Optimisation of Remote Monitoring Programmes in Heart Failure: Evaluation of Patient Drop-Out Behaviour and Healthcare Professionals' Perspectives.

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors' workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future.

Systemic Arterial Stiffness and Choroidal Microvascular Insufficiency on the Structural Progression of Normal Tension Glaucoma.

PURPOSE: To identify the role of systemic arterial stiffness and choroidal microvascular insufficiency on structural progression of normal-tension glaucoma (NTG). DESIGN: Retrospective cohort study. METHODS: A total of 107 early NTG eyes of 88 patients, who underwent pulse wave velocity (PWV) measurements and optical coherence tomography (OCT) angiography (OCT-A) at baseline, were categorized depending on the presence of peripapillary choroidal microvasculature dropout (MvD) and PWV. Differences in glaucomatous progression were analyzed. Structural progression rates were determined using the trend-based analysis of cirrus OCT. RESULTS: Thirty-two eyes displayed choroidal MvD (62.7 (95% CI 58.4-67.0) years old, 53.6% males), and 70 eyes did not show any MvD (59.9 (95% CI 57.1-62.6) years old, 53.3% males) at baseline. Patients were followed for 48.4 (95% CI 40.0-56.8) months. When they were further divided based on PWV (high PWV≥1400cm/sec), those with choroidal MvD and high PWV showed significantly faster thinning in macular ganglion cell-inner plexiform layer (GCIPL; P=0.023). In comparison to those with low PWV and no MvD, eyes with high PWV and MvD in the peripapillary area were likely to show fast structural progression (≤-1.2 µm/year) in the macular GCIPL by odds of 6.019 (95% CI 1.619-38.531, P=0.025). CONCLUSIONS: In NTG eyes, GCIPL thinning was faster when choroidal MvD and high systemic arterial stiffness were present. The simultaneous presence of regional and systemic vascular insufficiency may be associated with rapid glaucoma structural progression in eyes with low baseline intraocular pressure.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

The key reasons for dropout in Slovenian music schools - a qualitative study.

Music education often struggles to sustain students' long-term commitment, with many perceiving lessons as frustrating or unengaging, leading to discontinuation. To address this gap, our study aimed to elucidate the primary reasons for dropout from the perspectives of various stakeholders, including students, parents, teachers, and principals. Drawing upon the self-determination theory, our research comprehensively investigated external and internal factors contributing to dropout. Among external factors, competing extracurricular commitments, music theory and solfége lessons, and teacher's approach emerge as the most prominent. Among internal factors, our findings highlighted the critical role of autonomy, competency, and relatedness in shaping students' decisions to continue or discontinue music education. Inadequate teacher-student relationships, limited peer interactions, and uninspiring classroom atmospheres significantly impacted dropout. Moreover, challenges in the music school curriculum, such as difficulties with music theory and solfège, resource limitations, and excessive workloads, emerged as prominent barriers to student engagement. By addressing these multifaceted issues, our study underscores the importance of fostering supportive environments that cater to individual needs and interests, ultimately enhancing the overall music education experience and reducing dropout rates. This research represents the first systematic empirical study in Slovenian music education, laying the groundwork for future quantitative investigations to advance education practices in Slovenia.

Continuous Risk Score Predicts Waitlist & Post-Transplant Outcomes in Hepatocellular Carcinoma Despite Exception Changes.

BACKGROUND AND AIMS: Continuous risk stratification of candidates and urgency-based prioritization have been utilized for liver transplantation (LT) in non-hepatocellular carcinoma (HCC) patients in the United States. Instead, for HCC patients, a dichotomous criterion with exception points is still used. This study evaluated the utility of the hazard associated with LT for HCC (HALT-HCC), an oncological continuous risk score, to stratify waitlist dropout and post-LT outcomes. METHODS: A competing risk model was developed and validated using the UNOS database (2012-2021) through multiple policy changes. The primary outcome was to assess the discrimination ability of waitlist dropouts and LT outcomes. The study focused on the HALT-HCC score, compared to other HCC risk scores. RESULTS: Among 23,858 candidates, 14,646 (59.9%) underwent LT and 5,196 (21.8%) dropped out of the waitlist. Higher HALT-HCC scores correlated with increased dropout incidence and lower predicted five-year overall survival after LT. HALT-HCC demonstrated the highest AUC values for predicting dropout at various intervals post-listing (0.68 at six months, 0.66 at one year), with excellent calibration (R2=0.95 at six months, 0.88 at one year). Its accuracy remained stable across policy periods and locoregional therapy applications. CONCLUSIONS: This study highlights the predictive capability of the continuous oncological risk score to forecast waitlist dropout and post-LT outcomes in HCC patients, independent of policy changes. The study advocates integrating continuous scoring systems like HALT-HCC in liver allocation decisions, balancing urgency, organ utility, and survival benefit.

Spatially-informed insights into early marriage and school dropout: An advanced bivariate binary multilevel model for understanding Ethiopia's context.

The phenomenon of school dropout, which entails the failure to meet the minimum educational requirements, and early marriage, which involves the marital union of girls prior to attaining 18 years of age, constitute crucial issues in Ethiopia. This research endeavor sought to identify the determinants of these two outcomes. A weighted sample of 3091 girls who had experienced early marriage and school dropout was drawn from the 2016 Ethiopian Demographic and Health Survey (EDHS) dataset and analyzed utilizing bivariate binary multilevel models featuring spatial effects. The prevalence rates of early marriage and school dropout were 62.9 % and 75.4 %, respectively. We observed non-uniform spatial distributions of early marriage and school dropout across Ethiopia. The odds ratio of the association between early marriage and school dropout was 1.39, indicating a significant interdependence of these two outcomes. The probability of early marriage and school dropout was estimated to be 1.63 and 1.18 times higher, respectively, for girls hailing from rural areas and 1.70 and 1.23 times higher, respectively, for those classified in the poorest wealth index, as compared to their counterparts. Therefore, stakeholders and policymakers must prioritize hotspots, socio-economic, and demographic factors to achieve a meaningful reduction in the incidence of early marriage and school dropout.

Achieving optimal trade-off for student dropout prediction with multi-objective reinforcement learning.

Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness. Recognizing the limitations of existing methods, this study introduces a novel approach leveraging multi-objective reinforcement learning (MORL) to optimize the trade-off between prediction accuracy and earliness in SDP tasks. By framing SDP as a partial sequence classification problem, we model it through a multiple-objective Markov decision process (MOMDP), incorporating a vectorized reward function that maintains the distinctiveness of each objective, thereby preventing information loss and enabling more nuanced optimization strategies. Furthermore, we introduce an advanced envelope Q-learning technique to foster a comprehensive exploration of the solution space, aiming to identify Pareto-optimal strategies that accommodate a broader spectrum of preferences. The efficacy of our model has been rigorously validated through comprehensive evaluations on real-world MOOC datasets. These evaluations have demonstrated our model's superiority, outperforming existing methods in achieving optimal trade-off between accuracy and earliness, thus marking a significant advancement in the field of SDP.

Clusters Based on Within-Treatment Symptom Trajectories as Predictors of Dropout in Treatment for Posttraumatic Stress Disorder and Substance Use Disorder.

Objective: Dropout rates are high in treatments for co-occurring posttraumatic stress disorder (PTSD) and substance use disorders (SUDs). We examined dropout predictors in PTSD-SUD treatment. Methods: Participants were 183 veterans receiving integrated or phased motivational enhancement therapy and prolonged exposure. Using survival models, we examined demographics and symptom trajectories as dropout predictors. Using latent trajectory analysis, we incorporated clusters based on symptom trajectories to improve dropout prediction. Results: Hispanic ethnicity (integrated arm), Black or African American race (phased arm), and younger age (phased arm) predicted dropout. Clusters based on PTSD and substance use trajectories improved dropout prediction. In integrated treatment, participants with consistently-high use and low-and-improving use had the highest dropout. In phased treatment, participants with the highest and lowest PTSD symptoms had lower dropout; participants with the lowest substance use had higher dropout. Conclusions: Identifying within-treatment symptom trajectories associated with dropout can help clinicians intervene to maximize outcomes. ClinicalTrials.gov Identifier: NCT01211106.

Predictors of dropout, time spent on the program and client satisfaction in an internet-based, telephone-assisted CBT anxiety program among elementary school children in a population-based sample.

Increasing evidence has shown that childhood anxiety can be effectively treated by Internet-based cognitive behavioral therapy (ICBT). Being able to predict why participants decide to drop out of such programs enables scarce resources to be used appropriately. The aim of this study was to report dropout predictors for a population-based ICBT intervention aimed at children with anxiety, together with the time they and their parents spent on the program and client satisfaction rates. The study focused on 234 Finnish children aged 10-13 who received an ICBT intervention, with telephone support, for anxiety symptoms, as a part of a randomized control trial. Their parents also had access to Internet-based material and participated in the weekly telephone calls with the coach. Possible drop out factors were explored and these included various family demographics, child and parent psychopathology and therapeutic alliance. Just under a fifth (23.9%) of the children dropped out of the intervention. The risk was higher if the child did not fulfill the criteria for any anxiety diagnosis or reported a poorer therapeutic alliance. Family demographics and the COVID-19 pandemic did not increase the risk. The families spent an average of 127 min on the webpage each week and an average of 32 min on the phone calls. The overall satisfaction with the program was 87% for the children and 95% for the parents. Both the children and the parents found the telephone calls helpful. These findings are important in clinical practice when assessing a family's eligibility for ICBT.

Predictive modelling of student dropout risk: Practical insights from a South Korean distance university.

Distance education supports lifelong learning and empowers individuals in rapidly changing societal conditions, yet it encounters high dropout rates due to a range of individual and societal obstacles. This study addresses the challenge of creating a practical prediction model by analyzing extensive real-world time-point data from a well-established online university in Seoul. Covering 144,540 instances from 2018 to 2022, the study integrates diverse datasets to compare the accuracy of models based on longitudinal, semester-wise, and gender-specific datasets. The demographic, academic, and online metrics identified significant dropout indicators, including age (particularly when binned), residential area, specific occupations, GPA, and LMS log metrics, using a stepwise backward elimination process. The study revealed that, despite societal changes, recent data from the last four semesters can be effectively used for stable prediction training. Gender-based analysis showed different factors influencing dropout risk for males and females. The Light Gradient Boosting Machine (LGBM) algorithm excelled in prediction accuracy, with the ROC-AUC metric affirming its superiority. However, logistic regression also showed its competitive performance and offered in-depth interpretation. In South Korea's distinct educational setting, merging advanced algorithms like LGBM with the interpretive strength of logistic regression is key for effective student support strategies.

Machine learning predicts upper secondary education dropout as early as the end of primary school.

Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students' academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators' processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

A Bayesian convolutional neural network-based generalized linear model.

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.

The Involvement of Academic and Emotional Support for Sustainable Use of MOOCs.

MOOCs, the Massive Open Online Courses, are online educational courses that offer open access to a large number of participants globally. However, online engagement during MOOC learning remains a problem, as reflected in relatively high dropout rates. This paper involves academic and emotional support, aiming to explore whether they contribute to users' sustainable use of the MOOC platform. A total of 410 college students learning English as a foreign language (EFL) and with MOOC learning experience participated in this study. Employing the structural equation modeling (SEM) techniques, we examined the relationships among five factors in the EFL MOOC learning context: academic support (AS), emotional support (ES), perceived usefulness (PU), perceived ease of use (PEoU), and platform reputation (PR). The results indicate that academic support influences learners' perceptions of the usefulness and ease of use of the MOOC platform, as well as enhancing learners' feelings of being emotionally supported. Simultaneously, platform reputation plays a crucial role in influencing learners' perceptions of MOOC platforms. However, results suggest that emotional support does not have a statistically significant impact on the perceived usefulness and perceived ease of use of the platform in EFL MOOC learning contexts.

Retention in Junior Giants, a sport-based youth development program: what factors are associated with intentions to return?

Introduction: While evaluation research shows that physical activity-based youth development (PA-PYD) programs can have a positive impact on social and emotional growth, less is known about which participants return year after year and what factors are associated with their continued participation. The Junior Giants is a sport-based youth development program for 5-18-year-old boys and girls that is non-competitive and free to participate. The 8-week program uses baseball and softball as platforms for teaching life skills and fostering social emotional competencies. This mixed-methods study evaluated quantitative factors associated with intentions to return to the program the following year and qualitative reasons why parents/caregivers intended not to re-enroll their child. Method: Parents/caregivers of Junior Giants participants (N = 8,495) completed online surveys about their child's demographics, social emotional climate and learning, character development, and intentions to return the following year. Results: Descriptive data illustrated that parents/caregivers reported quite positive outcomes and experiences for their child. Chi-square and t-test analyses revealed significant differences (p < .001) between intended returners (n = 7,179, 84.5%) and those who reported no/undecided on returning (n = 1,316, 15.5%). Intended returners were significantly more likely to be identified as Latino and be in their second year of participation. Significant predictors of a binomial logistic regression [χ2 (df = 22) = 1,463.25, p < .001] included age, race/ethnicity, years played, character development, reading, league experiences, physical activity, and perceived support, with small to medium effect sizes. Using responses from a subset of 217 parents/caregivers who reported their child would not return to the program, a thematic analysis resulted in seven themes: Lack of Organization and Communication; Dissatisfied with Coaching, Didn't Learn Baseball/Softball, Not Competitive Enough, Skill Levels Not Matched, Aged Out, and Non-Program Related Reasons. Discussion: Quantitative results contribute to the literature on predictors of retention in youth development programs, while qualitative findings echo common motives cited for dropout in youth sport. Both provide opportunities for reflection and potential changes to future programming.

A dataset on Whatsapp groups effectiveness in inducting first years to university.

WhatsApp as the most popular instant messaging technology that has created opportunities for online cooperation and teamwork among students in the university context [1] and allows students to communicate even outside school hours [2]. Transitioning from high school to university can be a challenging experience for many students, particularly those who are entering a new academic atmosphere with greater autonomy and higher workload [3]. This data publication [4] presents a dataset capturing the perceptions of first-year university students on the utilization of WhatsApp as a means of continuous induction into university life. The dataset includes responses from participants on 7 background questions and 14 Likert scale questions, measuring agreeableness on the academic dimension of the academic dropout wheel [5]. The questionnaire aimed to investigate the effectiveness of WhatsApp groups established by academic support staff in facilitating the integration of first-year students into both formal academic structures and informal social systems within the campus. The data may offer valuable insights into the potential benefits of WhatsApp as an educational tool and its impact on fostering a positive academic environment, ultimately helping to reduce dropout rates in institutions of higher learning. Researchers, educators, and policymakers can utilize this dataset to gain a deeper understanding of the role that WhatsApp can play in enhancing student engagement and promoting successful integration into university life. By harnessing the information from this dataset, institutions can develop targeted strategies to provide effective support systems for first-year students, thereby promoting academic success and retention.

Adherence to Online Interventions for Family Caregivers of People With Dementia: A Meta-Analysis and Systematic Review.

OBJECTIVE: Online interventions hold promise in supporting the well-being of family caregivers and enhancing the quality of care they provide for individuals with long-term or chronic conditions. However, dropout rates from support programs among specific groups of caregivers, such as caregivers of people with dementia, pose a challenge. Focused reviews are needed to provide more accurate insights and estimates in this specific research area. METHODS: A meta-analysis of dropout rates from available online interventions for family caregivers of people with dementia was conducted to assess treatment acceptability. A systematic search yielded 18 studies involving 1,215 caregivers. RESULTS: The overall pooled dropout rate was 18.4%, with notable heterogeneity indicating varied intervention adherence. Interventions incorporating human contact, interactive features, and personalization strategies for specific types and stages of dementia predicted significantly lower dropout rates. Methodological assessment revealed variability in study quality. CONCLUSION: Findings support the effectiveness of social support, personalization strategies, and co-design in enhancing intervention adherence among dementia family caregivers. Further research is needed to explore factors influencing dropout rates and conduct robust trials to refine the implementation of future interventions.

Regulatory Issues in Electronic Health Records for Adolescent HIV Research: Strategies and Lessons Learned.

BACKGROUND: Electronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance. OBJECTIVE: As part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines. METHODS: Eight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements. RESULTS: Due to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites. CONCLUSIONS: The process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes.

Risk factors for dropout from psychological substance use disorder treatment programs in criminal justice settings.

BACKGROUND: Substance use disorders are highly prevalent in people within the criminal justice system. Psychological programs are the most common type of treatment available and have been shown to decrease recidivism, but dropping out of treatment is common. Risk factors associated with treatment dropout remain unclear in this setting, and whether the risk factors differ by treatment form (group-based vs. individual). METHODS: Outcome (treatment dropout) was defined as not finishing the program due to client's own wish, misbehavior, no-shows, or because program leader found client to be unsuitable. Predictors of treatment dropout included a comprehensive set of individual-level clinical, socioeconomic, and crime-related pre-treatment characteristics. Multivariable regression models were used to estimate the associations between predictors and dropout. FINDINGS: The study cohort included 5239 criminal justice clients who participated in a psychological treatment program (group-based or individual). Multivariable logistic regression models showed that female sex (OR=1.64, 95% CI 1.20-2.25), age (0.99, [0.97-1.00]), sentence length (0.98, [0.97-0.98]), higher education (0.54, [0.28-1.00]), number of violent offenses (1.03, [1.01-1.05]), and anxiety disorders (1.32, [1.01-1.72]) were associated with dropout from the individual treatment program. For the group-based program, age (OR=0.98, 95% CI 0.96-1.00), sentence length (OR=0.96, 95% CI 0.94-0.98), stimulant use disorder (OR=1.48, 95%, 1.00-2.19), and self-harm (OR 1.52, 95% CI 1.00-2.34) were associated with dropout. CONCLUSIONS: We identified certain sociodemographic, crime-related, and clinical characteristics that were particularly important in predicting dropout from psychological treatment. Further, we find that there are similarities and differences in predictors of dropout from group-based and individual treatment forms.

Comparative features of superior versus inferior hemisphere microvasculature dropout in open-angle glaucoma.

PURPOSE: This study aimed to investigate differences in microvasculature dropout (MvD) between the superior and inferior hemispheres in glaucoma patients. STUDY DESIGN: Retrospective and cross-sectional. METHODS: Fifty-eight eyes of 58 open-angle glaucoma patients (age 61.12 ± 10.19 years, mean deviation - 7.32 ± 6.36 dB) were included. MvD was detected with en face images from swept-source optical coherence tomography angiography. Blood flow at the optic nerve head was measured with laser speckle flowgraphy, represented as the mean blur rate in tissue (MBRT). Logistic and linear regression models adjusted for age, intraocular pressure, axial length, and circumpapillary retinal nerve fiber layer thickness were used to investigate the relationship between various factors and MvD angle in each hemisphere. RESULTS: The presence of inferior MvD was related to peripapillary atrophy-ß area (odds ratio = 14.10 [2.49-234.00], P = 0.019). Superior MvD angle was significantly related to MBRT in the superior quadrant (ß = -0.31 [- 0.60 - -0.02], P = 0.037). Inferior MvD angle was significantly related to peripapillary atrophy-ß area (ß = 0.49 [0.21-0.77], P = 0.001). CONCLUSIONS: Only superior MvD demonstrated a significant relationship with reduced ocular blood flow. In contrast, inferior MvD was associated with mechanical stress. These findings may suggest a potential difference in pathophysiology between superior and inferior MvD.

Dropout in cognitive behavioral treatment in adults living with overweight and obesity: a systematic review.

Obesity is a chronic, complex, and multifactorial disease resulting from the interaction of genetic, environmental, and behavioral factors. It is characterized by excessive fat accumulation in adipose tissue, which damages health and deteriorates the quality of life. Although dietary treatment can significantly improve health, high attrition is a common problem in weight loss interventions with serious consequences for weight loss management and frustration. The strategy used to improve compliance has been combining dietary prescriptions and recommendations for physical activity with cognitive behavioral treatment (CBT) for weight management. This systematic review determined the dropout rate and predictive factors associated with dropout from CBT for adults with overweight and obesity. The data from the 37 articles selected shows an overall dropout rate between 5 and 62%. The predictive factors associated with attrition can be distinguished by demographics (younger age, educational status, unemployed status, and ethnicity) and psychological variables (greater expected 1-year Body Mass Index loss, previous weight loss attempts, perceiving more stress with dieting, weight and shape concerns, body image dissatisfaction, higher stress, anxiety, and depression). Common reasons for dropping out were objective (i.e., long-term sickness, acute illness, and pregnancy), logistical, poor job conditions or job difficulties, low level of organization, dissatisfaction with the initial results, lack of motivation, and lack of adherence. According to the Mixed Methods Appraisal quality analysis, 13.5% of articles were classified as five stars, and none received the lowest quality grade (1 star). The majority of articles were classified as 4 stars (46%). At least 50% of the selected articles exhibited a high risk of bias. The domain characterized by a higher level of bias was that of randomization, with more than 60% of the articles having a high risk of bias. The high risk of bias in these articles can probably depend on the type of study design, which, in most cases, was observational and non-randomized. These findings demonstrate that CBT could be a promising approach for obesity treatment, achieving, in most cases, lower dropout rates than other non-behavioral interventions. However, more studies should be conducted to compare obesity treatment strategies, as there is heterogeneity in the dropout assessment and the population studied. Ultimately, gaining a deeper understanding of the comparative effectiveness of these treatment strategies is of great value to patients, clinicians, and healthcare policymakers. Systematic review registration: PROSPERO 2022 CRD42022369995 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022369995.