During the COVID-19 pandemic, as health care services shifted to video- and phone-based modalities for patient and provider safety, the Veterans Affairs (VA) Office of Connected Care widely expanded its video-enabled tablet program to bridge digital divides for veterans with limited video care access.This study aimed to characterize veterans who received and used US Department of VA–issued video-enabled tablets before versus during the COVID-19 pandemic.We compared sociodemographic and clinical characteristics of veterans who received VA-issued tablets during 6-month prepandemic and pandemic periods . Then, we examined characteristics associated with video visit use for primary and mental health care within 6 months after tablet shipment, stratifying models by timing of tablet receipt.There was a nearly 6-fold increase in the number of veterans who received tablets in the pandemic versus prepandemic study periods . Compared to the prepandemic period, tablet recipients during the pandemic were more likely to be older (mean age 64 vs 59 years), urban-dwelling , and have a history of housing instability . Pandemic recipients were more likely to use video care  and did so more frequently (5.6 vs 2.3 average encounters) within 6 months of tablet receipt. In adjusted models, pandemic and prepandemic video care users were significantly more likely to be younger, stably housed, and have a mental health condition than nonusers.Although the COVID-19 pandemic led to increased distribution of VA-issued tablets to veterans with complex clinical and social needs, tablet recipients who were older or unstably housed remained less likely to have a video visit. The VA’s tablet distribution program expanded access to video-enabled devices, but interventions are needed to bridge disparities in video visit use among device recipients. Many US veterans experience geographic and health-related barriers to accessing care. Nearly 1 in 3 of the approximately 9 million veterans enrolled in the US Department of Veterans Affairs (VA) live in rural, highly rural, or insular areas, and many more experience transportation challenges and financial insecurity that impede their use of VA services ..14].other) due to small numbers.To account for the association between past and future use of VA health care, we assessed the use of VA health care in the 365 days prior to and excluding the date of tablet shipment for all patients in both cohorts. We categorized health care use as low use, moderate use, or high use corresponding to 0th to 25th, 25th to 75th, and 75th to 100th percentiles. Prior in-person and video visits were dichotomized . Prior use of remote patient monitoring, specialty care, and rehabilitation care was combined into one category . We adjusted for patient demographics, clinical characteristics, region, and previous use of VA health care. We report adjusted incident risk ratios (RRs) and 95% CIs for all models.Additional analyses examined the relative proportion of encounters by modality  and care category  within each cohort. We also assessed the time between tablet shipment and recipients’ first mental health or primary care video visit as well as the total number of mental health or primary care video encounters recipients completed within 6 months of tablet shipment for both prepandemic and pandemic cohorts. All statistical analyses and graphical output were completed using Stata statistical software .This evaluation was completed as part of a VA Quality Enhancement Research Initiative and was designated as nonresearch quality improvement by VA’s Office of Rural Health, local institutional review board, and VA Research Administration.There was a nearly 6-fold increase in VA tablet recipients between the prepandemic study period in 2019 (n=6784) and the pandemic study period in 2020 . Conversely, pandemic tablet recipients had fewer average in-person care encounters compared to prepandemic tablet recipients . The overall trends in prior use for all tablet recipients were very similar for in-person and primary care encounters. Other baseline use patterns are presented in Adjusted models stratified by prepandemic and pandemic tablet receipt  showed tOur results demonstrated that tablet distribution patterns changed significantly during the COVID-19 pandemic, with higher clinical and social need veterans being more likely to receive a tablet; however, barriers remain in increasing use of video care among these veterans. VA’s rapid expansion of the tablet distribution program during the COVID-19 pandemic resulted in a nearly 6-fold increase in the number of veterans who received a tablet during 6-month comparison periods. This evaluation of the tablet distribution program highlights changes in sociodemographic and clinical characteristics of tablet recipients before and during the pandemic. Overall, findings suggest that VA’s tablet distribution program facilitated critical access to video care compatible devices for veterans with high levels of clinical and social needs, although a digital divide persisted in terms of how the devices were used.The fact that a higher proportion of tablet recipients during the pandemic were older and had low income (based on VA priority status) suggests that the expansion of the tablet distribution program, alongside the implementation of interventions aimed at facilitating technology literacy, may have facilitated a narrowing of the digital divide for these groups during the pandemic. Older veterans and veterans with a history of housing instability are groups that often face barriers to telemedicine and thus may have been more heavily targeted for tablet distribution due to higher risks of severe COVID-19 illness in these populations . AdditioBased on encounter data, tablet recipients during the pandemic (vs the prepandemic period) were more likely to have a video encounter within 6 months of receiving their tablet, although phone visits remained the most common form of telehealth among pandemic recipients, in line with a growing body of evidence illustrating this pattern across multiple countries . VeteranFindings from this study expand on previous analyses that showed that VA patients aged older than 45 years or those with a history of housing instability had a reduced likelihood of video care use during the first 3 months pandemic, despite these populations having higher clinical and social needs . We founWe also identified use patterns that may have been influenced by the social impacts and hotspot trajectory of the pandemic. Despite being proportionally less likely than men to receive a tablet during the pandemic, recipients who were women had an increased likelihood to use video care. Although this is consistent with previous studies showing that women veterans are higher users of all types of video care , these tOne point of concern is the potential for video care to increase disparities in health care access among racial and ethnic groups who may have health care access barriers prior to the pandemic -10,25. IUltimately, findings suggest a need for targeted interventions aimed at increasing uptake of video care among older and unstably housed veterans. Future research should also consider the unique impact of the COVID-19 pandemic and the expansion of video care on health care access for specific subgroups of veterans as well as potential areas for program improvement for veterans with higher clinical or social needs. Additionally, our analyses showed that nearly one-third of tablet recipients included in this study did not have any video visits within 6 months of receiving their tablet. Further investigations into demographic trends associated with video care disuse and methods of increasing video care uptake among tablet recipients would provide valuable information for further improvements to the tablet program.There are several limitations to this study that should be noted. Although findings indicate that VA’s tablet distribution program advanced accessibility of video care among veterans both before and during the pandemic, video visits accounted for only a minority of all encounters; future work should consider the balance of video, phone, and in-person care. In addition, it would be of value to understand the impact of tablets of different specialties of care during the pandemic. Although the impact of tablets on mental health video care use during the pandemic has been described , future In summary, this large, nationally representative evaluation characterized the reach and impact of VA’s effort to scale up distribution of video-enabled tablets to veterans during the COVID-19 pandemic. We found that the pandemic was associated with striking changes in the characteristics of tablet recipients and use. Notably, veterans with indicators of increased clinical and social need were more likely to receive tablets during the pandemic, although this need did not translate into greater use of video care. These findings suggest that health systems aiming to enhance access through video-capable devices should consider organizational, external, and patient-level factors influencing the uptake of video care.


Despite the higher prevalence of problem drinking in the UK military compared to the general population, problem recognition appears to be low, and little is known about which groups are more likely to recognise a problem. This study examined prevalence of problem drinking recognition and its associations.We analysed data from 6400 regular serving and ex-serving personnel, collected in phase 3 (2014–2016) of the King's Centre for Military Health Research cohort study.Participants provided sociodemographic, military, health and impairment, life experiences, problem drinking, and problem recognition information. Problem drinking was categorised as scores ≥ 16 in the AUDIT questionnaire. Associations with problem recognition were examined with weighted logistic regressions.Among personnel meeting criteria for problem drinking, 49% recognised the problem. Recognition was most strongly associated (ORs ≥ 2.50) with experiencing probable PTSD , social impairment due to physical or mental health problems , adverse life events , ever being arrested  and reporting symptoms of alcohol dependence . To a lesser extent, recognition was also statistically significantly associated with experiencing psychosomatic symptoms, feeling less healthy, probable common mental health disorders, and increased scores on the AUDIT.Half of UK military personnel experiencing problem drinking does not self-report their drinking behaviour as problematic. Greater problem drinking severity, poorer mental or physical health, and negative life experiences facilitate problem recognition.The online version contains supplementary material available at 10.1007/s00127-022-02306-x. The prevalence of harmful drinking or probable dependence in the UK is 10% among serving and ex-serving personnel  and miliLack of alcohol-specific problem recognition is a key barrier for seeking treatment, as shown for mental health help seeking among UK ex-serving personnel , 11 and Results from non-military studies suggest that factors that are associated with higher AUD recognition are greater alcohol consumption , 16, greTo address this issue, the present study uses data from phase 3 of a large UK military cohort to identify the prevalence of problem drinking recognition among people meeting criteria for problem drinking (defined as harmful or probable dependent drinking), and to examine associations with health and impairment, life experiences, and alcohol use.Participants were drawn from the third phase of a military cohort study of the health and well-being of the UK Armed Forces, established in 2003 by the King’s Centre for Military Health Research (KCMHR). Sampling and recruitment for this phase are described elsewhere .In brief, phase three took place between October 2014 and December 2016 and included 8,093 respondents (44.3% response rate) [The sample for this study was restricted to 6400 regular military personnel, full-time reservists, and ex-serving personnel from phase three of the cohort study. We excluded 1688 volunteer reservists  and five respondents who transferred to the Australian military, as we did not consider them representative of UK military drinking culture and norms.Participants provided information on gender , marital status (in relationship/not in relationship), whether they had children younger than 18 years old (yes/no), level of education , a 10-iAUDIT total scores from all items were used to derive the following levels of severity: (a) hazardous drinking (8–15), (b) harmful drinking (16–19), and (c) probable dependence (AUDIT ≥ 20). Participants falling in the last two categories (16 or above) were categorised as meeting caseness for problem drinking.The AUDIT sub-domains were used to evaluate drinking patterns. These sub-domains are hazardous alcohol use , alcohol-related harm  and dependence . Cut-off score were ≥ 10 for hazardous drinking  and ≥ 4 Regarding caseness for problem drinking, we linked our data with results from the previous phase of the cohort (phase 2) to derive a measure of previous problem drinking. This was analysed only among participants who participated in both phases.Common mental disorders (CMD) and post-traumatic stress disorder (PTSD) were evaluated with the General Health Questionnaire (GHQ-12);  and the Somatic symptoms  in the past month were assessed using the somatoform disorders scale of the Patient Health Questionnaire (PHQ) . The scaRespondents completed six items from the SF-36 Health Survey . One iteAdverse life events relating to military-related experiences, general adverse life events, and childhood experiences were examined in relation to recognition. To assess military-related experiences, respondents completed a single-item question asking if they experienced any major problems when returning from their most recent Iraq or Afghanistan deployment (yes/no), and a 14-item scale on combat experiences during their most recent deployment  , 29. TotGeneral adverse life events in the past 3 years were measured using an adapted version of a 10-item stressful life-events checklist  previously used in military populations . Items oMeasures of family relationship adversities and antisocial behaviours in childhood were adapted from the Adverse Childhood Exposure study scale . We calcRespondents were asked "Have you had any alcohol problems in the last three years?" (yes/no), to measure subjective self-identification of an alcohol-related problem. For the main analysis, recognition of problem drinking was defined as meeting criteria for problem drinking  and self-reporting an alcohol problem. However, self-reporting of alcohol problems is also reported here across all levels of AUD severity .All percentages, odds ratios (ORs), and confidence intervals (CIs) were weighted, using sampling and response weights. Sampling weights represented the inverse probability of a respondent being sampled from a specific sub-population and response weights represented the inverse probability of participating, based on factors associated with response . A single study weight was then calculated by multiplying sampling weights with response weights . FrequeFactors associated with problem drinking recognition were examined with weighted logistic regressions. Univariable weighted logistic regressions were used to identify sociodemographic and military variables significantly associated with problem drinking recognition, which were then added as covariates in the multivariable models (Online resource 1—Socio-demographic and military variables associated with problem drinking recognition among respondents meeting criteria for problem drinking).Multivariable weighted logistic regression models examined the associations between key variables of interest  and the outcome of interest (problem drinking recognition). Each variable was tested in a univariable model and then adjusted for the following covariates: (a) age and gender, and (b) education and service status (identified in the sociodemographic univariable models), and (c) caseness for CMD to account for psychological distress.For post-deployment experiences and past problem drinking, analyses were restricted to personnel who had deployed to Iraq or Afghanistan or had taken part to both phases 2 and 3 of the cohort study, respectively. According to results from the sociodemographic univariable models in these sub-samples, multivariable models were adjusted for a) age, gender, and education, and b) age, gender, and marital status, respectively.To avoid problems of multicollinearity, the association of probable PTSD with problem drinking recognition was not adjusted for CMD. Harmful drinking patterns (AUDIT sub-domain) were not included as a predictor of problem recognition as almost all respondents who met problem drinking criteria endorsed this pattern of alcohol use (96%).In a post hoc exploratory analysis, AUDIT scores were added as an additional covariate in the models that examined associations with problem drinking recognition. This was to examine whether greater severity of problem drinking could account for some of the associations . For life experiences, we included only adverse life events experienced in the last 3 years, as we did not consider this mechanism to be relevant to the other life experience factors .The analysis presented above was not pre-registered and the results should be considered exploratory.N = 6400) and for respondents meeting criteria for problem drinking . Both samples were predominantly male, in a relationship, with A level education or higher. Most were serving or had served in the Army in a combat (service) support role as NCOs and had deployed to either Iraq or Afghanistan.Table Among the full sample (N = 6400), 10% met criteria for problem drinking, of whom 49% self-reported alcohol problems in the last 3 years.Table Table Table Table Online resources 2 and 3 present the adjusted ORs and 95% CI for the post hoc exploratory analysis where AUDIT scores were added as a covariate in the models examining the association of health and impairment factors and life experiences factors with problem drinking recognition. The effects described above were not attenuated to non-significance, indicating that they were not fully accounted by problem drinking severity.Approximately half of the respondents in this study who met problem drinking criteria (AUDIT ≥ 16) recognised a problem. Recognition was higher among personnel experiencing more mental health problems, impairment, problem drinking severity, and stressors in life.Problem drinking recognition in our sample was higher compared to findings from the same UK cohort 7 years ago  and theProblem drinking recognition was more likely among respondents with more severe problem drinking, as previously found in non-military samples , 18, 38,Recognition was also more likely among respondents experiencing poorer health and greater impairment, as in non-military studies , 17. TheRespondents with problem drinking and mental health comorbidities were more likely to recognise an alcohol problem than those with problem drinking only, as found in non-military studies , 19. OurFinally, recognition was more likely among respondents who had experienced greater stressors recently in life. These stressors might be the outcome of problem drinking, increasing awareness of alcohol's adverse effects on one's life. For instance, problem drinking may lead to relationship breakdown or legal problems , orA strength of the study is the use of a large cohort of UK military personnel, allowing for robust, generalisable estimates. A limitation of the cross-sectional analysis is that, although it is appropriate for assessing the strength of associations, it does not identify unidirectional relationships. Another limitation comes from the time mismatch between the objective measure of problem drinking  and the subjective measure of self-perceived problems with alcohol (last 3 years). It is possible that some discordant responses may have been, because someone subjectively reported a problem occurring in the last 3 years, but at the time of completing the AUDIT, they were categorised as a lower risk or non-drinker and had cut down or were abstaining due to the previous problem. However, in the main analysis, these participants would not have been included, because it was restricted to those who met current criteria for problem drinking. It is additionally possible that participants with a recent increase in drinking may not have self-reported this due to the wording of the question relating to 3 years. A final limitation was that problem recognition was captured via a binary outcome, which could be less sensitive to change in problem recognition compared to dimensional AUD problem recognition scales.The main implication of our findings is that greater support might be required to facilitate problem drinking recognition in those drinking at a problematic level but not experiencing current health or social harms from their drinking. To that end, it might be helpful to challenge binary perceptions of alcohol problems  andEfforts to facilitate alcohol-problem recognition will have wide reaching benefits given that almost half of those who drink at a harmful or probable dependence level do not recognise their drinking as problematic and those who do not report having additional health and social issues are less likely to perceive their drinking as problematic. It is encouraging that problem recognition in the UK military has increased over a 7-year period, compared to previous studies in the general population.Supplementary file1 (DOCX 20 KB)Supplementary file2 (DOCX 16 KB)Supplementary file3 (DOCX 15 KB)Below is the link to the electronic supplementary material.


Current scientific evidence points to the importance of studying the link between mental health and problematic video game use in adolescents. The aim of this study was to analyse the correlation between gender and stage of adolescence and problematic video game use, as well as to study the correlation between internalizing and externalizing symptomatology, prosocial behaviour and video game use, and the correlation between video gaming and mental health issues in Spanish 12- to 18-year-olds . For this purpose, the Strengths and Difficulties Questionnaire (SDQ) and the Video Game-Related Experiences Questionnaire (VGEQ) were administered to 1448 secondary school students in Extremadura (Spain), of which 50.8% were women and 49.8% men. The results show that (a) males present more problematic video game use, (b) prosocial behaviour negatively correlates with problematic video game use, and (c) mental health issues and problematic video game use correlate in a negative way. However, the stage of adolescence was not seen to have any effect on the problematic video game use. In conclusion, this study points the effects that problematic video game use can have on the mental health of adolescents and the possible protective role that prosocial behaviours can have on the prevention of problematic video game use. From early infancy, through interaction in a wide variety of social contexts, a series of physical, socioemotional, and cognitive changes take place in humans which affect their personality, interpersonal relationships, and ultimately their mental health . The WorCurrent data (published by WHO in 2022) indicate that 13% of the world’s young population (aged 10–19) suffers from some type of mental health condition. This is a considerable risk factor for their future adulthood, since such conditions could potentially turn into mental disorders and diminish their quality of life [These data point to the need for a global approach to identifying, evaluating, and treating mental health problems at an early stage in order to prevent the development of more serious mental conditions in adulthood. This could help to prevent risky behaviour, failed interpersonal relationships, stress, dissatisfaction with life, depression, etc. . To thisThere is a correlation between mental health and problematic video game use. Depression and loneliness are linked with problematic video game use. In addition, physical aggression is an antecedent of problematic video game use and anxiety is a consequence of it .When discussing mental health conditions, different taxonomic classifications can be used as a reference, such as the mental disorder classification used in the Diagnostic and Statistical Manual of Mental Disorders DSM-5) , and/or  , and/orRegarding prevalence, behaviour patterns correlated with emotional difficulties and mental disorders are more common in 10- to 24-year-olds than in older age groups, regardless of gender . With reIt is also important, however, to look for variables that can modulate the internalizing and externalizing problems in adolescents. To this end, much research has focused on analysing behaviour, particularly prosocial behaviour, in the early stages of childhood and adolescence ,9,10,11.While prosocial behaviour has a positive correlation with the psychosocial life of teenagers, certain other behaviours, such as problematic video game use, have a negative correlation ,15,16. VMost Spanish ‘gamers’ are aged between 11 and 14, only 20% of which are female . While vBesides, being young can be another predictor of problematic video game use . RegardiHigh rates of problematic video game use have been reported, particularly MMORPGs, where players create avatars and interact virtually with other gamers to accomplish missions and move up levels . Groups In view of the above, there is a need for new, more probing scientific information on the correlation between gender and stage of adolescence and problematic video game use. This study uses data to shed light on the correlation between teenage mental health  and prosocial behaviour and problematic video game use, an area which has received little attention to date. Understanding this connection should help in the development of suitable, cost-effective educational and health strategies. Finally, the correlation between problematic video game use and the mental health of adolescents is analysed in detail. This should allow appropriate restrictions to be established regarding their use.Therefore, the present study aims to (1) analyse the correlation between gender and age on problematic video game use and examine its main symptoms, and (2) analyse the correlation between internalizing and externalizing symptoms and prosocial behaviour and video game use and examine how video game use can affect the mental health of Spanish12- to 18-year-olds.In this sense, the hypotheses of the present study are the following: (1) Males will obtain higher scores in the problematic use of video games compared to females. (2) There will be a negative correlation between prosocial behaviours and problematic video game use and there will be a positive correlation between internalizing and externalizing symptomatology and problematic video game use. (3) There will be a negative correlation between problematic video game use and age.A sample of 1448 ESO (Spanish Compulsory/Junior Secondary Education) and Baccalaureate (Pre-University/Senior Secondary Education) students was selected, 708 of whom were male (48.90%), with a mean age of 14.5 years (SD = 1.57). Previously, a complete list of the secondary schools and number of pupils in Caceres and Badajoz was obtained from the local branches of the Extremadura board of education. A random selection of 8 schools was taken from this list. Once access to the school had been granted, the questionnaires were administered to all the years in all the schools. When there was more than one stream per year , one was chosen at random; 51.8% of the students in the final sample came from rural areas, while 48.2% came from urban backgrounds. Regarding distribution by year, 22% were in the first year of junior secondary, 21.8% in the second year, 20% in the third year, and 21.9% in the fourth; 14% were in senior secondary school, 53.7% attended state-run schools, while 46.3% attended state-subsidized schools.The participants’ rights were respected in accordance with the professional code of ethics, including the interviewees’ right to privacy and personal data protection, as per Organic Law 3/2018 of 5 December 2018. All the teachers involved signed an informed consent document before taking part in the survey. Moreover, the parents signed the permission to participate. Finally, the ethical code of the University of Extremadura was adhered to, guaranteeing the impartiality and responsibility of the researchers in the collection and processing of all the data .The Strengths and Difficulties Questionnaire (SDQ) ,32,33. TFollowing the SDQ guidelines, the total score was obtained by adding the scores of the first four scales: emotional difficulties, conduct problems, hyperactivity, and peer relationship problems; the prosocial scale was not included. The Spanish version of the questionnaire was used in this study ,35,36. TThe Video Game-Related Experiences Questionnaire (VGEQ) . The VGEOther sociodemographic data were collected via an initial questionnaire to determine the following variables: gender  and age interval (early adolescence (12–13 years), mid-adolescence (14–17 years), and late adolescence (18 and over) .As the sample in the study was large (n = 1448) ,40, paraIn addition, a robust one-factor ANOVA was performed using Welch’s method (heterogeneous variances) to study the problematic video game use differences in the mental health of minors. Three similar-sized groups were formed based on their problematic video game use scores to make sure the sample was balanced. Thus, one group was made up of 495 students with the lowest problematic video game use scores (the ‘never’ group), another group consisted of 483 people with intermediate VGEQ scores (the ‘sometimes’ group), and yet another group comprised 469 people with the highest problematic video game use scores (the ‘a lot’ group). Finally, linear regression was performed  and standardized effect sizes were calculated. Data analysis was performed with the SPSS24 statistical program.w 1, 1445 = 411.308, p = 0.00). The mean of the male group  was significantly higher than that of the female group .First, descriptive statistics (mean and standard deviation) were calculated for problematic video game use , as well as for internalizing  and externalizing symptomatology  and prosocial behaviour . Significant higher mean of problematic video game use scores were obtained among the males surveyed used ANOVA. A contrast statistic is obtained . When the ‘mid- ‘and ‘late’ adolescence were put in the same group, still no differences were found between early adolescence and the joint group, so it would seem that the stage of adolescence is not a determinant factor in problematic video game use.In order to determine differences related to age or stage of adolescence, no statistically significant differences were found by early, mid-, or late adolescence group . Post hoc multiple comparisons were performed using the Games–Howell test . Post hoc multiple comparisons were performed using the Games–Howell test as the variances were heterogeneous . Post hoc multiple comparisons were performed using the Games–Howell test . Post hoc multiple comparisons were performed using Tukey’s test  was calculated, obtaining a coefficient close to 1 for all variables, which indicates that there is no relationship between the predictor variables see .Finally, multiple correlation was calculated using gender, age, and externalizing and internalizing symptomatology as predictor variables, with problematic video game use as the criterion variable. A coefficient of multiple determination (R-squared) of 29% was found. This coefficient of multiple determination indicates that 29% of the variance of problematic video game use is explained by the set of predictor variables. Likewise, the coefficient of multiple determination is significant see .The aim of this study was to determine problematic video game use in adolescents by identifying gender and age-related differences among gamers and by analysing the correlation between internalizing, externalizing, and prosocial behaviour and video game use. Finally, an attempt was made to determine how video game use may be linked with the mental health of Spanish adolescents aged 12 to 18 in the Autonomous Community of Extremadura (Spain).In terms of gender, males scored significantly higher than females in problematic video game use and also in its main associated symptoms. These results coincide with studies carried out by Sans  and GonzOther previous studies  indicateIndeed, the relationship between video game use and mental health has been the subject of much research in the post-pandemic era ,48,49 anHowever, a gender bias towards males has also appeared in recent studies . Boys teGiven that video game use is a massively widespread leisure activity, addressing it during adolescence is an important step towards preventing the potential disorders listed in the DSM-5. Therefore, it is essential to monitor the environment and control the amount of time children and teenagers spend on gaming ,53,54,55Regarding future research projects, it would be useful to include more sociodemographic characteristics such as social class and access to and availability of video games in the home. Such characteristics may play a part in problematic video game use according to several studies ,57. OtheIt is relevant to consider the content of video games, since video games with violent content may affect aggression, increasing antisocial behaviour and negative thoughts and decreasing prosocial behaviour, empathy, and sensitivity to aggression .As practical implications, the present study would raise awareness among teachers and families of the effects that problematic video game use can have on the mental health of adolescents and the possible protective role that prosocial behaviours can have on the prevention of problematic video game use. Likewise, it is important to consider the differences in terms of gender and age in the problematic use of video games. However, further research would be necessary to make these assertions.One of the main limitations of this study is the use of self-reporting for the gathering of data, given that the social desirability bias could potentially be a drawback. However, the size of the sample and the use of a validated mental health assessment scale offset the likelihood of any such bias. Other versions of the SDQ for collecting data from other informants could also be used with a view to comparing the information provided by the adolescents with that of their parents and/or teachers. It should also be pointed out that this was a cross-sectional study, so causal relationships cannot be established. Likewise, it would have been desirable to include more controls, such as family environment and personality styles. The fact that the prosocial subscale has low reliability is also indicated as a limitation.Regarding the research objective of this study, male adolescents were seen to score higher than female adolescents in problematic video game use. The adolescents who scored higher in internalizing and externalizing symptomatology also scored higher in problematic video game use. By contrast, adolescents with higher scores in prosocial behaviour scored lower in problematic video game use. Finally, adolescents with greater mental health difficulties scores also scored higher on problematic video game use. Given these results, it seems important to highlight the fact that males suffer more from problematic video game use and are at equal risk of developing the problem throughout their entire adolescence. Similarly, this study shows that not only does problematic video game use correlated with the mental health of adolescents negatively, but it also shows that there is a negative correlation between prosocial behaviour and the problematic use of video games.


COVID-19 has resurfaced health inequalities but also provides new opportunities for remote healthcare. Minority ethnic service users (SUs) are substantially under-represented in secondary mental health services due to gaps in understanding needs of this priority group. We aimed to assess and identify any differences in characteristics and acceptability, with a focus on minority ethnic mental health SUs.A prospective, online feedback questionnaire was developed with the help of SUs. This was built into video consultations (VCs), using the secure Attend Anywhere platform through a survey link. We present results between July 2020 and January 2022, during which, a total of 2,565 SUs completed the online questionnaire after VCs. SPSS (version 27) was used for descriptive statistical analysis. Chi-squared test, using 5% level of significance, was conducted to test differences between the two (minority Vs majority ethnic) SU groups.2=5.476, p < 0.05). By age group, almost half (48.7%) of minority ethnic SUs were less than 25 years old, compared with those from White British ethnicity (29.2%). In contrast, only 2.5% minority ethnic SUs were aged ≥65 years with none ≥80 years old .Of 2,565 SUs, 119 (4.6%) were from minority ethnic groups , 2,398 (93.5%) were White British, and 48 (1.9%) preferred not to disclose. A higher percentage of SUs were females from both minority (55.6%) and White British (66.1%) ethnic groups , their needs were met  and felt supported . However, according to care team, a significantly higher percentage of minority ethnic SUs (43%) had remotely consulted Specialist  services compared with those of majority ethnicity (29%) .No significant differences were found for video technical quality, such as waiting area, joining the video call, sound, and video quality. Similar findings were observed for video care delivery aspects with no significant differences between (minority ethnic and White British) SUs. Overall, both groups felt comfortable during the video call (ϰBoth minority ethnic and White British SUs found video care to be acceptable, with positive experiences. A significantly high proportion of minority ethnic SUs was younger and had remotely consulted Specialist services, with none in the 80-plus age group. These findings highlight priority areas to address among this massively underrepresented group in mental healthcare services.


Head impacts are highly prevalent in sports and there is a pressing need to investigate the potential link between head impact exposure and brain injury risk. Wearable impact sensors and manual video analysis have been utilized to collect impact exposure data. However, wearable sensors suffer from high deployment cost and limited accuracy, while manual video analysis is a long and resource-intensive task. Here we develop and apply DeepImpact, a computer vision algorithm to automatically detect soccer headers using soccer game videos. Our data-driven pipeline uses two deep learning networks including an object detection algorithm and temporal shift module to extract visual and temporal features of video segments and classify the segments as header or nonheader events. The networks were trained and validated using a large-scale professional-level soccer video dataset, with labeled ground truth header events. The algorithm achieved 95.3% sensitivity and 96.0% precision in cross-validation, and 92.9% sensitivity and 21.1% precision in an independent test that included videos of five professional soccer games. Video segments identified as headers in the test data set correspond to 3.5 min of total film time, which can be reviewed through additional manual video verification to eliminate false positives. DeepImpact streamlines the process of manual video analysis and can help to collect large-scale soccer head impact exposure datasets for brain injury research. The fully video-based solution is a low-cost alternative for head impact exposure monitoring and may also be expanded to other sports in future work. However, there are limited data to quantify the correlation between impact exposure and brain health consequences. In soccer, head impacts are common since players often use their head to redirect the ball. In fact, soccer heading represent approximately 90% of head impacts in soccer, and the remaining impacts are mostly unintentional player-to-player or head-to-ground impacts4. It is unclear whether intentional soccer heading, which are more frequent but mostly lower-severity than unintentional head impacts, may have deleterious effects on the brain. Exposure to a single session of controlled heading has been associated with increases in reported concussion symptoms5, alterations in postural control6, and increases in corticomotor inhibition with impaired memory function7. For players that self-reported long-term exposure of 885 to 1800 headers per year, brain white matter microstructure abnormality and neurocognitive dysfunction has been found8. However, some studies have not found significant neurocognitive performance or neuropsychological changes associated with short-term soccer heading exposure10. Using self-reported impact estimates in some studies can be a potential reason for the mixed results in the literature. Such subjective exposure reports may not provide accurate estimates of head impact count11. Because of the inconclusive results of repeated soccer head impact exposure, longitudinal research to correlate accurate exposure measurements with brain outcomes is recommended12. Therefore, collection of accurate large-scale head impact exposure data is a vital step to study brain injury mechanisms.Repetitive sports head impact exposure may not only increase the risk for sustaining acute brain injury such as concussion13. These sensors contain inertial measurement units (IMUs) to measure head kinematics, and typically set a linear acceleration threshold (e.g. 10g) to detect and record impact events16. Despite the simplicity of this method, it has limited accuracy in head impact detection as it fails to record any information about the events occurring below the threshold. Therefore, selecting the right threshold is important but it is also highly subjective, which can introduce bias in the exposure data19. To accurately quantify head impact exposure, methods that can detect most impacts (high sensitivity) without a substantial number of false positive detections (high precision) are required. Applying a low acceleration threshold may be a strategy to increase impact detection sensitivity at a cost of high false positive rate, while increasing the threshold to improve precision can lead to increased false negative rate20. Furthermore, limited kinematic accuracy in sensors leads to uncertainty in the estimated head accelerations for impact triggering22. Some sensors have combined the simple acceleration threshold method with additional filtering algorithms to remove false positive recordings. However, laboratory and field evaluation studies have shown poor performance of such algorithms25. Table Some studies have used wearable head impact sensors to objectively and quantitatively monitor impact exposure34. Most studies use video data only to confirm recordings of the sensor31, which is limited since this approach does not identify potential false negatives and may underestimate impact exposure. Therefore, other studies have conducted independent analysis of the video to identify all potential exposure events based on human reviewer observation35. Video information has also been used to extract contextual factors characterizing head impacts, such as impact location, impacting object and impact type20, which cannot be fully informed by IMU-based impact sensing. However, video analysis is a time-consuming process that requires substantial human resources. In a previous study, fourteen trained raters reviewed 163 hours of video to fully verify 217 head impacts20. Generating large-scale head impact exposure data using independent video analysis would be a cost-prohibitive and lengthy task.Considering the limitations of impact sensors, video analysis is recommended to improve the accuracy of impact exposure data36. Computer vision algorithms, more specifically deep learning (DL) techniques, have been applied to automate tasks such as player/ball detection and tracking, player pose estimation, game reconstruction, and generation of game statistics36. In this work, the feasibility of a video-based approach for automatic soccer header detection through DL algorithms is investigated. The goal of this study is to develop a more efficient and less resource-intensive approach for video-based head impact exposure estimation.Automatic analysis of sports games using video information has been extensively studied in computer vision researchDeepImpact, a DL algorithm with the novel application of automatic header impact exposure tracking using a single-view broadcast soccer video is proposed. This data-driven algorithm uses video information and computer vision algorithms to detect headers, which are defined as any contact between a player’s head and the soccer ball. Header detection in the video can be categorized as a video understanding problem in computer vision research, which consists of the identification and temporal localization of actions in a video.39. The descriptor is then used to train an action classifier. This is a complete data-driven approach in which the algorithm is trained on a dataset comprised of video samples from each action.The video data has two types of information: visual information present in each frame of the video as well as dynamic temporal information that can be obtained from consecutive frames. DL-based research in video understanding follows a general structure to use this spatiotemporal information. The common method comprises of extracting visual features in the spatial domain from each frame using convolutional neural networks (CNN), then temporally aggregating the features of consecutive frames to generate a video descriptorFigure Frames of the input video were extracted and segmented based on the selected segment length, which is the number of frames in each segment. Segment lengths between 5 to 15 frames were selected and the performance of the algorithm was evaluated for each length using cross validation on the full dataset see “”. A segm40. YOLOv5 is currently one of the best performing object detection algorithms. A series of convolutional layers are applied to the input image to extract its visual features for object identification inside the image40. The algorithm detects each object by estimating a bounding box around it as well as a probability that represents its confidence in the detected category of the bounding box. YOLOv5 has 4 model sizes, YOLOv5s/m/l/x, which have different numbers of learnable network parameters. A larger model with more learnable parameters can achieve higher accuracy considering that the training dataset is large enough to avoid over-fitting. However, more parameters require higher graphics processing unit (GPU) memory in the training process, which limits the possible highest batch size. In this study, YOLOv5l was used for the ball detector network because it was the largest size that allowed a minimum batch size of 32 on an NVIDIA RTX 3080 Ti GPU.The ball detection algorithm used in this work is a modification of the DL-based object detection algorithm, You Only Look Once v5 (YOLOv5)41 dataset was used to train the ball detection network. SoccerDB is a large-scale soccer video understanding dataset, which contains videos of 53 full-match and 18 half-match professional male soccer games. It has annotations for object detection, including 64,232 images with ground truth (GT) bounding box of the soccer ball. Furthermore, SoccerNet-v242 dataset was used to achieve a larger dataset size as well as higher variability in the images of the training dataset. SoccerNet-v2 is a soccer activity recognition dataset that contains high quality videos of 500 professional male soccer games. Ball positions in 55,000 randomly selected images that were extracted from the SoccerNet-v2 were labeled. Overall, the soccer ball dataset contained ground truth ball position in 119,232 images from 221 soccer games. These images contained a diverse set of scenes including different stadiums, lighting conditions, camera positions/angles, ball size/color/location, and scenes with a partially covered ball. This diversity helped to train a robust ball detection network.SoccerDB43. COCO is a large-scale object detection dataset that contains 330,000 images with 80 object categories43. Weights of the pre-trained network were used to initialize the ball detector network. The pre-training helps to achieve a more accurate ball detection network and reduce the necessary training time44. The last layer of the network was modified for two object categories . The network was then trained on the soccer ball dataset.The ball detection network was first pre-trained on the Common Objects in COntext (COCO) dataset45. Performance of the network was evaluated after each epoch and the best performing set of weights with the highest AP were used for the next steps of the header detection process. The network provides a confidence score for its detections. F1-score46, which combines sensitivity and precision into one metric was calculated to determine the best threshold on this confidence. Detections with a confidence lower than the selected threshold were considered as background.The suggested training hyper-parameters of YOLOv5 were used in the training process. Initial learning rate was set to 0.001 and the Adam optimizer was used in all experiments. Batch size was set to 32 and the network was trained for 300 epochs. The soccer ball dataset was randomly split into 100,000 images for training and 19,232 images for validation. Average precision (AP) was selected as the validation metric, which is commonly used to evaluate the performance of object detection algorithms48 and the same approach was followed in this work. A constant speed model was used to model ball movement, with the state vector defined asu and v denote the horizontal and vertical coordinates of the center of the bounding box, respectively, with time derivatives 49. In this application, the ball detection network provided the bounding box location in each frame as the measurement of the KF and the constant speed model provided the prediction of the ball location. The parameters of the measurement covariance matrix were set to 15 pixels for horizontal and vertical coordinates to optimize performance.Some frames could contain false positive ball detections or fail to detect the ball. To reduce the effect of such noisy detections, a Kalman filter (KF) based ball tracking method was implemented. KF has been extensively used for visual object trackingu and v, the detected ball with the highest confidence score in the first frame of the video segment was used, with To initialize u or v distance was higher than a threshold value d, the KF innovation  using the bicubic interpolation technique, which employs polynomial-based interpolation to enlarge the image51 (TSM) was used for spatiotemporal feature aggregation. TSM is an efficient method used for video understanding, which compares well with other potential approaches for temporal modeling of video such as 3D CNNs52, recurrent neural networks38, and attention mechanisms53. TSM employs 2D CNNs to extract visual features of each input image and fuses the extracted features using a temporal shift mechanism51. Using this algorithm, spatiotemporal features of each video segment can be learned using one neural network in an efficient manner. Because TSM employs 2D CNNs, common deep CNN structures such as VGG54 and ResNet55 could be used for visual feature extraction. ResNet-50 structure, which is 50 layers deep was used for visual feature extraction. Using deeper ResNet-101 structure did not make any improvement on the results.A deep learning algorithm called temporal shift moduleThe size of the input tensor to the feature aggregation network was , in which 11 is the number of frames, 3 is the number of color channels in each frame, and  is the resolution of the image. The network maps this input to a For training the feature aggregation network, a dataset containing both header and nonheader events was generated using broadcast soccer video. Three source datasets were used to obtain these events: SoccerNet-v2, SoccerDB, and publicly available soccer videos from the internet. Table 56 dataset, which is a general human action recognition dataset with 306,245 short video clips of 400 action classes was used. Parameters of the pre-trained network were used as the initialization point of the feature aggregation network and training was continued on the soccer header dataset. For training, the learning rate was set to 0.001 using a stochastic gradient descent optimizer. 0.0005 was used as the weight decay rate of the optimizer. The network was trained for 30 epochs with a batch size of 32. For the hyper-parameters of the TSM mechanism, the same settings as the original TSM work was used51. Performance of the algorithm was assessed on the validation dataset after each epoch using the evaluation metrics and the network weights that yielded the best metrics result were selected.Similar to the ball detection network, a network pre-trained on the KineticsFor evaluating the performance of DeepImpact algorithm in differentiating the header events from nonheaders, its detections were compared with the ground truth label of each video. Performance metrics of sensitivity, precision, specificity, and accuracy were calculated using the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Sensitivity and precision are especially important for assessing the performance of the header detection algorithm. Sensitivity shows classifier’s performance in identifying all header events of the video while precision reflects the number of nonheader events that are classified as headers (FP). These metrics are defined below.The performance of the header detection network was evaluated by conducting 5-fold cross validation. The header dataset consists of a diverse set of different soccer games. Cross validation can reduce potential bias in assessment of the performance. The unshuffled dataset was split to 5 partitions and each partition was used as the validation set in one pass of training. The averaged metrics were used for assessment.To further evaluate the performance of the header detection algorithm, this network was tested on 5 unseen independent soccer games from the SoccerNet-v2 dataset. The entire duration of the test games were processed into header and nonheader event segments, which is different from the training/validation dataset where a selected number of random nonheader events were used from each game. Using this analysis, the performance of the header detection algorithm in its final application scenario, where headers should be detected in full soccer videos was assessed.To evaluate the contribution of each element of the proposed header detection algorithm on its overall performance, the network was trained and validated in different settings, as described below.Setting 1 - Classification of Single Uncropped Frame: In the simplest setting, video segmentation, ball detection, frame cropping, and temporal feature aggregation steps were removed. An image classification network (ResNet-50) was trained to classify each frame of the video as a header or nonheader event. No temporal information was used in this setting and full-size frames were used without cropping them around the ball position.Setting 2 - Classification of Uncropped Video Segments: In this setting, ball detection and frame cropping steps were removed from the pipeline. Video segments consisting of multiple full-size frames were used to train the TSM network. While temporal information was used in this setting, potentially irrelevant and distracting spatial information were included as frames were not cropped using ball detection.Setting 3 - Full Algorithm: All elements of the algorithm were included in this setting. Other two settings were compared with this setting to determine the relative contributions of temporal information and frame cropping on header detection performance.45 (IoU) of 0.5  and temporal information  of video segments to accurately identify header events. As shown in Table In this work, we present a fully video-based method for detection of soccer header events. Our proposed solution does not require head kinematics data and only uses a single-view video footage of soccer games to identify header events. The DeepImpact algorithm is based on deep learning techniques and uses a data-driven approach to learn the visual and temporal features of header and nonheader events present in the video data. Using this methodology, video-based features can replace usual head kinematics-based features for header detection. To train and validate the algorithm, we also contribute a sizable labeled soccer header video dataset.In cross validation, our method yielded over 95% performance on all evaluation metrics. In addition, we tested the algorithm in its real use case on five independent full soccer games. In the independent test, 92.9% of the headers were detected with an average of 8 missing events per game. Considering 20 players on the field , the 8 missing headers correspond to approximately 0.4 missing headers per player per game. In comparison, wearable sensor studies that conducted independent video analysis to verify sensor performance have found lower sensitivity Table .31.In the independent full-game test, the precision dropped from the cross validation performance of 96% to 21.1%, with an average of 379 false positive events per game. The difference between validation and test performance can be explained by comparing the validation and test datasets. Header events correspond to a short duration of the total video time of the soccer games, and having a highly imbalanced dataset can complicate network training. We reduced the imbalance in headers/nonheaders during training and validation by including 7 nonheaders for each header event. It is best that both validation and test datasets have similar distribution of classes. In our full-game test dataset, we had an average of 110 nonheader segments for every header segment, i.e. a more imbalanced dataset. Therefore, there was a higher possibility for nonheader events to be identified as headers, leading to relatively higher proportions of false positives. While the precision seems low, it is similar to the performance of some wearable sensors. In fact, sensor overestimation of head impact exposure due to high false positive rate is a common problem, which leads to the best practices approach of further applying video verification on sensor-detected impactsWe would like to note that the false positive detection of nonheader video segments could be mitigated by a quick secondary review. Based on the test results, the algorithm could identify around 480 events per game (both TP and FP) as potential header events that require further verification by a video rater. With each event segment consisting of only 11 frames, the total review duration is approximately 3.5 minutes, which is significantly shorter than a full soccer game. As such, although the current network may not yet fully automate accurate header detection, it would streamline and enable efficient video-based measurement of head impact exposure in soccer.Efficient video-based head impact exposure measurements can contribute datasets substantially larger than currently available sensor-based datasets to study the mechanisms of cumulative brain injury with low resource requirements, especially considering that video recordings are already available for many sports events. Wearable sensors, on the other hand, have limited utility in large-scale exposure studies due to high cost of sensors, substantial logistics burden, user compliance issues, and the necessity of video verification. Therefore, a streamlined process to estimate impact exposure only using video data is a promising solution to address the limitations of wearable sensors. Moreover, video data can further provide information such as impact type and context that are not measurable by impact sensors.Although video data can be a strong solution to measure impact frequency, an inherent disadvantage of this data is that it cannot be used to obtain accurate impact magnitude information. Both impact frequency and magnitude contribute to our understanding of brain injury. Therefore, combining wearable sensors and our proposed video-based algorithm can provide a promising solution for efficient collection of both impact frequency and magnitude information. While the proposed algorithm can efficiently assess sensor’s sensitivity and collect impact frequency information, wearable sensors can measure the magnitude of the verified impacts.57. Several factors contribute to the difficulty, including small ball size, variable ball shape due to fast ball/camera movement, occlusion, and ball possession by players for extended durations. While the ball detection network performed well when header events occurred closer to the camera, it generally did not identify the ball in distant events from the camera or when multiple players were in the scene. Since we included nearly 100,000 labeled ball images in the training dataset, containing a diverse set of possible ball locations and sizes, the difficulty in ball detection cannot be fully explained by the size or quality of the training dataset. As shown in Fig. 57 may help enhance header detection performance. For each video segment, the camera angle with the best performance in detection of ball position can be selected and used for the next steps of the algorithm.Our framework for video-based header detection utilized two sources of information in video: ball position and temporal information of video frames. Among these elements, accurate ball detection was the most challenging task. In general, ball detection in broadcast soccer videos is not a trivial task58 and temporal segment networks (TSN)59 for temporal feature aggregation. TSM showed the best performance in these early trials. While TSN had a considerably better performance than LSTM in all the metrics, TSM could achieve a slightly better performance than TSN. Thus, we used TSM to conduct our experiments on the full header dataset. Using temporal information for activity recognition in video is an active area of research in computer vision. Future improved temporal feature aggregation techniques may also aid in improving the DeepImpact algorithm.In this work, we used TSM methodology to extract and utilize temporal information of the video segments. In our early trials on smaller versions of the header dataset, we tested long short-term memory (LSTM) networksThe dataset used for training DL-based algorithms is an essential element for their successful implementation. The dataset should be large enough and well representative of the real use case of the algorithm. Considering this point, the SoccerNet-v2 and SoccerDB datasets were highly valuable for this work. Using these datasets, we had access to large-scale open-source video datasets, which was crucial to training an effective network in the current project.In this work, we used single-view broadcast video footage to train our network. This is a limited source of video information. First, 64% of videos in the dataset had low 720p video quality. Second, the broadcast camera angle changes constantly to follow the ball, which can cause blurry ball and players in the images. Third, one video angle provides limited information for distant events happening on the opposite side of the camera location. In addition, impact events may be invisible in the single video view and remain undetected. Fourth, current dataset was selected from videos with different frame per second rates to enlarge the dataset. Having a consistent frame rate could improve the performance of the algorithm. To evaluate the header detection algorithm, we tested the developed network on an independent set of games. We should note that these videos were selected from similar professional-level broadcast soccer games that were used in training the network. Further testing using different video sources would be necessary to evaluate the generalizability of the proposed solution.The current solution does not identify the player involved in each header. Therefore, in addition to removing false positive detections, further video analysis by raters would be necessary to conduct individual exposure estimates. Furthermore, the DeepImpact algorithm is designed to only detect header events. Other types of impacts such as head to head, head to body, and head to ground will not be identified by the current network. Although the majority of head impacts are header events in soccer, other sports such as American football have different impact scenarios that may require different considerations in network design. In addition, an inherent limitation of video-based impact exposure measurements is the lack of impact kinematics information. It would be substantially more challenging to use video information to estimate the severity of impacts compared to sensor information.60, its successful implementation has two advantages. First, having players’ position information can complement the DeepImpact algorithm to identify the player in the header event. Second, players’ positions, along with their pose information, may not only provide additional information to confirm headers but also identify other types of impacts such as falling or head-body contacts. Human pose estimation using video has been extensively researched in the computer vision community61 and powerful algorithms have been developed for 2-dimensional and 3-dimensional estimation of human pose. Adaption of these algorithms for soccer can be used for impact detection and potentially kinematics estimation.A helpful future step in this research is to add player tracking into the system. Although player tracking using video information alone is a challenging taskFuture work may also benefit from a multi-view soccer video dataset with better-defined camera position and angle. In fact, with multi-view information, we anticipate that a video-based header detection algorithm would be capable of fully automating accurate identification of header events, as potential false positive and false negative detections may be cross-verified using the multiple views. Another extension of the current work is to adapt the system for head impact detection in other sports such as American football. Head impact events in the video can be characterized differently in each sport. For example, helmet contact with other objects such as other helmets, ground, or another player’s body is the main characteristic of head impacts in American football. These characteristics can be used to modify and adapt the header detection algorithm. Expanding this work to other sports would enable multi-sport head impact exposure measurements, especially for sports where brain injuries are more prevalent.We developed DeepImpact, an algorithm to identify header events in soccer using only video data. This algorithm achieved 95.3% sensitivity and 96% precision in cross validation, and 92.9% sensitivity and 21.1% precision on an independent test dataset. This test performance corresponds to correct identification of most header events, with an average of 3.5 minutes of each game video requiring further verification by a video rater to eliminate false positive detections. Our solution is a first step towards video-based head impact exposure quantification in soccer, and can help streamline the process of video analysis for independent or sensor-based impact exposure measurements. Such a system may enable the collection of an accurate large-scale head impact exposure dataset to study potential cumulative brain injury risk, and may also be further developed into an automated exposure monitoring tool for brain injury management in sports.


Micro/nanorobots have several potential biomedical applications, such as drug delivery, minimal invasiveness, and moving within narrow and complex areas. To achieve these desirable applications, precise path tracking and controlling magnetic micro/nanorobots within blood vessels is a crucial but challenging point. In this paper, a three-dimensional electromagnetic actuation system composed of three pairs of Helmholtz coils and three pairs of Maxwell coils is proposed. A closed-loop control algorithm is proposed to enhance trajectory tracking of a micro/nanorobot. Different simulation experiments were carried out using Simulink to verify the performance of the proposed algorithm. Different trajectories were tested in tracking two-dimensional and three-dimensional reference trajectories. The results showed that by using the developed algorithm and electromagnetic actuation system, a micro/nanorobot can follow the desired trajectory within a maximum error of 13 μm. Biomedical applications have significant potential for micro/nanorobots (MNRs) that operate wirelessly. These untethered, programmable, and powered MNRs can revolutionize conventional treatments, for example, minimally invasive therapies, drug delivery, and focused therapy ,2,3. EleMultiple groups have developed various electromagnetic actuation systems for actuating magnetic MNR within an electromagnetic field.The two-dimensional EMA system proposed in Ref.  is compoAnother EMA was proposed, in which a ferromagnetic locomotive microrobot was controlled in three dimensions using typical magnetic resonance imaging (MRI) . The uniThe motion control of a five DoFs microrobot (3-DOF position and 2-DOF pointing orientation) was proposed and named the OctoMag electromagnetic system . The OctDifferent EMAs  were presented and simulated, and the obtained result of the combined Helmholtz and Maxwell coils showed better actuation of the MNR than that of DCC and the one coil EMA, respectively .This paper proposes six-dimensional system (3D for position and 3D for orientation) consisting of three pairs of Helmholtz coils and three pairs of Maxwell coils. The Helmholtz coils are used to produce a uniform magnetic field, while the Maxwell coils are used to generate a uniform magnetic field gradient. The design and configuration of the proposed EMA are described in The main contribution points of the paper are as followsX, Y, and Z plane and, three dimensions for position. Three pairs of Helmholtz coils are used for the orientation and the other three pairs of Maxwell coils are used for positioning and MNR in the X, Y, and Z plane. The current supplied to the Helmholtz coils controls the uniform magnetic field generated from these coils to align an MNR to the desired orientation. Moreover, the proposed EMA can actuate the MNR to the desired orientation at any instant;The developed navigation of the MNR describes the pose of the robot in six-dimensions (6D), three dimensions for orientation of an MNR within the The design of the proposed EMA, in terms of the number of turns and coils’ radii, can actuate the small sizes of micro/nanorobots with scales less than 0.2 mm;3;The proposed and designed EMA can actuate and navigate micro/nanorobots within a relatively large region of interest (ROI) 40 × 40 × 40 mmThe error in the simulation results for different experiments tests was 13 μm and 8 μm.F) : (X-Y-Z) plane, three pairs of Maxwell coils are needed. So, the axial gradient magnetic field will be calculated as in Equation (For actuating an MNR in 3D Equation .(11)∇BMll Coils .Equation  expresseY and Z axes.The gradient magnetic field produced from three orthogonal pairs of Maxwell coils is shown below in Equation .(13)∇BMTo achieve MNR actuation control under the influence of a magnetic field, 3D EMA coil system is required to generate a controllable magnetic field in 3D space. By adjusting the currents that flow through the coils, it is possible to control the magnetic field produced by the coils, and thus the movement of the magnetic MNR. This idea motivates us to utilize Helmholtz coils and Maxwell coils to create a 3D EMA system consisting of three sets of Helmholtz coils and three sets of Maxwell coils arranged in three orthogonal axes. Helmholtz coils are responsible for the orientation of the MNR, while the Maxwell coils are responsible for the actuation and moving of the MNR. The main parameters of the proposed EMA are listed in 3 produced from the Helmholtz coils, and a uniform magnetic gradient along the region of interest produced from the Maxwell coils [The proposed EMA shown in ll coils .The MNR locomotion is affected in the vertical axis by two forces, its gravitational weight Equation : (14)Fm+R is the MNR’s radius, and v is the MNR’s speed. The drag force is not a constant value, since it depends on the speed of the MNR, which depends on the intensity of the magnetic field gradient.The microrobot is typically placed in a liquid with a low Reynolds number, and, consequently, its inertial force can be neutralized. Therefore, the drag force Both the gravitational and buoyancy forces are constant.V is the MNR’s volume, g is the gravity constant.The resultant of both gravitation force Since the proposed magnetic actuation systems are based on Helmholtz and Maxwell coil configurations, which can generate a uniform magnetic field and uniform magnetic gradients, respectively, this section’s analysis is crucial. It will be explained in the following section how this can be used to control MNRs. We will assume that the MNR is located in any plane in the three dimensional space, with the angle Equation  demonstrSo, if both In order to extend the dimensionality from 3D to 6D, another pair of Maxwell coils is added to represent the position in the Z-axis as well as a pair of Helmholtz coils to generate an additional angle, which will help in combination to conclude the six dimensions. Another angle For the purpose of generating the desired orientation in the x, y z plane, adjusting the currents applied to the three pairs of Helmholtz coils, the desired magnetic field will be generated, resulting in aligning the MNR to the desired orientation in the x, y, and z plane. Therefore, the desired magnetic field B to align the orientation can be obtained as follows:Maxwell coils produce a uniform magnetic gradient that generates a propulsion force, which is expressed in Equation .M is constant, so the components of the magnetization is described as follows: The magnitude of the MNR’s total magnetization saturation Since both the volume and the magnetization of the MNR are known, the three components of the magnetic driving force are derived as follows:Such that So, from Equation , any twoSince both the gravity and buoyancy forces in the z direction are constant,  deduced ,17.(26)From Equation , it can Equation , Fzg is The proposed system is intended for use in biomedical applications such as blood vessel navigation. The MNR is constructed of Neodymium (N52). The MNR has a cylindrical shape with a 1.5 mm diameter and length of 2 mm. The parameters of the MNR robot are depicted in By regulating the position of the MNR, the control architecture relies on a closed-loop feedback system using the PID controller. The reference input is the desired position, i.e., MNR’s desired coordinate. A set of coordinates are provided to the system in order to attain the desired trajectory. The position error serves as the controller’s input. The output of the PID controller aims to manipulate the current for the Maxwell pairs of coils.First, extract the coordinates  with corner coordinates : --- and finally to the starting point .μ in the y-axis. As shown in The second example is following a 3D trajectory, and the response of the controller and the EMA system is shown in The root mean square error for the three axes was calculated for the trajectory in A simulation test was carried out to check the ability of the proposed EMA to orient the MNR at any position, and the result is shown in In this paper, we introduced a three-dimensional (3D) system for position and a 3D system for orientation to control an MNR by developing an EMA consisting of three pairs of Helmholtz coils and three pairs of Maxwell Coils. The motion analysis of the MNR in both position and orientation was conducted and discussed. PID-based closed loop control technique with tuned parameters was used to improve EMA position tracking along a desired trajectory. Simulation tests were carried out to test the developed system. The results show that MNR can navigate and track its position along a desired trajectory with a maximum error of 13 μm. In addition, the simulation results confirm the ability of the closed loop EMA system to actuate an MNR as required. Future work is currently undergoing to experimentally validate the simulation results.


Among AVM cTTE, non-AVM cTEE, and AVM cTEE, the RLS grade evaluation results were inconsistent, with significant differences (p < 0.05). Non-AVM cTEE had RLS underestimation or false negatives. Compared with non-AVM cTEE, AVM cTEE and AVM cTTE had better consistency in evaluating PFO RLS . Aortic root downward movement could be used as a novel method for judging the effectiveness of VM, which is critical for the detection of PFO in cTEE. Concerning effectiveness and convenience, this method should be promoted during the clinical detection of PFO.The Valsalva maneuver (VM) is the most sensitive auxiliary method for the detection of patent foramen ovale (PFO), but it is difficult to assess whether the maneuver is adequately performed during transesophageal echocardiography (TEE). In this study, we tried to use aortic root downward movement as a novel method for judging whether VM was adequate or not, and to evaluate whether this novel method can increase the sensitivity of detecting PFO. A total of 224 patients with clinically suspected PFO were enrolled in this study. These patients were injected with activated normal saline to detect the right-to-left shunt (RLS), in the following three conditions: contrast-enhanced transthoracic echocardiography under adequate VM (AVM cTTE), contrast-enhanced TEE under non-adequate VM (non-AVM cTEE), and cTEE under adequate VM (AVM cTEE). A novel method in which the aorta root moves downward (movement range ≥16 mm) in the cTEE judged whether AVM was performed. The PFO detection rate and sensitivity of AVM cTEE were better than that of non-AVM cTEE (detection rate: 108 PFOs [48.2%] vs. 86 PFOs [38.4%],  Patent foramen ovale (PFO) is an important cause of cryptogenic stroke, especially in young patients whose morbidity is as high as 46% . Even inAlthough right heart catheterization and the confirmation of the guidewire passing through the septum are the most accurate ways to confirm the presence of PFO, considering its invasiveness, it is not feasible to perform right heart catheterization to diagnose PFO . At presAs we know, when the VM is performed, the chest pressure rises, the diaphragm decreases, and the mediastinum structures such as the heart and the root of the aorta will move downward. We have also found this phenomenon in clinical TEE operations, that is, when patients perform VM, the downward movement of the heart and aortic root can be observed. Compared with the heart, the movement of the aortic root is easier to locate and measure. Therefore, in this study, we observed whether it was effective to use aortic root downward movement in the cTEE for determining AVM and further for detecting PFO.A total of 233 patients with clinically highly suspected PFO were examined for cTTE and cTEE from May 2018 to December 2020. The exclusion criteria were atrial fibrillation, significant heart disease including moderate and severe valvular disease, moderate and severe left ventricular (LV) systolic dysfunction, congenital heart disease except for PFO, and other cardiac sources of embolism such as thrombi, tumors, or vegetation. Before the echocardiographic examination, each subject needed to be trained in the Valsalva movement. The sign of AVM is: use a pressure gauge to measure the pressure and increase the chest pressure by ≥40 mmHg (1 mmHg = 0.133 kPa) ,14. EighIn accordance with the established guidelines of the American Society of Echocardiography ,15, eachDuring the cTEE examination, when the subject performed the VM, TEE observed the interatrial septum (IAS) on the fossa ovale cut surface to see that the aorta root downward moved from the resting position ≥16 mm, and at the end of the VM moved up to the original resting position, which was defined as the downward movement of the aorta root during the cTEE examination and was judged to be an AVM A,B. WhenA pre-experiment was conducted to define the cutoff value of aortic root downward movement. Fifty persons of different ages and genders who underwent TTE in our hospital were randomly selected. During the examination, the pressure gauge method to measure the pressure and increase the chest pressure by ≥40 mmHg, and the position changes of the aorta root were observed. In order to correspond to the TEE section, after the previous multi-section test, a non-standard view, which was obtained from a slightly skewed the short-axis view of the great artery showing the atrial septum, was finally selected as the transthoracic observation section of aortic root downward movement C,D. The When using agitated saline contrast injection to see at least one clear microbubble in the left atrial (LA) or LV in the recorded movie, the diagnosis was positive for RLS. The number of microbubbles in LA (TEE) and LV (TTE) is counted in a single still frame showing the maximum number of microbubbles, and the amount of RLS is determined by the number of microbubbles appearing in the left ventricular cavity on the still single frame image. Quantification of LA opacification was regarded as grade 0 (zero microbubbles), grade 1 , grade 2  or grade 3  ,17.Clinical data included age, sex, body surface area, body mass index, migraine headache, atrial septal aneurysm, documented diagnosis of hypertension, transient ischemic attack, and stroke .RLS in PFO and the consistency between observers and within observers were evaluated. cTTE, non-AVM cTEE, and AVM cTEE were also evaluated to determine the degree of RLS classification and the consistency between observers, within observers, and between the three conditions.p values of <0.0167 were considered statistically significant (with the correction of Bonferroni)). The consistency between observers, within observers, and between two different inspection methods was tested by Kappa. p < 0.05 was considered to indicate statistical significance. Statistical analyses were performed using IBM SPSS V.20.0 software.Categorical variables are presented as a number and percentage (%). Continuous variables are expressed as the mean ±SD. We applied the chi-square test to compare the rates of detection. AVM cTEE is used as the gold standard for the diagnosis of PFO, comparing the sensitivity of AVM cTTE, non-AVM cTEE, and AVM cTEE. The Friedman test was used to analyze the data of the quantification of the shunt, and then we compared the results of each examination with those by the Wilcoxon matched pairs test (p = 0.036). The sensitivity of detecting PFO for AVM cTTE, non-AVM cTEE, and AVM cTEE was 92.6%, 79.6%, and 100%, respectively.All 224 subjects with suspected PFO underwent cTTE and cTEE examinations, of which 108 patients (48.2%) were diagnosed with PFOs by AVM cTEE, 86 patients (38.4%) by non-AVM cTEE, and 100 patients (44.6%) by AVM cTTE. The PFO detection rate of AVM cTEE washigher than non-AVM cTEE (p < 0.05), and a statistically significant difference was evident from the results between AVM cTEE and non-AVM cTEE (p < 0.01), between AVM cTTE and non-AVM cTEE (p < 0.01), and between AVM cTTE and AVM cTEE (p > 0.01).The number of cases at each level detected by the three conditions ispresented in p = 0.038). The consistency of the other two groups was poor .AVM cTEE and AVM cTTE have better consistency of RLS . AVM cTEE had good intraoberver and interoberver agreement when judging the RLS grade ; non-AVM cTEE had good intraoberver and interoberver agreement when judging the RLS grade ; 100% consistency was used in determining whether there was RLS between the intraobserver and interobserver groups in 3 conditions .AVM cTTE showed good intraobserver and interobserver agreement when judging the RLS grade  and cough . If the Combining our experience and thinking in clinical practice, in this study, we tried to use aortic root downward movement as a new method for judging whether VM is adequate or not in TEE examination. AVM will cause the diaphragm to move downward due to the increased pressure in the thoracic cavity during deep inhalation. When the diaphragm moves downward, the position of the heart and aortic root will also move downward, as seen in the TEE section. At that time, part of the reflux of the inferior vena cava into the right atrium isblocked, and the right heart preload isreduced; after deep exhalation, the sudden drop in thoracic pressure causes the diaphragm to move up, and the aorta root also returns to its original position. At this time, the sudden increase in venous return causes the right-to-left shunt. The increase in precardiac load results in a transient increase in the pressure gradient between the right and left atrium. If PFO is present, it can show the signal of right-to-left shunt microbubbles, thereby verifying the diagnosis of PFO. The downward movement of the diaphragm during AVM cannot be observed on TEE, while the accompanying obvious downward movement of the aorta root can be observed on TEE, and it can be achieved in the PFO diagnostic view. Therefore, it is feasible in principle to judge whether the patient has performed AVM by aortic root downward movement. In this study, we judged the Valsalva maneuver as AVM and non-AVM in cTEE according to whether there was aortic root downward movement.In the current study, we compared the PFO detection rate and sensitivity of AVM cTTE, non-AVM cTEE, and AVM cTEE. The effects of different conditions on the grade of PFO RLS were observed. Many previous studies have compared the use of cTEE as the gold standard or the use of cTTE- or cTEE-confirmed PFO diagnosis to compare the accuracy of TTE and TEE in diagnosing intracardiac shunts. These studies show different conclusions. Thanigaraj and Yue L et al. showed that the sensitivity of TTE to TTE or TEE diagnosed with PFO is higher than that of TEE  ,23. AnotIn patients with PFO-associated stroke, the presence of a large shunt or atrial septal aneurysm has been suggested to convey high risk of stroke recurrence . The resThe current study presents several limitations. First, this study was conducted in a single center and needs to be further verified by a large-scale multicenter study. Second, this study used an echocardiographic machine with the same image setting and vendor, which may prevent us from generalizing the results to other centers using different machines. third, there was selection bias because the subjects of this study were people who were clinically highly suspected of PFO and underwent adequate Valsalva maneuvers before echocardiography. Finally, because not all PFO positive subjects underwent PFO closure in this study, and the right heart catheterization was an invasive operation, cTEE with AVM was selected as the gold standard for the diagnosis of PFO.Adequate to the Valsalva maneuver (with aortic root downward movement), cTEE has a higher disease detection rate and sensitivity to PFO. Compared with AVM cTEE and AVM cTTE, non-AVM cTEE assesses RLS grade as a small shunt or false negatives, and AVM cTEE and AVM cTTE have better consistency in assessing the degree of PFO RLS. Aortic root downward movement (range of movement ≥16 mm) can be used as a novel method for cTEE to determine an AVM, which is simple and convenient, can be visually judged in real-time on the same view of ultrasound, and can be guided in real-time during the TEE inspection process, thereby further improving the detection rate of PFO and the accuracy of RLS grade, which is helpful to guide the diagnosis and treatment of clinical PFOs.


Clinical reports demonstrate that urinary Mg2+ excretion is low in PPI users with hypomagnesemia, suggesting a compensatory mechanism by the kidney for malabsorption of Mg2+ in the intestines. However, the exact mechanism by which PPIs cause impaired Mg2+ absorption is still unknown. In this review, we show that current experimental evidence points toward reduced Mg2+ solubility in the intestinal lumen. Moreover, the absorption pathways in both the small intestine and the colon may be reduced by changes in the expression and activity of key transporter proteins. Additionally, the gut microbiome may contribute to the development of PPI‐induced hypomagnesemia, as PPI use affects the composition of the gut microbiome. In this review, we argue that the increase of the luminal pH during PPI treatment may contribute to several of these mechanisms. Considering the fact that bacterial fermentation of dietary fibers results in luminal acidification, we propose that targeting the gut microbiome using dietary intervention might be a promising treatment strategy to restore hypomagnesemia in PPI users.Proton pump inhibitors (PPIs) reliably suppress gastric acid secretion and are therefore the first‐line treatment for gastric acid‐related disorders. Hypomagnesemia (serum magnesium [Mg Moreover, we aim to describe the molecular mechanisms underlying the disease, as significant progress has been made toward our understanding of PPI‐induced hypomagnesemia. Based on recent advances, we will propose novel therapeutic approaches toward the treatment of PPI‐induced hypomagnesemia.2+, K+‐ATPase) of the epithelial cell lining in the mucosa of the stomach.+, K+‐ATPase. This mechanism contributes to the specificity of PPIs for the gastric H+, K+‐ATPase and reduces inhibition of non‐gastric H+, K+‐ATPases.2RAs) or anticholinergics.2RAs.PPIs prevent gastric acid secretion by direct inhibition of the gastric proton‐potassium ATPase  tract .2+ intake is low.Approximately 30%–50% of the daily Mg2+ absorption  family. This absorption pathway is transcellular and secondary active. Transcellular transport of Mg2+ accounts for ~30% of total Mg2+ absorption in normal physiological conditions.+‐Mg2+ exchangers on the basolateral side of the intestinal epithelial cell.2+ uptake.2+.2+ levels (0.1–0.3 mmol/L).2+ absorption. Moreover, it was shown that TRPM6 in the intestine, but not in the kidney, is essential to maintain systemic Mg2+ balance in mice.Trpm6 in mice caused severe hypomagnesemia due to a defect in intestinal Mg2+ absorption.In the colon and distal segments of the ileum, fine‐tuning of Mg32+ absorption along the intestinal tract. Here, we will critically describe the evidence for these mechanisms as potential underlying cause for the development of PPI‐induced hypomagnesemia.Over the last years, several experimental studies have addressed the putative molecular mechanisms by which PPIs affect Mg3.12+ absorption in the small intestine depends on passive paracellular diffusion. Consequently, two factors are essential to consider: Mg2+ availability and tight junction permeability. Both factors are potentially compromised by PPI treatment.Mg3.1.12+ transport in duodenum, jejunum, and ileum was reduced by 81%, 71%, and 69%, respectively.2+ absorption as result of a higher luminal pH.2+ absorption can be explained by the decreased solubility of Mg2+ at a higher pH.2+ in the GI tract is partially bound to proteins and negatively charged ions such as Cl− and PO43−. This is also the reason why Mg2+‐salts are effective oral phosphate binders and are used for the treatment of chronic kidney disease (CKD).2+ salts bind more phosphate in an alkaline environment.2+ solubility and consequently absorption  for 14 and 21 days showed reduced paracellular transport as measured by Mg3.22+ transport is absorbed in healthy subjects. Moreover, absorption in the colon can compensate for reduced Mg2+ absorption in the small intestine. Although the colon is spatially separated from the stomach where PPIs predominantly act, a growing body of evidence demonstrates that colon is also affected by PPI therapy.The colon has been the main focus of studies toward the mechanisms of PPI‐induced hypomagnesemia. In this segment, a significant amount of Mg3.2.1+, K+‐ATPase (ATP12A) is a close homolog of the gastric H+, K+‐ATPase (ATP4A).+, K+‐ATPases, resulting in a less acidic local pH. Indeed, omeprazole treatment significantly increased the mRNA expression of colonic H+, K+‐ATPases in PPI‐treated mice.2+, as discussed in previous sections. Moreover, pH may also directly affect the activity of the Mg2+ channels TRPM6 and TRPM7 in the colon. However, whether PPIs have direct effects on the colonic H+, K+‐ATPase is still heavily debatable, as PPIs will not accumulate in these cells, in contrast to the parietal cells of the stomach.The colonic H3.2.22+ channel in the colon.2+ absorption  have an increased risk for the development of hypomagnesemia in response to PPI treatment.The pH in the colon may also determine the activity TRPM6/7, which represents the luminal Mgn Figure . Omepraz3.2.32+ 43. Therefore, it is interesting that PPIs have consistently been shown to change the composition of the gut microbiome (Table Clostridium difficile (CDI) infection  or small intestinal bacterial overgrowth (SIBO) .Importantly, bacterial fermentation results in acidification of the colon. This has been shown beneficial for the solubility and absorption of Mg3.2.42+ is not caused by direct effects of SCFAs, but likely by changes in the luminal environment of the colon. Targeting the gut microbiota with prebiotic inulin fibers (20 g/day) improved serum Mg2+ levels in patients with PPI‐induced hypomagnesemia.2+ solubility from 13% to 75%–95% compared to fructose treatment. These changes significantly increased the Mg2+ absorption compared to a fiber‐free diet in rats.2+ in the colon.Bacterial fermentation has been associated with increased production of important end‐metabolites, including short‐chain fatty acids . However, in a recent animal study, omeprazole did not affect the concentrations of colonic SCFAs in mice with hypomagnesemia, despite the profound effects of PPIs on the gut microbiome composition.3.32+ supplementation is often insufficient and causes diarrhea, nausea, and abdominal cramping at high concentrations.Currently, there are no adequate treatment strategies to restore hypomagnesemia in PPI users. PPI withdrawal still remains the gold standard.3.3.12+ levels restore to physiological concentrations upon PPI withdrawal,2RAs, could be considered. However, it is still debatable whether H2RAs also cause hypomagnesemia. Kieboom et al. demonstrated a positive correlation between H2RA use and hypomagnesemia ,Clinical case reports show that serum Mg3.3.22+ supplementation does not fully restore serum Mg2+ levels in patients with PPI‐induced hypomagnesemia. Indeed, high dose (30–40 mmol/day) oral Mg2+ supplements only partly restored serum Mg2+ levels in two PPI users with severe hypomagnesemia.2+ infusions did not correct the Mg2+ deficiency in PPI users as shown by the consistently low levels of Mg2+ in the urine.2+ supplementation only shortly maintained serum Mg2+ levels within the normal range.2+ supplementation often causes diarrhea, nausea, and abdominal cramping, this treatment option is both ineffective and poorly tolerated.Oral Mg3.3.3Lactobacillus reuteri reduced the prevalence of enteric infections after 12 weeks of treatment in children with GERD.Bacillus subtilis and Enterococcus faecium.PPIs greatly affect the luminal pH of the GI tract. Dietary fibers might therefore be a promising treatment strategy to target the microbiome and acidify the lumen of the colon. Recent intervention studies demonstrate that pre‐ and probiotic approaches can reduce PPI‐induced side effects, such as enteric infections and mineral deficiencies. Supplementation with 2+ and Mg2+ in postmenopausal women.2+ absorption by 18% after 36 days in adolescent girls using a stable isotope technique.2+ levels.Moreover, prebiotic fibers have been previously shown to improve mineral absorption. Inulin supplementation successfully increased Ca42+ in the GI tract as underlying cause for the development of hypomagnesemia. This observation sets PPI‐induced hypomagnesemia apart from all other forms of drug‐induced hypomagnesemia that are characterized by renal Mg2+ wasting, as seen in users of gentamycin, calcineurin inhibitors, diuretics, and anti‐diabetic drugs.Hypomagnesemia is a well‐known side effect of PPIs. Treatment duration (>1 year) and daily dose are important contributing factors for the development of PPI‐induced hypomagnesemia. Current clinical and experimental studies point to malabsorption of Mg2+ absorption in both small intestine and colon. Although the exact molecular mechanism remains to be elucidated, most evidence points toward reduced Mg2+ solubility in the small intestine or changes in the composition and function of the gut microbiome in the colon. This is likely caused by the increase of the luminal pH during PPI treatment. Future studies using wireless pH monitoring capsules are required to better understand the physiological pH range of different intestinal segments as well as the direct effects of PPIs on the luminal pH. This would allow to determine the optimal pH range for Mg2+ solubility. Considering the fact that high oral Mg2+ supplementation does not recover serum Mg2+ levels in PPI users with hypomagnesemia it is very likely that PPIs mainly impair active Mg2+ absorption in the colon rather than passive absorption in the small intestine.In this review, we set out numerous mechanisms by which PPIs affect Mg2+ supplementation or the use of different acid suppressants, are less effective. The gut microbiome might be a novel target to ameliorate PPI‐induced side effects using prebiotic strategies. This might rely on common mechanisms: (i) diets rich in fibers have been associated with increased bacterial diversity2+ absorption in PPI users with hypomagnesemia.To date, the cornerstone of hypomagnesemia treatment is still PPI withdrawal. However, this is not possible for patients who are dependent on PPIs. Alternative treatment options, such as oral MgNo conflicts of interest.