Food-Preparation Skills of Adults With ADHD and the Mediating Effect of Executive Functions

Abstract

Little is known about the relationships among attention-deficit/hyperactivity disorder (ADHD) symptoms, food-preparation skills, executive functions (EFs), and quality of life (QoL) in adults. This study examined differences between adults with and without ADHD regarding food-preparation skills and their relationships with EFs and QoL. The study included 63 adults with ADHD (mean age = 39 ± 11 years) and 101 without ADHD (mean age = 43 ± 10 years). Participants completed standardized self-report measures of ADHD symptoms (Adult ADHD Self-Report Scale [ASRS]), food-preparation skills (Cooking and Food Provisioning Action Scale [CAFPAS]), EFs (Behavior Rating Inventory of Executive Function-Adult Version [BRIEF-A]), and QoL (World Health Organization Quality of Life [WHOQOL]). Adults with ADHD reported lower food-preparation self-efficacy (d = −0.47, p < .001). An integrated mediation model showed a differentiated pattern of indirect associations, with metacognitive abilities more consistently linked to food-preparation and behavioral regulation more salient for QoL. Results advance understanding of occupation by demonstrating how EFs mediate engagement in food preparation and suggest exploring additional factors influencing this daily activity.

Plain Language Summary

Food-Preparation Skills of Adults with ADHD and the Role of Cognitive Functioning

Adults with attention-deficit/hyperactivity disorder (ADHD) often experience challenges that affect daily activities and participation. These challenges can be related to the symptoms of the disorder itself, as well as to difficulties in executive functioning. Executive functions are cognitive functions needed for goal-directed behaviors. They include solving novel problems, modifying behavior in response to new information, generating strategies, and sequencing complex actions. Food preparation is a meaningful and complex daily occupation that requires these higher cognitive functions and supports health, independence, and quality of life. Yet, little is known about how adults with ADHD experience this activity or about the role of executive functions in this context. This study included 164 adults, 63 of whom had a formal diagnosis of ADHD. Participants completed questionnaires assessing ADHD symptoms, executive functions, food-preparation abilities, and quality of life. Differences between adults with and without ADHD were examined, as well as the relationships between these factors. Adults with ADHD reported higher levels of ADHD symptoms, greater difficulties in executive functions, and lower quality of life compared to adults without ADHD. While self-reported food-preparation skills were similar between groups, adults with ADHD felt significantly less confident in their ability to prepare food. Difficulties in executive functions helped explain the association among ADHD symptoms, reduced confidence in food preparation, and lower quality of life. These findings suggest that interventions for adults with ADHD should focus on supporting executive function strategies to promote food-preparation skills and improved quality of life and to explore additional factors influencing this daily activity.

Keywords

adult ADHD food preparation executive functions quality of life

Introduction

The daily activity of preparing food for consumption can be described using various terms, such as food skills, cookery, or meal planning and preparation (Hingst et al., 2024). Food skills are defined as complex, interrelated, and person-centered sets of abilities required to provide and prepare nutritious and culturally appropriate meals for all household members (Fernandez et al., 2020). Cooking is a common activity of daily living that integrates cognitive, physical, and psychosocial processes, thereby influencing biopsychosocial health (Hingst et al., 2024). Planning and preparing meals involve interaction with the social environment, the use of executive functions (EFs), and higher-level cognitive abilities (American Occupational Therapy Association, 2020; Romero-Ayuso et al., 2021). Active engagement in these daily occupations promotes, facilitates, supports, and sustains health and well-being (American Occupational Therapy Association, 2020). Previous literature has indicated that adults diagnosed with attention-deficit/hyperactivity disorder (ADHD) differ from those without the diagnosis in eating behaviors and meal-planning skills, often exhibiting unhealthy eating patterns (Björk et al., 2023; Hershko et al., 2022).

ADHD is a common neurodevelopmental condition characterized by symptoms of inattention and/or hyperactivity-impulsivity that interfere with development and daily functioning (American Psychiatric Association [APA], 2022). Typically diagnosed in childhood, ADHD often persists in adolescence and adulthood (Agnew-Blais et al., 2016; Caye et al., 2016). Recent prevalence estimates suggest that persistent adult ADHD (with childhood onset) affects 2.58% and symptomatic adult ADHD (regardless of childhood onset) affects 6.76% of the total adult population worldwide (Song et al., 2021). Although ADHD symptoms generally decrease from childhood to adulthood (Döpfner et al., 2015), adults with ADHD typically display higher levels of impulsivity than healthy controls (Malloy-Diniz et al., 2007). Motor restlessness (i.e., hyperactivity), common in childhood, often evolves into inner restlessness in adolescence and young adulthood (Krauss & Schellenberg, 2022). In contrast, inattention symptoms tend to persist into adolescence and may resemble the pattern typically observed in adults with ADHD (e.g., Tischler et al., 2010).

Consistent with those symptoms, people with ADHD may have difficulties managing their daily lives (Björk et al., 2023). Studies have frequently reported an association between ADHD symptoms in adulthood and dysregulated eating behaviors, including irregular eating patterns and food addiction (Björk et al., 2023; Samela et al., 2021; Weissenberger et al., 2017). An unhealthy preoccupation with food may contribute to comorbid obesity, a common health condition associated with ADHD in adolescence and adulthood (APA, 2022; Cortese et al., 2016; Cortese, Ramos Olazagasti, et al., 2013; Cortese & Tessari, 2017; Weissenberger et al., 2017). Therefore, it is important to deepen understanding of the habits and challenges of food preparation in this population (Boop et al., 2020; Fernandez et al., 2020).

In addition to ADHD symptoms, the literature has suggested that unhealthy lifestyles commonly seen in individuals with ADHD may be associated with poor executive functioning (Allom et al., 2018; Weissenberger et al., 2017). As higher-order cognitive functions, EFs are needed for adaptive goal-directed behaviors (Salehinejad et al., 2023). Thus, they are essential for independence in everyday activities (Finnanger et al., 2022). Examples of EFs include solving novel problems, modifying behavior in light of new information, generating strategies, and sequencing complex actions (Connor & Maeir, 2011; Katz & Maeir, 2011; Lezak, 2004). Multidimensional constructs, such as EFs, are difficult to measure (Banich, 2009). Although it is important to view EFs as metacognitive control systems rather than as functions tied to particular cognition domains, they are commonly described in terms of specific cognitive functions (Salehinejad et al., 2023). Thus, in this study, we use the operational definitions of the metacognitive index (MI) and the behavioral regulation index (BRI) of the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A; Roth et al., 2005).

Adults with ADHD often experience deficits in EF-related skills, such as response inhibition, planning, problem-solving, modulation of attention and impulsiveness, self-monitoring, and emotion regulation (Onandia-Hinchado et al., 2021; adult samples aged 18+). Decreased EFs create challenges for adults with ADHD in maintaining a daily schedule and planning activities to achieve goals due to difficulties in initiating, structuring, and organizing everyday tasks (Björk et al., 2023; age 18+; Ek & Isaksson, 2013; ages 21–38; Roselló et al., 2020; ages 18–24). Moreover, impaired EFs are significantly associated with lower self-perceived quality of life (QoL) among adults with ADHD (Stern et al., 2013). Adults with ADHD have generally lower QoL than neurotypical adults in both early and later adulthood (Krauss & Schellenberg, 2022; Thorell et al., 2019). However, limited research has examined the relationships between food-preparation skills, EFs, and QoL among adults with ADHD. Hence, this study examines whether EFs mediate the relationships among ADHD symptoms, food-preparation skills, and QoL in this population.

We developed and tested a theoretical mediation model. Figure 1 illustrates the conceptual model. The central hypothesis states that EFs mediate the associations between ADHD symptom severity, perceived food-preparation skills, and QoL among adults with ADHD. We examined one integrated model that tested the mediating role of metacognition (BRIEF-A MI) and of behavioral regulation (BRIEF-A BRI). The model assessed whether the two EF components differ in their mediating strength.

Figure 1.

The conceptual integrated mediation model.

Hence, the current study’s objectives were to (a) examine differences between adults with and without ADHD regarding self-perceived food-preparation skills and (b) investigate the relationships between ADHD symptoms, perceived food-preparation skills, EFs, and QoL.

Methods

Study Design

This study used a cross-sectional design with a convenience sampling method.

Participants

Sample size estimation was conducted for Objective 1, which involved between-group comparisons. Given the limited prior evidence on expected group differences in food-preparation skills among adults with ADHD, Cohen’s conventional medium effect size (d = 0.50) was used as a pragmatic a-priori planning assumption (Cohen, 1988). Using G*Power version 3.1.9 (Faul et al., 2007) and assuming α = .05 and 1-β = .80, the required sample size was estimated at 51 participants per group. This power calculation did not address Objective 2; therefore, the subsequent mediation findings should be interpreted as exploratory, especially as the cross-sectional design does not allow conclusions about temporal ordering.

The final sample comprised 164 participants, of whom 63 (38%) had a formal diagnosis of ADHD and 101 (62%) did not. Among the participants with ADHD, 35 (56%) were diagnosed by a neurologist, 19 (30%) by a psychiatrist, and 9 (14%) by other qualified health care professionals. Table 1 presents the background and sociodemographic characteristics of the total sample by group (ADHD vs. non-ADHD).

Table 1.

Background and Sociodemographic Characteristics of the Study Sample and Groups.

Variable	Whole sample	ADHD	Non-ADHD	Sig. test
Gender (n, %)				χ²₍₁₎ = 2.27, p = .17
Male	23 (14)	12 (19)	11 (11)
Female	140 (85)	50 (81)	90 (89)
Missing values	1 (1%)
Age in years (M ± SD)	41 ± 11	39 ± 11	43 ± 10	t₍₁₆₂₎ = −1.91, p = .06
Education level (n, %)				χ²₍₂₎ = 10.26, p = .01
High school graduate	25 (15)	14 (24)	11 (11)
Bachelor’s degree	63 (38)	28 (48)	35 (36)
Master’s degree or higher	67 (41)	16 (28)	51 (53)
Missing values	9 (6%)

Note. ADHD = attention-deficit/hyperactivity disorder; Sig. = significance.

Procedure

The Human Subjects Research Committee at Ben-Gurion University of the Negev approved the study. We recruited participants through advertisements posted in relevant social media groups. After providing informed consent, participants completed an online anonymous battery of self-report questionnaires via Qualtrics, a web-based survey program that meets high standards for data security (https://www.qualtrics.com/security-statement/). The program randomly selected the order of the questionnaires. Participation in the entire study took approximately 30 minutes. After participants completed the survey, we offered them the opportunity to enter a raffle to win one of ten $60 electronic gift vouchers. Participation in the raffle was optional and not linked to survey responses.

Measures

Demographic Questionnaire

This questionnaire includes questions regarding self-reported gender, age, religious affiliation, marital status, socioeconomic status, and years of education.

Adult ADHD Self-Report Scale

The Adult ADHD Self-Report Scale (ASRS‑v1.1, Hebrew version; Kessler et al., 2005) is a self-report screening instrument that identifies current ADHD symptoms. It was developed by the World Health Organization (WHO) and the Work Group on Adult ADHD (Kessler et al., 2005). The scale contains 18 symptoms of inattention, hyperactivity, and impulsivity, defining ADHD according to the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text revision (APA, 2000) and 5th ed. (APA, 2013). The severity of the symptoms is reported on a 5-point Likert-type scale from 0 (never) to 4 (very often), with a total range of 0–72; higher scores indicate more ADHD symptoms.

Studies have often used an unweighted sum of responses across all 18 items to effectively predict clinical syndrome classifications. A cutoff score of 24 often indicates significant ADHD symptoms (Adler et al., 2006; Kessler et al., 2005; Silverstein et al., 2018). High factorial discriminant validity was reported between participants with and without an independent clinician’s ADHD diagnosis, and the test–retest reliability was excellent, with good internal consistency (Cronbach’s α = .79–.89; Adler & Newcorn, 2011; Silverstein et al., 2018). The ASRS‑v1.1 demonstrated excellent internal consistency in the study sample (α = .94).

Cooking and Food Provisioning Action Scale

The Cooking and Food Provisioning Action Scale (CAFPAS; Lahne et al., 2017) is a 28-item scale consisting of three subscales: self-efficacy (13 items; e.g., “When preparing food, I am confident that I can deal with unexpected results”), attitude (10 items; e.g., “I find cooking a very fulfilling activity”), and structure (five items; “I wish that I had more time to plan meals”). Items are answered using 7-point Likert-type scales from 1 (strongly disagree) to 7 (strongly agree). Twelve items (1, 15–17, 19, 21, 23–28) are reverse-coded. Higher scores indicate better perceived cooking and food-preparation skills. Scores for each subscale are calculated as the sum of responses, and an overall raw CAFPAS score is the sum of the three subscale scores. The CAFPAS was translated into Hebrew for this study with the authors’ permission. The translated version is currently undergoing validation. The total CAFPAS score demonstrated excellent internal consistency in the study sample (α = .90), with subscale alphas as follows: self-efficacy (α = .91), attitudes (α = .84), and structure (α = .83). The CAFPAS is a relatively new measure and the primary variable of interest in this study; thus, all 28 items, together with the relevant descriptive and comparative statistics, are presented in Supplemental Table S1.

Behavior Rating Inventory of Executive Function-Adult Version

BRIEF-A Hebrew version (Roth et al., 2005) is a standardized self-report measure that captures adults’ views of their EFs in everyday environments. It is designed for adults with various developmental disorders and systemic, neurological, and psychiatric illnesses. The BRIEF-A is composed of 75 items rated on a 3-point scale that encompass nine theoretically and empirically derived clinical scales measuring various aspects of executive functioning. These form two indices: the MI and the BRI. The MI includes the following clinical scales: initiate, working memory, plan/organize, task monitor, and organization of materials. The BRI includes the clinical scales: inhibit, shift, emotional control, and self-monitor (Roth et al., 2013). The global executive composite (GEC) is an overall summary score. The t scores are calculated for each scale, with higher scores indicating greater impairment. A score above 65 signifies clinical impairment. The BRIEF-A has moderate to high internal consistency (α = .73–.98). It demonstrated excellent internal consistency in the study sample, with α = .97 for the GEC, α = .94 for the BRI, α = .96 for the MI, and α ranging from .75 to .92 for the subscales.

World Health Organization Quality of Life-Brief Version

The World Health Organization Quality of Life-Brief Version (WHOQOL-BREF; WHO, 2004) is a shortened version of the WHOQOL-100. It is a self-report questionnaire comprising 26 items assessing four key domains of QoL: physical health, psychological health, social relationships, and environment. The tool uses a 5-point Likert-type scale, with higher scores reflecting higher self-perception of QoL. The WHOQOL-BREF demonstrated good to excellent internal reliability, inter-items correlations, discriminant validity, and construct validity based on factor analysis (Skevington et al., 2004). The internal consistency of the four WHOQOL-BREF domains was acceptable to good, with α coefficients ranging from .61 to .78.

Data Analysis

We used descriptive statistics to summarize the data, reported categorical variables as frequencies and percentages, and summarized continuous variables using means, standard deviations, medians, and interquartile ranges. We assessed internal consistency of the study instruments using Cronbach’s α. A coefficient of α ≥ .70 was considered indicative of acceptable reliability (Tavakol & Dennick, 2011). We conducted independent samples t-test to examine group differences across study variables and applied Levene’s test to assess the assumption of homogeneity of variances. When this assumption was violated, we adjusted the t values to ensure accurate comparisons. To control for multiple comparisons, we applied the false discovery rate (FDR) correction (Benjamini & Hochberg, 1995). We used Cohen’s d as a measure for effect size, which was interpreted as follows (absolute values): negligible effect from 0.00 to 0.20, small effect from 0.20 to 0.50, medium effect from 0.50 to 0.80, and large effect of ≥0.80 (Cohen, 1988). Group differences on individual CAFPAS items were also examined using the Mann–Whitney test. We conducted these analyses using IBM SPSS (version 26). Mediation analyses were performed using the lavaan package in R software (version 4.5.0). The model was estimated using robust maximum likelihood estimation. We determined the significance of direct, indirect, and total effects using robust standard errors, and interpreted results at the p ≤ .05 significance threshold.

Results

Sample Characteristics

Participants’ mean age was 41 ± 11 years. No significant difference in age was found between the two groups (M = 39 ± 11 vs. M = 43 ± 10), t(162) = −1.91, p = .06. A smaller proportion of participants with ADHD reported tertiary education compared to those without ADHD (p = .01). Participants with ADHD were more likely to report medication use (63.5%) than those without ADHD (35.6%), with significant association between ADHD status and medication use, χ²(1, n = 164) = 12.10, p < .001, φ = .27. There were no significant group differences in their body mass index (BMI), t(97) = 1.00, p = .32 (ADHD group: M = 27, SD = 7; control group: M = 26, SD = 5).

As expected, the mean ASRS-v1.1 score for the ADHD group was 41.83 ± 9.60 (M_d = 44, 25%–75% = 36–49), whereas the values for the non-ADHD group were 25.45 ± 11.52 (M_d = 23, 25%–75% = 18–33). This difference was statistically significant, t(162) = 9.42, p < .001, with a large effect size (d = 1.51). Significant group differences were observed for all BRIEF-A scores, including the GEC (p < .001), both indices (p < .001), and across all subscales (Supplemental Table S2). Finally, participants with ADHD reported significantly lower QoL scores in the physical health, psychological health, social relationships, and environment domains (p < .001, p = .001, p = .003, and p = .04, respectively; Supplemental Table S3).

Group Differences in Perceived Food-Preparation Skills

We initially found a significant difference in the CAFPAS total score between the ADHD and non-ADHD groups. However, this difference did not remain significant after correction for multiple comparisons. As for subscales, a significant difference was found on the self-efficacy subscale (d = −0.47, p < .001), indicating that ADHD participants felt less confident in their food-preparation abilities (Table 2).

Table 2.

Group Differences on the CAFPAS Subscales.

	ADHD	Non-ADHD	Sig. test
CAFPAS subscale	M ± SD	M ± SD			FDR correction	Effect size (d)
Self-efficacy	66.41 ± 13.99	72.62 ± 12.75	t₍₁₆₁₎ = −2.91	p = .00	Yes	−0.47
Attitudes	41.68 ± 11.18	43.33 ± 11.04	t₍₁₆₁₎ = −0.92	p = .36	No	−0.15
Structure	21.30 ± 7.57	21.11 ± 6.30	t₍₁₆₁₎ = 0.17	p = .86	No	0.03
Total score	129.40 ± 24.61	137.06 ± 23.69	t₍₁₆₁₎ = −1.98	p = .05	No	−0.32

Note. CAFPAS = Cooking and Food Provisioning Action Scale; ADHD = attention-deficit/hyperactivity disorder; Sig. = significance; FDR = false discovery rate.

Integrated Mediation Model

As expected, the analysis revealed a strong positive association between the ASRS-v1.1 and both EF domains (β = .79, p < .001 for MI; β = .75, p < .001 for BRI), indicating that greater ADHD symptomatology was associated with reduced executive functioning.

A summary of the mediation analysis is presented in Table 3. As shown, MI demonstrated the clearest indirect associations with food-preparation outcomes, particularly CAFPAS self-efficacy (β = −.35, p < .001) and attitudes (β = −.31, p = .01). In contrast, QoL outcomes showed a more mixed pattern: BRI was more salient for the physical health (β = −.27, p < .001) and psychological health (β = −.37, p < .001), whereas MI was more salient for the environment domain (β = −.27, p = .02).

Table 3.

Summary of the Integrated Mediation Model Analysis.

		Total effect (C)		Direct effect (C’)		Combined indirect effect (via MI and BRI)		Indirect effect (a*b)
Predictor	Outcome	β	p-value	β	p-value	β	p-value	mediator	β	p-value	Interpretation
ASRS	CAFPAS–Attitude	−.03	.70	.27	.03	−.30	.01	Thru BRI	.00	.97	Indirect association via MI, with a remaining direct ASRS effect
ASRS								Thru MI	−.31	.01
ASRS	CAFPAS–Self-efficacy	−.23	.004	.19	.10	−.43	<.001	Thru BRI	−.08	.36	Indirect association primarily via MI; direct ASRS effect not significant
ASRS								Thru MI	−.35	<.001
ASRS	CAFPAS–Structure	−.20	.02	−.38	<.001	.18	.08	Thru BRI	.07	.46	Association appears primarily direct rather than indirect
ASRS								Thru MI	.11	.27	Association appears primarily direct rather than indirect
ASRS	WHOQOL Physical health	−.42	<.001	.04	.71	−.47	<.001	Thru BRI	−.27	<.001	Indirect association via both MI and BRI; direct ASRS effect not significant
ASRS								Thru MI	−.19	.03
ASRS	WHOQOL Psychological health	−.45	<.001	.00	.99	−.45	<.001	Thru BRI	−.37	<.001	Indirect association primarily via BRI; direct ASRS effect not significant
ASRS	WHOQOL Psychological health							Thru MI	−.08	.40
ASRS	WHOQOL Social relationships	−.29	<.001	−.10	.48	−.19	.13	Thru BRI	−.02	.83	No evidence of indirect association via MI or BRI
ASRS	WHOQOL Social relationships							Thru MI	−.17	.13	No evidence of indirect association via MI or BRI
ASRS	WHOQOL Environment	−.17	.03	.20	.17	−.37	.00	Thru BRI	−.10	.31	Indirect association primarily via MI; direct ASRS effect not significant
	WHOQOL Environment							Thru MI	−.27	.02

Note. ASRS = Adult ADHD Self-Report Scale; CAFPAS = Cooking and Food Provisioning Action Scale; BRI = behavioral regulation index; MI = Metacognitive index; WHOQOL = World Health Organization Quality of Life

Bold values indicate statistically significant results (p < 0.05).

In addition, several residual covariances between food preparation and QoL remained significant. These significant residual associations were observed for specific food-preparation subscales and QoL domains rather than across all outcomes. Specifically, CAFPAS self-efficacy was associated with physical health (β = .19, p = .02) and environment (β = .28, p < .001). CAFPAS attitudes was associated with physical health (β = .20, p = .01), and CAFPAS structure with environment (β = .32, p < .001). This pattern suggests that the relationship between food preparation and QoL may involve additional shared associations not captured in the present model.

Finally, the R² values of the model indicated that it explains substantial variance in the EF mediators (R² = 0.62 for MI and 0.56 for BRI), moderate variance in selected QoL domains (R² = 0.29 and 0.33 for physical and psychological health, respectively), and relatively limited variance in the food-preparation outcomes (R² = 0.06–0.15).

Discussion

The current study provides important insights into adult ADHD self-perceived food-preparation skills by examining their associations with EFs and QoL within the same model. Overall, the analysis revealed direct relationships between ADHD symptoms, perceived food-preparation skills, and QoL, and that EFs mediated these relationships. Our results emphasize the need to support EF strategies for adults with ADHD to improve food-preparation skills and QoL.

The significant differences found between the ADHD and non-ADHD groups on ASRS-v1.1—reflecting higher ADHD symptom severity among participants diagnosed with ADHD—support the validity of the group classification. These findings are consistent with prior research demonstrating that adults with ADHD report higher levels of inattentive and hyperactive–impulsive symptoms compared to controls (e.g., Adler et al., 2019; Brevik et al., 2020). The significant difference found between groups in education level is consistent with existing literature, as academic performance difficulties are a hallmark of ADHD (e.g., Pagespetit et al., 2025; Voigt et al., 2017). In addition, the observed group differences in EFs and QoL align with the existing literature, indicating that ADHD might be associated with persistent impairments in EFs that contribute to reduced daily functioning and well-being (Onandia-Hinchado et al., 2021; Roselló et al., 2020; Stern et al., 2013; Thorell et al., 2019). Together, these results suggest that the present sample is representative of the broader ADHD population described in previous empirical studies, reinforcing the reliability and ecological validity of the findings.

Food preparation may depend on the individual’s time availability, skills, food and nutrition knowledge, and lifestyle, as well as their household’s characteristics (Fernandez et al., 2020). To the best of our knowledge, no literature examined the interaction between these components among adults with ADHD. The results of the current study indicate that adults with ADHD reported lower self-efficacy regarding food preparation, including grocery shopping, compared to adults without ADHD. This reflects reduced confidence in their ability to plan and execute meal preparation. Items in this subscale refer to the use of various EFs (e.g., planning, decision-making, and problem-solving) in daily food preparation (Supplemental Table S1). In addition, both BRIEF-A indices correlated with the CAFPAS self-efficacy subscale. The act of cooking a meal requires several EFs, including multitasking, planning, using prospective memory, and maintaining and completing both sub-goals and overall goals within a strict timeframe (Craik & Bialystok, 2006). These EFs might be limited among adults with ADHD, leading to decreased planning and performance of home routines (e.g., Björk et al., 2023; Ek & Isaksson, 2013), including cooking and meal preparation.

Specifically, metacognitive abilities were more closely related to food-preparation skills, whereas behavioral regulation played a more substantial role in shaping QoL. This suggests that each EF component may play a different role in explaining food preparation and QoL, underscoring the need for further research to clarify these distinct contributions. Nevertheless, although there were differences between the BRIEF-A indices, it is important to note that these indices represent primarily “cold” EFs. Current knowledge suggests that EFs can be divided into “cold” EFs, which are considered purely cognitive, and “hot” EFs, which are emotionally laden and involved in motivation, reward sensitivity, and affect-driven decision-making (Salehinejad et al., 2023; Ward, 2019). Given that eating behaviors and meal preparation may also occur in emotionally charged or reward-based contexts, future research should explore whether “hot” EFs also contribute to reduced confidence and challenges in everyday meal preparation among adults with ADHD. Such investigations may clarify whether food-related self-efficacy is shaped not only by cognitive EFs but also by motivational and emotional processing mechanisms.

Alongside the well-documented challenges, there are positive aspects of ADHD, such as creativity, hyperfocus, flexibility, and energy (e.g., Nordby et al., 2023; Schippers et al., 2022; Sedgwick et al., 2019). Further investigation of how these strengths may support adults with ADHD in the context of food preparation is warranted. Understanding underlying executive and other abilities may help clinicians work with their clients to manage EF impairments that translate into difficulties in their everyday occupational performance (Connor & Maeir, 2011; Finnanger et al., 2022). Therefore, future studies, including qualitative methods, should examine whether interventions that target EF strategies while facilitating positive abilities and strengths can improve everyday meal preparation and cooking.

The lack of group differences on the CAFPAS attitudes subscale may reflect the participants’ overall lifestyle and eating habits. No group differences were observed in BMI, and the mean BMI among adults with ADHD was 27 (SD = 7), which suggests a range from normal weight to mild obesity. This distribution appears broadly consistent with the patterns observed in the general adult population, in which more than half the adults are overweight or obese (Organisation for Economic Co-Operation and Development, 2023). Therefore, it is possible that participants with ADHD in our sample did not experience substantial lifestyle-related or dietary difficulties. Although some research has reported higher rates of obesity and unhealthy eating behaviors in adults with ADHD compared with those without the disorder (Cortese et al., 2016; Cortese, Ramos Olazagasti, et al., 2013; Cortese & Tessari, 2017; Weissenberger et al., 2017), other studies have indicated that BMI differences may not always be marked across all adult subgroups, and BMI distributions in individuals with ADHD can span from normal to mildly elevated ranges, consistent with general population data (Cortese, Faraone, et al., 2013).

Interestingly, we found no significant group differences on the CAFPAS structure subscale, which assesses perceived time-related barriers. One possible explanation is that adults in this sample may not be primarily responsible for meal preparation in their households. Support from others may serve as a coping strategy to manage ADHD-related challenges, as social and personal factors have been shown to support everyday functioning in adults with ADHD (Atique et al., 2025). This may reduce the perception of time constraints and task burden.

Another possible explanation could be differences in self-awareness. Adults with ADHD may recognize difficulties in their abilities or self-efficacy (Butzbach et al., 2021) without necessarily perceiving external situational barriers. Finally, even adults without an ADHD diagnosis can experience daily life demands and cognitive load that interfere with their ability to execute cooking preparations as they would ideally wish. Together, these explanations suggest that food-preparation challenges among adults with ADHD may stem more from internal factors, such as executive functioning and task initiation, than from actual environmental constraints. Future research, including qualitative studies, could provide deeper insight into how internal and environmental factors interact with compensatory strategies to shape everyday meal preparation among adults with ADHD.

Extending this perspective, the few residual correlations between specific CAFPAS subscales and QoL domains indicate that, even after controlling for ADHD symptoms and EFs, there are still associations between food-preparation skills and QoL. The CAFPAS self-efficacy subscale was associated with physical health and environmental QoL, the attitudes subscale with physical health QoL, and the structure subscale with environmental QoL. These results suggest that the relationship between food preparation and QoL may involve additional shared influences or reflect a direct association between these constructs that is not accounted for in the present model. These findings highlight the importance of considering additional individual and environmental factors when examining everyday functioning, which warrants further investigation.

Limitations and Future Directions

We acknowledge several limitations of this study. First, the sample included more women than men, which should be considered when interpreting the generalizability of our findings. However, no significant between-group differences were found for gender. Second, we did not collect information on the specific types of medications used, nor did we assess comorbid conditions; both may influence food-preparation skills, EFs, and QoL and should be considered in future research. Third, although the sample size meets commonly accepted criteria for between-group comparisons, we did not specifically address power for detecting indirect effects in the integrated mediation model. Therefore, the indirect effects should be interpreted as exploratory. Fourth, the examination of multiple indirect pathways across several food-preparation and QoL outcomes increases the risk of Type I error, even though the pairwise group comparisons reported in Objective 1 were adjusted using FDR correction. These considerations warrant caution in interpreting the indirect associations and underscore the need for replication in larger samples. Fifth, although we based the group assignment on an ADHD self-report, we asked participants to confirm that they had received a formal diagnosis from a qualified medical professional, and significant group differences found on the ASRS-v1.1 validated this allocation. Finally, the CAFPAS was translated into Hebrew for this study, and this was its first use among a population in Israel. Internal consistency was good to high, yet further research on this measure in general, and in Hebrew particularly, is required.

Conclusions

Overall, the findings contribute to understanding occupation and health by presenting the challenges in the daily living activity of food preparation among adults with ADHD. The findings suggest that these challenges may lie primarily in the domain of food-preparation skills themselves, which rely on various cognitive processes, particularly “cold” EFs. Nevertheless, our study suggests that contextual and environmental factors may also play a role in shaping food-preparation performance and QoL in this population. By focusing on a practical daily living skill that has been relatively underexplored in adults with ADHD, our findings highlight the functional challenges this population faces and offer potential avenues for occupational therapy interventions.

Interventions for individuals with ADHD could benefit from targeting specific EF components to improve food-preparation skills and QoL. Occupational therapists should evaluate the environmental and contextual factors that support daily functioning and reduce task burden. Incorporating strategies that leverage positive ADHD-related traits and strengths may further enhance engagement and confidence in food preparation. More research is needed to gain a broader understanding of the multiple factors linked to eating habits, food-preparation skills, and cooking among adults with ADHD. This could inform interventions that integrate cognitive, emotional, and contextual strategies to better support the daily living skills of food preparation.

Supplemental Material

sj-docx-1-otj-10.1177_15394492261459937 – Supplemental material for Food-Preparation Skills of Adults With ADHD and the Mediating Effect of Executive Functions

Supplemental material, sj-docx-1-otj-10.1177_15394492261459937 for Food-Preparation Skills of Adults With ADHD and the Mediating Effect of Executive Functions by Adi Stern and Kineret Sharfi in OTJR: Occupational Therapy Journal of Research

Footnotes

Acknowledgements

The authors would like to thank the participants who voluntarily participated in this study. The authors also thank Dr. Amihai Rigbi for his valuable statistical consultation and guidance throughout the data analysis process.

ORCID iDs

Adi Stern

Kineret Sharfi

Ethical Considerations

The Human Subjects Research Committee at Ben-Gurion University of the Negev approved this study (approval no. 871-2) on October 20, 2024.

Consent to Participate

Respondents gave written consent prior to participation in the study.

Consent for Publication

All authors have read and approved the final version of the manuscript and consent to its publication.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author on request.

AI Use Statement

We use a ChatGPT tool solely to assist with language editing and improvement of clarity in the preparation of this manuscript. No generative AI tools were used for data analysis, result interpretation, drawing scientific conclusions, or creating original content. The authors reviewed and verified the accuracy of all content and take full responsibility for the work.

Supplemental Material

Supplemental material for this article is available online.

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