N = 151) were infants with or without an elevated likelihood for ADHD (due to a family history of ADHD and/or ASD). A multi-method assessment protocol was used to assess infant attention and activity level at 10 months of age that included behavioural, cognitive, physiological and neural measures. Preschool ADHD traits were measured at 3 years of age using the Child Behaviour Checklist (CBCL) and the Child Behaviour Questionnaire (CBQ). Across a broad range of measures, we found no significant group differences in attention or activity level at 10 months between infants with and without a family history of ADHD or ASD. However, parent and observer ratings of infant activity level at 10 months were positively associated with later preschool ADHD traits at 3 years. Observable behavioural differences in activity level (but not attention) may be apparent from infancy in children who later develop elevated preschool ADHD traits.Mapping infant neurocognitive differences that precede later ADHD-related behaviours is critical for designing early interventions. In this study, we investigated (1) group differences in a battery of measures assessing aspects of attention and activity level in infants with and without a family history of ADHD or related conditions (ASD), and (2) longitudinal associations between the infant measures and preschool ADHD traits at 3 years. Participants ( Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition estimated to affect 3–5% of the population ,2 acrossA number of possible causal pathways for ADHD have been proposed , all of Greater insight into the early development of ADHD is also required to develop early intervention approaches ,22, withTwo domains that are conceptually related to the core features of ADHD are focused attention and activity level. Across a number of studies, increased activity levels during the preschool years have been shown to associate with both concurrent  and lateOne way to identify early differences in cognition and brain functioning associated with ADHD is to study infants who have an elevated likelihood of developing later ADHD because they have a family member with ADHD or a related condition (such as ASD). This study design leverages the fact that ADHD is highly heritable, with increased prevalence in individuals with first degree relatives with ADHD ,38 or AShttps://osf.io/kyc46, registered on 28 July 2017).Focused attention, in particular, is challenging to assess during infancy. Focused attention undergoes substantial development over the first year of life , and itsAt the global behavioural level, in line with previous prospective studies of infants with a family history of ADHD ,46, we aGlobal measures of behaviour are comprehensive but may miss more subtle changes that may occur early in the development of ADHD. Semi-structured play-based assessments afford an opportunity for more fine-grained analysis of attention-related behaviors than parent or observer report. Behavioural-cognitive measures of infant focused attention, such as the duration of time the infant spends manipulating objects whilst looking at them, show some continuity to later focused attention in the toddler and preschool years . AmongstWhilst observational measures of behaviour provide insight into subtle differences in attention in a natural setting, measures of visual attention taken through eye-tracking can provide more fine-grained dissection of the cognitive processes underlying behaviour change. In the present study, two eye-tracking measures of infant visual attention were used to capture focused attention. In developmental research, “peak look duration”  is ofteEpochs of looking can be associated with different levels of engagement with the stimulus being fixated; sometimes, infants may stare “blankly” at an object and this may not represent truly focused attention. Examining physiological arousal can provide further insight into periods of infant focused attention . MoreoveFinally, we also examined infant attention at the neural level. Specifically, we focused on EEG theta power. Infant theta oscillations have been associated with infant attention in a number of studies ,70,71,72https://osf.io/kyc46, registered on 28 July 2017) were that in comparison to infants with no family history of ADHD, infants with a family history of ADHD would show: higher parent and observer reported scores of activity level, lower behavioural attention during toy play, shorter peak look durations and greater reaction time variability during eye-tracking, higher mean heart rate and greater head motion during experimental paradigms designed to elicit attention, increased mean frontal theta and decreased change in frontal theta power in response to novel videos.The primary aim of our study was to investigate whether a family history of ADHD or related conditions (ASD) was associated with differences in early attention and activity level at 10 months of age. We focused on 10 months of age, as the latter part of the first year of life is proposed to be a critical period of development for attentional focus . This maThe secondary aim of our study was to investigate longitudinal associations between infant activity level and attention at 10 months of age and ADHD-related behaviours at 3 years . These analyses were hypothesis-driven but not pre-registered. We predicted that parent and observer reported activity level, reaction time variability, heart rate during naturalistic play, head motion and mean frontal theta would be positively associated with preschool ADHD traits. Further, we predicted that behavioural attention (parent and observer report), peak look duration  and change in frontal theta would be negatively associated with ADHD-related behaviours.Finally, in exploratory analysis, we also examined the extent to which our pre-registered measures of infant activity and attention converged. This exploratory analysis has value not only in interpreting the results of our own study, but also in contributing more broadly to the field in terms of the measurement of attention in infancy.Participants were recruited as part of the Studying Autism and ADHD Risks (STAARS) project. STAARS is an ongoing longitudinal study following the development of infants who either have a first-degree relative with a diagnosis or probable research diagnosis of ADHD, and/or a diagnosis of ASD (and thus are at elevated likelihood of ADHD), or who have no family history of ADHD and ASD . We had originally preregistered analyses to be conducted with infants at typical likelihood of ADHD and infants with a family history of ADHD only; however, in line with current moves towards transdiagnostic approaches in studying the early development of neurodevelopmental conditions ,74 we suFamilies were invited to visit the laboratory at the Centre for Brain and Cognitive Development (CBCD) when their infant reached 5, 10 and 14 months of age, and King’s College London when their infant reached ~3 years of age. Infants who met any of the following criteria based on parent report at screening were excluded: (1) serious medical or developmental conditions, (2) significant uncorrected vision or hearing problems, (3) significant prematurity (less than 36 weeks’ gestation), (4) genetic conditions such as Down’s syndrome or Fragile X syndrome.N = 151); see The sample for the present study comes from the first wave of participants, which comprises infants who attended their first visit to the laboratory for the project prior to June 2017 (N = 10), as part of a randomised controlled trial [Demographic characteristics for the study sample at 10 months and 3 years are shown in ed trial .N = 9), but this had not yet been confirmed by clinical services. For those who reported suspected ADHD, screening questionnaires were used to examine the probable existence of ADHD  or ADHD (“FH-ADHD”) in a first-degree relative (parent or older sibling). This was confirmed by interview with parents at a subsequent visit to the lab. A proportion of children/parents had suspected ADHD . Missing data per measure is described in We assessed infant activity level and attention across six modalities, as described below. For each modality, all infants with valid data for that modality were included, regardless of whether they had data for other modalities. For data exclusion thresholds see  (1)Global ratings of activity levelParent-report. Parents were asked to complete the Infant Behavioural Questionnaire-Revised (IBQ-R) shortened version [ version . The IBQ version . In our Observer report. Each 10-month visit at the CBCD lasted approximately 5 h, generally spanning both morning and afternoon, led by two researchers. Following each laboratory visit, the two researchers discussed and agreed a consensus code for the infant’s attentiveness and activity level across the whole 10-month visit (ranging from 1–7). For activity level this ranged from 1 (low activity level) to 7 (high activity level). To ensure consistency across infants, at least 1 of these researchers was a “Core consensus coder” (1 of 2 researchers who contributed to every 10-month consensus code). Core consensus coders had PhD level training. (2)Global ratings of attentionParent-report. Parents were asked to complete the IBQ-R, as described above. We selected the Duration of Orienting scale. This subscale measures infants’ attention and/or interaction with a single object for extended periods, and includes items such as “How often during the last week did the baby play with one toy or object for 5–10 minutes”, and “How often during the last week did the baby look at pictures in books and/or magazines for 5 minutes or longer at a time”. Previous reports with typically developing infants have found moderate inter-parent agreement and homotypic continuity over spans of up to 7 months for the Duration of Orienting Level scale of the IBQ-R [he IBQ-R . In our Observer report. As described above, consensus codes of infant attentiveness across the whole 10-month visit was rated by two researchers. For attentiveness this ranged from 1 (low attentiveness) to 7 (high attentiveness). (3)Behavioural measures of attentionLaboratory Temperament Assessment Battery (LabTAB). The task orientation episode from the Laboratory Temperament Assessment Battery, LabTAB [, LabTAB  was used (4)Eye tracking measures of attentionTwo eye-tracking tasks were administered as part of a larger battery (~30 minutes) of eye tracking tasks, using a Tobii TX-300 eye tracker  sampling at 120 Hz. The screen had a diagonal size of 23” , a native resolution of 1920 × 1080 pixels and an aspect ratio of 16:9. Stimuli were presented on Apple  Macbook Pro computers, using our custom-written stimulus presentation framework , running in Matlab R2020b  using Psychtoolbox 3 ,78.Face pop-out task . Infants were presented with a series of six annular visual arrays each composed of five objects in different locations on the screen. Each array contained: a face with direct gaze; a visual “noise” image generated from the same face presented within the array by randomising the phase spectra of the face whilst keeping the amplitude and colour spectra constant to act as a control for the low-level visual properties of the face stimuli [https://osf.io/kyc46 (Appendix 2) (registered on 28 July 2017) for a full description of analysis steps and data processing pipelines for this task. Previous work has shown that infants’ average duration of fixations shows high test-retest reliability [ stimuli ; and an iability  and thatiability .Gap-overlap task . For reaction time variability, our selected variable was the coefficient of variation for saccadic reaction times in the overlap condition of the gap-overlap task. All stimuli were presented at a size of 3 cm × 3 cm (2.86° × 2.86° at 60 cm viewing distance). In the overlap condition, each trial started with the onset of a central stimulus (CS), a cartoon image of an analogue clock accompanied by an alerting sound. This pulsed on screen at 3 Hz between 3 cm–5 cm (2.86°–4.77°) until fixated by the participant. The CS then rotated at 500° per second for a random 500–700 ms ISI. After 200 ms, a peripheral stimulus (PS) was presented, while the CS remained on screen for the duration of the rest of the trial. The PS was a cartoon cloud that appeared on either the left or the right side of the screen and was accompanied by a sound, 3 cm (2.86°) from the edge, rotating at 500° per second until fixated by the participant. A reward stimulus was then presented at the location of the PS for 1000 ms. The reward was a randomly chosen cartoon image of either a star, a sun, a dog, cat, pig, tiger or tortoise, accompanied by a sound. To encourage engagement the reward stimulus was animated to either spin on the spot, spin and shrink, or to pulse. Data were analysed offline. Each trial was inspected automatically to determine trial validity and calculate a saccadic reaction time (SRT) to shift attention from the CS to the PS, relative to PS onset. A trial was valid if the following conditions were met: (1) gaze fell on the CS; (2) no gaps of missing data longer than 200 ms were present during the CS period (before PS onset); (3) there was at least one sample of gaze on the CS within 50 ms either side of PS onset; (4) no gaps of missing data longer than 100 ms were present during the PS period (between PS onset and reward onset); (5) SRT was longer than 150 ms and shorter than 1200 ms; (6) gaze did not go in the opposite direction to the side of the PS; (7) gaze did not enter the PS Area Of Interest (AOI) after engagement with the CS but before PS onset. Trials that failed any of the above criteria were invalidated and removed from further analysis. See https://osf.io/kyc46 (Appendix 3) for a full description of analysis steps and data processing pipelines for this task. Previous work indicates that SRTs for the baseline condition of the Gap-overlap task show moderate test-retest reliability across a 1-week period amongst typically developing 10-month-old infants [ infants . (5)Physiological measures of attentionHeart rate. Average heart rate during the LabTAB task orientation episode (as described above) was used as a physiological measure of attention and arousal during toy play. Heart rate data was collected using the RSPEC BioNomadix , attached to a MP150 amplifier, sampled at either 100 or 1000 Hz. Three electrodes were placed in a lead-II position on the child’s back. The transmitter was placed in the pocket of a one-piece baby suit designed for the study. AcqKnowledge  was used for data acquisition. R-peaks were visually identified in AcqKnowledge and exported to Matlab. Missing R-peaks were corrected up to 3 consecutively missing beats by dividing the interbeat interval. Data was analysed in 30-second epochs, which were marked invalid if four or more consecutive beats were missing. The average of all valid epochs is used as the participant’s average heart rate for the task. Any 30-second epochs with more than 10% of peaks missing were excluded. See Head motion. Head motion was analysed during the face pop-out eye tracking task. The face pop-out task was selected as it is distributed throughout the eye tracking battery, involves passive viewing, and does not involve exogenous “attention getters”. During the face pop-out eye tracking task, head motion was derived from the 3D coordinates of each eye, as reported by the eye tracker and down-sampled to 30 Hz. Distance is the first derivative (the Euclidean distance between consecutive samples), and velocity is the second derivative. To normalise for quantity of valid, binocular samples  we used mean velocity as the derived variable to indicate amount of head motion. After smoothing, sample-by-sample velocity was derived and the mean velocity calculated for each trial. See  (6)Neural measures of attentionEEG. EEG was recorded while infants were presented with two 1-minute videos  each presented twice during the EEG session. In the social video, infants were presented with two women (one at a time) telling nursery rhymes with gestures. In the non-social condition, infants were present with child appropriate toys moving on the screen , with no social content. The order of the videos was counterbalanced across infants. EEG data was acquired using a 128 electrode Hydrocel Geodesic Sensor Net 1.0, recorded online with reference to the vertex, digitized at 500 Hz, amplification at 1000×. Using Netstation 4 (version 4.5.6)  band pass filtering at 0.1 to 100 Hz was applied. The EEG data was segmented into 1 second segments. Segments where infants were not looking at the video were removed from further analysis. Artefact detection was completed using hand editing . Data was referenced to the average reference, and the resulting segmented data was imported into Matlab. Within Matlab, segments were detrended and subjected to a Fast Fourier Transform (FFT). Power values were averaged across artefact-free segments and electrodes, within a priori of topographical groups. The topographical group of interest for this study were the frontal electrodes , and Time 2: the second half of the video (30–60 seconds)—for each 1 minute video. Natural logs were calculated to reduce skew. Logged power values were averaged across the theta (3 to 6 Hz) frequency range. Primary analyses collapsed across social and non-social conditions. The following formula was used to calculate change in frontal theta power averaged across the first presentation of the social and non-social video (Power30–60 s − Power0–30 s)/Power0–30 s × 100%. Only the first video repetition was used, because the second repetition would be contaminated by familiarity . For froChild Behaviour Checklist (CBCL). We used the DSM-oriented ADHD subscale of the CBCL (1 ½–5) to measure preschool ADHD traits at 3 years of age [s of age . This 6-s of age .Child Behaviour Questionnaire (CBQ). We used the CBQ short version to measure broader temperament dimensions related to the preschool ADHD phenotype at 3 years of age [s of age . The CBQEffect of family history of neurodevelopmental disorders at 10 months.The effect of family history on each of the six modalities described above was evaluated in a series of multivariate analyses of variance (MANOVAs), using the measures listed above as dependent variables such that there were two dependent variables for each MANOVA . Family history classification  were used as fixed factors in each MANOVA. For each modality, all infants with valid data for all measures in that modality were included, regardless of whether they had data for other modalities. Missing data per measure is described in https://osf.io/kzsyg/ (accessed on 29 March 2021) and set to 0 for negative values—and interpret both values using the heuristics suggested by Cohen (1988) [https://osf.io/kyc46, registered on 28 July 2017), see Partial eta squared values indicate the proportion of variance associated with membership of that group  after controlling for other factors in the model . As this is considered a biased estimate , we alson (1988)  whereby Longitudinal associations of infant attention and activity level with preschool ADHD traits at 3 years.Participant characteristics for the sample retained at 3 years are shown in Associations between measures of attention and activity level at 10 months.p-values were not corrected for multiple testing.We used Spearman’s correlations to investigate the association between our measures of attention and activity level at 10 months. Given the exploratory nature of this analysis, In our primary analyses, the MANOVA revealed no significant effect of family history of ADHD or ASD on any of the behavioural or neurocognitive measures of activity level and attention at 10 months of age, see Informed by the previous literature, our 10-month measures were selected to assess infant activity and attention at both the behavioural and neurocognitive level. Across the full sample of infants, we used Spearman’s correlations to investigate the association between our measures of attention and activity level at 10 months of age, see p = 0.027, partial η2 = 0.87. Consistent with our prediction, global measures of activity level at age 10 months were positively associated with ADHD-related behaviours at age 3 years. For the full cohort, robust regression showed that global ratings of activity level at 10 months of age (Model 1) was a significant predictor of CBCL ADHD traits at 3 years, F  = 9.70, p < 0.001, with parent-reported activity level (IBQ-R) contributing significantly to the model , and observer ratings of activity level more weakly contributing , p = 0.33; behavioural measures of attention, F  = 0.30, p = 0.74; eye tracking measures of attention, F  = 0.84, p = 0.44; physiological measures of attention, F  = 1.22, p = 0.30; or neural measures of attention, F  = 0.09, p = 0.91.Descriptive statistics for the 3-year outcome measures are shown below in p-value of 0.0125 was applied. Infant global activity ratings (from parents and observers) were entered as the predictor variables in all models. Robust regression showed that global ratings of activity level at 10 months was a significant predictor of Activity Level, F  = 5.18, p = 0.007, Impulsivity, F  = 3.70, p = 0.028, and Inhibitory Control, F  = 9.59, p < 0.001, at 3 years, though the Impulsivity model did not survive correction for multiple testing. For Activity Level, parent-reported activity level contributed significantly to the model , but not observer ratings of activity level . For Inhibitory Control, both parent-reported activity level  and observer-reported activity level  contributed to the model. Similar results were found for Impulsivity but with weaker effects . Global ratings of activity level at 10 months of age did not predict Attentional Focusing at 3 years, F  = 1.65, p = 0.20. For scatterplots of infant activity level and each CBQ outcome, see For the significant model (Model 1), we further investigated the relationship of infant activity with broader temperament dimensions conceptually related to the ADHD phenotype at 3 years. Four separate robust regression models were conducted with each selected CBQ subscale as the outcome variable . A corrected ADHD is a heritable neurodevelopmental condition that is typically diagnosed in middle childhood, but which is likely influenced by genes that act prenatally, as well as early environmental factors . IdentifThe lack of significant differences that we observed between infants with and without a family history of ADHD/ASD at 10 months in this study contrasts with the small body of existing literature that has found alterations in attention and activity level in infants at elevated likelihood of ADHD within the first year of life ,42. UsinThere are several possible explanations for why we did not find evidence for early alterations in attention and activity level amongst infants at elevated likelihood of ADHD at 10 months. First, it is possible that we did not select the most appropriate or sensitive measures in this study. It may be that trajectories of infant attention and activity across development are more informative in understanding individual differences in later outcomes ,59, rathAnother possible explanation of the difference between our findings and that of previous studies is sampling variation. Both autism and ADHD are heterogenous conditions, and many factors including (but not limited to) genetic variation, co-occurring conditions, psychosocial and environmental factors  may also influence the development of attention and activity level . PreviouOur categorisation approach used in this study is similar to recent studies using a prospective longitudinal design to study infants at elevated likelihood of ADHD . HoweveWe found that activity level at 10 months (as rated by parents and observers) was predictive of preschool ADHD traits at 3 years, measured using the DSM ADHD subscale of the CBCL. These results are consistent with previous research that has associated infant activity level from 7 months of age with later ADHD , both inIn general, parent-ratings of infant activity level were more strongly predictive of 3-year outcomes than observer ratings. Parents are often with their child on a regular basis and are therefore more likely to be able to give a representative view of their child’s behaviour across different contexts, compared to an observer who is only viewing the child on a single day . MoreoveWhile we found that infant activity level associated with preschool ADHD traits at 3 years, we did not find the same relationship for infant attention. Infant attention measured both at the behavioural and neurocognitive level did not associate with preschool ADHD traits at 3 years. We preregistered our selection of neurocognitive measures of attention based on both what is known about the developing infant attention system, and neurocognitive differences that have been associated with the later ADHD phenotype . However, some research suggests that the pattern of neurocognitive markers associated with conditions such as ADHD or ASD may change over development . Thus, wFurthermore, some studies have demonstrated low convergence between commonly used measures of infant attention ,102,103.For triangulation, we further examined the association between infant activity level and emerging ADHD traits, by investigating associations with broader temperament dimensions related to the later ADHD phenotype at 3 years including impulsivity, attentional focusing, inhibitory control and activity level (measured using the CBQ). Though the CBQ is a validated and widely used measure of temperament in preschool aged children , our resA limitation of our study is that this cohort has so far only been followed up to the age of 3 years. This allowed us to investigate the relationship between infant markers of attention and activity level, and preschool ADHD traits at age 3. However, while there is some evidence of continuity from the preschool years ,19, ADHDAn additional limitation of our study is that a subset of the infants included in this study received a 9-week attention training programme following their 10-month lab visit, as part of a randomised controlled trial . Our priUsing a multi-method, pre-registered assessment protocol, we found no significant group differences in activity level or attention in 10-month-old infants with and without an elevated likelihood of ADHD. Limitations of the study were noted, including the possibility that we did not select the most sensitive measures of infant attention in this study. In secondary longitudinal analyses, parent and observer ratings of infant activity level at 10 months significantly predicted ADHD-related behaviours at 3 years, including preschool ADHD traits (CBCL), and broader temperament dimensions related to the ADHD phenotype . Our findings support previous findings that early differences in activity level may be apparent in the first year of life in children who later develop elevated ADHD traits. It is important to acknowledge that infant activity level varies in the population , and hig


Attention-deficit hyperactivity disorder (ADHD) is common among people with intellectual disability. Diagnosing ADHD in this clinically and cognitively complex and diverse group is difficult, given the overlapping psychiatric and behavioural presentations. Underdiagnoses and misdiagnoses leading to irrational polypharmacy and worse health and social outcomes are common. Diagnostic interviews exist, but are cumbersome and not in regular clinical use.We aimed to develop a screening tool to help identify people with intellectual disability and ADHD.A prospective cross-sectional study, using STROBE guidance, invited all carers of people with intellectual disability aged 18–50 years open to the review of the psychiatric team in a single UK intellectual disability service (catchment population: 150 000). A ten-item questionnaire based on the DSM-V ADHD criteria was circulated. All respondents’ baseline clinical characteristics were recorded, and the DIVA-5-ID was administered blinded to the individual questionnaire result. Fisher exact and multiple logistic regressions were conducted to identify relevant questionnaire items and the combinations that afforded best sensitivity and specificity for predicting ADHD.Of 78 people invited, 39 responded , of whom 30 had moderate-to-profound intellectual disability and 38 had associated comorbidities and on were medication, including 22 on psychotropics. Thirty-six screened positive for ADHD, and 24 were diagnosed . Analysis showed two positive responses on three specific questions to have 88% sensitivity and 87% specificity, and be the best predictor of ADHD.The three-question screening is an important development for identifying ADHD in people with intellectual disability. It needs larger-scale replication to generate generalisable results. The study population was people with intellectual disability aged 18–50 years, open to review of the psychiatric team of a specialist National Health Service (NHS) intellectual disability service in the UK, covering a catchment of 152 000 people. Those aged >50 years or with a diagnosis of dementia were not included.16The service was developing pathways for various neurodevelopmental conditions and was using screening tools developed by clinicians as part of routine practice. For ADHD, a ten-item questionnaire was developed based on the current DSM-V ADHD diagnostic criteria, with an additional question relating to challenging behaviour .1617 The items that were left out relied on the patient possessing a higher level of functioning.The ten items were chosen based on a previous study highlighting that certain items used in the process of diagnosing ADHD in general population  were not reliable in people with intellectual disability.The recruitment process is provided in http://www.hra-decisiontools.org.uk/research/index.html) was used to confirm that no ethics approval was needed for this project (Supplementary File 1 available at https://doi.org/10.1192/bjo.2021.1023). No author had access to any patient-identifiable information other than that of their own clients in their service. All survey recipients had been informed of the reasons for the survey, that consent was given via return of the survey, that data would be used for both clinical and research use and all data would be anonymised before sharing outside of the clinical team.The project was done with anonymised data from a single centre. No patient data was shared outside of the clinical team. Data were collected as part of ongoing service evaluation, formally registered with the host NHS organisation. The NHS Health Research Authority tool  included all variables where the odds ratio was >2. A second approach (model 2) only included questions in the final model if the P-value was <0.2. For each variable in the two models, results are presented as odds ratios for each question, with their corresponding confidence intervals. These give the odds of a positive DIVA-5-ID result when the answer was ‘yes’ compared with odds when the answer was ‘no’.The DIVA-5-ID result was used as the outcome variable, with the screening questions as the predictor variables, using multiple logistic regression. To simplify the regression model, variables not found to be associated with the outcome were omitted from the model. This was done with a backward-selection approach, with the question with the largest Score 1: All questionnaire questions with >20% difference in positive DIVA-5-ID rates between responses, regardless of statistical significance.Score 2: Questionnaire questions with an odds ratio of >2 from the multiple logistic regression analysis, regardless of statistical significance.P-value of <0.2 from the multiple logistic model.Score 3: Questionnaire questions with a Score 4: As score 3, but giving more weight to questions more associated with the DIVA-5-ID result.The next stage aimed to determine a score based on the screening questions that could best be used to predict the DIVA-5-ID result. The regression coefficients for each question were calculated. A rule was established that for any question found to be significant, they would be multiplied by their effect size based on their individual regression coefficients.The final analyses examined the ability of the collective scores (1–4) to predict the DIVA-5-ID result. Receiver operating characteristic (ROC) curves were used to examine the predictive ability, with the area under the ROC curve quantifying the performance. The ROC curves were used to choose an optimal cut-off point for each score that could best predict the DIVA-5-ID result. The cut-off point was chosen so as to give the best combination of sensitivity and specificity. The positive and negative predictive values and diagnostic odds ratio have also been showcased.The recruitment numbers by stage are outlined in Appendix 2. There were 78 eligible people open to the psychiatric team. Of the 78 questionnaires sent out, 39 (50%) were returned; 26 male (66%) and 13 female (33%) respondents, with a mean age of 38 years. Thirty had moderate-to-profound intellectual disability (77%) and nine had mild intellectual disability (33%). Only one person did not have a comorbid health issue. Of the 38 respondents with comorbid health issues, 14 had one (36%), 14 had two (36%) and ten had three or more (26%) comorbid conditions. Specific psychiatric comorbidities and the number of patients with the diagnosis are provided in Just one person was not on any medication. Seven people were on one medication, 15 were on two medications and 16 were on three or more medications. Looking specifically at psychotropics, 22 people were prescribed antipsychotics (56%), 17 were prescribed antidepressants (44%), 12 were prescribed anti-seizure medication (31%) and 10 were prescribed mood stabilisers (26%); 21 people were prescribed two or more psychotropics (54%). The specific drugs and number of patients prescribed are provided in Of the 39 respondents, 36 (92%) had answered at least one question of the questionnaire positively, with three replying negatively to all questions. The corresponding DIVA-5-ID of all 39 respondents was completed, which identified 24 (61%) as having ADHD, of whom 16 were male (67%) and eight were female (33%).P < 0.05) with the DIVA-5-ID were found for questions 1, 2, 3 and 6, and borderline significance (P = 0.06) for question 8 (Significant associations (estion 8 . For theP < 0.05). As per the statistical plan, variables with larger P-values (≥ 0.2) were removed from model 1, leaving three questions for model 2 . Question 2 was the most associated with DIVA-5-ID result, with borderline significance (P = 0.05). For question 2, the odds ratio was 16 times higher for a positive DIVA-5-ID result and a ‘yes’ response compared with a ‘no’ response.The logistic regression of model 1, comprising five questions , had a greater than two times odds of a positive DIVA-5-ID result with a ‘yes’ compared with a ‘no’ response . Only quScore 1: Q1 + Q2 + Q3 + Q5 + Q6 + Q7 + Q8 + Q9 + Q10Score 2: Q1 + Q2 + Q3 + Q6 + Q7Score 3: Q1 + Q2 + Q6Score 4: Q1 + 2.Q2 + Q6.Each eligible question in the scores 1–4 was allotted one point for a ‘yes’ response and no points for a ‘no’ response. The exception was for score 4, where a ‘yes’ response to question 2 was allotted 2 points, as this variable was found to be most associated with a positive DIVA-5-ID score, as the regression coefficient for question 2 was approximately twice that of questions 1 and 6, which were similar. The scores were as follows, where Q indicates question:The diagnostic performance of each of the four scores for the prediction of the DIVA-5-ID result was evaluated . The resDiagnosing ADHD in adults with intellectual disability can be challenging, and there are many barriers to diagnosis . Using tThis tool has the potential to influence positive change in supporting the health needs of people with intellectual disability. The obvious benefit is to quickly identify people with intellectual disability with suspected ADHD for further diagnostic work-up, i.e. the DIVA-5-ID. Other benefits include reduction of misdiagnosis and, by extension, reduction in polypharmacy, particularly in the prescribing of inappropriate psychotropics in a vulnerable population. As shown, the majority of the study sample pre-screening were on psychotropic medication, with over half (54%) on multiple drugs. It could be that these drugs were prescribed to manage presenting symptoms of ADHD in the individual, without consideration of an ADHD diagnosis. This could have led to limited symptom relief. Better screening can lead to improved diagnosis, improving both health and quality of life for the patient. Furthermore, it is carer- or patient-led, thus having positive implications on clinician resource and time.Because of cognitive deficits, people with intellectual disability can be marginalised in their ability to interactively participate in a clinical process. Three-quarters of the patients in this study (77%) had a moderate-to-profound disability, which would affect their meaningful contribution to the diagnostic or treatment assessments. This disadvantage can be further compounded by the presence of ADHD. In these circumstances, clinicians often have to depend on the narrative from informants. This information from family members or professional carers could potentially bring an emotional and subjective bias to the diagnostic process. The availability of a structured questionnaire allows better evidence-based synthesis to the clinical formulation about the presence or absence of ADHD. The questionnaire will allow for stepwise discussion with the patient and other stakeholders and planning, including the justification for trialling ADHD medication.19 Further development and national adoption of the screening tool could help to mitigate these adverse outcomes.ADHD remains an unrecognised comorbidity in people with intellectual disability. This has, in many cases, led to overprescribing of psychotropic medication, which can lead to several unwanted effects on well-being, morbidity and mortality.The lack of a screening tool despite the need in this vulnerable population could also be because of the heterogeneous complex presentation of this population, particularly with regards to multimorbidity, polypharmacy and intellectual disability-specific characteristics confounding the picture. The current questionnaire looks to overcome these challenges of heterogeneity by using statistical concepts, and has shown potential for further study. In particular, multi-site implementation of score 4  needs to be conducted to see if the clinical yield is similar to this pilot study. Other psychometric properties of these three questions may need further research.Another potential area to explore would be to examine if the screening questions not only suggest ADHD, but have any discriminating validity in ruling out other reasons for ADHD symptoms, such as other serious mental illness.This is a pragmatic single-site study carried out as part of routine clinical practice. Hence, no advance power calculations on sampling were made. However, it is worth recognising that the study was prospective and the clinician investigators were blinded to the questionnaire results. The identified questions  are predominantly focused on observed behaviour, which could also be a presentation of other medical and psychological conditions such as drug-induced akathisia, autism spectrum disorder, etc. However, it is the study premise that this is not a diagnostic tool but a screening tool. It is not suggested to replace the gold standard of inquiry into such behavioural patterns.Score 2  was the other potential alternative as it had higher specificity (93%), higher positive predictive value (95%) and a better odds ratio (70) than score 4. However, as the focus is on screening, score 4 had a better negative predictive value and better sensitivity, in addition to only being three questions . It is worth noting that these three questions also feature in score 2. In future, larger field trials, it might be worth considering a more inclusive screening set (i.e. score 2), to revisit comparisons between score 2 and score 4 as to which would be better suited.P-value of 0.2 was taken for stage 2 instead of P < 0.05 or P < 0.10, as the data-set was small and likely to be predisposed to big associations for some questions that did not reach statistical significance. With a bigger sample, it is likely significant results would be obtained with similar effect size. The threshold here was relaxed to allow more variables into the score. It was felt that to make the scores simpler to calculate in practice, a simpler strategy of assigning points to each factor should be used. Using the exact coefficients would mean that more involved calculations would be required to calculate the score for each patient, which may limit the use of the scoring system. As outlined in the paper, the first scores used equal weighting for each variable. Approximate regression coefficients were used to give different weightings to the questions for the last of the scores.In the statistical work-up, assumptions had to be made based on the sample size and expected associations. A It is possible that more people who are engaged or interested in the support of ADHD responded to the questionnaire than people who are not. Also, it was not possible to explore the characteristics of non-responders. This may have introduced bias in the data. However, the response rate of 50% suggests that this is unlikely. Also, some questions might be perceived as ambiguous and there may be some overlap between questions. Carers relying on retrospective memory and reports are likely to lead to approximations. Despite its limitations, the study has captured critical knowledge and evidence hitherto unavailable in scientific literature.In conclusion, there is a significant level of underdiagnoses of ADHD in people with intellectual disability. This has led to misdiagnosis and polypharmacy, particularly for psychotropics. This study delivers three evidence-based screening questions to assist carers and clinicians to consider further ADHD diagnostic work-up: ‘Does he/she find hard to sit in one place for long?’, ‘Does he/she pace up and down most of the time?’ and ‘Is he/she easily distractible by busy environments?’.These three questions could be easily incorporated into any preliminary inquiry into a referral for a psychiatric or behavioural assessment of a person with intellectual disability, to help consider ADHD and provide better clinical formulation and bespoke treatment of their needs.


To examine the association between low birth weight (LBW) and cardiometabolic diseases  in adulthood, and to explore whether genetic, early-life environmental and healthy lifestyle factors play a role in this association.A population-based twin study.Twins from the Swedish Twin Registry who were born in 1958 or earlier participated in the Screening Across the Lifespan Twin (SALT) study for a full-scale screening during 1998–2002 and were followed up until 2014.19 779 twin individuals in Sweden with birthweight data available (mean age: 55.45 years).CMDs were assessed based on self-reported medical records, medication use and records from the National Patient Registry. A lifestyle index encompassing smoking status, alcohol consumption, exercise levels and Body Mass Index was derived from the SALT survey and categorised as unfavourable, intermediate or favourable. Data were analysed using generalised estimating equation (GEE) models and conditional logistic regression models.Of all participants, 3998 (20.2%) had LBW and 5335 (27.0%) had incident CMDs (mean age at onset: 63.64±13.26 years). In GEE models, the OR of any CMD was 1.39 (95% CI 1.27 to 1.52) for LBW. In conditional logistic regression models, the LBW–CMD association became non-significant . The difference in ORs from the two models was statistically significant (p<0.001). In the joint effect analysis, the multiadjusted OR of CMDs was 3.47 (95% CI 2.72 to 4.43) for participants with LBW plus an unfavourable lifestyle and 1.25 (95% CI 0.96 to 1.62) for those with LBW plus a favourable lifestyle.LBW is associated with an increased risk of adult CMDs, and genetic and early-life environmental factors may account for this association. However, a favourable lifestyle profile may modify this risk. This study provides an extraordinary opportunity to explore the association between low birth weight (LBW) and cardiometabolic diseases (CMDs) by using a twin study design to control for some unmeasured confounders.The investigation into factors that might compensate for the risk effect of LBW on CMDs is unique.Birth weight was based on self-reports, and non-differential misclassification among different birthweight groups could not be ruled out, possibly leading to an underestimation of the observed associations.Some prenatal factors  could not be controlled for, as information on these factors was not available.Potential variations of lifestyle factors during the follow-up also could not be assessed.With population ageing has come an increase in the prevalence of chronic diseases, especially heart diseases , stroke and type 2 diabetes mellitus (T2DM).6 7Recently, beyond the effects of some traditional risk factors including age, smoking, drinking and Body Mass Index (BMI) on individual CMDs, the role of early-life experiences in the future development of chronic diseases have drawn special attention.CMDs are complex genetic and lifestyle-related disorders,In the present study, we aimed to (1) verify the relationship between LBW and risk of CMDs using population-based Sweden twin data and (2) explore whether genetic, early-life environmental and healthy lifestyle factors play a role in this association.This prospective, nested case–control study included twins from the nationwide Swedish Twin Registry (STR), which started in the 1960s.10.1136/bmjopen-2020-048030.supp1Supplementary dataData on age, sex, educational attainment, marital status and zygosity status were collected through the SALT survey.Information on medical conditions including heart disease, stroke, T2DM and hypertension was derived from the National Patient Registry (NPR), which covers all inpatient diagnoses in Sweden from the 1960s and outpatient  diagnoses from 2001 to 2014.Data on birth weight was collected based on self-reports from SALT or STR. Generally, LBW was defined as birth weight of <2500 g in singletons.In the current analysis, CMDs included heart disease (coronary heart disease and heart failure), stroke (ischaemic stroke and haemorrhagic stroke) and T2DM, all of which were diagnosed based on self-reported medical records, medication use and NPR data. The detailed ICD codes for each disease are shown in the CMD status was categorised as CMD-free and any CMD . The any CMD group was further classified as only one CMD , any two CMDs , and three or more CMDs .2) and classified as underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9) and obesity (≥30) according to the WHO classification. Obesity was merged with overweight , and underweight was merged with normal weight as non-overweight (BMI<25).Information on lifestyle factors  was obtained from the SALT survey. In detail, smoking status was dichotomised as non-smoking versus former/current smoker. Alcohol consumption was categorised as no/mild drinking versus heavy drinking based on the survey question asking whether participants have ever drunk excessively over a period. Data on physical exercise were collected by a question on average exercise with seven response options: (1) ‘almost never’, (2) ‘much less than average’, (3) ‘less than average’, (4) ‘average’, (5) ‘more than average’, (6) ‘much more than average’ and (7) ‘maximum’In the current study, on the basis of the data availability, the following four factors were considered as healthy lifestyle factors: (1) non-smoking, (2) no/mild alcohol consumption, (3) active physical exercise and (4) non-overweight in adulthood.2 tests for categorical variables and one-way analysis of variance/Kruskal-Wallis H test for continuous variables. Missing values on education level (n=92), smoking status (n=77), alcohol consumption (n=117), marital status (n=2), physical exercise (n=1179) and BMI (n=290) were imputed using Rubin’s rule for pooling estimates to obtain valid statistical inferences.24The characteristics of participants in different groups were compared using χIn our study, two analytical strategies were applied. First, generalised estimating equation (GEE) models were used for unmatched case–control analysis. GEE models are conceptually equivalent to logistic regression for the analysis of classic case–control design but control for the clustering of twins within a pair. Second, conditional logistic regression models were used for cotwin matched case–control analysis using a pair of twins that was discordant for the outcome. Cotwin matched design  appeared more informative since cases and controls were comparable with respect to genetic background and early-life environmental factors such as intrauterine environment, prenatal and postnatal nutritional status, and childhood socioeconomic status.Considering information on lifestyle factors was obtained from the SALT questionnaire during 1998–2002, we excluded 1748 participants who developed CMDs before the SALT recruitment, and thus 18 031 participants remained for the joint effect analysis. The combined effect of the LBW (no vs yes) and lifestyle index (unfavourable/intermediate/favourable) on the risk of CMDs was assessed by creating dummy variables based on the joint exposures to both factors. The presence of an additive interaction was examined by estimating relative excess risk due to interaction, the attributable proportion (AP) and the Synergy Index (S).All the models were basic adjusted for age, sex and education, and further adjusted for smoking, alcohol consumption, marital status, physical exercise, BMI and hypertension. P values less than 0.05 were considered statistically significant. All statistical analyses were performed using SAS statistical software V.9.4 and IBM SPSS Statistics V.20.0.Patients and the public were not involved in the design, or conduct, or reporting of this study.Among all participants (n=19 779), 3998 (20.2%) had LBW. The average age at recruitment was 55.45 (±9.05) years. Compared with MBW individuals, those with LBW were more likely to be older, male, monozygotic twins, single, have lower education, have higher BMI, be physically inactive and have hypertension. Participants who had HBW were more likely to be male, dizygotic twins, smokers, heavy drinkers and have higher BMI .In the multiadjusted GEE model, compared with participants with MBW, those with LBW had a significantly higher risk of coronary heart disease, heart failure, ischaemic stroke and T2DM, which were further combined as CMDs (n=5335), as shown in Compared with non-LBW, the OR for the association between LBW and any CMD was 1.37 (95% CI 1.25 to 1.50). The multiadjusted ORs of LBW were 1.28 (95% CI 1.17 to 1.41) for only one CMD, 1.48 (95% CI 1.28 to 1.72) for any two CMDs and 1.82 (95% CI 1.37 to 2.42) for three or more CMDs (reference: CMD-free), indicating the LBW–CMD risk became higher when multiple CMDs were co-occurring (p for trend<0.001) . FurtherIn the cotwin matched case–control analysis consisting of 845 dizygotic pairs and 290 monozygotic pairs, the association between LBW and any CMD was attenuated compared with the GEE model and became non-significant . The ORs for the associations were 1.34 (95% CI 0.96 to 1.89) in dizygotic pairs and 1.07 (95% CI 0.66 to 1.73) in monozygotic pairs .The difference in ORs from the GEE model versus conditional logistic model was statistically significant , which suggested that genetic and early-life environment factors might play an important role in LBW–CMD association.In basic-adjusted and multiadjusted GEE models, not smoking, no/moderate alcohol drinking, active physical exercise and being non-overweight were individually related to a decreased risk of any CMD. When combined as a lifestyle index , compared with an unfavourable lifestyle profile, an intermediate and a favourable lifestyle profile were significantly associated with a lower risk of any CMD, ORs were 0.62 (95% CI 0.55 to 0.69) and 0.40 (95% CI 0.35 to 0.47), respectively .In the joint effect analysis, the multiadjusted ORs of any CMD were 1.25 (95% CI 0.96 to 1.62) for participants with LBW plus a favourable lifestyle profile, 1.94 (95% CI 1.64 to 2.28) for those with LBW plus an intermediate lifestyle profile, and 3.47 (95% CI 2.72 to 4.43) for those with LBW plus an unfavourable lifestyle profile (reference: those with non-LBW plus a favourable lifestyle profile) .The additive interaction between the unfavourable lifestyle profile and LBW on CMDs was statistically significant , indicating that if people with LBW have a favourable or intermediate lifestyle, the risk of LBW on CMDs can be reduced by 20% .The results were not much altered compared with those from the initial analysis when we repeated the following analyses after: (1) stratifying by sex to address possible sex differences in the CMDsIn this large-scale, prospective, population-based nested case–control study of Swedish twins, we found that (1) LBW was associated with an increased risk of CMDs including coronary heart disease, heart failure, ischaemic stroke and T2DM in adulthood, and the risk became higher when multiple CMDs were co-occurring; (2) genetic background and early-life environmental factors appear to account for the LBW–CMD association; (3) a favourable lifestyle profile may modify the risk effect of LBW on CMDs.et al found that higher birth weight was associated with a higher risk of coronary heart disease.Over the past two decades, the relationship between birth weight and T2DMThe potential contribution of genetic susceptibility and early-life environmental factors to the LBW–CMD association is still unclear. Previous twin cohort studies have shown that LBW is associated with an increased risk of CVD and T2DM when twins were considered as independent individuals. This association only held in outcome-discordant dizygotic twins but not in monozygotic twin pairs, suggesting that genetic mechanisms played a role in this association.Modifiable lifestyle factors  deserve to be studied in the context of the LBW–CMD association. To date, only a few studies have investigated the joint effect of LBW with lifestyle factors on T2DM.Several mechanisms may explain the relationship between LBW and the risk of CMDs. Twins have a unique and highly distinctive pattern of fetal growth. Although there is a higher rate of preterm birth among twins50Notable strengths of our study involve the large nationwide population-based twin cohort, which provided an extraordinary opportunity to explore the association between LBW and the risk of CMDs in adulthood by controlling for some unmeasured confounders, such as genetic background and early-life environmental factors. Furthermore, our investigation of potential compensatory factors against the LBW–CMD association is unique. Nevertheless, some limitations should be pointed out. First, hypertension was defined only based on self-reported data from the NPR, and subjects with undiagnosed hypertension might have been misclassified as hypertension-free. Thus, hypertension was not categorised as a CMD in the current study. Second, the assessment of birth weight was based on self-report so potential information bias could not be ruled out. However, such bias is more likely to be non-differential misclassification resulting in underestimation for the given associations. Third, data on gestational age and other prenatal factors  were not available and could not be fully controlled for. In addition, potential variations in lifestyle factors during follow-up could not be assessed. Fourth, diet could be partially taken into account, as it is closely associated with other lifestyle factors such as smoking, alcohol consumption, physical exercise and BMI.This study provides evidence that LBW is associated with increased risk of CMDs including coronary heart disease, heart failure, ischaemic stroke and T2DM. The risk of CMDs related to LBW tends to increase with the number of co-occurring CMDs. Further, genetic and early-life environmental factors play an important role in the LBW–CMD association. However, a favourable lifestyle involving not smoking, no/mild alcohol consumption, active physical exercise and a BMI of <25 may compensate for the risk effect of LBW on CMDs. Our findings highlight the need for monitoring and controlling LBW for the prevention of CMDs, and the importance of maintaining a favourable lifestyle profile in people with LBW in adulthood to reduce the risk of CMDs.


To improve on several methodological issues regarding current literature investigating the relationship between ADHD symptomatology and academic success in adults and examine the relative contributions of specific dimensions of ADHD symptomatology  to post-secondary academic success.A large sample of 3,688 post-secondary students were examined using a longitudinal design. The Conners’ Adult ADHD Rating Scale (CAARS) was used to assess adult ADHD symptoms and academic success was assessed using students’ official academic records .Students with greater inattention symptomatology at the start of their academic program showed consistently poorer long-term academic success , regardless of gender.Inattention symptoms are the primary driver of the relationship between ADHD symptomatology and academic underachievement in adults. Post-secondary education institutions should target and prioritize educational programming for inattention symptoms of ADHD in at-risk post-secondary students. ADHD is defined in the DSM-5 as a persistent pattern of inattention and/or hyperactivity-impulsivity that shows clear evidence of interference with social, academic, or occupational functioning in two or more settings  Examine the relationship between ADHD symptomatology and a variety of objective academic success indicators  in a large sample of emerging adults separately for men and women, and (2) evaluate the relative importance of different core symptom dimensions of ADHD  as predictors of academic success indicators separately for men and women.M = 19.29, SD = 1.18) at the start of their studies were included. The majority of participants (87%) were Caucasian, with 5% reporting Asian ethnicity, 2% African, 1% Hispanic, 1% Native, and 4% Other. Participants were from a diverse range of academic programs at the university, including the sciences , social sciences , and humanities .Participants were 3,688 first-year undergraduate students  from three consecutive cohorts of full-time students attending a small liberal arts university in Central Ontario. To control for the effect of age on ADHD symptomatology, only participants between the ages of 18 to 25 years  and degree completion status .During undergraduate orientation week in the first year of study, participants were recruited for the present study. Participation was voluntary; however, many participants were compensated with randomly drawn prizes. For each cohort, over 95% of students provided informed consent and participated in the study, thus providing a representative sample of the population of university students at the host university. Demographic information  was collected using a brief 8-item questionnaire. Participants then completed the 66-item CAARS as part of a larger battery of self-report measures. Six-years following this initial data collection phase, academic records were matched (using student ID numbers) to each participant’s scores on the CAARS. Six-year graduation rates are a common bench-mark for monitoring or comparing academic achievement rates in Canada and the United States . The stuTo assess the effects of ADHD symptoms on university graduation rates and maximize the ease of interpretation, a gender by graduation status by ADHD symptom type mixed ANOVA was conducted with mean-item scores for each of the three ADHD symptom scales as the dependent variable.To assess the effects of ADHD symptoms on the final GPAs of students who completed their degrees, several structural equation models were tested. In the first series of structural equation models, a single latent ADHD symptom variable, constructed from scores on the three ADHD subscales, was used to predict final GPAs separately by gender. In a second series of structural equation models, three correlated latent variables for inattention, hyperactivity, and impulsivity symptoms, constructed from items on each of the three ADHD subscales, were used to predict final GPAs, separately by gender.Estimation of each model was done using the Asymptotic Distribution-Free Gramian (ADFG) estimation method in order to account for the ordinal nature of the indicator variables. The following goodness-of-fit indices were used to evaluate model fit: the McDonald Fit Index (MFI), the Standardized Root Mean-Squared Residual (SRMR), and the Root Mean-Square Error of Approximation (RMSEA). Given the lack of universally accepted “gold standards” for interpreting goodness-of fit indices , the folF = 19.94, p < .001, F = 357.43, p < .001, F = 39.28, p < .001, F = 4.09, p = .017, d = 0.10), but not hyperactivity or impulsivity symptoms. According to Cohen’s (1992) conventions, all significant effects were small.Results for the structural equation models are presented in Supplemental Table 1). For men, MFI = 0.75, SRMR = 0.11, RMSEA = 0.06, RMSEA 90% CI , and for women, MFI = 0.83, SRMR = 0.08, RMSEAS = 0.05, and RMSEA 90% CI . As is evident from The models for the three correlated ADHD symptom dimensions similarly showed overall acceptable fit for men and women separately  as predictors of a variety of academic success indicators in a large sample of students. In general, our findings support the view that individuals with higher levels of ADHD symptomatology at the start of their academic program show poorer long-term academic success, regardless of gender. However, there is a differential impact of specific dimensions of ADHD symptomatology.The most consistent finding is the important role that inattention symptoms play in the academic success of both men and women, regardless of how academic success is operationalized. For instance, results regarding student degree completion showed that students who withdrew before completing their degrees had significantly greater inattention symptoms, but not different hyperactivity or impulsivity symptoms, than students who completed their degrees. Although there are many reasons for why students may choose to not complete their degrees, these findings are consistent with previous research that has demonstrated an increased likelihood of those with higher levels of ADHD symptoms to withdraw from classes and drop out of post-secondary education completely . HoweverBeyond degree completion rates, we were also interested in examining whether ADHD symptomatology, and specific core symptom dimensions, would predict academic success for students who successfully completed their degrees. Results from a series of structural equation models showed that overall ADHD symptomatology is indeed a modest predictor of final GPA, regardless of gender. However, consistent with our findings regarding degree completion, results from both three-factor models  and two-factor models  of ADHD revealed that only inattention symptoms emerged as a significant (modest) predictor of final GPAs. These findings are consistent with previous research that has pointed to ADHD symptomatology as contributing to a continued impairment in academic achievement from childhood and adolescence to adulthood . MoreoveDespite previous research highlighting inconsistencies in the prevalence of ADHD symptomatology among men and women , the conTaken together, these findings underscore the important role that inattention symptoms of ADHD can play in the cumulative struggles that many students face over the course of their post-secondary experiences. Consistent with this perspective are the findings of qualitative studies that demonstrate the many ways symptoms of inattention may impact post-secondary students with ADHD on a day-to-day basis . For insA number of recent evaluations of educational programs specifically designed to enhance students’ organizational skills have been shown to be effective interventions for students with ADHD . For insDespite the important implications of the present study, our results should be understood in the context of a number of limitations. Firstly, the present study made use of self-reported ADHD symptoms, which may be subject to response bias. For instance, Additionally, analyses in the present study did not control for a number of other variables known to effect academic success in university students, including symptoms of anxiety and depression, learning disorders, substance and medication use, and IQ . Future Nonetheless, the results of the present study add to the literature on adult ADHD symptomatology in a number of ways. Firstly, this study made several improvements on the methodological shortcomings of studies in the current literature, including using a large, representative sample of first year undergraduate students, a longitudinal design, and a robust assessment of academic success indicators , as well as restricting analyses to a homogeneous sample of emerging adults . These methodological improvements on previous studies allowed for more generalizable conclusions about post-secondary students in general and a more robust examination of the relationships between ADHD symptoms and academic success across the post-secondary experience. The present study also adds to the existing literature by demonstrating the important role that inattention symptoms play as the primary driver of the relationship between ADHD symptomatology and academic success in post-secondary education settings, regardless of gender, suggesting it as a priority focus for intervention programming.Click here for additional data file.Supplemental material, sj-docx-1-jad-10.1177_10870547211036758 for ADHD and Academic Success in University Students: The Important Role of Impaired Attention by Colin Henning, Laura J. Summerfeldt and James D. A. Parker in Journal of Attention Disorders


Attention deficit hyperactivity disorder is a disorder in which a person is unable to control behavior due to difficulty in processing neural stimuli, accompanied by an extremely high level of motor activity. The prevalence is much higher ranging from 8 to 77% among children with seizure disorders than in the general population. When attention deficit hyperactivity disorder presents in children with seizure disorder, it makes the treatment complicated and the prognosis poor. Hence, understanding the magnitude of attention deficit hyperactivity disorder and associated factors would be important to have a policy intention towards these people and to design appropriate interventions. Therefore, the current study was conducted to determine the comorbidity of attention deficit hyperactivity disorder and associated factors in children with seizure disorders. A hospital-based cross-sectional study was conducted by taking 260 children who have follow ups in the pediatric seizure clinic. The systematic random sampling technique was used to recruit participants. A structured, pretested and interviewer-administered questionnaire which included questions on associated factors and standard disruptive behavioral disorder rating scale was used to collect data. Data were coded, entered and cleaned by using the Epi-Data version 3.1 and exported to SPSS version 20 for further analysis. The multivariate binary logistic regression was used to check the association between independent and dependent variables. Variables with significant associations were identified based on adjusted odds ratio, with a 95% CI and p-value of < 0.05 will be considered as statistically significant. The prevalence of attention deficit hyperactivity disorder among epileptic children was found to be 115 (44.2%),with a confidence interval of (38.1–50.5),out of which only 3 (2.6%) were detected as having mental health problems by the clinician. The predominant subtype was inattentive type 96 (61.1%). Factors significantly associated with attention deficit hyperactivity disorder were male sex (AOR = 2.70 CI 1.46–4.97), family history of seizure disorder (AOR = 2.42 CI 1.26–4.65), family history of mental illnesses (AOR = 4.14 CI 1.76–9.68), sudden onset of the seizure (AOR = 2.37 CI 1.32–4.27), and uncontrolled seizure (AOR = 2.55 CI 1.41–4.61). Attention deficit hyperactivity disorder was common among children with seizure disorders in the study area. Male sex, sudden onsets of seizure, family history of seizure, and that of other psychiatric disorders as well as uncontrolled seizures were factors that increased the odds of attention deficit hyperactivity disorder. Therefore, interventions that would address such factors would help to overcome further complications. It is one of the most common mental disorders that develop in children and becomes apparent in preschool and early school years. It is characterized by pervasive and impairing symptoms of inattention, hyperactivity, and impulsiveness that occur before 7 years of ageSymptoms of ADHD may increase risk-taking behavior. Consequently, the quality of life of individuals with ADHD may be impaired. ADHD can also be associated with a substantial economic burden for the individual, their family and societal healthcare services.2. The prevalence is much higher ranging from 8 to 77% among children with epilepsy than in the general population3. The prevalence of ADHD among children with epilepsy in high-income countries ranges from 23 to 70%5. Clinical studies show that when ADHD presents in children with seizure disorders, it exerts its effect on the treatment and prognosis of the disorder. It can also have negative impacts on the affected child’s behavioral, learning, and social development. The studies suggest that 30–40% of children with epilepsy also present with ADHD7. Despite this high prevalence, ADHD often remains unrecognized and left without treatment.The prevalence of attention deficit hyper activity disorder (ADHD) in the general population ranges between 3 and 5%8, 42.2% in China9, 27.7% in Israel10, 29.1% in Brazil11, 12% in UK12, 28% in Norway13, 26.4% in USA14 and 60.4% in Iran15.Studies identified that the magnitude of the problem in children with epilepsy varied across the globe such as, 6.9% in Korea16.Another study conducted in central China showed that the magnitude of ADHD among children with epilepsy was 24.7%17.A recent study in the Indian tertiary medical center revealed that the comorbidity of ADHD among epileptic children was 23%18.According to a study in Tanzania, the comorbidity of ADHD among children with epilepsy showed that attention problem were more prevalent in children with epilepsy 53% compared with control groups (19%)16, earlier epilepsy onset, a longer period of antiepileptic medication, epileptic children’s receiving a combination of antiepileptic drugs7, and the frequency of seizures15 were the most common associated factors of ADHD among epileptic children.Previous studies revealed that age, sex, type of epilepsy syndromeMany of the studies were outside Africa, and the magnitude of ADHD among epileptic children in Ethiopia, particularly in the study area was poorly understood. Understanding the magnitude of ADHD and associated factors would be important to formulate policies and design appropriate interventions. Hence, this study aimed to assess the prevalence of comorbidity of ADHD and associated factors among epileptic children who have follow-ups at University of Gondar referral hospital’s pediatric seizure clinic.A hospital-based cross-sectional design was used to assess ADHD and associated factors among children with seizure disorders attending the pediatric seizure clinic of the University of Gondar referral hospital. Gondar has located about 723 km from Addis Ababa and 65 km north of Lake Tana. The University of Gondar referral hospital provides tertiary care to the population of Gondar and its neighboring regions. The data were collected from September 13 to November 25, 2016.All Children attending the pediatric seizure clinic and aged between 6 and 17 years were taken as the source population while children who were available during data collection were considered as the study population. As per the information obtained from the pediatric seizure clinic, about 40 epileptic children with mothers or caregivers used to visit the clinic per week. The single population proportion formula with an assumption of 95% confidence level, a 5% margin of error and 50% prevalence and a 10% non-response rate was taken to determine the sample size as there were no similar studies in our country. Because the total population was less than 10,000, the correction formula was used to get the final sample size of 265.Data were collected from parents or caregivers by using the interview technique with the standard DBD rating scale for investigating the presence of ADHD. The scale consists of 45 items representing symptoms of Disruptive Behavior Disorders that are conduct disorder (CD), oppositional defiant disorder (ODD) and ADHD. Of the 45 items, only 18 were used in this study.Children whose responses to six of the nine questions on hyper activity were “pretty much” or “very much” were considered as positive for the ADHD hyperactive subtype, while children whose responses to the same number of inattentive questions were “pretty much” or “very much” were considered as positive for the ADHD inattentive sub type. A structured questionnaire was developed to identify factors associated with ADHD, and patient charts were revised to get seizure related factors. The internal validity of the instrument was also checked  and found to be acceptable.The collected data were coded, entered and cleaned by using Epi-Data version 3.1 and exported to SPSS version 20. Frequencies and cross tabulations were used to summarize descriptive statistics of the data and tables and graphs were used for presentation. Bivariate logistic regression was first fitted to identify potential confounding factors and variables with p-values less than 0.2 were entered in to the multiple logistic regression model using the backward selection method to identify factors associated with depression. The adjusted odds ratio with a 95% confidence interval was calculated to report the strength and significance of the association.Data quality was managed by training and appropriate supervision of data collectors by the principal investigator. A pre-test was done on 42 children at Feleg-Hiwot hospital pediatric seizure clinic, Bahirdar. During analyzing the pre-test The Cronbach’s Alpha for the pretest was 0.631 and Appropriate modifications were made on parts of the questionnaire which ask about family related factors and seizure related variables before the actual data collection.Ethical clearance was obtained from the University of Gondar Ethical Review committee (IRB). A written support letter was secured from the hospital chief executive officer and medical director. The benefit of the study for their children and the possible risks of participating in the study were discussed with parents of caregivers. Also the right to refuse and discontinue the interview at any time was kept. Informed written consent was sought from participant’s parents or caregivers before starting the data collection. Parents of children confirmed to have ADHD were advised and linked to the psychiatry clinic of the hospital.All methods were performed following relevant guidelines and regulations of scientific studies.A total of 260 children on follow up at Gondar University hospital pediatric seizure clinic participated in the study with a response rate of 98.1%. Out of the total participants, 158 (60.8%) were male sex, with a mean age of 10.5 ± 2.7 years , and 41 (15.8%) of the children had a family history of seizure disorder and psychiatric illness, respectively, while 32 (12.3%) had severe medical illness before the age of 7 years  of the children were diagnosed with a generalized tonic–clonic seizure; among 135 (51.9%) participants’ seizure started suddenly. More than half 173 (66.5%) of the children had a seizure at least once per month before they were started medication. The seizure was not controlled among more than half 142 (54.6%) of the participants , family history of seizure disorder (AOR = 2.42 CI 1.26–4.65), family history of other mental illnesses (AOR = 4.14 (1.76–9.68), sudden onsets of seizure (AOR = 2.37 (1.32–4.27) and uncontrolled seizures, 2.55 (1.41–4.61) 8, Israel (27.7%)10, Brazil (29.1%)11, Norway (28%)13, USA (26.4%)14, Central China (24.7%)16 and India (23%)17. The differences were due to variations in sample sizes, study populations and screening tools. The sample sizes in the other studies were lower than that of the current study. The studies conducted in Korea and India used the DSM-IV criterion which was a diagnostic manual, and epileptic children were observed to confirm the presence of ADHD symptoms. In addition, they excluded children who had an intellectual disability, other psychiatric disorders and chronic medical illnesses. In the USA, only children with new onsets of epilepsy diagnosed in the past 12 months were taken.The prevalence of ADHD among children with seizure disorder was 44.2%  which is lower than those of previous studies conducted in Iran in which the prevalence of ADHD was 60.417. This was different from what was reported from Korea8 and the USA14.The current study was identified different factors that had associations with ADHD during the course of seizure disorders. Male sex was one of the associated factors of the magnitude of ADHD, a result in line with that of a study conducted in IndiaThe other factor that had a significant association with ADHD was family history of seizure disorder. If a child had a family history of seizure disorders, the illness would be very severe, and the severity of the seizure disorder might increase the risk of developing childhood psychiatric disorders, including ADHD. Previous studies we reviewed did not assess family history of seizure disorder as an associated factor.17, family history of psychiatric illness was significantly associated with the magnitude of ADHD among children with seizure disorders. That was evidenced by the role of genetic predisposition for psychiatric disorders. Other studies did not assess family history of psychiatric disorders as a contributing factor.In contrast to a study in India17. It was however different from what were reported in Korea8 and USA14 which showed that children’s seizure status had no a significant associations with the prevalence of ADHD.The other contributing factor was an uncontrolled seizure. Children who had seizure episodes during the last 6 months, had more than two times increased risk for ADHD compared with children who were seizure-free for more than 6 months, similar to the finding in IndiaSudden onset of seizure disorder is among the associated factors of the magnitude of seizure disorders. That was because when the onset is sudden the illness becomes severe. This result was different from what was noted in USA, where seizure onsets were not significant associated factor of the prevalence of ADHD.8, USA14 and India17 age in this work had no significant association with the magnitude of ADHD. The number of antiepileptic drugs, age at onset and prolonged antiepileptic treatments were not associated significantly with ADHD in the current study in distinction from the result of a study conducted in China9.Like studies conducted in Korea8 and Tanzania18, the type of epilepsy syndrome had no significant association with the magnitude of ADHD in our study. The institution-based cross-sectional design of our study has limited its generalizability as well as its capacity of establishing temporal relationships.In contrast to the studies conducted in KoreaThe magnitude of ADHD among children with seizure disorders was relatively higher in this attempt than most study reports worldwide. Despite this high prevalence, only a few children were diagnosed as having ADHD. Male sex, sudden onsets of seizure, family history of seizure and that of other psychiatric disorders as well as uncontrolled seizures were factors that increased the odds of ADHD. To Increasing an early detection and management of co-morbid ADHD training should be given about mental health problems for health professionals working in pediatric seizure clinics. Treating seizure disorder appropriately and controlling seizure could minimize the risk of having co-morbid ADHD.


Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition, which may be associated with life-enduring cognitive dysfunction. It has been hypothesized that age-related cognitive decline may overlap with preexisting deficits in older ADHD patients, leading to increased problems to manage everyday-life activities. This phenomenon may mimic neurodegenerative disorders, in particular Mild Cognitive Impairment (MCI). This cross-sectional study aims to assess cognitive and behavioral differences between older subjects with ADHD and MCI.Methods: A total of 107 older participants  underwent clinical, cognitive, and behavioral assessments by a multidisciplinary team at the Memory Clinic, D'Or Institute for Research and Education, Rio de Janeiro, Brazil. Mean scores in neuropsychological tasks and behavioral scales were compared across groups.Results: Participants with ADHD showed poorer performances than controls in episodic memory and executive function with large effect-sizes. Performances were comparable between MCI and ADHD for all domains.Discussion: MCI and ADHD in older individuals are dissociated clinical entities with overlapping cognitive profiles. Clinicians ought to be aware of these converging phenotypes to avoid misdiagnosis. Originally regarded as exclusive to childhood and adolescence, Attention-Deficit/Hyperactivity Disorder (ADHD) has been recognized as a lifelong neurodevelopmental condition, persisting into old age in 3–4% of the cases , 2. HoweAlternatively, the idea that ADHD may accelerate or predispose to pathological neurogenerative processes is attractive, yet unproven . It coulWith that, characterizing the cognitive and behavioral profile of older subjects with ADHD is needed to allow further insight on potentially converging pathological mechanisms in neurodevelopmental and neurodegenerative disorders in late-life and to avoid misdiagnosis of conditions with resembling clinical manifestations. In addition, therapeutic implications of accurately distinguishing these clinical entities could exist, considering that the effectiveness of treatment for ADHD in adults has been proven . Hence, Participants were volunteers referred for clinical and cognitive assessment due to cognitive complaints by health care services and senior centers in Rio de Janeiro, Brazil. Subjects older than 55 years old and with four or more years of formal education were assessed between January 2014 and November 2020 in the Memory Clinic, D'Or Institute for Research and Education, Rio de Janeiro, Brazil. Those were eligible for the study if none of the following criteria were met: current delirium , historyParticipants underwent clinical, neuropsychological and language assessments by a multidisciplinary team. Initially, subjects were interviewed for current and past medical history by a physician. Neuropsychologists and speech-language therapists conducted neuropsychological and language protocols. Validated instruments for assessing cognitive changes in older Brazilian population were applied, including: (1) the Mini-Mental State Examination (MMSE), an estimate of global cognitive function , 24; (2)Additionally, the Brazilian-Portuguese versions of the Geriatric Depression Scale (GDS) and the Geriatric Anxiety Scale (GAI) were applied to the participants –34. The Diagnosis of ADHD was established by board-certified psychiatrists based on the DSM-5 criteria , indepenpost-hoc test (2). Mean differences in neuropsychological performances across groups were assessed using analysis of covariance  or rank analysis of covariance  (2); those values were interpreted according to benchmarks suggested by Cohen as follows: small, medium and large effect sizes were defined as partial η2 of 0.01, 0.06, and 0.14, respectively .Normality of data was assessed using Kolmogorov-Smirnov test. Mean differences in continuous sociodemographic variables were analyzed using one-way analysis of variance (ANOVA), followed by Games-Howell hoc test ; or Krusriables) , adjustiectively . BonferrAll the participants provided an informed consent prior to the inclusion in the study. The project was approved by the Research Ethics Committee of the D'Or Institute under the protocol no. 226/11. The principles of the Resolution no. 510/2016 of the Brazil's National Health Council, which regulates research involving human beings in the country, as well as those of the latest version of the Declaration of Helsinki, were followed.n = 24) of participants in the MCI group were classified as amnestic MCI.A total of 107 participants  were included in the analyses. Sixty percent (p < 0.05). Scores in GDS were significantly more elevated in ADHD participants than in controls (p < 0.05). Presence of clinically relevant depressive symptoms (GDS > 5) did not differ across groups (p = 0.14). Neither scores on GAI nor presence of clinical anxiety (GAI > 13) was distinct among diagnostics clusters. MCI group was significantly older and less educated than controls and ADHD participants. A higher proportion of females were found in the control group (82.9%) than among MCI (60.0%) and ADHD (46.2%) subjects (2 = 0.13), effect-sizes were large for all these variables . No mean difference was observed between ADHD and MCI in the cognitive tasks. Those findings are shown in After adjustment for age, sex, years of schooling and GDS, mean scores in all memory indices , TMT B, Digit Span Forward and Digit Span Backwards were significantly different between controls and MCI subjects. Controls performed significantly better than ADHD group in RAVLT A1-A5, RAVLT A7, RAVLT Rec, TMT B and Digit Span Forward. Except for Digit Span Backwards  can be found below: The studies involving human participants were reviewed and approved by Research Ethics Committee of the IDOR. The patients/participants provided their written informed consent to participate in this study.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This research was supported by intramural grants from the D'Or Institute for Research and Education and Rede D'Or São Luiz Hospital Network, Conselho Nacional de Pesquisa, Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior and Fundação de Amparo à Pesquisa do Rio de Janeiro.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. 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