The rheology in terms of viscosity was higher for the fluid with 1% dispersed ZnO NPs additives at all temperatures for the entire range of shear rate values. Additionally, rheological correlation function models were derived for the synthesized fluids using statistical analysis methods. Subsequently, Herschel–Bulkley models were developed for optimum fluids depending on rheological correlation models. In the last section of the study, the pressure-drop estimation method is described using given group equations for laminar flow in a pipe depending on Herschel–Bulkley-model parameters have been identified for optimum fluids are consistency, flow index and yield stress values.Surfactant-based viscoelastic (SBVE) fluids are innovative nonpolymeric non-newtonian fluid compositions that have recently gained much attention from the oil industry. SBVE can replace traditional polymeric fracturing fluid composition by mitigating problems arising during and after hydraulic fracturing operations are performed. In this study, SBVE fluid systems which are entangled with worm-like micellar solutions of cationic surfactant: cetrimonium bromide or CTAB and counterion inorganic sodium nitrate salt are synthesized. The salt reagent concentration is optimized by comparing the rheological characteristics of different concentration fluids at 25 °C. The study aims to mitigate the primary issue concerning these SBVE fluids: significant drop in viscosity at high temperature and high shear rate (HTHS) conditions. Hence, the authors synthesized a modified viscoelastic fluid system using ZnO nanoparticle (NPs) additives with a hypothesis of getting fluids with improved rheology. The rheology of optimum fluids of both categories: with (0.6 M NaNO Hydraulic fracturing technology is a frequently used method for fracturing in low permeability rock formations ,2. TheseThe fracturing fluid compositions vary based on reservoir rocks and other surrounding factors. The primary function of the fracturing fluid is to fracture the rock and transport proppants in the fracture. So, the fluids should be able to carry and transport sand proppants from surface facilities to the newly created fractures in the subsurface and then break them down so that the proppants can be settled in the fracture gaps. At the same time, the remaining fluid should flow back to the surface. The conventional polymeric fracturing fluids have many issues, such as polymeric and crosslinker residue in the formation, which leads to damage , substanNot only good viscosity, but also having a good elasticity enable SBVE fluids to be a perfect alternative candidate to transport proppants . The worHowever, these cylindrical micelles are highly susceptible to hydrocarbons. During the completion stage of the hydraulic fracturing operation, the carrier liquid will be destroyed by the influence of the formation hydrocarbon and can be easily removed from the fractures. Consequently, the high permeable path of the fracture will be achieved for the formation fluids to flow. Nonetheless, application of SBVE fluids at high temperature conditions in deep wells is a huge challenge . The visTherefore, developing improved viscoelastic systems of SBVE fluids using other additives is necessary ,12,13. TOver the last few years, researchers have realized that nanoparticles can improve surfactant-based viscoelastic fluids’ performance. The nanoparticles establish electrostatic bridges to surfactant micelles which modify the microstructural behaviours and rheology of the viscoelastic fluid system. The nanoparticles strengthen the entanglements of worm-like micelles providing increased micellar length and consequently improving rheological characteristics such as viscosity ,41,42,433− of Sodium Nitrate), the surfactant solutions give a remarkable viscosity increase due to rapid growth in rod-shaped micelles even at low surfactant and salt concentrations  Br) with a molecular weight of 364.447 gm/mol. The cationic surfactant CTAB (98% pure) of Loba Chemie Pvt. Ltd. was obtained from Sigma Aldrich. AR grade Sodium Nitrate salt (with a minimum assay of 99%) for anionic nitrate counter ion was obtained from ACS chemicals  formation and branching in the micellar solution of Cetyltrimethylammonium bromide (CTAB) [The aqueous solutions of cationic surfactants such as hexadecyltrimethylammonium bromide (CTAB) form long worm-like micelles (WLMs) upon adding specific salts, strongly binding counter-ions or cosurfactants. The enthalpy of micellization and Gibs free energy for micellization seems to be the lowest for NO3− ,48 compa3−  investige (CTAB) .The surfactant solution was prepared with a fixed CTAB concentration of 0.1 M in demineralized water, and different viscoelastic fluids were prepared by varying salt concentrations from 0.2 M to 2.0 M.The transparent surfactant solution (d) was prepared by ultrasonication bathing (c) of white solution of demineralized solvent water (a) and solute cetrimonium bromide (b), as shown in Initially, the 1 wt% ZnO NP dispersion nanofluid solution was prepared using demineralized water and adding 20 wt% ZnO nanoparticle dispersion fluid (in water). The surfactant solution was prepared with a fixed 0.1 M CTAB concentration and nanofluid solution. Then different viscoelastic fluids were prepared by varying salt concentrations from 0.2 M to 2 M.Initially, a homogenous 1% weight of ZnO nanoparticle dispersion in water (1 wt% ZnO nanofluid) (a) was prepared using demineralized water and 20% weight ZnO nanoparticle dispersion in water obtained from Sigma Aldrich. The prepared fluid goes through an ultrasonic bath (h) which removes air bubbles in the fluid, and a homogeneous viscoelastic surfactant fluid (i) is prepared. The slightly white surfactant solution (e) was prepared by ultrasonication bathing (d) of white solution of solvent nanofluid (b) and solute Cetrimonium bromide (c). Then the inorganic sodium nitrate salt reagent was added, and the prepared solution (f) was mixed by heating and stirring (g) using a magnetic stirrer, as shown in −1 at a difference of 5 Sec−1 at different temperature conditions from 25 °C to 75 °C at 10 °C intervals.The focus of this research is to investigate the ability of nanoparticles to improve the viscoelastic fluid by improving its rheological characteristics. The rheometric characterization and analysis of the synthesized viscoelastic fluids were performed using the Anton-Par rotational rheometer (MCR2Model). Then, the viscosity was observed by varying shear rates from 1 to 500 Sec3 salt reagent concentration at constant temperature and increasing shear rate conditions (1 to 500 Sec−1) were plotted at 25 °C. The graphical plots depict that with increasing salt concentration, the viscosities of the fluids increases until 0.8 M NaNO3 salt concentration and give similar viscosities for 1.0 M salt concentration, see 3 concentration, the plot showed a decrease in viscosity values. So, 1.0 M and 0.8 M concentration are candidates for optimum concentration. We can decide the optimum concentration depending on maintained better rheology or viscosity of the fluid at elevated temperature and shear rate conditions.  and (2)) ,53,54 for the optimum fluid of type1 category fluids without nanoparticle additives, fluid with 0.8 M NaNO3 concentration can be expressed as below. The values have been taken from Similarly, the Herschel–Bulkley model equation (after yield stress has been reached) for the optimum fluid of type2 category fluids with nanoparticle additives, fluid with 0.6 M NaNOChilton and Stains represented a set of equations (Equations (7)–(11)) to calculate the pressure drop for laminar flow for such fluids . The equP is the Pressure Drop in Pa, L is the pipe length in meters, and D is the diameter of the pipe in meters.Here a, b, and c in Therefore, as suggested by Chilton and Stains, the pressure drop during laminar flow in a pipe can be estimated for both optimum fluids of type1 and type2 groups using factors calculated Micellar solution of surfactants having wormlike micelles or cylindrical micelles changes to viscoelastic fluids with good rheological characteristics under certain conditions due to entanglements in micelles. These viscoelastic systems are sensitive to changes in conditions. So, these viscoelastic systems are not able to maintain rheology under high temperature and high shear rate (HTHS) conditions. The nanoparticle additives can assist to maintaining or improving their rheology even at HTHS conditions.In this study, initially, a micellar solution of cationic surfactant of cetrimonium bromide or CTAB was prepared which had long cylindrical or worm-like micelles. The micellar solution formed a highly viscous, viscoelastic fluid system in the presence of counterion sodium nitrate salt reagents due to entanglements in long WLMs of CTAB. The rheology was analyzed using a rotational rheometer with varying temperature and shear rates. The represented rheology of the fluids was found to be high enough that the fluids can be implemented successfully on the field for hydraulic fracturing operations  at all temperature conditions: 25 °C, 35 °C, 45 °C, 55 °C, 65 °C and 75 °C respectively as illustrated in The viscosity values of the type2 optimum fluid  with ZnO nanoparticles additives are higher when compared to type1 (without nano additives) fluids of 0.6 and 0.8 (optimum) salt concentration for entire range of shear rates  can be added to them to cover remaining important technical aspects for a successful on-field applications during hydraulic fracturing operations.


Methylphenidate is a widely used first-line treatment for attention deficit/hyperactivity disorder (ADHD), but the underlying circuit mechanisms are poorly understood. Here we investigate whether a single dose of osmotic release oral system methylphenidate can remediate attention deficits and aberrancies in functional circuit dynamics in cognitive control networks, which have been implicated in ADHD. In a randomized placebo-controlled double-blind crossover design, 27 children with ADHD were scanned twice with resting-state functional MRI and sustained attention was examined using a continuous performance task under methylphenidate and placebo conditions; 49 matched typically-developing (TD) children were scanned once for comparison. Dynamic time-varying cross-network interactions between the salience (SN), frontoparietal (FPN), and default mode (DMN) networks were examined in children with ADHD under both administration conditions and compared with TD children. Methylphenidate improved sustained attention on a continuous performance task in children with ADHD, when compared to the placebo condition. Children with ADHD under placebo showed aberrancies in dynamic time-varying cross-network interactions between the SN, FPN and DMN, which were remediated by methylphenidate. Multivariate classification analysis confirmed that methylphenidate remediates aberrant dynamic brain network interactions. Furthermore, dynamic time-varying network interactions under placebo conditions predicted individual differences in methylphenidate-induced improvements in sustained attention in children with ADHD. These findings suggest that a single dose of methylphenidate can remediate deficits in sustained attention and aberrant brain circuit dynamics in cognitive control circuits in children with ADHD. Findings identify a novel brain circuit mechanism underlying a first-line pharmacological treatment for ADHD, and may inform clinically useful biomarkers for evaluating treatment outcomes. ClinicaMethylphenidate is a stimulant that is widely used as a first-line medication for the treatment of ADHD , and hasBrain systems involved in cognitive control are important targets for the investigation of functional circuit mechanisms by which methylphenidate remediates attentional deficits. These include, most prominently, the salience network (SN), frontoparietal network (FPN) and default mode network (DMN), which play a crucial role in virtually all tasks that require moment-by-moment changes in adaptive cognitive control . The SN,Here we use a randomized placebo-controlled double-blind crossover design  to inves2.2.1.This study protocols were approved by the Ethics Committee of the University of Fukui (Assurance no. 20170005). All participants and the parent(s) provided written informed consent and assent for participation in this study. This study is registered with the University Hospital Medical Information Network (UMIN000027533).Children with ADHD were scanned twice, in a randomized placebo-controlled double-blind crossover design. During the first visit, they were administered a single dose of osmotic release oral system methylphenidate (OROS-MPH) (1.0mg/kg: 1.0 ± 0.1mg/kg) or placebo (lactose) under double-blind conditions, as previous studies . We usedDuring the second visit, within 1 to 6 weeks after the first visit, they underwent a resting-state fMRI scan and performed the CPT after they took the second medicine: children with ADHD who took a single dose of OROS-MPH at the first visit took the placebo at the second visit under double-blind conditions, and vice versa. OROS-MPH condition was defined as ADHD-MPH, and placebo condition was defined as ADHD-Placebo in this study. TD controls were scanned once without OROS-MPH or placebo, and the CPT was not administered to the TD controls. Children with ADHD took their regularly prescribed medications between the two visits, but stopped medication prior to each MRI session, as described above.2.2.A standardized CPT was administered to children with ADHD under both the methylphenidate and placebo conditions. The task consisted of a Go/NoGo paradigm in which children were presented with either a target or non-target stimulus on the screen for 100 msec, once every 2 seconds for 15 minutes across three 5-minute blocks. The target stimulus was a triangle, while the non-target stimulus was either a circle or a square. Children were required to press the button when a target stimulus was presented, and withhold response to non-targets. The percentage of target stimuli varied between 22%, 50% and 78% across blocks . The tes2.3.Functional images were acquired with a T2*-weighted gradient-echo echo-planar imaging (EPI) sequence via a 3-T scanner  and a 32-channnel head coil. In total, 201 volumes were acquired for a total scanning time of 7 minutes 42 seconds. Each volume consisted of 40 slices, with a thickness of 3.5 mm and a 0.5-mm gap to cover the entire brain. The time interval between each successive acquisition of the same slice  was 2300 ms, with an echo time (TE) of 30 ms, and a flip angle (FA) of 81°. The field of view (FOV) was 192 × 192 mm, and the matrix size was 64 × 64, yielding volume dimensions of 3 × 3 mm. The participants were instructed to stay awake but close their eyes and think of nothing in particular. Participant movement was further minimized by the placement of memory-foam pillows around their head, as previously reported .2.4.http://www.fil.ion.ucl.ac.uk/spm/), including slice-timing correction, realignment, normalization, spatial smoothing (6-mm smoothing kernel), regression of nuisance variables , and bandpass filtering (0.008 Hz < f < 0.1Hz) (A standard preprocessing procedure was implemented using SPM12 (< 0.1Hz) .http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC). The number of components was set to 30, and four components , left and right frontoparietal (FPN), and default mode (DMN) networks) were identified using a quantitative template-matching procedure  to identify large-scale networks in the combined population  computes Fisher z-transform of Pearson’s correlation (CC) between ROI timeseries. Thus for instance, f computes Fisher z-transform of the Pearson’s correlation between the time series of the SN and FPN. f and f were computed separately and then their average was used as f . Larger NII values reflect more segregated cross-network interactions between the SN-FPN and SN-DMN systems in the context of the triple-network model.CC is Pearson’s correlation between the time series of two component networks, e.g., CCWe computed an NII for each sliding window and averaged NIIs for the windows corresponding to the same dynamic brain state. We next computed the mean and variability (measured by standard deviation) of time-varying NIIs across all the dynamic brain states for each participant.2.6.t-tests.First, we examined administration-related changes in sustained attention, as indexed by CPT performance measures, between the ADHD-MPH and ADHD-Placebo conditions using paired t-tests. Next, we examined differences by contrasting the ADHD-Placebo and TD control groups using two-sample t-tests. Finally, we examined administration-related improvement using two-sample t-tests contrasting the ADHD-MPH and TD control groups. A Bonferroni correction was used to correct for multiple comparisons across groups.Second, we examined administration-related changes in mean dwell times and mean and variability of dynamic time-varying NII between the ADHD-MPH and ADHD-Placebo conditions using paired caret R package (https://cran.rproject.org/web/packages/caret/).Third, to investigate the robustness of our findings in the context of methylphenidate-related improvement, which is the focus of the present study, we examined whether multivariate dynamic time-varying NII measures could distinguish ADHD-MPH, ADHD-Placebo, and TD control groups. We used a Regression Tree classifier  and leavPearson’s correlation, similar to our previous study  could predict the effect of methylphenidate on sustained attention. The composite performance score in CPT was used as measures of sustained attention, and was set as dependent variables. Mean and standard deviation of dynamic time-varying NII under placebo were set as independent variables with age, FSIQ, mean FD as confounding factors.t-test (comparison between ADHD-MPH and ADHD-Placebo) and Welch’s t-test (comparison with TD controls) for numerical variables and chi-square tests for categorical valuables. All statistical tests  were parametric test and two-tailed; p-values less than 0.05 were considered statistically significant.Demographic data are expressed as the mean ± SD. The clinical values were compared using paired 3.3.1.ps > 0.05). The two groups showed significant differences in inattention, hyperactivity/impulsivity and FSIQ . Mean FD in the ADHD-MPH was significantly lower than the ADHD-Placebo and TD groups . There were no differences in mean FD between the ADHD-Placebo and TD groups (p = 0.450). Detailed information about study participants, including comorbidity and medication history, is provided in Data analyses involved data from 76 male subjects, comprising 27 patients with ADHD  and 49 TD controls  . There w3.2.ps < 0.001, ts (26) = 4.03, 3.87, 5.05, Cohen’s ds = 0.78, 0.75, 0.97, respectively; p = 0.857, t (26) = 0.18, Cohen’s d = 0.03). We then examined composite scores, computed using all four behavioral measures, and found that it was significantly lower in the ADHD-MPH, compared to the ADHD-Placebo, condition  = 4.60, Cohen’s d = 0.89; To investigate the behavioral consequences of methylphenidate administration, we first examined sustained attention, assessed using the CPT in the ADHD-MPH and ADHD-Placebo conditions. Omission errors, mean RT, and standard deviation of RT were significantly lower in the ADHD-MPH, compared to the ADHD-Placebo, condition  = 4.37, Cohen’s d = 0.84). Compared to the TD group, mean dwell times were significantly longer in the ADHD-Placebo group  = 3.94, Cohen’s d = 1.15), but was not significantly different in the ADHD-MPH group  = 0.54, Cohen’s d = 0.12)  .p = 0.132, Bonferroni corrected, t (26) = 2.12, Cohen’s d = 0.41). Mean of time-varying NII was significantly higher in the ADHD-Placebo than the TD group  = 3.94, Cohen’s d = 0.87), but was not significantly different between the ADHD-MPH and TD groups  = 1.69, Cohen’s d = 0.40) (We next probed mean and variability of dynamic time-varying cross-network interactions among the three groups .This ana = 0.40) .p = 0.019, Bonferroni corrected, t (26) = 2.97, Cohen’s d = 0.57). Compared to the TD group, NII variability was significantly lower in the ADHD-Placebo  = 5.75, Cohen’s d = 1.30), but was not significantly different in the ADHD-MPH  = 2.43, Cohen’s d = 0.56). Additional analyses confirmed that our main findings held even after controlling for comorbidity, FSIQ and mean FD as potential confounds , and between the ADHD-Placebo and TD groups with an accuracy of 78% (p = 0.009). In contrast, no differences were observed between the ADHD-MPH and TD groups . These results further demonstrate that methylphenidate improves dynamic connectivity patterns associated with the SN, FPN and DMN. Furthermore, we found dynamic time-varying cross-network interactions could distinguish ADHD vs TD groups from the two independent ADHD-200 cohorts used in our prior study  and hyperactivity/impulsivity . Mean of time-varying NII was correlated with inattention  and hyperactivity/impulsivity . Variability of time-varying NII was correlated with inattention  and hyperactivity/impulsivity   . These r3.6.β = −26.57, p = 0.043, Finally, we used multiple linear regression analysis to determine whether mean and variability of dynamic time-varying NII, as well as age, FSIQ, and mean FD, under the placebo condition could predict individual differences in treatment response. Results showed that variability of time-varying NII in the ADHD-Placebo group predicted MPH-induced changes in composite CPT scores . The NII is a parsimonious metric based on the triple-network model , which sOur findings suggest that a single dose of methylphenidate can remediate aberrant dynamic brain network interactions among the SN, FPN and DMN. Furthermore, we found the relation between dynamic cross-network interactions and ADHD symptoms. These results suggests the behavioral relevance of dynamic cross-network interactions between the SN, FPN and DMN in the present sample, consistent with a previous report in childhood ADHD , and bro4.3.Our findings inform theoretical models of cognitive control circuits in childhood ADHD and their alteration by dopaminergic medication. The triple-network model proposes that the SN facilitates access to attention and cognitive control resources and plays a crucial role in switching its interaction with the FPN and DMN involved in externally-oriented attention and internally-oriented mental processes . Promine4.4.Our study examined the effects of a single dose of OROS-MPH administration on sustained attention and dynamic brain circuits involved in cognitive control. Immediate release methylphenidate is not approved for clinical use in Japan. Critically, methylphenidate is the main active ingredient in both osmotic and immediate release medications. In our study, the resting-state fMRI was acquired 5–8 hours after medication when methylphenidate concentrations in the blood generally reach their maximum (American Psychiatric Association 2007), thus, matching immediate release methylphenidate as closely as possible. However, no studies have directly compared brain response to osmotic release versus immediate-release methylphenidate, so the precise correspondence remains to be investigated.While our design allowed us investigate mechanisms by which cognitive control is improved by methylphenidate, it did not include follow up clinical assessments, so the overall real-life longitudinal clinical effects are not known. Further studies with multi-dose methylphenidate administration, treatment duration and stimulant formulations, and follow up assessments of inattention, hyperactivity, and impulsivity are needed to determine the long-term clinical impact of our findings .In the present study, we found that dynamic time-varying NII between the SN, FPN and DMN was lower in children with ADHD under placebo condition compared to TD controls at baseline, an opposite pattern of those reported in a previous study . This diOur NII metric focuses on three core cognitive control systems involving the SN, FPN and DMN. Missing here are interactions basal ganglia and reward pathways implicated in ADHD, which need to be incorporated into future work. As with most studies of ADHD, children with ADHD in our study were not drug naïve, were male, and spanned a wide range from 5 to 16. Specifically, all participants were medication-free prior to MRI for at least 5 half-lives. For ethical reasons we could not stop children’s medication between study days. Our randomized placebo-controlled double-blind crossover design involved similar washout protocols for the methylphenidate and placebo groups, thus controlling for the effects of use of medication between study days. The effect of medication on cognition and brain activity are likely to be influenced by administration procedures, the length of washout and medication dosage. Larger studies that include drug naïve males and females with ADHD are needed to determine how medication history, sex, and development stage along with other medication factors modulate methylphenidate effects on aberrant cognitive control circuits . For eth4.5.Our randomized placebo-controlled double-blind crossover study demonstrates, for the first time, that a single dose of methylphenidate can improve sustained attention and remediate aberrant brain circuit dynamics in cognitive control circuits in children with ADHD. Our findings provide novel insights into brain mechanisms underlying successful methylphenidate administration in ADHD and may lead to clinically useful biomarkers for evaluating ADHD treatment. More generally, salience network-related cross-network dynamics provides a novel and parsimonious quantitative systems-neuroscience based template for investigating the neural consequences of therapies that treat cognitive and attention deficits in psychiatric and neurological disorders. Importantly, our study addresses fundamental gaps in our knowledge of dopaminergic action on cognitive control circuits in children with ADHD.1


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder in a child with three symptoms, which include inattention, hyperactivity and impulsiveness that may persist into adulthood for some conditions. Parenting style is thought to be one part that determines the risk of ADHD in children. This study aims to analyse the relationship between parenting styles and the risk of ADHD in children.P-value < 0.05.Employing a cross-sectional design, this study was conducted in Surabaya from November 2020 to January 2021. The respondents of the study were parents who had children at risk of ADHD with a total of 55 samples recruited using the purposive sampling technique. The questionnaires used are the demographic questionnaire, Abbreviated Conners Rating Scale (ACRS), and the Parenting Style Questionnaire for Children and Adolescents (KPAA), which were filled out online by the respondents. The data were processed and analysed using the bivariate analysis test, Pearson’s chi-squared test, which has a significant value if the P < 0.001 for authoritarian and permissive styles and P = 0.005 for an authoritative style.The majority of the parents used the authoritative style (94.5%). There was a significant relationship between parenting style and the risk of ADHD in children with There is a significant relationship between parenting style and the risk of ADHD in children. This indicates the importance of early diagnosis of ADHD and treating the children with ADHD in the context of family environment, especially from parenting style. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder causing emotional and behavioural disorders in children. The three symptoms of ADHD are inattention, hyperactivity and impulsivity that typically occur before a child turns 12 years old and in some conditions can persist into adulthood. The symptoms of ADHD occur in two different situations and these conditions can cause problems in social, educational and work aspects. In the United States, the prevalence of ADHD ranges from 3% to 7%  Semolowaru I/261 Elementary School; ii) Semolowaru IV/614 Elementary School; iii) Keputih 245 Elementary School; iv) Kertajaya Elementary School and v) Baratajaya Elementary School. Research data were obtained from the Pediatric Psychiatry Unit of Dr. Soetomo General Academic Hospital, Surabaya, which reported 72 cases of ADHD in 2008 and 103 cases in 2009 .The study design was cross-sectional and all data were taken using a questionnaire that the parents of the students filled in through an online platform from November 2020 to January 2021.Calculation of the minimum sample used the following parameters:The demographic questionnaire contains data of students and data of parents. The Abbreviated Conners Rating Scale (ACRS) questionnaire which consists of 10 questions was used to assess the risk of ADHD in children listed in the Stimulation, Detection, and Early Intervention of Child Growth and Development (SDIDTK) book . The valP-value was < 0.05.The data were processed and analysed using univariate analysis techniques to see the frequency and percentage of variables and bivariate analysis to see the relationship between parenting style and risk of ADHD in children using the Pearson’s chi-squared test, considered significant if the The socio-demographic data of children and parents showed that male children dominated, which was 56.4% and the average age was of 10 years old, which was 32.7%. Most parents were between 31 years old and 40 years old (60%). Most dominant education background is senior high school (70.9%). Most of the respondents were housewives (47.3%) and the majority income is between IDR0 and IDR1,500,000 (50.9%) . The risP = 0.000 for authoritarian and permissive style, P = 0.005 for authoritative style)  . An anale style) .The results of this study found 55 samples of children who were at high risk of ADHD in this study  with a gThe age selection of children in this study follows the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) which explains that ADHD symptoms will be seen before turning 12 years old of age . NovrianThe results of this study indicate that the types of parenting styles applied in this study are authoritative and authoritarian parenting with a greater frequency of authoritative parenting than authoritarian parenting, which is 94.5% . FollowiP-value = 0.000 and P-value = 0.005 (P < 0.05). It can be concluded that there is a significant relationship between parenting styles and ADHD in children  using a larger sample size so that it can produce more general conclusions not only on the population or sample used; (ii) observation on the socio-demographic conditions of parents on the risk of ADHD in children. This is not discussed further in this study considering the wide range of inclusion criteria used.There is a significant relationship between parenting styles and ADHD in children. The dominant parenting style in this study is authoritative parenting. Parents who have children at high risk of ADHD mostly applied authoritarian parenting. Lack of parental attention through parenting can increase dopamine and norepinephrine levels in children, which is one of the causes of hyperactivity and will increase the risk of ADHD in children. More information about the condition of ADHD in children, the types of parenting as well as the advantages and disadvantages of each type of parenting is needed, so that parents can apply parenting styles appropriate to the child’s condition. Future research should focus on risk factors other than parenting that are thought to affect the risk of ADHD in children so that this condition can be minimised by early management.


The present study objectives were to determine the prevalence of attention-deficit/hyperactivity disorder symptoms (ADHD-like symptoms) in children and adolescent with d-transposition of great artery (D-TGA) after arterial switch operation (ASO) and examine associated risk factors and adverse personal, family dysfunctions.This cohort study included 103 patients with D-TGA who underwent ASO in early infancy at Shanghai Children’s Medical Center between 2011 and 2016 and then follow-up. Data analysis was conducted from September 2020 to April 2022. A standardized Swanson, Nolan, and Pelham IV (SNAP-IV) questionnaire is used to evaluate inattention and hyperactivity symptoms. Demographic, preoperative, intraoperative, and postoperative factor were collected. Univariate and multivariable regression analyses were performed with odds ratios (OR) and 95% confidence intervals (CIs).P = 0.03) and delayed sternal closure (P = 0.02) were risk factors of ADHD-like symptoms; increased preoperative oxygen saturation (SpO2) (P = 0.01) and surgical height (P = 0.01) and TGA subtype (VSD) (P = 0.02) were protective factor of ADHD-like symptoms. Multivariable analysis showed that delayed sternal closure (DSC)  is a risk factor for the occurrence of ADHD-like symptom while increased preoperative oxygen saturation  is a protective factor of ADHD-like symptom.Prevalence of ADHD-like symptoms was 27.18% (28/103). Attention-deficit  symptom was the predominant subphenotype. After underwent TGA surgery, 39% of patients with ADHD-like symptoms receive remedial special academic services. There is none had repeated grade. Univariate analysis showed that, positive inotropic drug score (The children and adolescents with D-TGA after ASO were at high risk of ADHD-like symptoms. Preoperative hypoxic status and postoperative DSC became predominant risk factors. Modification of the risk factors may be helpful to relieve ADHD-like symptoms for these patients. Congenital heart disease (CHD) is the most common congenital malformation, accounting for about 2–3% of population. D-transposition of the Great Arteries (TGA) is a misalignment of the main ventricular arteries, showing that the main artery starts from the right ventricle while the pulmonary artery starts from the left ventricle. This anatomical anomaly makes the systemic and pulmonary circulation become two sets of parallel cycles. Shunt which depending on the levels of atria, ventricles, or aorta allows children to survive with mixed blood between the two circulations. TGA accounted for about 3% of all CHD and nearly 20% of all cyanosis CHD. Without timely treatment, the mortality rate of d-transposition of great artery (D-TGA) children within 1 year is 90% . HoweverADHD is a common neurobehavioral and psychiatric disorder characterized by inattention and/or hyperactivity and impulsivity with an estimated prevalence rate around 5% in children, and frequently persists into adult, estimated to be around 2.5% , 8. It cn = 341) mainly diagnosed with transposition of TGA were retrospectively identified from the hospital database in Shanghai Children’s Medical Center between 2011 and 2016 and follow-up. Data analysis was conducted from September 2020 to April 2022. All patients diagnosed with TGA in their infancy were approached to participate. Children with double-outlet right ventricle and outflow tract or arch obstruction were excluded from the analysis. Patients with spontaneous genetic disorders, congenital anomalies, severe intellectual disabilities, or severe nerve injury  not related to open chest surgery were also excluded. Parents fill out the SNAP-IV questionnaire by an electronic questionnaire and a telephone interview. Pediatricians will further understand the general details and disease status of the child by telephone interview. The study was approved by the Ethics Review Committee of Shanghai Children’s Medical Center (number: SCMCIRB-K2022042-1). This study has been registered with the Chinese Clinical Trials Registry1 as ChiCTR2200062243.In this cohort study, consecutive patients  questionnaire, which is designed to quantify ADHD symptoms as described by the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. This tool enables the separate measurement of inattention and hyperactivity . BrieflyWe analyzed the prevalence of ADHD-like symptoms based on the SNAP-IV results. This involved the quantification of positive symptom scores  of the inattention and hyperactivity/impulsivity items. Each item was rated using a 4-point Likert scale, with 0 indicating “never,” 1 “occasional,” 2 “regular,” and 3 “always.” If participants reported more than six positive symptoms in any of the two subscales, they were diagnosed with ADHD-like symptoms of predominantly inattentive or hyperactive/impulsive subtypes. If a participant presented symptoms of both inattention and hyperactivity/impulsivity, a combined subtype of ADHD symptoms was diagnosed . The ADH2), surgical age, surgical weight, surgical height, alprostadil use, DHCA (Deep Hypothermic Circulatory Arrest), CPB (cardiopulmonary bypass), total cardiac support time, delayed sternal closure, positive inotropic drug score, and length of stay.The primary outcomes were high-risk scores for inattention and hyperactivity based on the parental answers in the SNAP-IV questionnaire. For the risk analysis of adverse primary outcomes, identified perioperative variables included sex, gestational age, low birth weight, preoperative oxygen saturation  with 95% confidence intervals (95% CIs). Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 26.0 .In the baseline data, categorical variables were analyzed using the chi-square and Fisher’s precision tests. The distributions of continuous variables were detected using histograms and QQ plots. Two-sample independent The final analysis included 103 patients (83 boys and 20 girls) who were above 6 years . Among t2 in patients with ADHD-like symptoms was 62.54% compared with 71.17% in patients without ADHD-like symptoms (95% CI: 2.07–14.86). The medium surgical weight at the time of surgery in patients with TGA and ADHD-like symptoms was 3,500 g vs. 3,800 g in patients without ADHD-like symptoms (95% CI: 0.009–0.013). Likewise, the medium surgical height was 50 cm in TGA patients with ADHD-like symptoms and 51 cm in those without ADHD-like symptoms .Of the patients who had delayed sternal closure, 21 patients developed ADHD-like symptoms, accounting for 38.2% of those patients. By contrast, among the patients without delayed sternal closure, only 7 (14.6%) children developed ADHD-like symptoms . Moreover, postoperative ventilator use was more common in ADHD-like symptoms patients. The interquartile range of positive inotropic drug score was 17.55 μg/kg/min in patients with ADHD-like symptoms vs. 14.5 μg/kg/min than in those without . Among the parameters with significant group differences were also the total time of extracorporeal circulation and myocardial ischemia time. A comparison of the clinical variables for patients with and without ADHD-like symptoms is provided in The prevalence of ADHD-like symptoms in patients after TGA surgery was 27.18%. Inattention, hyperactivity/impulsivity, and combined symptoms were present in 17.5, 5.8, and 3.8% of TGA patients, respectively . The per2 , surgical height  and TGA subtype (VSD)  were protective factors. After multivariate regression analysis, only delayed sternal closure  remained independent risk factors and preoperative increased SpO2  is a protective factor  is a protective factor  in below average study performance and above average performance in group of ADHD-like symptoms and Non-ADHD like symptoms. Besides, the ADHD-like symptoms group had a higher percentage of mathematics performance below average, there was statistical difference between the two groups   .We also counted the percentage of children who had completed surgery with well below average academic performance in study level is 20.4% and in mathematical level is 12.6%.According to our research, only 14.3% of these 28 probable patients had ever sought help in special preschool education. Furthermore, 60.7% of the parents of CHD children with ADHD-like symptoms did not notice considerable problems. In addition, only 25% of these patients were receiving special education . In the 2 and postoperative DSC are two independent risk factors for ADHD-like symptoms.In this study we found 27.2% of a cohort of patients with D-TGA after ASO presented ADHD-like symptoms. Clinical multivariable analysis showed that decreased preoperative SpOThe high occurrence of ADHD symptoms have been described in children with variety of CHD. Mahle et al. reported 69.5% of children with hypoplastic left heart syndrome had the symptoms of inattention, hyperactivity, or both . KirshboD-TGA is one of the most extensively studied cyanotic CHD with regard to neurodevelopmental outcomes. Long-term follow-up of these children who underwent the ASO reveals multiple neurologic deficits in cognitive function, motor skills, academic achievement, and language ability in infancy and early childhood, and as they were followed into their teenage years, behavioral problems and subsequently psychiatric problems became increasingly prominent , 30. BelIn our study, it was found that inattention, hyperactivity and the combination of the two accounted for about 64.3, 28.6, and 14.3% of ADHD-like symptoms, respectively. Consistent with other studies, inattention was the main symptom. Less than 15% of those children with ADHD-like symptoms requiring special education received additional education.Our findings are different from those of the Boston Circulatory Arrest Study, which prospectively followed a cohort of infants with dextrotransposition of TGA. Different to its report, one-third of the children were receiving school support, and 10% of the children had already repeated a grade, our research found that twenty five percent received remedial education, and none had repeated grades.Mahle and Wernovsky  reportedThe mechanisms for neurodevelopmental impairment in CHD are multifactorial. Early investigators attributed neurologic injury to a direct consequence of surgical repair and particularly exposure to cardiopulmonary bypass or circulatory , and latIn fetus and unrepaired neonates with D-TGA, oxygen supply to brain tissue reduces due to abnormal ventricular-arterial connection and abnormal hemodynamics. Chronic hypoxia may cause brain developmental immaturity and cerebral white matter injury seen on MRI neuroimaging, which are related to behavioral changes in children with CHD . MoreoveInterestingly, DSC was also identified to be an independent risk factor for ADHD-like symptoms occurrence in this study. As a management strategy for hemodynamic instability and severe coagulopathy after ASO, DSC could decrease the early surgical mortality and morbidity of patients with D-TGA , though So we also could take some measures to help these patients reduce the risk of ADHD-like symptoms. First of all, using some appropriate vasoconstriction and enhanced blood pressure drugs for some preoperative children. Appropriate blood transfusion can be used to increase ventilation and improve the oxygen carrying capacity of red blood cells. However, due to the defection of the current research mechanism, this method should be used with caution in combination of childrens’ condition.Secondly, the protection of myocardium can be strengthened by selecting myocardial protective fluid during surgery.Thirdly, after the operation, we should strengthen the caring for the serious degree of CHD and use timely anticoagulant drug to reduce the duration of infection risk CICU.Patients with D-TGA undergoing ASO, constitute a group receiving a homogeneous treatment regimen. This facilitates the assessment of the relative contributions of preoperative, intraoperative and postoperative risk factors to neurological outcomes. The main finding in our study is that the prevalence of ADHD-like symptoms was significantly higher than that in Western countries , 33. MeaIn this study, preoperative decreased hypoxia and DSC were identified to be two potentially modifiable risk factors for ADHD-like symptoms, which imply that ASO should be performed as soon as possible to shorten the chronic hypoxic time from birth to surgery and reduce the risk of brain injury from preoperative acute hypoxic events , 35, andOur study has some limitations. First, the screen of ADHD was based on SNAP-IV. However, these criteria are highly sensitivity, lacking of some specialty , so we sIn this study we concluded that children and adolescents with D-TGA undergoing ASO are at high risk of ADHD-like symptoms, especially inattention-related symptoms. Preoperative hypoxia and postoperative DSC are two high-risk factors. It is vital to carry out further research and strengthen our evaluation, with regard to surveillance and management of neurodevelopmental function, especially ADHD-like symptoms in children and adolescents with D-TGA after ASO.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Written informed consent was not obtained from the minor(s)’ legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.HTC: writing and designing original draft. YY: formal analysis. CL and XZ: methodology. GW: validation of research. ZJ: conducting a research. GS and XH: investigation. XT and HWC: supervision. ZZ: conceptualization and revise original draft. All authors contributed to the article and approved the submitted version.


Post-hoc pairwise testing identified four attentional strengths with significantly higher rates in ASD than in ADHD. Attentional strength scores were not associated with impairment or poor cognitive flexibility, but predicted levels of perseveration/perfectionism. The effect of attentional strengths on impairment and cognitive flexibility did not differ between autistic and Control children, but the higher perseveration/perfectionism scores seen in ASD were not found in Control children. ASD is associated with a pattern of attentional strengths that is not found in ADHD Characterizing the full range of attentional abilities in autistic children may explain variability in outcomes such as quality-of-life indicators and identify protective factors, providing targets for strength-based behavioral interventions. The clinical and etiological implications of the subgroup of autistic children with attentional strengths require further investigation.The comorbidity of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) diagnoses is well established. An ASD diagnosis is associated with elevated ADHD traits and symptoms, as well as strengths in attention. In the ASD literature, attentional strengths have been described as maladaptive , in contrast with positive portrayals in the typically developing population . The objective of this study was to (1) compare profiles of attentional strengths and weaknesses in ASD and ADHD and (2) determine whether attentional strengths in ASD are associated with impairment, poorer cognitive flexibility, and perseveration/perfectionism. In a community sample of 5,744 children and youth, 131 children were reported as having a diagnosis of ASD (mean age 10.3 years) and 346 children were reported as having a diagnosis of ADHD (mean age 10.7 years). We used the Strengths and Weaknesses of Attention-Deficit/Hyperactivity-symptoms and Normal-behaviors (SWAN) rating scale to calculate attentional and hyperactive/impulse control strength and weakness counts and scores. The Autism-Spectrum Quotient Switching factor served as a measure of cognitive flexibility. Impairment was assessed with the Columbia Impairment Scale. We used the symmetry/ordering factor on the Toronto Obsessive-Compulsive Scale as a measure of perseveration/perfectionism. No differences were found between the ADHD and ASD groups in SWAN weakness scores, symptoms, or hyperactive/impulse control strengths; however, autistic children had higher rates of attentional strengths [odds ratio: 5.7, 95% CI ,  Autism spectrum disorderIn contrast to this negative perspective on over-focused attention in ASD and ADHD, the state of “flow” described in typically developing people is described similarly as intense absorption to the point of losing track of time and perception of the outside world but without the negative connotation associated with hyperfocus. The term “flow” was coined by Csikszentmihalyi  in referAshinoff and Abu-Akel  proposedopposite extreme of the high, symptomatic end of continuous ADHD traits . Typical ADHD rating scales cannot identify strengths because they are limited to questions about the presence or absence of individual symptoms or deficits. Informants can rate a person as “not having a symptom” but cannot rate them as having an apparent strength in that trait . The development of the Strengths and Weaknesses of Attention-Deficit/Hyperactivity-symptoms and Normal-behaviors  and reduced impairment as measured using the Columbia Impairment Scale  in univariable models , e models . Howeverp = 0.0003] but not attentional strengths ] corresponding to a response of “very little problem” and, in the Control group, was −2.41 [95% CI: ], just above a response of “no problem” providing little room for improvement with increasing attentional strength scores .There was no evidence that higher scores on the TOCS symmetry/ordering factor in ASD was driven by individuals with a comorbid diagnosis of OCD. Only two autistic children reported a comorbid OCD diagnosis: one child had an attentional strength score of 0.8 and the second child had an attentional strength score of 0, and neither reported OCD symptoms in the symmetry/ordering domain. Although TOCS symmetry/ordering scores increased significantly with higher attentional strength scores, the average TOCS symmetry/ordering item predicted from the multivariable linear regression model across the range of observed attentional strength scores all fell below the cut-off for an OCD symptom.p = 0.0007, p = 0.002, p = 0.005, In a univariable model, higher levels of attentional strengths were significantly associated with higher average AQ-short-C numbers and patterns scores [β = 0.90, 95% CI: , In a sensitivity analysis, we repeated all the multivariable models in the ASD–ADHD sample only and in the parent respondent sample . ResultsThe goal of this study was to characterize attentional strengths in ASD. We took advantage of the unique structure of the SWAN ADHD questionnaire to create measures of ADHD weaknesses, ADHD strengths, ADHD symptoms and overall total ADHD scores. The ASD group had comparable levels of ADHD symptoms and ADHD weaknesses as the ADHD group consistent with the literature showing high rates of comorbid ADHD and high subthreshold ADHD traits among ASD individuals. However, when we expanded the focus to the full range of ADHD trait scores measuring both strengths and weaknesses, we discovered that children on the autism spectrum had significantly lower total scores than those with ADHD. These differences in trait profiles were driven by an increase in apparent attentional strengths in autistic children. Four attentional strengths were significantly more common in ASD than in ADHD: “sustain attention on tasks or play activity,” “engage in tasks that require sustained mental effort,” “remember daily activities,” and “give close attention to detail/avoid careless mistakes.” ADHD and ASD did not differ in the proportion of hyperactive/impulsive control strengths. These results were replicated in an independent sample, revealing a consistent profile of apparent attentional strengths in ASD across different samples.The four apparent attentional strengths identified as being more common in ASD than in ADHD are well established ASD traits. Both the Weak Central Theory of autism  and the In much the same way that hyperfocus describes attentional strengths through a negative lens and flow through a positive lens, the Weak Central Theory of autism describes a preference for local processing (attention to detail) stemming from difficulties understanding the big picture, in contrast with the hyper-systemizing theory where strengths in attention to detail are an asset used in support of recognizing patterns and rules that govern systems . Given tWhile there is no doubt that attentional strengths can be seen as beneficial in ASD, as one autistic adult explains, “I believe the unwavering focus to a subject has aided me academically” , autistiThe attentional strengths identified as more common in ASD than ADHD are well established ASD behaviors, yet research on comorbid ASD and ADHD has focused on weaknesses that are common to both disorders and not on the apparent attentional strengths that may serve to discriminate between them. More research is needed to characterize the co-occurrence of apparent strengths and weaknesses to inform ADHD diagnostic decisions in individuals on the autism spectrum. The ability to enter a state of intense focus depends on task engagement and does not preclude the presence of difficulties with focus in other contexts. That is, the same individual may sometimes exhibit attentional strengths and at other times attentional weaknesses on the same SWAN item, depending on their interest in and enjoyment of their current activity. This apparent contradiction may underlie some cases of poor respondent agreement on ADHD questionnaires. In their expert consensus guidelines, Young et al.  warn thaOur findings highlight the importance of designing questionnaires that measure both extremes of a behavioral trait. Such questionnaires can improve the homogeneity of empirically defined phenotypic subgroups, increasing our power to identify susceptibility genes in linkage studies  as well Hyperfocus or flow? Our failure to find a significant association between attentional strength scores with impairment and cognitive flexibility in either Control or autistic children belies the conventional belief that high levels of focused attention in Control children is a strength, as represented by flow, but invariably maladaptive in autistic children. Whether this is because some children on the autism spectrum experience high levels of strengths without the challenges associated with hyperfocus while others exclusively experience hyperfocus, or whether both positive and negative levels of attention can be seen within the same child remains to be determined. Compared to flow research, the research in hyperfocus is in its infancy. With more study, a consensus on the distinguishing features, overlap, and boundaries between flow and hyperfocus can be established, facilitating research into the conditions that lead to more positive flow experiences rather than negative hyperfocus ones.We characterized a broad range of attentional strengths in autistic children as described by parents using a single score to describe the prevailing trend across each item in the previous 6-month period. As such, we were unable to determine if individuals can present with both strengths and weaknesses for the same item across different contexts. Individuals with high strength scores may present with different profiles of strengths, not all of which are characteristic of hyperfocus . Participants visiting a science center may not be representative of or generalizable to the general population. Notably, there were fewer individuals from the lowest SES quintile and more high SES individuals participating in the 2009–2010 study than across the surrounding area , Susan Bradley Health Clinician Scientist Endowment (JC) and the Alberta Innovates Translational Health Chair in Child and Youth Mental Health (PA).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background and Objectives: the comorbidity of personality disorders in patients who use psychoactive substances is common in psychiatric practice. The epidemiology of disharmonious personality traits in patients with ADHD and addictions in adulthood is still insufficiently researched. The study investigated the typology of personality traits in a group of adult patients consuming psychoactive substances, in whom symptoms of ADHD were identified. Materials and Methods: the study evaluates a group of 104 patients with chronic psychoactive substances abuse, in whom symptoms of ADHD were identified in early adulthood, in terms of comorbid personality traits. Results: statistically significant data have been obtained regarding the presence of clinical traits characteristic for cluster B personality disorders, the patients presenting lower levels of self-control, self-image instability, difficulties in the areas of social relationships and own identity integration. Conclusions: ADHD symptomatology precedes the clinical traits of personality disorders in patients with addictions, negatively influencing chronic evolution and quality of life. Substance Use Disorder (SUD) is a psychiatric condition, which involves a modification of cerebral physiology through epigenetic mechanisms, during the transition from occasional, recreational consumption to repetitive, addictive behaviours of psychoactive substances . MultiplDuring the past few years, numerous studies on the comorbidity of substance abuse and ADHD (Attention Deficit Hyperactivity Disorder) have been published, however, research on the relationship between substance use, ADHD and personality disorders continues to be poorly developed.Studies that have investigated the co-occurrence of these disorders, report comorbidity rates ranging from 25% to 55% in adults with ADHD who had a history of psychostimulant abuse/dependence . If we cAddiction negatively affects the evolution of ADHD with age, through complications such as cognitive deterioration and memory problems. On the other hand, substance abuse can mimic the symptoms of ADHD and in this way lead to overdiagnosis. The objective is to establish a set of ADHD symptoms, in addition to the symptoms related to the onset, nature, and severity of substance abuse, so that patients with comorbidities can receive adequate treatment. Addiction in patients with ADHD has a more severe and chronic course compared to those who have addictions but do not associate this disorder as well .The presence of ADHD and personality disorder comorbidity, in patients with psychoactive substances abuse can be explained by the common impulsivity factor . The oveDue to the fact that the diagnosis of ADHD in adults is a relatively new one, the overlapping impulsivity, mood instability, and aggressivity symptoms can easily lead to the diagnosis of personality disorder without the suspicion of ADHD being taken into account . The invCertain personality traits based on impulsivity have been reported in multiple studies that have investigated patients with ADHD and SUD, such as the deficient self-control abilities , repeatePersonality disorders have a prevalence reported in the literature between 10 and 15% in the general population , the diaThe main objective of this study is to determine whether there is a significant relationship between personality disorders and drug use/ADHD. The secondary objective was to establish the most common type of personality disorder involved in this relationship.Our current research is a continuation of a previously published study, where we highlighted that in a group of 104 patients with SUDs, that 46% of them met the criteria for adult ADHD .The same group of 104 patients was included in this study. The patients, aged between 18–28 years, were selected from an addiction psychiatry unit at “Prof. Dr. Alexandru Obregia” Clinical Psychiatry Hospital in Bucharest, Romania. The age of the adult patients included in the study is between 18–25 years. 75% of the subjects were between 18 and 22 years old, with 21 years old being the age with most representatives. This age range was selected as the ward within which we conducted our research is dedicated to younger adults. Moreover, the majority of those who struggle with these disorders and who are referred to the aforementioned addictions clinic are aged 18–25 and they were generally more inclined to cooperate and consent to the inclusion in the study. The selection protocol of the subjects included in the study and research methodology were applied with the agreement of the Ethics Committee within “Carol Davila” University of Medicine and Pharmacy and “Prof. Dr. Alexandru Obregia” Clinical Psychiatry Hospital. In this cross-sectional, observational study we wanted to evaluate the pathological personality processes that were associated with the symptomatic picture of ADHD, and substance abuse rates.-Control group: patients with SUD and no ADHD symptoms, consisting of 60 participants.-Study group: patients with SUD and ADHD symptoms, consisting of 44 participants.Thus, the subjects were divided into two groups:All the participants underwent a comprehensive psychiatric assessment performed by a psychiatrist, consisting of the medical and developmental history, the educational history, the mental status examination, a Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnosis and the treatment history.All patients from this study used drugs chronically.The evaluation of ADHD symptoms was carried out with DIVA 2.0, a structured interview approved in Romania, that investigates ADHD symptoms in adults, conceptualized in 2010 by J.J.S. Kooij .The evaluation of maladaptive personality traits was performed applying Severity Indices of Personality Problems (SIPP-SF), developed in 2008 by Verheul and Collaborators . This set-test for independent samples. For comparison of proportions, we used the z test, with a p < 0.05 significance level.Statistical analysis: compiling the variables database was carried out with Microsoft Office Excel 2007. Statistical data were obtained using IBM SPSS Statistics 20, by performing descriptive and inferential statistical methods. We used histograms for graphical representation of the discrete quantitative variables. Descriptive analysis of the quantitative variables was performed, and the relationship between 2 quantitative variables was detailed using Pearson correlation coefficient and the relationship between a quantitative and a qualitative variable was tested with t-test for independent samples showed that there was a statistically significant difference between the average of the scores for the self-control field in those without ADHD and those with ADHD   .There was no statistically significant difference between the two groups regarding the scores obtained for Identity integrity and Responsibility sections.t-test for independent samples showed that there was a statistically significant difference between the 2 averages   .t-test for independent samples showed that there was no statistically significant difference between the 2 averages   .p-value are represented. There was a linear, positive, moderate, statistically significant correlation between the values obtained by the subjects for the Self-control section and those for the Identity Integration and Relational Skills domains. There was also a linear, positive, strong, statistically significant correlation between the values obtained by the subjects for the field of Self-control and the areas of Responsibility and Social Adequacy. There was a linear, positive, moderate, statistically significant correlation between the values for the Identity Integration section and those for the Responsibility domain. There was also a linear, positive, strong, statistically significant correlation between the values of the Identity Integration domain and those of the Relational Skills and Social Adequacy domains.In The present study aimed to evaluate the presence of ADHD specific symptoms in a group of patients diagnosed with SUDs, hospitalized in an addiction ward within a clinical psychiatry hospital. Subsequently, after identifying the patients who met the diagnostic criteria for ADHD, the group was divided into two subgroups in order to compare the profile of substance use and comorbidities according to the presence or absence of ADHD symptoms. The secondary objective of the study was to characterize and compare the two subgroups in terms of personality traits and underlying personality disorders.The lower level of self-control reported by adults with ADHD compared to those without ADHD is explained by the clinical picture and the specific symptomatology of this disorder. Thus, the impulsiveness characteristic of ADHD in the adult is manifested by low tolerance to frustration, irritability, hurried responses, difficulties in waiting for their turn, easily entering into conflicts, impulsive expenses . PreviouThis self-regulating deficit is reflected in the scores obtained in the personality traits questionnaire, Self-control section.The lower scores obtained by the subjects in the group with ADHD for the Relationship skills items compared to the group without ADHD, is explained by the difficulties of people with this disorder to establish and maintain relationships with the people around them. So, both in the case of children with ADHD and in adults, patients have difficulties maintaining relationships because of impulsivity and inadequate reactions, and because they lose interest and become bored easily. Moreover, these people tend to change environments, jobs, relationships out of impulsivity .Previous research investigating the relationship between identity development and substance abuse, suggested that individuals with a non-integrated identity status are more likely to experiment drugs or alcohol at a younger age, to stay abstinent for a shorter time period, and to register a low compliance to the therapeutic process ,30. In oEven though one of the characteristics of addictions is the inability to take responsibility, in our study there was no significant statistical difference between the two groups regarding the scores obtained for Responsibility sections. These findings may suggest that in our group the pathological personality traits can be integrated in a Cluster B Personality Disorder, more specific in a narcissist or antisocial type. The clinical characteristics suggestive for Borderline Personality Disorder  were not statistically significant in our group. Considering this results in comparison with previous studies, we concluded that there is still controversy over the hypothesis that ADHD is a precursor to the development of Borderline Personality Disorder, as there are authors who consider that these two disorders are so frequently associated because they overlap with the same genetic and environmental aetiology .One studOur findings are consistent with other previous results, which highlight the presence of Antisocial Personality Disorder in patients with ADHD in young adulthood ,35. We cSevere personality disorders frequently associate comorbid ADHD, and the presence of this pathology predisposes to the development of accentuated disruptive behaviours, and an increased degree of impulsivity. According to data from precedent studies, the development of disharmonious personality characteristics in children overlapped with a history of oppositional defiant and/or aggressive behaviour. Moreover, an overlap with the three criteria  of antisocial personality was identified in 90% of patients with ADHD. The combined type or the hyperactivity/impulsivity type increases the risk of developing Antisocial Personality Disorder .Most studies reported in the literature on the association of substance use with ADHD pathology in adults, were performed on groups of patients diagnosed with ADHD to investigate the presence of behavioural disorders secondary to psychostimulant use. The present research brings as a novelty the retrospective investigation of ADHD symptoms in adulthood in a group of patients admitted to a psychiatric service with an addiction profile, underlining the importance of raising awareness regarding the identification and accurate diagnosis of ADHD in adult psychiatric services. However, thorough analysis of each case is needed, with investigation o the presence of ADHD symptomatology during childhood, as symptoms declared by adult patients, which can be superimposed over the ADHD criteria, may be part of the clinical picture of another psychiatric disorder, such as bipolar affective disorder or personality disorder, which have late onset, adolescence or adulthood. The diagnosis of ADHD has criteria applicable to adults only with DSM-5 and is still an underdiagnosed and untreated disorder, which further increases the risk of chronicity. All of the above results may increase awareness of the use of the diagnosis of ADHD in adult psychiatric services. Early diagnosis of ADHD and comorbidities could better guide the intervention  in these patients in order to improve their quality of life.The clinical picture in the adult with ADHD and Substance Abuse Disorder is very complex and of great symptomatic diversity. The more psychiatric comorbidities there are, the harder they are to treat and more difficult it is to achieve remission despite proper interventions. In the case of patients with antisocial personality traits that are difficult to treat it is important to consider them during evaluation, as symptoms of an undiagnosed ADHD, because it can provide more perspectives on intervention.The hypothesis that ADHD is a precursor to the development of Antisocial Personality Disorder is becoming increasingly valid. The developmental trajectory and the factors that modulate the course of this comorbidity throughout life are very little understood and require further study.


Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior.To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management.A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed.A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance  in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform.ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection.ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982RR1-10.2196/40189 Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood . ADHD isThe diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior ,5. AdditTo address the limitations of the existing approaches, ADHD360 focuses on developing an integrated technology solution that includes a serious game for a probabilistic prediction indicating the presence of ADHD in players using machine learning (ML) models and a mobile app for monitoring ADHD behaviors. The platform aims to (1) facilitate early detection of ADHD characteristics and (2) serve as an adjunct intervention for ADHD management. The first goal of the project is to analyze the gameplay behavior of the users with respect to their diagnosis . Thus, discriminative gameplay patterns among the groups (ADHD or non-ADHD) are explored to estimate the users’ likelihood of having ADHD characteristics. The second goal is to investigate the effectiveness of the platform as an intervention for ADHD management.Discrepancies regarding ADHD prevalence have emerged over time and among studies, leading to mixed conclusions about the possible underdiagnosis or overdiagnosis of the disorder . MultiplIn terms of the medical aspects, the access to health providers , limitedAlthough diagnostic challenges could be addressed through a detailed history of prenatal conditions, family status and school/academic life ,13, stanAs early detection of ADHD could ameliorate the disorder’s development, diminish its long-term impact , improveFor addressing the existing challenges, ADHD360 aims to develop an integrated platform comprising a serious game and a mobile app for monitoring ADHD behaviors in a SMART  way ,25 as thA 2-phase nonrandomized controlled pilot study was performed in the context of the ADHD360 project. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted to the new conditions imposed by the pandemic, providing remote participation. The procedures for each of these 2 ways of participation are described thoroughly below.The first part of the pilot study begins with a first meeting between the involved members of the research team and the parents of each participant at the Laboratory of Medical Physics and Digital Innovation, School of Medicine of Aristotle University of Thessaloniki (iMedPhys). The aim of this first meeting is to thoroughly inform the parents and the children regarding the nature and scope of the project, as well as the exact procedures that take place according to the research protocol. In this context, parents can ask questions, express any concerns regarding the project, and sign the informed consent form. Afterward, the participants are subjected to a brief neuropsychological assessment by an experienced psychologist from the research team. This neuropsychological assessment includes 6 subtests of the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V), an intelligence test that measures a child’s intellectual ability, and 5 cognitive domains that impact performance . The 6 sSubsequently, the participants interact with the serious game “Pizza on Time” that has been specifically developed for the ADHD360 project for approximately 30-45 minutes at iMedPhys. During this interaction with the serious game, a form is completed regarding the conditions, the duration and the behaviors that will possibly be occurred. Participants interact again with the serious game for 30-45 minutes at iMedPhys, continuing from the level at which they stopped at the previous visit to complete all the levels of the first setting (the urban setting).After the aforementioned procedures, there is a telephone conversation between the participants’ parents and the psychologist who conducted the neuropsychological evaluation to discuss if there are any behaviors characteristic of ADHD that affect the daily functioning of the participants. Once the psychologist has reached a consensus with the parents that there are certain behaviors that could be observed, the behaviors are introduced into the participant's account in the mADHD360 app. Instructions regarding the installation and use of the mobile app are provided by the research team through email. The mADHD360 app can be used on a smartphone or tablet. An observation plan is scheduled, including at least 2-4 times per week of behavioral monitoring regarding the frequency or the duration of these behaviors during the time they usually spend with the participant. Each round of behavior monitoring has a duration of 12 minutes. The duration of this phase is determined to be 2 weeks. During the behavior monitoring phase, the participants do not interact with the serious game.Once behavior monitoring is complete, the research team instructs the participants to continue playing the game using the second (jungle) and third (space) settings on the premises of iMedPhys. The mean duration of this part is 10 weeks with a frequency of at least 2 times a week, depending on the participants’ availability. In this second part of the pilot testing phase, the goal is to exploit the serious game as an intervention to improve certain ADHD symptoms.Next, a second round of behavior monitoring is performed following the same procedures as the previous one. Furthermore, a neuropsychological assessment is conducted following the same procedures as those during the first evaluation in the beginning of their participation.All the procedures are identical to those followed during in-person participation, apart from the interactions with the serious game that are done remotely from their home using their personal computers.The eligibility criteria were reviewed by a member of the research team before assignment to the study. Inclusion criteriaParticipants should be between 7 and 16 years old.Participants of the attention deficit hyperactivity disorder (ADHD) group should be diagnosed by an approved body of the Ministry of Health.Participants should be willing to follow the study protocol and procedures.The ADHD symptoms should not be attributed to organic disease.Parents must voluntarily provide written consent for their child’s participation in the study.Exclusion criteriaParticipants suffering from comorbid conditions other than ADHD are excluded.Children whose parents are not willing to provide written consent for participation in the study are excluded.All participants are comprehensively informed regarding the procedures of the study through a document named “Participant Information and Consent Form.” Moreover, the participants are informed about the purpose of the study as well as the following aspects of their participation:Their participation is voluntary.They can ask questions about the study procedures before participating in the study.They will be aware of the underlying risk or burden associated with their participation.They will know who will benefit from conducting this research.They will know how data collection and data protection will be performed during the project’s lifetime as well as whether data will be destroyed or reused (in case there is a possibility to reuse them).They will be fully informed, and they will agree for further use of their data.They can leave the study at any time and withdraw their data from the study.They will be aware of the possible commercial exploitation of the research findings.Each informed consent is signed by the researcher who informs the participant and the principal investigator of the study. The informed consent explicitly states that the study was approved by the Committee for Bioethics and Ethics of the Medical School at the Aristotle University of Thessaloniki and all personal details are anonymized using a unique user identification code.The study protocol  was approved by the Ethics and Bioethics Committee of the School of Medicine at the Aristotle University of Thessaloniki (reference number: 6.225/29.7.2020).t test analysis using a power of 0.8, a significance level of 0.05, and an effect size equal to 0.2. The total sample size was estimated to be 199 participants. As proposed by Billingham et al [We initially considered recruiting at least 20 participants (10 ADHD and 10 non-ADHD) in our pilot trials. We estimated the sample size for the definite study by performing power analysis using GPower software . We conducted a paired am et al , the samam et al  and is sam et al .The ADHD group is mainly composed of patients from the Community Center for Mental Health of Children and Adolescents at the General Hospital of Thessaloniki “G. Papanikolaou,” which supports the implementation of pilot trials. Furthermore, dissemination activities such as social media posts, mass media interviews, information events open to the public, and project presentations at conferences and workshops are considered essential to recruit participants for pilot trials.The ADHD360 platform consists of 2 main components, a serious game and a mobile app for behavior monitoring.Game design covers a wide range of activities in designing games, including story, aesthetics, mechanics, and technology . These vThe mechanics-dynamics-aesthetics framework standardizes the basic components of a game into distinct parts such as rules, systems, and fun. This distinction helps us in the design of the mechanisms because the basic components of the game can be translated into game design elements such as mechanics, dynamics, and aesthetics that are visible to both the end users and the game designer, but from different perspectives. There are some games that have been developed specifically to improve ADHD symptoms, whereas commercial game titles have also been used for research purposes to measure the performance of users with or without ADHD diagnosis .The 2 games that focus on improving ADHD symptoms are Plan it Commander  and AntoFor bridging this gap, we focused on developing a serious game called “Pizza on Time” , which iThe ADHD360 monitoring app (mADHD360) developed in the project provides teachers, parents, and health professionals with features to easily monitor specific targeted behaviors related to ADHD. Although several related information and communications technology solutions were implemented to replace traditional pen-and-paper observation charts in the past years , not allThe process of creating a new subject profile and monitoring behavior can be described as follows:First, the associate user  creates an account and enters the basic data of the subject to be monitored in the mADHD360 app. There is a strict policy regarding the sensitive personal data of the children; it does not allow real names to be used or any other significant detail that can reveal the identity of the children in the real world.The next step is to create a network of people, usually people that spend time with the child (such as a relative or a class teacher) and a health professional. The app supports a directory of ADHD experts, which can be added to the child’s observation network. Inviting other people is a very simple process, and it can be performed via email.A limited number of ADHD-related behaviors are associated with the child. The associations come from a predefined vocabulary , but theFinally, the efficacy of each treatment can be evaluated by health professionals through visual analysis of the data gathered by the network members during the assessment periods.The mADHD360 app is based on the Web Health Application for ADHD Monitoring (WHAAM) [From a technology point of view, the mADHD360 app is designed to run on mobile devices, smartphones, and tablets. It is delivered for free as a progressive web app with the main game.P=.05. Continuous variables are explored for normality by the Shapiro-Wilk test to calculate the appropriate descriptive statistics. Continuous variables that are approximately normally distributed are reported as means (SDs) whereas those that are not normally distributed as medians and IQRs . Neuropsychological data expressed as raw scores are treated as continuous variables. Categorical data are described as frequencies and percentages.Statistical analysis is performed using SPSS  and the statistical significance level is set at State-of-the-art ML algorithms are used to analyze labeled gameplay data. Specifically, we attempt to classify game-generated data based on the user’s known label (ADHD or non-ADHD). All in-game events of a gameplay session are recorded to form a time series that one can use to reconstruct the whole gameplay session. These events correspond to player actions and environmental parameters, such as performing a jump/move, left or right moving action, obstacle collision, and coin collection. Informative feature extraction is performed by splitting players’ time series depending on the game level. Different feature extraction techniques are assumed for each level depending on its type. In the main runner game, several aggregates are calculated by capturing different events during the gameplay . In the mini games, feature extraction is based on the characteristic game mechanics and events of the respective mini game. The final feature vector of each player is produced by concatenating the individual-level feature vectors.k-fold cross-validation (CV) with k=3, an ML pipeline, and data evaluation was designed for experimental purposes. The train-test splitting is used for model specification , model selection, and training of the final model. Modeling decisions are made on the training set. Thus, the test set is used only to evaluate and report the statistics of the algorithm’s performance. The purpose of 3-fold CV is to fine-tune the hyperparameters of the supervised learning algorithms (classifiers) that we investigate.A schema that includes train-test splitting with a 75:25 split ratio, F value between label and feature), (3) principal component analysis for dimensionality reduction, and (4) classifier training. Therefore, the hyperparameters of all the steps are tuned  in the 3-fold CV process. The F1 score is used as the optimization criterion in the 3-fold CV, and the best configuration of each method's pipeline is then used on the entire training set (fully trained pipeline).For each classifier, an ML pipeline was designed to include the following procedures: (1) removal of features with 0 variance, (2) univariate feature selection according to a statistical criterion  , logistiIn total, 43 participants were recruited with a mean age of 11.82 (SD 2.81) years. Among these, 28 (65%) were male and 15 (35%) were female. Of the 43 participants, 18 (42%) were diagnosed with ADHD and 25 (58%) participants were considered controls (non-ADHD). Approximately 30 participants (70%) completed the first part of the pilot phase remotely.Neuropsychological data were obtained from 30 participants. They were evaluated using WISC-V by the psychologist of the ADHD360 team or by an approved national body within the last 2 years. Among the 30 participants, 10 (33%) were diagnosed with ADHD and 20 (67%) were considered as healthy controls (non-ADHD). There were 21 males (70%) and 9 females (30%). In the ADHD group, all participants were male, whereas in the non-ADHD group, 11 (55%) were male and 9 (45%) were female. The mean age of the screened participants was 11.40 (SD 2.85) years. In the ADHD group, the mean age of the participants was 12.90 (SD 2.64) years, whereas that of the participants in the non-ADHD group was 10.65 (SD 2.70) years.According to the data from the first neuropsychological assessment, the mean score in the figure weights subtest in the ADHD group was 14.00 (SD 5.79), whereas the median score of this subtest in the non-ADHD group was 19 (IQR 13-24). The mean scores in the subtests block design, similarities, digit span, coding, and vocabulary are presented in The mean score of the participants in the inattention subscale of the ADHD Rating Scale-IV was 12.70 (SD 7.04), whereas that in the hyperactivity-impulsivity subscale was 10.97 (SD 6.65). In the full group of participants, the mean total score was 23.67 (SD 12.56). The mean scores on the ADHD Rating Scale-IV obtained by grouping the participants based on their diagnosis are presented in Following the methodology described above, we present the results of a preliminary analysis of gameplay data collected during pilot trials. Gameplay data were collected from all recruited participants. However, technical difficulties  led to insufficient or no data generation in 13 of the 43 participants (30%). Therefore, gameplay data collected from 30 (70%) participants were used in the following preliminary analysis. We comprehensively describe the CV results for a training set consisting of data collected from 22 of the 30 participants (73%) as well as the selection of the best model. Subsequently, we evaluate the predictive performance on a test set consisting of data gathered from 8 (27%) participants using point estimates and CIs.F1 results for each learning method is shown in F1 score and the other performance measures for the CV procedure are given in A graphical illustration of the CV F1 score above 0.5. We observed significant overfitting for RF, kNN, and NB, with a difference of 0.1 between the training and validation scores. The test performance scores are given in Most classifiers achieved an F1 score (0.66). (2) Although SGD achieves the same validation score as SVM, the latter also achieves a better fit with a difference of approximately +0.07 between training and validation, which is approximately –0.09 for the former.The previously mentioned models seem to be substandard solutions for this classification problem, with all of them dominated by LogReg, SGD, SVM, and PAC. The latter models feature a tradeoff between validation performance and goodness of fit. Among these methods, we selected SVM for the following reasons: (1) It achieves the highest validation Even though the results are indicative of the potential that ML has in the domain of ADHD prediction based on gameplay data, we presume that experimenting with a larger data set could give more accurate and concrete conclusions.Limitations of the existing screening procedures for ADHD diagnosis are summarized as high dependence on pen-and-paper practices, subjectivity bias, difficulty in monitoring in different settings, and risk of data loss. The ADHD360 platform, consisting of the serious game “Pizza on Time” and the mADHD360 app, attempts to offer a holistic technological solution for early detection of ADHD characteristics and serve as a training tool against ADHD-related symptoms. It facilitates real-time data collection in different settings and from different individuals involved in the user’s care. Moreover, the ADHD360 platform enables quantitative data analysis assessing the behavior and game performance of the user while providing a more integrated view of the user's behavioral characteristics. Investigating different state-of-the-art ML methods revealed that our platform is characterized by a notable capacity to discriminate players based on their in-game patterns as those who have ADHD characteristics and those who do not. Thus, it is expected to serve as a complementary screening tool benefiting health care professionals, educators, and people with ADHD.However, the health emergency imposed by COVID-19 affected the implementation of pilot trials by forcing adaptation of the protocol to remote conditions. Moreover, lockdowns and social distancing may have contributed to the low sample size and the high number of dropouts during the study. Additionally, the long duration of the experimental protocol and the need for commitment to the study procedures may have negatively affected users’ adherence to pilot trials. Moreover, ADHD has been characterized by “a dislike of mental effort” . ConsideEven though preliminary analyses of the collected data have shown promising results, the platform can be further improved by training the ML models on a larger data set that can be developed by recruiting more participants. This would allow the learning models to further improve their accuracy in correctly distinguishing ADHD gameplay behaviors from non-ADHD gameplay behaviors. The monitoring app will soon have a dedicated enrollment service for health experts; parents will be able to see a list of related professionals near their area and contact a person to join their child’s network. Moreover, we will add asynchronous chatting capabilities and a strict schedule-monitoring functionality, with device and calendar notifications. The “Pizza on Time” game is planned to be released to the public for both Android and iOS devices, as well as for PCs (Windows and Mac).