisons or benchmarking. Hubs require time to realign existing information systems and other infrastructure or to build new data collection systems if needed. Given the detailed Operational Guidelines, and the heterogeneity of hub data sources and systems, concrete examples of how to calculate the metrics should be developed for each new metric.Finally, to achieve optimal benefit from the resources necessary for metric development and implementation requires a parallel emphasis on applying the performance improvement framework. CTSA hubs need to ensure that they understand the underlying causes behind current performance, prioritize the multiple opportunities for improvement, and recognize performance barriers and facilitators. They must identify a range of potential strategies, and select those that are actionable, impactful, and consistent with their mission and goals. They must also be sensitive to resource requirements, costs, and benefits. Over time, an emphasis on action planning and on repeated measurement to determine program effectiveness at both the local hub and CTSA Consortium levels will support the Common Metrics Initiative in achieving its objectives.


Poor mental health contributes significantly to global morbidity. The evidence regarding physical benefits of running are well-established. However, the mental health impacts of running remain unclear. An overview of the relationship between running and mental health has not been published in the last 30 years. The purpose of this study was to review the literature on the relationship between running and mental health. Our scoping review used combinations of running terms  and mental health terms . Databases used were Ovid(Medline), Ovid(Embase), ProQuest and SportDiscus. Quantitative study types reporting on the relationships between running and mental health were included. Database searches identified 16,401 studies; 273 full-texts were analysed with 116 studies included. Overall, studies suggest that running bouts of variable lengths and intensities, and running interventions can improve mood and mental health and that the type of running can lead to differential effects. However, lack of controls and diversity in participant demographics are limitations that need to be addressed. Cross-sectional evidence shows not only a range of associations with mental health but also some associations with adverse mental health (such as exercise addiction). This review identified extensive literature on the relationship between running and mental health. Poor mental health contributes significantly to the global health burden . The strThere is substantial evidence to support the relationship between physical activity (PA) and various mental health outcomes across the lifespan ,6,7. TheWhile the evidence base for the benefits of running on physical health is well-established, the mental health changes from running remain unclear. Addressing the gap within this knowledge is valuable as running is a form of PA popular among many population groups . InclusiIn recent years, there has been a transition within healthcare to focus on disease morbidity rather than disease mortality, in particular with a drive to improve global mental health . There i(1)to provide an overview of what is known regarding the relationship between running and mental health outcomes in all age groups and populations(2)to highlight current knowledge gaps and research prioritiesTo the best of the authors’ knowledge, no recent reviews of the relationship between running and mental health are available. The synthesis provided by this review will enable healthcare practitioners, psychologists and policy makers to better advise on running for mental health. It will also identify key gaps in the literature for future research. The aims of this scoping review are the following:A scoping review was concluded to be the most appropriate to address the research aims as it provides an overview of the volume and distribution of the evidence base as well as highlights where more research is warranted. The review followed the five-stage scoping review framework proposed by Arksey and O’Malley and was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scoping review extension checklist  17,18].,18.17,18Research questions were developed to address the research aims: “What is known about the effects of running on mental health outcomes?” and “What are the current knowledge gaps?”. Research question formulation was guided by item 4 in the PRISMA scoping review extension checklist . The defMental health outcomes were informed by Kelly et al. 2018) [ [8], whoAny geographical locationAll years between 1970 and 2019○Preventive effects (negative)○Health promotion effects (positive)○Intervention effectsQuantitative effects of running on predetermined mental health outcomesAny age group or sexHuman studiesDesigns including primary research Studies that mentioned walking as well as running were included because it is not possible to differentiate walkers from runners in events such as Parkrun.Studies were included based on the following criteria:Specialist groups including elite, professional or competitive athletes.General physical or aerobic activity, rather than exclusively runningQualitative and ethnographic designsSystematic and scoping reviews Editorials, opinion pieces, magazine/newspaper articles, case reports and papers without primary dataFocus on secondary mental health within clinical groups with specific physical or mental conditions that is not the condition being treated with running Evidence types including guidelines, unpublished and ongoing trials, annual reports, dissertations and conference proceedingsAnimal studiesUnavailable in EnglishRunning intervention was part of a wider study where differentiating the individual effect of running was not possible .Conference abstracts that were not published as full articlesStudies were excluded based on the following criteria:Databases searched were Ovid (Medline), Ovid (Embase), ProQuest and SportDiscus. Databases were searched for titles and abstracts that included at least one running term with one mental health outcome term. Appropriate truncation symbols were used to account for search term variations. Common running terms were combined. Search terms and the full search syntax can be found in https://www.covidence.org), and duplicates were automatically removed. Titles and abstracts were screened, with 20% cross-checked early in the process to assess agreement between authors. Full texts were reviewed by 2 authors.All identified records were uploaded to Covidence ((1)Author(s), year of publication and geographical location of study(2)Mental health conditions examined(3)Sample size and population details(4)Study design(5)Measures used to quantify any change in mental health outcome(s)(6)Running dose (if applicable) and compliance (if applicable)(7)Whether running was beneficial and the main findingsData extraction was completed by the lead author (F.O.) with 5% double screened by a second author (J.R.). The data extraction form was pilot tested with the first 20 studies and informed the following standardised extraction agreed upon by all authors:In studies that used “Profile of Mood States” (POMS) as a measurement of mood state, total mood disturbance was used in this review if reported by the authors. If the authors only reported one/some of the POMS subdimensions, these data were extracted instead.Included studies were organized into 3 categories: cross-sectional studies, acute  bouts of running, and long-term running interventions. For each of these 3 categories, the results were presented in two ways: (a) a descriptive numerical analysis to highlight the prevailing domains of research regarding geographical location, mental health outcomes and research methods and (b) a narrative summary of the key findings.From initial searches, 29,851 papers were identified. Following removal of duplicates, 16,401 were screened at the title and abstract levels and 273 papers were retained for full-text assessments. Ultimately, 116 papers met the inclusion criteria for this review. Forty-seven studies utilised cross-sectional designs (with and without non-running comparison groups)  29,30,3,73,74,75Sixteen of the 47 studies directly compared measures of mental health in runners and non-running comparisons ,63,64,73Nineteen studies only included runners ,74,75,76A further eight studies compared groups of runners ,60,68,69Two studies compared runners to individuals with diagnosed eating disorders but neither indicated that habitual running led to development of disordered eating or body-image problems ,59.One study found that habitual runners prevented from running by illness or injury had significantly greater overall psychological distress, depression and mood disturbance than continuing runners as well as significantly lower self-esteem and body-image .A study comparing negative addiction in runners versus gym exercisers found significant association between years of participation in running and gym exercise with negative addiction, regardless of activity type .Consistent evidence was found for a positive association between positive mental health outcomes and habitual or long-term recreational running compared to non-runners. In contrast, there was evidence that high or extreme levels of running (high frequency and long distance including marathon running) were associated with markers of running ill-health compared to levels of moderate running.Narrative description of findings of the 35 studies with an acute bout of running are included within ) [Twenty-three studies incorporated a design using a single bout of running to compare pre-post measurements of mood and short-term measures of mental health  [77,78,7) ,97,98,99) .Eleven studies used a single bout of treadmill running, and all found positive pre-post differences in mental health outcomes ,93,97,99Three studies used a single bout of track running and found significant decreases in anxiety ,87 and tThere were significant improvements for self-esteem, stress and total mood disturbance following a 5-km Parkrun , while aThere were nine studies that had a double-bout design ,107,108 Four studies compared park/rural versus urban running, and all found measures of mental health including anxiety, depression, mood and self-esteem improved post-run ,105,107.Three studies used three bouts of running  109,110110,111. Overall, these studies suggest that acute bouts of running can improve mental health and that the type of running can lead to differential effects. The evidence suggests that acute bouts of treadmill, track, outdoor and social running (2.5–20 km and 10–60 min) all result in improved mental health outcomes. There were few differences between high and low intensities. Studies consistently show that any running improves acute/short-term mood markers, but the lack of inactive comparison conditions is a limitation to the strength of the evidence. Little variation in the demographics of participants and small sample sizes limit generalizability and precision of findings.Thirty-four studies investigated the effects of more than three bouts of running on measures of mental health ranging from 2-week interventions to 1-year marathon training programmes  112,113,143,144.Eight studies used 2–8 week running interventions ,137,139.Eleven studies used 10–20 week running interventions ,140,143.A number of studies looked at specific populations. One investigated the impact of 10 organised runs on homeless people and found significant positive correlation with perceived self-sufficiency . Two invNine studies used subjects with known psychiatric disorders and found that longer-term interventions generally improved markers of mental health in psychiatric populations, particularly markers of depression ,142,144.Overall, running interventions of 2–20 weeks generally show improved markers of a range of mental health outcomes compared to non-running controls, including mental health outcomes in psychiatric and homeless populations. The risk of longer-term running interventions on adverse mental health outcomes remains unclear.The key findings of the each of the three categories of studies are summarised in n = 116 included studies resulted in the following gaps being identified:lack of studies in those aged under 18 (Only four acute bout studies ,107,108 lack of studies in those aged over 45;lack of gender-specific approaches;few studies investigating clinical populations; andlimited diversity in patient demographics.As well as reporting the available evidence, this review also aimed to identify key gaps in the evidence base for running and mental health. Consideration of sample demographics in the There is a growing body of literature exploring the relationships of running on certain mental health outcomes. There were variations in methods and outcomes studied, but there were similar overall beneficial trends. Generally, evidence supported positive effects of a range of lengths and intensities of running on mental health. However, there was limited diversity in participant demographics. Attribution was also compromised by the limited number of studies with comparisons/control groups. Synthesis of quantified effects is made challenging by large variations in reporting methods. Consistency and appropriateness of mental health measures was also varied throughout the literature.The review identified a smaller evidence-base focused on clinical populations. Behaviour change and compliance can be challenging in populations with clinical depressive disorders , and theOur findings suggest that, throughout cross-sectional evidence, acute bouts of running and longer-term running interventions are associated with improvements in a range of mental health outcomes. This is likely explained by running supplying a sufficient dose of moderate to vigorous PA to stimulate the known mental health benefits associated with PA. These benefits are thought to be mediated by neurobiological, psychosocial and behavioural mechanisms, all of which an effective running intervention of any genre has the potential to influence . The difThis review does not present running and mental health as a novel idea. As early as 1979, scholars discussed the relationship between psychotherapy and running . An earlStudies from 1986  and 1991This review agrees with these earlier findings but is the first to use systematic scoping review methods. This means that it presents a transparent search and inclusion strategy and is less prone to bias in terms of included studies and resulting findings. As such, this review has contributed to the evidence base by demonstrating that the weight of evidence up to 2019 favours positive mental health relationships with running.The authors acknowledge the limitation that this review was designed to assess the behaviour of running but that there are fields of studies including treadmill-based exercise which our review may not have picked up. However, the strength of this review is that the review does not focus on laboratory-based exercise but instead on what a healthcare professional may recommend to a free-living patient or the general public for mental health benefits. However, subjective measures of running intensity were not considered in detail, which may impact the conclusions of the review. The authors acknowledge that the results were not separated by means of running type due to the method of prioritization used to report the results, and thus, this remains a research gap. As with any scoping review, it is possible that the search and inclusion strategy led to omission of some key research.Synthesis of quantified effects was also made challenging by the large range of reporting methods used within the studies. This scoping review did not attempt to undertake quality appraisal of the included studies. The wide range of study designs and methods included within the review does not allow a statistical synthesis of the effectiveness of the studies., With increasing pressures on healthcare budgets, PA offers an augmentative therapeutic option for mental health management [Pharmacological management is often used as a first-line of defence for mental health disorders ; howevernagement . It is lThis review presents the effects of running on mental health and can inform healthcare professionals and psychologists who advise on management of mental health conditions. The authors’ interpretation of the evidence base is that, with appropriate clinical judgement, practitioners may identify patients with an interest in running or previous history of running as an ideal candidate for running as a form of psychotherapy. Findings from this review indicate that characteristics of running to be recommended may include self-pacing, distance and time feasibility to the individual, and being within the lactate threshold. There were consistent trends within findings despite a variety of running interventions, which suggest that it would be appropriate to recommend track running, outdoor urban and rural running, and treadmill running to improve mental health. However, a large number of studies used healthy, active college-aged participants, which may limit the relevance of these recommendations to other population groups. It is acknowledged that running will not be a suitable recommendation for everyone and that prescription of running is not as simple as just instructing people to run; it will require clinical expertise with regard to mental health in the way it is prescribed .This review identifies research gaps regarding patient demographics, but we have further recommendations about increasing sample sizes, quantitative study design and more coherent mental health outcomes. There was great variability in mental health outcome measures, particularly within the acute bout studies, where short-term measures of mental health could have equally been defined as mood and affect. We recommend that future research seeks more clarity on appropriate outcome measures. A comparison of types, settings and intensities of running is needed to better inform running and mental health recommendations.Recommendations for future research include addressing the effect of running on mental health of those under 18, those over 50s and clinical populations. A meta-analysis of the subset of study types such as interventions should be carried out. While the appropriateness of running interventions in those over 50 may be questioned, there is evidence that older adults do also benefit from the anti-depressive effect of exercise . We knowThis review is the most recent to comprehensively report the breadth of literature on the relationship between running and mental health. We conclude that running has important positive implications for mental health, particularly depression and anxiety disorders, but synthesis of quantified effects is made challenging by variation in reporting methods and remains a gap. This scoping review may have consequences for researchers, practitioners and relevant organisations and may inform the practice of healthcare professionals. Knowledge gaps concerning running on the mental health of children, older adults and clinical populations provide guidance for future research


Detection of early esophageal cancer is important to improve patient’s survival, but accurate diagnosis of superficial esophageal neoplasms is difficult even for experienced endoscopists. Computer-aided diagnostic system is believed to be an important method to provide accurate and rapid assistance for endoscopists in diagnosing esophageal neoplasms. We developed a single-shot multibox detector using a convolutional neural network for diagnosing esophageal cancer by using endoscopic images and the aim of our study was to assess the ability of our system. Our system showed good diagnostic performance in detecting as well as differentiating esophageal neoplasms and the accuracy can achieve 90%. Differentiating different histological grades of esophageal neoplasm is usually conducted by magnified endoscopy and we confirm that artificial intelligence system has great potential for helping endoscopists in accurately diagnosing superficial esophageal neoplasms without the necessity of magnified endoscopy and experienced endoscopists.Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system. A total of 936 endoscopic images were used as training images, and these images included 498 white-light imaging (WLI) and 438 narrow-band imaging (NBI) images. The esophageal neoplasms were divided into three classifications: squamous low-grade dysplasia, squamous high-grade dysplasia, and squamous cell carcinoma, based on pathological diagnosis. This AI system analyzed 264 test images in 10 s, and the sensitivity, specificity, and diagnostic accuracy of this system in detecting esophageal neoplasms were 96.2%, 70.4%, and 90.9%, respectively. The accuracy of this AI system in differentiating the histological grade of esophageal neoplasms was 92%. Our system showed better accuracy in diagnosing NBI (95%) than WLI (89%) images. Our results showed the great potential of AI systems in identifying esophageal neoplasms as well as differentiating histological grades. Esophageal cancer is a highly aggressive cancer with a poor prognosis, and around 508,000 esophageal cancer-related deaths were recorded globally in 2018. It is also the seventh most common cancer and the sixth most common cause of cancer-related death [Esophagogastroduodenoscopy (EGD) is the most sensitive examination approach as well as being the gold standard for diagnosis of esophageal cancer and precancerous lesions; nevertheless, the diagnosis of esophageal precancerous lesions and superficial cancer still presents great challenges for endoscopists, as these lesions are easily overlooked in conventional white-light imaging (WLI) and about 40% of lesions might be missed . Even thWith the development of computer technology, artificial intelligence (AI) has been widely studied in the endoscopic field in diagnosing gastrointestinal tract diseases, especially cancer . One majIn the present study, we aimed to test the ability of an AI-assisted image analysis system in differentiating histological grades of esophageal neoplasms, including low-grade squamous dysplasia, high-grade squamous dysplasia, and squamous cell carcinoma (SCC).p = 0.61).A total of 264 images were used as a test image set, including 112 WLI and 152 NBI images, and SSD required 10 s for analysis. The comprehensive SSD accurately diagnosed 202 images of 210 images of esophageal neoplasms and 38 images of 54 images of a normal esophagus . The senThe detailed results of our SSD in analyzing different histological grades of esophageal neoplasms are shown in Our AI system showed good diagnostic performance for the detection of esophageal neoplasms, and the accuracy, sensitivity, and PPV of our SSD were 90.9%, 96.2%, and 92.7%, respectively. Previous studies using other AI systems in diagnosing esophageal neoplasms showed the accuracy, sensitivity, and PPV of different AI systems were about 56–93%, 89–98%, and 46–86%, respectively ,18,19,20In addition to the detection of esophageal neoplasm, our SSD system also demonstrated the capacity to differentiate histological grades of neoplasms into low-grade dysplasia, high-grade dysplasia, and cancer. The accuracy of our SSD system for the diagnosis of histological grades of esophageal neoplasm was 92%, and NBI (95%) showed higher accuracy than WLI (89%). To the best of our knowledge, this study is the pilot study using an AI system to differentiate between histological grades of esophageal neoplasm, and a diagnostic system that not only detects esophageal neoplasm but also identifies the histological grade of esophageal neoplasm was constructed.Most previous studies used two nominal classifications in testing the diagnostic performance of an AI system for esophageal neoplasm, including cancer and non-cancer or a region of interest and a region of non-interest ,10,14,20In this pilot study, we found the severity of histological grade influenced the diagnostic performance of our AI system. A trend of diagnostic performance of comprehensive analysis between cancer and low-grade dysplasia was observed; our AI system showed the highest PPV and F1 score in diagnosing cancer, on the other hand, low-grade dysplasia had the lowest PPV and F1 score. The same finding was also observed in the WLI model. Discussing the reasons as to why histological grade influences the diagnostic performance of the AI system is problematic because deep learning did not explain the predictions; however, this phenomenon was not observed in the NBI model. Our explanation is that the classification of esophageal neoplasms is made by pathological diagnosis, and it is supposed that some image features of advanced esophageal neoplasms could be identified by the AI system in the WLI model, while the image differences between early and advanced lesions would be diminished by the NBI model as NBI magnifies some features that could not be demonstrated on WLI.The specificity rate of our AI system was 70.4%, and previous studies have shown specificity rates of about 68–96% ,14,20,22There are several limitations in our study. Firstly, our SSD system was constructed by images from a single center using fewer training images as compared to previous studies. In addition, images were still used from regular endoscopes  and standard endoscopic video systems , rather than non-high-resolution endoscopy images and system. Whether the diagnostic performance of our SSD system could be improved by higher quality images from a newer model machine or magnified endoscopy is unknown; however, based on the results of the present study, our SSD system shows non-inferior diagnostic performance in detecting esophageal neoplasms and has a great potential in differentiating neoplasms into different histological grades. Furthermore, a newer machine or magnified endoscopy is not necessary for our system, and this advantage might be of great help in institutions without these devices. Secondly, as mentioned before, fewer images of a normal esophagus were used in our training set and data imbalance between different groups might have interfered with the model optimization; moreover, the number of images from each lesion was not consistent, and this might have also influenced the learning effect of our SSD system. Thirdly, suboptimal images with poor quality were excluded in both training and testing sets, which might have caused selection bias; and fourthly, we focused mainly on esophageal squamous dysplasia and squamous cell carcinoma, and images of Barrett’s esophagus and esophageal adenocarcinoma were not collected for analysis. Because of the small sample size, we believe that constructing an AI system merely focused on squamous cell neoplasms would provide better diagnostic accuracy. Further study might be warranted to evaluate our SSD system in diagnosing Barrett’s esophagus as well as esophageal adenocarcinoma.To construct our SSD  system for the diagnosis of esophageal neoplasm, we retrospectively collected EGD images from 46 patients with esophageal neoplasms at Kaohsiung Medical University Hospital, and there were 10 patients with esophageal low-grade dysplasia, 20 patients with high-grade dysplasia, and 16 patients with SCC. The classification of esophageal neoplasm was based on pathological reports. A total of 936 images were collected for training images, including 162 images of a normal esophagus, 165 images of low-grade squamous dysplasia, 282 images of high-grade squamous dysplasia, and 327 images of esophageal cancer (SCC). The number of WLI and NBI images was 498 and 438, respectively. An additional 264 images were also obtained for the test set, and these images included 112 WLI and 152 NBI images. We excluded low-quality images caused by blurring, defocusing, mucus, and poor air blowing. This study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUH) (KMUHIRB-E(I)-20180338). Written informed consent was waived because of the retrospective, anonymized nature of study design.A convolutional neural network (CNN) architecture, called the single-shot multibox detector (SSD) model, was used in constructing a diagnosis system based on AI ,27,28,29The crucial feature of SSD is that it can provide at least one order of magnitude and a default box with different locations, scales, and aspect ratios compared with the existing methods; additionally, the SSD architecture utilizes multiscale convolutional bounding-box output with multiple feature maps ,30. The Several rectangular default boxes with different sizes and positions can be obtained through the settings above. However, the predicted results of the model output only needed a few boundary boxes to match the ground truth. Therefore, SSD required a mechanism that could match or eliminate redundant default boxes. The matching principles between the default boxes and the ground truth are defined as follows. First, how the ground truth of the training images matches the default boxes must be determined. The matching degree between the default boxes and the ground truth is determined by calculating the Intersection over Union (IOU) value and used in ensuring that each ground truth corresponds to a unique default box . The bouWhen the model detector detects an esophageal neoplasm from the input data of the test image, the disease name  is assigned, and a rectangular frame is displayed in the endoscopic image to surround the area of the esophageal neoplasm. In addition, if the area is normal, the rectangular frame is not displayed. p value below 0.05 was considered statistically significant. To compare the diagnostic accuracy of SSD in using WLI and NBI in differentiating histological grade of esophageal neoplasm, the kappa coefficient was used to assess interrater diagnostic agreement between SSD and pathological diagnoses. All statistical operations were performed using STATA 15 statistical software.Fisher’s exact test was used to compare the diagnostic accuracy of SSD in using WLI and NBI for the detection of esophageal neoplasm, while a Our SSD system showed good diagnostic performance in detecting neoplasm as well as classifying histological grade. An AI system might have potential in diagnosing the early stages of esophageal neoplasms without the necessity of magnified endoscopy and an experienced endoscopist. It is hoped that early and accurate diagnosis of esophageal neoplasm will provide a less harmful therapeutic option and improve patient prognosis.


Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types . ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins ( Small-scale fisheries (SSF) are a critical means of subsistence and livelihood in many regions of the world. They provide needed sources of protein, food security, and poverty alleviation , 2, and SSF, like other fisheries sectors, incurs fisheries bycatch. Bycatch refers to the unintended capture of non-target species , 17, andRecent innovations in geospatial technology have demonstrated success in supporting sustainable fisheries and marine mammal conservation. Global positioning systems coupled with unmanned aerial and marine drones equipped with laser, thermal, and acoustic sensors now enable scientists and conservation practitioners to track marine megafauna movements –30 and SA risk assessment evaluates the likelihood, or probability, of an event happening and the magnitude of the consequences if the event happens , 40. SpeDespite advances in GIS technology for data collection, spatial analysis, and risk assessment, there remains a need for tools that incorporate the spatio-temporal dimension of SSF and include both bycatch exposure and its consequences to resident marine mammal populations. For this reason, we developed the Bycatch Risk Assessment (ByRA), a tool for spatially explicit risk assessment tailored specifically for marine mammal bycatch in data-limited fisheries. ByRA combines existing SSF and marine mammal data within an open source GIS-based framework. Most importantly, outputs from the ByRA, which include bycatch risk maps and plots describing species-gear interactions during different seasons and scenarios, are produced in accessible interactive web visualization and summary table formats. These products can therefore easily be communicated to non-expert stakeholders and vetted by experts for iterative improvement that augments the understanding of local fisheries, identifies and fills knowledge gaps, and can be used for designing strategies to meet sustainability objectives , 52, 53.Here, we present the results from an application of ByRA in Southeast Asia, specifically Malaysia and Vietnam. In these countries, fish are a major source of nutrition and livelihoods , 55 and (Orcaella brevirostris), and the latter has a significant dugong (Dugong dugon) population. At each of the field sites, we had the support of and worked collaboratively with local researchers and resource management agencies to undertake this work and use their data on marine mammal and small-scale fisheries vessel occurrence.To develop the ByRA tool, we used three case study sites in Southeast Asia and the bycatch of two species of marine mammals. The sites were Kien Giang Biosphere Reserve (Vietnam), Kuching Bay and the Mersing Archipelago  . The firBycatch risk was assessed from existing GIS data on marine mammal occurrence and fishing activities. These were mapped as habitat suitability and kernel density estimations, respectively. Fishing data were organized into five general gear categories . Drivers of bycatch risk included environmental and sociopolitical factors, specifically seasonal weather  patterns and the current status of fisheries management that influence the distribution of marine mammal populations and SSFs. In Kuching Bay, Malaysia we assessed three scenarios  to capture seasonal drivers known to change fishing patterns and Irrawaddy dolphin occurrence over time and, therefore, vary their exposure to bycatch risk, including encounter rates and timing of overlap with different gears in space. In all sites we identified consequences (impacts) of such interactions, including life history stages affected by gear-specific threats and local conservation status of the species. The following sections describe three key steps we took to leverage existing data with the ByRA tool: (i) engage stakeholders and acquire existing knowledge, (ii) build bycatch scenarios, and (iii) analyze and visualize bycatch risk and data uncertainty.Our methodological approach emphasized transparency and building collaborative relationships prior to acquiring data, including representatives from provincial governments, non-governmental organizations (NGOs), and scientists from local universities . When reThe following questions posed during the data acquistion phase yielded the most useful information to perform the ByRA: (i) What kinds of surveys and technology do you use to track marine mammals and fishing vessels? (ii) Which fisheries are present and what fishing gears are used? (iii) Which spatial data are available/exist for your field site to help understand risk of bycatch ? (iv) Does the area have an existing fisheries observer program, stranding network or other indications of work related to marine mammals and, if so, in which season(s) is monitoring conducted?Based on the spatial coverage of existing marine mammal and fisheries surveys, we structured the risk assessment by delineating areas of interest (AOI) that extended 10 km beyond locations with high SSF and marine mammal occurrence. This minimized edge effects in the geospatial calculations and focused on known areas of bycatch concern. To summarize and compare ByRA findings within each site, we defined between 4 and 8 subregions  that difWe acquired information about fishing gears known to entangle, cause strandings or mortality of the two coastal marine mammals of interest. All fishing methods were organized into five broad but distinct categories: (1) nets, (2) trawls, (3) pots and traps, (4) longlines, and (5) hook and line . Combininaturalearthdata.com), (2) GADM (gadm.org) and (3) GPS Nautical Charts  offered free reference layers and viewers to identify available nautical charts for purchase. Monitoring efforts by local partners documented sightings of animals and SSF during aerial and boat-based surveys between 2008 and 2016. These data included GPS location (latitude/longitude), individuals observed (number) or gear type (name), and time of year (season) for each recorded sighting  to characterize the coastal-marine environment in the study areas. Three online sources, in particular, (1) Natural Earth  to map suitable environmental conditions. Maxent needs 30 or more sightings for reasonable statistical power [When marine mammal sightings were available, we applied the Maxent modeling software  for a correlative model in Vietnam. Instead, we employed a ‘low-data’ approach to map suitable habitat areas for Irrawaddy dolphins in KGBR based on Briscoe et al. [There were insufficient observations of Irrawaddy dolphins , dry season (May to September), and pre-monsoon (September to December). In SBTI, aerial surveys to monitor dugongs and SSF around the Sibu-Tinggi Islands were conducted during the dry season only, typically from November to February, to avoid the northeast monsoon . FishingTo map fishing intensity by gear type, we used kernel density estimation (KDE), an interpolation technique available in QGIS for mapping hot spots that estimates location, spatial extent, and intensity of fishing activity . This nonaturalcapitalproject.stanford.edu/software/invest) [exposure , and its consequence, which is the gear-specific impact to a species. For two additional exposure criteria unique to bycatch risk, we defined: (1) likelihood of interaction, as the probability that the animal will encounter a fishing gear if spatial overlap was detected, and (2) catchability, as the likelihood of animal capture by a gear type when this overlap occurs. Similar to Samhouri and Levin [consequence attributes were defined as: (a) the resilience of a species to a stressor  and (b) its sensitivity .To assess risk of bycatch in each site, we combined geospatial layers of (1) species distribution, based on habitat suitability, and (2) fisheries presence, organized by gear type, distribution, and intensity of use . The cor/invest) . Here, w/invest) , 75, whend Levin , speciesSpecies-gear interactions for a total of twelve bycatch exposure and consequence criteria were scored as guided by field observations, literature review, and, subsequently, expert opinion . In Auguintensity of gear use, (ii) current status of management, and (iii) likelihood of interaction between the gear and species (mmbycatchtoolbox.org) to automate the necessary GIS operations (i.e. unions and definition queries) for preparing ByRA SEC input layers. Each output was reclassified  using a Jenks natural breaks algorithm to minimize the variation within each class. Encounter rates, or ‘likelihood of interaction’, were calculated as the sum of overlapping layers for habitat suitability (1–3) and gear intensity (1–3), where a sum total of 6 or 5 = high, 4 = medium, and 3 or 2 = low likelihood of interaction between gear and species. Current status of management was scored as “1” if implemented and “2” if identified for a given area. Areas where no management or regulation was identified were scored “3”, which was the maximum score (highest contribution to exposure) for this criterion.When available GIS data could be used to characterize species-gear interaction rates, spatially explicit criteria (SEC) layers were created to differentiate rating scores over space  and consequence (C), to quantify bycatch risk  = 0 for the grid cell being evaluated.Two common methods for measuring environmental risk based on expert opinion are Euclidean distance and multiplicative functions. Cumulative impact mapping studies tend to use a multiplicative approach , 77, whetch risk . If a stid = data quality weight and iw = attribute weight–to account for data uncertainty and substantiate the species-gear interaction ratings for each site  and the density of fishing activities that were found to overlap suitable marine mammal habitat areas . Areas with high marine mammal occurrence and fisheries activity were predicted as highest risk of SSF bycatch. Specific to the three field sites, areas where nets and trawls were used  had been overcome.In-person meetings with stakeholders in August 2017 yielded a diagnostic for data uncertainty, showing a gradient of data input quality across the three field sites . This waByRA map outputs were also error checked by in-country collaborators with certain areas flagged as potential over- or underestimates of fishing activity and habitat suitability, two important drivers of bycatch risk in the tool. We could only characterize data uncertainty and validate outputs because local experts were present to corroborate bycatch risk estimates based on information previously provided by researchers and knowledge holders. The visualization of uncertainty across a range of data inputs and field sites assisted researchers in Malaysia, Vietnam, and Cambodia to chart a path forward by concentrating limited resources in areas with substantial information gaps and consider monitoring protocols and technologies that had already yielded substantial returns in neighboring regions  despite subregion 4 being the most expansive , closest to the main fishing village (Mersing), and least protected (no fisheries management identified). ByRA revealed this and other non-obvious patterns that would be difficult to uncover without a spatio-temporally explicit risk assessment framework. For instance, risk coordinates plotted for each species-gear interaction and range of habitat suitability highlighted that nets deployed in some areas of SBTI posed an intermediate level of bycatch risk, similar to other SSF gears. On the other hand, risk from nets was highest inside subregion 3  field site, seasonal snapshots depicted how bycatch risk is likely to change during the year and within subregions . For theThe most data-scarce of the three SEA sites, KGBR Vietnam represents a template for ByRA users applying the tool in places where existing data are severely limited  identified as highest level of bycatch risk to dugongs and dolphins. For these areas of concern, nets and trawls were the gear types associated with the highest bycatch risks in large part due to greater exposure (distribution and intensity relative to other SSF gears) and consequences  when these fisheries encounter marine mammals. The spatially and temporally explicit scenarios in Kuching Bay showed patterns of risk that shifted to and from the estuarine and coastal waters across seasons. By integrating information from fisher interviews and line-transect surveys, we mapped the likelihood of species-gear interactions over space and time and at local scales. In parallel, geospatial analysis techniques such as participatory mapping with local scientists and agency experts were used to build habitat suitability layers by site and season. Finally, SSF gear and species interaction rates were scored to assess and map bycatch risk and data uncertainty. These ByRA outputs demonstrated the potential of a new tool to co-create knowledge and garner insights about bycatch exposure in data-limited small-scale fisheries.In this study, we present spatially explicit estimates of bycatch risk in three SE Asian field sites. A total of 10 810 kmCharacterizing the effects of marine mammal bycatch through space and time can be compared to finding a needle in a haystack–as bycatch is difficult to observe and quantify , 79. HowAgainst a backdrop of the importance of SSF as nutrition source and livelihood for coastal communities , 80, effAn obvious information gap in our study was bycatch data from onboard observers, a common requirement in European and US fisheries policy , 84, andAnother consideration for future research is the need to account for antagonistic or synergistic effects, that may better reflect the total risk of bycatch to the species . While BMarine mammal scientists and conservation groups in the SEA region continue to collect data on environmental habitat variables to understand seasonality, cetacean behaviors, and enhance the effectiveness of protection and management measures , 56. ThiSpecies-gear interactions and their impacts vary widely by location and across small-scale fisheries , 36, 82,There are also opportunities to apply ByRA for multispecies assessment, which can illuminate high risk gears across species by season or scenario. This may include comparing risk between fishing areas, how different gears contribute to risk, or evaluating alternative management strategies under consideration , 47, 92.We created a spatially explicit management tool (ByRA) to better understand and characterize risk of bycatch posed by common fishing gears in data-limited small-scale fisheries. Three unique field sites, where substantial marine mammal bycatch has been reported, were systematically screened using existing data and a powerful form of visualization to map areas and seasons of concern in a region where distinct spatio-temporal patterns of bycatch risk had not been identified. ByRA employed a range of geospatial and participatory engagement techniques–including specific methods tailored for data scarce areas–to compile and analyze existing information about small-scale fisheries and better plan further research, bycatch mitigation, and species recovery and protection. This information enables managers to establish baselines, deliberate with stakeholders on the next steps for data acquisition, and identify interventions that are likely to mitigate bycatch risk in small-scale fisheries. It may also help these fisheries comply with European Commission and U.S. regulations , 96 thatS1 Data(DOCX)Click here for additional data file.


We highlight the similarities and differences in primary and lateral root growth, focusing on the differential impact that phytohormones and environmental cues have on these.  The root system architecture describes the shape and spatial arrangement of roots within the soil. Its spatial distribution depends on growth and branching rates as well as directional organ growth. The embryonic primary root gives rise to lateral (secondary) roots, and the ratio of both root types changes over the life span of a plant. Most studies have focused on the growth of primary roots and the development of lateral root primordia. Comparably less is known about the growth regulation of secondary root organs. Here, we review similarities and differences between primary and lateral root organ growth, and emphasize particularly how external stimuli and internal signals differentially integrate root system growth. The root system’s extensive epidermal surface facilitates the interphase between the plant and soil environment, supplying the plant with water and nutrients . A robusIn both monocots and dicots, the PR develops from an embryonically formed meristem . Each LROryza sativa) and maize (Zea mays), seems largely similar to their development in dicots, the overall root system architecture (RSA) is more complex in monocots  in response to external stimuli, such as nutrients and other environmental cues, as well as developmentally programmed signals, such as phytohormones.Plant hormones coordinate root growth in response to developmental  and environmental  cues. Here we mainly focus on auxin, cytokinin (CK), abscisic acid (ABA), brassinosteroids (BRs), ethylene, gibberellin (GA), salicylic acid (SA), and jasmonate (JA), and their specific synthesis and signal transduction pathways. Extensive crosstalk between these hormone signalling pathways defines the development and growth of LRs and PRs. In the following, we will discuss how these hormones and their interactions determine PR and LR growth.In addition to its central role in PR and LR initiation (reviewed in Very low auxin concentrations promote PR elongation in Arabidopsis and maize . ConversSolanum lycopersicum). This could indicate that there are diversified responses to auxin input in LRs of different species.Little is known about the direct effects of auxin concentration on cell division and elongation in LRs. When young Arabidopsis seedlings were transferred to medium containing auxin, the elongation of LRs was inhibited  mutants display a wide range of lateral root phenotypes   proteins  are conserved among PRs and LRs.CK and auxin interact both synergistically and antagonistically during plant development. Like auxin, exogenously applied CK inhibits organ growth of the PR in Arabidopsis , and ricIt would be interesting to assess how CK signalling, metabolism, transport, and/or distribution mechanisms are distinct in PRs and LRs. In addition, the possibly distinct contributions of environmental stimuli to CK signalling in PRs and LRs remain to be addressed.Though ABA is mainly known for its role in seed germination and responses to water shortage , and ricThe growth responses of LRs are more sensitive to ABA signalling than those of PRs. Low concentrations of ABA show a quantitatively stronger impact on LR elongation when compared with the PR  inhibits the elongation of PRs in monocots and dicots  to produce amino acids and proteins is one of the key requirements for growth and plays a major role in defining plant productivity. N is primarily taken up through the root system in its inorganic (ammonium and nitrate) forms but can also be taken up in its organic  forms. In several plant species, different responses to nitrate and ammonium have been described.Hordeum vulgare) and Arabidopsis , but not NRT1.2, is also able to transport auxin and has been shown to transport auxin to the tip of LRs under low N availability to promote growth  is a major component of nucleic acids, phospholipids, ATP, and other molecules, and is therefore crucial for plant growth and development. Because phosphate is the only source of phosphorus in the soil, RSA is highly responsive to phosphate starvation. Pi is immobile in soil, resulting in an uneven distribution and low accessibility by plant roots. Additionally, Pi can be bound by soil components or converted to organic phosphate (O. sativa) show enhanced growth under Pi-deficient conditions (The response of roots to low Pi varies strongly between different species and genotypes. Numerous studies have shown a reduction in PR growth in the Arabidopsis Columbia (Col-0) accession during Pi starvation , 2006. HInterestingly, the PR response to low Pi is linked to excessive iron accumulation that occurs in these conditions . AccordiPhaseolus vulgaris), as well as monocots such as barley, rice, and maize display increased branching to form tertiary and higher order LRs  and PHT1;4 showed a faster LR elongation, but slower PR growth under low Pi . FurtherFinally, recent work in maize suggests that auxin and BR might play a role in Pi starvation. Altering the levels of the Arabidopsis homologue SERINE/THREONINE PROTEIN PHOSPHATASE 2A (ZmPP2AA1) 1 can change the response of PRs and LRs under phosphate starvation , several oak (Quercus) species, Arabidopsis, and soybean (Glycine max) (Vicia faba (bean) accessions from drier regions have a larger root system than those from well-irrigated, wet regions  mutants with drought-resistant LR elongation ] do not show any growth differences when grown under increased temperature (Helianthus annuus), temperatures below 20 °C and above 35 °C favour PR growth, while temperatures in the 20–35 °C range favour LR growth (Solanum tuberosum), PR growth at high temperatures was considerably reduced compared with LR growth , CaIn contrast to PRs, LRs partially suppress positive gravitropic growth and maintain a gravitropic set point angle (GSA) that allows radial expansion of the root system (plagiotropism) . The horThough hormonal mechanisms tuning angular LR growth have been recently explored, many open questions remain about how developmental and environmental signals modulate hormone signals and transcriptional cascades to modulate angular LR growth. In particular, it will be crucial to uncover how these mechanisms interact to produce distinct directional growth responses in PRs and LRs.RSA is highly dynamic, responding to external and endogenous cues to adapt to constantly fluctuating conditions . The mecMost of the here described responses are often dependent on the developmental context of the root organ. While this may be more apparent when studying LRs, most studies on PRs concentrate on a single time point. Hence, a better understanding of age-dependent responses in primary and lateral roots is needed to substantiate which of the responses of PRs and LRs underlie truly distinct developmental programmes. A challenging step will be to understand the transcriptional and post-translational mechanisms that define the distinct nature of PRs and LRs because they are likely to be key to understanding the distinct hormonal, physiological, and developmental outputs.