Next Article in Journal
Targeting Cancer with Paris’ Arrow: An Updated Perspective on Targeting Wnt Receptor Frizzled 7
Previous Article in Journal
Dapagliflozin and Silymarin Ameliorate Cisplatin-Induced Nephrotoxicity via Nrf2/HO-1 Upregulation: A Preclinical Mechanistic Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sense of Coherence Is Associated with Functional Impairment in Individuals Diagnosed with ADHD

1
Paul Baerwald School of Social Work and Social Welfare, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel
2
Seymour Fox School of Education, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel
*
Author to whom correspondence should be addressed.
Submission received: 20 January 2025 / Revised: 4 April 2025 / Accepted: 30 April 2025 / Published: 8 May 2025
(This article belongs to the Section Sports Science and Medicine)

Abstract

:
Individuals diagnosed with ADHD are at a heightened risk of antisocial behaviors, substance abuse, emotional distress, and diminished happiness. Identifying protective factors that reduce the likelihood of these functional impairments in ADHD is vital. Research has shown that a strong sense of coherence (SOC) can serve as a protective factor against various risks and health issues. Therefore, investigating the relationship between SOC and functioning among adults and adolescents with ADHD is essential. A study involving 468 participants aged 15–50 who had reported being diagnosed with ADHD was conducted and analyzed by structural equation modeling. Individuals who reported higher SOC levels also reported lower levels of functional impairment across all domains. This association remained fairly consistent across gender and age groups. The results suggest that SOC is associated with reduced functional impairments in individuals with ADHD. Recognizing this association can aid in identifying those at higher risk within the ADHD population. Further research is warranted to test the causal relationship between SOC and functional impairment and explore potential therapeutic approaches to bolster SOC in individuals with ADHD.

1. Introduction

ADHD is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity [1]. ADHD in children and adolescents persists into adulthood at significant rates, continuing to cause various functional impairments [2,3]. The correlation between ADHD and unfavorable outcomes, such as criminal behavior and negative life events, remains even after controlling for confounding variables and risk factors [4,5,6]. Despite being a distinct risk factor, outcomes are not the same among all those diagnosed with ADHD [7,8]. Tools are needed to help identify subgroups within this population that are at increased risk of poor outcomes. Additionally, in the research literature, much is known about risk factors for ADHD functional impairments, though only a little is known about protective factors [9,10,11,12,13,14,15,16,17,18]. The present study focuses on examining the contribution of the protective factor “sense of coherence” in identifying those with ADHD who are at low risk of a variety of functional impairments, including antisocial behavior, substance abuse, emotional distress, and low happiness, among different age and gender groups.

1.1. ADHD, Antisocial Behavior, and Emotional Distress

Numerous studies have indicated that ADHD is associated with antisocial behavior and delinquency (e.g., [6,19,20]). These studies have demonstrated that compared to individuals without ADHD, adolescents and adults, both men and women, with ADHD are more often involved in the criminal justice system. Thus, they are more likely to be arrested, convicted, and incarcerated and to be so at an earlier age. They also show an increased risk of criminal recidivism [21].
ADHD is also associated with various forms of substance abuse, including tobacco and marijuana smoking, alcohol drinking, and illicit drug use [2,22,23,24]. Several meta-analyses have linked childhood ADHD to abuse and dependence on nicotine, alcohol, marijuana, cocaine, and other substances [25,26,27].
In addition to its association with delinquency and substance abuse, ADHD was found to be a risk factor for emotional dysregulation and distress [16,28,29]. Relatedly, individuals with ADHD have a higher tendency for comorbid mental health disorders, including anxiety, depression, behavioral disorders, and personality disorders [30,31]. Finally, as well-being and happiness are considered the ultimate goal of health [32], it is notable that ADHD is also associated with low well-being and happiness [33,34,35].

1.2. Protective and Risk Factors Predicting ADHD-Related Functional Impairment

Although ADHD is a neurodevelopmental disorder, its presentation and functional impairment depend on various environmental, personality, cognitive, and social factors [36] (Weissenberger et al., 2017). Previous studies have examined risk and protective factors that correlate with ADHD-related functional impairment in the domains on which this study focuses: antisocial behaviors and mental health problems (e.g., [22,37,38]).

1.2.1. Factors Predicting ADHD-Related Antisocial Behavior

Various variables predicting the risk of developing antisocial behavior patterns among people with ADHD have been examined in the research literature. For example, ADHD symptom severity, deviant peer affiliation, and low non-verbal intelligence were all associated with higher antisocial behavior [37,38,39]. Similarly, agreeable personality, religiosity, and targeted pharmacotherapy were found as factors that decrease this risk [14,20,21,40,41,42].

1.2.2. Factors Predicting ADHD-Related Mental Health Problems

Various variables predicting levels of distress and happiness among people with ADHD have been examined in the research literature [13]. For example, being married, being physically active, and using spirituality to cope with challenges were positively associated with complete mental health among adults with ADHD. Complete mental health was defined as an absence of mental illness and substance dependence and a presence of happiness and well-being [13]. Additionally, adverse childhood experiences, debilitating pain, and a history of depression and anxiety were negatively associated with complete mental health [13]. Moreover, higher education and work participation were related to a lower probability of comorbid psychiatric disorders in a clinical sample of adults with ADHD [30].

1.3. Sense of Coherence

The Salutogenic Model of Health assumes that health is under constant threat from various risk factors, thus focusing on factors contributing to defense mechanisms that help to generate health [43,44]. This model offers the concept of the “sense of coherence” (SOC) as a protective factor for various risk situations, including diseases and disorders [45]. SOC consists of three components (usually treated as a unified concept): Comprehensibility (the cognitive component)—the degree to which one perceives the world as logical, consistent, and predictable; Meaningfulness (the emotional component)—the degree to which one sees one’s life as meaningful and worthy of effort; Manageability (the behavioral component)—the degree to which one views oneself as competent and capable of influencing reality [44,46]. The theory contends that individuals with high SOC, who see their lives as logical, meaningful, and manageable, are more resistant to various risk factors.
Numerous studies have found associations between high SOC and physical, mental, social, and behavioral health [47,48,49]. Some studies have demonstrated an association between an increased SOC and improved emotional state among patients with cancer [50]. It was also shown that interventions that increased SOC effectively reduced disease symptoms [51]. Similarly, a few studies have pointed out the relationship between SOC and normative behavior [52]. For example, it was found that poor SOC was associated with a larger number of criminal offenses in young males and with recidivism [53,54]. High SOC was associated with decreased antisocial behavior, including violent behavior, smoking, and drinking [55,56]. Moreover, a large-sample study showed that patterns of substance use in peer groups predicted the substance use of adolescents better in participants with low SOC than in participants with high SOC [57], suggesting that SOC is a moderator of the link between risk factors and antisocial behavior. The present study aims to examine the extent to which SOC is associated with a higher level of functioning in people diagnosed with ADHD.

1.4. SOC as a Protective Factor Predicting ADHD-Related Functional Impairment

In a recent study we conducted among general population children and adults, it was found that SOC moderated the association between ADHD symptoms and antisocial behavior, such that for people with high levels of SOC, ADHD symptoms were a weaker risk factor for antisocial behavior [58]. However, high levels of ADHD symptoms are not equivalent to an ADHD diagnosis since an ADHD diagnosis relies on the presence of both ADHD symptoms and functional impairment [59]. Therefore, in the present study, we first aimed to examine whether SOC is associated with lower levels of antisocial behavior among people diagnosed with ADHD.
Another line of inquiry for the present study focuses on whether SOC predicts better mental health and happiness. As reviewed earlier, high SOC is associated with better mental health and well-being in both healthy people and patients [50,60]. However, the role of SOC in predicting mental health and happiness among individuals with ADHD has not been studied yet.
Moreover, studies that measured SOC across different age and gender groups found that the measurement model remains consistent [61]. SOC is known to be present in the individual from a young age and continues to develop over the years [62]. Additionally, SOC is a resilience factor that was found to predict mental health even from adolescence [63]. Hence, another aim of the present study is to examine whether the protective role of coherence varies among different age and gender groups.
To summarize, individuals diagnosed with ADHD are at risk of functional impairments. Previously, we reported that SOC moderates the association between ADHD symptoms and antisocial behavior in the general population. In light of previous findings that have demonstrated the protective role of SOC against antisocial and mental health problems in clinical populations, the current study sought to examine three hypotheses that have not been tested yet: a. whether SOC predicts decreased delinquency among people who have been diagnosed with ADHD; b. whether SOC predicts better mental health among that population; and c. whether the protective role of SOC is stable across different age and gender groups.

2. Materials and Methods

2.1. Participants

For the current study, a subsample was selected from a sample used in a previous study [58]. The study’s participants were recruited by convenience sampling, using an online questionnaire distributed on social networks in Israel in October–November 2020. Three thousand four hundred fifteen participants between the ages of 15 and 50 responded to the questionnaire. Four hundred fifty-five participants were excluded since they did not fully answer the questionnaires that referred to the main variables: ADHD, delinquency, and SOC. From the remaining 2960 participants, a subsample of those who responded “yes” to the question “Have you ever been diagnosed with a diagnosis of attention-deficit/hyperactivity disorder (ADHD)?” was selected for the current study, resulting in 486 participants included in the statistical analysis. For analysis purposes, participants were grouped into three age groups: adolescence (aged below 18), emergent adulthood (aged between 18 and 29), and adulthood (aged above 29). Table 1 summarizes the demographic characteristics of the sample.

2.2. Measures

2.2.1. Antisocial Behavior

Delinquency was measured using the Self-Report Delinquency (SRD) scale [64]. In its full version, the scale includes 47 items that describe illegal or non-normative actions, and the respondent is requested to indicate the number of times these actions were performed in the last year. The scale ranges from 1 = “Never” to 5 = “More than 10 times”. The test–retest reliability of the original scale was high (r = 0.8–0.99). The scale was translated into Hebrew using the back-translation method; the internal consistency of the Hebrew version was high (Cronbach’s α = 0.908) [65]. The scale included 27 items that referred to the following measures: violent crimes (15 items: 4 on serious physical assaults (ω = 0.729), 4 on mild physical assaults (ω = 0.769), 4 on verbal assaults (ω = 0.790), and 3 on indirect violence (ω = 0.559)); property crimes (5 items, ω = 0.784); crimes against public order (3 items, ω = 0.738); and cybercrimes (4 items, ω = 0.623). As the internal consistency of the indirect violence scale was poor, this scale was not further analyzed.
Substance use was measured using a scale developed by Johnston, O’Malley, and Bachman [66], the Hebrew translation of which is widely used in Israel [67]. Participants were asked to indicate their substance use in the last year. Specifically, they rated their use of cannabis (two items), alcohol (two items), and cigarette smoking (one item) on a 7-point Likert scale (0 = never to 5 = 30 times or more). The internal consistency of the scale was high (ω = 0.756).

2.2.2. Mental Health

Emotional distress was measured by the Strengths and Difficulties Questionnaire, which probes emotional and social adaptation (SDQ) [68]. The questionnaire consists of 25 items comprising 5 subscales. The scale ranges from 1 = “Not true” to 3 = “Certainly true”. We used the emotional problems subscale as a covariate in the present study. The scale was found to be valid and reliable for children [69] and adults [70]. In the current study, the internal consistency of the scale was high (ω = 0.727).
Happiness was assessed using a single question: “In general, how do you feel about your life at present?”. Predefined response categories were: “I feel very happy” (15.7% of responses), “I feel quite happy” (45.1%), “I don’t feel very happy” (31.9%), and “I am not happy at all” (7.3%) [71].

2.2.3. Sense of Coherence

SOC was measured using the 13-item short version of the original Antonovsky’s scale [43]. The 13 items were found to be highly correlated with items in the original long version of the scale [44]. The scale ranges from 1 = “Seldom or never” to 5 = “Very often”. This scale has a high internal consistency (α = 0.83) and validity [43,44]. The internal consistency in the current study was also high (ω = 0.888). The scale consists of three components (comprehensibility, meaningfulness, and manageability), but the scale’s 13 items were analyzed as one variable, as suggested [46].

2.2.4. ADHD Symptoms

The Hebrew version of the Adult ADHD Self-Report Scale (ASRS-v1.1) was used to measure the level of ADHD symptoms [29,72], with a scale ranging from 1 = “Never” to 5 = “Very often”. In order to measure positive attention and impulse regulation behaviors in the general population, the wording of the 18 ADHD symptoms was phrased positively, in a similar manner to the SWAN questionnaire [73]. The internal consistency of the scale was good (w = 0.887). The convergent validity of the scale was confirmed by a strong correlation (r = 0.655) with the Hyperactivity Problems subscale of the Strengths and Difficulties Questionnaire.

2.2.5. Socio-Demographic Characteristics

The participants filled out a socio-demographic questionnaire that included variables related to antisocial behavior. For the sake of the current study, we used as covariates only the variables that are comparable between adolescents and adults, i.e., age and gender. Age was categorized as adolescence (15–17), emergent adulthood (18–29), and adulthood (30–50).

2.3. Analytic Approach

The overall score of each scale was obtained by averaging the items of each scale for each respondent. We computed the descriptive statistics of the dependent variables, independent variables, and covariates, as well as the first-order non-parametric correlations among the variables. We applied a path analysis using the R lavaan package with a maximum-likelihood estimation method [74] to examine the research hypotheses. Path analysis has several advantages over a series of regressions (e.g., the PROCESS macro for mediation by Andrew F. Hayes [75]), as it can simultaneously test multiple hypotheses regarding multiple dependent variables in a single model and thus avoid alpha inflation. Another advantage of path analysis is its ability to treat missing data using the full information maximum likelihood. We therefore modeled SOC as a predictor of the four outcome measures. The level of ADHD symptoms, age group (adolescent/emergent adult/adult), and gender (man/woman) were modeled as covariates. The four predictors and the four dependent variables were allowed to covary to account for their potential associations. Model fit was assessed using the chi-square (χ2) test, the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR) [76]. We tested the significance of the parameter estimates using a 95% CI based on 5000 bias-corrected and accelerated bootstrap samples, which are more robust to violating distributional assumptions.
To further explore whether the effect of coherence is stable across age and gender groups, we used a multi-group analysis in path analysis. Specifically, we contrasted two models, one in which the effect was freely estimated in the three age groups (or two genders) and the second in which the effects in the age groups (or two genders) were constrained to equality. A significant chi-square difference between the two models (free vs. constrained) indicates that age/gender further modifies the effect of SOC. In contrast, a nonsignificant chi-square difference suggests that the effect of SOC is equivalent across age/gender groups. To identify which paths differ between groups, we examined the differences between groups in parameter estimates in the unconstrained models.

3. Results

3.1. Descriptive Statistics

The distributions of the demographic and study variables were as follows: in the sample, 217 individuals were women (45.1%). The mean age was 23.62 ± 8.47; 133 participants were adolescents (27.4%). The means and standard deviations of the study variables are summarized in Table 1.

3.2. Correlations

As the distribution of part of the study variables was assumed not to be normal (e.g., delinquency), the correlations were analyzed using non-parametric tests. Spearman’s rho correlation coefficients were computed between all the study variables and are summarized in Table 2. Of note, SOC correlated positively with happiness and negatively with ADHD symptoms, delinquency, and emotional problems, but not with drug use.

3.3. Path Analysis

We used path analysis to examine the effects of SOC on the outcome measures. The theoretical model, containing age and gender groups as covariates, is a saturated model and, therefore, perfectly fits the data: χ2(0) = 0, CFI = 0.1, TLI = 0.1, RMSEA = 0, SRMR = 0. SOC significantly predicted all four outcome measures: higher SOC was associated with lower delinquency, drug use, and emotional problems and with higher happiness levels. In addition, high levels of ADHD symptoms were associated with more delinquency and emotional problems; young age was significantly associated with less delinquency and more drug use; and being a woman was significantly associated with less delinquency, drug use, and happiness and with more emotional problems. Figure 1 summarizes the pathways represented by standardized coefficients.
To examine whether the effect of SOC is stable across age groups, we contrasted two models, one in which the effects were freely estimated in the two age groups and the second in which the effects in the age groups were constrained to equality. The constrained model had an acceptable fit (χ2(24) = 39.316, p = 0.025, CFI = 0.977, TLI = 0.941, RMSEA = 0.063, SRMR = 0.053). A significant chi-square difference between the two models (free vs. constrained) indicated that age further modifies the effect of SOC (χ2(24) = 39.136, p = 0.025). To identify which paths differ between the age groups, we examined the differences in parameter estimates in the unconstrained model. The following differences in estimates were significantly different: higher ADHD symptoms were more strongly associated with delinquency in adolescence than in adulthood, being a man was more strongly associated with higher delinquency in adolescence than in adulthood, being a man was more strongly associated with higher drug use in adolescence and emergent adulthood than in adulthood, and being a woman was associated with lower happiness only in emergent adulthood. No significant differences were found in the effects of SOC on any of the outcome measures.
To examine whether the effect of SOC is stable across gender groups, we contrasted two models, one in which the effects were freely estimated in the two gender groups and a second in which the effects in the gender groups were constrained to equality. The constrained model had an acceptable fit (χ2(12) = 21.802, p = 0.040, CFI = 0.985, TLI = 0.947, RMSEA = 0.058, SRMR = 0.040). A significant chi-square difference between the two models (free vs. constrained) indicated that age further modifies the effect of SOC (χ2(12) = 21.802, p = 0.040). To identify which paths differ between the gender groups, we examined the differences in parameter estimates in the unconstrained model. The following differences in estimates were significantly different: higher age was associated with higher drug use only in women, and lower age was more strongly associated with higher delinquency in men than in women. SOC was associated with delinquency in both genders, though marginally more strongly in men than in women.

4. Discussion

This study examined the variation in the severity of functional impairment in relation to SOC levels in the population of young and adult individuals diagnosed with ADHD. It was found that individuals who reported high SOC levels also reported less antisocial behavior, less substance abuse, less emotional distress, and more happiness. Although ADHD symptoms are negatively associated with resilience in general and with SOC in particular [58], SOC remains an independent predictor of social and emotional functioning among individuals with ADHD. Multi-group analyses revealed that the associations between SOC and functional domains were similar among men and women and among adolescents and adults.
Although the contribution of SOC to coping with various diseases and risk factors is well established in the literature, there is a lack of research regarding ADHD. In a previous study, we found that SOC moderated the link between ADHD symptoms and antisocial behavior in the general population [58]. The current study employed a subset of the same database to examine additional issues. First, diagnosed and non-diagnosed people are two different populations in terms of functional impairment, as functional impairment is necessary for diagnosing ADHD. Hence, whether SOC continues to relate to lower functional impairment even in a subsample consisting of individuals with an ADHD diagnosis, i.e., who have significant functional impairment, remained an open question. Second, in the current study, functional impairment was also studied in the mental health domain, not only the antisocial domain.
Considering the high rates of ADHD diagnosis and the serious functional impairments attributed to the disorder, it is important to develop tools that will help target subgroups that are at greater risk of developing poor functional outcomes. The current study found that examining the level of SOC can significantly predict who has likely developed poor functional outcomes.
Previous studies have indicated that the association of poor functional outcomes with ADHD depends on additional background variables. The current study focused on SOC, an early personality factor [77]. Additionally, some studies have focused on factors less amenable to intervention, such as past childhood experiences, the level of intelligence, and religiosity [38,39,77]. In the current study, the focus was on an amenable protective factor that has been proven to be effective in other diseases (for a review, see [78]) and can also be applied in the context of ADHD.
These findings are consistent with the research literature demonstrating that although ADHD is a major risk factor, it is not exclusive in causing the results attributed to it, and protective factors can significantly reduce functional impairments. This approach corresponds to the WHO’s International Classification of Functioning, Disability and Health (ICF) manual [79]. The ICF distinguishes between several concepts: impairments—problems in body function and structure, such as significant deviation or loss; activity limitations—difficulties an individual may have in executing activities; and participation restrictions—problems an individual may experience in involvement in life situations. The individual’s functioning and health consist of the interactions between these three components and personal and developmental factors [79].
The ICF model has been implemented in previous studies of ADHD [80,81]. In these implementations, ADHD symptoms comprise impairments in body function, and the academic and social problems comprise the activity limitations and the participation restrictions. Personal and family factors were found to protect against the risk of academic and social problems. For example, it was found that among children with ADHD, family coherence and community support show protective effects on academic and social outcomes [10]. The current study suggests that the ICF model can also be implemented in the context of ADHD and other functional domains, such as antisocial behavior and emotional problems. Importantly, this implementation implies that the association between ADHD and antisocial behavior is not inevitable but rather depends on risk and protective factors. Specifically, the current study suggests that the personal factor SOC interacts with ADHD symptoms, reducing the risk of functional impairment.

4.1. Limitations

Notably, since this research is cross-sectional, one cannot infer the directionality and causality of the link between SOC and functional impairment, and these issues need to be examined using longitudinal and interventional designs. The findings of previous longitudinal studies indicate that SOC constitutes a protective factor for other outcome variables. For example, it was found that a strong SOC moderates the risk of long-term ADHD persistence [82]. In addition, the phrasing of the items in the present study may suggest that higher levels of SOC preceded the decrease in antisocial behavior and distress. While participants were requested to refer to a general SOC in their life, they were requested to refer to the previous year on the delinquency scale and emotional distress scales, the previous month on the substance use scale, and those days on the happiness scale.
The measuring of antisocial behavior relied on participants’ self-reporting without collateral support from other sources, such as partners’ reports or criminal records. However, it allows antisocial behaviors to be referred to that are not documented in criminal records. Notably, criminal records might contain a certain rate of false convictions due to false confessions, which are more prevalent among individuals with ADHD [83].
Another limitation is that the study examined ADHD symptoms in the past six months. Referring only to adulthood ADHD symptoms might supply a partial picture since ADHD symptoms tend to be less conspicuous in adults [84]. Additionally, measuring ADHD symptoms using only self-reporting tends to lessen the actual level of ADHD symptoms [77]. Finally, the study was conducted during a global pandemic, which might have influenced the level and severity of antisocial behavior [85].

4.2. Research Contribution and Recommendations

The main contribution of this study is showing that ADHD constitutes a risk factor for antisocial behavior and mental health problems, depending on other factors, as implicated by the ICF model [79]. The study suggests that the antisocial behavior and mental health problems stemming from ADHD are a product of the interaction between ADHD symptoms and a personal factor, namely SOC. This finding is consistent with the growing trend in the research literature calling for the examination of resilience factors for the risk of functional impairments associated with ADHD [9,10,11,12,13,14,15,16,17,18].
These findings have several implications for research, policy, and clinical practice. Assuming that the findings are repeated in a longitudinal study, it will be possible to use a SOC questionnaire to estimate the participants’ prognosis and detect those at increased risk who require more intensive intervention. This also has implications for public health policy since by measuring SOC levels, it is possible, with a relatively small investment (short and accessible self-report questionnaire for children, adolescents, and adults), to locate among the ADHD-diagnosed population those subgroups that are at an excess risk of developing emotional or social distress and to focus the intervention efforts on them.
In addition, it was suggested that new theoretical characterizations of ADHD should be developed and validated to provide novel treatment design opportunities [86]. This study suggests that SOC is an important factor associated with functional outcomes in ADHD and invites consideration of new forms of intervention with an emphasis on increasing SOC. The research findings suggest that interventions that increase SOC may be beneficial for people with ADHD, as has been demonstrated in the context of other medical and social conditions, such as patients with diabetes and receiving dialysis, ICU survivors, and older people in the community [51,87,88,89]. In light of the research findings, there is a need to develop and test an intervention study targeting SOC among individuals with ADHD.

Author Contributions

Conceptualization, H.D., M.K.-K. and Y.P.; methodology, H.D., M.K.-K. and Y.P.; writing—original draft preparation, H.D.; writing—review and editing, M.K.-K. and Y.P.; supervision, M.K.-K. and Y.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The research was conducted in accordance with internationally accepted ethical guidelines and was approved by the ethics committee of The Paul Baerwald School of Social Work and Social Welfare at the Hebrew University of Jerusalem (Ethical Approval Reference Number: 15052020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available because they contain self-reported criminal behavior.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Faraone, S.V.; Banaschewski, T.; Coghill, D.; Zheng, Y.; Biederman, J.; Bellgrove, M.A.; Newcorn, J.H.; Gignac, M.; Saud, N.M.A.; Manor, I.; et al. The world federation of ADHD international consensus statement: 208 evidence-based conclusions about the disorder. Neurosci. Biobehav. Rev. 2021, 128, 789–818. [Google Scholar] [CrossRef] [PubMed]
  2. Di Lorenzo, R.; Balducci, J.; Poppi, C.; Arcolin, E.; Cutino, A.; Ferri, P.; D’Amico, R.; Filippini, T. Children and adolescents with ADHD followed up to adulthood: A systematic review of long-term outcomes. Acta Neuropsychiatr. 2021, 33, 283–298. [Google Scholar] [CrossRef] [PubMed]
  3. Kooij, J.J.S.; Bijlenga, D.; Salerno, L.; Jaeschke, R.; Bitter, I.; Balazs, J.; Thome, J.; Dom, G.; Kasper, S.; Filipe, C.N.; et al. Updated European Consensus Statement on diagnosis and treatment of adult ADHD. Eur. Psychiatry 2019, 56, 14–34. [Google Scholar] [CrossRef] [PubMed]
  4. Dayan, H.; Shoham, R.; Berger, I.; Khoury-Kassabri, M.; Pollak, Y. Features of attention deficit/hyperactivity disorder and antisocial behaviour in a general population-based sample of adults. Crim. Behav. Ment. Health 2023, 33, 172–184. [Google Scholar] [CrossRef]
  5. Garcia, C.R.; Bau, C.H.D.; Silva, K.L.D.; Callegari-Jacques, S.M.; Salgado, C.A.I.; Fischer, A.G.; Victor, M.M.; Sousa, N.O.; Karam, R.G.; Rohde, L.A.; et al. The burdened life of adults with ADHD: Impairment beyond comorbidity. Eur. Psychiatry 2012, 27, 309–313. [Google Scholar] [CrossRef]
  6. Mohr-Jensen, C.; Bisgaard, C.M.; Boldsen, S.K.; Steinhausen, H.C. Attention-deficit/hyperactivity disorder in childhood and adolescence and the risk of crime in young adulthood in a Danish nationwide study. J. Am. Acad. Child Adolesc. Psychiatry 2019, 58, 443–452. [Google Scholar] [CrossRef]
  7. Schubiner, H.; Katragadda, S. Overview of epidemiology, clinical features, genetics, neurobiology, and prognosis of adolescent attention-deficit/hyperactivity disorder. Adolesc. Med. State Art Rev. 2008, 19, 209–215. [Google Scholar]
  8. Spencer, T.J.; Biederman, J.; Mick, E. Attention-deficit/hyperactivity disorder: Diagnosis, lifespan, comorbidities, and neurobiology. J. Pediatr. Psychol. 2007, 32, 631–642. [Google Scholar] [CrossRef]
  9. Chan, E.S.; Groves, N.B.; Marsh, C.L.; Miller, C.E.; Richmond, K.P.; Kofler, M.J. Are there resilient children with ADHD? J. Atten. Disord. 2021, 26, 643–655. [Google Scholar] [CrossRef]
  10. Duh-Leong, C.; Fuller, A.; Brown, N.M. Associations between family and community protective factors and attention-deficit/hyperactivity disorder outcomes among US children. J. Dev. Behav. Pediatr. 2020, 41, 1–8. [Google Scholar] [CrossRef]
  11. Dvorsky, M.R.; Langberg, J.M. A review of factors that promote resilience in youth with adhd and adhd symptoms. Clin. Child Fam. Psychol. Rev. 2016, 19, 368–391. [Google Scholar] [CrossRef] [PubMed]
  12. Freire, J.V.C.; Cerqueira, R.A.; Sousa, D.F.; Novaes, J.F.A.; Farias, T.M.; Cal, S.F.M. Resilience and ADHD: What is New. Res. Trends Chall. Med. Sci. 2021, 9, 1–15. [Google Scholar]
  13. Fuller-Thomson, E.; Ko, B.K.; Carrique, L.; MacNeil, A. Flourishing Despite Attention-Deficit Hyperactivity Disorder (ADHD): A Population Based Study of Mental Well-Being. Int. J. Appl. Posit. Psychol. 2022, 7, 227–250. [Google Scholar] [CrossRef]
  14. Giannotta, F.; Rydell, A.M. The prospective links between hyperactive/impulsive, inattentive, and oppositional-defiant behaviors in childhood and antisocial behavior in adolescence: The moderating influence of gender and the parent–child relationship quality. Child Psychiatry Hum. Dev. 2016, 47, 857–870. [Google Scholar] [CrossRef]
  15. Jia, R.M.; Mikami, A.Y.; Normand, S. Social Resilience in Children with ADHD: Parent and Teacher Factors. J. Child Fam. Stud. 2021, 30, 839–854. [Google Scholar] [CrossRef]
  16. Lee, Y.C.; Yang, H.J.; Chen, V.C.H.; Lee, W.T.; Teng, M.J.; Lin, C.H.; Gossop, M. Meta-analysis of quality of life in children and adolescents with ADHD: By both parent proxy-report and child self-report using PedsQL™. Res. Dev. Disabil. 2016, 51, 160–172. [Google Scholar] [CrossRef]
  17. Lesch, K.P. ‘shine bright like a diamond!’: Is research on high-functioning adhd at last entering the mainstream? J. Child Psychol. Psychiatry 2018, 59, 191–192. [Google Scholar] [CrossRef]
  18. Schoenfelder, E.N.; Kollins, S.H. Topical review: ADHD and health-risk behaviors: Toward prevention and health promotion. J. Pediatr. Psychol. 2016, 41, 735–740. [Google Scholar] [CrossRef]
  19. Pollak, Y.; Shoham, R.; Dayan, H.; Gabrieli-Seri, O.; Berger, I. Symptoms of ADHD Predict Lower Adaptation to the COVID-19 Outbreak: Financial Decline, Low Adherence to Preventive Measures, Psychological Distress, and Illness-Related Negative Perceptions. J. Atten. Disor. 2022; Advance online publication. [Google Scholar] [CrossRef]
  20. Retz, W.; Ginsberg, Y.; Turner, D.; Barra, S.; Retz-Junginger, P.; Larsson, H.; Asherson, P. Attention-deficit/hyperactivity disorder (adhd), antisociality and delinquent behavior over the lifespan. Neurosci. Biobehav. Rev. 2020, 120, 236–248. [Google Scholar] [CrossRef]
  21. Young, S.; Cocallis, K. ADHD and offending. J. Neural Transm. 2021, 128, 1009–1019. [Google Scholar] [CrossRef]
  22. Fuller-Thomson, E.; Lewis, D.A.; Agbeyaka, S. Attention-Deficit/Hyperactivity Disorder and Alcohol and Other Substance Use Disorders in Young Adulthood: Findings from a Canadian Nationally Representative Survey. Alcohol Alcohol. 2022, 57, 385–395. [Google Scholar] [CrossRef] [PubMed]
  23. Oliva, F.; Mangiapane, C.; Nibbio, G.; Berchialla, P.; Colombi, N.; Vigna-Taglianti, F.D. Prevalence of cocaine use and cocaine use disorder among adult patients with attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. J. Psychiatr. Res. 2021, 143, 587–598. [Google Scholar] [CrossRef] [PubMed]
  24. Zulauf, C.A.; Sprich, S.E.; Safren, S.A.; Wilens, T.E. The complicated relationship between attention deficit/hyperactivity disorder and substance use disorders. Curr. Psychiatry Rep. 2014, 16, 1–11. [Google Scholar] [CrossRef]
  25. Lee, S.S.; Humphreys, K.L.; Flory, K.; Liu, R.; Glass, K. Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: A meta-analytic review. Clin. Psychol. Rev. 2011, 31, 328–341. [Google Scholar] [CrossRef]
  26. Charach, A.; Yeung, E.; Climans, T.; Lillie, E. Childhood attention-deficit/hyperactivity disorder and future substance use disorders: Comparative meta-analyses. J. Am. Acad. Child Adolesc. Psychiatry 2011, 50, 9–21. [Google Scholar] [CrossRef] [PubMed]
  27. Groenman, A.P.; Janssen, T.W.; Oosterlaan, J. Childhood psychiatric disorders as risk factor for subsequent substance abuse: A meta-analysis. J. Am. Acad. Child Adolesc. Psychiatry 2017, 56, 556–569. [Google Scholar] [CrossRef]
  28. Hirsch, O.; Chavanon, M.; Riechmann, E.; Christiansen, H. Emotional dysregulation is a primary symptom in adult Attention-Deficit/Hyperactivity Disorder (ADHD). J. Affect. Disord. 2018, 232, 41–47. [Google Scholar] [CrossRef]
  29. Kessler, R.C.; Adler, L.; Barkley, R.; Biederman, J.; Conners, C.K.; Demler, O.; Faraone, S.V.; Greenhill, L.L.; Howes, M.J.; Secnik, K.; et al. The prevalence and correlates of adult ADHD in the United States: Results from the National Comorbidity Survey Replication. Am. J. Psychiatry 2006, 163, 716–723. [Google Scholar] [CrossRef]
  30. Anker, E.; Bendiksen, B.; Heir, T. Comorbid psychiatric disorders in a clinical sample of adults with ADHD, and associations with education, work and social characteristics: A cross-sectional study. BMJ Open 2018, 8, e019700. [Google Scholar] [CrossRef]
  31. Ottosen, C.; Larsen, J.T.; Faraone, S.V.; Chen, Q.; Hartman, C.; Larsson, H.; Petersen, L.; Dalsgaard, S. Sex differences in comorbidity patterns of attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 2019, 58, 412–422. [Google Scholar] [CrossRef]
  32. Howell, R.T.; Kern, M.L.; Lyubomirsky, S. Health benefits: Meta-analytically determining the impact of well-being on objective health outcomes. Health Psychol. Rev. 2007, 1, 83–136. [Google Scholar] [CrossRef]
  33. Agarwal, R.; Goldenberg, M.; Perry, R.; IsHak, W.W. The quality of life of adults with attention deficit hyperactivity disorder: A systematic review. Innov. Clin. Neurosci. 2012, 9, 10–21. [Google Scholar] [PubMed]
  34. Pinho, T.D.; Manz, P.H.; DuPaul, G.J.; Anastopoulos, A.D.; Weyandt, L.L. Predictors and moderators of quality of life among college students with ADHD. J. Atten. Disord. 2019, 23, 1736–1745. [Google Scholar] [CrossRef] [PubMed]
  35. Stickley, A.; Koyanagi, A.; Takahashi, H.; Ruchkin, V.; Inoue, Y.; Yazawa, A.; Kamio, Y. Attention-deficit/hyperactivity disorder symptoms and happiness among adults in the general population. Psychiatry Res. 2018, 265, 317–323. [Google Scholar] [CrossRef]
  36. Weissenberger, S.; Ptacek, R.; Klicperova-Baker, M.; Erman, A.; Schonova, K.; Raboch, J.; Goetz, M. ADHD, lifestyles and comorbidities: A call for an holistic perspective–from medical to societal intervening factors. Front. Psychol. 2017, 8, 454. [Google Scholar] [CrossRef]
  37. Breuer, D.; von Wirth, E.; Mandler, J.; Schürmann, S.; Döpfner, M. Predicting delinquent behavior in young adults with a childhood diagnosis of ADHD: Results from the Cologne Adaptive Multimodal Treatment (CAMT) Study. Eur. Child Adolesc. Psychiatry 2022, 31, 553–564. [Google Scholar] [CrossRef]
  38. Thapar, A.; Van den Bree, M.; Fowler, T.; Langley, K.; Whittinger, N. Predictors of antisocial behaviour in children with attention deficit hyperactivity disorder. Eur. Child Adolesc. Psychiatry 2006, 15, 118–125. [Google Scholar] [CrossRef]
  39. García, B.H.; Vázquez, A.L.; Moses, J.O.; Cromer, K.D.; Morrow, A.S.; Villodas, M.T. Risk for substance use among adolescents at-risk for childhood victimization: The moderating role of ADHD. Child Abus. Negl. 2021, 114, 104977. [Google Scholar] [CrossRef]
  40. Dew, R.E.; Kollins, S.H.; Koenig, H.G. ADHD, religiosity, and psychiatric comorbidity in adolescence and adulthood. J. Atten. Disord. 2022, 26, 307–318. [Google Scholar] [CrossRef]
  41. Novis-Deutsch, N.; Dayan, H.; Pollak, Y.; Khoury-Kassabri, M. Religiosity as a moderator of ADHD-related antisocial behaviour and emotional distress among secular, religious and Ultra-Orthodox Jews in Israel. Int. J. Soc. Psychiatry 2022, 68, 773–782. [Google Scholar] [CrossRef]
  42. Sagar, S. The Role of Protective Factors in Relation to Attentional Abilities in Emerging Adults. Electronic Theses and Dissertations. 2021. Available online: https://scholar.uwindsor.ca/etd/8573 (accessed on 31 May 2022).
  43. Antonovsky, A. Unraveling the Mystery of Health: How People Manage Stress and Stay Well; Jossey-Bass: San Francisco, CA, USA, 1987. [Google Scholar]
  44. Antonovsky, A. Complexity, conflict, chaos, coherence, coercion and civility. Soc. Sci. Med. 1993, 37, 969–974. [Google Scholar] [CrossRef] [PubMed]
  45. Langeland, E.; Vinje, H.F. Applying Salutogenesis in Mental Healthcare Settings. In The Handbook of Salutogenesis; Springer: Cham, Switzerland, 2022; pp. 433–439. [Google Scholar]
  46. Eriksson, M.; Lindström, B. Validity of Antonovsky’s sense of coherence scale: A systematic review. J. Epidemiol. Community Health 2005, 59, 460–466. [Google Scholar] [CrossRef] [PubMed]
  47. Eriksson, M.; Lindström, B. Antonovsky’s sense of coherence scale and the relation with health: A systematic review. J. Epidemiol. Community Health 2006, 60, 376–381. [Google Scholar] [CrossRef] [PubMed]
  48. Lewin, A.; Mitchell, S.J.; Ronzio, C.R. Developmental differences in parenting behavior: Comparing adolescent, emerging adult, and adult mothers. Merrill-Palmer Q. 2013, 59, 23–49. [Google Scholar] [CrossRef]
  49. Mittelmark, M.B.; Bull, T.; Daniel, M.; Urke, H. Specific resistance resources in the salutogenic model of health. In The Handbook of Salutogenesis; Springer: Cham, Switzerland, 2017; pp. 71–76. [Google Scholar]
  50. Winger, J.G.; Adams, R.N.; Mosher, C.E. Relations of meaning in life and sense of coherence to distress in cancer patients: A meta-analysis. Psycho-Oncology 2016, 25, 2–10. [Google Scholar] [CrossRef]
  51. Uzdil, N.; Ceyhan, Ö.; Şimşek, N. The effect of salutogenesis-based care on the sense of coherence in peritoneal dialysis patients. J. Clin. Nurs. 2022, 31, 184–195. [Google Scholar] [CrossRef]
  52. Eriksson, M.; Lindström, B. Antonovsky’s sense of coherence scale and its relation with quality of life: A systematic review. J. Epidemiol. Community Health 2007, 61, 938–944. [Google Scholar] [CrossRef]
  53. Kishi, K.; Suzuki, J.; Monma, T.; Asanuma, T.; Takeda, F. Psychosocial and criminological factors related to recidivism among Japanese criminals at offender rehabilitation facilities. Cogent Soc. Sci. 2018, 4, 1489458. [Google Scholar] [CrossRef]
  54. Ristkari, T.; Sourander, A.; Ronning, J.; Helenius, H. Self-reported psychopathology, adaptive functioning and sense of coherence, and psychiatric diagnosis among young men. Soc. Psychiatry Psychiatr. Epidemiol. 2006, 41, 523–531. [Google Scholar] [CrossRef]
  55. Mattila, M.-L.; Rautava, P.; Honkinen, P.-L.; Ojanlatva, A.; Jaakkola, S.; Aromaa, M.; Suominen, S.; Helenius, H.; Sillanpää, M. Sense of coherence and health behaviour in adolescence. Acta Paediatr. Int. J. Paediatr. 2011, 100, 1590–1595. [Google Scholar] [CrossRef]
  56. Nilsson, K.W.; Starrin, B.; Simonsson, B.; Leppert, J. Alcohol-related problems among adolescents and the role of a sense of coherence. Int. J. Soc. Welf. 2007, 16, 159–167. [Google Scholar] [CrossRef]
  57. García-Moya, I.; Jiménez-Iglesias, A.; Moreno, C. Sense of coherence and substance use in Spanish adolescents. Does the effect of SOC depend on patterns of substance use in their peer group? Adicciones 2013, 25, 109–117. [Google Scholar] [CrossRef] [PubMed]
  58. Dayan, H.; Khoury-Kassabri, M.; Pollak, Y. The link between ADHD symptoms and antisocial behavior: The moderating role of the protective factor sense of coherence. Brain Sci. 2022, 12, 1336. [Google Scholar] [CrossRef] [PubMed]
  59. World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: Diagnostic Criteria for Research; World Health Organization: Geneva, Switzerland, 1993. [Google Scholar]
  60. del-Pino-Casado, R.; Espinosa-Medina, A.; López-Martínez, C.; Orgeta, V. Sense of coherence, burden and mental health in caregiving: A systematic review and meta-analysis. J. Affect. Disord. 2019, 242, 14–21. [Google Scholar] [CrossRef]
  61. Grevenstein, D.; Bluemke, M. Measurement invariance of the SOC-13 Sense of Coherence Scale across gender and age groups. Eur. J. Psychol. Assess. 2022, 38, 61. [Google Scholar] [CrossRef]
  62. Eriksson, M.; Contu, P. The sense of coherence: Measurement Issues. In The Handbook of Salutogenesis; Springer: Cham, Switzerland, 2022; pp. 79–91. [Google Scholar]
  63. Carlén, K.; Suominen, S.; Lindmark, U.; Saarinen, M.M.; Aromaa, M.; Rautava, P.; Sillanpää, M. Sense of coherence predicts adolescent mental health. J. Affect. Disord. 2020, 274, 1206–1210. [Google Scholar] [CrossRef]
  64. Elliott, D.S.; Ageton, S.S. Reconciling race and class differences in self-reported and official estimates of delinquency. Am. Sociol. Rev. 1980, 45, 95–110. [Google Scholar] [CrossRef]
  65. Elizur, Y.; Spivak, A.; Ofran, S.; Jacobs, S. A gender-moderated model of family relationships and adolescent adjustment. J. Clin. Child Adolesc. Psychol. 2007, 36, 430–441. [Google Scholar] [CrossRef]
  66. Johnston, L.; O’Malley, P.M.; Bachman, J.G. National Survey Results on Drug Use from the Monitoring the Future Study, 1975–1994: College Students and Young Adults (No. 95); National Institute on Drug Abuse, US Department of Health and Human Services, Public Health Service, National Institutes of Health: Washington, DC, USA, 1995. [Google Scholar]
  67. Schiff, M.; Benbenishty, R.; Hamburger, R. Adolescents’ Exposure to Negative Life Events and Substance Use: RISK and Protective Factors—Comparison Between Adolescents who Were Born in the Former Soviet Union and Those who Were Born in Israel; Final Report; Israel Anti-Drug Authority (Hebrew): Jerusalem, Israel, 1972. [Google Scholar]
  68. Goodman, R. The Strengths and Difficulties Questionnaire: A research note. J. Child Psychol. Psychiatry 1997, 38, 581–586. [Google Scholar] [CrossRef]
  69. Goodman, R. Psychometric properties of the strengths and difficulties questionnaire. J. Am. Acad. Child Adolesc. Psychiatry 2001, 40, 1337–1345. [Google Scholar] [CrossRef]
  70. Brann, P.; Lethbridge, M.J.; Mildred, H. The young adult Strengths and Difficulties Questionnaire (SDQ) in routine clinical practice. Psychiatry Res. 2018, 264, 340–345. [Google Scholar] [CrossRef] [PubMed]
  71. Natvig, G.K.; Albrektsen, G.; Qvarnstrøm, U. Associations between psychosocial factors and happiness among school adolescents. Int. J. Nurs. Pract. 2003, 9, 166–175. [Google Scholar] [CrossRef] [PubMed]
  72. Zohar, A.H.; Konfortes, H. Diagnosing ADHD in Israeli adults: The psychometric properties of the adult ADHD Self Report Scale (ASRS) in Hebrew. Isr. J. Psychiatry Relat. Sci. 2010, 47, 308. [Google Scholar] [PubMed]
  73. Swanson, J.M.; Schuck, S.; Porter, M.M.; Carlson, C.; Hartman, C.A.; Sergeant, J.A.; Clevenger, W.; Wasdell, M.; McCleary, R.; Lakes, K.; 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. [Google Scholar]
  74. Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 2012, 48, 1–36. [Google Scholar] [CrossRef]
  75. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 2nd ed.; Guilford publications: New York, NY, USA, 2017. [Google Scholar]
  76. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  77. Idan, O.; Braun-Lewensohn, O.; Lindström, B.; Margalit, M. Salutogenesis: Sense of coherence in childhood and in families. In The Handbook of Salutogenesis; Springer: Cham, Switzerland, 2017; pp. 107–121. [Google Scholar]
  78. Suárez Álvarez, Ó.; Ruiz-Cantero, M.T.; Cassetti, V.; Cofino, R.; Álvarez-Dardet, C. Salutogenic interventions and health effects: A scoping review of the literature. Gac. Sanit. 2022, 35, 488–494. [Google Scholar] [CrossRef]
  79. World Health Organization. How to Use the Icf-A Practical Manual for Using the International Classification of Functioning, Disability and Health; World Health Organization: Geneva, Switzerland, 2013. [Google Scholar]
  80. de Schipper, E.; Mahdi, S.; Coghill, D.; De Vries, P.J.; Gau, S.S.F.; Granlund, M.; Holtmann, M.; Karande, S.; Levy, F.; Almodayfer, O.; et al. Towards an ICF core set for ADHD: A worldwide expert survey on ability and disability. Eur. Child Adolesc. Psychiatry 2015, 24, 1509–1521. [Google Scholar] [CrossRef]
  81. Loe, I.M.; Feldman, H.M. Academic and educational outcomes of children with ADHD. J. Pediatr. Psychol. 2007, 32, 643–654. [Google Scholar] [CrossRef]
  82. Edbom, T.; Malmberg, K.; Lichtenstein, P.; Granlund, M.; Larsson, J.O. High sense of coherence in adolescence is a protective factor in the longitudinal development of ADHD symptoms. Scand. J. Caring Sci. 2010, 24, 541–547. [Google Scholar] [CrossRef]
  83. Gudjonsson, G.H.; Gonzalez, R.A.; Young, S. The risk of making false confessions: The role of developmental disorders, conduct disorder, psychiatric symptoms, and compliance. J. Atten. Disord. 2021, 25, 715–723. [Google Scholar] [CrossRef] [PubMed]
  84. Cherkasova, M.V.; Roy, A.; Molina, B.S.; Scott, G.; Weiss, G.; Barkley, R.A.; Biederman, J.; Uchida, M.; Hinshaw, S.P.; Owens, E.B.; et al. Adult outcome as seen through controlled prospective follow-up studies of children with attention-deficit/hyperactivity disorder followed into adulthood. J. Am. Acad. Child Adolesc. Psychiatry 2022, 61, 378–391. [Google Scholar] [CrossRef] [PubMed]
  85. Cheung, L.; Gunby, P. Crime and mobility during the COVID-19 lockdown: A preliminary empirical exploration. N. Z. Econ. Pap. 2022, 56, 106–113. [Google Scholar] [CrossRef]
  86. Champ, R.E.; Adamou, M.; Tolchard, B. The impact of psychological theory on the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in adults: A scoping review. PLoS ONE 2021, 16, e0261247. [Google Scholar] [CrossRef]
  87. Jensen, J.F.; Egerod, I.; Bestle, M.H.; Christensen, D.F.; Elklit, A.; Hansen, R.L.; Knudsen, H.; Grode, L.B.; Overgaard, D. A recovery program to improve quality of life, sense of coherence and psychological health in icu survivors: A multicenter randomized controlled trial, the rapit study. Intensive Care Med. 2016, 42, 1733–1743. [Google Scholar] [CrossRef]
  88. Odajima, Y.; Kawaharada, M.; Wada, N. Development and validation of an educational program to enhance sense of coherence in patients with diabetes mellitus type 2. Nagoya J. Med. Sci. 2017, 79, 363. [Google Scholar] [CrossRef]
  89. Tan, K.K.; Chan, S.W.C.; Wang, W.; Vehviläinen-Julkunen, K. A salutogenic program to enhance sense of coherence and quality of life for older people in the community: A feasibility randomized controlled trial and process evaluation. Patient Educ. Couns. 2016, 99, 108–116. [Google Scholar] [CrossRef]
Figure 1. Path analysis of the study variables: A path analysis model presents the significant direct effect between sense of coherence and the function domains of delinquency, drug use, emotional distress, and happiness. Rectangles represent observed measured variables. Values are standardized path coefficients.
Figure 1. Path analysis of the study variables: A path analysis model presents the significant direct effect between sense of coherence and the function domains of delinquency, drug use, emotional distress, and happiness. Rectangles represent observed measured variables. Values are standardized path coefficients.
Sci 07 00060 g001
Table 1. Demographic characteristics of the study participants (N = 486).
Table 1. Demographic characteristics of the study participants (N = 486).
CharacteristicCategoryN%
Age GroupAdolescence, 15–1713327.4
Emergent adulthood, 19–2925251.9
Adulthood, 30–5010120.8
GenderWomen21745.1
Men26954.9
Country of BirthIsrael46896.3
Other183.7
Marital StatusSingle23448.2
Married/In a relationship22045.3
Divorced275.5
Other51.0
Religious IdentityUltra-orthodox18738.5
Orthodox11022.6
Traditional5310.9
Non-religious12225.1
Not defined142.9
EducationElementary/High school16233.3
Up to 14 years17736.5
Graduate14730.2
Economic StatusVery low6513.4
Low13828.3
Medium23748.7
High408.2
Very high71.5
Employment StatusNot working19640.4
Part-time6012.3
Full-time23047.3
Table 2. Descriptive statistics of and Spearman correlation coefficients among the study variables.
Table 2. Descriptive statistics of and Spearman correlation coefficients among the study variables.
VariableM/NSD/%1234567
1. Delinquency0.590.54
2. Drug Use1.081.360.31 **
3. Emotional Distress1.680.520.19 **−0.03
4. Happiness1.690.82−0.22 **−0.03−0.45 **
5. Gender (Women)21745.1−0.12 **−0.19 **0.27 **−0.2 **
6. Age Group (Adolescents)13327.4−0.33 **0.11−0.080.070.04
7. ADHD Symptoms1.840.680.26 **0.040.36 **−0.27 **0.11 *−0.10
8. Sense of Coherence4.371.15−0.41 **−0.07−0.61 **0.51 **−0.14 **0.22 **−0.38 **
Note. M and SD are used to represent mean and standard deviation, respectively. * = p < 0.01, ** = p < 0.005.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Dayan, H.; Khoury-Kassabri, M.; Pollak, Y. Sense of Coherence Is Associated with Functional Impairment in Individuals Diagnosed with ADHD. Sci 2025, 7, 60. https://doi.org/10.3390/sci7020060

AMA Style

Dayan H, Khoury-Kassabri M, Pollak Y. Sense of Coherence Is Associated with Functional Impairment in Individuals Diagnosed with ADHD. Sci. 2025; 7(2):60. https://doi.org/10.3390/sci7020060

Chicago/Turabian Style

Dayan, Haym, Mona Khoury-Kassabri, and Yehuda Pollak. 2025. "Sense of Coherence Is Associated with Functional Impairment in Individuals Diagnosed with ADHD" Sci 7, no. 2: 60. https://doi.org/10.3390/sci7020060

APA Style

Dayan, H., Khoury-Kassabri, M., & Pollak, Y. (2025). Sense of Coherence Is Associated with Functional Impairment in Individuals Diagnosed with ADHD. Sci, 7(2), 60. https://doi.org/10.3390/sci7020060

Article Metrics

Back to TopTop