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Development and validation of a Disorganised Thoughts Scale: a new measure to assess thinking difficulties in the general population

BMC Psychology volume 12, Article number: 492 (2024) Cite this article

Abstract

Background

Disordered thinking is a condition that can manifest in not only clinical cases (e.g., psychotic disorders), but also the wider general population. However, there is no current method to measure the specific cognitive processes experienced during such a condition. Therefore, this study aimed to develop a new self-report measure, the Disorganised Thoughts Scale (DTS), that can assess disorganised thinking in the general population.

Methods

To achieve this aim, a survey was developed and shared online with four independent samples, including a sample of Australians in the general population (N = 321) and three samples (N = 200 each) that were controlled for their substance use (i.e., frequent alcohol and cannabis use; non-frequent substance use). Exploratory and confirmatory factor analyses, and reliability analyses, were used to test the internal validity, whilst correlational analyses were implemented to examine the external validity.

Results

The exploratory factor analysis revealed a two-factor structure (10 items each) measuring Positive thought disorder (i.e., accelerated, uncontrollable, and incongruent thinking) and Negative thought disorder (i.e., inhibited, disjointed, and disorientated thinking). This internal structure was confirmed with subsequent confirmatory factor and reliability analyses (α = 0.90 to 0.97) in the three substance-controlled groups. Concurrent validity was also supported, as the DTS exhibited strong correlations with established measures of general cognitive difficulties, specific self-regulatory dysfunctions, and psychopathological symptomology. Finally, the measure was also shown to be significantly higher in cohorts who exhibited a higher degree of psychological distress and who frequently used substances (i.e., alcohol and cannabis).

Conclusions

Overall, this study provided preliminary evidence to suggest that the DTS is a sound measure of disorganised thought that is linked to psychopathology and substance use in non-clinical populations. The measure could be used in future research which seeks to better understand how thinking effects, and is affected by, various psychological conditions.

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Introduction

The ability to be aware of and control (i.e., self-regulate) thinking plays a significant role in the functioning of daily life and general well-being [38]. This ability is central to how effectively and safely people can perform specific daily tasks, such as driving [33]. Formal thought disorder (FTD) is a psychological condition whereby an individual struggles to construct structured, coherent, and logical sequences of thought, and is a characteristic commonly found in the broader spectrum of psychotic (e.g., schizophrenia) disorders [58]. The symptomology of FTD has been categorised into two clusters known as positive FTD (e.g., thought: tangentiality; derailment; incoherence; illogicality) and negative FTD (e.g., poverty of thought and speech content; Bora et al., [8], which have shown to be distinct components of thought dysfunction [27]. Research has generally found that people who experience FTD, particularly among those populations with schizophrenia, have severe deficits in their working memory, attention, planning, processing speed, executive functioning, and processing of semantic information [8, 25]; with the symptoms often being observable in patterns of speech [45].

Although FTD has been largely approached as a clinical issue and diagnostic criteria specific to schizophrenia, the prevalence of the disorder has been noted in other populations, such as those with affective disorders (e.g., bipolar, depression, anxiety; Roche et al., [43] Yalincetin et al., [58], those with neurocognitive deficits [8], users of cannabis [2], and even among non-clinical populations [6, 43]. These findings suggest that FTD symptomology is not confined to specific diagnostic criteria, as the construct itself could be operationalised and measured on a continuum. However, as highlighted by Roche et al. [43], following the observable patterns in speech deficits among those experiencing FTD symptomology, it is likely that this continuum exists on an exponential curve of increasing thought disorganisation, as opposed to following a linear pattern of dysfunction.

Throughout the history of research on FTD, several potential causes, or links, to the condition have been identified. First and foremost, FTD is thought to be a product of neurocognitive deficiencies, which are often observed as deficits or ‘symptoms’ associated with specific disorders, such as schizophrenia [8]. Another identified potential cause for FTD (and its associated deficiencies) is a history of substance use [43]. Recent reviews and meta-analyses have indicated that problematic use of substances, such as alcohol [1], cannabis [21, 22], and methamphetamines [50], are well-established predictors of psychotic symptomology. Explanations for this link are bidirectional, in that those who lack sufficient self-regulatory capacity are more likely to engage in using substances as a coping mechanism [55]; and that the misuse of substances has been evidenced to deteriorate several domains (e.g., executive functioning; learning and memory; attentional processing; impulse control) of cognitive functioning [9, 29, 34].

Given the links between mental disorder and FTD, research has also sought to identify the impact of general psychopathology (i.e., trait emotion and arousal) on dysregulated thinking styles. Whilst not specific to the FTD, research has found connections between experiences of anxiety, depression, and stress on specific psychological functions, such as racing thoughts [7], cognitive processing capacity [15], attentional processing and bias [59], cognitive flexibility [49], impulsivity [37], and the experience of flow state (i.e., an optimal mental state for performance; Peifer et al., [41]. In conjunction with the apparent FTD continuum, such relationships leads to question the theoretical overlap between symptomology defined in FTD, and theoretical constructs (e.g., metacognitive control; detached mindfulness; perseverative thinking) described in the broader psychological and self-regulatory literature.

Despite the adverse impact that various forms of thought disorder have on general well-being and task performance, there is very little research that investigates the links between factors such as substance use, negative affect, self-regulation, and the conceptual constructs of thought disorder (i.e., thought disorganisation). This limitation appears to extend to the lack of ability to measure the specific components of the condition in self-report form. Currently, the measurement of psychotic symptomology such as thought disorder is largely limited to clinician-rated measures [30], given the specific cognitive-related symptomology of these disorders (i.e., a lack of ability to understand or control thinking). This is despite studies showing a degree of alignment between clinician-rated and patient-rated measures of psychosis [6, 39]. Of the few self-rated subjective measures, the focus has been on the broader spectrum of clinical symptomatology (e.g., the Prodromal Questionnaire; Loewy et al., [32], or on difficulties specific to speech (e.g., the Formal Thought Disorder–Self-Report Scale [5]; Thought Disorder Questionnaire [51]; the Thought and Language Disorder scale [26]). However, such scales may not be a true reflection of thought disorganisation, nor capture the specific cognitive deficits being experienced in the condition, when considering the impact of both social anxiety and language skills specifically.

In response to these limitations, the current study aimed to develop and provide preliminary validation for a new self-report questionnaire that measures specific aspects of thought disorder (i.e., the Disorganised Thoughts Scale; DTS) in the general population. Four objectives were formed to achieve this research aim. The first was to develop the factorial structure and internal reliability of the DTS. The second was to retest the internal validity of the DTS in three substance-controlled groups (i.e., frequent alcohol users; frequent cannabis users; non-frequent substance users). The third was to investigate the external (concurrent) validity of the DTS. The final objective was to compare the DTS across groups of participants with varying levels of psychological distress (i.e., mild; moderate; severe) and between the different substance-controlled groups.

Method

Procedure and participants

Phase one sample

Following institutional ethical approval (#A232014; #A231998) from the University of the Sunshine Coast Human Ethics Committee, four groups of participants were recruited. Each group completed a survey, taking approximately 10 to 15 min and containing a research project information sheet, a consent form, questions about mental health difficulties and substance use, the DTS, and several measures used for concurrent validity. The first group (N = 336) was recruited online through Facebook advertising services, which shared a link to a survey hosted via Qualtrics. The survey was open to Australian residents aged 18 years or older and consisted of measures relating to general cognitive dysfunction and psychopathology. Upon completion of the survey, participants were offered the opportunity to go into the draw to win one of five $50.00 (AUD) electronic gift vouchers. Of note, this recruitment strategy returned a very high proportion of females (91.4%) and those who were experiencing significant psychological distress (67.6% = severe distress), which may have been due to the focus of the study. While this proportion may have affected the means of the DTS, it was not expected to significantly alter the factorial structure, as they are a valid reflection of persons’ lived experiences drawn from the broader population. Further, the factorial structure was retested in subsequent more gender balanced cohort.

Phase two sample

The substance-controlled groups were recruited as part of a larger project investigating the impact of substance use on psychological functioning. Given the specific nature of the groups, Footprints Market Research was enlisted for participant recruitment purposes. This entity employs diverse strategies, such as print media, social media, and personal invitations, to engage a broad spectrum of participants. The included measures for this survey contained items relating to specific self-regulatory dysfunction and psychopathology. Participants received tokens as compensation for survey completion, which could be exchanged for various rewards on the Footprints Market Research website. In addition to the inclusion criteria employed for the first group, participants had to have met one of three conditions related to substance use: (a) they used alcohol on a daily or almost daily basis (termed frequent alcohol users; N = 200) but did not use any other substances more than once a month, (b) they used cannabis on a daily or almost daily basis (termed frequent cannabis users; N = 200) but did not use any other substances more than once a month, or (c) they did not use any substance more than once a month (termed non-frequent users; N = 200). The characteristics of each group are displayed in Table 1.

Table 1 Information about the groups’ demographics, mental health, and substance use
Full size table

Primary measure

Thought disorganisation

The measure developed, the DTS, was constructed by the authors, who together share significant experience in the areas of clinical and cognitive psychology. Relevant literature related to formal thought disorder, psychosis, schizophrenia, attention-deficit disorders, and various forms of problematic thinking (e.g., worry), were all reviewed when developing the initial pool of items. After the authors had agreed on the final item pool, a pilot survey of the DTS was shared with 10 colleagues to review the suitability and clarity of the items. In total, the DTS consisted of 25 items that were split evenly among five intended dimensions of thought disorganisation, including: disjointed thinking (e.g., “my thoughts felt disconnected or fragmented”), disorientated thinking (e.g., “it was hard for me to grasp the content of my thoughts”), incongruent thinking (e.g., “I experienced thoughts that were unrelated to the task at hand), inhibited thinking (e.g., “I experienced sudden interruptions in my thoughts, as if they were being cut off or blocked”), and accelerated thinking (e.g., “it was difficult for me to slow down or quiet my thoughts”). Participants were asked to rate each item based on how often they experienced each item in the past month and were scored using a five-point scale ranging from 0 (not at all) to 4 (almost always). This method of scoring was chosen because other prominent and well-validated measures, such as the DASS-21 [35], GAD-7 [46], PHQ-9 [28] and DAR-5 [17] have used similar scoring anchors to measure recent psychological symptomology.

Concurrent validity measures – general cognitive dysfunction

Disordered speech

To measure aspects of disordered speech patterns, the Formal Thought Disorder-Self Scale (FTD-SS; Barrera et al., [6] Barrera et al., [5] was implemented. The FTD-SS contains 29 items across three measured factors including: odd speech (15 items; e.g., “I have found myself talking in ways that other people may find strange”), alogia (poverty of speech; seven items; e.g., “I find it hard to put into words what I want to say”); and working memory deficit (seven items; e.g., “I tend to forget the point I was trying to make in a conversation”). Each item is scored on a four-point Likert scale, where 1 = almost never, and 4 = almost always. Higher scores were indicative of more frequent occurrences of formal thought disorder symptomology, as expressed in speech. The FTD-SS has been shown to demonstrate good internal consistency across its subscales (α = 0.82 to 0.87) and for the total scale. (α = 0.93; Barrera et al., [6].

Racing and crowded thoughts

The 13-item short version [54] of the Racing and Crowded Thoughts Questionnaire (RCTQ-13; Weiner et al., [53] was used to measure a tendency for racing and crowded thoughts. The RCTQ-13 contains three subscales including: thought overactivation (four items; e.g., “I have too many thoughts at the same time”, a burden of thought overactivation (four items; e.g., “my brain cannot manage all these thoughts that arise at the same time”), and thought overexcitability (five items; e.g., “there is not enough time to grasp the meaning of a thought, as new ones immediately arise”). However, for the current study, the total scale score was used. Each item is rated on a scale on a 5-point scale from 1 (not at all) to 5 (completely agree), as to how much participants agree with the statement. Higher scores indicate a higher degree of racing and overcrowded thoughts. Internal consistency for this measure has been noted as excellent across each of the subscales and total scale (α = 0.92 to 0.95; Weiner et al., [53].

Attentional control

The 20-item Attentional Control Scale (ACS; Derryberry & Reed [16], was used to measure participants’ ability to control their attention. While the total scale provides an aggregate measure of attentional control, the ACS does contain two subscales relating to attention focusing (nine items; e.g., “when I need to concentrate and solve a problem, I have trouble focusing my attention”) and attention shifting (11 items; e.g., “it is easy for me to alternate between two different tasks”). Participants are asked to rate how often each of the statements occurs for themselves on a four-point scale, where 1 = almost never and 4 = almost always. After accounting for the reverse-scored items, higher scores on the ACS indicate a greater degree of control over attention. The internal consistency of the ACS has been demonstrated to be good (α = 0.88; Derryberry & Reed [16], .

Concurrent validity measures – specific self-regulatory dysfunctioning

Worry

To assess participants’ tendency to worry, the brief version [18] of the Penn State Worry Questionnaire (PSWQ; Meyer et al., [36] was used. This concise scale comprises eight items delineating a proclivity for worry (e.g., “many situations make me worry”), which participants are asked to rate on a five-point scale (1 = not at all typical of me; 5 = very typical of me), based on their self-perceived relevance, with higher scores indicating an increased tendency to worry. The abbreviated form of this questionnaire has undergone thorough validation, demonstrating strong internal consistency (α = 0.92; Gladstone et al., [18].

Anger rumination

The angry afterthoughts subscale from the Anger Rumination Scale (ARS; Sukhodolsky et al., [47] was used to identify participants’ tendency for anger rumination. The angry afterthoughts subscale consists of six items (e.g., “whenever I experience anger, I keep thinking about it for a while”) that measure the tendency to uncontrollably ruminate about past negative experiences. Participants are asked to rate each item on a four-point frequency scale (1 = almost never; 4 = almost always), with higher scores being indicative of more frequent engagement in anger rumination. This scale has been shown to have good internal consistency (α = 0.86; Sukhodolsky et al., [47] and was chosen over the total scale, as it is concerned with issues of the controllability of thoughts specifically.

Impulsivity

To gauge participants’ challenges in regulating emotional impulses, we utilised the abbreviated positive and negative urgency subscales [14] derived from the original the Urgency, Premeditation, Perseverance, and Sensation Seeking Impulsive Behavior Scale [57] were implemented. These subscales assess the inclination to lack self-control in response to positive (e.g., “when I am in a great mood, I tend to get into situations that could cause me problems”) and negative (e.g., “Sometimes when I feel bad, I can’t seem to stop what I am doing even though it is making me feel worse”) emotions. Participants rated their agreement with each item on a four-point scale, where 0 = strongly disagree and 3 = strongly agree. While the shortened subscales have demonstrated a robust factor structure and internal reliability (α = 0.78 to 0.85), they also exhibit satisfactory psychometric properties as a comprehensive measure of emotion-based impulsivity [14], as applied in the present study.

Mindlessness

To assess participants’ level of attentional awareness, we employed the acting with awareness subscale from the Five Facet Mindfulness Questionnaire [4]. Comprising eight items, this subscale gauges the propensity to lack vigilance in everyday life (e.g., “I find myself doing things without paying attention”). Participants rated the extent to which each statement resonated with them on a five-point scale (1 = never or very rarely true; 5 = very often or always true), with higher scores indicative of increased mindlessness. The acting with awareness subscale, widely utilised across various research domains, has exhibited robust internal consistency (α = 0.87; Baer et al., [4].

Concurrent validity measures – psychopathological symptomology

Psychological distress

Psychological distress was evaluated by employing the 10-item version [20] of the Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond [35], . The abbreviated DASS-10 can be used to either identify general levels of psychological distress or as a means to pinpoint recent symptomology associated with (a) anxiety and stress (six items, e.g., “I found it difficult to relax”) and (b) depression (four items, e.g., “I felt downhearted and blue”). Respondents rated the frequency of experiencing each symptom over the past week on a four-point scale ranging from 0 (not at all) to 3 (almost always), with higher scores indicating more frequent occurrences of anxiety and depression. Cut-off scores for mild (≤ 6), moderate (7–12), and severe (≥ 13) psychological distress have been established for the total scale. The recent validation of the DASS-10 [20] showed that the scale contains a good internal consistency across anxiety (α = 0.83), depression (α = 0.85), and total distress scales (α = 0.89).

Anger

The five-item shortened version [17] of the Dimensions of Anger Reactions Scale (DAR; Novaco, 1975) was used to measure recent anger symptomology. The DAR-5 includes items relating to recent anger symptoms (e.g., “when I got angry, I got really mad”) individuals have experienced over the past four weeks. Each item is rated on a four-point scale, where 1 = not at all; and 4 = nearly every day. Higher scores on the DAR-5 are indicative of increased anger symptomology. The DAR-5 has demonstrated to have a high internal consistency (α = 0.90) and strong concurrent validity to other established measures of anger [17].

General measures

Substance use frequency

Participants were also asked to record how often they consumed various substances, including alcohol, marijuana (including THC-dominant prescription medicine), cocaine, gamma-hydroxybutyrate acid, heroin, inhalants (e.g., cleaning fluids; nitrous oxide; paint; glue; aerosols), ketamine or dissociative anesthetics, methyl-​enedioxy​methamphetamine, methadone, methamphetamines, psychedelics (e.g., LSD; acid; psilocybin/magic mushrooms), prescription opioids (e.g., oxycodone), prescription sedatives (e.g., Valium; Xanax), and prescription amphetamines (e.g., Desoxyn; Adderall; Ritalin). Participants were required to indicate the frequency of their substance use over the past 12 months, using a scale where 0 = never, 1 = less than monthly, 2 = monthly or more, 3 = weekly or more, and 4 = daily or almost daily.

Demographic information

To identify the characteristics of the groups, participants were asked to complete a short demographic questionnaire. Participants were asked about their gender, age, employment status, and if they had received a mental disorder diagnosis in the previous 12-month period.

Data analysis

Following data collection, all responses were transferred into the statistical package, SPSS (version 29), where they was manually inspected and then summed into the variables using the compute function. Averaged scores were used over totalled scores for the majority of variables, as these scores are more interpretable to the original scoring anchors. However, the total scores were used for the psychopathological measures (i.e., the DASS-10; DAR-5, and DTS), as these scales have meaningful scoring ranges, and could be used as comparative data for future research. The normality, linearity, and homoscedasticity of the computed variables were assessed using descriptive statistics and plots; whilst internal consistency was assessed using reliability analyses.

Objective one

Using the data from the general population cohort, exploratory factor analysis (EFA; principal axis factoring) was conducted with the initial 25 items of the DTS. The Kaiser-Meyer-Olkin (KMO) statistic and inter-item correlations were first examined to assess the adequacy and suitability of the data. Factor retentions were based on recommendations by Howard [23], who suggest that eigenvalues should typically be higher than 1.00, factors should demonstrate observable variances in the scree plot, and factor loadings should be a minimum of 0.40, have a maximum secondary loading of 0.30, and retain a 0.20 margin between primary and secondary loadings. Item retention was also based on communalities being higher than 0.40, as suggested by Costello and Osborne [13]. Parallel analysis using SPSS dialog from Costello [12] was also used to help determine component retentions. An oblique rotation (i.e., ProMax) was chosen over an orthogonal given the theoretical alignment between the items, and that this rotation is considered optimal in cases where the items are well correlated [40]. The authors aimed for a minimum sample size of 250, as established guidelines recommended a 10:1 ratio of participants to items [19, 52].

Objective two

For the second objective of retesting the factor structure of the DTS, the data relating to the substance-controlled groups were imported into SPSS AMOS (version 29), where confirmatory factor analysis (CFA) was used to test the revised factor structure of the DTS on each group. Although chi-square is a primary indicator of model fit, this value is sensitive to sample size, and so alternative fit indices were also examined, including: (a) RMSEA, values < 0.06 = good fit, < 0.08 = acceptable fit, and 0.08 to 0.10 = marginal fit; (b) comparative fit index (CFI) and Tucker-Lewis index (TLI), values ≥ 0.95 = good fit, and ≥ 0.90 = acceptable fit; and (c) standardised root mean square residual (SRMR), values < 0.08 = sound [10, 24]. Finally, factor loadings of more than 0.40 were considered acceptable, as per the EFA. Prior research has suggested that a sample of 150 is sufficient for CFA [31, 48], particularly where data is normally distributed and there are more than three observed variables used per latent construct [31].

Objectives three and four

For the third objective pertaining to concurrent validation, bivariate correlations were used to test the associations between the DTS and general cognitive dysfunction measures in phase one sample (the ACS; FTD-SS; RCTQ-13; DASS-10), as well as a specific self-regulatory dysfunction measures in the phase two sample (the DASS-10; DAR-5; PSWQ; ARS; UPPS; FFMQ). The correlations were interpreted based on guidelines presented by Cohen [11], where 0.10 = small, 0.30 = medium, and 0.50 = large for Pearson’s r. Finally, for the fourth objective, ANOVAs were used to compare the DTS scoring across the groups of participants with varying psychological distress and substance use patterns. Post-hoc Bonferroni analyses were used for identification of specific group differences. The effect sizes (η2) for the comparisons were interpreted as per recommendations by Cohen [11], where 0.01 = small, 0.06 = medium, 0.14 = large. Prior power analyses using GPower (version 3.1.9.7) indicated that a minimum sample of N = 200 would allow the detection of small to medium effect sizes (α = 0.95; 1 − β = 0.80) for the correlational and comparative analyses.

Results

Objective one: factor structure and reliability of the DTS

Factor structure

The EFA was run on the initial 25-item pool of the DTS (Appendix 1). An inspection of the KMO (0.968), correlation matrix (r = .34 to 0.80) and communalities (0.438 to 0.746) suggested that the sample and corresponding items were adequate for analyses. Further inspection of the eigenvalues and scree plot indicated that a three-factor solution was plausible; however, the pattern matrix contained multiple overlapping variables between two of the factors, and therefore a two-factor solution was chosen, which the parallel analysis also supported. The EFA was the re-run and showed that three items (items 6; 19; 4) continued to load across the two factors. These items were sequentially removed leaving 11 items per factor. Although the scale demonstrated adequate statistical fit, examination of the item content revealed that the two lowest loading variables of each factor contained had minor inconsistencies with the remainder of the items. For example, item 25 (communality = 0.484, loading = 0.533) was concerned with feelings of being overwhelmed, as opposed to the rest of the items, which were tied to thought difficulties specifically. Alternatively, item 12 (communality = 0.447, loading = 0.452) related to expressing thoughts to others (i.e., communication), rather than internal difficulties specifically. Therefore, to increase the quality and clarity of the scale, these two items were also removed.

The final EFA resulted in two factors, each containing ten items (Table 2). Inspection of the KMO (0.962), communalities (≥ 0.509), Eigen values (≥ 1.576), and the scree plot (two-factor inflection point), indicated that all items and the identified factor structure were statistically supported. Of note, a post-hoc inspection of the final item pool with a single factor (i.e., the total TDS) also demonstrated sound loadings across the items (0.622 to 0.823).

Upon examination of the content from the factorised items, distinct theoretical themes emerged across the two factors. Factor 1 contained items that were related to accelerated and incongruent thinking, overall portraying a condition where individuals’ thinking is disconnected from reality because their thoughts are racing, jumping to different topics, and difficult to control. This factor was therefore labelled Positive thought disorder. In contrast, factor 2 included items that represented disjointed, disorientated, and inhibited thinking patterns, portraying a condition characterised by a difficulty to understand thinking and form cohesive strings of thought. This factor was therefore labelled Negative thought disorder. Positive and negative thought disorder have been well established as distinct components of FTD within the literature, representing polarised but interrelated symptomology [8]. In essence, the two factors within the DTS characterised a lack of ability to understand and regulate thinking.

Internal consistency

To test the internal consistency of the DTS subscales, reliability analyses were performed and showed that both the Positive thought disorder (α = 0.90) and the Negative thought disorder (α = 0.91) subscales had excellent internal consistency. Reliability analysis was also performed for the total item pool, representing a general thought disorganisation, which showed excellent internal consistency (α = 0.96). The results of the final EFAs and reliability analyses are presented in Table 2.

Table 2 Factors loadings, communalities, eigenvalues, and reliability statistics of the DTS
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Objective two: retesting the internal validity of the DTS

Factor structure

The proposed two-factor structure of the DTS was retested across three independent groups according to their substance use patterns, including: a sample of frequent alcohol users, a sample of frequent cannabis users, and a control sample of non-frequent substance users. The CFA showed the two-factor structure of the DTS demonstrated a good fit to the data for the frequent alcohol-using sample (X2 = 359.29, Df = 169, p < .001, TLI, = 0.953, CFI = 0.958, RMSEA = 0.075, SRMR = 0.028) and frequent cannabis using sample (X2 = 312.85, Df = 169, p < .001, TLI, = 0.950, CFI = 0.956, RMSEA = 0.065, SRMR = 0.039). However, the RMSEA value, which is indicative loading misspecification, was marginally high for the control sample (X2 = 582.19, Df = 169, p < .001, TLI, = 0.903, CFI = 0.913, RMSEA = 0.111, SRMR = 0.041).

Inspection of modification indices indicated there were a number of correlated errors between the within-factor variables. Given the strong theoretical similarities between items within the DTS subscales and the notion that each subscale contained more than one interrelated construct (i.e., accelerated and incongruent thinking; disjointed, disorientated, and inhibited thinking), covariances were allowed to be sequentially placed on the largest covariances. In total, three covariances were placed on items within the Negative thought disorder factor (DTS16-DTS21; DTS1-DTS24; DTS2-DTS17) and four within the Positive thought disorder factor (DTS2-DTS4; DTS3-DTS10; DTS4-DTS8; DTS4-DTS10). Following these additions, the revised model was indicative of an adequate fit (X2 = 473.92, Df = 162, p < .001, TLI, = 0.923, CFI = 0.935, RMSEA = 0.098, SRMR = 0.037).

The final factor loadings of each model (Fig. 1) were also shown to be sound, ranging from β = 0.81 to 0.90 for the alcohol sample, β = 0.73 to 0.86 for the cannabis sample, and β = 0.79 to 0.93 for the control sample. Of note, due to the phase one results indicating strong correlations between the DTS subscales and a high internal consistency for the total scale, a priori hierarchical CFAs, with an overarching latent factor representing the total scale, were also run. However, such models did not demonstrate meaningful improvements in model fit and were therefore disregarded.

Fig. 1
figure 1

Factor loadings for each item within the DTS. Notes: All loadings were significant (p < .001). Red = control group loadings; blue = alcohol group loadings; green = cannabis group loadings

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Factor loadings for each item within the DTS

Internal consistency

Finally, reliability tets were performed on the DTS scales across the three groups. The results showed that the Positive thought disorder (α = 0.94 to 0.96) and Negative thought disorder (α = 0.94 to 0.97) subscales, as well as the total scale (α = 97 to 0.98), all exhibited excellent internal consistency. The DTS questionnaire, along with the scoring and instructions can be found in Appendix 2.

Objective three: concurrent validity of the DTS

Data preparation

Prior to the formal analyses involving the computed variables, plots were examined and indicated that normality, linearity, and homoscedasticity were present among the variables. Descriptive data supported the indication of normality as skew (≤ 0.80) and kurtosis (≤ 0.69) fell within acceptable ranges across all of the variables for both groups. Of note, the mean scores of the DTS scales appeared to vary between the phase one sample (total M = 42.74), which was recruited via Facebook and from the general population; and the combined phase two sample (total M = 22.13; see objective four comparisons), which was recruited via Footprints and controlled for substance use. Finally, reliability analyses suggested that all of the variables exhibited an acceptable to excellent internal consistency (α = 0.71 to 0.98). The descriptive statistics and reliability coefficients are displayed in Table 3.

Concurrent validity

The bivariate correlations (Table 3) first showed evidence of convergent validity, in that there were strong positive correlations between the DTS scales and (a) the general measures of cognitive dysfunction, including: thought-overaction (r = .64 to 75), odd speech (r = .64 to 0.69), working memory deficit (r = .61 to 0.72), and alogia (r = .52 to 0.58); (b) the specific measures of self-regulatory dysfunction, including anger rumination (r = .72 to 0.76), worry (r = .71 to 0.75), impulsivity (r = .74 to 0.75), and mindlessness (r = .80 to 0.85); and (c) measures of psychopathology, including anxiety and stress (r = .61 to 0.86), depression (r = .51 to 0.80), anger (r = .73 to 0.76), and total psychological distress (r = .62 to 0.86). Second, evidence of discriminant validity was present, as the DTS scales exhibited negative medium to strong relationships with attentional focusing (r = − .48 to − 0.52) and shifting (r = -30 to − 0.33). Notably, there small associations between the DTS scales and being female (r = .14 to 0.19), but no associations with age.

Table 3 Descriptive statistics, reliability coefficients, and bivariate correlations of the variables
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Objective four: comparisons of the DTS Across differing levels of psychological distress and substance use

Comparative analyses (ANOVA) were then performed with the DTS scales being compared across groups of participants with varying levels of psychological distress (mild, moderate, and severe) and substance use (frequent alcohol use, frequent cannabis use, and non-frequent substance use). For both ANOVAs, Levene’s tests indicated that all variables demonstrated an inequality of variances between the groups, and thus Browns-Forsythe (BF) statistics were substituted in all cases. The first ANOVA revealed that there were significant group differences across the psychological distress groups (BF = 665.83–774.61, p < .001, η2 = 0.590 − 0.630). Post-hoc Bonferroni tests confirmed that the severe distress group scored significantly the highest across the DTS scales (p < .001), followed by the moderate distress group (p < .001) and the mild distress group (p < .001). The second ANOVA also showed significant group differences in the DTS, between the substance user groups (BF = 665.83–774.61, p < .001, η2 = 0.590 − 0.630). Specifically, post-hoc Bonferroni tests indicated that the frequent cannabis users scored the highest across all of the variables (p < .001), followed by alcohol users (p < .001), and then the control sample (p < .001). The group means, standard deviations, and comparative statistics from the ANOVAs are displayed in Table 4.

Table 4 Comparative statistics for the DTS across Levels of Psychological Distress and substance use
Full size table

Discussion

Despite advancements in the literature that examines self-regulatory dysfunction and psychopathology, there is currently no method to measure the general psychological condition of disorganised thinking (i.e., FTD). This study therefore sought to develop and provide preliminary validation for a new self-report scale (The DTS) that measures specific aspects of FTD. The items of the DTS were subject to EFA in a general Australian sample, and then CFA in three substance-controlled groups. Reliability tests were also used to confirm the internal consistency of the resultant factors. The concurrent validity of the DTS was also tested via bivariate correlations with established measures of general cognitive difficulties, specific self-regulatory dysfunctions, and psychopathology. Finally, the scores of the DTS were compared across groups of varying levels of psychology (i.e., mild, moderate, and severe), and substance use (i.e., frequent alcohol users, frequent cannabis users, and non-frequent substance users).

The Disorganised Thought Scale: a valid measure of disordered thinking

With regards to testing the internal validity of the DTS, the factor analytical techniques and reliability analyses showed that two prominent and internally consistent factors emerged, representing the known clusters of positive and negative symptomology with FTD. The Positive thought disorder subscale consisted of ten items that were originally aimed to represent accelerated and incongruent thinking; while the Negative thought disorder subscale contained items that represented disoriented, inhibited, and disjointed thinking. Given the items of the DTS were developed in the grounding of relevant theoretical literature, these subscales and the content they represent are supported by previous research, as positive and negative thought disorder are well-established independent, but interrelated components of FTD [8, 27]. In addition to the two-factor structure, the total scale representing the general construct of FTD was also demonstrated to have sound psychometric properties and internal consistency across the final item pool.

In addition to internal validity, the DTS also demonstrated external (concurrent) validity, as the total scale and subscales were shown to exhibit strong relationships with general cognitive dysfunctions, including constructs such as thought-overaction, working memory deficits, alogia, and attentional control. Such measures were chosen for construct validity as they measured specific cognitive dysfunctions that have been previously associated with FTD [8]; Kerns & Berenbaum [25]. The DTS also exhibited strong relationships with specific self-regulatory dysfunctions, such as worry, anger rumination, impulsivity, and mindlessness. Whilst comparisons are difficult to make with existing literature, these findings provide grounding for the DTS in the generalised cognitive-behavioural literature, as such factors are conducive to FTD manifestations and have been linked to various forms of psychopathology and problematic behaviour [3, 42, 44, 56].

The correlations also demonstrated that the DTS was linked to various psychopathological symptomology, including, anger, anxiety, depression, and general psychological distress. Follow-up comparative analyses coincided this finding in that the DTS scores were significantly higher in groups that were characterised by greater psychological distress. Such findings are supportive of previous literature, which suggests that FTD symptomology can be a product of psychological dysfunction and is a prevalent condition in populations characterised by mental disorder [8, 43, 58]. These general measures were chosen as they represent a continuum of symptomology that underlie more complex disorders (e.g., affective disorders; psychotic disorders) known to be associated with FTD. Further, there is literature that suggests negative affect has an aversive impact on array of cognitive functions, such as attentional processing, flexibility, bias, and control [7, 15, 49, 59].

Finally, the DTS was also shown to significantly differ across the substance-controlled groups, in that frequent cannabis users displayed a higher thought disorder, followed by frequent alcohol users, and non-frequent substance users. These groups were controlled for the frequent use of other substances and thus the findings may suggest that there is a relationship between frequent alcohol and cannabis use with thought disorganisation. As discussed, prior investigations have identified substance misuse as an indicator of FTD [43] and psychotic symptomology [1, 21, 22] and is associated with deficits in cognitive functioning [9, 29, 34]. Notably, the results also showed the relevance and continuum of FTD symptomology within the general population, considering the mean scores of the DTS in both the controlled groups and the general population sample were considerably high.

Implications, limitations, and future directions

This study developed and provided preliminary validation for a new self-report measure of disordered and disorganised thinking. As discussed, existent measures relating to thought disorder primarily rely on clinician observations or are tied to communication deficits specifically, and thus the DTS provides a novel form of measurement for FTD that could help advance research further in this space. This measure also provides a nuanced ability to measure dysfunctional, disorganised, and uncontrolled thinking outside of other generalised measures of cognitive difficulties, such as rumination, worry, and attentional control. Whilst the DTS was designed as a general measure of thought disorganisation, it could also be used in future research to investigate and better understand how FTD can present or manifest in various forms of mental disorder and psychological dysfunctions. As such, the DTS could be used by clinicians and researchers to evaluate if interventions are efficacious in modifying thought disorder. The measure may also provide a new avenue for investigation in better understanding how different patterns of substance use can impact thinking and behaviour. Together, such research findings could help inform interventions about how to better approach conditions characterised by disorganised thinking and how substance misuse may be exacerbating such conditions.

Although this study has exhibited strong preliminary support for the DTS as a sound psychometric measure, limitations were present that need to be highlighted. First and foremost, was that the original sample returned a very high proportion of respondents that were female and exhibited high levels of psychological distress, which may have inflated the means, given that scores on the DTS were comparatively higher compared to the other three groups. Nonetheless, the factor structure and relationships between the items appeared to be unaffected by this disproportion, as the CFAs exhibited acceptable model fit across to the proposed factor structure. Second, the data collection methods used were restricted to online mediums (e.g., social media) and may have reduced the potential to capture the entirety of the targeted populations, limiting the generalisability of the findings. Third, the substance-controlled groups were only controlled for by the frequency in which they consumed alcohol and cannabis (i.e., daily). This may have limited the ability to interpret how actual problematic use (i.e., abuse) may be affecting thought disorder. A final notable limitation is that longitudinal indicators such as test-retest reliability and predictive validity were not assessed in the current study, leaving the support for concurrent validity reliant on preliminary cross-sectional evidence.

In response to the potential implications and limitations, several approaches are recommended for future research. First, the DTS would benefit from further testing and validation. Exploration of its internal validity in unique clinical and substance use cohorts could provide support for the use of the DTS in specific populations, whilst utilisation of longitudinal designs would afford evidence for the predictive utility and reliability of the DTS. Further investigation into factors that are linked to the manifestation and progression of disorganised thinking patterns, such as substance misuse, trauma, and psychopathology, may also be a beneficial avenue of research, with real-world implications for treatment. Finally, researchers may wish to further ground the DTS in theoretical frameworks, by investigating alternative self-regulatory factors (e.g., metacognitive beliefs; detached mindfulness; executive functioning) that are associated with the scale, in order to explore what other forms of thought disorder may be co-occurring with the identified subscales in the DTS.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Data availability

The data that support the findings of this study are available on request from the corresponding author, SL. The data are not publicly available due to ethical restrictions.

References

  1. Archibald L, Brunette MF, Wallin DJ, Green AI. Alcohol Use Disorder and Schizophrenia or Schizoaffective Disorder. Alcohol Res. 2019;40(1):arcr.v40.1.06. https://doi.org/10.35946/arcr.v40.1.06.

  2. Argote M, Sescousse G, Brunelin J, Fakra E, Nourredine M, Rolland B. Association between formal thought disorder and cannabis use: a systematic review and meta-analysis. Schizophrenia. 2022;8(1):78. https://doi.org/10.1038/s41537-022-00286-0.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Asghari M, Abdollahi MH, Shahgholian M. The role of impulsivity, executive functions and dispositional mindfulness in the occurrence of risky-taking behaviours of young people. J Psychol Stud. 2021;17(3):137–56.

    Google Scholar 

  4. Baer RA, Smith GT, Hopkins J, Krietemeyer J, Toney L. Using self-report assessment methods to explore facets of mindfulness. Assessment. 2006;13(1):27–45.

    Article  PubMed  Google Scholar 

  5. Barrera A, McKenna PJ, Berrios GE. Two new scales of formal thought disorder in schizophrenia. Psychiatry Res. 2008;157(1–3):225–34.

    Article  PubMed  Google Scholar 

  6. Barrera Á, Handel A, Kondel TK, Laws KR. Formal thought disorder: self-report in non-clinical populations. Int J Psychol Psychol Ther. 2015;15(1):155–67.

    Google Scholar 

  7. Bertschy G, Weibel S, Giersch A, Weiner L. Racing and crowded thoughts in mood disorders: a data-oriented theoretical reappraisal. L’encephale. 2020;46(3):202–8.

    Article  PubMed  Google Scholar 

  8. Bora E, Yalincetin B, Akdede BB, Alptekin K. Neurocognitive and linguistic correlates of positive and negative formal thought disorder: a meta-analysis. Schizophr Res. 2019;209:2–11.

    Article  PubMed  Google Scholar 

  9. Brennan SE, McDonald S, Page MJ, Reid J, Ward S, Forbes AB, McKenzie JE. Long-term effects of alcohol consumption on cognitive function: a systematic review and dose-response analysis of evidence published between 2007 and 2018. Syst Rev. 2020;9(1):33. https://doi.org/10.1186/s13643-019-1220-4.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Byrne BM. Structural equation modeling with Mplus: basic concepts, applications, and programming. routledge; 2013.

    Book  Google Scholar 

  11. Cohen J. Statistical power analysis for the behavioral sciences. Routledge; 2013.

    Book  Google Scholar 

  12. Costello S. MonteCarlo Parallel Analysis for PCA: An SPSS Dialog. 2016.

    Google Scholar 

  13. Costello AB, Osborne J. Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval. 2005;10(1):7.

    Google Scholar 

  14. Cyders MA, Littlefield AK, Coffey S, Karyadi KA. Examination of a short English version of the UPPS-P Impulsive Behavior Scale. Addict Behav. 2014;39(9):1372–6. https://doi.org/10.1016/j.addbeh.2014.02.013.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Derakshan N, Eysenck MW. Anxiety, processing efficiency, and cognitive performance: new developments from attentional control theory. Eur Psychol. 2009;14(2):168–76.

    Article  Google Scholar 

  16. Derryberry D, Reed MA. Anxiety-related attentional biases and their regulation by attentional control. J Abnorm Psychol. 2002;111(2): 225.

    Article  PubMed  Google Scholar 

  17. Forbes D, Alkemade N, Mitchell D, Elhai JD, McHugh T, Bates G, Novaco RW, Bryant R, Lewis V. Utility of the dimensions of anger Reactions–5 (DAR-5) scale as a brief anger measure. Depress Anxiety. 2014;31(2):166–73.

    Article  PubMed  Google Scholar 

  18. Gladstone GL, Parker GB, Mitchell PB, Malhi GS, Wilhelm KA, Austin M-P. A brief measure of worry severity (BMWS): personality and clinical correlates of severe worriers. J Anxiety Disord. 2005;19(8):877–92.

    Article  PubMed  Google Scholar 

  19. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis: a global perspective (7 ed.). London: Pearson Education; 2010.

  20. Halford WK, Frost ADJ. Depression anxiety stress Scale-10: a brief measure for routine psychotherapy outcome and progress assessment. Behav Change. 2021;38(4):221–34.

    Article  Google Scholar 

  21. Hasan A, von Keller R, Friemel CM, Hall W, Schneider M, Koethe D, Leweke FM, Strube W, Hoch E. Cannabis use and psychosis: a review of reviews. Eur Arch Psychiatry Clin NeuroSci. 2020;270(4):403–12. https://doi.org/10.1007/s00406-019-01068-z.

    Article  PubMed  Google Scholar 

  22. Hindley G, Beck K, Borgan F, Ginestet CE, McCutcheon R, Kleinloog D, Ganesh S, Radhakrishnan R, D’Souza DC, Howes OD. Psychiatric symptoms caused by cannabis constituents: a systematic review and meta-analysis. Lancet Psychiatry. 2020;7(4):344–53. https://doi.org/10.1016/s2215-0366(20)30074-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Howard MC. A review of exploratory factor analysis decisions and overview of current practices: what we are doing and how can we improve? Int J Hum Comput Interact. 2016;32(1):51–62.

    Article  Google Scholar 

  24. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equat Model: Multidisciplin J. 1999;6(1):1–55.

    Article  Google Scholar 

  25. Kerns JG, Berenbaum H. Cognitive impairments associated with formal thought disorder in people with schizophrenia. J Abnorm Psychol. 2002;111(2): 211.

    Article  PubMed  Google Scholar 

  26. Kircher T, Krug A, Stratmann M, Ghazi S, Schales C, Frauenheim M, Turner L, Fährmann P, Hornig T, Katzev M, Grosvald M, Müller-Isberner R, Nagels A. A rating scale for the assessment of objective and subjective formal Thought and Language Disorder (TALD). Schizophr Res. 2014;160(1):216–21. https://doi.org/10.1016/j.schres.2014.10.024.

    Article  PubMed  Google Scholar 

  27. Kircher T, Bröhl H, Meier F, Engelen J. Formal thought disorders: from phenomenology to neurobiology. Lancet Psychiatry. 2018;5(6):515–26.

    Article  PubMed  Google Scholar 

  28. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kroon E, Kuhns L, Cousijn J. The short-term and long-term effects of cannabis on cognition: recent advances in the field. Curr Opin Psychol. 2021;38:49–55.

    Article  PubMed  Google Scholar 

  30. Kumari S, Malik M, Florival C, Manalai P, Sonje S. An assessment of five (PANSS, SAPS, SANS, NSA-16, CGI-SCH) commonly used symptoms rating scales in schizophrenia and comparison to newer scales (CAINS, BNSS). J Addict Res Ther. 2017;8(3):324. https://doi.org/10.4172/2155-6105.1000324.

  31. Kyriazos TA. Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology. 2018;9(08):2207.

    Article  Google Scholar 

  32. Loewy RL, Bearden CE, Johnson JK, Raine A, Cannon TD. The prodromal questionnaire (PQ): preliminary validation of a self-report screening measure for prodromal and psychotic syndromes. Schizophr Res. 2005;79(1):117–25. https://doi.org/10.1016/j.schres.2005.03.007.

    Article  PubMed  Google Scholar 

  33. Love S, Larue G, Rowland B. Development and validation of the Driver Attention Regulation Scale: A measure of the perceived ability to regulation attention on the road. Transportation Research Part F: Traffic Psychology and Behavior. 2023;102(2024):199-212. https://doi.org/10.1016/j.trf.2024.02.015.

  34. Love S, Nicolls M, Rowland B, Davey J. The Impact of Methamphetamine Use and Dependence: A Systematic Review on the Cognitive-Behavioural Implications for Road Safety. 2024;103(2024):480-99. https://doi.org/10.1016/j.trf.2024.05.002.

  35. Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression anxiety stress scales (DASS) with the Beck Depression and anxiety inventories. Behav Res Ther. 1995;33(3):335–43.

    Article  PubMed  Google Scholar 

  36. Meyer TJ, Miller ML, Metzger RL, Borkovec TD. Development and validation of the penn state worry questionnaire. Behav Res Ther. 1990;28(6):487–95.

    Article  PubMed  Google Scholar 

  37. Moustafa AA, Tindle R, Frydecka D, Misiak B. Impulsivity and its relationship with anxiety, depression and stress. Compr Psychiatr. 2017;74:173–9. https://doi.org/10.1016/j.comppsych.2017.01.013.

    Article  Google Scholar 

  38. Mutlu E, Abaoğlu H, Barışkın E, Gürel ŞC, Ertuğrul A, Yazıcı MK, Akı E, Yağcıoğlu AEA. The cognitive aspect of formal thought disorder and its relationship with global social functioning and the quality of life in schizophrenia. Soc Psychiatry Psychiatr Epidemiol. 2021;56(8):1399–410. https://doi.org/10.1007/s00127-021-02024-w.

    Article  PubMed  Google Scholar 

  39. Niv N, Cohen AN, Mintz J, Ventura J, Young AS. The validity of using patient self-report to assess psychotic symptoms in schizophrenia. Schizophr Res. 2007;90(1):245–50. https://doi.org/10.1016/j.schres.2006.11.011.

    Article  PubMed  Google Scholar 

  40. Osborne JW. What is rotating in exploratory factor analysis? Pract Assess Res Eval. 2015;20(1):2.

    Google Scholar 

  41. Peifer C, Schulz A, Schächinger H, Baumann N, Antoni CH. The relation of flow-experience and physiological arousal under stress—can u shape it? J Exp Soc Psychol. 2014;53:62–9.

    Article  Google Scholar 

  42. Reynolds BW, Basso MR, Miller AK, Whiteside DM, Combs D. Executive function, impulsivity, and risky behaviors in young adults. Neuropsychology. 2019;33(2):212.

    Article  PubMed  Google Scholar 

  43. Roche E, Creed L, MacMahon D, Brennan D, Clarke M. The epidemiology and associated phenomenology of formal thought disorder: a systematic review. Schizophr Bull. 2015;41(4):951–62. https://doi.org/10.1093/schbul/sbu129.

    Article  PubMed  Google Scholar 

  44. Salguero JM, García-Sancho E, Ramos‐Cejudo J, Kannis‐Dymand L. Individual differences in anger and displaced aggression: the role of metacognitive beliefs and anger rumination. Aggressive Behav. 2020;46(2):162–9.

    Article  Google Scholar 

  45. Sarzynska-Wawer J, Wawer A, Pawlak A, Szymanowska J, Stefaniak I, Jarkiewicz M, Okruszek L. Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res. 2021;304:114135.

    Article  PubMed  Google Scholar 

  46. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

    Article  PubMed  Google Scholar 

  47. Sukhodolsky DG, Golub A, Cromwell EN. Development and validation of the anger rumination scale. Pers Indiv Differ. 2001;31(5):689–700.

    Article  Google Scholar 

  48. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics (Vol. 6). Boston, MA.: pearson; 2013.

    Google Scholar 

  49. Tirch DD, Leahy RL, Silberstein LR, Melwani PS. Emotional schemas, psychological flexibility, and anxiety: the role of flexible response patterns to anxious arousal. Int J Cogn Ther. 2012;5(4):380–91.

    Article  Google Scholar 

  50. Voce A, Calabria B, Burns R, Castle D, McKetin R. A systematic review of the symptom profile and course of methamphetamine-associated psychosis: substance use and misuse. Subst Use Misuse. 2019;54(4):549–59.

    Article  PubMed  Google Scholar 

  51. Waring EM, Neufeld RWJ, Schaefer B. The thought disorder questionnaire. Can J Psychiatry. 2003;48(1):45–51.

    Article  PubMed  Google Scholar 

  52. Watkins MW. Exploratory factor analysis: a guide to best practice. J Black Psychol. 2018;44(3):219–46.

    Article  Google Scholar 

  53. Weiner L, Weibel S, de Sousa Gurgel W, Keizer I, Gex-Fabry M, Giersch A, Bertschy G. Measuring racing thoughts in healthy individuals: the Racing and Crowded Thoughts Questionnaire (RCTQ). Compr Psychiatr. 2018;82:37–44.

    Article  Google Scholar 

  54. Weiner L, Ossola P, Causin J-B, Desseilles M, Keizer I, Metzger J-Y, Krafes EG, Monteil C, Morali A, Garcia S. Racing thoughts revisited: a key dimension of activation in bipolar disorder. J Affect Disord. 2019;255:69–76.

    Article  PubMed  Google Scholar 

  55. Weiss NH, Kiefer R, Goncharenko S, Raudales AM, Forkus SR, Schick MR, Contractor AA. Emotion regulation and substance use: a meta-analysis. Drug Alcohol Depend. 2022;230: 109131. https://doi.org/10.1016/j.drugalcdep.2021.109131.

    Article  PubMed  Google Scholar 

  56. Wells A. Breaking the cybernetic code: understanding and treating the human metacognitive control system to enhance mental health. Front Psychol. 2019;10: 2621.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Whiteside SP, Lynam DR, Miller JD, Reynolds SK. Validation of the UPPS impulsive behaviour scale: a four-factor model of impulsivity. Eur J Pers. 2005;19(7):559–74.

    Article  Google Scholar 

  58. Yalincetin B, Bora E, Binbay T, Ulas H, Akdede BB, Alptekin K. Formal thought disorder in schizophrenia and bipolar disorder: a systematic review and meta-analysis. Schizophr Res. 2017;185:2–8. https://doi.org/10.1016/j.schres.2016.12.015.

    Article  PubMed  Google Scholar 

  59. Zsidó AN. The effect of emotional arousal on visual attentional performance: a systematic review. Psychol Res. 2023;88(1):1–24. https://doi.org/10.1007/s00426-023-01852-6.

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Acknowledgements

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Funding

This research was funded by the Motor Accident Insurance Commission.

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  1. MAIC/UniSC Road Safety Research Collaboration, School of Law and Society, University of the Sunshine Coast, 90 Sippy Downs Dr, Sippy Downs, Queensland, 4556, Australia

    Steven Love & Kerry Armstrong

  2. School of Health, University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, Queensland, 4556, Australia

    Lee Kannis-Dymand

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All authors conceptualised the study design and methodology. Steven Love was responsible for the data collection, formal analyses and writing ta draft of the manuscript. Kerry Armstrong and Lee Kannis-Dymand provided intellectual input throughout the course of the project and reviewed the final manuscript.

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Love, S., Kannis-Dymand, L. & Armstrong, K. Development and validation of a Disorganised Thoughts Scale: a new measure to assess thinking difficulties in the general population. BMC Psychol 12, 492 (2024). https://doi.org/10.1186/s40359-024-01988-z

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