Stroke is the result of a cerebrovascular event that, depending on the affected brain areas, causes a variety of focal neurological deficits  and is broadly distinguished into ischemic or hemorrhagic. The first results from arterial occlusion, causing reduced blood flow, and the second from the rupture of arteries, causing subarachnoid or intracerebral hemorrhages . It is prevalent among elderlies, and its lifetime risk is 24.9% (95% uncertainty interval, 23.5–26.2%) . Globally, in 2019, there were 12.2 million incidents and 101 million prevalent stroke cases, and it is the second leading cause of death and the third leading cause of death and disability combined . Stroke’s global burden has increased significantly in the last decade, due to population aging and to the increased exposure to modifiable risk factors . Among them, sedentary lifestyle, smoking, alcohol consumption, hypertension, diabetes, or cardiovascular diseases are the most relevant [4,5].
Despite research has documented complex psychosocial consequences after stroke, the outcome measures used in rehabilitation research to address its consequences are mostly measures of impairment . Health-related outcomes, such as the return to usual activities, psychological and cognitive resources, disability, and patients’ health-related quality of life (HRQoL) have been less considered . This is critical as stroke often causes significant worsening in HRQoL, which is associated with a reduction in physical and psychological health, functional independence, and social relationships [7,8].
Literature findings highlighted that HRQoL after stroke is determined by age [9,10], time elapsed from stroke , depression , comorbidities , functional abilities [9,10], social support, caregiver characteristics [13–15], and socioeconomic status [16,17].
Assessing and understanding the factors that can affect patients’ HRQoL after stroke allows to highlight the domains that can contribute towards improving patients’ health, which should, therefore, be considered in the care pathway of stroke survivors. A proper evaluation of such complexity requires to account for the joint effect of several candidate predictors. However, studies addressing predictors of HRQoL after stroke often rely on models with few variables [9–17]. This is critical, also because potential predictors of patients’ HRQoL might be interrelated (e.g. cognitive functioning and grip strength , or depression and anxiety ).
The objective of this study, which is purely exploratory, is to assess the role of health status, health habits, environment’s features, and social support in predicting HRQoL in stroke survivors with a stable clinical condition.
Study design and patients’ selection
This was a cross-sectional, observational, and monocentric study. A consecutive series of both inpatients and outpatients were enrolled between 2015 and 2019 at the ‘Carlo Besta’ Neurological Institute in Milan.
Inclusion criteria were: history of radiologically confirmed ischemic or hemorrhagic stroke, which occurred at least 6 months before the date of inclusion; clinically stable situation, and a National Institutes of Health Stroke Scale (NIHSS)  score ≤ 10; age ≥ 45 years; and adequate comprehension of Italian language. Exclusion criteria were: the presence of severe language and cognitive deficits, which limited the completion of questionnaires (according to the judgement of neurologists who screened the patients); musculoskeletal disorders that could interfere with the grip strength test; comorbidity for psychiatric diseases of psychotic area or for neuromuscular, neurodegenerative, neoplastic or systemic diseases; corticosteroid treatment during the week before recruitment; and being resident in a nursing home for the elderly or disabled.
Eligible patients were invited to participate to the study and signed a specific informed consent form. The study was approved by the Besta Institute Ethical Committee (protocol no. 650/2014).
The research protocol
The WHO Quality of Life Questionnaire for old-Age subjects (WHOQOL-AGE) was used to assess HRQoL. The questionnaire includes 13 items forming a superordinate factor. Each item has a score from 1 (totally/very dissatisfied) to 5 (completely/very satisfied): the total questionnaire score ranges from 0 to 100, with higher values indicating better HRQoL .
A set of additional elements included sociodemographic variables, stroke-related variables, health status measures, risk factors, and healthy behaviors, and a final set of outcomes included social support, the perceived impact of stroke, mobility, and functional independence.
Sociodemographic variables included age, sex, marital status (herein reported as married/cohabitating vs. never married, separated/divorced, and widowed), years of formal education, education level (herein reported as high school or university vs. up to secondary), and employment status (herein reported as employed vs. nonemployed for any reason).
Stroke-related clinical variables
Stroke type was herein defined as ischemic or hemorrhagic, based on clinical records. The severity of stroke outcome was evaluated with the NIHSS, a 15-item scale that evaluates stroke-related neurological deficits . Each item score has 3–5 grades of severity, with 0 indicating normal functioning, and the total NIHSS score ranges from 0 to 42.
Measures of patients’ health status
Measures of health status included cognitive functioning, symptoms of anxiety and depression, handgrip strength, BMI, presence of raised blood pressure, and comorbidities.
Cognitive functioning was assessed with the Montreal Cognitive Assessment (MoCA) , a global measure of cognitive functioning adopted to identify mild or early signs of cognitive impairment. MoCA score of 25 or less indicates the presence of cognitive dysfunction.
Symptoms of anxiety were measured with State-Trait Anxiety Inventory (STAI), which evaluated patients’ levels of apprehension, stress and nervousness, and included both temporary and stable anxiety levels in two separate scales of 20 items each. Raw scores were converted into T-scores (mean, 50; SD, 10) on the basis of age and sex-based Italian normative . Higher values correspond to greater anxiety, with scores of at least 61 indicating clinically relevant anxiety.
Symptoms of depression were assessed using the Beck Depression Inventory-II (BDI-II), which includes 21 items evaluating cognitive and somatic mood symptoms. BDI-II total score range is 0–63, with higher scores related to greater symptoms’ severity : scores in the range 14–19, 20–28, and ≥29 indicate, respectively, mild, moderate, and severe depressive symptoms .
Handgrip was measured with Jamar’s hydraulic dynamometer. The test was performed on both hands (three trials, with the highest value as definitive), and the best of the two hands was used for the analyses. Based on age and sex normative values , patients were classified as weak or strong if they fell, respectively, below or above the 95% confidence interval (CI) bounds of normative values, and of average-level strength if they fell within the 95% CI.
BMI was based on objective measures of height and weight, obtained using a stadiometer and a SECA scale, respectively. BMI is defined as kg/m2 and was herein reported also in categories, BMI ≥ 30, between 25 and 29.9, and BMI ≤ 24.9 identified obesity, overweight, and normal weight, respectively.
Blood pressure was measured twice (2 min apart), after the patient had been sitting for at least 10 min, with a digital automatic sphygmomanometer (model OMRON M3): pressure values above 140/90 mmHg indicated raised blood pressure .
The presence and impact of comorbidities were assessed using Self-administered Comorbidity Questionnaire (SCQ) . It is based on a predefined list, which included 12 main broad conditions and enables adding up to three further ones. For each condition, patients were asked to refer on its presence, on the use of medications or other treatments, and on the presence of limitations due to it: each condition is assigned 0–3 points; therefore, total SCQ score ranges between 0 and 45. In addition to the SCQ score, we also reported the amount of conditions per patient.
Risk factors and healthy behaviors
Risk factors included smoking and alcohol consumption; healthy behaviors included fruit and vegetable consumption, and physical activity.
Smoking was assessed with questions evaluating if the patient currently smokes or if he/she has smoked in the past. Current smokers were asked how many cigarettes (or other products) they smoked per day; nonsmokers or past smokers were assigned zero.
Alcohol consumption was assessed by asking patient if he/she has consumed alcoholic beverages in the previous week and how many ‘standard drinks’ (which corresponds to 10 g of ethanol) he/she has consumed, dividing them between weekdays and weekends to enhance recall .
Consumption of fruit and vegetables was assessed using WHO guidelines’ threshold of five servings of fruits and vegetables per day .
Physical activity was assessed using European Prospective Investigation into Cancer and Nutrition–Physical Activity Questionnaire (EPIC-PAQ) , which allows to estimate the metabolic-equivalent intensity values and classify people as inactive, moderately inactive, moderately active, and active. EPIC-PAQ consists of four questions about physical activity in the last year: type of work activity; physical activities carried out in the summer/winter periods; participation in activities with vigorous/nonvigorous physical effort; flights of stairs climbed per day.
A final set of outcomes included social support, the perceived impact of stroke, mobility, and functional independence.
Social support was measured using Medical Outcome Study–Social Support Survey (MOS-SSS), which consists of 19 items addressing patients’ perceived social support and the amount of people who might provide support when needed .
The perceived impact of stroke was measured with the Perceived Disease Impact Scale (PDIS), a 20-item scale aimed to address the influence of illness on various life domains, for example, activities, relationships, lifestyle personality, and trust in own body. Items are rated on a 7-point Likert scale ranging from ‘very negatively’ (−3) to ‘very positively’ (+3), and the total score is based on the mean to the responses .
Mobility was assessed using the single item ‘Walk-Wheelchair’ of the functional independence measure . The item measured patients’ ability to move on a flat surface, asking about their most frequently adopted way of locomotion on a scale from 1 (total dependence) to 7 (complete independence).
The Barthel Index was used as a measure of functional independence in daily activities . It is composed of 10 items on motor tasks – such as dressing, mobility, and grooming – rated on a 0–20 weighted ordinal scale, with higher scores indicating higher independence.
We used frequencies and percentages for categorical variables, and means and SD for continuous ones.
To assess the variation in HRQoL, a linear regression analysis was fitted targeting WHOQOL-AGE as outcome, and all the other variables as predictors. To select predictors, Pearson’s correlation analysis was at first carried out, and candidate variables significantly correlated to the WHOQOL-AGE were retained and entered in a univariable linear regression model. Those who predicted WHOQOL-AGE variation were entered into a multivariable linear regression analysis in which a backward procedure was applied, using P < 0.05 as a criterion for retention. In this way, only predictors significantly associated with WHOQOL-AGE were retained in the final model.
Data were analyzed with SPSS 27.0, IBM, Armonk, New York, USA.
A total of 122 patients, of whom 97 with ischemic stroke, were enrolled: Table 1 reports a comprehensive description of patients’ characteristics.
Table 1 -
Characteristics of enrolled. patients with stroke
||Results as mean (sd) or N (%)
||64.1 ± 11.0
|Marital status: married/cohabitating
|Years of formal education
||11.6 ± 4.2
|Education level: high school completed or university
||2.9 ± 2.4
| Stroke type: ischemic
| Distance from acute event (years)
||5.1 ± 5.2
|Health status measures
| MoCA tot
||22.2 ± 4.7
| Cognitive dysfunction (MoCA ≤ 25)
||49.8 ± 10.4
| Relevant state anxiety symptoms
||50.8 ± 10.9
| Relevant trait anxiety symptoms
||11.9 ± 9.0
| Relevant depressive symptoms
| Handgrip strength/kg (stronger hand)
||33.7 ± 11.0
|Stronger hand (age and sex normative)
| Weak (below 95% CI lower bound)
| Average (within 95% CI)
| Strong (above 95% CI upper bound)
||25.9 ± 3.7
| Normal weight
|Raised blood pressure
||8.2 ± 3.3
||2.5 ± 1.6
|Risk factors and healthy behaviors
| Past smokers
| Current smokers
| No. cigarettes/day
||1.8 ± 4.9 (10.2 ± 7.0 among current smokers)
| Standard drinks/week
||3.2 ± 4.8 (6.5 ± 5.1 among active drinkers)
| Fruits (servings/day)
||2.4 ± 1.4
| Vegetables (servings/day)
||1.6 ± 0.8
| At least 5 servings/day of fruit and vegetables
||87.9 ± 71.7
|Sex-based total physical activity index
| Moderately inactive
| Moderately active
| MOS-SSS overall support index
||77.9 ± 18.6
| Social network size
||5.7 ± 4.7
||−3.3 ± 16.7
||6.6 ± 0.8
||95.0 ± 11.5
||57.5 ± 13.7
BDI-II, Beck Depression Inventory-II; CI, confidence interval; METs, metabolic-equivalent intensity values; MoCA, Montreal Cognitive Assessment; MOS-SSS, Medical Outcome Study–Social Support Survey; NIHSS, National Institutes of health Stroke Scale; PDIS, Perceived Disease Impact Scale; SCQ, Self-administered Comorbidity Questionnaire; STAI, State-Trait Anxiety Inventory; WHOQOL-AGE, WHO Quality of Life Questionnaire for old-Age subjects.
Pearson’s correlation showed that STAI-State, STAI-Trait, BDI-II, MOS-SSS overall score, Barthel index, SCQ, PDIS, BMI, and EPIC-PAQ correlated to the WHOQOL-AGE with P < 0.05, and were, therefore, retained for the regression analysis.
Table 2 reports the different steps of the regression. BMI and EPIC-PAQ were not independent predictors of WHOQOL-AGE in univariable regression and were excluded from the multivariable analysis; SCQ and PDIS were instead excluded during the iteration phase. Thus, the final multivariable regression model was composed of STAI-State, STAI-Trait, BDI-II, MOS-SSS overall score, and Barthel index, and it accounted for 54.9% of WHOQOL-AGE variation.
Table 2 -
Univariable and multivariable linear regression
models predicting variation of WHO Quality of Life
Questionnaire- for old-Age subjects in Stroke
||Univariable linear regressions
||Multivariable linear regression: initial model
||Multivariable linear regression: final model
2 = 0.376; F = 74.0 (P < 0.001)
2 = 0.544; F = 21.6 (P < 0.001)
2 = 0.549; F = 30.5 (P < 0.001)
2 = 0.144; F = 21.3 (P < 0.001)
2 = 0.385; F = 77.6 (P < 0.001)
2 = 0.069; F = 10.0 (P = 0.002)
2 = 0.297; F = 52.1 (P < 0.001)
2 = 0.149; F = 22.1 (P < 0.001)
||Not retained in the model
2 = 0.062; F = 9.0 (P = 0.003)
2 = 0.002; F = 1.3 (P = 0.26)
||Not retained in the model
2 = −0.008; F = 0.02 (P = 0.89)
BDI-II, Beck Depression Inventory-II; EPIQ-PAQ, metabolic equivalents intensity values; METs, metabolic-equivalent intensity values; MOS-SSS, Medical Outcome Study–Social Support Survey; PDIS, Perceived Disease Impact Scale; SCQ, Self-administered Comorbidity Questionnaire.
In this study, we evaluated the role of a large set of candidate variables addressing health status, risk factors, healthy behaviors, environment’s features, and social support degree in HRQoL in stroke survivors. Our results showed that anxiety, depression, functional independence, and social support independently predicted around 55% of HRQoL variation.
Previous literature showed that high anxiety and depression rates can slow down the recovery process and worsen the functional independence of stroke patients since the first month after stroke . Stroke survivors can experience various psychological consequences, especially mood disorders, which affect up to 35% of patients and can compromise the recovery process likewise physical disability : a reciprocal relationship between depression and physical disability has in fact been highlighted, suggesting the need for interventions tackling both physical impairment and depression . Anxiety is a further common mental health symptom. As recently shown by Rafsten’s meta-analysis based on 13 756 patients, one-third of the patients experience it during the first year poststroke, and it is also associated with reduced HRQoL . Moreover, symptoms of anxiety and depression are often interrelated, and thus, they should be jointly managed [19,37].
The limitations in functional independence after stroke may be caused by reduced muscle strength and increased fatigue that may also determine falls and fractures, which in turn negatively impact on health outcomes and mood state . Moreover, reduced muscle strength has also been associated with cognitive functioning : although cognitive functioning was not found as a predictor of reduced HRqOL in our study, its role cannot be completely ignored. Around 70% of our patients were cognitively impaired: so, it can be presumed that such a variable did not fit in the model in reason of its limited variability.
Rehabilitation interventions, which include physical activities and physical fitness training, enhancing walking and taking steps during leisure time too , should be offered to patients as they can enhance patients’ functional independence and HRQoL; previous studies have shown that improvement in muscle strength and quality during rehabilitation also correlates with increased functional independence . Psychological symptoms, motor impairment, and functional dependence together contribute to the decline in social participation of stroke patients . Stroke survivors may experience difficulties in maintaining their relations, because many health domains are compromised and, thus, social networks are hampered, which in turn is negative for HRQoL [9,41,42]. Therefore, enhancing emotional and practical social support can be a relevant facilitator for patients’ functioning, supporting daily life activities and, thus, improving HRQoL. In addition to this family relation, counseling should be taken into account to address the potential benefit arising out of the improvement of social ties towards enhanced quality of life in aging populations . Themes connected to loss of relations are known among families of stroke survivors, potentially leading to depressed mood , and recent research has shown that engagement in social activities is associated with lower depression levels .
Thus, the improvement of HRQoL in stroke survivors needs to take into account different elements, which act either independently or through the mediation of other outcomes. Most often in literature, the relation between HRQoL and its predictors is based on univariate models, or on multivariate models based on few candidate predictors . This, however, does not allow to capture the complexity of stroke consequences. Exceptions to this include few studies published in the last decade [10,45–48], which in part share the same predictors found in our study and multivariable analyses, such as depression and anxiety [10,47] – with higher symptoms’ severity predicting lower HRQoL – and functional independence [45,48], where, on the contrary, higher score predicted better HRQoL. Other factors from literature predicting poststroke HRQoL are also advanced age , higher education, higher monthly family income, comorbidities , or opportunities of career counseling . Addressing different determinants of HRQoL enables to highlight the influence and the potential impact of treating domains that might not be strictly connected to the vascular event: a common aspect between our study and the aforementioned ones is that the portion of HRQoL explained is not irrelevant: thus, there is a large room for improving HRQoL after stroke.
Some limitations have to be taken into account. First, the sample was enrolled in a single center, where patients attend follow-up examinations after the acute and postacute phases. Second, patients were out of the postacute phase and had a stable condition with a relatively low severity, as defined by an NIHSS score of 10 or less: this made the sample homogeneous, excluding acute and subacute patients with unstable conditions, but our results might not be extended to patients who are closer to the acute event, or who had any kind of recent variation in their overall clinical profile. Third, the cross-sectional study design does not allow to address a strict causal relationship toward explaining HRQoL variation. Fourth, information about the stroke site was not included in our analyses.
The results of this study show that symptoms of anxiety and depression, functional independence, and social support have a considerable impact on patients’ HRQoL: thus, addressing these elements in a multidimensional approach might help enhancing a relevant portion of the HRQoL of stroke survivors.
Ethics approval and consent to participate: the study was approved by the Besta Institute Ethical Committee (protocol no. 650/2014).
Conflicts of interest
There are no conflicts of interest.
1. Hankey GJ. Stroke
. Lancet 2017; 389:641–654.
2. Campbell BCV, Khatri P. Stroke
. Lancet 2020; 396:129–142.
3. GBD 2016 Lifetime Risk of Stroke
Collaborators. Global, regional, and country-specific lifetime risks of stroke
, 1990 and 2016. N Engl J Med 2018; 379:2429–2437.
4. GBD 2019 Stroke
Collaborators. Global, regional, and national burden of stroke
and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol 2021; 20:795–820.
5. Donkor ES. Stroke
in the 21st
century: a snapshot of the burden, epidemiology, and quality of life
Res Treat 2018; 2018:3238165.
6. Sveen U, Thommessen B, Bautz-Holter E, Wyller TB, Laake K. Well-being and instrumental activities of daily living after stroke
. Clin Rehabil 2004; 18:267–274.
7. Cerniauskaite M, Quintas R, Koutsogeorgou E, Meucci P, Sattin D, Leonardi M, et al. Quality-of-life and disability in patients with stroke
. Am J Phys Med Rehabil 2012; 91:S39–S47.
8. Nelson M, McKellar KA, Yi J, Kelloway L, Munce S, Cott C, et al. Rehabilitation
evidence and comorbidity: a systematic scoping review of randomized controlled trials. Top Stroke
Rehabil 2017; 24:374–380.
9. Wang R, Langhammer B. Predictors of quality of life
for chronic stroke
survivors in relation to cultural differences: a literature review. Scand J Caring Sci 2018; 32:502–514.
10. Boudokhane S, Migaou H, Kalai A, Jellad A, Borgi O, Bouden A, et al. Predictors of quality of life
survivors: a 1-year follow-up study of a Tunisian sample. J Stroke
Cerebrovasc Dis 2021; 30:105600.
11. Van Mierlo M, van Heugten C, Post MWM, Hoekstra T, Visser-Meily A. Trajectories of health-related quality of life
: results from a one-year prospective cohort study. Disabil Rehabil 2018; 40:997–1006.
12. Chaturvedi P, Tiwari V, Singh AK, Qavi A, Thacker AK. Depression
impedes neuroplasticity and quality of life
. Fam Med Prim Care Rev 2020; 9:4039–4044.
13. Klinedinst NJ, Gebhardt MC, Aycock DM, Nichols-Larsen DS, Uswatte G, Wolf SL, et al. Caregiver characteristics predict stroke
survivor quality of life
at 4 months and 1 year. Res Nurs Health 2009; 32:592–605.
14. Kruithof WJ, van Mierlo ML, Visser-Meily JM, van Heugten CM, Post MW. Associations between social support
survivors’ health-related quality of life
–a systematic review. Patient Educ Couns 2013; 93:169–176.
15. Alshahrani AM. Quality of life
and social support
: perspectives of Saudi Arabian stroke
survivors. Sci Progr 2020; 103:36850420947603.
16. Jun HJ, Kim KJ, Chun IA, Moon OK. The relationship between stroke
patients’ socio-economic conditions and their quality of life
: the 2010 Korean community health survey. J Phys Ther Sci 2015; 27:781–784.
17. Twardzik E, Clarke P, Elliott MR, Haley WE, Judd S, Colabianchi N. Neighborhood socioeconomic status and trajectories of physical health-related quality of life
18. Kang JH, Kim YH, Choi YA. Montreal cognitive assessment and frontal assessment battery test as a predictor of performance of unaffected hand function after subcortical stroke
. Int J Rehabil Res 2021; 44:45–50.
19. Rafsten L, Danielsson A, Sunnerhagen KS. Anxiety
: a systematic review and meta-analysis. J Rehabil Med 2018; 50:769–778.
20. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke
21. Caballero FF, Miret M, Power M, Chatterji B, Tobiasz-Adamczyk S, Koskinen M, et al. Validation of an instrument to evaluate quality of life
in the aging population: WHOQOL-AGE. Health Qual Life Outcomes 2013; 11:177.
22. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53:695–699.
23. Predabissi L, Santinello M. Spielberger’s state-trait anxiety
inventory (Form Y) Italian Version. Giunti O.S.; 1989.
24. Ghisi M, Flebus GB, Montano A, Sanavio E, Sica C. Beck depression
inventory II. Italian Version. Giunti O.S.; 2006.
25. Beck AT, Steer RA, Brown GK. Beck depression
inventory manual. 2nd ed. The Psychological Corporation; 1996.
26. Bohannon RW, Peolsson A, Massy-Westropp N, Desrosiers J, Bear-Lehman J. Reference values for adult grip strength measured with a Jamar dynamometer: a descriptive meta-analysis. Physiotherapy 2006; 92:11–15.
27. Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G, et al. ESH-ESC practice guidelines for the management of arterial hypertension: ESH-ESC task force on the management of arterial hypertension. J Hypertens 2007; 25:1751–1762.
28. Sangha O, Stucki G, Liang MH, Fossel AH, Katz JN. The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research. Arthritis Rheumatol 2003; 49:156–163.
29. World Health Organization. International guide for monitoring alcohol consumption and related harm. World Health Organization; 2000.
30. World Health Organization. Global strategy on diet, physical activity and health. World Health Organization; 2004.
31. Cust AE, Smith BJ, Chau J, van der Ploeg HP, Friedenreich CM, Armstrong BK, et al. Validity and repeatability of the EPIC physical activity questionnaire: a validation study using accelerometers as an objective measure. Int J Behav Nutr Phys Act 2008; 5:33.
32. Giangrasso B, Casale S. Psychometric properties of the medical outcome
study social Heidelberg. Soc Indic Res 2014; 116:185–197.
33. Mols F, Vingerhoets AJ, Coebergh JW, van de Poll-Franse LV. Well-being, posttraumatic growth and benefit finding in long-term breast cancer survivors. Psychol Health 2009; 24:583–595.
34. Kidd D, Stewart G, Baldry J, Johnson J, Rossiter D, Petruckevitch A, et al. The functional independence
measure: a comparative validity and reliability study. Disabil Rehabil 1995; 17:10–14.
35. Mahoney FI, Barthel DW. Functional evaluation: the Barthel index. Md Med 1965; 14:61–65.
36. Hackett ML, Pickles K. Part I: frequency of depression
: an updated systematic review and meta-analysis of observational studies. Int J Stroke
37. Kutlubaev MA, Hackett ML. Part II: predictors of depression
and impact of depression
on stroke outcome
: an updated systematic review of observational studies. Int J Stroke
38. Saunders DH, Sanderson M, Hayes S, Kilrane M, Greig CA, Brazzelli M, et al. Physical fitness training for stroke
patients. Cochrane Database Syst Rev 2016; 3:CD003316.
39. de Jong AU, Smith M, Callisaya ML, Schmidt M, Simpson DB. Sedentary time and physical activity patterns of stroke
survivors during the inpatient rehabilitation
week. Int J Rehabil Res 2021; 44:131–137.
40. Irisawa H, Mizushima T. Assessment of changes in muscle mass, strength, and quality and activities of daily living in elderly stroke
patients. Int J Rehabil Res 2022; 45:161–167.
41. De Belvis AG, Avolio M, Sicuro L, Rosano A, Latini E, Damiani G, et al. Social relationships and HRQL: a cross-sectional survey among older Italian adults. BMC Public Health 2008; 8:348.
42. Raggi A, Corso B, Minicuci N, Quintas R, Sattin D, De Torres L, et al. Determinants of quality of life
in ageing populations: results from a cross-sectional study in Finland, Poland and Spain. PLoS One 2016; 11:e0159293.
43. Robinson-Smith G, Mahoney C. Coping and marital equilibrium after stroke
. J Neurosci Nurs 1995; 27:83–89.
44. Bui Q, Kaufman KJ, Munsell EG, Lenze EJ, Lee JM, Mohr DC, et al. Smartphone assessment uncovers real-time relationships between depressed mood and daily functional behaviors after stroke
. J Telemed Telecare2022. doi: 10.1177/1357633X221100061.
45. Safaz I, Kesikburun S, Adigüzel E, Yilmaz B. Determinants of disease-specific health-related quality of life
in Turkish stroke
survivors. Int J Rehabil Res 2016; 39:130–133.
46. Lourenço E, Sampaio MRDM, Nzwalo H, Costa EI, Ramos JLS. Determinants of quality of life
in southern Portugal: a cross sectional community-based study. Brain Sci 2021; 11:1509.
47. Schneider S, Taba N, Saapar M, Vibo R, Kõrv J. Determinants of long-term health-related quality of life
in young ischemic stroke
patients. J Stroke
Cerebrovasc Dis 2021; 30:105499.
48. Zhu W, Jiang Y. Determinants of quality of life
in patients with hemorrhagic stroke
: a path analysis. Medicine (Baltimore) 2019; 98:e13928.