The Groundskeeper Gaming Platform as a Diagnostic Tool for Attention-Deficit/Hyperactivity Disorder: Sensitivity, Specificity, and Relation to Other Measures

Assessment of executive functions using a 3D-video game in children and adolescents with ADHD

Objective

Executive functions are important factors that affect the well-being of children with ADHD. Therefore, inclusion of a convenient assessment of executive dysfunction in diagnosis and treatment of ADHD patients is warranted. However, executive dysfunction assessment presently relies on lab-based neuropsychological tests and symptom rating scales. The present study examined the potential of a 3-D action puzzle video game to reflect ecologically valid executive functioning in pediatric ADHD patients.

Methods

Participant gameplay metrics were compared to both their Cambridge Neuropsychological Test Automated Battery (CANTAB) and Conners 3 Parent Form's executive functioning subscale scores. Participants consisted of 33 ADHD and non-ADHD patients aged 8-21.

Results

Metrics from gameplay were associated with distinct CANTAB test scores, and a composite score from gameplay was significantly correlated with executive dysfunction from Conners 3.

Conclusion

For children with ADHD, cognitive domains related to executive function and overall real-life executive functioning appear to both be measurable via video games. It may be possible to develop individualized behavioral therapy based on the quantitative data obtained from the video game used in this study.

Tools for the Diagnosis of ADHD in Children and Adolescents: A Systematic Review

CONTEXT

Correct diagnosis is essential for the appropriate clinical management of attention-deficit/hyperactivity disorder (ADHD) in children and adolescents.

OBJECTIVE

This systematic review provides an overview of the available diagnostic tools.

DATA SOURCES

We identified diagnostic accuracy studies in 12 databases published from 1980 through June 2023.

STUDY SELECTION

Any ADHD tool evaluation for the diagnosis of ADHD, requiring a reference standard of a clinical diagnosis by a mental health specialist.

DATA EXTRACTION

Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.

RESULTS

In total, 231 studies met eligibility criteria. Studies evaluated parental ratings, teacher ratings, youth self-reports, clinician tools, neuropsychological tests, biospecimen, EEG, and neuroimaging. Multiple tools showed promising diagnostic performance, but estimates varied considerably across studies, with a generally low strength of evidence. Performance depended on whether ADHD youth were being differentiated from neurotypically developing children or from clinically referred children.

LIMITATIONS

Studies used different components of available tools and did not report sufficient data for meta-analytic models.

CONCLUSIONS

A valid and reliable diagnosis of ADHD requires the judgment of a clinician who is experienced in the evaluation of youth with and without ADHD, along with the aid of standardized rating scales and input from multiple informants across multiple settings, including parents, teachers, and youth themselves.

Attention and impulsivity assessment using virtual reality games

The assessment of cognitive functions is mainly based on standardized neuropsychological tests, widely used in various fields such as personnel recruitment, education, or health. This paper presents a virtual reality game that allows collecting continuous measurements of both the performance and behaviour of the subject in an immersive, controllable, and naturalistic experience. The application registers variables related to the user's eye movements through the use of virtual reality goggles, as well as variables of the game performance. We study how virtual reality can provide data to help predict scores on the Attention Control Scale Test and the Barratt Impulsiveness Scale. We design the application and test it with a pilot group. We build a random forest regressor model to predict the attention and impulsivity scales' total score. When evaluating the performance of the model, we obtain a positive correlation with attention (0.434) and with impulsivity (0.382). In addition, our model identified that the most significant variables are the time spent looking at the target or at distractors, the eye movements variability, the number of blinks and the pupil dilation in both attention and impulsivity. Our results are consistent with previous results in the literature showing that it is possible to use data collected in virtual reality to predict the degree of attention and impulsivity.

Evaluating the validity of game-based assessments measuring cognitive function among children and adolescents: A systematic review and meta-analysis

Games offer advantages over traditional methods of assessing cognitive function among children and adolescents. However, the validity of game-based assessments has not been systematically evaluated. We conducted a systematic review and meta-analysis to assess the validity of game-based assessments measuring cognitive function among children and adolescents. We systematically searched several databases using pre-defined inclusion criteria. For papers that met the criteria, we extracted and analyzed the cognitive functions measured by each study, the correlation coefficients between game-based and traditional assessments, and factors that could influence the validity of game-based assessments. Our review identified 19 articles featuring 20 studies, 18 games, and 378 unique correlations between game-based and traditional assessments of cognitive function. Game-based assessments yielded significant correlations (n=282, 75%) with traditional assessments, over half of which were in the low to medium range in strength (r=0.3-0.69, n=227, 80%). Factors related to the child, such as age, gender, and prior gaming experience, may influence the validity of game-based assessments by modifying performance on game-based assessments. In addition, we found that game-based assessments that measured cognitive functions across more than one neurocognitive domain and used a prediction model for scoring were more likely to yield significant correlations. In contrast, including a narrative storyline in a game-based assessment was less likely to yield significant correlations. Most studies were of good quality, although the lack of sample size justification was a limiting factor. Further research is needed to elucidate the influence of identified factors on the validity of game-based assessment to justify the wide adoption of game-based assessments of cognitive function among children and adolescents.

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

OBJECTIVE

Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied.

METHODS

A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered.

RESULTS

We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances.

CONCLUSION

Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

Ahead of the (ROC) Curve: A Statistical Approach to Utilizing Ex-Gaussian Parameters of Reaction Time in Diagnosing ADHD Across Three Developmental Periods

INTRODUCTION

Performance on executive function (EF) tasks is only modestly predictive of a diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), despite the common assumption that EF deficits are ubiquitous to the disorder. The current study sought to determine whether ex-Gaussian parameters of simple reaction time are better able to discriminate between children and adults with and without ADHD, compared with traditional measures of inhibitory control.

METHODS

Receiver Operating Characteristic (ROC) analyses and the area under the curve (AUC) were used to examine the ability of performance on two commonly used tasks of inhibitory control (i.e. stop signal reaction time (SSRT) and go-no-go tasks) to predict ADHD status in preschool (N = 108), middle childhood (N = 309), and young adulthood (N = 133).

RESULTS

Across all samples, SSRT, go-no-go percentage of failed inhibits, and standard deviation of reaction (SDRT) time to "go" trials, all successfully discriminated between individuals with and without ADHD. Ex-Gaussian decomposition of the RT distribution indicated that both larger tau and larger sigma drove findings for SDRT. Contrary to predictions, traditional measures of inhibitory control were equal if not better predictors of ADHD status than ex-Gaussian parameters.

CONCLUSIONS

Findings support ongoing work to quantify the separate contributions of cognitive subprocesses that drive task performance, which in turn is critical to developing and improving process-based approaches in clinical assessment.

Attention Deficit Hyperactivity Disorder Assessment Based on Patient Behavior Exhibited in a Car Video Game: A Pilot Study

Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observation and communication skills of caregivers, and the experience of the medical professional. Attempts to obtain additional measures to support the medical diagnosis, such as reaction time when performing a task, can be found in the literature. We propose an information recording system that allows to study in detail the behavior shown by children already diagnosed with ADHD during a car driving video game. We continuously record the participants' activity throughout the task and calculate the error committed. Studying the trajectory graphs, some children showed uniform patterns, others lost attention from one point onwards, and others alternated attention/inattention intervals. Results show a dependence between the age of the children and their performance. Moreover, by analyzing the positions by age over time using clustering, we show that it is possible to classify children according to their performance. Future studies will examine whether this detailed information about each child's performance pattern can be used to fine-tune treatment.

Serious Video Games: Angels or Demons in Patients With Attention-Deficit Hyperactivity Disorder? A Quasi-Systematic Review

OBJECTIVE

To carry out a quasi-systematic review of the use of serious video games for health as a cognitive rehabilitative tool in patients diagnosed with attention-deficit hyperactivity disorder.

METHOD

A quasi-systematic review of serious video games used as an evaluative and rehabilitative tool in patients with ADHD was conducted. It included behavioral patterns in the use of video games and addiction problems in this population. For its elaboration the PRISMA GUIDES were followed. The search was carried out in three PubMed databases, MEDLINE, and PsycInfo using the keywords: [game OR serious game OR computer game) AND (psychotherapy OR rehabilitation OR intervention OR mental disorders) AND (adhd)], [(adhd) AND (Video game addiction)]. All articles written in English, Spanish, or Portuguese from January 1970 to June 2021 were included: those in which reference was made to the use of video games and/or new technologies as a therapeutic and evaluative tool in children and adults diagnosed with ADHD, as well as those that referred to behavioral and clinical patterns in the use of video games.

RESULTS

We found 605 articles of which 128 were reviewed (44 observational studies, 26 quasi-experimental studies, 26 experimental studies, 8 systematic reviews, 9 narrative texts, 6 case reports, 7 pilot studies, 8 systematic reviews, and 2 meta-analyses). Serious video games can be used to ameliorate ADHD symptoms while improving adherence to treatment. Some serious video games show high accuracy properties assessing ADHD features.

CONCLUSION

Serious video games for health are increasingly being used as a cognitive rehabilitation tool in patients with attention-deficit hyperactivity disorder (ADHD).

SYSTEMATIC REVIEW REGISTRATION

[www.crd.york.ac.uk/prospero], identifier [CRD42021247784].

Video games for the assessment and treatment of attention-deficit/hyperactivity disorder: a systematic review

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent and serious disorder among children. Video games have shown potential for aiding in child healthcare. Video games could contribute to the assessment and management of ADHD, but there are no previous reviews on this topic. Here, we systematically review the evidence about video game-based assessment tools and interventions for children diagnosed with ADHD. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The review protocol was registered in PROSPERO database. We searched four databases-PubMed, PsycInfo, Embase and clinicaltrials.gov-to identify original studies exploring either video game-based interventions or video game-based assessment tools in children with ADHD. After initial screening, full text revision and study selection, 22 articles were finally included in the review. Most studies used PC as platform, with a minority using a video console, pad, or 3D device. Video game-based assessment tools were generally effective in discriminating ADHD cases from controls, and in discriminating between ADHD subtypes. Video game-based therapeutic interventions were well accepted and generally effective in improving cognitive areas and decreasing ADHD symptoms. Gamification and cognitive training could be the main mechanisms underlying the usefulness and effectiveness of video game-based assessment tools and interventions. Software optimization and greater collaboration between developers and healthcare professionals are some of the priorities for future research in this area.

Trends, Limits, and Challenges of Computer Technologies in Attention Deficit Hyperactivity Disorder Diagnosis and Treatment

Attention deficit hyperactivity disorder (ADHD) is a neurobiological condition that appears during an individual's childhood and may follow her/him for life. The research objective was to understand better how and which computer technologies have been applied to support ADHD diagnosis and treatment. The research used the systematic literature review method: a rigorous, verifiable, and repeatable approach that follows well-defined steps. Six well-known academic data sources have been consulted, including search engines and bibliographic databases, from technology and health care areas. After a rigorous research protocol, 1,239 articles were analyzed. For the diagnosis, the use of machine learning techniques was verified in 61 percent of the articles. Neurofeedback was ranked second with 9.3 percent participation, followed by serious games and eye tracking with 5.6 percent each. For the treatment, neurofeedback was present in 50 percent of the articles, whereas some studies combined both approaches, accounting for 31 percent of the total. Nine percent of the articles reported remote assistance technology, whereas another 9 percent have used virtual reality. By highlighting the leading computer technologies used, their applications, results, and challenges, this literature review breaks ground for further investigations. Moreover, the study highlighted the lack of consensus on ADHD biomarkers. The approaches using machine learning call attention to the probable occurrence of overfitting in several studies, thus demonstrating limitations of this technology on small-sized bases. This research also presented the convergence of evidence from different studies on the persistence of long-term effects of using neurofeedback in treating ADHD.

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

Objective Assessment of Attention-Deficit Hyperactivity Disorder (ADHD) Using an Infinite Runner-Based Computer Game: A Pilot Study

In the last few years, several computerized tasks have been developed to increase the objectivity of the diagnosis of attention-deficit hyperactivity disorder (ADHD). This article proposes the "running raccoon" video game to assess the severity of inattention in patients diagnosed with ADHD. Unlike existing tests, the proposed tool is a genuine video game in which the patient must make a raccoon avatar jump to avoid falling into different gaps. The distance to the gap is recorded for each jump. To evaluate the proposed game, an experiment was conducted in which 32 children diagnosed with ADHD participated. For each participant, the median and interquartile range of these distances were calculated, along with the number of omissions. Experimental results showed a significant correlation between the participants' inattention (measured by the Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior rating scale (SWAN) inattention subscale) with each of these three measures. In addition to its accuracy, other benefits are its short duration and the possibility of being run on both standard computers and mobile devices. These characteristics facilitate its acceptance in clinical environments or even its telematic use. The obtained results, together with the characteristics of the video game, make it an excellent tool to support clinicians in the diagnosis of ADHD.

Virtual prognostication: When virtual alcohol choices predict change in alcohol consumption over 6-months

Narrative games, in which users interact with virtual agents, are increasingly being used in health interventions to change targeted behaviors. In virtual social interactions, based on similar real-life contextual cues, past behavior can predict virtual choices. Here, based on theories in learning and interactivity, we examined the whether following a virtual intervention, choices in social interactions may be particularly diagnostic of future behavior changes. To test this, we needed to: (1) leverage a contextualized risk (e.g., involving alcohol consumption) scenario (e.g., having one more drink with my partner) given a target audience (e.g., sexually risky young men who have sex with men (YMSM)), (2) include within this context an evidence-based virtual intervention (e.g., promoting alcohol reduction), (3) instantiate and record a virtual choice (water or alcohol) in a virtual dating game scenario intervention with IA for that target audience, and (4) assess pre and 6-months post-intervention YMSM's alcohol use. Using a Socially Optimized Learning Environment (SOLVE) intervention game with IA and alcohol use measures, we found that virtual water choice (versus virtual alcohol choice) significantly predicted real-life alcohol consumption change. Furthermore, personality factors (e.g., Behavioral Approach System) predicted virtual choices and alcohol consumption change. Implications of these findings are discussed.