Psychometric properties of the self-report version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale in a sample of Hungarian adolescents and young adults

The Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) measures the full spectrum of attention and activity symptoms, not just the negative end of the distribution. Previous studies revealed strong psychometric properties of the parent and teacher report versions; however, there is little research on the new self-report form of the SWAN. Therefore, our research aimed to explore the psychometric characteristics of the SWAN self-report. A non-clinical sample of young women (N = 664, mean age: 20.01 years, SD: 3.08 years) completed the SWAN self-report, the Strengths and Difficulties Questionnaire (SDQ) and the Mental Health Continuum Short Form (MHC-SF). We tested several models using confirmatory factor analyses to assess the factorial validity of the SWAN self-report. Distributional characteristics, convergent, and predictive validity were assessed. A bifactor model with a general factor and a specific inattention factor (bifactor-1) provided the best fit in our data (CFI = 0.977, TLI/NFI = 0.972, RMSEA = 0.053 [90% CI: 0.047 – 0.059], SRMR = 0.061, ω = 0.90). The reliability of the general ADHD factor was good (ω_h = 0.87), and the specific inattention factor was acceptable (ω_h = 0.73). The distribution of the SWAN self-report scores did not differ from the normal distribution. A strong correlation between the SWAN and the SDQ Hyperactivity subscale was found. The analyses revealed good predictive validity. Our results suggest that the SWAN self-report is a valuable tool for assessing symptoms of ADHD in adolescents and young adults.

1 Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by developmentally inappropriate symptoms of inattention, hyperactivity and impulsivity that lead to impairment in at least two domains of functioning (1). According to a recent meta-analysis (2), the prevalence of ADHD is 7.6% in children and 5.6% in adolescents. In adults, it was found to be 2.6% (3), with a risk as high as 70–75% for coexisting psychiatric disorders (4, 5), including mood and anxiety disorders, disruptive disorders, bipolar disorder, substance use, behavioural addictions, insomnia, and personality disorders (6–13). Though many environmental and genetic factors with small effects have already been identified, there are no definitive causal factors that can reliably predict ADHD (14–17). In their highly important work, Kapteyn and van Uven (18) proved that a high number of independent factors of small effects tend to cause normal distributions, whereas processes with few factors and large effects tend to cause more skewed distributions. In line with this, ADHD-related traits follow a continuous, normal distribution in the population (19, 20).

In children, the diagnosis is mainly based on parent and teacher ratings, as well as on the direct observation of the symptoms (21). The importance of self-reporting increases with age, however. Though parent-report predicts outcomes better than self-reports even in adolescents and college students (22, 23), by adulthood, self-report is the most valuable source of information in the diagnostic process (24). It should be noted that symptom list-based approaches are the predominant means of assessing adult ADHD through rating scales, such as the Adult AD/HD Rating Scale [AARS (25)], the Barkley Adult ADHD Rating Scale–IV [BAARS-IV (26)], the Conners’ Adult ADHD Rating Scale [CAARS (27)], and the Adult ADHD Self-Report Scale [ASRS (28)]. Although these measures have strong psychometric properties, they indicate normative functioning by the absence of symptoms, resulting in a skewed and truncated distribution of scores. The lack of fit to normal distribution causes difficulty in establishing the clinical cutoffs of symptom scales, which can lead to lower specificity and type 1 errors (29). Therefore, it is important to take the strengths in attention, activity, and impulse control into consideration as well to see the full picture (19, 20).

The Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale [SWAN (20)] was shown to satisfy this requirement. SWAN is an 18-item questionnaire with a 7-point Likert scale that examines traits related to ADHD symptoms. The scale is anchored to average behaviour, namely that –3 means the respondent observed far below average capability in the trait measured, 0 means average behaviour, and +3 means far above average ability that can be a strength in self-control or executive functioning (i.e., lower scores indicate greater difficulties). Consequently, the SWAN scores were shown to follow a normal distribution (20, 30–32), and, therefore, it was found to have just as good sensitivity but better specificity than other ADHD symptom scales (33). Originally, parent-, and teacher-report versions have been developed (20), and translated into several languages (30, 31, 33–37). More recently, Blume and colleagues (38) created a self-report version the German language, extending the SWAN family.

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5 (1)] conceptualizes ADHD with two primary dimensions; consequently, predominantly inattentive, predominantly hyperactive/impulsive, and combined presentations are described. However, it has been debated whether the two-factor structure best represents ADHD (39). Factor-analytic studies have reported the best fit for different measurement models, with mixed support for one-, two-, and three-factor models or more complex hierarchical or bifactor models. In terms of fit indices, most evidence seems to support the bifactor models (40–43). In bifactor models, a latent general ADHD construct may explain the diagnostic stability of ADHD, while the specific factors are in line with the clinical observation that symptom presentation may change over time (44). Converging results revealed a strong and reliable general ADHD factor in different age groups and across different measures (40). A specific inattention factor which acquired sufficient specificity and stability for interpretation beyond the general ADHD factor has also been supported by several studies, especially in clinical samples (42). However, a specific hyperactivity/impulsivity factor (bifactor-2 model) or separate hyperactivity and impulsivity factors (bifactor-3 models) were shown to explain only a small amount of additional variance and had low reliabilities and low construct replicabilities. It is important to note that, bifactor-3 models performed better when talks excessively (modulating verbal activity) was treated as a part of impulsivity rather than hyperactivity (41, 43). In line with this several authors argued that the hyperactivity and impulsivity symptoms listed in the DSM-5 (1) could be better grouped into motor and verbal hyperactivity/impulsivity (45). The weakness of specific factors led some researchers to view bifactor models as not interpretable, clinically irrelevant, or even statistical artefacts (46, 47). In that way, they suggested the use of either a total score of the scales (the general ADHD factor only) (46) or the acceptance of more traditional non-hierarchical two- or three-factor models (47) or hierarchical models (39), despite of their inferiority to bifactor models in term of fit indices. However, the hierarchical models had negative factor loadings and low internal consistencies which is a known issue for models with a general factor (48). The usage of bifactor-1 model was found to be a viable alternative by previous ADHD researchers (49, 50).

The factor structure of the SWAN has also been explored. In studies on the parent- and teacher-report versions, exploratory factor analysis resulted in a two-factor structure (inattention and hyperactivity/impulsivity) in different samples (20, 31). In studies using confirmatory factor analysis (31, 35, 51, 52), bifactor models were reported to have a better fit than non-hierarchical models; however, similar to studies with other measures, the results suggested a strong and reliable general ADHD factor but mostly weak or unreliable specific factors. A single study under peer review (38) reported similar results for the new self-report version of the SWAN. Blume et al. found an inferior fit of a non-hierarchical two-factor model in comparison to a bifactor model with a general and two specific factors; however, five of nine item-loadings of the specific hyperactivity/impulsivity factor were nonsignificant.

To gain more information about the self-report version of the SWAN, our study aimed to examine the psychometric properties of the Hungarian version in a sample of adolescents and young adults.

2 Methods

2.1 Sample and procedures

The Institutional Research Ethics Committee of the Psychological Institute Eötvös Loránd University approved the study (Nr. 2022/344). There was a 24-hour-long campaign through Mélylevegő Projekt (Deep Breath Project), a Hungarian psychoeducational social media page. Mélylevegő Projekt was founded during the 2020 COVID-19 lockdown by graduate psychology students as a platform connect and provide mental health related information for teens and young adults. Participants gave their informed consent before completing the online questionnaire.

In total, 2597 participants agreed to fill out the questionnaire, of whom 717 completed it. Completers’ gender distribution was highly skewed (N = 664 [92.6%] women, N = 47 [6.6%] men, N = 6 [0.8%] other), therefore, data from 664 women (age range: 14 − 25 years, mean age: 20.01 years, SD: 3.08) were analyzed (Table 1).

Table 1

Table 1 Demographic characteristics (N = 664).

2.2 Measure

The self-report version of the SWAN (38) was used to assess ADHD symptoms. Blume et al. (38) reported good internal reliabilities for the German version (α = 0.85 – 0.90). Up to now, only the parent and teacher versions of the SWAN have been validated in the Hungarian language showing good internal consistencies (α = 0.87 – 0.93) and convergent validity with the Hyperactivity subscale of the Strengths and Difficulties Questionnaire [SDQ (53)] (r = 0.67 - 0.74) (30). The Hungarian version of the SWAN self-report was created by Paulina et al. (54). In their pilot study, they reported good internal reliability (α = 0.89) for the total score, but the factorial validity has not been evaluated yet.

The SDQ (53) assesses emotional and behavioral problems with four problem scales (Emotional problems, Conduct problems, Hyperactivity, Peer problems) and prosocial skills. The SDQ Hyperactivity subscale measures hyperactivity with two items (SDQ#2, SDQ#10), but includes an impulsivity item (SDQ#20) and two inattention items (SDQ#15, SDQ#25) as well. Higher scores indicate more emotional and behavioral problems. Internal reliabilities of the self-report version were α_SDQ-ES = 0.66, α_SDQ-CP = 0.60, α_SDQ-H = 0.67, α_SDQ-PP = 0.41, α_SDQ-P = 0.66 (53). Psychometric properties of the Hungarian version have been reported by Turi et al. (55). In our sample, the Hyperactivity and Emotional problems subscales had acceptable reliability (α_SDQ-H = 0.76 and α_SDQ-ES = 0.68), but the Conduct problems and Peer problems subscales had poor reliabilities (α_SDQ-CP = 0.47, α_SDQ-PP = 0.45), therefore, we used only the Hyperactivity and Emotional problems subscales for further analyses.

The Mental Health Continuum - Short Form [MHC-SF (56)] measures emotional, psychological and social well-being with 14 Likert-type items measuring the frequency of indicators of positive mental health (from 1 = never to 6 = every day). Both the original (α_MHC = 0.74) and the Hungarian versions (α_MHC = 0.89) showed good internal consistencies (56, 57). In the present sample, the internal reliability was α_MHC = 0.85.

2.3 Statistical analyses

Confirmatory factor analyses were conducted to assess the factorial validity of the SWAN self-report. Because of the significance of the Shapiro-Wilk Test for Multivariate Normality and the Mardia’s coefficients for multivariate skewness and kurtosis, we used the Diagonally Weighted Least Squares (DWLS) estimator (58). We compared several models: a non-hierarchical two-factor model and a bifactor model with a general ADHD factor and two specific factors for inattention and hyperactivity/impulsivity (bifactor-2), a bifactor model with a general factor and a specific inattention factor (bifactor-1), and two bifactor models with a general and three specific factors: inattention, hyperactivity + “Modulating verbal activity”, and impulsivity (bifactor-3-IA/H[MVA]/I), and inattention, hyperactivity, and hyperactivity+ “Modulating verbal activity” (bifactor-3-IA/H/I[MVA]). The analyses were conducted using the lavaan (version 0.6–15) package of JASP version 0.17.1 and R version 4.2.1. Factor scaling was assumed to be based on factor variances. Maximum likelihood estimators were used. Comparative Fit Indices (CFI) and Tucker-Lewis Indices/Normed Fit Indices (TLI/NFI) were calculated with a minimum acceptable threshold of 0.900 (59, 60). Root Mean Square Error of Approximation (RMSEA) was calculated with a maximum acceptable threshold of 0.07 (61). Standardized Root Mean Square Residual (SRMR) was calculated with a maximum acceptable threshold of 0.08 (62). Composite reliabilities were calculated with ω_h > 0.50 as the criteria of minimal acceptability and ω_h > 0.75 as the preferred reliability index (63). Explained common variance was calculated for the bifactor models to compare the factor loadings of the general factor with that of the one-factor model (64). Hancock’s H coefficient was calculated for each factor to measure how well each latent variable is represented by its group of items. The criterium of stability was H > 0.70, which means the factor is unlikely to fluctuate across samples (47, 65).

Normality was evaluated by the Shapiro-Wilk statistics, skewness, and kurtosis. Convergent validity was evaluated by computing the Person’s correlational coefficient of the SWAN and the SDQ Hyperactivity subscale. Predictive validity was examined by means of linear regression analyses with the MHC-SF and the SDQ Emotional problems subscale as dependent variables.

3 Results

3.1 Confirmatory factor analyses

First, the non-hierarchical two-factor model was evaluated. The usage of Chi-square goodness of fit was largely eschewed due to the large sample size (66). Composite reliability of the two-factor model was ω_h = 0.85 for inattention, ω_h = 0.87 for hyperactivity/impulsivity, and ω = 0.92 for the total model. Construct replicability was good for both inattention (Hancock’s H-index = 0.86) and hyperactivity/impulsivity (Hancock’s H-index = 0.87) factors. The model showed a good fit to our data (CFI = 0.972, TLI/NFI = 0.968, RMSEA = 0.057 [90% CI: 0.051 – 0.063], SRMR = 0.066). All item loadings were significant (Table 2A).

Table 2A

Table 2A Factor loadings and fit indices of the two-factor, bifactor-2, and bifactor-1 models.

The bifactor-2 model showed a very good fit (CFI = 0.999, TLI/NFI = 0.998, RMSEA = 0.013 [90% CI: 0.000 – 0.024], SRMR = 0.036), and the SRMR has shown a considerable decrease compared to the two-factor model (ΔSRMR = 0.030). All item loadings were statistically significant. Items 15–18 demonstrated significant, negative loadings on the hyperactivity/impulsivity factor. The overall reliability of the bifactor-2 model was good (ω = 0.89). The reliability of the general ADHD factor was also good (ω_h = 0.87), the specific inattention factor was minimally acceptable (ω_h = 0.73), and the reliability of the specific hyperactivity/impulsivity factor was poor (ω_h = 0.10). The explained common variance of the model was ECV = 0.62, suggesting that the factor loadings of the general ADHD factor were slightly similar to those that might be obtained with the one-factor model. Analysis of construct replicability indicated that the general ADHD factor (Hancock’s H-index = 0.90) and the specific inattention factor (Hancock’s H-index = 0.76) were stable, but the specific hyperactivity/impulsivity factor was not (Hancock’s H-index = 0.56).

The bifactor-1 model showed a good fit (CFI = 0.977, TLI/NFI = 0.972, RMSEA = 0.053 [90% CI: 0.047 – 0.059], SRMR = 0.061). All factor loadings were significant, ranging from 0.28 to 0.75. The factor loadings of six of nine inattention-symptom items were substantially larger than their loadings on the general factor (Δb > 0.1). The overall reliability of the bifactor-1 model was good (ω = 0.90). The reliability of the general ADHD factor was also good (ω_h = 0.87), and the specific inattention factor was minimally acceptable (ω_h = 0.73). The explained common variance was 0.71, suggesting that the general ADHD factor explained more than two-thirds of the common variance. Analysis of construct replicability indicated that both the general ADHD factor (Hancock’s H-index = 0.89) and the inattention factor (Hancock’s H-index = 0.75) were stable.

Both bifactor-3 models (Table 2B) showed very good fit [bifactor-3-IA/H[MVA]/I: CFI = 0.995, TLI/NFI = 0.994, RMSEA = 0.025 [90% CI: 0.015 – 0.033], SRMR = 0.041; bifactor-3-IA/H/I[MVA]: CFI = 0.998, TLI/NFI = 0.998, RMSEA = 0.015 [90% CI: 0.000 – 0.025], SRMR = 0.037)], with the bifactor-3-IA/H/I[MVA] performing slightly better. “Modulating verbal activity” had a negative loading on the Hyperactivity factor (bifactor-3-IA/H[MVA]/I model) but loaded significantly and positively on the Impulsivity factor (bifactor-3-IA/H/I[MVA] model). In both models, the overall reliability of the model was good (ω = 0.91 for both); the reliability of the general ADHD factor was also good (ω_h = 0.86 for both), and the specific inattention factor was minimally acceptable (ω_h = 0.70 and 0.68, respectively). However, in both bifactor-3 models, the reliabilities of the hyperactivity and impulsivity factors were poor (< 0.70). The explained common variances of the models were 0.58 and 0.56, respectively, suggesting the general ADHD factor explained about half of the common variance in the bifactor-3 models. Construct replicability indicated that, both the general ADHD factor (Hancock’s H-index = 0.88 and 0.87, respectively) and the inattention factor (Hancock’s H-index = 0.73 and 0.71, respectively) were stable in both bifactor-3 models. The hyperactivity factor was unstable in both models (Hancock’s H-index = 0.65); however, the impulsivity factor did not show stability in both bifactor-3 models (Hancock’s H-index = 0.27 and 0.49, respectively).

Table 2B

Table 2B Factor loadings and fit indices of t of the bifactor-3 models.

We compared the models by computing several Scaled Chi-Square Difference Tests. All bifactor models were proven to be significantly better than the two-factor model. However, the bifactor-2 (Δχ²(9) = 229.286, p < 0.05), and both the bifactor-3-IA/H[MVA]/I (Δχ²(9) = 195.086, p < 0.05) and bifactor-3-IA/H/I[MVA] models (Δχ²(9) = 227.101, p < 0.05) were significantly better than the bifactor-1 model.

3.2 Distributional characteristics of the SWAN

Shapiro-Wilk statistics were non-significant for both adolescents (W = 0.994, p = 0.700) and young adults (W = 0.995, p = 0.137), indicating normality. The skewness of the SWAN scores in the adolescent and young adult samples was found to be 0.03 and 0.02, respectively, indicating a close fit to symmetrical distribution. The kurtosis of SWAN in the adolescent and young adult samples was found to be –0.39 and –0.44, respectively.

3.3 Convergent validity

Descriptive statistics and bivariate relationships of study variables are shown in Table 3. The SWAN subscales showed significant negative correlations of large effect size with the SDQ Hyperactivity subscale indicating good convergent validity (Table 3).

Table 3

Table 3 Descriptive statistics, reliabilities and intercorrelations of study variables (Pearson’s correlational coefficients).

3.4 Predictive validity

In a linear regression model with MHC-SF as the dependent variable and the total score of the SWAN self-report scale as an independent variable, the analysis resulted in a significant model (adjusted R² = 0.125, F(1,663) = 96.125, p < 0.001). The SWAN was a significant predictor (β = 0.356, p < 0.001), explaining 12.5% of the variance of the dependent variable. When choosing the SDQ Hyperactivity subscale as the independent variable, the regression model was significant (adjusted R² = 0.079, F(1,662) = 57.486, p < 0.001). The SDQ Hyperactivity was a significant predictor (β = -0.283, p < 0.001), explaining 7.9% of the variance of the dependent variable.

The next model, with SDQ Emotional problems as dependent and the SWAN as the independent variable, was significant (adjusted R² = 0.133, F(1,662) = 102.694, p < 0.001). The SWAN was a significant predictor (β = –0.366, p < 0.001). Finally, the regression analysis with SDQ Emotional problems as dependent and the SDQ Hyperactivity scale as an independent variable resulted in a significant model (adjusted R² = 0.132, F(1,662) = 101.770, p < 0.001). The SDQ Hyperactivity was a significant predictor (β = 0.365, p < 0.001).

4 Discussion

There is a solid theoretical foundation for the relevance of measuring the full spectrum of ADHD symptoms, not just the negative end of the distribution (19, 20, 32). Also, several empirical studies using parent or teacher-report versions of the SWAN scale have demonstrated the merits of this approach [e.g (30, 67). (31, 34, 33) (35–37)]. Up to now, there was no self-report instrument available that would measure both strengths and weaknesses in attention, activity, and impulse control. Blume and colleagues (38) have filled this gap by developing a self-report version of the SWAN. Since they reported promisingly good psychometric characteristics for the German version of the SWAN self-report, it was worthwhile to conduct further research with the Hungarian version. Therefore, our research aimed to explore the factor structure, and distributional characteristics of the SWAN self-report, and to assess its convergent validity with the SDQ Hyperactivity subscale. Furthermore, we assessed its predictive validity in linear regression models by analyzing how well it can explain the variance in mental well-being and emotional problems.

4.1 The factor structure of the SWAN self-report

Though all models tested showed acceptable fit, the bifactor models were superior to the non-hierarchical two-factor model in multiple fit indices: they had higher CFI and NFI/TLI and lower 90%CI for the RMSEA and SRMR. However, though the bifactor-2 and bifactor-3 models have shown a better fit than the bifactor-1, neither has shown an acceptable composite reliability nor Hancock’s H-index for each of their factors. The specific hyperactivity/impulsivity factor of the bifactor-2 model was shown to have poor composite reliability of ωh = 0.10. The items of the specific factor thus share 1% in common variance, the rest being accountable for measurement error. The bifactor-3 models were also shown to have poor composite reliabilities. Bifactor-3-IA/H[MVA]/I’s specific impulsivity factor was ωh = 0.18, having its items share 3.2% in common variance. Bifactor-3-IA/H/I[MVA]’s specific verbal hyperactivity/impulsivity factor was ωh = 0.43 having its items share 18.5% in common variance. The model with the best fit to show good composite reliability was the bifactor-1 model.

When evaluating the different factors, we concluded that both the general ADHD factor and the specific inattention factor had acceptable composite replicability and stability in the bifactor models. Our results suggested that more than two-thirds of the variance was explained by a single common source, i.e., a general ADHD factor, and there was also a substantial proportion of unique variance associated with the inattention factor. However, after partitioning out the variability explained by the general factor, there was little variance remaining that could have been attributed to the (joint or separated) specific hyperactivity and impulsivity factors. The comparison of the bifactor-3 models revealed that “modulating verbal activity” fitted better to the impulsivity than the hyperactivity factor, as suggested in previous research (41).

These findings converge with the results of previous studies exploring the structure of ADHD symptoms in different age groups and across different measures (40, 52, 68) and are in line with the findings of the single study on the SWAN self-report (38). On the other hand, diverging interpretations of similar findings have been formulated by different researchers. Some concluded that the bifactor models support the unidimensionality of ADHD symptoms and suggested using the total score (46). In contrast, Park and colleagues (47) recalled research findings showing different associations of attention deficit and hyperactivity/impulsivity factors with comorbid conditions, cognitive variables, and different domains of functional impairment (69), underlining the importance of different ADHD domains. They have argued that bifactor models only perform better because of their complexity, and in this way, they can be considered statistical artefacts and are not clinically useful. Our results support the notion that the only use of a general ADHD factor would lead to missing important information about the heterogeneity of the phenomenon; the inclusion of a separate inattention factor in the model is at least warranted. On the other hand, similarly to previous findings, our analysis found the hyperactivity and impulsivity factors to be problematic.

On one hand, these results may be related to the questionable validity or at least the low prevalence of predominantly hyperactive/impulsive presentation of ADHD, especially in adolescents and adults when hyperactivity typically decreases (70). For example, in a large sample of adults with ADHD, the prevalence of a clinical diagnosis of ADHD with hyperactive/impulsive presentation was very rare (3%), and this group had ratings of inattention comparable to the group with a clinical diagnosis of ADHD inattentive presentation (71). Furthermore, latent class analysis did not reveal a group with predominantly hyperactivity and impulsivity in children and adolescents aged between 6 and 18 years old (72). These results suggest that hyperactivity/impulsivity symptoms are accompanied by inattention symptoms in the vast majority of the cases, in that way, in factor-analytic studies they would be captured by the general ADHD factor.

On the other hand, DSM-based rating scales follow a relatively simplistic conceptualization of impulsivity as a single domain, however, impulsivity has been defined as a multidimensional construct in previous research (73, 74). Furthermore, all but one symptom of impulsivity in the DSM-5 symptom list (75) is specifically related to the verbal modality, while the assessment of motor impulsivity is very limited. These shortcomings may prevent the definition of an interpretable impulsivity factor in studies using these rating scales.

4.2 The distributional characteristics and the validity of the SWAN self-report

In line with previous research using different versions of the SWAN (30, 38), we found that the distribution of the SWAN self-report scores did not differ significantly from the normal distribution. Our analysis also confirmed the good convergent validity of the SWAN self-report by its very strong correlation with the SDQ Hyperactivity subscale. The correlation was even higher than the correlation of SWAN self-report with the Conners’ Adult ADHD Rating Scale (38) and that of the parent-report versions of the SWAN and the SDQ Hyperactivity subscale (34) in previous research.

Both measures explained a significant proportion of variance in emotional problems and mental health. In the case of the MHC-SF, the predictive power of the SWAN was better than that of the SDQ Hyperactivity. In previous research using the parent-report version, the association of the SWAN and SDQ Emotional problems was somewhat lower than in our study (34), which may be due to the different informants used.

4.3 Limitations

The results can be viewed considering the limitations of the study. The cross-sectional design does not allow us to explore causality. Self-reports of ADHD symptoms and mental health may be biased by social desirability, insight, and cognitive abilities. Our sample consisted solely of women which may be related to the data collection method; young women may be more interested in mental health topics covered by the Mélylevegő Project website than men. Therefore, our results cannot be generalized to the population, future research should address these psychometric properties in more diverse demographic groups. Gender differences have been reported in symptom presentation, prevalence, comorbid profile, and social perception of ADHD symptoms (76, 77). Symptoms of inattention are more likely to be present than hyperactive symptoms in women, sometimes leading to delayed referral and diagnosis (77, 78). However, factor analytic studies reported gender invariance in adolescent (40) and adult (35) samples, suggesting that gender differences might not have a substantial influence on the results. On the other hand, cultural differences have been reported in the structure of ADHD symptoms; more research is needed in different cultural contexts (79).

5 Conclusion

Taken together, our results suggest that the SWAN self-report is a valuable tool for assessing symptoms of ADHD in adolescents and young adults. It has comparably good psychometric properties to measures in the SWAN family.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://osf.io/frvsw/?view_only=b9e81e7a8d824881b32070984b2a0ae6.

Ethics statement

The Institutional Research Ethics Committee of the Psychological Institute Eötvös Loránd University approved the study (Nr. 2022/344). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

KV: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Visualization, Writing – original draft, Writing – review & editing. LP: Conceptualization, Methodology, Writing – review & editing. ST: Methodology, Writing – review & editing, Supervision. AA-V: Methodology, Supervision, Writing – review & editing. JS: Methodology, Supervision, Writing – review & editing. MM: Methodology, Supervision, Writing – review & editing, Conceptualization, Formal analysis, Writing – original draft.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Research, Development, and Innovation Office (Nr. ÚNKP-22–2-I-ELTE-854).

Acknowledgments

The authors thank YouthInMind for providing us the license to use the online version of the SDQ (license number 101833). The authors thank Mélylevegő Projekt (Deep Breath Project) for their help in data collection.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1330716/full#supplementary-material

References

1. APA. Diagnostic and Statistical Manual of Mental Disorders (2023). Available online at: https://www.appi.org/dsm-5-tr.

Google Scholar

2. Salari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, et al. The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Ital J Pediatr. (2023) 49:48. doi: 10.1186/s13052-023-01456-1

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health. (2022) 11:04009. doi: 10.7189/jogh.11.04009

CrossRef Full Text | Google Scholar

4. Katzman MA, Bilkey TS, Chokka PR, Fallu A, Klassen LJ. Adult ADHD and comorbid disorders: clinical implications of a dimensional approach. BMC Psychiatry. (2017) 17:302. doi: 10.1186/s12888-017-1463-3

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Biederman J. Impact of comorbidity in adults with attention-deficit/hyperactivity disorder. J Clin Psychiatry. (2004). 65(Suppl 3):3–7.

Google Scholar

6. McGough JJ, Smalley SL, McCracken JT, Yang M, Del’Homme M, Lynn DE, et al. Psychiatric comorbidity in adult attention deficit hyperactivity disorder: findings from multiplex families. Am J Psychiatry. (2005) 162:1621–7. doi: 10.1176/appi.ajp.162.9.1621

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Matthies S, Philipsen A. Comorbidity of personality disorders and adult attention deficit hyperactivity disorder (ADHD)–review of recent findings. Curr Psychiatry Rep. (2016) 18:33. doi: 10.1007/s11920-016-0675-4

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Schiweck C, Arteaga-Henriquez G, Aichholzer M, Thanarajah SE, Vargas-Cáceres S, Matura S, et al. Comorbidity of ADHD and adult bipolar disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev. (2021) 124:100–23. doi: 10.1016/j.neubiorev.2021.01.017

PubMed Abstract | CrossRef Full Text | Google Scholar

9. van Emmerik-van Oortmerssen K, van de Glind G, van den Brink W, Smit F, Crunelle CL, Swets M, et al. Prevalence of attention-deficit hyperactivity disorder in substance use disorder patients: A meta-analysis and meta-regression analysis. Drug Alcohol Depend. (2012) 122:11–9. doi: 10.1016/j.drugalcdep.2011.12.007

PubMed Abstract | CrossRef Full Text | Google Scholar

10. El Archi S, Barrault S, Brunault P, Ribadier A, Varescon I. Co-occurrence of adult ADHD symptoms and problematic internet use and its links with impulsivity, emotion regulation, anxiety, and depression. Front Psychiatry. (2022) 13:792206. doi: 10.3389/fpsyt.2022.792206

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Fadeuilhe C, Daigre C, Richarte V, Grau-López L, Palma-Álvarez RF, Corrales M, et al. Insomnia disorder in adult attention-deficit/hyperactivity disorder patients: clinical, comorbidity, and treatment correlates. Front Psychiatry. (2021) 12. doi: 10.3389/fpsyt.2021.663889

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Nunez NA, Coombes BJ, Romo-Nava F, Bond DJ, Vande Voort J, Croarkin PE, et al. Clinical and genetic correlates of bipolar disorder with childhood-onset attention deficit disorder. Front Psychiatry. (2022) 13:884217. doi: 10.3389/fpsyt.2022.884217

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Slobodin O, Crunelle CL. Mini review: socio-cultural influences on the link between ADHD and SUD. Front Public Health. (2019) 7:173. doi: 10.3389/fpubh.2019.00173

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Núñez-Jaramillo L, Herrera-Solís A, Herrera-Morales WV. ADHD: reviewing the causes and evaluating solutions. J Pers Med. (2021) 11:166. doi: 10.3390/jpm11030166

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Bonvicini C, Faraone SV, Scassellati C. Attention-deficit hyperactivity disorder in adults: A systematic review and meta-analysis of genetic, pharmacogenetic and biochemical studies. Mol Psychiatry. (2016) 21:872–84. doi: 10.1038/mp.2016.74

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Li JJ, He Q. Polygenic scores for ADHD: A meta-analysis. Res Child Adolesc Psychopathol. (2021) 49:297–310. doi: 10.1007/s10802-021-00774-4

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Nikolas MA, Burt SA. Genetic and environmental influences on ADHD symptom dimensions of inattention and hyperactivity: a meta-analysis. J Abnorm Psychol. (2010) 119:1. doi: 10.1037/a0018010

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Kapteyn JC, van Uven MJ. Skew frequency curves in biology and statistics. 2nd paper. Groningen: Hoitsema Bros (1916). 69 p.

Google Scholar

19. Coghill D, Sonuga-Barke EJS. Annual Research Review: Categories versus dimensions in the classification and conceptualisation of child and adolescent mental disorders – implications of recent empirical study. J Child Psychol Psychiatry. (2012) 53:469–89. doi: 10.1111/j.1469-7610.2011.02511.x

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Swanson JM, Schuck S, Porter MM, Carlson C, Hartman CA, Sergeant JA, et al. Categorical and dimensional definitions and evaluations of symptoms of ADHD: history of the SNAP and the SWAN rating scales. Int J Educ Psychol Assess. (2012) 10:51–70.

PubMed Abstract | Google Scholar

21. Pelham WE, Fabiano GA, Massetti GM. Evidence-based assessment of attention deficit hyperactivity disorder in children and adolescents. J Clin Child Adolesc Psychol. (2005) 34:449–76. doi: 10.1207/s15374424jccp3403_5

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Du Rietz E, Cheung CHM, McLoughlin G, Brandeis D, Banaschewski T, Asherson P, et al. Self-report of ADHD shows limited agreement with objective markers of persistence and remittance. J Psychiatr Res. (2016) 82:91–9. doi: 10.1016/j.jpsychires.2016.07.020

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Dvorsky MR, Langberg JM, Molitor SJ, Bourchtein E. Clinical utility and predictive validity of parent and college student symptom ratings in predicting an ADHD diagnosis. J Clin Psychol. (2016) 72:401–18. doi: 10.1002/jclp.2016.72.issue-4

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Zucker M, Morris MK, Ingram SM, Morris RD, Bakeman R. Concordance of self- and informant ratings of adults’ current and childhood attention-deficit/hyperactivity disorder symptoms. Psychol Assess. (2002) 14:379–89. doi: 10.1037//1040-3590.14.4.379

PubMed Abstract | CrossRef Full Text | Google Scholar

25. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale—IV: Checklists, norms, and clinical interpretation. New York, NY: Guilford press (1998).

Google Scholar

26. Barkley RA. Barkley Adult ADHD Rating Scale-IV (BAARS-IV). New York, NY: Guilford Press (2011).

Google Scholar

27. Conners CK, Erhardt D, Sparrow EP. Conners’ adult ADHD rating scales (CAARS): technical manual. North Tonawanda, NY: Multi-Health Systems (1999).

Google Scholar

28. Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. Int J Methods Psychiatr Res. (2007) 16:52–65. doi: 10.1002/mpr.208

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Mulraney M, Arrondo G, Musullulu H, Iturmendi-Sabater I, Cortese S, Westwood SJ, et al. Systematic review and meta-analysis: screening tools for attention-deficit/hyperactivity disorder in children and adolescents. J Am Acad Child Adolesc Psychiatry. (2022) 61:982–96. doi: 10.1016/j.jaac.2021.11.031

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Lakatos K, Birkás E, Tóth I, Gervai J. Gyermekkori viselkedési problémák felismerésének lehetőségei rövid kérdőívekkel II: A SWAN (Strength and Weakness of ADHD-symptoms and Normal-behavior) skála magyar változata a figyelemhiányos/hiperaktivitás zavar szűrésére. Psych Hung. (2010) 25:493–502.

Google Scholar

31. Schulz-Zhecheva Y, Voelkle M, Beauducel A, Buch N, Fleischhaker C, Bender S, et al. ADHD traits in German school-aged children: validation of the German strengths and weaknesses of ADHS symptoms and normal behavior (SWAN-DE) scale. J Atten Disord. (2019) 23:553–62. doi: 10.1177/1087054716676365

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Arnett AB, Pennington BF, Friend A, Willcutt EG, Byrne B, Samuelsson S, et al. The SWAN captures variance at the negative and positive ends of the ADHD symptom dimension. J Atten Disord. (2013) 17:152–62. doi: 10.1177/1087054711427399

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Robaey P, Amre D, Schachar R, Simard L. French version of the strengths and weaknesses of ADHD symptoms and normal behaviors (SWAN-F) questionnaire. J Can Acad Child Adolesc Psychiatry. (2007) 16:80–9. doi: 10.1037/t77132-000

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Lakes KD, Swanson JM, Riggs M. The reliability and validity of the English and Spanish strengths and weaknesses of ADHD and normal behavior rating scales in a preschool sample: continuum measures of hyperactivity and inattention. J Attention Dis. (2012) 16(6):510516. doi: 10.1177/1087054711413550

CrossRef Full Text | Google Scholar

35. Trejo S, Andaverde-Vega AA, Villalobos-Gallegos L, Swanson JM, Salum GA. Factor structure, measurement invariance, and scoring practices of the strengths and weaknesses of ADHD–symptoms and normal behavior. Psychol Assess. (2022) 3(3):269279. doi: 10.1037/pas0001201

CrossRef Full Text | Google Scholar

36. Hurtig T, Taanila A, Ebeling H, Miettunen J, Moilanen I. Attention and behavioural problems of Finnish adolescents may be related to the family environment. Eur Child Adolesc Psychiatry. (2005) 14:471–8. doi: 10.1007/s00787-005-0492-5

PubMed Abstract | CrossRef Full Text | Google Scholar

37. So CYC, Leung PWL, Hung SF. Treatment effectiveness of combined medication/behavioural treatment with Chinese ADHD children in routine practice. Behav Res Ther. (2008) 46:983–92. doi: 10.1016/j.brat.2008.06.007

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Blume F, Buhr L, Kühnhausen J, Köpke R, Weber LA, Fallgatter AJ, et al. Validation of a self-report version of the German strengths and weaknesses of ADHD symptoms and normal behavior scale (SWAN-DE-SB). Assessment. (2020) 10731911241236699. doi: 10.31234/osf.io/x6bpd

CrossRef Full Text | Google Scholar

39. Nichols JQVA, Shoulberg EK, Garner AA, Hoza B, Burt KB, Murray-Close D, et al. Exploration of the factor structure of ADHD in adolescence through self, parent, and teacher reports of symptomatology. J Abnorm Child Psychol. (2017) 45:625–41. doi: 10.1007/s10802-016-0183-3

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Gomez R. DSM-IV ADHD symptoms self-ratings by adolescents: test of invariance across gender. J Atten Disord. (2013) 17:3–10. doi: 10.1177/1087054711403715

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Gomez R, Liu L, Krueger R, Stavropoulos V, Downs J, Preece D, et al. Unraveling the optimum latent structure of attention-deficit/hyperactivity disorder: evidence supporting ICD and hiTOP frameworks. Front Psychiatry. (2021) 12:666326. doi: 10.3389/fpsyt.2021.666326

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Arias VB, Ponce FP, Núñez Daniel E. Bifactor models of attention-deficit/hyperactivity disorder (ADHD): an evaluation of three necessary but underused psychometric indexes. Assessment. (2018) 25:885–97. doi: 10.1177/1073191116679260

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Arildskov TW, Virring A, Lambek R, Carlsen AH, Sonuga-Barke EJS, Østergaard SD, et al. The factor structure of attention-deficit/hyperactivity disorder in schoolchildren. Res Dev Disabil. (2022) 125:104220. doi: 10.1016/j.ridd.2022.104220

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Martel MM, von Eye A, Nigg JT. Revisiting the latent structure of ADHD: is there a ‘g’ factor? J Child Psychol Psychiatry. (2010) 51:905–14. doi: 10.1111/j.1469-7610.2010.02232.x

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Stanton K, Forbes MK, Zimmerman M. Distinct dimensions defining the Adult ADHD Self-Report Scale: Implications for assessing inattentive and hyperactive/impulsive symptoms. Psychol Assess. (2018) 30:1549–59. doi: 10.1037/pas0000604

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Morin AJ, Tran A, Caci H. Factorial validity of the ADHD Adult Symptom Rating Scale in a French community sample: Results from the ChiP-ARD Study. J Atten Disord. (2016) 20:530–41. doi: 10.1177/1087054713488825

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Park J, Silveira M, Elliott M, Savalei V, Johnston C. Confirmatory factor analysis of the structure of adult ADHD symptoms. J Psychopathol Behav Assess. (2018) 40:573–85. doi: 10.1007/s10862-018-9698-y

CrossRef Full Text | Google Scholar

48. Eid M, Geiser C, Koch T, Heene M. Anomalous results in G-factor models: Explanations and alternatives. Psychol Methods. (2017) 22:541. doi: 10.1037/met0000083

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Burke JD, Johnston OG. The bifactor S-1 model: a psychometrically sounder alternative to test the structure of ADHD and ODD? J Abnorm Child Psychol. (2020) 48:911–5. doi: 10.1007/s10802-020-00645-4

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Burns GL, Geiser C, Servera M, Becker SP, Beauchaine TP. Application of the bifactor S – 1 model to multisource ratings of ADHD/ODD symptoms: an appropriate bifactor model for symptom ratings. J Abnorm Child Psychol. (2020) 48:881–94. doi: 10.1007/s10802-019-00608-4

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Normand S, Flora DB, Toplak ME, Tannock R. Evidence for a general ADHD factor from a longitudinal general school population study. J Abnorm Child Psychol. (2012) 40:555–67. doi: 10.1007/s10802-011-9584-5

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Gomez R, Vance A, Stavropoulos V. Measurement invariance of maternal ratings of ADHD symptoms across clinic referred children’s with and without ADHD. Adv Dev Educ Psychol. (2019) 1:7–17. doi: 10.25082/ADEP.2019.01.002

CrossRef Full Text | Google Scholar

53. Goodman R. Psychometric properties of the strengths and difficulties questionnaire. J Am Acad Child Adolesc Psychiatry. (2001) 40:1337–45. doi: 10.1097/00004583-200111000-00015

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Paulina LR, Csejtei I, Miklosi M. [Attention deficit/hyperactivity symptoms, quality of sleep, and social media use in young adults during the COVID-19 epidemic]. Neuropsychopharmacol Hung. (2023) 25:70–81.

PubMed Abstract | Google Scholar

55. Turi E, Gervai J, Áspán N, Halász J. A Képességek és Nehézségek Kérdőív (SDQ-Magy) validálása serdülőkorú klinikai populációban = Validation of the Hungarian Strengths and Difficulties Questionnaire in an adolescent clinical population. Psychiatr Hung. (2013) 28:165–79.

PubMed Abstract | Google Scholar

56. Keyes CL. Mental illness and/or mental health? Investigating axioms of the complete state model of health. J Consult Clin Psychol. (2005) 73:539. doi: 10.1037/0022-006X.73.3.539

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Reinhardt M, Horváth Z, Morgan A, Kökönyei G. Well-being profiles in adolescence: psychometric properties and latent profile analysis of the mental health continuum model – a methodological study. Health Qual Life Outcomes. (2020) 18:95. doi: 10.1186/s12955-020-01332-0

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Muthén BO. Goodness of fit with categorical and other nonnormal variables. Newbury Park, CA: SAGE Focus Ed (1993) 154: 205–5.

Google Scholar

59. Hair JF, Black WC, Babin BJ. Multivariate Data Analysis: A Global Perspective. New York, NY: Pearson Education (2010). 800 p.

Google Scholar

60. Afthanorhan WMAW. A Handbook on SEM Zainudin Awang -Universiti Sultan Zainal Abidin ANALYZING THE SEM STRUCTURAL MODEL 4.1 STEPS INVOLVED IN PERFORMING THE STRUCTURAL EQUATION MODELING . Available online at: https://www.academia.edu/34981108/A_Handbook_on_SEM_Zainudin_Awang_Universiti_Sultan_Zainal_Abidin_ANALYZING_THE_SEM_STRUCTURAL_MODEL_4_1_STEPS_INVOLVED_IN_PERFORMING_THE_STRUCTURAL_EQUATION_MODELING_SEM.

Google Scholar

61. Steiger JH. Understanding the limitations of global fit assessment in structural equation modeling. Pers Individ Differ. (2007) 42:893–8. doi: 10.1016/j.paid.2006.09.017

CrossRef Full Text | Google Scholar

62. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model Multidiscip J. (1999) 6:1–55. doi: 10.1080/10705519909540118

CrossRef Full Text | Google Scholar

63. Reise SP, Bonifay WE, Haviland MG. Scoring and modeling psychological measures in the presence of multidimensionality. J Pers Assess. (2013) 95:129–40. doi: 10.1080/00223891.2012.725437

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Rodriguez A, Reise SP, Haviland MG. Applying bifactor statistical indices in the evaluation of psychological measures. J Pers Assess. (2016) 98:223–37. doi: 10.1080/00223891.2015.1089249

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Cudeck R, Jöreskog K, Jöreskog KG, Du Toit SHC, Sörbom D eds. Structural equation modeling: present and future ; a festschrift in honor of Karl Jöreskog. In: Rethinking construct reliability within latent variable systems. SSI, Scientific Software International, Lincolnwood, IL. 598 p. (Methodology in the social sciences).

Google Scholar

66. Kyriazos TA. Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology. (2018) 9:2207–30. doi: 10.4236/psych.2018.98126

CrossRef Full Text | Google Scholar

67. Swanson JM, Wigal T, Lakes K, Volkow ND. Attention Deficit Hyperactivity Disorder: Defining A Spectrum Disorder and Considering Neuroethical Implications. In: Illes J, Sahakian BJ, editors. Oxford Handbook of Neuroethics. Oxford: Oxford University Press (2011). doi: 10.1093/oxfordhb/9780199570706.013.0075

CrossRef Full Text | Google Scholar

68. Allan DM, Lonigan CJ. Examination of the structure and measurement of inattentive, hyperactive, and impulsive behaviors from preschool to grade 4. J Abnorm Child Psychol. (2019) 47:975–87. doi: 10.1007/s10802-018-0491-x

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Willcutt EG. The prevalence of DSM-IV attention-deficit/hyperactivity disorder: A meta-analytic review. Neurotherapeutics. (2012) 9:490–9. doi: 10.1007/s13311-012-0135-8

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Faraone SV, Biederman J, Mick E. The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies. Psychol Med. (2006) 36:159–65. doi: 10.1017/S003329170500471X

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Gibbins C, Weiss MD, Goodman DW, Hodgkins PS, Landgraf JM, Faraone SV. ADHD-hyperactive/impulsive subtype in adults. Ment Illn. (2010) 2:41–5. doi: 10.4081/mi.2010.e9

CrossRef Full Text | Google Scholar

72. de Nijs PFA, Ferdinand RF, Verhulst FC. No hyperactive–impulsive subtype in teacher-rated attention-deficit/hyperactivity problems. Eur Child Adolesc Psychiatry. (2007) 16:25–32. doi: 10.1007/s00787-006-0572-1

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Sharma L, Markon KE, Clark LA. Toward a theory of distinct types of “impulsive” behaviors: A meta-analysis of self-report and behavioral measures. Psychol Bull. (2014) 140:374–408. doi: 10.1037/a0034418

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Reynolds B, Ortengren A, Richards JB, de Wit H. Dimensions of impulsive behavior: Personality and behavioral measures. Pers Individ Differ. (2006) 40:305–15. doi: 10.1016/j.paid.2005.03.024

CrossRef Full Text | Google Scholar

75. American Psychiatric Association DS, Association AP. Diagnostic and statistical manual of mental disorders: DSM-5 Vol. 5. Washington, DC: American psychiatric association (2013). doi: 10.1176/appi.books.9780890425596

CrossRef Full Text | Google Scholar

76. Attoe DE, Climie EA. Miss. Diagnosis: A systematic review of ADHD in adult women. J Atten Disord. (2023) 27(7):645–57. doi: 10.1177/10870547231161533

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Williamson D, Johnston C. Gender differences in adults with attention-deficit/hyperactivity disorder: A narrative review. Clin Psychol Rev. (2015) 40:15–27. doi: 10.1016/j.cpr.2015.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Young S, Adamo N, Ásgeirsdóttir BB, Branney P, Beckett M, Colley W, et al. Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identification and treatment of attention-deficit/hyperactivity disorder in girls and women. BMC Psychiatry. (2020) 20:404. doi: 10.1186/s12888-020-02707-9

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Toplak ME, Sorge GB, Flora DB, Chen W, Banaschewski T, Buitelaar J, et al. The hierarchical factor model of ADHD: invariant across age and national groupings? J Child Psychol Psychiatry. (2012) 53:292–303. doi: 10.1111/j.1469-7610.2011.02500.x

PubMed Abstract | CrossRef Full Text | Google Scholar