Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images Using Artificial Intelligence Anatomy Recognition.

Objective: To construct a convolutional neural network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background: Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical toward optimal outcomes. Therefore, new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute toward future AI-assisted RARP and surgeon guidance. Methods: Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. Two hundred sixty-four frames were annotated according to prostate, urethra, ligated plexus, and catheter. Thirty annotated images from different RARP videos were used as a test data set. The dice (similarity) coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results: The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes, respectively, with a Hd95 of 29.27 and 72.62, respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 to 1.86 mm difference vs human annotators, but with significant deviation (standard deviation = 3.28-3.56). Conclusion: This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared with human annotators, but with a small mean difference (<2 mm). This is a promising development for further research on AI-assisted RARP.

Feasibility of generating structured motivational messages for tailored physical activity coaching.

Tailored motivational messages are helpful to motivate people in eHealth applications for increasing physical activity, but it is not sufficiently clear how such messages can be effectively generated in advance. We, therefore, put forward a theory-driven approach to generating tailored motivational messages for eHealth applications for behavior change, and we examine its feasibility by assessing how motivating the resulting messages are perceived. For this, we designed motivational messages with a specific structure that was based on an adaptation of an existing ontology for tailoring motivational messages in the context of physical activity. To obtain tailored messages, experts in health psychology and coaching successfully wrote messages with this structure for personas in scenarios that differed with regard to the persona's mood, self-efficacy, and progress. Based on an experiment in which 60 participants each rated the perceived motivational impact of six generic and six tailored messages based on scenarios, we found credible support for our hypothesis that messages tailored to mood, self-efficacy, and progress are perceived as more motivating. A thematic analysis of people's free-text responses about what they found motivating and demotivating about motivational messages further supports the use of tailored messages, as well as messages that are encouraging and empathetic, give feedback about people's progress, and mention the benefits of physical activity. To aid future work on motivational messages, we make our motivational messages and corresponding scenarios publicly available.

Reducing social diabetes distress with a conversational agent support system: a three-week technology feasibility evaluation.

Background: People with diabetes mellitus not only have to deal with physical health problems, but also with the psycho-social challenges their chronic disease brings. Currently, technological tools that support the psycho-social context of a patient have received little attention. Objective: The objective of this work is to determine the feasibility and preliminary efficacy of an automated conversational agent to deliver, to people with diabetes, personalised psycho-education on dealing with (psycho-)social distress related to their chronic illness. Methods: In a double-blinded between-subject study, 156 crowd-workers with diabetes received a social help program intervention in three sessions over three weeks. They were randomly assigned to receive support from either an interactive conversational support agent (n=79) or a self-help text from the book "Diabetes burnout" as a control condition (n=77). Participants completed the Diabetes Distress Scale (DDS) before and after the intervention, and after the intervention, the Client Satisfaction Questionnaire (CSQ-8), Feeling of Being Heard (FBH), and System Usability Scale (SUS). Results: Results indicate that people using the conversational agent have a larger reduction in diabetes distress (M=-0.305, SD=0.865) than the control group (M=0.002, SD=0.743) and this difference is statistically significant (t(154)=2.377, p=0.019). A hypothesised mediation effect of "attitude to the social help program" was not observed. Conclusions: An automated conversational agent can deliver personalised psycho-education on dealing with (psycho-)social distress to people with diabetes and reduce diabetes distress more than a self-help book. Ethics Study Registration and Open Science: This study has been preregistered with the Open Science Foundation (osf.io/yb6vg) and has been accepted by the Human Research Ethics Committee - Delft University of Technology under application number 1130. The data and analysis script are available: https://surfdrive.surf.nl/files/index.php/s/4xSEHCrAu0HsJ4P.

Breaking Down Barriers to a Suicide Prevention Helpline: Protocol for a Web-Based Randomized Controlled Trial.

BACKGROUND: Globally, suicide is among the leading causes of death, with men being more at risk to die from suicide than women. Research suggests that people with suicidal ideation often struggle to find adequate help. Every month, around 4000 people fill in the anonymous self-test for suicidal thoughts on the website of the Dutch suicide prevention helpline. This self-test includes the Suicidal Ideation Attributes Scale (SIDAS), which educates users about the severity of their suicidal thoughts. The vast majority (70%) of people who complete the self-test score higher than the cutoff point (≥21) for severe suicidal thoughts. Unfortunately, despite this, less than 10% of test-takers navigate to the web page about contacting the helpline. OBJECTIVE: This protocol presents the design of a web-based randomized controlled trial that aims to reduce barriers to contacting the suicide prevention helpline. The aim of this study is 2-fold: (1) to measure the effectiveness of a brief barrier reduction intervention (BRI) provided in the self-test motivating people with severe suicidal thoughts to contact the Dutch suicide prevention helpline and (2) to specifically evaluate the effectiveness of the BRI in increasing service use by high-risk groups for suicide such as men and middle-aged people. METHODS: People visiting the self-test for suicidal thoughts on the website of the suicide prevention helpline will be asked to participate in a study to improve the self-test. Individuals with severe suicidal thoughts and little motivation to contact the helpline will be randomly allocated either to a brief BRI, in which they will receive a short tailored message based on their self-reported barrier to the helpline (n=388) or care as usual (general advisory text, n=388). The primary outcome measure is the use of a direct link to contact the helpline after receiving the intervention or control condition. Secondary outcomes are the self-reported likelihood of contacting the helpline (on a 5-point scale) and satisfaction with the self-test. In the BRI, participants receive tailored information to address underlying concerns and misconceptions of barriers to the helpline. A pilot study was conducted among current test-takers to identify these specific barriers. RESULTS: The pilot study (N=1083) revealed multiple barriers to contacting the helpline. The most prominent were the belief that a conversation with a counselor would not be effective, fear of the conversation itself, and emotional concerns about talking about suicidal thoughts. CONCLUSIONS: Our study will provide insight into the effectiveness of a brief BRI designed to increase the use of a suicide prevention helpline provided in a self-test on suicidal thoughts. If successful, this intervention has the potential to be a low-cost, easily scalable, and feasible method to increase service use for helplines across the world. TRIAL REGISTRATION: ClinicalTrials.gov NCT05458830; https://clinicaltrials.gov/ct2/show/NCT05458830. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41078.

Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance.

Goal-setting is often used in eHealth applications for behavior change as it motivates and helps to stay focused on a desired outcome. However, for goals to be effective, they need to meet criteria such as being specific, measurable, attainable, relevant and time-bound (SMART). Moreover, people need to be confident to reach their goal. We thus created a goal-setting dialog in which the virtual coach Jody guided people in setting SMART goals. Thereby, Jody provided personalized vicarious experiences by showing examples from other people who reached a goal to increase people's confidence. These experiences were personalized, as it is helpful to observe a relatable other succeed. Data from an online study with a between-subjects with pre-post measurement design (n=39 participants) provide credible support that personalized experiences are seen as more motivating than generic ones. Motivational factors for participants included information about the goal, path to the goal, and the person who accomplished a goal, as well as the mere fact that a goal was reached. Participants also had a positive attitude toward Jody. We see these results as an indication that people are positive toward using a goal-setting dialog with a virtual coach in eHealth applications for behavior change. Moreover, contrary to hypothesized, our observed data give credible support that participants' self-efficacy was lower after the dialog than before. These results warrant further research on how such dialogs affect self-efficacy, especially whether these lower post-measurements of self-efficacy are associated with people's more realistic assessment of their abilities.

Addressing people's current and future states in a reinforcement learning algorithm for persuading to quit smoking and to be physically active.

Behavior change applications often assign their users activities such as tracking the number of smoked cigarettes or planning a running route. To help a user complete these activities, an application can persuade them in many ways. For example, it may help the user create a plan or mention the experience of peers. Intuitively, the application should thereby pick the message that is most likely to be motivating. In the simplest case, this could be the message that has been most effective in the past. However, one could consider several other elements in an algorithm to choose a message. Possible elements include the user's current state (e.g., self-efficacy), the user's future state after reading a message, and the user's similarity to the users on which data has been gathered. To test the added value of subsequently incorporating these elements into an algorithm that selects persuasive messages, we conducted an experiment in which more than 500 people in four conditions interacted with a text-based virtual coach. The experiment consisted of five sessions, in each of which participants were suggested a preparatory activity for quitting smoking or increasing physical activity together with a persuasive message. Our findings suggest that adding more elements to the algorithm is effective, especially in later sessions and for people who thought the activities were useful. Moreover, while we found some support for transferring knowledge between the two activity types, there was rather low agreement between the optimal policies computed separately for the two activity types. This suggests limited policy generalizability between activities for quitting smoking and those for increasing physical activity. We see our results as supporting the idea of constructing more complex persuasion algorithms. Our dataset on 2,366 persuasive messages sent to 671 people is published together with this article for researchers to build on our algorithm.

A randomized controlled trial to pilot the efficacy of a computer-based intervention with elements of virtual reality and limited therapist assistance for the treatment of post-traumatic stress disorder.

Although well-established therapies exist for post-traumatic stress disorder (PTSD), barriers to seek mental health care are high. Technology-based interventions may play a role in improving the reach of efforts to treat, especially when therapist availability is low. The goal of the current randomized controlled trial was to pilot the efficacy of a computer-based trauma intervention with elements of virtual reality (VR; 3MR system) and limited therapist involvement for the treatment of PTSD in a childhood sexual abuse (CSA) and war veteran sample and to compare this to "treatment as usual" (TAU). TAU consisted of evidence-based approaches such as imaginal exposure, EMDR, or narrative exposure therapy. A total of 44 patients with PTSD were included and randomly assigned to 12 sessions of 3MR intervention or TAU (completer n 3MR = 12, TAU = 18). Several measures (PCL-5, BDI-II, OQ-45-2, and the M.I.N.I. 5.0.0.) were administered to measure symptoms of PTSD and depression and scores of overall well-being at pre, post, and a three-month follow-up measurement. Analyses suggest that symptoms of PTSD and depression in the 3MR condition decreased, and overall well-being increased between pre and post measurements. Results did not indicate any clear differences between the treatment conditions over time which suggests that treatment gains of the 3MR intervention seem no less than those of TAU. Finally, both treatment conditions produced similar remission rates of PTSD and depression. Therefore, the 3MR intervention could possibly constitute an appropriate treatment alternative. The small sample size as well as evident drop-out rates in the 3MR condition (45%) do warrant further research. The procedures of this study were approved by the Medical Ethical Research Committee (MERC) of the Erasmus Medical Center in Rotterdam (MEC-NL46279.078.13) and pre-registered via ClinicalTrials.gov (Protocol Record CI1-12-S028-1).

Using a conversational agent for thought recording as a cognitive therapy task: Feasibility, content, and feedback.

E-mental health for depression is increasingly used in clinical practice, but patient adherence suffers as therapist involvement decreases. One reason may be the low responsiveness of existing programs: especially autonomous systems are lacking in their input interpretation and feedback-giving capabilities. Here, we explore (a) to what extent a more socially intelligent and, therefore, technologically advanced solution, namely a conversational agent, is a feasible means of collecting thought record data in dialog, (b) what people write about in their thought records, (c) whether providing content-based feedback increases motivation for thought recording, a core technique of cognitive therapy that helps patients gain an understanding of how their thoughts cause their feelings. Using the crowd-sourcing platform Prolific, 308 participants with subclinical depression symptoms were recruited and split into three conditions of varying feedback richness using the minimization method of randomization. They completed two thought recording sessions with the conversational agent: one practice session with scenarios and one open session using situations from their own lives. All participants were able to complete thought records with the agent such that the thoughts could be interpreted by the machine learning algorithm, rendering the completion of thought records with the agent feasible. Participants chose interpersonal situations nearly three times as often as achievement-related situations in the open chat session. The three most common underlying schemas were the Attachment, Competence, and Global Self-evaluation schemas. No support was found for a motivational effect of providing richer feedback. In addition to our findings, we publish the dataset of thought records for interested researchers and developers.

Users' needs for a digital smoking cessation application and how to address them: A mixed-methods study.

Background: Despite their increasing prevalence and potential, eHealth applications for behavior change suffer from a lack of adherence and from dropout. Advances in virtual coach technology provide new opportunities to improve this. However, these applications still do not always offer what people need. We, therefore, need a better understanding of people's needs and how to address these, based on both actual experiences of users and their reflections on envisioned scenarios. Methods: We conducted a longitudinal study in which 671 smokers interacted with a virtual coach in five sessions. The virtual coach assigned them a new preparatory activity for quitting smoking or increasing physical activity in each session. Participants provided feedback on the activity in the next session. After the five sessions, participants were asked to describe barriers and motivators for doing their activities. In addition, they provided their views on videos of scenarios such as receiving motivational messages. To understand users' needs, we took a mixed-methods approach. This approach triangulated findings from qualitative data, quantitative data, and the literature. Results: We identified 14 main themes that describe people's views of their current and future behaviors concerning an eHealth application. These themes relate to the behaviors themselves, the users, other parties involved in a behavior, and the environment. The most prevalent theme was the perceived usefulness of behaviors, especially whether they were informative, helpful, motivating, or encouraging. The timing and intensity of behaviors also mattered. With regards to the users, their perceived importance of and motivation to change, autonomy, and personal characteristics were major themes. Another important role was played by other parties that may be involved in a behavior, such as general practitioners or virtual coaches. Here, the themes of companionableness, accountability, and nature of the other party (i.e., human vs AI) were relevant. The last set of main themes was related to the environment in which a behavior is performed. Prevalent themes were the availability of sufficient time, the presence of prompts and triggers, support from one's social environment, and the diversity of other environmental factors. We provide recommendations for addressing each theme. Conclusions: The integrated method of experience-based and envisioning-based needs acquisition with a triangulate analysis provided a comprehensive needs classification (empirically and theoretically grounded). We expect that our themes and recommendations for addressing them will be helpful for designing applications for health behavior change that meet people's needs. Designers should especially focus on the perceived usefulness of application components. To aid future work, we publish our dataset with user characteristics and 5,074 free-text responses from 671 people.

Multidisciplinary integrated care pathway for von Hippel-Lindau disease.

BACKGROUND: Clinical pathways are care plans established to describe essential steps in the care of patients with a specific clinical problem. They translate (inter)national guidelines into local applicable protocols and clinical practice. The purpose of this article is to establish a multidisciplinary integrated care pathway for specialists and allied health care professionals in caring for individuals with von Hippel-Lindau (VHL) disease. METHODS: Using a modified Delphi consensus-making process, a multidisciplinary panel from 5 Dutch University Medical Centers produced an integrated care pathway relating to the provision of care for patients with VHL by medical specialists, specialized nurses, and associated health care professionals. Patient representatives cocreated the pathway and contributed quality criteria from the patients' perspective. RESULTS: The panel agreed on recommendations for the optimal quality of care for individuals with a VHL gene mutation. These items were the starting point for the development of a patient care pathway. With international medical guidelines addressing the different VHL-related disorders, this article presents a patient care pathway as a flowchart that can be incorporated into VHL expertise clinics or nonacademic treatment clinics. CONCLUSIONS: Medical specialists (internists, urologists, neurosurgeons, ophthalmologists, geneticists, medical oncologists, neurologists, gastroenterologists, pediatricians, and ear-nose-throat specialists) together with specialized nurses play a vital role alongside health care professionals in providing care to people affected by VHL and their families. This article presents a set of consensus recommendations, supported by organ-specific guidelines, for the roles of these practitioners in order to provide optimal VHL care. This care pathway can form the basis for the development of comprehensive, integrated pathways for multiple neoplasia syndromes.

Improving Robotic Skills by Video Review.

Background: Since the transition of surgical robot systems into the medical field, physicians have had to develop new dexterity skills. The ideal learning environment for robotic surgery has yet to be discovered. Virtual reality (VR) simulation is a possible safe and economic method. In VR simulator training human feedback is hardly used, and an alternative may be found in video revision. The purpose of this study is to investigate whether adding video review to VR simulation-based training in novice physicians improves their ability to complete a complex robot task. In addition, the secondary goal is to investigate whether the skills learned on the robotic simulator can be transferred to a real robotic system. Materials and Methods: Forty participants, medical students and, medical-PhD candidates, from one university hospital were included. Baseline dexterity skills were measured through completion of a vesicourethral anastomosis on a VR robot simulator and the da Vinci robot. Participants were randomized into a video and control group. The video group practiced skills on the robot simulator with intermediate video revision, whereas the control group had intermediate pause instead. Postintervention dexterity skills were measured using the same exercises as the baseline tests. Results: No significant differences were found in baseline performance. Postintervention results on the VR simulator show that the video group commits significantly fewer injuries to the urethra and sutures at a greater optimal depth. The control group was significantly faster, had less camera travel, and had their instruments less out of view. On the da Vinci robot, participants in both groups performed significantly faster and had better global evaluative assessment of robotic skill score after the training sessions on the VR simulator. Conclusions: Video revision significantly improves the quality of robotic skills in novice surgeons on the VR simulator, although at the expense of time. Furthermore, both groups demonstrated enhanced skills on the da Vinci robot after training sessions, which advocates transferability of skill.

Factors Affecting User's Behavioral Intention and Use of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Small-Scale UTAUT Analysis.

A mobile app could be a powerful medium for providing individual support for cognitive behavioral therapy (CBT), as well as facilitating therapy adherence. Little is known about factors that may explain the acceptance and uptake of such applications. This study, therefore, examines factors from an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) model to explain variation between people's behavioral intention to use a CBT for insomnia (CBT-I) app and their use-behavior. The model includes eight aspects of behavioral intention: performance expectancy, effort expectancy, social influence, self-efficacy, trust, hedonic motivation, anxiety, and facilitating conditions, and investigates further the influence of the behavioral intention and facilitating conditions on app-usage behavior. Data were gathered from a field trial involving people (n = 89) with relatively mild insomnia using a CBT-I app. The analysis applied the Partial Least Squares-Structural Equation Modeling method. The results found that performance expectancy, effort expectancy, social influence, self-efficacy, trust, and facilitating conditions all explained part of the variation in behavioral intention, but not beyond the explanation provided by hedonic motivation, which accounted for R2 = 0.61. Both behavioral intention and facilitating conditions could explain the use-behavior (R2 = 0.32). We anticipate that the findings will help researchers and developers to focus on: (1) users' positive feelings about the app as this was an indicator of their acceptance of the mobile app and usage; and (2) the availability of resources and support as this also correlated with the technology use.

Natural language processing for cognitive therapy: Extracting schemas from thought records.

The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen's κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.

Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study.

BACKGROUND: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block. OBJECTIVE: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer's block when they encounter difficult situations in chats with help-seekers. METHODS: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors' chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system's utility and usability were measured, and the validity of the algorithm was examined. RESULTS: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=-0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (ß=.30, P<.001). CONCLUSIONS: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer's block.

Identifying the relationship between postoperative urinary continence and residual urethra stump measurements in robot assisted radical prostatectomy patients.

OBJECTIVE: To investigate the feasibility of urethral stump length and width measurements in recorded videos of robot assisted radical prostatectomy procedures using the Kinovea software and to assess if these measurements could be used as predictors of postoperative urinary continence. METHODS: Fifty-three patients were selected from an institutional database of 1400 cases and included in the study. All videos were analysed using the computer software 'Kinovea'. All measurements were performed using the inserted bladder catheter as a reference point. RESULTS: The reference point (bladder catheter) was available in 33 out of 53 patients. The median surgical urethral length (SUL) was significantly higher in the continent group (1050 vs. 1294 mm, p = 0.018). The urethral width measurements did not show a difference between the groups. In order to validate the Kinovea software as an accurate tool for the measurement of the urethral stump length and width results were correlated with the magnetic resonance imaging measurements of the urethra. CONCLUSIONS: The results of this study showed a significantly longer median SUL incontinent patients.

Analysis of the video motion tracking system "Kinovea" to assess surgical movements during robot-assisted radical prostatectomy.

BACKGROUNDS: Robot-assisted surgery facilitated the possibility to evaluate the surgeon's skills by recording and evaluating the robot surgical images. The aim of this study was to investigate the possibility of using a computer programme (Kinovea) for objective assessment of surgical movements in previously recorded in existing robot-assisted radical prostatectomy (RARP) videos. METHODS: Twelve entire RARP procedures were analysed by a trained researcher using the computer programme "Kinovea" to perform semi-automated assessment of surgical movements. RESULTS: Data analysis showed Kinovea was on average able to automatically assess only 22% of the total surgical duration per video of the robot-assisted surgery. On average, it lasted 4 hours of continued monitoring by the researcher to assess one RARP using Kinovea. CONCLUSION: Although we proved it is technically possible to use the Kinovea system in retrospective analysis of surgical movement in robot-assisted surgery, the acquired data do not give a comprehensive enough analysis of the video to be used in skills assessment.

Technological State of the Art of Electronic Mental Health Interventions for Major Depressive Disorder: Systematic Literature Review.

BACKGROUND: Electronic mental (e-mental) health care for depression aims to overcome barriers to and limitations of face-to-face treatment. Owing to the high and growing demand for mental health care, a large number of such information and communication technology systems have been developed in recent years. Consequently, a diverse system landscape formed. OBJECTIVE: This literature review aims to give an overview of this landscape of e-mental health systems for the prevention and treatment of major depressive disorder, focusing on three main research questions: (1) What types of systems exist? (2) How technologically advanced are these systems? (3) How has the system landscape evolved between 2000 and 2017? METHODS: Publications eligible for inclusion described e-mental health software for the prevention or treatment of major depressive disorder. Additionally, the software had to have been evaluated with end users and developed since 2000. After screening, 270 records remained for inclusion. We constructed a taxonomy concerning software systems, their functions, how technologized these were in their realization, and how systems were evaluated, and then, we extracted this information from the included records. We define here as functions any component of the system that delivers either treatment or adherence support to the user. For this coding process, an elaborate classification hierarchy for functions was developed yielding a total of 133 systems with 2163 functions. The systems and their functions were analyzed quantitatively, with a focus on technological realization. RESULTS: There are various types of systems. However, most are delivered on the World Wide Web (76%), and most implement cognitive behavioral therapy techniques (85%). In terms of content, systems contain twice as many treatment functions as adherence support functions, on average. Furthermore, autonomous systems, those not including human guidance, are equally as technologized and have one-third less functions than guided ones. Therefore, lack of guidance is neither compensated with additional functions nor compensated by technologizing functions to a greater degree. Although several high-tech solutions could be found, the average system falls between a purely informational system and one that allows for data entry but without automatically processing these data. Moreover, no clear increase in the technological capabilities of systems showed in the field, between 2000 and 2017, despite a marked growth in system quantity. Finally, more sophisticated systems were evaluated less often in comparative trials than less sophisticated ones (OR 0.59). CONCLUSIONS: The findings indicate that when developers create systems, there is a greater focus on implementing therapeutic treatment than adherence support. Although the field is very active, as evidenced by the growing number of systems developed per year, the technological possibilities explored are limited. In addition to allowing developers to compare their system with others, we anticipate that this review will help researchers identify opportunities in the field.

A Prospective, Observational, Multicentre Study Concerning Nontechnical Skills in Robot-assisted Radical Cystectomy Versus Open Radical Cystectomy.

INTRODUCTION AND HYPOTHESES: valuation of surgical skills, both technical and nontechnical, is possible through observations and video analysis. Besides technical failures, adverse outcomes in surgery can also be related to hampered communication, moderate teamwork, lack of leadership, and loss of situational awareness. Even though some surgeons are convinced about nontechnical skills being an important part of their professionalisation, there is paucity of data about a possible relationship between nontechnical skills and surgical outcome. In robot-assisted surgery, the surgeon sits behind the console and is at a remote position from the surgical field and team, making communication more important than in open surgery and conventional laparoscopy. A lack of structured research makes it difficult to assess the value of the different analysis methods for nontechnical skills, particularly in robot-assisted surgery. Our hypothesis includes the following: (1) introduction of robot-assisted surgery leads to an initial decay in nontechnical skills behaviour during the learning curve of the team, (2) nontechnical skills behaviour is more explicitly expressed in experienced robot-assisted surgery teams than in experienced open surgery teams, and (3) introduction of robot-assisted surgery leads to the development of different forms of nontechnical skills behaviour compared with open surgery. DESIGN: This study is a prospective, observational, multicentre, nonrandomised, case-control study including bladder cancer patients undergoing either an open radical cystectomy or a robot-assisted radical cystectomy at the Catharina Hospital Eindhoven, the Netherlands, or at the Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam. All patients are eligible for inclusion; there are no exclusion criteria. The Catharina Hospital Eindhoven, the Netherlands, performs on average 35 radical cystectomies a year. The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital Amsterdam, performs on average 100 radical cystectomies a year. PROTOCOL OVERVIEW: The choice of treatment is at the discretion of the patient and the surgeon. Patient results will be obtained prospectively. Pathology results as well as complications occurring within 90 d following surgery will be registered. Surgical complications will be registered according to the Clavien-Dindo system. MEASUREMENTS: Nontechnical skills will be observed using five different methods: (1) NOTSS: Nontechnical Skills for Surgeons; (2) Oxford NOTECHS II: a modified theatre team nontechnical skills scoring system; (3) OTAS: Observational Teamwork Assessment for Surgery; (4) Interpersonal and Cognitive Assessment for Robotic Surgery (ICARS): evaluation of nontechnical skills in robotic surgery; and (5) analysis of human factors. Technical skills in robot-assisted radical cystectomy will be analysed using two different methods: (1) GEARS: Global Evaluative Assessment of Robotic Skill and (2) GERT: Generic Error Rating Tool. SAFETY CRITERIA AND REPORTING: Formal ethical approval has been provided by Medical research Ethics Committees United (MEC-U), The Netherlands (reference number W19.048). We hope to present the results of this study to the scientific community at conferences and in peer-reviewed journals. STATISTICAL ANALYSIS: Frequency statistics will be calculated for patient demographical data, and a Shapiro-Wilk test with p > 0.05 will be used to define normal distribution. Univariate analysis will be conducted to test for statistically significant differences in observation scores between open radical cystectomy and robot-assisted radical cystectomy cohorts across all variables, using independent sample t tests and Mann-Whitney U testing, as appropriate. A variable-selection strategy will be used to create multivariate models. Binary logistic regression will be conducted to calculate odds ratios and 95% confidence intervals for significant predictors on univariate analysis and clinically relevant covariates. Statistical significance is set at p < 0.05 based on a two-tailed comparison. SUMMARY: This study uses a structured approach to the analysis of nontechnical skills using extracorporeal videos of both open radical cystectomy and robot-assisted radical cystectomy surgeries, in order to obtain detailed data on nontechnical skills during open and minimally invasive surgeries. The results of this study could possibly be used to develop team-training programmes, specifically for the introduction of the surgical robot in relation to changes in nontechnical skills. Additional analysis of technical skills using the intracorporeal footage of the surgical robot will be used to elucidate the role of surgical skills and surgical events in nontechnical skills.

Correction: Controlling Social Stress in Virtual Reality Environments.

[This corrects the article DOI: 10.1371/journal.pone.0092804.].