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Article

Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study

by
Agnese Capodieci
1,*,†,
Valeria Olla
2,
Chiara Tonasso
3,
Marianna Campana
3,
Annalisa Morsiani
3,
Agnese Zambelli
3 and
Giulia Guidetti
3
1
Dipartimento di Psicologia (IUSVE), Istituto Universitario Salesiano di Venezia, 30174 Venezia, Italy
2
Studio di Psicologia, 20831 Seregno, Italy
3
Gruppo Clinico e di Ricerca ADHD (GCR-ADHD), 40137 Bologna, Italy
*
Author to whom correspondence should be addressed.
†
This author is the first and corresponding author.
Appl. Sci. 2025, 15(15), 8741; https://doi.org/10.3390/app15158741
Submission received: 21 June 2025 / Revised: 21 July 2025 / Accepted: 1 August 2025 / Published: 7 August 2025
(This article belongs to the Special Issue Assistive Technology for Rehabilitation)
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Abstract

ADHD is a childhood neurodevelopmental disorder, but it can persist into adolescence and adulthood and become detrimental to the individual’s well-being. It is known that many individuals with ADHD manifest executive functioning problems that affect their adaptive functioning. In the evaluation phase, it is, therefore, useful to consider these aspects as well. The diagnosis of ADHD is purely clinical in adults: it is based on anamnesis and the completion of questionnaires on the history of symptoms and current symptomatology. In recent years, the tele-assessment has become a valuable and accessible tool for diagnostic framing and intervention planning; however, there are currently few tele-assessment tools that enable the in-depth analysis of young adults. In this study, a group of 34 young adults with ADHD was compared with 35 typically developing peers using a tele-assessment tool for executive functioning (TeleFE, Anastasis). This research can be considered a pilot study to evaluate the differences in these tasks between the two populations and open the possibility of standardizing the tool for young adults. The use of this tool to assess executive functioning in individuals with ADHD in this age group would enable clinicians to plan more individualized interventions.

1. Introduction

Since the 1960s, the integration of information and communication technologies (ICT) into healthcare—referred to as e-health—has steadily grown in both clinical settings and educational contexts [1,2]. E-health plays a crucial role in delivering healthcare services remotely [3], helping to overcome barriers related to geographic distance or the specific needs of families and individuals [4,5]. The use of tele-health solutions has notably expanded for both therapeutic purposes (e.g., tele-rehabilitation and tele-intervention) and evaluations (e.g., tele-assessment, online assessments, and videoconferencing assessments) of cognitive functions.
Cognitive functioning encompasses the mental activities involved in acquiring, processing, storing, and retrieving information from the environment. These include a broad range of abilities, such as intelligence, language, learning, memory, attention, executive functioning, perception, visual–spatial skills, motor coordination, and academic abilities.
E-health tools are regarded as practical and dependable alternatives for cognitive assessments. Compared to traditional paper-and-pencil tests, these digital formats can offer more engaging, multi-sensory environments that boost motivation and cooperation, especially among children and young adults [6].
Additionally, as digital technologies have become an integral part of daily life, using them for cognitive evaluation adds ecological validity by reflecting real-world conditions. Lastly, these tools enable the collection of digital data on individuals with special needs, which can support clinical and diagnostic decision making [7].

2. Assessment in Adults with ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that tends to persist into adulthood, albeit in different forms and modalities [8].
In recent years, various tools have been developed to investigate the presence of ADHD in adults [9]. Specifically, self-report questionnaires are commonly used to assess ADHD in adulthood. At the same time, neuropsychological tests—especially those conducted online—are scarce, and few have been validated in the scientific literature for this clinical population.
Regarding self-report tools for the assessment of ADHD symptoms, they predominantly rely on the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV and DSM-5) [10,11]. Among these, the Adult ADHD Self-Report Scale (AARS) is an 18-item instrument based on the DSM-IV criteria, with a shorter six-item version designed for screening purposes [12,13]. Another commonly used tool is the ADHD Rating Scale-IV, which consists of 18 items based on the DSM-IV criteria [14]. The Current Symptom Scale (CSS), also based on DSM-IV, is composed of 18 items, of which 9 assess inattention and 9 assess hyperactivity/impulsivity [15].
The Conners Adult ADHD Rating Scale (CAARS) offers three versions: a full scale with 66 items, a brief form with 30 items, and a screening version with 28 items [16]. The ADHD Investigator Symptom Rating Scale (AISRS), based on a structured interview, comprises 18 items, with 9 measuring inattention and 9 assessing hyperactivity/impulsivity [17]. Another tool, the Barkley Adult ADHD Rating Scale-IV (BAARS-IV), provides a comprehensive evaluation of adult ADHD and is available in both self-report and observer-report formats [18]. The Adult ADHD Symptom Scale for DSM-5 (AASS-5) consists of 21 items and evaluates the presence of the ADHD criteria according to the DSM-5 guidelines [19]. Finally, the primary semi-structured interview used in the assessment of ADHD is the Diagnostic Interview for Adult ADHD (DIVA-5) based on the DSM-5 criteria [20,21,22]. This interview assesses the criteria related to inattention (criterion A1 of the DSM-5) and hyperactivity/impulsivity (criterion A2 of the DSM-5), the age of onset, and the impairment caused by ADHD symptoms.
The Screening Inventory (IR-ADHD) is a questionnaire containing 119 items, divided into two subscales: one for inattention and one for impulsivity [23]. Lastly, the Wender Utah Rating Scale (WURS) and the Self-Reported Wender–Reimherr Adult Attention Deficit Disorder Scale (SR-WRAADDS) follow the criteria outlined in the Utah model. The WURS includes 61 items [24], while the SR-WRAADDS comprises 30 items [25].
The Utah criteria for ADHD—also referred to as the Wender’s Utah Criteria—represent a diagnostic framework specifically used to identify ADHD in adulthood. These criteria underscore the importance of a childhood history of combined-type ADHD, with particular emphasis on the persistence of inattention, hyperactivity, and impulsivity from childhood into adulthood.
Some questionnaires are specifically used to evaluate the perception of executive functioning in adults with ADHD. Among these scales, we identify the Barkley Deficits in Executive Functioning Scale (BDEFS), an 89-item questionnaire [26]; the Barkley Functional Impairment Scale (BFIS), which measures psychosocial impairment in patients with ADHD across various domains of functioning [27]; the Adult Executive Functioning Inventory (ADEXI), a 14-item questionnaire consisting of two subscales that assess working memory and response inhibition, respectively [28]; the Behavior Rating Inventory of Executive Function (BRIEF-A), a 75-item questionnaire [29]; the Questionnaire for Experiences of Attention Deficits (FEDA), a questionnaire divided into three subscales: distractibility and cognitive slowing, fatigue and slowing during practical activities, and avolition; and the Mind Excessively Wandering Scale (MEWS), a 15-item questionnaire that evaluates the frequency of difficulties in thought processing in patients with ADHD [30,31].
There are other questionnaires validated in the literature that assess additional dimensions. Specifically, the Subjective Distress Associated with Adult ADHD (SDAA-SR) measures the level of distress perceived by adult patients with ADHD [31]. The scale consists of 33 items divided into three subscales: distress caused by inattention/disorganization, distress caused by hyperactivity/impulsivity, and distress caused by low self-esteem. The Sensory Gating Inventory (SGI) evaluates the patient’s ability to control their sensitivity to sensory stimuli and is composed of 36 items [32]. The Everyday Life Attention Scale (ELAS) is designed to assess the patient’s self-perception of attention in various daily life situations. The scale contains 30 items [33]. The ADHD Cognition Scale (ACS) measures the frequency of automatic thoughts in patients with ADHD. The scale consists of seven items, to which patients respond using a five-point Likert scale [34]. Questionnaires that measure the quality of life in patients with ADHD include the Adult ADHD Quality of Life (AAQoL), which comprises 29 items [35], and the Impairment Rating Scale (IRS), consisting of 12 items [36].
Turning to digital tools developed to remotely assess executive functions (EFs) in adult populations, the following can be cited [37,38,39].
The MOXO d-CPT is a computerized continuous performance test that incorporates visual and auditory distractors to simulate real-world conditions. It evaluates attention, impulsivity, hyperactivity, and timing in individuals aged 6–70 and has shown strong ecological validity in both clinical and tele-assessment contexts, particularly with ADHD populations [40,41].
CNS Vital Signs is a well-established computerized neurocognitive battery used to assess the executive function, processing speed, and attention. It is supported by large normative datasets and can be administered remotely [42]. Similarly, the Cambridge Brain Sciences (CBS) Battery offers gamified, online tasks measuring working memory, reasoning, inhibition, and planning, with applications in both healthy and clinical adult populations [43].
Other tools, such as Cogstate (New Haven, CT, USA) and Neurotrack (Redwood City, CA, USA), offer brief, scalable cognitive assessments, including EF-related tasks, and are used primarily for aging populations or the early detection of cognitive decline. Both platforms support remote use and are increasingly used in clinical trials and longitudinal monitoring [44,45].
Although these tools vary in their scope and target population, they reflect a broader shift towards a remote performance-based assessment of executive functioning in adulthood, offering new possibilities for clinical diagnostics, research, and intervention planning.
As can be seen, there are numerous self-report and informant-based questionnaires for assessing symptoms, executive functioning, and the impact of ADHD symptoms on daily functioning in the adult population [46,47,48,49,50,51]. However, the same cannot be said for digital tools that evaluate executive functions, which are standardized for an adult population and allow for an analysis of the executive functioning profile of young adults with ADHD.

3. Executive Functions in Adults with ADHD

Executive functions (EFs) refer to a collection of cognitive control processes that help manage thoughts and behaviors, particularly in situations that are complex or unfamiliar. According to fractionated models of EFs, e.g., [52,53], there are three core components: inhibition, which involves the ability to resist inappropriate actions or distractions; working memory updating, which entails actively manipulating information held in memory; and shifting (or cognitive flexibility), which is the capacity to adapt one’s thinking or actions in response to changing rules or goals [54].
Assessing executive functions poses several challenges [55,56]. While the models developed by Miyake and Diamond suggest that these EF components are distinct, they are also closely interconnected and can influence each other [52,57]. For example, tasks requiring cognitive flexibility also demand working memory updates and the inhibition of previous strategies. Moreover, researchers have pointed out inconsistencies in how EF components are defined across studies, as well as concerns about the validity of the tools used to assess them, e.g., [55,58,59]. Each EF component likely includes multiple sub-processes that differ in how well they align with the main construct being measured.
In this context, selecting suitable tools to evaluate the various components of executive functioning (EF) proves to be a complex task, which can influence the study outcomes.
This issue is particularly important due to the bidirectional relationship between EF impairments and clinical symptoms, e.g., [60]. On the one hand, deficits in the EF can lead to or worsen certain symptoms; on the other hand, the primary symptoms of a disorder may also negatively affect the EF performance [61]. Executive function impairments are widely recognized as a core feature of ADHD in both pediatric and adult populations. In a comprehensive review of the literature, Rincon, Morals, and Sandoval [9] conducted a documentary analysis of 33 peer-reviewed studies published within the past decade, focusing on executive functioning in adults with ADHD. The review examined a range of variables, including the publication year and country, diagnostic criteria, participant demographics, comorbid conditions, ADHD subtypes, medication status, assessment instruments, and specific executive function processes evaluated. When comparing individuals with ADHD to a control group without any medical or psychiatric conditions, significant differences were observed in several executive function (EF) areas, particularly in inhibitory control, working memory, planning, and sustained attention. These findings align with previous research by Barkley [62,63,64] and Senkowski et al. [65], who argue that individuals with ADHD have impaired inhibitory control. This impairment negatively impacts overall executive functioning, leading to the reduced regulation of motor and emotional responses to stimuli, difficulty suppressing impulsive actions, and challenges in planning, focusing, and problem-solving. However, no notable differences in the processing speed, verbal fluency, or reaction time were found between the two groups. The findings indicate a substantial prevalence of executive function deficits among adults with ADHD, as evidenced by their lower performance on standardized assessments compared to both neurotypical individuals and those with other psychiatric conditions. However, outcomes have varied across studies, reflecting the heterogeneous nature of ADHD and underscoring the influence of methodological differences and participant characteristics. The review also identified commonly employed diagnostic criteria and assessment tools for evaluating the executive function in this population. While the results substantiate the established association between ADHD, executive dysfunction, and challenges in major life domains, the authors note several methodological limitations. These include the inadequate control of confounding variables such as comorbidities, a lack of differentiation between ADHD subtypes, and the use of assessment tools with limited specificity, all of which may compromise the validity of the findings.

4. Tele-Assessment of Executive Functions

The Cognitive Tele-Assessment (CTA) approach has been extensively applied in adults across clinical, research, and screening contexts. In recent decades, the CTA has also emerged as a valuable method for assessing neurodevelopmental disorders in both healthcare and educational environments, leading to the development of new assessment tools. Compared to the traditional In-Person Assessment (IPA), the CTA offers several potential benefits, including improved accessibility, shorter waiting times, reduced travel and time-related costs, and enhanced infection control through limited physical interaction. The research findings indicate comparable performance outcomes between the CTA and IPA, along with high levels of participant cooperation [66].
Although an executive function (EF) evaluation is mandatory for individuals with ADHD [2], it is not routinely conducted in young adults with this diagnosis. This may largely stem from the lack of clearly defined assessment protocols and the need to streamline functional evaluations into fewer sessions. In this context, a tele-assessment using computerized tasks represents a promising approach for profiling EF components in ADHD.
Research comparing remote and in-person EF assessments has shown no significant differences between the two methods, indicating that a remote assessment is feasible during the developmental stages [67]. Additionally, a computerized tele-assessment offers several advantages: it allows for a precise, standardized, and replicable administration, which helps reduce scoring errors and shortens the administration time. This facilitates more straightforward comparisons across different tasks and conditions [68]. These benefits are particularly crucial for the EF assessment, since the EF encompasses multiple cross-modal components that must be evaluated under varying conditions, with the accurate measurement of both the response accuracy and reaction time, e.g., [68,69].
A recent line of research investigated the effects of environmental distractors on ADHD symptoms using the MOXO d-CPT, a continuous performance test (CPT) with visual and auditory distractors [37,70,71]. Continuous performance tests (CPTs) are considered a gold standard for ADHD evaluation and are currently the most widely used objective laboratory measures to support the clinical diagnosis of the disorder because they can detect the patient’s typical behavior in the context of daily life [72,73,74,75]. Indeed, Epstein et al. [76] showed that children diagnosed with ADHD exhibit more variable reaction times and produce more errors of commission and omission than undiagnosed children in sustained attention tasks. The MOXO d-CPT is a recent version of the CPT task that integrates visual and auditory distractors, making it even more ecologically valid than the classic CPT.
The results of studies using the MOXO d-CPT for children and adults showed that subjects with ADHD had greater distractibility than controls when performing the CPT, measured by errors of omission in the presence of distracting visual and auditory stimuli, as well as a combination of distracting stimuli employing both sensory channels [41,77]. In addition, the results showed that auditory and visual distractors had different effects on sustained attention and inhibitory control [38].
The MOXO d-CPT is based on a go/no-go task that requires the subject to maintain attention on a continuous stream of stimuli and respond to a predetermined target stimulus. In addition, contrary to other CPTs, the MOXO d-CPT includes a measurable and environmentally friendly distractor system. Distractors are visual stimuli in motion and auditory stimuli associated with them, which can appear separately or together and simulate events and sounds typical of everyday life. Two different versions of the MOXO d-CPT have been designed, with different stimuli and distractors, as well as different durations. The Teens and Adults version is designed for the 13–70-year age group and lasts 18 min. Neither version involves the use of letters or numbers between stimuli. This is important because people with ADHD frequently have learning disabilities (e.g., dyslexia and dyscalculia) that may interfere with their performance on CPTs employing numbers and/or letters [78]. The MOXO d-CPT assesses the individual’s attentional skills through four different indices: Attention, Timeliness, Impulsivity, and Hyperactivity. These indices assess different aspects of ADHD: an attention deficit, problems with time performance, impulsivity, and hyperactivity [52]. The measurement of these four indices enables the clinician to obtain a specific profile of attentional skills for everyone, allowing them to identify the subject’s strengths and weaknesses [79].
The description of this tool, the only example of a standardized tele-assessment test of attention and hyperactivity/impulsivity in adults, demonstrates the utility of proposing and standardizing additional tools for the in-depth study of executive functions in adults. Considering the impact of EF problems on the quality of life of people with ADHD, it is important to better understand executive dysfunctions in young adults with this disorder to establish neuropsychological profiles and enable the development of more effective interventions.
The present study aimed to provide an overall picture of the EF profile of young adults with ADHD through the analysis of EF components. In particular, the objectives were to analyze differences in the performance of tele-assessment EF tasks between adults with and without ADHD and consider these differences according to the type of conditions and stimuli. It was expected that young adults with ADHD would show a more compromised EF profile than young adults without ADHD.

5. Materials and Methods

5.1. Participants

This study involved 34 young adults with ADHD, diagnosed by the public or private health service of Italy with different modalities and questionnaires (mean age in years = 25.75 (3.96), 16 males and 18 females), and 35 young adults without ADHD (mean age in years = 24.72 (3.39), 18 males and 17 females). All young adults involved had intelligence within the normal range (with ADHD: IQ > 85 at the WISC-IV [80], with M = 109.82 and DS = 12.93 [54]; without ADHD: z score > −1 SD at the SPM [81]). The two groups were comparable in terms of age (t= −1.15, p > 0.05) and gender (χ2 = 0.13, p > 0.05). This study involved a sample of young adults (aged between 20 and 30 years) to ensure greater homogeneity in their cognitive development levels. This age group represents a stage in which cognitive abilities are fully developed, but not yet affected by age-related decline. Moreover, individuals in this age range are generally more proficient in the use of digital technologies, a relevant factor in the context of the present research, which is based on tele-assessment tools.
No adults with ADHD had a comorbidity with a Specific Learning Disorder (SLD) or autism spectrum disorder (ASD). Nineteen of them had received treatment: thirteen received psychological therapy and six received both psychological and medication therapy, not for ADHD symptoms, but for emotional problems. In both groups, there were young adults with some symptoms of internalized disorder, but none of them had a psychiatric diagnosis. The exclusion criteria were comorbidities with neurodevelopmental, behavioral, or psychiatric disorders and an IQ < 85.

5.2. Procedure

The evaluation was carried out on an individual basis by psychologists who had received specific training in using the TeleFE platform (Anastasis Cooperativa, Bologna, Italy, 2023) [82] for online executive function assessments. These psychologists, referred to as “remote operators”, conducted the sessions from their homes or clinics. The participants—young adults with and without ADHD—were instructed to situate themselves in a quiet room or classroom with a stable and strong Internet connection. Before each session, their Internet access (via Ethernet, Wi-Fi, or hotspot) was tested to ensure connectivity.
Each assessment session lasted approximately one hour, which included a brief familiarization period. This initial phase was crucial to engage participants, helping them become comfortable with the tasks while fostering their curiosity and participation. To begin the session, the remote operator sent a one-time access link to the participant, allowing them to log in to the TeleFE platform. Once connected, participants shared their screens so that the remote operator could monitor their activity. The operator then read the task instructions aloud, which the participants could also read on their screens.

5.3. Measures

To tele-assess EF components, four classical paradigms from the TeleFE platform were used (three experimental tasks and one ecological task). For all tasks, participants sat 30–50 cm from the PC screen. The screen size was usually 11 inches (range 9–13). The stimulus size was calibrated before the assessment began by matching a 1 EUR coin with its image on the screen.
Go/NoGo task. The Go/NoGo task is designed to assess the response inhibition (RI) or a person’s capacity to suppress impulsive actions in favor of those that align with the task rules [83,84]. During the task, the participant is presented with a geometric shape—either a yellow or blue circle or triangle—displayed at the center of the screen.
The task comprises four blocks, each containing 50 trials (35 Go trials and 15 NoGo trials). The conditions are structured as follows:
  • Block 1: Go stimuli are yellow; NoGo stimuli are blue.
  • Block 2: Go stimuli are blue; NoGo stimuli are yellow.
  • Block 3: Go stimuli are circles; NoGo stimuli are triangles.
  • Block 4: Go stimuli are triangles; NoGo stimuli are circles.
Each stimulus appears for 500 milliseconds. If the participant presses the spacebar within this window, the stimulus disappears immediately. Otherwise, it remains on the screen until the end of the 500 ms, followed by a neutral black screen (interstimulus interval: 500, 750, or 1000 ms) and then the next stimulus. The measures derived from the task are as follows:
  • Go CR (Correct Responses): the average number of accurate responses to the Go stimuli.
  • NoGo CR (Correct Rejections): the average number of correct inhibitions in response to the NoGo stimuli.
  • Go RT (Reaction Time): the average response time to the Go stimuli (calculated only if the accuracy on the Go trials exceeds 20%).
The interpretation of the metrics:
  • NoGo CR serves as a direct index of the inhibitory control accuracy.
  • Go RT reflects the processing speed of the inhibitory control.
Flanker task. This task assesses two key executive functions: interference control (CI), defined as the ability to suppress responses to irrelevant information, and cognitive flexibility (FC), or the capacity to adapt behavior according to two distinct rule sets depending on the stimulus features [85,86,87].
In the task, five horizontally aligned arrows appear on the screen. The participant is instructed to identify the direction of the target arrow while ignoring the others.
The task is divided into three blocks:
  • Block 1 and Block 2 each consist of 8 practice trials and 40 test trials.
  • Block 3 includes 64 test trials.
In Block 1, all arrows are blue; in Block 2, all are orange; and in Block 3, half are blue and half are orange. Each block includes 50% congruent trials (all arrows point in the same direction) and 50% incongruent trials (the central arrow points in the direction opposite to the flanking arrows).
  • In Block 1 (central target condition), the participant must press the ‘S’ key if the central arrow points left and ‘L’ if it points right.
  • In Block 2 (peripheral target condition), the response is based on the direction of the flanking arrows.
  • In Block 3 (mixed-rule condition), the response depends on the color of the arrows: blue arrows require a response based on the central target; orange arrows require a response based on the peripheral targets.
Each trial begins with a fixation cross displayed at the center of the screen for 600–1200 ms, followed by a blank screen lasting 600 ms. Then, the stimulus array is presented for up to 1500 ms. A response is considered valid if made between 200 ms after stimulus onset and before its disappearance. For the single-rule blocks (Blocks 1 and 2), there are general measures:
  • CI accuracy: the accuracy related to interference control, derived from the performance in incongruent trials in the first two (single-rule) blocks of the Flanker task.
  • CI RT: the speed related to the interference control, calculated based on reaction times in incongruent trials from the first two blocks of the Flanker task.
  • There are also specific measures:
  • CR congruent: the number of correct responses in congruent trials (central and peripheral targets).
  • CR incongruent: the number of correct responses in incongruent trials.
  • RT incongruent: The mean reaction time for correct responses in incongruent trials.
For the mixed-rule block (Block 3), the general indices are as follows:
  • FC accuracy: the accuracy related to cognitive flexibility, measured from incongruent trials in the mixed-rule (third) block of the Flanker task.
  • FC RT: the speed related to cognitive flexibility, based on reaction times in incongruent trials from the mixed-rule block of the Flanker task.
The specific measures are as follows:
  • CR mixed congruent: the number of correct responses in congruent trials.
  • RT mixed congruent: the mean reaction time for correct congruent responses.
  • CR mixed incongruent: the number of correct responses in incongruent trials.
  • RT mixed incongruent: the mean reaction time for correct incongruent responses.
The performance in incongruent trials is interpreted as a measure of interference control in the single-rule task and as a measure of cognitive flexibility in the mixed-rule task. The performance in congruent trials reflects basic processing abilities, such as multi-stimulus integration and task compliance.
N-back. The N-back task is a well-established measure of the updating component of working memory [88,89].
In this task, participants are presented with a continuous sequence of stimuli, each shown individually at the center of the screen. Their task is to press the spacebar whenever the current stimulus matches the one presented steps 1 or 2 earlier.
The task comprises six blocks:
  • Blocks 1 and 2 present colored stimuli (yellow, blue, green, and red circles).
  • Blocks 3 and 4 use geometric shapes (a triangle, circle, square, rhombus, and pentagon).
  • Blocks 5 and 6 feature letters (l, m, g, t, and b) shown in both uppercase and lowercase.
The participants in each block perform a 1-back (respond when the stimulus matches the one immediately preceding it) and 2-back task (respond when the stimulus matches the one shown two steps earlier).
Each block contains 52 trials: 16 targets (stimuli that require a response) and 36 non-targets. Stimuli are displayed for 1500 milliseconds, followed by a 1000 ms interstimulus interval (ISI). The participants may respond at any time from the stimulus onset to the end of the ISI.
The performance in span-1 and span-2 was interpreted as reflecting updating efficiency under low and high working memory loads, respectively. The general indices are as follows:
  • WM1 accuracy: the accuracy of working memory updating under a low cognitive load, calculated based on the performance in the 1-back blocks.
  • WM-1 RT: the speed of working memory updating under a low cognitive load, derived from reaction times in the 1-back blocks.
  • WM-2 accuracy: the accuracy of working memory updating under a high cognitive load, based on the performance in the 2-back blocks.
  • WM-2 RT: the speed of working memory updating under a high cognitive load, measured through reaction times in the 2-back blocks.
Other specific variables are the accuracy and RT for each block.
Planning Task (TPQ). This task, adapted from the work of Sgaramella, Bisiacchi, and Falchero [90], as well as Schweiger and Marzocchi [91], assesses a person’s planning ability, defined as the capacity to select and organize a sequence of actions to achieve a specific goal.
In the task, participants are asked to plan the order of a series of daily activities for a hypothetical day, making sure to include all of them while respecting logical and temporal constraints and aiming to follow the shortest route possible. A visual map displaying streets, houses, and buildings is shown on screen, along with a list of 11 activities. Each activity must be completed at a specific location and under given constraints (e.g., “Math homework must be done before 5 p.m.” or “You must buy a bus ticket before going to tennis class”). The task proceeds in stages:
  • The list of activities is read aloud.
  • The participant is asked to recall as many activities as possible.
  • The participant estimates how much time each activity may take.
  • The participant organizes the activities into a sequence.
  • They must also estimate the travel time between activities, using the provided map.
The main outcome measures of the TPQ are as follows:
  • Planning accuracy: a score reflecting the correctness of task planning.
  • Planning speed: a score based on the time taken to complete the planning phase.
Other outcome measures include the following:
  • Recall: accuracy in memorizing and recalling planned activities.
  • Time estimation: accuracy in estimating the duration required to complete the planned activities.
  • Map planning consistency: consistency between planning with and without the use of a map.
  • Map time consistency: consistency in time estimation between mapped and non-mapped planning.
  • Map temporal constraints: accuracy in adhering to time constraints when estimating durations for tasks and travel.
  • Map minimal path: a score evaluating the efficiency of planning in terms of minimizing travel or movement during map-based planning.
The order of administration of the TeleFE tasks was defined according to a Latin square procedure.

6. Statistical Analysis

Data were analyzed to provide a comprehensive and detailed EF profile of young adults with ADHD in comparison to young adults without ADHD. Descriptive and inferential statistics were computed by using Jamovi Software 2.2.3.0. The normality of the distribution (skewness cut-off = 2; kurtosis cut-off = 3) was analyzed for all measures. To investigate the presence of significant differences between the groups, ANOVAs on the different measures of each EF task were performed for normally distributed measures, while Kruskal–Wallis tests were used for non-normally distributed measures. The effect size was expressed by partial eta squared (ηp2) and xi (ξ) values.
The Kruskal–Wallis test was chosen as it allows for the comparison of two or more independent groups based on an ordinal or non-normally distributed interval/ratio variable. Although only two groups were included in this analysis (ADHD vs. controls), the Kruskal–Wallis test, as a generalization of the Mann–Whitney U test, will provide greater methodological consistency if the analysis is extended to more than two groups in future studies. Additionally, the Kruskal–Wallis test yields a chi-square statistic and an effect size index (ε2), facilitating the interpretation of the effect magnitude in between-group comparisons.

7. Results

The results are first presented concerning the general indices of executive functioning, which provide a descriptive profile of young adults with ADHD compared to their typically developing peers. This is followed by a more detailed analysis of group differences in specific indices.
The analysis of normality revealed that not all variables were normally distributed.
Response inhibition showed a significant group difference in accuracy (χ2 = 4.74, p = 0.030, ξ = 0.070), with young adults with ADHD performing significantly worse than their typically developing counterparts. No significant differences in reaction times were found (p > 0.05, see Table 1 and Figure 1).
Regarding interference control, no significant group differences were observed for either the accuracy or reaction time index (p > 0.05, see Table 2 and Figure 2).
For cognitive flexibility, the difference in accuracy between the two groups approached statistical significance (χ2 = 3.19, p = 0.074, ξ = 0.047), with young adults with ADHD showing a worse performance compared to their typically developing peers, whereas no significant differences in the reaction time were found (p > 0.05, see Table 3 and Figure 3).
In the case of working memory, a significant difference emerged between the two groups for the reaction time in the high-cognitive-load condition (χ2 = 4.66, p = 0.031, ξ = 0.070), with young adults with ADHD showing a slower performance compared to their typically developing peers. No significant differences were found for either the accuracy or reaction time in the low-cognitive-load condition (p > 0.05, see Table 4 and Figure 4).
Considering planning abilities, the differences in accuracy approached significance (χ2 = 3.03, p = 0.083, ξ = 0.044); in contrast, the difference in the recall time estimation accuracy between the two groups was highly significant (χ2 = 13.61, p < 0.001, ξ = 0.200), with young adults with ADHD showing a worse performance in the estimated duration required to complete the planned activities compared to their typically developing peers (see Table 5). In the map temporal constraints variable, a difference emerged between the groups (χ2 = 4.65, p = 0.031, ξ = 0.068), with individuals with ADHD showing difficulties in adhering to time constraints for tasks and travel. No other variables in the TPQ test reached significance (see Table 5 and Figure 5).
To assess the internal consistency of the executive function tasks included in the TeleFE battery, Cronbach’s alpha was calculated across 12 variables representing the accuracy and reaction time percentiles for key domains (i.e., inhibition, interference control, cognitive flexibility, working memory, and planning). The analysis yielded a Cronbach’s alpha of 0.65, indicating moderate internal consistency, which is acceptable given the exploratory and multidimensional nature of the battery.
To further examine the construct validity, a standardized correlation matrix was computed among the same variables. The results showed meaningful associations within individual EF domains: the RI accuracy and RI time: r = −0.2, p = 0.10, and the HLWM accuracy and HLWM time: r = −0.17, p = 0.16; these results highlight the internal convergence between the accuracy and speed within tasks. Additionally, moderate inter-domain correlations were observed, such as the RI time and IC time (r = 0.47, p < 0.001) and IC accuracy and CF accuracy (r = 0.21, p = 0.08), suggesting an overlap in the cognitive processing speed across inhibition and interference control and in accuracy across interference control and cognitive flexibility.
Conversely, weak or null associations were found between some planning-related measures and other EF domains (P time and CF accuracy: r = 0.04, p = 0.75), possibly reflecting differences in the task structure, measurement sensitivity, or individual variability.
Overall, the pattern of correlations supports the construct validity of the TeleFE battery, indicating that while tasks within the same domain are related, they also capture distinct cognitive components, aligning with a multidimensional model of executive functioning.
  • The following text describes specific measures emerging from individual tasks.
Go/NoGo task. Considering the specific result for this task, a significant difference emerged in Block 2, both in the response time for the wrong answer (χ2 = 10.25, p < 0.001, ξ = 0.155; see Table 6) and in commission errors (χ2 = 6.30, p = 0.012, ξ = 0.10; see Table 7); the young adults with ADHD showed a longer reaction time (273.58 ms vs. 90.82 ms) and more commission errors (1.03 vs. 0.26).
Flanker task. In this task, a significant difference in the correct responses (CR) emerged between the two groups in the congruent and incongruent trials of Block 2, specifically the number of correct responses in both congruent and incongruent trials in the peripheral target condition measuring interference control (respectively, χ2 = 8.48, p = 0.004, ξ = 0.129 and χ2 = 3.89, p = 0.049, ξ = 0.059; see Table 8). In both cases, the group of young adults with ADHD had a worse performance compared to their TD peers (respectively, 96.81 vs. 100 and 92.12 vs. 95.58). A significant difference also emerged in the CR of congruent trials of Block 3—the mixed-rule condition measuring cognitive flexibility (χ2 = 8.25, p = 0.004, ξ = 0.123, TD group = 96.76 vs. ADHD group = 94.50; see Table 8).
N-back. In the working memory task, the difference emerged in the ‘WM-2 RT’ variables of Block 6, corresponding to the speed in working memory updating under a high cognitive load with letters as the stimuli (χ2 = 6.01, p = 0.014, ξ = 0.091; see Table 9). In this case, the group of young adults with ADHD showed a slower reaction time compared to their TD peers (692.64 ms vs. 579.44 ms).

8. Study Limitations

This research is a pilot study with some peculiarities that will be outlined below. The estimated power with the available sample size, calculated through power analysis, was approximately 55%, which reflects the exploratory nature of the present investigation but raises some methodological limitations of the research that need to be mentioned.
First, the sample size, while adequate for pilot purposes, resulted in lower statistical power than the conventional threshold of 80%, increasing the risk of a Type II error, i.e., the possibility of failing to detect true effects due to insufficient statistical sensitivity. Consequently, the results should be interpreted with caution and considered preliminary evidence to inform future, larger-scale studies. Second, while the use of the Kruskal–Wallis test was appropriate given the non-normal distribution of the data, this non-parametric approach is known to be less powerful than its parametric counterparts, potentially contributing to an underestimation of between-group differences.
Another limitation concerns the cross-sectional design, which does not allow for causal inferences or the examination of EF trajectories over time. Additionally, the decision to include only young adults (between 20 and 30 years old), though methodologically sound for reducing developmental variability and age-related decline and difficulties with technology, limits the generalizability of the findings to other age groups. Finally, although standardized tools were used to assess the EF, we cannot completely rule out the influence of uncontrolled variables (e.g., comorbidities, the variability of treatment, medication use, and educational background) that may have affected participants’ cognitive performance.
In light of these limitations, we recommend interpreting the results with caution and suggest that future studies include larger samples and adopt longitudinal or multimodal approaches to provide a more comprehensive understanding of the cognitive differences associated with ADHD.

9. Discussion

This pilot study provides preliminary insights into the executive functioning profile of young adults with ADHD, highlighting specific weaknesses in response inhibition, high-load working memory, and time estimation. The use of a tele-assessment battery enabled the detection of statistically significant differences between the clinical and control groups, supporting the value of computerized testing not only for its accessibility, but also for its precision in identifying specific cognitive vulnerabilities. Furthermore, the pattern of correlations supports the construct validity of the TeleFE battery, indicating that while tasks within the same domain are related, they also capture distinct cognitive components, aligning with a multidimensional model of executive functioning.
The emerging patterns suggest that, while not all executive components yielded significant group differences, interactions among the task type, stimulus modality, and cognitive load may play a key role in the performance outcomes. These findings reinforce the importance of multicomponent and multimodal assessments capable of providing a more comprehensive and ecologically valid cognitive profile [90,91].

10. Conclusions

The aim of the present study was to investigate the EF profile of adults with ADHD to provide a global picture of their basic EF components.
For this purpose, a multicomponent EF model [32,33] and a tele-assessment battery, TeleFE (Anastasis Cooperativa, Bologna, Italy, 2023), were adopted, allowing the automatic scoring of the performance and an accurate comparison across the different measures of each EF component. Since the tasks in the TeleFE battery were chosen and developed to provide measures of the different EF components, with simple stimuli and little dependence on the information processing efficiency (verbal or visuospatial), it was hypothesized that TeleFE could be a useful tool for assessing the EF profile of adults with ADHD.
This research is configured as a pilot study; however, the results highlight significant aspects. The tele-assessment proves to be a useful tool for collecting data, which is registered in real time and, therefore, ensures greater objectivity. This ecological tool (TeleFE) creates a real situation that is challenging for individuals with ADHD, helping subjects verbalize perceived obstacles and enabling the recognition of their experiences. TeleFE has proven to be effective in capturing the typical characteristics of the disorder, and it is valid and standardizable for both the clinical assessment and treatment of adults. In addition, combined with self-report questionnaires, it confirms the patient’s experience and treatment needs. The returned results provide patients with a clear and detailed representation of their executive profile, vulnerabilities, and subjective resources, and clinicians can more precisely organize an intervention strategy suited to the individual characteristics of the subject. Moreover, the results indicate the possible utility of standardizing this tele-assessment method, which would make it a concrete and reliable support in daily clinical practice. Therefore, TeleFE is configured as a tool that can potentially be integrated into broader differential diagnosis and multimodal intervention pathways for monitoring symptoms pre- and post-treatment.
For future research, it would be useful to expand the sample size and include adults over the age of 30, approaching the age range of the MOXO, to replicate this study for more detailed analyses. A larger sample would highlight the differences between subjects undergoing pharmacological therapy for ADHD symptoms or psychotherapeutic treatment and those who are not, while also allowing for the design of pre- and post-intervention studies using TeleFE to verify its effectiveness as an assessment and monitoring tool. A further objective would be to contribute to the creation of guidelines for the assessment and treatment of ADHD, thus making the evaluation process homogeneous using a single protocol based on its effectiveness in improving executive functioning. Finally, future research may explore the possibility of integrating this tool with the assessment of sustained attention using the MOXO, also a computerized tool, which allows for the assessment of the timeliness as well as the accuracy of the participants’ responses.

11. Outlook

The present pilot study offers preliminary yet meaningful insights into the executive functioning profile of young adults with ADHD using a tele-assessment approach. While several group differences were detected—particularly in the response inhibition, high-load working memory, and time estimation—other components appeared less sensitive to group differences, possibly due to individual variability or task specificity [92,93,94].
These findings highlight both the promise and limitations of current tele-assessment methods. Future research should aim to expand the sample size, include a broader age range, and differentiate between treatment subgroups (e.g., medication, psychotherapy, and untreated), which would allow for more robust analyses of TeleFE’s sensitivity and ecological validity across subpopulations.
Moreover, integrating additional standardized tools—such as the MOXO d-CPT—could provide a complementary profile of attentional and timing processes, enriching the interpretive power of the assessment. Given the feasibility and diagnostic utility demonstrated, longitudinal studies could also explore how executive function profiles evolve over time and in response to intervention.
The diagnosis of ADHD was based on multiple tools. Future research should identify a primary diagnostic instrument to reduce heterogeneity and further validate the applicability of TeleFE in a sample with unified diagnostic criteria. In the long term, the standardization of TeleFE may contribute to the development of shared protocols for ADHD evaluation, helping clinicians implement more individualized, efficient, and data-informed interventions for adults with ADHD.

Author Contributions

Conceptualization, A.C. and V.O.; methodology, A.C.; validation, C.T., M.C., A.M., A.Z. and G.G.; formal analysis, A.C. and V.O.; investigation and resources, all authors; data curation, C.T., M.C., A.M., A.Z. and G.G.; writing—original draft preparation, A.C.; writing—review and editing, V.O., C.T., M.C., A.M., A.Z. and G.G.; visualization and supervision, A.C. and V.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of IUSVE of 2 February 2024.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

Data are available upon request to interested researchers.

Acknowledgments

The authors would like to express their sincere gratitude to Vania Espinosa, Alessia Volpato, Nicolò Zaggia, Marianna Borelli, Irene Novarini, and Elisabetta Manfredini for their valuable contribution in collecting data and all participants for their availability and commitment.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 08741 g001
Figure 1. Box plots comparing the group with ADHD and the TD group on Response Inhibition (RI) in accuracy and reaction time.
Figure 1. Box plots comparing the group with ADHD and the TD group on Response Inhibition (RI) in accuracy and reaction time.
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Applsci 15 08741 g002
Figure 2. Box plots comparing the group with ADHD and the TD group on Interference Control (IC) in accuracy and reaction time.
Figure 2. Box plots comparing the group with ADHD and the TD group on Interference Control (IC) in accuracy and reaction time.
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Applsci 15 08741 g003
Figure 3. Box plots comparing the group with ADHD and the TD group on Cognitive Flexibility (CF) in accuracy and reaction time.
Figure 3. Box plots comparing the group with ADHD and the TD group on Cognitive Flexibility (CF) in accuracy and reaction time.
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Applsci 15 08741 g004
Figure 4. Box plots comparing the group with ADHD and the TD group on Low (LLWM) and High cognitive Load Working Memory (HLWM) in accuracy and reaction time.
Figure 4. Box plots comparing the group with ADHD and the TD group on Low (LLWM) and High cognitive Load Working Memory (HLWM) in accuracy and reaction time.
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Figure 5. Box plots comparing the group with ADHD and the TD group on Planning (P), Recall (R) and Map (M) in accuracy and time.
Figure 5. Box plots comparing the group with ADHD and the TD group on Planning (P), Recall (R) and Map (M) in accuracy and time.
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Table 1. Non-parametric ANOVA with Kruskal–Wallis for response inhibition (RI).
Table 1. Non-parametric ANOVA with Kruskal–Wallis for response inhibition (RI).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
RI accuracy percentile73.7 (21.40)82.3 (22.89)4.73610.030 *0.06964
RI reaction time percentile74.5 (21.30)74.4 (26.65)0.12710.7220.00189
Note: RI = response inhibition; SD = Standard Deviation; * significant.
Table 2. Non-parametric ANOVA with Kruskal–Wallis for interference control (IC).
Table 2. Non-parametric ANOVA with Kruskal–Wallis for interference control (IC).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
IC accuracy percentile51.0 (23.99)60.0 (21.73)2.78010.0950.04088
IC reaction time percentile76.8 (30.62)81.3 (28.99)0.52010.4710.00765
Note: IC = interference control; SD = Standard Deviation.
Table 3. Non-parametric ANOVA with Kruskal–Wallis for cognitive flexibility (CF).
Table 3. Non-parametric ANOVA with Kruskal–Wallis for cognitive flexibility (CF).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
CF accuracy percentile64.7 (28.76)78.1 (23.77)3.1910.074 +0.0469
CF reaction time percentile59.6 (35.85)69.1 (29.04)1.4610.2270.0215
Note: CF = cognitive flexibility; SD = Standard Deviation; + approached significance.
Table 4. Non-parametric ANOVA with Kruskal–Wallis for low- (LLWM) and high-cognitive-load working memory (HLWM).
Table 4. Non-parametric ANOVA with Kruskal–Wallis for low- (LLWM) and high-cognitive-load working memory (HLWM).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
LLWM accuracy percentile73.1 (26.52)76.4 (22.64)0.28710.5920.00422
LLWM reaction time percentile71.8 (24.18)73.1 (28.70)1.36810.2420.02012
HLWM accuracy percentile80.3 (19.24)80.1 (26.75)0.97010.3250.01447
HLWM reaction time percentile52.3 (30.52)68.3 (25.75)4.66010.031 *0.06955
Note: LLWM = low-cognitive-load working memory; HLWM = high-cognitive-load working memory; SD = Standard Deviation; * significant.
Table 5. Non-parametric ANOVA with Kruskal–Wallis for TPQ variables: Planning (P), Recall (R), and Map (M).
Table 5. Non-parametric ANOVA with Kruskal–Wallis for TPQ variables: Planning (P), Recall (R), and Map (M).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
P accuracy percentile50.0 (1.00)48.3 (5.68)3.00310.083 +0.04416
P time percentile43.7 (26.95)47.3 (33.09)0.26910.6040.00395
R accuracy76.1 (21.89)75.7 (15.42)0.28810.5920.00423
R estimation time accuracy87.8 (10.83)96.1 (5.02)13.6101<0.001 *0.20015
M accuracy64.9 (16.79)73.2 (12.12)4.65110.031 *0.06839
M time constraints accuracy44.1 (18.69)40.1 (17.61)0.58510.4440.00861
Note: P = Planning; R = Recall; M = Map; SD = Standard Deviation; + approached significance; * significant.
Table 6. Mean reaction time (RT) for correct and wrong answers in the four blocks of the Go/NoGO task.
Table 6. Mean reaction time (RT) for correct and wrong answers in the four blocks of the Go/NoGO task.
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
Block 1 mean RT correct answer342.0606 (29.866)347.2353 (41.281)0.079710.7780.00121
Block 1 mean RT wrong answer209.1212 (186.121)153.5882 (142.344)1.891010.1690.02865
Block 2 mean RT correct answer351.8485 (68.258)363.2353 (35.863)0.0066510.9351.01 × 10−4
Block 2 mean RT wrong answer273.5758 (291.567)90.8235 (129.341)10.2457310.001 *0.155
Block 3 mean RT correct answer317.9394 (204.911)319.2647 (289.348)0.32110.5710.00486
Block 3 mean RT wrong answer22.4242 (85.457)67.7941 (158.605)0.94510.3310.01432
Block 4 mean RT correct answer259.2121 (218.735)283.5588 (145.812)1.62710.2020.02465
Block 4 mean RT wrong answer18.8788 (79.474)48.0882 (148.029)0.14510.7030.00220
* Significant.
Table 7. Commission errors in the four different blocks of the Go/NoGO task.
Table 7. Commission errors in the four different blocks of the Go/NoGO task.
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
Block 1 commission errors0.9091 (1.156)0.6176 (0.888)1.445710.2290.02191
Block 2 commission errors1.0303 (2.604)0.2647 (0.567)6.301210.012 *0.09547
Block 3 commission errors0.4848 (1.093)0.5000 (0.826)0.813210.3670.01232
Block 4 commission errors0.5758 (1.370)0.4118 (0.821)0.087510.7670.00133
* Significant.
Table 8. Mean reaction time (RT) of correct responses (CR) in the congruent and incongruent trials of the three blocks of the Flanker task (Block 1 central condition, Block 2 peripheral condition, and Block 3 mixed-rule condition).
Table 8. Mean reaction time (RT) of correct responses (CR) in the congruent and incongruent trials of the three blocks of the Flanker task (Block 1 central condition, Block 2 peripheral condition, and Block 3 mixed-rule condition).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
Fb1 mean RT CR congruent483.3030 (153.436)497.1765 (133.552)0.0308310.8614.67 × 10−4
Fb1 mean RT CR incongruous495.4848 (174.124)509.9412 (150.192)0.0039310.9505.96 × 10−5
Fb1 accuracy CR congruent96.8182 (17.402)100.0000 (0.000)2.09210.1480.03169
Fb1 accuracy CR incongruous92.1212 (24.013)95.5882 (17.090)0.14010.7090.00212
Fb2 mean RT CR congruent98.1818 (3.0150)99.7059 (1.715)1.57 × 10−410.9902.38 × 10−6
Fb2 mean RT CR incongruous96.8182 (4.812)98.6765 (3.328)0.18710.6650.00284
Fb2 accuracy CR congruent96.8182 (17.402)100.0000 (0.000)8.4810.004 *0.1285
Fb2 accuracy CR incongruous92.1212 (24.013)95.5882 (17.090)3.8910.049 *0.0590
Fb3 mean RT CR congruent872.8824 (203.359)851.2059 (155.161)0.063210.8019.44 × 10−4
Fb3 mean RT CR incongruous1006.0000 (1006.0000)982.7059 (171.574)0.353910.5520.00528
Fb3 accuracy CR congruent94.5000 (5.572)96.7647 (8.648)8.2510.004 *0.1232
Fb3 accuracy CR incongruous87.2059 (10.907)91.2647 (10.103)3.0610.0800.0456
* Significant.
Table 9. Accuracy and mean reaction time (RT) of correct answers and of commission errors in the 6 blocks of the N-back task (Blocks 1, 3, and 5—low cognitive working memory; Blocks 2, 4, and 6—high cognitive working memory).
Table 9. Accuracy and mean reaction time (RT) of correct answers and of commission errors in the 6 blocks of the N-back task (Blocks 1, 3, and 5—low cognitive working memory; Blocks 2, 4, and 6—high cognitive working memory).
MeasuresMean (SD)
ADHD Group		Mean (SD)
TD Group		χ2gdlpε2
Nb1 accuracy percentile99.5152 (1.176)99.6176 (1.206)1.38610.2390.02100
Nb1 mean RT correct answer451.7273 (65.628)458.0882 (110.665)0.42510.5140.00644
Nb1 mean RT commission error236.0606 (555.298)104.4412 (267.691)0.59210.4420.00896
Nb2 accuracy percentile89.8485 (8.228)90.9412 (8.410)0.57110.4500.00866
Nb2 mean RT correct answer637.0606 (169.752)571.0000 (132.517)2.99510.0840.04538
Nb2 mean RT commission error433.2727 (475.476)299.4412 (381.555)1.59310.2070.02413
Nb3 accuracy percentile97.6364 (2.434)98.1765 (2.052)0.77310.3790.01172
Nb3 mean RT correct answer527.3333 (79.606)513.7059 (96.726)1.54210.2140.02336
Nb3 mean RT commission error216.0000 (474.310)147.2647 (419.910)0.25710.6120.00389
Nb4 accuracy percentile87.4848 (6.906)88.9412 (8.312)0.88710.3460.0134
Nb4 mean RT correct answer685.0303 (168.650)618.5000 (127.204)3.17210.0750.0481
Nb4 mean RT commission error699.2424 (433.948)615.1176 (413.598)1.63510.2010.0248
Nb5 accuracy percentile96.9091 (4.537)98.0882 (2.789)1.41100.2360.0213
Nb5 mean RT correct answer577.3636 (85.127)558.6765 (115.367)1.1610.2810.0176
Nb5 mean RT commission error279.9697 (471.621)141.2059 (346.392)2.4610.1170.0373
Nb6 accuracy percentile91.8788 (7.236)92.2941 (7.375)0.033410.8555.06 × 10−4
Nb6 mean RT correct answer692.6364 (193.281)579.4412 (134.726)6.012210.014 *0.09109
Nb6 mean RT commission error529.9394 (496.458)618.3824 (626.537)0.183310.6690.00278
* Significant.
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Capodieci, A.; Olla, V.; Tonasso, C.; Campana, M.; Morsiani, A.; Zambelli, A.; Guidetti, G. Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study. Appl. Sci. 2025, 15, 8741. https://doi.org/10.3390/app15158741

AMA Style

Capodieci A, Olla V, Tonasso C, Campana M, Morsiani A, Zambelli A, Guidetti G. Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study. Applied Sciences. 2025; 15(15):8741. https://doi.org/10.3390/app15158741

Chicago/Turabian Style

Capodieci, Agnese, Valeria Olla, Chiara Tonasso, Marianna Campana, Annalisa Morsiani, Agnese Zambelli, and Giulia Guidetti. 2025. "Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study" Applied Sciences 15, no. 15: 8741. https://doi.org/10.3390/app15158741

APA Style

Capodieci, A., Olla, V., Tonasso, C., Campana, M., Morsiani, A., Zambelli, A., & Guidetti, G. (2025). Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study. Applied Sciences, 15(15), 8741. https://doi.org/10.3390/app15158741

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