Duration Model Research Articles

Hazard perception test among young inexperienced drivers and risk analysis while driving through a T-junction

In this article, the hazard-based accident duration model and the reaction time due to various types of distractions are considered. This analysis is mainly considered T-junction or T-junctions like all other forms of road geometries which are more prone to accident. The model proposed is constructed by using a electronics devices along with driving simulator, to study the behavior of the yang inexperienced drivers as well as an experienced driver about their reaction by calling their phone from unknown numbers intentionally. The results show that drivers distracted by mobile phones uses the hard breaking due to least available time to respond after identification of an event. Some of the research and theory bearing on decision making and risk perception, driver situation awareness, and possible mediators of risk-taking is also analyzed here.

Surgical procedure prediction using medical ontological information

Background and Objective: Predicting the duration of surgical procedures is an important step in scheduling operating rooms. Many factors have been shown to influence the duration of a procedure, in this research we aim to use medical ontological information to improve the predictions.Methods: This paper presents two methods for incorporating the medical information about a surgical procedure into the prediction of the duration of the procedure. The first method uses the Systematised Nomenclature of Medicine Clinical Terms to relate different procedures to each other. The second uses simple text fragments. The relationships between types of procedures are included in a regression model for the procedure duration. These methods are applied to data from New Zealand healthcare facilities and the accuracy of the estimations of the durations is compared. In addition a simulation of scheduling the procedures in an operating room is performed.Results: It is shown that both of the methods provide an improvement in the prediction of procedure durations. When compared to a traditional categorical encoding, the ontological information provides an improvement in the continuous ranked probability scores of the prediction of procedure durations from 18.4 min to 17.1 min, and from 25.3 to 21.3 min for types of procedures that are not performed very often.Conclusions: Different methods for encoding medical ontological information in surgery procedure duration predictions are presented, and show an improvement over traditional models. The improvement in duration prediction is shown to improve the efficiency of scheduling in a simple simulation.

Need-based approach for modeling multiday activity participation patterns and identifying the impact of activity/travel conditions

People consume time and space given to them in order to participate in various activities, and the range of time and space that individuals can consume differs according to activity/travel conditions such as the working time, commuting time, the quality of transport infrastructure, etc. The accessibility toward opportunities to participate in these activities may have an impact on individual’s quality of life, and the accessibility to non-work activities has a larger impact than accessibility to mandatory activities. Therefore, the research objective of this paper was to examine how activity/travel conditions affected activity participation patterns.The mandatory activities that are not significantly affected by changes in activity/travel conditions were excluded from the research and the focus was on the changes in patterns of discretionary activities. To consider discretionary activity patterns with large day-to-day variability, multiday analysis was performed. For multiday analysis, the need-based theory suggested by prior studies was used as a theoretical background. To statistically analyze the need-based theory, a hazard-based duration model was utilized, and the general hazard-based duration model was extended to analyze the heterogeneity between latent classes. The time-use survey was conducted to collect multiday activity/travel diary data, and in order to relieve fatigue of survey respondents, the survey was simplified and conducted online.The results indicated that people's discretionary activity patterns are affected by various socio-demographic attributes, time budgets, and space budgets. In most of the activities, differences in activity patterns between the latent classes were identified. Furthermore, in this paper, the changes in activity patterns due to the opening of GTX-A (the express railway planned for the Seoul metropolitan area) were predicted using the model estimation results. Accordingly, it was found that as the GTX-A line opens and the time–space budget expands, the frequency of discretionary activities increases, i.e., induced demand occurs.

A dynamical scan-path model for task-dependence during scene viewing.

In real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan-path statistics across individual observers. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Application of the thermal time model of embryonic development duration to the culture of octopod cephalopods

Data about the effect of temperature on the duration of embryonic development, or the incubation process, are relatively abundant for octopod cephalopods. Experimental data can be used to fit the thermal time classic model of development in order to predict the duration of the process and the range of temperatures within which development proceeds according to the model and with high survival (permissive range), with potential applications for basic research and production activities. The present work is aimed at fitting this classical model to fourteen data sources dealing with eleven species of octopods, seven of them not being previously analyzed, and discussing the constraints and applications of the model. As a result, it is possible to hold that the thermal time model of development duration suitably fits experimental data in octopods, and can be used to schedule hatching events in a production facility. Nevertheless, the range of temperatures tested in the literature is usually too limited to allow an appropriate estimation of the permissive range, so an extension of the temperature range to the known limits of each species is suggested. In addition, when the model is applied to whole spawnings with culture purposes, it is recommended to set the very first hatchings as the criterion for the end of the incubation. Further research is needed to ascertain if temperature fluctuations within and/or out of the permissive range can have implications for the parameterization of the model in octopod species.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

BackgroundExisting studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents. MethodsThe traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models. Results(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %. ConclusionsCompared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

Analysis of cumulative outcomes and influencing factors of patients with discrepancies between age and AMH levels in the early follicular phase prolonged protocol.

To explore the cumulative outcomes and influencing factors of patients with discrepancies between age and Anti-Müllerian hormone (AMH) levels in the early follicular phase prolonged protocol. A total of 1282 cycles of in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) assisted pregnancy with the early follicular phase prolonged protocol in the Reproductive Medicine Center of the Third Affiliated Hospital of Zhengzhou University from September 2015 to December 2020 were retrospectively analyzed. They were divided into the young low-AMH group (n=1076) and the older high-AMH group (n=206). The primary outcomes included cumulative clinical pregnancy rate (CCPR) and cumulative live birth rate (CLBR). Secondary outcomes included the number of oocytes retrieved, number of available embryos, clinical pregnancy rate (CPR), live birth rate (LBR), miscarriage rate (MR), pregnancy complications, and neonatal outcomes. The CPR (68.7% vs. 59.4%) and the LBR (60.7% vs. 43.1%) in the young low-AMH group were higher than those in the older high-AMH group. In contrast, the number of oocytes retrieved (11 vs. 17), number of available embryos (5 vs. 8), and MR (10.6% vs. 18.3%) in the young low-AMH group were lower. There was no significant difference between the two groups in the CCPR, CLBR, pregnancy complications, and neonatal outcomes. Logistic regression analysis showed that infertility duration, basal follicle-stimulating hormone (FSH), and antral follicle count (AFC) correlated with CCPR, while maternal age, type of infertility, basal FSH, AFC, and infertility duration correlated with CLBR. The area under the receiver operating characteristic curves (ROC) curve for the combined model of infertility duration, AFC, and basal FSH to predict cumulative pregnancy was 0.629 (95%CI:0.592-0.666), while the combined model of maternal age, AFC, basal FSH, infertility duration, and type of infertility to predict cumulative live birth was 0.649 (95%CI:0.615-0.682). Although AMH levels are low by contrast, young patients have a favorable outcome after IVF/ICSI. In patients with discrepancies between age and AMH levels in the early follicular phase prolonged protocol, maternal age correlates better with cumulative live birth. The model that combines maternal age and other factors can help predict cumulative live birth, but its value is limited.

Evolution and copula modelling of drought duration and severity over Africa using CORDEX‐CORE regional climate models

AbstractThis study assessed the dependence of drought severity and duration across four subregions (Southern, Western, Eastern and Central) in Africa using copula modelling. The analysis was carried out for the reference period 1991–2020 and the future period 2071–2099. Simulated daily precipitation at a horizontal resolution of 0.22° were obtained from three regional climate models (RCMs) participating in the Coordinated Output for Regional Evaluations within the Coordinated Regional Downscaling Experiment (CORDEX‐CORE). The RCMs were downscaled by three global climate models and validated using three high‐resolution gridded daily precipitation products obtained from The Climate Hazards Group InfraRed Precipitation with Stations data (CHIRPS), Climate Prediction Center Africa Rainfall Climatology Version 2.0 (CPC‐ARC2) and Tropical Applications of Meteorology using SATellite data and ground‐based observations (TAMSAT). Comparison of precipitation from the RCMs with the three gridded reference observations shows relatively good performance across the different regions with median value within the range of 3–5 mm·day−1 in Central Africa, 1.5–4.5 mm·day−1 in Western Africa, 1.8–2.8 mm·day−1 in Eastern Africa and 0.5–2.2 mm·day−1 in Southern Africa. On the other hand, the correlation values of drought duration–severity from CORDEX‐CORE models in the different regions of Africa exhibit predominantly strong values greater than 0.8 for both historical and projected climate. The analysis also considered two families of copulas: Archimedean (Frank, Clayton, Gumbel) and Elliptical (Gaussian, Student's t). The performance of the copula functions were estimated using the Akaike information criteria (AIC). Generally, there is a good agreement in the distribution of observed precipitation among the three observational data products across the subregion of Africa, with slight differences attributed to the different processing algorithms of the products. Across West Africa, mean precipitation values from CPC, CHIRPS and TAMSAT was 2.86, 3.16 and 3.07 mm·day−1, respectively. The CCLM‐NCC underestimated the mean values of precipitation reported in the observation data, while CCLM‐MPI and CCLM‐HAD overestimated the mean values of precipitation in the West Africa region.

Modelling braking behaviour of distracted young drivers in car-following interactions: A grouped random parameters duration model with heterogeneity-in-means

Braking is an important characteristic of driving behaviour that has a direct relationship with rear-end collisions in a car-following task. Braking becomes more crucial when drivers’ cognitive workload increases because of using mobile phones whilst driving. This study, therefore, investigates and compares the effects of using mobile phones whilst driving on braking behaviour. Thirty-two young licenced drivers, evenly split by gender, faced a safety–critical event, that is, leader’s hard braking, in a car-following situation. Each participant drove the CARRS-Q Advanced Driving Simulator and was required to respond to a braking event in the simulated environment in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A random parameters duration modelling approach is employed to (i) model drivers’ braking (or deceleration) times using a parametric survival model, (ii) capture unobserved heterogeneity associated with braking times, and (iii) account for repeated experiment design. The model identifies the handheld phone condition as a random parameter whilst vehicle dynamics variables, hands-free phone condition, and driver-specific variables are found as fixed parameters. The model suggests that most distracted drivers (in the handheld condition) reduce their initial speeds more slowly than undistracted drivers, reflecting their delayed initial braking that may lead to abrupt braking to avoid a rear-end collision. Further, another group of distracted drivers exhibits faster braking (in the handheld condition), recognising the risk associated with mobile phone usage and delayed initial braking. Provisional licence holders are found to be slower in reducing their initial speeds than open licence holders, indicating their risk-taking behaviour because of their less experience and more sensitivity to mobile phone distraction. Overall, mobile phone distraction appears to impair the braking behaviour of young drivers, which poses significant safety concerns for traffic streams.

Tax Progressivity and Self-Employment Dynamics

AbstractAnalysis of the relationship between taxes and self-employment should account for the interplay between responses in self-employment and wage employment. To this end, we estimate a two-state multispell duration model which accounts for both observed and unobserved heterogeneity using a large longitudinal administrative data set for Norway for 1993 to 2011. Our findings confirm theoretical predictions and are robust to various changes to definitions and sample selections. A policy experiment simulating a flatter tax schedule in the year 2000 is found to encourage self-employment, delivering a net increase of predicted inflow into self-employment from 2.8% to 5.3%.

Development of situation models for duration of production cycles of manufacturing finished rolled products. Message 2

Development of situation models for duration of production cycles of manufacturing finished rolled products. Message 2

Are Patients with Axial Spondyloarthritis Who Were Breastfed Protected against the Development of Severe Disease?

Breastfeeding is recognized as one of the most influential drivers of the gut microbiome. In turn, alterations in the gut microbiome may play a role in the development and severity of spondyloarthritis (SpA). We aimed to analyze different disease outcomes in patients with axial SpA (axSpA) based on the history of breastfeeding. A random sample was selected from a large database of axSpA patients. Patients were divided based on history of breastfeeding and several disease outcomes were compared. Both groups were also compared based on disease severity. Adjusted linear and logistic regression statistical methods were used. The study included 105 patients (46 women and 59 men), and the median age was 45 years (IQR: 16-72), and the mean age at diagnosis was 34.3 ± 10.9 years. Sixty-one patients (58.1%) were breastfed, with a median duration of 4 (IQR: 1-24) months. After the fully adjusted model, BASDAI [-1.13 (95%CI: -2.04, -0.23), p = 0.015] and ASDAS [-0.38 (95%CI: -0.72, -0.04), p = 0.030] scores were significantly lower in breastfed patients. Forty-two percent had severe disease. In the adjusted logistic model for age, sex, disease duration, family history, HLA-B27, biologic therapy, smoking, and obesity, breastfeeding had a protective effect against the development of severe disease (OR 0.22, 95%CI: 0.08-0.57, p = 0.003). The selected sample size was sufficient to detect this difference with a statistical power of 87% and a confidence level of 95%. Breastfeeding might exert a protective effect against severe disease in patients with axSpA. These data need further confirmation.

Joint Modelling of Drought Severity and Duration using Copula Theory: A Case Study of Ghana

Analysing and understanding the occurrence and development of droughts is of great significance in mitigating drought impacts. This study assessed the possible changes in the joint distribution of drought duration and severity in two major cities of Ghana, Accra and Yendi. The duration and severity of droughts, estimated using the Standardized Precipitation Index (SPI), were determined based on run theory. The best-fitted Copula models were used to combine the drought duration and severity to analyse the drought return period. The gamma, lognormal and Weibull distributions were considered to select the marginal distributions for the duration and severity, while the normal, t, Gumbel, Joe, Clayton and Frank copulas to select the best-fit Copula model. Bias corrected climate simulations of six Global Climate Models (GCMs) of the CMIP6 were used to project drought characteristics for the near and far futures. The results showed the Clayton and Frank copulas as the most suitable for fitting the joint distribution of drought duration and severity at Accra and Yendi, respectively. Lognormal and Weibull distributions were the most suitable for the marginal distributions of severity and duration, respectively. The joint return periods of droughts showed almost no change in the future compared to the historical period in Accra with a historical mean of 11.36 and a near and far future mean of 12.26 and 10.30 respectively but significantly reduced return periods in the future in Yendi with a near and far future means of 1.47 and 2.13 respectively compared to a historical mean of 17.40. The drought risks estimated for different future periods can provide useful information in planning, management, and assessing the adequacy of the water structures in the region.

Incorporating real-time weather conditions into analyzing clearance time of freeway accidents: A grouped random parameters hazard-based duration model with time-varying covariates

To minimize non-recurrent congestion, a better understanding of the factors that affect accident clearance time is crucial, in order to optimize incident management strategies. A number of methods have been developed to predict incident clearance duration, but few of those have considered the time-varying nature of certain observed factors. In addressing this gap in the literature, this study developed a grouped random parameters hazard-based duration model with time-varying covariates, while accounting for unobserved heterogeneity. Data on accidents, traffic, road inventory, and real-time weather condition were compiled for the Kaiyang freeway in 2014. Comparison of candidate models shows that the proposed model with Weibull distribution exhibits the best fit performance. The results suggest that the effects of rear-end accident, involvements of trucks or other vehicles, evening hours, and shoulder blockage on the hazard function are heterogeneous across observations. Other variables such as angle accident, injury severity, traffic volume and composition, morning or pre-dawn hours, and blockage of overtaking lane were also found to have significant but homogenous effects on accident clearance time. More importantly, the results also reveal the significant effects of the time-varying covariates (wind speed, temperature, and humidity). Accordingly, the viability and superiority of the proposed model in analyzing accident clearance time are confirmed. Overall, the results of this study are expected not only to improve traffic incident management by allowing government agencies to better understand factors affecting accident clearance times, but also to facilitate incident clearance through the recognition of time-varying pattern.

Modeling daily in-home activities using machine learning techniques

Activity-based modeling for travel demand forecasting have mainly focused on out-of-home activities. However, out-of-home (OH) and in-home (IH) activities are closely related decisions. For example, individuals working-from-home for longer duration are less likely to make commute trips to the workplace. Furthermore, COVID-19 has forced individuals to replace their OH with IH activities, which further indicates the need for in-home activity modelling. To address the inter-dependencies and better predict travel demand, efforts are required to model in-home activities. This paper develops machine learning (ML) models to investigate in-home activities. We have considered in-home activities in the four thematic categories: sleeping, leisure and discretionary, household and personal maintenance, and mandatory activities. Models were developed for: 1) activity participation and 2) activity duration. The former is focused on modeling what type of in-home activities individuals would perform during a typical weekday, where the latter models the duration for each in-home activity. Several machine learning models were applied, including artificial neural network (ANN), regression trees (RT), Ensembles, support vector machine (SVM), k-nearest neighbor (KNN), and Gaussian process regression (GPR). For each technique, we seek the best model by fine-tuning the modeling architecture. We also compared the prediction speed of the models to understand how they would perform in practice. The results of the participation models had an overall accuracy above 95%, where the activity duration models had an R2 between 0.74 and 0.94. This research demonstrates how machine learning models are robust and can be adopted to accurately predict activity participation and duration.

Assessing impacts of the built environment on mobility: A joint choice model of travel mode and duration

This paper introduces a joint choice model for travel mode and duration to quantify the mobility impacts of urban design changes on the built environment. The model is formulated as a Random Forest classifier that predicts the mode-duration probabilities of a given trip. A novel series of predictor features are proposed which measure the urban form, demographics, and service densities on different scales of the transportation network. Through a sensitivity analysis and a proof-of-concept case study, we find that a dense, mixed-use environment with good coverage of a multi-modal mobility network can significantly promote active transportation and public transit use. However, we also find that ultra-dense, centralized developments can lead to increased travel time and increased vehicle use in the urban periphery. Our modeling and analysis method provides a simplified and effective way to assess urban design and planning scenarios from different mobility perspectives and facilitates data-driven, mobility-aware urban design and planning that can help identify better solutions more quickly.

Revisiting fertility regulation and family ties in Tunisia

BackgroundWe revisit fertility regulation in Tunisia by examining the role of the extended family. As marriage is the exclusive acknowledged childbearing context, we examine fertility analysis in Tunisia through the sequence: woman’s marriage age, post-marriage delay in the first use of contraception, and past and current contraceptive use. We trace the family socio-economic influences that operate through these decisions.MethodsUsing data from the 2001 PAP-FAM Tunisian survey, we estimate the duration and probability models of these birth control decisions.ResultsIn Tunisia, family ties and socio-cultural environment appear to hamper fertility regulation that operates through the above decisions. This is notably the case for couples whose marriages are arranged by the extended family or who benefit from financial support from both parental families.ConclusionThis calls for family planning policies that address more the extended families.

Development of situation models for duration of production cycles of manufacturing finished rolled products. Message 1

The problems of system analysis and development of a complex set of models necessary for constructing an algorithm for situational (multivariant) estimation of the duration of production of different batches of rolled products in the conditions of a modern automated medium-sized shop (object of study), including rigidly connected production sections with continuous and cyclic technological processes (feeding blanks, heating in method furnaces, rolling, cutting the rolled stock into strips, cooling, trimming the ends of the bundles of rolled stock, cutting to cut lengths, forming and tying bundles of finished rolled products, loading bundles into railway cars). The study of the functioning of the rolling shop was carried out in the following order: the study of technological processes; technical characteristics of the main and auxiliary equipment; analysis of technical and economic indicators; performance of chronometric and monitoring observations of the metal flow, the operation of units and equipment; decomposition of the production process into operations, elements and trace elements; analysis of the influence of the human factor in the organization of production and operational management of processes; analysis of expert assessments of real and possible production situations related to the fulfillment of orders for finished products. The duration of production cycles is proposed to be classified as follows: piece (corresponding to a workpiece, a pack of workpieces, a package of finished rolled products); batch (corresponding to the shipment of products); subsystem and system (related to sections, departments, workshop as a whole); technically possible, normative, planned, predictive and actual. Based on the preliminary development of situational batch models of the duration of production cycles of the departments of the shop, a situational system model of the duration of the production cycle for manufacturing a batch of finished rolled products is determined by selecting the models of the unit with the maximum cycle duration. To bring the duration of the production cycles of subsystems and the system as a whole into a comparable form, the quantitative characteristics of rolled products batches are determined. Mathematical models of the duration of the production cycle of the warehouse of blanks are built; technological line. The clock models of work of the warehouse of blanks, the production line, the warehouse of finished products have been formed.

Changes in behavior patterns or demographic structure? Re-estimating the impact of higher education on the average age of the first marriage.

During the last few decades, China implemented college enrollment expansion to accelerate the process of urbanization. However, most existing papers blaming that receiving higher education may delay people choosing to enter the age of first marriage, which in turn results in the age of the population. In this paper, we argued that the previous papers confused the total impact of higher education on the average age of the first marriage with the influence on individual's behavior change, and thus led to overestimating the delayed effect of higher education on the age choosing behavior of first marriage. The present paper re-estimated the impact of higher education on the average age of the first marriage in China with both extensive and intensive margins using the duration model and qualified the pure effect on the behavior patterns change after removing macroeconomic factors. The results show that: (1) changes in either the demographic structure or behavior patterns due to higher education explain 63.41% or 36.59%, respectively, of the average marriage age delay; (2) the macro factors would delay the age of first marriage; (3) after controlling for demographic structure and macro factors, 3 years or more of higher education would only delay the choosing behavior of entering the first marriage by 0.84 years. Thus, we concluded that higher education does not completely squeeze the time of marriage, and the expansion of college enrollment could improve social and economic benefits.

Revisión sistemática y metaanálisis de intervenciones transdiagnósticas en línea de tercera generación

Existing meta-analyses on the effect of online psychological interventions (OPIs) have found small to medium effect sizes for the treatment of anxiety and depression. On the other hand, third-generation trans-diagnostic OPIs are very rare and, due to the large variability among disorders, symptoms or target populations, it is difficult to assess their overall effect. Other systematic reviews and meta-analyses have overly broad inclusion criteria that make the understanding of the findings more difficult. The current study aims to analyze the empirical evidence for third-wave trans-diagnostic OPIs designed to decrease symptoms and promote psychological flexibility, including studies that compare a OPI to some control condition ( e. g. , waiting list, treatment as usual or other that should not have any effect) and include a general symptomatology scale as dependent variable. A search without filters or timeframe was performed on Scopus and 1 408 articles were found, among which 21 were reviewed in depth and 6 were included for meta-analysis. Risk of bias was assessed by a quality and heterogeneity assessment. Separate meta-analyses were performed for general distress and psychological flexibility at post-treatment and last follow-up. Risk of bias analysis suggest low risk of threats to validity and attribute heterogeneity to between-study attrition rates. Additionally, meta-regression models for duration, attrition rate, and mean age are proposed for each time point. The results show significantly large effect sizes for both variables at both time points. According to the meta-regression models attrition rates are a mediating variable for the effect on general distress both at completion and at the last follow-up. On the other hand, duration, age and attrition rate are all mediating variables for the effect on psychological flexibility at the end of treatment. The findings suggest that the high attrition rates observed on tele-psychology need to be mitigated; if this is not possible, intention-to-treat approaches should be adopted for data analysis. https://doi.org/10.16888/interd.2023.40.2.5