Maintaining nutrition and exercise strategies after rehabilitation can be difficult for older people with malnutrition or limited mobility. A technical assistance system such as an e-coach could help to positively influence changes in dietary and exercise behavior and contribute to a sustainable improvement in one’s nutrition and mobility status. Most apps do not provide a combination of nutrition and exercise content. In most cases, these apps were evaluated with healthy individuals aged <70 years, making transferability to vulnerable patients, with functional limitations and an assumed lower affinity for technology, in geriatric rehabilitation unlikely.This study aims to identify the potential for optimization and enhance usability through iterative test phases to develop a nutrition and mobility e-coach suitable for older adults (≥65 years) based on individual health behavior change stages in a rehabilitation setting.Iterative testing was performed with patients aged ≥65 years in a rehabilitation center. During testing, participants used an e-coach prototype with educational elements and active input options on nutrition and mobility as a 1-time application test. The participants performed navigation and comprehension tasks and subsequently provided feedback on the design aspects. Hints were provided by the study team when required, documented, and used for improvements. After testing, the participants were asked to rate the usability of the prototype using the System Usability Scale (SUS).In all, 3 iterative test phases (T1-T3) were conducted with 49 participants . Improvements were made after each test phase, such as adding explanatory notes on overview screens or using consistent chart types. The use of the user-centered design in this specific target group facilitated an increase in the average SUS score from 69.3  at T1 to 78.1  at T3. Fewer hints were required for navigation tasks  than for comprehension questions . However, the proportion of unsolved tasks, calculated across all participants in all tasks, was higher for navigation tasks  than for comprehension tasks .The extensive addition of explanatory sentences and terms, instead of shorter keywords, to make it easier for users to navigate and comprehend the content was a major adjustment. Thus, good usability (SUS: 80th-84th percentile) was achieved using iterative optimizations within the user-centered design. Long-term usability and any possible effects on nutritional and physical activity behavior need to be evaluated in an additional study in which patients should be able to use the e-coach with increasing independence, thereby helping them to gain access to content that could support their long-term behavior change. Different demographic, clinical, biological, and lifestyle factors contribute to the development of frailty and sarcopenia in older populations. The accumulation of these risk factors leads to a reduction in resistance to health stressors. In addition to a decline in independence, there is an increased risk of falls and mortality ,2. In maTechnical assistance systems, such as health apps, could help ensure that dietary and exercise behaviors are implemented and changed after rehabilitation. In a survey in Germany with older adults (aged >65 years) in 2020, up to 22% reported that they used health apps to track fitness data, and 16% used apps to obtain information about health, fitness, and nutrition topics. However, the proportion of seniors interviewed who could envision using such apps was more than twice as high for both types of health apps . FactorsApps are one type of technical tool for improving, assisting, or supporting people. The various realizations, such as tele-visits, exergames, or health websites, can be summarized under the term eHealth. A review of the use of eHealth in the context of geriatric rehabilitation revealed that most studies (68%) involved people with neurological diseases. In addition, only 8% of all identified studies assessed the use of health apps as an eHealth intervention. However, the results on the applicability of eHealth with the target group indicated that interventions are feasible if adequate training takes place, and if the eHealth intervention is simple and has good usability . For exaAs previously described, a nutrition intervention in combination with an exercise intervention could lead to more significant effects than would an exercise intervention alone in older people with frailty or sarcopenia . An app Ergonomics of human–system interaction—Part 210: Human-centered design of interactive systems [To develop an age-adapted device and health app (e-coach) for older adults with deficits in nutritional status and physical activity needs, we followed the German International Organization for Standardization 9241-210:2019 These concepts first require an analysis of the context of use and, as a next step, a specification of the use requirements. The context of use was described in a previous study by performing and analyzing focus groups with older adults as well as experts . In all,The information gained was used to derive the user and design requirements for the e-coach. For geriatric patients in rehabilitation, educative content from the areas of nutrition and physical activity focused on the changes and demands of aging that should be included in the e-coach. The older adults would also like the e-coach to be able to provide them with exercises and thus support them in their training. The feedback and evaluation of input regarding nutrition and exercise are described as helpful but should not be an admonition. The results indicate that, as many patients in this age group have little experience with technology and usually use other sources of information, it is important to develop a nutrition and mobility e-coach, particularly given the easy handling and provision of clear information to individual users on the advantages of the e-coach. It is also important for older adults to avoid barriers, such as small font, low video volume, or poor contrast.The e-coach needs to be integrated into users’ daily lives without stressing or restricting them. Moreover, it must be possible to adapt the content to the physical abilities of the users, and because of the heterogeneity of older people in terms of previous knowledge and willingness to change their behavior, appropriate strategies should be used. A recent umbrella review of eHealth interventions suggested that applications involving behavior change techniques may have promising effects on physical activity, sedentary behavior, and healthy eating. However, it is not yet known which theoretical construct is the most effective . In thisOn the basis of the findings from the focus groups, it was possible to further differentiate the settings for teaching the use of the e-coach. To give older adults time to familiarize themselves with the system and generally introduce them to its use, this introduction should already take place in the rehabilitation center. In a real health care situation, it would be easier to explain the technology to the patient; in case of questions or if further explanations are necessary, it would be more uncomplicated to address these points in a personal appointment. In addition, patients would also be able to repeat relevant content that they may not have been able to remember completely from the seminars at their own pace. Therapists should have the possibility to adapt the e-coach to the needs of the patients and their TTM phase. As, in the context of rehabilitation, the therapies take place directly between the physiotherapist or nutritionist and the patient, and the interventions are also strongly influenced by the interactions between the professionals and patients, complete automation of the e-coach would not be efficient. Adaptations of the e-coach to the patient’s previous knowledge and support needs should therefore be made by a physiotherapist or a nutritionist.This paper aims to describe the design process and an iterative evaluation of the developed content. The aim of this study is to identify optimization potentials and enhance usability through iterative test phases to develop a nutrition and mobility e-coach based on individual health behavior change stages, usable for older adults (≥65 years) in a rehabilitation setting.The e-coach prototypes were evaluated with older adults in 3 iterative test phases, using a between-subject design. User experience was reflected by the System Usability Scale (SUS)  and partTo detect and analyze usability problems in more detail, at least 10 patients were included in each iterative test phase . Then, bThe study was approved by the Ethical Review Board of the Carl von Ossietzky University Oldenburg (registration number: 2018–132). We conducted the study in accordance with the Declaration of Helsinki and the underlying data protection regulation.Patients in rehabilitation, from geriatric and cardiology wards, were eligible based on the following inclusion criteria: (1) participants aged ≥65 years and (2) participants were able to speak and understand German. Exclusion criteria were (1) severe visual impairment , (2) severe hearing impairment , or (3) inability to understand study information and provide informed written consent . Participants were recruited by placing flyers in the patients’ wards.The e-coach screens were designed in Adobe XD  for a 10-inch tablet in landscape mode. Design guidelines for apps for older adults were used to take into account specific requirements, such as a decline in vision or decreased motor abilities when developing the prototype ,30. To emobility and nutrition. Both topics offered five modules in total, which were identified from previous focus group discussions with patients and experts. The e-coach was designed to support behavior change in older, vulnerable patients in rehabilitation by providing information, education about risk factors, and strategies for implementing and maintaining nutrition and physical activity recommendations. Different elements, such as videos, texts, or quizzes, were used to provide the information. In addition, the e-coach included educational elements and active input options, such as feedback on nutrient intake through entries in a nutrition diary or instructions and documentation of physical exercises, and an overview of the achievement of exercise goals. The modules entitled interesting facts, recommendations, and execution were elements of both main themes, although with different subthemes . The modules and their content differed according to the phase of the TTM. For example, by focusing more on educational content in the precontemplation and contemplation phases, strategies to increase problem awareness, environmental re-evaluation, and emotional experience are promoted, whereas, in the planning, action, and maintenance phases, more content was introduced that enabled the maintenance of new behaviors, such as enhancing self-efficacy or offering specific action guidance. The module content and the composition of modules for each TTM phase were compiled in advance by members of the study team who have expertise in physiotherapy and nutritional therapy. The content of the nutrition modules was based on the recommendations of the European Society for Clinical Nutrition and Metabolism guidelines on clinical nutrition and hydration in geriatrics [Deutsche Gesellschaft für Ernährung (German Nutrition Society) entitled Essen und Trinken im Alter (Eating and drinking in old age) [Mangelernährung im Alter  [Älter werden in Balance  program of the Federal Center for Health Education and the recommendations from the IN FORM initiative run by Bundesarbeitsgemeinschaft der Seniorenorganisationen  [The e-coach contained two main topics: riatrics , as wellold age)  and Mangold age) . The conzations) . Physicazations) , Lifestyzations) , and homzations)  programsData were collected in the form of in-person testing in patients’ rooms at the rehabilitation center. After the patients had been informed of the content and the procedure of the study by a study team member (LH or MS) and had signed the informed consent, a survey was conducted on sociodemographic data, data on nutrition and physical activity, the phases of behavioral change, and technology commitment. A usability test was subsequently performed. The entire process of data collection took approximately 45 minutes to 60 minutes per patient.Nutritional status was assessed using the Mini Nutritional Assessment-Short Form (MNA-SF)  and a suPatients were classified separately into the TTM for physical activity and nutrition based on the data from the dietary protocol and the PASE. Patients who did not achieve the defined target criteria in the areas of physical activity or nutrition were asked whether they had thought about changing their behavior (contemplation) and, if so, whether they had already planned to do so in any specific way (planning). Depending on their answers, patients were then categorized into the phases of precontemplation, contemplation, or planning. Patients who were already performing the target behavior were asked whether they had been doing so for a short time (action) or for a longer period (maintenance). On the basis of their answers, the patients were classified into the phases of action or maintenance.In addition, the patients’ technology commitment was assessed using a questionnaire developed by Neyer et al . In thisThe test procedure was explained in detail, and the contents and navigation options were shown in advance. Before testing the usability task, the patients were told that the aim of the study was not to test their abilities, but the quality and usability of the e-coach . MoreoveTesting also took place in the patient’s room at the rehabilitation center. During the test, the examiner (LH or MS) and the participant sat at a table. The tablet with the app could be placed on the integrated stand of the device, placed on the table, held in the hand, or laid down by the participant. The examiner read the tasks to the participant and then observed the participant.Usability tasks in three different domains were defined and used for each iterative testing period: navigation, comprehension, and design. Navigation tasks were used to determine whether users were able to find their way through the e-coach and use the buttons correctly. In the case of quizzes, we tested whether the screens were structured such that they could be used successfully by the participants. On quiz screens, the question was highlighted at the top of the screen  and below it, two to three answer options were shown  along with a prompt (press the correct answer). Comprehension tasks were intended to test whether screen content and information were correctly interpreted and understood. The aim of the design questions was to identify visual barriers such as a font that was too small or an acoustic problem, such as an extremely fast rate of speech in videos. An example of questions and tasks is shown in successful and unsuccessful. Furthermore, the content of the hints given and the number of hints given were recorded.The results from the usability tasks were reported in three different categories  to evaluate usability problems in more detail. The performance of the particular task was evaluated in the categories of For the usability test, patients were instructed to simultaneously speak their thoughts aloud while performing the tasks. The concurrent think-aloud method was intended to immediately identify and specify problems for older adults using the e-coach . AttentiThe tasks in the test sequence were always carried out in the same order, but patients had the option of skipping tasks at any time or stopping the usability test. The tests and verbal feedback during the tasks were recorded by taking notes.After the usability test, patients were finally interviewed using the SUS. The questionnaire contained 10 statements about the usability of a system rated on a 5-point Likert scale. The values of individual items were added together and then multiplied by 2.5, resulting in a score between 0 and 100. The results provided a general overview of product usability .simpler tasks such as finding the next page; in phases 2 and 3, we increased the number of navigation steps required in some tasks and tested other functions such as the use of the help button. To minimize the contact time and number of contacts with the study team in the context of the increasing incidence of COVID-19 in our region in Germany, it was decided to test the optimized elements and new content in the nutrition section only after completion of the second iterative test phase. As no further problems were found concerning the exercise part, but some were found concerning navigation from screens or the interpretation of nutritional diagrams, the last iterative phase focused on the questions from the nutrition part. A total of 3 iterative test phases were used. Before each test phase, we refined the content, design, and potential functionality of the elements. The first test phase mainly tested the basic functionality of the chosen navigation structure with the target group, with Statistical analyses were performed using the software SPSS .Participants’ characteristics, success rates of the tasks, and SUS scores were analyzed using descriptive statistics; they were presented as frequencies, means, SDs, and percentages.The hints given during the usability test were presented as the total number of hints required for this task type in the iterative phase. The total number of unsolved tasks and percentages of unsolved tasks for all participants that performed this task type in the iterative phase were also reported.The notes from the concurrent thinking aloud during the usability test were used to derive aspects that the participants noticed during the test. These aspects were discussed by the study team (LH and MS) to identify specific problems in the tasks and to derive possibilities for optimization.F test was used if the assumption of homogeneity of variances was violated or if the data were not normally distributed.An explorative ANOVA was conducted to compare the SUS scores among the 3 iterative test phases. The robust Welch U test for ordinal and not normally distributed variables, and the chi-square test was performed for nominal variables. Statistical significance was set at P<.05, for all explorative analyses.The group was divided into 2 equally sized subgroups to test for group differences in successful task completion. Half of the participants who performed the tasks better were compared with the other half. The variables of age, sex, BMI, MNA-SF classification, technology affinity, TTM phase nutrition, TTM phase mobility, and SUS score were tested for group differences. The explorative analysis was performed using the Mann–Whitney A total of 49 patients who were aged 66-94 years  participated in the study. Patient characteristics per iterative test phase are presented in Navigation tasks required fewer hints (14.1%-26.5%) per task than comprehension tasks (20%-30.5%) in all iterative test phases . The perMost participants understood how to use the buttons correctly. In 86.9% of all tests, participants consistently selected the arrow button for navigation to the next screen as intended. When navigating back to previous screens, this was done completely correctly in 84.7% of all tasks.Many participants were able to successfully interpret the active input options such as the drinking protocol , the diagram of consumed food groups , and the exercise diary .Some participants had difficulties in interpreting the content they would expect to find based on the names of the modules or the themes. The contents that needed to be optimized are shown in Many design elements on the overview screens and the educational features were rated positively by older adults. For educational content with texts, the font sizes, readability, and length of the text were positively rated in almost all corresponding tasks (92.3%).P=.02). The participants who were able to solve more than 90% of the tasks also rated the e-coach significantly better with the SUS score (P=.04).Moreover, more than half of the participants  were able to successfully solve more than 90% of the tasks. Exploratory analysis of group differences suggested that those who solved more than 90% of the tasks had significantly higher technology affinity .Because the normality assumptions of the ANOVA were violated, a 1-way Welch-ANOVA was performed to determine whether the SUS score was significantly different among the test phases. The improvement in the SUS score was not statistically significant among the 3 test phases  to 78.1 , indicating good usability of the e-coach and is comparable with the SUS results on eHealth application use in other studies with older adults -47. MoreComparing the completion of tasks among the 3 iterations, it is noticeable that in the first iterative test phase, hints were needed, especially for comprehension questions (30.5%), and a few tasks were not fulfilled by all participants in this test (1.9%). In this iterative phase, hints were often needed for comprehension questions, such as what can be done on certain screens and also concerning the interpretation of what was meant by certain titles . These difficulties were solved by adding more information to the descriptive texts and partly by changing the wording of individual terms.In the second iterative test phase, the proportion of required hints in the navigation tasks (26.5%) increased noticeably compared with the first iterative test phase (14.1%), and 15.2% of all navigation tasks could not be solved by the participants in the second iterative test phase. In the first iterative test phase, many navigation tasks included easier functions, such as navigation to the next screen, and only a few tasks with more complex navigation steps. In contrast, in the second iterative test phase, more complex tasks were added, requiring, for example, 2 navigation steps, and the proportion of simpler navigation tasks decreased accordingly . It seemIn the third iterative test phase, hints were also required more often for navigation tasks that required several navigation steps. Although there were some tasks in the last iteration phase that required more than one navigation step, only 17% (2/12) of all participants needed help with all navigation tasks, and 4.3% of all navigation tasks in the third iterative test phase were not successfully completed. However, in the comprehension tasks, the need for hints was almost similar in the last iterative phase (20%) compared with the second iterative phase (21.6%), but more tasks could not be solved (2.5%) than in the second iterative phase. Compared with the first iterative test phase, almost all participants  in the last iterative test phase were able to derive the following content from the labeling of the modules. In addition, the question about what can be done on the overview screens was also answered correctly by all participants. Nevertheless, some participants found it difficult to interpret diagrams, meaning that they required hints, and one participant was unable to solve the task.Other aspects should be considered as possible reasons for the different performance in the tasks, in addition to the increase in complexity. There were fewer tasks and fewer participants in the third iterative test phase because increasing COVID-19 case numbers forced us to reduce contact times with the participants. In addition, all the comprehension questions that could not be answered successfully in the last test were questions about protein and energy intake shown in a diagram. This topic could be difficult for people with little existing knowledge of the subject.P=.02). A significant correlation between the use of health apps by older people and a higher affinity for technology has also been found in other studies [P=.09).Furthermore, in our exploratory analysis of group differences between half of the participants who solved more than 90% of the tasks correctly and those who solved less than 90% of the tasks correctly, we found indications that those who solved more than 90% of the tasks correctly had a significantly higher affinity for technology  points in the group of participants who correctly solved more than 90% of the tasks and at a median of 3.0 (0.6) in the group of people who correctly solved less than 90% of the tasks. This value seems to correspond approximately to the technology readiness of people in this age group in Germany. Rasche et al [To increase the usability of e-coaches in health care, as well as for less technically inclined older persons, detailed instructions on how to use the e-coach could be applied. Older people seem to have greater benefits from step-by-step instructions when learning to use new technologies. To address this problem in a real-world setting in terms of use of the e-coach by older adults, guidance  should be additionally offered . Furtherprotein or foodgroup is more difficult for people with less existing knowledge. However, even someone without existing technical knowledge may have already achieved the nutrition and physical activity goals we used. Therefore, it is not practical to draw conclusions from the behavior change phase alone about the ability to use or the general understanding of the app based on our results.The data on the TTM phases of the participants demonstrate that geriatric patients in rehabilitation are at different phases of the behavioral change process. In the iterative tests with nutrition content as well as with physical activity content, the proportion of participants in the first 2 phases of TTM (precontemplation and contemplation) and the remaining phases  was quite balanced. As such, the readiness and implementation of behavior change in the areas of nutrition and physical activity appear to be heterogeneous among the target group, requiring the use of different strategies in the e-coach to support behavior changes. No indications of significant group differences between the half of the participants who were able to solve more than 90% of their tasks and those who were able to solve less than 90% of the tasks were found for the TTM phase distribution in the area of nutrition. For the TTM phase in the physical activity domain, no testing for group differences was conducted because of the small sample size resulting from missing data from the last iterative test. A correlation between certain phases of behavior change and the ability to successfully solve tasks could be explained only to a limited extent. In the case of tasks for operating the app, such as finding specific screens or using buttons, personal readiness to change one’s nutritional or physical activity behavior should have little influence. It could be possible that the interpretation of specific content with terms such as When developing the e-coach, design recommendations for the target group ,30 were In a few tests, it was noticed that a user wanted to select the correct button, but the pressure on the button was not recognized by the device. Besides a possible malfunction of the touchscreen, there are 2 points that have been described in further studies ,55 and aIt is likely that more people who already had a general interest in nutrition, physical activity, or technology participated in the study. Therefore, a selection bias cannot be ruled out even if at least the measured technology commitment also corresponds to the figures from another study with older people in Germany with varying degrees of experience in the use of technology . In addiData collection was conducted by study team members who were involved in the development of the e-coach. An uninvolved person would have had to conduct usability tests to increase the objectivity of these tests. This was not possible because of the financial resources of the project.Moreover, the test situation itself may have biased the results. On the one hand, it is likely that older adults were more nervous and made more mistakes than if they had used the e-coach unobserved. On the other hand, the possibility of receiving hints and help from the examiner could also have led to a situation in which help was requested more quickly and, if necessary, tasks would have been solved after some time without the help of the study team member. In addition, it cannot be ruled out that unintentional nonverbal responses were given by the study team  that were not recorded in the documentation.In the context of this study, the time spent on the task was not determined. The time it takes a participant to complete a task can be an indicator of usability, as quick performance can also indicate ease of use. However, there is also evidence from a study by Sonderegger et al  that theThis study only evaluated whether e-coach elements are generally usable for older adults. It provides an indication of the e-coach’s usability for first-time users but not for a longer period of use.Ergonomics of human–system interaction—Part 210: Human-centered design of interactive systems [This study involved older people undergoing inpatient rehabilitation in iterative optimization and usability testing for an e-coach according to the German International Organization for Standardization 9241-210:2019  systems . It has As the target group is particularly vulnerable, and an individual’s willingness to continuously use the e-coach may impose an additional burden on them, it is essential to evaluate the acceptance, willingness, and adherence for long-term use of the system in a further study. Previous studies have shown that it can be very difficult to recruit patients for such long-term use of technical devices in this target group, and that good usability, as well as subjective benefits for patients, must necessarily be present .


The complexity of intimate partner violence and the impossibility of understanding it from single factors have been studied from different biological, psychological, and socio-cultural factors. A cross-sectional study was conducted on 187 men involved in legal proceedings for problems of violence in their intimate partner relationships in the Dominican Republic in order to explore whether insecure attachment represents a risk factor for alexithymia in men with violent behaviors. The attachment style was determinate by the Casullo and Fernández-Liporace Attachment Styles Scale, and alexithymia was assayed using the Latin American Consensual Toronto Alexithymia Scale (LAC TAS-20), a modification of the Toronto Alexithymia Scale (TAS-20). Chi-square test and multiple binary logistic regression analysis were performed to explore the phenomena of alexithymia and attachment styles in the context of a confinement center for male intimate partner offenders in the Dominican Republic. The results showed that insecure attachment represents a risk factor for alexithymia, being highest for avoidant attachment in the population studied. The results also highlight the influence of other factors such as education and maternal–familial relationships as a factor risk for alexithymia. Intimate partner violence (IPV), the most common form of violence that women experience globally, is an act of violence committed by a male intimate partner or ex-partner that results in either physical, sexual, or psychological harm directed towards females . TraditiViolence in intimate relationships is a serious social problem in the Dominican Republic, as it is the most reported crime and claims many lives every year. According to official sources, in 2019, 152 women were murdered in the country in the intimate partner environment and 19,178 complaints were received. This number constitutes a rate of 2.7 per 100,000 women, the third-highest in Latin America, only surpassed by Honduras (6.2) and Salvador (3.3) [Attachment and alexithymia are emotional factors that have been regularly linked to violent behaviors in the male population and that have a direct impact on partner relationships. Although both have been studied in a possible relationship to male aggressors, they have been always studied separately ,10,11.Attachment could be defined as a special bond that constitutes an adaptive phenomenon. It is an organized behavioral system whose function is to maintain the proximity of care, and which acts as a homeostatic control system. Briefly, attachment theory establishes that both the resilience capacity of children and their subsequent behavior and emotional development are directly related to the type of bond that children established with their parents in the early years. According to this, attachment can be classified into two different styles: secure and insecure attachment (which can be divided into anxious and avoidant insecure attachment). Secure attachment occurs when the child feels unconditional on the part of her parents and has the certainty that they will not fail them. Within insecure attachments, for anxious attachment, the child does not trust their caregivers and grows up with a feeling of uncertainty and insecurity, which generates anguish. On the other hand, avoidant attachment occurs when caregivers do not provide sufficient security for the child, causing the child to develop an emotional distance from them .Attachment is responsible for maintaining the stability of the individual in their environment . In the On the other hand, alexithymia is a common personality trait among individuals with different health problems, suggesting that alexithymia could be a risk factor for medical or psychiatric illness . AlexithSeveral demographic and psychosocial factors appear to be associated with alexithymia, although these findings appear to be inconsistent. The academic level, a factor that could reduce the possibilities of developing the emotional skills that alexithymia lack, seems to be the most determining factor ,21,22. TPrevious studies have found that insecure attachment and alexithymia are associated with different kinds of violence in populations with mental health problems, such as depression, anxiety, somatoform disorders, personality disorders, or dissociative disorders ,25,26. TA cross-sectional study was conducted on a cohort of 187 men  who were undergoing a therapeutic process at the Behavioral Intervention Center for Men of the Attorney General’s Office of the Dominican Republic as part of a coercive program for violence against their partners. Data were collected between January to June of 2019, at the beginning of the therapeutic program of the Center for Behavioral Intervention for Male Aggressors (early skills phase). Individuals selected were men over 18 years of age who had been detained at the Center during the previous year and voluntarily agreed to participate in the study. Participants had to be able to read and write and to have the capacity to understand the questions. Participants were not previously evaluated with the same instruments used in this study. A therapist interviewed all participants individually. There were two screening tests  that lasted an average time of 25 min each. In addition, according to institutional protocols, some questions about socio-demographic parameters were asked to obtain key information. Individuals who volunteered to participate in the study signed an informed consent form after being informed about the objectives of the study and their right to drop from the study at any time. The study was approved by the Bioethics Commission of the Institute of Human Sexuality belonging to the School of Medicine of the Autonomous University of Santo Domingo in the Dominican Republic. All the procedures were performed following the ethical standards of the Helsinki Declaration.The evaluation began with the application of an interview to collect information on the socio-demographic characteristics of every participant. The questionnaire applied was designed by the Center for Behavioral Intervention for Men of the Attorney General’s Office of the Dominican Republic in 2008. This questionnaire was used since to collect information on the main variables such as age, marital status, occupation, educational level, type of violence exerted, and quality of the relationship with the mother. The Casullo and Fernández-Liporace Scale  was usedTo evaluate alexithymia, the Latin American Consensual Toronto Alexithymia Scale (LAC TAS-20) was used, for which the Spanish version of the Toronto Alexithymia Scale (TAS-20) was previously validated . The aleEvery question on the LAC TAS-20 was a Likert scale statement that could range between 1 and 5 points: (1) strongly disagree; (2) moderately disagree; (3) neither agree nor disagree; (4) moderately agree; (5) strongly agree. The evaluation of the scale is done by assigning the scores obtained. From the sum of these scores, the total score, and the score of each of the three factors  could be obtained. Thus, the total score ranged from a minimum of 20 to a maximum of 100 points. Initially, the TAS-20 scale divided individuals into three groups: those scoring 56 points or more , those scoring between 41 and 55 , and those scoring less than 40 points . However, for this study, this variable was dichotomized as the presence of alexithymia  or absence of alexithymia (scores below 40 points), as undefined alexithymia was considered no alexithymia being present. The scale presented a Cronbach’s alpha of 0.78. p < 0.05. The IBM-SPSS statistical software package (SPSS 26.0 for Windows) was used for all the statistical analyses.A descriptive analysis of the continuous variables was carried out using means and standard deviations, whereas absolute and relative frequency distributions were calculated for categorical variables. Categorical variables were compared using the Chi-square test. Multiple binary logistic regression analysis was used to assess the risk of having alexithymia adjusted for variables that were considered to influence the statistical model based on the bivariate analysis. Insecure attachment (in the two manifestations: anxious and avoidant) was taken as an independent variable. The indicators of a warm relationship with the mother were predominance of affection, stimulus-centered education, and unconditional acceptance of the children. On the other hand, alexithymia was the dependent variable and was measured based on its presence or absence in the men evaluated. The level of statistical significance was established for a value of n = 90) or not (n = 97). The study group did not differ regarding possible confounding factors such as age, marital status, period with the partner, employment status, and the number of children they had with the victims (p = 0.001). All participants were of Dominican nationality and the majority had medium studies . Regarding the kind of violence perpetrated by the aggressors, the most predominant was psychological violence (55%), while physical violence was 45.5% (data not shown).A total of 187 men receiving treatment for gender-based violence at the center of behavioral intervention for male aggressors participated in the study. These men were divided into two groups according to whether they had a previous diagnosis of alexithymia  could influence the violent behavior of men with this condition. This association could be useful in programs for the re-education and reintegration of IPV offenders, proposing personalized programs according to the offender’s profile.The main finding of this study is that insecure attachment, in any of its manifestations, avoidant or anxious, represents a risk of alexithymia in the population of aggressor men studied. These results complement the findings of other research in which unsafe attachment suggests a risk of alexithymia in a different population ,32,33. BPrevious studies have established a possible relationship between the type of violence and certain behaviors lacking emotional regulation ,36,37. OThe indirect effect of attachment and alexithymia on violence and its mediating role through impulsivity have also been previously considered. The inability to regulate emotions is directly related to alexithymia, and alexithymia is directly related to impulsivity and violence ,40. The In this study, although the direct relationship between alexithymia and violence has not been explored, a higher risk of alexithymia has been found in violent men when some manifestations of insecure attachment (distant or anxious) are present. Evidence confirms that safe attachment improves parental bonds and these, in turn, increase the indicators of emotional intelligence. The ability to identify emotions is strongly associated with the attachment style .Both avoidant insecure and anxious insecure attachment are associated with alexithymia. The results confirm the associations between attachment and alexithymia in the population studied, but not the relationship of any of them with violence. A possible explanation for these results lies in the complexity of violent behavior in partner relationships, which makes them irreducible in its explanation of linear emotional processes. Therefore, the less likely the possibility of an agreement in the face of conflict leads alexithymic husbands to have lower satisfaction, less expression of affection, low cohesion, and poorer dyadic adjustment . AccordiThe quality of the relationship with parents, especially with the mother, has already been recognized as a protective factor for alexithymia . In thisCertain studies suggest that the deterioration of relationships with progenitors in early years negatively influences the model of relations that will be established later . AlthougCertain studies also report that securely attached individuals have lower levels of psychological distress and highlight the poor experience of relationships with the mother in alexithymia problems . AccordiThe present study did not find an association between any of the demographic factors studied and alexithymia, except for the educational factor. Several studies have found an association between alexithymia and demographic variables such as age, marital status, occupation, and educational level, but most of them are inconsistent ,4. HowevThe main strength of the study was the exploration of the relevance of insecure attachment as a risk factor for alexithymia in a population of male abusers. This research highlights the importance of assessing alexithymia and attachment style in IPV perpetrators. The results of the study will encourage the search for therapeutic methods for an approach focused on male aggressors according to every personal attachment style. These interventions could have an impact on alexithymia, reducing the deregulatory effects as well as promoting strategies for emotional self-regulation, triggering a better adjustment for escalating situations that often lead to explosions.Furthermore, this study promotes the insertion of instruments to measure attachment styles and the presence of alexithymia within the evaluations used in the initial stages of the attention programs for male aggressors. These instruments could be used for assessment purposes and in planning the treatment and rehabilitation plan for program participants.However, the study also has several limitations. The main limitation is the lack of a control group, as only IPV perpetrators were included in the study, which avoids establishing a causal relationship comparing the rates between IPV perpetrators and those who were not. Another limitation is that, as the study was a cross-sectional study, it did not allow for establishing a causal linear relationship, depending on whether the insecure attachment determined the origin of the alexithymia. For this purpose, a longitudinal study should have been considered. On the other hand, some of questions raised were self-report responses and, as it was a population in conflict with the law, some individuals would appeal to social desirability in their responses. In addition, the study was carried out in an official center that belongs to the Dominican Republic’s Justice System, with a very limited study population, so the results obtained could not be extrapolated to the whole male population who have the status of perpetrators. The impossibility of measuring certain variables associated with IPV  could be considered as other limitation of the study. It could lead to misinterpretation of the results and be taken as empirical support for some wrong myths  that minimize the importance of IPV and could lead to exonerating them from responsibility . In addiThe present study provides evidence on the relationship between the two forms of manifestations of insecure attachment (avoidant and anxious) and the condition of alexithymia in the aggressor population. On this basis, both alexithymia and insecure attachment could be related to the mechanism that explains certain violent behavior in intimate relationships and that it could be related to emotional factors, although it is not completely clear. This relationship allows us to propose the evaluation of alexithymia and personal attachment style as a therapeutic complement for the assessment of the male perpetrator’s profile that will support proposal rehabilitation plans adjusted to each profile. This study also highlighted the possibility that the presence of insecure attachment may represent a risk factor for alexithymia in the aggressor population. Finally, the present study also confirms that both maternal–filial relationships and a medium level of education are indicative of alexithymia among the population of IPV perpetrators studied. In this sense, quality mother–child relationships and high levels of education show a protective effect against the development of alexithymia. However, further research is necessary to confirm the conclusions that can be drawn from this study. Future research should consider the potential effects of including a control group more carefully.


Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death worldwide. Early identification of CHD can assist to reduce death rates. When it comes to prediction using traditional methodologies, the difficulty arises in the intricacy of the data and relationships. This research is aimed at applying recent machine learning technology to identify heart disease from past medical data to uncover correlations in data that can greatly improve the accuracy of prediction rates using various machine learning models. Models have been implemented using naive Bayes, random forest algorithms, and the combinations of two models such as naive Bayes and random forest methods. These methods offer numerous attributes associated with heart disease. This proposed system foresees the chance of rising heart disease. The proposed system uses 14 parameters such as age, sex, quick blood sugar, chest discomfort, and other medical parameters which are used in the proposed system. Our proposed systems find the probability of developing heart disease in percentages as well as the accuracy level (accuracy of 93%). Finally, this proposed method will support the doctors to analyze the heart patients competently. The heart is the only organ that pumps blood to all of the body's tissues via the arteries, veins, and capillaries in order to provide oxygen and nutrition. Vital organs such as the brain and kidney suffer if the heart's pumping motion becomes ineffective, and if the heart stops working entirely, death happens within minutes. Heart disease has been around for a long time, and it has been the most complex and life-threatening human diseases on the planet. Life is beautiful in and out itself. The efficient operation of the heart is fully reliant on it. The following are some of the signs and symptoms of cardiac disease, namely, breathing trouble, physical weakness, left hand paining, and exhaustion are all indicators of heart illness, as are other signals such as high jugular vein pressure and peripheral edema produced by functional cardiac or no cardiac problems. The early inquiry procedures are used to detect the heart disease. Heart problem prevention and therapy are extremely difficult especially in rural areas, due to the scarcity of investigative equipment and specialists, as well as other factors that impede good heart disease prediction and treatment .Nowadays, the artificial intelligence and machine learning are widely acknowledged to show important role in the medical field for diagnosing the disease and classify or forecast the outcomes, it utilizes a variety of machine learning and deep learning models. Machine learning algorithms can quickly adopt thorough genetic data analysis. Medical records may be changed and studied more severely for well estimation, and improved models can be identified for good prediction. Various researchers have reported on the prediction of heart problems using a different algorithm .The common drawback in machine learning is high dimensionality of data which contain large amounts of data and sometimes cannot be seen even in 3D, which is known as the curse of dimensionality. As a result, the overfitting occurs due to more operations done with data analysis and due to this reason, it needed a lot of memory. In addition to reducing redundancy in the dataset, the weighting features can also help reduce the processing time of the execution. The dataset can be reduced in dimensionality using a variety of feature analysis strategies. Hence, the heart disease prediction systems based on machine learning will be precise and reduce risks. This suggested that the proposed algorithm makes a contribution by developing a hybrid machine learning method in medical intelligent decision supporting system for heart disease detection [To identify the heart disease, the process involved highly on several key steps such as preprocessing, feature analysis, and finally classificationA good automated hybrid proposed algorithm model was built to analyze the cardiovascular diseaseThis automatic proposed algorithm suggests which features are suitable for designing a good intelligent system for identifying heart disease and healthy peopleFinally, to evaluate the performance, various machine learning models were compared with the proposed algorithm with various similarity metrics to ensure the metrics were improved or notThis proposed method reduces the human error and achieved very good diagnosisFor this research works, the major contributions are listed below:The remaining sections of the paper are divided into four groups. The introduction is in Several studies have been conducted, and several machine learning models have been used to categories and predict the changes in the heart. Using a machine learning-based technique, various research investigations have reported on the detection of cardiac disease. The classification of performance related to multiple machine learning algorithms on a heart disease dataset was described in a literature study.Numerous procedures are currently used in heart disease for detection by applying computer vision. The heart disease detection by using and extracting relevant feature with standard dataset used by authors in , 4 has bThe authors in  have devAccording to studies, compared to women's, approximately the males have a heart problem during their lifetime. The higher risk maintained even after correcting for established heart disease risk factors such blood pressure and diabetes. The four important datasets, namely, Cleveland, Switzerland, Hungary, and Long Beach V, are investigated from 1998 by various researchers with important characteristics for heart disease prediction [The dataset used in this investigation was the common health dataset, which was created in 1988 in Cleveland. It includes a genuine dataset of 300 data samples with a total of 14 important features  such as blood pressure, type of chest discomfort and ECG result as shown in Data preprocessing technique is required for the efficient data representation using machine learning, which must be trained and tested thoroughly. For successful use in the classifiers, the preprocessing approaches which follow are missing value removal, standard scalar, and Minmax Scalar. In real life, there are a lot of missing and noisy data to eliminate such errors and produce confident forecasts; the data is preprocessed. Feature selection is one of the vital roles to select the important feature from the input variable to create a predictive model . From thThe naive Bayes classifier is a simple and an effective classification method which is used for creating machine learning models quickly, which can generate accurate predictions. It works by predicting the probability of an object based on its characteristics. It is based on the strong (naive) attribute independence assumption . The BayP(X/Y), P(X), and P(Y). P(X) designates the class prior probability, P(Y) represents the predictor prior probability, and P(Y) refers to likelihood. In terms of data categorization algorithms, the naive Bayes algorithm is a simple, easy-to-use, nonlinear, and complex approach. However, there is a loss of accuracy because it is based on assumptions and class conditional independence. The dataset's 14 attributes were used to achieve an accuracy of 83.49%.There are four probability expressions in the probability hierarchy: Input choice in random forestRandom blend forestCombination of input choice in random forest and random blend forestA strategy for supervised classification is the random forest algorithm. In this process, several trees are used to form a forest. The random forest emits a class expectation from each tree, and the class with the most votes becomes the model's forecast. A random forest classifier's accuracy improves as the number of trees increases. The following are the three most prevalent methodologies:It may be used for classification as well as regression, but it excels in classification and overcomes missing variables . The resThe confusion matrix is a performance evaluator for machine learning classification models. The confusion matrix was used to assess the performance of all of the produced models. The confusion matrix shows how many times our models properly predict and how many times they mistakenly guess. True positives and true negatives were assigned to successfully anticipated values, whereas false positives and false negatives were assigned to incorrectly predicted values. The model's performance was evaluated using accuracy, precision, and recall after all of the predicted values were organized in the matrix.The confusion matrix is a performance evaluator for machine learning classification models. This calculation assesses the correct performance and shows the performance of the different models. This confusion matrix shows how many times our models properly predict and how many times they mistakenly guess . Here, tIn this section, the final results of different models are discussed below with graphical representation.This paper presents a new proposed method of modified naive Bayes and random forest classifier algorithm. It is an extension of the naive Bayes and random forest classifier model. Both these classifiers are used to construct the new classifier algorithm. This classifier gathers all standing instance and classifies new occurrences of similarity based on new correlations through new distance as shown in equation . This noThe proposed new algorithm is described in According to the WHO, the leading death rates globally increased due to the high impact of heart failure in this COVID situation, estimated at 17.9 million in each year, because of high-risk factors such as stress, unhealthy diet, diabetes, blood pressure, and smoking. In many circumstances, preserving a patient's life hinges on the time between seeing a doctor and obtaining the necessary hospitalization, so providing physicians with constant updates on their patients' health status will significantly reduce the number of deathsThis present work has provided the automatic diagnosis of heart disease prediction and keeps the updated medical status. In the future, the other disorder such as blood pressure, diabetics, and the pulse rate can also be considered related to heart failure and the medical records can also updated using IoTN number of patients affected heart problems day by day. So, there is a requirement for a technology that can automatically detect cardiac failure from the symptoms. For this purpose, our proposed algorithm involved various methods to identify heart diseases such as preprocessing, feature extraction, and classification. The dataset has been displayed, and the missing values have been filled in. Some extraneous features have been removed from the data as part of the preprocessing process. Standards were developed to make it easier to incorporate the values into machine learning models. The different performance metrics such as accuracy, recall, and a confusion matrix were utilized to ensure the performance of the proposed model. Finally, this proposed method achieved 92% accuracy compared to other existing methods.The


Handwashing with soap has received considerable attention due to its importance in the prevention and interruption of the transmission of diseases. Regardless of the positive effects of handwashing with soap, developing countries still have a low rate of handwashing.The study aimed to determine the individual, household and community-level factors associated with handwashing behavior among households in EswatiniUsing the Eswatini Multiple Indicator Cluster Survey conducted in 2014, a secondary analysis was done of the households surveyed. A total of 1,520 households nested in communities with complete data on handwashing practices were included in the analysis. Univariate, bivariate analysis and multivariate multilevel logistic regression were used to establish the factors that were associated with handwashing behavior.The prevalence of handwashing among households was 56% in 2014. Households whose heads were aged 35–54 and 55 years and older were more likely to practice handwashing ; and  compared to those aged 15–34 years. Households with a pit latrine or no toilet facility at all, were less likely to practice handwashing ;  respectively compared to those with a flush toilet. Region of residence was a community-level variable associated with lower odds of handwashing, with those from the Hhohho  and Manzini region  compared to Lubombo region. Households from communities where access to mass media was high were more likely to practice handwashing  compared to those from communities where access to mass media was lowHouseholds headed by young adults, with pit latrine or no toilet facility at all and lived in the Hhohho and Manzini regions and with low access to mass media, should be targeted for interventions aimed at improving handwashing practices. Globally, approximately 6.3 million children died, and over 500 thousand deaths were due to diarrhea in 2013Handwashing with soap (HWWS) has received considerable attention due to its importance in the prevention and interruption of transmission of viruses and bacteria causing diarrhea, common flue, ARIs, pneumonia, and (COVID-19), hence reducing the magnitude of diseasesThe practice of HWWS is founded on the global agenda of Sustainable Development Goals (SDGs), especially target 6.2 of goal 6, which aims to achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations by 2030Even though several studies have been conducted in other countries to investigate the factors associated with handwashing behavior, they applied a single-level analysisRegardless of the importance of HWWS as a public health intervention, few studies have been conducted in Sub-Saharan Africa (SSA) that investigated the correlates of handwashing behavior let alone the application of multilevel approaches. In Eswatini, only descriptive reports The study adopted a secondary cross-sectional analysis using the 2014 Eswatini Multiple Indicator Cluster Survey (EMICS). The MICS is an international initiative that is nationally representative of the population and is utilized to assist countries in monitoring and tracking indicators on children, women, and men's health and development. MICS uses standardized structured survey questionnaires administered through face to face interviews at the household levelThe 2014 EMICS adopted a two-staged sampling strategy that involved systematic sampling of enumeration areas (EAs) and randomly selecting 15 households from each EAIn the survey, there were 5,211 households selected; however, 4,865 (93.4%) completed the interview. Out of the households that were interviewed, only 1,809 had a place for handwashing observed during the household interview; 1,533 had water available in the household, and 272 did not have water in the household. The total number of households included in the analysis were 1,520.Outcome variable: The study outcome variable is handwashing behavior. The household was deemed to be practicing handwashing if it had a place for handwashing, and water was available with soap/ detergent at the specific place coded as 1. In contrast, those that had a place for washing hands but without water and soap or detergent in the particular place were coded 0. These were assessed through rapid observation during the household interview and is recommended to be the most efficient methods in community surveys Explanatory variables: To understand the determinants of handwashing behavior, several individual, households, and community-level factors were utilizedStata 15 was used for the analysis. To account for the complex MICS design, weights were applied through the svy command. Frequencies and percentages were used to describe the study sample. Bivariate analysis with chi-square test statistics was performed to test the independence of the distribution between the independent variables and handwashing behavior. The vector inflation factor (VIF) was used to test for strongly correlated explanatory factors, and no factors were strongly correlated to each other (see Table S1). The command melogit was used to fit the multilevel logistic regression to identify the factors that were significantly associated with handwashing behavior. Multilevel analysis was considered appropriate to account for the hierarchical nature of the MICS and to be able to estimate community-level effects on the outcome variableIn the final model , at the At the household level, the toilet facility was associated with handwashing behavior. Consistent with model 3, in the final model, households with a pit latrine and no toilet facility at all were less likely to practice handwashing, ;  respectively compared to households with a flush toilet, holding other factors constant in the model.At the community level, even after controlling for the individual and household level factors, households from Hhohho and Manzini regions were less likely to practice handwashing, , and  respectively compared to households located in the Lubombo region. In the final model, households from communities where access to mass media was high were more likely to practice handwashing,  compared to those from communities where access to mass media was low.The random-effects results were also reported . There wThe study found that overall the practice of handwashing was 56% in Eswatini and that several factors were associated with handwashing behavior. The findings showed a 9 points increase in the prevalence of handwashing with soap in 2014 when compared to 47% in 2010 Evidence from the literature suggests that the older the household head, the more likely the practice of handwashing in the householdEvidence in the literature reported a positive relationship between education and handwashing behaviorSeveral studies found a significant association between the type of toilet facility in the household and handwashing behaviorThis study also found that households from the Hhohho and Manzini regions were less likely to practice handwashing when compared to the Lubombo region. The higher odds of handwashing behavior in the Lubombo region are surprising when compared to the other regions, given that it is the least developed, followed by ShiselweniExposure to mass media has been found to play a significant role in the adoption of a positive attitude towards handwashing behavior. In a study done in Kenya, media exposure was associated with the formation of hygiene behaviors, including handwashingThere are some limitations associated with the study. First: social desirability bias could affect the findings since the data was self-reported This study confirms the relationship between age of the household head, type of toilet facility, region of residence, the proportion of households with access to mass media in the community, and handwashing practices. To achieve remarkable progress on handwashing practices by 2030, intervention should target households headed by young adults, with pit latrines or no toilet facility at all, and live in the Hhohho and Manzini region. This study highlights that individual, household, and community-level factors have a significant role in determining handwashing behavior among households. The study results suggest that handwashing behavior could be improved if interventions consider the individual, household, and community factors that influence handwashing. Therefore, interventions should be integrative of the individual, household and community-based approaches especially in disadvantaged communities


The outbreak of COVID-19 has significantly restricted the productive capacity of society and resulted in a shortage of supplies to maintain survival. Lightening the burden not only depends on government agencies, but also needs extensive social organization participation. However, few studies focus on how to promote social cooperation to support the provision of emergency supplies. This study aimed to find out the theoretical mechanism to expand the cooperative networks of supply support organizations during the epidemic. Data from the emergency response to the COVID-19 pandemic in China were used. Three cooperative networks from a progressive perspective were constructed based on the cooperative relationships among organizations. The expansion mechanism was verified by the exponential random graph model. The results show that when the institutional network expands into an interactive network, the composition of organization types has changed, but the cooperative network’s efficiency does not improve much. The matching effect of the organizational type and the Matthew effect of nodes are both effective paths to promote cooperative network expansion, however, the structure effect shows that complex relationship structure is not a critical factor. Our findings highlight the importance of core organizations and the function of different types of organizations in building cooperative network as well as providing theoretical frameworks for policymakers to use in guiding and motivating social cooperation in emergency supplies. The extensive global outbreak of COVID-19 has led to severe shortages of basic supplies. The World Health Organization reported that nearly a quarter of the world’s population was under quarantine, and markets for basic supplies and key medical supplies had contracted quickly by the end of March 2020. Due to strict specifications and a limited number of manufacturers and suppliers, it is complicated to produce a large amount of supplies in a short period of time. The Covid-19 Supply Chain System (CSCS) was built to bring together UN agencies, public health partners, suppliers and NGOs to provide material support to troubled areas. The emergence of a major public health issue is one of the greatest threats to social development, and supply support is a vital task for the government to deal with hazards and maintain people’s livelihoods .The production, procurement, delivery, and distribution of emergency supplies  as well The expansion of such networks explains the path and mechanism by which multiple organizations are effectively incorporated into the emergency organization system from the perspective of network actors. However, directly merging and constructing a cooperative network will ignore the essential differences between institutionalization and socialization in cooperative relationships. Therefore, by adopting a progressive perspective from institutionalization to socialization, this paper develops three cooperative networks of institutional, interactive and social emergency supply support organizations. Taking the COVID-19 epidemic situation in China as a study scenario, this paper constructs an exponential random graph model (ERGM) to discuss the expansion mechanism of networks to wider social cooperation in an attempt to provide theoretical assistance for emergency supply support activities and the accumulation of emergency social capital. This paper makes the following contributions. First, three emergency networks are constructed with the idea of continuous integration and superposition, and the socialization characteristics of cooperation for emergency supply support are explained. Second, based on the embeddedness of social capital, this paper presents a comparative analysis of the network structure of the three networks above. Third, this paper proposes an expansion mechanism of networks for emergency supply support in the process of socialization, providing a theoretical supplement to policy making.This paper is organized as follows: the second section collates and summarizes previous studies as the theoretical background. The third section proposes the research framework and describes the data and methods. Next, network analysis and model calculation are conducted, and the research results are presented. The results of the model are discussed considering the reality of emergency management. The last section presents our conclusions.Since the 1980s, human beings have moved irreversibly into a risk society. Many types of risks and crises occur frequently, such as earthquakes, tsunamis and epidemics. Emergencies are unpredictable [Traditional emergency management organizations are mainly composed of government agencies and authoritative organizations, which establish connections and communication rules through planning policies ,17. ExisSupply chain research can be regarded as a perspective of emergency organization expansion. Singh et al. focused on the issue of supply chain disruptions for emergency supplies, suggesting that during a pandemic, essential items include food and medical equipment and that a flexible food and medical supply chain is needed . Their sIn recent years, scholars have integrated emergency organizations into a network form to address severe crises  because When nonprofit organizations, private organizations and even individuals join emergency management, the boundary of the original cooperation system collapses . The fuzIn the field of social network analysis, the ERGM is established to explore structural effects. The ERGM uses structural types such as chains, stars and rings as well as relational types such as transfer and sharing to explain the formation mechanism of organizational networks ,38. ThusOverall, current research on cooperative networks for emergency management is flourishing. A consensus on the participation of numerous organizations has gradually been reached, motivating abundant research achievements in relevant fields. The social network blurs the original boundary of the system, which is the structural basis for the expansion of emergency supply support cooperation. Emergency supply support requires social participation in emergency management activities ,43, howeBased on the objectives of this research, we designed a research framework that includes theoretical and practical processes, as shown in From the perspective of the theoretical process, whether as a theoretical tool or a practical element, social capital has become an essential component in the field of emergency management . Social From the perspective of the practical process, when organizations participating in emergency supply support gradually expand from governmental institutions to social organizations, enterprises and volunteers, the rules of action gradually accommodate administrative constraints, reciprocal cooperation and voluntary donation . In thisBased on social capital theories  and the One of the main viewpoints of social network analysis is that network edges are interdependent ; that isThe principle of homogeneity and the “like-me” hypothesis in social capital theory propose that “social interaction tends to occur between similar individuals” . The corThe position in the network affects opportunities to acquire and utilize social capital . The tenCOVID-19 first appeared in Wuhan, Hubei Province, China, in late 2019 and later developed into a global public health event. Faced with multiple tasks, such as scale differentiation, sudden fluctuations in the epidemic, and the fundamental life needs of residents in isolation, national and local governments have used the application and approval process to organize several enterprises as key producers of materials for epidemic prevention and control to enhance the emergency management capacity of society. A relevant special audit report shows that from the outbreak of the COVID-19 pandemic to 31 August 2020, the Red Cross Society of China received donations worth more than 800 million yuan, including approximately 600 million yuan in funds and 200 million yuan in materials. A total of 42 batches of materials were donated, including drugs, masks, protective clothing, goggles, gloves, nucleic acid detection agents, consumables, and disinfection and sterilization products for daily use . To addrBased on web crawler collected a large amount of network data by setting the “epidemic prevention” and “materials” as keywords. (1) The official policy documents from National Health Commission’s official website and the State Council website database were collected, with the acquisition time being from January 2020 to February 2021. (2) Large news and information websites were used to screen the news, and the collection time was January 2020–February 2021. (3) Sina Weibo was used to search institutional microblogs directly related to keywords and their follow lists, and screen key users and their friends.To transform textual data into structured data that can be used to build a network, it is necessary to first identify the subjects in the text and to then identify whether the relationship between the subjects can be established. The processing procedure is shown in We identify organizations in the following categories. The emergency supply support organizations in this emergency mainly include national and local government-related organizations that allocate and coordinate emergency supplies in the form of administration and command; social organizations, enterprises, media and other nongovernmental organizations that perform the tasks of material production, circulation, distribution, scheduling and information dissemination in a spontaneous or semi-spontaneous form ; and indThe ERGM, which is developed based on relationships, provides an important research method for the expansion mechanism of cooperative networks ,47,52. Tk can ensure that network structure expansion has a probability range of 0~1. Y represents the matrix network, and y is the observation value of the matrix network. A includes all network structures, while Aη represents the parameters associated with A. If and only if the variables in A are conditionally independent does Aη = 0. Additionally, Ag(y) is the network statistic related to A. If the structure in A can be observed in network y, then Ag(y) = 1; otherwise, Ag(y) = 0.The formula is as follows:The construction of the ERGM starts from a simple stochastic graph model, namely, a null model. The null model usually only investigates the edge index of the network. Then, the attributes of the members for emergency supply support are incorporated into the model by adding main effects and interactive statistics, generating a binary independent model. Finally, the main effects of geometric statistics and the complement of interaction statistics are incorporated into the model. Variables are proposed based on the research framework and research methods, as shown in Three networks were constructed to examine the expansion mechanism. First, the institutional network of emergency supply support (Net1) is built based on the website of the National Health Commission and the policy database of the State Council. The interactive network (Net2) consists of Net1 and the organization are filtered from information from news websites. Finally, the social network (Net3) is composed of Net2 and organizations from Sina Weibo data. Gephi software   is used The organizations provided by the policy database and the official website of the government can be distinguished into diverse types. The membership in the institutional network (Net1) and the interactive network (Net2) are counted to obtain the proportion of types, as shown in Indicators of overall network characteristics parameters are calculated, as shown in The results in The network characteristic parameters reflect the different transmission mechanisms of the three networks through further analysis. The institutional network (Net1) with a larger average degree and a smaller average path length indicates that the construction of governmental institutions and departments provides mandatory guarantees for emergency supplies. The clustering coefficient in the interactive network (Net2) is the largest and three typical cluster structures  are displayed as detail amplifications in Structure is the skeleton formed by the network, and the number of each structure is statistically shown in In general, in contrast to the institutional network (Net1), the number of various structures in the interactive network (Net2) has increased significantly, while the structures in the social network (Net3) have not improved significantly compared with the interactive network (Net2). Based on the distribution of structures shown in The statnet tool in R software  was used, and Monte Carlo maximum likelihood estimation (MLE) results are shown in The estimation results show that the cooperative relationship of emergency supply support is relatively loose. The null model can be used to analyze the expansion situation based on the cooperative relationships in the organizational network. The negative coefficient in the null model indicates that the network density is less than 50% . Therefop = 0.285) in the institutional network (Net1) but are significant  in the interactive network (Net2). This illustrates that organizations of the same type are more inclined to establish cooperative relationships in the interactive network. However, the institutional network does not confirm this rule. This reflects the insufficiency of horizontal cross-organizational connections in governmental departments. The interactive network (Net2) consists of government agencies, enterprises and social organizations that carry out the entire process from upstream production to downstream distribution. It is possible that there is a close relationship between homogeneous types of social organizations and between homogeneous types of governmental institutions but not between the two types of members.The matching effect is significant in the interactive network (Net2), which demonstrates that type matching contributes to expanding the cooperative network of emergency supply support organizations. In this paper, the institutional network (Net1) and the interactive network (Net2) are selected to conduct the matching test. The estimators of the matching effect are not significant . The estimators using the clustering coefficient as the index are significant in both the interactive network (Net2) and the social network (Net3) . The clustering coefficient of a node quantifies the degree to which its neighboring nodes cluster together to form local connections. Based on the above analysis, there is no obvious tendency of network expansion with node degree as an indicator, while the connection in local cliques can be strengthened after social enterprise organizations and social platform organizations are joined.In general, the Matthew effect, which takes the node degree as an indicator, plays no obvious role in the network expansion of emergency supply support organizations, but the local connection tendency plays a significant role. The degree and clustering coefficient are selected as the basic indexes for the estimation of the Matthew effect in this paper. The results using the degree as an indicator show that the Matthew effect is not significant in the three networks, although it is slightly significant in the institutional network (Net1), having a low value (−0.027, Effective and efficient emergency supplies are of great value in relieving social pressure and maintaining social stability in a crisis. With the active participation of government institutions, enterprises, other social organizations and the public in solving public affairs, an emergency organization action system is composed of numerous participants. Social capital can also compensate for deficiencies in government management. Based on the developing requirements of supply support cooperation in public health emergencies from institutionalization to socialization, this paper constructed three networks: an institutional network, an interactive network and a social network. The expansion mechanism of the organizational network was revealed by considering the network characteristics.The network characteristics indicate that the cooperative network of emergency supply support organizations in China is relatively loose. Comparing the interactive network with the institutional network, this study finds that the composition of organizational types is significantly different and that social enterprises have become an essential constituent for emergency supply support systems. This study also finds that the institutional network established by policy norms is relatively balanced, with the highest coordination efficiency from the perspective of network structure. This result is closely related to the emergency response rules and drills conducted in governmental organizations, which provide institutional guarantees for national emergency management. However, the socialization of the institutional network remains weak. After forming the interactive network and the social network, there are more obvious features of block factions and core nodes. The importance of network attributes such as the relationship structure and member position begin to manifest. The interactive network, with increased enterprise and social organization, facilitates more complex relationship structures, but the social network with organizations from social platforms has not yet increased the complex relationship structures. In conclusion, the addition of diversified social organizations and enterprises has greatly changed the cooperative network structure of emergency supply support organizations, but the expansion of networks has not distinctly promoted efficiency.Our analysis of the expansion mechanism shows that it is difficult for the current network to expand through an increase in various complex relationship structures. The interactive network embodies a distinct matching effect and Matthew effect, indicating that the network expands after the extensive participation of diversified enterprises and social organizations. First, the same types of organizations tend to develop cooperative relationships. Then, the Matthew effect shows that the members in the dominant position of the network have strong attraction and are more inclined to establish connections with other members. Thus, two ways to expand the cooperative network of emergency supply support organizations are indicated. In addition, although the formation of a complex relationship structure has difficulty providing an obvious extension to the network, there has been a trend of strengthening the small group structure. Therefore, network expansion can also be facilitated by the increasing cooperation of emergency supply support organizations in minor factions.The study case in this paper describes the situation in China, and some of the results are characteristic of the typical Chinese scenario. However, since the COVID-19 outbreak has become a global pandemic crisis, a more general discussion will take place. In a public health emergency, market failure is very likely to occur . The outCompared with existing studies, our study consisted of several different concerns. The existing research includes institutional network, planned network, actual network, communication network and even risk perception network. Such studies tend to focus on and compare the performance of networks. By adopting a progressive perspective from institutionalization to socialization, we developed three cooperative networks of institutional, interactive and social emergency supply support organizations. The research method of ERGM gives us a relatively complete and straightforward framework, which is of great help to the study of network mechanism. In various studies, different structures and embeddedness have been studied. According to the research results of the existing literature, the density of cooperation networks formed by emergency organizations is generally not high, and the cooperation tends to be matched. The Matthew effect has not yet been tested in emergency management networks, and we have closely examined the effect of bars on a broad network. But it may exist in more liberal networks, such as entirely social networks, collaborative networks of innovation. Our study is expected to establish a broader emergency supply base, so that the Matthew effect may be better reflected in the future.This paper is an important attempt to research the cooperative network of emergency supply support. One possible limitation may stem from the processing of the data obtained from the sources used in this study. Although the data obtained from the sources used in this study have also been used in previous studies, we are still aware that some standards for data construction and screening are not perfect in the process of implementation. Relatively explicit processes are also established to reduce their impact on the results, but there may still be some bias. However, they mainly affect the absolute scale of the organizational network, and previous studies have demonstrated the feasibility of research based on such data.Supply support is a vital task for dealing with major public emergencies. Seeking the extensive integration of socialized organizations is a requirement for improving practical ability based on governmental departments maintaining reliable action power. In the face of the demand to socially expand emergency supply support, this paper establishes a research framework that includes three successively increasing networks. Using the ERGM, this paper proposes the theoretical expansion mechanism of cooperative networks based on the structural effect, matching effect and Matthew effect. Our results can enrich theoretical references for the government and society to develop strategies for emergency supply support.Our results show that the parameters related to the network scale, such as diameter and the average path length, all increase with the expansion of the scale of the cooperative organization system for emergency supply support. However, the parameters closely related to network efficiency, such as network density, the average degree and the clustering coefficient, show a decreasing trend. Compared with the institutional network, the number of shared structures in the interactive network dominated by social enterprises increases. The analysis of network characteristics implies that the three networks increase in order and that the complex relationship structure increases; however, but network efficiency does not improve. The ERGM results show that the matching effect and the Matthew effect, which take the type as the index, influence network expansion. The structural effect is consistent with the parameter expression of network characteristics.Cooperative relationships are the theoretical basis of this paper and the essential element for forming social networks. However, in some fields, the strength of a relationship can be used to measure social capital. This paper takes the network structure characteristics and network expansion as the research objective and weakens the influence of relationship strength to some extent. There may be differences in the social capital stock among different cooperative networks of emergency supply support organizations. In other words, the same change in density in the institutional network and the social network may affect different social capital. This issue will constitute an important line of research.