Abstract
The Cognitive–Functional Intervention for Adults (Cog–Fun–A) may offer an effective metacognitive occupation-based nonpharmacological intervention for adults with ADHD.
Attention deficit hyperactivity disorder (ADHD) is a common neuropsychiatric disorder characterized by inattention, impulsivity, and hyperactivity (American Psychiatric Association, 2013). Its prevalence among adults ranges from 1.4% to 3.6% (Kooij et al., 2019). Symptoms arise from executive dysfunction (e.g., inhibition and working memory; Barkley, 2015) that directly hinder goal-directed behavior (Munro et al., 2018) and impair the intentional self-regulation of cognition, emotion, and behavior (Silverstein et al., 2020). Therefore, executive function (EF) has been identified as a target for intervention among this population (Faraone et al., 2021; Kooij et al., 2019).
Adults with ADHD face challenges in their daily functioning that affect their work, relationships, and health management, leading to a lower quality of life (QoL) compared with that of their peers without ADHD (Brod et al., 2015; Faraone et al., 2021). ADHD is deemed a chronic health condition that requires intentional, lifelong management (Faraone et al., 2021). Effective management of ADHD necessitates an intervention framework that emphasizes treatment adherence, continuity of care throughout life, and the integration of pharmacological and nonpharmacological interventions (Faraone et al., 2021). Although pharmacological treatments are commonly used as firstline interventions for ADHD, up to half of patients discontinue treatment within the first 3 yr because of side effects and ineffectiveness (Kooij et al., 2019). Few nonpharmacological interventions have been studied, focusing on cognitive–behavioral therapy, which emphasizes cognitive restructuring and modifying maladaptive behavioral strategies, and dialectical behavior therapy, which incorporates similar techniques alongside mindfulness and principles of acceptance and self-compassion. These interventions have shown some evidence of improving ADHD symptoms and psychological outcomes (Fullen et al., 2020; Nimmo-Smith et al., 2020); however they do not specifically target the functional challenges and EF deficits that significantly affect daily life activities for people with ADHD. Additionally, some occupational therapy interventions have been explored. These target cognitive regulatory skills, implement environmental adaptations, or combine these approaches with routine construction (Gutman et al., 2020; Lindstedt & Umb-Carlsson, 2013; Stern et al., 2016). However, evidence on their effectiveness remains limited, which highlights the need for a comprehensive intervention framework that addresses cognitive and symptom management, environmental factors, psychosocial aspects, and daily functioning within life roles.
The Cognitive–Functional Intervention for Adults (Cog–Fun A; Maeir et al., 2018) is a metacognitive, functional occupation–based intervention designed for adults with ADHD. The primary goal of the Cog–Fun A is to addresses the chronic effects of ADHD by facilitating the self-management of ADHD-related challenges in daily life. To operationalize these goal, the Cog–Fun A follows a structured process. Adults with ADHD ▪ develop self-awareness regarding the chronic nature of ADHD, the profile of adults with EF deficit, and their implications for daily participation; ▪ acquire coping strategies within meaningful occupational contexts through guided-discovery processes; ▪ cultivate an adaptive occupational identity by integrating self-compassion and acceptance of personal strengths and limitations in daily life; and ▪ prepare for long-term self-management by establishing sustainable strategies beyond the intervention setting (Maeir et al., 2018).
A pilot study found improvements from pre- to postintervention in EF, awareness, occupational performance, and QoL after the Cog–Fun A intervention, with QoL changes sustained at a 3-mo follow-up. However, this study was conducted in a controlled research setting, had a small sample of 14 adults, and did not thoroughly examine the clinical benefit at the individual level (Kastner et al., 2022). This calls for further research to assess the Cog–Fun A intervention using a larger sample in a real-world context.
This study is rooted in the concept of practice- based evidence, which emphasizes the importance of researching the effectiveness of interventions within real-world contexts as they are implemented in community settings (Barkham & Mellor-Clark, 2003; Macey et al., 2015). Controlled trials may not capture the dynamic nature of routine clinical practice and individual differences, because they use controlled environments and selected samples that differ from diverse real-world patient profiles. Thus, interventions that appear successful in controlled trials at the group level may not benefit all participants, and some participants may even experience negative effects (Jackson & Jordan, 2022).
Different approaches to measuring changes in outcomes may lead to varying conclusions about an intervention’s effectiveness. Traditional methods, such as examining the mean difference between pre- to postintervention measurements at the group level, do not necessarily imply clinical significance at the individual level. As a result, there is a growing call to incorporate additional methods for examining intervention effectiveness (Blampied, 2022; Wolpert et al., 2015). Methods to measure clinically meaningful change at the individual level include crossing a clinical threshold and measuring reliable change that surpasses measurement error (Wolpert et al., 2015).
Furthermore, in line with the concept of practice-based evidence, it is important to understand what constitutes the most suitable care for each patient. As far as we know, no published studies specifically investigated predictors of intervention effectiveness and noncompletion of interventions among adults with ADHD. Therefore, in this study, we used a case-series design including several methods at both the group and individual levels to evaluate the effectiveness of the Cog–Fun A on QoL and EF in adults with ADHD and to identify demographic and clinical predictors of QoL improvement and intervention adherence.
Method
Research Design
In this study, we used a retrospective case-series design with therapist-reported outcome measures from medical records. This design was chosen because it allows for the examination of real-world clinical data, reflecting the complexities of practice-based evidence. This design enables the identification of patterns and potential treatment effects in a naturalistic setting, which may not be feasible in a prospective study or controlled trial because of logistical or feasibility constraints. Additionally, this design minimizes ethical concerns related to withholding treatment and reduces participant burden (Talari & Goyal, 2020).
Intervention
The intervention consisted of 24 one-hour weekly sessions structured into four units. In the first unit (approximately seven sessions), participants engaged in a shared preintervention assessment of EF and QoL to develop awareness of their ADHD and EF profile and its impact on daily functioning. This was achieved through a joint review of assessment outcomes and psychoeducation, allowing participants to recognize functional challenges and set preliminary occupational goals. The second unit (approximately seven sessions) focused on acquiring strategies to promote occupational goals. Strategies were identified through structured retrospective activity monitoring, followed by strategy efficacy monitoring. This process included identifying barriers and modifying or replacing strategies as needed to optimize effectiveness. The third unit (approximately seven sessions) focused on fostering an adaptive occupational identity. Participants continuously monitored goal attainment, which enabled them to recognize the balance between occupational demands and the limitations of effortful strategies. Additionally, sessions addressed self-compassion and self-advocacy practices, as well as the use of external resources. The fourth unit (approximately three sessions) focused on preparing participants for long-term self-management of ADHD symptoms. We conducted a reassessment process to evaluate progress and consolidate optimal strategies. Participants developed a maintenance plan that included identifying ongoing occupational concerns and exploring medical and therapy options for continued support. Further details regarding intervention practices can be found in Kastner et al.’s (2022) work.
The Cog–Fun A intervention was delivered in a private practice setting by licensed occupational therapists undergoing training to become certified Cog–Fun A practitioners. The training included 49 hr of education, followed by an internship period. During the internship, therapists implemented the Cog–Fun A intervention protocol with two clients; they followed a standardized manual to ensure consistency in its application. They received ongoing supervision, meeting with their instructor once every two sessions per client, during which the instructor monitored adherence (fidelity) to the protocol. Sessions were audiotaped to support the supervision process.
Participants
Participants included adults ages 18–65 yr with a self-reported ADHD diagnosis by a licensed medical professional who attended at least 50% of the Cog–Fun A sessions with an occupational therapist in training and who gave signed consent to use their anonymous data for research purposes. Participants with additional health conditions were not excluded, to reflect the heterogeneous adult population with ADHD wherein comorbidities are highly common (Kooij et al., 2019) in real-world practice.
Measures
The demographic questionnaire for the occupational therapist included two questions related to gender and years of experience, and the demographic questionnaire for the client included questions about gender; age; marital and work status; and medical information, such as medication treatment and frequency.
The Adult ADHD Quality-of-Life Scale (AAQoL; Brod et al., 2005) assesses QoL in adults with ADHD. Comprising 29 items rated on a 5-point scale, the AAQoL assesses the effect of ADHD symptoms on function and generates both total and domain scores (life productivity, life outlook, relationships, and psychological health). Raw scores are converted on a scale ranging from 0 to 100, with higher scores indicating better QoL. The AAQoL presents with excellent internal consistency (α = .93) and test–retest reliability (intraclass correlation = .86). Although an 8-point (0.5-SD) change is considered clinically significant (Tanaka et al., 2019), this study used a stringent 16-point (1-SD) criterion because of the retrospective design.
The Behavior Rating Inventory of Executive Function–Adult Version (BRIEF–A; Roth et al., 2005) is a self-report questionnaire that comprises 75 items designed to assess EF. It generates an overall summary score, the Global Executive Composite score, along with two index scores—the Behavioral Regulation Index (BRI) and the Metacognition Index (MI)—each consisting of several scales. Raw scores were converted into T scores according to age, with higher scores indicating greater executive dysfunction; a T score of 65 of higher indicates a clinical impairment. The BRIEF–A demonstrates moderate to high internal consistency (αs = .73–.98) and high test–retest reliability (rs = .82–.94). Although a 5-point (0.5-SD) change is considered clinically significant, this study used a stringent 10-point (1-SD) criterion because of the retrospective design.
Procedure
On receiving approval from the Hebrew University Institutional Review Board (No. 29022024), we advertised the study on the Cog–Fun A professional social media networks, with permission from group administrators. Interested therapists provided digitally signed informed consent to participate and then anonymously provided demographic information about themselves and their clients and the clients’ AAQoL and BRIEF–A baseline and postintervention scores.
Statistical Analyses
Statistical analysis was conducted using IBM SPSS Statistics (Version 28), with statistical significance set at p < .05. Descriptive statistics were calculated for the demographic characteristics. Because all study variables followed a normal distribution (based on skewness and kurtosis), changes in QoL (measured with the AAQoL), behavioral regulation, and metacognition (EF; measured with the BRIEF–A) from pre- to postintervention were assessed using one-way repeated-measures multivariate analysis of variance for within-participant differences. Additionally, the percentage of participants who demonstrated a clinically significant change of more than 1 SD (Norman et al., 2003) was calculated for all AAQoL domains and BRIEF–A scores. The Reliable Change Index (RCI; Jacobson & Truax, 1991; Jackson & Jordan, 2022) was calculated for the total scores (AAQoL and BRIEF–A MI and BRI scores) to determine whether an adult’s change from pre- to postintervention was statistically significant beyond random measurement error. Reliable change was considered if the RCI score was less than 1.96 in the AAQoL total score or −1.96 or less in the BRIEF–A BRI and MI scores.
To identify potential predictors of QoL improvement, we performed a logistic regression analysis. All independent variables were entered simultaneously, including therapist experience, participant demographics (age, gender, and marital status), productive role (nonworker or nonstudent = 0; worker, student, or both = 1), pharmacological treatment, and baseline MI and BRI subscale scores. The dependent variable was the total AAQoL RCI score classification (no change versus reliable change). Additionally, to identify potential predictors of intervention adherence (Full completers attended all 24 sessions versus Partial completers, who attended at least 50% of sessions but did not complete the full intervention), a logistic regression analysis was performed with the same independent variables.
Results
Study Population
One hundred fifteen client participants met the study inclusion criteria, of which 107 completed the full intervention, and 8 did not. Forty-five (39.1%) participants reported as male, and ages ranged from 19 to 70 yr (M = 34.7, SD = 10.5). Sixty-one participants (53%) worked either full-time (56; 48.7%) or part-time (5; 4.3%), 21 (18.3%) were students or in professional training, 20 (17.4%) were students and worked, and 13 (11.3%) were unemployed or retired. Thirty-four (29.6%) participants were single, 13 (11.3%) were in a long-term relationship, 62 (53.9%) were married, and 6 (5.2%) were divorced. Most participants (83; 72.1%), used pharmacological treatment of ADHD, with 51 (44.3%) reporting daily use and 32 (27.8%) using medication occasionally, as needed.
Seventy-one occupational therapists participated in the study. Their clinical experience varied in that 9 (12.7%) had 0–5 yr, 19 (26.8%) had 6–10 yr, 7 (9.9%) had 11–15 yr, and 36 (50.7%) had 16 yr or more of experience.
Effectiveness
In a comparison of participant change in outcome measures from pre- to postintervention, the multivariate results showed an overall significant increase of AAQoL domains, with a large effect size indicating an improvement of QoL, F(4, 103) = 34.752, p < .001 (Wilks’s λ = 0.426, η2 p = 0.534). Regarding the total QoL score, 45% of the participants demonstrated a clinically significant improvement, and 34% presented a reliable change. Regarding the AAQoL domains, the univariate tests (Table 1) showed that there were significant improvements from pre- to postintervention in all four domains. Participants displayed the highest (47.7%) clinically significant improvement in the psychological health domain, compared with the life outlook domain, which was the least improved (37.4%).
Mean AAQoL and BRIEF–A Scores and Changes from Pre- to Postintervention
Note. n = 107. Clinically significant change = 1 SD or more. AAQoL = Adult ADHD Quality-of-Life Scale; ADHD = attention deficit hyperactivity disorder; BRI = Behavioral Regulation Index; BRIEF–A = Behavioral Rating Inventory of Executive Function–Adult Version; MI = Metacognition Index.
Similarly, the multivariate results showed an overall significant decrease of BRI scores, F(5, 102) = 19.862, p < .001 (Wilks’s λ = .507, η2 p = .493), and MI scores, F(4, 103) = 19.521, p < .001 (Wilks’s λ = .569, η2 p = .431); with a large effect size, indicating an improvement of behavioral regulation and metacognitive function, respectively. Participants demonstrated a clinically significant improvement of 31.8% in the BRI scores and a 10% reliable change. Similarly, 40.2% demonstrated a clinically significant improvement in the MI scores, compared with 29% who demonstrated a reliable change. The univariate tests (Table 1) showed that there was a significant improvement from pre- to postintervention with respect to all BRIEF–A subscales. The largest clinically significant improvement of functions related to behavioral regulation was demonstrated on the Initiate subscale (40.2%), and the lowest was on the Emotional control subscale (24.3%). The largest clinically significant improvement of functions related to metacognitive function was demonstrated on the Plan and organize subscale (43.9%), and the lowest was on the Organizing materials subscale (28%).
Predictors of Reliable Change of Quality of Life
The first logistic regression model predicting reliable change in QoL (see statistical analyses) was statistically nonsignificant, χ2(8, N = 107) = 2.681, p = .953. The model explained only 3.5% (Nagelkerke R 2) of the variance in reliable change and correctly classified 68.2% of cases. None of the independent variables were statistically significant in predicting the odds of reliable change outcome.
Predictors of Intervention Adherence
The second logistic regression model was statistically significant, χ2(10, N = 115) = 25.743, p = .041. The model explained 50.6% (Nagelkerke R 2) of the variance in completion of the intervention and correctly classified 93.9% of cases. Gender, pharmacological treatment, and baseline MI score were identified as significant predictors. Specifically, a female participant was 10 times more likely to complete the intervention, compared with a male participant (odds ratio [OR] = 10.84; 95% confidence interval [CI[ = [1.09, 108.29]). Pharmacological treatment was found to be significant (OR = 3.37; 95% CI [1.01, 11.24]), indicating that participants who reported pharmacological treatment of ADHD were three times more likely to complete the intervention compared with those who reported no treatment. Additionally, those who reported daily pharmacological treatment were three times more likely to complete the intervention, compared with those who reported occasional treatment. Finally, the baseline MI score (OR = 0.81; 95% CI [0.68, 0.97]) was a significant predictor, showing that participants with higher scores, indicating greater metacognitive executive dysfunction, had a lower likelihood of completing the intervention.
Discussion
In this retrospective case-series study, we aimed to explore practice-based evidence regarding the effectiveness of the Cog–Fun A intervention in improving patient-reported QoL and EF. We used multiple statistical methods to evaluate the efficiency of the Cog–Fun A at both the individual and group levels, with a focus on clinical change and treatment outcomes. The results demonstrated statistically significant improvements across all QoL domains (AAQoL) and EF scores (BRIEF–A) after the Cog–Fun A intervention, with large effect sizes. Pharmacological treatments for adults with ADHD (De Crescenzo et al., 2017), as well as other nonpharmacological treatments such as mindfulness, dialectical behavior therapy, and cognitive–behavioral therapy, have shown moderate to large effect sizes (Fullen et al., 2020). However, these studies used prospective controlled designs (De Crescenzo et al., 2017) so comparison with them is limited.
Contrary to the large effect size found on the group level, on the individual level, only 45% of participants demonstrated clinically significant improvement in overall QoL, with 34% showing reliable change. These findings highlight the importance of setting realistic expectations for treatment outcomes. Although a substantial proportion of participants benefited from the intervention, a significant number did not achieve reliable change, suggesting that additional supports may be necessary to enhance treatment effectiveness. Moreover, clinicians may need to consider the use of supplemental therapeutic approaches or strategies.
It is interesting that the effectiveness on the individual level is comparable with that of other nonpharmacological mental health interventions, not specific to ADHD, with the RCI (Martinmäki et al., 2023; Vaz et al., 2013). Although nonpharmacological treatments may be beneficial, their impact varies among patients. This underscores the need for further research to identify the factors that contribute to the success of these interventions for different people. Furthermore, comparing these factors across different interventions may enable comparisons between them.
These findings align with previous research on the Cog–Fun A approach (Kastner et al., 2022). This study expands on the pilot research by including a larger and more diverse sample, as well as multiple therapists, reflecting real-world clinical practice. The involvement of multiple therapists (rather than two therapists, as in the pilot study) suggests that the effects are not therapist specific. Additionally, this study added nuanced understanding on the possible treatment impact, based on the combination of group-level pre-post comparisons and clinical gain at the individual level. Additionally, unlike in the pilot study, participants were not excluded on the basis of co-occurring mental health conditions, which possibly supports the feasibility of the Cog–Fun A for the diverse ADHD population encountered in everyday practice.
The positive effects observed may be attributed to the extended session format recommended in initial studies, which showed that larger effect sizes (Kastner et al., 2022) may allow for better skill acquisition and integration into daily life (DeKeyser, 2020). These effects may also be explained by several key mechanisms of change. One fundamental mechanism is the development of self-awareness regarding the chronicity of ADHD and its impact on daily life. Previous research has shown positive pre–post changes in both self-awareness and strategy-based awareness after implementation of the Cog–Fun A (Kastner et al., 2022), which suggests that greater awareness of coping strategies contributes to improvements in EF and QoL. Furthermore, self-awareness regarding the chronicity of ADHD and its impact on daily life may also account for the observed enhancements in psychological health. By incorporating self-compassion and acceptance of the lifelong implications of ADHD, the intervention may help people cultivate a more adaptive occupational identity.
A second mechanism of change involves the acquisition of personalized coping strategies through guided discovery (rather than providing a fixed set of strategies). The intervention tailors strategies to each person’s unique profile and life roles, refining them through an iterative process of guided self-exploration. This process enhances strategy ownership, facilitating their application across different contexts. Research on adults with ADHD suggests that self-awareness influences strategy selection in daily life (Ben-Dor Cohen et al., 2024), highlighting the importance of this personalized, discovery-based approach. This personalized client-driven strategy acquisition process may be related to the greater improvements observed in metacognitive functions compared with behavioral regulation. People often prioritize practical goals, which are closely linked to metacognitive EFs (i.e., planning, organizing, and working memory), whereas behavioral regulation (i.e., emotional dysregulation) may be less explicitly addressed in short-term intervention, because of social stigma (Godfrey et al., 2021).
The highest rates of clinically significant improvement were observed in the psychological health domain for QoL (47.7%) and the MI score (BRIEF–A) for EF (40.2%). The improvement in psychological health symptoms after functional gains may be understood through the core belief among occupational therapists in the relationship between occupation and health. Successfully engaging in meaningful activities and roles has been linked with positive psychological health and overall QoL (American Occupational Therapy Association, 2020). Regarding the greater improvement in the MI index over the BRI index, metacognitive skills (measured with the MI) are more cognitive in nature and may be more amenable to direct instruction and practice. These skills include planning, organizing, initiating tasks, working memory, and monitoring. In contrast, behavioral regulation skills (measured with the BRI) such as inhibition, shifting, and emotional control may be more ingrained patterns of behavior that are harder to change in a shorter period (Toplak et al., 2008).
Predictors of Intervention Adherence and Reliable Change in Quality of Life
The analysis of predictors for intervention adherence (completers versus partial completers) yielded several interesting findings. Compared with male participants, female participants were significantly more likely to complete the intervention. This gender difference may reflect varying symptom presentations between men and women with ADHD (Oroian et al., 2023). Additionally, sociocultural factors may influence treatment adherence. Women tend to be more inclined to adhere to treatment because of social expectations and compliance (Esteban-Gonzalo et al., 2020). In contrast, men younger than age 65 may face greater stigma around help-seeking behaviors and are less likely to complete treatment, compared with women (Baker, 2018). Further research is recommended to explore potential gender-specific barriers to treatment adherence and to tailor interventions accordingly.
Pharmacological treatment was identified as a significant predictor of intervention completion, with participants who used medication (either occasionally or daily) being more likely to complete the intervention, compared with those not who did not use medication. This finding aligns with clinical best practices for ADHD treatment, recommending combined pharmacological and nonpharmacological treatment to improve focus, reduce impulsivity, and enhance treatment adherence (Faraone et al., 2021).
It is interesting that individuals with lower baseline MI scores, indicating less impairment, were associated with completing the intervention. This finding suggests that people with less severe metacognitive executive dysfunction at baseline may have better resources or strategies to support treatment adherence (Toplak et al., 2008). Conversely, those with more significant metacognitive challenges may struggle with the EFs necessary to consistently attend sessions, manage time, and remember appointments. This insight highlights the importance of providing additional support and accommodations for people with more severe EF deficits to help promote treatment adherence.
None of the variables that were examined significantly predicted reliable changes in QoL outcomes. This may be due to the limitation in sample representation, which can affect variability and representativeness in retrospective studies (Talari & Goyal, 2020). Moreover, only limited predictors were examined, because of the limited demographic and medical information that was accessible, considering that the data reports were based on clinical records.
It should be noted that the therapists’ experience was not a significant predictor in explaining the variance related to treatment adherence or reliable change. This may be due to the fact that, despite the various levels of professional experience, all of the therapists were novices to delivering treatment using the Cog–Fun A protocol. Furthermore, training and frequency supervision of occupational therapists may have reduced the effect of experience while enhancing fidelity between therapists.
Strengths and Limitations
The main strengths of this study are its large sample size and use of multiple methods to evaluate the effectiveness of the Cog–Fun A at the individual level, using data collected from clinical practices of occupational therapists in community settings. Clients with comorbidities were not excluded, reflecting real-world client profiles, as comorbidities among adults with ADHD are common (Kooij et al., 2019).
However, the retrospective case-series design has inherent limitations. First, the retrospective design includes selection bias; this includes overrepresentation of successful cases and underrepresentation of negative or neutral outcomes, skewing the data and potentially making the Cog–Fun A appear more effective than it might be in a broader sample, thus limiting the generalizability of results (Talari & Goyal, 2020). Second, the retrospective design, as well as the lack of a control group, does not allow for establishing causal relationships between the intervention and the changes observed (Talari & Goyal, 2020), which makes it difficult to rule out extraneous variables such as spontaneous recovery or placebo effects. Additionally, it relied solely on medical record reports, which restricted the available data on participants (e.g., whether ADHD diagnoses were confirmed using standardized criteria, comorbid diagnosed or undiagnosed mental health conditions). The absence of data beyond the immediate posttreatment period limits understanding of the intervention’s sustained impact. Finally, the study relied exclusively on the self-report measures of QoL and EF (rather than real-world functional assessments and performance-based neuropsychological tests); the therapists were not blinded to them, and this may have introduced bias.
Further research is recommended to include prospective randomized controlled trials to provide more robust evidence of the Cog–Fun A’s efficacy and controlling for potential confounding variables. These should incorporate outcome measures beyond subjective QoL and EF, such as performance-based EF tests and measures of awareness and knowledge of ADHD. Additionally, incorporating longer term follow-up assessments and comparing the intervention with other evidence-based treatments for adult ADHD to explore more specific effects of Cog–Fun A and provide insights into the sustainability of improvements is recommended. Moreover, future research should explore a wider range of potential predictors, such as psychosocial factors, comorbidities, and specific occupational challenges, for a better understanding of other possible predictors for QoL outcomes.
Implications for Occupational Therapy Practice
The Cog–Fun–A intervention may be an effective metacognitive, occupation-centered approach for adults with ADHD, supporting improvement in both EF and QoL. The results of this study have the following implications for occupational therapy education: ▪ Because pharmacological treatment appears to enhance intervention adherence, it is important to collaborate with medical professionals and discuss medication use with clients and, when appropriate, coordinating care with prescribing providers. ▪ Additional supports and accommodations may be necessary for people with more severe baseline metacognitive deficits to promote treatment completion. This may include extended treatment duration or supplementary resources. ▪ Given that men were less likely to complete the intervention, tailored engagement strategies may improve retention. ▪ The intervention appears effective when implemented by multiple therapists, possibly suggesting its broad applicability in diverse clinical settings. Training and frequent supervision may help ensure fidelity to the approach.
Conclusion
The Cog–Fun–A intervention may be a potential metacognitive, occupation-centered approach for improving EF and QoL in adults with ADHD. The large effect sizes and significant clinical changes observed across multiple QoL domains and EFs may support its potential as a nonpharmacological treatment option at both the group and individual levels. Factors such as gender, medication use, and baseline metacognitive functioning appeared to influence treatment adherence; however, further research on individual applicability is recommended. These findings contribute to the growing body of evidence supporting occupation-based interventions for adults with ADHD and highlight the important role that occupational therapy can play in addressing the functional challenges associated with this condition.
Footnotes
Acknowledgments
This study is dedicated to Carmel Gat, a beloved master’s student at the School of Occupational Therapy at the Hebrew University, whose passion for learning and commitment to helping others inspired all who knew her. Carmel was taken hostage on October 7, 2023, and subsequently killed in captivity by her captors. Her absence is deeply felt. We honor her strength, her spirit, and her enduring impact on our professional community.
Authors’ Contributions
The first author (Jennifer Budman) and last author (Shahar Zaguri-Vittenberg) contributed equally to this published work.