The sex-specific incidence was higher in boys . In adolescents, more events occurred on the street (25.9%), whereas home remained the most common locale in girls of all ages. In the multivariable regression analysis, abdominal and multiple injuries were risk factors for major trauma.Children and adolescents are vulnerable to non-accidental injury. Early identification and prevention rely on detailed epidemiological studies, which are limited in Asia. This retrospective study used the registry data of Pan-Asian Trauma Outcome Study (PATOS) from October 1, 2015 to December, 31, 2020. Pediatric patients (aged < 20 years) with non-accidental injuries were enrolled, which were divided by age into preschool (0–6 years), child (7–12 years), and adolescent (13–19 years) groups. Baseline characteristics, injury epidemiology, and excess mortality ratio-adjusted injury severity score (EMR-ISS) were collected. Major trauma was defined as an EMR-ISS score > 24. The study enrolled 451 patients with non-accidental injuries, accounting for 2.81% of pediatric trauma events presented to an emergency department in the PATOS registry. The overall mortality rate was 0.9%, similar to those in Western countries. Mortality rate was high in preschool children (8.7%,  Children and adolescents are especially vulnerable to non-accidental events5.Non-accidental injury events are an important public health concern. These events cause significant physical injury, medical resource consumption, and future loss of productivity7. The majority of patients in the pediatric population were adolescents, followed by infants5. The mortality rate of non-accidental injuries ranges from 0.1 to 8% and is associated with risk factors such as sex, age, and ethnicity8. Those who are injured in non-accidental injury events often seek medical help in the emergency department (ED)10. Early identification of the victims would help in the timely involvement of the social protection system, facilitate the judicial system to maintain justice, and prevent further damages caused by the offenders. Furthermore, understanding the demographics of non-accidental injuries in a vulnerable populations would help develop preventive strategies, reduce the incidence of injury, and plan resource allocation9.The epidemiology of non-accidental pediatric injuries is influenced by age and sex. In previous studies, non-accidental injuries accounted for 1.1 to 20% of all injuries and caused significant morbidity and mortality in the pediatric population11. Data of the Asian pediatric population remain unclear, especially in the ED setting11. In addition, firearm injuries is a severe threat in the pediatric population in the United States13. The epidemiology of firearm injuries in Asia, where the firearm control policy is stricter than that in the United States, remains unclear. Thus, the aim of our study was to delineate the incidence, patient characteristics, and severity of non-accidental injuries in children and adolescents presented to the ED in Asia.Therefore, the characteristics of non-accidental injuries in children and adolescents are of great importance. However, the epidemiological profiles of injury patterns and outcomes of non-accidental injuries are limited15, which records patient data, including baseline characteristics, injury epidemiology, prehospital care, emergency department care, hospital care, injury severity, and outcomes. Data were recorded in an unified manner. The registry used a standardized electronic data form with consensus variables. The study protocol we devised adhered to ethical guidelines of the 1975 Declaration of Helsinki, securing a priori approval of Research Ethics Committee Office of National Taiwan University Hospital, which approved the study protocol and waived the need for written informed consent. All the other centers in the PATOS collaboration independently obtained ethical approval.This retrospective study using registered data was conducted at the participating centers of the Pan-Asian Trauma Outcome Study (PATOS). The study period was from October 1, 2015, to December 31, 2020, registering 127,715 injury events. Phase I data enrolled 71,262 injury events from October 2015 to November 2018, and phase II data enrolled 56,453 events from December 2018 to December 2020, respectively. The PATOS is a clinical research network with a multicenter trauma registry in AsiaIn the screening process, only participants younger than 20 years at the time of injury and had “assault” as a recorded intention of the injury or “sexual assault” as a recorded mechanism of injury were eligible for inclusion. Participants with injuries not due to non-accidental injury or unknown age were excluded.The baseline characteristics of the participants, including age, sex, and country, were collected. Data on injury epidemiology were collected, including mechanism of injury, type of injury, location of the body in the injury, and place where the injury occurred. Data on alcohol and psychoactive drug/substance use in the events of patients and injurers were also collected.17. For the purpose of analysis of major trauma and its risk factors, events with moderate EMR-ISS were combined with those with mild EMR-ISS as non-major trauma group as previous studies19. Major trauma was defined as EMR-ISS > 24. The characteristics were further compared, and the risk factors for severe injuries were analyzed.To better understand the effect of age on the epidemiology of non-accidental injuries, we divided the patients into three groups: preschool children (0–6 years), children (7–12 years), and adolescents (13–19 years). Injury severity was categorized on the basis of the excess mortality ratio-adjusted injury severity score (EMR-ISS) as mild (1 ≤ EMR-ISS ≤ 8), moderate (9 ≤ EMR-ISS ≤ 24), and severe and critical (EMR-ISS > 24)U test and the chi-square or Fisher exact test was used, respectively. For the risk factor analysis for major trauma, basic characteristics, including age, sex, and variables of injury mechanism and injured body region with p values < 0.05 in the univariable analysis were selected for the multivariable regression analysis. A p value less than 0.05 was considered statistically significant.Statistical analyses were performed using SPSS Statistics version 20 for Windows  and R version 4.0.3. In our presentation, all the continuous variables are presented as median , and the categorical variables are presented as numbers (%), unless otherwise specified. For comparing continuous and categorical variables, the Mann–Whitney In the Phase I and Phase II PATOS databases, 127,715 injury events were registered. A total of 451 injuries during the study period were related to non-accidental injuries, which were considered for further analysis  and major trauma (EMR-ISS > 24) were studied  of preschool non-accidental injuries and 40% (2 out of 5) of preschool major traumas.Four patients died due to non-accidental injuries in the study population. The overall mortality rate was 0.9% (4 out of 451). None of the patients had pre-existing comorbidities before the events. Among the four patients, three were male, two were infants, and the rest were adolescents. One infant had a skull fracture, traumatic subarachnoid hemorrhage and epidural hemorrhage due to a fall, and the other had an organ injury in the thorax due to hanging/choking. One adolescent had an organ injury on the head due to a fall, and the other had cuts in the neck and upper arm. Three of the events took place at home, and one occurred in a commercial area. All the patients required cardiopulmonary resuscitation before arriving at the ED, and half of them remained asystole at triage. All the patients received cardiopulmonary resuscitation, and three died in the ED. The details of the four patients were in the Supplementary Table We report the following important findings: (1) non-accidental injury events accounted for 2.81% of the events among all the injury cases presented to ED in Asian countries; (2) male predominance was noted in the Asian cohort, especially in adolescents; (3) the places where non-accidental injury events took place varied with respect to sex and age; (4) the overall mortality rates for non-accidental injury in children and adolescents were relatively low, but they were high in preschool children; and, (5) abdominal and multiple injuries were risk factors for major trauma. To the best of our knowledge, this is the first report of non-accidental pediatric injuries across Asian countries.7. In the present study, non-accidental injuries accounted for 2.81% of all injuries presenting to the ED, which is consistent with the figures in literature. The incidence during the study period remained consistent, whereas there was a decreasing trend in non-accidental events in the ED in the United States11. A decrease in pediatric assault injury hospitalizations was also observed in Australia, despite protection orders for children continued to rise20. The differences in the incidence trends of non-accidental injury between the United States and Asia might stem from the implementation of evidence-based prevention strategies that reach the youth in the United States. A similar prevention policy may have beneficial effects in Asia.In literature, non-accidental injuries accounted for 1.1 to 20% of all injuries presented to the ED in the Western countries21. Higher male-to-female ratio for non-accidental events has been observed in older children and adolescents in the literature22. In our study, the incidence in male adolescents was higher than that in female adolescents. Therefore, education and interventions to reduce violence are particularly important in boys.Our study found that male predominance was significant, especially in adolescents; however, sex was not associated with the risk of developing major trauma or mortality. Previous reports worldwide have also shown a male predominance in non-accidental injuries20. In the present study, preschool children were most likely to be injured at home, but adolescents were more likely to be injured on the streets. This finding is consistent with that of other studies24. Furthermore, for the majority of female patients, regardless of age, most non-accidental injury events occurred at home. In contrast, the places of incidence varied more with age for male patients, with the schools and streets being the most common places in male children and adolescents. It was reported in the United States that female accounted for 55.9% when non-accidental events occurred at home, and 29% when on the streets11. The variations in the places of incidence with age and sex have not been reported in detail in the literature. Although sex was not associated with major injury outcome, our study provides valuable data on the detailed differences in injury epidemiology among age and sex subgroups, which might be beneficial in the development of injury prevention strategies.Many studies have shown that the places of injury events differed by age and ethnicity, but sex differences were less discussed22 which could be attributed to the vulnerability of young children and delay in the provision of medical care if the caregiver was the assailant25. Head trauma is common in pediatric non-accidental trauma6, which causes high morbidity and mortality in victims27. In our data, two infants having major trauma were diagnosed of intracranial hemorrhage, who might be victims of abusive head trauma, including shaken baby syndrome, making up a significant proportion of preschool major traumas (40%) and mortality (25%). Intervention programs dedicated to preventing violence inflicted by caregivers are of paramount importance.Although preschool children comprised the least proportion of the population in our study, their mortality rate was higher than that of adolescents. Higher mortality or injury severity in young children as compared to other age groups was also reported in previous studies22. The different variables analyzed in each study partly explained the difference; injured body region was not analyzed in the Israeli study, and ethnicity was not recorded in this study. The epidemiology of firearm injuries may also make a difference. Firearm injuries constituted 5.5% of non-accidental injuries in the Israeli study, but only one case (0.2%) in this pan-Asian study. Firearm injury is a major issue in Western countries, which accounted for 4.6% of all pediatric ED injury visits, and it is the second most common cause of death in children in American trauma centers29. However, this condition was rare in Asia. This difference reflects the influence of the firearm control policy in injury epidemiology. Gunfire was prohibited in most countries participating in the PATOS study. The low percentage of firearm injuries may also have contributed to the lower mortality rate in our cohort. In addition to firearm, alcohol is also a risk factor for non-accidental injury. Alcohol use by victims has been reported to increase interpersonal violence30. In our study, alcohol was more frequently used in adolescents than in preschooler and children. However, we had no information on the prevalence of alcohol use in adolescent population; therefore, it was difficult to assess how alcohol influence the incidence of non-accidental injuries. The effect of alcohol use in pediatric non-accidental injury needs more investigation. The epidemiological profile reported by our study is thus valuable in countries where public safety policies are required.We found that abdominal and multiple injuries were risk factors for major trauma, whereas facial and struck/hit injuries predicted non-major trauma in pediatric non-accidental injuries. In comparison, an Israeli study showed that age less than 1 year and firearm injuries were risk factors for severe violence-related injury, whereas stabbing and unarmed brawling were least likely to cause severe injury31. Furthermore, there was no missing data in the intention and mechanism categories, and data coded as unknown comprised 1.5% and 2.1% of each category. Thus, non-accidental injury based on this definition was considered representative of the study purpose. Second, the PATOS database included only patients in the ED. Patients who had been treated in outpatient departments or other non-ED venues and those who did not avail medical care could not be evaluated. Third, the PATOS database is a multi-country, hospital-based database and not a population surveillance database. Although future population-based research representing the national situation is warranted, the present (first report) pan-Asian study still provides valuable information for pediatric care and public health in the reduction and prevention of non-accidental injury in Asian countries.Our study has valuable findings despite certain limitations. First, the definition of non-accidental injury was based on the intentions and mechanisms of the recorded registry data. The detailed judgment and diagnostic processes could not be reviewed. Abusive head trauma, including shaken baby syndrome, needs a thorough evaluation for a definite diagnosis, which might include social worker intervention and judicial investigation. Thus, the diagnosis might not be promptly established and recorded in the EDs. However, such limitations exist in most database studies on non-accidental injuriesIn this pan-Asian study, the background incidence of pediatric non-accidental injuries presenting to the ED was 2.81%, and the places of the incidents and events varied with age and sex. Although the overall mortality rate was lower than that in Western countries, it remained high among preschool children. Abdominal and multiple injuries were associated with major trauma.Supplementary Information.


Domestic violence (DV) are one of the important risk factors for women’s health outcomes. The aim of this study was explored the risk of DV association with the poor pregnancy outcomes (PPOs), including premature delivery, abortion, and stillbirth.A nested case–control study was applied. Data from the Taiwan National Health Insurance Research Database were collected from 2000 to 2015. A total of 41,730 participants were included in this study, including 8,346 participants in the case group and 33,384 age- and index year-matched control group. Assessments of DA and PPOs were determined according to the International Classification of Diseases, 9th Revision. We conducted a conditional logistic regression analysis to estimate the effect of DV on PPOs.The mean age was 35.53 in the 41,730 female participants. The overall incidence rate of PPOs of the participants, who had experienced DV, was 84.05 per 100,000 person-years. which was significantly higher than that for the controls . The risk of PPOs was higher in the participants who had experienced DV than in the controls , including for premature delivery , abortion  and stillbirth . The results showed that the longer a participant has been suffering DV, the risk of PPOs was higher.Present results reaved the risk of PPOs associated with DV. Especially, the longer a woman has been experiencing DV, the risk of PPOs was higher, showed a dose–response effect.The online version contains supplementary material available at 10.1186/s12905-023-02602-x. Domestic violence (DV) is a significant risk factor that negatively impacts women’s lives , 2. RecePrevious studies have examined the association between DV during pregnancy and adverse birth outcomes, including abortion, stillbirth, low birth weight, premature birth, and neonatal death , 8, 9. WMost published findings indicate that DV is a risk factor for adverse fetal and pregnancy outcomes in fertile women. However, few studies have examined the association between abnormal pregnancy outcomes and the intensity and frequency of DV. Therefore, the aim of this study was to investigate the association between DV and poor pregnancy outcomes (PPOs), including premature delivery, abortion, and stillbirth.We used Taiwan National Health Insurance Research Database (NHIRD) managed by government, which involved medical treatment information for the public in Taiwan; the NHIRD is population-based claims database covering over 99.9% of Taiwan’s population . In thisThis study employed a population-based nested case-control design. The case group was defined as participants who experienced PPOs identified by ICD-9-CM codes for premature delivery (ICD-9-CM code 644.21), abortion (ICD-9-CM code 634.9), or stillbirth (ICD-9-CM code 656.43) and at least once with principal or secondary diagnoses indicating PPOs between 2000 and 2015.The control group was selection of the participants with a control-to-case ratio of 4:1 used propensity scores  between 2000 and 2015.Comorbidities were defined as at least one of the following primary or secondary diagnoses: type 1 diabetes, type 2 diabetes, anxiety, cardiovascular disease, depression, eclampsia, gestational diabetes mellitus, obesity, preeclampsia, syphilis, thalassemia, and alcoholism according to ICD-9-CM codes. Additionally, based on previous study, urbanization levels 1 (highest) to 4 (lowest) were defined .2 test was applied to estimate differences in the categorical variables, and Student’s t test was used to calculate the differences in the continuous variables. Conditional logistic regression was performed to estimate the correlation between previous DV history and the occurrence of PPOs among female participants. Data are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Adjusted odds ratio (AOR) included age, comorbidities, urbanization level, parity, and delivery type. The P values provided are two-sided, with the level of significance at 0.05.The SPSS  was applied in this study to execute all data analyses. The rate of the outcomes was calculated by the total follow-up duration (person-years). The χAs shown in Table Table Table Table Our results demonstrated an association between DV and PPOs. Specifically, women who experienced DV had a 3.31 times higher risk of PPOs compared to controls. These findings are consistent with previous studies , 16. FurDV involves multiple complex factors and causes adverse health impacts in the female population. The true pathway by which DV increases the risk of PPOs remains unknown, prior reports have provided some reasons those may describe these results. First, previous animal research demonstrated that the repeated exposure of pregnancy rats to stress may change hypothalamic–pituitary–adrenal (HPA) function , and altIn this study, a stratified method was employed to control for the effect of age, revealing that the highest prevalence of PPOs was observed in the age group of 45–49 years old. This can be attributed to the fact that the risk of adverse pregnancy outcomes tends to increase with age . FurtherGenerally, DV can have both direct and indirect impacts on pregnancy outcomes. Some similarities included DV can individual a pregnant woman to emotional stress, potentially affecting the developing fetus and pregnancy outcomes. Secondly, DV can endanger the health of pregnant women, and physical abuse or trauma associated with DV can pose risks to their well-being, consequently impacting the fetus outcomes , 22). HoOur results highlight the key point of health organizations in DV screening and support for women with a history of DV who occur to experience a disproportionate burden of high-risk pregnancy events. Other studies have made similar viewpoints . Health The effective identification of the risk and protective factors for DV is critical to addressing the problem early. This will help reduce the impact of DV and help develop prevention and control strategies. Health care providers play a key role as a resource linker. Through assessment, early detection, and linkage of related resources, women’s sense of security can be enhanced, and their physical and mental anxiety and uncertainty can be reduced, becoming one of women’s support systems, to prevent the recurrence of DV and its irreversible health risks to pregnant women . Some evThis study revealed that maternal exposure to DV was associated with a significantly increased risk of PPOs. These findings emphasize the relationship between the frequency and intensity of DV and the increased risk of PPOs. This research is one of the few to apply diagnostic criteria for DV and PPOs in the analysis of a population-based nested case–control study. However, several limitations of this study should be addressed. First, the NHIRD does not provide detailed information on socioeconomic factors, such as the level of education, level of income, or use of alcohol and drugs. Second, the NHIRD data does not included detailed information on the type of DV. Third, the study participants were selected using medical records from the NHIRD. Data for participants who had experienced DV but did not undergo treatment or assessment in the medical institution were not recorded in the NHIRD, the prevalence of exposure to DV during pregnancy in our study 0.52%) was lower than that found in previous study [2% was loThese findings are important from the maternal health policy and human rights perspectives. Women’s exposure to DV is associated with adverse consequences for birth outcomes. A decrease in the burden of DV against women is not only likely to improve the health and quality of life but is also likely to improve poor delivery events.Present study supported that DV is significant factor for poor birth outcomes in women. We recommend early detection via nursing assessment for the signs of family violence and providing the properly protect care for women.Below is the link to the electronic supplementary material.Supplementary Material 1


SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals , their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents’ daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models  in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility ( Specifications Table•The data, consisting of a synthetic population and households, are crucial inputs to develop simulation models, including agent-based models in various research areas such as transportation, land use, energy, economics, etc.•The data allows for generating simulations that realistically reflect heterogeneous human mobility behaviour without violating personal privacy.•The data is created using publicly accessible datasets from Swedish authorities, such as census data and the national travel survey. The methodology can be applied to similar data in other regions.•The data can be used for models evaluating emerging technologies in transportation in which individual decision-making strongly affects policy outcomes, such as the adoption of electric vehicles or the use of shared autonomous mobility.•Our datasets enhance research integrity, promote overall transparency, and allow peers to evaluate research outcomes. They provide valuable resources for evidence-based policymaking and scientific research, particularly in the context of national-wide datasets.1This paper describes a dataset of Sweden's synthetic population with individual activity-travel patterns and the methodology. Synthetic populations are crucial inputs for developing simulation models, particularly agent-based models, widely used in many research fields, such as transportation, land use, economics, and epidemiology. Although many countries have created synthetic populations  model This paper contains three datasets: Person, Households, and Activity-travel patterns for the year 2018, generated from the Synthetic Sweden Mobility , [180K, 300K), [300K, 420K), [420K, 1000K)}. The income classes are based on the Swedish national income quartiles up to 1 million SEK.The synthetic population is generated based on data from Statistics Sweden (SCB) 2.22.33This section discusses the input datasets and key modelling concepts. The methodology consists of three major steps: 1) population synthesis, 2) activity generation and 3) location and mode assignment. The first component, population synthesis, produces the Person and Household tables. The second and third components produce the Activity-travel table. We also briefly present the assessment and validity of data in this section.3.1The creation of a synthetic population of Sweden in this paper uses four primary input sources: statistical data of socio-demographic information from Statistics Sweden (SCB), Swedish national travel survey from Transport Analysis, Origin-Destination (OD) matrices from Trafikverket (Swedish Transport Administration)’s model - Sampers, and buildings from Lantmäteriet (Swedish Land Survey Authority).3.1.1SCB To synthesise the population, we also use statistical data at the municipal level SCB also publishes population density data over square kilometre grids. To deduce agents’ residential zones, we use the square kilometre grid data. This grid system presents statistics on the registered population in 114161 square areas covering only populated areas within Sweden.3.1.2Swedish national travel survey 3.1.3The OD matrices from the Swedish transport model, Sampers 3.1.4We use the building data, produced from the property registers by Lantmäteriet 3.1.5The agents are generated in three steps . First, We synthesise all agents with the basic attributes, i.e., the age, gender, civil status, residential zone (DeSO), and municipality attributes. The numbers of individuals by gender and age group are available at the DeSO level. At the municipality level, we know the number of individuals with a given combination of gender, age group, and civil status. Using the given statistical data and IPF technique In the household creation step, we create households using synthesised agents’ age and civil status attributes and the data on the number of household types  at the DeSO zone level. We begin with the ’couple’ households. For each DeSO zone, we divide individuals with couple marital status by two gender groups. We sort the first group in ascending order of age and the second group in ascending order of an age proxy.The last step of the population synthesis is advanced attributes assignment, i.e., employment and student statuses, personal income class, and the number of vehicles for each agent. To deduce these advanced attributes, we use a generative model that combines ML, IPF and probabilistic sampling . The genWe first jointly assign agents employment and studenthood statuses using the generative model, where an NNC is trained on the Swedish national travel survey 3.2The activity generation component has four steps to create an activity schedule for each agent . We starhome (H), work (W), school (S), and other (O), like visiting shops, restaurants, etc. We assume that each agent visits their home at least once a day. Given the agent's socio-demographic attributes, we use a generative probabilistic model to create a set of activity types, similar to the approach illustrated in For each agent in the synthetic population, we first assign a set of activity types showing the activity participation during the given day. Four types of activities are considered: The activity duration of each activity type is determined through a two-step method. Using another generative probabilistic model, we first jointly deduce broad duration classes  for the different activity types, capturing the correlation between the duration of the different activity types and socio-demographic attributes. We then estimate the total daily travel time for each agent in the form of range classes, H,tW,tS,tOt) of individuals. For an agent, the survey participant with the smallest Euclidean distance is selected, and then their sequence is directly assigned to the agent.Next, we assign an activity sequence such as home-work-other-home to the agents with known activity types and their durations. An activity sequence comprises the type, order, and frequency of activities participated in a day. To achieve the task, we assume that individuals with similar socio-demographic attributes and activity type duration would have similar activity sequences After activity types, durations, and sequences are ready, we generate the agents’ activity schedule that contains all their activity instances’ start and end times. We assume the day starts and ends at 3 AM since this is the time of day with the least amount of individuals in motion, according to the travel survey.We first model the start and end times of the 3 AM activity, which helps arrange the remaining activities using the assigned activity sequences and duration. An NNC is trained to predict 3 AM activity's start and end times. To determine the start and end times of the remaining activities in a sequence , we equally distribute the activity's total duration among its instances in the sequence. For example, if an agent participates in a work activity, followed by other activity types, and then returns to work, the duration of work activities in both instances will be equal.3.3This component assigns locations to all activities in the daily activity schedules and determines travel modes to access these activities . First, 22, from which the size of the population living in the virtual zones are derived. Each household in the DeSO zones is randomly assigned to one of the virtual zones corresponding to the household's DeSO zone. Subsequently, we assign households to detached houses or apartment buildings within each virtual zone by correlating the household size with building type. For example, a larger household is more likely than a single one to live in a detached house.First, we place households in residential buildings. After the population synthesis (Section 2.2), agents and households have their residences in DeSO zones. To increase the accuracy of the population distribution, we created smaller "virtual zones" by overlapping DeSO and grids of 1 kmAfter assigning home locations, we place non-home activities in the activity sequence and assign the travel mode between the activities. To deduce destination zones and mode, we initially calculate the OD probability matrices for each activity type and travel mode. These matrices provide the probability distribution of an activity located in a zone, given the origin (home) zone, activity type, and travel mode. The OD probability matrices are deduced using OD matrices from the Sampers model or a variant of a gravity model based on the Swedish national travel survey.We first assign travel modes to agents’ destination locations. Given the OD probability matrices and the agent's socio-demographic attributes, we use a generative probabilistic model to predict modes, similar to the approach illustrated in 3.4While synthetic populations are overall useful for various applications, there are several limitations to consider, such as data availability, bias, lack of transparency, and limitations in predicting behaviours. Accurate and comprehensive data is necessary to create realistic populations; incomplete, inaccurate, or outdated data can limit the model's accuracy. The synthetic population can also be biased if the underlying data is biased. The methodology used to generate the population can be complex, and transparency may be lacking. It may be difficult for researchers to understand how the population was generated and what assumptions were made. Synthetic populations can provide insight into demographic and statistical trends, but they may not accurately predict individual behaviour, especially regarding future technology and behavioural change.There are certain limitations associated with this dataset. The most important one is the estimation of travel time. Travel durations are estimated based on the time gap between activities. Even though the location assignment for the corresponding activities is performed by correlating travel distances with travel duration, it is not an accurate estimation of the travel time. To better estimate travel durations and the subsequent activities' start-end times, one needs to simulate activity travel plans with an actual road network using a traffic simulation platform such as MATSim Because of the limitations listed above, the data should be used with caution and in conjunction with other data sources and analytical methods, and their limitation should be clearly communicated to any potential users.3.5We perform data assessments for each model component that generates the data. This subsection provides a brief overview of some of the evaluations. For further details, readers can refer to the model evaluation and assessment chapter in the model documentation First, we compare the synthesised population against the official statistics at the DeSO zone level and calculate the percentage difference in the number of individuals for each attribute. We found that in over 92% of DeSO regions, the difference in the number of people assigned an incorrect gender by the statistical data is a range between -0.5% to 0.5%. Similarly, the percentage difference for age is within -1% to 1% for more than 78% of DeSO zones. The evaluations show that the attribute distributions of the synthetic population are in good agreement with the statistical data.We also compare the generated activity schedules to the travel survey by the activities' duration and temporal profiles and quantify distribution similarity using Jensen-Shannon (JS) distance. JS distances take values in the range from 0 to 1, with 1 indicating the maximum distance. Finally, we evaluate the performance and validity of synthetic individuals' travel patterns by comparing them with other models, such as the Sampers The authors declare that this work does not involve the use of human subjects, social media data, or experimentation with animals.Çağlar Tozluoğlu: Conceptualization, Methodology, Software, Validation, Data curation, Writing – original draft, Writing – review & editing. Swapnil Dhamal: Conceptualization, Methodology, Software, Validation, Data curation, Writing – original draft, Writing – review & editing. Sonia Yeh: Conceptualization, Methodology, Project administration. Frances Sprei: Conceptualization, Methodology, Writing – review & editing, Project administration. Yuan Liao: Conceptualization, Writing – review & editing. Madhav Marathe: Conceptualization. Christopher L. Barrett: Conceptualization. Devdatt Dubhashi: Conceptualization.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Supplemental Digital Content is Available in the Text. P < 0.001). Subgroup analyses showed no significant differences between type of pain, ie, VR effects were similar in acute, chronic, and procedural pain conditions. Univariate and multivariate meta-regression analyses were performed to investigate the effect of intervention, patient, and pain characteristics on VR. Virtual reality effectively reduced pain, especially in patients reporting moderate to severe pain and in younger subjects. Studies comparing VR with a control group receiving no distraction methods were associated with higher effect sizes. The effect of VR was not related to a specific frequency or duration of use. Type of software and interaction level were related to VR effects in the univariable, but not in the multivariable, meta-regression analysis. Heterogeneity was considerable for all meta-analyses, and risk of bias was moderate to high in most included studies. Studies on mechanisms behind VR analgesia in younger patients and patients reporting moderate to severe pain are recommended to confirm our hypotheses while taking into account risk of bias and the comparator. Optimal application of VR using treatment modules for long-term pain conditions are an important issue for future research.There is a rapidly growing body of evidence for the application of virtual reality (VR) in pain management, however, with varying effectiveness. Little is known about patient-related and VR-related factors affecting efficacy of VR. A systematic review and meta-analysis was performed including 122 randomised controlled trials (9138 patients), reporting on subjectively reported pain scores comparing an immersive VR intervention to a non-VR control group. Virtual reality significantly reduced pain in the pooled analysis (standardized mean difference = −0.65, 95% CI −0.76 to −0.54,  Virtual reality holds promise as an attractive tool to reduce chronic analgesics use due to the low frequency and impact of side effects compared with traditional analgesics.39 Previous studies mainly focus on effect sizes in specific patient populations, such as children or adults,38 or specific pain conditions including burn wound care or procedural pain in general.38 The evidence on potential effect modifiers is limited. Some previous studies have suggested that younger patients might benefit more from VR; however, results are conflicting.37 Another important potential effect modifier is the type of VR software used because different applications can vary substantially in interactivity and content provided.48 The relatively small studies available provide only limited insights into potentially modifying factors in a specific patient population. A systematic review of studies including a pooled analysis, with a multifactorial design, focusing on multiple effect modifiers, might contribute to a better understanding of how VR can be personalized and implemented in pain management.Virtual reality for pain management seems effective; however, studies vary in design, outcome measures, VR applications, and population. In addition, it remains unclear which patient characteristics, pain conditions, VR treatment aspects, and context factors contribute to the effect of VR. In contrast to drug treatments, for which the mechanism of action is roughly similar between patients, the effect of digital treatments such as VR depends more on individual physical, mental, and cognitive characteristics, and environmental factors.The aim of this systematic review and meta-regression analysis was to identify potential effect modifiers related to intervention, patient, and pain characteristics and detect those patients who are most eligible for use of VR in pain management.The review protocol of this systematic review and meta-(regression) analysis has been registered on PROSPERO (CRD42020178930). We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in reporting the results.clinicaltrials.gov), and the Science Citation Index for articles up to December 1, 2021. We used search terms, synonyms, MeSH terms, or Embase subject headings (Emtree) for “Virtual Reality” and “Pain” in title, abstract, and keywords. In addition, we searched in reference lists of included studies and previous reviews for studies that were not included in our search. The full search strategy can be found in Appendix 1: Search Strategy . Deduplication was performed in EndNote X9 .We searched PubMed, Embase, CENTRAL, the WHO trial registry, the International Clinical Trials Registry Platform (Study selection was performed by 2 independent reviewers (E.J.L. and E.M.S.), first on title and abstract and in a second round on full text of selected studies. Discrepancies were discussed with a third researcher (M.d.V.) until consensus was reached. Inclusion criteria were randomised controlled trials (RCT) with a parallel group design or cross-over design, reporting on the effect of immersive VR on self-reported pain scores compared with a non-VR control group. Immersive VR was defined as any intervention using a head-mounted device (HMD) that completely blocks the view of the users' real-world surroundings. This is in contrast to nonimmersive hardware such as tablets, screens, and glasses that allow the user to stay aware and keep control of their physical environment. Exclusion criteria were (1) studies using experimental pain stimuli in volunteers; (2) studies with no control group, VR control group, or a “nonrepresentative” control group ; (3) studies reporting no pain-related outcome measure or an outcome that is not self-reported by the patient  or outcome measure not specified for VR users vs nonusers; (4) studies using an intervention not meeting our definition of immersive VR; (5) publications other than randomised controlled studies; and (6) full text or outcome data not available. In case of different publications by the same group using overlapping cohorts, the most recent study was included. In case of conference abstracts or study registries with complete enrollment, we searched for corresponding full-text articles by searching on title, registration number, and authors separately. When no corresponding study was found, authors were approached by email to verify whether the results of the study were published in a journal. If full text was not available, the study was excluded. No language restrictions were applied.Two reviewers independently extracted the data from included studies (E.J.L. and E.M.S.), and discrepancies were resolved by re-examination of the study with a third researcher (M.d.V.). We extracted information on first author name, year of publication, study design, number of groups, number of participants (per group), the type of control group, and country. For outcomes, mean or median values, measures of spread , and type of outcome were extracted. Furthermore, we extracted details on intervention, patient, and pain characteristics for regression analyses (refer below). If data on key variables, that is, outcome measures or number of participants, were missing, the corresponding author was contacted. If the missing information could not be provided, the study was excluded.Risk of bias was assessed by 2 independent reviewers (E.J.L. and E.M.S.), using the Cochrane Risk of Bias 2.0 tool for parallel studies and cross-over trials. According to this tool, studies were assessed on bias arising from the randomisation process, period and carry over effects (in cross-over studies), deviation from the intervention, missing outcomes, outcome measurements, and reporting bias. Publication bias was assessed with a funnel plot, and the Egger test was used to test for funnel plot asymmetry.16 In studies with more than one comparison, intervention groups not related to VR or control were excluded. Studies comparing 2 VR intervention groups vs a control group were included as 2 separate studies; the control group was included twice. In studies with more than one VR session or outcome measurement, the measurement closest to the end of the VR therapy was extracted. In studies reporting more than one outcome, VAS  pain scores were extracted.The primary outcome of interest was the standardized mean difference (SMD) in pain scores in the intervention group compared with the control group. Standardized mean difference was chosen to correct for the differences in pain scales used. If mean and SD could not be retrieved to calculate SMD, studies reporting medians were included as if they reported mean values, and SDs were approximated from reported measures of spread. If no SD could be approximated, the largest SD of existing data was imputed. One study seemed as an extreme outlier compared with other included studies, and reconstruction of the study data suggested incorrect measures of spread that could not be verified with the authors; therefore, we decided to impute the largest SD of existing data.2 tests; an I2 value greater than 75% was considered substantial heterogeneity.20 Sensitivity analyses were performed to explore the impact of studies of which the control group was included twice, studies with imputed SDs, studies reporting medians instead of means, studies with a high risk of bias, and studies reporting worst pain scores instead of average pain scores. A mixed-effects subgroup analysis  was performed for study design, separating parallel design and cross-over studies. A second subgroup analysis was performed for type of pain to analyse differences between acute pain, chronic pain (pain >3 months), and procedural pain (short-term pain experience arising from tissue injury associated with diagnostic or treatment procedures).A random-effects meta-analysis model was used for the pooled analysis because we expected high heterogeneity between studies. Standardized mean differences of all studies were plotted in forest plots. Heterogeneity was measured by ITo explore potential effect modifiers, univariate meta-regression analyses (mixed-effects model) were conducted including prespecified covariates related to VR intervention, patient, and pain characteristics. We also assessed some study characteristics to check whether such factors interacted with VR efficacy. Categories were a priori developed in such a way that each subcategory would contain at least 10 studies.Regarding features of the VR intervention, we analysed the frequency of intervention (1 session or more than one session), duration of intervention and total time in VR (in minutes), and VR software type. For type of software, the studies were divided into 5 subcategories. First, two-dimensional (2D) videos included, for example, cartoon animations. In 3D videos, the user was guided through the video, whereas in a 3D 360° VR environment, the user was able to choose the directions of the views and movements through the digital environment. Virtual reality games were defined as a digital environment in which a competitive element was incorporated. Virtual reality therapies included applications with some kind of  therapy integrated in VR, including patient education, hypnosis, and physiotherapy. Applications were marked as “interactive” if the user could interact with the digital environment, eg, by actively changing elements of the virtual world, apart from exploring the digital environment by changing the point of view.For patient characteristics, we analysed mean age, age range of participants , the percentage of male , type of patient (inpatient or outpatient subjects), and diagnosis. For diagnosis, we explored whether different indications for VR analgesia would affect VR efficacy, including dental care procedures , wound care (both dressing changes and physiotherapy), vascular access (venipuncture and intravenous cannulation), and studies not matching with one of these subcategories marked as “other” diagnoses.Regarding pain characteristics, we analysed the pain score of the control group (<4 or ≥4) and the use of coanalgesia .For study characteristics, we analysed year of publication  and number of participants . To investigate whether sociocultural, geographical, and socioeconomical differences would affect efficacy of VR, we also explored Continent and World bank income class  of the country where the study was conducted. Finally, we categorized the type of control intervention into standard of care or no distraction, other distraction methods, or standard therapy.P < 0.20) were included into an explorative multivariate model, together with significant variables in subgroup and sensitivity analyses. Analysis was performed using a stepwise backward selection, excluding variables until all variables in the model remained significant (P < 0.05).Potential effect modifiers in univariate meta-regression analyses , using R studio 1.3.1093.0 and packages meta, metafor, dmetar, and ggplot2.http://links.lww.com/PAIN/B788).A total of 5945 citations were retrieved from the search, and 3 records were identified from references. After deduplication and screening for title and abstract, 711 articles were included for full-text screening. After excluding 589 articles that did not meet the eligibility criteria, 122 articles were included, corresponding with 137 study cohorts. Four articles were excluded during data extraction because outcome data were not available for these citations. The results of search and screening are summarized in Figure http://links.lww.com/PAIN/B788).Included studies were published between 2000 and 2021. A total of 9138 patients were included, with 4596 patients in the control group and 4542 in the VR intervention group. One hundred cohorts were parallel cohorts and 37 had a cross-over study design. Most study cohorts (n = 100) investigated the effect of VR on procedural pain, 14 studied chronic pain, and 23 studied acute pain conditions. The characteristics of VR interventions varied widely across studies, especially for the type of software used. Twenty-two study cohorts used applications with 2D videos and 26 cohorts used 3D video applications. Thirty study cohorts used a 3D 360° VR environment and 35 cohorts applied VR game applications. Twenty-four studies were classified as VR therapy, using hypnotherapy, physiotherapy, or patient education integrated in VR. The duration of VR interventions varied from 2 to 120 minutes, with a median duration of 10 minutes per session (IQR 5-20 minutes). In 118 study cohorts, a single VR session was used, and in 19 studies, multiple sessions were applied, with a maximum of 64 prescribed sessions. Forty-three study cohorts reported that none of the participants received any pain medication; another 47 study cohorts reported that participants received nonopioids; and in 22 study cohorts, ≥75% of participants received opioids. Twenty-five studies did not specify the use of analgesia. A summary of main study characteristics and references of included studies are provided in Appendix 2 . In the overall risk of bias assessment, 12 studies (9.8%) were judged as low risk of bias, 82 (67.2%) as some concerns (moderate risk), and 28 (22.9%) as high risk of bias. There were some concerns about the randomisation process in 59 studies (48.3%), mostly because authors reported a general statement of randomisation, without specifications on randomisation or allocation concealment. Four studies (3.2%) had a high risk of bias in this domain because they did not properly randomise groups. In cross-over studies, 14 of 30 studies (46.7%) had a high risk of bias because of a short wash-out period, ie, both intervention and control were assessed in the same session within minutes after each other (Domain S: Bias arising from period and carry-over effects). Most studies (74.5%) scored “some concerns” in measurement of the outcome because both participants and outcome assessors were not blinded owing to the nature of the intervention.A summary of risk of bias assessments is provided in Appendix 3  and the result of Egger test (P < 0.01) indicated that bias existed in the publication of studies; studies reporting larger effect sizes in favour of the control group were lacking.Both the funnel plot  , corresponding with a medium to large effect.6 The I2 value of 84.1% indicated considerable heterogeneity. The results were robust throughout sensitivity analyses . A subgroup analysis to account for study design did not show a significant difference between the effect of VR in cross-over or parallel design studies . A second subgroup analysis to account for type of pain revealed larger effect sizes for chronic pain conditions  compared with acute pain conditions  and procedural pain conditions . However, the difference between subgroups was not significant, indicating that VR effects were similar in acute, chronic, and procedural pain conditions (P = 0.35) . Because other studies also consider acute and procedural pain as 1 subtype of pain, we repeated the analysis for chronic pain compared with acute  pain conditions; however, this did not change the results {acute [procedural] pain conditions } vs chronic pain conditions .The pooled effect of 137 study cohorts showed a significant pain reduction in favour of VR . Full results and bubble plots of significant variables are shown in Appendix 7, available as supplemental digital content at http://links.lww.com/PAIN/B788.Univariate meta-regression analyses revealed 8 potential effect modifiers . Type of diagnosis was also not found to be a significant effect modifier, ie, no differences were observed in VR efficacy between dental care procedures, vascular access procedures, wound care, and other indications for VR analgesia.For patient-related potential effect modifiers, mean age of participants as a continuous variable was observed as a potential effect modification. No potential effect modification was observed for age categories, ie, children, adolescents, and adults. Furthermore, results suggested no differences in effect modification for the percentage of male participants and between type of patients , the variables indicated as potential effect modifiers, except mean age and type of software, remained significant.After Bonferroni correction for multiple testing  and accounted for 20.05% of the heterogeneity : type of control group, mean age of participants, and pain score of the control group.In a multivariate meta-regression model, 3 variables remained significant (P > 0.05); these variables did not significantly interact with efficacy of VR in our multivariate meta-regression model. Table Results on pain score in the control group were similar to univariate regression analysis; a larger analgesic effect was found in patients reporting pain scores ≥4. Regarding mean age of participants, the largest VR effects were observed in younger participants. For type of control group, studies comparing VR with control groups receiving standard of care or no distraction, or control groups receiving standard therapy were associated with larger effect sizes; the smallest effect sizes were observed in studies comparing VR with other distraction methods . Type of patient, continent, World Bank income class, type of software, and interactivity level were excluded from the model by backward selection , specific procedures and diagnoses , or specific patient populations . In accordance with our finding of an overall positive effect of VR on pain, previous reviews with a more narrow scope also demonstrated beneficial effects for the different conditions and populations included.37 One review with a narrow scope of paediatric patients aged 4 to 12 undergoing vascular access procedures did not observe an effect of age in their meta-regressions.37 A second review including both children and adults using immersive and nonimmersive VR for chronic and acute pain conditions reported larger effect sizes in juvenile patients (<18 years) compared with adults in a subgroup analysis.23 The third review including paediatric patients of 21 years or younger undergoing medical procedures while using a HMD found a significantly larger effect size in studies with a lower mean age of participants.11 In general, VR is believed to be more engaging for children, eg, due to higher levels of imagination and magical thinking in children. In the elderly patient groups, different mechanisms might affect efficacy of VR. On the one hand, prolonged wonder at the virtual environment might increase effects of VR, as supported by some experimental data.27 On the other hand, lower digital literacy among older patients can interfere with the correct application and subsequent user experience.19 These issues can partly be resolved by good instructions and user support, as well as applying more simple to use or passive applications in short treatments. Finally, the low number of studies in elderly populations and the relatively small number of older subjects participating in studies may have obscured older age as a modifier.We found a marginal effect of age on the efficacy of VR. Mean age did independently affect results, showing a declining efficacy of VR with rising age. The effect per year of age was small, and categorizing age groups , which is generally seen as a more reliable reflection of age distribution, did not significantly affect VR efficacy in our meta-regression analyses. Three reviews have assessed the impact of age on efficacy of VR in more depth; however, closer examination of these studies does not provide an explanation for this difference, eg, both mean age and categorized age groups have been reported as significant factors.12 Previous reviews have not investigated such differences; however, some individual studies have also observed that VR was especially effective in patients reporting severe baseline (pain score ≥7), including similar pain reductions of approximately 30% in these patients.42 In this review, the number of studies reporting severe pain scores on baseline or in the control group was too small to create a separate subgroup for severe pain to investigate whether pain reduction would further increase with more intense pain. Although the mechanism behind this larger benefit of VR is still unclear, it is encouraging to observe that VR is effective in moderate to severe pain as well.Results of the meta-analyses revealed that baseline pain score and type of comparator both had a relatively large impact on the efficacy of VR. The impact of baseline pain score is partly grounded in methodological reasons. A proportionally similar reduction in the pain score in a patient with a higher baseline pain score will result in a larger absolute pain score reduction compared with the pain score reduction in a patient with lower baseline pain score. We found a pain reduction of 25.3%  in studies reporting a pain score in the control group of 4 and above.45 In clinical studies, however, evidence is contradicting, with some studies reporting larger effects in interactive VR compared with passive VR, or virtual environments compared with videos, but other studies found similar effects in different types of software.48 A potential explanation for the limited added value of interactive computer-generated environments as opposed to passive VR software applications in this review might be found in the relatively large number of studies on periprocedural pain using an intervention providing video content by a HMD. Although the type of pain was also included in the multivariate regression analysis, the number of studies investigating chronic pain conditions was small, as was the number of studies investigating therapeutic modules integrated in VR. This review did not demonstrate a major impact of these applications; however, this subgroup included a heterogeneous collection of therapies and the therapies have been used for a variety of pain conditions, complicating further analyses. It should be mentioned that the small number of studies in other subgroups also limited our ability to perform additional analyses, for example, on interaction factors that might explain some of the results. Although we expected the model to correct for most baseline differences, an interaction factor between variables affecting the results cannot be completely ruled out.In the analysis of type of software, we observed modest to no effects for different factors relating to the type of VR intervention applied, including type of software and interactivity. This finding was somewhat unexpected given our hypotheses and results of laboratory studies. In VR, we expected larger efficacy in studies using software with high levels of immersion and interactivity, which has previously been reported in several laboratory studies using an experimentally induced pain stimulus.44In the analysis of type of comparator we found that, although VR was effective compared with all types of comparators, the effect was much smaller when VR was compared with control groups exposed to distractive methods in addition to standard of care. We hypothesize that during short periods of pain, such as periprocedural pain, distraction might be the major component in pain relief related to VR. Based on our findings, we suggest that any VR application that provides good distraction seems effective to reduce such pain, whenever using an immersive VR device. However, mechanisms of distraction conceptually seem insufficient to achieve long-lasting results.40 In addition, new VR software modules have been developed that may specifically address needs of patients with chronic pain. Such treatment modules can be deployed as part of pain education, providing patients with tools to cope with pain outside the periods that they are actually using the VR. Furthermore, modules can contain interactive elements that are part of pain reprocessing therapies, including aspects of cognitive behavioral therapy, EMDR therapy (Eye Movement Desensitization and Reprocessing), and mindfulness. Another aspect includes the integration of embodiment in VR therapies, through which the illusion of ownership of a virtual body is used to change body perception. Virtual embodiment combines the feeling of presence with high levels of immersion and interactivity, thereby creating a strong perceptual illusion that goes beyond distraction analgesia.44 Virtual embodiment using avatars has already been reported to reduce pain in experimental studies in healthy volunteers and patients with chronic neuropathic pain. Although conceptually promising, the efficacy of such modules is yet to be evaluated in studies.44Virtual reality is a rapidly developing technology and some of its features are still in early development. Over the past few years, many improvements have been made for hardware specifications including display quality or field of view, and more recent developments include the possibility of monitoring and interacting with heartbeat and eye tracking. These features may increase the level of immersion and interactivity and, therefore, also contribute to increased efficacy of VR.47 Placebo effects related to group assignment cannot be ruled out because VR cannot be provided in a blinded fashion. A placebo effect could significantly modulate treatment effects for conditions in which VR is increasingly used, including pain and anxiety.38 Nevertheless, we performed many subgroup and sensitivity analyses and the beneficial effects of VR were consistent throughout all these different analyses. Regarding carry-over effects, VR effects are believed to act shortly; however, carry-over effects could not be ruled out. In many cross-over studies, a wash-out period was only a few minutes between sessions. The lack of wash-out periods is expected to result in an underestimation of the effects of VR, which is considered a less dangerous bias than overestimation. Furthermore, there was no difference in effect size compared between parallel and cross-over studies, suggesting the impact of this potential source of bias is low.Some limitations and features of this meta-analysis should be discussed as they affect the interpretation of the evidence. This review provides important insights into the efficacy of VR and which characteristics might be used to select individual patients who are most eligible for use of VR in pain management. However, it should be noted that results of this review can mostly be used to generate hypotheses. An individual patient data (IPD) meta-analysis would have provided stronger evidence, especially in analysing potential effect modifiers; however, obtaining individual patient data was not feasible. Second, to generate these hypotheses on potential effect modifiers, we included a large diversity of studies, which might explain the high level of heterogeneity observed. However, the analyses performed could explain heterogeneity only partly; heterogeneity in subgroup analyses and meta-regression analyses was still of substantial or considerable level. Risk of bias was also substantial with less than 1 in 10 studies scoring as low risk of bias. Major aspects affecting the risk of bias were lack of blinding and carry-over effects that could not be ruled out. Because of the nature of the intervention, the results of this review include the effects of a nonblinded intervention. Together with self-reported outcomes, this might have introduced ascertainment bias.34Finally, for patients with chronic pain, outcomes related to pain intensity scores might not be the most appropriate measure to determine success of treatment because they fluctuate and are also dependent of functioning and participation. Measures of pain disability and social functioning seem to be more relevant outcome measures for these patients.In conclusion, VR significantly reduced pain in acute, chronic, and procedural pain conditions, in particular, in patients reporting moderate to severe pain and, to a smaller extent, in younger patients. To confirm our hypotheses, studies on mechanisms behind VR analgesia in these patient groups are recommended. Optimal application of VR using new VR treatment modules in the setting of prolonged use and chronic pain conditions is an important topic for future research. Study characteristics with regard to risk of bias and type of control group should be taken into account in designing VR research.The authors declare that they have no competing interests.http://links.lww.com/PAIN/B788.Supplemental digital content associated with this article can be found online at


Post-Polio Syndrome (PPS) is a chronic condition characterized by the emergence of new symptoms and functional decline in individuals who previously had polio. Despite advances in medical understanding, management of PPS remains challenging. This study aimed to evaluate the use of neurobiological modulation treatments using Radio Electric Asymmetric Conveyer (REAC) technology on fatigue and muscle strength. An open-label study was conducted with 17 patients submitted to four neuromodulation protocols: Neuro Postural Optimization (NPO), Neuro Psycho Physical Optimization (NPPO), Neuro Psycho Physical Optimization—Cervico Brachial (NPPO-CB), and Neuromuscular Optimization (NMO). The Time Up and Go (TUG) test, Handgrip Strength Test, and Revised Piper Fatigue Scale (RPFS) were used to assess participants’ fatigue and muscle strength, being applied at the beginning and end of each protocol. The results obtained from the improvement in strength, physical endurance, and particularly the RPFS behavioral dimension, affective dimension, and psychological sensory dimension, through the utilization of REAC neurobiological modulation treatments, highlight this correlation. These results suggest that these treatments could be considered as a potential therapeutic approach for PPS. Post-Polio Syndrome (PPS) is a long-term condition  that affEmerging muscle weakness and fatigue are among the most frequent and debilitating late symptoms of poliomyelitis, causing significant functional impairment in the functional capacity of people with PPS ,10,11. IThe combination of new symptoms and primary polio-related deficits results in significant limitations, and unexpected impairments and/or disabilities can have a psychological impact. These symptoms can be psychologically devastating. Individuals may experience a recurrence of symptoms similar to those they experienced during their acute illness, which they thought they had permanently overcome .The uncertainty and unpredictability of symptom progression in PPS can generate anxiety about the future and concerns regarding the ability to manage the physical and emotional challenges associated with the condition.Until this day, the treatment of fatigue and muscle strength in PPS is based on lifestyle changes, physiotherapy, training programs, and the prevention of secondary complications . PatientIn this sense, it is important to investigate targeted therapeutic strategies that can effectively address the unique challenges faced by individuals with PPS. By focusing on these key aspects, Radio Electric Asymmetric Conveyer (REAC) biotechnology was developed with the intention of offering new therapeutic strategies aimed at recovering the correct mechanisms of neurobiological functions that may have been altered by dysfunctional phenomena of epigenetic adaptation, in particular at the psychophysical level.One of the notable advantages of REAC treatments is their non-invasiveness and absence of adverse effects, in addition to being painless and quick to apply (from fractions of seconds to a few minutes), making it a safe option for diverse populations. Specifically, this investigation aims to evaluate the effects of the REAC Neuro Postural Optimization (NPO), Neuro Psycho Physical Optimization (NPPO), Neuro Psycho Physical Optimization—Cervico Brachial (NPPO-CB) and Neuromuscular Optimization (NMO) neurobiological treatments on strength, fatigue, and functional capacity in individuals with PPS.Open-label study, conducted in accordance with the Declaration of Helsinki  and apprAfter conducting a sample power analysis using G*Power  ,16 and sDue to the great heterogeneity of this population, we aimed to make the studied population as homogeneous as possible.Ambulatory subjects with PPS were included if they had: confirmed history of poliomyelitis with electroneuromyography; confirmed diagnosis of PPS; new muscle weakness for at least one year; aged between 18 and 65 years, male or female; ability to walk at least three meters with or without additions and orthoses; preserved capacity for verbal communication; ability to understand the information about the study and document their decision about their participation in the study in the free and informed consent form.The criteria for exclusion were: individuals with a degree of muscle strength less than four were excluded; patients with associated diseases that also lead to motor neuron involvement , diabetes mellitus, alcoholism, thyroid disease, multifocal conduction block, Multiple Sclerosis, nutritional deficiency, autoimmune diseases, exogenous intoxication .The study was conducted in the Division of Neuromuscular Diseases, Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil.The study comprised 17 PPS patients. The treatments  prior to the study were maintained according to medical advice.Once patients had been recruited in accordance with the inclusion and exclusion criteria and informed consent had been obtained, the study commenced, with patients undergoing four treatment protocols  divided into different stages. It is important to note that each treatment protocol was different in its application and administration.T0: Pre-intervention period, all assessments were performed at this time;T1: Administration of the NPO protocol, which consists of a single treatment of approximately 1 s applied via the metal tip of the REAC probe to a specific point in the external auditory pavilion (ear).T2: Post-REAC NPO/pre-NPPO assessments: The NPPOs were administered concurrently, with patients receiving the NPPO initially, followed by the NPPO-CB, entailing 18 treatment cycles using radiofrequency pulses. The NPPO protocol was implemented with a metal tip on seven specified points of the auricle. The NPPO-CB procedure involved placing a smooth probe covered by a tissue plate onto the cervicobrachial area, secured with a tubular mesh, and left in position briefly. The NPPO-CB application was administered immediately following each NPPO application. This resulted in eight daily applications (4 NPPO + 4 NPPO-CB) separated by a one-hour interval between each.T3: Post-REAC NPPOs and Pre-NMO assessments.The NMO treatment method involves the application of radiofrequency pulses through probes covered by tissue plates that are fixed over the lower limbs’ treated area. The treatment plan consists of 10 applications divided into three cycles. During the first two cycles, four applications were carried out each day, while in the third cycle, there were two applications, with a one-hour interval between them.T4: Post-REAC NMO assessments.During the neuromodulation treatment cycles and the observation phase of REAC-NPPO, no participant received any additional concurrent treatments. A seven-day interval between each protocol was strictly observed. Considering the time demands and application schedule of NPPO, NPPO-CB, and NMOs, the participants were split into two groups with treatments on different days of the week but at the same time in the morning. The protocol spanned a period of 12 weeks, during which assessments and applications were conducted. All evaluations were carried out before and after each stage of the research, as illustrated in The TUG is a widely utilized assessment tool for evaluating mobility and functional performance in individuals . It measThe TUG yields valuable information about multiple aspects of mobility and functional ability, including balance, gait speed, agility, and the performance of basic mobility tasks. It is particularly beneficial in evaluating individuals who may be at risk of falls or experience mobility limitations due to various conditions, such as aging, neurological disorders, or musculoskeletal impairments ,18,20.Interpreting the results of the TUG is based on the time required to complete the test. Generally, shorter completion times indicate better mobility and functional performance, while longer times suggest limitations or difficulties in performing the tasks. However, interpretation may vary depending on the specific population being assessed and the designated cutoff values ,20,21.In our study, patients were given prior instructions on how to perform the test and were instructed to walk at their habitual speed and stride. Three measurements were conducted with a one-minute interval, and the best performance (shortest time) was considered as the final measurement.Time values below 10 s indicate individuals who possess complete freedom and independence. Patients who complete the test within 10 to 19 s are deemed independent, demonstrating reasonable balance and walking speed. Most of them are capable of walking more than 500 m, climbing stairs, and independently leaving their homes. Those who take between 20 and 29 s fall into a “gray area”, signifying challenges with daily tasks that can vary significantly depending on different situations encountered, necessitating good balance, a minimum walking speed of 0.5 m/s, and functional capacity. Subjects with a time score of 30 s or more tend to be fully dependent on many basic and instrumental activities of daily living, such as rising from a chair, self-feeding, changing clothes, bathing, and walking ,20,21.The HGST is a common dynamometry test employed for assessing the maximum force exerted by an individual’s hand and forearm muscles . This te® manual digital dynamometer was used.The HGST was conducted using a handheld dynamometer, a device specifically designed to measure grip strength. The dynamometer was adjusted to accommodate the individual’s hand size and was positioned in a manner that allowed a comfortable grip. InstruthermPrior to the test, participants were provided with clear instructions on proper hand placement and technique. They were instructed to hold the dynamometer in their dominant hand or/least affected limb with a firm grip, avoiding excessive squeezing or slackness. The forearm was positioned neutrally, and the arm was extended along the body. Participants were advised to refrain from using their body or non-tested hand for support during the test.Each participant performed the test three times, with a brief resting period of one minute between trials. During each trial, participants were instructed to exert maximum force while squeezing the dynamometer for a duration of approximately five seconds. The highest recorded grip strength value from the three trials was considered the representative measurement for that hand.To ensure accurate and consistent measurements, the dynamometer was calibrated before the commencement of the study, following the manufacturer’s guidelines. The researcher administering the test was trained in proper testing procedures to minimize inter-operator variability.The RPFS  is a sciRPFS has been widely used in clinical trials, research studies, and clinical practice to evaluate fatigue levels, track changes over time and assess the effectiveness of interventions aimed at managing or reducing fatigue.The REAC technology is an innovative therapeutic approach that utilizes radio electric fields asymmetrically conveyed to promote healing processes and restore physiological balance within the human body .It is based on a sophisticated system that generates carefully calibrated radio electric fields asymmetrically conveyed. These fields are designed to create an asymmetrical radio electric gradient within the body, with the specific purpose of optimizing cellular endogenous bioelectric activity (EBA). This optimization of EBA is essential for influencing cellular processes and biochemical reactions, ultimately leading to improved therapeutic effectiveness.REAC induces the optimization of EBA, which in turn triggers various physiological mechanisms. These mechanisms include the activation of cellular signaling pathways, facilitation of ion transport, and stimulation of protein synthesis, among others. By modulating cellular activity through these mechanisms, REAC technology has the potential to enhance tissue repair and promote overall well-being.The technology holds promising potential for enhancing neurobiological functions that are compromised in various pathological conditions. This innovative approach progressively promotes the processes of recovery, healing, and overall well-being .By leveraging REAC technology, it becomes possible to address the underlying neurobiological dysfunctions associated with different pathologies. This includes promoting the restoration of disrupted neural processes, facilitating neuroplasticity, and optimizing neuronal communication. These mechanisms contribute to the overall improvement of neurological functions, leading to enhanced recovery and well-being ,29.Furthermore, the application of REAC technology has the potential to support the healing processes within the nervous system. By stimulating cellular and molecular responses, it can enhance tissue regeneration, mitigate inflammation, and optimize the neuroimmune system’s functioning. These factors collectively contribute to the overall improvement of neurological health and well-being .One of the remarkable benefits of REAC therapies is their non-invasiveness and lack of side effects, rendering them a secure alternative for diverse patient populations.Moreover, REAC treatments can be easily integrated into existing medical treatments, complementing traditional approaches and enhancing their outcomes.REAC NPO is a non-invasive neuromodulation treatment that focuses on improving postural control and stability by modulating the neurobiological system. NPO recognizes the essential role of the central nervous system (CNS) in maintaining postural stability and movement control. By enhancing CNS function, NPO aims to optimize postural control and stability, which can provide relief from musculoskeletal condition, dysfunctional adaptations of muscle balance and postural distortions . DetaileThe specific REAC treatments employed in this study NPPO and NPPO-CB represent an advanced neuromodulation treatment that focuses on optimizing human performance by acknowledging the intricate relationship between the nervous system, psychological well-being, and physical body .Clinical evidence supports the effectiveness of REAC NPPOs treatments in addressing various mood and behavioral disorders , includiComprehensive descriptions of the administration methods have been extensively documented in prior publications .NMO is the term used to encompass a set of therapeutic protocols for neurobiological modulation within the REAC technology aimed at optimizing muscle management, particularly the functional balance between agonist and antagonist muscles, in both pathological and dysfunctional conditions.NMO therapies are performed by applying a special probe called asymmetric conveyer probe to the targeted areas, which is connected to the REAC device. Typically, multiple treatment cycles are implemented, varying based on the individual’s health or disease state. This approach aims to optimize muscle management and movement, enabling a gradual and progressive correction of neuro-psycho-motor alterations and the restoration of proper muscular and postural control .To ensure the replicability of the study, the REAC device  used for administering the NPO, NPPOs, and NMO treatments employs fixed parameters that are set by the manufacturer. These parameters cannot be modified by the operators, thereby ensuring consistency across treatment administrations.For the statistical evaluation of the study, the G*Power  and SPSS 22 software  were used.p-value of p < 0.50 to be statistically significant. The Analysis of Variance of Repeated Measures (ANOVA-RM) was performed on the scores of the TUG, HGST, and RPFS at four different time points: T0, T2, T3, and T4. A Bonferroni post-hoc test was used for a posteriori analysis when necessary. The effect size was expressed as η2 in all statistical analyses.To verify the normal distribution of the data, we used the Wilcoxon test and the sign test. Given the sample size, we consider the The sample consisted of 5 men, mean age: 52.60, and 11 women, mean age: 56.70. One subject was unable to perform the last assessment due to COVID-19 infection. In this case, exclusively, the evaluations were considered up to T3 for statistical analysis.p < 0.01; η2 = 0.51) when comparing the final time (T4) to the initial time (T0). A posteriori analysis (Bonferroni post-hoc) showed that there was a significant difference in the T0 moment  in relation to the others: T2 , T3 , and T4 . The results also showed that the T2 assessment was not statistically different at T3 (p = 0.66) and T4 (p = 0.35). Furthermore, T3 did not differ statistically when compared to T4 (p = 1.00).When evaluating the TUG, a decrease in scores from T0 to T4 can be observed, as shown in p < 0.01; η2 = 0.34).In the manual dynamometry evaluation, an Analysis of Variance of Repeated Measures (ANOVA-MR) was performed, with the objective of evaluating the scores of four moments: T0, T2, T3, and T4, as shown in p = 0.01) and also, from condition T2  in relation to T4 (p = 0.02). Regarding the other comparisons, there was no significant difference (p > 0.05).A posteriori analysis (Bonferroni post-hoc) showed that there was a significant difference in the scores of the evaluated conditions. In this sense, there was a significant difference between T0  in relation to T4 . When evaluating the general fatigue through the RPFS, a statistically significant difference was observed when comparing the evaluation carried out at T0 in relation to moments T2, T3, and T4. The other comparisons performed did not show statistically significant differences, as shown in Post-hoc Bonferroni analysis showed the following differences in the scores of the RPF dimensions in the conditions evaluated, as shown in p < 0.01; η2 = 0.57), affective  = 29.08, p < 0.01; η2 = 0.45), sensory  = 11.9, p < 0.01; η2 = 0.44), and fatigue  = 31, 81, p < 0.01; η2 = 0.56).A Repeated Measures Analysis of Variance (ANOVA-MR) was performed with the objective of evaluating the RPFS scores according to the dimensions in four moments: T0, T2, T3, and T4, as shown in The a posteriori analysis with post-hoc Bonferroni showed the following differences in the scores of the dimensions of the RPFS in the evaluated conditions, according to One of the primary reasons for the therapeutic difficulties in managing PPS is the lack of specific treatment options. Currently, there is no cure for PPS, and interventions primarily focus on symptom management and improving patients’ quality of life. The main symptoms of PPS are fatigue, pain, and new weakness. Rehabilitation programs for this population aim to minimize functional decline and maximize independence.The intricate interplay between the physical, psychological, and epigenetic aspects of PPS poses additional challenges in providing the best and most appropriate therapies. In this context, the results found in the present study are of great importance since improvements in mobility and functional performance, handgrip strength, and general fatigue and in the dimensions assessed  were demonstrated when compared to the initial assessment (T0) with the final assessment (T4).p < 0.01).Considering the mobility and functional performance, the participants presented improvement when comparing T0 on TUG to the other evaluations . The test is commonly used to measure mobility and functional performance in people with PS and PPS ,34. The In fact, when evaluating the results presented, an improvement in the performance of the TUG was obtained, that is, a reduction in the time measured by the scale. This result may infer an improvement in their gait functionality, safety, and efficiency. Furthermore, a progressive improvement in the score was presented in the second reevaluation (T3), demonstrating an improvement in the gait rhythm after performing the NPPO and NPPO-CB, despite not showing a statistically significant difference. However, after applying the NMO protocol, there was stability in the TUG score (T3–T4).This result is important as individuals with PPS may experience a decline in their capacity to swiftly navigate obstacles while walking. This can contribute to a perceived decrease in balance and an increased sense of apprehension regarding the risk of falling . FallingTo decrease this incidence of falling, many strategies are adopted, such as: walking aids, wheelchair, motorized wheelchair, adapting the patient’s home environment, and exercises to control balance and strength , but mosIt was demonstrated in an fMRI study that a 250-millisecond REAC NPO pulse was capable of producing changes in brain activity that were apparent 40 min after the stimulus . FurtherAn important finding was that the application of the REAC protocols that were part of the present study resulted in an increase in handgrip strength when comparing T0 with T4 . This result is of great importance since it is well established that handgrip exhibits a robust correlation with an individual’s overall muscle strength . FurtherThese data gain even more significance when we take into account that the sample population consisted of individuals over the age of 60, with an average age of 52.6 for women and 56.7 for men. Research on the degradation of muscle strength reveals an annual decline of 1% in the general population after the age of 50, while individuals with PPS experience a decline of 2% per year ,41. DespMany studies correlated handgrip strength to functionality ,43,44, ap < 0.0001). These results demonstrate the effectiveness of REAC neurobiological modulation in people with PPS, even though fatigue is a complex and debilitating symptom of this neurodegenerative disease. The RPFS presents three main topics: affective/sensorial, psychological, and behavioral.Another aspect evaluated in the present work was fatigue, which is one of the most frequent, incapacitating, and debilitating symptoms and causes the greatest interference in the everyday activities of the patients ,45. The Interestingly, it was believed that the application of NPO (T1), which aims to optimize neuro psychomotor strategies through functional and epigenetic adaptive processes ,31, woulp < 0.0001) was observed in the fatigue indices of this dimension when comparing the moment T0  with T4 . Throughout the treatment, a slight fluctuation of the symptom was observed with a small increase in the symptom from T2 to T3 (Post-NPPO). When evaluating the classification indicated by the instrument, it did not change. The same results were found in the sensory/psychological dimension, reporting moderate initial fatigue (T0 = 4.33 ± 2.56) evolving to mild fatigue in the other phases of the study even with small fluctuations . This dimension brings together self-perception, emotional and cognitive components to the presence of fatigue. These findings suggest that enhancing neuro psychomotor strategies, even without the explicit involvement of the patient, can result in substantial improvements in the investigated neuropsychic domains assessed by the RPFS. The fluctuation in fatigue rates, with a slight increase, occurred in the treatments of NPPOs (moments T2 to T3) in the affective and sensory/psychological dimensions despite them having been administered precisely to treat mood and behavioral disorders. It was expected that these treatments would produce more noticeable results on the RPFS, but statistically significant improvements were observed mainly in motor aspects, as demonstrated above. These findings suggest that, despite the administration of treatments with NPPOs, known for their broad and demonstrated efficacy, improvement in several mood and behavioral disorders [The affective dimension seeks to identify the interpretation or meaning attributed to fatigue. A statistically significant improvement  and demonstrated the greatest reduction at the end of the study (Mild fatigue T4: 1.75 ± 1.97). It represents functional capacity-related components that can be impaired by fatigue, such as personal issues, social activities, and sexual relationships. It is interesting to note that REAC did not propose any physical changes in these patients; the deficiency remains present, but their behavior towards these deficiencies is modified.Treatments targeting NPPOs have been administered to treat mood and behavioral disorders ,29. It wLikewise, the NMO protocol is specifically designed as an advanced way to recover the correct facilitation and inhibition mechanisms, both ipsilateral and contralateral to the muscle groups. However, it did not have statistically positive effects when evaluated separately, despite presenting maintenance of the values obtained in strength assessments and gait function (TUG and HGST) in addition to improvement.On the other hand, the results obtained in this study point to an interaction between the treatments since the NMO protocol promoted a reduction in fatigue and the application of the neuropsychic aspects protocols (NPPO and NPPO-CB) played an important action on motor control items and functionality (TUG and HGST). Corroborating the above , report All this, when applied to the reality of patients with PPS, helps us better understand how neurobiological modulation treatments, such as REAC NPO, NPPOs, and NMO, can represent a new therapeutic strategy to assist PPS patients in both physical and psychological aspects.It is worth mentioning that the population studied consists of people who suffer from a chronic degenerative disease in which there is a series of adaptations, whether physical, emotional and/or behavioral, already installed. This reinforces the importance of the data found in such a short intervention period of the protocols used in the present study. Furthermore, despite the search for other therapeutic strategies to manage fatigue and improve functional performance, results as significant as those demonstrated in the present study were not found for this population.The limitations of this study lie in its open nature, with a relatively small but still significant sample, and patients who, despite sharing the diagnosis of PPS, present varying degrees of disability. Therefore, it is necessary to carry out further research in order to verify whether specific treatments using REAC neurobiological modulation treatments can represent a valid strategy for improving fatigue, functional performance and, consequently, quality of life in patients with PPS.Given the data presented, it can be inferred that the intervention through the REAC NPO, NPPOs, and NMO treatments demonstrated positive effects in improving the fatigue index, functional performance of gait, and handgrip strength. Furthermore, the use solely of the NPO protocol proved to be a good therapy for improving the fatigue index and functional gait performance. The results also suggest the need for new studies with longer intervention times, as well as an expansion of the sample and a different design  in order to better investigate the effects of the interventions.