Abstract
Background:
ADHD affects 8% of children and adolescents globally, marked by significant deficits in cognitive abilities, which leads to various emotional, behavioral, and adjustment issues. Traditional methods like medication and behavior therapy fall short in managing ADHD’s cognitive domains, urging the adoption of innovative approaches like cognitive training programs specifically adopting the emerging technology such as ReadON.ai. However, the precise influence of computerized cognitive retraining on ADHD remains understudied.
Aim:
To study the feasibility of computerized cognitive retraining (ReadON.ai) in enhancing cognitive abilities in children diagnosed with attention deficit hyperactivity disorder.
Materials and Methods:
The study employs a pre- and post-intervention design including six participants (7–11 years), diagnosed with ADHD according to DSM-5 criteria. Each participant underwent 30 hours of computerized cognitive retraining (ReadON.ai) over ten weeks, targeting attention and concentration, working memory, memory and learning, perceptual abilities, and reasoning skills. Assessments before and after intervention included tools like Conners’ 4TM Parent version and ReadON.ai CSA. Statistical analysis was conducted using IBM SPSS version 28.
Results:
Paired t-test results revealed a significant difference in pre-test and post-test means of attention and concentration (t = -6.873, P < 0.001), working memory (t = -5.771, P < 0.001), learning and memory (t = -12.491, P < 0.001), perception (t = 14.398, P < 0.004), reasoning (t = -3.464, P < 0.018), hyperactivity (t = 11.073, P < 0.001), impulsivity (t = 11.948, P < 0.001), emotional dysregulation (t = 8.242, P < 0.001), anxious thoughts (t = 2.67 P = 0.219), depressed mood (t = 2.924, P = 0.020), school work (t = 7.387, P = 0.001) and peer interaction (t = 4.632, P = 0.006) with medium to large effect size.
Conclusion:
Computerized cognitive retraining through ReadON.ai is feasible in enhancing cognitive abilities like attention and concentration, working memory, memory and learning, perception, and reasoning among children with ADHD.
Keywords: ADHD, attention, computerized cognitive retraining, working memory
Cognitive abilities are crucial for any individual to think, deal, and act effectively in their environment, especially for children where development is dependent on their ability to acquire new knowledge, retain information, interpret cues, understand cause-effect relationships, solve problems, and make decisions. These skills are known to be compromised in neurodevelopmental disorders like attention-deficit/hyperactivity disorder (ADHD).[1,2,3,4] It has direct impact on the child’s functioning across various domains of life, hampering academic progress and social integration. Quintessentially, core deficits of ADHD are managed through pharmacological and psychotherapeutic interventions. However, cognitive abilities require special targeted and individualized intervention based on task repetition or training in which computerized methods have been more efficient.[5] Cognitive retraining has been employed to enhance cognitive functioning in several disabling conditions stretching from organic disorders like Alzheimer’s,[6] mild cognitive impairment[7], and multiple sclerosis,[8] to developmental disorders like dyslexia,[9] ADHD, and autism spectrum disorder[10] and in the stabilization process of traumatic experiences.[11] With the advent of technology and artificial intelligence, some researchers have attempted to integrate it with cognitive training and developed some intervention packages of computerized cognitive retraining. One such computerized cognitive retraining model is ReadON.ai.
Computerized Cognitive Retraining Model: ReadON.ai is an artificial intelligence-based cognitive retraining model designed to assist young students with difficulty in reading.[12] The software leverages the power of engagement with stimulating gameplays, boosts phonemic awareness by separating words into individual sounds and gradually introducing blends, uses a targeted multisensory approach to create connections between auditory and visual pathways required for fluent reading, process-oriented gameplays specifically designed to strengthen coding and decoding skills, strengthens eye tracking and movement, enhances executive functioning through multiple simultaneous demands, performance feedback, and problem-solving. It targets auditory processing, visual processing, ocular motor control, visual sequential memory, executive functions, working memory, visual closure, phonological awareness, decoding and encoding, morphology, timing, and multisensory activities. In view of the paucity of studies in this area, the present study was undertaken to test the feasibility of computerized cognitive retraining (ReadON.ai) in enhancing cognitive abilities like attention and concentration, working memory, memory and learning, perception, and reasoning among children with ADHD.
MATERIAL AND METHODS
Participants
Six school-aged children (ages 7–11 years, mean age = 8 years) diagnosed with ADHD according to DSM-5 criteria by a licensed mental health professional were recruited from a public school in Delhi. Participants were selected for the study based on the inclusion criteria that required participants to have been enrolled in and attending formal education for at least one year prior to the study, have no current or past diagnosis of other neurodevelopmental or psychiatric disorders that could interfere with the intervention process, have not previously undergone cognitive retraining, and have not experienced any recent changes in their school environment.
The project was undertaken after obtaining permission from the institutional ethical committee. Informed consent was obtained from their guardians and ascent from children. They were informed about the nature of the study, including the risks and benefits involved. They were assured of confidentiality and a non-judgmental space to express themselves. Participation was voluntary, and they had the freedom to withdraw from the study at any time.
Procedure
The study began by obtaining permission from the school authorities to conduct therapy sessions with children. The purpose and details of the therapy were thoroughly explained to the authorities. With the help of a special educator and school counselor, children diagnosed with ADHD were identified. Initially, 35 children with ADHD were interviewed. Out of these, only six students expressed willingness to participate in the therapy sessions. Details of the disorder, detailed etiology, tertiary prevention, and treatment plan were discussed with the parents/guardians. Data regarding information about the selected participants, such as gender, age, education, religion, and family type, was recorded. Conners’ 4sTM (Parent version) assessment tool, assessing various domains such as inattention, hyperactivity, impulsivity, emotional dysregulation, anxious thoughts, depressed mood, schoolwork, and peer interaction was used to confirm the diagnosis.
The ReadON.ai academic cognitive skill assessment (CSA-ACAD) was then used to evaluate the participants’ baseline attention and concentration, working memory, memory and learning, perception, and reasoning ability scores. Each individual session lasted 25–30 minutes, and an individual report was generated for each participant. Subsequently, each participant underwent thirty sessions of one-hour duration computerized cognitive retraining, with 3–4 sessions held every week using the ReadON.ai Software. These 30 sessions were completed over ten weeks, scheduled on an individual rotation basis every week from 7 am to 5 pm. The session was conducted in the computer laboratory of the school. Each session consisted of these eight tests which increased in complexity overtime. Initially, the children took multiple pauses, but the frequency of the pauses was reduced in later sessions. Details of CSA-ACAD and ReadON.ai cognitive retraining modules have been discussed in Figures 1 and 2, respectively.
Figure 1.
Details of Academic Cognitive Skill Assessment (CSA- ACAD) of ReadON.ai
Figure 2.
Details of intervention module ReadON.ai
After the intervention, Conners’ 4™ (Parent version) and ReadON.ai’s CSA were conducted again to collect post-intervention scores. Data collected from the assessments were analyzed using IBM SPSS Statistics version 28.
RESULTS
The socio-demographic profile of the participants was analyzed using descriptive statistics. The pre- and post-interventions were then analyzed using paired t-test to examine changes in the outcomes over time to see the effect of the computerized cognitive retraining (ReadON.ai). Effect sizes (Cohen’s d) were calculated to determine the magnitude of intervention effects. The findings have been summarized in Tables 1-3 and Figure 3.
Table 1:
Socio-demographic characteristics of the sample (6)
| Variable | n (%) | M±SD |
|---|---|---|
| Age (years) | 8.166±1.169 | |
| 7 | 2 (33.3) | |
| 8 | 2 (33.3) | |
| 9 | 1 (16.7) | |
| 10 | 1 (16.7) | |
| Gender | ||
| Male | 6 (100) | |
| Female | 0 (0) | |
| Education | ||
| Third grade | 2 (33.3) | |
| Fourth grade | 1 (16.7) | |
| Fifth grade | 2 (33.3) | |
| Sixth grade | 1 (16.7) | |
| Family | ||
| Joint | 2 (33.33) | |
| Nuclear | 3 (50) | |
| Extended | 1 (16.7) |
The total sample was 6. n=Number of participants; % = Percentage of participants; M=Mean; SD=Standard deviation
Table 3:
Comparison of pre- and post-treatment means in behavioral concerns among children diagnosed with ADHD as measured by Conners’ 4™
| Cognitive Abilities | Pre-treatment |
Post-treatment |
t (6) | P | d | ||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | ||||
| Hyperactivity | 84.166 | 3.920 | 52.500 | 4.505 | 11.073 | 0.001 | 4.521 |
| Impulsivity | 83.166 | 4.708 | 52.666 | 3.444 | 11.948 | 0.001 | 4.878 |
| Emotional dysregulation | 69.333 | 11.893 | 45.666 | 7.763 | 8.242 | 0.011 | 3.365 |
| Anxious thoughts | 52.166 | 7.678 | 43.000 | 0.000 | 2.167 | 0.219 | 1.194 |
| Depressed mood | 49.833 | 7.494 | 43.833 | 2.041 | 2.924 | 0.020 | 0.885 |
| Schoolwork | 84.666 | 7.229 | 59.166 | 2.483 | 7.387 | 0.001 | 3.016 |
| Peer interaction | 63.833 | 5.492 | 49.833 | 9.108 | 4.632 | 0.006 | 1.891 |
Figure 3.
Comparison of pre- and post-treatment group averages
As observed, the mean age of participants was about 8 years, and all were male participants. Nearly 33.3.7% were in third grade, 16.7% in fourth grade, 33.3% in fifth grade, and 16.7% in sixth grade. Total of 50% belonged to nuclear family setup, 33.33% represented joint family, and 16.7% were from extended families.
The mean and standard deviation of study variables, including attention and concentration, working memory, memory and learning, perception, and reasoning are listed in Table 2. The findings of the study following the ten-week treatment period suggest that the intervention through ReadON.ai has led to statistically significant difference in cognitive abilities.
Table 2:
Comparison of pre- and post-treatment means in cognitive domains of children diagnosed with ADHD
| Cognitive Abilities | Pre-treatment |
Post-treatment |
t (6) | P | d | ||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | ||||
| Attention and concentration | 183.500 | 125.555 | 457.166 | 124.385 | -6.873 | 0.001 | 2.806 |
| Working memory | 9.500 | 2.810 | 475.830 | 196.329 | -5.771 | 0.001 | 2.356 |
| Learning and memory | 60.166 | 16.400 | 553.333 | 88.350 | -12.491 | 0.000 | 5.100 |
| Perception | 204.333 | 53.406 | 440.833 | 143.023 | 14.398 | 0.004 | 1.880 |
| Reasoning | 266.166 | 104.845 | 472.166 | 161.609 | -3.464 | 0.018 | 1.414 |
Note. N=6. M=Mean, SD=Standard deviation, t=Paired sample t-test, d=Cohen’s d effect size, P<0.05
Table 3 summarizes scores as measured by Conners’ 4TM. Among clinical features of ADHD assessed by Conner’s 4TM.
DISCUSSION
The primary aim of this study was to evaluate the feasibility and efficacy of using ReadON.ai, a computerized cognitive retraining program to enhance cognitive abilities and manage clinical symptoms among children with ADHD. The rationale behind this study is grounded in the increasing need for effective, accessible, and individualized interventions that address the unique cognitive and behavioral challenges faced by children with ADHD. Prior research has demonstrated that deficits in working memory, attention, and reasoning significantly impact academic performance, problem-solving, and overall learning in children with ADHD.[13,14] Thus, there is a pressing need for interventions that can mitigate these deficits and improve overall functioning. The current study contributes significantly to the existing body of knowledge by providing promising evidence that computerized cognitive retraining can be an effective tool in managing ADHD. The individual nature of ReadON.ai allows for personalized interventions, catering to the unique needs of each child. Chakraborty and Halder also reported in favor of tailored cognitive remediation for school-going children with ADHD.[7] Studies conducted so far have emphasized the importance of individualized interventions that cater to the unique needs and profiles of children with ADHD.[4]
The findings indicate that computerized cognitive retraining through ReadON.ai leads to significant enhancement in various cognitive domains, including attention, working memory, learning and memory, perception, and reasoning abilities. Enhanced attention and concentration directly impact retention, storage and recall abilities, facilitating a more organized and systematic approach to tasks.[13] A meta-analysis investigating the effects of computerized cognitive training on clinical, neuropsychological, and academic outcomes in individuals with ADHD found that this modality can lead to short-term improvements in working memory with small clinical effects.[14] Similarly, a study of sixty children diagnosed with ADHD and reported improvements in academic performance and working memory due to structured virtual games among children with ADHD.[15]
The relevance of these findings lies in the potential application of computerized cognitive retraining as a feasible intervention for children with ADHD. The study supports previous research, indicating that such interventions can lead to short-term improvements in working memory, attention, and inhibitory control, although with small clinical effects.[16] A study reported that reasoning ability is directly related to problem-solving, logical thinking, and the ability to make informed decisions, skills often compromised in individuals with ADHD.[17] Enhancing this ability facilitates larger cognitive improvements and adaptive functioning.
The recent research indicates that computerized cognitive retraining produces notable differences between pre- and post-treatment groups regarding ADHD-related behaviors such as hyperactivity, impulsivity, emotional regulation, mood, academic performance, and social interactions. Enhancing cognitive abilities appears promising in addressing the fundamental behavioral and emotional aspects of ADHD. While earlier studies have demonstrated improvements in depressive symptoms through such interventions, their effectiveness in managing emotional challenges in children with ADHD warrants further investigation.[18] Markedly hyperactivity decreased significantly among participants in this study, alighting with findings a previous study suggested that virtual reality-based interventions can help rehabilitate children with ADHD by integrating behavioral and physical components.[19] Utilizing computerized cognitive retraining to address these facets may lead to substantial decreases in disruptive behavior or thought patterns as noted.[20,21,22]
One of the notable advantages of computerized cognitive retraining is its feasibility and low dropout rate. The structured and engaging nature of virtual games and tasks keeps children motivated and actively involved in the intervention, leading to better adherence and outcomes.[23] The proactive design of ReadON.ai ensures that children are continually challenged and supported, minimizing the likelihood of dropout and enhancing the overall effectiveness of the program.
Despite the positive outcomes, it is essential to acknowledge potential drawbacks and limitations. Additionally, while significant improvements were found in several domains, the intervention did not produce significant improvement changes in anxious thoughts, indicating a need for complementary approaches to address anxiety-related issue.[19]
Overall, this study provides compelling evidence supporting the feasibility of using ReadON.ai for cognitive enhancement and symptoms’ management in children with ADHD. The individualized and structured nature of intervention shows promise in improving cognitive abilities and reducing behavioral symptoms, thus contributing to better academic performance and overall quality of life.
Limitation and future directions
While this study was able to achieve its primary objective to examine the feasibility of ReadON.ai, it is limited by its small sample size and lack of a control group. This may have directly impacted the large effect size in the study. The tasks were repetitive and time-consuming in actual practice. The phonetics utilized in the intervention mandated proficiency in English language. Moreover, this tool is not readily accessible currently due to lack of training, work overload labelling, and limited sensitivity towards clinically relevant psychological states. Additionally, parent and teacher involvement could have been enhanced for smoother administration of the treatment. Additionally, extended research to other age groups, such as young adults and middle and late adolescents with ADHD can provide insight into its broader applicability and long-term benefits. Follow-up study which is in the process will help in further commenting on the longevity of the benefits. Future research should continue to explore the long-term effects and potential integration of such interventions with other therapeutic approaches to comprehensively support children with ADHD.
CONCLUSION
The present study highlights the potential of AI-generated computerized cognitive retraining interventions like ReadON.ai in addressing cognitive deficits in children with ADHD. It offers a promising approach to supplement traditional interventions. The findings have implications for educational curricula and future research exploring the efficiency and feasibility of computerized cognitive retraining for children with ADHD to explore its integration into school settings to further assess its impact on academic and behavioral outcomes.
Data availability
Data will be made available on reasonable request
Author's contribution
All authors have contributed equally to the study design, data collection, preparing the entire manuscript.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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Associated Data
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Data Availability Statement
Data will be made available on reasonable request
