Abstract
Objective
To evaluate domain-specific quality of life and its associations with functional severity, selected comorbidities, and social factors among children with cerebral palsy attending a tertiary referral centre in Sri Lanka.
Results
Among 223 children, 61.9% were male and the mean age was 6.99 years. Quality-of-life scores varied markedly across domains. Daily activities, school activities, movement and balance, eating activities, and speech and communication showed wide score dispersion, whereas pain and fatigue showed ceiling effects. Scores declined progressively with increasing Gross Motor Function Classification System level across analysed domains, with strong monotonic trends observed (Kendall’s τ − 0.57 to − 0.72; all p < 0.001). Epilepsy, cognitive impairment, cortico-visual impairment, hearing impairment, and gastro-oesophageal reflux disease were associated with lower overall or domain-specific scores in unadjusted analyses. In adjusted models focused on measured social factors, age, sex, living arrangement, and parental education were not independently associated with the analysed domain scores. Domain-level assessment may provide clinically informative quality-of-life profiles that are not captured by overall scores alone.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13104-026-07943-y.
Keywords: Cerebral Palsy, Quality of Life, Gross Motor Function Classification System (GMFCS), Paediatric Quality of Life Inventory (PedsQL), Low-and middle-income Countries (LMICs)
Introduction
Cerebral palsy (CP) is the most common cause of childhood physical disability and is associated with lifelong motor impairment, activity limitation, and participation restriction [1]. Beyond survival and motor outcomes, quality of life (QoL) has emerged as a critical outcome in children with CP, reflecting their ability to function, participate in daily activities, and interact with their environment [1, 2]. QoL is increasingly recognised as a key indicator informing clinical care, rehabilitation planning, and health policy in paediatric neurodisability [3, 4].
QoL in children with CP is highly heterogeneous, even among those with similar clinical diagnoses [5, 6]. This variability highlights the limitations of purely diagnostic or symptom-based approaches and underscores the importance of considering functional severity and contextual factors that shape everyday experiences [7]. Functional impairment, commonly classified using the Gross Motor Function Classification System (GMFCS), has consistently been associated with differences in physical functioning, independence, and participation [8–10]. In addition, children with CP frequently experience epilepsy, intellectual impairment, sensory impairments, gastro-oesophageal reflux disease, and autism spectrum disorder, which may further restrict participation and negatively influence QoL [11, 12].
Socioeconomic and family-related factors may also affect QoL outcomes, particularly in low- and middle-income countries (LMICs), where access to rehabilitation services, assistive devices, and inclusive education may be limited [8, 13, 14]. However, existing evidence regarding the relative contribution of social factors beyond functional severity remains inconsistent [6, 15]. Moreover, most studies rely on overall or summary QoL scores, which may obscure clinically meaningful differences across functional and participation-related domains [2, 3, 15].
Data on QoL in children with CP from LMIC settings remain limited, despite the majority of affected children globally residing in these contexts [10, 16]. This research note therefore presents a domain-specific analysis of QoL and its associations with functional severity, comorbidities, and selected social factors among children with CP attending a tertiary referral centre in Sri Lanka.
Methods
Study design, setting, and participants
An analytical cross-sectional study was conducted among children with cerebral palsy (CP) attending the paediatric neurology clinics at Teaching Hospital Kurunegala, Sri Lanka, over a one-year period (March 2022–February 2023. Children aged 2–17 years with a confirmed diagnosis of CP based on standard diagnostic criteria [17] were eligible. Written informed consent was obtained from parents or legal guardians, with assent obtained from children where appropriate.
Sampling and eligibility
All eligible and consented children attending the clinic during the study period were recruited consecutively (n = 226). Exclusion criteria included acute medical illness at assessment, receipt of palliative care, major non-CP neurological or genetic disorders likely to independently affect functional status or quality of life, and incomplete essential data being either a confirmed diagnosis of CP or data required for the confirmation of the diagnosis of CP.
Acute medical illness was defined as an intercurrent illness requiring active treatment at the time of assessment, such as febrile illness, acute respiratory infection, or another clinically unstable condition.
Data collection instruments
Data were collected using a structured three-section data collection form comprising socio-demographic information, clinical characteristics, and quality-of-life assessment. The study-specific socio-demographic and clinical sections were developed for this study and pretested in a pilot sample of 20 eligible children to assess clarity, feasibility, and completeness. An English-language version of the study-specific data collection form is provided as Additional file 1.
Quality of life was assessed using the caregiver-proxy Paediatric Quality of Life Inventory Cerebral Palsy Module, a standard previously published instrument. Permission to use the PedsQL™ Cerebral Palsy Module was obtained from Mapi Research Trust (Special Terms No. 128497) on behalf of the copyright holder, James W. Varni [18]. The present study represents a secondary analysis of data obtained from the licensed parent observational study, focusing specifically on quality-of-life outcomes.
Quality of life assessment
Quality of life was assessed using the Paediatric Quality of Life Inventory (PedsQL) [18], which includes 35 items across seven domains rated on a 5-point Likert scale. Item scores were reverse-scored and transformed to a 0–100 scale, with higher scores indicating better quality of life. Validated Sinhala [19] and Tamil [20] versions of the PedsQL were used, following standard forward–backward translation procedures.
Data collection procedure
The principal investigator collected socio-demographic data through caregiver interview and clinical data through caregiver interview and review of available clinic records. Clinical diagnoses were independently confirmed, and functional severity was classified by a trained investigator using the GMFCS Expanded and Revised system [21]. Caregivers completed the PedsQL in a quiet, private setting to minimise distraction and ensure confidentiality.
Clinical variables and comorbidity classification
Clinical variables included CP motor type, GMFCS level, and selected comorbidities relevant to functioning and QoL. Comorbidities were recorded as binary variables according to their presence or absence at assessment. The comorbidities assessed included epilepsy, cognitive impairment, cortico-visual impairment, hearing impairment, gastro-oesophageal reflux disease, and autism spectrum disorder. Comorbidity status was obtained from caregiver interview and verified, where available, using clinic records and specialist documentation.
The classification and distribution of assessed comorbidities are provided in Supplementary Table S1.
Statistical analysis
Analyses were conducted using complete cases for the primary QoL outcomes. Of 226 recruited children, 223 had complete PedsQL domain data and were included in the analytic cohort. No imputation was performed because only three records were excluded and the primary outcome required complete domain-level PedsQL scoring. Continuous variables were summarised as mean with standard deviation (SD) or median with interquartile range (IQR), and categorical variables as frequencies and percentages.
As PedsQL domain scores were seriously skewed, non-parametric methods were used throughout.
Differences in overall and domain-specific quality of life across GMFCS levels were assessed using the Kruskal–Wallis test, with ordinal trends examined using Kendall’s τ. Associations between specific binary comorbidities and PedsQL scores were evaluated using the Mann–Whitney U test. For each comorbidity-specific analysis, children were grouped according to the presence or absence of the individual comorbidity being assessed. Thus, for epilepsy, children with epilepsy were compared with children without epilepsy; for cognitive impairment, children with cognitive impairment were compared with children without cognitive impairment; and the same approach was applied to each assessed comorbidity. The “without” group therefore referred only to absence of the specific comorbidity under analysis and did not represent children without any comorbidity. Median differences were calculated as the median PedsQL score among children with the comorbidity minus the median score among children without that comorbidity.
Associations with socio-demographic variables were examined using Spearman correlation for continuous predictors and Mann–Whitney U or Kruskal–Wallis tests for categorical predictors.
Multivariable linear regression models were fitted separately for PedsQL domains that demonstrated adequate score dispersion, including daily activities, school activities, movement and balance, and speech and communication. Pain and fatigue domains were excluded from regression modelling because of marked ceiling effects and limited variability. Each domain score was entered as a continuous outcome variable. Models included age, sex, living arrangement, maternal education, paternal education, maternal occupation, and paternal occupation as covariates. Regression coefficients with 95% confidence intervals and p-values were reported. All analyses were two-sided, with statistical significance set at p < 0.05.
Results
Demographic and clinical characteristics
A total of 226 children with cerebral palsy were recruited, of whom 223 had complete PedsQL data and were included in the final analysis. The majority were male (61.9%), with a mean (SD) age of 6.99 (3.91) years. Participants were represented across all GMFCS levels, and spastic CP was the predominant motor type. Epilepsy, cortico-visual impairment, and cognitive impairment were the most frequent comorbidities. Detailed demographic characteristics, including age, sex, living arrangement, parental education and clinical characteristics, including Limb involvement, GMFCS level, and comorbidities are provided in Supplementary Table S2.
Distribution of domain-specific quality of life scores
Quality-of-life scores demonstrated substantial heterogeneity across functional and participation-related domains. Daily activities, school activities, movement and balance, eating activities, and speech and communication showed wide interquartile ranges, whereas pain and fatigue exhibited pronounced ceiling effects with minimal variability. Domain-specific score distributions are presented in Supplementary Table S3.
Association between functional severity (GMFCS) and quality of life
Domain-specific QoL scores declined progressively with increasing functional severity. Visualization of the data using heat map (Fig. 1) demonstrated lower median QoL scores across all functional and participation-related domains with increasing GMFCS level. These differences were statistically significant for all analysed domains (Kruskal–Wallis test; all p < 0.001), with strong monotonic trends observed on ordinal analysis (Kendall’s τ range − 0.57 to − 0.72; all p < 0.001). Pain and fatigue domains exhibited marked ceiling effects and were therefore excluded from inferential analyses and retained for descriptive reporting only.
Fig. 1.
Domain-specific Quality of Life scores across GMFCS levels. Heat map showing median PedsQL Cerebral Palsy Module domain scores across GMFCS levels I–V. Lower scores indicate poorer quality of life. Functional and participation-related domains showed a significant monotonic decline with increasing GMFCS level (Kendall’s τ range − 0.57 to − 0.72; all p < 0.001)
Association between comorbidities and domain-specific quality of life
In unadjusted analyses, several specific clinically documented comorbidities were significantly associated with lower overall and domain-specific quality of life (Table 1).
Table 1.
Association of functional comorbidities with overall and domain-specific quality of life
| Comorbidity | QoL outcome | Specific comorbidity present, Median (IQR) | Specific comorbidity absent, Median (IQR) | Median difference† |
|---|---|---|---|---|
| Epilepsy | Overall QoL | 45.71 (28.93–77.50) | 84.29 (51.43–95.71) | −38.58 |
| Daily activities | 27.78 (0.00–63.89) | 72.22 (33.33–94.44) | −44.44 | |
| School activities | 25.00 (0.00–100.00) | 100.00 (75.00–100.00) | −75.00 | |
| Movement & balance | 55.00 (20.00–80.00) | 80.00 (55.00–100.00) | −25.00 | |
| Speech & communication | 75.00 (18.75–100.00) | 100.00 (75.00–100.00) | −25.00 | |
| Cognitive impairment | Overall QoL | 42.86 (26.43–77.14) | 91.79 (84.82–98.04) | −48.93 |
| Daily activities | 11.11 (0.00–50.00) | 83.33 (55.56–100.00) | −72.22 | |
| School activities | 0.00 (0.00–75.00) | 100.00 (100.00–100.00) | −100.00 | |
| Movement & balance | 55.00 (13.75–76.25) | 92.50 (75.00–100.00) | −37.50 | |
| Speech & communication | 68.75 (18.75–100.00) | 100.00 (100.00–100.00) | −31.25 | |
| Cortico-visual impairment | Overall QoL | 37.14 (23.04–72.14) | 77.86 (43.04–92.86) | −40.72 |
| School activities | 0.00 (0.00–75.00) | 100.00 (75.00–100.00) | −100.00 | |
| Speech & communication | 50.00 (12.50–87.50) | 100.00 (75.00–100.00) | −50.00 | |
| Hearing impairment | Overall QoL | 22.86 (17.86–27.86) | 77.86 (42.14–90.71) | −55.00 |
| Speech & communication | 25.00 (0.00–50.00) | 93.75 (56.25–100.00) | −68.75 | |
| GERD | Overall QoL | 23.57 (22.50–30.89) | 80.71 (51.43–93.39) | −57.14 |
| Eating activities | 10.00 (0.00–31.25) | 80.00 (56.25–100.00) | −70.00 |
Values are median (IQR). Only statistically significant associations are shown. For each row block, “specific comorbidity present” and “specific comorbidity absent” refer only to the comorbidity listed in the first column. The absent group may include children with other assessed comorbidities; it does not represent a group with no comorbidity
† Median difference = median QoL score (with specific comorbidity present) − median QoL score (specific comorbidity absent)
For each assessed comorbidity, children were compared according to the presence or absence of that specific condition. For example, children with epilepsy were compared with children without epilepsy, irrespective of whether other comorbidities were present. Only statistically significant associations are presented. Children with epilepsy, cognitive impairment, cortico-visual impairment (CVI), hearing impairment, and gastro-oesophageal reflux disease had significantly lower median overall PedsQL scores than children without these conditions (all p < 0.001).
Domain-specific analyses revealed distinct and clinically coherent patterns of impairment. Epilepsy and cognitive impairment were consistently associated with lower scores across multiple functional and participation-related domains, including daily activities, school activities, movement and balance, and speech and communication (all p ≤ 0.002). Cognitive impairment showed particularly pronounced associations with school-related quality of life. CVI was selectively associated with poorer school activities and speech and communication domains, while hearing impairment predominantly affected speech and communication. Gastro-oesophageal reflux disease was primarily associated with lower eating-related quality of life, with smaller effects observed in other domains.
Social determinants of QoL
In unadjusted analyses, overall PedsQL scores were not significantly associated with age, sex, or living arrangement, nor with paternal education or occupation (all p > 0.05). In contrast, maternal education and maternal occupation demonstrated significant unadjusted associations with overall quality of life (p = 0.012 and p = 0.002, respectively), indicating variation in QoL across maternal socio-economic strata prior to adjustment (Supplementary Table S4).
Multivariate analyses were conducted to examine associations between social determinants and domain-specific quality-of-life outcomes. Models were fitted separately for movement and balance, daily activities, school activities, and speech and communication domains and adjusted for age, sex, living arrangement, and parental education (Fig. 2).
Fig. 2.
Adjusted associations between social determinants and domain specific Quality of Life. Adjusted unstandardized regression coefficients (B) with 95% confidence intervals are shown from separate multivariable linear regression models for four PedsQL Cerebral Palsy Module domains. Models included age, sex, living arrangement, maternal education level, and paternal education level. The vertical line indicates no association. Confidence intervals crossing zero indicate no statistically significant independent association
Across all domains, none of the examined social or demographic variables demonstrated statistically significant independent associations with quality of life after adjustment. Effect estimates were consistently close to the null, with confidence intervals crossing zero in all domain-specific models. These findings indicate that, after accounting for basic demographic and social context, variation in domain-specific quality of life among children with cerebral palsy is not independently explained by the measured social determinants.
Discussion
This research note provides a domain-specific evaluation of quality of life (QoL) in children with cerebral palsy (CP) within a LMIC context and examines associations with functional severity, comorbidities, and selected social factors.
Marked heterogeneity was observed across functional and participation-related QoL domains, consistent with the multidimensional nature of QoL in children with CP [2]. Domains reflecting daily activities, school participation, movement and balance, and speech and communication showed wide interquartile ranges, indicating substantial variability among children with the same diagnosis. In contrast, pain and fatigue demonstrated pronounced ceiling effects, limiting their discriminatory capacity. Similar domain-specific variability has been reported previously, including comparisons with typically developing children, even when functional limitations are comparable [2]. Limitations in mobility and physical functioning restrict participation in home, school, and community settings, contributing to lower QoL in participation-related domains [22], and movement, communication, and functional abilities are therefore recognised as critical determinants of QoL in CP [11, 23]. These findings support the use of domain-specific analyses, as overall QoL scores may obscure clinically meaningful heterogeneity across functional domains [2, 11, 16].
Functional severity, classified using the Gross Motor Function Classification System (GMFCS), was a strong and consistent correlate of QoL. Increasing GMFCS levels were associated with progressively lower QoL across overall, functional, and participation-related domains, demonstrating a clear dose–response relationship in line with existing evidence [24, 25]. The impact of motor impairment extended beyond physical mobility to include communication, school participation, and self-care [26], supporting the conceptualisation of CP as a condition with multisystem functional consequences rather than isolated motor impairment [22, 27].
Epilepsy and cognitive impairment were associated with widespread reductions across daily activities, school participation, movement and balance, and communication, consistent with known participation restrictions [11, 12]. Cognitive impairment showed a particularly strong association with school-related QoL, highlighting vulnerability in educational participation, as previously reported [11]. Sensory impairments demonstrated selective effects: cortico-visual impairment was associated with poorer school and communication-related QoL, reflecting its impact on learning and classroom participation [28], while hearing impairment predominantly affected speech and communication-related QoL, consistent with established effects on language acquisition and academic participation [29, 30].
Gastro-oesophageal reflux disease (GERD) was primarily associated with poorer eating-related QoL, with comparatively smaller effects on other domains. Feeding disorders, including GERD and oropharyngeal dysfunction, are common in children with CP and can adversely affect nutritional status, daily functioning, and caregiver burden [31, 32]. The selective domain-specific impact observed underscores the importance of identifying and addressing feeding and gastrointestinal problems within comprehensive CP care.
Although maternal education and occupation were associated with QoL in unadjusted analyses, these effects were not sustained after adjustment. The absence of independent social effects may reflect the dominant influence of functional severity and comorbidity burden on QoL in CP [25], as well as the selective impact of comorbidities across domains [11, 12]. Limited socioeconomic variability within a public-sector tertiary-care cohort may also reduce the ability to detect independent social gradients. Broader psychosocial and contextual factors, such as caregiver stress, family coping, and access to rehabilitation, were not directly assessed and may represent more proximal social determinants of QoL than educational or occupational status alone [3, 14]. Previous studies have shown that household income, caregiver education, and family structure influence participation and care continuity, particularly in resource-limited settings [8, 13].
Strengths and limitations
Strengths of this study include the use of a well-characterised clinical cohort, application of a validated, age-appropriate quality-of-life instrument, and domain-specific analyses that allow nuanced interpretation of functional impact. The study additionally addresses an under-represented population from a LMIC, contributing context-specific evidence to a literature that is predominantly derived from high-income settings.
Several limitations should be acknowledged. First, the cross-sectional design precludes inference regarding temporal or causal relationships between functional severity, social determinants, and quality-of-life outcomes. Second, the study was conducted at a single tertiary-care centre, which may limit generalisability to children with cerebral palsy managed in primary or secondary care settings or in community-based programmes. Third, quality-of-life data were caregiver-reported, which may be influenced by caregiver perceptions, psychological state, and caregiving burden, and may not fully capture the child’s subjective experience. Fourth, although comorbidities were common, they were not included in multivariate models due to collinearity with functional severity, limiting the ability to disentangle their independent contributions to quality-of-life outcomes. Finally, the study relied on clinical and questionnaire-based assessments without objective measures such as polysomnography, which may have resulted in under-recognition or misclassification of sleep-related disturbances where relevant.
Conclusions
Quality of life in children with cerebral palsy is highly domain-specific and is strongly influenced by functional severity and comorbidity profiles. Gross motor impairment demonstrates a clear dose–response relationship with multiple QoL domains, while comorbidities exert selective, clinically meaningful effects on participation and daily functioning. Measured social determinants did not independently explain QoL variation after adjustment in this LMIC cohort.
Supplementary Information
Acknowledgements
The authors wish to thank the children and caregivers who participated in this study and the staff of the paediatric neurology clinics at Teaching Hospital Kurunegala for their support during data collection.
Author contributions
SACD and CD conceptualised the study. SACD conducted data collection. CD verified clinical diagnoses and functional classification. TCMGDPC contributed to data analysis and interpretation. TCMGDP and SACD drafted the manuscript. All authors critically revised the manuscript and approved the final version.
Funding
This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due to the inclusion of sensitive clinical and socio-demographic information that could compromise participant confidentiality. De-identified data are available from the corresponding author upon reasonable request and subject to approval by the relevant ethics committee.
Declarations
Ethics approval and consent to participate
Ethical approval was obtained from the Ethics Review Committee of the Sri Lanka College of Paediatricians (SLCP/ERC/2023/25). Written informed consent to participate was obtained directly from all participants aged 16 years and over. For participants younger than 16 years, written informed consent was obtained from a parent or legal guardian. The study was conducted in accordance with the principles of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due to the inclusion of sensitive clinical and socio-demographic information that could compromise participant confidentiality. De-identified data are available from the corresponding author upon reasonable request and subject to approval by the relevant ethics committee.