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Open Access

Peer-reviewed

Research Article

Mediation and moderation analysis of the association between physical capability and quality of life among stroke patients

  • Xue Yang ,

    Contributed equally to this work with: Xue Yang, Hongmei Zhang

    Roles Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Department of Rehabilitation Medicine, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    ⨯
  • Hongmei Zhang ,

    Contributed equally to this work with: Xue Yang, Hongmei Zhang

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Department of Rehabilitation Medicine, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    ⨯
  • Qian Liu,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliation Department of Rehabilitation Medicine, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    ⨯
  • Yihuan Lu,

    Roles Conceptualization, Data curation, Investigation, Methodology

    Affiliation Department of Rehabilitation Medicine, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    ⨯
  • Liqing Yao

    Roles Project administration, Supervision

    yaoliqing98731@163.com

    Affiliation Department of Rehabilitation Medicine, the Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    ⨯

Abstract

To estimate the extent to which physical capability, depressive symptoms, balanced self-efficacy (BSE), and other risk factors, that are interrelated with stroke, influence the quality of life (QoL) in stroke survivors. A theoretical model based on Wilson and Cleary’s model tested the specific hypotheses: 1) physical capability has an indirect effect on QoL mediated by BSE; 2) physical capability has an indirect effect on QoL by depressive symptoms; 3) stroke risk factors (hypertension/diabetes/gender) moderate the above relationship. Six hundred and seventy stroke survivors were enrolled from ten different hospitals in Yunnan province from 2019 to 2021. Patients’ mental and physical function was assessed using the Brunnstrom recovery stage (BRS), mini-balance evaluation system test (Mini-BEST), Barthel index (BI), Activities-specific Balance Confidence scale (ABC), and Hamilton depression scale (HAM-D). The structural equation model (SEM) was used to test the moderated mediation model in Mplus 8.0 software. The model showed a good fit (RMSEA =  0.075, SRMR =  0.010). BSE significantly mediated the relationship between physical capability and QoL (β =  0.322, p =  0.002). Hypertension was found a significant moderator of all the direct paths from physical capability to QoL through depressive symptoms (В =  0.412, p =  0.015; В =  0.831, p =  0.020, respectively). This study provides a better insight into the relationship between physical capability and QoL via BSE in stroke survivors, which may help establish appropriate treatment for these individuals.

Citation: Yang X, Zhang H, Liu Q, Lu Y, Yao L (2025) Mediation and moderation analysis of the association between physical capability and quality of life among stroke patients. PLoS One 20(6): e0320833. https://doi.org/10.1371/journal.pone.0320833

Editor: Tanja Grubić Kezele, University of Rijeka Faculty of Medicine: Sveuciliste u Rijeci Medicinski fakultet, Croatia

Received: November 7, 2024; Accepted: February 25, 2025; Published: June 10, 2025

Copyright: © 2025 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The work was supported by the Major Science and Technology Project of Yunnan Province (Grant numbers[2018zf016]), Rehabilitation Clinical Medical Centre of Yunnan Province (Grant numbers[zx2019-04-02]), National Key Research and Development Program (Grant numbers[2018YFC2002301]), and Jiajie Expert Workstation of Yunnan Province (Grant numbers[2019IC034]), and Kunming Medical University Postgraduate Innovation Fund (Grant numbers[2022B11]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Stroke is a primary cause of long-term neurological disability, with more than 80 million stroke survivors globally in 2016 [1]. Compared to Europe, America, and Australia, Asia has the largest stroke burden, especially in China due to its large and aging population [2,3]. Stroke decreases the survivors’ quality of life (QoL) because it can cause physical and cognitive impairments, emotional distress, and fatigue[4,5]. Additionally, it can also lead to social isolation and a loss of independence[6]. Therefore, it is a serious health problem worldwide, especially in Asia [7]. The reduction in QoL often accompanies the increased risk of lifestyle diseases, including hypertension, diabetes, and an array of physical dysfunctions. Thus, improving QoL can improve the overall health and physical ability of stroke survivors. Various factors, including physical [8] and psychological [9], affect the QoL of stroke survivors, which must be taken into account by rehabilitation professionals to improve the QoL.

A recent study showing a strong relationship between physical capability and QoL in stroke survivors has raised serious concerns [10]. Physical capability denotes one’s capacity to completely undertake a task. Normally, it is evaluated based on the assessment of an individual’s balance and motor functions, such as the MiniBest scale [11], and Brunnstrom stages of stroke recovery [12]. Earlier research has shown that better physical capability is an important predictor of stroke survivors’ QoL [8]. Additionally, physical capability is associated with balance function [13], which may also pose risks to patients’ QoL [14,15].

Balance impairment could lead stroke survivors to develop anxiety-related or depression-related distress behaviors such as fear of falling [16,17]. The fear of falling in stroke survivors frequently leads to activity and participation limitations as a consequence of poststroke physical disability [17]. Therefore, enhancing the balance confidence may improve patients’ QoL [18]. Balance confidence can be assessed using the “Activities-specific Balance Confidence Scale (ABC)” questionnaire [19]. It is also called Balance self-efficacy (BSE), which means an individual’s belief in his or her ability to accomplish a task [20]. Studies in the last few decades have produced fairly convergent results, indicating that higher BSE is linked to better QoL of stroke survivors [21–23]. French et al. proposed a theoretical model to explain the mediating role of BSE on the relationship between the patient’s physical capability and participation [20]. Using the above theoretical model, we propose the first hypothesis, considering the mediation effect of BSE on the relationship between physical capability and QoL.

Additionally, poor QoL has been found to be strongly associated with severe depressive symptoms [24,25]. Evidence suggests that depressive symptoms after stroke harm motor outcomes [26]. A study reported that there is a negative relationship between depression and the perception of QoL [27]. Conversely, physical impairments contribute to post-stroke depressive disorders [28]. Thus, we propose the second hypothesis that the relationship between physical capabilities and QoL may also be mediated by depressive symptoms.

Besides, other risk factors such as hypertension, diabetes, and gender may further impact these relationships among physical capability, depressive symptoms, BSE, and QoL [29,30]. Hypertension is a leading risk factor for stroke in Asia [3,7], significantly increasing the risk of ADL disability [31]. Hypertension with other comorbid conditions lowers the patient’s QoL and physical capability [32,33]. Interestingly, one study demonstrated that enhancing the self-efficacy of stroke survivors can help them improve blood pressure [34]. Concerning diabetes, ischemic stroke is a well-known macrovascular complication of diabetes. A study reported that diabetic stroke survivors have poorer outcomes than non-diabetic stroke survivors [35]. Notably, one study indicated gender association with health-related QoL [35]. Therefore, this study also examined the effect of these moderators (hypertension, diabetes, gender) on the relationships among physical capability, BSE, depressive symptoms, and QoL. Accordingly, here, we propose the third hypothesis that hypertension/diabetes/gender may act as moderators affecting the mediated relationship in stroke survivors [36].

To fully elucidate the complex relationships among factors that contribute to QoL in stroke, Wilson and Cleary’s 1995 model (W-C model) is applied to link physical function (physical capability), general health perception (BSE, depressive symptoms), and QoL [37]. General health perception refers to self-reported health assessment [38]. A hypothetic and theoretic model of the expected moderated mediation is described in Fig 1. The model tests specific hypotheses: (1) physical capability has an indirect effect on QoL mediated by BSE; (2) physical capability has an indirect effect on QoL by depressive symptoms; (3) stroke risk factors (hypertension/diabetes/gender) moderate the above relationship.

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Fig 1. The theoretical model is based on Wilson and Cleary’s model.

①: Hypothesis 1 of the current study; ②: Hypothesis 2 of the current study; ③: Hypothesis 3 of the current study. BSE: Balance Self-Efficacy; HRQL: Health-Related Quality of Life; QoL: Quality of Life. The arrows represent the hypothesized directional relationships between the variables in the model.

https://doi.org/10.1371/journal.pone.0320833.g001

Methods

Study design

The research was a multicenter cross-sectional study conducted from April 2019 to April 2020. In total, 1,765 participants were recruited using the consecutive sampling method, consisting of inpatients from ten different rehabilitation institutions. Ethical approval was obtained from the local ethics committees (approval No. PJ-2019-04) and the research was performed according to the Declaration of Helsinki [39]. Written informed consent was obtained from all patients before participation.

Participants

Patients’ participation criteria were as follows: (I) age, > 18 years; (II) diagnosis of hemorrhagic or ischemic stroke for >  1 month; (III) stable vital signs, and (IV) signed informed consent. The participant exclusion criteria were as follows: (I) presence of any mental diagnosis before stroke according to the Diagnostic and Statistical Manual of Mental Disorders classification (i.e., major depressive disorder, bi-directional affective disorder, and obsessive-compulsive disorder, etc.); (II) malignant tumors, and/or; (III) dementia, loss of consciousness, or severe cardiopulmonary, liver, or kidney dysfunctions; (IV) pre-existing physical disability from any cause, such as fracture, osteoarthritis, etc. Out of the 1,765 participants, 245 did not meet the inclusion criteria, and 850 were excluded based on exclusion criteria. Finally, 670 participants were included in the structural equation model analysis.

Procedure

The case data used in this cross-sectional study were obtained from stroke patients across ten rehabilitation facilities in Yunnan Province, China. Initially, ten hospitals were invited to participate in a training program conducted every three months. Subsequently, two trained researchers visited various hospitals periodically to collect data and perform quality control [13]. After patients provided informed consent, data collectors conducted a standardized assessment, which included a neurological evaluation covering demographic information, clinical data, and both neurological and neuropsychological functional assessments. Assessment outcomes and patient information were entered into the electronic database of our hospital department (Department of Rehabilitation, the Second Affiliated Hospitals of Kunming Medical University).

Measurement

Physical capability: The Brunnstrom recovery stage (BRS) [40] is conducted to evaluate post-stroke motor recovery in the affected limbs [12]. The evaluation consists of six levels, Stage 1 (flaccid paresis) to Stage 6 (approximately normal paresis). The total score is the summation of all scores of the limbs. A high score suggests a positive recovery. The Rasch reliability coefficient was above 0.90 [41]. Mini-BEST, a mini-balance evaluation system test [11], assesses the dynamic balance of the patient. The mini-BEST had high internal consistency and interrater reliability [42]. It includes 14 items, each rated from 0 (severe) to 2 (normal). The maximum score is 28 points. A higher score indicates better balance and postural control.

Quality of life (QoL): The Barthel index (BI) [43], a functionally independent tool, evaluates the patient’s performance in daily life activities, with a total score ranging from 0 to 100. A high score indicates a better ability to live independently. BI demonstrates good validity and reliability [44].

Balance self-efficacy (BSE): ABC, an Activities-specific Balance Confidence scale, evaluates individuals’ balance confidence while carrying out specific activities [45]. ABC shows excellent internal consistency and test-retest reliability [46]. This 11-point scale ranges from 0 (no confidence) to 100 (complete confidence). The total score denotes the average score.

Depressive symptoms: The Hamilton depression scale (HAM-D) is widely used to assess the severity of depression [47]. HAM-D demonstrates satisfactory reliability [48]. It included 17 items that were scored on a scale of 0 to 4. A higher score suggests a higher severity of depression.

Statistics analysis

Descriptive statistics and bivariate correlation analysis were conducted with IBM SPSS 22.0. Normal data distribution was confirmed from the normal probability plots. Continuous data, with normal or near normal distribution, are expressed as mean ±  SD; median and inter-quartile ranges [P25-P75] are reported in case of nonnormal data distribution. Categorical data are expressed as rates and percentages. Pearson or Spearman correlation analyses were conducted to examine the correlations among BRS, Mini-BEST, QoL, BSE, and depressive symptoms.

The Mplus 8.0 program [49] was used to employ structural equation modeling (SEM) to test the hypothesized model. Some variables (<5%) had missing data. Missing data imputation uses a process of multiple imputations assuming data missing at random [50]. The maximum likelihood estimation method was applied to assess the structural equation model. The goodness-of-fit indices are widely used for structural equation models [51]. χ2/df ≤  5 (the ratio of χ2 to the degree of freedom) indicates an acceptable fitting of the data. The comparative five indexes (CFI) and the Tucker-Lewis index (TLI) of >  0.95 represent an excellent model fit. The SRMR (standardized root mean square residual) and RMSEA (root mean square error of approximation) indices of ≤  0.08 indicate an acceptable fit of the model. p <  0.05 denotes the statistical significance of the data.

Additionally, a recommended bias-corrected bootstrapped method was employed to examine the mediators of BSE and depressive symptoms on the relationship between physical capability and QoL and generate 95% bias-corrected confidence intervals (CIs) [52], with the procedure of 5,000 samples. A multi-group comparison test was employed to examine the indirect effects of physical capability and QoL differed between BSE and depressive symptoms. Also, the possible moderating effects of hypertension, diabetes, and gender among simple paths in the multiple-mediation model were examined. A statistically significant difference in the path coefficients between subgroups indicates the moderating effect of the path [36].

Results

Demographic characteristics

Six hundred and seventy participants (438 males, 232 females, mean age 61.4 ±  11.9 years) were involved in this study. The majority of participants (556, 83.0%) had an ischemic stroke, while hemorrhagic- and other strokes were reported in 101 (15.1%) and 13 (1.9%) patients respectively. Of the total number, 292 (43.6%) had a left-sided stroke (right hemiparesis), and 297 (44.3%) had a right-sided stroke (left hemiparesis). Patients with hypertension accounted for 68.5% (459), while patients with diabetes accounted for 11.3% (76).

Correlations

The median score of the ABC was 88.13, whereas the mean assessment scales of mini-BEST, BRS, BI, and HAM-D are outlined in Table 1. We found significant correlations between BRS, Mini-BEST, QoL, BSE, and depressive symptoms (p <  0.001) (Table 2). BRS showed a positive correlation with Mini-BEST (r =  0.696), QoL (r =  0.633), and BSE (r =  0.644), but was negatively correlated with depressive symptoms (r =  -0.438). Additionally, Mini-BEST showed a significantly positive correlation with BSE, and QoL (r = 0.789, r =  0.701), but a negative correlation with depressive symptoms (r =  -0.492). BSE was positively correlated with QoL (r =  0.758) and negatively correlated with depressive symptoms (r =  -0.496). QoL was negatively associated with depressive symptoms (r =  -0.535).

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Table 1. Demographic and clinical information.

https://doi.org/10.1371/journal.pone.0320833.t001

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Table 2. Correlations among variables.

https://doi.org/10.1371/journal.pone.0320833.t002

Fitting of the theoretical model

The SEM analysis showed that the study model fit indices indicated satisfactory fit (χ2 =  14.233, χ2/df =  4.744, p = 0.002. RMSEA =  0.075, SRMR =  0.010, CFI =  0.994, TLI =  0.981).

Path analysis

As shown in Fig 2, physical capability was the independent variable; BSE and depressive symptoms were the mediators and QoL was the dependent variable in the multiple mediation model. The standardized path coefficients, interpreted similarly to regression coefficients, are presented in Fig 2. All the pathways between physical capability and QoL were significant in the model. This model explained a total of 66.4% of the variance in QoL. Physical capability was a positive predictive factor of QoL (β =  0.470, p <  0.000). BSE contributed most to the total variance of QoL (71.8%), while Depressive symptoms accounted for 31.6% of the variance of QoL. The mediator of BSE positively predicted QoL (β =  0.299, p <  0.000). However, depressive symptoms were the negative predictor of QoL (β =  -0.122, p <  0.000). Additionally, physical capability positively predicted self-efficacy (β =  0.847, p <  0.000) but was a negative of depressive symptoms (β =  -0.562, p <  0.000) (Table 3).

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Table 3. Path analysis.

https://doi.org/10.1371/journal.pone.0320833.t003

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Fig 2. Model fit (χ2 =  14.233,χ2/df =  4.744, p =  0.002. RMSEA =  0.075, SRMR =  0.010, TLI =  0.981, CFI =  0.994).

Mini-BEST: mini-Balance Evaluation Systems Test; BRS: Brunnstrom; BSE: Balance self-efficacy; QoL: Quality of Life. This model is consistent with hypotheses 1 and 2 of the theoretical model. BSE and Depressive symptoms are mediating variables between Physical capability and QoL.

https://doi.org/10.1371/journal.pone.0320833.g002

Mediation effect analysis

As shown in Table 4, we found significant total, direct, and indirect effects in the multiple mediation model. The specific indirect effect of physical capability on QoL via BSE was statistically significant (β =  0.254, p =  0.003). However, the other mediations of depressive symptoms were not statistically significant (β =  0.069, p =  0.136). These findings negated the hypothesis that depressive symptoms mediate the relationship between physical capability and QoL. Thus, BSE significantly mediates the association between physical capability and QoL (β =  0.322, p =  0.002).

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Table 4. The total, direct, and indirect effects.

https://doi.org/10.1371/journal.pone.0320833.t004

Moderation effect analysis

The analysis presented in Table 5 shows that hypertension significantly moderated the indirect effect of physical capability on QoL (В =  -0.953, p =  0.014). We examined the moderation effect using the pick-a-point approach to estimate the conditional direct effects of physical capability on QoL via BSE and depressive symptoms on QoL at different levels of hypertension. The results indicated that the interaction terms (physical capability × hypertension and depressive × hypertension) in the pathways from physical capability to QoL through depressive symptoms were significant (В =  0.412, p =  0.015; В =  0.831, p =  0.020, respectively). The plots of the interactions between physical capability, depressive symptoms, and QoL are presented in Fig 3. In all, hypertension significantly moderates the association between physical capability and QoL toward depressive symptoms, supporting our hypothesis. However, diabetes and gender showed no significant effect on the indirect path (В =  0.943, p =  0.570; В =  0.022, p =  0.961, respectively). Thus, these findings do not support the hypothesis that diabetes/gender moderates the aforementioned association.

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Table 5. Moderated analysis.

https://doi.org/10.1371/journal.pone.0320833.t005

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Fig 3. Interactive effect of hypertension on the relationship between physical capability, QoL, and depressive symptoms.

This is consistent with the assumptions of Theoretical Model 3. Hypertension acts as a moderating variable between Physical capability and QoL.

https://doi.org/10.1371/journal.pone.0320833.g003

Discussion

This study primarily explored a multiple mediation model of the ‘physical capability to QoL via BSE and depressive symptoms’ pathway in a group of stroke survivors from China based on the Wilson and Cleary model. Our findings are consistent with a previous study, showing the significant mediation effect of BSE on the relationship between physical capability and QoL [20]. As hypothesized, the physical capability showed an indirect positive correlation with QoL through BSE; a higher level of physical capability indicated higher BSE, thereby improving the patient’s QoL.

However, we found no mediation effect of depressive symptoms on the relationship between physical capability and QoL. According to French et al. [19], depressive symptoms moderate the relationships between physical capability, self-efficacy, and participation in stroke survivors. Although the hypothesis about depressive symptoms mediating the relationship between physical capability and QoL was not supported, all the paths in our model were significant. The present findings suggest that a high level of physical capability can predict a lower severity of depressive symptoms and a better QoL, while a high severity of depressive symptoms predicts a lower QoL. This supports our hypothesis.

Our results also demonstrated that hypertension moderated the relationship between physical capability, depressive symptoms, and QoL in stroke survivors. The patients with hypertension had decreased physical capability, which in turn increased depressive symptoms. This result is consistent with Sok et al. showing a strong association between depressive symptoms and hypertension [35]. Accordingly, another study reported that depressed patients with hypertension had poor control of blood pressure [53]. The possibility exists that antidepressant therapy interferes with blood pressure control in persons with hypertension, but further research on this topic is warranted. Additionally, a recent study showed that increased walking (physical) activity lowers hypertension risk in postmenopausal women [54]. Our study also indicated that physical activity positively influences hypertension risk, even in a mixed-gender population. Conversely, patients with high blood pressure were found to be less active than those without hypertension. Furthermore, QoL negatively correlates with stroke risk factors such as hypertension [55, 56]. In general, hypertension is an important moderator of relationships between physical capability, depressive symptoms, and QoL.

However, there is a lack of literature examining the relationship between hypertension and BSE, which must be explored in both a qualitative and quantitative manner. Similarly, we found no evidence that hypertension moderated the ‘physical capability to QoL via BSE’ pathway. Although our findings did not show that diabetes and gender moderated the relationship between physical capability and QoL, this may be attributable to the relatively small number of individuals with diabetes and females in this cohort. Therefore, further research is needed with a larger and more diverse sample.

The theoretical model in this study was built based on a previous model suggesting that BSE is a significant mediator and hypertension has a moderating effect on the ‘physical capability to QoL through depressive symptoms’ pathway. The mediating effect of BSE and the moderating effect of hypertension may help interpret the mechanisms of the relationship between physical capability and QoL. Our conceptual model facilitates the understanding of these associations to make targeted strategies promoting QoL and rehabilitation. Patients show improved balance function, which naturally enhances balance confidence, leading to a significant improvement in QoL [18]. Health professionals should incorporate interventions to enhance balance confidence and the control of blood pressure in their management of physical and mental rehabilitation.

Limitations

There are some limitations to our research. First, we did not consider the complex nature of the QoL index, which may complicate relationships among the various missing data of clinical variables. Researchers should implement rigorous strategies to minimize missing data and place greater emphasis on exploring the intricacies of the QoL index in more detail, taking into account its multiple dimensions. Second, we did not take into account the impact of stroke subtypes, a possible confounding parameter in this study. Future research should consider stratifying stroke survivors by subtype, which would facilitate a more precise analysis of how different stroke types may influence the outcomes. Third, the cross-sectional study design restricts the interpretation and generalization of our results. An additional longitudinal study may help verify the conclusions of this study.

Conclusion

This study provided a better insight into the relationship between physical capability and QoL via BSE in stroke survivors, which may help establish appropriate treatment for these individuals. Our results uncovered the underlying moderating effect of hypertension on relationships between physical capability, depressive symptoms, and QoL. An effective rehabilitation strategy can achieve concrete targets for stroke risk patients via BSE, improving their QoL [57].

Acknowledgments

The authors would like to thank all the participants in the study and MJEditor (www.mjeditor.com) for their linguistic assistance during the preparation of this manuscript.

References

  1. 1. GBD 2016 Stroke Collaborators. Global, regional, and national burden of stroke, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 2019;18(5):439–58. pmid:30871944
  2. 2. Chen Z, Jiang B, Ru X, Sun H, Sun D, Liu X, et al. Mortality of stroke and its subtypes in China: results from a Nationwide Population-Based Survey. Neuroepidemiology. 2017;48(3–4):95–102. pmid:28586776
  3. 3. Kim AS, Cahill E, Cheng NT. Global stroke belt: geographic variation in stroke burden worldwide. Stroke. 2015;46(12):3564–70. pmid:26486867
  4. 4. Jurgens CY, Lee CS, Aycock DM, Masterson Creber R, Denfeld QE, DeVon HA, et al. State of the science: the relevance of symptoms in cardiovascular disease and research: a scientific statement from the American Heart Association. Circulation. 2022;146(12):e173–84. pmid:35979825
  5. 5. Labeit B, Michou E, Hamdy S, Trapl-Grundschober M, Suntrup-Krueger S, Muhle P, et al. The assessment of dysphagia after stroke: state of the art and future directions. Lancet Neurol. 2023;22(9):858–70. pmid:37596008
  6. 6. Braun LT, Grady KL, Kutner JS, Adler E, Berlinger N, Boss R, et al. Palliative care and cardiovascular disease and stroke: a policy statement from the American Heart Association/American Stroke Association. Circulation. 2016;134(11):e198-225. pmid:27503067
  7. 7. Turana Y, Tengkawan J, Chia YC, Nathaniel M, Wang J-G, Sukonthasarn A, et al. Hypertension and stroke in Asia: A comprehensive review from HOPE Asia. J Clin Hypertens (Greenwich). 2021;23(3):513–21. pmid:33190399
  8. 8. Nor Azlin MN, Aziz NA, Saperi BS, Aljunid SM. Functional limitation and health-related quality of life, and associated factors among long term stroke survivors in a Malaysian community. Med J Malaysia. 2016;71(6):313–21. pmid:28087954
  9. 9. Haghgoo HA, Pazuki ES, Hosseini AS, Rassafiani M. Depression, activities of daily living and quality of life in patients with stroke. J Neurol Sci. 2013;328(1–2):87–91. pmid:23522526
  10. 10. Muren MA, Hütler M, Hooper J. Functional capacity and health-related quality of life in individuals post stroke. Top Stroke Rehabil. 2008;15(1):51–8. pmid:18250074
  11. 11. Franchignoni F, Horak F, Godi M, Nardone A, Giordano A. Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. J Rehabil Med. 2010;42(4):323–31. pmid:20461334
  12. 12. Naghdi S, Ansari NN, Mansouri K, Hasson S. A neurophysiological and clinical study of Brunnstrom recovery stages in the upper limb following stroke. Brain Inj. 2010;24(11):1372–8. pmid:20715900
  13. 13. Yang X, Fu Y, Fan H, Ou J, Chen J, Yao L. Exploring the complex pathways between somatosensory impairment, physical ability and affective symptoms following stroke: mediation analyses. Ann Palliat Med. 2022;11(7):2395–409. pmid:35927774
  14. 14. Di Monaco M, Trucco M, Di Monaco R, Tappero R, Cavanna A. The relationship between initial trunk control or postural balance and inpatient rehabilitation outcome after stroke: a prospective comparative study. Clin Rehabil. 2010;24(6):543–54. pmid:20511303
  15. 15. Schmid AA, Van Puymbroeck M, Altenburger PA, Miller KK, Combs SA, Page SJ. Balance is associated with quality of life in chronic stroke. Top Stroke Rehabil. 2013;20(4):340–6. pmid:23893833
  16. 16. Schmid AA, Van Puymbroeck M, Knies K, Spangler-Morris C, Watts K, Damush T, et al. Fear of falling among people who have sustained a stroke: a 6-month longitudinal pilot study. Am J Occup Ther. 2011;65(2):125–32. pmid:21476359
  17. 17. Schmid AA, Arnold SE, Jones VA, Ritter MJ, Sapp SA, Van Puymbroeck M. Fear of falling in people with chronic stroke. Am J Occup Ther. 2015;69(3):6903350020. pmid:25871606
  18. 18. Torkia C, Best KL, Miller WC, Eng JJ. Balance confidence: a predictor of perceived physical function, perceived mobility, and perceived recovery 1 year after inpatient stroke rehabilitation. Arch Phys Med Rehabil. 2016;97(7):1064–71. pmid:27060032
  19. 19. French MA, Miller A, Pohlig RT, Reisman DS. Depressive symptoms moderate the relationship among physical capacity, balance self-efficacy, and participation in people after stroke. Phys Ther. 2021;101(12):pzab224. pmid:34636909
  20. 20. French MA, Moore MF, Pohlig R, Reisman D. Self-efficacy mediates the relationship between balance/walking performance, activity, and participation after stroke. Top Stroke Rehabil. 2016;23(2):77–83. pmid:26653764
  21. 21. Liang YY, Wang L, Yang Y, Chen Y, Mok VCT, Ungvari GS, et al. Association between behavioral dysexecutive syndrome and the health-related quality of life among stroke survivors. Front Psychiatry. 2020;11:563930. pmid:33101083
  22. 22. Hellström K, Lindmark B, Wahlberg B, Fugl-Meyer AR. Self-efficacy in relation to impairments and activities of daily living disability in elderly patients with stroke: a prospective investigation. J Rehabil Med. 2003;35(5):202–7. pmid:14582550
  23. 23. Szczepańska-Gieracha J, Mazurek J. The role of self-efficacy in the recovery process of stroke survivors. Psychol Res Behav Manag. 2020;13:897–906. pmid:33177896
  24. 24. Koleck M, Gana K, Lucot C, Darrigrand B, Mazaux JM, Glize B. Quality of life in aphasic patients 1 year after a first stroke. Qual Life Res. 2017;26(1):45–54. https://10.1007/s11136-016-1361-z pmid:27405871
  25. 25. Lin W-Q, Huang T-Y, Liu L, Yang Y-O, Li Y-H, Sun M-Y, et al. Prevalence and related factors of depression and falls among the elderly living in rural communities of Guangzhou. Psychol Health Med. 2020;25(8):980–8. pmid:31967487
  26. 26. Lin C, Babiker A, Srdanovic N, Kocherginsky M, Harvey RL. Depressive symptoms after stroke are associated with worse recovery. Int J Psychiatry Med. 2020;55(4):227–38. pmid:32050815
  27. 27. León LP de, Mangin JPL, Ballesteros S. Psychosocial determinants of quality of life and active aging. a structural equation model. Int J Environ Res Public Health. 2020;17(17):6023. pmid:32824975
  28. 28. Robinson RG, Starr LB, Kubos KL, Price TR. A two-year longitudinal study of post-stroke mood disorders: findings during the initial evaluation. Stroke. 1983;14(5):736–41. pmid:6658957
  29. 29. Heiland EG, Welmer A-K, Wang R, Santoni G, Fratiglioni L, Qiu C. Cardiovascular risk factors and the risk of disability in older adults: variation by age and functional status. J Am Med Dir Assoc. 2019;20(2):208-212.e3. pmid:30006016
  30. 30. Tusa BS, Geremew BM, Tefera MA. Heath related quality of life and associated factors among adults with and without diabetes in Adama city East Shewa, Ethiopia 2019; using generalized structural equation modeling. Health Qual Life Outcomes. 2020;18(1):83. pmid:32228598
  31. 31. Li Z-H, Lv Y-B, Kraus VB, Yin Z-X, Liu S-M, Zhang X-C, et al. Trends in the incidence of activities of daily living disability among Chinese older adults from 2002 to 2014. J Gerontol A Biol Sci Med Sci. 2020;75(11):2113–8. pmid:31603986
  32. 32. Qian J, Ren X. Association between comorbid conditions and BADL/IADL disability in hypertension patients over age 45: Based on the China health and retirement longitudinal study (CHARLS). Medicine (Baltimore). 2016;95(31):e4536. pmid:27495110
  33. 33. Katan M, Luft A. Global burden of stroke. Semin Neurol. 2018;38(2):208–11. pmid:29791947
  34. 34. Zhang X-P, Pan J-H, Wan L-H, Liu Z-Y, Mo M-M, Wang M-Y, et al. Factors influencing health behaviour, blood pressure control, and disability in hypertensive ischaemic stroke patients after a comprehensive reminder intervention. J Adv Nurs. 2020;76(6):1384–93. pmid:32128865
  35. 35. Sok SR, Yim E, Sim H, Sim HJ. Relationships among self-care competency, presence of depressive symptom, and health-related quality of life of Korean stroke patients. Clin Nurs Res. 2021;30(5):670–9. pmid:32207330
  36. 36. Preacher KJ, Rucker DD, Hayes AF. Addressing moderated mediation hypotheses: theory, methods, and prescriptions. Multivariate Behav Res. 2007;42(1):185–227. pmid:26821081
  37. 37. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA. 1995;273(1):59–65. pmid:7996652
  38. 38. Saengsiri A, Hacker ED. Conducting quality of life research in people with coronary artery disease in non-English-speaking countries: conceptual and operationalization issues. J Cardiovasc Nurs. 2015;30(1):74–84. pmid:24365869
  39. 39. Association W. World medical association declaration of helsinki ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–4.
  40. 40. Sawner KJL. Brunnstrom movement therapy in hemiplegia: a neurophysilogical approach. 2nd ed. Philadelphia: JB Lippincott; 1992.
    • 41. Huang C-Y, Lin G-H, Huang Y-J, Song C-Y, Lee Y-C, How M-J, et al. Improving the utility of the Brunnstrom recovery stages in patients with stroke: Validation and quantification. Medicine (Baltimore). 2016;95(31):e4508. pmid:27495103
    • 42. Tsang CSL, Liao L-R, Chung RCK, Pang MYC. Psychometric properties of the Mini-Balance Evaluation Systems Test (Mini-BESTest) in community-dwelling individuals with chronic stroke. Phys Ther. 2013;93(8):1102–15. pmid:23559522
    • 43. Florence I, Mahoney DWB. Functional evaluation: the Barthel index. Maryland State Medical Journal. 1965;14:56–61.
    • 44. Collin C, Wade DT, Davies S, Horne V. The Barthel ADL Index: a reliability study. Int Disabil Stud. 1988;10(2):61–3. pmid:3403500
    • 45. Powell LE, Myers AM. The Activities-specific Balance Confidence (ABC) scale. J Gerontol A Biol Sci Med Sci. 1995;50A(1):M28-34. pmid:7814786
    • 46. Zhang D, Tian F, Gao W, Huang Y, Huang H, Tan L. The Chinese short version of the activities-specific balance confidence scale: its validity, reliability, and predictive value for future falls in community-dwelling older adults. Clin Interv Aging. 2022;17:1483–91. pmid:36212511
    • 47. Fava GA, Kellner R, Munari F, Pavan L. The hamilton depression rating scale in normals and depressives. Acta Psychiatr Scand. 1982;66(1):26–32. pmid:7124430
    • 48. Müller MJ, Dragicevic A. Standardized rater training for the Hamilton Depression Rating Scale (HAMD-17) in psychiatric novices. J Affect Disord. 2003;77(1):65–9. pmid:14550936
    • 49. Linda K, Muthen BOM. Mplus User’s Guide. Los Angeles: CA: Muth´en & Muth´ en; 2012.
      • 50. Dong Y, Peng C-YJ. Principled missing data methods for researchers. Springerplus. 2013;2(1):222. pmid:23853744
      • 51. Hooper DJC, Mirchael. MR. Structural equation modelling guidelines for determining model fit. JBusiness Res Methods. n.d.;6:53–60.
      • 52. Kline RB. Principles and Practice of Structual Equation Modeling. 4th ed. New York: Guilford Press; 2016.
        • 53. Scalco AZ, Scalco MZ, Azul JBS, Lotufo Neto F. Hypertension and depression. Clinics (Sao Paulo). 2005;60(3):241–50. pmid:15962086
        • 54. Miller CR, Wactawski-Wende J, Manson JE, Haring B, Hovey KM, Laddu D, et al. Walking volume and speed are inversely associated with incidence of treated hypertension in postmenopausal women. Hypertension. 2020;76(5):1435–43. pmid:32981366
        • 55. Yao H, Zhang J, Wang Y, Wang Q, Zhao F, Zhang P. Stroke risk and its association with quality of life: a cross-sectional study among Chinese urban adults. Health Qual Life Outcomes. 2021;19(1):236. pmid:34627278
        • 56. Alotaibi SM, Alotaibi HM, Alolyani AM, Abu Dali FA, Alshammari AK, Alhwiesh AA, et al. Assessment of the stroke-specific quality-of-life scale in KFHU, Khobar: a prospective cross-sectional study. Neurosciences (Riyadh). 2021;26(2):171–8. pmid:33814370
        • 57. Silva SM, Corrêa FI, Faria CDC de M, Pereira GS, Attié EADA, Corrêa JCF. Reproducibility of the items on the stroke specific quality of life questionnaire that evaluate the participation component of the international classification of functioning, disability and health. Disabil Rehabil. 2016;38(24):2413–8. pmid:26879175