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Abstract

Maladaptive daydreaming (MD) is an urge-driven mental activity characterized by excessive, immersive daydreaming that disrupts daily life and leads to functional distress. However, MD is not formally recognized in diagnostic manuals, and urgent work is required to better understand its conceptualization and underlying mechanisms. MD has been linked to various mental health and neurodevelopmental conditions, including obsessive-compulsive disorder (OCD), attention-deficit hyperactivity disorder (ADHD), and autism. Yet, disentangling their individual contributions to MD remains challenging due to their frequent co-occurrence. This study investigated whether OCD, ADHD, and autistic traits significantly predicted MD traits and examined their relative contributions, while accounting for their co-occurrence. An adult sample (N = 294) completed self-report trait measures of OCD, ADHD, autism, and MD. Multiple regression analyses indicated that both OCD and ADHD traits, but not autistic traits, significantly predicted MD traits, with ADHD traits being the strongest predictor. Further analyses identified two subcomponents of OCD (obsessing, neutralizing) and both subcomponents of ADHD (inattention, hyperactivity) as significant predictors of MD traits. These findings suggest that OCD and, particularly, ADHD are more central to understanding the development of MD, providing insights into potential shared mechanisms that could inform future assessments and interventions for MD.

Lay Abstract

Maladaptive daydreaming (MD) is characterized by a strong urge to create vivid, immersive stories in one's mind. As these daydreams can be highly absorbing and engaging, they can interfere with daily life and often lead to distress. MD has not yet been recognized as a clinical condition, and more research is needed to better understand the mental processes that drive this condition. MD has been linked with other conditions, such as obsessive-compulsive disorder (OCD), attention-deficit hyperactivity disorder (ADHD), and autism. These three conditions often occur together, making it unclear which one influences people's level of MD the most. To better understand these relationships, we recruited 294 adults and statistically examined how traits typical of OCD, ADHD, and autism contribute to MD, while accounting for the overlap between these conditions. We found that ADHD traits were the most strongly linked to people's level of MD, and there was a weaker association between OCD traits and level of MD. In contrast, people's autistic traits did not predict their level of MD. Looking at the data more closely, we found that both ADHD traits associated with inattention and those linked to hyperactivity/impulsivity were related to people’s level of MD. When it came to OCD, only traits related to obsessing and those linked to neutralizing were associated with MD. This study provides new insights into MD and highlights potential characteristics that may contribute to this behavior. Crucially, our work helps to improve our understanding of MD as a condition and informs future assessment and interventions for MD.

Keywords

maladaptive daydreaming OCD ADHD autism

Introduction

Maladaptive daydreaming (MD) is a mental activity characterized by excessive, immersive daydreaming that interferes with an individual's daily functioning. While most people engage in daydreaming as a normal part of their cognitive experience, MD is distinct in its intensity and associated distress (Soffer-Dudek et al., 2025; Somer et al., 2025). Despite its prevalence and functional impact, MD remains under-researched and is not yet formally recognized as a clinical condition in current diagnostic manuals. Existing research has primarily focused on defining and identifying MD, leaving a critical knowledge gap in understanding the underlying mechanisms that contribute to its development. Crucially, investigating the extent to which psychological and neurodevelopmental traits contribute to MD can offer valuable insights that enhance our understanding of this phenomenon.

While daydreaming is typically regarded as a form of simple mind-wandering, psychological research in the early 2000s began to identify a qualitatively distinct form of daydreaming. This phenomenon was described as a highly focused and absorbing mental activity, where one's mind was deeply engaged in inventing vivid, fantastical plots and stories that were emotionally evocative, unrealistic, and typically detached from the daydreamer's actual life (Schupak & Rosenthal, 2009; Somer, 2002). This activity was further characterized as purposeful rather than spontaneous, driven by a strong urge to engage in the daydream, and accompanied by short-term enjoyment and pleasure. However, due to its immersive and absorbing qualities, this form of daydreaming could persist for several hours at a time, significantly interfering with daily functioning and thus highlighting its potentially maladaptive nature (Bigelsen & Schupak, 2011). Following these initial case studies and cohort-based reports showing that daydreaming can evolve into an extreme and maladaptive behavior (Bigelsen & Schupak, 2011; Schupak & Rosenthal, 2009; Somer, 2002), research has started to highlight the need to view MD as a clinically significant condition.

To facilitate the collection of generalizable evidence on MD, Somer and colleagues developed a measure of MD—the Maladaptive Daydreaming Scale (MDS; Somer, Lehrfeld et al., 2016). This scale was based on both the reports of daydreaming experiences from individuals seeking support for MD, as well as input from daydreaming experts (e.g., clinical practitioners, researchers). Among the versions of this scale, the widely used 16-item version (MDS-16) includes items referring to four core aspects of MD: yearning (i.e., an intense urge to daydream), impairment, kinesthesia (i.e., repeated, ritualized movements), and a need for evocative music, which together capture the uniqueness of MD (Somer et al., 2017b). Using the MDS-16 to quantitatively measure MD and identify individuals with elevated levels of MD, researchers have provided three important pieces of evidence that support the need for MD to be considered a distinct condition.

First, engaging in MD has been consistently linked to significant impairment in everyday functioning across various research designs. Both case studies and qualitative studies of individuals seeking help for MD have highlighted an association between MD and emotional and functional distress (Chaudhary et al., 2022; Chefetz et al., 2023; Pietkiewicz et al., 2018; Shanbhag & Pothiyil, 2024; Somer, 2018; Somer, Lehrfeld et al., 2016). Similarly, correlational studies have demonstrated a statistical link between higher levels of MD traits and poor well-being (e.g., Abu-Rayya et al., 2019; Pietkiewicz et al., 2023; Schimmenti et al., 2019). Recently, a meta-analysis including 24,977 individuals across 40 studies revealed that MD traits are positively associated with emotion regulation difficulties, loneliness, and negatively associated with self-efficacy and self-esteem (Somer et al., 2025). Longitudinal studies further highlight MD traits as a risk factor for increased psychological distress and heightened negative emotions (Musetti et al., 2023; Soffer-Dudek & Somer, 2018). More broadly, MD has been associated with worse life outcomes, including elevated rates of unemployment and attempted suicide (Soffer-Dudek & Somer, 2018; Somer et al., 2017a).

Second, studies have revealed a unique presentation of symptoms in MD. The core distinguishing feature of MD is dissociative absorption, where an individual deeply immerses themselves into a vivid inner world, focusing their attention primarily inward rather than to the outward environment (Ross et al., 2020; Soffer-Dudek & Somer, 2018). This internal absorption can be enhanced and/or triggered by external, evocative stimuli like music. People engaging in MD report performing kinesthetic movements such as pacing, facial expressions, and limb stretching, as well as listening to music while daydreaming (Bigelsen & Schupak, 2011; Bigelsen et al., 2016; Somer, 2024). Importantly, while MD has been linked to various conditions, such as attention-deficit hyperactivity disorder (ADHD), anxiety disorders, depressive disorders, dissociative disorders, and obsessive-compulsive disorder (OCD) (Somer et al., 2025), the range of MD symptoms cannot be fully explained by any other known condition, suggesting that this symptomology is unique to MD (Bigelsen et al., 2016; Ross et al., 2020; Soffer-Dudek et al., 2025; Somer et al., 2017a).

Third, a recent epidemiological study on MD showed its prevalence to be approximately 4.2%, which is comparable to the prevalence of common internalizing conditions, such as generalized anxiety disorder (GAD) and social anxiety disorder (Soffer-Dudek & Theodor-Katz, 2022). This finding underscores the importance of recognizing MD as a clinical condition, as it affects a notable proportion of the population who would benefit from access to formal support.

Despite establishing the validity and reliability of MD as a unique construct, MD has yet to be considered a distinct condition in diagnostic manuals (Soffer-Dudek et al., 2025). This lack of recognition presents a notable problem for people with MD symptoms as they cannot receive an accurate diagnosis and, most importantly, cannot access targeted treatment, leading to further loneliness and distress (Bershtling & Somer, 2018). Individuals seeking help for their MD symptoms report that their symptoms have been summarily dismissed or contested by healthcare professionals who provided misdiagnoses and unsuccessfully attempted to treat them for more prevalent diagnoses, such as anxiety (Somer & Soffer-Dudek, 2025; Somer, Somer et al., 2016). To facilitate the recognition of MD as a clinical condition and better support those experiencing related challenges, it is imperative to develop a clearer ontological conceptualization of MD. This includes pinpointing the potential pathways and mechanisms underlying its development and manifestation.

One approach in which research can address this issue is by studying the co-occurrence of MD with other established conditions. Identifying associations between MD and these conditions can provide insights into shared underlying mechanisms, which could, in turn, highlight validated interventions that may benefit individuals with MD (Soffer-Dudek & Somer, 2018). A particularly notable pattern of co-occurrence that remains under-explored is the relationship between MD and other established mental health and neurodevelopmental conditions, namely OCD, ADHD, and autism spectrum disorder (hereafter autism).

Focusing on the link between OCD and MD, studies have indicated that individuals reporting symptoms of MD show elevated OCD symptoms (Bigelsen et al., 2016), with approximately 55% meeting the clinical threshold for OCD (Salomon-Small et al., 2021; Somer et al., 2017a). Moreover, studies across the general and clinical populations have shown a positive correlation between MD traits and OCD traits (e.g., Conte et al., 2022; Gemignani et al., 2025; Ross et al., 2020; Somer et al., 2020; see Somer et al., 2025 for a meta-analysis), including separate associations with both obsession and compulsive traits of OCD (Salomon-Small et al., 2021; Somer, Lehrfeld et al., 2016). Further evidence from individuals with elevated MD traits has found both MD traits to be a significant predictor of OCD traits (Gemignani et al., 2025; Salomon-Small et al., 2021) and OCD traits to be a significant predictor of MD traits (Ross et al., 2020). Notably, OCD traits have been shown to moderate the relationship between general psychopathological symptoms and the degree to which MD interferes with daily life (Chirico et al., 2024). A longitudinal study by Soffer-Dudek and Somer (2018) collecting daily reports from individuals with MD over 14 days further showed that OCD symptoms were the only consistent temporal antecedent of later MD symptoms. Collectively, these findings indicate a strong association between OCD and MD traits, supporting the possibility of overlapping psychological processes that involve urge-driven cognition and impaired control over intrusive thoughts.

Studies exploring the link between ADHD and MD have also highlighted notable overlaps between the two conditions, with individuals with MD endorsing higher rates of ADHD symptoms (Bigelsen et al., 2016). Specifically, research has shown that 23–37% of an ADHD adult sample meet the criteria for MD (Pyszkowska et al., 2025; Theodor-Katz et al., 2022) and 77% of a MD adult sample meet the criteria for ADHD (Somer et al., 2017a). In line with this, when examined through a trait-based continuous approach, ADHD traits and MD traits were consistently found to be significantly and positively associated across non-clinical samples, neurodivergent groups (e.g., individuals with ADHD or autism), and samples exhibiting clinical levels of MD (Conte et al., 2022; Metin et al., 2022; Somer et al., 2020; Theodor-Katz et al., 2022; West et al., 2022a, 2022b; see Somer et al., 2025 for a meta-analysis). Further evidence from clinical samples has found both MD traits to be a significant predictor of ADHD traits (Kandeğer et al., 2025) and ADHD traits to be a significant predictor of MD traits (Pyszkowska et al., 2025). These findings indicate that, although MD is a distinct condition from ADHD, they may share subclinical underlying mechanisms, such as difficulties in sustaining attention on dull stimuli and the ability to hyperfocus.

Although fewer studies have explored the relationship between autism and MD, there is some evidence of an overlap between these conditions. Specifically, 41–43% of autistic adults in sampled populations reported elevated symptoms of MD (Pyszkowska et al., 2025; West et al., 2022b), and early studies on MD found that 24% of individuals in an MD sample reported impaired social skills and difficulties with relationships, which are core features of autism (Bigelsen & Schupak, 2011). Moreover, a positive association between autistic traits and MD traits has been observed both in individuals reporting MD (West et al., 2022b) and in a sample of individuals with ADHD, autism, or both (Pyszkowska et al., 2025). Importantly, while autistic traits are linked to MD traits, research indicates that loneliness and emotion regulation difficulties significantly mediate this relationship (West et al., 2022a), suggesting these factors may serve as underlying mechanisms connecting the two.

While the links between OCD, ADHD, and autistic traits with MD have each been individually explored, investigating these conditions simultaneously is a crucial next step. This is because the three conditions frequently co-occur (Antshel et al., 2016; Sharma et al., 2021), making it difficult to isolate their individual contributions to MD. Addressing the significant overlap among these traits is essential for determining whether particular traits independently predict MD or if their predictive power is influenced by their co-occurrence with other traits. Disentangling the links between OCD, ADHD, autistic, and MD traits would provide crucial insights into clarifying the shared underlying mechanisms of MD, advancing its clinical conceptualization. This approach could have important implications for the clinical assessment and future intervention for MD.

Current Research

To address gaps in the existing literature, the current study aimed to examine the individual contributions of OCD, ADHD, and autistic traits to MD traits, while accounting for the effects of each trait construct on the others. Due to the lack of clinical validation for MD diagnoses, recruiting sufficiently large clinical samples to achieve adequate statistical power remains a significant barrier in MD research. Moreover, finding samples with MD that are diagnosed with co-occurring OCD, ADHD, and/or autism poses further challenges for a closer examination of these links. To overcome these limitations and ensure well-powered research, this study adopted a trait-based approach. This approach allows for a larger and more accessible sample from the general population, which is suitable for investigating the complex interplay of OCD, ADHD, and autism in relation to MD (Krueger & Piasecki, 2002; Layinka et al., 2024; Lyall, 2023), given their dimensional distribution across the general population (Fullana et al., 2010; Robinson et al., 2011; Vogel et al., 2018). Drawing on existing literature, we hypothesized that all three trait constructs would significantly predict MD traits, with their relative predictive strengths explored.

Method

Participants

Two hundred ninety-four adults (208 female; 86 male), aged 21–80 years (M = 42.79, SD = 13.81), were recruited via Prolific (www.prolific.co), an online participant recruitment platform with multiple verification processes. This sample size was sufficient to achieve 95% power to detect small-to-medium effects (f² = 0.08) in our regression analyses (α = .05, two-tailed) (Faul et al., 2007). All participants were residents of the United Kingdom (UK) and had passed both of the two attention checks embedded in the survey.

All participant data were pseudonymized via Prolific IDs, which are 16-character alphanumeric codes. These IDs do not contain personally identifiable information and were used solely for managing participation and compensation. The research team did not have access to any personal identifying details. Prolific IDs were removed prior to data analysis and before making any data publicly available. Participants provided informed consent electronically for study participation and open-access sharing of their anonymized data (i.e., without their Prolific IDs) and received financial compensation for their time.

All procedures performed were in accordance with the ethical standards of the institutional committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Ethical approval for this study was granted by the University of Bath Department of Psychology Research Ethics Panel (REF: 1832–2327).

Measures and Procedure

Demographic information, including age, sex, ethnicity, education, employment, and mental health diagnoses, was collected to characterize the sample. Participants completed the following self-report measures in a randomized order, all of which showed good-to-excellent internal consistency in the present study (see Table S1 in the Supplemental Materials).

OCD traits were measured using the revised 18-item Obsessive–Compulsive Inventory (OCI-R; Foa et al., 2002). Participants rated the extent to which different experiences had distressed or bothered them over the past month on a five-point Likert scale ranging from 0 (not at all) to 4 (extremely). The OCI-R produces a total OCD trait score between 0 and 60, along with five subtotal scores between 0 and 12, that reflect washing, obsessing, ordering, checking, and neutralizing. Higher scores indicated more OCD symptoms. To align with DSM-5 classification and focus specifically on OCD, the hoarding subscale was not included in this study, and the total score excluded items from this subscale (see Wootton et al., 2015).

ADHD traits were measured using the 18-item Adult ADHD Self-Report Scale (ASRS; Kessler et al., 2005). Participants rated the frequency of symptoms related to inattention and hyperactivity/impulsivity over the past six months using a 5-point Likert scale ranging from 0 (never) to 4 (very often). This produces a total score between 0 and 72, along with two subscale scores between 0 and 36 that reflect attention and hyperactivity/impulsivity. Higher scores indicated more ADHD traits.

Autistic traits were measured using the abridged 28-item version of the Autism-Spectrum Quotient (AQ-Short; Hoekstra et al., 2011). Participants indicated their agreement with statements related to social and non-social autistic traits on a 4-point Likert scale ranging from 1 (definitely agree) to 4 (definitely disagree). The AQ-Short produces a total score ranging from 28 to 112, along with five subscales of varying number of statements and subtotal score ranges: difficulties with social skills (seven items, 7–28), routine (four items, 4–16), attention switching (four items, 4–16), imagination (eight items, 8–32), and a fascination for numbers/patterns (five items, 5–20). Higher scores indicated more autistic traits.

MD traits were measured using the 16-item Maladaptive Daydreaming Scale (MDS-16; Somer, Lehrfeld et al., 2016; Somer et al., 2017b), which assesses the strength of the urge to daydream, degree of vocational, social, or functional impairment, kinesthetic activity during daydreaming, and the triggering effect of music on daydreaming. Participants rated the extent to which each statement reflected their experiences over the preceding month on an 11-point Likert scale ranging from 0% (not at all) to 100% (the fullest extent). A total MD trait score is obtained by averaging across all items. This produces a total score between 0 and 100, with higher scores indicating more MD traits.

Data Analysis

Data were analyzed in JASP (version 0.18.3; JASP Team, 2024), and the open data, variable dictionary, and analysis syntax are provided as Supplemental Materials. The interrelationships among all variables were first examined using Pearson's (for continuous variables) and point-biserial (for binary variables) correlations. Multiple linear regression using a Forced Entry approach was then conducted to determine whether OCD, ADHD, and autistic traits independently predicted MD traits, while controlling for participants’ age and sex given their potential effects on MD (Soffer-Dudek & Theodore-Katz, 2022; Somer et al., 2025).

Table S2 presents the full results of the assumption checks. Overall, the data met the assumption of independent errors, linearity, and lack of multicollinearity. Two datapoints were identified as potential outliers based on their studentized residuals; however, as removing them did not alter the results (see Tables S3 and S4 for sensitivity analyses), they were retained in the dataset (Sullivan et al., 2021). To address the potential violations of normality and homoscedasticity assumptions, 95% bootstrap confidence intervals (5000 resamples) were reported to complement the ordinary least squares estimates. Where any of the three key variables of interest (OCD, ADHD, autistic traits) significantly predicted MD traits, additional exploratory regression analyses were performed to further explore their subcomponents as predictors.

To determine which predictor has a stronger influence on MD traits, both standardized beta coefficients (β) and squared semi-partial correlations (sr²) were used. The standardized beta coefficients reflect changes in the standard deviations of a predictor and indicate whether these changes lead to a significant effect on MD traits, with all other predictors held constant. The magnitude of the standardized beta coefficient represents the size of the predictor's effect. On the other hand, squared semi-partial correlations measure the unique contribution of each predictor to the variance in MD traits, providing an estimate of how much variance is explained by a single predictor, independent of other predictors. Squared semi-partial correlation values of ≥0.01, ≥0.09, and ≥0.25 are interpreted as small, medium, and large effects, respectively (Cohen et al., 2003). By examining both indices, we assessed which predictors had the strongest individual effects and unique contributions to MD traits (Field, 2018).

Results

Descriptive statistics of the sample characteristics and study measures are summarized in Table 1. Of the 294 participants, 40 (13.61%) scored above the cut-off score of 40 on the MDS-16, suggestive of suspected clinical-level MD (Somer, Lehrfeld et al., 2016; Theodor-Katz et al., 2022).

Table 1.

Demographic Characteristics of the Sample.

Characteristic	n (%)
Sex
Male	86 (29.25)
Female	208 (70.75)
Ethnicity
White	266 (90.48)
Non-White	28 (9.52)
Education
<Bachelors degree	114 (38.78)
≥Bachelors degree	180 (61.22)
Employment
Employed	228 (77.55)
Unemployed	66 (22.45)
Mental health diagnoses
OCD	5 (1.70)
ADHD	8 (2.72)
Autism	8 (2.72)
Other	132 (44.90)
No diagnosis	157 (53.40)
	M (SD)
Age	42.79 (13.81)
OCD traits (OCI-R)	12.51 (10.62)
Washing	1.54 (2.48)
Obsessing	3.06 (3.25)
Ordering	3.53 (3.25)
Checking	2.98 (2.84)
Neutralizing	1.40 (2.04)
ADHD traits (ASRS)	29.40 (13.38)
Inattention	16.17 (6.96)
Hyperactivity/Impulsivity	13.23 (7.24)
Autistic traits (AQ-Short)	64.60 (11.92)
Social skills	17.31 (4.91)
Routine	10.39 (2.58)
Attention switching	9.52 (2.40)
Imagination	16.45 (4.03)
Numbers/Patterns	10.92 (3.74)
MD traits (MDS-16)	18.70 (16.49)

Intercorrelations between all predictors and the outcome for the main analysis are summarized in Table 2. More OCD traits, ADHD traits, and autistic traits were all correlated with more MD traits (all rs ≥ 0.35, ps < 0.001), while older age was correlated with fewer MD traits (r = −0.26, p < 0.001). Sex was not significantly correlated with MD traits. In line with the expected overlap between OCD, ADHD, and autistic traits, the three variables were positively correlated with each other (all rs ≥ 0.41, ps < 0.001), indicating that more traits in one construct were associated with more traits in the others. Moreover, older age was significantly associated with more traits in all three constructs (OCD, ADHD, and autistic traits; all rs ≤ −0.15, ps ≤ 0.012).

Table 2.

Correlations Between Predictor and Outcome Variables.

Measures	1.	2.	3.	4.	5.
1. OCD traits	-
2. ADHD traits	0.53***[0.44, 0.60]	-
3. Autistic traits	0.50***[0.41, 0.58]	0.41***[0.31, 0.50]	-
4. Age	−0.31***[−0.41, −0.20]	−0.33***[−0.43, −0.23]	−0.15*[−0.26, −0.03]	-
5. Sex	−0.02 ^NS[−0.14, 0.09]	−0.02 ^NS[−0.14, 0.09]	0.06 ^NS[−0.06, 0.17]	−0.06 ^NS[−0.18, 0.05]	-
6. MD traits	0.51***[0.42, 0.59]	0.54***[0.45, 0.61]	0.35***[0.24, 0.44]	−0.26***[−0.37, −0.16]	−0.02 ^NS[−0.13, 0.10]

Note. N = 294. Sex is coded as 0 = female, 1 = male. ^NS p > .05, * p < .05, ** p < .01, *** p < .001. 95% confidence intervals for the correlation coefficient are shown in square brackets.

The multiple linear regression assessing the unique associations of OCD, ADHD, and autistic traits with MD traits whilst accounting for participants’ age and sex as covariates is shown in Table 3. The model explained a significant proportion of variance in MD traits (F(5, 288) = 32.57, p < 0.001, R² = 0.36). Both OCD traits and ADHD traits were found to be significant unique predictors of MD traits (both ps < 0.001). Specifically, having more OCD traits and having more ADHD traits were both associated with higher levels of MD. While both predictors showed moderate strength in predicting MD traits, ADHD traits demonstrated a stronger influence (β = 0.35, sr² = 0.080), as indicated by their higher standardized beta coefficient and squared semi-partial correlation compared to OCD traits (β = 0.27, sr² = 0.045). In contrast, autistic traits, age, and sex did not significantly predict MD traits (all ps ≥ 0.262).

Table 3.

Main Multiple Regression Results for the Collected Data.

Predictor	B (SE)	95% CIs B	β	p	sr²
OCD traits	0.43 (0.09)	[0.21, 0.64]	0.27	<0.001	0.045
ADHD traits	0.43 (0.07)	[0.25, 0.59]	0.35	<0.001	0.080
Autistic traits	0.09 (0.08)	[−0.06, 0.24]	0.06	0.262	0.003
Age	−0.07 (0.06)	[−0.18, 0.06]	−0.06	0.282	0.003
Sex	−0.38 (1.72)	[−3.56, 3.14]	−0.01	0.825	<0.001

Note. 95% bias-corrected and accelerated bootstrap confidence intervals for B using 5,000 resamples. Sex is coded as 0 = female, 1 = male. SE: standard error; CI: confidence interval.

Building on the main findings, two additional exploratory multiple regression analyses using a Forced Entry approach were performed to determine which subcomponents of OCD and ADHD contributed to their unique influence on MD traits, controlling for the overall traits of the other construct, autistic traits, age, and sex. Both models explained a significant proportion of variance in MD traits (F(9, 281) = 24.77, p < 0.001, R² = 0.44 and F(6, 284) = 30.63, p < 0.001, R² = 0.39, respectively).

As shown in Table 4, only obsessing traits and neutralizing traits of OCD were significant and unique predictors of MD traits (both ps ≤ 0.036), while accounting for ADHD traits, autistic traits, age, and sex. Specifically, more obsessing traits and neutralizing traits of OCD were associated with more MD traits. Notably, obsessing traits demonstrated a stronger contribution to MD traits (β = 0.27, sr² = 0.037), as indicated by their higher standardized beta coefficient and squared semi-partial correlations, compared to neutralizing traits (β = 0.13, sr² = 0.009). Washing, ordering, and checking traits of OCD did not significantly predict MD traits (all ps ≥ 0.052). This pattern of results remained in the sensitivity analysis following the exclusion of three potential outliers, with washing traits also reaching significance (see Table S6).

Table 4.

Multiple Regression Results Including Subcomponents of OCD for the Collected Data.

Predictor	B (SE)	95% CI B	β	p	sr²
Washing traits	0.78 (0.40)	[−0.24, 1.97]	0.12	0.052	0.008
Obsessing traits	1.35 (0.32)	[0.60, 2.17]	0.27	<0.001	0.037
Ordering traits	−0.39 (0.30)	[−1.02, 0.27]	−0.08	0.190	0.004
Checking traits	−0.23 (0.36)	[−1.10, 0.57]	−0.04	0.511	<0.001
Neutralizing traits	1.01 (0.48)	[0.07, 2.18]	0.13	0.036	0.009
ADHD traits	0.40 (0.07)	[0.22, 0.57]	0.32	<0.001	0.065
Autistic traits	0.08 (0.08)	[−0.07, 0.24]	0.06	0.304	0.002
Age	−0.03 (0.06)	[−0.14, 0.09]	−0.02	0.626	<0.001
Sex	−0.51 (1.70)	[−3.74, 3.11]	−0.01	0.765	<0.001

Note. Sex is coded as 0 = female, 1 = male. SE: standard error; CI: confidence interval. Assumption checks are summarized in Table S5, and sensitivity analyses are reported in Table S6.

As shown in Table 5, both inattention traits and hyperactivity traits of ADHD were significant and unique predictors of MD traits (both ps ≤ 0.032), while controlling for OCD traits, autistic traits, age, and sex. Specifically, more inattention traits and more hyperactivity traits of ADHD were associated with more MD traits. Notably, the two subcomponents showed similar influence on MD traits, as indicated by their standardized beta coefficients and squared semi-partial correlations (inattention traits: β = 0.20, sr² = 0.015; hyperactivity traits: β = 0.17, sr² = 0.010). This pattern of results was largely retained in the sensitivity analysis following the exclusion of three potential outliers, although the association for hyperactivity traits was reduced to a trend level (see Table S8).

Table 5.

Multiple Regression Results Including Subcomponents of ADHD for the Collected Data.

Predictor	B (SE)	95% CI B	β	p	sr²
OCD traits	0.43 (0.10)	[0.20, 0.64]	0.28	<0.001	0.045
Inattention traits	0.48 (0.19)	[0.07, 0.86]	0.20	0.011	0.015
Hyperactivity traits	0.38 (0.18)	[0.02, 0.78]	0.17	0.032	0.010
Autistic traits	0.08 (0.08)	[−0.06, 0.24]	0.06	0.291	0.002
Age	−0.06 (0.06)	[−0.18, 0.05]	−0.05	0.289	0.003
Sex	−0.38 (1.72)	[−3.73, 3.01]	−0.01	0.827	<0.001

Note. Sex is coded as 0 = female, 1 = male. SE: standard error; CI: confidence interval. Assumption checks are summarized in Table S7, and sensitivity analyses are reported in Table S8.

Discussion

This is the first study to investigate whether OCD, ADHD, and autism are linked to MD at the trait level, while accounting for their co-occurrence. Crucially, we found that, among OCD, ADHD, and autistic traits, ADHD traits were the strongest predictor of MD traits in our general population sample, with OCD but not autistic traits also significantly predicting MD traits. Furthermore, we found some evidence to suggest that specific subcomponents of both ADHD (inattention and hyperactivity) and OCD (obsessing and neutralizing) were uniquely associated with MD. Overall, these findings add nuance to our current understanding of MD by highlighting its complex links with other established conditions.

Our findings are consistent with previous literature and demonstrate a positive, trait-level association between OCD and MD (Chirico et al., 2024; Conte et al., 2022; Gemignani et al., 2025; Ross et al., 2020; Salomon-Small et al., 2021; Soffer-Dudek & Somer, 2018; Somer et al., 2025; Somer, Lehrfeld et al., 2016). We further elucidated this relationship by demonstrating that OCD traits have a significant unique contribution to MD traits, above and beyond their shared variance with ADHD and autistic traits (Antshel et al., 2016; Sharma et al., 2021). Critically, our exploratory analyses showed that this association was driven by its obsessing and, to a lesser extent, neutralizing subcomponents.

This pattern of results aligns with the limited prior research indicating that both obsessive (e.g., obsessing traits) and compulsive aspects (e.g., neutralizing traits) of OCD are related to MD traits. It also suggests that MD may be more closely associated with obsession than compulsion tendencies (Salomon-Small et al., 2021; Somer, Lehrfeld et al., 2016). One possible psychological mechanism that could explain this overlap between MD and obsessions is impaired cognitive control. Indeed, Salomon-Small et al. (2021) suggest that a reduced ability to direct cognitive functions and behaviors in line with one's internal goals could manifest as the difficult-to-control obsessive mental urge present in both conditions (see also Soffer-Dudek & Somer, 2023).

Our findings regarding the trait-level association between ADHD and MD also align with the existing literature (Conte et al., 2022; Kandeğer et al., 2025; Pyszkowska et al., 2025; Somer et al., 2025; Somer, Lehrfeld et al., 2016; Theodor-Katz et al., 2022; West et al., 2022a; 2022b). Importantly, we expanded our understanding of this relationship by demonstrating that ADHD traits have a significant individual contribution to MD traits, even after accounting for their overlap with OCD and autistic symptoms (Antshel et al., 2016; Sharma et al., 2021). Moreover, in our study, it was ADHD traits that were found to be the strongest predictor across all models.

Further support for the idea of potential shared mechanisms between MD and ADHD was offered by our exploratory analysis, which found some preliminary evidence that both the inattention and the hyperactivity/impulsivity subcomponents of ADHD were significant predictors of MD traits with comparable strength. This novel finding, though tentative, indicates that both subcomponents of ADHD may contribute to driving its association with MD, potentially challenging the previously proposed assumption that shared problems with sustaining attention on dull stimuli are the sole basis for their association (Theordor-Katz et al., 2022). The link between inattention and MD could be driven by hyperfocus, which is the intense focusing of attention. The experience of hyperfocus, a common symptom of ADHD (see Ozel-Kizil et al., 2016), may relate to the intense attentional focus on daydreaming that people with MD exhibit. Specifically, a tendency to focus intensely on a given task (in ADHD) or imagined scenario (in MD) may underlie the difficulties with time perception and shifting attention away from the object of hyperfocus seen in both conditions. These traits likely increase the time individuals with MD spend on daydreaming, contributing to the maintenance of the condition (Ashinoff & Abu-Akel, 2021; Soffer-Dudek & Somer, 2023). In contrast, the tentative link between hyperactivity and MD may be driven by kinesthetic movement, a strategy sometimes used by individuals with ADHD to help sustain their attention (e.g., Hartanto et al., 2015). In a similar manner, performing kinesthetic movements could potentially prolong daydreaming emersion in people with MD. Nevertheless, further confirmatory research is now needed to follow up on these exploratory findings.

In contrast to OCD and ADHD, we did not find consistent evidence for a significant trait-level association between autism and MD. Though these constructs were correlated in our zero-order analysis, in line with past research (West et al., 2022a; 2022b; Pyszkowska et al., 2025), this link was no longer significant once we accounted for the effects of OCD traits, ADHD traits, age, and sex. One explanation for our findings is that by controlling for OCD and ADHD traits, loneliness and emotional dysregulation difficulties, which have previously been identified as driving the association between autism and MD traits, were also indirectly accounted for. This is plausible given that both OCD and ADHD overlap with autism and are also strongly related to emotional dysregulation difficulties (Antshel et al., 2016; Greene et al., 2020; Sharma et al., 2021; Shaw et al., 2014) and loneliness (Friedman-Ezra et al., 2024; Jong et al., 2024).

Another plausible explanation for the discrepancy between our findings and those of previous studies regarding the link between autistic traits and MD traits lies in variations in sample characteristics and methodology. While prior research relied on clinical populations with formal diagnoses (e.g., Pyszkowska et al., 2025), our study used a non-clinical sample, characterized by milder or subclinical levels of autistic traits (see Hoekstra et al., 2011). It is possible that the association between autistic traits and MD traits becomes more pronounced at higher, clinically significant levels of autistic traits, where sensory sensitivities and cognitive styles like rumination are more marked (Cooper & Russell, 2024; Normansell-Mossa et al., 2021). In contrast, participants in our sample may not exhibit the intensity of traits required to drive a strong link with MD, particularly considering the role of sensory input such as music and internally focused, repetitive thought patterns in initiating and sustaining immersive daydreaming. This reflects a potential threshold effect.

Additionally, whereas past studies investigating the link between autism and MD used the 10-item and the full 50-item versions of the AQ (Pyszkowska et al., 2025; West et al., 2022b), our study employed the 28-item abbreviated version of the AQ (i.e., AQ-Short). Our study, thus, did not measure traits related to sensory sensitivities and attention to detail (e.g., “I often notice small sounds when others do not”) as previous studies did, which, again, could be important drivers of MD. Together, replication of this study in clinical samples with more extensive self-report measures, such as those that measure sensory sensitivities (e.g., the Comprehensive Autistic Trait Inventory, English et al., 2021; the Ritvo Autism Asperger Diagnostic Scale, Ritvo et al., 2011), would help clarify whether specific autistic trait domains are more predictive of MD.

Notably, although beyond the scope of this study, we found that age was negatively correlated with MD traits, which is consistent with previous literature (Soffer-Dudek & Theodore-Katz, 2022). However, this association was no longer significant when OCD, ADHD, and autistic traits were accounted for. Looking at the correlational results, we speculate that, given the associations between age and these predictors in our sample, the correlation between age and MD traits may be driven by age differences across OCD, ADHD, and autistic traits. Specifically, younger people tend to report more OCD and ADHD traits (Ramakrishnan et al., 2022; Vogel et al., 2018), and thus may have more characteristics that drive MD behaviors.

To our knowledge, this is the first study to simultaneously explore the contributions of OCD, ADHD, and autism to MD. We provide novel insights into the conceptualization of MD and highlight its overlap with key mental health and neurodevelopmental conditions, offering potential avenues for future theoretical research into MD. Our study also has the potential to inform future interventions for MD, a population that is currently unable to access appropriate support due to a lack of clinical recognition. Specifically, our exploratory analyses suggest that interventions targeting obsessing traits in OCD and management strategies for inattention and, to a lesser extent, hyperactivity/impulsivity in ADHD may have the greatest potential to help individuals with MD. A promising direction for intervention could build on existing self-guided programs designed to manage MD symptoms (e.g., Herscu et al., 2023), drawing on our findings that highlight the significant roles of OCD and ADHD traits in the expression of MD. Specifically, mindfulness with self-monitoring strategies already embedded in these programs could be further tailored to address the feelings of urges and impaired thought control, and to enhance attentional grounding in the external rather than internal environment. Moreover, in light of our findings, incorporating questions about MD traits into the clinical assessment of OCD and ADHD could enable practitioners to obtain a more comprehensive profile of patients’ experiences, crucial for identifying and addressing their more specific unmet needs.

In recruiting a large, general population sample, we were also able to estimate the prevalence of MD in the UK for the first time. Based on the suggested cut-off of ≥40 on the MDS-16, we observed a prevalence rate of 13.61%, which is notably larger than the previously reported prevalence of 4.2% in the general Jewish–Israeli population (Soffer-Dudek & Theodor-Katz, 2022). One possible reason for this is that 47% of participants in our sample reported having one or more mental health or neurodevelopmental diagnoses. Previous research has shown that a highly clinical MD cohort is characterized by a higher number of reported clinical disorders (Somer et al., 2017a). Our observed prevalence of MD in the UK offers a preliminary estimate that can inform and guide future, more comprehensive epidemiological research on MD prevalence.

Further research is also required to address some methodological limitations of our study. First, this study relied on self-report measures of psychological traits provided by the participants, which depended on the participants’ perceptions of their own characteristics and experiences that could be influenced by biases or inaccuracies. Indeed, recent research has highlighted that people may overestimate the extent of their difficulties when self-reporting their ADHD traits compared to their performance on attention control tasks (Waldren et al., 2024). Thus, to complement and validate self-reports, conceptual replication using more objective approaches (e.g., semi-structured interviews, behavioral observations, cognitive tasks) is warranted. Such methods would help clarify whether the associations we observed hold when assessed through more objective measures of OCD, ADHD, autism, and MD traits.

Second, our study only provides a cross-sectional snapshot of the associations of OCD, ADHD, and autism with MD, and thus, we were unable to establish a causal link between these constructs. Longitudinal research spanning childhood, adolescence, and adulthood is needed to examine the temporal precedence of OCD and ADHD traits in the development of MD.

Finally, though our study represents an important first step in elucidating the overlap between MD and other mental health and neurodevelopmental conditions, our findings may not generalize to individuals with clinical-level MD. To better understand MD as a clinical diagnosis and to provide further impetus for its official adoption into diagnostic manuals, our study must be replicated in clinical samples. Especially important for understanding the shared underlying mechanisms between MD, OCD and ADHD would be comparing individuals with only MD to those with MD and a comorbid clinical level of OCD/ADHD.

Overall, our findings highlight that OCD and ADHD, but not autism, are significantly associated with MD at the trait level, with ADHD traits emerging as the strongest predictor. Unpacking this further, we found that the link between OCD traits and MD traits was likely driven by the obsessing and neutralizing subcomponents of OCD. In contrast, both the inattention and, to a lesser extent, the hyperactivity/impulsivity subcomponents of ADHD appeared to be relevant in its link with MD traits. These findings imply possible shared mechanisms between OCD, ADHD, and MD, which add nuance to the current conceptualization of MD. Additionally, this study could have important implications for the development of interventions for MD, highlighting the potential utility of current OCD treatments and ADHD symptom management strategies. While further research is necessary to replicate these findings using more objective measures of OCD, ADHD, autistic, and MD traits and in participants with clinical-level MD, OCD, ADHD, and autism, our study provides important novel insights into the complex interplay between psychological and neurodevelopmental constructs in MD.

Supplemental Material

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Footnotes

Acknowledgments

We are grateful to all participants who took the time to participate in this research. We also thank Brianne Lee and Olujolagbe Layinka for assistance with study setup and data collection.

ORCID iDs

Aleksandra Kowalczyk

Punit Shah

Luca D. Hargitai

Florence Y. N. Leung

Ethical Approval and Informed Consent Statements

This study was granted ethical approval by the University of Bath Department of Psychology Research Ethics Panel (REF: 1832-2327), and informed consent was obtained from all participants prior to their participation.

Authors’ Contribution

AK: conceptualization, formal analysis, investigation, methodology, writing—original draft; PS: conceptualization, funding acquisition, supervision, writing—review and editing; LDH: data curation, investigation, methodology, supervision, validation, writing—original draft, writing—review and editing; FYNL: conceptualization, data curation, investigation, methodology, project administration, supervision, validation, writing—original draft, writing—review and editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by the University of Bath undergraduate student dissertation fund. LDH is supported by a doctoral studentship from the Economic and Social Research Council (ES/P000630/1).

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

The data and analysis syntax are accessible in the Supplemental Materials.

Supplemental Material

Supplemental material for this article is available online.

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Investigating OCD,ADHD,and Autistic Traits as Predictors of Maladaptive Daydreaming

Abstract

Lay Abstract

Keywords

Introduction

Current Research

Method

Participants

Measures and Procedure

Data Analysis

Results

Discussion

Supplemental Material

sj-docx-1-ndy-10.1177_27546330251363392 - Supplemental material for Investigating OCD, ADHD, and Autistic Traits as Predictors of Maladaptive Daydreaming

Supplemental Material

sj-csv-2-ndy-10.1177_27546330251363392 - Supplemental material for Investigating OCD, ADHD, and Autistic Traits as Predictors of Maladaptive Daydreaming

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sj-docx-3-ndy-10.1177_27546330251363392 - Supplemental material for Investigating OCD, ADHD, and Autistic Traits as Predictors of Maladaptive Daydreaming

Supplemental Material

sj-docx-4-ndy-10.1177_27546330251363392 - Supplemental material for Investigating OCD, ADHD, and Autistic Traits as Predictors of Maladaptive Daydreaming

Footnotes

Acknowledgments

ORCID iDs

Ethical Approval and Informed Consent Statements

Authors’ Contribution

Funding

Declaration of Conflicting Interests

Data Availability

Supplemental Material

References

Supplementary Material