Digital mental health interventions offer a scalable solution that reduces barriers to seeking care for clinical depression and anxiety.We aimed to examine the effectiveness of a 12-week therapist supported, app-based cognitive behavioral therapy program in improving symptoms of depression and anxiety within 9 months.A total of 323 participants with mild to moderately severe depression or anxiety were enrolled in a 12-week digital cognitive behavior therapy program. The analysis was restricted to participants who provided at least one follow-up assessment after baseline. As a result, 146 participants (45.2%) were included in the analysis—74 (50.7%) participants completed assessments at 3 months, 31 participants (21.2%) completed assessments at 6 months, and 21 participants (14.4%) completed assessments at 9 months. The program included structured lessons and tools  as well as one-on-one weekly video counseling sessions with a licensed therapist for 12 weeks and monthly check-in sessions for 1 year. The clinically validated Patient Health Questionnaire (PHQ-8) and Generalized Anxiety Disorder Scale (GAD-7) were used to assess depression and anxiety, respectively. Linear mixed-effects modeling was employed to examine changes in depression and anxiety over time.P<.001) and anxiety scores . At the end of the 12-week intervention, we observed an average reduction of 3.76 points (95% CI –4.76 to –2.76) in PHQ-8 scores. Further reductions in depression were seen at program month 6  and program month 9 . A similar pattern of improvement emerged for anxiety, with a 3.17-point reduction at the end of the 12-week intervention (95% CI –4.21 to –2.13). These improvements were maintained at program month 6  and program month 9 . In addition, greater program engagement during the first 12 weeks predicted a greater reduction in depression We observed a significant positive effect of program time on improvement in depression (β=–0.12, The results suggest that digital interventions can support sustained and clinically meaningful improvements in depression and anxiety. Furthermore, it appears that strong initial digital mental health intervention engagement may facilitate this effect. However, the study was limited by postintervention participant attrition as well as the retrospective observational study design. Telemedicine delivered through smartphone-based apps is fast redefining how care is sought and delivered for disease prevention and management. The emergence of digital health technology is particularly relevant for mental health services. With a 12-month prevalence rate of 6.7% and 3.1% for major depressive disorder and generalized anxiety disorder, respectively, in the United States  significWith the rapid development and application of digital technologies such as smartphone apps, telemedicine has emerged as a compelling solution for disease treatment and management. Digital mental health interventions are growing in appeal because they offer scalable solutions and can improve uptake rates among those who are less inclined to seek mental health services. Digital mental health interventions are an effective means of delivering evidence-based psychological treatment to patients who are unable or unlikely to seek in-person treatment with a provider . In addiGiven the potential benefits of digital mental health interventions, there has been increased research focusing on their effectiveness. In their meta-analytic review, Richards et al  observedWhile early evidence is promising, digital mental health interventions are not monolithic in their treatment approach, design, or delivery. They can be game-based, focus primarily on psychoeducation, or incorporate human interactions. Digital mental health interventions can entail one-on-one interactions in real time with a therapist via audio or video conferencing tools and involve both synchronous and asynchronous text messaging.Interestingly, Firth et al  observedDigital mental health interventions are not without their drawbacks. Importantly, it is still unclear if all digital psychological treatments are equally effective. For instance, in their review of digital mental health interventions for students, Lattie et al  found thIn summary, recent interest and demand for remotely delivered mental health interventions has led to an increase in app-based solutions. Technology-based mental health programs have been shown to be effective in improving access and treating common mental health conditions such as depression. However, given the heterogeneity of digital mental health interventions in terms of treatment approach and modality and continued challenges of attrition, their overall effectiveness remains an open question. Though a fairly robust body of academic research supports the efficacy of digital mental health interventions in the treatment of mild to moderate depression and anxiety, there remain gaps. Specifically, commercially available, app-based mental health programs have received less research scrutiny, particularly, in evaluating medium-term and long-term outcomes. Second, hybrid models that combine a cognitive behavior therapy–based digital intervention with one-on-one remote therapist support have been assessed to a lesser extent.In this study, we evaluated the Vida Health therapy program, a digital therapeutic intervention for mild to moderate depression and anxiety. Vida Health is a commercially available, app-based digital health program for mental health and cardiometabolic conditions. The program is available direct to consumers, as an employee-based benefit or via select health plans. The Vida Health app offers tailored digital content paired with remote therapy and health coaching with licensed therapists, registered dieticians, certified diabetes educators or health education specialists. Vida Health program offerings include cognitive behavior therapy, mindfulness-based stress management, weight management, and chronic disease  management. Available in all 50 states, the therapy program that is the focus of this study is based on cognitive behavior therapy, a therapeutic approach that has shown the strongest evidence for treating depression and anxiety ,16. CognA retrospective observational study design was employed to evaluate changes in depression and anxiety following Vida Health’s app-based cognitive behavior therapy program that included one-on-one therapist-supported counseling. The study was approved by an independent institutional review board . The institutional review board waived the informed consent because the study was identified as having minimal risk and the data were fully anonymized before retrospective analysis.Depression was assessed using the clinically validated 8-item Patient Health Questionnaire (PHQ-8). Although the PHQ-9 may be more widely used, we utilized the PHQ-8 in this program. The key distinction between the two instruments is the inclusion of the suicidality item in the PHQ-9. In lieu of screening via app, all participants were evaluated for suicidality by a licensed therapist during the initial biopsychosocial assessment and intake. The 7-item Generalized Anxiety Disorder (GAD-7) scale was used to evaluate anxiety. These validated self-reported instruments are commonly used in clinical practice to assess depression and generalized anxiety disorder ,19. The The study included adults (18 years or older) who owned a smartphone or tablet, were fluent in spoken and written English, with mild to moderately severe depression or mild to moderate anxiety. Participants were recruited from companies based in the California Bay Area and in the state of Washington that offered the Vida Health cognitive behavior therapy program as a benefit to employees and spouses. Although Vida Health therapy program patients are currently distributed across the United States, this study focused on participants drawn from organizations where participants had been enrolled in the program for at least 6 months. Between February 2017 and January 2020, 323 participants were recruited through a combination of email announcements, paper flyers, and onsite events at their employers . ParticiPrior to enrolling in the Vida Health digital cognitive behavior therapy program, participants completed a brief set of registration questions that included name, contact information, basic demographics , and existing health conditions. At program start, participants completed the Patient Health Questionnaire (PHQ-8) and Generalized Anxiety Disorder scale (GAD-7). Study enrollment and participant flow are also presented in Study participants who were eligible were then paired with a licensed therapist based on their state of residence and their preferred times for consultations. Therapists were mental health professionals licensed by their states’ respective licensure boards and employed by Vida Health. Therapists were licensed in one or more states. License titles and types, and scope of license-practicing privileges varied by state. Therapists had one or more of the following license types : licensed clinical social workers, licensed medical clinical social workers, licensed independent social worker, licensed marriage and family therapists, licensed professional counselors, licensed professional clinical counselors, licensed mental health counselor, licensed clinical psychologists, licensed practicing psychologist, and clinical mental health counselor.Prior to program intake, all participants received and signed an informed consent to psychotherapy form. Consent to psychotherapy is a standard of care practice at the start of therapy. Its purpose is to inform clients of the expectations of therapy, their rights to confidentiality, and limits to confidentiality (including mandated reporting requirements as stipulated by the therapist’s licensing board and state regulations) and supply provider license information. The consent form also included details about the termination process, accessibility through digital text messaging, and video conferencing; the cancellation policy; limitations to teletherapy; and consent to engage in teletherapy. During the first appointment, the therapist conducted a biopsychosocial intake questionnaire that reviewed history of previous treatment or diagnosis, family history of mental health conditions and physical health conditions, current support systems, current medical conditions, current prescribed medications, history of substance use, strengths, current interests, spirituality or religiousness, sleep quality and quantity, appetite, current presenting problem, presenting symptoms, and mental status examination . After completing the intake, the therapist generated an initial diagnostic impression and determined if a participant was eligible to continue participating in the program. If a participant presented with mild to moderate anxiety or depression, they were eligible to continue the program.The Vida Health cognitive behavior therapy program is a HIPAA-compliant structured digital program that connects adults living with mild to moderate depression or anxiety with a licensed therapist. Although the core structure, duration, and focus of the digital cognitive behavior therapy program remained the same, certain app-features, improved functionality, and app-enhancements were introduced to the cognitive behavior therapy program between 2017 and 2020, resulting in 3 different program iterations. These program versions were included as a random factor in the final linear mixed methods model, to account for any potential differences in version effectiveness.As part of the intervention, participants were sent audio-, video-, and text-format lessons, activities and practices based on cognitive behavior therapy by their therapist through the app. Cognitive behavior therapy addresses maladaptive thinking patterns by exploring the relationship between thoughts, emotions, and behavior . The matThe therapist reviewed the completed lessons prior to every weekly consult, and in discussion with the participant, reviewed strategies for applying the concepts and skills that had been covered. The therapist would check-in with the participant about their current mood, set goals for the week, and prepare them for the upcoming lessons and activities as homework for the week ahead. Over the course of the program, the therapist met with the participants on a weekly basis for a duration of 30 minutes over video or phone call. Every week, the therapist generated a personalized treatment plan that reviewed the participant’s short-term and long-term goals as well as the habits, actions, and homework they planned to engage in for the week. These treatment plans were reviewed with the participant during the core intervention phase. Toward the end of the intensive phase (at approximately week 10), the therapist sent the participant lessons and tools to help generate a Wellness Recovery Action Plan. The Wellness Recovery Action Plan is a plan that identifies known triggers and coping strategies that the participant has identified to support their management of symptoms . The WelAcross all participants, the mean survey completions were 5.75 (SD 3.90) and 5.50 (SD 3.50) for GAD-7 and PHQ-8, respectively. The study design intended to obtain at least one assessment every fortnight. Based on this expectation, we could have received as many 1106 completed PHQ-8 surveys, assuming one survey completion per participant per fortnight; we received 698 PHQ-8 assessments, indicating a survey completion rate of 63.11%. Likewise, out of the 1185 possible GAD-7 survey completions (one per participant per fortnight), we received a total of 744 surveys indicating a completion rate of 62.78%. The temporal resolution of changes in PHQ-8 or GAD-7 scores was far higher for some participants than others. Although the automated assessments were administered every 2 weeks , a participant could complete the survey at any time during the 2-week period. As a result, a participant could have completed more than one survey within a fortnight. For example, a participant could complete a survey received on week 12, on the day of receipt or 8 days thereafter. The week of survey completion was calculated as the difference in weeks from program start. As a result, on any given program week, only a fraction of participants contributed data, with no consistent cadence. To overcome this data sparsity, curve fitting was applied as a data imputation technique for each participant. All data preparation was performed using the curve_fit function  . The objCare was taken to ensure fidelity of data due to known issues with automated digital data acquisition platforms. Specifically, during onboarding, duplicate entries that could have occurred by participants inadvertently submitting the survey twice were ignored. Furthermore, partially completed assessments were not included in the analysis. For each participant, the timeseries of available PHQ-8 scores was compiled after removing duplicate entries. Three different functional forms were fit to this data: linear, quadratic, and sigmoidal , and the fit that yielded the lowest root mean squared error was chosen as each participant’s trajectory. A nonlinear fit was applied only in instances where 6 or more data points were available. Furthermore, for the sigmoidal fit, the magnitude of the coefficients was capped. These two steps were necessary to prevent overfitting. In order to preserve temporal resolution inherent in the data, during curve fitting, predicted scores were calculated for each program day. Predicted scores were then aggregated by program week or month, as applicable, with a month being defined as a 4-week period. The same method was applied for GAD-7 scores.P values<.05 were deemed significant.To determine the effect of program time on changes in depression and anxiety scores, conventional methods such as ordinary least squares can be employed. However, these methods do not account for heterogeneity in the data, specifically, possible differences in the effectiveness across provider and program versions. In this study, participants were drawn from four companies, nested within three cognitive behavior therapy program versions that were nested across 30 providers. Hence, we employed a linear mixed-effects model where company, cognitive behavior therapy program version, and provider were treated as random effects. All analyses were performed using StatsModels (Python) . P valueThe data sets analyzed for this study are available from the corresponding author upon request.P=.26). Likewise, no baseline differences in GAD-7 scores were observed between groups . Furthermore, chi-square tests revealed no significant baseline differences between groups in the occurrence of self-reported chronic health conditions such as diabetes, obesity, hypertension, and hyperlipidemia (P=.59).The study included 323 participants enrolled in a digital cognitive behavioral intervention between February 2017 and January 2020 with baseline PHQ-8 scores or GAD-7 scores ≥5. The 9-month study cohort recruitment period ended in October 2019. These analyses were restricted to participants who had at least two valid survey submissions between baseline and program month 9. While the comorbidity of depression with anxiety was prevalent in the majority of participants, there were 7 participants with anxiety but subclinical levels of depression. Therefore, unless otherwise noted, results are reported by condition. Program nonstarters were defined as participants for whom no survey data was available beyond their initial baseline week. Of the 323 participants, 139 participants (43.0%) completed a follow-up PHQ-8 during the 9-month period, and 146 participants (45.2%) completed a GAD-7. There were no significant baseline differences in PHQ-8 between the treatment cohort and program nonstarters . For depression, 54.7% (76/139) of the cohort had baseline PHQ-8 scores that corresponded to moderate to severe depression (PHQ-8 score≥10). For anxiety, 41.8% (61/146) of the participants had GAD-7 scores≥10 corresponding to moderate to severe anxiety.Demographics and baseline characteristics for depression and anxiety are shown in P=.58). Likewise, there were no company-based baseline differences between groups (P=.91). Company and cognitive behavior therapy program versions were included as the top-level group variables in the model. Providers were included in the model as a nested group and specified as a variance component.The available PHQ-8 score assessed at the start of the program served as the intake PHQ-8 and were treated as the baseline. Change from baseline, the outcome variable, was defined as the difference between the baseline and the predicted score derived from curve-fitting to the participant’s data. Program time was included as a fixed effect as it was hypothesized that time engaged with cognitive behavior therapy program would predict reduction in depression scores. Baseline PHQ-8 and the composite engagement score were also predictors. As mentioned earlier, the trial period included 3 cognitive behavior therapy program versions. Pairwise comparisons revealed no significant baseline differences in depression or anxiety scores between groups (P<.001). A total of 69 PHQ-8 completions at the end of the intensive phase , 29 completions at month 6 , and 13 completions by month 8  were included in the analyses. Although 77.3% (51/66) and 58.1% (25/43) of participants remained active  in the program at months 6 and 9, respectively, participants did not complete the assessments. There was an average reduction of 3.76 points  by the end of the intensive phase of the program (month 3). Further improvements in depression were seen during the maintenance phase of the intervention, with an average reduction of 4.75 points  at program month 6. By program month 9, we observed a 6.42-point average reduction (95% CI –7.15 to –5.70).P<.001). A total of 50.7% of participants (74/146) completed the GAD-7 assessment at the end of the intensive phase, 23.5% of participants (31/132) who had been in the program for at least 6 months completed the GAD-7 assessment, and 12.8% of participants (14/109) who had been in the program for at least 9 months completed the GAD-7 assessment; 51.5% of participants (68/132) who had been in the program for 6 months were active in the program. Likewise, 43.1% of participants (47/109) who were in the program for at least 9 months remained active. We observed a 3.17-point reduction in GAD-7 scores by program month 3, the end of the intensive phase of the intervention . Further improvements in anxiety were noted at program month 6  and program month 9 .We observed a similar pattern of improvement in anxiety see . There wP=.001). A similar trend was observed for depression, but the effect did not reach significance .In the case of both depression and anxiety, participants with higher symptom severity at baseline appeared to show greater postintervention improvement. Higher anxiety scores at baseline predicted greater reduction in GAD-7 scores  of the program are shown by condition in P=.01) and anxiety . Engagement was added in the linear mixed-effects model as a composite factor and was defined a priori, so as to avoid cherry picking factors. In a supplementary analysis, each scaled engagement factor was entered into the model separately. This analysis revealed that therapist consultation, lesson completions, and the number of survey completions were each a significant predictor of PHQ-8 reduction (P<.001). However, the number of messages sent to the therapist, while positively associated with the other engagement factors, was not a significant predictor of change in depression and anxiety scores. In other words, activities that did not involve direct interaction with the therapist appear to have also moderated improvement in depression and anxiety. However, as shown in One of the current open questions around digital mental health interventions has been the role of program engagement in facilitating treatment and symptom improvement. We examined if the extent of usage and interaction with the Vida Health cognitive behavior therapy platform was related to mental health outcomes. Indeed, increased program engagement during the intensive phase of the intervention was associated with greater improvement in depression .Overall, the extent of engagement  declined with program time. However, 77.3% (51/66) of participants who completed an assessment at the end of the intensive phase, remained active in the program at month 6 and 58.1% (25/43) were active at program month 9. It was possible that this effect was due to survival bias, in that the program retained participants who were improving. We tested this hypothesis using a logistic regression model to examine if greater reductions in depression at the end of the intensive phase predicted the likelihood of remaining active in the program at month 6. However, changes in depression scores at the end of program month 3 failed to predict the likelihood of remaining active in the program  suggests that the interaction with the digital platform beyond just therapist contact was a component in the mechanism of action. However, this study lacked a control or comparison group, and instead, employed an observational study design, thereby preventing the drawing of any causal inferences.Vida Health’s digital cognitive behavior therapy program is an app-based structured intervention that includes one-on-one sessions with a remote licensed therapist. The objective of the study was to assess the effectiveness of Vida Health’s digital therapy program in the treatment of mild to moderate depression and anxiety. The analyses revealed significant and clinically meaningful reductions in PHQ-8 and GAD-7 scores relative to baseline, postintervention at 12 weeks, that were then sustained at month 6 of the program. While there is a growing body of evidence that suggests that digital mental health interventions are as efficacious as face-to-face interventions [g=0.72 [d=1.1) and depression (Cohen d=0.41) [Nevertheless, the results of the present study are consistent with findings from previous research. A meta-analysis evaluating the effectiveness of cognitive behavior therapy-based digital mental health intervention for the treatment of anxiety and depression among young adults reported a mean effect size Hedges g=0.72 . Other m d=0.41) . While t d=0.41) ,27, it r d=0.41)  observedPrevious research also suggests that digital mental health interventions that are self-contained may facilitate improved program outcomes compared to those of hybrid interventions that integrate therapist support and app-based content delivery ,10. In tThis study utilized a nonrandomized observational design. As such, this approach precludes any causal conclusions on the effectiveness of the intervention on the observed mental health outcomes. Self-selection bias and the lack of random assignment increase the likelihood of an overestimation of the effect size and limit generalizability of the findings. Furthermore, it was noted earlier that 54.7% of participants (177/323) completed the initial mental health assessment but failed to engage in the intervention, and as a result, these participants were excluded from all subsequent analyses. Attrition is a crucial study limitation and remains a continuing challenge for digital health interventions, particularly those focused on mental health. Fleming et al  have notP=.45) and 9 (P=.86). It, therefore, seems unlikely that only participants who improved were motivated to complete future assessments. Furthermore, other tools and activities such as tracking moods, anxiety episodes, and mindfulness minutes were also available throughout the program. It is possible that, in some cases, participants used a multitude of these tools instead of the assessment to track progress. App-based activities in Vida Health therapy program included guided lessons and interactive trackers . Supplementary analyses revealed that lessons pertaining to maladaptive thinking patterns had the highest rates of completion across participants compared to content related to mindfulness or sleep. It is possible that altering or expanding content to include topics with the greatest uptake may be associated with improved program retention. We also noted that lessons were consistently utilized at a greater rate than tools throughout the study duration. In the intensive phase , participants utilized 2.64 more lessons than tracking tools. During the maintenance phase of the program, participants completed 3.21 more lessons than tracking tools. While beyond the scope of this paper, additional research is warranted to understand if specific patterns of lesson and tool usage are associated with improved retention, and moreover, if specific guided lessons that incorporate interactive tracking tools or videos are associated with greater program engagement.The Vida Health cognitive behavior therapy program can be completed without interaction with a therapist; however, the program is designed to be therapist supported. It is possible that participants who failed to engage were seeking a self-guided approach. It is also important to note that patients may disengage from the program as symptoms improve; therefore, lack of engagement or incomplete key outcome measures, may not necessarily imply lack of improvement. Another key limitation to the generalizability of this study is the low completion rate of the assessment during the maintenance phase. While participants were encouraged by their therapist and through in-app messaging to complete the assessments, survey completion was mandatory only at the time of program enrollment and was optional at all future time-points. However, it is possible that the semimonthly assessment for the entire study duration prompted survey fatigue. Baseline score severity and changes in depression scores at the end of the intensive phase did not increase the likelihood of assessment completion at program month 6  to multidimensional understanding. In this study, engagement was defined as the combined additive effect of app-based behaviors and the extent of therapist contact. However, dimensionality reduction techniques such as principal component or factor analysis may provide more nuanced insight into patterns of engagement and possible changes with time.As health care increasingly leverages technology for delivering remote on-demand care and interventions, research on their effectiveness has largely taken a single condition, single outcome approach. However, mental health conditions such as depression and anxiety are often comorbid with other chronic conditions such as obesity and type 2 diabetes. Approximately 40% of adults are living with multiple chronic conditions in the United States . It is eIt is estimated that 7.1% of all Americans have experienced at least one major depressive episode, with increased prevalence among women and the highest prevalence observed among young adults (18-25 years) . Similar


The incidence of type 2 diabetes and gestational diabetes shows an increasing trend worldwide, and women diagnosed with gestational diabetes are more likely to develop type 2 diabetes within 5 years of delivery. This is closely related to lifestyle habits such as dietary intake and physical activity; hence, self-management should be continuously practiced. However, after childbirth, women find it challenging to practice self-management due to physical discomfort and child rearing. Therefore, it is necessary to develop an intervention program that is tailored to the characteristics of each participant and allows them to practice self-health management efficiently without time and space restrictions. This study aimed to develop a self-management mobile virtual reality program and investigate its efficacy in preventing type 2 diabetes after childbirth among women diagnosed with gestational diabetes. Intervention with the self-management mobile virtual reality program was performed for 12 weeks. The data of 57 participants in the experimental group and 62 participants in the control group were analyzed. After 12 weeks, the body weight, body fat, hemoglobin A1c, and fasting glucose were decreased in the experimental group compared with the control group. In addition, the dietary habits and health-promoting lifestyle profile were improved in the experimental group compared with the control group. These findings demonstrated that a self-managed mobile virtual reality program could be used as an intervention method for health promotion. To verify the effectiveness of intervention with the self-management mobile virtual reality program, a follow-up study with a larger number of research subjects should be conducted in the future. Gestational diabetes mellitus (GDM) is diagnosed based on the results of an oral glucose tolerance test conducted between 24 and 28 weeks of pregnancy for women without a history of diabetes ,2,3. In GDM etiology is related to pancreatic beta-cell dysfunction and marked insulin resistance secondary to placental hormonal release . InsulinDuring pregnancy and delivery, a long journey of around 280 days, women experience many changes both physically and psychologically . In partAlthough stress levels are known to increase in women with gestational diabetes after childbirth, current research and interventions targeting women with gestational diabetes have mainly focus on pregnancy, and studies involving interventions after childbirth remain insufficient . ConsequVirtual reality (VR) was developed by the United States Aeronautics and Space Administration  to create a visual virtual environment for astronaut training, and since the 2000s, it has been developed in three dimensions (computer-based or mobile-based) to achieve an immersive experience . VR inteVR simulation programs are increasingly used in the field of airplane maneuvering or combat, and they have been applied in various forms in games, sports, architecture, engineering, medicine, and nursing . With thThus far, studies using VR programs for health problems have mainly aimed to support rehabilitation. The effectiveness of VR programs has been reported in an intervention study aiming to restore the motor function and physical function of patients with brain damage, stroke patients, and adults with disabilities . InterveThe present study investigated the efficacy of intervention using a self-management mobile VR program (mobile VR program), which may be useful for the postpartum management of gestational diabetic women considering that women are physically weak after childbirth and have difficulties in visiting the hospital due to their responsibility of raising children. The hypotheses of this study were as follows:Hypothesis 1.Mobile VR program may have positive effects on physiological variables.Hypothesis 2.Mobile VR program may be associated with a higher level of diabetes knowledge.Hypothesis 3.Mobile VR program may have positive effects on eating habits.Hypothesis 4.Mobile VR program may be associated with a better health-promoting lifestyle profile.Hypothesis 5.Mobile VR program may be associated with lower parenting stress.This was a quasi-experimental study that investigated the effects of mobile VR intervention on women with gestational diabetes . In thisWe developed the mobile VR program on Android A. The moThe exercise program included 123 types of exercises ranging from light stretching to strength training that can be selected and performed by the participants B, and weIn this study, two exercise trainers, two clinical dietitians, two diet experts, two neonatal specialists, two nurses, and three laughter therapists participated in the development of the self-management mobile VR program. Moreover, during the program development process, we received advice from gynecologists, sports medicine specialists and psychiatrists, and three professors in the nursing department. The developed mobile VR program was registered with the Korea Copyright Commission for copyright protection.The participants assigned to the experimental group were asked to download and install the mobile VR program on a mobile phone the day before the scheduled delivery date or the day after delivery. Moreover, we provided VR headsets necessary for the mobile VR program as well as gave a presentation on how to use this program with supporting educational content. In the hospital, the participants assigned to the control group were provided with written educational materials on GDM in pregnant women, and the physiological variables of both the experimental group and the control group were measured before intervention. Mobile VR intervention took place from May 2018 to February 2019, and the intervention period was 12 weeks after childbirth. Seven days after the mobile VR program intervention (13 weeks after childbirth), postpartum physiological variables were measured. After the completion of the experimental intervention in this study, the participants in the control cohort were also offered the chance to use the mobile VR program.Physiological variables. The physiological variables measured in this study were body weight (kg), body fat (%), fasting glucose (mg/L), and HbA1c (%). The reliability was Cronbach’s α = 0.89.Diabetes knowledge. The level of diabetes knowledge was evaluated with 24 items [Dietary habits. The assessment of dietary habits was performed with 20 items rated on a 5-point scale based on the dietary assessment  of KoreaHealth-promoting lifestyle profile. The health-promoting lifestyle profile was evaluated with 52 items on a 4-point scale ; the higParenting stress. Parenting stress was evaluated with 23 items on a 5-point scale ; the lowThis study was conducted after obtaining approval from the Institutional Review Board of Women’s Medical Center (CGH-IRB-2017-44). After explaining the purpose of the study, confidentiality of the data, and disposal of the data after study completion, we received written consent for voluntary participation from our study participants. The participants were informed that they can withdraw from the study at any time and that there were no penalties if participation was withdrawn. Moreover, they were informed that the collected research data will not be used for any purpose other than for this research and will be discarded according to the Bioethics Act.t-test or chi-square test was performed for each sociodemographic and baseline variable to ensure the equivalence of groups. Furthermore, the difference in the intervention effect between the experimental group and the control group was analyzed by independent t-test.The data were analyzed using the SPSS 26.0 program . The participants’ general characteristics, physiological variables, diabetes knowledge, dietary habits, health-promoting lifestyle profile, and parenting stress were analyzed using the frequency, percentage, mean, and standard deviation. In addition, Among the participants in this study, the number of older women aged 35 years or older was 62 (52.1%), and the number of primiparous women was 80 (67.2%). In addition, 72 women (60.5%) had normal vaginal delivery, and 35 women (29.4%) had a family history of diabetes .Based on the results of the homogeneity test between the two groups for the dependent variables measured in the preliminary survey, there was no difference in the body weight, body fat, fasting glucose, hemoglobin A1c, diabetes knowledge, dietary habits, health-promoting lifestyle profile, and parenting stress. Thus, the two groups were found to be homogeneous .(1)Following intervention with the mobile VR program, the body weight (−5.65 ± 12.90), body fat (−2.52 ± 7.09), fasting glucose (−3.26 ± 16.88), and hemoglobin A1c (−0.18 ± 0.38) were significantly lower in the experimental group than in the control group; thus, hypothesis 1 was supported.(2)Following intervention with the mobile VR program, the level of diabetes knowledge was increased in both the experimental group (0.11 ± 0.90) and the control group (0.10 ± 0.09); however, hypothesis 2 was rejected as there was no statistically significant difference between the two groups.(3)Following intervention with mobile VR program, the score for dietary habits was significantly higher in the experimental group (0.74 ± 0.63) than in the control group (0.34 ± 0.58); thus, hypothesis 3 was supported.(4)Following intervention with the mobile VR program, the total mean score of health-promoting lifestyle profile was significantly higher in the experimental group (0.34 ± 0.49) than in the control group (0.18 ± 0.39); thus, hypothesis 4 was supported.(5)Following intervention with the mobile VR program, parenting stress was increased in both the experimental group (0.02 ± 0.60) and the control group (0.10 ± 0.81), and there was no statistically significant difference between the two groups; thus, hypothesis 5 was rejected.The outcomes in the experimental group and the control group following intervention with the mobile VR program for 12 weeks are shown in Here, we developed a mobile VR program for women diagnosed with GDM and used it for 12 weeks to determine its efficacy. As the mobile VR program developed in this study provides auditory and visual feedback according to the participants’ behavior, the participants were assigned a health trainer, clinical nutritionist, and medical staff to aid their health care process. Therefore, the overall exercise routine in the experimental group lasted for an average of 20 min with 2.5 times per day , and the dietary recording was used 1.8 times per day . After 12 weeks of mobile VR intervention, the physiological variables  in the experimental group were significantly different compared with the variables in the control group. These results are consistent with those of a previous study showing that regular exercise and lifestyle regulation centered on a proper diet are important elements for type 2 diabetes treatment; additionally, these elements are meaningful because they have a positive effect . An inteThe meal quality of the participants in the experimental group was improved in this study. The mobile VR program showed the calories, carbohydrates, proteins, fats, and essential nutrients to be consumed per day for each subject and indicated visually whether they met the nutrient requirements according to their dietary record. This finding is in line with a previous study; for example, in the study of motivation enhancement for obesity patients in South Korea , it showAccording to the health-promoting lifestyle profile of the experimental group in this study, there were statistically significant differences in the scores of physical activity, nutrition, spiritual growth, and interpersonal relation. Reasonable lifestyle habits may have a positive effect . EvidencHowever, although the level of diabetes knowledge was increased in both the experimental group and the control group, the results were not statistically different. It is possible that the hospital has provided sufficient education orally or in writing about diabetes to women diagnosed with GDM. Additionally, GDM can cause complications in the fetus, such as intrauterine fetal death, macrosomia, and fetal growth restriction, and the women may be anxious about the health of their fetus ,2,3. ThuFurthermore, there was no significant difference in parenting-related stress between the two groups. These findings are in agreement with those of a previous study, which revealed that the biggest concerns of Korean parents with young children these days were parenting, low birth rates, and avoidance of marriage by South Korean women . In addiThis study has few limitations. First, the mobile VR program developed in this study cannot run on the iOS system (iPhone) with a VR device (headset and gear), because it is developed on the Android operating system. Moreover, to date, both Android and iOS have limitations with regard to running VR applications; hence, this issue needs to be considered during intervention. Second, VR can usually cause cybersickness. As most VR devices are developed to continuously change the field of view using 3D or 360° techniques to improve the immersive experience, motion sickness syndromes such as car sickness, sea sickness, and air sickness may occur . When usIn this study, a self-management mobile VR program was developed for women who gave birth after being diagnosed with gestational diabetes, and its effectiveness was evaluated. The mobile VR program consisted of an exercise program, a diet program, laughter therapy, and a neonatal first aid program, and 64 participants in the experimental group used it for 12 weeks. The physiological variables, dietary habits, and health-promoting lifestyle profile of the experimental group showed statistically significant improvements compared with those of the control group. Although further long-term studies with higher-level evidence on the effectiveness of self-management mobile VR program intervention are needed, this study is meaningful in that it provided preliminary data to support self-management through a VR program.


Phenylketonuria (PKU) screening is a public health measure taken to diagnose and treat the patients with PKU to prevent severe neurological disorders in them. The present study was aimed at analyzing the policies of the national PKU screening (NaPS) program in Iran.PKU screening program policies were analyzed in compliance with the policy triangle model. Document review and 38 semi-structured interviews were used for data collection. Document review data were analyzed using content analysis, and interview data were analyzed using framework analysis.The national PKU screening (NaPS) program was a decision made at the genetics department of Ministry of Health and Medical Education (MOHME) in Iran. Many internal and external stakeholders were involved in it and valid evidence was used to formulate the policies. Despite some opposition and insufficient support, the program was implemented due to the continuous persistence of parents, interested executives, formulated valid content and a top-down approach. The main barriers included rapid substitution of managers, shortage of Phe-free milk, little awareness of patients’ families, social stigma, and inadequate co-operation of some hospital administrators.The policy triangle framework contributed to explaining the different components of the PKU screening program. A successful PKU screening program requires more stability of senior managers in MOHME, enough human resources and Phe-free milk, educating patients’ families, and commitment of hospitals administrators. Meanwhile, all the stakeholders need to be involved in the program effectively.The online version contains supplementary material available at 10.1186/s12913-021-06116-w. Phenylketonuria (PKU) is the most common hereditary metabolic disorder in the world . PatientNeonatal screening is a population-based public health screening program implemented for early detection . PKU scrThe PKU screening program in Iran launched in 2006. In this program blood samples were taken from all infants on day 3 to 5 after birth for colorimetric screening. And people with phenylalanine levels of 4 mg/dl or higher were referred to be confirmed by HPLC test. Then regular follow-up is performed for people who have phenylalanine levels equal to or greater than 4 mg/dl, and if the phenylalanine levels are 7 mg/dl or more, the diet begins with restricted phenylalanine. Dietary supplements of Iron, zinc, selenium, carnitine, vitamins and essential fatty acids are prescribed for all children up to 2 years of age .Policy analysis includes plans and actions aimed at achieving health care goals by defining a vision for the future, setting goals and objectives, setting priorities, and identifying the roles of different groups. Health policy analysis helps to understand the success or failure in implemented policies so that they can be useful for planning future policies. Health policy analysis helps policymakers to improve their successful implementation of future policies and provide opportunities to produce policy documents . Policy content includes policy goals, operational policies, and so on. The word “actors” refers to executives and influential organizations. The context refers to social, economic, political, cultural and other environmental conditions. The process consists of four parts: agenda setting, policy formulation, policy implementation, and policy evaluation [This qualitative study was conducted from May 2015 to January 2016 using the Walt and Gilson triangle Kingdon’s multiple streams models. Kingdon model is an instrument for understanding the policies agenda-setting. Based on this model, the interaction of three streams problem, policy and politics will result in a policy window and policy entrepreneurs can use the opportunities for policy development . The trialuation .Fig. 1Bthe Health Commission of the Islamic Consultative Assembly,Ministry of Health and Medical Education (MOHME) ,the Iranians Health Insurance organization,the policymaking council,faculty members in universities of medical sciences,children’s specialized hospitals,Pasteur Institute,Welfare Organization, andPKU Patients Support Association.Study participants included 38 individuals from different sectors related to the study in Iran:Target groups were selected based on purposive sampling. Their selection criteria included knowledge and experience in phenylketonuria screening programs, active participation in phenylketonuria screening programs, and interest in research participation.Semi-structured interviews were conducted to collect data using the interview guideline. Credibility, transferability, dependability, and conformability were used such as criteria of rigor and trustworthiness of this study –22. InteFramework Analysis was used to analyze the data, which included five basic steps: Familiarization, Identifying a Thematic Framework, Indexing, Charting and Mapping and Interpretation. In addition, Document Analysis was used to validate the findings of the interviews and to benefit from the evidence available in the policy process. Documents were collected purposefully by referring to the organizations involved in NaPS program and reviewing relevant internet sites. The content analysis method was used for data analysis of documents.Findings were reported based on four categories of policy triangle framework . These categories are explained as follows Fig. :Fig. 2TContent included three factors: political, economic, and social and cultural factors.Political commitment and stability of top-level healthcare administrators of MOHME will contribute to the continuity of the approach of fair access to health services for all. According to Article 89 of Iran’s Fourth Development Plan (1963–1972), the MOHME was required to design a system of minimum standardization of health services based on the levels of services in order to ensure fair access to health services. In this regard, the NaPS program with specific level was launched at the lowest levels of the network, was fairly implemented in cities and villages. But the long-term plan for the future has not been defined. Because of the political changes in government, the minister, his deputies and other senior managers of the MOHME change relatively fast."The system should not change with the change of managers. It must be stable. After the management changes, specialized staff also change. Then the problem emerges "(P.19).“Iran sanctions face us with limited drug access and low access to technology” (P.17).Sanctions have had a major impact on the import of Phe-free milk for patients”(P.4).““We had trouble publishing the article on this project, so one of the journals responding to our article replied: we cannot publish your article because you are under sanctions” (P.24).Economic sanctions will exert the most pressure on the needy and less privileged /under-privileged and susceptible patients- those who cannot afford the diagnostic and therapeutic costs of PKU. The economic sanctions restricted the currency exit from country and the imports of drugs and Phe-free milk and even publication of scientific articles related to the PKU program in foreign journals. Because most families could not afford the diagnostic and therapeutic costs of the disease, the cost of screening and Phe-free milk was subsidized by government.“With age, withdrawal from treatment increases and disease control becomes more difficult. Many patients develop seizures because they have not followed the diet and phenylalanine levels in their blood have risen.” (P.28).“Disease-related social stigma makes it difficult to care and follow up. The parents of one of the patients moved and they would not be followed up as they do not provide their new address. A person who has a child with PKU doesn’t even tell his sister and brother that his child has PKU” (P.36).Adequate public education on PKU- e.g. mental retardation, prevention, and follow up- and knowledge of healthcare providers on the PKU-associated social stigma will help decrease the impacts of the disorder and achieve the goals of the program. People welcomed the diagnosis program as it dealt with concerns about the likelihood of mental retardation and the possibility of preventing the disease, but some families do not regularly follow up on treatment regimens. Parenting skills of patients’ parents and self-care of patients are poor. Patients and their families have little knowledge of the disease, and they are not provided with adequate education. The tendency for family marriages in Iran leads to an increased risk of the disease. The social stigma associated with disease has made it difficult to advance the program.To help NaPS program to be effective, different specialized and administrative departments at country, provincial and national levels and universities need to fulfil their duties, responsibilities, and follow regulations in this regard. The purpose of NaPS program was to reduce physical disability, mental retardation and family damage. PKU screening is not just a test, but it is a system designed to identify, treat, and fully care for patients. Therefore, the program was organized at county, provincial, and national levels.The genetics department of the MOHME and medical sciences universities were determined as responsible for implementing the program in national and provincial levels. In health part, health houses, urban and rural health centers, district health centers and disease prevention and control unit in the deputy of health of the medical sciences universities were considered to provide health services. In treatment part, blood sampling centers, provincial health reference laboratory and selected PKU hospitals were considered to provide diagnostic and therapeutic services.drafting the national program guidelines,developing the monitoring checklists,forming the national technical committees,notifying all universities in the country of the program,collecting the annual reports from medical sciences universities,evaluating implemented programs,following up the necessary facilities,engaging relevant organizations and departments and making coordination with them,following up laws and approvals,developing the educational packages,anticipating funding for program implementation and reporting to high level officials.Chief among the duties of genetics department are:Chief among the duties of medical sciences universities are:▪ planning and discussing the program at the genetics committee,▪ organizing meetings of the PKU academic committee,▪ monitoring implementation of approvals,▪ monitoring performance, and▪ periodically reporting to health and treatment deputies.The duties of the provincial health reference laboratory are:❖ receiving and registering all screening samples,❖ performing all screening tests,❖ reporting positive screening tests  and❖ requiring re-testing  to the “disease prevention and control unit” in the province.○ specialist physician periodic visit,○ laboratory services for treatment control,○ expert nutrition services,○ pharmaceutical services (delivery of Phe-free milk and other nutrition items to patients),○ clinical psychology and social care services, and○ parents’ education.The number of patients in each province is relatively limited, so such a large system cannot be implemented in all cities. In this way, at least one children’s specialized hospital as PKU selected hospital in each province provides all required services to patients. In PKU selected hospitals, all required services are provided:immediate referral of newly-diagnosed patients to the selected hospitals,follow-up in absence of treatment, collection, registration and transmission of information,participation in educational programs, andeducating pregnant mothers.The duties of the health centers and health houses include:In order to implement the program, guidelines of laboratory, care, clinical, nutritionist, genetic diagnosis and prevention, non-classic sampling guidelines and supplementary regulations of clinical psychologists were developed.The results of policy process of PKU program consists of five sections: 1) Agenda setting, 2) Formulation of PKU screening policies, 3) Implementation of PKU screening policies, 4) Evaluation of PKU screening policies, and 5) Challenges of implementing a PKU screening program.Problem Stream: Identifying the problems behind the necessity of implementing a national PKU screening program;Policy Stream: Support of insurance organizations and treatment department of MOHME; andThree different streams are needed to set up an agenda for the policy process of PKU program:3. Political Stream: Adequate support of patients’ families and members of the Iranian Society of Pediatrics to implement NaPS program.First Stream).There was a problem. Every year a number of patients were added to PKU patients. They suffered from severe physical and mental disabilities, reduced IQs and complications such as seizures and increased financial burden for the families and health system. On the other hand, the disease could be diagnosed early and be cared for” (P.1).“During 1997–2010, the outbreak ratio of PKU was calculated 1:8000 in three big provinces of Tehran, Fars, and Mazandaran in Iran . If the Second stream).“We had already experienced congenital hypothyroidism screening. Sampling time, sampling method and sending to the lab are the same as phenylketonuria screening” (P.14).The above-mentioned problems have led health policymakers to explore ways to solve these problems. Therefore, with the successful implementation of the hypothyroid screening program and the use of infrastructure of this program for phenylketonuria screening, as well as executive and scientific efforts, PKU screening program was developed .We, patients’ families, corresponded with the Vice-Chancellor of Health Minister. We met him. We talked to him about our problems. He was very upset about our situation. He instructed the relevant organizations to serve all PKU patients” (P.22).“One of the reasons for the success of this program was the presence of a capable, interested, and persistent responsible person” (P.16).“Despite the opposition of the Network Development Office with regard to integrating the program, insufficient support of insurance organizations and deputy of treatment of MOHME, the NaPS program was implemented due to the continuous persistence of patients’ families and members of the Iranian Society of Pediatrics to initiate screening and therapeutic interventions and formulated content, and talented and interested executives who convinced the policymakers (first stage), successful implementation of the hypothyroid screening program and ability to run the program based on the created infrastructure at national level (second stage), exerting pressure on the patients’ families, members of the Scientific Committee on Children and following up by executives (third stage) concurrently led to agreement of health policy makers with the program, and policy window was opened.Problems associated with the disease .““One of the problems is that hospital information is not recorded very well” (P.2).Different sections of a health care system should actively cooperate to fulfil the successful implementation of PKU screening program. A national screening decision was made in 2011, but was delayed due to laboratory equipment problems in medical sciences universities in 2012. Monitoring and follow-up of PKU patients have lower priority than other programs in some selected hospitals. Selected hospitals suffered from a shortage of milk powder. It was not appropriate to record hospital and non-hospital information. Qualitative evaluation of selected hospitals was not performed well due to inadequate co-operation of some hospital administrators and the lack of a supervisory system for providing PKU services in hospitals.Genetics department,Health Network Development Center,Office of Population & Family Health,Health Reference Laboratory,Children Health Department,Deputies of Health Universities of Medical Sciences,Deputies of treatment Services,Selected Hospitals of PKU,Pasteur Institute of Iran,Food and Drug Administration,and related research centersInternal stakeholders involved in the NaPS program include:the Welfare Organization,Health Insurance Organization,PKU Patient Support Association,the Post office andthe Registration Office.External stakeholders involved were:The purpose of the Phenylketonuria screening program in Iran was to reduce physical disability and mental retardation and medical and nonmedical costs. Given the relatively high prevalence of PKU  comparedBased on the results of the present study, to implement the program nationwide, provincial laboratories were equipped. The surveillance system for the affected patients was organized in health networks. PKU selected hospitals were designated in the capital of each province and a treatment team consisting of a pediatrician, a nutritionist, a psychologist, a social worker and a secretary was formed in these hospitals. There are some differences between Iranian structure of human resources for PKU screening and that of the other countries. New Zealand, which launched the Metabolic Disorders Screening Program since 2005, used a consulting group including pediatricians, patients’ families and other medical teams before implementing the program . The resBased on the results, a “top-down” approach for policy implementation was adopted. Ham and Hill have considered two approaches for policy implementation; the main difference between these approaches is the degree of actors’ involvement in the policy cycle . In the The results of the study indicated the impact of political changes in the country and consequently structural and managerial changes in the health system which have an adverse impact on the implementation of NaPS policies and programs. This result is in line with the results of the studies by Romain et al. in 2015 in Tunisia . ManagerAnother political factor was the impact of international sanctions on imports of dietary milk and medicine. Studies by Masoumi et al. in 2015 , GeorgiaOne of the important cultural factors was the family’s lack of awareness of PKU. This result is consistent with the results of a study by Hong et al. in Southern Taiwan in 2005  and BaroThis study had some strength. To use a qualitative approach, internal and external stakeholders participated in the study with different backgrounds and explained their own professional experience individually. Two well-known conceptual framework  used were able to collect relevant data including process, content and context and actors to interpret development policies.Nonetheless, this study suffers from some limitations. The study, rather than being longitudinal was a cross-sectional study. Since this study was the first research to analyze the PKU screening policies, other restrictions of the study was limited access to documents.The present study was conducted to analyze the policies of PKU Screening in Iran. The analysis helped to identify the different components of policymaking cycle and effective factors on formulation, implementation, and evaluation of policies. A successful PKU Screening program requires more stability of senior managers in the MOHME, enough human resources and Phe-free milk, educating patients’ families, and commitment of hospitals administrators and also, all of the stakeholders should be involved in the program effectively.Additional file 1.


Testicular cancer predominantly affects young adult men and is the most common cancer affecting this demographic. An important prognostic factor for early-stage disease is the presence of tumours within blood vessels or lymphatic channels, which is termed lymphovascular invasion. This is identified by careful microscopic examination of the tumour after orchidectomy, which is frequently challenging and time-consuming. We trained a proof-of-concept deep learning artificial intelligence algorithm to automatically identify areas suspicious for lymphovascular invasion in digital whole slide images from testicular tumours. Our study demonstrates that automated detection of areas suspicious for lymphovascular invasion by artificial intelligence algorithms is feasible and may prove useful in the context of a decision support tool.Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment. Testicular cancer is the most common cancer in men under 45, with the vast majority being testicular germ cell tumours (TGCT). With modern therapeutic regimes, these tumours have an extremely high cure rate greater than 90% overall, but challenges still remain . CurrentPatients are usually treated with primary orchidectomy, and the tumour type is ascertained histologically using the World Health Organisation (WHO) classification system , where tThe generally good prognosis of these tumours makes powering of studies to evaluate prognostic factors difficult, with much evidence based on large cohort studies. One of the few parameters that is a powerful predictor for metastasis or disease recurrence in stage I disease  is the pTGCT are often managed in supra-regional networks. For example, in the United Kingdom (UK), these cover a population of 2–4 million and manage 50–100 new patients per year. This means that expertise, including pathological expertise, is concentrated in specialist centres. Specialist assessment for LVI is valuable, as identification of genuine LVI is often challenging. Tumour may be artefactually displaced into vessels during specimen cut up or processing. Atypical histiocytes within vessels, intratubular tumour and retraction artefact, may also be mistaken for LVI ,17,18. CLVI can be present within the tumour, spermatic cord, tunica albuginea or hilar soft tissue. Regardless of location, its presence is regarded as TNM category pT2 ,19. As tAssessment for LVI by pathologists is inherently limited by being undertaken by human observers. Examination of large areas of tumour for LVI is time-consuming and challenging, as foci of LVI may be small and subjective. Nonetheless, the presence of LVI may markedly affect patient management when present, and identification of a single focus deemed to be genuine is enough to mark a case as positive. As such, automated identification of foci likely to represent LVI may be of significant clinical utility.Digital pathology (DP) refers to the generation of whole slide images from histology slides, which can be viewed on a screen to form a diagnostic report. Histological diagnosis and pathological staging by cellular pathologists have traditionally been achieved using glass slides and microscopy ,21. TherAI algorithms utilising convolutional neural networks (CNNs) for image analysis have already shown significant promise in the pathological assessment of a range of tumours, including screening for prostate cancer in prostate biopsies ,28, provThere is relatively sparse literature on the use of AI in TGCT, due to the challenges of training and validating algorithms in these heterogeneous tumours, as well as the relative rarity of these tumours and their concentration in specialist centres. One AI study evaluated the ability of a deep learning model to assess tumour infiltrating lymphocytes (TILs) in both seminoma and NSGCT. An AI algorithm was able to evaluate lymphocyte density in tumours beyond the capacity of human visual assessment, counting more than 100,000 cells per sample. Although previous studies involving human observers had failed to identify lymphocyte density as a significant prognostic factor, the AI tool was able to use lymphocyte density to predict clinical stage and disease relapse in seminoma .In this study, we demonstrate a proof-of-concept AI algorithm that aims to highlight foci of likely LVI for the attention of the reviewing pathologist, who then ultimately makes the decision as to the presence or absence of LVI.This study was conducted under the Oxford Radcliffe Biobank (ORB) Research Ethics Approval (reference 19/SC/0173). A total of 29 cases of primary TGCT were retrospectively selected and included in sequence from the period January 2019 to July 2020 from the Cellular Pathology Department archives of the John Radcliffe Hospital, Oxford, after a check of the research consent section of the procedure consent form. These were cases that had primary management within the trust . This reflects the typical number of cases seen of this tumour type in the department. Sixteen of the tumours were pure seminomas, and 13 were NSGCTs (including 10 mixed germ cell tumours and 1 spermatocytic tumour). One prepubertal type teratoma was also included to increase the cohort size, acknowledging that it is not a malignant TGCT. Ten patients had metastatic disease at presentation, and one patient later developed metastatic disease. Thirteen of the patients went on to have adjuvant chemotherapy.Three hundred and two archived whole slide images from these patients were exported in TIFF format to the Visiopharm platform, using the Philips De-ID tool  with all personally identifiable information removed. Images were imported into our in-house annotation platform, Annotation of Image Data by Assignments (AIDA) ,36. OnlySlides were derived from 4–5 µm thick sections cut from formalin-fixed, paraffin-embedded blocks of tissue, stained with H&E. Slides were scanned using the Philips IntelliSite Ultrafast Scanner using a 40× objective.Information from the deidentified reports was used to split the patient cohort  into a tThree hundred and fifty candidate foci were manually annotated by a pathologist (AG) on 141 of the 184 training whole slide images using the Visiopharm platform. Annotated slides were then exported to the AIDA platform. Each candidate focus was then reviewed by two specialist uropathologists  and classified as to whether LVI was considered present, equivocal or not present as per the International Collaboration on Cancer Reporting (ICCR) classification . EquivocThe Visiopharm AI module uses manually classified annotations to train a CNN for automatic segmentation of image structures. The Deeplabv3+ semantic segmentation architecture was used . This neFollowing initial training, the model was applied to the remaining 43 training slides, and parameters were empirically adjusted to select for areas with a high prediction confidence for LVI only. These areas were initially reviewed by another pathologist (AG) to remove any clearly misclassified areas and add additional candidate foci to produce 121 more foci for review. These foci were then reviewed by three specialist uropathologists  and again classified depending on whether LVI was considered present, equivocal or not present. The uropathologists used for final classification have 13 years (CV), 2 years (RC) and 11 years (LB) experience post-specialist registration and regularly review cases for the supraregional germ cell MDT. CV is the supraregional pathology lead for the Thames Valley germ cell tumour network.Eighteen foci that were considered not appropriate to flag by all reviewers were then removed as labels on Visiopharm, and a further round of training performed on the entire labelled training set. The final model was configured to highlight pixels with high prediction confidence only and predicted foci dilated to combine those in close proximity to each other and aid subsequent human review. An overview of the training process is shown in The model algorithm was applied to the validation set of 118 whole slide images. A quality check was performed on the image and the detected foci to exclude tissue processing artefacts. One NSGCT case, encompassing 14 whole slide images, was excluded due to failure of the algorithm within a stroma-rich tumour, a morphology that was not represented in the training set (See results). A final set of 104 whole slide images was used for testing algorithm performance.Thirty-four foci within 12 slides were identified by the algorithm for evaluation. Each of these foci was then evaluated by the three uropathologists using the AIDA platform. The pathologists were instructed to classify each focus depending on whether LVI was considered present (including cases where confirmatory immunohistochemistry (IHC) would be used), equivocal or not present. An overview of the validation process is shown in Each focus was categorised based on the majority vote from the three reviewers. If a focus was considered to contain LVI or was considered equivocal for LVI by two or more of the three reviewers, the focus was considered appropriate to flag. If a focus was considered to contain LVI by two or more of the three reviewers, the focus was considered to contain LVI by consensus. To reflect the real-world usage of the tool, the overall precision was calculated, defined as the number of appropriate foci identified divided by the total number of foci identified. Precision was also calculated separately for consensus LVI.The detected foci were ordered by area on the basis that multiple adjacent high probability foci may have been combined during post-processing, and these areas should represent the first foci to review, as these are the areas most likely to be true positives. Precision-recall curves were used to assess performance on the ordered results. To estimate sensitivity  in the validation set for the construction of a precision-recall curve, a total of 124 more candidate foci, in addition to those identified by the algorithm, were annotated on the validation slides by another pathologist (AG). These foci were independently reviewed by an expert uropathologist (CV) and classified depending on whether LVI was considered present, equivocal or not present. Estimated recall for appropriate areas was defined as the number of appropriate areas detected, divided by this figure plus the total number of additional appropriate foci identified on review by the single expert pathologist. Similarly, estimated recall for LVI was defined as the number of areas of consensus LVI detected, divided by this figure plus the total number of additional definite LVI foci identified on review by a single expert pathologist.Interobserver variability was estimated by Fleiss kappa statistics, performed on the classification of each focus by the three independent uropathologists. Kappa statistics were calculated separately based on the classification of foci as LVI or not, as well as the classification of foci as appropriate to flag or not. Interpretation of the kappa statistic was based on previously published thresholds, suggesting; <0 as poor or no agreement, 0–0.2 as slight agreement, 0.21–0.40 as fair agreement, 0.41–0.60 as moderate agreement, 0.61–0.80 as substantial agreement, and 0.81–1.00 as almost perfect agreement .For the purposes of statistical evaluation, no distinction was made between foci that were classified as definite LVI and those that a pathologist would classify as definite LVI with the aid of immunohistochemistry.t-test.The number of foci of consensus LVI was compared between cases with metastatic disease at presentation (or who developed metastatic disease subsequently) and those without metastatic disease using the unpaired The deep-learning classifier identified 34 foci across 104 validation whole slide images. Examples of the detected areas are presented in Twenty-nine of the 34 identified foci were peritumoural, and 5 were intratumoural. Twenty-three of the 34 identified foci were assessed as being appropriate to flag based on consensus expert review, comprising foci that were categorised as LVI (including those that would be confirmed using immunohistochemistry), and those which were considered equivocal (but ultimately negative) ,7,16. ThNineteen of the 34 identified foci were assessed as containing LVI (including those that would be confirmed using immunohistochemistry), and all of these contained embryonal carcinoma. Of the 15 foci not considered LVI, 5 contained smear artefact  was 0.56 .The kappa statistic for interobserver agreement between three expert pathologists based on the categorisation of each focus as appropriate to flag or not was 0.62 . Unanimous agreement for appropriateness to flag was seen in 25 foci (74%).The kappa statistic for interobserver agreement between three expert pathologists based on the categorisation of each focus as LVI or not was 0.57 (moderate agreement). Unanimous agreement for the presence or absence of LVI was seen in 23 foci (66%).The 34 foci identified by the deep-learning classifier were ranked based on focus size. The top five foci were all classified as appropriate to flag, with LVI deemed to be present in each on consensus review. The overall precision of the top 10 foci for categorisation as appropriate to flag or not was 0.7, and all of the appropriate foci identified in this top 10 were deemed to be LVI on consensus review. This is summarised in p = 0.96).There was no evidence of a significant association between the number of LVI foci identified and the incidence of metastatic disease at presentation in this study . TheFurthermore, it is possible to rank retrieval using a variety of parameters. In this study, we have ranked each focus by size, based on the rationale that many very high probability pixels in close proximity are more likely to represent a human-appreciable area of genuine LVI. When evaluating the highest-ranked foci in this way, the largest five foci were all classified as LVI on consensus review. Other methods of ranking foci could include the distance from the main tumour mass, as foci of tumour in vessels distant from the mass may be more likely to be interpreted as genuine LVI. Further work is required to investigate automated ranking in this way. Rational ranking of identified foci is likely to increase the precision of such a tool in practice greatly.The challenging nature of agreeing on genuine LVI was demonstrated by measuring interobserver variability in the validation slides. Moderate agreement was reached when deciding whether LVI was present or not , and there are no molecular tests that currently guide clinical practice as in other tumour types, which would make identification of mutations of high importance. These tumours show a low rate of mutations compared to common cancers, which would make the prediction of mutations by AI more challenging ,45, althWe have shown in this study that deep learning algorithms have the potential to detect features including LVI, which are considered subjective even by human pathologists. In addition to potential workflow and efficiency benefits in the context of a fully digitised system, such algorithms may prove useful as decision support tools to improve diagnostic reliability.


Departing from the high relevance that loyalty plays in the promotion of tourist destinations, the study of the variables that result in loyalty is key. For this reason, there are myriads of studies devoted to the analysis of the variables that result in loyalty, especially considering either or both attitudinal and behavioral loyalty; in turn, relatively less frequent are the studies that also/instead consider active and passive loyalty. Within this research line, we go one step further and propose the importance of focusing on the study of the variables that are conducive to attitudinal and behavioral loyalty; moreover, within attitudinal loyalty, we also acknowledge the distinction of two further types of loyalty, that is, active attitudinal and passive attitudinal loyalty. This opinion paper aims at adding knowledge to the field of consumer behavior in tourism, proposing the importance of studying the variables that are able to drive loyalty in a very differentiated way .Loyalty is a key variable in all economic sectors and industries, so there is no doubt that looking for loyalty is a key priority. In fact, it has been largely proven how loyalty is essential for achieving key company indicators such as long-term competition, profitability, and survival  may be limited due to other variables, such as “search for variety.” However, a tourist who does not repeat a visit to the same destination may have an important attitudinal loyalty toward that destination and be willing to strongly recommend his visit. Therefore, this opinion paper has been aimed at showing how in the field of consumer behavior in tourism marketing, it is still possible to propose new future lines of research. One of them is to analyze the differences between active and passive attitude loyalty. Although some previous work in this area had shown differences between the active and passive behavior of consumers as opposed to the use of information sources (Ganesh et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


Slow deformations associated with a subducting slab can affect quasi-static displacements and seismicity over a wide range of depths. Here, we analyse the seismotectonic activities in the Tonga subduction zone, which is the world’s most active area with regard to deep earthquakes. In our study, we combine data from global navigation satellite systems with an earthquake catalogue. We focus on the deep earthquakes that are below 400 km at the lower part of the Wadati–Benioff zone. We find that trenchward transient displacements and quiescence of deep earthquakes, in terms of background seismicity, were bounded in time by large intraslab earthquakes in 2009 and 2013. This “slow deformation event” between 2009 and 2013 may have been triggered by a distant and shallow M8.1 earthquake, which implies a slow slip event at the plate interface or a temporal acceleration of the subduction of the Pacific Plate. These findings provide new insights into the relationship between shallow and deep earthquakes in the subduction zone. In particular, slow deformations associated with a subducting slab can affect quasi-static displacements and seismicity through changes in the stress state, like the variety of slow earthquakes that have been found at the Circum-Pacific subduction zones4, over a wide range of depths.Several seismicity studies of subduction zones have recently suggested relationships between deep and shallow earthquakes as results of possible slow deformations5, belongs to the Circum-Pacific subduction zones. It is well known for frequent occurrences of earthquakes much larger than M7 (magnitude 7). Figure 6 and shows the seismicity map around the Tonga subduction zone. Among all the earthquakes, two deep intraslab earthquakes with dynamic triggering (M7.6 and M7.7) occurred on 19 August 20027. Other shallow intraslab earthquakes occurred near the Tonga Trench on 3 May 2006 (M8.0) and 19 March 2009 (M7.6)8. A normal fault earthquake (M8.1) in the outer trench-slope occurred at the northern end of the Tonga subduction zone, possibly with two interplate thrust subevents, on 29 September 200911. Figure The Tonga subduction zone, that is the most active area of deep earthquakes in the world12. Although this region has few observation stations providing continuous GNSS monitoring, we can analyse temporal fluctuations of the surface displacements. Here, using GNSS time series and earthquake catalogue data, we analyse the seismotectonic activities in the Tonga subduction zone.Monitoring the surface displacements using a Global Navigation Satellite System (GNSS) at the Tonga subduction zone revealed a rapid plate convergence (approximately 240 mm/year) and back-arc extension13 provided by the Nevada Geodetic Laboratory14 allow us to analyse transient surface displacements at the Tonga subduction zones after 2002. The locations of the observation stations around the Tonga Trench are shown in Fig. GNSS time series data in the ITRF2014/IGS14 Reference Frame15 for the timespan of 2002–2017 and the spatial range of 12–25 degrees south latitude and 178–192 degrees east longitude  Comprehensive Earthquake Catalog (ComCat) dataude Fig. . The magFigure To extract signals of transient surface displacements in the GNSS time series, we first remove the offsets reflecting equipment changes for the GNSS stations by estimating the differences in median values on five days before and after the offset times. If the terms for the offset estimation are separated by more than two weeks, we do not perform the offset correction.Then, we detrend the time series focusing on the transient displacements after the M8-class large shallow earthquakes (on 3 May 2006 and 29 September 2009). Namely, the detrended time series data are obtained by removing linear trends in the one-year period preceding the 2006 earthquake. Supplementary Fig. 16. The ETAS model separates the seismicity rate into the background seismicity rate 17.To exclude the effects of aftershock clustering from the earthquake catalogue data, we use the Epidemic-Type Aftershock Sequence (ETAS) model to extract the background seismicity rate We perform the ETAS analysis on yearly data. For the parameter estimation, the initial values are assumed to be  to that of the coseismic offset of the 2006 M8.0 earthquake (~ 5.8 cm) as shown in Supplementary Fig. 22.For P2 in Fig. In contrast, for P3, the transient displacements linearly increased from the beginning of P3 (the 2009 M8.1 earthquake) and suddenly ceased around the 2013 M7.4 earthquake Fig. . The traFurthermore, Fig. Figure In order to extract the characteristics of the deeper and shallower earthquake activities, we took the ratio of the background seismicity rate tio Fig.  revealedtio Fig.  ceased a10. The mentioned study modelled the viscoelastic relaxation of the 2009 M8.1 earthquake to explain the postseismic deformation. It was based on the GNSS and Gravity Recovery And Climate Experiment (GRACE) data. In another region of the central Kuril Islands with near-trench major earthquakes23, no additional trenchward movement similar to that estimated in this study was reported24.The trenchward transient displacements at the upper plate, which are measured by the TONG and VAVS stations Fig. , were no10 using the same numerical code VISCO1D25. Here is a brief overview of that simulation: The code calculates viscoelastic relaxation of the asthenosphere following the earthquake on a layered spherical Earth. The 2009 M8.1 earthquake was modelled as a combination of normal and thrust faultings  temporal acceleration of the subduction of the Pacific Plate to recover the force balance of plate tectonics was triggered and induced the changes in the displacement field, as in the case of the 2003 M8.0 Tokachi-oki earthquake in Japan27. Unfortunately, it is difficult to distinguish between the two possible processes from the present data set. We expect that the GRACE and its follow-on data will contribute to solving this problem.During the slow deformation event, the trenchward displacements, whuch cannot be explained by the viscoelastic relaxation mechanism, were triggered by stress changes due to the 2009 M8.1 intraslab earthquake in the far north through the following possible processes: (i) A slow slip event at the plate interface was triggered as in the case of the 2016 M7.8 Kaikoura earthquake in New Zealand28 or densification of metastable slab29 might cause the large-scale phenomena. In any case, the findings of the present study provide new insights into the relationship between shallow and deep earthquakes in the subduction zone.The mechanisms for the slow deformation event that finished around the time of the 2013 M7.4 intraslab earthquake are not yet understood. One speculation is that the slab deformations due to the 2013 M7.4 earthquake regained the force balance before the event series. Besides, the possible effects of sudden fluid exchangeAt the Tonga subduction zone, we identified a slow deformation event of the trenchward transient displacements and the quiescence of the deeper earthquakes. The time-related accordance of both phenomena and the large intraslab earthquakes implied an interconnected system between earthquakes from shallow to deep parts of the subduction zone.Supplementary Information.