Analyzing Dropout in Alcohol Recovery Programs: A Machine Learning Approach.

Background: Retention in treatment is crucial for the success of interventions targeting alcohol use disorder (AUD), which affects over 100 million people globally. Most previous studies have used classical statistical techniques to predict treatment dropout, and their results remain inconclusive. This study aimed to use novel machine learning tools to identify models that predict dropout with greater precision, enabling the development of better retention strategies for those at higher risk. Methods: A retrospective observational study of 39,030 (17.3% female) participants enrolled in outpatient-based treatment for alcohol use disorder in a state-wide public treatment network has been used. Participants were recruited between 1 January 2015 and 31 December 2019. We applied different machine learning algorithms to create models that allow one to predict the premature cessation of treatment (dropout). With the objective of increasing the explainability of those models with the best precision, considered as black-box models, explainability technique analyses were also applied. Results: Considering as the best models those obtained with one of the so-called black-box models (support vector classifier (SVC)), the results from the best model, from the explainability perspective, showed that the variables that showed greater explanatory capacity for treatment dropout are previous drug use as well as psychiatric comorbidity. Among these variables, those of having undergone previous opioid substitution treatment and receiving coordinated psychiatric care in mental health services showed the greatest capacity for predicting dropout. Conclusions: By using novel machine learning techniques on a large representative sample of patients enrolled in alcohol use disorder treatment, we have identified several machine learning models that help in predicting a higher risk of treatment dropout. Previous treatment for other substance use disorders (SUDs) and concurrent psychiatric comorbidity were the best predictors of dropout, and patients showing these characteristics may need more intensive or complementary interventions to benefit from treatment.

People Place Larger Bets When Risky Choices Provide a Postbet Option to Cash Out.

After a risky choice, decision makers must frequently wait out a delay period before the outcome of their choice becomes known. In contemporary sports-betting apps, decision makers can "cash out" of their bet during this delay period by accepting a discounted immediate payout. An important open question is how availability of a postchoice cash-out option alters choice. We investigated this question using a novel gambling task that incorporated a cash-out option during the delay between bet and outcome. Across two experiments (N = 240 adults, recruited via Prolific), cash-out availability increased participants' bet amounts by up to 35%. Participants who were more likely to cash out when odds deteriorated were less likely to cash out when odds improved. Furthermore, the effect of cash-out availability on bet amounts was positively correlated with individual differences in cash-out propensity for bets with deteriorating odds only. These results suggest that cash-out availability may promote larger bets by allowing bettors to avoid losing their entire stake.

Episodic future thinking improves intertemporal choice and food choice in individuals with higher weight: A systematic review and meta-analysis.

Episodic future thinking (EFT) strengthens self-regulation abilities by increasing the perceived value of long-term reinforcements and reducing impulsive choice in delay discounting tasks. As such, EFT interventions have the potential to improve dietary and eating-related decision-making in individuals with obesity or binge eating symptoms, conditions associated with elevated delay discounting. Here, we meta-analyzed evidence from 12 studies that assessed whether EFT interventions improve delay discounting and real-world food choice compared to control interventions. Included studies involved 951 adults with overweight or obesity (body mass index [BMI] ≥25). There were no studies involving participants with binge eating disorder. EFT intervention pooled effects were significant, improving delay discounting with a medium effect, g = 0.55, p < 0.0001, and subsequent food choice outcomes with a small effect, g = 0.31, p < 0.01. Notably, our review is the first to analyze mechanisms of effect in this population, demonstrating that improvements were greater when temporal horizons of EFT episodes were aligned with delay discounting tasks and more distant horizons predicted far-transfer to subsequent dietary and eating-related choices. Our findings thus show that EFT is an effective intervention for individuals with higher weight at risk of adverse health consequences.

Testing behaviour change with an artificial intelligence chatbot in a randomized controlled study.

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. Yet research on the feasibility and effectiveness of chatbot-administered behaviour change interventions is sparse. The effectiveness of established behaviour change interventions when implemented in chatbots is not guaranteed, given the unique human-machine interaction dynamics. We pilot-tested chatbot-based behaviour change through information provision and embedded animations. We evaluated whether the chatbot could increase understanding and intentions to adopt protective behaviours during the pandemic. Fifty-nine culturally and linguistically diverse participants received a compassion intervention, an exponential growth intervention, or no intervention. We measured participants' COVID-19 testing intentions and measured their staying-home attitudes before and after their chatbot interaction. We found reduced uncertainty about protective behaviours. The exponential growth intervention increased participants' testing intentions. This study provides preliminary evidence that chatbots can spark behaviour change, with applications in diverse and underrepresented groups.

The effectiveness of the TRACE online nutrition intervention in improving dietary intake, sleep quality and physical activity levels for Australian adults with food addiction: a randomised controlled trial.

BACKGROUND: Few interventions for food addiction (FA) report on dietary intake variables. The present study comprised a three-arm randomised controlled trial in adults with symptoms of FA. The aim was to evaluate dietary intake, sleep and physical activity resulting from a dietitian-led telehealth intervention at 3 months. METHODS: Adults with ≥3 symptoms of FA and a body mass index > 18.5 kg/m2 were recruited. Dietary intake including energy, nutrients and diet quality were assessed by a validated food frequency questionnaire in addition to sleep quality and physical activity (total min) and compared between groups and over time. Personalised dietary goals set by participants were examined to determine whether improvements in percent energy from core and non-core foods were reported. RESULTS: The active intervention group was superior compared to the passive intervention and control groups for improvements in percent energy from core (6.4%/day [95% confidence interval (CI) -0.0 to 12.9], p = 0.049), non-core foods (-6.4%/day [95% CI -12.9 to 0.0], p = 0.049), sweetened drinks (-1.7%/day [95% CI -2.9 to -0.4], p = 0.013), takeaway foods (-2.3%/day [95% CI -4.5 to -0.1], p = 0.045) and sodium (-478 mg/day [95% CI -765 to -191 mg], p = 0.001). CONCLUSIONS: A dietitian-led telehealth intervention for Australian adults with FA found significant improvements in dietary intake variables. Setting personalised goals around nutrition and eating behaviours was beneficial for lifestyle change.

Chemical cousins with contrasting behavioural profiles: MDMA users and methamphetamine users differ in social-cognitive functions and aggression.

Methamphetamine (METH, "Crystal Meth") and 3,4-methylenedioxymethamphetamine (MDMA, "Ecstasy") share structural-chemical similarities but have distinct psychotropic profiles due to specific neurochemical actions. Previous research has suggested that their impact on social cognitive functions and social behaviour may differ significantly, however, direct comparisons of METH and MDMA users regarding social cognition and interaction are lacking. Performances in cognitive and emotional empathy (Multifaceted Empathy Test) and emotion sensitivity (Face Morphing Task), as well as aggressive social behaviour (Competitive Reaction Time Task) were assessed in samples of n = 40 chronic METH users, n = 39 chronic MDMA users and n = 86 stimulant-naïve controls (total N = 165). Self-reports and hair samples were used to obtain subjective and objective estimates of substance use patterns. METH users displayed diminished cognitive and emotional empathy towards positive stimuli, elevated punitive social behaviour regardless of provocation, and self-reported heightened trait anger relative to controls. MDMA users diverged from the control group only by exhibiting a distinct rise in punitive behaviour when faced with provocation. Correlation analyses indicated that both higher hair concentrations of MDMA and METH may be associated with reduced cognitive empathy. Moreover, greater lifetime MDMA use correlated with increased punitive behaviour among MDMA users. Our findings confirm elevated aggression and empathy deficits in chronic METH users, while chronic MDMA users only displayed more impulsive aggression. Dose-response correlations indicate that some of these deficits might be a consequence of use. Specifically, the dopaminergic mechanism of METH might be responsible for social-cognitive deficits.

Common genetic factors for uncontrolled eating mechanisms.

OBJECTIVE: Reward-based eating drives are putative mechanisms of uncontrolled eating implicated in obesity and disordered eating (e.g., binge eating). Uncovering the genetic and environmental contributions to reward-related eating, and their genetic correlation with BMI, could shed light on key mechanisms underlying eating and weight-related disorders. METHOD: We conducted a classical twin study to examine how much variance in uncontrolled eating phenotypes and body mass index (BMI) was explained by genetic factors, and the extent that these phenotypes shared common genetic factors. 353 monozygotic twins and 128 dizygotic twins completed the Reward-based Eating Drive 13 scale, which measures three distinct uncontrolled eating phenotypes (loss of control over eating, preoccupation with thoughts about food, and lack of satiety), and a demographic questionnaire which included height and weight for BMI calculation. We estimated additive genetic (A), common environmental (C), and unique environmental (E) factors for each phenotype, as well as their genetic correlations, with a multivariate ACE model. A common pathway model also estimated whether genetic variance in the uncontrolled eating phenotypes was better explained by a common latent uncontrolled eating factor. RESULTS: There were moderate genetic correlations between uncontrolled eating phenotypes and BMI (.26-.41). Variance from the uncontrolled eating phenotypes was also best explained by a common latent uncontrolled eating factor that was explained by additive genetic factors (52%). DISCUSSION: These results suggest that uncontrolled eating phenotypes are heritable traits that also share genetic variance with BMI. This has implications for understanding the cognitive mechanisms that underpin obesity and disordered eating. PUBLIC SIGNIFICANCE: Our study clarifies the degree to which uncontrolled eating phenotypes and BMI are influenced by shared genetics and shows that vulnerability to uncontrolled eating traits is impacted by common genetic factors.

Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach.

Neuroscience has contributed to uncover the mechanisms underpinning substance use disorders (SUD). The next frontier is to leverage these mechanisms as active targets to create more effective interventions for SUD treatment and prevention. Recent large-scale cohort studies from early childhood are generating multiple levels of neuroscience-based information with the potential to inform the development and refinement of future preventive strategies. However, there are still no available well-recognized frameworks to guide the integration of these multi-level datasets into prevention interventions. The Research Domain Criteria (RDoC) provides a neuroscience-based multi-system framework that is well suited to facilitate translation of neurobiological mechanisms into behavioral domains amenable to preventative interventions. We propose a novel RDoC-based framework for prevention science and adapted the framework for the existing preventive interventions. From a systematic review of randomized controlled trials using a person-centered drug/alcohol preventive approach for adolescents, we identified 22 unique preventive interventions. By teasing apart these 22 interventions into the RDoC domains, we proposed distinct neurocognitive trajectories which have been recognized as precursors or risk factors for SUDs, to be targeted, engaged and modified for effective addiction prevention.

Model-based and model-free mechanisms in methamphetamine use disorder.

People with methamphetamine use disorder (MUD) struggle to shift their behaviour from methamphetamine-orientated habits to goal-oriented choices. The model-based/model-free framework is well suited to understand this difficulty by unpacking the computational mechanisms that support experienced-based (model-free) and goal-directed (model-based) choices. We aimed to examine whether 1) participants with MUD differed from controls on behavioural proxies and/or computational mechanisms of model-based/model-free choices; 2) model-based/model-free decision-making correlated with MUD symptoms; and 3) model-based/model-free deficits improved over six weeks in the group with MUD. Participants with MUD and controls with similar age, IQ and socioeconomic status completed the Two-Step Task at treatment commencement (MUD n = 30, Controls n = 31) and six weeks later (MUD n = 23, Controls n = 26). We examined behavioural proxies of model-based/model-free decisions using mixed logistic regression, and their underlying mechanisms using computational modelling. At a behavioural level, participants with MUD were more likely to switch their choices following rewarded actions, although this pattern improved at follow up. At a computational level, groups were similar in their use of model-based mechanisms, but participants with MUD were less likely to apply model-free mechanisms and less likely to repeat rewarded actions. We did not find evidence that individual differences in model-based or model-free parameters were associated with greater severity of methamphetamine dependence, nor did we find that group differences in computational parameters changed between baseline and follow-up assessment. Decision-making challenges in people with MUD are likely related to difficulties in pursuing choices previously associated with positive outcomes.

Digital Health for Myocardial Infarction: Research Topics and Trends.

We aimed to map the topics and trends of research on digital health for myocardial infarction over the past ten years. This can inform future research directions and newly emerging topics for myocardial infarction care, diagnosis and monitoring. The Web of Science database was searched for papers related to digital health for myocardial infarction. 1,344 retrieved records were used for visualisation through bibliometrics and co-occurrence network analysis of keywords. Our mapping revealed several emerging topics in recent years, including artificial intelligence and deep learning. Higher emphasis on automated and artificially intelligent digital health systems in recent years can inform future clinical practice and research directions for myocardial infarction.

Leveraging Online Treatment to Re-examine the Association Between Alcohol Use and Disinhibition.

BACKGROUND: Cognitive disinhibition underpins alcohol and drug use problems. Although higher-risk substance use is consistently associated with poorer disinhibition, current findings may be limited by narrow recruitment methods, which over-represent individuals engaged in traditional treatment services with more severe presentations. We embedded a novel gamified disinhibition task (the Cognitive Impulsivity Suite; CIS) in a national online addiction support service ( https://www.counsellingonline.org.au/ ). METHOD: Participants aged 18 to 64 ( N = 137; 109 women) completed the Alcohol-Use Disorders Identification Test (AUDIT) and Drug Use Disorders Identification Test (DUDIT) along with the CIS, which measures three aspects of disinhibition (Attentional Control, Information-Sampling, and Feedback Monitoring/Shifting). The majority of the sample comprised people with alcohol use, and AUDIT scores were differentiated into 'higher-risk' or 'lower-risk' groups using latent-class analysis. These classes were then regressed against CIS performance measures. RESULTS: Compared to lower-risk, higher-risk alcohol use was associated with poorer attentional control and feedback monitoring/shifting. While higher-risk alcohol use was associated with slower information accumulation, this was only observed for older adults, who appeared to compensate with a more conservative response criterion. CONCLUSIONS: Our results reveal novel relationships between higher-risk alcohol use and specific aspects of disinhibition in participants who sought online addiction help services.

A three-arm randomised controlled trial of a telehealth intervention targeting improvement in addictive eating for Australian adults (the TRACE program).

There is a substantial research base for addictive eating with development of interventions. The current 3-arm RCT aimed to investigate the efficacy of the TRACE (Targeted Research for Addictive and Compulsive Eating) program to decrease addictive eating symptoms and improve mental health. Participants (18-85 yrs) endorsing ≥3 addictive eating symptoms were randomly allocated to 1) active intervention, 2) passive intervention, or 3) control group. Primary outcome was change in addictive eating symptoms 3-months post-baseline measured by the Yale Food Addiction Scale. Depression, anxiety and stress were also assessed. A total of 175 individuals were randomised. Using Linear Mixed Models, from baseline to 3-months, there was significant improvement in symptom scores in all groups with mean decrease of 4.7 (95% CI: -5.8, -3.6; p < 0.001), 3.8 (95% CI: -5.2, -2.4; p < 0.001) and 1.5 (95% CI: -2.6, -0.4; p = 0.01) respectively. Compared with the control group, participants in the active intervention were five times more likely to achieve a clinically significant change in symptom scores. There was a significant reduction in depression scores in the active and passive intervention groups, but not control group [-2.9 (95% CI: -4.5, -1.3); -2.3 (95% CI: -4.3, -0.3); 0.5 (95% CI: -1.1, 2.1), respectively]; a significant reduction in stress scores within the active group, but not passive intervention or control groups [-1.3 (95% CI: -2.2, -0.5); -1.0 (95% CI: -2.1, 0.1); 0.4 (95% CI: -0.5, 1.2), respectively]; and the reduction in anxiety scores over time was similar for all groups. A dietitian-led telehealth intervention for addictive eating in adults was more effective than a passive or control condition in reducing addictive eating scores from baseline to 6 months. Trial registration: Australia New Zealand Clinical Trial Registry ACTRN12621001079831.

Leveraging Online Treatment to Re-examine the Association Between Alcohol Use and Disinhibition / Tratamiento Online Como Plataforma Para Re-examinar la Relación Entre el Uso de Alcohol y la Desinhibición Cognitiva

Background: Cognitive disinhibition underpins alcohol and drug use problems. Although higher-risk substance use is consistently associated with poorer disinhibition, current findings may be limited by narrow recruitment methods, which over-represent individuals engaged in traditional treatment services with more severe presentations. We embedded a novel gamified disinhibition task (the Cognitive Impulsivity Suite; CIS) in a national online addiction support service (https://www.counsellingonline.org.au). Method: Participants aged 18 to 64 (N = 137; 109 women) completed the Alcohol-Use Disorders Identification Test (AUDIT) and Drug Use Disorders Identification Test (DUDIT) along with the CIS, which measures three aspects of disinhibition (Attentional Control, Information-Sampling, and Feedback Monitoring/Shifting). The majority of the sample comprised people with alcohol use, and AUDIT scores were differentiated into ‘higher-risk’ or ‘lower-risk’ groups using latent-class analysis. These classes were then regressed against CIS performance measures. Results: Compared to lower-risk, higher-risk alcohol use was associated with poorer attentional control and feedback monitoring/shifting. While higher-risk alcohol use was associated with slower information accumulation, this was only observed for older adults, who appeared to compensate with a more conservative response criterion. Conclusions: Our results reveal novel relationships between higher-risk alcohol use and specific aspects of disinhibition in participants who sought online addiction help services.(AU)

Antecedentes: El uso de alcohol se asocia a mayor desinhibición, pero estos hallazgos podrían no ser representativos de toda la población ya que predominan estudios en contextos especializados y casos severos. Aquí, incorporamos una nueva batería de evaluación de la desinhibición (Cognitive Impulsivity Suite o CIS) en una web de tratamiento online con acceso a una población más amplia (https://www.counsellingonline.org.au). Método: Participantes de 18 a 64 años (N = 137; 109 mujeres) completaron vía web el “Alcohol-Use Disorders Identification Test” y la CIS, que evalúa tres componentes de la desinhibición (Control Atencional, Acumulación de Información y Monitorización / Cambio). Clasificamos en grupos de alto-riesgo versus bajo-riesgo aplicando un análisis de clases latentes sobre las puntuaciones del AUDIT. Usamos análisis de regresión para asociar las dos clases resultantes con las medidas de la CIS. Resultados: Alto-riesgo en el consumo de alcohol se asoció con peor rendimiento en Control Atencional y Monitorización / Cambio. La pertenencia al grupo de alto-riesgo se asoció con menor eficiencia en la acumulación de información en participantes de mayor edad. Conclusiones: Revelamos nuevas asociaciones entre el consumo de alcohol de riesgo y el rendimiento cognitivo en distintos componentes de la desinhibición en participantes que buscaban asistencia en una web de tratamiento online.(AU)

Early life factors and structural brain network in children with overweight/obesity: The ActiveBrains project.

BACKGROUND: The aims of this study were to investigate the association of early life factors, including birth weight, birth length, and breastfeeding practices, with structural brain networks; and to test whether structural brain networks associated with early life factors were also associated with academic performance in children with overweight/obesity (OW/OB). METHOD: 96 children with OW/OB aged 8-11 years (10.03 ± 1.16) from the ActiveBrains project were included. Early life factors were collected from birth records and reported by parents as weight, height, and months of breastfeeding. T1-weighted images were used to identify structural networks using a non-negative matrix factorization (NNMF) approach. Academic performance was evaluated by the Woodcock-Muñoz standardized test battery. RESULTS: Birth weight and birth length were associated with seven networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate nucleus, putamen, pallidum, nucleus accumbens, and amygdala. No associations were found for breastfeeding practices. None of the networks linked to birth weight and birth length were linked to academic performance. CONCLUSIONS: Birth weight and birth length, but not breastfeeding, were associated with brain structural networks in children with OW/OB. Thus, early life factors are related to brain networks, yet a link with academic performance was not observed. IMPACT: Birth weight and birth length, but not breastfeeding, were associated with several structural brain networks involving the cerebellum, cingulate gyrus, occipital pole, and subcortical structures including hippocampus, caudate, putamen, pallidum, accumbens and amygdala in children with overweight/obesity, playing a role for a normal brain development. Despite no academic consequences, other behavioral consequences should be investigated. Interventions aimed at improving optimal intrauterine growth and development may be of importance to achieve a healthy brain later in life.

Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.

BACKGROUND: Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior. AIMS: It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity. METHOD: We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data. RESULTS: We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls. CONCLUSIONS: Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification.

Digital Health Secondary Prevention Using Co-Design Procedures: Focus Group Study With Health Care Providers and Patients With Myocardial Infarction.

BACKGROUND: Myocardial infarction (MI) is a debilitating condition and a leading cause of morbidity and mortality worldwide. Digital health is a promising approach for delivering secondary prevention to support patients with a history of MI and for reducing risk factors that can lead to a future event. However, its potential can only be fulfilled when the technology meets the needs of the end users who will be interacting with this secondary prevention. OBJECTIVE: We aimed to gauge the opinions of patients with a history of MI and health professionals concerning the functions, features, and characteristics of a digital health solution to support post-MI care. METHODS: Our approach aligned with the gold standard participatory co-design procedures enabling progressive refinement of feedback via exploratory, confirmatory, and prototype-assisted feedback from participants. Patients with a history of MI and health professionals from Australia attended focus groups over a videoconference system. We engaged with 38 participants across 3 rounds of focus groups using an iterative co-design approach. Round 1 included 8 participants (4 patients and 4 health professionals), round 2 included 24 participants (11 patients and 13 health professionals), and round 3 included 22 participants (14 patients and 8 health professionals). RESULTS: Participants highlighted the potential of digital health in addressing the unmet needs of post-MI care. Both patients with a history of MI and health professionals agreed that mental health is a key concern in post-MI care that requires further support. Participants agreed that family members can be used to support postdischarge care and require support from the health care team. Participants agreed that incorporating simple games with a points system can increase long-term engagement. However, patients with a history of MI emphasized a lack of support from their health care team, family, and community more strongly than health professionals. They also expressed some openness to using artificial intelligence, whereas health professionals expressed that users should not be aware of artificial intelligence use. CONCLUSIONS: These results provide valuable insights into the development of digital health secondary preventions aimed at supporting patients with a history of MI. Future research can implement a pilot study in the population with MI to trial these recommendations in a real-world setting.

Fenneman et al.'s (2022) review of formal impulsivity models: Implications for theory and measures of impulsivity.

In Fenneman et al.'s (2022) review of theories and integrated impulsivity model, the authors distinguish between information impulsivity (i.e., acting without considering consequences) and temporal impulsivity (i.e., the tendency to pick sooner outcomes over later ones). The authors find that both types of impulsivity can be adaptive in different contexts. For example, when individuals experience scarcity of resources or when they are close to a minimum level of reserves (critical threshold). In this commentary, we extend their findings to a discussion about the measurement of impulsivity. We argue that a common method for measuring temporal impulsivity in which people make decisions between outcomes that are spaced out in time (intertemporal choice tasks), puts individuals in a specific context that is unlikely to generalize well to other situations. Furthermore, trait measures of impulsivity may only be modestly informative about future impulsive behavior because they largely abstract away from important context. To address these issues, we advocate for the development of dynamic measures of the two types of impulsivity. We argue that measuring temporal impulsivity in naturalistic contexts with varying environmental and state parameters could provide insights into whether individuals (i.e., humans and nonhuman animals) react to environmental changes adaptively, while trait measures of impulsivity more generally should collect and provide more contextual information. Dynamic measurement of different types of impulsivity will also allow for more discussion about adaptive impulsive responses in different contexts, which could help combat the stigmatization of various disorders associated with impulsivity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies. METHODS: This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online. RESULTS: Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor. CONCLUSIONS: Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.