Statistical Covariance Research Articles

Effects of the Stanford Chronic Conditions Model on Behavioral and Clinical Indicators in Saudi Arabia: A Prospective Quasi-Experimental Study.

This study aimed to evaluate the 6-month impact of the Chronic Disease Self-Management Program based on the Stanford chronic condition model on behavioral and clinical indicators in individuals with chronic illnesses. This prospective, quasi-experimental study was conducted in primary healthcare centers located in Riyadh, Saudi Arabia. A total of 110 adults aged 18 years or older, living with at least one chronic disease, and receiving treatment at a primary healthcare center were included. We compared patients who received the Chronic Disease Self-Management Program with those who received the usual care from primary healthcare centers. Data analysis included analysis of descriptive and covariance statistics. The analysis of covariance indicated that individuals who received the Chronic Disease Self-Management Program had significantly lower systolic (F=5.60, p<0.05) and diastolic blood pressure (F=7.60, p<0.01). These patients were more likely to adopt healthy behaviors to manage their chronic illnesses (F=11.17, p<0.01). However, no significant differences were observed in the HbA1c values of the patients. We recommend incorporating the Stanford Chronic Disease Self-Management Program into patient education to foster peer support for effective chronic disease management.

Accurate initial field estimation for weather forecasting with a variational constrained neural network

Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems.

Efficient Attacks on Strong PUFs via Covariance and Boolean Modeling

The physical unclonable function (PUF) is a widely used hardware security primitive. Before hacking into a PUF-protected system, intruders typically initiate attacks on the PUF as the first step. Many strong PUF designs have been proposed to thwart non-invasive attacks that exploit acquired CRPs. In this work, we propose a general framework for efficient attacks on strong PUFs by investigating from two perspectives, namely, statistical covariances in the challenge space and the design dependency among PUF compositions. The framework consists of two novel attack methods against a wide range of PUF families, including XOR APUFs, interpose PUFs, and bistable ring (BR)-PUFs. It can also exploit the knowledge of reliability information to improve attack efficiency with gradient optimization. We evaluate our proposed attacks through extensive experiments, running both software-based simulation and hardware implementations on FPGAs to compare with corresponding SOTA works. Considerable effort has been made in ensuring identical software/hardware conditions for a fair comparison. The results demonstrate that our framework significantly outperforms SOTA results. Moreover, we show that our framework can efficiently attack diverse PUF families built from entirely different types, while almost all existing works solely focused on attacking one or very limited number of PUF designs.

Sleep disorders are closely associated with coronary heart disease in US adults (≥20 years): A cross-sectional study.

The purpose of this research was to assess the association between sleep disorders and coronary heart disease (CHD) using data from the National Health and Nutrition Examination Survey (NHANES) database. This cross-sectional study included 9886 eligible participants with valid data on sleep disorders and CHD from the NHANES from 2011 to 2014. The complex NHANES sampling led to use of sample weights in analyses. Various statistical methods and covariates were utilized. Significance was set at P < .05. Receiver operating characteristic curves were used to assess the diagnostic efficacy of sleep disorders in relation to CHD. Sleep disorders were significantly associated with CHD (P < .001). In the model corrected for age, sex, race, hypertension, diabetes, and uric acid as covariates, sleep disorders and CHD remained significantly associated (P < .001, odds ratio = 1.83 [95% confidence interval: 1.31-2.58]). The correlation between sleep disorders and CHD varies by age and gender. Sleep disorders have some predictive value for CHD (0.5 < area under curve ≤ 0.7). Sleep disorders were associated with and predictive of CHD risk, warranting consideration in clinical assessments.

Roles of Two Learning Methods in the Perceived Competence of Surgery and Quality of Teaching: A Quasi-experimental Study among Operating Room Nursing Students.

Nowadays, Clinical courses are meticulously structured to give students essential opportunities to elevate their professional qualifications,so that the patients' safety is protected and their conditions improve. Given the many challenges in the clinical environment of the operating room, this study was conducted to compare the impact of team-based and task-based learning methods in the clinical settings on the perceived competence of surgery and the quality of training from the operating room nursing students' point of view. This quasi-experimental study was conducted on fifty 5th semester operating room technology students at Hamadan University of Medical Sciences in 2023. In this study, students were selected using the convenience sampling method and placed in two educational groups (team-based and task-based) of 25 subjects using the matching method. After implementing the training process in the operating room setting, the data related to the study were collected using the valid questionnaires of perceived competence in surgery (Cronbach's alpha=0.86) and quality of education (Cronbach's alpha=0.94). Also, the data analysis was conducted at the descriptive and inferential (included independent t-test and analysis of covariance) statistics level using SPSS version 16 software. Findings showed that the mean clinical training quality score was significantly higher in the team-based learning group than in the other group (P=0.014). Also, after the median intervention, the perceived competence score of surgery was higher in the task-based learning group than in the team-based group, and the difference in the average change of the competence score between the two groups was statistically significant (P<0.001). Based on the results, it is suggested that a task-based learning method should be used for the clinical instructors to increase level of the perceived competence of the surgery among operating roon nursing students.

Response of agrin, mTOR and AKT proteins in dual resistance-cognitive exercises of healthy people

Introduction: Considering the importance of agrin, mTOR and AKT proteins in cognitive and physiological function, knowing the effects of different exercise programs is of particular importance. Aim: The purpose of this study was to investigate the responses of agrin, mTOR and AKT proteins in dual resistance-cognitive exercises of healthy people. Method: The research method is semi-experimental. 10 healthy men were randomly divided into two groups (resistance-cognitive group of 5 people and resistance group of 5 people). Resistance contraction included eccentric and concentric knee extension with maximum strength and speed. The contractions included 12 sets of 10 repetitions for the right leg, and in the cognitive group, cognitive task exercises included counting numbers in reverse, multiplying and dividing numbers, counting the months of the year, and counting coins simultaneously with facial resistance exercises. Took A biopsy was performed at the beginning and end of the study. To analyze the data, dependent t statistical method and covariance test were performed in SPSS21 software. Results: The results showed that the intra-group changes of mTOR and AKT proteins after one activity session were significant in the resistance-cognitive and resistance groups (p≤0.05). However, the inter-group changes of agrin, mTOR and AKT proteins showed no difference between the two groups. Conclusion: The present study showed that a session of resistance and resistance-cognitive activity leads to changes in the factors involved in the strength and hypertrophy of skeletal muscles. In addition, these changes are generally more resistance-cognitive in the resistance group.

Engagement predicts computational thinking skills in unplugged activity: Analysis of gender differences

Engagement is critical to computational thinking and programming education. However, as multi-dimensional variables, when analyzing how different aspects of engagement in computational thinking tasks influence skills development, it becomes crucial to discern precisely which engagement components intersect with the progression of computational thinking skills. This discernment is pivotal because engagement can vary based on the task nature, the context, and the individual's preferences. Therefore, the purpose of this study is to explore the gender differences in engagement and computational thinking skills and the predictive relationships among these sub-dimensions for boys and girls in an unplugged environment. In this study, a quasi-experiment for 90 sixth-grade students from a north Chinese primary school was performed. By partial least squares–structural equation modeling, students’ models of computational thinking skills and engagement were analyzed, and comparisons of boys and girls were made by analysis of covariance statistics. Results indicated that compared with boys, girls reported higher computational thinking performance and more engagement in unplugged contexts. Furthermore, girls’ computational thinking skills were only predicted by their cognitive engagement, whereas boys’ computational thinking skills were predicted by their behavioral, emotional, and cognitive engagement. Teachers can adopt these findings to design computational thinking teaching interventions that combine the advantages of unplugged activity and focus on the engagement of boys and girls.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Confounded Local Inference: Extending Local Moran Statistics to Handle Confounding

Local statistical analysis has long been of interest to social and environmental scientists who analyze geographic data. Research into local spatial statistics experienced a step-change in the mid-1990s, which provided a large class of local statistical methods and models. The local Moran statistic is one commonly used local indicator of spatial association, able to detect both areas of similarity and observations that are very dissimilar from their surroundings. From this, many further local statistics have been developed to characterize spatial clusters and outliers. These statistics have seen limited adoption because they do not sufficiently model the relationships involved in confounded spatial data, where the analyst seeks to understand the local spatial structure of a given outcome variable that is influenced by one or more additional factors. Recent innovations used to do joint multivariate local analysis also do not model this kind of conditional local structure in data. This article provides tools to rigorously characterize confounded local inference and a new and different class of multivariate conditional local Moran statistics that can account for confounding. To do this, we return to the Moran scatterplot as the critical tool for local Moran-style covariance statistics. Extending this concept, a new method is available directly from a “Moran-form” multiple regression. We show the empirical and theoretical properties of this statistic, show how some existing heuristic approaches arise naturally from this framework, and show how the use of conditional inference can change interpretations in an empirical analysis of rent and housing stock in a rapidly changing neighborhood.

Optimization of the Path of Collaborative Parenting between Civic Work and Student Management in Colleges and Universities under the Background of Informatization

Abstract This paper uses the statistical analysis model and covariance analysis model to study the teaching effect of Civics and Politics in colleges and universities from the dimensions of students’ values, academic situation analysis, and classroom participation, investigates the students’ satisfaction with the comprehensive quality of the student management workers, their management attitude and management effect, and evaluates the overall effect of the management of students in colleges and universities. Finally, we analyzed the relationship between college civic politics and student management and created an innovative approach to cultivating college civic politics and student management synergistically. The results indicate that the average value of each dimension of student values in Civic and Political Teaching is greater than 4 points. The students were satisfied with the overall quality of managers, management attitude, and management effect, with a satisfaction rate of 82.41%, 75.68%, and 68.10%, respectively. This study provides a comprehensive theoretical system and reference materials for managing moral education in colleges and universities and serves as a guide for future students’ moral education management.

The mechanical strength of which in operation directly of the introduction into highly filled halogen-free plastics production

With the introduction into highly filled halogen-free plastics production, the mechanical strength of which in operation directly depends on the flame retardant content and application technology, it becomes important to control the cable sheath mechanical characteristics in a fireproof design. Polymeric materials and their compositions are viscoelastic materials for which the mechanical properties depend on the technological process of extrusion. The results of estimating the elongation at break of samples from the uniaxial stretching. It is shown that the inner and outer layers of the halogen-free plastic cable sheath have significantly different values of the plasticity normative parameter: differences evidence in the polymer structure in the inner and outer layers of the sheath due to the forced deformation process during extrusion, which is forced polymer structure orientation. It is shown that the inner and outer layers of the halogen-free plastic cable sheath have significantly different values of the relative change in specific mass of polymer samples. Analysis of the statistical covariance of the relative elongation at break and of the relative change in specific mass of polymer samples different in structure similar filled halogen-free polymers. With the introduction into highly filled halogen-free plastics application technology, it becomes important to control the cable sheath mechanical characteristics in fireproof design for the inner and outer layers

Group Based Learning And Academic Achievement Of Upper Basic 2 Students’ In Pre-Vocational Studies In Umuahia North Local Government Area Of Abia State

The study assessed the effect of Group based instructional strategy on upper basic two (junior secondary school two) students’ achievement in Prevocational studies in Umuahia North Local Government of Abia State. The study adopted a quasi-experimental control design; specifically the pre-test post-test non-equivalent control group design was used. Two research questions were posed, and one hypothesis formulated to guide the study which was carried out in a Government co-education school in Umuahia North Council Area in Umuahia Education Zone of Abia State. Eighty-seven (87) JSS two students were used for the study. The study is a quasi- experimental study which employed pretest posttest control design and a 2x2 factorial design. Instrument used for data collection was Pre-vocational Studies Achievement Test (PSAT) developed by the researchers. PSAT was validated by three experts and the reliability coefficient using Kuder Richardson (KR-20) was found to be 0.81. Mean and standard deviation were used to answer the research questions while Analysis of Covariance (ANCOVA) statistics was used to test the hypothesis at 0.05 significance level. The result revealed a significant factor on students’ achievement in Pre-vocational studies. Thus, it was confirmed that students taught Pre-vocational studies using Group based instructional strategy performed better than those taught using the lecture method. The result also revealed that gender was not a factor in achievement of students in Pre-vocational studies. It was recommended among other things that efforts should be made by curriculum experts to incorporate Group based instructional strategy, teaching strategy into the teaching of pre-vocational studies.&#x0D; &#x0D;

Distinct developmental changes in regional gray matter volume and covariance in individuals with attention-deficit hyperactivity disorder: A longitudinal voxel-based morphometry study

BackgroundVery few studies have investigated longitudinal clinical cohorts of attention-deficit/hyperactivity disorder (ADHD). Moreover, how baseline brain changes could affect the development of ADHD symptoms later in life remains elusive. Therefore, we aimed to fill this gap by exploring brain and clinical changes in youth with ADHD using a longitudinal design. MethodsThis prospective study consisted of 74 children and adolescents with ADHD and 50 age-, sex-, intelligence-matched typically developing controls (TDC), evaluated at baseline (aged 7–19 years) and re-evaluated 5.3 years later (a mean follow-up latency). We applied voxel-based morphometry to characterize brain structures, followed by both mass-univariate and multivariate structural covariance statistics to identify brain regions with significant diagnosis-by-time interactions from late childhood/adolescence to early adulthood. We used the cross-lagged panel model to investigate the longitudinal association between structural brain metrics and core ADHD symptoms. ResultsThe mass-univariate statistic revealed significant diagnosis-by-time interactions in the right striatum and the sixth lobule of the cerebellum. This was expressed by increased striatal and decreased cerebellar volume in ADHD, while TDC showed inverse volume changes over time. The multivariate method showed significant diagnosis-by-time interactions in a structural covariance network consisting of the regions involved in the functional sensory-motor and default-mode networks. Higher baseline right striatal and cerebellar volumes were associated with elevated ADHD symptoms at follow-up. ConclusionsOur findings suggest a temporal association between the divergent development of striatal and cerebellar regions and dynamical ADHD phenotypic expression through young adulthood. These results highlight a potential brain marker of future outcomes.

The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics

Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.

Assessing the Accuracy of 3D-VAR in Supercell Thunderstorm Forecasting: A Regional Background Error Covariance Study

Data assimilation (DA) integrates observational data with numerical weather predictions to enhance weather forecast accuracy. This study evaluates three regional background error (BE) covariance statistics for numerical weather prediction (NWP) via a variational data assimilation (VAR) scheme. The best practices in DA are highlighted, as well as the impact of BE covariance calculation in DA procedures by employing the Weather Research and Forecasting (WRF) model. Forecasts initialized at different intervals were used to compute distinct regional background error statistics utilizing three control variable (CV) methodologies over a span of 20 days. These statistics are used by the three-dimensional VAR DA process of WRF DA software, producing analysis fields that lead to forecasts for a distinct convective supercell event during the summer of 2019 over northern Greece. This high-impact convective event underscores the importance of selecting appropriate BE over complex terrain areas. The results emphasize the significance of BE usage in DA, proposing the optimal DA approach for simulations of convective systems.

The height premium: a literature review and meta-analysis

The association between adult height and labor-market wages, or the “height premium” (HP), is an important input for quantifying potential economic benefits of nutritional interventions promoting growth. A large economics literature has evaluated this association; however, HP estimates differ greatly depending on the study populations and statistical methodologies used. We conducted a meta-analysis of HP estimates to describe the differences in estimates with different statistical methodologies and to examine potential effect modification of the HP by sex and country income category. We performed meta-analyses for studies using instrumental variables (IV) and ordinary least squares (OLS) methods, separately. OLS estimates were separated into those that were “low-adjusted” for confounding variables and “high-adjusted” for at least one common mediator variable, specifically cognition or schooling. Overall, in a total of 12 studies, the pooled estimates for IV studies indicated that each centimeter increase in height was associated with 3.58% greater wages (95% CI: 1.62-5.54%; I2=97.5%, p&lt;0.001)). In the 24 total OLS studies, low-adjusted estimates indicated an HP of 1.06% (95% CI: 0.85-1.28%, I2=95.5%, p&lt;0.001), while for high-adjusted estimates the HP was only 0.57% (95% CI: 0.41-0.73%, I2=95.8%, p&lt;0.001). Further, the meta-analysis found evidence of effect modification by sex in OLS estimates but not IV, and for both IV and OLS for country income category. Overall, the literature suggests a robust association between adult height and wages; however, the magnitude of the estimate appears to be dependent on statistical methods and covariates selected for multivariable models. Our findings also suggest there may be differences by sex and country income category. Additional analyses are needed taking into account a causal inference framework and, if adult height is being used to capture the cumulative effect on wages of nutritional exposures from conception through adulthood, studies should not adjust for potential mediators including cognition and schooling.

Increasing load carriage and running speed differentially affect the magnitude, variability and coordination patterns of muscle forces

The study aims to investigate the effects of different loads and speed during running on inter- and intra-individual muscle force amplitudes, variabilities and coordination patterns. Nine healthy participants ran on an instrumentalized treadmill with an empty weight vest at two velocities (2.6 m/s and 3.3 m/s) or while carrying three different loads (4.5, 9.1, 13.6 kg) at 2.6 m/s while kinematics and kinetics were synchronously recorded. The major lower limb muscle forces were estimated using a musculoskeletal model. Muscle force amplitudes and variability, as well as coordination patterns were compared at the group and at the individual level using respectively statistical parametric mapping and covariance matrices combined with multidimensional scaling. Increasing the speed or the load during running increased most of the muscle force amplitudes (p < 0.01). During the propulsion phase, increasing the load increased muscle force variabilities around the ankle joint (modification of standard deviation up to 5% of body weight (BW), p < 0.05) while increasing the speed decreased variability for almost all the muscle forces (up to 10% of BW, p < 0.05). Each runner has a specific muscle force coordination pattern signature regardless of the different experimental conditions (p < 0.05). Yet, this individual pattern was slightly adapted in response to a change of speed or load (p < 0.05). Our results suggest that adding load increases the amplitude and variability of muscle force, but an increase in running speed decreases the variability. These findings may help improve the design of military or trail running training programs and injury rehabilitation by progressively increasing the mechanical load on anatomical structures.

A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.

Adaptations in athletic performance and muscle architecture are not meaningfully conditioned by training free-weight versus machine-based exercises: Challenging a traditional assumption using the velocity-based method.

Although the superior effectiveness of free-weight over machine-based training has been a traditionally widespread assumption, longitudinal studies comparing these training modalities were scarce and heterogeneous. This research used the velocity-based method to compare the effects of free-weight and machine-based resistance training on athletic performance and muscle architecture. Thirty-four resistance-trained men participated in an 8-week resistance training program allocated into free-weight (n = 17) or machine-based (n = 17) groups. Training variables (intensity, intraset fatigue, and recovery) were identical for both groups, so they only differed in the use of a barbell or specific machines to execute the full squat, bench press, prone bench pull, and shoulder press exercises. The velocity-based method was implemented to accurately adjust the planned intensity. Analysis of covariance and effect size (ES) statistics were used to compare both training modalities on a comprehensive set of athletic and muscle architecture parameters. No between-group differences were found for any athletic (p ≥ 0.146) and muscle architecture (p ≥ 0.184) variable. Both training modalities significantly and similarly improved vertical jump (Free-weight: ES ≥ 0.45, p ≤ 0.001; Machine-based: ES ≥ 0.41, p ≤ 0.001) and lower limb anaerobic capacity (Free-weight: ES ≥ 0.39, p ≤ 0.007; Machine-based: ES ≥ 0.31, p ≤ 0.003). Additionally, the machine-based group meaningfully enhanced upper limb anaerobic power (ES = 0.41, p = 0.021), whereas the free-weight group significantly improved the change of direction (ES = -0.54, p = 0.003) and 2/6 balance conditions analyzed (p ≤ 0.012). Changes in sprint capacity (ES ≥ -0.13, p ≥ 0.274), fascicle length, and pennation angle (ES ≤ 0.19, p ≥ 0.129) were not significant for either training modality. Adaptations in athletic performance and muscle architecture would not be meaningfully influenced by the resistance modality trained.

Comparing the effectiveness of treatment based on acceptance and commitment and mindfulness therapy on chronic pain in people with drug addiction

Introduction: Not only can the experience of pain be a factor for drug use, but it can also be a motivation to use drugs again during and after treatment. Aim: The purpose of the present study was to compare the effectiveness of acceptance and commitment-based therapy and mindfulness-based therapy on chronic pain in People with drug addiction. Method: The research method was semi-experimental with a pre-test-post-test design and a control group with one-month follow-up. The statistical population included people suffering from drug abuse who referred to addiction treatment clinics in Arak city in 2021. Using available sampling method, 60 people were selected and randomly put/ placed in experimental and control groups (20 people in each group). The research tool included the revised McGill Pain Questionnaire (2009). Mindfulness therapy was implemented in 8 sessions (90 minutes each session) and acceptance and commitment based therapy was also implemented in 8 sessions (90 minutes each session). Data were analyzed using SPSS-25 statistical software and covariance analysis. Results: The results of covariance analysis for chronic pain in the post-test and follow-up phase showed/ demonstrated a significant difference (P=0.001). Both treatments had a significant effect on the improvement of chronic pain, and it is worth noting that this effect was greater for the treatment group for whom the treatment was based on acceptance and commitment (P=0.01). Conclusion: Treatment based on acceptance and commitment showed more effectiveness in improving chronic pain compared to mindfulness. Therefore, the use of acceptance and commitment-based therapy is recommended to improve chronic pain in people suffering from drug abuse.