While numerous reviews examine digital interventions, including computer game-based, evidence regarding their effectiveness for ADHD symptoms remains mixed. Focusing on computer game-based interventions, this study aims to identify specific intervention features and trial-related factors that influence outcomes in pediatric ADHD symptoms through a systematic review and meta-analysis of randomized controlled trials.

Comprehensive literature searches were conducted in Web of Science, PubMed and PsycINFO. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.

Seventeen studies were included. Beyond common game characteristics, some interventions incorporated additional elements-such as neurofeedback, physical exercise via virtual reality (VR) and social components-allowing subgroup analyses. The neurofeedback subgroup showed the largest effect size (standardized mean difference (SMD)=-0.51, 95 % confidence interval (CI) [-0.88, -0.15], heterogeneity index (I2)=45.85 %, p < 0.05, very low certainty of evidence), followed by the physical exercise/VR subgroup (SMD=-0.40, 95 %CI [-0.76, -0.04], I2=0.00 %, p < 0.05, very low certainty of evidence). Other subgroups, including those with social components or without additional elements, did not show significant effect sizes. The physical exercise/VR subgroup demonstrated small-to-medium effect sizes in both inattention and hyperactivity/impulsivity subscales, whereas the neurofeedback subgroup showed similar effects only in inattention. Trial-level factors, including participant demographics and intervention duration, showed no significant relationship with SMD in meta-regression.

Although inconclusive, synergizing with game dynamics, neurofeedback and physical exercise/VR may enhance effectiveness of computer game-based interventions in addressing pediatric ADHD symptoms. Particularly, interventions with physical exercise/VR have potential to address both inattention and hyperactivity/impulsivity.

Virtual reality (VR) technology is increasingly utilized in the medical field, including interventions for attention deficit hyperactivity disorder (ADHD) in children. However, evidence on the efficacy of VR-based interventions in improving attention remains limited and inconclusive.

This systematic review synthesizes evidence from randomized controlled trials (RCTs) to evaluate the effectiveness of VR-based interventions in addressing attention deficits in children with ADHD and identifies key methodological insights.

Six databases were searched for English-language RCTs involving children aged 6-12 years with ADHD receiving VR interventions. The Cochrane bias risk assessment tool was used to assess methodological quality. Statistical analyses included meta-analysis for effect size estimation and publication bias testing.

Eleven RCTs involving 640 participants were included. Meta-analysis revealed a moderate reduction in attention deficit symptoms (SMD = -0.33, 95%CI [-0.58, -0.09], p = 0.008). No significant publication bias was detected.

VR-based interventions show potential in alleviating attention deficits in children with ADHD. However, study heterogeneity and limited long-term data warrant caution. Future research should focus on large-scale, standardized trials with extended follow-ups to validate these findings.

Artificial Intelligence (AI), has garnered attention in research on attention deficit hyperactivity disorder (ADHD). In the future, AI may have clinical applications in ADHD, particularly in facilitating the objective diagnosis and classification of ADHD. This study aimed to comprehensively analyze the current status and research frontiers of AI applications in ADHD, identifying hotspots and trends to guide future research directions and promote clinical advancements in this field.

Articles in the field of AI applications in ADHD were from the Web of Science Core Collection (WoSCC) database. Analysis was conducted using CiteSpace 6.3.R.1. Additionally, high-impact articles were analyzed.

A total of 342 articles from 50 countries and regions were included. The United States led with 103 articles, having the highest H-index of 21, followed by China with 69 articles, and England with 34 articles. The State University of New York System produced the most articles (11), and Frontiers in Psychiatry had the most articles (12). Burst keywords in 2022-2024 included "diagnosis," "network," "attention deficit hyperactivity disorder" and "artificial intelligence."

AI technologies have become a prominent topic in ADHD research, with the United States, China, and England leading in articles and influence. The State University of New York System was the most influential institution, while Frontiers in Psychiatry stood out as the key journal. Utilizing networks and other AI technologies for diagnosing ADHD represents current hotspots and future trends, potentially offering objective indicators for ADHD.

Attention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children's academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8-12 with ADHD. Participants received either 12 weeks of AI-driven therapy or a placebo intervention. Assessments were conducted pre- and post-intervention and magnetoencephalography (MEG) analyzed brain activity. Results showed significant reductions in impulsiveness and inattentiveness scores in the treatment group, associated with normalized MEG spectral profiles, indicating neuromaturation. Notably, improvements in inhibitory control correlated with spectral profile normalization in the parieto-temporal cortex. Improvements in inhibitory control, linked to normalized spectral profiles, suggest AI-driven digital cognitive therapy can reduce impulsiveness in ADHD children by enhancing neurophysiological efficiency. This emphasizes personalized, technology-driven ADHD treatment, using neurophysiological markers for assessing efficacy.

Supports for adaptive functioning in individuals with neurodevelopmental conditions (NDCs) is of umost importance to long-term outcomes. Artificial intelligence (AI)-assistive technologies has enormous potential to offer efficient, cost-effective, and personalized solutions to address these challenges, particularly in everday environments. This systematic review examines the existing evidence for using AI-assistive technologies to support adaptive functioning in people with NDCs in everyday settings. Searches across six databases yielded 15 studies meeting inclusion criteria, focusing on robotics, phones/computers and virtual reality. Studies most frequently recruited children diagnosed with autism and targeted social skills (47%), daily living skills (26%), and communication (16%). Despite promising results, studies addressing broader transdiagnostic needs across different NDC populations are needed. There is also an urgent need to improve the quality of evidence-based research practices. This review concludes that AI holds enormous potential to support adaptive functioning for people with NDCs and for personalized health support. This review underscores the need for further research studies to advance AI technologies in this field.

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity. Individuals with ADHD often encounter heightened emotional and behavioral challenges. This study aims to conduct a meta-analysis of the efficacy of digital interventions in improving symptoms of ADHD.

This study searched 6 databases for English articles from database construction until December 4, 2023. Randomized controlled trials employing digital interventions for ADHD were gathered. Following the Cochrane Collaboration criteria, Review Manager 5.3 software was utilized to evaluate the risk of bias present in the included papers. Mean differences for post-intervention and follow-up data were standardized using Stata 18.0 software. Subgroup analysis was employed to investigate the sources of heterogeneity. The study adhered to the PRISMA guidelines and was registered on the PROSPERO platform (CRD42024504134).

This review included a total of 25 randomized controlled trials (RCTs). The combined study population comprised 1780 cases. In the digital intervention groups, there was a significant decrease in overall ADHD symptoms (SMD = -0.33; 95 % CI = [-0.51, -0.16]) compared to the control groups. Furthermore, there was a significant reduction in inattention symptoms (SMD = -0.31; 95 % CI = [-0.46, -0.15]) and hyperactivity/impulsivity symptoms (SMD = -0.15; 95 % CI = [-0.29, -0.02]) within the digital intervention groups.

The digital interventions proved beneficial for individuals with ADHD by alleviating symptoms of ADHD, inattention, and hyperactivity/impulsivity.

People are able to voluntarily suppress unwanted thoughts or memories, a phenomenon known as suppression-induced forgetting or memory suppression. Despite harmful alcohol use, such as binge drinking, has been linked to impaired inhibitory control (IC) and augmented alcohol-cue reactivity, no study to date has assessed memory inhibition abilities towards alcohol-related cues in binge drinkers (BDs). Thus, the present preregistered study aimed to evaluate the behavioral and neurofunctional mechanisms associated with memory inhibition, specifically those related to the suppression of alcohol-related memories, in young BDs. For this purpose, electroencephalographic activity was recorded in eighty-two college students aged between 18 and 24 years old from the University of Minho (50% females; 40 non/low-drinkers [N/LDS] and 42 BDs) while they performed the Think/No-Think Alcohol task. Brain functional connectivity (FC) was calculated using the phase locking value and, subsequently, a dynamic seed-based analysis was conducted to explore the FC patterns between IC and memory networks. Comparatively to N/LDs, BDs exhibited decreased alpha-band FC between the anterior cingulate cortex and the left fusiform gyrus during attempts to suppress non-alcohol memories, accompanied by unsuccessful forgetting of those memories. Conversely, BDs displayed augmented gamma-band FC between the IC network and memory regions -i.e., hippocampus, parahippocampus and fusiform gyrus- during suppression of alcohol-related memories. Inhibitory abnormalities in BDs may lead to hypoconnectivity between IC and memory networks and deficient suppression of non-alcohol-related memories. However, while suppressing highly salient and reward-predicting stimuli, such as alcohol-related memories, BDs display a hyperconnectivity pattern between IC and memory networks, likely due to their augmented attention towards intrusive alcoholic memories and the attempts to compensate for potential underlying IC deficits. These findings hold important implications for alcohol research and treatment, as they open up new avenues for reducing alcohol use by shifting the focus to empowering suppression/control over alcohol-related memories. CLINICAL TRIAL REGISTRATION: [http://www.ClinicalTrials.gov], identifier [NCT05237414].

Many youth experience mental health problems and digital games hold potential as mental health interventions. This systematic review provides an overview of randomised controlled studies assessing the effectiveness of digital applied and casual games for improving mental health in youth aged 6-24 years. A systematic search of PsycINFO, Web of Science and Pubmed yielded 145 eligible studies. Studies on (sub)clinical participant samples (n = 75) most often focused on attention-deficit/hyperactivity disorder (ADHD), autism and anxiety. Applied games were found most effective for improving social skills, verbal memory and anxiety, whereas casual games were found most effective for improving depression, anxiety and ADHD. Studies involving healthy youth (n = 70) were grouped into papers examining anxiety in medical settings, momentary effects on positive and negative affect, and papers employing a longitudinal design measuring mental health trait outcomes. Promising results were found for the use of games as distraction tools in medical settings, and for applied and casual games for improving momentary affect. Overall, our findings demonstrate the potential of digital games for improving mental health. Implications and recommendations for future research are discussed, such as developing evaluation guidelines, clearly defining applied games, harmonising outcome measures, including positive outcomes, and examining nonspecific factors that may influence symptom improvement as well.

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.

Artificial Intelligence-based Medical Devices (AI-based MDs) are experiencing exponential growth in healthcare. This study aimed to investigate whether current studies assessing AI contain the information required for health technology assessment (HTA) by HTA bodies.

We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to extract articles published between 2016 and 2021 related to the assessment of AI-based MDs. Data extraction focused on study characteristics, technology, algorithms, comparators, and results. AI quality assessment and HTA scores were calculated to evaluate whether the items present in the included studies were concordant with the HTA requirements. We performed a linear regression for the HTA and AI scores with the explanatory variables of the impact factor, publication date, and medical specialty. We conducted a univariate analysis of the HTA score and a multivariate analysis of the AI score with an alpha risk of 5 %.

Of 5578 retrieved records, 56 were included. The mean AI quality assessment score was 67 %; 32 % of articles had an AI quality score ≥ 70 %, 50 % had a score between 50 % and 70 %, and 18 % had a score under 50 %. The highest quality scores were observed for the study design (82 %) and optimisation (69 %) categories, whereas the scores were lowest in the clinical practice category (23 %). The mean HTA score was 52 % for all seven domains. 100 % of the studies assessed clinical effectiveness, whereas only 9 % evaluated safety, and 20 % evaluated economic issues. There was a statistically significant relationship between the impact factor and the HTA and AI scores (both p = 0.046).

Clinical studies on AI-based MDs have limitations and often lack adapted, robust, and complete evidence. High-quality datasets are also required because the output data can only be trusted if the inputs are reliable. The existing assessment frameworks are not specifically designed to assess AI-based MDs. From the perspective of regulatory authorities, we suggest that these frameworks should be adapted to assess the interpretability, explainability, cybersecurity, and safety of ongoing updates. From the perspective of HTA agencies, we highlight that transparency, professional and patient acceptance, ethical issues, and organizational changes are required for the implementation of these devices. Economic assessments of AI should rely on a robust methodology (business impact or health economic models) to provide decision-makers with more reliable evidence.

Currently, AI studies are insufficient to cover HTA prerequisites. HTA processes also need to be adapted because they do not consider the important specificities of AI-based MDs. Specific HTA workflows and accurate assessment tools should be designed to standardise evaluations, generate reliable evidence, and create confidence.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that commonly occurs in childhood. The aim of this meta-analysis was to summarize the available evidence for the efficacy of digital therapeutics in children and adolescents with ADHD.

We searched the MEDLINE, EMBASE, Cochrane Library (Cochrane Database of Systematic Reviews), and Web of Science (science and social science citation index) databases for relevant studies and used Stata 15.0 software to carry out the meta-analysis.

A total of 31 studies involving 2169 participants (1665 boys and 504 girls) aged 4-17 years old were included in the final analysis. The meta-analysis results showed that digital interventions improved the symptoms of inattention with an effect value of -0.20 (95% confidence interval [CI] -0.36, -0.04) and decreased the continuous performance task (CPT) reaction time (effect, -0.40, 95% CI -0.73, -0.07) in ADHD patients. The score for impulsive hyperactivity was slightly decreased (effect, -0.07, 95% CI -0.23, 0.09). Moreover, executive function was improved (effect, 0.71, 95% CI 0.37, 1.04). The capability of working memory appeared to be increased (effect, 0.48, 95% CI 0.21, 0.76) between the two groups. Visual appraisal of the sensitivity analysis suggested the absence of heterogeneity, and no obvious publication bias was detected.

Based on the existing literature evidence, we conclude that digital therapy can be a promising therapeutic strategy for ADHD patients.

This meta-analysis investigated the effects of computerized cognitive training (CCT) on clinical, neuropsychological and academic outcomes in individuals with attention-deficit/hyperactivity disorder (ADHD). The authors searched PubMed, Ovid, and Web of Science until 19th January 2022 for parallel-arm randomized controlled trials (RCTs) using CCT in individuals with ADHD. Random-effects meta-analyses pooled standardized mean differences (SMD) between CCT and comparator arms. RCT quality was assessed with the Cochrane Risk of Bias 2.0 tool (PROSPERO: CRD42021229279). Thirty-six RCTs were meta-analysed, 17 of which evaluated working memory training (WMT). Analysis of outcomes measured immediately post-treatment and judged to be "probably blinded" (PBLIND; trial n = 14) showed no effect on ADHD total (SMD = 0.12, 95%CI[-0.01 to -0.25]) or hyperactivity/impulsivity symptoms (SMD = 0.12, 95%[-0.03 to-0.28]). These findings remained when analyses were restricted to trials (n: 5-13) with children/adolescents, low medication exposure, semi-active controls, or WMT or multiple process training. There was a small improvement in inattention symptoms (SMD = 0.17, 95%CI[0.02-0.31]), which remained when trials were restricted to semi-active controls (SMD = 0.20, 95%CI[0.04-0.37]), and doubled in size when assessed in the intervention delivery setting (n = 5, SMD = 0.40, 95%CI[0.09-0.71]), suggesting a setting-specific effect. CCT improved WM (verbal: n = 15, SMD = 0.38, 95%CI[0.24-0.53]; visual-spatial: n = 9, SMD = 0.49, 95%CI[0.31-0.67]), but not other neuropsychological (e.g., attention, inhibition) or academic outcomes (e.g., reading, arithmetic; analysed n: 5-15). Longer-term improvement (at ~6-months) in verbal WM, reading comprehension, and ratings of executive functions were observed but relevant trials were limited in number (n: 5-7). There was no evidence that multi-process training was superior to working memory training. In sum, CCT led to shorter-term improvements in WM, with some evidence that verbal WM effects persisted in the longer-term. Clinical effects were limited to small, setting specific, short-term effects on inattention symptoms.

In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.

The transition from adolescence to adulthood is a complex period in which multiple changes take place (education, work, independent living, and social relations). This stage is especially difficult for adolescents suffering from attention deficit hyperactivity disorder (ADHD), who have to move on from child and adolescent mental health services to adult mental health services. This review analyzes developmental and environmental risk and protective factors as well as critical variables such as executive functioning and self-monitoring that influence the course of ADHD in transitional age youth and guide the priorities for an optimal transition of care. The influence of the COVID-19 pandemic is also discussed. We reflect on the unmet needs for an optimal transition of care and propose practice and policy recommendations to achieve this goal.