Abstract
Health-related Quality of Life (HRQoL) is a multi-faceted construct influenced by a myriad of environmental, demographic, and individual characteristics. Our understanding of these influencers remains highly limited in neurodevelopmental conditions. Existing research in this area is sparse, highly siloed by diagnosis labels, and focused on symptoms. This review synthesized the evidence in this area using a multi-dimensional model of HRQoL and trans-diagnostically across neurodevelopmental conditions. The systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Checklist, was completed in June 2023 using Medline, PsycInfo, Embase, PubMed, and Cochrane Library. Our search revealed 78 studies that examined predictors of HRQoL in neurodevelopmental conditions. The majority of these studies focused on autism and ADHD with a paucity of literature in other conditions. Cross-diagnosis investigations were limited despite the fact that many of the examined predictors transcend diagnostic boundaries. Significant gaps were revealed in domains of biology/physiology, functioning, health perceptions, and environmental factors. Very preliminary evidence suggested potentially shared predictors of HRQoL across conditions including positive associations between HRQoL and adaptive functioning, male sex/gender, positive self-perception, physical activity, resources, and positive family context, and negative associations with diagnostic features and mental health symptoms. Studies of transdiagnostic predictors across neurodevelopmental conditions are critically needed to enable care models that address shared needs of neurodivergent individuals beyond diagnostic boundaries. Further understanding of HRQoL from the perspective of neurodivergent communities is a critical area of future work.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Background
Neurodevelopmental conditions refer to a group of heterogeneous attributes that manifest early in life and can be associated with differences and disability in personal, social, occupational, or academic functioning (“Neurodevelopmental Disorders”, 2013). These conditions include autism spectrum disorder (autismFootnote 1; prevalence 1 in 66; Ofner et al., 2018), attention-deficit/hyperactivity disorder (ADHD; prevalence 1 in 20; Polanczyk et al., 2014), intellectual disability (ID; prevalence up to 63 in 1000), communication disorders (prevalence up to 1 in 10), learning disorders, including impairments in reading, writing and mathematics (LD; prevalence up to 1 in 10), and motor disorders (including tic disorders, and stereotypic disorders; prevalence up to 17 in 100) (Francés et al., 2022). Considerably large within-condition heterogeneity and cross-condition overlap exist in aetiology, neurobiology, and phenotypes associated with neurodevelopmental conditions (Anholt et al., 2010; Antshel et al., 2013; Astle et al., 2021; Kushki et al., 2019). These conditions can also be associated with transdiagnostic challenges that can further increase the heterogeneity of presentation and outcomes (e.g. mental health conditions (DeFilippis, 2018; Moritz, 2008; Schatz & Rostain, 2006), sleep difficulties (Díaz-Román et al., 2015, 2018), and differences in learning (DuPaul et al., 2004; Estes et al., 2011; Fischer-Terworth, 2013), and motor skills (Abramovitch et al., 2011; Damme et al., 2015). These differences and disabilities, combined with societal barriers, can lead to decreased quality of life (QoL); (Becker et al., 2011; Coales et al., 2019; Kuhlthau et al., 2010; Lack et al., 2009; Lin, 2019; Wanni Arachchige Dona et al., 2023), as one’s satisfaction in relation to their culture, value systems, goals, expectations, standards, and concerns (World Health Organization, Division of Mental Health and Prevention of Substance Abuse 2012). Further narrowing this definition, Health-Related Quality of Life (HRQoL) reflects QoL in the context of an individual’s health status, excluding the non-health-related categories such as cultural or political measurements (Ferrans et al., 2005).
Mirroring the diversity in neurodevelopmental conditions, HRQoL outcomes are highly variable in these conditions. In this context, several studies have attempted to characterize predictors of HRQoL in neurodivergent individuals. Among these, diagnostic clinical features of neurodevelopmental conditions, including features associated with autism (Ayres et al., 2018; Lin 2019), and ADHD (Danckaerts et al., 2010), have been suggested to be correlates of HRQoL. Mental health symptoms have also been associated with decreased quality of life across neurodevelopmental conditions (Lawson et al., 2020; Lin, 2019; Mason et al., 2018; Orm et al., 2023). To our knowledge, no reviews exist on transdiagnostic predictors of HRQoL in neurodevelopmental conditions, and none within the last five years on HRQoL predictors in individual diagnoses (Agarwal et al., 2012; Ayres et al., 2018; Chiang & Wineman, 2014; Danckaerts et al., 2010). A recent review is critically needed given the emerging interest in this area as demonstrated by several recent publications on predictors of HRQoL in neurodevelopmental conditions. Further, individual studies of HRQoL are almost entirely conducted in diagnostic siloes, and very little is known about transdiagnostic predictors of HrQoL in neurodevelopmental conditions. This transdiagnostic approach is critically needed in the light of the growing concern that our existing, discrete, diagnostic categories do not adequately capture experiences, align with underlying biological mechanisms, or guide the choice of supports (Anholt et al., 2010; Antshel et al., 2013; Astle et al., 2021; Kushki et al., 2019). To address this gap, the objective of the present study was to characterize the state of the literature on transdiagnostic predictors of HRQoL in neurodevelopmental conditions and generate hypotheses for future research in this area.
HRQoL is a multi-dimensional and interconnected construct which can be influenced by a multitude of biological, phenotypic, environmental, and sociodemographic variables. To reflect this, we grounded our review in the theoretical framework of Wilson and Cleary, a conceptual model which links HRQoL to biological and psychosocial variables (Wilson & Cleary, 1995). For this review, we used Ferrans et al.’s revised Wilson and Cleary Model of HRQoL predictors (Ferrans et al., 2005; Fig. 1). In this model, HRQoL is impacted by four domains: (1) biological and physiological factors (functioning of one’s human body on a cellular, organ, or organ system level), (2) symptoms (physical or mental features of the human body as a whole), (3) functioning (an individual’s ability to complete physical, social, or psychological tasks), and (4) general health perceptions (the subjective feeling of health). Each of these domains is impacted by characteristics of the individual and the environment (Wilson & Cleary, 1995). Individual factors in this model include demographic group (e.g. sex, gender, age, ethnicity), biological features (e.g. body mass index, skin colour, family medical history), and psychological characteristics (e.g. cognitive appraisal, affective response, motivation; Ferrans et al., 2005). Environmental characteristics include social factors (e.g. influence of family, friends, and healthcare providers), and physical factors (e.g. neighbourhood and school; Ferrans et al., 2005). Given this theoretical grounding, our specific research question for this review was: across neurodevelopmental conditions, what are the transdiagnostic predictors of HRQoL within the domains of the revised Wilson and Cleary model?
Methods
This systematic review protocol was designed and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Checklist (Moher et al., 2009). The full review protocol is provided in the Supplementary Materials and was registered in PROSPERO (Reg. No. CRD42023431150). Ethics approval was not needed as this review used previously completed studies. There were no published systematic reviews on this topic in the Cochrane library or PROSPERO at the time the review was designed.
Search Strategy
Five databases were used for the search: Medline, PsycInfo, Embase, PubMed, and Cochrane. The search terms included neurodevelopmental disorders as defined in the Diagnostic and Statistical Manual of Mental disorders (DSM-5; autism/ASD, attention-deficit/hyperactivity disorder/ADHD, intellectual disorder, intellectual disability, intellectual developmental disorder, global developmental delay, communication disorder, language disorder, speech disorder, speech sound disorder, fluency disorder, stutter, learning disorder, impairment in reading, impairment in written expression, impairment in mathematics, motor disorder, developmental coordination disorder, stereotypic movement disorder, tic disorder, or Tourette), quality of life/QoL, and predict/determinant (see detailed list in Supplementary Table 1). The search was completed on 23 June 2022.
All articles were imported to Covidence to undergo screening, review, and extraction by the authors (screening and extraction: MM, TP, HB, JC; full-text review: TP, HB, JC). Inter-rater screening reliability was determined on a subset of 300 articles with the goal of greater than 80% consensus among all reviewers. For title and abstract screening, each study was assessed by two reviewers and disagreements resolved through deliberation.
Inclusion and Exclusion Criteria
Our inclusion criteria were the following: (1) primary peer-reviewed literature published in English, (2) employed a validated measure of HRQoL in populations with neurodevelopmental disorders as defined in the DSM-5, and (3) statistically examined the association between a predictor(s) variable and a total HRQoL score. Studies that employed qualitative methods were excluded, as they did not provide a statistical quantification of the effect of a predictor on HrQoL. Theses/dissertations, conference/poster abstracts, and randomized control trials were excluded.
Data Extraction and Analysis
Data were extracted using personalized extraction templates on Covidence (Covidence Systematic Review Software, Veritas Health Innovation, Melbourne, Australia., n.d.). The extracted data included the following: title, year, HRQoL outcome measure, informant (self or proxy), country, and sample characteristics (total sample size, diagnosis, gender, age, family/self-income, parental/self-education, socioeconomic status, and race/ethnicity). Other data extracted included analysis methods, significant/non-significant predictors of HRQoL, and the associated statistics. For data extraction, one reviewer extracted the data, and a second reviewer cross-checked the extracted data. Due to the heterogeneity of the study designs, a narrative synthesis of the results took place. Risk of bias assessment was completed using an adapted Cochrane template since the review included more than one study design (see Supplementary Table 2).
The Revised Wilson and Cleary model of HRQoL predictors (Fig. 1) guided the synthesis of predictor variables. Predictors were categorized under the main domains of the model (biology/physiology, symptoms, functioning, general health perceptions), or the external domains (environmental and individual characteristics) through consensus among co-authors (Supplementary Table 3). Each domain was operationalized as follows:
-
Biology/Physiology: variables measuring functioning of cells, organs, or organ systems.
-
Symptoms: core-domain features of neurodevelopmental conditions as well as co-occurring symptoms in domains of behaviour and mental health. Predictors related to physical health and health care needs were also included in this category.
-
Functioning: operationalized as adaptive functioning or the ability to complete demands of everyday life.
-
General Health Perceptions: predictors related to the subjective feeling of health.
-
Individual characteristics: variables related to demographics, psychological characteristics, healthfulness behaviours, and birth-related and anthropometric variables.
-
Environmental Characteristics: birth/prenatal characteristics, parental/sibling characteristics, social and physical environment,, and access to healthcare resources.
Results
Literature Search
The search revealed 4025 articles after duplicates were removed. For abstract and title screening, the per cent agreement between all 3 reviewers was 81%. Title and abstract screening deemed 3582 studies as irrelevant. The most common reasons for exclusion were as follows: 1. study did not include a neurodivergent population, 2. study did not assess HRQoL, and 3. study was a review or meta-analysis.
Following this, 478 full-text studies were assessed for eligibility. For full-text review, the agreement between the reviewers was 87.8%. Upon full-text review, studies were removed due to non-English language (n = 13), study population not including a neurodevelopmental condition (n = 38), absence of total HRQoL assessment (n = 65), no predictors of HRQoL (n = 94), and study designs not meeting inclusion criteria (n = 155; qualitative studies, thesis/dissertations, reviews, conference/poster abstract, editorial, commentary, letter, proposals, protocols, and case reports). After these exclusions, 78 studies were included in the review as shown in the PRISMA diagram in Fig. 2.
Study Characteristics
Of the studies included in the review, the majority (n = 71) had a low risk of bias, with only six and one studies with medium and high risks of bias, respectively. The most frequently identified sources of biases included sample selection and description, description of statistical methods, and reporting of statistical results. Table 1 provides the details of the reviewed studies.
Study Populations
The most frequently studied diagnoses were autism (n = 23) and ADHD (n = 22), followed by intellectual disorder (n = 14), tic disorders (n = 11), and stutter (n = 5). The number of studies investigating pediatric (< 21 years), and adult groups were 37 and 39, respectively, with one study examining both groups. Of the reviewed studies, only two reported HRQoL predictors across multiple diagnosis categories. This included one study on tic disorders, autism, ADHD, and learning disorder, and another on autism and intellectual disability.
For the studies that reported sex and/or gender (total participants 16,639), there were 3924 female (24%), 12,685 male (76%), 6 non-binary (< 1%), and 24 not-specified/other (< 1%) participants. Twenty-one studies reported socioeconomic status indicators (composite scores, income, employment, or education).
HRQoL Measurement
Across the reviewed studies, the most frequently used instrument used to assess HRQoL was the Pediatric Quality of life inventory (Varni et al., 2001) (PedsQL; n = 25), followed by the Quality of Life Questionnaire (QoL-Q; n = 11). Beyond these, the measures used in the reviewed literature were highly heterogeneous.
Analytical Approaches
To quantify the association between HRQoL and predictors, a wide variety of methodological approaches were employed in the reviewed studies. These included computation of correlation coefficients, comparisons of groups defined on predictor variables (e.g. analysis of variance, t tests), and regression analysis.
Predictors of HRQoL
With reference to the Revised Wilson and Cleary model, the most frequently studied predictors of HRQoL were in domains of symptoms and individual factors. Significant gaps were evident in studies examining predictors in domains of biology/physiology, functioning, environment, and general health perceptions, within and across conditions, as described below.
Biology/Physiology (Table 2)
Four of the 78 studies reported on predictors related to this domain (3 autism; 1 ID), with a focus on physical health/wellbeing (e.g. physical health conditions, sensory disorders, chronic pain, migraines or headaches), and microcephaly and dysmorphology. These studies revealed positive or null associations between physical health variables and HRQoL.
Symptoms (Table 3)
This domain was the most frequently studied predictor of HRQoL (autism: 20, ADHD: 19, ID: 3, tic disorder: 9, stutter: 5, learning disorder: 1, cross-diagnosis: 1). We grouped the symptoms investigated into four categories: (1) symptoms/features associated with core domains of each neurodevelopmental condition, (2) mental health/behavioural features, (3) physical symptoms, and (4) interventions aimed at reducing symptom intensity/impact. The existing literature on core domains was heavily focused on features associated with autism (15 studies) and ADHD (22 studies). Cross-diagnosis studies of symptoms were scarce and limited to investigation of ADHD symptoms (autism: 2 studies, learning disorders: 1 study, tic disorders: 5 studies) and autism features (ADHD: 3 studies). Overall, several studies reported a negative association between symptom intensity in the core domains and HRQoL across diagnoses (n = 21), although null findings were common (n = 32).
In terms of mental health/behaviour, the impact of mental health symptoms on HRQoL was most frequently investigated, with a significant focus on anxiety (19 studies) and depression (18 studies). These symptoms were overwhelmingly associated with decreased HRQoL across diagnoses (31 studies), with a small number of studies reporting null findings (13 studies). Studies examining the impact of interventions on HRQoL mainly included participants with ADHD (5 studies), followed by autism (4 studies). This very small body of literature showed a differential impact of interventions in ADHD and autism, with very preliminary suggestion of potentially positive impact in ADHD, and null or negative findings in autism. Studies of physical health were relatively limited and restricted to autism and ADHD.
Functioning (Table 4)
The literature on predictors of HRQoL related to functioning was very sparse and included investigations of daily living skills and performance of everyday activities (autism: 2, ADHD: 2, ID: 3, LD:1). The majority of the reviewed studies suggested a positive association between adaptive functioning skills and HRQoL across neurodevelopmental conditions.
General Health Perceptions (Table 5)
There was very limited investigation of the impact of health perceptions on HRQoL across neurodevelopmental conditions (ADHD: 1, stutter: 1). The studied predictors included concerns about illness/condition, beliefs and perceptions about cause, perceived duration of symptoms, and identity.
Individual Characteristics (Table 6)
Forty-eight studies investigated the variables related to individual characteristics (autism: 19, ADHD: 10, stutter: 5, LD: 1, TD: 4, ID: 9). We grouped the variables investigated as predictors into four categories: (1) demographics, (2) psychological factors, (3) anthropomorphic, and (4) healthfulness behaviours. Demographics variables were most frequently investigated across diagnoses, with a focus on age (autism: 11, ADHD: 7, stutter: 3, LD: 1, TD: 2, ID: 5), sex/gender (autism: 7, ADHD: 3, stutter: 3, LD: 1, TD: 2, ID: 3), and employment (autism: 1, ADHD: 1, ID: 3). The effects of age on HRQoL were mixed, whereas male gender and employment were most frequently associated with increased HRQoL. Beyond age and sex/gender, there was a paucity of studies examining the effects of demographics such as race/ethnicity and sexual orientation.
In terms of psychological factors, IQ was most frequently studied as an individual factor; however, these studies were mainly limited to autism (9 studies), with 1 study related to ADHD and 1 study focused on intellectual disability. Of these, seven studies reported null associations between IQ and HRQoL, consistent with the findings in ADHD (1 study) and ID (1 study). Positive self-perception was also studied in four publications, with reports of positive association with HRQoL. For healthfulness behaviours, physical activity was examined in 4 studies (autism: 2, ID: 1, TD: 1), with all studies reporting either a positive or null association between physical activity and HRQoL.
Environmental Characteristics (Table 7)
Our results revealed 36 studies which examined the association between HRQoL and environmental characteristics across diagnostic groups (autism: 9, ADHD: 8, stutter: 3, tic disorder: 4, LD: 1, ID: 11). The predictors examined in these studies were clustered into six categories: (1) prenatal/birth factors, (2) social environment, (3) social supports, (4) physical environment, (5) resources, and (6) family context. The literature on prenatal/birth factors was limited to two studies. Among predictors related to the social environment, socioeconomic status was most commonly investigated across diagnoses, with highly mixed findings reported within and across diagnoses (positive, null, and negative associations). The impact of social supports on HRQoL was also frequently examined across diagnoses (autism: 3, ADHD, 2, stutter: 1, ID, 4), with eight studies reporting positive and five studies reporting null associations. Predictors related to resources included healthcare resources, and academic and physical environments. Across diagnoses, these resources were associated with positive impact on HRQoL across the majority of studies. Finally, variables related to family context were investigated in 16 studies, with the majority suggesting an association between positive family context (e.g. parental mental health, family function) and improved HRQoL.
Discussion
We conducted this systematic review to synthesize the literature findings related to transdiagnostic predictors of HRQoL across neurodevelopmental conditions. Our review revealed less than 30 published studies for each condition meeting our review criteria. These studies mainly focus on autism and ADHD, with a significant paucity of literature on HRQoL predictors in communication disorder, language disorder, speech disorder, speech sound disorder, fluency disorder, motor disorder, developmental coordination disorder, or stereotypic movement disorder. This is a critical gap given the prioritization of quality of life as an outcome by clinicians (Lord et al., 2022) and the neurodivergent communities (Oakley et al., 2021).
Cross-diagnosis investigation of HRQoL predictors was highly limited in the literature, despite the fact that many of the examined variables transcend diagnostic boundaries. This is a significant gap as many symptoms overlap largely among neurodevelopmental conditions (Craig et al., 2016; Stern & Robertson & 1997, Hulsbosch et al., 2021; Nippold & Schwarz, 1990). Similarly, influencers related to adaptive functioning, health perceptions, and demographics, and environmental context can also be shared across individuals with neurodevelopmental conditions.
The results of this review provide very preliminary suggestions on potentially shared predictors of HRQoL across HRQoL. In particular, very early patterns were observed to suggest positive associations between HRQoL and adaptive functioning, male sex/gender, positive self-perception, physical activity, resources, and positive family context, and negative associations with core and mental health symptoms. It is important to note that although these predictors may also be relevant to HRQoL in neurotypical populations, neurodivergent populations may be more likely to experience negative predictors and at greater intensity (e.g. mental health). Reducing exposure to these factors through timely access to care and environmental adaptations and supports can therefore contribute to greater HRQoL.
The only domain where preliminary differential effects were observed across conditions was the impact of interventions. These results suggested a pattern of positive association in ADHD and null or negative findings in autism. Although very preliminary, these patterns are consistent with previous literature suggesting increases in QoL associated with medication use in ADHD (Agarwal et al., 2012; Coghill, 2010; Coghill et al., 2017) and mixed perceptions of interventions in autism (Schuck et al., 2022). These results must be interpreted with caution given that we did not carry out a meta-analysis to quantify effect sizes.
Measurement and Analysis
The most frequently used instrument used for measuring HRQoL in the reviewed literature was the Pediatric Quality of Life Inventory (PedsQL). The PedsQL is a 23-item questionnaire investigating HRQoL across four domains of physical functioning, emotional functioning, school functioning, and social functioning (Varni et al., 2001). This measure includes both self- and parent-report versions, and age-appropriate versions for children 2–18 years old. In adult HRQoL studies, QoL-Q (Schalock & Keith, 1993) was most commonly used. This is a 40-item questionnaire with four subscales: personal life and satisfaction, competence and productivity, empowerment and independence, and social belonging and community integration. Overall, we found a heterogeneity of instruments used, which challenged the interpretation and compatibility of results across studies. It is also important to note that our understanding of the validity of existing HRQoL measures in neurodivergent communities is very limited as these measures are often not co-created or validated with neurodivergent individuals. This is critical as perceptions of HRQoL may differ between neurotypical and neurodivergent populations. For example, the subdomains related to social functioning may be valued differently by neurotypical and neurodivergent populations. These suggestions are similar to those in existing reviews critiquing HRQoL studies in neurodevelopmental populations, suggesting that an NDD-specific HRQoL instrument is needed (Evers et al., 2022). We are aware of one study (McConachie et al., 2018) which addresses these challenges by examining the psychometric properties of the WHOQoL-BREF in autistic adults and co-created nine additional autism-specific items. Additional studies to further understand HRQoL from the perspectives of other neurodivergent communities are an important area for future research.
In addition to differences in instruments used, a variety of analytical approaches were employed in the reviewed literature to quantify the associations between HRQoL and the hypothesized predictors. These methodological differences, including differences in assumptions of linearity and normality, and inclusion of covariates and interaction terms, may contribute to the heterogeneity of findings in this field.
Predictors of HRQoL
HRQoL is a multi-dimensional construct and impacted by several interacting domains. To reflect this complexity, we grounded our analyses in a theoretical model of HRQoL, the Revised Wilson and Cleary model (Ferrans et al., 2005). With reference to this model, the most commonly investigated predictors of HRQoL were in the symptom domain. This included both studies examining core features of neurodevelopmental conditions as well as co-occurring symptoms. For the latter, our results suggest that mental health, and specifically anxiety and depression, may be transdiagnostic domains which negatively impact HRQoL in neurodevelopmental conditions. Given the high prevalence of these symptoms in neurodevelopmental conditions [e.g. In autism, 20 and 11% prevalence of anxiety and depressive disorder, respectively (Lai et al., 2019)], future research in this area, including a meta-analysis, is highly encouraged.
Physical health is also a key area for future research in neurodivergent children as there is a sizable body of evidence in community samples suggesting that physical health may positively impact HRQoL (Cordova et al., 2021; Davies et al., 2019; Gu et al., 2020; Redondo-Tebar et al., 2019; Schafer et al., 2016; Tsiros et al., 2017), but our review found very few studies on this topic.
In addition to symptoms, our review revealed that individual characteristics were also frequently studied as predictors of HRQoL across neurodevelopmental populations, with a significant focus on age and sex/gender. Despite a growing body of literature examining the impact of age on HRQoL, the findings were highly mixed. For sex/gender, our results suggest a potential association of male sex/gender with increased HRQoL across neurodevelopmental conditions. Future studies in this area are needed to better understand the nature of this association. Additionally, these findings must be interpreted in the context that the majority of studies did not differentiate between sex as a biological variable and gender as a social identity, and study samples did not include gender-diverse participants, with less than 1% of the sample across all studies having a non-binary gender identity. There was also a significant gap in understanding the impact of other demographic variables, such as race/ethnicity/Indigeneity, immigration status, and other dimensions of identity. These can impact well-being through access to health resources (Khanlou et al., 2017), intergenerational trauma (Czyzewski, 2011), and experiences of discrimination (Benner et al., 2018). In terms of other individual predictors, the literature reports were sparse, but a handful of studies suggested positive associations between HRQoL and positive self-perception and physical activity. Future studies are needed to further understand these associations.
In the domain of environmental predictors, our review found highly mixed findings with respect to SES. Our results highlighted social supports and family functioning as potential avenues for future investigation as preliminary positive associations with HRQoL were reported. At the same time, our results revealed gaps in understanding other environmental influencers, such as access to care and resources, accommodations, inclusion, and acceptance, social and environmental barriers, as well as other factors that impact the person-environment fit (Lord et al., 2022). Timely access to healthcare resources and social support also significantly impacts outcomes in neurodevelopmental conditions and likely predict HRQoL. These findings are in line with other reviews investigating predictors in single neurodevelopmental conditions (Chiang & Wineman, 2014; Sevastidis et al., 2023).
A significant literature gap was also found in the domain of functioning (ability to complete tasks of daily life). This is a key area for future studies of HRQoL in neurodevelopmental conditions as functioning may help to disentangle the distinction between individual differences and disability.
Lastly, most reviewed studies focused on predictors in single domains impacting HRQoL in isolation. This isolated study of HRQoL predictors does not reflect the multi-dimensional nature of HRQoL and the interconnectedness among the various influences. Given the complexity of the HRQoL construct, future studies should consider the interrelations among the various domains impacting HRQoL. Examples include examining the effect of sociodemographic and environmental variables as moderators of the associations among HRQoL, symptoms, and functioning. Grounding such investigating in a theoretical model can further contextualize the findings of future studies.
Strengths and Limitations
This study had various strengths. The transdiagnostic approach of this study allows the exploration of HRQoL predictors that transcend diagnostic boundaries and reflects the large overlap among neurodevelopmental conditions. In addition, grounding our analyses in a theoretical model allowed us to explore HRQoL predictors with a global and multi-dimensional lens.
The findings of this review should be interpreted in the context of several limitations. We considered HRQoL as a single dimensional construct (total score). This choice was made due to the large heterogeneity in domains included in various HRQoL instruments, limiting the ability to capture subscales. Additionally, we did not consider interactions among HRQoL domains or their predictors. Further, this review focused on cross-sectional studies of HRQoL and inferences about the predictors of long-term outcomes or predictors of changes in quality of life are not possible. Qualitative research and non-peer reviewed papers were excluded from the search which may have limited the evidence collected. In addition, the exclusion of non-English articles may have geographically and ethnically limited the sample of studies reviewed. Finally, due to the sparsity of studies and heterogeneity of methods and measures, a meta-analysis was not possible to quantify the effect of each predictor across the reviewed studies.
Conclusion
We found significant gaps in understanding predictors of HRQoL in neurodevelopmental conditions, especially outside of autism and ADHD. Cross-condition studies of these predictors are critically needed to enable care models that address shared needs of neurodivergent individuals transcending diagnostic boundaries. Outside of symptoms, our review identified several such need areas that may be associated with HRQoL outcomes, including mental health, social determinants of health, access to care, family context, and positive self-perceptions. Further understanding of HRQoL from the perspective of neurodivergent communities is highly needed.
Notes
In this paper, we will adopt identify-first language (“autistic individuals”) where possible, recognizing that these preferences may vary across the community.
Abbreviations
- ADHD:
-
Attention-deficit/hyperactivity disorder
- HRQoL:
-
Health-related quality of life
- OCD:
-
Obsessive–compulsive disorder
- PedsQL:
-
Pediatric quality of life inventory
- PRISMA:
-
Preferred reporting items for systematic reviews and meta-analysis checklist
- QoL:
-
Quality of life
- SES:
-
Socioeconomic status
References
Abramovitch, A., Dar, R., Schweiger, A., & Hermesh, H. (2011). Neuropsychological impairments and their association with obsessive-compulsive symptom severity in obsessive-compulsive disorder. Archives of Clinical Neuropsychology, 26(4), 364–376.
Adams, D., Clark, M., & Keen, D. (2019). Using self-report to explore the relationship between anxiety and quality of life in children on the autism spectrum. Autism Research, 12(10), 1505–1515.
Adams, D., Clark, M., & Simpson, K. (2020). The relationship between child anxiety and the quality of life of children, and parents of children, on the autism spectrum. Journal of Autism and Developmental Disorders, 50(5), 1756–1769. https://doi.org/10.1007/s10803-019-03932-2
Agarwal, R., Goldenberg, M., Perry, R., & IsHak, W. W. (2012). The quality of life of adults with attention deficit hyperactivity disorder: A systematic review. Innovations in Clinical Neuroscience, 9(5–6), 10–21.
Ahnemark, E., Di Schiena, M., Fredman, A.-C., Medin, E., Söderling, J. K., & Ginsberg, Y. (2018). Health-related quality of life and burden of illness in adults with newly diagnosed attention-deficit/hyperactivity disorder in Sweden. BMC Psychiatry, 18(1), 223. https://doi.org/10.1186/s12888-018-1803-y
Albuquerque, C. P. (2012). Psychometric properties of the portuguese version of the quality of life questionnaire (QOL-Q). Journal of Applied Research in Intellectual Disabilities: JARID, 25(5), 445–454. https://doi.org/10.1111/j.1468-3148.2012.00685.x
Anholt, G. E., Cath, D. C., van Oppen, P., Eikelenboom, M., Smit, J. H., van Megen, H., et al. (2010). Autism and ADHD symptoms in patients with OCD: Are they associated with specific OC symptom dimensions or OC symptom severity? Journal of Autism and Developmental Disorders, 40(5), 580–589.
Antshel, K. M., Zhang-James, Y., & Faraone, S. V. (2013). The comorbidity of ADHD and autism spectrum disorder. Expert Review of Neurotherapeutics, 13(10), 1117–1128.
Astle, D. E., Holmes, J., Kievit, R., & Gathercole, S. E. (2021). Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders. Journal of Child Psychology and Psychiatry. https://doi.org/10.1111/jcpp.13481
Ayres, M., Parr, J. R., Rodgers, J., Mason, D., Avery, L., & Flynn, D. (2018). A systematic review of quality of life of adults on the autism spectrum. Autism, 22(7), 774–783.
Balboni, G., Mumbardó-Adam, C., & Coscarelli, A. (2020). Influence of adaptive behaviour on the quality of life of adults with intellectual and developmental disabilities. Journal of Applied Research in Intellectual Disabilities: JARID, 33(3), 584–594. https://doi.org/10.1111/jar.12702
Becker, A., Roessner, V., Breuer, D., Döpfner, M., & Rothenberger, A. (2011). Relationship between quality of life and psychopathological profile: Data from an observational study in children with ADHD. European Child and Adolescent Psychiatry, 20(S2), 267–275.
Ben-Dor Cohen, M., Eldar, E., Maeir, A., & Nahum, M. (2021). Emotional dysregulation and health related quality of life in young adults with ADHD: A cross sectional study. Health and Quality of Life Outcomes, 19(1), 270. https://doi.org/10.1186/s12955-021-01904-8
Benner, A. D., Wang, Y., Shen, Y., Boyle, A. E., Polk, R., & Cheng, Y. P. (2018). Racial/ethnic discrimination and well-being during adolescence: A meta-analytic review. American Psychologist, 73(7), 855.
Bernard, B. A., Stebbins, G. T., Siegel, S., Schultz, T. M., Hays, C., Morrissey, M. J., Leurgans, S., & Goetz, C. G. (2009). Determinants of quality of life in children with Gilles de la Tourette syndrome. Movement Disorders: Official Journal of the Movement Disorder Society, 24(7), 1070–1073. https://doi.org/10.1002/mds.22487
Boyle, M. P. (2015). Relationships between psychosocial factors and quality of life for adults who stutter. American Journal of Speech-Language Pathology, 24(1), 1–12. https://doi.org/10.1044/2014_AJSLP-14-0089
Capal, J. K., Macklin, E. A., Lu, F., & Barnes, G. (2020). Factors associated with seizure onset in children with autism spectrum disorder. Pediatrics, 145(Suppl 1), S117–S125. https://doi.org/10.1542/peds.2019-1895O
Caron, V., Jeanneret, N., Giroux, M., Guerrero, L., Ouimet, M., Forgeot d’Arc, B., Soulières, I., & Courcy, I. (2022). Sociocultural context and autistics’ quality of life: A comparison between Québec and France. Autism: The International Journal of Research and Practice, 26(4), 900–913. https://doi.org/10.1177/13623613211035229
Carter, A., Breen, L., Yaruss, J. S., & Beilby, J. (2017). Self-efficacy and quality of life in adults who stutter. Journal of Fluency Disorders, 54, 14–23. https://doi.org/10.1016/j.jfludis.2017.09.004
Cavanna, A. E., David, K., Orth, M., & Robertson, M. M. (2012). Predictors during childhood of future health-related quality of life in adults with Gilles de la Tourette syndrome. European Journal of Paediatric Neurology, 16(6), 605–612. https://doi.org/10.1016/j.ejpn.2012.02.004
Chiang, H.-M., & Wineman, I. (2014). Factors associated with quality of life in individuals with autism spectrum disorders: A review of literature. Research in Autism Spectrum Disorders, 8(8), 974–986. https://doi.org/10.1016/j.rasd.2014.05.003
Chou, Y. C., Schalock, R. L., Tzou, P. Y., Lin, L. C., Chang, A. L., Lee, W. P., & Chang, S. C. (2007). Quality of life of adults with intellectual disabilities who live with families in Taiwan. Journal of Intellectual Disability Research: JIDR, 51(Pt 11), 875–883. https://doi.org/10.1111/j.1365-2788.2007.00958.x
Coales, C., Heaney, N., Ricketts, J., Dockrell, J. E., Lindsay, G., Palikara, O., & Charman, T. (2019). Health-related quality of life in children with autism spectrum disorders and children with developmental language disorders. Autism & Developmental Language Impairments, 4, 239694151985122. https://doi.org/10.1177/2396941519851225
Coghill, D. (2010). The impact of medications on quality of life in attention-deficit hyperactivity disorder. CNS Drugs, 24(10), 843–866. https://doi.org/10.2165/11537450-000000000-00000
Coghill, D. R., Banaschewski, T., Soutullo, C., Cottingham, M. G., & Zuddas, A. (2017). Systematic review of quality of life and functional outcomes in randomized placebo-controlled studies of medications for attention-deficit/hyperactivity disorder. European Child & Adolescent Psychiatry, 26(11), 1283–1307. https://doi.org/10.1007/s00787-017-0986-y
Corbera, S., Wexler, B. E., Bell, M. D., Pearlson, G., Mayer, S., Pittman, B., Belamkar, V., & Assaf, M. (2021). Predictors of social functioning and quality of life in schizophrenia and autism spectrum disorder. Psychiatry Research, 303, 114087. https://doi.org/10.1016/j.psychres.2021.114087
Cordova, M. A. C., Stausberg, D., Wang, B., Becker, A., Rothenberger, A., Herrmann-Lingen, C., et al. (2021). Headache is associated with low systolic blood pressure and psychosocial problems in german adolescents: Results from the population-based german kiggs study. Journal of Clinical Medicine, 10(7), 1492.
Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. [Internet]. Available from: www.covidence.org
Craig, F., Margari, F., Legrottaglie, A. R., Palumbi, R., de Giambattista, C., & Margari, L. (2016). A review of executive function deficits in autism spectrum disorder and attention-deficit/hyperactivity disorder. Neuropsychiatric Disease and Treatment, 12, 1191–1202. https://doi.org/10.2147/NDT.S104620
Cramm, J. M., & Nieboer, A. P. (2012). Longitudinal study of parents’ impact on quality of life of children and young adults with intellectual disabilities. Journal of Applied Research in Intellectual Disabilities: JARID, 25(1), 20–28. https://doi.org/10.1111/j.1468-3148.2011.00640.x
Crawford, C., Burns, J., & Fernie, B. A. (2015). Psychosocial impact of involvement in the special olympics. Research in Developmental Disabilities, 45–46, 93–102. https://doi.org/10.1016/j.ridd.2015.07.009
Czyzewski, K. (2011). Colonialism as a broader social determinant of health. International Indigenous Policy Journal. https://doi.org/10.18584/iipj.2011.2.1.5
Damme, T. V., Simons, J., Sabbe, B., & van West, D. (2015). Motor abilities of children and adolescents with a psychiatric condition: A systematic literature review. World Journal of Psychiatry, 5(3), 315–329.
Danckaerts, M., Sonuga-Barke, E. J. S., Banaschewski, T., Buitelaar, J., Dopfner, M., Hollis, C., Santosh, P., Rothenberger, A., Sergeant, J., Steinhausen, H.-C., Taylor, E., Zuddas, A., & Coghill, D. (2010). The quality of life of children with attention deficit/hyperactivity disorder: A systematic review. European Child and Adolescent Psychiatry, 19(2), 83–105. https://doi.org/10.1007/s00787-009-0046-3
Davies, G. A., Winn, C. O. N., Mackintosh, K. A., Eddolls, W. T. B., Stratton, G., McNarry, M. A., et al. (2019). Effect of high-intensity interval training in adolescents with asthma: The exercise for Asthma with Commando Joe’s (X4ACJ) trial. Journal of Sport and Health Science, 10, 488.
de Vries, M., Verdam, M. G., Prins, P. J., Schmand, B. A., & Geurts, H. M. (2018). Exploring possible predictors and moderators of an executive function training for children with an autism spectrum disorder. Autism: The International Journal of Research and Practice, 22(4), 440–449. https://doi.org/10.1177/1362361316682622
DeFilippis, M. (2018). Depression in children and adolescents with autism spectrum disorder. Children, 5(9), 112.
Díaz-Román, A., Mitchell, R., & Cortese, S. (2018). Sleep in adults with ADHD: Systematic review and meta-analysis of subjective and objective studies. Neuroscience and Biobehavioral Reviews, 1(89), 61–71.
Díaz-Román, A., Perestelo-Pérez, L., & Buela-Casal, G. (2015). Sleep in obsessive–compulsive disorder: A systematic review and meta-analysis. Sleep Medicine, 16(9), 1049–1055.
Dijkhuis, R. R., Ziermans, T. B., Van Rijn, S., Staal, W. G., & Swaab, H. (2017). Self-regulation and quality of life in high-functioning young adults with autism. Autism: The International Journal of Research and Practice, 21(7), 896–906. https://doi.org/10.1177/1362361316655525
Doja, A., Bookwala, A., Pohl, D., Rossi-Ricci, A., Barrowman, N., Chan, J., & Longmuir, P. E. (2018). Relationship between physical activity, tic severity and quality of life in children with tourette syndrome. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 27(4), 222–227.
Dolgun, G., Savaşer, S., & Yazgan, Y. (2014). Determining the correlation between quality of life and self-concept in children with attention deficit/hyperactivity disorder. Journal of Psychiatric and Mental Health Nursing, 21(7), 601–608. https://doi.org/10.1111/jpm.12114
DuPaul, G. J., Volpe, R. J., Jitendra, A. K., Lutz, J. G., Lorah, K. S., & Gruber, R. (2004). Elementary school students with AD/HD: Predictors of academic achievement. Journal of School Psychology, 42(4), 285–301.
Eapen, V., Snedden, C., Črnčec, R., Pick, A., & Sachdev, P. (2016). Tourette syndrome, co-morbidities and quality of life. The Australian and New Zealand Journal of Psychiatry, 50(1), 82–93. https://doi.org/10.1177/0004867415594429
Eddy, C. M., Cavanna, A. E., Gulisano, M., Agodi, A., Barchitta, M., Calì, P., Robertson, M. M., & Rizzo, R. (2011). Clinical correlates of quality of life in Tourette syndrome. Movement Disorders, 26(4), 735–738. https://doi.org/10.1002/mds.23434
Edvinsson, D., & Ekselius, L. (2018). Six-year outcome in subjects diagnosed with attention-deficit/hyperactivity disorder as adults. European Archives of Psychiatry and Clinical Neuroscience, 268(4), 337–347. https://doi.org/10.1007/s00406-017-0850-6
Engel-Yeger, B. (2022). Emotional status and quality of life in women with ADHD during COVID-19. OTJR: Occupational Therapy Journal of Research, 42(3), 219–227. https://doi.org/10.1177/15394492221076516
Estes, A., Rivera, V., Bryan, M., Cali, P., & Dawson, G. (2011). Discrepancies between academic achievement and intellectual ability in higher-functioning school-aged children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 41(8), 1044–1052.
Evans, S., Sciberras, E., & Mulraney, M. (2020). The relationship between maternal stress and boys’ ADHD Symptoms and quality of life: An Australian prospective cohort study. Journal of Pediatric Nursing, 50, e33–e38. https://doi.org/10.1016/j.pedn.2019.09.029
Evers, K., Maljaars, J., Schepens, H., Vanaken, G., & Noens, I. (2022). Conceptualization of quality of life in autistic individuals. Developmental Medicine & Child Neurology, 64(8), 950–956. https://doi.org/10.1111/dmcn.15205
Ferrans, C. E., Zerwic, J. J., Wilbur, J. E., & Larson, J. L. (2005). Conceptual model of health-related quality of life. Journal of Nursing Scholarship, 37(4), 336–342.
Fischer-Terworth, C. (2013). Obsessive-compulsive disorder in children and adolescents: Impact on academic and psychosocial functioning in the school setting. Life Span and Disability, 16(2), 127–155.
Flor, J., Bellando, J., Lopez, M., & Shui, A. (2017). Developmental functioning and medical co-morbidity profile of children with complex and essential autism. Autism Research: Official Journal of the International Society for Autism Research, 10(8), 1344–1352. https://doi.org/10.1002/aur.1779
Folostina, R., Iacob, C. I., & Syriopoulou-Delli, C. K. (2023). Physical activity, sedentary behaviour and quality of life in children with autism: Insights from Romania and Greece. International Journal of Developmental Disabilities, 69(3), 432–441. https://doi.org/10.1080/20473869.2023.2204574
Francés, L., Quintero, J., Fernández, A., Ruiz, A., Caules, J., Fillon, G., Hervás, A., & Soler, C. V. (2022). Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5: A systematic review in accordance with the PRISMA criteria. Child and Adolescent Psychiatry and Mental Health, 16(1), 27. https://doi.org/10.1186/s13034-022-00462-1
Galloway, H., Newman, E., Miller, N., & Yuill, C. (2019). Does parent stress predict the quality of life of children with a diagnosis of ADHD? A comparison of parent and child perspectives. Journal of Attention Disorders, 23(5), 435–450. https://doi.org/10.1177/1087054716647479
Georgiadou, I., Vlachou, A., & Stavroussi, P. (2022). Quality of life and vocational education service quality in students with intellectual disability. International Journal of Developmental Disabilities, 68(5), 681–691. https://doi.org/10.1080/20473869.2021.1887435
Gerlach, H., Chaudoir, S. R., & Zebrowski, P. M. (2021). Relationships between stigma-identity constructs and psychological health outcomes among adults who stutter. Journal of Fluency Disorders, 70, 105842. https://doi.org/10.1016/j.jfludis.2021.105842
Gortz-Dorten, A., Breuer, D., Hautmann, C., Rothenberger, A., & Dopfner, M. (2011). What contributes to patient and parent satisfaction with medication in the treatment of children with ADHD? A report on the development of a new rating scale. European Child & Adolescent Psychiatry, 20, S297–S307. https://doi.org/10.1007/s00787-011-0207-z10.1007/s00787-011-0207-z
Grenwald-Mayes, G. (2002). Relationship between current quality of life and family of origin dynamics for college students with attention-deficit/hyperactivity disorder. Journal of Attention Disorders, 5(4), 211–222. https://doi.org/10.1177/108705470100500403
Gu, X., Zhang, T., Chen, S., Keller, M. J., & Zhang, X. (2020). School-based sedentary behavior, physical activity, and health-related outcomes among hispanic children in the united states: A cross-sectional study. International Journal of Environmental Research and Public Health, 17(4), 1197.
He, J. A., Antshel, K. M., Biederman, J., & Faraone, S. V. (2019). Do personality traits predict functional impairment and quality of life in adult ADHD? A controlled study. Journal of Attention Disorders, 23(1), 12–21. https://doi.org/10.1177/1087054715613440
Hematian, K., Alborzi, S., & Khayyer, M. (2009). Quality of life of Iranian vocational students with and without intellectual disability. Psychological Reports, 105(3 Pt 1), 738–746. https://doi.org/10.2466/PR0.105.3.738-746
Hesapçıoğlu, S. T., Tural, M. K., & Kandil, S. (2014). Quality of life and self-esteem in children with chronic tic disorder. Turk Pediatri Arsivi, 49(4), 323–332. https://doi.org/10.5152/tpa.2014.1947
Hulsbosch, A.-K., De Meyer, H., Beckers, T., Danckaerts, M., Van Liefferinge, D., Tripp, G., & Van der Oord, S. (2021). Systematic review: Attention-deficit/hyperactivity disorder and instrumental learning. Journal of the American Academy of Child & Adolescent Psychiatry, 60(11), 1367–1381. https://doi.org/10.1016/j.jaac.2021.03.009
Isaacs, D. A., Riordan, H. R., & Claassen, D. O. (2021). Clinical correlates of health-related quality of life in adults with chronic tic disorder. Frontiers in Psychiatry, 12, 619854. https://doi.org/10.3389/fpsyt.2021.619854
Jahan, M. U., Karim, M. R., Rahman, A., & Akhter, S. (2015). Cognitive functions and health related quality of life of institutional autism spectrum disorder children in Dhaka city. Bangladesh Medical Research Council Bulletin, 41(3), 151–159.
Karande, S., & Venkataraman, R. (2012). Self-perceived health-related quality of life of Indian children with specific learning disability. Journal of Postgraduate Medicine, 58(4), 246–254. https://doi.org/10.4103/0022-3859.105442
Karci, C., Toros, F., Yolga Tahiroglu, A., & Metin, Ö. (2018). Effects of methylphenidate treatment on quality of life in adolescents. Dusunen Adam: The Journal of Psychiatry and Neurological Sciences, 31, 11–20. https://doi.org/10.5350/DAJPN2018310101
Khanlou, N., Haque, N., Mustafa, N., Vazquez, L. M., Mantini, A., & Weiss, J. (2017). Access barriers to services by immigrant mothers of children with autism in Canada. International Journal of Mental Health and Addiction, 15(2), 239–259.
Kim, P. J. (2019). Social determinants of health inequities in indigenous canadians through a life course approach to colonialism and the residential school system. Health Equity, 3(1), 378–381. https://doi.org/10.1089/heq.2019.0041
Klang, A., Westerberg, B., Humble, M. B., & Bejerot, S. (2022). The impact of schizotypy on quality of life among adults with autism spectrum disorder. BMC Psychiatry, 22(1), 205. https://doi.org/10.1186/s12888-022-03841-2
Koedoot, C., Bouwmans, C., Franken, M.-C., & Stolk, E. (2011). Quality of life in adults who stutter. Journal of Communication Disorders, 44(4), 429–443. https://doi.org/10.1016/j.jcomdis.2011.02.002
Kuhlthau, K. A., McDonnell, E., Coury, D. L., Payakachat, N., & Macklin, E. (2018). Associations of quality of life with health-related characteristics among children with autism. Autism, 22(7), 804–813. https://doi.org/10.1177/1362361317704420
Kuhlthau, K., Orlich, F., Hall, T. A., Sikora, D., Kovacs, E. A., Delahaye, J., et al. (2010). Health-related quality of life in children with autism spectrum disorders: Results from the autism treatment network. Journal of Autism and Developmental Disorders, 40(6), 721–729.
Kushki, A., Anagnostou, E., Hammill, C., Duez, P., Brian, J., Iaboni, A., et al. (2019). Examining overlap and homogeneity in ASD, ADHD, and OCD: A data-driven, diagnosis-agnostic approach. Translational Psychiatry, 9(1), 318.
Lachapelle, Y., Wehmeyer, M. L., Haelewyck, M.-C., Courbois, Y., Keith, K. D., Schalock, R., Verdugo, M. A., & Walsh, P. N. (2005). The relationship between quality of life and self-determination: An international study. Journal of Intellectual Disability Research: JIDR, 49(Pt 10), 740–744. https://doi.org/10.1111/j.1365-2788.2005.00743.x
Lack, C. W., Storch, E. A., Keeley, M. L., Geffken, G. R., Ricketts, E. D., Murphy, T. K., et al. (2009). Quality of life in children and adolescents with obsessive-compulsive disorder: Base rates, parent–child agreement, and clinical correlates. Social Psychiatry and Psychiatric Epidemiology, 44(11), 935–942.
Lai, M. -C., Kassee, C., Besney, R., Bonato, S., Hull, L., Mandy, W., Szatmari, P., & Ameis, S. H. (2019). Prevalence of co-occurring mental health diagnoses in the autism population: A systematic review and meta-analysis. The Lancet Psychiatry, 6(10), 819–829. https://doi.org/10.1016/S2215-0366(19)30289-5
Lawson, L. P., Richdale, A. L., Haschek, A., Flower, R. L., Vartuli, J., Arnold, S. R., & Trollor, J. N. (2020). Cross-sectional and longitudinal predictors of quality of life in autistic individuals from adolescence to adulthood: The role of mental health and sleep quality. Autism: The International Journal of Research and Practice. https://doi.org/10.1177/1362361320908107
Lee, H., Park, S., Lee, J., & Lee, M.-S. (2020). Relationships among tic symptoms, expressed emotions, and quality of life in tic disorder patients. Journal of Child and Family Studies. https://doi.org/10.1007/s10826-019-01651-x
Lee, J.-H., Maeng, S., Lee, J.-S., Bae, J.-N., Kim, W.-H., & Kim, H. (2022). The difference in the quality of life of Korean children with attention-deficit/hyperactivity disorder between before and after COVID-19. Journal of Child & Adolescent Psychiatry, 33(4), 113–121. https://doi.org/10.5765/jkacap.220019
Lin, Y. C. (2019). Quality of life and its related factors for adults with autism spectrum disorder. Disability and Rehabilitation, 41(8), 896–903.
Liu, F., Wang, G., Yao, B., Ye, J., Wang, J., Wang, H., & Liu, H. (2023). Cross-sectional investigation of quality of life determinants among children with tic disorders: The roles of family environmental and clinical factors. Heliyon, 9(2), e13228. https://doi.org/10.1016/j.heliyon.2023.e13228
Logrieco, M. G., Casula, L., Ciuffreda, G. N., Novello, R. L., Spinelli, M., Lionetti, F., Nicolì, I., Fasolo, M., Giovanni, V., & Stefano, V. (2022). Risk and protective factors of quality of life for children with autism spectrum disorder and their families during the COVID-19 lockdown, An Italian study. Research in Developmental Disabilities, 120, 104130. https://doi.org/10.1016/j.ridd.2021.104130
Lord, C., Charman, T., Havdahl, A., Carbone, P., Anagnostou, E., Boyd, B., et al. (2022). The lancet commission on the future of care and clinical research in autism. The Lancet, 399(10321), 271–334.
Lucey, J., Evans, D., & Maxfield, N. D. (2019). Temperament in adults who stutter and its association with stuttering frequency and quality-of-life impacts. Journal of Speech, Language, and Hearing Research: JSLHR, 62(8), 2691–2702. https://doi.org/10.1044/2019_JSLHR-S-18-0225
Malow, B. A., Katz, T., Reynolds, A. M., Shui, A., Carno, M., Connolly, H. V., Coury, D., & Bennett, A. E. (2016). Sleep difficulties and medications in children with autism spectrum disorders: a registry study. Pediatrics, 137(Suppl 2), S98–S104. https://doi.org/10.1542/peds.2015-2851H
Mason, D., McConachie, H., Garland, D., Petrou, A., Rodgers, J., & Parr, J. R. (2018). Predictors of quality of life for autistic adults. Autism Research, 11(8), 1138–1147. https://doi.org/10.1002/aur.1965
Mazon, C., Sauzeon, H., Fage, C., Consel, C., Amestoy, A., Bouvard, M., Etchegoyhen, K., & Hesling, I. (2019). Cognitive mediators of school-related socio-adaptive behaviors in ASD and intellectual disability pre-and adolescents: A pilot-study in french special education classrooms. Brain Sciences, 9(12), 334. https://doi.org/10.3390/brainsci9120334
McConachie, H., Mason, D., Parr, J. R., Garland, D., Wilson, C., & Rodgers, J. (2018). Enhancing the validity of a quality of life measure for autistic people. Journal of Autism and Developmental Disorders, 48(5), 1596–1611. https://doi.org/10.1007/s10803-017-3402-z
McGuire, J. F., Park, J. M., Wu, M. S., Lewin, A. B., Murphy, T. K., & Storch, E. A. (2015). The impact of tic severity dimensions on impairment and quality of life among youth with chronic tic disorders. Children’s Health Care, 44(3), 277–292. https://doi.org/10.1080/02739615.2014.912944
Meral, B. F., & Fidan, A. (2015). Measuring the impact of feeding covariates on health-related quality of life in children with autism spectrum disorder. Research in Autism Spectrum Disorders, 10, 124–130. https://doi.org/10.1016/j.rasd.2014.11.009
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 21(339), b2535.
Moritz, S. (2008). A review on quality of life and depression in obsessive-compulsive disorder. CNS Spectrums, 13, 16–22.
Mulraney, M., Giallo, R., Sciberras, E., Lycett, K., Mensah, F., & Coghill, D. (2019). ADHD symptoms and quality of life across a 12-month period in children with ADHD: A longitudinal study. Journal of Attention Disorders, 23(13), 1675–1685.
Neurodevelopmental Disorders. In: Diagnostic and statistical manual of mental disorders [Internet]. American Psychiatric Association; 2013 [cited 2021 May 10]. (DSM Library). Available from: https://doi.org/10.1176/appi.books.9780890425596.dsm01
Nicholson, E., Guerin, S., Keogh, F., & Dodd, P. (2019). Comparing traditional-residential, personalised residential and personalised non-residential respite services: quality of life findings from an Irish population with mild–moderate intellectual disabilities. British Journal of Learning Disabilities, 47(1), 12–18. https://doi.org/10.1111/bld.12237
Nippold, M. A., & Schwarz, I. E. (1990). Reading disorders in stuttering children. Journal of Fluency Disorders, 15(3), 175–189. https://doi.org/10.1016/0094-730X(90)90017-M
Oakley, B. F., Tillmann, J., Ahmad, J., Crawley, D., San José Cáceres, A., Holt, R., et al. (2021). How do core autism traits and associated symptoms relate to quality of life findings from the longitudinal European autism project. Autism, 25(2), 389–404.
Ofner M, Coles A, Decou ML (2018) Autism spectrum disorder among children and youth in Canada 2018: a report of the national autism spectrum disorder surveillance system [Internet]. [Cited 2020 Nov 17]. Available from: https://www.deslibris.ca/ID/10096072
Orm, S., Oie, M. G., Fossum, I. N., Fjermestad, K., Andersen, P. N., & Skogli, E. W. (2023). Predictors of quality of life and functional impairments in emerging adults with and without ADHD: A 10-year longitudinal study. Journal of Attention Disorders, 27(5), 458–469. https://doi.org/10.1177/10870547231153962
Ozboke, C., Yilmaz, I., & Yanardag, M. (2021). Exploring the relationships between motor proficiency, independence and quality of life in adolescents with autism spectrum disorder. International Journal of Developmental Disabilities. https://doi.org/10.1080/20473869.2021.1900506
Park, B.-E., Lee, J.-S., Kim, H.-Y., Bae, J.-N., Kim, W.-H., Kim, H.-Y., Rim, M.-R., Kang, S.-G., & Choi, S.-H. (2019). The influence of depression and school life on the quality of life of Korean child and adolescent patients with attention-deficit/hyperactivity disorder: a comparison of the perspectives of the patients and their caregivers. Journal of Child & Adolescent Psychiatry, 30(1), 2–8. https://doi.org/10.5765/jkacap.180027
Payakachat, N., Tilford, J. M., Kuhlthau, K. A., van Exel, N. J., Kovacs, E., Bellando, J., Pyne, J. M., & Brouwer, W. B. F. (2014). Predicting health utilities for children with autism spectrum disorders. Autism Research, 7(6), 649–663. https://doi.org/10.1002/aur.1409
Pearlman-Avnion, S., Cohen, N., & Eldan, A. (2017). Sexual well-being and quality of life among high-functioning adults with autism. Sexuality and Disability, 35(3), 279–293. https://doi.org/10.1007/s11195-017-9490-z
Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434–442. https://doi.org/10.1093/ije/dyt261
Ragab, M. M., Eid, E. M., & Badr, N. H. (2020). Effect of demographic factors on quality of life in children with adhd under atomoxetine treatment: 1-year follow-up. Journal of Child Science, 10(1), E163–E168. https://doi.org/10.1055/s-0040-1717104
Randall, K. N., Bernard, G., & Durah, L. (2023). Association between employment status and quality of life for individuals with intellectual or developmental disability. Journal of Applied Research in Intellectual Disabilities: JARID, 36(2), 270–280. https://doi.org/10.1111/jar.13053
Redondo-Tebar, A., Ruiz-Hermosa, A., Martinez-Vizcaino, V., Cobo-Cuenca, A. I., Bermejo-Cantarero, A., Cavero-Redondo, I., et al. (2019). Associations between health-related quality of life and physical fitness in 4–7-year-old Spanish children: The MOVIKIDS study. Quality of Life Research, 28(7), 1751–1759.
Renty, J. O., & Roeyers, H. (2006). Quality of life in high-functioning adults with autism spectrum disorder: The predictive value of disability and support characteristics. Autism: The International Journal of Research and Practice, 10(5), 511–524. https://doi.org/10.1177/1362361306066604
Rimmerman, A., Yurkevich, O., Birger, M., & Araten-Bergman, T. (2005). Quality of life of men and women with borderline intelligence and attention deficit disorders living in community residences: A comparative study. Journal of Attention Disorders, 9(2), 435–443. https://doi.org/10.1177/1087054705281765
Rimmerman, A., Yurkevich, O., Birger, M., Azaiza, F., & Elyashar, S. (2007). Quality of life of Israeli adults with borderline intelligence quotient and attention-deficit/hyperactivity disorder. International Journal of Rehabilitation Research, 30(1), 55–60. https://doi.org/10.1097/MRR.0b013e328013d8a0
Roestorf, A., Howlin, P., & Bowler, D. M. (2022). Ageing and autism: A longitudinal follow-up study of mental health and quality of life in autistic adults. Frontiers in Psychology, 13, 741213. https://doi.org/10.3389/fpsyg.2022.741213
Sahan, N., Esen, H. T. C., Parmaksiz, A., & Uysal, S. A. (2020). Self- and parent-rated quality of life in school aged children with ADHD: The impact of common comorbid psychiatric disorders and motor proficiency. Psychiatry and Clinical Psychopharmacology, 30(4), 403–414. https://doi.org/10.5455/PCP.20200818114525
Sasinthar, K., Sugumaran, A., Boratne, A. V., & Patil, R. K. (2022). Health-related quality of life of intellectually disabled children attending a special school in Puducherry—A cross-sectional study. Journal of Family Medicine and Primary Care, 11(8), 4549–4554. https://doi.org/10.4103/jfmpc.jfmpc_520_21
Schafer, T. K., Herrmann-Lingen, C., & Meyer, T. (2016). Association of circulating 25-hydroxyvitamin D with mental well-being in a population-based, nationally representative sample of German adolescents. Quality of Life Research, 25(12), 3077–3086.
Schalock, R. L., & Keith, K. D. (1993). Quality of life questionnaire. European Journal of Psychological Assessment. https://doi.org/10.1037/t10624-000
Schatz, D. B., & Rostain, A. L. (2006). ADHD with comorbid anxiety: A review of the current literature. Journal of Attention Disorders, 10(2), 141–149.
Schuck, R. K., Tagavi, D. M., Baiden, K. M. P., Dwyer, P., Williams, Z. J., Osuna, A., Ferguson, E. F., Jimenez Muñoz, M., Poyser, S. K., Johnson, J. F., & Vernon, T. W. (2022). Neurodiversity and autism intervention: Reconciling perspectives through a naturalistic developmental behavioral intervention framework. Journal of Autism and Developmental Disorders, 52(10), 4625–4645. https://doi.org/10.1007/s10803-021-05316-x
Sevastidis, A., Dona, S. W. A., Gold, L., Sciberra, E., Coghill, D., & Le, H. N. D. (2023). Social gradient in use of health services and health-related quality of life of children with attention-deficit/hyperactivity disorder: A systematic review. JCPP Advances, 3(3), e12170. https://doi.org/10.1002/jcv2.12170
Sorkhi, N., Akbarzade, I., Nedjat, S., Khosravi, M., Nazemipour, M., Memari, A.-H., & Mansournia, M. A. (2022). Validity and reliability of the persian version of the world health organization quality of life disabilities module. Journal of Intellectual Disabilities: JOID. https://doi.org/10.1177/17446295221123867
Stern, J. S., & Robertson, M. M. (1997). Tics associated with autistic and pervasive developmental disorders. Neurologic Clinics, 15(2), 345–355. https://doi.org/10.1016/S0733-8619(05)70317-0
Stoeckel, D., Brkić, M., & Vesić, Z. (2022). Supported housing services for people with intellectual disabilities and mental health problems in Serbia-Social and community integration or “mini-institutions.” Health & Social Care in the Community, 30(5), e2917–e2927. https://doi.org/10.1111/hsc.13736
Torrente, F., López, P., Alvarez Prado, D., Kichic, R., Cetkovich-Bakmas, M., Lischinsky, A., & Manes, F. (2014). Dysfunctional cognitions and their emotional, behavioral, and functional correlates in adults with attention deficit hyperactivity disorder (ADHD): Is the cognitive-behavioral model valid? Journal of Attention Disorders, 18(5), 412–424. https://doi.org/10.1177/1087054712443153
Tsiros, M. D., Samaras, M. G., Coates, A. M., & Olds, T. (2017). Use-of-time and health-related quality of life in 10- to 13-year-old children: Not all screen time or physical activity minutes are the same. Quality of Life Research, 26(11), 3119–3129.
Ueda, R., Okada, T., Kita, Y., Ozawa, Y., Inoue, H., Shioda, M., Kono, Y., Kono, C., Nakamura, Y., Amemiya, K., Ito, A., Sugiura, N., Matsuoka, Y., Kaiga, C., Kubota, M., & Ozawa, H. (2021). Psychological status associated with low quality of life in school-age children with neurodevelopmental disorders during COVID-19 stay-at-home period. Frontiers in Psychiatry, 12, 676493. https://doi.org/10.3389/fpsyt.2021.676493
van Asselt-Goverts, A. E., Embregts, P. J. C. M., & Hendriks, A. H. C. (2015). Social networks of people with mild intellectual disabilities: Characteristics, satisfaction, wishes and quality of life. Journal of Intellectual Disability Research: JIDR, 59(5), 450–461. https://doi.org/10.1111/jir.12143
van der Kolk, A., Bouwmans, C. A. M., Schawo, S. J., Buitelaar, J. K., van Agthoven, M., & Hakkaart-van Roijen, L. (2014). Association between quality of life and treatment response in children with attention deficit hyperactivity disorder and their parents. The Journal of Mental Health Policy and Economics, 17(3), 119–129.
Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQLTM 4.0: reliability and validity of the pediatric quality of life inventoryTM version 4.0 generic core scales in healthy and patient populations. Medical Care, 39(8), 800–12.
Vincent, A., Da Fonseca, D., Baumstarck, K., Charvin, I., Alcaraz-Mor, R., & Lehucher-Michel, M.-P. (2020). The quality of life and the future of young adults with asperger syndrome. Disability and Rehabilitation, 42(14), 1987–1994. https://doi.org/10.1080/09638288.2018.1544297
Wanni Arachchige Dona, S., Badloe, N., Sciberras, E., Gold, L., Coghill, D., & Le, H. N. D. (2023). The impact of childhood attention-deficit/hyperactivity disorder (ADHD) on children’s health-related quality of life: A systematic review and meta-analysis. Journal of Attention Disorders, 27(6), 598–611. https://doi.org/10.1177/10870547231155438
White, K., Flanagan, T. D., & Nadig, A. (2018). Examining the relationship between self-determination and quality of life in young adults with autism spectrum disorder. Journal of Developmental and Physical Disabilities, 30(6), 735–754. https://doi.org/10.1007/s10882-018-9616-y
Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. JAMA, 273(1), 59–65.
Wong, I. Y. T., Hawes, D. J., & Dar-Nimrod, I. (2019). Illness representations among adolescents with attention deficit hyperactivity disorder: Associations with quality of life, coping, and treatment adherence. Heliyon, 5(10), e02705. https://doi.org/10.1016/j.heliyon.2019.e02705
World Health Organization, Division of Mental Health and Prevention of Substance Abuse (2012). WHOQOL User Manual. World Health Organization. https://iris.who.int/bitstream/handle/10665/77932/WHO_HIS_HSI_Rev.2012.03_eng.pdf?sequence=1
Yarar, E. Z., Roestorf, A., Spain, D., Howlin, P., Bowler, D., Charlton, R., & Happé, F. (2022). Aging and autism: Do measures of autism symptoms, co-occurring mental health conditions, or quality of life differ between younger and older autistic adults? Autism Research, 15(8), 1482–1494. https://doi.org/10.1002/aur.2780
Zinner, S. H., Conelea, C. A., Glew, G. M., Woods, D. W., & Budman, C. L. (2012). Peer victimization in youth with Tourette syndrome and other chronic tic disorders. Child Psychiatry and Human Development, 43(1), 124–136. https://doi.org/10.1007/s10578-011-0249-y
Funding
Canadian Institutes of Health Research.
Author information
Authors and Affiliations
Contributions
MM conceptualized and designed the study; collected, screened, reviewed, extracted, and analysed the study data, drafted the original manuscript; and revised the manuscript. RC contributed to the design of the study; screened, reviewed, and extracted the study data; generated the manuscript figures; and reviewed and revised the manuscript. TP, HB, JC, and SK contributed to the design of the study and screened, reviewed, and extracted the study data. EA, BA, and MP contributed to the concept and design of the study and critically reviewed and revised the manuscript. AK conceptualized and designed the study, supervised the data collection, and critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.
Corresponding author
Ethics declarations
Competing Interests
AK and EA are the inventor of a software called the “holly” (formerly, “Anxiety Meter”.) They are involved in commercializing the holly (patents US 9,844,332 B2 and US 16/276,208 (pending)) and will financially benefit from its sales. AK served on the board of advisors for Shaftesbury, a media company developing virtual reality products for autistic children, from February 2020—February 2021, and was compensated financially for this role. AK has received donations of hardware for her research programme from Samsung Canada. AK also reports personal fees from DNAStack. EA reports grants from Roche, personal fees from Roche, personal fees from Quadrant, personal fees from Wiley, book royalties from Springer, book royalties from APPI, and non-financial support from AMO Pharma outside the submitted work. The other authors report no potential conflicts of interest.
Ethical approval
This systematic review adheres to all relevant ethical guidelines and principles. No human or animal subjects were involved in this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Mahjoob, M., Paul, T., Carbone, J. et al. Predictors of Health-Related Quality of Life in Neurodivergent Children: A Systematic Review. Clin Child Fam Psychol Rev 27, 91–129 (2024). https://doi.org/10.1007/s10567-023-00462-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10567-023-00462-3