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Objective: This study investigated if patients' experience of an initial assessment may be associated with outcome expectations, and with subsequent treatment attendance., Method: The sample comprised n  = 6051 patients with depression/anxiety disorders, nested within k  = 148 assessing therapists. Multilevel modelling (MLM) was used to examine therapist effects on treatment initiation and subsequent dropout, adjusting for patient-level characteristics. We tested associations between early outcome expectancy measured at an initial assessment with attendance at a first therapy session, and with dropout after initiation. Variability in mean expectancy ratings in the caseloads of assessing therapists was examined using the intracluster correlation coefficient (ICC)., Results: Therapist effects partly explained the variance in treatment initiation and dropout. Pre-treatment outcome expectations significantly predicted treatment initiation but not dropout for the subgroup of patients who started treatment. Approximately 16% of variability in mean expectancy ratings was explained by therapist effects (ICC = 0.159) after controlling for patient-level covariates., Conclusions: Patients assessed by some therapists are more likely to have higher outcome expectations, which influences their decision to initiate treatment thereafter. Once patients start therapy, early expectancy measured at assessment no longer influences their attendance, but the "first impression" from an initial assessment does influence their subsequent likelihood of dropout.

Background: Dropout is common and affects the statistical power and randomization balance of randomised controlled trials (RCTs)., Aims: To estimate the dropout rate in RCTs of metabolic dysfunction-associated steatohepatitis (MASH) and to examine factors associated with dropout in placebo-treated participants., Methods: PubMed and Cochrane databases were searched for phase 2-4 MASH RCTs with placebo arms through November 24, 2024. Dropout was defined as the attrition of patients included in the intention-to-treat analysis but did not complete treatment. RCTs were qualitatively reviewed to assess the expected and observed dropouts. Generalised linear mixed model was used to estimate pooled dropout rates., Results: Sixty RCTs with 3230 placebo-treated participants with MASH were analysed. Thirty-three RCTs reported the dropout rate used to estimate the effect size. Of these, 60.6%, 36.4%, and 3.0% had an expected dropout rate that was higher, lower, and similar, respectively, than the observed dropout rate in the placebo arm. Overall, the dropout rate was 11.06% (95% confidence interval [CI] 9.07 to 13.42), with a higher rate in phase 3-4 trials than in phase 2 trials. The corresponding rates due to adverse events, loss to follow-up and patient choice were 2.41% (95% CI 1.67 to 3.48), 1.79% (95% CI 1.06 to 2.99) and 4.06% (95% CI 2.97 to 5.53), respectively. Meta-regression determined that the dropout rate increased with longer treatment duration., Conclusion: Placebo dropout in MASH RCTs is significant, mainly due to patient choice. Factors such as trial phase and treatment duration should be considered when calculating sample size in future clinical trials., (© 2025 John Wiley & Sons Ltd.)

Background: Fully automated digital interventions delivered via smartphone apps have proven efficacious for a wide variety of mental health outcomes. An important aspect is that they are accessible at a low cost, thereby increasing their potential public impact and reducing disparities. However, a major challenge to their successful implementation is the phenomenon of users dropping out early., Objective: The purpose of this study was to pinpoint the factors influencing early dropout in a sample of self-selected users of a virtual agent (VA)-based behavioral intervention for managing insomnia, named KANOPEE, which is freely available in France., Methods: From January 2021 to December 2022, of the 9657 individuals, aged 18 years or older, who downloaded and completed the KANOPEE screening interview and had either subclinical or clinical insomnia symptoms, 4295 (44.5%) dropped out (ie, did not return to the app to continue filling in subsequent assessments). The primary outcome was a binary variable: having dropped out after completing the screening assessment (early dropout) or having completed all the treatment phases (n=551). Multivariable logistic regression analysis was used to identify predictors of dropout among a set of sociodemographic, clinical, and sleep diary variables, and users' perceptions of the treatment program, collected during the screening interview., Results: The users' mean age was 47.95 (SD 15.21) years. Of those who dropped out early and those who completed the treatment, 65.1% (3153/4846) were women and 34.9% (1693/4846) were men. Younger age (adjusted odds ratio [AOR] 0.98, 95% CI 0.97-0.99), lower education level (compared to middle school; high school: AOR 0.56, 95% CI 0.35-0.90; bachelor's degree: AOR 0.35, 95% CI 0.23-0.52; master's degree or higher: AOR 0.35, 95% CI 0.22-0.55), poorer nocturnal sleep (sleep efficiency: AOR 0.64, 95% CI 0.42-0.96; number of nocturnal awakenings: AOR 1.13, 95% CI 1.04-1.23), and more severe depression symptoms (AOR 1.12, 95% CI 1.04-1.21) were significant predictors of dropping out. When measures of perceptions of the app were included in the model, perceived benevolence and credibility of the VA decreased the odds of dropout (AOR 0.91, 95% CI 0.85-0.97)., Conclusions: As in traditional face-to-face cognitive behavioral therapy for insomnia, the presence of significant depression symptoms plays an important role in treatment dropout. This variable represents an important target to address to increase early engagement with fully automated insomnia management programs. Furthermore, our results support the contention that a VA can provide relevant user stimulation that will eventually pay out in terms of user engagement., (© María Montserrat Sanchez Ortuño, Florian Pecune, Julien Coelho, Jean Arthur Micoulaud-Franchi, Nathalie Salles, Marc Auriacombe, Fuschia Serre, Yannick Levavasseur, Etienne De Sevin, Patricia Sagaspe, Pierre Philip. Originally published in JMIR Mental Health (https://mental.jmir.org).)

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 fourth (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., Competing Interests: Declarations. Ethical approval: The funders have played no role in the planning of the study. The Research Centre for Child Psychiatry at the University of Turku initiated, planned, and managed the study. Competing interests: Andre Sourander is CEO in Digifamilies Inc. which provides clinical consultation and digital interventions for specialized care. The other authors have no competing interests to declare that are relevant to the content of this article., (© 2024. The Author(s).)

Background: Despite advancements in reducing cardiovascular disease, it remains a major health concern. Cardiac rehabilitation (CR) has a positive impact on morbidity, mortality, and functioning, but faces high dropout rates especially among vulnerable patients, due to social inequalities and insufficiently tailored interventions. To address this, we developed the Heart Priority Programme targeting and supporting cardiac patients at risk of dropout. This study outlines the development of the Heart Priority Programme and share findings from a feasibility test., Methods: An iterative three-stage process involving cardiac patients and healthcare professionals from three community healthcare services, guided the development of the programme. Stage 1 included reviewing evidence, consulting stakeholders, and observing practice. In stage 2, a project-group of researchers and healthcare professionals was established to co-produce the programme. Stage 3 included field-testing and local adaptions. Subsequently, the programme was feasibility tested in a single-arm follow-up study across two community healthcare services. Over six months, process data on implementation, acceptability, and mechanism of impact were gathered., Results: The Heart Priority Programme was development from January 2023 to June 2023, comprising two main parts: 1) an evidence-based identification form with 13 risk factors to identify patients at risk of dropout (referred to as priority patients), and 2) an add-on intervention targeted these patients with three core elements-assigning a contact person, systematic communication, and follow-up. During a six-month feasibility phase, 260 patients were included. Of these, 91 (35%) were identified as priority patients. CR teams found that the programme was relevant, easy to integrate into daily workflow, and applicable in practice. Patient consultations yielded positive feedback on the three core elements of the add-on intervention., Conclusions: This paper outlines the development and feasibility test of the Heart Priority Programme, co-produced to identify, and support priority patients. The programme, developed through a three-stage iterative co-production process, was found relevant and easy to implement in community healthcare services. CR teams valued its structured approach to supporting priority patients, and patients found it aligned with their needs. Although initial results are promising, further research is needed to evaluate the programme's effectiveness and suitability for widespread implementation., Trial Registration: ClinicalTrials.gov NCT06575764, registered retrospectively on 28 August 2024., Competing Interests: Declarations. Ethics approval and consent to participate: The study was registered and approved by the Data Protection Agency of Central Denmark Region (1–16-02–121-24). According to Danish legislation and the Act on Biomedical Research Ethics Committee System in Denmark, research without human biological material does not require approval from an ethics committee (§14 Sect. 2). All participants received oral and written information regarding the study and provided written informed consent to participate in this study. Anonymity was ensured for all participants during the analysis and in the presentation of the findings. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Premature dropout from psychotherapy can harm patients and increase mental health costs. This study identified predictors of dropout in brief online psychotherapy for essential workers during the COVID-19 pandemic. This was a secondary analysis of a randomized trial on 4-week CBT or IPT protocols. Participants provided sociodemographic data and completed the Patient-Reported Outcomes Measurement Information System and Burnout Assessment Tool Short-Form. Predictors were analyzed in three blocks: sociodemographic, clinical, and therapist characteristics using bivariable and multivariable analyses. The sample included 804 individuals who attended at least the first session of either CBT (n = 403) or IPT (n = 401). A total of 17.2% (n = 138) of the participants dropped out during the protocol. Significant predictors of dropout included having children (IRR = 1.48; 95% CI: 1.07-2.05; p = 0.016), residing in specific regions of Brazil (Northeast IRR = 1.44; 95% CI:1.04-2.00; p = 0.02 and Midwest IRR = 1.73; 95%CI: 1.13-2.64; p = 0.01), therapist male sex (IRR = 2.04; 95% CI: 1.47-2.83; p = < 0.001), second wave of Covid-19 (IRR = 1.54; 95% CI: 1.01-2.34; p = 0.04) and low life satisfaction (IRR = 1.63; 95% CI: 1.06-2.50; p = 0.02). Our findings underscore the necessity for culturally tailored strategies, support for those with children, and targeted therapy for individuals with low life satisfaction. Implementation of these strategies may reduce dropout rates and improve treatment outcomes for essential workers in crisis., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Objective: To investigate the prevalence and moderators of dropout rates among adults with knee or hip osteoarthritis participating in exercise randomized controlled trials (RCTs)., Data Sources: Two authors searched Embase, CINAHL, PsycARTICLES, and PubMed up to 01/09/2023., Study Selection: We included RCTs of exercise interventions in people with knee or hip osteoarthritis that reported dropout rates., Data Extraction: Dropout rates from exercise and control conditions and exerciser/participant, provider, and design/implementation related moderators., Data Synthesis: In total, 209 RCTs involving 277 exercise arms in 13,102 participants were included (mean age at study level=64 years; median prevalence of men participants=26.8%). The trim-and-fill-adjusted prevalence of dropout across all RCTs was 17.5% (95% CI=16.7%-18.2%), which is comparable with dropout observed in control conditions (trim-and-fill-adjusted odds ratio=0.89; 95% CI=0.71-1.12, P=.37). Higher prevalence of antidepressant use at study-level predicted higher dropout (R 2 =0.75, P=.002, N RCTs=6, n exercisers=412). Supervision by an exercise professional was associated with lower dropout rates, with a trim-and-fill-adjusted rate of 13.2% (95% CI=11.7%-14.9%) compared with 20.8% without supervision (95% CI=18.3%-23.5%) (P<.001)., Conclusions: Dropout rates for exercise in RCTs are comparable with control conditions, suggesting that exercise is a generally well-accepted intervention. However, interventions should be supervised by an exercise professional, such as a physiotherapist or exercise physiologist, to further minimize the risk of dropout. Health professionals should consider participants' use of antidepressants as a risk factor for dropout from exercise., (Copyright © 2024 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness., Competing Interests: Declarations. Competing interest: C. Hu, and A. Kondic, are employees of Bristol Myers Squibb and own stock/stock options. A. Roy is an employee of PumasAI., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Detecting early dropout from digital interventions is crucial for developing strategies to enhance user retention and improve health-related behavioral outcomes. Bricker and colleagues proposed a single metric that accurately predicted early dropout from 4 digital tobacco cessation interventions based on log-in data in the initial week after registration. Generalization of this method to additional interventions and modalities would strengthen confidence in the approach and facilitate additional research drawing on it to increase user retention., Objective: This study had two research questions (RQ): RQ1-can the study by Bricker and colleagues be replicated using data from a large-scale observational, multimodal intervention to predict early dropout? and RQ2-can first-week engagement patterns identify users at the greatest risk for early dropout, to inform development of potential "rescue" interventions?, Methods: Data from web users were drawn from EX, a freely available, multimodal digital intervention for tobacco cessation (N=70,265). First-week engagement was operationalized as any website page views or SMS text message responses within 1 week after registration. Early dropout was defined as having no subsequent engagement after that initial week through 1 year. First, a multivariate regression model was used to predict early dropout. Model predictors were dichotomous measures of engagement in each of the initial 6 days (days 2-7) following registration (day 1). Next, 6 univariate regression models were compared in terms of their discrimination ability to predict early dropout. The sole predictor of each model was a dichotomous measure of whether users had reengaged with the intervention by a particular day of the first week (calculated separately for each of 2-7 days)., Results: For RQ1, the area under the receiver operating characteristic curve (AUC) of the multivariate model in predicting dropout after 1 week was 0.72 (95% CI 0.71-0.73), which was within the range of AUC metrics found in the study by Bricker and colleagues. For RQ2, the AUCs of the univariate models increased with each successive day until day 4 (0.66, 95% CI 0.65-0.67). The sensitivity of the models decreased (range 0.79-0.59) and the specificity increased (range 0.48-0.73) with each successive day., Conclusions: This study provides independent validation of the use of first-week engagement to predict early dropout, demonstrating that the method generalizes across intervention modalities and engagement metrics. As digital intervention researchers continue to address the challenges of low engagement and early dropout, these results suggest that first-week engagement is a useful construct with predictive validity that is robust across interventions and definitions. Future research should explore the applicability and efficiency of this model to develop interventions to increase retention and improve health behavioral outcomes., (©Linda Q Yu, Michael S Amato, George D Papandonatos, Sarah Cha, Amanda L Graham. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.11.2024.)

Background: The abandonment of psychotherapeutic treatments is influenced by various factors, including patient characteristics, therapist traits, and the therapeutic relationship. Despite the well-documented importance of these factors, limited empirical research has focused on the role of the therapeutic relationship and the characteristics of therapist-patient dyads in predicting treatment dropout. This study protocol outlines a longitudinal research project aimed at predicting dropout and non-response in psychotherapy for individuals with personality disorders. The research seeks to identify predictive factors related to psychotherapy outcomes, focusing on patient, therapist, and dyadic elements. Specifically, the study will examine the influence of therapist characteristics (e.g., personality traits, countertransference, responsiveness) on treatment outcomes, explore the impact of relational factors (e.g., treatment expectations, epistemic trust, therapeutic alliance) on therapy effectiveness, and assess how the therapeutic alliance within therapist-patient dyads affects the likelihood of dropout and non-response., Methods: The longitudinal study will include 100 therapist-patient dyads (200 participants) recruited from various Mental Health Services in Milan, Italy. Validated instruments will be administered to both patients and therapists at four-time points: T0 (baseline), T1 (3 months), T2 (6 months), and T3 (1 year). Data will be collected at baseline and at the one-year mark to evaluate the relationships between therapist, patient, and dyadic factors and treatment outcomes., Discussion: Identifying predictive variables associated with high dropout rates can help preempt treatment discontinuation, reducing the financial and operational burdens on mental health services. Understanding these factors will enable the development of targeted interventions to improve treatment engagement and reduce attrition. This approach could enhance outcomes for individuals with personality disorders and lead to more efficient resource allocation and sustainable delivery of mental health care., (© 2024. The Author(s).)

Objectives: Although treatments of patients with borderline personality disorder (BPD) were historically associated with relatively high dropout rates, dropout rates in contemporary evidence-based treatments for BPD are typically substantially lower. However, only a few studies have investigated dropout rates in mentalization-based treatment (MBT), and even fewer have investigated predictors of dropout in this type of treatment. In this study, we investigated dropout rates in two types of MBT (day hospital MBT [MBT-DH] and intensive outpatient MBT [MBT-IOP]) using data from a recent multicenter randomized clinical trial. Given the central importance of attachment considerations in MBT, we also investigated the relationship between dropout in these two treatments and attachment dimensions., Design: Within a multicenter randomized clinical trial, 114 BPD patients were randomized to MBT-DH (n = 70) or MBT-IOP (n = 44)., Methods: Dropout in both types of MBT was investigated using descriptive analyses, and its association with attachment anxiety and attachment avoidance, as measured by the Experiences in Close Relationships questionnaire at baseline, was investigated using regression analyses., Results: Dropout rates were relatively low (10.5% across both types of MBT) and did not significantly differ between groups (11.4% in MBT-DH, 9.1% in MBT-IOP). Attachment avoidance and attachment anxiety did not impact dropout, nor did their interaction or the interaction with the type of MBT., Conclusions: Low dropout rates in both types of MBT indicate a high level of engagement of patients in both programmes. Attachment dimensions were not associated with dropout, consistent with the principle that MBT is tailored to each individual's needs. More research is needed, however, to investigate to what extent attachment is a dynamic context-bound adaptive process rather than a static personality feature., (© 2023 The British Psychological Society.)

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., (© 2024 John Wiley & Sons Ltd.)

According to the main international guidelines, patients with obesity and psychiatric/psychological disorders who cannot be addressed to surgery are recommended to follow a nutritional approach and a psychological treatment. A total of 94 patients (T0) completed a battery of self-report measures: Symptom Checklist-90-Revised (SCL-90-R), Barratt Impulsiveness Scale-11 (BIS-11), Binge-Eating Scale (BES), Obesity-Related Well-Being Questionnaire-97 (ORWELL-97), and Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Then, twelve sessions of a brief psychodynamic psychotherapy were delivered, which was followed by the participants completing the follow-up evaluation (T1). Two groups of patients were identified: Group 1 ( n = 65), who fully completed the assessment in both T0 and T1; and Group 2-dropout ( n = 29), who fulfilled the assessment only at T0 and not at T1. Machine learning models were implemented to investigate which variables were most associated with treatment failure. The classification tree model identified patients who were dropping out of treatment with an accuracy of about 80% by considering two variables: the MMPI-2 Correction (K) scale and the SCL-90-R Phobic Anxiety (PHOB) scale. Given the limited number of studies on this topic, the present results highlight the importance of considering the patient's level of adaptation and the social context in which they are integrated in treatment planning. Cautionary notes, implications, and future directions are discussed.

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., Competing Interests: Declaration of Competing Interest Authors declare no conflicts of interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Randomized controlled trials and intent-to-treat analyses are important for infertility clinical studies. Dropouts or crossovers during the study process will disrupt the randomization design and affect the intent-to-treat analysis. In this review, we have briefly introduced the occurrence of dropout and crossover from our previous Reproductive Medicine Network and other related studies and provided some experience obtained from these studies on how to minimize and reduce the occurrence of dropout and crossover for infertility randomized clinical studies., (Copyright © 2024 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Patient experiences and survival outcomes can be influenced by the circumstances related to dialysis initiation and subsequent modality choices. This systematic review and meta-analysis aimed to explore the rate and reasons for peritoneal dialysis (PD) dropout following haemodialysis (HD) to PD switch., Method: This systematic review conducted searches in four databases, including Medline, PubMed, Embase, and Cochrane. The protocol was registered on PROSPERO (study ID: CRD42023405718). Outcomes included factors leading to the switch from HD to PD, the rate and reasons for PD dropout and mortality difference in two groups (PD first group versus HD to PD group). The Critical Appraisal Skills Programme (CASP) checklist and the GRADE tool were used to assess quality., Results: 4971 papers were detected, and 13 studies were included. On meta-analysis, there was no statistically significant difference in PD dropout in the PD first group (OR: 0.81; 95%CI: 0.61, 1.09; I 2  = 83%; P = 0.16), however, there was a statistically significant reduction in the rate of mortality (OR: 0.48; 95%CI: 0.25, 0.92; I2 = 73%; P = 0.03) compared to the HD to PD group. The primary reasons for HD to PD switch, included vascular access failure, patient preference, social issues, and cardiovascular disease. Causes for PD dropout differed between the two groups, but inadequate dialysis and peritonitis were the main reasons for PD dropout in both groups., Conclusion: Compared to the PD first group, a previous HD history may not impact PD dropout rates for patients, but it could impact mortality in the HD to PD group. The reasons for PD dropout differed between the two groups, with no statistical differences. Psychosocial reasons for PD dropout are valuable to further research. Additionally, establishing a consensus on the definition of PD dropout is crucial for future studies., (© 2024. The Author(s).)

Background: Conversational agents (CAs) or chatbots are computer programs that mimic human conversation. They have the potential to improve access to mental health interventions through automated, scalable, and personalized delivery of psychotherapeutic content. However, digital health interventions, including those delivered by CAs, often have high attrition rates. Identifying the factors associated with attrition is critical to improving future clinical trials., Objective: This review aims to estimate the overall and differential rates of attrition in CA-delivered mental health interventions (CA interventions), evaluate the impact of study design and intervention-related aspects on attrition, and describe study design features aimed at reducing or mitigating study attrition., Methods: We searched PubMed, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and Web of Science, and conducted a gray literature search on Google Scholar in June 2022. We included randomized controlled trials that compared CA interventions against control groups and excluded studies that lasted for 1 session only and used Wizard of Oz interventions. We also assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool 2.0. Random-effects proportional meta-analysis was applied to calculate the pooled dropout rates in the intervention groups. Random-effects meta-analysis was used to compare the attrition rate in the intervention groups with that in the control groups. We used a narrative review to summarize the findings., Results: The systematic search retrieved 4566 records from peer-reviewed databases and citation searches, of which 41 (0.90%) randomized controlled trials met the inclusion criteria. The meta-analytic overall attrition rate in the intervention group was 21.84% (95% CI 16.74%-27.36%; I 2 =94%). Short-term studies that lasted ≤8 weeks showed a lower attrition rate (18.05%, 95% CI 9.91%- 27.76%; I 2 =94.6%) than long-term studies that lasted >8 weeks (26.59%, 95% CI 20.09%-33.63%; I 2 =93.89%). Intervention group participants were more likely to attrit than control group participants for short-term (log odds ratio 1.22, 95% CI 0.99-1.50; I 2 =21.89%) and long-term studies (log odds ratio 1.33, 95% CI 1.08-1.65; I 2 =49.43%). Intervention-related characteristics associated with higher attrition include stand-alone CA interventions without human support, not having a symptom tracker feature, no visual representation of the CA, and comparing CA interventions with waitlist controls. No participant-level factor reliably predicted attrition., Conclusions: Our results indicated that approximately one-fifth of the participants will drop out from CA interventions in short-term studies. High heterogeneities made it difficult to generalize the findings. Our results suggested that future CA interventions should adopt a blended design with human support, use symptom tracking, compare CA intervention groups against active controls rather than waitlist controls, and include a visual representation of the CA to reduce the attrition rate., Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022341415; https://www.crd.york.ac.uk/prospero/display&#95;record.php?ID=CRD42022341415., (©Ahmad Ishqi Jabir, Xiaowen Lin, Laura Martinengo, Gemma Sharp, Yin-Leng Theng, Lorainne Tudor Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.02.2024.)

Dropout is a ubiquitous psychotherapy outcome in clinical practice and treatment research alike, yet it remains a poorly understood problem. Contemporary dropout research is dominated by models of prediction that lack a strong theoretical foundation, often drawing on data from clinical trials that report on dropout in an inconsistent and incomplete fashion. In this article, we assert that dropout is a critical treatment outcome that is worthy of investigation as a mechanistic process. After briefly describing the scope of the dropout problem, we discuss the many factors that limit the field's present understanding of dropout. We then articulate and illustrate a transdiagnostic conceptual framework for examining psychotherapy dropout in contemporary research, concluding with recommendations for future research. With a more comprehensive understanding of the factors affecting retention, research efforts can shift toward investigating key processes underlying treatment dropout, thus, boosting prediction and informing strategies to mitigate dropout in clinical practice., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Evidence-based treatments exist to address traumatic stress related symptoms for children, however dropout from trauma-focused treatment remains a concern. This study examined use of an alliance building dropout management program for a group of children ages 3-17 who received an evidence-based trauma-focused treatment. Logistic regression analysis was conducted to examine the relationships between child gender, race, ethnicity, age, guardianship, externalizing behaviors, participation in a dropout management program and the dose of treatment received. The final model was significant and participation in the dropout management program as well as a child's placement in foster care were significant individual correlates with full completion of treatment. Use of an Alliance Building Dropout Management program may help decrease overall dropout over and above the contribution of other variables known to impact treatment completion.

Ambivalence towards change is an expected, recurrent process in psychological change. However, the prolonged experience of ambivalence in psychotherapy contributes to client disengagement, which could result in treatment dropout. Considering the negative effects of premature termination of therapy and the convenience of the identification of clients who are at risk of dropping out before achieving good-outcome, the current study explored the predictive power of ambivalence for premature therapy termination using a multilevel time-backwards model (i.e., considering the session of the dropout as session zero and then modelling what occurred from the dropout until session 1). Participants included a total of 96 psychotherapy clients (38 dropouts) treated in a university-based clinic following the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders. Multilevel modelling using a time-backwards model to analyse dropout data provided evidence of the predictive power of ambivalence evolution throughout treatment on the decision to prematurely discontinue treatment (p < .0001; R 2  = .29). Specifically, good-outcome dropouts presented a decreasing ambivalence trend throughout treatment, whereas poor-outcome dropouts tended to experience the same levels of ambivalence before deciding to drop out (time × dropout; β adj  = .29). Specifically, good-outcome dropouts presented a decreasing ambivalence trend throughout treatment, whereas poor-outcome dropouts tended to experience the same levels of ambivalence before deciding to drop out (time × dropout; β 11  = .64, p = .014). Additionally, poor-outcome dropouts presented higher levels of ambivalence (β 01  = 9.92, p < .0001) in the last session. The results suggest that the pattern of client ambivalence towards change is a predictor of premature termination of therapy. Implications for clinical and research contexts are discussed., (© 2021 John Wiley & Sons, Ltd.)

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (eg, due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. We investigate the use of the trimmed means (TM) estimator for the case of univariable missingness in one continuous outcome. The TM estimator operates by setting missing values to the most extreme value, and then "trimming" away equal fractions of both groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the "strong MNAR" and "location shift" assumptions. We derive formulae for the TM estimator bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted TM estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA and TM estimates, to inform sensitivity analyses. The TM approach is illustrated in a sensitivity analysis of the CoBalT RCT of cognitive behavioral therapy (CBT) in 469 individuals with 46 months follow-up. Results were consistent with a beneficial CBT treatment effect, with MI estimates closer to the null and TM estimates further from the null than the CCA estimate. We propose using the TM estimator as a sensitivity analysis for data where extreme outcome value dropout is plausible., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Dropout from psychotherapy is common and can have negative effects for patients, providers, and researchers. A better understanding of when and why patients stop treatment early, as well as actionable factors contributing to dropout, has the potential to prevent it. Here, we examined dropout from a large randomized controlled trial of transdiagnostic versus single-diagnosis cognitive-behavioral treatment (CBT) for patients with anxiety disorders (n = 179; Barlow et al., 2017). We aimed to characterize the timing of and reasons for dropout and test whether participants who dropped out had different symptom trajectories than those who completed treatment. Results indicated that overall, the greatest risk of dropout was prior to the first treatment session. In single-diagnosis CBT, dropout risk was particularly elevated before the first session and after other early sessions, whereas in transdiagnostic CBT, dropout risk was low and stable before and during treatment. Participants most often dropped out due to failure to comply with study procedures or dissatisfaction with or desiring alternative treatment. Results from multilevel models showed that trajectories of anxiety symptoms did not significantly differ between dropouts and completers. These findings suggest that there may be specific time windows for targeted and timely interventions to prevent dropout from CBT., (Copyright © 2021. Published by Elsevier Ltd.)

Introduction: More than two-thirds of the pregnant women in Africa have at least one antenatal care contact with a health care provider. However, to achieve the full life-saving potential that antenatal care promises for women and babies, four visits providing essential evidence-based interventions - a package often called focused antenatal care are required. Hence, identifying the factors associated with dropout of maternal health care utilization would have meaningful implications. The study aimed to assess antenatal care dropout and associated factors among mothers delivering in the public health facilities of Dire Dawa town, Ethiopia., Methods: Facility-based cross-sectional study was conducted from January 1 to 30, 2020. Proportionate sampling and simple random sampling techniques were used to select 230 women. Data were collected using a structured and pretested interview administered questionnaire during delivery. The data were entered into Epidata version 3.1 and analyzed using SPSS version 20. A binary logistic regression model with a 95 % confidence interval was used to analyze the results. Bivariable analysis (COR [crude odds ratio]) and multivariable analysis (AOR [adjusted odds ratio]) was used to analyze the results. From the bivariable analysis, variables with a p-value < 0.25 were entered into the multivariable logistic regression analysis. From the multivariable logistic regression analysis, variables with a significance level of p-value < 0.05 were taken as factors independently associated with ANC dropout., Result: The proportion of antenatal care dropouts was 86 (37.4 %) (95 % CI: 31.3-43.9). In logistic regression analyses, those who had no past antenatal care follow up were more likely to have ANC dropout (AOR = 7.89; 95 % CI: 2.109-29.498) and those who had no professional advice were more likely to have antenatal care dropout (AOR = 4.64 95 % CI: 1.246-17.254)., Conclusions: This study indicates that a high number of women had antenatal care dropout. Having no past ANC follow-up and professional advice were the major factors of ANC service utilization dropout. Hence, giving more information during the ANC visit is important to reduce the dropout rate from the maternity continuum of care., (© 2021. The Author(s).)

Background: Despite the established evidence base of psychological interventions in treating PTSD in children and young people, concern that these trauma-focused treatments may 'retraumatise' patients or exacerbate symptoms and cause dropout has been identified as a barrier to their implementation. Dropout from treatment is indicative of its relative acceptability in this population., Objective: Estimate the prevalence of dropout in children and young people receiving a psychological therapy for PTSD as part of a randomized controlled trial (RCT)., Methods: A systematic search of the literature was conducted to identify RCTs of evidence-based treatment of PTSD in children and young people. Proportion meta-analyses estimated the prevalence of dropout. Odds ratios compared the relative likelihood of dropout between different treatments and controls. Subgroup analysis assessed the impact of potential moderating variables., Results: Forty RCTs were identified. Dropout from all treatment or active control arms was estimated to be 11.7%, 95% CI [9.0, 14.6]. Dropout from evidence-based treatment (TFCBTs and EMDR) was 11.2%, 95% CI [8.2, 14.6]. Dropout from non-trauma focused treatments or controls was 12.8%, 95% CI [7.6, 19.1]. There was no significant difference in the odds of dropout when comparing different modalities. Group rather than individual delivery, and lay versus professional delivery, were associated with less dropout., Conclusions: Evidence-based treatments for children and young people with PTSD do not result in higher prevalence of dropout than non-trauma focused treatment or waiting list conditions. Trauma-focused therapies appear to be well tolerated in children and young people., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Background: Missing data are a notable problem in Alzheimer's disease clinical trials. One cause of missing data is participant dropout. The Research Attitudes Questionnaire is a 7-item instrument that measures an individual's attitudes toward biomedical research, with higher scores indicating more favorable attitudes. The objective of this study was to describe the performance of the Research Attitudes Questionnaire over time and to examine whether Research Attitudes Questionnaire scores predict study dropout and other participant behaviors that affect trial integrity., Methods: The Research Attitudes Questionnaire was collected at baseline and weeks 26 and 52 from each member of 119 participant/study partner dyads enrolled in a Phase 2, randomized, double-blind, placebo-controlled mild-to-moderate Alzheimer's disease clinical trial. Within-subject longitudinal analyses examined change in Research Attitudes Questionnaire scores over time in each population. Logistic regression analyses that controlled for trial arm and clustering in trial sites were used to assess whether baseline Research Attitudes Questionnaire scores predicted trial completion, study medication compliance, and enrollment in optional substudies., Results: Participants and study partners endorsed statistically similar ratings on the Research Attitudes Questionnaire that were stable over time. Participants with baseline Research Attitudes Questionnaire scores above 28.5 were 4.7 (95% confidence interval = 1.01 to 21.95) times as likely to complete the trial compared to those with lower scores. Applying the same cutoff, baseline study partner Research Attitudes Questionnaire scores were similarly able to predict study completion (odds ratio = 4.2, 95% confidence interval = 1.71 to 10.32). Using a score cutoff of 27.5, higher participant Research Attitudes Questionnaire scores predicted study medication compliance (odds ratio = 5.85, 95% confidence interval = 1.34 to 25.54). No relationship was observed between Research Attitudes Questionnaire score and participation in optional substudies., Conclusion: This brief instrument that measures research attitudes may identify participants at risk for behaviors that cause missing data.

Objective: . Exercise has established efficacy in people with anxiety and stress-related disorders. Dropouts from randomized controlled trials (RCT's) pose a threat to the validity of the evidence, with dropout rates varying across studies. We conducted a meta-analysis to investigate the prevalence and predictors of dropout rates among adults with anxiety and stress-related disorders participating in exercise RCT's., Methods: . Two authors searched major electronic databases up to 07/2020. We included RCT's of exercise interventions in people with anxiety and stress-related disorders that reported dropout rates. A random effects meta-analysis and meta-regression were conducted., Results: . Fourteen RCT's involving 16 exercise interventions (n=369, mean age 20.7 to 67.7years; 38.4% male) were included. The trim-and-fill-adjusted prevalence of dropout across all studies was 22.4% (95%CI = 15.0% to 32.0%). Applying controlled motivation strategies (P<0.001) predicted higher dropout. Supervision during all sessions and by an expert in exercise prescription and applying autonomous motivation strategies predicted lower dropout (all P<0.001). Dropout was similar in exercise versus control conditions (OR = 0.84, 95%CI = 0.54 to 1.29, p = 0.42, I 2  = 0%; N=16)., Limitations: . Potentially important moderators of dropout, such as the severity of mental health symptoms and illness duration were insufficiently available., Conclusions: . Exercise is well tolerated by people with anxiety and stress-related disorders and drop out in RCT's is comparable to control conditions. Thus, exercise is a feasible treatment, in particular when autonomous motivation strategies are included and when the intervention is delivered by healthcare professionals with expertise in exercise prescription., (Copyright © 2021. Published by Elsevier B.V.)

Purpose : Eating disorders (ED) are complex and severe illnesses where evidence-based treatment is needed to recover. However, about half of the patients with ED do not respond to treatments currently available, which call for efforts to expand the portfolio of treatments. The aim of this study was to explore experiences from patients who dropped out of a new treatment for bulimia nervosa and binge ED, combining p hysical e xercise and d ietary t herapy (PED-t). Methods : We conducted open-ended face-to-face interviews. The interviews were transcribed verbatim and the data were analysed with a phenomenological hermeneutical approach. Results : Three themes emerged: "standing on the outside", "unmet expectations" and "participation not a waste of time". Feelings of standing on the outside were elicited by being different from other group members and having challenges with sharing thoughts. Unmet expectations were related to treatment content and intensity, as well as the development of unhealthy thoughts and behaviours. Finally, some positive experiences were voiced. Conclusion : A need to clarify pre-treatment expectations and refining criteria for treatment suitability is indicated. The findings have contributed to the chain of clinical evidence regarding the PED-t and may lead to treatment modifications improving the treatment and thereby reducing drop out.

Background: To describe the associated factors for non-medical reasons for dropout in peritoneal dialysis (PD) patients., Methods: A retrospective cohort study was performed using registry data of adult patients commencing PD as their initial renal replacement therapy in one hospital-facilitated PD center in Taiwan between 2014 and 2018. The collected data included socio-demographics and relevant medical and PD-related parameters. Kaplan-Meier analysis was used to determine the impact of non-medical reasons and medical reasons on PD dropout., Results: The analysis included 224 PD patients, of whom 37 dropped out for non-medical reasons and 187 for medical reasons during the study period. There was significant difference between the two cohorts in age (62.3 years vs. 56.1 years, P = 0.010) and PD vintage (median 3.4 years vs. 4.8 years, P = 0.001). Diabetes was more predominant in the cohort for non-medical reasons than in the one for medical reasons (54.1% vs. 27.3% respectively, P = 0.001). In non-medical reason cohort, two leading reasons given for dropping out were lacking of caregivers (n = 12) and losing confidence (n = 10), whereas PD-related peritonitis (n = 101) was the main medical reason for PD dropout. Using Kaplan-Meier curve analysis, patients in the non-medical reason cohort demonstrated higher cumulative dropout rate compared to patients in the medical reason cohort during a 10-year period (P < 0.001)., Conclusions: The main characteristics of PD dropout patients for non-medical reasons are age, diabetes, patients' perception and caregiver support.

Emerging adults are less likely to participate in and more likely to drop out of behavioral weight loss programs. Thirty-five female emerging adults who dropped out of a behavioral weight loss program, Weight Loss For Life, completed an online survey. Main reasons for dropout relate to insufficient behavioral skills and unique characteristics of emerging adults, especially when in college (e.g., citing working and getting good grades to be more important than losing weight). Most desired >50% of program online, having virtual groups, and using small groups to model desirable behaviors. Around $140 and $180 seemed sufficient to them to encourage participation in all scheduled treatment sessions and reaching overall weight loss goal, respectively. Future behavioral weight loss programs for emerging adults may consider helping develop time management and task management, decision-making that focuses on longer term outcomes, and immediate tangible rewards similar to what college students typically receive after they complete each class assignment.

There is general consensus that alcohol abuse is a risk factor to be considered in batterer intervention programs. Intimate partner violence perpetrators with alcohol abuse problems are more likely to dropout of batterer intervention programs. However, there is little research on intimate partner violence perpetrators with alcohol abuse problems completing batterer intervention programs. In this study, we analyze drop-out rates among perpetrators with alcohol abuse problems and explore whether perpetrators with alcohol abuse problems completing a batterer intervention program differ from those who do not have alcohol abuse problems in a number of outcomes. The sample was 286 males convicted for intimate partner violence against women, attending a community-based batterer intervention program. Final (i.e., recidivism) and proximal (i.e., risk of recidivism, responsibility attributions, attitudes toward violence, sexism, psychological adjustment, and social integration) intervention outcomes were analyzed. Chi-square test, binary logistic regression, and one-way ANOVA were conducted. Results confirmed higher dropout rates among perpetrators with alcohol abuse problems. Results also showed a reduction in alcohol abuse among perpetrators with alcohol abuse problems completing the batterer intervention program. Finally, results showed that, regardless of alcohol abuse problems, perpetrators who completed the batterer intervention program showed improvements in all intervention outcomes analyzed. Perpetrators both with and without alcohol abuse problems can show positive changes after completing an intervention program and, in this regard, the present study highlights the need to design more effective adherence strategies for intimate partner violence perpetrators, especially for those with alcohol abuse problems.

Objective: Beliefs that exposure with response prevention (ERP) is excessively distressing and will result in client dropout from treatment are commonly-cited reasons for clinicians not providing evidence-based treatment. This meta-analysis examined treatment attrition for ERP for youth with obsessive compulsive disorder (OCD) compared with other treatment modalities., Method: A systematic literature search identified 11 randomized controlled trials (RCTs) comparing ERP to active or waitlist control conditions, 9 comparing pharmacotherapy to control, and 3 comparing ERP to pharmacotherapy for youth with OCD., Results: Attrition rates were low for ERP (10.24%) compared to pharmacotherapy (17.29%), active control (e.g., relaxation, metacognitive therapy; 20.63%), and pill placebo (23.49%). ERP had lower risk of attrition compared to active control conditions (RR = 0.60; p = .02), and was not significantly different to waitlist (RR = 0.80; p = .59). In head-to-head trials, there was no difference between the risk of attrition from ERP and pharmacotherapy (RR = 1.26; p = .74). Of the pharmacotherapy trials, risk of attrition from serotonin reuptake inhibitors treatment was not significantly different compared to placebo (RR = 0.94; p = .76), with no difference between antidepressants and clomipramine (p = .19). Attrition from ERP was primarily for logistical reasons, compared to lack of efficacy for relaxation and/or adverse reactions from pharmacotherapy., Conclusions: Attrition from ERP is low, and is generally lower than non-ERP interventions. Given favorable attrition and efficacy data, there is little justification for appropriately-trained clinicians not to offer ERP as a first-line treatment for youth with OCD., (© 2019 Wiley Periodicals, Inc.)

Background: Effective treatments for posttraumatic stress disorder (PTSD) (e.g., prolonged exposure (PE); cognitive processing therapy (CPT)) exist and are widely adopted by the Departments of Veterans Affairs (VA) and Defense (DoD). Unfortunately, dropout from these treatments regularly exceeds 30%. However, in a recent survey of patients who dropped out of PE, approximately half indicated a greater likelihood of completion if a peer who had completed treatment were available to help with the in vivo exposure homework., Methods: We will use a between-groups randomized controlled design with repeated assessment at baseline, post treatment, and 3- and 6-month follow-up across measures of PTSD, depression, and functioning with 150 veterans who have indicated that they intend to drop out of treatment. Participants will be randomly assigned to one of two PE + Peer Support conditions: (1) a peer will offer support directly during in vivo exposure homework for 3-4 weeks; vs (2) a peer will call weekly for 3-4 weeks to offer general support and to check in on treatment progress., Discussion: The present study was designed to test the hypothesis that dropout from exposure-based PTSD treatment may be mitigated by using peers as support agents directly during PE in vivo homework experiences. Specifically, we intend to determine: whether patients who have dropped out of PE and are offered the "in vivo peer" adjunctive component to PE therapy will (1) return and complete treatment and (2) evince reduced PTSD symptomatology, compared to the same PE treatment, but with general peer support more reflective of current VA practices., Trial Registration: This study protocol is approved and information is available at ClinicalTrials.gov, ID: NCT03485391. Registered on 2 April 2018.

Background/aims: The Intent to Attend is a brief questionnaire recommended by the National Research Council to address dropout concerns and improve prediction of missing data in clinical trials, although implementation has been very limited. As a formative study in pediatric research, the relationship between caregiver intentions and study compliance was investigated in a 180-day trial of dietary supplementation of preterm toddlers. Treatment effect estimation in the context of missing data was also explored., Methods: Study compliance (i.e. study completion, supplement adherence, and diary completion) was tracked over three study visits. Baseline questionnaires asked caregivers about intentions concerning study completion via the Intent to Attend , screened for mental health symptoms (depression, trait anxiety), and captured family demographics. Simple and multiple logistic regression models were built to examine associations between caregiver intent and compliance outcomes. The Intent to Attend was also employed as an auxiliary variable to account for missing data within mixed models estimating the treatment effect on the primary outcomes., Results: Of the 316 caregiver-child dyads included, 95% of caregivers with low intentions had a child complete the study, but only 87% of caregivers with high intentions had a child complete the study. Low intentions to complete the study were associated with a more than 60% lower odds of study non-completion, but the confidence interval included the null (odds ratio: 0.36; 95% confidence interval: 0.11, 1.20). No effect measure modification by caregiver mental health, child sex, or annual income was detected. Income was the only significant predictor of study non-completion; the lowest income group was almost four times more likely to be study non-completers compared with the highest income group, even after adjustment for child sex and caregiver mental health (adjusted odds ratio = 3.59, 95% confidence interval: 1.38, 9.31). When using Intent to Attend as an auxiliary variable, similar results were obtained when compared with the original treatment effect estimates on the primary outcomes., Conclusion: Contrary to prior adult studies, there is no clear relationship between caregiver intentions and study compliance. Findings elucidate the complexities of caregiver-child interactions during pediatric trial participation.

Premature treatment termination in offender treatment is linked to negative consequences for clients, practitioners, and the criminal justice system. Therefore, identifying predictors of treatment attrition is a crucial issue in offender rehabilitation. Most studies on this topic focus on adult offenders; less is known about adolescent offenders. In our study, therapy attrition and engagement were predicted via logistic and linear regression to examine the link between pretreatment variables, engagement, and treatment failure in 161 young offenders treated in a social-therapeutic unit in Germany. Engagement could be predicted by motivation, disruptive childhood behavior, low aggressiveness, and higher age. In turn, low motivation, substance abuse, and young age predicted attrition, but their impact diminished when engagement was added to the model with only substance abuse remaining significant. The effect of substance abuse on attrition disappeared, when the offender's initial motivation was high. Implications for assessment and treatment planning are discussed.

Background and Aims: Extended-release formulations of naltrexone have emerged as effective treatment options for opioid use disorder. This post-hoc analysis examined the temporal relationship between episodes of opioid use and subsequent dropout in a placebo-controlled trial of extended-release injection naltrexone (XR-NTX) to draw inferences about the mechanism by which extended blockade of opioid receptors translates into clinical effectiveness., Design: This was a 24-week multiple-site, double-blind, randomized trial of monthly XR-NTX versus placebo injections. We analyzed time to dropout from treatment using survival analysis with an extended Cox model as a function of treatment (XR-NTX versus placebo) and with weekly urine drug test (UDT) results for opioids at each week as a time-dependent covariate., Setting: Thirteen addiction treatment programs in Russia, 2008-09., Participants: A total of 250 adults with opioid use disorder who had completed in-patient detoxification., Intervention: XR-NTX injection or placebo injection every 4 weeks with weekly clinic visits and biweekly counseling., Measurements: Urine toxicology for opioids measured weekly and week of dropout from treatment., Findings: The Cox model yielded a significant interaction of time-dependent urine toxicology by treatment (P = 0.024). Among patients receiving placebo, a positive UDT in a given week increased the risk for dropout from treatment in the subsequent week [hazard ratio (HR) = 6.25; 95% confidence interval (CI) = 3.6-10.0], whereas among patients receiving XR-NTX, a positive UDT result showed no significant effect on risk for dropout (HR = 1.67; 95% CI = 0.6-4.5). The proportion of patients who completed all 24 weeks without any positive UDT result was 31% on XR-NTX compared with 20% on placebo (P = 0.051)., Conclusions: Extended-release injection naltrexone was effective at reducing the risk of dropout from opioid use disorder treatment after an episode of opioid use. Just under a third of patients (31%) on XR-NTX had no opioid-positive urine tests across the trial, but the hypothesis that this would differ from placebo (20%) was not confirmed., (© 2019 Society for the Study of Addiction.)

Background and Aims: Relapse rates for psychosocial substance use disorder (SUD) treatments are high, and dropout is a robust predictor of relapse. This study aimed to estimate average dropout rates of in-person psychosocial SUD treatments and to assess predictors of dropout., Design: A comprehensive meta-analysis of dropout rates of studies of in-person psychosocial SUD treatment. Studies included randomized controlled trials (RCTs) and cohort studies., Setting: Studies conducted anywhere in the world that examined SUD treatment and were published from 1965 to 2016, inclusive., Participants/cases: One hundred and fifty-one studies, 338 study arms and 299 dropout rates including 26 243 participants., Measurements: Databases were searched for studies of SUD treatment that included an in-person psychosocial component. Meta-analyses and meta-regressions were conducted to estimate dropout rates and identify predictors of dropout, including participant characteristics, facilitator characteristics and treatment characteristics. Pooled estimates were calculated with random-effects analyses accounting for the hierarchical structure of study arms nested within studies., Findings: The average dropout rate across all studies and study arms was 30.4% [95% confidence interval (CI) = 27.2-33.8 and 95% prediction interval (PI) = 6.25-74.15], with substantial heterogeneity (I 2  = 93.7%, P < 0.0001). Studies including a higher percentage of African Americans and lower-income individuals were associated with higher dropout rates. At intake, more cigarettes/day and a greater percentage of heroin use days were associated with lower dropout rates, whereas heavier cocaine use was associated with higher dropout rates. Dropout rates were highest for studies targeting cocaine, methamphetamines and major stimulants (broadly defined) and lowest for studies targeting alcohol, tobacco and heroin, although there were few studies on methamphetamines, major stimulants and heroin. Programs characterized by more treatment sessions and greater average session length were associated with higher dropout rates. Facilitator characteristics were not significantly associated with dropout., Conclusions: On average, approximately 30% of participants drop out of in-person psychosocial SUD treatment studies, but there is wide variability. Drop-out rates vary with the treated population, the substance being targeted, and the characteristics of the treatment., (© 2019 Society for the Study of Addiction.)

Dropout from children's mental health services has negative impacts on children, families and service providers. To target interventions aimed to reduce dropout, it is essential to predict individuals who drop out. This study compares predictors of dropout using a novel need-based definition, to existing definitions of dropout. Children (N = 650; 61% male) aged 5-13 attended five children's mental health agencies in Ontario. A mixed effects logistic regression was used to model binary outcome variables (i.e., dropout or treatment completion), for each definition of dropout. Using the need-based definition, older child age, lower child problem presentation, higher child risk behaviors, higher caregiver needs, and more child strengths predicted an increased likelihood of dropout. The need-based definition results in different predictors of dropout than existing definitions in the literature. High caregiver needs was the only predictor of dropout across all definitions. Caregiver needs represent a prospective target when distributing interventions aimed to reduce dropout.

To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms ( N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived "good enough" dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.

Psychotherapy dropout is often regarded as an indicator of treatment failure; however, evidence of a relationship between dropout and outcome has not been well established. The current research consisted of three meta-analytic studies, the results of which found (a) individuals who dropped out began treatment more distressed than those who completed therapy, (b) individuals who dropped out of therapy were more distressed at posttreatment than individuals who completed therapy, and (c) treatments with higher rates of dropout were also less effective for the treatment completers. Dropout may particularly signal poor outcomes for shorter treatments. The continued ambiguity in the meaning of dropout is discussed as well as the promising potential for future research in the area of dropout as it relates to outcome.

Objective: We sought to elucidate the timing of symptom change and treatment dropout in a leading evidence-based psychotherapy for posttraumatic stress disorder, Cognitive Processing Therapy (CPT). Despite its efficacy across numerous populations, treatment nonresponse and premature dropout are common in CPT and other trauma-focused interventions, particularly among military veterans. Advancements are therefore needed to reduce dropout and increase retention., Method: Survival analysis was used to identify the temporal probability of symptom amelioration at each session of CPT (with written trauma account; CPT-A) and compare this with the cumulative, session-by-session probability of dropout. Data were obtained from 194 veterans seeking outpatient treatment at a Veterans Affairs specialty clinic., Results: Overall, 49-61% of veterans reported meaningful symptom reduction in the course of CPT-A and 40% dropped out prematurely. The cumulative probability of dropout exceeded the probability of symptom change beginning after session six of therapy. Secondary analyses indicated that this six session rule generalized well across subgroups., Conclusions: When symptom amelioration is not observed by the midway point in CPT-A, a change in therapeutic approach appears warranted for preempting dropout among veterans and boosting treatment retention., (Published by Elsevier Ltd.)

Trauma-focused therapies, including Cognitive Processing Therapy (CPT; Resick et al., 2016), are effective at reducing clients' PTSD symptoms. A limitation to these treatments, however, is client completion of them. The current study examined temporal patterns of treatment non-completion and the relationships among non-completion, PTSD, and overall mental health functioning outcomes, among clients in a randomized controlled CPT implementation trial. Two models of symptom change were tested: 1) dose-effect model (i.e., clients uniformly improve with additional sessions at a negatively accelerating rate); and 2) the good-enough level model (i.e., clients remain in therapy until they have achieved sufficient improvement, thus clients who attend fewer sessions improve at quicker rates). Results indicated that 42% of clients did not complete treatment, with most discontinuing between sessions two and five. Data did not fit the dose-effect or good-enough level model. Rather, clients who improved at a greater rate in their PTSD symptoms and overall mental health functioning attended more sessions. The average client had the best outcomes when they completed all 12 sessions. Identifying clients who may be at risk for discontinuing treatment, and making a concerted effort toward retaining them, is imperative to reduce non-completion rates and ultimately improve client outcomes., (Published by Elsevier Ltd.)

Although evidence-based practice is becoming more widely accepted, the issue of patient preference has been relatively ignored. As noted by Holmes and colleagues (2019), when delivered in a community setting, Cognitive Processing Therapy (CPT) can have a relatively high dropout rate (42 % in the Holmes et al., 2019). In this commentary, issues about the conceptualization of treatment dropout as one index of patient preference are discussed. Dropout can be conceptualized as a potential reflection of poor fit between the patient and a specific empirically-supported treatment. Consideration of ways in which an empirically-supported treatment can be personalized, while remaining true to its underlying principles, are discussed using CPT as an example., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common-Sense Model of Self-Regulation (E-CSM)., Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout., Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard devition 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor., Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this -chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout.

Objective: High therapy dropout rates among adolescents have been reported, but little is known about whether dropout is associated with poor outcomes. This study aimed to examine clinical outcomes in adolescents with depression who dropped out of psychological therapy and to determine whether this varied by treatment type., Method: Data were drawn from the Improving Mood with Psychoanalytic and Cognitive Therapies (IMPACT) study, a randomized controlled trial, comparing a brief psychosocial intervention, cognitive-behavioral therapy, and short-term psychoanalytic psychotherapy in the treatment of adolescent major depression. The sample comprised 406 adolescents with a diagnosis of major depression, 169 of whom dropped out of treatment before the planned end of therapy. Primary outcome was self-report Mood and Feelings Questionnaire (MFQ); secondary outcomes were Health of the Nation Outcome Scale for Children and Adolescents, Revised Children's Manifest Anxiety Scale, Modified Leyton Obsessional Inventory, and clinical diagnosis., Results: During follow-up, there was a nonsignificant trend for dropouts to report higher depressive symptoms than completers. However, modeling showed insufficient evidence for an association between dropout and outcomes., Conclusion: In contrast to studies of adult therapy, there was no strong evidence that adolescent patients who dropped out had poorer clinical outcomes compared with those who completed therapy, when dropout was defined as ending treatment without agreement of the therapist. This challenges us to understand why adolescents stop going to therapy, how dropout should be defined, and whether what is prescribed is what is always needed., Clinical Trial Registration Information: Improving Mood and Preventing Relapse With Psychoanalytic Psychotherapy and Cognitive Behavior Therapy; http://www.isrctn.com/; 83033550., (Copyright © 2019 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: Dropout is one factor that might limit the effectiveness of interpersonal psychotherapy (IPT). Improved understanding of IPT dropout is an important research priority. This meta-analysis examined dropout rates from IPT in randomized controlled trials. Method: Seventy-two trials met inclusion criteria. Results: The weighted mean dropout rate from IPT was 20.6% (95% CI = 17.4-24.2). Dropout rates were similar for depressive (20.9%; 95% CI = 17.2-25.2), anxiety (16.1%; 95% CI = 11.1-22.9), and eating disorders (18.7%; 95% CI = 11.6-28.8). Dropout was highest when more stringent definitions of dropout were applied (e.g., failure to complete the entire IPT protocol versus failure to complete at least 50% of sessions) and was lowest when adolescent patients were sampled. There was some evidence that IPT was associated with significantly lower rates of dropout than both CBT and non-specific supportive therapies. These effects were generally replicated when analysing trials that provided a clear definition of treatment (rather than study) dropout. Conclusions: Overall, findings provide preliminary evidence to suggest that IPT may be an accepted and tolerated treatment option for patients with common mental health disorders. This review also highlights the need for future trials to rigorously report detail pertaining to patient dropout.

Aim: Previous study findings have shown that more frequent contacts with the diabetes care team predict better diabetes control. It is unknown whether this is true also for previous dropouts with type 2 diabetes (T2D). The aim of this study was to evaluate if those previous dropouts with T2D who succeeded to improve their glycaemic control had more frequent contacts with health care professionals in the public primary diabetes health care system than those dropouts who did not show improvement., Methods: In this "real life" retrospective cohort study, we identified 115 dropouts with T2D who were contacted by trained diabetes nurses and who returned to a public T2D-care system. Those previous dropouts who had baseline haemoglobin A 1c ≥53mmol/mol (7%) and had a reduction in HbA 1c ≥6mmol/mol (0.5%) during the follow-up were compared with those with unsatisfactory change in HbA 1c (baseline HbA 1c ≥53mmol/mol and change <6mmol/mol, or HbA 1c <53mmol/mol at the baseline measurement but above that in the end of the study period) or with those who remained at good glycaemic control over the study period. Trained diabetes nurses collected quantitative data from the patient records about visits and contacts during the follow-up., Results: Previous dropouts showing improvement had more visits to the diabetes nurse (p=0.003) and other nurses (p<0.001) than those with no improvement or those with satisfactory glycaemic control. Telephone calls not focusing on diabetes (p<0.001) were also more frequent among previous dropouts with improvement than among the others., Conclusions: Especially previous dropouts with T2D who had poor glycaemic control, may benefit from more frequent contacts including visits and telephone calls. Recalling dropouts does not seem to lead to overuse of the T2D care-system by those recalled patients whose glycaemic control does not require special care., (Copyright © 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.)

Objective: A lack of understanding of the causes of attrition in longitudinal studies of older adults may lead to higher attrition rates and bias longitudinal study results. In longitudinal epidemiological studies of Alzheimer's disease and related dementias, high rates of attrition may cause a systematic underestimation of dementia prevalence and skew the characterization of the disease. This can compromise the generalizability of the study results and any inferences based on the surviving sample may grossly misrepresent the importance of the risk factors for dementia. The National Institute on Aging outlined a National Strategy for Recruitment and Participation in Alzheimer's Disease Clinical Research to address this problem, providing evidence of the magnitude of this problem., Method: To explore predictors of attrition, this study examined the National Alzheimer's Coordinating Center (NACC) Uniform Data Set, a repository of observations of older adults spanning 11 years, using survival analysis. Four samples were examined: the full sample (n = 30,433), the alive subsample excluding those who died (n = 24,231), the MRI sample [participants with complete MRI data (n = 1104)], and the alive MRI subsample [participants with MRI data excluding those who died (n = 947)]., Results: Worsening cognitive impairment, neuropsychiatric symptoms, and difficulty with functional activities predicted attrition, as did lower hippocampal volume in the MRI subsample. Questionable co-participant reliability and an informant other than a spouse also increased risk of attrition., Discussion: Special considerations exist in recruiting and retaining older adults in longitudinal studies, and results of baseline psychological, functional, and cognitive functioning should be used to identify targeted retention strategies.

Background: Treatment with opioid agonists is effective for opioid use disorder, but early discontinuation of treatment is a major obstacle to success. Intensive longitudinal methods - which take many repeated measurements over time, usually in the field- have provided unique insight into the effects of stress, mood and craving on drug use while people are being treated; these methods might also be useful for studying the processes that lead people to drop out of treatment., Methods: Ecological momentary assessment (EMA) was conducted for up to 17 weeks by obtaining multiple electronic diary entries per day from 238 participants being treated with methadone or buprenorphine-naloxone. Survival analysis was used to study two outcomes: dropping out of treatment and noncompliance with EMA self-report requirements. Self-reports of stress, craving, and mood were used as time-varying predictors. Demographic and psychosocial variables measured with the Addiction Severity Index at the start of treatment were used as time-invariant predictors., Results: Dropping out of treatment was more likely in participants with more reported hassles (a measure of stress), higher levels of cocaine craving, lower levels of positive mood, a recent history of emotional abuse, a recent history of being bothered frequently by psychological problems, and with buprenorphine rather than methadone as their medication. In contrast, study noncompliance was not significantly associated with any of the variables analyzed., Conclusions: Assessment of stress, craving and mood during treatment might identify people who are at greater risk of dropping out, and therapeutic interventions targeting these processes might increase retention., (Published by Elsevier B.V.)

In this study we test the efficacy of Moment-by-Moment in Women's Recovery (MMWR), a mindfulness-based intervention adapted to support women with substance use disorder (SUD) while in residential treatment. We use a parallel-group randomized controlled trial with a time-matched psychoeducation control to test MMWR effects on residential treatment retention. We used clinical staff-determined residential site discharge status and discharge date from the SUD treatment site record to determine retention. We tested for study group differences in retention defined as time to treatment non-completion without improvement (i.e., patient left treatment before completion of the treatment plan and made little or no progress toward achieving treatment goals based on clinical team determination), as well as differences in self-report of study intervention mechanisms of action (i.e., mindfulness, perceived stress, distress tolerance, emotion regulation, distress, affect, and drug and alcohol craving). The analytic timeframe for the survival analysis was from study intervention start date to 150 days later. The sample (N = 200) was female, majority amphetamine/methamphetamine users (76%), Hispanic (58%), with a history of incarceration (62%). By the 150-day analytic endpoint, the sample had 74 (37%) treatment Completers, 42 (21%) still In-residence, 26 (13%) Non-completers with satisfactory progress, and 58 (29%) Non-completers without satisfactory progress. Survival analysis of the intent-to-treat sample showed the risk of non-completion without improvement was lower in MMWR as compared to the control group (adjusted hazard ratio = 0.42, 95% CI: 0.16-1.08, p = .07). Both groups improved on select self-reported mechanism measure scores at immediate post-intervention, but only in the MMWR group did class attendance (dosage) have a large-size correlation with improved mindfulness (r = .61, p < .01), distress tolerance (r = 0.55, p < .01) and positive affect (r = 0.52, p < .01) scores. The hazard ratio for retention was of medium-to-large effect size, suggesting the clinical relevance of adding MMWR to an all-women's, ethnoracially diverse, SUD residential treatment center. An extended curriculum may be helpful considering the protective benefits of class attendance on psychological health indicators., (Published by Elsevier Ltd.)

Objective: This manuscript explores the characteristics of individuals diagnosed with an eating disorder who dropped out of treatment, compared with those who completed it., Method: The participants were 196 patients diagnosed with eating disorders (according to DSM-IV-TR criteria) who consecutively began treatment for the first time in an eating disorders unit. They were assessed at baseline with a set of questionnaires evaluating eating habits, temperament, and general psychopathology. During the follow-up period, patients who dropped out were re-assessed via a telephone interview., Results: In the course of a 2-year follow-up, a total of 80 (40.8%) patients were labeled as dropouts, and 116 (59.2%) remaining subjects were considered completers. High TCI scores in the character dimensions of Disorderliness (NS4) (p < .01) and total Novelty Seeking (NST), along with low scores in Dependency (RD4), were significantly associated with dropout in the course of 2 years. Once the results were submitted to logistic regression analysis, dropout only remained associated with high scores in Disorderliness (NS4) and, inversely, with an initial Anorexia Nervosa (AN) diagnosis (p < .05). Reasons for dropout stated by the patients included logistic difficulties, subjective improvement of their condition, and lack of motivation., Discussion: Clinicians should handle the first therapeutic intervention with particular care in order to enhance their understanding of clients and their ability to rapidly identify those who are at risk of dropping out of treatment., Level of Evidence: Level III: Cohort Study.