Behavior Data Research Articles

Predicting changes in task difficulty perception based on visual behavior in mobile health information search

With the proliferation of online health resources, mobile health information search has become the new norm, in which the task difficulty perception in search affects the user's search experience. This study aimed to investigate the relationship between visual behavior that reflects users' cognitive processing and changes in perceived task difficulty, thereby predicting such changes.This study was conducted through a controlled experiment. 46 participants were recruited to complete four tasks. Visual behavior data were collected through eye-tracking technology, and changes in task difficulty perception were measured through pre-task and post-task questionnaires. The mobile health information search process is divided into three search activities: querying, browsing, and viewing activities. Predictors were inspected from the overall session and individual search activity levels using the Mann-Whitney U test, and then K-Nearest Neighbor, Extreme-Trees, Naive Bayesian, Support Vector Machine, Logistic Regression algorithms were used to predict and evaluate prediction effects.The results showed significant differences in participants' fixation and saccade behaviors between increases and decreases in task difficulty, both at the overall session and individual search activity level. The logistic regression algorithm demonstrated the highest predictive performance, Furthermore, visual behavioral indicators for the browsing activity proved to perform better relative to the other search activities.This study highlights the importance of visual behavioral indicators as reliable predictors of changes in users' perceived task difficulty in mobile health information search. It can help health information providers and administrators to provide timely and targeted assistance and implement effective guidance strategies.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0.72) and Davies-Bouldin index (1.05), demonstrating higher clustering effectiveness and model stability. Particularly noteworthy is the recall rate (0.82), a key performance indicator, where the ACO model accurately captures different behavioral characteristics of athletes, validating its effectiveness and reliability in athlete behavior analysis. The innovation lies not only in the application of the ACO algorithm to address practical issues in the field of sports but also in showcasing the advantages of the ACO algorithm in handling complex, high-dimensional sports data. However, its generality and efficiency on a larger scale or different types of sports data still need further validation. In conclusion, through the introduction and optimization of the ACO clustering model, this study provides a novel and effective approach for a deeper understanding and analysis of athlete behavior characteristics. This study holds significant importance in advancing sports science research and practical applications.

Analysing HCV incidence trends in people who inject drugs using serial behavioural and seroprevalence data: A modelling study

IntroductionThe introduction of new direct-acting antivirals for hepatitis C virus (HCV) infection, has enabled the formulation of a HCV elimination strategy led by the World Health Organisation (WHO). Guidelines for elimination of HCV target a reduction in incidence, but this is difficult to measure and needs estimating. MethodsSerial cross-sectional bio-behavioural sero-surveys provide information on an individual's infection status and duration of exposure and how these change over time. These data can be used to estimate the rate of first infection through appropriate statistical models. This study utilised updated HCV seroprevalence information from the Unlinked Anonymous Monitoring survey, an annual survey of England, Wales and Northern Ireland monitoring the prevalence of blood borne viruses in people who inject drugs. Flexible parametric and semiparametric approaches, including fractional polynomials and splines, for estimating incidence rates by exposure time and survey year were implemented and compared. ResultsIncidence rates were shown to peak in those recently initiating injecting drug use at approximately 0.20 infections per person-year followed by a rapid reduction in the subsequent few years of injecting to approximately 0.05 infections per person-year. There was evidence of a rise in incidence rates for recent initiates between 2011 and 2020 from 0.17 infections per person-year (95 % CI, 0.16–0.19) to 0.26 infections per person-year (0.23–0.30). In those injecting for longer durations, incidence rates were stable over time. ConclusionsFractional polynomials provided an adequate fit with relatively few parameters, but splines may be preferable to ensure flexibility, in particular, to detect short-term changes in the rate of first infection over time that may be a result of treatment effects. Although chronic HCV prevalence has declined with treatment scale up over 2016–2020, there is no evidence yet of a corresponding fall in the rate of first infection. Seroprevalence and risk behaviour data can be used to estimate and monitor HCV incidence, providing insight into progress towards WHO defined elimination of HCV.

Analysis of Factors Influencing Online Learning Using the Decision Tree Method

Objective: With the continuous development of online learning, the analysis of students' online learning has become increasingly important. Understanding which factors can influence students' engagement in online learning plays a crucial role in improving their learning performance. Methods: By utilizing web crawling techniques, students' online learning behavior data was collected from the Chinese University’s massive open online courses (MOOC) platform. To address the imbalance in the dataset, a synthetic minority oversampling technique (SMOTE) was used. Course progress was used to reflect students' online learning status, which was categorized into interruptions and completions. Furthermore, to tackle the issue of low computational efficiency in the C4.5 decision tree algorithm, its calculation formula was improved to develop an improved version of C4.5. Findings: Of the several factors analyzed, the number of course chapters had the greatest impact on students' online learning, followed by the number of course evaluations and overall course scores. The classification of students’ online learning situations based on an improved C4.5 algorithm revealed that the improved method achieved the highest accuracy rate of 0.942 and the shortest classification time of 0.165 s compared to methods such as the naive Bayesian and random forest algorithms. Novelty: This study designed an improved version of C4.5 to analyze the influencing factors in online learning, and its reliability was demonstrated through experiments, providing a new effective method for data analysis in online learning. Doi: 10.28991/HIJ-2024-05-02-018 Full Text: PDF

Autistic-like behaviour and changes in thalamic cell numbers a rat model of valproic acid-induced autism; A behavioural and stereological study

The contribution of the thalamus to the development and behavioural changes in autism spectrum disorders (ASD), a neurodevelopmental syndrome, remains unclear. The aim of this study was to determine the changes in thalamic volume and cell number in the valproic acid (VPA)-induced ASD model using stereological methods and to clarify the relationship between thalamus and ASD-like behaviour.Ten pregnant rats were administered a single dose (600 mg/kg) of VPA intraperitoneally on G12.5 (VPA group), while five pregnant rats were injected with 5 ml saline (control group). Behavioural tests were performed to determine appropriate subjects and ASD-like behaviours. At P55, the brains of the subjects were removed. The sagittal sections were stained with cresyl violet and toluidine blue. The thalamic and hemispheric volumes with their ratios, the total number of thalamic cells, neurons and non-neuronal cells were calculated using stereological methods. Data were compared using a t-test and a Pearson correlation analysis was performed to examine the relationship between behaviour and stereological outcomes.VPA-treated rats had lower sociability and sociability indexes. There was no difference in social novelty preference and anxiety. The VPA group had larger hemispheric volume, lower thalamic volume, and fewer neurons. The highest percentage decrease was in non-neuronal cells. There was a moderate positive correlation between the number of non-neuronal cells and sociability, thalamic volume and the number of neurons as well as the time spent in the light box.The correlation between behaviour and stereological data suggests that the thalamus is associated with ASD-like behaviour.

A taxonomic revision of Arnot’s Chat Myrmecocichla arnotti (family Muscicapidae)

Arnot’s Chat Myrmecocichla arnotti in the family Muscicapidae is largely confined to the woodlands of the Zambezian biome of south-central Africa. This work assesses its taxonomic status, including comments on the so-called Ruaha Chat M. [a.] collaris (also named M. [a.] leucolaema). Based on an analysis of morphology, behaviour and molecular data, we recommend that M. arnotti be considered a single species with two subspecies: M. a. arnotti (including shelleyi, leucolaema and collaris) and M. a. harterti. A restricted type locality is proposed for Saxicola shelleyi Sharpe, 1877: Ngwesi (21°03′ S, 27°50′ E), southwestern Zimbabwe.

Real-time structural health monitoring of carbon fiber-reinforced plastic sandwich structures with carbon nanotube-dispersed core using electromechanical behavior data

Sandwich composite structures have been widely used in many industrial fields because of their high bending strength, especially in large structures. As such, the need for structural health monitoring of sandwich structures to reduce maintenance costs during their lifetime is highly sought after. Here, the structural health status of sandwich composite structures comprising carbon nanotube (CNT)-dispersed cores and carbon fiber-reinforced polymers (CFRPs) was monitored in real time using self-sensing data. Previous studies using self-sensing methods have only monitored the occurrence of damage and deformation in the elastic region. Here, we conjointly investigated various failure mechanisms in real time to propose a self-sensing health index system to determine the damage severity in sandwich structures. The self-sensing technique allows for the analysis of various damage types in both the skin and core of sandwich structures. Furthermore, the actual propagation of damage (i.e., the length of the core crack) in composite sandwich structures can be investigated. The results demonstrate that self-sensing structural health monitoring is an effective and reliable method for analyzing sandwich structures, with promising applications in aircraft components, wind turbines, and personal aerial vehicles.

Iodine Status of Infants and Toddlers under 2 Years of Age and Its Association with Feeding Behaviors and Maternal Iodine Status in Shanghai: A Quantile Regression Analysis.

It is crucial to provide adequate iodine nutrition to infants and toddlers for proper thyroid function and subsequent brain development. Infants are particularly vulnerable to iodine deficiency during the transition from a milk-based diet (breast milk and/or infant formula) to solid food. This study examines the current iodine levels of children during their first two years of life and investigates the association between these levels and feeding behaviors and the iodine status of their mothers in Shanghai, a city located in eastern China. A hospital-based cohort study was conducted to enroll mother-child pairs, where the child is aged 6-23 months, who visited community health service centers in the 16 districts of Shanghai, China. Data on socio-demographic factors and feeding behavior data were collected from the participants. The urinary iodine concentration (UIC) in both the young children and their mothers were analyzed. A total of 2282 mother-child pairs were included in this analysis. The median (p25-p75) UIC for lactating women, weaning women, and children were 121.3 μg/L (68.1-206.4 μg/L), 123.4 μg/L (58.4-227.2 μg/L), and 152.1 μg/L (75.8-268.3 μg/L), respectively. The UIC in children was found to be higher than that in their mothers (p < 0.001). Children who consumed less than 500 mL per day of formula milk in the last week had lower UICs compared with those who consumed 500 mL per day or more (p = 0.026). Furthermore, the children's UIC was positively correlated with the maternal UIC (rs = 0.285, p < 0.001). Multiple quantile regression analysis revealed a statistically significant positive association between maternal UIC and children's UIC between the 0.1 and 0.9 quantiles (all p < 0.001). We found that the iodine status of infants and toddlers, as well as of mothers, was sufficient. However, a large minority of children and mothers may be at risk of iodine deficiency. Furthermore, no associations between children's UIC and feeding behaviors were observed. Moreover, there was a positive correlation between the UIC of young children and their mothers.

Female Mate Choice in Wild Kenyan Blue Monkeys (Cercopithecus mitis).

Female mate choice may drive sexual selection, but discerning whether female behaviors reflect free expression of choice or responses to constraints can be difficult. We investigated the efficacy of female choice in wild blue monkeys using 10 years of behavior and paternity data (N = 178 male-female dyads). Although blue monkeys live modally in one-male polygynous groups, where male-biased intersexual power is expected, females can access multiple potential mates during seasonal male influxes and occasional intergroup encounters. Additionally, extra-group males sire offspring. We examined female resistance rates to male-initiated sexual interactions, and unsolicited proceptive behavior that females directed to males (corrected for male availability). Females seldom resisted male solicitation, but initiated sexual interactions more than males. Females generally preferred residents. Those who preferred non-residents tended to have residents with longer tenures, but neither female parity nor rank influenced the tendency to prefer non-residents vs. residents. The male most solicited by a particular female fathered that female's infant 82% of the time; odds of siring were 26 times higher for most vs. nonpreferred males. Female preference predicted paternity even more strongly among non-resident males, with odds of siring 33 times higher for most vs. nonpreferred non-residents. Neither female rank nor parity influenced her likelihood of having her preferred partner as sire. Paternity by preferred males did not affect infant survival. While we cannot fully discount the effect of male-male competition on paternity, these results suggest that blue monkey females can exercise choice successfully, even in a polygynous mating system.

Prediction Model of E-commerce Users' Purchase Behavior Based on Deep Learning

This study mainly uses Deep Learning (DL) technology to build a prediction model of e-commerce users' purchasing behavior, and evaluates its application effect on actual e-commerce data. Methodologically, Recurrent Neural Network (RNN) is used to capture the temporal dependence of user behavior, and the performance of the model is improved through detailed data preprocessing and feature engineering. The research results show that the DL model based on RNN has achieved remarkable advantages in predicting the purchase behavior of e-commerce users. Compared with other methods, RNN model can capture the temporal dependence of user behavior more accurately, thus improving the prediction accuracy. This advantage is especially obvious when dealing with complex and dynamic user behavior data. It provides new ideas and methods for accurate marketing and personalized service of e-commerce industry.

A systematic data-driven approach for targeted marketing in enterprise information system

ABSTRACT In the rapidly evolving landscape of digital marketing, leveraging data analytics within Enterprise Information Systems (EIS) has become crucial for businesses aiming to understand and engage their customers more effectively. This study presents a comprehensive analysis of data-driven marketing strategies, emphasising the critical role of diverse data sources in developing targeted, EIS-specific campaigns. Central to our investigation is a comparative analysis of various machine learning algorithms, including Random Forest, CART, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbours, and Naïve Bayes. Our selection and evaluation of these models are grounded in their ability to address the complex dynamics of customer behaviour and marketing data, demonstrating the superior efficacy of Random Forest and CART in this context.

User experience of english online classroom E-learning based on routing algorithm and data visualization analysis

With the development of the Internet, E-learning has become an important way of education. However, in online English classes, user experience problems still exist, such as network delay and slow video loading speed, which affect the learning effect and user engagement. Therefore, this paper hopes to improve E-learning user experience by studying routing algorithm and data visualization analysis technology. The routing algorithm is used to optimize the network connection. By analyzing the network topology and data transmission path, the optimal route is selected to reduce the network delay and bandwidth consumption. Data visualization technology is used to analyze and display the behavior and learning data of learners in the classroom in real time, so as to help learners better understand their learning situation. The experimental results show that the application of routing algorithm and data visualization analysis technology can significantly improve the user experience of online English classes. The stability of learners’ network connection in the classroom is improved, the video loading speed is accelerated, and the learning effect is also significantly improved. By optimizing network connectivity and providing real-time data analytics, it is possible to meet the individual needs of learners and improve their learning effectiveness and engagement.

Numerical simulation of axially loaded circular concrete-filled steel tubular short columns with localized corrosion

Concrete-filled steel tubular (CFST) columns are widely utilized in structures owing to their cost-effectiveness, high strength, ductility, and proven fire resistance. Despite these advantages, concerns persist regarding the performance of CFST columns with locally corroded steel tubes, impeding their widespread adoption in offshore structures. This research employs three-dimensional finite element (FE) simulations for circular CFST stub columns subjected to axial compression, validated against experimental data. Verification includes full load-displacement histories, ultimate axial strengths, and failure modes. The validated FE models are then employed to assess and compare the structural behavior of CFST columns with various shapes of localized corrosion, analyzing overall load-deformation curves, interaction stress-deformation responses, and composite actions. To acquire critical data for understanding stub column behavior, 81 specimens underwent non-linear finite element analysis, with local corrosion represented by artificial notches in steel tubes. Test parameters, such as notch orientations, lengths, depths, widths, and concrete compressive strength, are varied. Results elucidate the impacts of these parameters on different aspects of stub column responses with corroded steel tubes. Furthermore, the accuracy of existing design models in predicting the ultimate axial strengths of CFST columns with corroded steel tubes is assessed. Finally, through parametric analysis of test results, a practical model is proposed and verified for computing the peak resistance of a CFST stub column with a locally corroded steel tube.

Algorithms For Cold-Start Game Recommendation Based On GNN Pre-training Model

Abstract In the absence of sufficient user behavior data, game recommendation systems face the cold-start problem. To address this issue, this paper proposes a solution based on the Graph Neural Network pre-training model to alleviate the cold-start problem. The proposed model directly reconstructs cold-start user/game embeddings using a meta-learning setup based on dataset training simulations and uses an adaptive neighbor sampler to improve user interaction relations and thereby to improve game recommendation performance. Experimental results demonstrate the effectiveness and practicality of the recommendation model proposed in this study. Moreover, the proposed model is embedded in the game recommendation system to visualize the recommendation results.

White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis.

Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states,physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured usingan immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

The Safer Driver App Decreases Mobile Phone Induced Distracted Driving: Evidence From a Randomized Controlled Trial

When using a mobile phone while driving, people are more at risk of causing or being in a crash. To address distracted driving behaviours related to mobile phone use, we developed a mobile coaching app, Safer Driver. The app collects data on a person's driving behaviour and implements behavioural change techniques to help people make smart changes to reduce their distracted driving behaviour. Initially 814 participants were randomly assigned to a 30-day trial period of the Safer Driver app (intervention group, n=573) or the control app (control group, n=241). The control app did not provide any coaching and only collected driving behaviour data. We measured the relative distraction duration score as a proximal outcome. Only participants who were assessed as distracted drivers at the start of the trial were included in our main analysis. This resulted in 182 distracted drivers in the intervention condition who interacted with the Safer Driver app and 86 distracted drivers in the control condition. In line with our hypothesis, the relative distraction duration score was significantly lower at the end of the trial period for distracted drivers who received digital coaching via the Safer Driver app, compared to distracted drivers who received the control app. The findings from this trial indicate that digital coaching via a smartphone application has the potential to decrease mobile phone usage while driving. Digital coaching is a promising way of changing people's behaviour towards safer driving habits.

Status and influencing factors of returning to work 6 months after discharge from hospital with severe acute pancreatitis-a cross-sectional descriptive-analytical study in China.

To describe the return to work of patients with severe acute pancreatitis within 6 months after discharge, and to explore the influence of demographic, clinical, and psychosocial factors on their return to work. Prospective 6 months follow-up study. A third class hospital in Guizhou Province. Adult of severe acute pancreatitis(18-60years), with a job before admission, in the intensive care unit ≥ 24 h, were included. To study return to work and influencing factors one, three and six months severe acute pancreatitis patients discharge. several measurements were used, including General Health Questionnaire (Demographic, disease-related, job-related and health behavior data), Readiness for Return-To-Work Scale and the Hospital Anxiety and Depression Scale. Forty-three severe acute pancreatitis patients were included in our study, with mean age 41.53 years. Twenty-nine (67.44%) patients returned to work within 6 months, and fourteen patients did not return to work. The status of Readiness for Return-To-Work Scale: fourteen severe acute pancreatitis patients who did not return to work were mainly in the precontemplation dimension and prepared for action-self-evaluative dimension both 5 cases (35.71%), and the 29 patients who had returned to work were in the Proactive maintenance stage. The study showed that the independent risk factors for returning to work in SAP patients were chronic disease (OR, 0.095; 95% CI [0.011-0.822]; p=0.008), sepsis (OR, 0.071; 95% CI [0.015-0.339]; p=0.009), low education level (OR, 2.905; 95% CI [0.969-8.710]; p<0.001), and anxiety and depression at 6 months (OR, 1.418; 95% CI [0.996-2.019]; p=0.004). In conclusion, the return to work of patients with severe acute pancreatitis needs to be improved. Chronic diseases, sepsis, low level of education and higher degree of anxiety and depression at 6 months were important factors leading to their failure to return to work.

Exploring individual responses to welfare issues in growing-finishing pig feeding behaviour

The feeding behaviour of individual growing-finishing pigs can be continuously monitored using sensors such as electronic feeding stations (EFSs), and this could be further used to monitor pig welfare. To make accurate conclusions about individual pig welfare, however, it is important to know whether deviations in feeding behaviour in response to welfare issues are shown only on average or by each individual pig. Therefore, this study aimed (1) to quantify the individual variation in feeding behaviour changes in response to a range of welfare issues, and (2) to explain this individual variation by quantifying the responses to welfare issues for specific subgroups of pigs. We monitored four rounds of 110 growing-finishing pigs each (3–4 months per round). We collected feeding behaviour data using IVOG® EFSs and identified health issues and heat stress using climate sensors and twice-weekly health observations. For each pig, a generalised additive model was fitted, which modelled feeding behaviour through time and estimated the effect of each welfare issue that the pig had suffered from. The range of these effect estimates was compared between pigs to study the individual variation in responses. Subsequently, pigs were repeatedly grouped using physical and feeding characteristics, and, with meta–subset analysis, it was determined for each group whether a deviation in response to the welfare issue (i.e. their combined effect estimates) was present. We found that the range in effect estimates was very large, approaching normal distributions for most combinations of welfare issues and feeding variables. This indicates that most pigs did not show feeding behaviour deviations during the welfare issue, while those that did could show both increases and reductions. One exception was heat stress, for which almost all pigs showed reductions in their feed intake, feeding duration and feeding frequency. When looking at subgroups of pigs, it was seen that especially for lameness and tail damage pigs with certain physical characteristics or feeding strategies did consistently deviate on some feeding components during welfare issues (e.g. only relatively heavier pigs reduced their feeding frequency during lameness). In conclusion, while detection of individual pigs suffering from heat stress using feeding variables should be feasible, detection of (mild) health issues would be difficult due to pigs responding differently, if at all, to a given health issue. For some pigs with specific physical or behavioural characteristics, nevertheless, detection of some health issues, such as lameness or tail damage, may be possible.

The Impact of Behavior Change Counseling Delivered via a Digital Health Tool Versus Routine Care Among Adolescents With Obesity: Pilot Randomized Feasibility Study.

Youth overweight and obesity is a public health crisis and increases the risk of poor cardiovascular health (CVH) and chronic disease. Health care providers play a key role in weight management, yet few tools exist to support providers in delivering tailored evidence-based behavior change interventions to patients. The goal of this pilot randomized feasibility study was to determine the feasibility of implementing the Patient-Centered Real-Time Intervention (PREVENT) tool in clinical settings, generate implementation data to inform scale-up, and gather preliminary effectiveness data. A pilot randomized clinical trial was conducted to examine the feasibility, implementation, and preliminary impact of PREVENT on patient knowledge, motivation, behaviors, and CVH outcomes. The study took place in a multidisciplinary obesity management clinic at a children's hospital within an academic medical center. A total of 36 patients aged 12 to 18 years were randomized to use PREVENT during their routine visit (n=18, 50%) or usual care control (n=18, 50%). PREVENT is a digital health tool designed for use by providers to engage patients in behavior change education and goal setting and provides resources to support change. Patient electronic health record and self-report behavior data were collected at baseline and 3 months after the intervention. Implementation data were collected via PREVENT, direct observation, surveys, and interviews. We conducted quantitative, qualitative, and mixed methods analyses to evaluate pretest-posttest patient changes and implementation data. PREVENT was feasible, acceptable, easy to understand, and helpful to patients. Although not statistically significant, only PREVENT patients increased their motivation to change their behaviors as well as their knowledge of ways to improve heart health and of resources. Compared to the control group, PREVENT patients significantly improved their overall CVH and blood pressure (P<.05). Digital tools can support the delivery of behavior change counseling in clinical settings to increase knowledge and motivate patients to change their behaviors. An appropriately powered trial is necessary to determine the impact of PREVENT on CVH behaviors and outcomes. ClinicalTrials.gov NCT06121193; https://www.clinicaltrials.gov/study/NCT06121193.

Метакогнитивные читательские стратегии: анализ данных самоотчета и глазодвигательного поведения российских школьников

&lt;p&gt;The paper presents the results of a study aimed to investigate the correlations between oculomotor reading behavior and self-report data of Russian high school students on their practice of using metacognitive strategies. It was found that high school students tended to use problem-solving reading strategies, while seldom using supportive reading strategies. Differences in the use of metacognitive strategies were found between schools that emphasized differently the development of reading competence in middle school. The findings suggested that there were differences in oculomotor measures across groups with varying levels of metacognitive strategy use. The paper outlines possible directions for further research on this topic.&lt;/p&gt;