Psychometric Properties of The 12-Item Stroke-Specific Quality of Life Scale Among Stroke Survivors In Hong Kong

doi:10.21203/rs.3.rs-1305612/v1

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Psychometric Properties of The 12-Item Stroke-Specific Quality of Life Scale Among Stroke Survivors In Hong Kong

https://doi.org/10.21203/rs.3.rs-1305612/v1

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published 27 Jan, 2023

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The present study examined the psychometric properties of the 12-item Stroke-Specific Quality of Life Scale (SSQOL-12) as a measure of quality of life specific to stroke in 184 stroke survivors in Hong Kong. The participants also completed a questionnaire on short form health survey (SF-12) and validating variables at baseline and 148 of them completed SSQOL-12 two months later. Confirmatory factor analysis was performed to investigate the construct validity, reliability, and longitudinal invariance of SSQOL-12 across two months. Convergent and discriminant validity was examined with respect to hope, anxiety, depression, self-esteem, and SF-12. The original 2-factor model did not reveal a superior fit and a modified 1-factor model with 4 residual covariances provided an acceptable fit to data in both waves. The SSQOL-12 factor displayed substantial factor loadings (λ = 0.40–0.87), good reliability (Ω = 0.88), temporal stability (r = 0.70), and scalar measurement invariance across time. Stroke-specific quality of life was significantly associated with ischemic stroke, lower functional disability, anxiety, and depression and higher levels of hope of self-esteem. Most of the associations remained significant after controlling for SF-12. These findings provided support for validity and reliability of the unidimensional structure for SSQOL-12 among stroke survivors.

Assessment scale

Chinese version

Confirmatory factor analysis

Convergent validity

Correlation coefficients

Ischemic stroke

Longitudinal invariance

Mental well-being

Patients

Reliability

Stroke is a neurocardiovascular disease with two major types of ischemic and haemorrhagic stroke ¹. Stroke is an universal public health issue and carries significant economic burden to the societies with the associated deaths and disabilities. Although the incidence of stroke increases with age ², there has been an increase in the incidence of ischemic stroke in younger adults in the past decade ³. Stroke survivors typically experience substantial functional impairments and require intensive treatments and continuous rehabilitation regimens ⁴. It is essential to have a brief, valid, and reliable assessment tool for a precise evaluation of stroke-specific quality of life for researchers and practitioners.

The 49-item Stroke-Specific Quality of Life (SSQOL) Scale was developed by Williams, et al. ⁵ and has been validated in various samples of stroke survivors ^6-9. However, administration of this 49-item scale could be cumbersome for the stroke survivors in clinical settings. Post, et al. ¹⁰ derived a 12-item short version of the SSQOL (SSQOL-12) by selecting one item with the highest item total correlation from each of the 12 domains. Compared to the commonly used Short Form 12-item Health Survey ¹¹, the SSQOL-12 focuses on stroke-specific domains such as language, cognition, and vision. Previous studies ^12-14 have found satisfactory measurement properties for the SSQOL-12 in terms of reliability, criterion validity, and convergent validity.

In Hong Kong, Wong, et al. ¹⁵ derived the Chinese version of the SSQOL-12 in a sample of 186 haemorrhagic stroke survivors. Despite the satisfactory internal consistency and convergent validity for the SSQOL-12, there was less support for its construct validity. First, the scale development studies ^10,15 did not empirically test the factor structure. Although subsequent studies ^7,8 suggested a two-factor (physical and psychosocial) structure on the SSQOL-12, these studies are subject to methodological issues such as the use of principal component analysis and orthogonal varimax rotation ¹⁶. These obsolete analytic methods have been shown to be inappropriate in examining the latent structure ¹⁷ and could lead to an incorrect number of factors with distorted factor structures ¹⁸.

Second, a psychometric study ¹⁹ obtained a mediocre fit for the 2-factor model for Wong’s SSQOL-12 version. As shown in their Table 1 ¹⁹, most of the SSQOL-12 items displayed floor/ceiling effects with at least one-fourth of the respondents endorsing the lowest/highest response. Despite the considerable degree of deviation from normality assumption, the authors treated the items as skewed continuous variables using the robust maximum likelihood estimator. This practice failed to account for the items’ distributional asymmetry and could yield incorrect estimation results ²⁰. The factor structure of the SSQOL-12 should be examined using categorical methods on the ordinal items. Third, no existing studies have investigated the measurement invariance of the SSQOL-12 across time. Such an examination could test the stability of the factor structure and ensure unbiased comparisons of the latent SSQOL-12 scores across time. Longitudinal non-invariance in the item loadings and thresholds would bias our interpretation of the temporal changes ²¹.

In the clinical context, the SSQOL-12 offers a holistic assessment of functioning of stroke survivors in multiple domains such as cognition, mobility, language, and vision that are prognostic factors of successful stroke rehabilitation ^22-24. There is currently a lack of robust psychometric studies to determine essential measurement properties of the SSQOL-12 in terms of validity, reliability, and measurement invariance. Such a systematic evaluation is crucial for an accurate assessment of quality of life in stroke-specific domains. In view of the research gaps, the present study aimed to evaluate the psychometric properties of the SSQOL-12 in a sample of stroke survivors. The first objective of the study was to evaluate the construct validity and reliability of the SSQOL-12. The second objective was to investigate the measurement invariance of the SSQOL-12 factors across time to establish the stability of the factor structure. Patients with higher stroke-specific quality of life have shown better emotional functioning and coping ^25,26. The third objective aimed to examine the convergent validity of the SSQOL-12 with regard to emotional functioning (anxiety and depression) and coping (hope and self-esteem) and its discriminant validity with generic QOL measures.

Sample. The present study sample comprised 184 stroke survivors in Hong Kong, who were recruited under convenience sampling via centres of community rehabilitation network and a local hospital from 2019 to 2021. The present study had a larger sample size than those reported in previous psychometric studies (N = 114-146) ^27-29 and a subject to item ratio of 184:12 = 15.3. The inclusion criteria were experience of a stroke event, aged between 18 and 64, and ability to understand Chinese. The exclusion criteria were presence of psychiatric disorders that required hospitalization, diagnosis of no disability (modified Rankin Scale [mRS] score = 0) or severe disability (mRS score = 5). Participation was completely voluntary and all participants provided written informed consent. The participants received a HKD 50 (USD 6.5) voucher incentive upon completion of assessment in each wave.Ethical approval was obtained from the Human Research Ethics Committee of the University of Hong Kong (EA1702058).

Procedures and measures. The participants filled in a self-report questionnaire on the SSQOL-12 and measures on generic QoL and mental health variables at baseline (Time 0). The participants took approximately 10-15 minutes to complete the questionnaire. A total of 148 participants completed the SSQOL-12 two months later (Time 1). The short form of Stroke-specific Quality of Life (SSQOL-12) is a measure on the disease-specific quality of life in the stroke survivors ³⁰. The 12 items inquired the extent of interference in QoL as a result of stroke and were rated on a 5-point Likert format from 1 = “agree completely” to 5 = “disagree completely”. The scale yielded two subscales in physical QoL (7 items) and psychosocial domains (5 items). The modified Rankin Scale (mRS) measured the degree of disability in the daily activities of the respondents who suffered from stroke ³¹. The present study adopted the simplified mRS Questionnaire to access the level of disability with the degree of disability rated on a 6-point format from 0 = “no symptoms/disability” to 5 = “severe disability”.

The 12-item Short Form Health Survey ¹¹ was used to assess physical and mental QoL of the participants. Out of a theoretical range from 0 to 100, the mean scores for physical QoL and mental QoL ranged from 39.2 to 46.3 and 45.8 to 54.9, respectively, in previous studies among stroke survivors ^32-34. Anxiety and depressive symptoms were evaluated by the 14-item Hospital Anxiety and Depression Scale ³⁵. The items were answered on a 4-point Likert format. The two subscales had a theoretical range from 0 to 21 and a subscale score of ≥ 8 suggested possible anxiety and depression. The 6-item State Hope Scale ³⁶ was adopted to measure the respondents’ level of hope in terms of agency and pathways. The items were rated on an 8-point Likert format from 1 = “Definitely false” to 8 = “Definitely true”. The total hope score had a theoretical range from 8 to 48. Rosenberg Self-Esteem Scale ³⁷ was used to assess the respondents’ level of self-esteem. The 10 items were rated on a 4-point Likert format from 1 = “Disagree very much” to 4 = “Agree very much”. The total self-esteem score had a theoretical range from 10 to 40. In the present study, preliminary reliability analysis found satisfactory to good levels of internal consistency (Cronbach’s α = 0.74-0.91) for all of the measures on SF-12, anxiety, depression, hope, and self-esteem.

Ethical considerations. All procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Ethics approval was obtained from the Human Research Ethics Committee of the University of Hong Kong (Reference number: EA1702058). The objectives, data collection procedure, and issues regarding confidentiality and anonymity were explained to the participants. Informed consent was obtained from all of the study participants.

Data analysis. Four of the 12 SSQOL-12 items showed substantial ceiling effects with over 25% of the respondents endorsing the maximum level. The robust weighted least square (WLSMV) estimator is recommended over the maximum likelihood estimator for modeling items with asymmetric category thresholds to avoid biased factor loadings and standard errors ²⁰. The present study modeled the SSQOL items as ordinal categorical variables using the WLSMV estimator. Psychometric properties of the SSQOL-12 were examined in three steps.

Construct validity and reliability. Firstly, the 1-factor and 2-factor structure of the SSQOL-12 were examined in Time 0 via confirmatory factor analysis (CFA) in Mplus 8.5 ³⁸. Problematic items with no substantial factor loadings (λ < 0.40) were iteratively removed from the model ³⁹. Model fit was appraised using the following cutoff criteria ⁴⁰ on the fit indices: comparative fit index (CFI) and Tucker-Lewis index (TLI) ≥ 0.95, root mean square error of approximation (RMSEA) and standardized root mean square residuals (SRMR) ≤ 0.08. In case of inadequate model fit, modification indices were inspected together with the expected parameter changes. Model modification that had theoretical justifications would be iteratively incorporated into the CFA model. Model fit of nested models was compared using the chi-square difference test. The optimal factor structure derived in Time 0 would then be tested in Time 1. Reliability of the SSQOL-12 factors was evaluated using Mcdonald’s omega (Ω), with values of at least 0.75 indicating satisfactory composite reliability.

Measurement invariance tests. The second step evaluated the measurement invariance of the SSQOL-12 across Time 0 and Time 1. Multiple-group CFA estimated the configural invariance model with different factor loadings and item thresholds across time. Metric and scalar invariance models were then estimated to constrain the factor loadings and item thresholds to be equal across time, respectively ²¹. Model identification was achieved by fixing the factor variances at one and autoregressive residual correlations were specified for each SSQOL-12 item between the two time points. Temporal stability of the SSQOL-12 factors was examined via bivariate correlations across time. Model comparison was evaluated via the chi-square difference test and model fit indices ⁴¹. A change of ≥ -0.01 for ΔCFI and a change of ≤ 0.015 for ΔRMSEA constituted support for scalar measurement invariance ⁴². Structural invariance tests examined the equality of factor means across time on a standardized metric. Apart from the dropout from Time 0 to Time 1, missing data were minimal (≤ 3%) in the present study. Missing data were handled under the missing-at-random assumption ⁴³ and preliminary attrition analysis examined the missing completely at random assumption of the study dropout.

Convergent and discriminant validity. Thirdly, the convergent and discriminant validity of the SSQOL-12 factors were evaluated via path analysis in Time 0. In the path model, the SSQOL-12 factors were regressed on demographic factors such as gender and age and clinical characteristics such as the type of stroke, onset time of stroke event, and degree of disability. The four mental health variables (hope, anxiety, depression, and self-esteem) were regressed on the SSQOL-12 factors. Convergent validity was examined with reference to the associations with the degree of disability and mental health variables. Discriminant validity was evaluated via the associations between SSQOL-12 and generic QoL and the predictive power of SSQOL-12 after controlling for generic QoL. R-square denoted the proportion of explained variance of the outcome variables. Statistical significance was set at the 0.05 in the present study.

Sample profile and attrition. The mean age of the sample was 55.2 years (SD = 7.92). More than half of the sample was male (61%), married (60%), and attained at least post-secondary education (60%). More than half of the sample experienced ischemic stroke (57%) and the onset age of stroke was 51.4 years (SD = 8.43) for the participants with average stroke duration of 3.83 years (SD = 4.25). Over 90% of the participants reported comorbidities such as hypertension, hyperlipidemia, diabetes, and/or heart diseases. Half (51%) of the sample reported slight disability and one-forth of the sample (26%) reported moderate disability.

In the present sample, the mean score for anxiety and depressive symptoms was 6.73 (SD = 4.13) and 7.28 (SD = 3.89), respectively. Around half of the participants reported possible anxiety and depression (41% and 50%). The sample showed average levels of hope (M = 33.5, SD = 9.1) and self-esteem (M = 26.5, SD = 5.2) and a lower level of functioning in the physical domain (M = 36.6, SD = 8.5) than mental domain (M = 46.7, SD = 10.4). Attrition analysis did not reveal significant differences between the study completers (N = 148) and dropouts (N = 36) in the mental health variables (p = 0.06-0.85), quality of life (p = 0.19-0.79), and demographic and clinical characteristics (p = 0.26-0.89), except for a significantly lower degree of disability (Cohen d = 0.16, p = 0.028) for the dropouts (mRS score = 1.86) than the study completers (mRS score = 2.18).

Construct validity and reliability. At Time 0 and Time 1, the 2-factor CFA model provided a better model fit than the 1-factor model (Δχ² = 8.90-13.0, Δdf = 1, p < 0.01). From Table 1, neither model provided an adequate fit to the data with CFI and TLI < 0.95 and RMSEA > 0.10. Examination of modification indices suggested four residual correlations between item 1 (“fatigue”) and item 2 (“interference with personal life”), item 3 (“language”) and item 10 (“upper extremity”), item 5 (“mood”) and item 6 (“personality”), and item 7 (“self-care”) and item 12 (“housework”). Addition of these residual correlations led to an acceptable approximate fit (CFI and TLI ≥ 0.95, RMSEA ~ 0.08, and SRMR < 0.06) for the modified 1-factor model. The modified 2-factor model did not provide a superior fit over the modified 1-factor model (Δχ² = 0.12-2.24, Δdf = 1, p = 0.13-0.73). The two factors in the modified 2-factor model were extremely highly correlated (r = 0.95-0.99). The equivalent model fit and potential factor redundancy supported a unidimensional factor structure for the SSQOL-12.

[Insert Table 1 about here]

Figure 1 depicts the factor structure of the modified 1-factor model at both Time 0 and Time 1. All of the specified factor loadings were substantial (λ = 0.40-0.87) at Time 0 and Time 1. The four residual correlations among the SSQOL-12 items were significant and moderate (r = 0.25-0.45, p < 0.05). The SSQOL-12 factor showed good reliability at both Time 0 and Time 1 (Ω = 0.88-0.89).

[Insert Figure 1 about here]

Measurement invariance across time. Table 2 shows the fit indices of measurement invariance models of the SSQOL-12 across time. The configural invariance model provided an adequate fit (CFI ≥ 0.95, RMSEA = 0.06, and SRMR = 0.065) to the data. The metric invariance model, which constrained factor loadings to be equal across time, provided better model fit indices over the configural invariance model without significant difference (Δχ² = 19.4, Δdf = 12, p = 0.08) in chi-square difference test. The scalar invariance model fixed the four thresholds for each of the 12 items to be equal across time. This invariance model showed improved RMSEA and TLI over the metric invariance model without significant difference (Δχ² = 52.5, Δdf = 47, p = 0.27) in chi-square difference test. These findings supported scalar measurement invariance across time. The latent mean of the SSQOL-12 factor showed a non-significant increase from Time 0 to Time 1 (standardized mean difference = 0.16, SE = 0.08, p = 0.051). The SSQOL-12 factors were strongly correlated (r = 0.70, p < 0.01) across Time 0 and Time 1. Besides, the model with invariant residual covariances did not display significantly worse fit (Δχ² = 4.44, Δdf = 4, p = 0.35) than the scalar invariance model.

[Insert Table 2 about here]

Convergent and discriminant validity. The path model that associated the SSQOL-12 factor with mental health outcomes and demographic and clinical covariates provided a good fit (χ² = 24.2, df = 20, p = 0.24, CFI = 0.990, TLI = 0.982, RMSEA = 0.034, and SRMR = 0.030) to the Time 0 data. As shown in Table 3, gender, age, and onset time of stroke were not significantly associated (p = 0.25-0.52) with SSQOL. Participants with ischemic stroke showed significantly higher SSQOL (β = 0.34, SE = 0.15, p = 0.018) than those with hemorrhagic stroke. The degree of disability was negatively associated (β = -0.40, SE = 0.06, p < 0.01) with SSQOL. SSQOL was moderately linked with higher levels of hope and self-esteem (β = 0.42-0.51, SE = 0.05-0.06, p < 0.01) and lower levels of anxiety and depression (β = -0.37 to -0.62, SE = 0.05-0.07, p < 0.01). The SSQOL-12 factor explained 17.5%, 13.9%, 37.9%, and 26.2% of the total variance of hope, anxiety, depression, and self-esteem, respectively.

[Insert Table 3 about here]

The SSQOL-12 factor was significantly and moderately correlated (r = 0.42-0.48, p < 0.01) with the physical and mental SF-12. Males showed significantly higher levels of physical QoL (β = 0.27, SE = 0.13, p = 0.042) than females. Age, onset time of stroke, and type of stroke were not significantly associated (p = 0.42-0.80) with physical and mental QoLs. The degree of disability was significantly and negatively associated (β = -0.43, SE = 0.07, p < 0.01) with physical QoL but not with mental QoL (β = -0.02, SE = 0.09, p = 0.85). Mental QoL showed positive and moderate associations (β = 0.27-0.40, SE = 0.06-0.09, p < 0.01) with hope and self-esteem and negative and moderate associations (β = -0.58 to -0.42, SE = 0.05-0.06, p < 0.01) with anxiety and depression. Weaker associations (β = -0.19 to 0.18) were found between physical QoL and mental health outcomes.

Controlling for the effects of physical and mental QoLs, the association between SSQOL and anxiety became non-significant (β = -0.06, SE = 0.08, p = 0.45). SSQOL was still significantly and positively linked with hope and self-esteem (β = 0.24-0.26, SE = 0.07-0.09, p < 0.01) and negatively linked with depression (β = -0.35, SE = 0.07, p < 0.01). This model explained 23.0%, 40.4%, 51.8%, and 38.6% of the total variance of hope, anxiety, depression, and self-esteem, respectively.

The present study conducted a systematic evaluation of the psychometric properties of the SSQOL-12 in a sample of stroke survivors in Hong Kong. In terms of dimensionality, neither the original 1-factor nor 2-factor model provided an adequate fit at Time 0 and Time 1. The extreme inter-factor correlations (r = 0.95-0.99) imply factor redundancy and do not support the existence of two factors in the present sample. Modification indices suggested four residual correlations in the CFA model. The first residual correlation between item 1 (“fatigue”) and item 2 (“interference with personal life”) matches with the proximity between fatigue and interference in daily functioning among patients with chronic diseases such as cancer ⁴⁴ and inflammatory bowel disease ⁴⁵. As found in previous studies ^46-48, fatigue was prevalent among younger stroke survivors and was linked with worse functional outcome.

The second residual correlation between item 3 (“language”) and item 10 (“upper extremity”) is consistent with previous findings ⁴⁹ on the co-occurrence of language changes with motor changes in stroke survivors. The third residual correlation could be explained by the similarity in the item wordings (“lack of”) for item 5 (“lack of confidence in myself”) and item 6 (“lack of patience with others”). The fourth residual correlation between item 7 (“self-care”) and item 12 (“housework”) makes theoretical sense given the content overlap between preparation of food and doing housework. These residual correlations appeared to be the source of model misfit and their addition led to an approximate fit for the 1-factor CFA model at Time 0. The rejection of the χ² test of exact fit for all measurement models in Table 1 could be attributed to inflated Type I errors of χ² test for categorical CFA models with small sample size (N < 200) in a methodological study ⁵⁰. The present results suggest the use of the total score as a parsimonious composite score on quality of life in stroke-specific domains.

The same modified 1-factor structure was replicated in the follow-up data. The series of measurement invariance tests supported the equality of factor loadings, item thresholds, and residual correlations for the ordinal SSQOL-12 items across two months. These results lend support the construct validity and meaningful comparisons of its latent mean across time. On the one hand, the present sample did not exhibit notable differences in SSQOL across gender, age, onset time of stroke, and time. On the other hand, patients with haemorrhagic stroke showed a lower level of SSQOL than those with ischemic stroke. This is in line with previous findings ^51,52 on the worse functional outcome and greater depressive symptoms among patients with haemorrhagic stroke. Besides, participants with higher SSQOL displayed significantly higher levels of hope and self-esteem, a lower degree of disability, and fewer anxiety and depression symptoms. The moderate associations between the SSQOL-12 and clinical and mental health variables provide empirical support to the convergent validity.

The unidimensional factor structure aligns with recent literature ^53-55 and highlights the importance of mind-body connections among patients who have experienced a stroke. In the present sample, the SSQOL-12 factor was moderately correlated with generic QoL measures. This suggests conceptual overlap between the two measurement scales and that the two scales were not strongly associated. There existed no significant differences in physical and mental QoL across the type of stroke. Similarly, degree of disability was significantly and negatively associated with only physical QoL but not mental QoL. The associations between SSQOL and the clinical variables (the type of stroke and degree of disability) and mental health variables (hope, depression, and self-esteem) remained significant after controlling for physical and mental QoLs. These results imply an incremental predictive role for the SSQOL on these variables and support the discriminant validity of the SSQOL-12 in relation to generic QoLs.

From a clinical perspective, the present sample displayed a mean score of 3.29 for the SSQOL-12, which appears to be lower than the level (Mean score = 3.62) reported in a previous study ¹⁹. Similarly, the levels of physical and mental QoLs were apparently lower in the present sample than in previous studies of stroke survivors ^32-34 and nearly half of the respondents exhibited considerable symptoms in anxiety or depression. The worse physical and psychological well-being of the present sample could be attributed to the nature of younger stroke survivors. Previous studies ^56-59 have discussed the prevalence and risk factors of stroke among younger patients aged between 40 and 49. Apart from standard physiotherapy, the present findings highlight the potential service gap to provide tailored rehabilitation programs to address the psychosocial needs of the younger stroke survivors. Randomized controlled trials are recommended to systematically evaluate the effects of various mind-body interventions such as yoga, tai chi, and meditation in improving the mood and quality of life of the stroke survivors to facilitate their functional rehabilitation.

There are several limitations in the present study. First, the study participants were recruited via convenience sampling which might introduce self-selection biases. The selective attrition of those with a lower degree of disability could induce potential response bias. The present findings might not generalize to other samples of stroke survivors such as older stroke survivors. Further studies could examine the measurement invariance of the SSQOL-12 across age groups and cultural contexts. Second, the convergent and discriminant validity of the SSQOL-12 was examined only at Time 0. The cross sectional design could not rule out the likely reciprocal effects from emotional functioning and coping to SSQOL. The causal relationships between these variables remain to be clarified in future longitudinal studies.

Third, all of the measures on quality of life and mental well-being were self-reported by the stroke survivors. The common method variance may introduce bias to the parameter estimates. Alternative forms of assessments such as functional assessments and observer ratings are recommended. Further studies could investigate the predictive role of dysphagia severity {Everton, 2020 #1380} and life-space assessment {Ho, 2021 #1381} in relation to SSQOL among the stroke survivors. Fourth, Moshagen and Musch ⁶⁰ suggested that a sample size of around N = 200 could produce robust factor loadings and latent covariance for the WLSMV estimator. In the present study, the number of estimated parameters ranged from 64 to 69 in the CFA and path models. The ratio of sample size to number of parameters was relatively low (148/64 = 2.3) at Time 1 and this could bias the reliability of parameter estimates. Although the four residual correlations were successfully replicated at Time 1, they could arise as a result of the idiosyncratic sample characteristics. Future studies of larger sample size are needed to replicate the modified 1-factor structure.

The present study contributed to a better understanding of the psychometric properties of the SSQOL-12 in a sample of stroke survivors in Hong Kong. Empirical support was found for a robust 1-factor structure with satisfactory construct validity, reliability, adequate convergent validity, and scalar measurement invariance across two months. It is recommended to conceptualize the SSQOL-12 as a unidimensional measure of stroke-specific quality of life among stroke survivors in the Chinese context.

Acknowledgements: The authors would like to express their gratitude toward staff members of the community rehabilitation day centres, Queen Mary Hospital, Hong Kong Stroke Association, and Hong Kong Polytechnic University for their help in the recruitment of the stroke patients and all respondents for their participation in the survey. We would like to thank the team members of Centre on Behavioral Health for their help in the administration of the project. This study was supported by the General Research Fund of the Research Grants Council (GRF/HKU 17609417). The party was not involved in the study design, collection, analysis and interpretation of data, or the writing of the manuscript.

Author contributions: TF: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft. TL: Data Curation, Investigation, Project administration, Writing – review & editing, RH: Resources, Methodology, Funding acquisition, Software, Supervision, Writing – review & editing.

Competing interests: The authors declare no competing interests.

Data availability: All data generated or analysed during this study are included as the Supplementary Data file in this published article.

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Table 1. Fit indices of confirmatory factor analysis models for the Stroke-Specific Quality of Life Scale at baseline and 2-month follow-up

Model	Specification	χ²	df	RMSEA	CFI	TLI	SRMR	Δχ² test (Δ df)
Baseline (N = 184):								Model
1	1-factor	171.7*	54	0.109	0.927	0.911	0.062
2	2-factor	160.1*	53	0.105	0.934	0.918	0.060	1 vs 2	13.0* (1)
3	modified 1-factor	117.0*	50	0.085	0.959	0.946	0.053
4	modified 2-factor	115.2*	49	0.086	0.959	0.945	0.052	3 vs 4	2.24 (1)
2-month follow-up (N = 148):
5	1-factor	150.1*	54	0.110	0.940	0.927	0.060
6	2-factor	143.9*	53	0.108	0.943	0.929	0.060	5 vs 6	8.90* (1)
7	modified 1-factor	95.7*	50	0.079	0.971	0.962	0.048
8	modified 2-factor	95.5*	49	0.080	0.971	0.961	0.048	7 vs 8	0.12 (1)
* p < 0.01; df = degree of freedom; RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standardized root mean square residuals; Δχ² test = chi-square difference test.

Table 2. Measurement invariance tests of the modified 1-factor model for the Stroke-Specific Quality of Life Scale across time (N = 184)

Invariance model	χ²	df	RMSEA	CFI	TLI	SRMR	Δχ²	Δdf	p
Configural	385.1*	231	0.060	0.951	0.942	0.065
Metric	387.2*	243	0.057	0.954	0.948	0.070	19.4	12	0.08
Scalar	436.9*	290	0.052	0.953	0.956	0.071	52.5	47	0.27
Residual covariance	440.2*	294	0.052	0.954	0.956	0.071	4.44	4	0.35
* p < 0.01;χ² = chi-square; df = degree of freedom; RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standardized root mean square residuals; Δχ² = chi-square difference.

Table 3. Associations among the covariates, stroke-specific quality of life, and outcome variables at baseline without and with controlling for SF-12

	SSQOL only	SSQOL + SF-12
	SSQOL	SSQOL	physical SF-12	mental SF-12
Covariates	β (SE)	β (SE)	β (SE)	β (SE)
Male	.12 (.14)	.12 (.14)	.27 (.13)*	.17 (.16)
Age	.07 (.06)	.07 (.06)	.02 (.06)	.06 (.08)
Ischemic stroke	.34 (.15)*	.35 (.15)*	.07 (.15)	.11 (.16)
Onset time of stroke	-.04 (.06)	-.04 (.06)	.02 (.06)	-.06 (.09)
Modified Rankin Scale	-.40 (.06)**	-.40 (.06)**	-.43 (.07)**	-.02 (.09)
Outcome variables	β (SE)	β (SE)	β (SE)	β (SE)
Hope	.42 (.06)**	.24 (.09)**	.13 (.07)	.27 (.09)**
Anxiety	-.37 (.07)**	-.06 (.08)	-.16 (.07)*	-.58 (.05)**
Depression	-.62 (.05)**	-.35 (.07)**	-.19 (.06)**	-.42 (.06)**
Self-esteem	.51 (.05)**	.26 (.07)**	.18 (.06)**	.40 (.06)**
N = 184; * p < .05; ** p < .01; SSQOL = stroke-specific quality of life; SF-12 = short form health survey; β = standardized regression estimates; SE = standard error.

No competing interests reported.

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Psychometric Properties of The 12-Item Stroke-Specific Quality of Life Scale Among Stroke Survivors In Hong Kong

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