Psychological Assessment in Anorexia Nervosa: A Single Case Study on Predictive Factors of Dropout.

Anorexia nervosa (AN) has the highest mortality rate among psychiatric disorders. Adult AN patients have a chronic history of treatment dropout due to denial of their psychological and physical disease states, which may be connected to defense mechanisms. We developed an assessment protocol to evaluate the psychological functioning of patients undergoing a psychodynamic approach for eating disorders (PAED), aimed at identifying the psychological factors associated with intervention success or dropout. We analyzed the case of an adult patient who quit treatment at the start and discussed her psychological functioning profile. We present the case of a 45-year-old woman with enduring AN, who entered the PAED program at an Italian hospital. In adult AN patients, denial and acting out may have significant impacts on clinic compliance. This hampers establishing a relationship with the clinic and the success of the psychological work aimed at promoting mental awareness and insights into the disorder. This highlights the need to consider which aspects of the initial psychological assessment are predictive of dropout in AN patients.

Addiction Therapeutic Communities in Saudi Arabia: An Evaluation of the Resident Retention Rates and Their Correlation With the Type of Drug Used.

BACKGROUND: Long-term retention is a reliable, well-studied factor associated with enhanced outcomes in addiction therapeutic communities (ATCs). The aim of this study was to estimate resident retention rates of ATCs in Saudi Arabia at three and six months, completion of therapy, and early dropout, and investigate their correlation with the type of drug used and other social variables. MATERIAL AND METHODS: This was a cohort retrospective study and data of all residents admitted to all Saudi ATCs since their establishment in 2000 through September 2014 were collected from their files. There were five Saudi ATCs during the study period. The date of admission, discharge date, socio-demographics, and drug use were reported. Retention rates at three and six months and dropouts in the first week were calculated, and the correlation with the type of drug used was studied using multinomial binary logistic regression analysis. RESULTS: Out of a total of 2050 files, 2003 were suitable for analysis. All residents were male adults. The retention rate for three and six months was 45% and 28%, respectively, and 8.3% dropped out in the first week. The median duration of stay was 77 days. Unemployment and being a student were associated with the completion of treatment. The type of drug used showed no significant correlation with retention rates or dropouts. CONCLUSION: Three-month retention, treatment completion, and dropout within the first week were reasonable, comparable, and consistent with reported rates worldwide. These rates can be considered an indicator of successful Saudi ATC programs. The type of drug used does not make a difference in retention and dropout rates in the present study, which is in line with the ATC management system that does not view the type of drug as a main treatment outcome modifier.

First impressions count: Therapists' impression on patients' motivation and helping alliance predicts psychotherapy dropout.

OBJECTIVE: With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed. METHOD: We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (N = 1,691 and N = 12,473) using baseline and initial process variables reported by patients and therapists. RESULTS: Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients' motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout. CONCLUSIONS: Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists' first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients' motivation and the therapeutic alliance.

Predictors and Patterns of Dropout From Psychiatric Treatment Among Trauma-Affected Refugees: A Large Data Pool Analysis.

Dropout from mental health treatment is a substantial hindrance to relevant and effective treatment. Despite the high prevalence of PTSD among refugees, research into their treatment dropout has received limited attention. This study aimed to identify patterns and predictors of treatment dropout versus completion through different treatment stages. The sample included 940 patients with a refugee background undergoing outpatient treatment for PTSD in Denmark. All patients were offered 10 medical doctor sessions and 16-20 psychotherapy sessions. Dropout was analysed in three stages: (1) during the first six MD sessions, (2) during the first eight psychotherapy sessions upon completion of Stage 1, and (3) during psychotherapy sessions 9 to 16. A stepwise multiple regression analysis was conducted for each stage to identify predictors of stage-specific dropout. Counter to expectations, both early dropout and full completion were associated with better symptom outcomes, relative to late-treatment dropout. Key predictors varied by stage, with younger age predicting early dropout, whereas chronic pain and poor Danish proficiency predicted late dropout. Female gender and a clearly articulated motivation for active participation were predictors for full treatment completion. Practical advice is suggested to accommodate at-risk patients and to re-evaluate patient engagement after familiarisation with treatment.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations. RESULTS: To address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction. CONCLUSIONS: These results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.

This paper investigates uncertainty quantification (UQ) techniques in multi-class classification of chest X-ray images (COVID-19, Pneumonia, and Normal). We evaluate Bayesian Neural Networks (BNN) and the Deep Neural Network with UQ (DNN with UQ) techniques, including Monte Carlo dropout, Ensemble Bayesian Neural Network (EBNN), Ensemble Monte Carlo (EMC) dropout, across different evaluation metrics. Our analysis reveals that DNN with UQ, especially EBNN and EMC dropout, consistently outperform BNNs. For example, in Class 0 vs. All, EBNN achieved a UAcc of 92.6%, UAUC-ROC of 95.0%, and a Brier Score of 0.157, significantly surpassing BNN's performance. Similarly, EMC Dropout excelled in Class 1 vs. All with a UAcc of 83.5%, UAUC-ROC of 95.8%, and a Brier Score of 0.165. These advanced models demonstrated higher accuracy, better discriaminative capability, and more accurate probabilistic predictions. Our findings highlight the efficacy of DNN with UQ in enhancing model reliability and interpretability, making them highly suitable for critical healthcare applications like chest X-ray imageQ6 classification.

Determinants of measles second dose vaccination dropout among children aged 18-24 months in Ejere woreda, central Ethiopia; unmatched case-control study.

Background: Measles continues to pose a significant public health challenge, especially in low- and middle-income countries. Despite the implementation of national vaccination programs, measles outbreaks persist in some parts of Ethiopia, and the determinants of dropout from the second measles vaccine dose are not well understood. Hence, this study aimed to assess determinants of measles second dose vaccination dropout among children aged 18-24 months in Ejere woreda, central Ethiopia. Methods: A community-based unmatched case-control design was conducted in the Ejere Woreda of the Oromia regional state in Ethiopia between February 14 and April 6, 2023. Data were collected using a pre-tested structured questionnaire. The collected data were coded and entered into Epi-data version 3.1 and then transported to SPSS version 27 for statistical analysis. Descriptive analysis like frequency, mean, and percentage was calculated. Binary and multivariable logistic regression analysis was done. Finally, variables with a p-value <0.05 were considered statistically significant. Result: A total of 446 mothers/caregivers, comprising 110 cases and 336 controls, participated in this study, making the response rate 97.8%. Lack of a reminder for the measles vaccine during postnatal care (PNC) (AOR = 5.19; 95% CI: 2.34, 7.83), having ≤2 antenatal care (ANC) contacts (AOR = 4.95; 95% CI: 2.86, 9.24), long waiting times during previous vaccination (AOR = 2.78; 95% CI: 1.19, 4.38), children of mothers/caregivers without formal education (AOR = 6.46; 95% CI: 2.81, 11.71), mothers/caregivers of children who were unaware of the importance of the second dose of measles (AOR = 8.37; 95% CI: 4.22, 15.08), and mothers/caregivers whose children did not receive at least two doses of vitamin A (AOR = 4.05; 95% CI: 2.15, 8.11) were significant determinants of measles second dose vaccination dropout. Conclusion: Implementing targeted interventions during antenatal care and when mothers visit health facilities for other vaccines can significantly improve the uptake of the second dose of the measles vaccine. These strategies not only enhance overall vaccination coverage but also mitigate the risk of measles outbreaks in the community.

Dropout From Psychological Interventions for Pathological Health Anxiety: A Three-Level Meta-Analysis of Randomized Controlled Trials.

This study aimed to provide the first comprehensive evidence on the prevalence and predictors of dropout in psychological interventions for pathological health anxiety. A database search in Web of Science, EMBASE, PubMed, Scopus, PsycINFO and the Cochrane Central Register of Controlled Trials identified 28 eligible randomized controlled trials (40 intervention conditions; 1783 participants in the intervention condition), published up to 18 June 2024. Three-level meta-analytic results showed a weighted average dropout rate of 9.67% (95% confidence interval [CI] [6.49%, 14.17%]), with dropout equally likely from treatment and control conditions (odds ratio = 1.07, 95% CI [0.80, 1.44]). Moderator analyses indicated no statistically significant effects of study, participant, treatment or therapist characteristics, except for the country of study. These findings suggest that the average dropout rate is relatively low compared with those reported for other mental health conditions and highlight the importance of considering cultural and societal factors when evaluating treatment adherence. Future research should continue to explore the complex and multifaceted factors influencing dropout to improve the design and implementation of psychological interventions for pathological health anxiety.

Attrition of older adults in web-based health interventions: Survival analysis within an observational cohort study.

To identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults. We conducted an observational cohort study. Participants completed a pre-test questionnaire and got access to an eHealth intervention, called Stranded, for 4 weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affect drop-out. Ninety older adults started using Stranded. 45.6% participants continued their use for 4 weeks. 32.2% dropped out in the first week and 22.2% dropped out in the second or third week. The final multivariate Cox-regression model which predicts drop-out, consisted of the variables: perceived computer skills and level of external regulation. Predicting the chance of dropping out of an eHealth intervention is possible by using level of self-perceived computer skills and level of external regulation (externally controlled rewards or punishments direct behaviour). Anticipating to these factors can improve eHealth adoption.

No medication prescription and residential distance from the hospital are important factors associated with nonsurgical weight-loss treatment discontinuance in Japanese patients with high-degree obesity: a retrospective study.

BACKGROUND: Although the percentage of the population with a high degree of obesity (body mass index [BMI] ≥ 35 kg/m2) is low in Japan, the prevalence of obesity-related diseases in patients with high-degree obesity is greater than that in patients with a BMI < 35 kg/m2. Therefore, treatment for high-degree obesity is important. However, clinical studies have reported that 20-50% of patients with obesity discontinue weight-loss treatment in other countries. The circumstances surrounding antiobesity agents are quite different between Japan and other countries. In this study, we investigated the predictors of treatment discontinuation in Japanese patients with high-degree obesity. METHODS: We retrospectively reviewed the medical charts of 271 Japanese patients with high-degree obesity who presented at Toho University Sakura Medical Center for obesity treatment between April 1, 2014, and December 31, 2017. The patients were divided into non-dropout and dropout groups. Patients who discontinued weight-loss treatment within 24 months of the first visit were defined as "dropouts." Multivariate Cox proportional hazards regression analysis and Kaplan-Meier survival analysis were performed to examine the factors predicting treatment withdrawal. RESULTS: Among the 271 patients, 119 (43.9%) discontinued treatment within 24 months of the first visit. The decrease in BMI did not significantly differ between the two groups. No prescription of medication and residential distance from the hospital exceeding 15 km were the top contributors to treatment discontinuation, and the absence of prescription medication was the most important factor. The dropout-free rate was significantly higher in patients with medication prescriptions than in those without and in patients who lived within 15 km of the hospital than in those who lived farther than 15 km from the hospital. CONCLUSIONS: No medication prescription and longer residential distance from the hospital were associated with treatment dropout in Japanese patients with high-degree obesity; therefore, the addition of antiobesity medications and telemedicine may be necessary to prevent treatment discontinuation in such patients.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Meibomian Gland Dysfunction and Dropout in Diabetic Patients with Non-Proliferative Diabetic Retinopathy.

This study aims to compare meibomian gland (MG) dropout and MG dysfunction (MGD) between patients with diabetes mellitus (DM) with moderate-severe non-proliferative diabetic retinopathy (NPDR) and patients with no diabetes (NDM). This prospective, transversal, age, and gender-matched case-control study included 98 DM and 106 NDM eyes. Dry eye disease (DED) and MGD evaluations were performed, including meibography (Keratograph 5M®). The objective MG dropout percentage was obtained by analyzing meibography images with ImageJ software (v. 1.52o, National Institutes of Health, Bethesda, MD, USA) and was subsequently graded with Arita's meiboscore. The DM duration was 18 ± 9 years. The mean meiboscore (3.8 ± 0.8 vs. 3.4 ± 1.0, p = 0.001), meiboscore severity (p = 0.016), and MG dropout (45.1 ± 0.1% vs. 39.0 ± 0.4%, p < 0.001) were greater in DM than in NDM. All patients showed MG dropout (meiboscore > 1). Lower eyelids showed greater MG dropout in both groups. A correlation with age (r = 0.178, p = 0.014) and no correlations with DM duration or gender (p > 0.005) were observed. Patients with diabetes showed greater corneal staining (1.7 ± 1.3 vs. 0.9 ± 1.1; p < 0.001), reduced corneal sensitivity (5.4 ± 1.1 vs. 5.9 ± 0.4; p < 0.001), lower MG expressibility (3. 9 ± 1.6 vs. 4.4 ± 2.1; p = 0.017), and worse meibum quality (1.9 ± 0.8 vs. 1.7 ± 0.5; p = 0.019). Tear breakup time, osmolarity, MMP-9, Schirmer, and the Ocular Surface Disease Index showed no significant differences. In conclusion, patients with DM with NPDR have greater MG dropout and meiboscore, as well as more severe MGD and DED parameters than persons with NDM.

Gray matter volume differences in intimate partner violence perpetrators and its role in explaining dropout and recidivism.

AIM: Psychological instruments that are employed to adequately explain treatment compliance and recidivism of intimate partner violence (IPV) perpetrators present a limited ability and certain biases. Therefore, it becomes necessary to incorporate new techniques, such as magnetic resonance imaging (MRI), to be able to surpass those limitations and measure central nervous system characteristics to explain dropout (premature abandonment of intervention) and recidivism. METHOD: The main objectives of this study were: 1) to assess whether IPV perpetrators (n = 60) showed differences in terms of their brain's regional gray matter volume (GMV) when compared to a control group of non-violent men (n = 57); 2) to analyze whether the regional GMV of IPV perpetrators before starting a tailored intervention program explain treatment compliance (dropout) and recidivism rate. RESULTS: IPV perpetrators presented increased GMV in the cerebellum and the occipital, temporal, and subcortical brain regions compared to controls. There were also bilateral differences in the occipital pole and subcortical structures (thalamus, and putamen), with IPV perpetrators presenting reduced GMV in the above-mentioned brain regions compared to controls. Moreover, while a reduced GMV of the left pallidum explained dropout, a considerable number of frontal, temporal, parietal, occipital, subcortical and limbic regions added to dropout to explain recidivism. CONCLUSIONS: Our study found that certain brain structures not only distinguished IPV perpetrators from controls but also played a role in explaining dropout and recidivism. Given the multifactorial nature of IPV perpetration, it is crucial to combine neuroimaging techniques with other psychological instruments to effectively create risk profiles of IPV perpetrators.

Exploring the impact of depression, anxiety, stress, academic engagement, and dropout intention on medical students' academic performance: A prospective study.

BACKGROUND: Depression, anxiety, and stress (DAS) have been linked to poor academic outcomes. This study explores the relationships among DAS, academic engagement, dropout intentions, and academic performance - measured by Grade Point Average (GPA) - in medical students. It aims to understand how these factors relate to each other and predict academic performance. METHODS: Data were collected from 351 medical students (74.9 % female) through an online survey. The average age was 20.2 years. Psychometric instruments measured DAS, academic engagement, and dropout intentions. Structural equation modeling was used to test the relationships between these variables and their prediction of GPA. RESULTS: DAS was negatively associated with academic engagement ß̂=-0.501p<0.001 and positively associated with dropout intentions ß̂=0.340p<0.001. Academic engagement positively predicted GPA ß̂=0.298p<0.001 and negatively associated with dropout intentions ß̂=-0.367p<0.001. DAS had a nonsignificant direct effect on GPA ß̂=-0.008p=0.912. However, the indirect effect of DAS - via academic engagement - on GPA and dropout intention was statistically significant. LIMITATIONS: The study's limitations include the use of a convenience sample and the collection of all variables, except GPA, at the same time point, which may affect the generalizability of the results. CONCLUSIONS: The study supports the important role of DAS in its association with academic engagement and dropout intentions, which can predict GPA. Addressing DAS could enhance academic engagement and reduce dropout rates, leading to better academic performance.

Rates and predictors of service disengagement in first episode psychosis: Results from a 2-year follow-up study in an Italian real-world care setting.

AIM: Service disengagement is a major problem for "Early Intervention in Psychosis" (EIP). Understanding predictors of engagement is also crucial to increase effectiveness of mental health treatments, especially in young people with First Episode Psychosis (FEP). No Italian investigation on this topic has been reported in the literature to date. The goal of this research was to assess service disengagement rate and predictors in an Italian sample of FEP subjects treated within an EIP program across a 2-year follow-up period. METHODS: All patients were young FEP help-seekers, aged 12-35 years, recruited within the "Parma Early Psychosis" (Pr-EP) program. At baseline, they completed the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF) scale. Univariate and multivariate Cox regression analyses were carried out. RESULTS: 489 FEP subjects were enrolled in this study. Across the follow-up, a 26 % prevalence rate of service disengagement was found. Particularly strong predictors of disengagement were living with parents, poor treatment adherence at entry and a low baseline PANSS "Disorganization" factor score. CONCLUSION: More than a quarter of our FEP individuals disengaged the Pr-EP program during the first 2 years of intervention. A possible solution to reduce disengagement and to facilitate re-engagement of these young patients might be to offer the option of low-intensity monitoring and support, also via remote technology and tele-mental health care.

Cluster Analysis in Victims of Perceived Cyberbullying at the Higher Education Level.

A number of studies have demonstrated the prevalence of cyberbullying in university settings. The objective of this research is to conduct a cluster analysis to categorize victims according to the nature of the behavior they have received and to examine the relationship between gender and intention to drop out. To this end, the Online Victimization Questionnaire was administered to a sample of 800 first-year students at a university in northern Spain who had opted to participate in the study. All analyses were conducted using the SPSS statistical software, version 27.0. Results indicate the presence of four clusters: Cluster 4 (73.625%) exhibited no instances of cyberbullying behaviors. Cluster 1 (21.875%), which exhibited low scores across all cyberbullying behaviors except identity manipulation, was the most prevalent. Cluster 2 (3.125%) demonstrated high scores for public aggression and social isolation. Finally, Cluster 3 (1.375%) exhibited high scores for all cyberbullying behaviors. Furthermore, gender differences play a significant role in the formation of these clusters. It is therefore evident that there are various profiles of cyberbullying victims, which both public policies and educational programs should be aware of in order to adapt their prevention strategies. This is also a factor that affects university dropout prevention programs.

Crowd Counting Using Meta-Test-Time Adaptation.

Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the specific conditions encountered during testing. The majority of current studies concentrate on unsupervised domain adaptation. These approaches commonly perform hundreds of epochs of training iterations, requiring a sizable number of unannotated data of every new target domain apart from annotated data of the source domain. Unlike these methods, we propose a meta-test-time adaptive crowd counting approach called CrowdTTA, which integrates the concept of test-time adaptation into the meta-learning framework and makes it easier for the counting model to adapt to the unknown test distributions. To facilitate the reliable supervision signal at the pixel level, we introduce uncertainty by inserting the dropout layer into the counting model. The uncertainty is then used to generate valuable pseudo labels, serving as effective supervisory signals for adapting the model. In the context of meta-learning, one image can be regarded as one task for crowd counting. In each iteration, our approach is a dual-level optimization process. In the inner update, we employ a self-supervised consistency loss function to optimize the model so as to simulate the parameters update process that occurs during the test phase. In the outer update, we authentically update the parameters based on the image with ground truth, improving the model's performance and making the pseudo labels more accurate in the next iteration. At test time, the input image is used for adapting the model before testing the image. In comparison to various supervised learning and domain adaptation methods, our results via extensive experiments on diverse datasets showcase the general adaptive capability of our approach across datasets with varying crowd densities and scales.

The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model.

To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time-frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

Framework Development for Reducing Attrition in Digital Dietary Interventions: Systematic Review and Thematic Synthesis.

BACKGROUND: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes. OBJECTIVE: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities. METHODS: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis. RESULTS: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective. CONCLUSIONS: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions. TRIAL REGISTRATION: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.

Navigating the transition: a multidisciplinary approach to inflammatory bowel disease in children.

PURPOSE: A multidisciplinary approach to Inflammatory Bowel Disease (IBD) has recently demonstrated a positive impact in pediatric patients, reducing dropout rates and facilitating the transition to adult care. Our study aims to evaluate how this approach influences disease activity, dropout rates, and transition. METHODS: We conducted a longitudinal observational study including all patients diagnosed with IBD during pediatric-adolescent age, with a minimum follow-up period of 12 months. For each patient, endpoints included therapeutic approach, need for surgery and transition features. RESULTS: We included 19 patients: 13 with Ulcerative Colitis (UC) and 6 with Crohn's disease (CD). Most patients required multiple lines of therapy, with over 50% in both groups receiving biological drugs. Compliance was good, with a single dropout in each group (10, 5%). The need for surgery was significantly higher in the CD group compared to the UC group (16% vs. 7.7%, p < 0.01). Mean age at transition was significantly higher in the UC group compared to the CD group (19.2 ± 0.7 years SD vs. 18.3 ± 0.6 years SD, p < 0.05). CONCLUSIONS: In our experience, the multidisciplinary approach to IBD in transition-age patients appears effective in achieving clinical remission, offering the potential to reduce therapeutic dropouts.