Co-occurring Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis

Co-occurring Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Co-occurring Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis Yasuhiro Ogawa, Hiroyuki Tanaka, Daisuke Haga, Yasuhiro Higashi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9286185/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Health professional students with autism spectrum disorder (ASD) and/or attention-deficit/hyperactivity disorder (ADHD) traits may experience poorer sleep and worse mental health. These difficulties are often attributed to problematic internet use, which may obscure underlying trait-related burdens. We examined whether differences in sleep quality and mental health across the trait-defined groups persisted after adjusting for the severity of problematic internet use. Methods We conducted a secondary cross-sectional analysis of questionnaire data from health profession students in Japan (N = 399). ASD traits were screened using the Adult Autism Spectrum Disorders Self-Rating Scale, and ADHD traits were screened using the Adult ADHD Self-Report Scale. Participants were classified into four groups: typical (below both thresholds), ASD only, ADHD only, and co-occurring ASD–ADHD. Sleep quality was assessed using the Pittsburgh Sleep Quality Index. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12) with binary scoring. Problematic internet use severity was measured using the Internet Addiction Test. Group differences were analyzed using multivariate general linear models with follow-up-adjusted comparisons, controlling for age and sex. Results Trait-defined groups differed significantly in the combined outcomes of sleep quality and mental health. This multivariate effect remained significant after adding the Internet Addiction Test score to the model, and no group × Internet Addiction Test interaction was detected. In adjusted comparisons, the co-occurring ASD–ADHD group exhibited the poorest sleep quality and mental health outcomes relative to the typical and single-trait groups, even after controlling for problematic internet use severity. Conclusions Sleep problems and poor mental health associated with ASD and/or ADHD traits, particularly their co-occurrence, persisted after adjustment for problematic Internet use (IAT), supporting the view that a device-first explanation alone may be insufficient. Therefore, effective support strategies should extend beyond device-focused interventions to include neurodevelopmentally informed accommodations, improved access to social support, and targeted sleep and mental health care. problematic internet use autistic traits ADHD traits health profession students sleep quality psychological distress Figures Figure 1 Background In higher education, the enrollment and recognition of ASD and ADHD students have increased, and universities are increasingly challenged to determine how best to support neurodivergent learners [ 1 ]. Alongside students with formal diagnoses, universities also host a substantial group of individuals with elevated ASD and ADHD traits that fall below diagnostic thresholds (often referred to as “subthreshold” or “grey-zone”), for whom routine support services may not be activated despite difficulties in time management, sleep–wake regulation, executive functioning, and emotion regulation [ 2 – 4 ]. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are relatively common among young adults and frequently co-occur [ 5 ]. When ASD and ADHD traits co-occur, their characteristics may offset or mask one another from external observers (for example, an autistic preference for sameness may be intermittently overridden by distractibility or apparent sociability may reflect impulsivity rather than genuine social ease), which contributes to the underrecognition of support needs despite a substantial internal burden. Empirical evidence suggests that an ADHD diagnosis may delay recognition of autism by several years [ 6 ]. In parallel, camouflaging—defined as compensatory strategies used to conceal or compensate for autistic characteristics—has been documented and is associated with poorer mental health [ 7 – 9 ], and similar compensatory behaviors have also been reported among adults with ADHD [ 10 ]. Taken together, these mechanisms suggest that the co-occurrence of ASD and ADHD traits may lead to distinct and often less invisible challenges in student life. Studies of university students indicate that individuals with high levels of both ASD and ADHD traits are at an increased risk of experiencing emotional difficulties [ 11 ]. Among university students, particularly those enrolled in health profession programs, sleep disturbances and poor mental health represent some of the most consistently reported health concerns. A large meta-analysis estimated that approximately 27% of medical students experienced depression or depressive symptoms, while about 11% reported suicidal ideation [ 12 ]. Similarly, a recent meta-analysis reported that approximately 55–57% of medical and health profession students experienced poor sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) [ 13 ]. Within these high-demand programs, early start times, irregular schedules, heavy academic workloads, and sustained interpersonal demands during skills training and clinical placements represent routine pressures that may impair sleep and increase psychological distress. From a person–environment fit perspective, such conditions may present particular challenges for students with neurodevelopmental traits, especially when interpersonal responsibilities, safety-critical tasks, and shift-like schedules are involved. Because co-occurring ASD and ADHD traits can obscure one another, related difficulties may be less likely to be recognized as disability-related, and affected students may be misperceived as unmotivated or noncompliant rather than being offered appropriate accommodations. At the same time, problematic or addictive Internet use (PIU/IA) is common among student populations and has been associated with poorer mental health and sleep outcomes in meta-analytic studies [ 14 , 15 ]. Importantly, neurodevelopmental traits have been associated with an elevated risk of problematic internet use (PIU), and meta-analyses as well as observational studies have reported a moderate association between ADHD and PIU. Longitudinal studies further suggest that ADHD symptoms may prospectively predict subsequent problematic internet use [ 16 ]. Individuals with autism also demonstrate increased tendencies toward compulsive or excessive online engagement (for example, gaming) compared with non-autistic peers [ 17 ]. Extending this literature, our previous report from the same sample of health profession students found that the co-occurring ASD and ADHD traits scored higher on the Internet Addiction Test (IAT) than the ASD-only, ADHD-only, and typical groups [ 18 ]. A growing body of research has also examined when and why students engage in online activities. Bedtime procrastination—defined as delaying intended sleep in the absence of external constraints—has been linked to smartphone and internet use and to poorer sleep outcomes, with evidence suggesting that bedtime procrastination may mediate the relationship between problematic smartphone use and sleep problems [ 19 , 20 ]. The mechanisms are important in practice because they may lead nocturnal device use to appear as the sole cause of students’ sleep and mood difficulties, thereby diverting attention from underlying neurodevelopmental factors. Taken together, these strands of evidence highlight a practical dilemma for educators and clinicians. Sleep and psychological difficulties in students are often attributed primarily to internet use, reflecting the implicit assumption that device-focused interventions will be sufficient. However, if co-occurring ASD–ADHD traits impose additional trait-linked burdens—potentially less visible due to mutual masking— internet-related mechanisms may explain only part of the variance in sleep and mental health outcomes. Notably, previous university-based studies have rarely examined whether differences in sleep and mental health across ASD and ADHD trait groups persist after accounting for PIU severity. To our knowledge, such analyses have not yet been conducted among health profession students. This gap led to the central question guiding the present study: Do group differences in sleep quality and mental health persist after adjustment for PIU severity? To address this question, we analyzed a previously collected cross-sectional dataset of health profession students in Japan recruited through an online survey. In this secondary cross-sectional analysis, four trait-defined groups—typical, ASD-only, ADHD-only, and co-occurring ASD–ADHD— were compared on two outcomes: sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) and mental health assessed using the 12-item General Health Questionnaire (GHQ-12). To examine whether internet-related mechanisms account for group differences, statistical models adjusted for IAT scores alongside age and sex, and evaluated whether any differences remained after accounting for PIU severity. We hypothesized a graded pattern in which the co-occurring group would demonstrate the poorest outcomes on the PSQI and GHQ-12. We further expected these differences to persist after adjustment of IAT scores, indicating that they could not be explained solely by PIU and likely reflect additional trait-related burdens. Materials and methods Design, Setting, and Participants We conducted a secondary cross-sectional analysis of an existing classroom-based survey administered by health-profession faculty in Japan. Recruitment and data collection took place between December 2021 and March 2022. After a brief in-class explanation of the study, students were invited to access the online questionnaire by scanning QR codes, and the survey remained open throughout the recruitment period. Of the 468 students approached, 401 provided electronic consent, and two patterned responders (i.e., participants who provided invariant responses) were excluded a priori, yielding a final analytic sample of n = 399. Because the survey platform required responses to all items, no item-level missing data were observed. Participation was voluntary, and no financial or material compensation was provided. This observational study was reported in accordance with the STROBE guidelines [ 21 ]. Measures Neurodevelopmental Traits and Group Definition Participants were classified into four groups based on screening thresholds: typical (below both thresholds), ASD-positive only, ADHD-positive only, and co-occurring ASD and ADHD. ADHD traits were screened using the six-item Adult ADHD Self-Report Scale version 1.1 (ASRS-6). According to the scoring guidelines, endorsement of four or more items at the threshold level constituted a positive screen for ADHD traits [ 22 ]. The reliability and validity of the Japanese version of the ASRS have been previously demonstrated [ 23 ]. In the present sample, Cronbach’s alpha for the ASRS was 0.795, indicating acceptable internal consistency. Autistic traits were assessed using the Adult Autism Spectrum Disorders Self-Rating Scale (A-ASD). The A-ASD is a self-report instrument developed in Japan consisting of 20 items for males and 23 items for females, and sex-specific cutoff scores from the manual were applied. According to the test manual, elevated autistic traits were defined using sex-specific thresholds (> 51 for males and > 58 for females) [ 24 ]. Participants whose scores exceeded these thresholds were classified as ASD-trait positive for grouping purposes rather than clinical diagnosis. Because the ASD trait measure uses sex-specific cut-off values, a robustness check was conducted for participants who selected “prefer not to answer” for sex (n = 5). Specifically, both male and female cutoff scores were applied to each participant’s total score. All five participants scored below both thresholds, indicating that the classification was unaffected by the choice of cutoff. In the present sample, the Cronbach’s alpha for the A-ASD was 0.843, indicating good internal consistency. Application of these screening thresholds resulted in four groups, which were used in all subsequent analyses. Sleep Quality Sleep quality was assessed using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J), in which higher total scores indicate poorer sleep quality [ 25 ]. The PSQI-J has demonstrated good reliability and validity in both clinical and non-clinical populations [ 26 ]. For secondary categorical analyses, poor sleep was defined as a PSQI score ≥ 6, consistent with the PSQI-J validation cut-point of 5.5 (rounded to the nearest integer for logistic modelling) [ 26 ]. Mental Health Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12) with binary scoring (0-0-1-1). The Japanese version of the GHQ-12 has been widely used and validated in prior studies, including research examining its factor structure and construct validity; binary scoring is the standard in screening applications [ 27 , 28 ]. For categorical analyses, GHQ-12 caseness was defined as a binary-scored sum ≥ 4 [ 29 ]. Problematic internet use Problematic or addictive internet use was assessed using the Japanese version of Young’s Internet Addiction Test (IAT), a 20-item self-report instrument measuring loss of control, preoccupation, and functional impairment related to internet use [ 30 ]. Items (e.g., ‘How often do you find that you stay online longer than you intended?’) were rated on a 5-point Likert scale ranging from 1 (rarely) to 5 (always), and responses were summed to obtain a global score (range: 20–100), with higher scores indicating greater severity. In the present sample, the internal consistency was excellent (Cronbach’s α = 0.901). Covariates All adjusted models included age (continuous) and sex (male, female, or prefer not to answer), which was dummy-coded for analysis. This coding approach preserved a separate category for non-responses rather than excluding those cases from analyses. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics for Windows, version 27. The analyses were designed to address two primary aims. 1. Continuous Outcomes (PSQI, GHQ-12) We first fitted a multivariate analysis of variance (MANOVA) model with group as the fixed factor and PSQI and GHQ-12 scores as dependent variables, adjusting for age and sex; Pillai’s Trace was prespecified because of its robustness to unequal covariance matrices. We subsequently estimated univariate general linear models (GLMs) for each outcome using the same covariate adjustment set and repeated the models after adding IAT scores to examine whether between-group differences persisted beyond PIU severity. For each model, we report F(df1, df2), p values, and partial η² as the effect size. Homogeneity of variance was assessed using Levene’s test. Because GHQ-12 scores showed heteroscedasticity and these outcomes may be skewed, we supplemented parametric inference with bias-corrected and accelerated (BCa) 95% bootstrap confidence intervals based on 1,000 resamples for GLM parameter estimates and estimated marginal means (EMMs). Omnibus F tests were reported with parametric p values, whereas bootstrap confidence intervals were used to evaluate the robustness of the estimates. We additionally examined a Group × IAT interaction to assess whether residual between-group differences after IAT adjustment reflected slope heterogeneity (moderation) rather than level differences alone. Because the co-occurring subgroup was small, interaction tests were considered exploratory. 2. Categorical Outcomes (Secondary Analyses) We created two binary outcomes: poor sleep (PSQI ≥ 6) and GHQ-12 caseness (≥ 4). Each outcome was modeled using binary logistic regression to estimate adjusted odds ratios (aORs) for the group membership (reference category = typical), adjusting for age, sex, and IAT scores. Results are presented with 95% confidence intervals; statistical significance was defined using a two-sided α = .05. No multiplicity adjustment was applied to the logistic regression models; however, pairwise comparisons of estimated marginal means in the GLM were Bonferroni-adjusted. Data quality. There were no missing item-level data because the survey used a forced response design. Two cases were excluded because of invariant response patterns. Consequently, the complete-case analysis corresponded to the full analytic sample (n = 399). Results Participants and Descriptive Characteristics The analytic sample comprised 399 students (men, n = 170; women, n = 224; preferred not to answer, n = 5). The trait-defined groups were as follows: typical, n = 260; ASD(+), n = 37; ADHD(+), n = 67; and co-occurring ASD–ADHD n = 35 (Fig. 1 ). As shown in Table 1 , mean scores for PSQI, GHQ-12 (0–0–1–1), and IAT were highest in the co-occurring ASD–ADHD group. Crude prevalences of poor sleep (PSQI ≥ 6) and GHQ-12 caseness (≥ 4) were also highest in this group. Detailed descriptive statistics and proportions are presented in Table 1 . Table 1 Descriptive statistics for sleep, mental health, and Internet-addiction scores by trait-defined group Measure Typical (n = 260) ASD(+) (n = 37) ADHD(+) (n = 67) ASD–ADHD (n = 35) PSQI, mean (SD) 5.48 (3.14) 6.84 (3.21) 7.28 (4.07) 9.26 (3.37) Poor sleep (PSQI ≥ 6), n/N (%) 111/260 (42.7%) 23/37 (62.2%) 44/67 (65.7%) 30/35 (85.7%) GHQ-12 (0–0–1–1), mean (SD) 2.17 (2.43) 4.86 (3.05) 4.12 (3.11) 5.37 (3.09) Poor mental health (GHQ-12 ≥ 4), n/N (%) 61/260 (23.5%) 22/37 (59.5%) 34/67 (50.7%) 27/35 (77.1%) IAT, mean (SD) 42.42 (12.15) 48.41 (11.63) 50.91 (13.20) 59.03 (11.23) Notes GHQ-12 was scored using the binary method (0–0–1–1). PSQI—Pittsburgh Sleep Quality Index; GHQ-12—12-item General Health Questionnaire; IAT—Internet Addiction Test. Of 468 students approached, 401 consented electronically, and 2 were excluded due to patterned responses, yielding an analytic sample of 399. Participants were assigned to four trait-defined groups based on screening thresholds: Typical (n = 260), ASD(+)-only (n = 37), ADHD(+)-only (n = 67), and co-occurring ASD–ADHD (n = 35). ASD: autism spectrum disorder; ADHD: attention-deficit/hyperactivity disorder. Multivariate and Univariate Models (Continuous Outcomes) Multivariate analysis of variance (MANOVA) revealed a significant overall group effect both before and after adjustment for IAT (Pillai’s trace p < .001 in both models; see Table 2 ). In univariate general linear models (GLMs), group effects were significant for both PSQI and GHQ-12 after adjustment for age and sex and remained significant after additional adjustment for IAT (Table 2 ). Assumptions and Robustness Checks. Table 2 Group effects on PSQI and GHQ-12 in general linear models with and without IAT adjustment Outcome Model F(df1, df2) p Partial η² Levene p PSQI Age/sex adjusted 14.347 (3, 393) < .001 0.099 0.296 PSQI + IAT 6.285 (3, 392) < .001 0.046 0.047 GHQ-12 Age/sex adjusted 25.265 (3, 393) < .001 0.162 0.015 GHQ-12 + IAT 18.383 (3, 392) < .001 0.123 0.018 Notes. F-tests are parametric. Where Levene p < .05 (GHQ-12; PSQI in + IAT), BCa 95% bootstrap CIs (1,000) were used for parameter/EMM estimates; inferences were unchanged. Levene’s tests indicated heteroscedasticity for GHQ-12 and borderline heterogeneity for PSQI after adjustment for IAT. Therefore, robustness was evaluated using 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals based on 1,000 resamples, which corroborated the primary inferences (see Methods ). We found no clear evidence of a Group×IAT interaction for either outcome (PSQI: F(3,389) = 1.686, p=.170, partial η²=.013; GHQ-12: F(3,389) = 0.675, p=.568, partial η²=.005). Accordingly, within the limits of power for interaction tests, the IAT–outcome association appeared broadly similar across groups, and adjusted group differences were observed across the IAT range. Pairwise Comparisons of Adjusted Means With IAT included in the model, Bonferroni-adjusted estimated marginal mean (EMM) contrasts showed that, for PSQI, the ASD–ADHD group scored significantly higher than the Typical group (Δ = 2.507, 95% CI 1.177–3.730, p = .001) and the ASD(+) (Δ = 1.640, 95% CI 0.010–3.184, p = .027). The ADHD(+) versus Typical difference was smaller but remained statistically significant (Δ = 1.116, 95% CI 0.105–2.146, p = .043), whereas the ASD(+) versus Typical comparison was not significant (Δ = 1.245, 95% CI − 0.224–2.193, p = .150). For GHQ-12, all elevated-trait groups had higher adjusted scores than the Typical group: ASD(+) (Δ = 2.446, 95% CI 1.493–3.497, p = .001), ADHD(+) (Δ = 1.669, 95% CI 0.892–2.488, p = .001), and ASD–ADHD (Δ = 2.859, 95% CI 1.512–3.675, p = .001). Full model estimates are provided in Table 3 . Table 3 Pairwise EMM contrasts by trait-defined group for PSQI and GHQ-12 (IAT-adjusted) Outcome Contrast (Typical − group) Adjusted mean difference 95% CI p (Bonferroni) PSQI Typical vs ASD(+) −1.245 −2.193 to 0.224 0.15 PSQI Typical vs ADHD(+) −1.116 −2.146 to − 0.105 0.043 PSQI Typical vs ASD–ADHD −2.507 −3.730 to − 1.177 0.001 GHQ-12 Typical vs ASD(+) −2.446 −3.497 to − 1.493 0.001 GHQ-12 Typical vs ADHD(+) −1.669 −2.488 to − 0.892 0.001 GHQ-12 Typical vs ASD–ADHD −2.859 −3.675 to − 1.512 0.001 Notes . Negative values mean higher adjusted means in the comparison group (vs Typical). EMMs from GLMs adjusted for age, sex, and IAT; Bonferroni-corrected. BCa 95% bootstrap CIs (1,000) supported results. Categorical Outcomes (Secondary Analyses) The binary logistic regression analyses adjusted for age, sex, and IAT are summarized in Table 4 . For poor sleep (PSQI ≥ 6), only the ASD–ADHD group showed significantly higher odds than the Typical group (aOR = 3.15, 95% CI 1.11–8.93, p = .031). The ASD(+) group was not significantly different from the Typical group, whereas the ADHD(+) group showed a nonsignificant trend toward higher odds. For poor mental health (GHQ-12 ≥ 4), all elevated-trait groups had higher odds compared with the Typical group: ASD(+) (aOR = 4.32), ADHD(+) (aOR = 2.81), and ASD–ADHD (aOR = 7.58). All associations were statistically significant (p ≤ .001) unless otherwise noted. Complete estimates and confidence intervals are reported in Table 4 . Table 4 Binary logistic models for PSQI ≥ 6 and GHQ-12 ≥ 4: adjusted odds by trait-defined group and IAT Outcome Group / Predictor (ref = Typical) aOR (Exp[B]) 95% CI p Poor sleep (PSQI ≥ 6) ASD(+) 1.55 0.74–3.28 0.248 ADHD(+) 1.69 0.93–3.07 0.088 ASD–ADHD 3.15 1.11–8.93 0.031 Poor mental health (GHQ-12 ≥ 4) ASD(+) 4.32 2.04–9.14 < .001 ADHD(+) 2.81 1.54–5.11 < .001 ASD–ADHD 7.58 3.01–19.08 < .001 Notes . Binary logistic regression adjusted for age, sex, and IAT (reference = Typical). aOR—adjusted odds ratio; CI—confidence interval; two-sided α = .05 Discussion In this cross-sectional sample of health profession students, individuals screening positive for co-occurring ASD–ADHD traits exhibited the poorest sleep quality and the highest levels of psychological distress. These between-group differences remained significant after adjusting for the IAT scores, suggesting that device-related severity alone was unlikely to explain the observed burden. Secondary analyses based on established clinical cut-offs largely replicated these patterns (poor sleep: PSQI ≥ 6; poor mental health: GHQ-12 ≥ 4). Post-hoc moderation analyses revealed no significant Group × IAT interaction for either outcome (PSQI: F(3,389) = 1.686, p = .170, partial η² = .013; GHQ-12: F(3,389) = 0.675, p = .568, partial η² = .005). This finding suggests broadly parallel associations between IAT scores and the outcome across groups and supports the interpretation that group differences persist across the full IAT range. Our findings align with previous studies linking autistic and/or ADHD traits, particularly when both are elevated, to increased emotional difficulties among university students [ 11 ]. These findings also support evidence that problematic or addictive internet use co-occurs with poorer sleep and worse mental health outcomes [ 14 , 15 ]. Previous research has also shown that ADHD and problematic internet use (PIU) are moderately associated, with some longitudinal evidence indicating that ADHD symptoms may prospectively predict later PIU [ 16 ]. A key contribution of the present study is that group differences in sleep and mental health remained evident even after adjustment for IAT scores. We observed this pattern among health profession students, a population that has previously been reported to exhibit high baseline rates of depression and poor sleep [ 12 , 13 ]. Because group differences remained after adjustment for IAT and did not vary across IAT levels, a simple device-first explanation— where internet problems are viewed as the primary driver of sleep and mood difficulties—appears insufficient. This pattern is consistent with theoretical perspectives suggesting that preexisting psychological strain may lead individuals to engage in online activities as a coping strategy. Conceptually, the cognitive–behavioral model of pathological internet use emphasizes maladaptive expectations and emotion-regulation motives [ 31 ]. Similarly, the compensatory internet use perspective proposes that individuals may go online to manage stress or fulfill unmet needs [ 32 ]. Empirical studies further indicate that avoidant coping mediates the association between psychological distress and PIU among adolescents and adults [ 33 ], supporting a pathway in which strain precedes and helps drive coping-motivated engagement. In university samples, social support—alongside factors such as stress, self-control, and anxiety—has emerged as a significant predictor of PIU [ 34 ]. If co-occurring ASD–ADHD traits are masked or compensated for, such that students’ needs remain underrecognized, these individuals may be less likely to access support. Reduced perceived support may subsequently increase the risk of internet addiction while leaving trait-related sleep and mental health needs insufficiently addressed. A common behavioral manifestation in student life is bedtime procrastination, often characterized by late-night smartphone or internet use, which has been associated with poorer sleep quality [ 19 , 20 ]. Although the findings challenge a purely device-first explanation, the cross-sectional design and potential mediation by IAT scores mean that these adjusted differences should be interpreted as conservative and non-causal. We caution against framing sleep and mood problems primarily as screen-time issues. For some students, online activities may function as self-regulation or coping for pre-existing psychological distress. This vulnerability may be particularly pronounced when ASD and ADHD traits co-occur and remain underrecognized. Accordingly, we emphasize attunement rather than admonition in responding to students’ late-evening internet use. Practically, this involves recognizing possible masking, inviting non-judgmental conversations about the timing and purpose of late-evening online use, and considering brief screening for neurodevelopmental traits, including their potential co-occurrence. Such approaches may help ensure that less visible difficulties are recognized and that appropriate support options can be explored collaboratively. Clinically, problematic internet use has been associated with several high-risk outcomes. For example, a recent meta-analysis reported a moderate positive association between problematic internet use and non-suicidal self-injury among adolescents [ 35 ]. This finding underscores the importance of looking beyond device-focused advice and addressing underlying psychological distress and access to support. Several important limitations should be acknowledged and pointed to concrete directions for future research. First, the cross-sectional design precludes conclusions regarding temporal ordering or causal mediation. Future research should employ multi-wave longitudinal or experience-sampling designs to examine time-ordered pathways. Such designs could also evaluate potential bidirectional relationships between psychological distress and problematic internet use (e.g., cross-lagged or longitudinal mediation models). Second, reliance on self-report measures may introduce common-method bias. Integrating self-reports with objective sleep measures (e.g., actigraphy or wearable devices), clinician-rated assessments of neurodevelopmental characteristics, and privacy-protected passive indicators of online activity would strengthen construct validity. Third, the single-institution sample of Japanese health profession students limits the generalizability of the findings. Multi-site, multi-program, and cross-national samples are required to examine whether this pattern holds across curricula and cultures. Fourth, the co-occurring group was relatively small (n = 35), which limits statistical precision, particularly the power to detect interaction effects. Future studies could oversample this subgroup, pool data across multiple sites, and consider Bayesian hierarchical modeling or preregistered equivalence tests (e.g., TOST) when evaluating claims of “no interaction”, guided by a priori power analyses. Fifth, interpretation is constrained by measurement choices: the GHQ-12 was scored using the binary (0–0–1–1) method, ADHD traits were screened using the six-item ASRS (≥ 4 threshold), and poor sleep was defined as PSQI ≥ 6, consistent with PSQI-J validation at 5.5. Sensitivity analyses using GHQ-12 Likert scoring, more comprehensive ADHD or autism trait instruments, alternative PSQI thresholds, and tests of measurement invariance (e.g., by sex) would help clarify the robustness of the findings. Finally, unmeasured confounding may have influenced the findings. Variables such as chronotype, medication use, anxiety or depression comorbidity, clinical placement load, living arrangements, social support, coping style (e.g., avoidant coping), and bedtime procrastination were not fully captured. Future studies should systematically measure these factors and assess the robustness of the findings using sensitivity analyses and cluster-robust standard errors. A further interpretive consideration is that IAT scores may partially mediate the relationship between neurodevelopmental traits and the observed outcomes. Consequently, adjusting for IAT scores represents a conservative analytic approach and may underestimate the magnitude of trait-related effects. Longitudinal mediation analyses incorporating motives and time-of-day patterns of online use would allow a more direct evaluation of the proposed self-regulation pathway. Conclusions Among health profession students, co-occurring ASD–ADHD traits were associated with poorer sleep quality and worse mental health outcomes, even after accounting for Internet Addiction Test (IAT) severity. Furthermore, these group differences did not appear to vary according to the level of IAT severity. These findings suggest that device-related mechanisms likely explain only a portion of the observed variance in sleep and mental health outcomes. Therefore, effective student support strategies should extend beyond device-focused interventions to include neurodevelopmentally informed accommodations, improved access to social support, and targeted sleep and mental health care. Abbreviations A-ASD Adult Autism Spectrum Disorders Self-Rating Scale ADHD Attention-deficit/hyperactivity disorder aOR Adjusted odds ratio ASRS Adult ADHD Self-Report Scale ASD Autism spectrum disorder BCa Bias-corrected and accelerated CI Confidence interval EMM Estimated marginal mean GHQ-12 12-item General Health Questionnaire GLM General linear model IAT Internet Addiction Test MANOVA Multivariate analysis of variance PIU Problematic internet use PSQI Pittsburgh Sleep Quality Index PSQI-J Pittsburgh Sleep Quality Index — Japanese version QR Quick response SPSS Statistical Package for the Social Sciences STROBE Strengthening the Reporting of Observational Studies in Epidemiology TOST Two one-sided tests Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Morinomiya University of Medical Sciences (approval number: 2021–141) and was conducted in accordance with the Declaration of Helsinki. Informed consent to participate was obtained electronically. Prior to participation, students received an oral explanation in class and were provided with study information via the online survey page, including the study purpose and procedures, the voluntary nature of participation, the potential risks and benefits (minimal risk and no guaranteed direct benefit), and data handling and confidentiality, including the anonymous nature of the survey. Because the survey was anonymous, submission of the completed questionnaire was taken as provision of consent. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this study. Author Contribution All authors contributed to the conception and design of the study. Material preparation, data collection, and analyses were performed by Ogawa Y., Yokota S., and Tano K. The first draft of the manuscript was written by Y. Ogawa. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors acknowledge the assistance of the participants, including the students involved in this study. The authors also acknowledge Editage (www.editage.jp) for the English language editing support. Data Availability The datasets supporting the conclusions of this study are not publicly available due to privacy and ethical restrictions. The data are securely stored at Morinomiya University of Medical Sciences. Access may be granted under specific conditions that comply with the university’s data protection policies and ethical guidelines. Researchers seeking access for legitimate academic purposes may contact the corresponding author with a detailed request. Approval for data access will be determined based on the proposed use, adherence to ethical standards, and agreement to maintain the privacy and confidentiality of participants. Authors’ information The authors are affiliated with a university department in rehabilitation sciences and have research interests in mental health and health behaviors among university students. References Clouder L, Karakus M, Cinotti A, Ferreyra MV, Fierros GA, Rojo P. Neurodiversity in higher education: A narrative synthesis. High Educ. 2020;80:757–78. https://doi.org/10.1007/s10734-020-00513-6 . Kwon SJ, Kim Y, Kwak Y. Difficulties faced by university students with self-reported symptoms of attention-deficit hyperactivity disorder: A qualitative study. Child Adolesc Psychiatry Ment Health. 2018;12:12. https://doi.org/10.1186/s13034-018-0218-3 . Southon C. The relationship between executive function, neurodevelopmental disorder traits, and academic achievement in university students. Front Psychol. 2022;13:958013. https://doi.org/10.3389/fpsyg.2022.958013 . Petti T, Gupta M, Fradkin Y, Gupta N. Management of sleep disorders in autism spectrum disorder with co-occurring attention-deficit hyperactivity disorder: Update for clinicians. BJPsych Open. 2023;10:e11. https://doi.org/10.1192/bjo.2023.589 . Rommelse NNJ, Franke B, Geurts HM, Hartman CA, Buitelaar JK. Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry. 2010;19:281–95. https://doi.org/10.1007/s00787-010-0092-x . Miodovnik A, Harstad E, Sideridis G, Huntington N. Timing of the diagnosis of attention-deficit/hyperactivity disorder and autism spectrum disorder. Pediatrics. 2015;136:e830–7. https://doi.org/10.1542/peds.201-2 . Hull L, Petrides KV, Allison C, Smith P, Baron-Cohen S, Lai MC, et al. Putting on My Best Normal: Social camouflaging in adults with autism spectrum conditions. J Autism Dev Disord. 2017;47:2519–34. https://doi.org/10.1007/s10803-017-3166-5 . Lai M-C, Lombardo MV, Ruigrok AN, Chakrabarti B, Auyeung B, Szatmari P, et al. Quantifying and exploring camouflaging in men and women with autism. Autism. 2017;21:690–702. https://doi.org/10.1177/1362361316671012 . Hull L, Levy L, Lai MC, Petrides KV, Baron-Cohen S, Allison C, et al. Is social camouflaging associated with anxiety and depression in autistic adults? Mol Autism. 2021;12:13. https://doi.org/10.1186/s13229-021-00421-1 . van der Putten WJ, Mol AJJ, Groenman AP, Radhoe TA, Torenvliet C, van Rentergem JAA, et al. Is camouflaging unique for autism? A comparison of camouflaging between adults with autism and ADHD. Autism Res. 2024;17:812–23. https://doi.org/10.1002/aur.3099 . Saito A, Matsumoto S, Sakata Y, Sugawara M. The relation between autism spectrum disorder traits, attention-deficit/hyperactivity disorder traits, and emotional problems in Japanese university students. Adv Neurodev Disord. 2022;7:525–34. https://doi.org/10.1007/s41252-022-00311-4 . Rotenstein LS, Ramos MA, Torre M, Segal JB, Peluso MJ, Guille C, et al. Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: A systematic review and meta-analysis. JAMA. 2016;316:2214–36. https://doi.org/10.1001/jama.2016.17324 . Jalali E, Vaddi A, Rengasamy K, Tadinada A. The worldwide prevalence of sleep problems among medical students: A meta-analysis. Cureus. 2023;15:e33452. https://doi.org/10.7759/cureus.33452 . Cai Z, Mao P, Wang Z, Wang D, He J, Fan X. Associations between problematic Internet use and mental health outcomes of students: A meta-analytic review. Adolesc Res Rev. 2023;8:45–62. https://doi.org/10.1007/s40894-022-00201-9 . Kokka I, Mourikis I, Nicolaides NC, Darviri C, Chrousos GP, Kanaka-Gantenbein C, et al. Exploring the effects of problematic Internet use on adolescent sleep: A systematic review. Int J Environ Res Public Health. 2021;18:760. https://doi.org/10.3390/ijerph18020760 . Wang BQ, Yao NQ, Zhou X, Liu J, Lv ZT. The association between attention deficit/hyperactivity disorder and internet addiction: A systematic review and meta-analysis. BMC Psychiatry. 2017;17:260. https://doi.org/10.1186/s12888-017-1408-x . Murray A, Koronczai B, Király O, Griffiths MD, Mannion A, Leader G, et al. Autism, problematic Internet use and gaming disorder: A systematic review. Rev J Autism Dev Disord. 2021;9:120–40. https://doi.org/10.1007/S40489-021-00243-0 . Ogawa Y, Tanaka H, Haga D, Higashi Y, Yokota S, Tano K. Relationship between co-occurring autism spectrum disorder and attention deficit hyperactivity disorder traits and internet addiction among college students in Japan. Curr Psychol. 2024;43:33382–9. https://doi.org/10.1007/s12144-024-06863-z . Kroese FM, de Ridder DTD, Evers C, Adriaanse MA. Bedtime procrastination: Introducing a new area of procrastination. Front Psychol. 2014;5:611. https://doi.org/10.3389/fpsyg.2014.00611 . Bozkurt A, Demirdöğen EY, Akıncı MA. The association between bedtime procrastination, sleep quality, and problematic smartphone use in adolescents: A mediation analysis. Eurasian J Med. 2024;56:69–75. https://doi.org/10.5152/eurasianjmed.2024.23379 . Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31–4. https://doi.org/10.4103/sja.SJA_543_18 . Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, et al. The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychol Med. 2005;35:245–56. Suzuki T, Wada K, Muzembo BA, Ngatu NR, Yoshii S, Ikeda S. Autistic and attention deficit/hyperactivity disorder traits are associated with suboptimal performance among Japanese university students. JMA J. 2020;3:216–31. Fukunishi H. Adult autism spectrum disorders self-rating scale (A-ASD). Chiba Test Center; 2016. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. https://doi.org/10.1016/0165-1781(89)90047-4 . Doi Y, Minowa M, Uchiyama M, Okawa M, Kim K, Shibui K, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97:165–72. https://doi.org/10.1016/s0165-1781(00)00232-8 . Doi Y, Minowa M. Factor structure of the 12-item General Health Questionnaire in the Japanese general adult population. Psychiatry Clin Neurosci. 2003;57:379–83. Fuchino Y, Mizoue T, Tokui N, Ide R, Fujino Y, Yoshimura T. Health-related lifestyle and mental health among inhabitants of a city in Japan. Nippon Koshu Eisei Zasshi. 2003;116:303–13. (article in Japanese). Honda S, Shibata Y, Nakane Y. Screening for psychiatric disorders using the GHQ-12 item questionnaire. Welf Indic. 2001;48:5–10. (article in Japanese). Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction – And a Winning Strategy for Recovery. New York: Wiley; 1998. Davis RA. A cognitive-behavioral model of pathological Internet use. Comput Hum Behav. 2001;17:187–95. Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Comput Hum Behav. 2014;31:351–4. McNicol ML, Thorsteinsson EB. Internet addiction, psychological distress, and coping responses among adolescents and adults. Cyberpsychol Behav Soc Netw. 2017;20:296–304. https://doi.org/10.1089/cyber.2016.0669 . Chen M, Zhang X. Factors influencing internet addiction among university students: The mediating roles of self-control and anxiety. Acta Psychol. 2024;250:104535. https://doi.org/10.1016/j.actpsy.2024.104535 . Li M, Liu F, Han X, Wang J, Li N. The association between Internet addiction and non-suicidal self-injury among adolescents: A meta-analysis. J Adolesc. 2025;97:1433–48. https://doi.org/10.1002/jad.12510 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Editor invited by journal 08 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9286185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622106705,"identity":"e1db528a-82b8-421f-a7c6-d09637a68391","order_by":0,"name":"Yasuhiro Ogawa","email":"data:image/png;base64,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","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Ogawa","suffix":""},{"id":622106706,"identity":"7ae94c12-5984-4387-8880-3dc13d452444","order_by":1,"name":"Hiroyuki Tanaka","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Tanaka","suffix":""},{"id":622106707,"identity":"32ab8545-57c3-4580-9e2c-4c1a03b12b3e","order_by":2,"name":"Daisuke Haga","email":"","orcid":"","institution":"ONEMORE Job Training Institution","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Haga","suffix":""},{"id":622106708,"identity":"d42963ca-3b7e-4e2a-b089-6b9ea16aa73b","order_by":3,"name":"Yasuhiro Higashi","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yasuhiro","middleName":"","lastName":"Higashi","suffix":""},{"id":622106709,"identity":"b4bfee44-861d-4c34-bc94-028ff229e1d1","order_by":4,"name":"Sakura Yokota","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sakura","middleName":"","lastName":"Yokota","suffix":""},{"id":622106710,"identity":"330f4f46-ab41-47d8-b2d6-2ce02623ca9a","order_by":5,"name":"Keiko Tano","email":"","orcid":"","institution":"Morinomiya University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Keiko","middleName":"","lastName":"Tano","suffix":""}],"badges":[],"createdAt":"2026-04-01 03:53:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9286185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9286185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107259337,"identity":"1ca1a51d-fe0d-4ebb-8ce3-cfb7b6a727f1","added_by":"auto","created_at":"2026-04-19 12:49:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16576,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram with trait-defined group breakdown.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9286185/v1/c117300265eae7eecf0b4084.png"},{"id":107482857,"identity":"fc5f63b4-2ae4-4ae6-b746-7fa7fbc0a86f","added_by":"auto","created_at":"2026-04-22 02:25:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":463635,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9286185/v1/8c33a94c-4f37-4b15-843e-445902619810.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Co-occurring Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eIn higher education, the enrollment and recognition of ASD and ADHD students have increased, and universities are increasingly challenged to determine how best to support neurodivergent learners [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Alongside students with formal diagnoses, universities also host a substantial group of individuals with elevated ASD and ADHD traits that fall below diagnostic thresholds (often referred to as \u0026ldquo;subthreshold\u0026rdquo; or \u0026ldquo;grey-zone\u0026rdquo;), for whom routine support services may not be activated despite difficulties in time management, sleep\u0026ndash;wake regulation, executive functioning, and emotion regulation [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are relatively common among young adults and frequently co-occur [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. When ASD and ADHD traits co-occur, their characteristics may offset or mask one another from external observers (for example, an autistic preference for sameness may be intermittently overridden by distractibility or apparent sociability may reflect impulsivity rather than genuine social ease), which contributes to the underrecognition of support needs despite a substantial internal burden. Empirical evidence suggests that an ADHD diagnosis may delay recognition of autism by several years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In parallel, camouflaging\u0026mdash;defined as compensatory strategies used to conceal or compensate for autistic characteristics\u0026mdash;has been documented and is associated with poorer mental health [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and similar compensatory behaviors have also been reported among adults with ADHD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Taken together, these mechanisms suggest that the co-occurrence of ASD and ADHD traits may lead to distinct and often less invisible challenges in student life. Studies of university students indicate that individuals with high levels of both ASD and ADHD traits are at an increased risk of experiencing emotional difficulties [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong university students, particularly those enrolled in health profession programs, sleep disturbances and poor mental health represent some of the most consistently reported health concerns. A large meta-analysis estimated that approximately 27% of medical students experienced depression or depressive symptoms, while about 11% reported suicidal ideation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, a recent meta-analysis reported that approximately 55\u0026ndash;57% of medical and health profession students experienced poor sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Within these high-demand programs, early start times, irregular schedules, heavy academic workloads, and sustained interpersonal demands during skills training and clinical placements represent routine pressures that may impair sleep and increase psychological distress. From a person\u0026ndash;environment fit perspective, such conditions may present particular challenges for students with neurodevelopmental traits, especially when interpersonal responsibilities, safety-critical tasks, and shift-like schedules are involved. Because co-occurring ASD and ADHD traits can obscure one another, related difficulties may be less likely to be recognized as disability-related, and affected students may be misperceived as unmotivated or noncompliant rather than being offered appropriate accommodations.\u003c/p\u003e \u003cp\u003eAt the same time, problematic or addictive Internet use (PIU/IA) is common among student populations and has been associated with poorer mental health and sleep outcomes in meta-analytic studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Importantly, neurodevelopmental traits have been associated with an elevated risk of problematic internet use (PIU), and meta-analyses as well as observational studies have reported a moderate association between ADHD and PIU. Longitudinal studies further suggest that ADHD symptoms may prospectively predict subsequent problematic internet use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Individuals with autism also demonstrate increased tendencies toward compulsive or excessive online engagement (for example, gaming) compared with non-autistic peers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Extending this literature, our previous report from the same sample of health profession students found that the co-occurring ASD and ADHD traits scored higher on the Internet Addiction Test (IAT) than the ASD-only, ADHD-only, and typical groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA growing body of research has also examined when and why students engage in online activities. Bedtime procrastination\u0026mdash;defined as delaying intended sleep in the absence of external constraints\u0026mdash;has been linked to smartphone and internet use and to poorer sleep outcomes, with evidence suggesting that bedtime procrastination may mediate the relationship between problematic smartphone use and sleep problems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The mechanisms are important in practice because they may lead nocturnal device use to appear as the sole cause of students\u0026rsquo; sleep and mood difficulties, thereby diverting attention from underlying neurodevelopmental factors.\u003c/p\u003e \u003cp\u003eTaken together, these strands of evidence highlight a practical dilemma for educators and clinicians. Sleep and psychological difficulties in students are often attributed primarily to internet use, reflecting the implicit assumption that device-focused interventions will be sufficient. However, if co-occurring ASD\u0026ndash;ADHD traits impose additional trait-linked burdens\u0026mdash;potentially less visible due to mutual masking\u0026mdash; internet-related mechanisms may explain only part of the variance in sleep and mental health outcomes. Notably, previous university-based studies have rarely examined whether differences in sleep and mental health across ASD and ADHD trait groups persist after accounting for PIU severity. To our knowledge, such analyses have not yet been conducted among health profession students. This gap led to the central question guiding the present study: Do group differences in sleep quality and mental health persist after adjustment for PIU severity?\u003c/p\u003e \u003cp\u003eTo address this question, we analyzed a previously collected cross-sectional dataset of health profession students in Japan recruited through an online survey. In this secondary cross-sectional analysis, four trait-defined groups\u0026mdash;typical, ASD-only, ADHD-only, and co-occurring ASD\u0026ndash;ADHD\u0026mdash; were compared on two outcomes: sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI) and mental health assessed using the 12-item General Health Questionnaire (GHQ-12). To examine whether internet-related mechanisms account for group differences, statistical models adjusted for IAT scores alongside age and sex, and evaluated whether any differences remained after accounting for PIU severity. We hypothesized a graded pattern in which the co-occurring group would demonstrate the poorest outcomes on the PSQI and GHQ-12. We further expected these differences to persist after adjustment of IAT scores, indicating that they could not be explained solely by PIU and likely reflect additional trait-related burdens.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign, Setting, and Participants\u003c/h2\u003e \u003cp\u003eWe conducted a secondary cross-sectional analysis of an existing classroom-based survey administered by health-profession faculty in Japan. Recruitment and data collection took place between December 2021 and March 2022. After a brief in-class explanation of the study, students were invited to access the online questionnaire by scanning QR codes, and the survey remained open throughout the recruitment period. Of the 468 students approached, 401 provided electronic consent, and two patterned responders (i.e., participants who provided invariant responses) were excluded a priori, yielding a final analytic sample of n\u0026thinsp;=\u0026thinsp;399. Because the survey platform required responses to all items, no item-level missing data were observed. Participation was voluntary, and no financial or material compensation was provided. This observational study was reported in accordance with the STROBE guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNeurodevelopmental Traits and Group Definition\u003c/h2\u003e \u003cp\u003eParticipants were classified into four groups based on screening thresholds: typical (below both thresholds), ASD-positive only, ADHD-positive only, and co-occurring ASD and ADHD.\u003c/p\u003e \u003cp\u003eADHD traits were screened using the six-item Adult ADHD Self-Report Scale version 1.1 (ASRS-6). According to the scoring guidelines, endorsement of four or more items at the threshold level constituted a positive screen for ADHD traits [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The reliability and validity of the Japanese version of the ASRS have been previously demonstrated [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the present sample, Cronbach\u0026rsquo;s alpha for the ASRS was 0.795, indicating acceptable internal consistency.\u003c/p\u003e \u003cp\u003eAutistic traits were assessed using the Adult Autism Spectrum Disorders Self-Rating Scale (A-ASD). The A-ASD is a self-report instrument developed in Japan consisting of 20 items for males and 23 items for females, and sex-specific cutoff scores from the manual were applied. According to the test manual, elevated autistic traits were defined using sex-specific thresholds (\u0026gt;\u0026thinsp;51 for males and \u0026gt;\u0026thinsp;58 for females) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Participants whose scores exceeded these thresholds were classified as ASD-trait positive for grouping purposes rather than clinical diagnosis. Because the ASD trait measure uses sex-specific cut-off values, a robustness check was conducted for participants who selected \u0026ldquo;prefer not to answer\u0026rdquo; for sex (n\u0026thinsp;=\u0026thinsp;5). Specifically, both male and female cutoff scores were applied to each participant\u0026rsquo;s total score. All five participants scored below both thresholds, indicating that the classification was unaffected by the choice of cutoff. In the present sample, the Cronbach\u0026rsquo;s alpha for the A-ASD was 0.843, indicating good internal consistency. Application of these screening thresholds resulted in four groups, which were used in all subsequent analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSleep Quality\u003c/h3\u003e\n\u003cp\u003eSleep quality was assessed using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J), in which higher total scores indicate poorer sleep quality [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The PSQI-J has demonstrated good reliability and validity in both clinical and non-clinical populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For secondary categorical analyses, poor sleep was defined as a PSQI score\u0026thinsp;\u0026ge;\u0026thinsp;6, consistent with the PSQI-J validation cut-point of 5.5 (rounded to the nearest integer for logistic modelling) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMental Health\u003c/h3\u003e\n\u003cp\u003eMental health was assessed using the 12-item General Health Questionnaire (GHQ-12) with binary scoring (0-0-1-1). The Japanese version of the GHQ-12 has been widely used and validated in prior studies, including research examining its factor structure and construct validity; binary scoring is the standard in screening applications [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For categorical analyses, GHQ-12 caseness was defined as a binary-scored sum\u0026thinsp;\u0026ge;\u0026thinsp;4 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProblematic internet use\u003c/h2\u003e \u003cp\u003eProblematic or addictive internet use was assessed using the Japanese version of Young\u0026rsquo;s Internet Addiction Test (IAT), a 20-item self-report instrument measuring loss of control, preoccupation, and functional impairment related to internet use [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Items (e.g., \u0026lsquo;How often do you find that you stay online longer than you intended?\u0026rsquo;) were rated on a 5-point Likert scale ranging from 1 (rarely) to 5 (always), and responses were summed to obtain a global score (range: 20\u0026ndash;100), with higher scores indicating greater severity. In the present sample, the internal consistency was excellent (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.901).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eAll adjusted models included age (continuous) and sex (male, female, or prefer not to answer), which was dummy-coded for analysis. This coding approach preserved a separate category for non-responses rather than excluding those cases from analyses.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics for Windows, version 27. The analyses were designed to address two primary aims.\u003c/p\u003e \u003cp\u003e1. Continuous Outcomes (PSQI, GHQ-12)\u003c/p\u003e \u003cp\u003eWe first fitted a multivariate analysis of variance (MANOVA) model with group as the fixed factor and PSQI and GHQ-12 scores as dependent variables, adjusting for age and sex; Pillai\u0026rsquo;s Trace was prespecified because of its robustness to unequal covariance matrices.\u003c/p\u003e \u003cp\u003eWe subsequently estimated univariate general linear models (GLMs) for each outcome using the same covariate adjustment set and repeated the models after adding IAT scores to examine whether between-group differences persisted beyond PIU severity. For each model, we report F(df1, df2), p values, and partial η\u0026sup2; as the effect size.\u003c/p\u003e \u003cp\u003eHomogeneity of variance was assessed using Levene\u0026rsquo;s test. Because GHQ-12 scores showed heteroscedasticity and these outcomes may be skewed, we supplemented parametric inference with bias-corrected and accelerated (BCa) 95% bootstrap confidence intervals based on 1,000 resamples for GLM parameter estimates and estimated marginal means (EMMs). Omnibus F tests were reported with parametric p values, whereas bootstrap confidence intervals were used to evaluate the robustness of the estimates.\u003c/p\u003e \u003cp\u003eWe additionally examined a Group \u0026times; IAT interaction to assess whether residual between-group differences after IAT adjustment reflected slope heterogeneity (moderation) rather than level differences alone. Because the co-occurring subgroup was small, interaction tests were considered exploratory.\u003c/p\u003e \u003cp\u003e2. Categorical Outcomes (Secondary Analyses)\u003c/p\u003e \u003cp\u003eWe created two binary outcomes: poor sleep (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6) and GHQ-12 caseness (\u0026ge;\u0026thinsp;4). Each outcome was modeled using binary logistic regression to estimate adjusted odds ratios (aORs) for the group membership (reference category\u0026thinsp;=\u0026thinsp;typical), adjusting for age, sex, and IAT scores.\u003c/p\u003e \u003cp\u003eResults are presented with 95% confidence intervals; statistical significance was defined using a two-sided α\u0026thinsp;=\u0026thinsp;.05. No multiplicity adjustment was applied to the logistic regression models; however, pairwise comparisons of estimated marginal means in the GLM were Bonferroni-adjusted.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData quality.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were no missing item-level data because the survey used a forced response design. Two cases were excluded because of invariant response patterns. Consequently, the complete-case analysis corresponded to the full analytic sample (n\u0026thinsp;=\u0026thinsp;399).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Descriptive Characteristics\u003c/h2\u003e \u003cp\u003eThe analytic sample comprised 399 students (men, n\u0026thinsp;=\u0026thinsp;170; women, n\u0026thinsp;=\u0026thinsp;224; preferred not to answer, n\u0026thinsp;=\u0026thinsp;5). The trait-defined groups were as follows: typical, n\u0026thinsp;=\u0026thinsp;260; ASD(+), n\u0026thinsp;=\u0026thinsp;37; ADHD(+), n\u0026thinsp;=\u0026thinsp;67; and co-occurring ASD\u0026ndash;ADHD n\u0026thinsp;=\u0026thinsp;35 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, mean scores for PSQI, GHQ-12 (0\u0026ndash;0\u0026ndash;1\u0026ndash;1), and IAT were highest in the co-occurring ASD\u0026ndash;ADHD group. Crude prevalences of poor sleep (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6) and GHQ-12 caseness (\u0026ge;\u0026thinsp;4) were also highest in this group. Detailed descriptive statistics and proportions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for sleep, mental health, and Internet-addiction scores by trait-defined group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical (n\u0026thinsp;=\u0026thinsp;260)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD(+) (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD(+) (n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASD\u0026ndash;ADHD (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.48 (3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.84 (3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.28 (4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.26 (3.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor sleep (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6), n/N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111/260 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23/37 (62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44/67 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30/35 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12 (0\u0026ndash;0\u0026ndash;1\u0026ndash;1), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.17 (2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.86 (3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.12 (3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.37 (3.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor mental health (GHQ-12\u0026thinsp;\u0026ge;\u0026thinsp;4), n/N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61/260 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22/37 (59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34/67 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27/35 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIAT, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.42 (12.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.41 (11.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.91 (13.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.03 (11.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNotes\u003c/strong\u003e \u003cp\u003eGHQ-12 was scored using the binary method (0\u0026ndash;0\u0026ndash;1\u0026ndash;1). PSQI\u0026mdash;Pittsburgh Sleep Quality Index; GHQ-12\u0026mdash;12-item General Health Questionnaire; IAT\u0026mdash;Internet Addiction Test.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOf 468 students approached, 401 consented electronically, and 2 were excluded due to patterned responses, yielding an analytic sample of 399. Participants were assigned to four trait-defined groups based on screening thresholds: Typical (n\u0026thinsp;=\u0026thinsp;260), ASD(+)-only (n\u0026thinsp;=\u0026thinsp;37), ADHD(+)-only (n\u0026thinsp;=\u0026thinsp;67), and co-occurring ASD\u0026ndash;ADHD (n\u0026thinsp;=\u0026thinsp;35). ASD: autism spectrum disorder; ADHD: attention-deficit/hyperactivity disorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate and Univariate Models (Continuous Outcomes)\u003c/h2\u003e \u003cp\u003eMultivariate analysis of variance (MANOVA) revealed a significant overall group effect both before and after adjustment for IAT (Pillai\u0026rsquo;s trace p \u0026lt; .001 in both models; see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In univariate general linear models (GLMs), group effects were significant for both PSQI and GHQ-12 after adjustment for age and sex and remained significant after additional adjustment for IAT (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssumptions and Robustness Checks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGroup effects on PSQI and GHQ-12 in general linear models with and without IAT adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF(df1, df2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial η\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevene p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge/sex adjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.347 (3, 393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ IAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.285 (3, 392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge/sex adjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.265 (3, 393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+ IAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.383 (3, 392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes.\u003c/em\u003e F-tests are parametric. Where Levene p \u0026lt; .05 (GHQ-12; PSQI in +\u0026thinsp;IAT), BCa 95% bootstrap CIs (1,000) were used for parameter/EMM estimates; inferences were unchanged.\u003c/p\u003e \u003cp\u003eLevene\u0026rsquo;s tests indicated heteroscedasticity for GHQ-12 and borderline heterogeneity for PSQI after adjustment for IAT. Therefore, robustness was evaluated using 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals based on 1,000 resamples, which corroborated the primary inferences (see Methods ).\u003c/p\u003e \u003cp\u003eWe found no clear evidence of a Group\u0026times;IAT interaction for either outcome (PSQI: F(3,389)\u0026thinsp;=\u0026thinsp;1.686, p=.170, partial η\u0026sup2;=.013; GHQ-12: F(3,389)\u0026thinsp;=\u0026thinsp;0.675, p=.568, partial η\u0026sup2;=.005). Accordingly, within the limits of power for interaction tests, the IAT\u0026ndash;outcome association appeared broadly similar across groups, and adjusted group differences were observed across the IAT range.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePairwise Comparisons of Adjusted Means\u003c/h2\u003e \u003cp\u003eWith IAT included in the model, Bonferroni-adjusted estimated marginal mean (EMM) contrasts showed that, for PSQI, the ASD\u0026ndash;ADHD group scored significantly higher than the Typical group (Δ\u0026thinsp;=\u0026thinsp;2.507, 95% CI 1.177\u0026ndash;3.730, p = .001) and the ASD(+) (Δ\u0026thinsp;=\u0026thinsp;1.640, 95% CI 0.010\u0026ndash;3.184, p = .027). The ADHD(+) versus Typical difference was smaller but remained statistically significant (Δ\u0026thinsp;=\u0026thinsp;1.116, 95% CI 0.105\u0026ndash;2.146, p = .043), whereas the ASD(+) versus Typical comparison was not significant (Δ\u0026thinsp;=\u0026thinsp;1.245, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.224\u0026ndash;2.193, p = .150). For GHQ-12, all elevated-trait groups had higher adjusted scores than the Typical group: ASD(+) (Δ\u0026thinsp;=\u0026thinsp;2.446, 95% CI 1.493\u0026ndash;3.497, p = .001), ADHD(+) (Δ\u0026thinsp;=\u0026thinsp;1.669, 95% CI 0.892\u0026ndash;2.488, p = .001), and ASD\u0026ndash;ADHD (Δ\u0026thinsp;=\u0026thinsp;2.859, 95% CI 1.512\u0026ndash;3.675, p = .001). Full model estimates are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise EMM contrasts by trait-defined group for PSQI and GHQ-12 (IAT-adjusted)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContrast (Typical\u0026thinsp;\u0026minus;\u0026thinsp;group)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted mean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep (Bonferroni)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ASD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.193 to 0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ADHD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.146 to \u0026minus;\u0026thinsp;0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ASD\u0026ndash;ADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.730 to \u0026minus;\u0026thinsp;1.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ASD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.497 to \u0026minus;\u0026thinsp;1.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ADHD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.488 to \u0026minus;\u0026thinsp;0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHQ-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTypical vs ASD\u0026ndash;ADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.675 to \u0026minus;\u0026thinsp;1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes\u003c/em\u003e. Negative values mean higher adjusted means in the comparison group (vs Typical). EMMs from GLMs adjusted for age, sex, and IAT; Bonferroni-corrected. BCa 95% bootstrap CIs (1,000) supported results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCategorical Outcomes (Secondary Analyses)\u003c/h2\u003e \u003cp\u003eThe binary logistic regression analyses adjusted for age, sex, and IAT are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For poor sleep (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6), only the ASD\u0026ndash;ADHD group showed significantly higher odds than the Typical group (aOR\u0026thinsp;=\u0026thinsp;3.15, 95% CI 1.11\u0026ndash;8.93, p = .031). The ASD(+) group was not significantly different from the Typical group, whereas the ADHD(+) group showed a nonsignificant trend toward higher odds. For poor mental health (GHQ-12\u0026thinsp;\u0026ge;\u0026thinsp;4), all elevated-trait groups had higher odds compared with the Typical group: ASD(+) (aOR\u0026thinsp;=\u0026thinsp;4.32), ADHD(+) (aOR\u0026thinsp;=\u0026thinsp;2.81), and ASD\u0026ndash;ADHD (aOR\u0026thinsp;=\u0026thinsp;7.58). All associations were statistically significant (p \u0026le; .001) unless otherwise noted. Complete estimates and confidence intervals are reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic models for PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6 and GHQ-12\u0026thinsp;\u0026ge;\u0026thinsp;4: adjusted odds by trait-defined group and IAT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup / Predictor (ref\u0026thinsp;=\u0026thinsp;Typical)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eaOR (Exp[B])\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor sleep (PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026ndash;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026ndash;3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD\u0026ndash;ADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u0026ndash;8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor mental health (GHQ-12\u0026thinsp;\u0026ge;\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04\u0026ndash;9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHD(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u0026ndash;5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD\u0026ndash;ADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.01\u0026ndash;19.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNotes\u003c/em\u003e. Binary logistic regression adjusted for age, sex, and IAT (reference\u0026thinsp;=\u0026thinsp;Typical). aOR\u0026mdash;adjusted odds ratio; CI\u0026mdash;confidence interval; two-sided α\u0026thinsp;=\u0026thinsp;.05\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional sample of health profession students, individuals screening positive for co-occurring ASD\u0026ndash;ADHD traits exhibited the poorest sleep quality and the highest levels of psychological distress. These between-group differences remained significant after adjusting for the IAT scores, suggesting that device-related severity alone was unlikely to explain the observed burden. Secondary analyses based on established clinical cut-offs largely replicated these patterns (poor sleep: PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6; poor mental health: GHQ-12\u0026thinsp;\u0026ge;\u0026thinsp;4). Post-hoc moderation analyses revealed no significant Group \u0026times; IAT interaction for either outcome (PSQI: F(3,389)\u0026thinsp;=\u0026thinsp;1.686, p = .170, partial η\u0026sup2; = .013; GHQ-12: F(3,389)\u0026thinsp;=\u0026thinsp;0.675, p = .568, partial η\u0026sup2; = .005). This finding suggests broadly parallel associations between IAT scores and the outcome across groups and supports the interpretation that group differences persist across the full IAT range.\u003c/p\u003e \u003cp\u003eOur findings align with previous studies linking autistic and/or ADHD traits, particularly when both are elevated, to increased emotional difficulties among university students [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings also support evidence that problematic or addictive internet use co-occurs with poorer sleep and worse mental health outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous research has also shown that ADHD and problematic internet use (PIU) are moderately associated, with some longitudinal evidence indicating that ADHD symptoms may prospectively predict later PIU [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A key contribution of the present study is that group differences in sleep and mental health remained evident even after adjustment for IAT scores. We observed this pattern among health profession students, a population that has previously been reported to exhibit high baseline rates of depression and poor sleep [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause group differences remained after adjustment for IAT and did not vary across IAT levels, a simple device-first explanation\u0026mdash; where internet problems are viewed as the primary driver of sleep and mood difficulties\u0026mdash;appears insufficient. This pattern is consistent with theoretical perspectives suggesting that preexisting psychological strain may lead individuals to engage in online activities as a coping strategy. Conceptually, the cognitive\u0026ndash;behavioral model of pathological internet use emphasizes maladaptive expectations and emotion-regulation motives [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similarly, the compensatory internet use perspective proposes that individuals may go online to manage stress or fulfill unmet needs [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Empirical studies further indicate that avoidant coping mediates the association between psychological distress and PIU among adolescents and adults [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], supporting a pathway in which strain precedes and helps drive coping-motivated engagement. In university samples, social support\u0026mdash;alongside factors such as stress, self-control, and anxiety\u0026mdash;has emerged as a significant predictor of PIU [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. If co-occurring ASD\u0026ndash;ADHD traits are masked or compensated for, such that students\u0026rsquo; needs remain underrecognized, these individuals may be less likely to access support. Reduced perceived support may subsequently increase the risk of internet addiction while leaving trait-related sleep and mental health needs insufficiently addressed. A common behavioral manifestation in student life is bedtime procrastination, often characterized by late-night smartphone or internet use, which has been associated with poorer sleep quality [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the findings challenge a purely device-first explanation, the cross-sectional design and potential mediation by IAT scores mean that these adjusted differences should be interpreted as conservative and non-causal.\u003c/p\u003e \u003cp\u003eWe caution against framing sleep and mood problems primarily as screen-time issues. For some students, online activities may function as self-regulation or coping for pre-existing psychological distress. This vulnerability may be particularly pronounced when ASD and ADHD traits co-occur and remain underrecognized. Accordingly, we emphasize attunement rather than admonition in responding to students\u0026rsquo; late-evening internet use. Practically, this involves recognizing possible masking, inviting non-judgmental conversations about the timing and purpose of late-evening online use, and considering brief screening for neurodevelopmental traits, including their potential co-occurrence. Such approaches may help ensure that less visible difficulties are recognized and that appropriate support options can be explored collaboratively. Clinically, problematic internet use has been associated with several high-risk outcomes. For example, a recent meta-analysis reported a moderate positive association between problematic internet use and non-suicidal self-injury among adolescents [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This finding underscores the importance of looking beyond device-focused advice and addressing underlying psychological distress and access to support.\u003c/p\u003e \u003cp\u003eSeveral important limitations should be acknowledged and pointed to concrete directions for future research. First, the cross-sectional design precludes conclusions regarding temporal ordering or causal mediation. Future research should employ multi-wave longitudinal or experience-sampling designs to examine time-ordered pathways. Such designs could also evaluate potential bidirectional relationships between psychological distress and problematic internet use (e.g., cross-lagged or longitudinal mediation models). Second, reliance on self-report measures may introduce common-method bias. Integrating self-reports with objective sleep measures (e.g., actigraphy or wearable devices), clinician-rated assessments of neurodevelopmental characteristics, and privacy-protected passive indicators of online activity would strengthen construct validity. Third, the single-institution sample of Japanese health profession students limits the generalizability of the findings. Multi-site, multi-program, and cross-national samples are required to examine whether this pattern holds across curricula and cultures. Fourth, the co-occurring group was relatively small (n\u0026thinsp;=\u0026thinsp;35), which limits statistical precision, particularly the power to detect interaction effects. Future studies could oversample this subgroup, pool data across multiple sites, and consider Bayesian hierarchical modeling or preregistered equivalence tests (e.g., TOST) when evaluating claims of \u0026ldquo;no interaction\u0026rdquo;, guided by a priori power analyses. Fifth, interpretation is constrained by measurement choices: the GHQ-12 was scored using the binary (0\u0026ndash;0\u0026ndash;1\u0026ndash;1) method, ADHD traits were screened using the six-item ASRS (\u0026ge;\u0026thinsp;4 threshold), and poor sleep was defined as PSQI\u0026thinsp;\u0026ge;\u0026thinsp;6, consistent with PSQI-J validation at 5.5. Sensitivity analyses using GHQ-12 Likert scoring, more comprehensive ADHD or autism trait instruments, alternative PSQI thresholds, and tests of measurement invariance (e.g., by sex) would help clarify the robustness of the findings. Finally, unmeasured confounding may have influenced the findings. Variables such as chronotype, medication use, anxiety or depression comorbidity, clinical placement load, living arrangements, social support, coping style (e.g., avoidant coping), and bedtime procrastination were not fully captured. Future studies should systematically measure these factors and assess the robustness of the findings using sensitivity analyses and cluster-robust standard errors.\u003c/p\u003e \u003cp\u003eA further interpretive consideration is that IAT scores may partially mediate the relationship between neurodevelopmental traits and the observed outcomes. Consequently, adjusting for IAT scores represents a conservative analytic approach and may underestimate the magnitude of trait-related effects. Longitudinal mediation analyses incorporating motives and time-of-day patterns of online use would allow a more direct evaluation of the proposed self-regulation pathway.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAmong health profession students, co-occurring ASD\u0026ndash;ADHD traits were associated with poorer sleep quality and worse mental health outcomes, even after accounting for Internet Addiction Test (IAT) severity. Furthermore, these group differences did not appear to vary according to the level of IAT severity. These findings suggest that device-related mechanisms likely explain only a portion of the observed variance in sleep and mental health outcomes. Therefore, effective student support strategies should extend beyond device-focused interventions to include neurodevelopmentally informed accommodations, improved access to social support, and targeted sleep and mental health care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eA-ASD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdult Autism Spectrum Disorders Self-Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention-deficit/hyperactivity disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdult ADHD Self-Report Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutism spectrum disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBias-corrected and accelerated\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated marginal mean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHQ-12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e12-item General Health Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral linear model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternet Addiction Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultivariate analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProblematic internet use\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI-J\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh Sleep Quality Index \u0026mdash; Japanese version\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuick response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTwo one-sided tests\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Ethics Committee of Morinomiya University of Medical Sciences (approval number: 2021\u0026ndash;141) and was conducted in accordance with the Declaration of Helsinki. Informed consent to participate was obtained electronically. Prior to participation, students received an oral explanation in class and were provided with study information via the online survey page, including the study purpose and procedures, the voluntary nature of participation, the potential risks and benefits (minimal risk and no guaranteed direct benefit), and data handling and confidentiality, including the anonymous nature of the survey. Because the survey was anonymous, submission of the completed questionnaire was taken as provision of consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception and design of the study. Material preparation, data collection, and analyses were performed by Ogawa Y., Yokota S., and Tano K. The first draft of the manuscript was written by Y. Ogawa. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the assistance of the participants, including the students involved in this study. The authors also acknowledge Editage (www.editage.jp) for the English language editing support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the conclusions of this study are not publicly available due to privacy and ethical restrictions. The data are securely stored at Morinomiya University of Medical Sciences. Access may be granted under specific conditions that comply with the university\u0026rsquo;s data protection policies and ethical guidelines. Researchers seeking access for legitimate academic purposes may contact the corresponding author with a detailed request. Approval for data access will be determined based on the proposed use, adherence to ethical standards, and agreement to maintain the privacy and confidentiality of participants.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are affiliated with a university department in rehabilitation sciences and have research interests in mental health and health behaviors among university students.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eClouder L, Karakus M, Cinotti A, Ferreyra MV, Fierros GA, Rojo P. Neurodiversity in higher education: A narrative synthesis. 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J Adolesc. 2025;97:1433\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jad.12510\u003c/span\u003e\u003cspan address=\"10.1002/jad.12510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"problematic internet use, autistic traits, ADHD traits, health profession students, sleep quality, psychological distress","lastPublishedDoi":"10.21203/rs.3.rs-9286185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9286185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth professional students with autism spectrum disorder (ASD) and/or attention-deficit/hyperactivity disorder (ADHD) traits may experience poorer sleep and worse mental health. These difficulties are often attributed to problematic internet use, which may obscure underlying trait-related burdens. We examined whether differences in sleep quality and mental health across the trait-defined groups persisted after adjusting for the severity of problematic internet use.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a secondary cross-sectional analysis of questionnaire data from health profession students in Japan (N\u0026thinsp;=\u0026thinsp;399). ASD traits were screened using the Adult Autism Spectrum Disorders Self-Rating Scale, and ADHD traits were screened using the Adult ADHD Self-Report Scale. Participants were classified into four groups: typical (below both thresholds), ASD only, ADHD only, and co-occurring ASD\u0026ndash;ADHD. Sleep quality was assessed using the Pittsburgh Sleep Quality Index. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12) with binary scoring. Problematic internet use severity was measured using the Internet Addiction Test. Group differences were analyzed using multivariate general linear models with follow-up-adjusted comparisons, controlling for age and sex.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTrait-defined groups differed significantly in the combined outcomes of sleep quality and mental health. This multivariate effect remained significant after adding the Internet Addiction Test score to the model, and no group \u0026times; Internet Addiction Test interaction was detected. In adjusted comparisons, the co-occurring ASD\u0026ndash;ADHD group exhibited the poorest sleep quality and mental health outcomes relative to the typical and single-trait groups, even after controlling for problematic internet use severity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSleep problems and poor mental health associated with ASD and/or ADHD traits, particularly their co-occurrence, persisted after adjustment for problematic Internet use (IAT), supporting the view that a device-first explanation alone may be insufficient. Therefore, effective support strategies should extend beyond device-focused interventions to include neurodevelopmentally informed accommodations, improved access to social support, and targeted sleep and mental health care.\u003c/p\u003e","manuscriptTitle":"Co-occurring Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder traits are associated with poorer sleep and mental health beyond problematic internet use among health profession students: a cross-sectional analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:49:15","doi":"10.21203/rs.3.rs-9286185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-27T18:44:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55021633887406409721449039454598460919","date":"2026-04-27T14:41:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T11:23:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T11:20:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T14:13:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T19:20:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-04-07T15:37:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2626147e-f481-41b9-a451-70e9d3182649","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T11:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:49:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9286185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9286185","identity":"rs-9286185","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}