IntroductionAccurate drug dose calculation in obese patients requires an estimation of lean body weight (LBW) for dosing hydrophilic medications. Inaccurate weight estimates during the management of critically ill obese adults may contribute to inaccurate drug doses and consequential poor outcomes. Existing methods of LBW estimation or measurement may be very difficult or impossible to use during emergency care. A point-of-care model that could provide rapid, accurate estimates of LBW would, therefore, be of significant clinical value.MethodsA model was derived based using the adult version of the PAWPER XL-MAC tape. This derived model used recumbent length and measured total body weight (TBW) to estimate LBW. The derived model was used to generate LBW estimations in a random sample from National Health and Nutrition Examination Survey (NHANES) datasets . The benchmark outcome measure was to achieve >95% of LBW estimations within 20% of DXA-measured fat-free mass (P20>95%) and >70% of estimations within 10% of DXA-measured fat-free mass (P10>70%).ResultsThe new model achieved a P20 of 99.7% and a P10 of 86.4% for LBW in the pooled sample and exceeded the minimum accuracy standards. This accuracy was maintained in both sexes, all ages, all ethnic groups, all lengths and in all habitus types.ConclusionsThe modified PAWPER XL-MAC model, using TBW as an input variable, proved to be an accurate method of LBW estimation. It could potentially have an important role in facilitating emergency drug dose calculations in critically ill or injured obese adult patients. BackgroundWeight-based drug therapy in obese patients is complicated by the need to use the appropriate weight scalar for each drug administered ,2. In geImportanceThe ability to estimate LBW easily and accurately is both relevant and important. It is relevant because obesity is common, with a prevalence of more than 37% in the USA, and doctors providing emergency or critical care will frequently encounter obese patients ,15. The In addition, improved access to methods of rapidly estimating LBW at the patient’s bedside could increase the use of correct dose scaling in clinical medicine as well as drug-related research. This could lead to a reduction in medication errors in obese patients and enhanced patient safety .Goals of this studyThe aim of this study was to evaluate the accuracy of LBW estimations using the PAWPER XL-MAC tape model in a new way, when actual TBW, rather than an estimate of TBW, is included in the model estimation methodology.This was a “virtual study,” in which the estimation of LBW by a new method of using the PAWPER XL-MAC method, was evaluated using data from a large database.PatientsThe National Health and Nutrition Examination Survey (NHANES) datasets from 1999-2000 to the 2017-2018 surveys were downloaded from the Centers for Disease Control (CDC) website [EthicsThe National Health and Nutrition Survey Examination studies were each approved by the Research Ethics Review Board of the National Center for Health Statistics. This current study was exempted from additional IRB review as the study only utilized anonymized, publicly available data.LBW estimation modelThe PAWPER XL-MAC tape is a 196 cm long tape, designed to be able to predict TBW, IBW and LBW in male and female adolescent and adult patients. There is insufficient reference data to establish weight estimation parameters in patients taller than 196cm. The tape is divided into 3 cm length segments. Figure The model described and evaluated in this paper varies slightly from the original methodology. Since estimations of LBW are likely to be more accurate if actual rather than estimated TBW is used in the estimation process, a modified model to estimate LBW was developed. In this new model, the tape is used to measure the patient’s length to identify their length segment, in the same way as the original method. However, once the patient’s length segment is identified, the new model follows a different pathway. The patient’s measured TBW is used to identify their HS, rather than using measured MAC . This is done by finding the HS (in the patient’s length segment) for which the TBW estimate is closest to the measured TBW. The LBW estimate for that HS can be read directly off the tape. Figure Generation of dataThe new model was used to generate estimates of weight from height and MAC for each individual in the dataset. Estimates of LBW were created in a spreadsheet (Microsoft Excel for Mac 2021) using measured TBW and height from the NHANES datasets as input data. The formulas used in the spreadsheet to calculate the estimates were custom developed. Height was used to determining the appropriate length segment of the tape to be used in the next step of the estimation process. In this second step, the TBW estimate on the tape closest to the actual TBW was established - this identified the participant’s HS category. The LBW assigned to that HS category on the tape was then used as the LBW estimate for the participant. This process was repeated to generate LBW estimates for each of the participants in the dataset.Analysis of data and outcomesIn the data analysis, the performance of the virtual PAWPER XL-MAC estimation of LBW was compared against the participants’ actual FFM , the gold standard for FFM measurement). In general, FFM and LBW are considered to be equivalent, although FFM may be slightly lower than LBW, as LBW includes the lipid mass in cell membranes, the central nervous system and bone marrow ,19. In tThe principal indicators of the performance were: mean percentage error (MPE) which is a measure of bias; the root mean squared percentage error (RMSPE) which is a measure of precision; and the percentage of weight estimations falling within 10% and 20% of actual measured FFM (P10 and P20 respectively) which is a measure of overall accuracy. The percentage of weight estimations falling within 10% and 20% is a standard descriptor used in weight estimation studies. It is clinically relevant as a substantial number of patients receiving a weight estimation with >10% error is at risk of harm from a medication error [To be able to compare the current model with other, older LBW estimation methods, the root means square error (RMSE) was calculated. This is not a good statistic to evaluate predictive performance or compare data from different studies, as its value is dependent on the actual weight of the study participants, and the range of BMIs in the study sample. If the weights of participants in other studies differ substantially, then the value of this metric is of limited value. However, it is the only potentially useful outcome measure that has been reported in the few previously published studies.Acceptable outcome criteriaThe primary outcome measure was the performance of the model when compared to the measured FFM. A P20 >95% and a P10 >70% were considered to be acceptable accuracy of estimation, as has previously been suggested for TBW estimations [A total of 33,215 individuals were included in the study, with 16,358 females (49.2%) and 16,857 males (50.8%). The basic demographic and anthropometric characteristics of the study population are shown in Table The results of the analyses of the performance of the new PAWPER XL-MAC tape model are shown in Table The relationship between BMI, age, and ethnicity with the overall accuracy of the model, as reflected by the P10 and P20 outcomes, is shown in Figure It is not yet clear how we should estimate alternate weight scalars, such as LBW, for obese patients in need of urgent weight-based drug administration . In clinIn this study, the PAWPER XL-MAC model achieved a high level of accuracy. It remained accurate even in morbidly obese and super-obese participants. In this respect, it outperformed the original PAWPER XL-MAC methodology for LBW estimation . The curThe original PAWPER XL-MAC methodology achieved an accuracy comparable to the current gold standard for clinical LBW estimation, the Janmahasatian formula ,24. The As a potential method to be used in emergency and critical care, a weight estimation system needs to be easy to use and must not require calculations in the methodology ,26. The The ability to accurately, and easily, estimate LBW has important implications for clinical care as well as for research. The use of the correct scalars has the potential to reduce medication errors in obese patients, with a reduction in patient harm . In termLimitationsThis was a virtual study, in which information from a database was used as the input data. The benefit and validity of virtual studies have previously been documented . NonetheThis preliminary study on the use of the PAWPER XL-MAC tape method to predict LBW (when TBW is known) showed promising results. The accuracy achieved by the model exceeded the predetermined benchmark (P20>95%), with nearly 100% of estimations within 20% of measured FFM. The model was accurate in all subpopulations, including in morbidly obese and super-obese individuals. The new model outperformed the original PAWPER XL-MAC model and was at least as accurate, if not more accurate, than the Janmahasatian method, arguably the current gold standard for LBW estimation. The ability to easily estimate LBW at the patient’s bedside has the potential to increase the use of correct drug dose scaling in obese patients requiring emergency or critical care. Future prospective studies will be required to further validate this system, including during real and simulated clinical scenarios.


Zero-inflated negative binomial regression was used to model days of LMM with adjustments for sociodemographic and health-related factors. In total, 20% of those with ADHD and 59% of those without had no days of LMM during the follow-up. The median of those with LMM days with and without ADHD was 312 and 98 days. Having an ADHD diagnosis was associated with a higher incidence of LMM days (incident rate ratios (IRRs) 2.7–3.1) with no differences across occupational branches. Adjustments for sociodemographic and health-related factors explained most of the differences (IRRs: 1.4–1.7). In conclusion, young, employed adults with ADHD had a higher incidence of LMM days than those without, but there were no substantial differences between branches, even after adjusting for sociodemographic and health-related factors.We compared labor market marginalization (LMM), conceptualized as days of unemployment, sickness absence and disability pension, across occupational branches , among young employees with or without attention deficit hyperactivity disorder (ADHD) and examined whether sociodemographic and health-related factors explain these associations. All Swedish residents aged 19–29 years and employed between 1 January 2005 and 31 December 2011 were eligible. Individuals with a first ADHD diagnosis ( Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder usually diagnosed during childhood, but from the 2000s it has more widely been recognized among adults, the working-age population ,2,3. TheThere have been relatively few studies so far that have investigated whether being diagnosed with ADHD in young adulthood influences a sustainable working life. Previous studies on young adults with mental disorders reported that many have problems of entering and being able to stay in the labor market ,10,13. YTherefore, by using nationwide register information, we aimed to investigate the association between ADHD diagnosis in adulthood and LMM, conceptualized from a social insurance perspective as unemployment, SA and disability pension (DP)  through We conducted a prospective register-based matched cohort study. We followed the STROBE guideline/checklist for reporting the study  Supplem. All indAll information used in this study was obtained as microdata from the following national registers and linked at an individual level through the unique personal identification number :The Longitudinal Integration Database for Health Insurance and Labor Market Studies (LISA), held by Statistics Sweden, contains information on sociodemographics, such as sex, year of birth, year of emigration, type of living area, country of birth, family composition, educational level, labor market participation and other work-related information such as occupational branch, income and unemployment . The NatOutcomes: labor market marginalization (LMM) days were measured as work disability, i.e., the sum of net days with SA, DP, or days of full-term unemployment during the follow-up period, in order to capture both medically certified and non-medically certified absence from work. Employees in Sweden are eligible for paying SA for the first 14 days of the sickness spell; a medical certificate is required from day 8. Time-restricted DP can be granted to any individuals between the ages of 19 and 29 whose work capacity/ability has been reduced by at least 25%. Temporary DP can become permanent after the age of 30 if the same conditions persist. Both SA and DP can be granted in part-time and full-time [ull-time  amounts.In secondary analysis, we also investigated the sum of SA/DP days and unemployment days separately. Unemployment days were calculated on an annual basis. SA/DP days were available on a daily basis; therefore, we could calculate this outcome from the exact cohort entry date .Predictors: the detailed categorization of the predictors is presented in www.scb.se, accessed on 5 June 2022) and were further categorized to the following wider occupational branches: (1) manufacturing, (2) construction, (3) trade and communication, (4) financial and business services, (5) education and research, (6) health and social care, (7) other . Sex , age, educational level (low (<10); medium (10–12); high (>12 years)), country of origin , family composition (married/cohabiting or single with children or without children), type of living area , blue- or white-collar job and income in quartiles were considered as sociodemographic variables. The following health-related variables were considered: History of SA was defined as any or no. The following diagnoses (based on ICD-10 codes) were considered as comorbidities: depression and bipolar disorders (ICD-10: F30–F34), anxiety- and stress-related disorders (ICD-10: F40–F48), autism spectrum disorders (ICD-10: F84), substance use (ICD-10: F10–F19), behavioral and emotional disorders (ICD-10: F91–F98), schizophrenia/non-affective psychoses (ICD-10: F20–F29), other mental disorders (ICD-10 other F-codes), musculoskeletal disorders (ICD-10: M01–M99) and other somatic disorders .2 statistics. As LMM days is a non-negative integer value with zero-inflated distribution, therefore, in descriptive statistics we presented the percentage of individuals with no LMM days during the follow-up, and the median and interquartile range (IQR) among those who had at least one LMM day during the follow-up. As conditional variance of the outcome exceeded its means, resulting in overdispersion and as there were excessive zero counts as most of the individuals had no LMM days, we used zero-inflated negative binomial regressions (zinb) to model the association between ADHD diagnosis and LMM [Differences in sociodemographic and health-related factors between individuals with ADHD and without ADHD were summarized and compared by calculating proportions and chi and LMM . Zero-in and LMM  and IRRsFour models, with adjustments for different sets of covariates, were run. Model 1 was only adjusted for age, model 2 was further adjusted for sociodemographic factors, model 3 was additionally adjusted for previous SA and somatic comorbidities and model 4 further for mental disorders. In a secondary analysis we ran the same models separately for the sum of SA/DP days and unemployment days. We also ran model 4 for the whole study sample twice, once without and once with adjustment for occupational branches as a categorical covariate, to examine how much differences between occupational branches explained the association between ADHD diagnosis and LMM. Finally, we stratified the analyses by individuals with a diagnosis of ADHD with and without any mental comorbidity while adjusting to occupational branches and other covariates.Data management was performed with SAS Base  and statistical analysis with R  using the “zeroinfl” function from the “pscl” package. The Regional Ethical Review Board of Stockholm, Sweden, approved our study.A diagnosis of ADHD was associated with a higher probability of LMM in all occupational branches . The higThe IRRs of LMM days were almost three times higher among individuals with diagnosed ADHD compared with individuals without the ADHD diagnosis in all occupational branches. Adjusting for sociodemographic variables and somatic disorders explained part of the observed differences. After adjusting for mental disorders, the IRRs decreased to 1.3–1.6 throughout the branches . The fulThe ORs of experiencing SA/DP when compared with those with and without ADHD diagnosis were highest in the “financial and business service” branch and lowest in the “health and social care” branch in the crude models . Adjustin = 4045 and n = 1985, respectively) are presented in The estimates for LMM comparing those with and without ADHD diagnosis did not change when occupational branches were included into the fully adjusted model as a covariate . The resYoung employed adults diagnosed with ADHD had a higher risk of LMM, both as a higher number of unemployment days and SA/DP, compared with their peers without ADHD in all occupational branches. These differences were to a large extent explained by sociodemographic and health-related factors. There were no substantial differences between the occupational branches concerning the risk of LMM. However, it has to be mentioned that in health and social care, the absolute difference of LMM days, especially SA/DP days, was large between individuals with and without ADHD.The similar relative differences in the risk of LMM between individuals with and without ADHD through occupational branches were in contradiction with our hypothesis that in occupational branches including occupations with high numbers of contacts with people the risk of LMM among individuals with ADHD is higher compared with other branches. This lack of relative difference might be due to the fact that individuals with ADHD to some extent adjust their occupational choices according to their ADHD symptoms and self-select occupations and occupational branches that fit better for their workability . ObserviSeveral studies have reported that individuals with ADHD diagnosed in childhood and young adulthood have difficulties completing their education , as wellOne of the main strengths of our study was that we used high quality, longitudinal register data of the whole population of Sweden, including a wide-range of sociodemographic and health-related covariates ,46. The One of the limitations of our study is that ADHD and comorbid disorders were captured by visits at specialized outpatient and inpatient health care facilities; therefore, conditions that were only treated in primary care might have been missed. However, in Sweden, ADHD that has severe enough symptoms to influence education, working life, social behaviour or other aspects of life is exclusively diagnosed and mainly treated in specialized healthcare centers, even if the first contact due to the symptoms may have been with primary care . AnotherAs our study only focused on outcomes among individuals who already had some attachment to the labor market, the results cannot be applied to all individuals diagnosed with ADHD. Often adults with high IQ and probably a supportive environment might function well in elementary or secondary education, and ADHD will first become a barrier when they reach higher level education or meet professional demands and are not able to further compensate for their symptoms . IndividThere were no substantial differences between occupational branches concerning LMM between young employed adults with and without ADHD. Differences in sociodemographic and health-related factors, especially comorbid mental disorders, accounted for a large part of the differences in LMM days similarly across all occupational branches. In some occupational branches the absolute difference in LMM days between individuals with and without ADHD was substantial. Focusing on the treatment of comorbid mental disorders, education and adjustments in the work environment might target interventions to prevent LMM in individuals with ADHD.


Cognitive biases are a core feature of psychotic disorders. Moreover, people with first episode of psychosis (FEP) have more difficulties in social cognition, in particular in theory of mind. On the other hand, deficits in processing speed and distractibility appear to be core features of attention deficit hyperactivity disorder (ADHD) and impairment in these basic processes can lead to deficits in more complex functions, that could induced to cognitive biases.To evaluate whether FEP with and without ADHD differ in the rate and type of cognitive biases.Instruments : The Diagnostic Interview for ADHD (DIVA) and the Cognitive Biases Questionnaire for Psychosis (CBQp) measuring 2 themes : anomalous perception (AP) and threatening events (TE) and 5 cognitive biases: Intentionalising (Int) , Catastrophising (Cat), Dichotomous thinking (DT), Jumping to conclusions (JTC) and Emotional reasoning (ER)Participants 121 FEP treated at the Early Intervention Service of Reus and aged between 14 and 28 years. 31 out 121 (25.6%) met criteria for childhood ADHD. Compared with FEP ADHD- , FEP-ADHD+ presented significant higher scores in the CBQp total score , the AP theme  , the TE theme  and DT bias - subjects, presenting more delusions, distress and a worse cognitive insight.Our findings support the fact that subjects with FEP-ADHD+ presented more cognitive biases than those ADHD-. So, FEP-ADHD+ subjects could represent a clinical subgroup with a worse prognosis than FEP-ADHDNo significant relationships.


Although psychoeducation is generally recommended for the treatment of adult attention-deficit hyperactivity disorder (ADHD), participation in clinical psychoeducation groups is impeded by waiting times and the constrained number of patients who can simultaneously attend a group. Digital psychoeducation attempts are promising, but the rapidly expanding number of apps lack evidence and are mostly limited to only a few implemented interactive elements.To determine the potential of digital, self-guided psychoeducation for adult ADHD, a newly developed interactive chatbot was compared with a previously validated, conventional psychoeducation app.Forty adults with ADHD were randomised, of whom 17 participants in each group completed self-guided psychoeducation based on either a chatbot or conventional psychoeducation app between October 2020 and July 2021. ADHD core symptoms were assessed before and after the 3-week interventions, using both the blinded observer-rated Integrated Diagnosis of ADHD in Adulthood interview and the self-rated ADHD Self-Assessment Scale (ADHS-SB).Observer- and patient-rated ADHD symptoms were significantly reduced from pre- to post-intervention . However, there were no group × intervention interaction effects that would indicate a stronger therapeutic benefit of one of the interventions. Likewise, administered psychoeducational knowledge quizzes did not show differences between the groups. No adverse events were reported.Self-guided psychoeducation based on a chatbot or a conventional app appears similarly effective and safe for improving ADHD core symptoms. Future research should compare additional control interventions and examine patient-related outcomes and usability preferences in detail. Compared with other treatments, psychoeducation has the advantage of having hardly any side-effects as well as being easily scalable through digital provision without significantly increasing costs. A psychoeducation mobile app, for instance, could significantly reduce the time and effort associated with conducting clinical psychoeducation.The basic principles of psychoeducation are to provide knowledge about the disorder and treatment procedures, as well as emphasise the patient's personal strengths and potential for growth. Although psychoeducation generally shows promising results, few rigorous examinations of treatment effects on ADHD core symptoms exist. One of these studies compared psychoeducation with mindfulness training in adults with ADHD,14 and be economically beneficial because of their low-cost scalability, especially in low-income countries.15 However, although mHealth is growing in popularity, scientific evidence of its efficacy is inconsistent, study quality is often low16 and, moreover, safety concerns related to incorrect information, lack of expert involvement and poor validation have been reported.17 Consequently, the risk of low-value care – that is, services that provide little benefit to patients or that even cause harm – is particularly high.18 This risk also applies to mental mHealth, where many applications seem to have no scientifically valid foundation,19 and can appear to suggest evidence-based treatments by using misleading scientific language.20 Regarding ADHD, for instance, Păsărelu et al.21 identified 109 apps, including 23 that focused on psychoeducation, but none provided proof of their effectiveness.In general, advances in digital health have led to the development of a substantial number of mobile health  apps, which can reduce the need for in-person meetings with a clinician, shorten waiting list times, promote self-care22 Although the app was more effective in improving ADHD symptoms and no adverse events were reported, we cannot directly transfer these results to self-guided psychoeducation without a concomitant psychoeducation group. Moreover, the app used may be less suitable for self-guided learning in ADHD because it was developed as a digital, but hardly interactive, instructional format. Thus, the potential benefits of mHealth applications, such as considering individual patient differences in learning behaviour, have not yet been incorporated.To address this issue, our research group recently conducted a randomised controlled trial (RCT), in which we evaluated an 8-week psychoeducation group programme that was either assisted by traditional paper brochures or by a newly developed psychoeducation app for adult ADHD.23 Regarding attention deficits, although not specifically addressing patients with ADHD, only one previous study conducted a chatbot-assisted psychoeducation, and found stronger improvements of ADHD-related symptoms than a self-help book control group.24Considering that motivation-related and dysfunctional learning behaviours occur in adult ADHD, the implementation of a psychoeducation chatbot may be of particular value. A chatbot  is a computer program that can simulate conversations with human users. Potential benefits for psychoeducation include the possibility of interactively self-guiding the learning path, and receiving individualised responses and feedback that are not achievable with a ‘conventional’ psychoeducation app. Although these properties appear valuable for several mental disorders, there is limited evidence for the use of chatbots in mental health.22In this study, we implemented a new chatbot that, based on validated psychoeducational content, interacts in such a way that the patient co-determines the topics addressed. We hypothesised that this approach might lead to greater symptom improvement than conventional module-based content presentation, given the increased potential for self-guidance through the preferred psychoeducational content, as well as the higher level of interaction and individualisation offered by a chatbot. For clinical evaluation, we conducted an RCT to evaluate the effects of a 3-week self-guided, chatbot-based psychoeducation (CBP) in adults with ADHD compared with our previously validated psychoeducation app, which is based on a module-by-module content presentation.https://doi.org/10.1192/bjo.2023.573). The study was advertised via the Department of Psychiatry and Psychotherapy of the University Hospital Bonn, via the website ‘Central ADHD Network’ , and via other publicly accessible media. Individuals were eligible to participate if they met DSM-5 ADHD diagnostic criteria,25 as assessed by the observer-rated Integrated Diagnosis of ADHD in Adulthood (IDA-R);26 were aged 18–65 years; had access to a smartphone with Android OS (version 5.0 or higher) and had sufficient command of the German language.A total of 139 adult out-patients with ADHD were contacted for study participation, of which 40 participants were randomised to the intervention groups and 34 participants completed the study .28 Intake of medication for ADHD was permitted, but had to be stable from 4 weeks preceding the start of study through to the final examinations. The participants received no compensation for their participation in the study.Individuals were ineligible to participate if they met the diagnostic criteria for schizophrenia or other psychotic disorders; severe affective disorder; moderate-to-severe substance use disorder, as assessed by the Brief Diagnostic Interview for Mental Disorders ;a priori sample calculation in G*Power version 3.1 for Windows29  was performed to determine the required sample size of 34 participants, which was based on an alpha error probability of 0.05, a power of 80% and a moderate effect size (f = 0.25). To account for study drop-out, a randomisation of n = 40 was pursued. The first participant was enrolled on 29 September 2020.The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human patients were approved by the local medical ethics committee of the University of Bonn (protocol number: 123/19). All participants provided written informed consent. The study was preregistered in the German Clinical Trials Register  on 13 August 2020. An Two interventions were compared in this parallel-group RCT: a self-guided CBP and a self-guided, app-based psychoeducation (ABP). All participants underwent an extensive baseline assessment (time point 0) and were allocated to one of the intervention groups by permuted block randomisation in blocks of two, to maintain balanced group sizes. Sequence generation and participant enrolment were performed by different study personnel. Participants were asked to engage with the psychoeducation content as much as possible during the self-guided 3-week psychoeducation period. Afterwards, a final assessment (time point 1) was conducted. The relative change in ADHD total symptom severity from time point 0 to time point 1, as examined by a blinded clinical rater on the IDA-R, was considered the primary outcome parameter of the study. Participants’ remarks about app specifics during the final assessment (time point 1), which provided indications of their respective assigned group, were the cause for not maintaining rater blinding for five cases in the CBP group and four cases in the ABP group. Participants were not blinded for assignment to CBP or ABP.26 self-rated ADHD symptoms were obtained via the ADHD Self-Assessment Scale (ADHS-SB).30 Further outcome parameters included the subscales of the World Health Organization Quality of Life questionnaire (WHOQOL)31 and the Depression, Anxiety and Stress Scale (DASS-21).32 The Multiple-choice Word Test (MWT-B) was conducted at time point 0 exclusively, to estimate verbal intelligence.33Besides observer-rated ADHD symptoms as measured via the IDA-R,Following baseline assessments at time point 0 (approximately 2 h), participants received a download link to either the conventional psychoeducation app or the chatbot, and time point 1 final assessments were scheduled. However, because of organisational constraints of some participants, these could not be exactly planned after 3 weeks in all cases. As a result, there were marginal differences in the duration of possible use between participants (see ‘Results’). Both the CBP and ABP groups were not limited in the amount or duration of material usage during the intervention period. Processing of all psychoeducational content was estimated to require about 16 h. Following the self-guided 3-week intervention periods, final assessments (approximately 1 h) of the outcome parameters were conducted.34 and consisted of eight separate modules that contained a comprehensive summary of the psychoeducation content, assignments and a content quiz. In line with a recent Delphi consensus study on digital psychoeducation for adult ADHD,35 a comprehensive summary on various aspects of ADHD was implemented, including multiple illustrations to facilitate understanding. The following topics were addressed: basic information about ADHD, personal resources, mindfulness and attention control, self-organisation, stress management, mood regulation and impulsive behaviour control, relationships and a final evaluation. Within each module, the conventional psychoeducation app presented the content linearly (i.e. module by module), whereas the chatbot interactively presented content based on user input  and has previously been presented in detail.22 The chatbot (CBP group) was based on the open-source conversational artificial intelligence platform Botpress version 12.2 for Windows .The conventional psychoeducation app of the ABP group is available in the Google Play Store  with group  as a between-subjects factor and time  as a within-subjects factor, were conducted for the following outcome parameters: IDA-R total, inattention (sum score of E1 items), hyperactivity (sum score of E2.1–E2.5 items) and impulsivity (sum score of E2.6–E2.9 items) scores; ADHS-SB total, inattention, hyperactivity and impulsivity scores; the sum scores of the DASS-21 subscales  and the sum scores of the WHOQOL subscales .P < 0.05). Therefore, the DASS-21 variables of these two scales were Johnson-transformed36 and subsequent tests for normal distributions revealed no violations . Transformed data were used for all statistical analyses.Data from the DASS-21 scales for symptoms of depression and anxiety were considerably right-skewed, resulting in non-normal distributions for scores at both time points . Visualisation of the correlation matrix was performed with R version 3.6.1 for Windows ,38 using the Corrplot package for R version 0.84.39 Reported statistical tests were two-sided and based on a significance level of α = 0.05.Statistical analyses were conducted with SPSS software version 21.0 for Windowsn = 17) and ABP (n = 17) groups. The exact duration from time point 0 to time point 1 in which the participants could access the psychoeducation content on their smartphones was 22.5 days (s.d. 2.2) in the CBP group and 23.8 days (s.d. 4.3) in the ABP group (t(32) = 1.16; P = 0.25).In total, 34 participants  completed the RCT between October 2020 and July 2021. P < 0.001; ηp2 = 0.58), with reductions of 20.8% in the CBP group and 23.9% in the ABP group. Neither a group × time interaction  = 0.04; P = 0.84), nor a significant main effect of group  = 3.47; P = 0.072) was found.Changes in observer- and self-rated ADHD symptoms from time point 0 to time point 1 are shown in P = 0.68), hyperactivity  = 0.16; P = 0.69) or impulsivity  = 0.15; P = 0.70), but only showed main effects of time. That is, across groups, inattention improved by 20.3%  = 30.30; P < 0.001; ηp² = 0.47), hyperactivity improved by 20.7%  = 18.30; P < 0.001; ηp² = 0.36) and impulsivity improved by 29.9%  = 34.90; P < 0.001; ηp² = 0.52) from time point 0 to time point 1. No main effect of group on any core symptom was revealed.In line with this, the separate analyses of each core symptom  showed no significant group × time interactions for inattention  = 0.17; P = 0.012; ηp² = 0.18), but no group × time interaction  = 0.03; P = 0.88) was observed. Here, ADHD total symptoms decreased by 12.6% in the CBP group and 16.4% in the ABP group, with no significant main effect of group on total symptom severity  = 1.93; P = 0.17).Self-rated ADHD total symptoms  also improved  over time  = 7.12; P = 0.014; ηp² = 0.18) and 19.2% for hyperactivity  = 7.64; P = 0.009; ηp² = 0.19), but only a descriptive reduction of 12.0% for impulsivity  = 1.65; P = 0.21; ηp² = 0.05). For impulsivity, in turn, a significant main effect of group  = 4.84; P = 0.035; ηp² = 0.13) was found, in that mean impulsivity symptoms were higher in the CBP group  than in the ABP group .In the separate analyses of self-rated ADHD core symptoms , no group × time interactions were found, but time had a significant main effect. Specifically, we observed improvements of 12.7% for inattention  = 6.77; t = −1.85, P = 0.078, d = −0.78). Both groups also performed similarly on the content quiz (t(32) = 0.62, P = 0.54, d = 0.22), as measured by the proportion of correct answers, which amounted to 76.4% in the CBP group and 79.4% in the ABP group.The percentages of correct and missing answers in the content quizzes are presented in Supplementary Fig. 2. Of the questions, 21.1% were not answered in the CBP group and 6.9% were not answered in the ABP group. This difference in the percentage of missing responses was, however, not statistically significant (P = 0.13), anxiety  = 0.72; P = 0.40) or stress  = 0.01; P = 0.94). Besides a significant group effect indicating higher stress symptoms in the CBP group  = 5.79; P = 0.02; ηp² = 0.15), we did not observe significant main effects of time or group (see Supplementary Table 1).The analyses of comorbid symptoms  did not reveal any group × time interactions with regard to symptoms of depression  = 2.47; P = 0.95), psychological health  = 0.47; P = 0.50), social relationships  = 0.36; P = 0.55) or environment  = 0.12; P = 0.73). Instead, the analysis of variance only revealed a significant main effect of group on quality of life concerning social relationships  = 4.27; P = 0.047; ηp² = 0.12). Concretely, the ABP group  reported higher quality of life than the CBP group .The domain-specific analysis of self-rated quality of life  demonstrated no significant group × time interactions for physical health  < 0.01; rCBP = 0.64, PCBP < 0.01; rABP = 0.59, PABP  < 0.05), and between changes in DASS-21 scores for symptoms of depression and anxiety . In the CBP group, inter alia, DASS-21 difference scores for depression symptoms were further positively correlated with those of stress , and larger difference scores of stress  and depression  were associated with larger difference scores of self-rated ADHD symptoms.A detailed matrix of Pearson correlations between time point 0 to time point 1 difference scores of primary and secondary outcome parameters is depicted in In each group, three participants did not complete the study for unknown reasons, as a reestablishment of contact was unsuccessful. However, no unintended consequences or adverse events related to any intervention were reported.In this RCT, we examined the efficacy of self-guided digital psychoeducation for adults with ADHD. Specifically, we compared a newly developed chatbot with a previously validated psychoeducation app in addressing ADHD symptoms in a 3-week psychoeducation. A total of 34 participants completed the study and, although both interventions yielded strong effects in the reduction of ADHD core symptoms, neither proved superior.40 However, although these results appear promising, it has to be considered that the two interventions in this study were not compared with further control conditions (e.g. a waiting list group). Therefore, we cannot completely rule out potential improvements caused by incidental treatment effects independent of our specific interventions.The symptom improvements found across intervention groups were evident in both observer ratings (approximately 22%) and patient ratings (approximately 15%), with underestimates of self-rated effects being a common finding in adult ADHD, according to a previous meta-analysis.22 However, although we previously assessed our conventional psychoeducation app in combination with an 8-week psychoeducation group, in the current study, we tested the app and the chatbot as self-directed psychoeducation approaches without face-to-face meetings or involvement of clinical experts in the treatment process. Therefore, although we earlier demonstrated the effectiveness of a psychoeducation app as an adjunct to a group intervention, here, we provide first evidence that ABP or CBP for adults with ADHD may also be effective in a self-guided setting without continuous clinical supervision.Notably, in the current study, we find similar effect sizes in terms of ADHD symptom reductions as in our previous app study.24 However, their generalisability may be limited given that they did not include patients with ADHD and only included individuals with attention deficits regardless of psychiatric diagnosis. Still, a recent pre–post feasibility study of a conventional mHealth app for CBT psychoeducation found that adults with ADHD viewed the content delivered via an app positively, and self-reported a decrease in ADHD symptoms after 7 weeks of use.41 Also, our results are generally consistent with other psychoeducation studies in adult ADHD, in which all core symptoms improved after the intervention.13Regarding symptom-specific effects, our analyses revealed significant and strong effects on observer-rated inattention, hyperactivity and impulsivity, as well as self-rated inattention and hyperactivity across intervention groups. These results contrast with the only other study that examined a psychoeducation chatbot in individuals with attention deficits, which primarily found significant improvements in impulsivity symptoms compared with reading a self-help book, but no improvements in inattention and hyperactivity in their per-protocol analysis.The evaluation of psychoeducational knowledge transfer showed similar results in terms of knowledge acquisition and content completion in both groups. This is contrary to our expectation, as we hypothesised that a greater emphasis on interaction and the individual selection of topics associated with the use of the chatbot would lead to a greater increase in psychoeducation content knowledge compared with the use of the conventional app. Yet, it is also conceivable that greater involvement and constant interaction may have instead led to a decreased learning capacity in patients with ADHD that overshadowed potential positive effects of individual learning pathways. Future psychoeducation research should additionally focus on the patients’ specific learning styles.22 which yielded improvements in ADHD core symptoms, but not in secondary outcomes , we found no enhancement in any secondary outcome in the present study. Although we used narrow inclusion criteria for affective disorders in both our studies and therefore did not expect great reductions in depressive symptoms, improvement in quality of life was particularly anticipated based on previous, non-digital psychoeducation in ADHD.13 One explanation for this difference could be that in these studies, quality of life was considered as health-related rather than global, as was done in this study by using the WHOQOL. In addition, our correlation analysis found that symptoms of ADHD and symptoms of depression and stress, as well as subdomains of quality of life, generally correlate well with each other. In particular, the health-related subdomains of the WHOQOL  were correlated with changes in other symptom scores. Hence, despite not finding time effects across groups, improvements in ADHD symptoms may be associated with improvements in other secondary domains. As this has relevance for clinical application, future research should target this issue and investigate the extent to which there are potential deviations from non-digital psychoeducation.Further secondary outcomes included changes in depression, anxiety, stress and quality of life. In line with our previous study,In general, both groups reported no adverse events or unintended consequences and had equal drop-out rates. As mentioned above, the chatbot was more prone to technical errors. Consequently, in this study, the conventional psychoeducation app appeared to have advantages in terms of overall usability. Given the similar clinical efficacy, this may also illustrate the potential therapeutic benefit that a more individualised approach, such as a chatbot, could have if the technical foundation allows for a flawless and natural flow of conversation. On the other hand, there is the possibility that the preferred method of content delivery, and ultimately the clinical efficacy, also depends on individual patient characteristics. For example, patients who have no prior experience with psychoeducation may prefer an app that offers a more structured format, whereas more experienced patients may have specific questions and interests that can be addressed more efficiently with a chatbot.This study has some limitations. First, the total duration of interventions was rather short and could therefore be responsible for the lack of improvements in secondary outcomes. In particular, changes in quality of life could possibly be perceived in a delayed manner. Second, for technical reasons, we could not limit the duration of app use to precisely 3 weeks. As a result, the average duration of app access was approximately 1 day longer in the ABP group than the CBP group. We assume only minor implications, as completing the amount of psychoeducational content was manageable within the intervention period. Moreover, we were technically unable to measure the exact amount of time each participant spent using the app. Future research should incorporate these measurements, as they may provide important insights into the behaviour of patients with ADHD. Third, although the conventional app worked without errors, the chatbot had some technical issues in one of the modules that eventually led to the exclusion of this module from the analysis and may have resulted in fewer symptom improvements, as well as negative associations with CBP among affected participants. Chatbots appear particularly susceptible in this regard, and conducting pilots with a wide range of devices is recommended. Fourth, the chatbot was compared with an active control intervention that had only been validated once, along with group psychoeducation. Further evaluation of the chatbot against a traditional group psychoeducation or treatment as usual is recommended. Finally, the relatively moderate sample size may have contributed to the failure to detect certain effects, which also complicates more detailed subgroup analysis. However, with respect to the primary end point, we assume that none of the interventions proved superior, given that effect sizes were considerably small.14 In addition, specific participant characteristics that might affect treatment outcomes should be examined, as, for instance, different age groups might have different preferences regarding usability and content presentation, or education level might be related to certain preferred learning styles. In addition, the generalisability of the present results may be limited by the above-average educational level of the sample, which should be addressed in future studies.Overall, taking into account the similar outcome of the digital psychoeducation forms examined in this study, a chatbot may offer the greater potential for development, especially considering the current pace of innovation in the non-clinical chatbot market. Future research should also focus on the implementation of different add-ons that might help patients organise their personal digital psychoeducation, such as notifications that provide exercise reminders and recap psychoeducational content to help deepen knowledge.In conclusion, we found both a conventional module-based psychoeducation app and an interactive chatbot to be safe, feasible and effective for self-guided psychoeducation, although neither can be favoured based on the present findings. Strong effects on ADHD core symptoms were observed, providing a first step toward implementing these scalable and cost-effective applications in clinical practice; for instance, to provide treatment during therapy waiting times or as an augmentation to medication. Secondary outcome effects, such as on symptoms of depression, anxiety and stress and quality of life, need to be given stronger consideration in the further development of digital psychoeducation, as they did not improve in this study.Selaskowski et al. supplementary material


Lactobacillus acidophilus LB (Lacteol Fort) on attention-deficit/hyperactivity disorder (ADHD) symptomatology and evaluate its influence on cognition function.This study was designed to examine the possible efficacy of the probiotic strain In this randomized controlled trial, 80 children and adolescents with ADHD diagnosis, aged 6–16 years, were included. The participants were randomly assigned to two groups: one group received probiotics plus atomoxetine, whereas the other group received atomoxetine only. ADHD symptomatology was assessed using the Conners Parent Rating Scale–Revised Long Version (CPRS-R-L) and Child Behavioral Checklist (CBCL/6–18). The participants were evaluated for their vigilance and executive function using Conner’s Continuous Performance Test (CPT) and Wisconsin Card Sort Test (WCST). Both groups were assessed at the beginning of the study and the end of the twelve weeks.p = 0.032, 0.024, respectively). Additionally, the probiotic group demonstrated improved focus attention  compared with the control group. An analysis of the Wisconsin Card Sorting Test (WCST) performance demonstrated that the probiotic group had significantly lower perseverative (p = 0.017) and non-perseverative errors (p = 0.044) but no significant differences compared to the control group.The probiotic group comprised 36 patients, whereas the control group comprised 40 patients in the final analysis after four patients dropped out of the trial. After 3 months of probiotic supplementation, a significant improvement in the CPRS-R-L and CBCL total T scores was observed compared with those in the control group . Registration date: 19–11-2019. Attention-deficit/hyperactivity disorder (ADHD) is the most common early-onset neurodevelopmental disorder in the pediatric population, affecting 7.2% of school-age children globally. It is characterized by deficits in the cognitive functioning pattern, with hyperactivity, impulsivity, and attention problems that are developmentally inappropriate and significantly impairing symptoms . The patRecently, a growing interest has been directed to the bidirectional pathway between the gut and brain—the gut–brain axis (GBA). Alterations and imbalances in the gut microbiota may play a role in developing and progressing neurodevelopmental disorders like ADHD. , 7. MounFurthermore, several reports showed an association between ADHD and low levels of brain-derived neurotrophic factor (BDNF), which is essential for neuronal development, suggesting that BDNF contributes to its pathophysiology –14. TherLactobacillus rhamnosus GG (LGG) early in life [Lactobacillus rhamnosus and Bifidobacterium species are the most researched strains, and their neurobehavioral impacts have been reported [Lactobacillus acidophilus LB, reduces cholesterol levels and has physiological and pharmaceutical benefits in preventing and treating certain disorders [Lactobacillus acidophilus strain in children with ADHD have not yet been investigated. Accordingly, the primary objective of this randomized controlled trial was to examine the potential effects of Lactobacillus acidophilus LB supplementation combined with atomoxetine on the core clinical symptoms of ADHD. The secondary objective was to investigate whether 12-week probiotic supplementation could improve cognitive functions in children with ADHD.Probiotics are bacteria that benefit the host body . Because in life . Lactobareported , 20. Anoisorders . HoweverThis study was a 12-week randomized controlled trial set as a prospective, parallel, open-label study conducted from June 2020 to October 2021 on pediatric and adolescent outpatients with ADHD. The trial was registered at ClinicalTrials.gov . The study’s reporting complies with the Consolidated Standards of Reporting Trials 2010 statement . The stuEighty children and adolescents aged 6–16 were recruited from the Developmental and Behavioral Pediatrics Clinic Children’s Hospital and Psychiatry Institute, Faculty of Medicine, Ain-Shams University, Cairo, Egypt. The participants fulfilled the diagnostic criteria for ADHD according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)  criterian = 40) or the control group (n = 40) using a random number generator from a computer-based randomization software. The allocation was concealed using opaque, sealed, sequentially numbered envelopes. After obtaining informed consent, the opaque sealed envelopes were unwrapped, and the participants were enrolled in the relevant group. Only the researchers collecting and analyzing the data were blinded to the study groups (assessor-blinded).A statistician, independent from the study investigators, randomly allocated the enrolled participants into the probiotic group  in a dose of two sachets disintegrated in 50-mL freshwater, twice daily, from the first day of the study until 3 months. Additionally, each parent received a daily text message reminding them to let their children take their supplements as directed and to report any side effects. The control (no probiotic) group did not receive any probiotics. All participants were on a stable pharmacological treatment for ADHD (atomoxetine) with a consistent dosage throughout the study (1.2 mg/kg/day).The participants with ADHD randomized to the study (Probiotic) group received a probiotic preparation  [(Arabic version [).PRS-R-L)  (Arabic  version ).The CPRS-R-L is an 80-item behavior rating scale that identifies children at risk of ADHD and assesses the severity of their ADHD symptoms. On a 4-point Likert scale, from 0 to 3, parents’ responses to their children’s behavior over the previous month were rated, with 0 indicating “not at all” and scores of 1–3 indicating “just a little” to “severely affected,” respectively. The raw scores were interpreted using T-values, with scores above 60 considered moderately elevated and those above 70 considered significantly high. The scale included seven subscales, three DSM-IV Symptom Indices, an ADHD Index, and three Conners’ Global Indices. Our study targeted the three DSM-IV subscales .CBCL/6–18, provided by the Achenbach System of Empirically Based Assessment Foundation  after signing a license agreement with them.The CBCL/school-aged (6–18 years) is a parent-rated questionnaire containing 113 items subdivided into three dimensions, noted as internalizing, externalizing, and total behavior problems, quantitatively assessing and providing dimensional insights concerning children’s psychopathology and behavioral functioning . ResponsPEBL version 2.0 of the CPT measures sustained attention and impulsivity in a 14-min computerized task. Participants are instructed to hit the button whenever any alphabet letter other than the X letter is shown. The test measures selective inattentiveness (missing target stimuli: omission errors), impulsivity , and sustained attention (reaction time and reaction time variability).PEBL version 2.0 of the WCST measures executive functioning, cognitive flexibility, and set-shifting abilities. It comprises two sets of cards: 64 reaction cards and four stimulus cards. Based on the patterns present on the cards, participants were instructed to categorize them. The rule for properly sorting the stimuli shifts regularly, and the ability to change strategies that vary according to the stimuli's color, number, or shape is recorded. The participant should first choose the proper sorting principle and stick with it throughout the test to perform successfully. Shifting the matching rule to another category occurs after ten successively correct matches in one category . The main outcome parameters are the correct responses, categories completed, perseverative errors, non-perseverative errors, total errors, and failure to maintain a set. Executive dysfunction is assumed to be reflected in preservative and non-preservative errors .Both groups were assessed at baseline and follow-up at twelve weeks. The primary outcomes were changes in the severity of ADHD symptoms and associated behavioral problems assessed using the CPRS-R-L  and CBCL , respectively. Secondary outcomes were improvements in sustained and focused attention, impulsivity, executive functioning, and set-shifting abilities based on the CPT and WSCT tasks.Using G*Power, the alpha error and study power were set at 5% and 80%, respectively. Assuming an effect size of 0.7 (Cohen’s d), a sample size of 40 cases per group was required, considering a dropout rate of 20%.The collected data were revised, coded, tabulated, and introduced to a personal computer using Statistical Package for the Social Sciences, version 25.Mann–Whitney U-test was used to assess the statistical significance of the difference in baseline and follow-up changes between the two study groups. The Wilcoxon signed-rank test was applied to evaluate the statistical significance of the difference in scores measured twice for the same group.Student’s t-test was used to evaluate the statistical significance of the mean difference between the two study groups. The chi-square test was used to compare the two study groups. The Of 100 children and adolescents, 80 (40 in each group) met the eligibility criteria and were willing to participate in the study. However, only 36 of the 40 probiotic group participants completed the study as we failed to follow up with four children who were reluctant to continue the investigation . Moreover, no significant difference in the IQ measurements was observed between the two groups.As shown in Table Primary outcomes.vs. -2.5 ± 6.7, -2.6 ± 12, and -2.5 ± 9.5; p < 0.001) were observed over 12 weeks . The absence of the required response (omission errors) for attention selectiveness was less in the probiotics group than in the control group, with a significant mean change difference between the two groups (p = 0.043). Although the probiotics group’s mean score decreased from baseline with a mean change over 12 weeks in reaction time (RT) on correct trials in ms (− 3.9 ± 96.56) and the standard deviation of RT (variability) (− 7.3 ± 134.7) compared with those of the control group , this difference did not reach statistical significance. Furthermore, the two groups did not differ in the mean and standard deviation of the error RT (p > 0.05). Addressing impulsivity, we found no significant difference between the two groups over 12 weeks in the inhibited RT during the entire number of trials (foil accuracy rate) (p = 0.606) and in the false required response (commission errors) (p = 0.559).Table p = 0.016, p = 0.027, p = 0.017, and p = 0.044, respectively), but not in the control group. However, the Mann–Whitney U-test analysis of the mean change difference between the two groups in these indices revealed no significant differences (p > 0.05). The remaining index, including failure to maintain the set, did not significantly improve for either group (p > 0.05) , and non-perseverative errors , reported decreased appetite. One patient from the probiotics group reported having diarrhea, which cleared up after a few days.Lactobacillus acidophilus LB strain as supplementation added to a weight-dependent dose of atomoxetine to examine its impact on the core symptoms, behavior, and cognition of children and adolescents with ADHD.As knowledge of the GBA has risen with emerging research highlighting this bidirectional relationship with a theoretical translation of animal models to human analyses, the core mechanism and which probiotics have a promising or negative result remain ambiguous. To the best of our knowledge, this study is the first randomized controlled trial to use a Twelve-week supplementation with L. acidophilus LB combined with a weight-dependent dose of atomoxetine could improve the symptoms and behavioral problems of ADHD, according to the parental reports of the CPRS-R-L and CBCL, respectively, relative to the control group.Our findings support the results of a recent trial by Ghanaatgar et al. . The stuBifidobacterium bifidum-688 (Bf-688) for 8 weeks reduced the clinical symptoms of patients with ADHD while increasing their body weights and body mass index. The Bf-688 supplement also markedly changed the composition of the gut flora [Another study investigated the effects of probiotics on the psychological health of children with ADHD. The study found that probiotic treatment for 8 weeks, using four bacterial strains, including Lactobacillus acidophilus, significantly improved the severity of ADHD symptoms and anxiety compared to a placebo. The improvement was measured using the ADHD and Hamilton Anxiety Rating scales. However, probiotics did not have an impact on depression. . Of noteut flora .Contradictory to our findings, Kumperscak et al.  evaluateAvailable reviewing research has broadened our understanding of the link between probiotic supplementation and its impact on ADHD clinical symptoms. However, their findings remain inconclusive to formulate any clinical recommendation or approaches. As the underlying etiopathogenesis of ADHD is still unclear, investigating the role of the complex messaging system between the microbiota, gut, and brain has drawn much attention , 36. FurConsidering that the GBA hypothesis has been linked to the pathophysiological pathways underlying ADHD , it may Lactobacillus acidophilus LB could have a favorable effect on focused attention as indicated by the CPT. However, we did not find a comparable improvement in impulsivity.Regarding the secondary outcomes under study, our current analysis found that the probiotic group revealed improvement in the target accuracy rate and omission errors compared with the control group (medium effect size), suggesting that Mounting evidence suggests that the cholinergic and dopaminergic systems contribute to the pathophysiology of selective attention . An inteOur results showed improvement in the executive functions in the probiotic group reflected in both perseverative and non-perseverative errors on WCST performance ; however, this did not reach a significant level of differences between the two groups.Impairments in executive functions  are considered a core deficit in the cognitive function of ADHD, which may play a crucial role in the challenging adaptation of ADHD , 43.The cognitive regulation of behavior and reward perception is modulated via the norepinephrine and dopamine circuits, which connect to the prefrontal cortex and striatum, and these pathways are considered fundamental in the pathophysiology of ADHD .Lactobacillus rhamnosus supplements demonstrated a favorable influence on cognitive function and a lowered risk of developing ADHD [Until now, limited randomized trials have investigated the impact of probiotic supplementation on cognitive performance . Althouging ADHD .Lactobacillus acidophilus EG004, Lacticaseibacillus rhamnosus, and Lacticaseibacillus paracasei. The three probiotic-fed testing groups demonstrated better cognitive function; however, the L. acidophilus-fed group was superior to the other two groups and scored the highest on cognitive–behavioral assessments. Focusing on understanding how the changed microbial diversity affects the brain, a 16S-23S rRNA sequencing of the gut microbiome of the L. acidophilus group was performed. It was found that the L. acidophilus group had an elevated proportion of L. acidophilus presence, suggesting that a good proportion of L. acidophilus can be adequately ingested without being harmed by the digestive juices. Researchers hypothesized that an increase in L. acidophilus in the intestines modifies neurotransmitters and neurotrophic factors, including dopamine, noradrenaline, gamma-aminobutyric acid (GABA), and serotonin, affecting an animal’s nervous system. Interestingly, they found that ingesting L. acidophilus increased SCFAs in the gut of experimental mice, indicating L. acidophilus’s ability to produce SCFAs, which may positively impact brain function. The microbially fermented compounds SCFAs, such as acetate, propionate, and butyrate, stimulate indirect signaling in the brain by modifying and inducing neurotransmitters and neurotrophic factors, such as GABA and BDNF [The linkage between ADHD and the microbiota can be understood in terms of how neurotransmitters function in cognition. A recent study has provided insights into the GBA and introduced a new strain that improves cognitive function through this axis. Using healthy mice, Jeon et al.  investigand BDNF , 48. ConIn the present study, most participants were males. The finding aligns with previous studies indicating that ADHD is more common in boys , 50. HowThis study is an open-label study without a placebo intervention. Given that the parents were knowledgeable of the treatment their child had experienced, this potentially may have affected how they responded to the questionnaires. However, parallel to the parent responses, we also included various objective measure evaluations , and the results showed the advantage of adding probiotics to the standard treatment alone. While our findings showed that a 3-month probiotic intervention was beneficial, there were no observable changes in some cognitive functions measured by the neuropsychological assessment battery, leading us to believe that the study’s duration was insufficient to track the improvements. Therefore, further research with a longer intervention time is needed. In this study, we did not investigate the socioeconomic status of the groups; however, SES should be assessed in microbiome studies, given that it can be an influential confounding variable that impacts the analysis of the study results . FinallyLactobacillus acidophilus LB (Lacteol Fort) added to a weight-dependent dose of atomoxetine improved the severity of symptoms, sustained attention, and executive functions in children and adolescents with ADHD. Considering this information, Lactobacillus acidophilus LB may be a desirable supplementary therapy for children with ADHD without side effects. Future research is recommended to verify the treatment impacts of the probiotic Lactobacillus acidophilus LB on the core symptoms and cognitive functions of ADHD.In conclusion, this study has demonstrated that 3 months’ supplementation of oral probiotics, such as


The differences in the LBW men with NAFLD included increased levels of ornithine and tyrosine (PFDR ≤ 0.1), as well as of triglycerides and phosphatidylcholines with shorter carbon-chain lengths and fewer double bonds. Pathway and network analyses demonstrated downregulation of transfer RNA (tRNA) charging, altered urea cycling, insulin resistance, and an increased risk of T2D in the LBW men with NAFLD. Our findings highlight the importance of increased liver fat in the pathogenesis of T2D in LBW individuals.The extent to which increased liver fat content influences differences in circulating metabolites and/or lipids between low-birth-weight (LBW) individuals, at increased risk of type 2 diabetes (T2D), and normal-birth-weight (NBW) controls is unknown. The objective of the study was to perform untargeted serum metabolomics and lipidomics analyses in 26 healthy, non-obese early-middle-aged LBW men, including five men with screen-detected and previously unrecognized non-alcoholic fatty liver disease (NAFLD), compared with 22 age- and BMI-matched NBW men (controls). While four metabolites (out of 65) and fifteen lipids (out of 279) differentiated the 26 LBW men from the 22 NBW controls ( We reth a LBW ,5,6. TheAnalysis of circulating metabolites and lipids can provide important insights into the metabolic signatures of NAFLD . DiffereIn a meta-analysis of seven cohorts, lower birth weight was associated with increased levels of aromatic and branched-chain amino acids as well as of saturated and monounsaturated fatty acids . FurtherHowever, whether non-obese LBW subjects with screen-detected NAFLD exhibit similar dysmetabolic traits as patients diagnosed with NAFLD in the clinics due to symptoms or elevated liver function tests is unknown. Moreover, the extent to which elevated hepatic fat content and/or overt NAFLD may contribute to or explain changes of the metabolome and/or lipidome in LBW subjects with increased risk of developing T2D remains to be explored. Thus, the main objective of the current study was to identify differential metabolomic and/or lipidomic signatures in healthy, early-middle-aged non-obese LBW subjects with and without NAFLD, compared with matched NBW controls without NAFLD.2, self-reported physical activity > 10 h/week, and alcohol intake above the national recommendation. The study was approved by the ethical committee of the Capital Region of Denmark (H-4-2014-128) and carried out according to the Declaration of Helsinki II, and written informed consent was obtained from all participants. ClinicalTrials.gov identifier: NCT02982408.The study included 48 healthy Caucasian males born at gestational weeks 39–41 between 1979 and 1980 and recruited from the Birth Registry at the Danish Health Data Authority, as described previously [The participants were standardized with regards to diet, alcohol intake, and physical activity for three days prior to examination. Fasting blood samples were obtained after a 10 h overnight fast and stored at −80 °C for later use. Clinical examinations, including magnetic resonance spectroscopy (MRS) of hepatic liver fat content and isotope tracers for glucose kinetics, were performed as described previously . A priorFasting serum samples were analyzed using two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS)  ,16 Supp. In all,Fasting serum samples were analyzed in a random order in positive and negative electrospray ionization modes using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS)  ,20,21,22Quality control of metabolomics and lipidomics data was completed prior to the analyses using R 4.1.0  .FDR) using the Benjamini–Hochberg method. The log2 transformation demonstrates the ratio between the mean metabolite abundance for two groups and is referred to as the log fold change. Metabolites and lipids with PFDR ≤ 0.1 were considered significant, whereas metabolites and lipids with p ≤ 0.05 (unadjusted) were considered suggestive. The models were not further adjusted, as the NBW and LBW groups were matched on sex, age, and BMI. Principal component analysis (PCA) plots based on metabolites and lipids with differential abundance between groups (PFDR ≤ 0.1 and p ≤ 0.05) were generated (R-package broom) to examine how individuals differed from each other within each group. Furthermore, a heatmap with the log2-normalized abundances of the 11 metabolites of interest (PFDR ≤ 0.1 and p ≤ 0.05), ordered by the three groups, was produced to illustrate the differential abundances (R-package ComplexHeatmap). A Pearson correlation analysis between the first principal component (PC1) and the second (PC2) and a panel of selected clinical variables associated with specific metabolic traits was conducted (R-package PCAtool) and FDR corrected. Volcano plots were created based on the lipidomics data to illustrate the differentially abundant lipids between the groups. A structural composition analysis of the lipids, assessing the importance of the lipid fatty acid carbon-chain lengths and the number of double bonds was performed (R-package LipidomeR) comparing both LBW w/o NAFLD and LBW w/NAFLD with NBW subjects.Differential abundances in the metabolomics and lipidomics data were analyzed in four comparisons between the three groups, as follows: LBW  vs. NBW; LBW with NAFLD (LBW w/NAFLD) vs. NBW; LBW without NAFLD (LBW w/o NAFLD) vs. NBW; and LBW w/NAFLD vs. LBW w/o NAFLD. The four comparisons were analyzed using linear regression models on log2-transformed and scaled omics data (R-package limma), adjusting for multiple testing with false discovery rate (FDR) , together with the Human Metabolome Data Base (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation IDs, were used to map enriched pathways using the Ingenuity Pathway Analysis application . IPA generated enriched metabolic pathways based on differences between the groups.All circulating metabolites, including their FDR ≤ 0.1) between identified key metabolites (n = 11) and all lipids (n = 279) in the NBW individuals were calculated (R-package correlation) [FDR ≤ 0.1) in the network created using Cytoscape 3.7.1 [Correlations  . The corpe 3.7.1 . Analysepe 3.7.1 .p = 0.02), fasting plasma TGs (p = 0.03), cholesterol (p = 0.05), and C-peptide (p = 0.02) [The LBW individuals w/NAFLD had a significantly increased hepatic fat content with a median hepatic fat content of 9.45% compared with a hepatic fat content of 0.80% and 0.78% in LBW subjects w/o NAFLD and NBW controls, respectively , as prevs p = 0.0, cholestp ≤ 0.05) between the LBW  and the NBW groups (p = 0.02), alpha-tocopherol (p = 0.03), and cholesterol (p = 0.04), and lower levels of hippuric acid (p = 0.04) in the LBW subjects.Out of the 65 semiquantified serum metabolites, four metabolites were differentially abundant (W groups . These ip = 0.001), tyrosine (p = 0.007), citrulline (p = 0.02), leucine (p = 0.02), and 2-oxoisovaleric acid (p = 0.03) in the LBW w/NAFLD subjects. Additionally, linoleic acid (p = 0.02) and arachidonic acid (p = 0.05) levels were significantly decreased in the LBW w/NAFLD subjects compared with the NBW subjects.Seven metabolites differed significantly between the LBW sub-group w/NAFLD and the NBW group before multiple-testing corrections , includip = 0.01) and an increase in alpha-tocopherol levels (p = 0.04) in the LBW w/o NAFLD subjects , ornithine (p = 0.003), citrulline (p = 0.02), and alpha-ketoglutaric acid (p = 0.04) in the LBW subjects w/NAFLD. Moreover, linoleic (p = 0.03) and arachidonic (p = 0.03) acid levels were decreased among the LBW subjects w/NAFLD  remained significant for the LBW w/NAFLD vs. the NBW subjects, while tyrosine (PFDR = 0.07) and ornithine (PFDR = 0.1) remained significant for the LBW w/NAFLD vs. LBW w/o NAFLD groups , before multiple-testing corrections, from the four differential abundance analyses as shown in In total, 11 differential metabolites were identified  are illustrated in FDR ≤ 0.001, r2 = 0.55) and hepatic fat content , whereas PC2 correlated with birth weight category , waist/hip ratio (WHR) , and levels of alanine aminotransferase (ALAT) , gamma-glutamyltransferase (GGT) , triglyceride , total cholesterol , and LDL cholesterol   C.FDR ≤ 0.1; Based on the 65 identified metabolites, IPA predicted the transfer RNA (tRNA) charging pathway as the top canonical pathway downregulated among the LBW w/NAFLD subjects compared with the other groups. Here, at least 75% of the examined amino acids (including leucine and tyrosine) were upregulated in the LBW w/NAFLD subjects compared with both the NBW and the LBW w/o NAFLD groups (Pp ≤ 0.05) between the LBW  and the NBW subjects before correcting for multiple testing   .p ≤ 0.05) in the LBW w/o NAFLD subjects compared with the NBW group, before FDR adjustment (n = 31) displayed a slight decrease in abundance in the LBW w/o NAFLD group , with the vast majority belonging to the PC and TG subclasses, as well as a few PEs, one SM, and one LPC. Only six lipid species displayed slight increased levels  in the LBW w/o NAFLD subjects, including one CER, one PC, PEs, and SMs  (FDR = 0.02).Lastly, comparing the LBW group w/NAFLD with the LBW group w/o NAFLD, 27 lipids were differentially abundant ( ≤ 0.05) . Twenty- ≤ 0.05) A. Only Cp ≤ 0.05 threshold)  . Group sreshold) .Interestingly, when comparing the concentration of the different lipid species based on lipid structure (carbon-chain length and number of double bonds), the LBW w/NAFLD group showed a distinct pattern compared with the NBW subjects and the LBW subjects w/o NAFLD B. In then = 11) and lipids (n = 56) onto the NBW reference network to identify significantly differing molecular patterns and communities between the LBW w/ and w/o NAFLD groups . Metabolites/lipids were clustered into communities, identifying functional groups of lipids having similar properties, and metabolites which were tightly connected due to shared regulatory processes [The correlation network analysis identified patterns highlighting the relationship between key metabolomic and lipidomic profiles that were enriched for the LBW individuals w/NAFLD and w/o NAFLD compared with the NBW controls. We mapped differentially abundant metabolites  cycling, development of insulin resistance, and inflammation, and have furthermore previously been shown to be associated with NAFLD, fibrosis, and T2D ,27. To tFour metabolites were differentially abundant in the LBW  subjects as compared with the NBW group, including higher levels of leucine, alpha-tocopherol, and cholesterol, together with lower levels of hippuric acid. Interestingly, increased levels of both leucine and cholesterol have been associated with low birth weight as well as with a higher risk of T2D ,28,29. FBoth ornithine and citrulline are critical intermediates of the urea cycle, which primarily takes place in the mitochondrial matrix of hepatocytes. It has been documented, that NAFLD affects plasma levels of urea-cycle intermediates, and that NAFLD with mild fibrosis has been associated with high citrulline and low ornithine concentrations . In contElevated concentrations of plasma tyrosine have previously been associated with lower birth weight . In the A pathway analysis was performed to understand biological processes associated with differentially abundant metabolites between the LBW subjects w/NAFLD compared with the other groups. In this analysis, the tRNA charging/aminoacyl-tRNA pathway was identified as the prime downregulated canonical pathway among the LBW subjects w/NAFLD. tRNA charging is essential for protein synthesis, and disturbance/dysfunction in this process is associated with T2D and aging ,40. PrevA total of 56 significant lipids were found across the four differential abundance analyses. Of these, TGs and PCs dominated the lipid species showing increased levels in the LBW subjects w/NAFLD compared with the LBW w/o NAFLD and NBW groups. The cholesteryl esters (CE) lipid subclass showed the largest decrease in abundance in the LBW w/NAFLD subjects compared with the groups w/o NAFLD, with only cholesteryl linoleate (CE(18:2)) being significantly lower after FDR correction when comparing the LBW w/NAFLD subjects with the LBW w/o NAFLD group. Investigations of the liver lipidome have revealed that TGs and CEs are among the lipid subclasses showing the most differential levels across disease states from simple steatosis to cirrhosis . This inOur finding of TGs and PCs with relatively fewer double bonds, reflecting increased saturation, as well as shorter carbon-chain length, in the LBW subjects w/NAFLD compared with LBW and NBW subjects without NAFLD, is in agreement with previous studies of NAFLD individuals ,13,48,51Finally, a correlation-based network analysis was performed in which a NBW reference network with combined metabolite–lipid patterns was created. Mapped onto the network were the key differentially abundant lipids and metabolites, identified from comparing both the LBW individuals w/ and w/o NAFLD with the NBW controls. Two distinct communities were identified. Community 1 included triglycerides with higher molecular weight and polyunsaturation, in addition to alpha-tocopherol and cholesterol, representing two of the most prominent component loadings of PC2, which separated the LBW w/o NAFLD group from the NBW controls. Community 2 consisted of the PC1 component loadings ornithine, tyrosine, and citrulline, which separated the LBW w/NAFLD group from the two other groups and, furthermore, correlated with hepatic fat content and NAFLD status. In particular, triglycerides with shorter carbon-chain length and fewer double bonds were observed in community 2, corresponding to the pattern seen for the LBW subjects w/NAFLD in the structural analysis. Interestingly, 67% of the identified differentially abundant metabolites and lipids belonged to one of the two central communities. The results collectively point towards a tight co-regulation among differentially abundant metabolites and lipids in the LBW w/NAFLD group, suggesting that these metabolites are likely to share a common pathophysiological pathway.This study has its strength in the deep-phenotyping and careful standardization of the study participants. The use of untargeted metabolomics and lipidomics is truly hypothesis generating and allows for discovery of novel circulating markers associated with NAFLD pathogenesis among LBW individuals. As the data are cross-sectional, interpretations of this study are only applicable to the specific timepoint. Our post hoc evaluation of the subgroup of LBW individuals w/NAFLD included only five individuals. None of the NBW individuals had screen-detected NAFLD, and thus we were not able to evaluate the independent effect of birth weight in the presence of NAFLD. Therefore, we suggest further validation in future larger studies including a group of NBW individuals with NAFLD.The observed differences in metabolomics and lipidomics between the LBW and the NBW subjects in this study appeared mainly to be driven by the LBW subjects with overt NAFLD exhibiting elevated serum levels of amino acids, as well as increased abundance of more-saturated, shorter-chained serum phosphatidylcholines and triglycerides. These differences collectively reflect impaired tRNA charging, altered urea cycling and insulin resistance, and support the hypothesis that elevated hepatic fat content and NAFLD contribute to the increased risk of developing T2D in LBW subjects.


Attention‐deficit/hyperactivity disorder (ADHD) is a prevalent, impairing, and highly heritable condition typically diagnosed in middle childhood. However, it is now recognized that symptoms emerge much earlier in development. Research focused on understanding—using multiple units of analysis—the cascade of early‐life  developmental changes that will later emerge as ADHD has the potential to transform early identification, prevention, and intervention. To this end, we introduce the recently established Early ADHD Consortium, an international network of investigators engaged in prospective, longitudinal studies of risk for ADHD beginning early in life, conducted within a developmental framework, and which incorporate multimethod approaches. This network seeks to harmonize measures and methodological approaches to increase the potential for data sharing and subsequent impact.This perspective paper highlights the importance of investigating pre‐diagnostic markers of ADHD, and potential models and mechanisms of ADHD risk and development, with the long‐term objective of facilitating development of preemptive interventions that will minimize the impact of ADHD symptoms on everyday functioning and maximize health and developmental outcomes.We selectively describe key challenges and questions for this field related to theoretical models and developmental mechanisms in ADHD and recommend next steps for the science, including methodological, measurement, and study design considerations. We then describe potential implications for preemptive intervention development. We conclude by considering other issues including ethical concerns and the critical value of incorporating stakeholder input.It is hoped that this perspective puts forth a research agenda that will enhance collaborative efforts and accelerate progress in understanding developmental mechanisms and the early ADHD phenotype, with implications for early intervention enhancement of healthy development for infants, young children, and their families. ADHD is a prevalent and impairing condition typically diagnosed around age 7, but it is increasingly acknowledged that symptoms emerge much earlier.Research focused on understanding the cascade of early‐life developmental changes that occur in ADHD across multiple levels of analysis has the potential to transform early identification and intervention.Early ADHD Consortium, a network of investigators engaged in prospective, longitudinal studies beginning early in life, conducted within a developmental framework, and which incorporate multimethod approaches; we describe key features of this work in an effort to outline a research agenda for the field.We established the We seek to more rapidly advance our understanding of early developmental pathways to ADHD, improving outcomes for infants/young children at risk.Attention‐deficit/hyperactivity disorder (ADHD) is an impairing neurodevelopmental disorder with global prevalence estimated at 3%–5%  identify individuals at risk through more complete early risk indicators, and (b) discover novel ways to intervene to maximize healthy brain development and prevent clinically impairing symptoms of ADHD and other conditions. A key goal is identifying how these factors shape human brain development and behavior, particularly during the fetal, infant, and toddler periods, when experience‐expectant and dependent neural plasticity is maximal, so that novel interventions can be developed that prevent cascading effects of early difficulties and optimize health and functioning. Understanding these early mechanisms may also point to more sensitive and specific markers of ADHD risk that will facilitate early identification more broadly, a topic we turn to later.Existing knowledge of early manifestations and precursors of ADHD comes from several sources including retrospective studies and chart reviews, large community‐based samples not selected for ADHD risk  better understanding causal mechanisms, developmental pathways, and predictive markers; (2) facilitating early identification; and (3) supporting the development of preemptive interventions that will shift trajectories to enable maximal health and development. We begin by briefly reviewing relevant theoretical models and mechanisms. We then describe promising methods, measurement strategies, and study designs with which to undertake this work. Next, we highlight the ways in which research focused on developmental mechanisms and the early ADHD phenotype could be used to inform the development of preemptive interventions. We conclude by considering ethical issues, as well as the critical role of stakeholder input. Throughout, we emphasize the importance of cross‐site collaborative work through research networks focused on these goals, including the newly‐formed Characterization of developmental features that presage later ADHD is critical to building and refining robust theoretical models. Because neurocognitive changes observed in children with ADHD could be secondary to experiential and environmental alterations evoked by early‐emerging symptoms, prospective studies are essential to identifying temporal precedence, a key component of causal models. Ideally, such studies would begin in pregnancy. Many etiological risk factors for ADHD are shared with other neurodevelopmental conditions, and transdiagnostic mechanisms, such as inflammation, have been suggested , complicating distinctions between genetic and environmental effects. For example, well‐designed adoption and sibling cohort studies have recently challenged the presumed causal role of maternal smoking on ADHD  and high surgency  and negative affect as risk markers for ADHD. Several small studies have found that high negative affect and low attentional control are related to familial ADHD risk as early as 6 months of age  power spectrum, which is theorized to reflect cortical excitation/inhibition balance , parental relationship status, and parental maltreatment, among others . Some studies have focused on pregnant mothers with ADHD, following their offspring over time , limiting measurement continuity in longitudinal research. Moreover, the validity of measuring Although some ADHD rating scales are validated for use as early as 24 months of age, the majority of reliable measures are normed for preschool‐age children or older . These methods are feasible for repeated administration across early development but have been used less frequently despite theoretical support  since dimensions are better suited to understanding associations between variables, and better capture the full range of phenotypic differences among those who do and do not meet diagnostic thresholds for specific neurodevelopmental conditions   reported no immediate treatment effect on attentional and inhibitory control processes, or on other early alterations associated with ADHD  are under‐explored but could be arguably more important than core ADHD symptom reduction in some cases. However, the large sample sizes required to demonstrate effects (particularly the absence of emergence of impairments), along with longer follow‐up periods required to reveal potential latent effects, remain challenges.Several ethical issues arise related to implications of research focused on early identification of, and intervention for, ADHD/ADHD risk, including concerns about potential risks of false positives. Although research on preschoolers with ADHD has demonstrated relatively high stability of preschool diagnoses in clinically‐referred samples (without consideration for subtype/presentation) into middle childhood Lahey, , stabiliAnother ethical consideration relates to medication, one of the two evidence‐based treatments for ADHD, but not for mere risk factors or precursive signs or for infants or toddlers. Current guidance  specifies behavioral interventions as the recommended first‐line treatment for ADHD in children under the age of 6. Concerns could be raised about whether earlier identification of very young children at markedly elevated risk for ADHD might lead to inappropriate prescribing of medication in very young children , conducted within a developmental framework, and which incorporate multimethod approaches. These studies will benefit from multi‐site, international collaborations and networks that may share common methods and objective measures and which incorporate stakeholder perspectives, such as our recently‐established Meghan Miller: Conceptualization; Project administration; Supervision; Writing – original draft; Writing – review & editing. Anne B. Arnett: Conceptualization; Writing – original draft; Writing – review & editing. Elizabeth Shephard: Conceptualization; Writing – original draft; Writing – review & editing. Tony Charman: Conceptualization; Writing – review & editing. Hanna C. Gustafsson: Conceptualization; Writing – original draft; Writing – review & editing. Heather M. Joseph: Conceptualization; Writing – original draft; Writing – review & editing. Sarah Karalunas: Conceptualization; Writing – original draft; Writing – review & editing. Joel T. Nigg: Conceptualization; Writing – review & editing. Guilherme V. Polanczyk: Conceptualization; Writing – original draft; Writing – review & editing. Elinor L. Sullivan: Conceptualization; Writing – original draft; Writing – review & editing. Emily J.H. Jones: Conceptualization; Writing – original draft; Writing – review & editing.Advances. Dr. Nigg has received royalties from Guilford Publications, he also serves on the JCPP Advances Editorial Advisory Board. Elizabeth Shephard serves on the JCPP Advances Editorial Advisory Board. Emily J.H. Jones is a Joint Editor for JCPP Advances. All other authors have declared that they have no competing or potential conflicts of interest.Dr. Charman has served as a paid consultant to F. Hoffmann‐La Roche Ltd. and Servier; and has received royalties from Sage Publications and Guilford Publications. Dr. Polanczyk has served as a consultant/speaker to Abbott, Ache, Medice, Novo Nordisk, and Takeda, and has received royalties from Editora Manole. He is also a Joint Editor for JCPP No ethics approvals were required for the preparation of this research review, as no patients or members of the public were involved.