Validation of the Adaptive Cognition and Behaviors-6 (ACBS-6) Scale in Individuals with Insomnia and its Association with Sleep- Related Safety Behaviors

Validation of the Adaptive Cognition and Behaviors-6 (ACBS-6) Scale in Individuals with Insomnia and its Association with Sleep- Related Safety Behaviors | 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 Validation of the Adaptive Cognition and Behaviors-6 (ACBS-6) Scale in Individuals with Insomnia and its Association with Sleep- Related Safety Behaviors Jeonghwan Lee, Mohd. Ashik Shahrier, Jangho Park, Seockhoon Chung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9078083/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 Insomnia is a chronic disorder in which dysfunctional beliefs and maladaptive safety behaviors contribute to its persistence. This study aimed to validate the Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6) and examine its psychometric properties and associations with dysfunctional beliefs, safety behaviors, and insomnia severity. Methods A total of 600 participants with insomnia were recruited through an online survey. Participants completed the ACBS-6, Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6), Insomnia Severity Index (ISI), and Sleep-Related Behavior Questionnaire-10 (SRBQ-10). For psychometric analysis, the Rasch model and graded response model within Confirmatory Factor Analysis (CFA) and Item Response Theory (IRT) were used. Reliability was assessed using McDonald's Omega, and associations with related constructs were examined using correlation, regression, and mediation analyses. Results As a result of the CFA, a two-factor structure (adaptive cognition and adaptive behavior) of the ACBS-6 was identified, and all items showed significant factor loadings. In the IRT analysis, item fit was good, with high reliability and discrimination; however, questions 3 and 4 provided limited information across the latent characteristics. The scale showed a significant negative correlation with the ISI (r = -0.15, p < 0.01), whereas the DBAS-6 (r = 0.47) and the SRBQ-10 (r = 0.49) showed positive correlations. In the regression analysis, ACBS-6 (β = -0.15), DBAS-6 (β = 0.29), and SRBQ-10 (β = 0.32 significantly predicted the severity of insomnia, and the model explained 32% of the variance. Mediation analysis showed that adaptive cognition and behavior partially buffered the effect of insomnia severity on dysfunctional beliefs and safe behavior. Conclusions The ACBS-6 demonstrated robust validity and reliability as a measure of adaptive sleep-related cognition and behavior in individuals with insomnia. Adaptive cognitions and behaviors were associated with lower insomnia severity and served as protective factors, attenuating the impact of dysfunctional beliefs and maladaptive safety behaviors. insomnia adaptive cognition dysfunctional beliefs safety behaviors Figures Figure 1 Figure 2 Figure 3 Introduction Insomnia is a common and chronic condition that makes it difficult to fall asleep or stay asleep and can lead to daytime impairments. Individual with insomnia frequently experience fatigue, decreased concentration, emotional pain, and poor quality of life beyond sleep deprivation itself [1]. Insomnia is not merely a night-specific problem but a 24-h condition. Many individuals with insomnia use maladaptive coping strategies or develop distorted beliefs about sleep in an attempt to solve the problem, which instead strengthens their sleep-related anxiety and perpetuates the disorder [2]. Consequently, there is a growing interest in cognitive models that emphasize psychological processes contributing to the maintenance of insomnia, moving beyond purely biomedical or behavioral explanations. Harvey (2002) proposed a cognitive model of insomnia, providing an integrated framework for explaining how insomnia is maintained through multiple interacting cognitive and behavioral processes [3]. The five maintenance mechanisms central to this model include cognitive arousal before going to sleep (e.g., worry, rumination), selective attention and monitoring of sleep-related threat signals, distorted perceptions of sleep and their consequent daytime outcomes, rigid and unhelpful beliefs about sleep, and counterproductive safety behaviors to avoid sleep failure or its consequences. These elements are intertwined to form a vicious cycle, heightening anxiety and arousal about sleep and strengthening distorted beliefs that block opportunities for reality testing. By elaborating on the key mechanisms sustaining insomnia, Harvey's model provides the theoretical basis for therapeutic intervention by emphasizing that cognitive behavioral therapy for insomnia (CBT-I) should cover cognitive content and functions beyond merely correcting sleep habits. Safety behaviors refer to the various coping strategies that individuals with insomnia perform to avoid sleep failure or the consequent negative daytime effects. Examples include taking naps to compensate for insufficient sleep, minimizing activities to save energy, canceling schedules or appointments, and improperly relying on alcohol or drugs for nighttime sleep. These behaviors may appear to be attempts to solve the problem, but in reality, they can raise the level of physical arousal, disturb the circadian rhythm, and make sleep more unstable [4]. In addition, these repetitive behaviors temporarily reduce anxiety related to sleep failure, but simultaneously contribute to the persistence of insomnia by strengthening negative perceptions and avoidance patterns related to sleep. Underlying these safety behaviors lies an unrealistic and rigid belief in sleep, called dysfunctional beliefs about sleep, which often excessively amplifies the perceived threat of sleep failure [5]. These beliefs take the form of excessive conviction, such as "I must sleep 8 h," "I completely ruin my day by staying up all night," and "I can't sleep alone," creating excessive expectations and a desire for control over sleep while increasing anxiety and arousal about the possibility of sleep failure. This heightened state of alertness negatively affects actual sleep initiation and maintenance, while strengthening the sense of helplessness that patients cannot control their sleep on their own. These psychological and physiological tensions lead to behavioral strategies and safety behaviors aimed at preventing or minimizing the effects of sleep failure. These beliefs attribute the cause of insomnia to external conditions rather than internal factors, making individuals dependent on specific measures (e.g., drugs, specific environments) and weakening self-efficacy in self-regulation and sleep recovery. Thus, CBT-I considers these patterns important treatment targets, and cognitive restructuring, accurately identifying dysfunctional beliefs and replacing them with more flexible and realistic thinking, is emphasized as a key component of the treatment. Recently, we developed a sleep-related adaptive cognition and behavior scale called the Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6). Although the conventional Dysfunctional Beliefs and Attitudes about Sleep-16 (DBAS-16) [6] and DBAS-6 [7] focus on assessing unrealistic and rigid beliefs, namely, dysfunctional beliefs about sleep, the ACBS-6 focuses on measuring attitudes and behaviors that accommodate and realistically address sleep problems [8]. The measure consists of six questions and includes two subfactors: adaptive sleep-related cognition and adaptive sleep-related behavior. The results of the study showed that the scale had a significant negative correlation with insomnia severity and was independent of dysfunctional beliefs. Therefore, it can be used as a clinical tool to help build more flexible and healthy sleep-related perceptions, beyond simply correcting distorted thinking in the course of CBT-I. However, the scale was not inversely correlated with insomnia severity in the general population, although it contributed inversely in the linear regression analysis. We interpreted the lack of correlation as a consequence of surveying the general population rather than individuals with insomnia. Thus, we sought to explore the reliability and validity of the scale in patients with insomnia. In this study, we aimed to validate the ACBS-6 scale among the general population with insomnia and to explore whether there is an association between sleep-related dysfunctional and functional cognitions, sleep-related safety behaviors, and insomnia severity. Methods 1. Participants and procedure The survey was conducted online among registered general population panels of the professional survey company EMBRAIN. The survey was conducted anonymously from June 18 to 24, 2025. We developed a survey form that included questions regarding participants' age, sex, marital status, and past psychiatric history. To collect individuals experiencing insomnia in the general population, participants were enrolled based on their affirmative response to the question, "Have you experienced difficulty falling asleep or maintaining sleep in the past 3 months?" In addition, to exclude participants with circadian rhythm sleep–wake disorders, eligibility required an affirmative response to the question, "Do you go to bed between 9 PM and 1 AM every day?" Participants were excluded if they responded affirmatively to the question, "Are you working a shift?" Finally, participants could proceed with the survey after providing their consent. The sample size was estimated at 600 participants, calculated by allocating 50 samples per 12 cells (sex × six age groups) based on the Central Limit Theorem [9]. A total of 7,558 emails were sent to 1.8 million registered panel members, of which 1,882 accessed the survey and 640 completed it. To ensure valid samples, the fastest 5% of respondents in each quota based on response time were excluded, along with responses in which the average time spent per question exceeded three times the overall average. Ultimately, the company delivered 600 de-identified responses to the researchers. The study protocol was approved by the Institutional Review Board of Asan Medical Center (2025-0607). 2. Measures 2.1. ACBS-6 The ACBS-6 is a self-report rating scale designed to measure an individual's adaptive cognition and behavior regarding sleep. It consists of six items, each rated on a scale from 0 ("strongly disagree") to 10 ("strongly agree"). The final score is the average of all six items, with higher scores reflecting more adaptive attitudes toward sleep-related thoughts and behaviors. The scale was developed and validated in the Korean general population, with McDonald's Omega reported as 0.734 in a previous study [8]. 2.2. DBAS-6 The DBAS-6 is a self-report rating scale designed to assess individuals' sleep-related dysfunctional beliefs and attitudes [6]. It is a shortened version of the DBAS-16 developed using a machine learning approach [7]. All six items were scored between 0 ("strongly disagree") and 10 ("strongly agree"). The final score is the average of all six items, with higher scores indicating greater levels of sleep-related dysfunctional cognition. The Cronbach's alpha for this sample was 0.768. 2.3. Insomnia Severity Index (ISI) The ISI is a self-report rating scale used to evaluate insomnia severity [10]. It comprises seven items, each rated on a 5-point Likert scale. Higher total scores indicate more severe insomnia. The validated Korean version of the scale was used [11], and Cronbach's alpha for this sample was 0.822. 2.4. Sleep-Related Behavior Questionnaire-10 (SRBQ-10) The SRBQ-10 is a self-report rating scale developed to assess maladaptive sleep-related behaviors in individuals. The SRBQ-10 is a shortened version of the original SRBQ [12], created using a machine learning approach to optimize item selection for brevity and psychometric validity [13]. Each of the 10 items is rated on a 5-point scale ranging from 0 (rarely) to 4 (almost always), reflecting the frequency of specific sleep-related behavior. The total score was calculated, with higher scores indicating more frequent engagement in maladaptive sleep behaviors. In this sample, the SRBQ-10 demonstrated good internal consistency, with a Cronbach’s alpha of 0.850. Statistical analyses The psychometric properties of the ACBS-6 in the general population reporting insomnia were explored using Classical Test Theory and modern psychometric approaches. We first assessed the construct validity of the ACBS-6 using confirmatory factor analysis (CFA) based on a predefined, two-factor model. The normality of the six items was evaluated using skewness and kurtosis values within the range of ±2. Sample adequacy and data suitability were confirmed using the Kaiser-Meyer-Olkin (> 0.60) and Bartlett's test of sphericity (p < 0.001). Model fit indices, including the Comparative Fit Index, Tucker-Lewis Index, Root Mean Square Error of Approximation, and Standardized Root Mean Square Residual, were used to evaluate the ACBS-6. Second, under the Item Response Theory (IRT) approach, we used the Rasch model and the graded response model (GRM) to evaluate the item-level properties, reflecting the appropriateness of the ACBS-6 items with the underlying latent construct. In the Rasch model, the match between the actual item response and the expected response was determined using infit and outfit mean square (MnSq) values. Infit and outfit MnSq values closer to 1 indicate a good fit, while values less than 0.5 and greater than 1.7 reflect a poor fit with the underlying latent trait measured [14, 15]. Furthermore, the person and item separation indices with their respective reliabilities were calculated to examine the scale’s discriminating abilities across different levels of the latent trait and to assess the consistency of the item difficulty estimates. Reliability coefficients greater than 0.80 and separation indices exceeding 1.50 were considered cutoffs for measurement precision and consistency of the scale, indicating the sufficient robustness of the ACBS-6 [16]. Under the GRM, we examined the item discrimination and difficulty parameters of ACBS-6 items. For discrimination parameters, the suggested cut-offs, ranging from 0.65 to 1.34, indicated moderate, 1.35 to 1.69 indicated high, and greater than 1.70 indicated very high, reflecting the ability of items to effectively distinguish between individuals across the latent trait [17]. The item difficulty parameters represent participants’ response patterns with a 50% probability of endorsing each item category across varying latent trait levels. Moreover, the item information curves (IICs) and test information function (TIF) of the ACBS-6 were examined to assess the extent of information provided by the scale items across the spectrum of the underlying latent construct. Third, internal consistency reliability was measured using McDonald's Omega, while convergent validity with pre-existing rating scales, such as the ISI, DBAS-16, and SRBQ-10, was assessed using Pearson's correlation coefficients. To explore whether dysfunctional or functional sleep-related cognitions interact with insomnia severity and sleep-related safety behaviors, linear regression and mediation analyses were conducted. Data were analyzed using JASP software, version 0.14.1.0 (JASP team, Amsterdam, The Netherlands). The R package “ltm” version 1.2.0 [18] was used for the GRM. Results A total of 600 participants reporting insomnia were included in the study with an average age of 49.1 years (standard deviation = 16.6), and men and women were included equally. Among the participants, 39.8% reported a history of mental health problems, such as depression, anxiety, or insomnia, or previous treatment for these conditions. All participants were currently experiencing insomnia, with 23.5% reporting difficulty falling asleep, 45.8% frequent awakening, and 30.7% reporting both. Additionally, 20.0% of the participants reported using sleeping pills or sedatives for sleep ( Table 1 ). Factor analysis We checked the skewness and kurtosis values to verify the assumption of normality. The skewness of each ACBS-6 item ranged between -0.31 and 0.69, and kurtosis ranged between -0.78 and -0.30, all within the range of ± 2.0, indicating that the assumption of normality was not significantly violated ( Table 2 ). The Kaiser-Meyer-Olkin value of 0.740 and Bartlett's test (p < 0.001) indicated that the samples were suitable for the factor analysis. CFA was performed to verify the construct validity of the ACBS-6 scale, and the results showed good overall model fit, with a Comparative Fit Index of 0.981, Tucker-Lewis Index of 0.964, Root Mean Square Error of Approximation of 0.059, and Standardized Root Mean Square Residual of 0.023. Two-factor structures were identified; factor 1 consisted of items 1, 2, 3, and 4, with factor loadings of 0.73, 0.84, 0.58, and 0.59, respectively, all exceeding the standard cut-off of 0.4, while factor 2 consisted of items 5 and 6, with factor loadings of 0.56 and 0.80, respectively ( Table 2 ). These results suggest that the ACBS-6 measures two distinct components, adaptive sleep-related cognition and adaptive sleep-related behavior, and that each item appropriately reflects its respective factor. IRT results from the Rasch model and the GRM The item-level psychometric characteristics of the ACBS-6 revealed that the infit and outfit mean square values ranged from 0.62 to 1.32 (Table 3), indicating an adequate model fit and good item fit with the underlying latent trait. As shown in Table 3 , the item and person reliabilities (0.93 for items and 0.79 for persons) and the corresponding separation indices (3.65 for items and 1.94 for persons) exhibited excellent measurement precision and strong differentiation in the item difficulty levels of the ACBS-6. The GRM framework for evaluating the item-level characteristics of the ACBS-6 demonstrated that the scale items had discriminative values ranging from 1.80 to 3.45 ( Table 4 ), indicating very high discriminative abilities of both factors to provide information and distinguish among individuals across the latent construct. Furthermore, for factor 1 (adaptive sleep-related cognition), the difficulty parameters ( Table 4 ) and the TIF ( Figure 1A ) revealed that the four items provided information across a polarized spectrum of ability ranges, with theta values ranging from -1 to +2. An inspection of the IICs in Figure 2A showed that items 1 and 2 (Factor I) provided the highest level of information about the underlying construct, with distinct abilities across varying levels of the latent continuum, whereas items 3 and 4 provided similar information with less distinction across a limited spectrum of the latent trait. For factor 2 (items 5 and 6), the difficulty parameters ( Table 4 ) and TIF ( Figure 1B ) indicated that adaptive sleep-related behaviors provided information across a moderate spectrum of ability ranges, with theta values ranging from -2 to +2. In addition, the IICs in Figure 2B show that items 5 and 6 (Factor 2) provided the highest level of information about the underlying construct (adaptive sleep-related behaviors), with distinct abilities across varying levels of the latent trait. Altogether, the scale items demonstrated excellent discriminating power and strong differentiation in item difficulty, with a recommendation to reconsider the under-represented response categories for items 3 and 4 by ensuring a more heterogeneous sample in future validation studies. Reliability and Evidence Based on Relations to Other Variables Pearson’s correlation analysis revealed that the ISI was significantly positively correlated with the DBAS-6 (r = 0.47, p < 0.01) and SRBQ-10 (r = 0.49, p < 0.01) scores, whereas it was negatively correlation with ACBS-6 Factor I (r = -0.15, p < 0.01) ( Table 5 ). This indicates that as insomnia severity increases, distorted beliefs and maladaptive behaviors increase, whereas adaptive thinking tends to decrease. Regression analysis showed that DBAS-6 (β = 0.29), SRBQ-10 (β = 0.32), and ACBS-6 (β = -0.15) had significant predictive power for ISI, with the overall model explaining 32% of the variance (Adjusted R² = 0.32, F = 71.6, p < 0.001) ( Table 6 ). Mediation analysis revealed that the ISI directly influenced the DBAS-6 and SRBQ-10, and the ACBS-6 inversely mediated the effects of the ISI on the DBAS-6 and SRBQ-10 ( Table 7 ). These pathways are illustrated in Figure 3 . This result supports that adaptive sleep-related cognition and behaviors can function as protective factors, buffering against cognitive distortions or maladaptive behaviors associated with insomnia. Discussion In this study, we confirmed that the ACBS-6 exhibited good psychometric characteristics in the general population reporting insomnia. The factor structure and item fit of the ACBS-6 were found to be stable, capturing an independent conceptual domain distinct from the DBAS-6 and SRBQ-10. These results suggest that in individuals with insomnia, adaptive cognition and behavior are not merely the opposite of dysfunctional beliefs but constitute separate and independent dimensions. Additionally, the ACBS-6 total score showed a significant negative correlation with the ISI score, supporting the idea that adaptive sleep-related cognition and behaviors are associated with symptom relief. Although DBAS-6 and SRBQ-10 were positively correlated with insomnia severity, the ACBS-6 functioned independently and simultaneously mitigated the negative effects of DBAS-6 and SRBQ-10 in the mediation model. In other words, this study empirically demonstrated that adaptive sleep-related cognition and behavior mediate the impact of dysfunctional beliefs and safety behaviors on insomnia severity. The item-level psychometric characteristics of the ACBS-6, evaluated using the Rasch and GRM models within the IRT framework, indicate that the newly validated ACBS-6 had excellent fit statistics for individuals reporting insomnia symptoms. The item fit with the underlying latent construct, assessed through infit and outfit MnSq values, along with highly acceptable item and person separation indices and reliabilities, established the ACBS-6 as a robust tool for insomnia complaints. Additionally, the discrimination and difficulty parameters demonstrated the measurement precision and consistency of the ACBS-6, providing information about how effectively the scale items discriminate among individuals with distinct abilities across a moderate spectrum of latent traits. However, an inspection of the item difficulty parameters, along with the ICCs and TIF, revealed that items 3 and 4 in factor 1 (adaptive sleep-related cognitions) provided a limited and similar level of information across the latent trait, whereas the other items (i.e., items 1 and 2 in factor 1 and items 5 and 6 in factor 2; adaptive sleep-related behaviors) provided a sufficient amount of information across varying ability ranges. This suggests that the 11-point response categories should be reconsidered, particularly focusing on the underreported categories for items with indistinct abilities. Reducing the response categories and ensuring greater sample heterogeneity in future validation studies could clarify whether the limited measurement precision of some items in factor 1 is sample-specific or reflects inherent measurement challenges. Overall, the fit statistics in the Rasch model, along with the very high discriminative ability and strong item differentiation in the GRM for the ACBS-6, suggest that no further revisions to the scale items are needed, except for reconsidering the response categories, highlighting the overall robustness of the scale for individuals reporting insomnia. In a previous study that examined the reliability and validity of the ACBS-6 among the general population [ 8 ], we observed a lack of association between the ACBS-6 and ISI scores. We considered that this lack of association may have been due to the relatively low insomnia severity in the study participants. In this study, we explored the association between insomnia in the general population and observed a significant inverse correlation between the ACBS-6 and ISI scores. However, a significant correlation between the ISI and ACBS-6 Factor 2 (adaptive sleep-related behaviors) was not observed. This lack of association may be due to the items in Factor 2 being unfamiliar. Items 5 ("To sleep for 7 h within a 24-h day, 17 h of activity is necessary") and 6 ("Being active during the day leads to better sleep rather than good sleep leading to better daytime activity") were developed based on previous studies [ 19 , 20 ]. These items may be useful in CBT-I for patients with insomnia, but participants in this study may have found it difficult to understand the exact meaning of the items. Similar to a previous study [ 8 ], a significant correlation between the ACBS-6 and DBAS-6 was not observed in this study. The findings indicate that the domains measured by the two scales reflect distinct and independent dimensions. In studies on coping styles, adaptive and maladaptive coping styles operate independently rather than at opposite ends of a single continuum. Thompson et al. [ 21 ] showed that high levels of adaptive coping buffer the adverse effects of maladaptive coping on depression. Brown et al. demonstrated that increased use of maladaptive strategies occurs when stress is appraised more negatively, whereas the use of adaptive coping strategies appears independent of the level of appraisal [ 22 ]. Similarly, adaptive sleep-related cognition operates independently of dysfunctional sleep-related cognition, which may explain the lack of associations. However, the mediation analysis showed a positive link between the ACBS-6 and DBAS-6 scores. Factor 2 of the ACBS-6 was significantly correlated with DBAS-6 in this study and in a previous study [ 8 ]. In addition, the SRBQ-10 score was positively correlated with factor 2 in this study. The implications of these associations require further exploration, especially considering the lack of association between factor 2 and the ISI, which was significantly associated with the DBAS-6 and SRBQ-10. Future studies should include larger samples and more detailed methodologies to clarify these relationships. The mediating model analysis revealed that adaptive sleep-related cognition and behaviors partially buffered the pathway from insomnia severity to dysfunctional beliefs (DBAS-6) and maladaptive sleep-related behaviors (SRBQ-10). This suggests that even when insomnia patients experience the same level of sleep disturbance, the degree of conversion into distorted beliefs or maladaptive coping behaviors may be reduced if they have more flexible and realistic cognitive and behavioral patterns. In other words, adaptive beliefs and behaviors may act as protective factors, mitigating cognitive distortions and safety behaviors, which are known to maintain insomnia. However, this study was based on a cross-sectional design and self-reported data, limiting causal interpretation. The association between the adaptive behavior subfactors of the ACBS-6 and insomnia severity was inconsistent, indicating that a more refined item composition and repeated validation across diverse samples are needed. Future studies should closely examine how adaptive beliefs and behaviors regulate dysfunctional beliefs and safety behaviors during insomnia treatment and whether their effects are sustained over the long-term. This study had certain limitations. First, this study was conducted as an online survey rather than a validation study. Compared to face-to-face interviews, online surveys may result in lower response rates or less accurate data. However, numerous recent validation studies in the field of sleep medicine have been conducted online [ 23 – 25 ]. Second, this study was conducted among the general population with insomnia. Although participants were recruited based on strict criteria, including experiencing sleep initiation or maintenance difficulties within the past 3 months, this does not guarantee that they truly have clinical insomnia. The validation of the ACBS-6 in a clinical insomnia sample is needed in future studies. In conclusion, the ACBS-6 is a useful tool for the simple and valid assessment of sleep-related adaptive cognition and behaviors in individuals with insomnia. Higher ACBS-6 scores were associated with lower insomnia severity, and the ACBS-6 partially mitigated the pathway from insomnia severity to dysfunctional beliefs (DBAS-6) and maladaptive sleep-related behavior (SRBQ-10). These results indicate that adaptive sleep-related cognition and behaviors act as protective factors that can weaken the vicious cycle of insomnia, suggesting that they may represent promising therapeutic targets for future CBT-I interventions. Declarations Acknowledgment None. Funding None. Authors’ contribution Conceptualization, methodology, statistical analysis, writing-original draft, writing-review and editing: Jeonghwan Lee, Mohd. Ashik Shahrier, Jangho Park, and Seockhoon Chung. Data curation: Seockhoon Chung. ORCID Jeonghwan Lee, https://orcid.org/0000-0001-9807-8583 Mohd. Ashik Shahrier, https://orcid.org/0000-0002-1204-7928 Jangho Park, https://orcid.org/0000-0001-5370-005X Seockhoon Chung, https://orcid.org/0000-0002-9798-3642 Data availability Data will be available from the authors upon request. Conflict of interest The authors have no competing interests to declare. Ethical statement and consent to participate The study protocol was approved by the Institutional Review Board of Asan Medical Center (2025-0607). This study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants prior to participation. Consent for publication Not applicable. References Dinges DF, Rogers NL, Baynard MD. 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Frontiers in Digital Health 2024;6:1394901. Ravyts SG, Dzierzewski JM, Perez E, Donovan EK, Dautovich ND. Sleep health as measured by RU SATED: a psychometric evaluation. Behavioral Sleep Medicine 2021;19:48-56. Dzierzewski JM, Donovan EK, Sabet SM. The sleep regularity questionnaire: development and initial validation. Sleep medicine 2021;85:45-53. Tables Tables 1 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files ACBS6Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 18 Mar, 2026 Editor invited by journal 13 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9078083","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619718390,"identity":"4d160903-17e9-4a1c-8d04-b4382d95d7c0","order_by":0,"name":"Jeonghwan Lee","email":"","orcid":"","institution":"Ulsan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jeonghwan","middleName":"","lastName":"Lee","suffix":""},{"id":619718392,"identity":"ba315761-c16b-414c-8614-f66c4dbf8f93","order_by":1,"name":"Mohd. Ashik Shahrier","email":"","orcid":"","institution":"University of Rajshahi","correspondingAuthor":false,"prefix":"","firstName":"Mohd.","middleName":"Ashik","lastName":"Shahrier","suffix":""},{"id":619718393,"identity":"0b4e3c76-6ad4-462b-b017-13a161b4c542","order_by":2,"name":"Jangho Park","email":"","orcid":"","institution":"Ulsan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jangho","middleName":"","lastName":"Park","suffix":""},{"id":619718394,"identity":"e1703d2c-fab3-4fda-916c-08d759b2494a","order_by":3,"name":"Seockhoon Chung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACHhBhA+fKQAQIa0lD4hKp5TAJWuTbzx6TLvh1Xl63/QDzhw8Vh4Fazj7Aq8XgTF6a9My+24bbziSwSc44A9TC226AX4sEj5k0b89txm03GNiYeduAWvjZCDhsBljLOXugFubPf4nRwnADqIXnx4FEoBYGaUaQFt42/DoMzuQYW/M2JCdvO5PYJtlzJp2HjecYAYe1nzG8zfPHznbb8cOHP/yosJbj50nDrwUMGMFOYWwAcwj5BAr+EKdsFIyCUTAKRigAAFPtPkqNtpZ5AAAAAElFTkSuQmCC","orcid":"","institution":"Asan Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Seockhoon","middleName":"","lastName":"Chung","suffix":""}],"badges":[],"createdAt":"2026-03-10 02:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9078083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9078083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106703282,"identity":"edc4bdda-ea12-43a2-a98d-08cacda2dd45","added_by":"auto","created_at":"2026-04-12 07:40:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTest information functions (TIFs) of the adaptive sleep-related cognitions (A) and adaptive sleep-related behaviors (B) of the ACBS-6\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9078083/v1/35ee1e89f87d426d49ad93c4.png"},{"id":106703281,"identity":"59c80b30-2e3e-405b-a2c7-0e31374e027f","added_by":"auto","created_at":"2026-04-12 07:40:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eItem information curves (IICs) of the adaptive sleep-related cognitions (A) and adaptive sleep-related behaviors (B) of the ACBS-6\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9078083/v1/cba18e626ee979355b667d3e.png"},{"id":106703283,"identity":"0bb1431a-3a17-48d5-aa85-f3f02cd523c3","added_by":"auto","created_at":"2026-04-12 07:40:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eItem information curves (A) and the test information function (B) of the ACBS-6\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9078083/v1/e26b3ee36ff84263abdabf31.png"},{"id":106703285,"identity":"401f5d1b-4c63-4915-a06b-85ff24505b90","added_by":"auto","created_at":"2026-04-12 07:40:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":929266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9078083/v1/b4ae54d2-862c-45ab-a50d-2ee8e7c3ee59.pdf"},{"id":106703280,"identity":"f1e946cc-570b-445d-8ba7-314e31828650","added_by":"auto","created_at":"2026-04-12 07:40:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36043,"visible":true,"origin":"","legend":"","description":"","filename":"ACBS6Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9078083/v1/352a97f5411a08b35754b94d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of the Adaptive Cognition and Behaviors-6 (ACBS-6) Scale in Individuals with Insomnia and its Association with Sleep- Related Safety Behaviors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInsomnia is a common and chronic condition that makes it difficult to fall asleep or stay asleep and can lead to daytime impairments. Individual with insomnia frequently experience fatigue, decreased concentration, emotional pain, and poor quality of life beyond sleep deprivation itself [1]. Insomnia is not merely a night-specific problem but a 24-h condition. Many individuals with insomnia use maladaptive coping strategies or develop distorted beliefs about sleep in an attempt to solve the problem, which instead strengthens their sleep-related anxiety and perpetuates the disorder [2]. Consequently, there is a growing interest in cognitive models that emphasize psychological processes contributing to the maintenance of insomnia, moving beyond purely biomedical or behavioral explanations.\u003c/p\u003e\n\u003cp\u003eHarvey (2002) proposed a cognitive model of insomnia, providing an integrated framework for explaining how insomnia is maintained through multiple interacting cognitive and behavioral processes [3]. The five maintenance mechanisms central to this model include cognitive arousal before going to sleep (e.g., worry, rumination), selective attention and monitoring of sleep-related threat signals, distorted perceptions of sleep and their consequent daytime outcomes, rigid and unhelpful beliefs about sleep, and counterproductive safety behaviors to avoid sleep failure or its consequences. These elements are intertwined to form a vicious cycle, heightening anxiety and arousal about sleep and strengthening distorted beliefs that block opportunities for reality testing. By elaborating on the key mechanisms sustaining insomnia, Harvey's model provides the theoretical basis for therapeutic intervention by emphasizing that cognitive behavioral therapy for insomnia (CBT-I) should cover cognitive content and functions beyond merely correcting sleep habits.\u003c/p\u003e\n\u003cp\u003eSafety behaviors refer to the various coping strategies that individuals with insomnia perform to avoid sleep failure or the consequent negative daytime effects. Examples include taking naps to compensate for insufficient sleep, minimizing activities to save energy, canceling schedules or appointments, and improperly relying on alcohol or drugs for nighttime sleep. These behaviors may appear to be attempts to solve the problem, but in reality, they can raise the level of physical arousal, disturb the circadian rhythm, and make sleep more unstable [4]. In addition, these repetitive behaviors temporarily reduce anxiety related to sleep failure, but simultaneously contribute to the persistence of insomnia by strengthening negative perceptions and avoidance patterns related to sleep.\u003c/p\u003e\n\u003cp\u003eUnderlying these safety behaviors lies an unrealistic and rigid belief in sleep, called dysfunctional beliefs about sleep, which often excessively amplifies the perceived threat of sleep failure [5]. These beliefs take the form of excessive conviction, such as \"I must sleep 8 h,\" \"I completely ruin my day by staying up all night,\" and \"I can't sleep alone,\" creating excessive expectations and a desire for control over sleep while increasing anxiety and arousal about the possibility of sleep failure. This heightened state of alertness negatively affects actual sleep initiation and maintenance, while strengthening the sense of helplessness that patients cannot control their sleep on their own. These psychological and physiological tensions lead to behavioral strategies and safety behaviors aimed at preventing or minimizing the effects of sleep failure. These beliefs attribute the cause of insomnia to external conditions rather than internal factors, making individuals dependent on specific measures (e.g., drugs, specific environments) and weakening self-efficacy in self-regulation and sleep recovery. Thus, CBT-I considers these patterns important treatment targets, and cognitive restructuring, accurately identifying dysfunctional beliefs and replacing them with more flexible and realistic thinking, is emphasized as a key component of the treatment.\u003c/p\u003e\n\u003cp\u003eRecently, we developed a sleep-related adaptive cognition and behavior scale called the Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6). Although the conventional Dysfunctional Beliefs and Attitudes about Sleep-16 (DBAS-16) [6] and DBAS-6 [7] focus on assessing unrealistic and rigid beliefs, namely, dysfunctional beliefs about sleep, the ACBS-6 focuses on measuring attitudes and behaviors that accommodate and realistically address sleep problems [8]. The measure consists of six questions and includes two subfactors: adaptive sleep-related cognition and adaptive sleep-related behavior. The results of the study showed that the scale had a significant negative correlation with insomnia severity and was independent of dysfunctional beliefs. Therefore, it can be used as a clinical tool to help build more flexible and healthy sleep-related perceptions, beyond simply correcting distorted thinking in the course of CBT-I. However, the scale was not inversely correlated with insomnia severity in the general population, although it contributed inversely in the linear regression analysis. We interpreted the lack of correlation as a consequence of surveying the general population rather than individuals with insomnia. Thus, we sought to explore the reliability and validity of the scale in patients with insomnia.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to validate the ACBS-6 scale among the general population with insomnia and to explore whether there is an association between sleep-related dysfunctional and functional cognitions, sleep-related safety behaviors, and insomnia severity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Participants and procedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey was conducted online among registered general population panels of the professional survey company EMBRAIN. The survey was conducted anonymously from June 18 to 24, 2025. We developed a survey form that included questions regarding participants' age, sex, marital status, and past psychiatric history. To collect individuals experiencing insomnia in the general population, participants were enrolled based on their affirmative response to the question, \"Have you experienced difficulty falling asleep or maintaining sleep in the past 3 months?\"\u0026nbsp;In addition, to exclude participants with circadian rhythm sleep–wake disorders, eligibility required an affirmative response to the question, \"Do you go to bed between 9 PM and 1 AM every day?\" Participants were excluded if they responded affirmatively to the question, \"Are you working a shift?\" Finally, participants could proceed with the survey after providing their consent. The sample size was estimated at 600 participants, calculated by allocating 50 samples per 12 cells (sex × six age groups) based on the Central Limit Theorem [9]. A total of 7,558 emails were sent to 1.8 million registered panel members, of which 1,882 accessed the survey and 640 completed it. To ensure valid samples, the fastest 5% of respondents in each quota based on response time were excluded, along with responses in which the average time spent per question exceeded three times the overall average. Ultimately, the company delivered 600 de-identified responses to the researchers. The study protocol was approved by the Institutional Review Board of Asan Medical Center (2025-0607).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. ACBS-6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ACBS-6 is a self-report rating scale designed to measure an individual's adaptive cognition and behavior regarding sleep. It consists of six items, each rated on a scale from 0 (\"strongly disagree\") to 10 (\"strongly agree\"). The final score is the average of all six items, with higher scores reflecting more adaptive attitudes toward sleep-related thoughts and behaviors. The scale was developed and validated in the Korean general population, with McDonald's Omega reported as 0.734 in a previous study [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. DBAS-6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DBAS-6 is a self-report rating scale designed to assess individuals' sleep-related dysfunctional beliefs and attitudes [6]. It is a shortened version of the DBAS-16 developed using a machine learning approach [7]. All six items were scored between 0 (\"strongly disagree\") and 10 (\"strongly agree\"). The final score is the average of all six items, with higher scores indicating greater levels of sleep-related\u0026nbsp;dysfunctional cognition. The Cronbach's alpha for this sample was 0.768.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Insomnia Severity Index (ISI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ISI is a self-report rating scale used to evaluate insomnia severity [10]. It comprises seven items, each rated on a 5-point Likert scale. Higher total scores indicate more severe insomnia. The validated Korean version of the scale was used [11], and Cronbach's alpha for this sample was 0.822.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Sleep-Related Behavior Questionnaire-10 (SRBQ-10)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SRBQ-10 is a self-report rating scale developed to assess maladaptive sleep-related behaviors in individuals. The SRBQ-10 is a shortened version of the original SRBQ [12], created using a machine learning approach to optimize item selection for brevity and psychometric validity [13]. Each of the 10 items is rated on a 5-point scale ranging from 0 (rarely) to 4 (almost always), reflecting the frequency of specific sleep-related behavior. The total score was calculated, with higher scores indicating more frequent engagement in maladaptive sleep behaviors. In this sample, the SRBQ-10 demonstrated good internal consistency, with a Cronbach’s alpha of 0.850.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe psychometric properties of the ACBS-6 in the general population reporting insomnia were explored using Classical Test Theory and modern psychometric approaches. We first assessed the construct validity of the ACBS-6 using confirmatory factor analysis (CFA) based on a predefined, two-factor model. The normality of the six items was evaluated using skewness and kurtosis values within the range of ±2. Sample adequacy and data suitability were confirmed using the Kaiser-Meyer-Olkin (\u0026gt; 0.60) and Bartlett's test of sphericity (p \u0026lt; 0.001). Model fit indices, including the Comparative Fit Index, Tucker-Lewis Index, Root Mean Square Error of Approximation, and Standardized Root Mean Square Residual, were used to evaluate the ACBS-6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, under the Item Response Theory (IRT) approach, we used the Rasch model and the graded response model (GRM) to evaluate the item-level properties, reflecting the appropriateness of the ACBS-6 items with the underlying latent construct. In the Rasch model, the match between the actual item response and the expected response was determined using infit and outfit mean square (MnSq) values. Infit and outfit MnSq values closer to 1 indicate a good fit, while values less than 0.5 and greater than 1.7 reflect a poor fit with the underlying latent trait measured [14, 15]. Furthermore, the person and item separation indices with their respective reliabilities were calculated to examine the scale’s discriminating abilities across different levels of the latent trait and to assess the consistency of the item difficulty estimates. Reliability coefficients greater than 0.80 and separation indices exceeding 1.50 were considered cutoffs for measurement precision and consistency of the scale, indicating the sufficient robustness of the ACBS-6 [16].\u003c/p\u003e\n\u003cp\u003eUnder the GRM, we examined the item discrimination and difficulty parameters of ACBS-6 items. For discrimination parameters, the suggested cut-offs, ranging from 0.65 to 1.34, indicated moderate, 1.35 to 1.69 indicated high, and greater than 1.70 indicated very high, reflecting the ability of items to effectively distinguish between individuals across the latent trait [17]. The item difficulty parameters represent participants’ response patterns with a 50% probability of endorsing each item category across varying latent trait levels. Moreover, the item information curves (IICs) and test information function (TIF) of the ACBS-6 were examined to assess the extent of information provided by the scale items across the spectrum of the underlying latent construct.\u003c/p\u003e\n\u003cp\u003eThird, internal consistency reliability was measured using McDonald's Omega, while convergent validity with pre-existing rating scales, such as the ISI, DBAS-16, and SRBQ-10, was assessed using Pearson's correlation coefficients. To explore whether dysfunctional or functional sleep-related cognitions interact with insomnia severity and sleep-related safety behaviors, linear regression and mediation analyses were conducted. Data were analyzed using JASP software, version 0.14.1.0 (JASP team, Amsterdam, The Netherlands). The R package “ltm” version 1.2.0 [18] was used for the GRM.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 600 participants reporting insomnia were included in the study with an average age of 49.1 years (standard deviation = 16.6), and men and women were included equally. Among the participants, 39.8% reported a history of mental health problems, such as depression, anxiety, or insomnia, or previous treatment for these conditions. All participants were currently experiencing insomnia, with 23.5% reporting difficulty falling asleep, 45.8% frequent awakening, and 30.7% reporting both. Additionally, 20.0% of the participants reported using sleeping pills or sedatives for sleep (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe checked the skewness and kurtosis values to verify the assumption of normality. The skewness of each ACBS-6 item ranged between -0.31 and 0.69, and kurtosis ranged between -0.78 and -0.30, all within the range of ± 2.0, indicating that the assumption of normality was not significantly violated (\u003cstrong\u003eTable 2\u003c/strong\u003e). The Kaiser-Meyer-Olkin value of 0.740 and Bartlett's test (p \u0026lt; 0.001) indicated that the samples were suitable for the factor analysis. CFA was performed to verify the construct validity of the ACBS-6 scale, and the results showed good overall model fit, with a Comparative Fit Index of 0.981, Tucker-Lewis Index of 0.964, Root Mean Square Error of Approximation of 0.059, and Standardized Root Mean Square Residual of 0.023. Two-factor structures were identified; factor 1 consisted of items 1, 2, 3, and 4, with factor loadings of 0.73, 0.84, 0.58, and 0.59, respectively, all exceeding the standard cut-off of 0.4, while factor 2 consisted of items 5 and 6, with factor loadings of 0.56 and 0.80, respectively (\u003cstrong\u003eTable 2\u003c/strong\u003e). These results suggest that the ACBS-6 measures two distinct components, adaptive sleep-related cognition and adaptive sleep-related behavior, and that each item appropriately reflects its respective factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRT results from the Rasch model and the GRM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe item-level psychometric characteristics of the ACBS-6 revealed that the infit and outfit mean square values ranged from 0.62 to 1.32 (Table 3), indicating an adequate model fit and good item fit with the underlying latent trait. As shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, the item and person reliabilities (0.93 for items and 0.79 for persons) and the corresponding separation indices (3.65 for items and 1.94 for persons) exhibited excellent measurement precision and strong differentiation in the item difficulty levels of the ACBS-6. The GRM framework for evaluating the item-level characteristics of the ACBS-6 demonstrated that the scale items had discriminative values ranging from 1.80 to 3.45 (\u003cstrong\u003eTable 4\u003c/strong\u003e), indicating very high discriminative abilities of both factors to provide information and distinguish among individuals across the latent construct.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, for factor 1 (adaptive sleep-related cognition), the difficulty parameters (\u003cstrong\u003eTable 4\u003c/strong\u003e) and the TIF (\u003cstrong\u003eFigure 1A\u003c/strong\u003e) revealed that the four items provided information across a polarized spectrum of ability ranges, with theta values ranging from -1 to +2. An inspection of the IICs in \u003cstrong\u003eFigure 2A\u0026nbsp;\u003c/strong\u003eshowed that items 1 and 2 (Factor I) provided the highest level of information about the underlying construct, with distinct abilities across varying levels of the latent continuum, whereas items 3 and 4 provided similar information with less distinction across a limited spectrum of the latent trait. For factor 2 (items 5 and 6), the difficulty parameters (\u003cstrong\u003eTable 4\u003c/strong\u003e) and TIF (\u003cstrong\u003eFigure 1B\u003c/strong\u003e) indicated that adaptive sleep-related behaviors provided information across a moderate spectrum of ability ranges, with theta values ranging from -2 to +2. In addition, the IICs in \u003cstrong\u003eFigure 2B\u0026nbsp;\u003c/strong\u003eshow that items 5 and 6 (Factor 2) provided the highest level of information about the underlying construct (adaptive sleep-related behaviors), with distinct abilities across varying levels of the latent trait. Altogether, the scale items demonstrated excellent discriminating power and strong differentiation in item difficulty, with a recommendation to reconsider the under-represented response categories for items 3 and 4 by ensuring a more heterogeneous sample in future validation studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability and\u003c/strong\u003e \u003cstrong\u003eEvidence Based on Relations to Other Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson’s correlation analysis revealed that the ISI was significantly positively correlated with the DBAS-6 (r = 0.47, p \u0026lt; 0.01) and SRBQ-10 (r = 0.49, p \u0026lt; 0.01) scores, whereas it was negatively correlation with ACBS-6 Factor I (r = -0.15, p \u0026lt; 0.01) (\u003cstrong\u003eTable 5\u003c/strong\u003e). This indicates that as insomnia severity increases, distorted beliefs and maladaptive behaviors increase, whereas adaptive thinking tends to decrease. Regression analysis showed that DBAS-6 (β = 0.29), SRBQ-10 (β = 0.32), and ACBS-6 (β = -0.15) had significant predictive power for ISI, with the overall model explaining 32% of the variance (Adjusted R² = 0.32, F = 71.6, p \u0026lt; 0.001) (\u003cstrong\u003eTable 6\u003c/strong\u003e). Mediation analysis revealed that the ISI directly influenced the DBAS-6 and SRBQ-10, and the ACBS-6 inversely mediated the effects of the ISI on the DBAS-6 and SRBQ-10 (\u003cstrong\u003eTable 7\u003c/strong\u003e). These pathways are illustrated in \u003cstrong\u003eFigure 3\u003c/strong\u003e. This result supports that adaptive sleep-related cognition and behaviors can function as protective factors, buffering against cognitive distortions or maladaptive behaviors associated with insomnia.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we confirmed that the ACBS-6 exhibited good psychometric characteristics in the general population reporting insomnia. The factor structure and item fit of the ACBS-6 were found to be stable, capturing an independent conceptual domain distinct from the DBAS-6 and SRBQ-10. These results suggest that in individuals with insomnia, adaptive cognition and behavior are not merely the opposite of dysfunctional beliefs but constitute separate and independent dimensions. Additionally, the ACBS-6 total score showed a significant negative correlation with the ISI score, supporting the idea that adaptive sleep-related cognition and behaviors are associated with symptom relief. Although DBAS-6 and SRBQ-10 were positively correlated with insomnia severity, the ACBS-6 functioned independently and simultaneously mitigated the negative effects of DBAS-6 and SRBQ-10 in the mediation model. In other words, this study empirically demonstrated that adaptive sleep-related cognition and behavior mediate the impact of dysfunctional beliefs and safety behaviors on insomnia severity.\u003c/p\u003e \u003cp\u003eThe item-level psychometric characteristics of the ACBS-6, evaluated using the Rasch and GRM models within the IRT framework, indicate that the newly validated ACBS-6 had excellent fit statistics for individuals reporting insomnia symptoms. The item fit with the underlying latent construct, assessed through infit and outfit MnSq values, along with highly acceptable item and person separation indices and reliabilities, established the ACBS-6 as a robust tool for insomnia complaints. Additionally, the discrimination and difficulty parameters demonstrated the measurement precision and consistency of the ACBS-6, providing information about how effectively the scale items discriminate among individuals with distinct abilities across a moderate spectrum of latent traits. However, an inspection of the item difficulty parameters, along with the ICCs and TIF, revealed that items 3 and 4 in factor 1 (adaptive sleep-related cognitions) provided a limited and similar level of information across the latent trait, whereas the other items (i.e., items 1 and 2 in factor 1 and items 5 and 6 in factor 2; adaptive sleep-related behaviors) provided a sufficient amount of information across varying ability ranges. This suggests that the 11-point response categories should be reconsidered, particularly focusing on the underreported categories for items with indistinct abilities. Reducing the response categories and ensuring greater sample heterogeneity in future validation studies could clarify whether the limited measurement precision of some items in factor 1 is sample-specific or reflects inherent measurement challenges. Overall, the fit statistics in the Rasch model, along with the very high discriminative ability and strong item differentiation in the GRM for the ACBS-6, suggest that no further revisions to the scale items are needed, except for reconsidering the response categories, highlighting the overall robustness of the scale for individuals reporting insomnia.\u003c/p\u003e \u003cp\u003eIn a previous study that examined the reliability and validity of the ACBS-6 among the general population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], we observed a lack of association between the ACBS-6 and ISI scores. We considered that this lack of association may have been due to the relatively low insomnia severity in the study participants. In this study, we explored the association between insomnia in the general population and observed a significant inverse correlation between the ACBS-6 and ISI scores. However, a significant correlation between the ISI and ACBS-6 Factor 2 (adaptive sleep-related behaviors) was not observed. This lack of association may be due to the items in Factor 2 being unfamiliar. Items 5 (\"To sleep for 7 h within a 24-h day, 17 h of activity is necessary\") and 6 (\"Being active during the day leads to better sleep rather than good sleep leading to better daytime activity\") were developed based on previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These items may be useful in CBT-I for patients with insomnia, but participants in this study may have found it difficult to understand the exact meaning of the items.\u003c/p\u003e \u003cp\u003eSimilar to a previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], a significant correlation between the ACBS-6 and DBAS-6 was not observed in this study. The findings indicate that the domains measured by the two scales reflect distinct and independent dimensions. In studies on coping styles, adaptive and maladaptive coping styles operate independently rather than at opposite ends of a single continuum. Thompson et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] showed that high levels of adaptive coping buffer the adverse effects of maladaptive coping on depression. Brown et al. demonstrated that increased use of maladaptive strategies occurs when stress is appraised more negatively, whereas the use of adaptive coping strategies appears independent of the level of appraisal [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, adaptive sleep-related cognition operates independently of dysfunctional sleep-related cognition, which may explain the lack of associations.\u003c/p\u003e \u003cp\u003eHowever, the mediation analysis showed a positive link between the ACBS-6 and DBAS-6 scores. Factor 2 of the ACBS-6 was significantly correlated with DBAS-6 in this study and in a previous study [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, the SRBQ-10 score was positively correlated with factor 2 in this study. The implications of these associations require further exploration, especially considering the lack of association between factor 2 and the ISI, which was significantly associated with the DBAS-6 and SRBQ-10. Future studies should include larger samples and more detailed methodologies to clarify these relationships.\u003c/p\u003e \u003cp\u003eThe mediating model analysis revealed that adaptive sleep-related cognition and behaviors partially buffered the pathway from insomnia severity to dysfunctional beliefs (DBAS-6) and maladaptive sleep-related behaviors (SRBQ-10). This suggests that even when insomnia patients experience the same level of sleep disturbance, the degree of conversion into distorted beliefs or maladaptive coping behaviors may be reduced if they have more flexible and realistic cognitive and behavioral patterns. In other words, adaptive beliefs and behaviors may act as protective factors, mitigating cognitive distortions and safety behaviors, which are known to maintain insomnia. However, this study was based on a cross-sectional design and self-reported data, limiting causal interpretation. The association between the adaptive behavior subfactors of the ACBS-6 and insomnia severity was inconsistent, indicating that a more refined item composition and repeated validation across diverse samples are needed. Future studies should closely examine how adaptive beliefs and behaviors regulate dysfunctional beliefs and safety behaviors during insomnia treatment and whether their effects are sustained over the long-term.\u003c/p\u003e \u003cp\u003eThis study had certain limitations. First, this study was conducted as an online survey rather than a validation study. Compared to face-to-face interviews, online surveys may result in lower response rates or less accurate data. However, numerous recent validation studies in the field of sleep medicine have been conducted online [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Second, this study was conducted among the general population with insomnia. Although participants were recruited based on strict criteria, including experiencing sleep initiation or maintenance difficulties within the past 3 months, this does not guarantee that they truly have clinical insomnia. The validation of the ACBS-6 in a clinical insomnia sample is needed in future studies.\u003c/p\u003e \u003cp\u003eIn conclusion, the ACBS-6 is a useful tool for the simple and valid assessment of sleep-related adaptive cognition and behaviors in individuals with insomnia. Higher ACBS-6 scores were associated with lower insomnia severity, and the ACBS-6 partially mitigated the pathway from insomnia severity to dysfunctional beliefs (DBAS-6) and maladaptive sleep-related behavior (SRBQ-10). These results indicate that adaptive sleep-related cognition and behaviors act as protective factors that can weaken the vicious cycle of insomnia, suggesting that they may represent promising therapeutic targets for future CBT-I interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, methodology,\u0026nbsp;statistical analysis, writing-original draft, writing-review and editing: Jeonghwan Lee, Mohd. Ashik Shahrier, Jangho Park, and Seockhoon Chung. Data curation: Seockhoon Chung.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJeonghwan Lee, https://orcid.org/0000-0001-9807-8583\u003c/p\u003e\n\u003cp\u003eMohd. Ashik Shahrier, https://orcid.org/0000-0002-1204-7928\u003c/p\u003e\n\u003cp\u003eJangho Park, https://orcid.org/0000-0001-5370-005X\u003c/p\u003e\n\u003cp\u003eSeockhoon Chung, https://orcid.org/0000-0002-9798-3642\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available from the authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Asan Medical Center (2025-0607). This study was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDinges DF, Rogers NL, Baynard MD. Chronic sleep deprivation. In. \u003cem\u003ePrinciples and practice of sleep medicine\u003c/em\u003e. Elsevier. 2005;67-76.\u003c/li\u003e\n\u003cli\u003eTang NK, Saconi B, Jansson‐Fr\u0026ouml;jmark M, Ong JC, Carney CE. Cognitive factors and processes in models of insomnia: A systematic review. \u003cem\u003eJournal of Sleep Research\u003c/em\u003e 2023;32:e13923.\u003c/li\u003e\n\u003cli\u003eHarvey AG. A cognitive model of insomnia. \u003cem\u003eBehaviour research and therapy\u003c/em\u003e 2002;40:869-93.\u003c/li\u003e\n\u003cli\u003eHarvey AG. Identifying safety behaviors in insomnia. \u003cem\u003eThe Journal of nervous and mental disease\u003c/em\u003e 2002;190:16-21.\u003c/li\u003e\n\u003cli\u003eWoodley J, Smith S. Safety behaviors and dysfunctional beliefs about sleep: Testing a cognitive model of the maintenance of insomnia. \u003cem\u003eJournal of Psychosomatic Research\u003c/em\u003e 2006;60:551-7.\u003c/li\u003e\n\u003cli\u003eMorin CM, Valli\u0026egrave;res A, Ivers H. Dysfunctional beliefs and attitudes about sleep (DBAS): validation of a brief version (DBAS-16). \u003cem\u003eSleep\u003c/em\u003e 2007;30:1547-54.\u003c/li\u003e\n\u003cli\u003eJo H, Jeon HJ, Ahn J, Jeon S, Kim JK, Chung S. Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning approach. \u003cem\u003eSleep Medicine\u003c/em\u003e 2024;119:312-8.\u003c/li\u003e\n\u003cli\u003eChung S, Yoo S, Suh S. Development of the Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6) for sleep-related conditions or behaviors diminishing insomnia severity. \u003cem\u003eSleep Medicine\u003c/em\u003e 2025:106703.\u003c/li\u003e\n\u003cli\u003eKwak SG, Kim JH. Central limit theorem: the cornerstone of modern statistics. \u003cem\u003eKorean journal of anesthesiology\u003c/em\u003e 2017;70:144.\u003c/li\u003e\n\u003cli\u003eBastien CH, Valli\u0026egrave;res A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. \u003cem\u003eSleep medicine\u003c/em\u003e 2001;2:297-307.\u003c/li\u003e\n\u003cli\u003eChung S, Ahmed O, Cho E, Bang YR, Ahn J, Choi H, et al. Psychometric properties of the insomnia severity index and its comparison with the shortened versions among the general population. \u003cem\u003ePsychiatry Investigation\u003c/em\u003e 2024;21:9.\u003c/li\u003e\n\u003cli\u003eRee MJ, Harvey AG. Investigating safety behaviours in insomnia: the development of the sleep-related behaviours questionnaire (SRBQ). \u003cem\u003eBehaviour Change\u003c/em\u003e 2004;21:26-36.\u003c/li\u003e\n\u003cli\u003eJeon S, Jeong EM, Bang YR, Ahn J, Yoo S, Kim JK, et al. Machine-Learning Validated Short Form of the Korean Version of the Sleep-Related Behaviors Questionnaire-10 Items: SRBQ-10. \u003cem\u003eBehavioral Sleep Medicine\u003c/em\u003e 2025:1-14.\u003c/li\u003e\n\u003cli\u003eWaugh RF, Chapman ES. An analysis of dimensionality using factor analysis (true-score theory) and Rasch measurement: what is the difference? Which method is better? \u003cem\u003eJournal of Applied Measurement\u003c/em\u003e 2005;6:80-99.\u003c/li\u003e\n\u003cli\u003eBond TG, Fox CM. \u003cem\u003eApplying the Rasch model: Fundamental measurement in the human sciences\u003c/em\u003e. Psychology Press, 2013.\u003c/li\u003e\n\u003cli\u003eDuncan PW, Bode RK, Lai SM, Perera S, Investigators GAiNA. Rasch analysis of a new stroke-specific outcome scale: the Stroke Impact Scale. \u003cem\u003eArchives of physical medicine and rehabilitation\u003c/em\u003e 2003;84:950-63.\u003c/li\u003e\n\u003cli\u003eBaker FB, Kim S-H. \u003cem\u003eThe basics of item response theory using R\u003c/em\u003e. Springer, 2017.\u003c/li\u003e\n\u003cli\u003eRizopoulos D. ltm: An R package for latent variable modeling and item response analysis. \u003cem\u003eJournal of statistical software\u003c/em\u003e 2007;17:1-25.\u003c/li\u003e\n\u003cli\u003eKline CE. The bidirectional relationship between exercise and sleep: implications for exercise adherence and sleep improvement. \u003cem\u003eAmerican journal of lifestyle medicine\u003c/em\u003e 2014;8:375-9.\u003c/li\u003e\n\u003cli\u003eCastelli L, Ciorciari AM, Galasso L, Mul\u0026egrave; A, Fornasini F, Montaruli A, et al. Revitalizing your sleep: the impact of daytime physical activity and balneotherapy during a spa stay. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 2024;12:1339689.\u003c/li\u003e\n\u003cli\u003eThompson RJ, Mata J, Jaeggi SM, Buschkuehl M, Jonides J, Gotlib IH. Maladaptive coping, adaptive coping, and depressive symptoms: Variations across age and depressive state. \u003cem\u003eBehaviour research and therapy\u003c/em\u003e 2010;48:459-66.\u003c/li\u003e\n\u003cli\u003eBrown LJ, Bond MJ. The pragmatic derivation and validation of measures of adaptive and maladaptive coping styles. \u003cem\u003eCogent Psychology\u003c/em\u003e 2019;6:1568070.\u003c/li\u003e\n\u003cli\u003eDe Moraes ACF, Concei\u0026ccedil;\u0026atilde;o da Silva LC, Lima BS, Marin KA, Hunt ET, Nascimento-Ferreira MV. Reliability and validity of the online Pittsburgh sleep quality index in college students from low-income regions. \u003cem\u003eFrontiers in Digital Health\u003c/em\u003e 2024;6:1394901.\u003c/li\u003e\n\u003cli\u003eRavyts SG, Dzierzewski JM, Perez E, Donovan EK, Dautovich ND. Sleep health as measured by RU SATED: a psychometric evaluation. \u003cem\u003eBehavioral Sleep Medicine\u003c/em\u003e 2021;19:48-56.\u003c/li\u003e\n\u003cli\u003eDzierzewski JM, Donovan EK, Sabet SM. The sleep regularity questionnaire: development and initial validation. \u003cem\u003eSleep medicine\u003c/em\u003e 2021;85:45-53.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"insomnia, adaptive cognition, dysfunctional beliefs, safety behaviors","lastPublishedDoi":"10.21203/rs.3.rs-9078083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9078083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInsomnia is a chronic disorder in which dysfunctional beliefs and maladaptive safety behaviors contribute to its persistence. This study aimed to validate the Adaptive Cognition and Behaviors about Sleep-6 (ACBS-6) and examine its psychometric properties and associations with dysfunctional beliefs, safety behaviors, and insomnia severity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 600 participants with insomnia were recruited through an online survey. Participants completed the ACBS-6, Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6), Insomnia Severity Index (ISI), and Sleep-Related Behavior Questionnaire-10 (SRBQ-10). For psychometric analysis, the Rasch model and graded response model within Confirmatory Factor Analysis (CFA) and Item Response Theory (IRT) were used. Reliability was assessed using McDonald's Omega, and associations with related constructs were examined using correlation, regression, and mediation analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAs a result of the CFA, a two-factor structure (adaptive cognition and adaptive behavior) of the ACBS-6 was identified, and all items showed significant factor loadings. In the IRT analysis, item fit was good, with high reliability and discrimination; however, questions 3 and 4 provided limited information across the latent characteristics. The scale showed a significant negative correlation with the ISI (r = -0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas the DBAS-6 (r\u0026thinsp;=\u0026thinsp;0.47) and the SRBQ-10 (r\u0026thinsp;=\u0026thinsp;0.49) showed positive correlations. In the regression analysis, ACBS-6 (β = -0.15), DBAS-6 (β\u0026thinsp;=\u0026thinsp;0.29), and SRBQ-10 (β\u0026thinsp;=\u0026thinsp;0.32 significantly predicted the severity of insomnia, and the model explained 32% of the variance. Mediation analysis showed that adaptive cognition and behavior partially buffered the effect of insomnia severity on dysfunctional beliefs and safe behavior.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe ACBS-6 demonstrated robust validity and reliability as a measure of adaptive sleep-related cognition and behavior in individuals with insomnia. Adaptive cognitions and behaviors were associated with lower insomnia severity and served as protective factors, attenuating the impact of dysfunctional beliefs and maladaptive safety behaviors.\u003c/p\u003e","manuscriptTitle":"Validation of the Adaptive Cognition and Behaviors-6 (ACBS-6) Scale in Individuals with Insomnia and its Association with Sleep- Related Safety Behaviors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-12 07:39:16","doi":"10.21203/rs.3.rs-9078083/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-15T13:14:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306039427369319336052663054992480747343","date":"2026-04-13T11:31:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T11:22:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T12:51:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-13T11:31:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T06:09:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-12T01:21:58+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":"1e4c0a63-b9c1-47b5-95fe-9a8325dae885","owner":[],"postedDate":"April 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-12T07:39:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-12 07:39:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9078083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9078083","identity":"rs-9078083","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}