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Abstract

Introduction

Post-stroke fatigue (PSF) is a known sequel after ischemic stroke (IS), which affects quality of life (QoL), however its incidence and contributing factors remain controversial or not enough established, especially in younger population in working age.

Objectives

To assess PSF in patients in working age (18–65 years) at 3 months after IS and evaluate its possible association to other factors.

Methods

Consecutive patients with IS in working age enrolled in the prospective FRAILTY (Factors Affecting the Quality of Life After Ischemic Stroke in Young Adults; ClinicalTrials.gov: NCT04839887) were analyzed. PSF was assessed using Fatigue Severity Scale (FSS) and Neuro_QoL_Fatigue scale, post-stroke depression (PSD) and anxiety (PSA) using Hospital Anxiety and Depression Scale at 3 months after IS. QoL was evaluate using Stroke Impact Scale (SIS), version 3.0. Logistic regression analysis (LRA) was performed to identify possible predictors of PSF.

Results

In total, 150 (54.0% males, mean age 51.2 ± 8.9 years) were analyzed and 132 (88%) had excellent clinical outcome after 3 months. PSF, based on the FSS, was present in 55.3% of patients and severe PSF in 41.3% of them. LRA showed the significant association between SIS domains memory, emotions, communication, mobility and participation and the presence of severe PSF (FSS score > 5). PSD and PSA were found as other predictors of severe PSF. No association was found between PSF and age, sex, and clinical outcome.

Conclusions

Despite overall excellent clinical outcome, severe PSF occurred in 41.3% of patients in working age after 3 months post-stroke. PSF affected strongly QoL and may have close relationship to psychosocial factors and cognition. Routine screening for PSF should be incorporated into post-stroke follow up, especially for working-age patients.

Keywords

stroke fatigue quality of life young adult work

Introduction

Post-stroke fatigue (PSF) occurs in nearly 50% of patients with ischemic stroke (IS) and has a negative impact on recovery after stroke (Zhan et al., 2023). PSF represents a disabling condition and affects significantly rehabilitation and other activities that promote recovery (English et al., 2024). It's not a simple “tiredness” or physical deconditioning, but rather a complexity of symptoms, being now defined as a feeling of exhaustion, weariness, or lack of energy that can be overwhelming, and which can involve physical, emotional, cognitive, and perceptual contributors, which is not relieved by rest and affects a person's daily life (English et al., 2024).

PSF is often present together with a depression or insufficient satisfaction with functional outcome after IS (Maaijwee et al., 2015; Pihlaja et al., 2014) and may occur also in patients with minor stroke (Vitturi et al., 2021). PSF affects health-related quality of life (QoL) and may limit the return back to work despite excellent neurological outcome after IS (Andersen et al., 2012). Many biopsychosocial factors are associated with PSF, but the causal relationships are unclear and the mechanisms of PSF remain still unknown (Zhang et al., 2021). Understanding these causative mechanisms is considered a key to developing effective therapeutic interventions. Currently, there is very limited evidence for any interventions to treat or manage PSF (Chen et al., 2023; Chu et al., 2023; English et al., 2024; Mead et al., 2023).

PSF is a frequent and disabling complication of IS, with a global prevalence estimated between 42% and 53% (Chen et al., 2023; Zhan et al., 2023). Epidemiological data indicate a rising incidence of IS among adults ≤65 years, with a concurrent increase in PSF (Ekker et al., 2018; Ibrahim et al., 2024). Over 40% of younger stroke survivors report PSF, which may persist for years and impede return to work, social reintegration, and role fulfillment (Boot et al., 2022; Rutkowski et al., 2021). Fatigue is frequently linked to a decreased sense of self-efficacy and functional independence (Lee & Kim, 2024).

Numerous factors have been associated with the occurrence and severity of PSF. Demographic characteristics such as female sex, higher age, being unmarried, and having a higher education level have been identified as potential risk factors (Zhan et al., 2023; Zhang et al., 2021). Lifestyle and clinical features, including low physical activity prior to stroke, poor sleep quality, insomnia, and shorter sleep duration, also correlate with PSF (Chen et al., 2023; English et al., 2024). Functionally, patients with neurological deficits, reduced mobility, or impaired activities of daily living tend to report more severe fatigue (Vollertsen et al., 2023). Psychological symptoms such as post-stroke depression (PSD) and post-stroke anxiety (PSA) are strongly associated with PSF. Depression often overlaps with fatigue in its psychological dimensions, while anxiety may exacerbate the physical components (Chen et al., 2023; Zhan et al., 2023).

A more evidence about PSF and its causative mechanisms, especially in younger stroke population, may contribute to better understanding PSF and to development of more effective treatment options. Better insight into its demographic, psychosocial, and clinical determinants—especially during the subacute phase—may support the identification of high-risk individuals and contribute to improved long-term functional and social outcomes. Therefore, the aim was to assess the occurrence of PSF in the patients in working age (18–65 years) at 3 months after IS and evaluate possible associations to PSD and PSA, and to other relevant clinical factors.

Subjects and Methods

Study Design and Study Sample

A prospective observational single center study design was used and the STROBE (The Strengthening the Reporting of Observational Studies in Epidemiology) checklist for observational studies was applied. The study was conducted at Comprehensive Stroke Centre in University Hospital Olomouc. Consecutive patients with IS in working age (18–65 years) enrolled in the prospective FRAILTY (Factors Affecting the Quality of Life After Ischemic Stroke in Young Adults; registered on ClinicalTrials.gov, Identifier: NCT04839887, registered on April 09, 2021) between May 2022 and September 2024 were included in this study analysis.

The inclusion criteria of the FRAILTY study were as follows: age 18 to 65 years, first-ever IS confirmed on computed tomography (CT) or magnetic resonance imaging (MRI) of the brain, ability to understand the content of the questionnaires, and to communicate and sign informed consent. The exclusion criteria were as follows: transient ischemic attack without progression to ischemic stroke; cerebral infarction caused by trauma; hemorrhagic stroke; history of cognitive impairment or communication disorder resulting in impaired ability to understand the questionnaires; a concomitant severe systemic illness that could affect QoL.

Data Collection Instruments

The neurological deficit was assessed using the National Institute of Health Stroke Scale (NIHSS); modified Rankin Scale (mRS) and Barthel Index (BI) were used for the assessment of the functional outcome after 3 months. Excellent clinical outcome was scored as 0 or 1 point in mRS and good clinical outcome was defined as the mRS score 0 to 2.

PSF was assessed using the Fatigue Severity Scale (FSS) (Lerdal & Kottorp, 2011) and Neuro_QoL_Fatigue scale (NQFs) (Cella et al., 2012). The following categorization of a fatigue level according to mean FSS score was applied for the study analysis: nonfatigue (FSS ≤ 4.0), borderline fatigue (4.0 < FSS < 5.0) and severe fatigue (FSS ≥ 5.0) (Johansson et al., 2008; Ottonello et al., 2016). Although these thresholds were originally proposed in studies of patients with multiple sclerosis, they have been widely adopted in PSF research to enable cross-condition comparability and to differentiate clinically relevant fatigue levels (e.g., Lerdal & Kottorp, 2011; Norlander et al., 2021). The NQFs “raw” score was a total sum of all scored scale items, and higher score indicated a worse (undesirable) status. The NQFs “raw” score was translated into the NQFs T-score using a conversion table. NQFs T-score distributions rescale raw scores into the standardized scores with a mean of 50 points and a standard deviation (SD) of 10 points. Thus, a person who had a T-score of 60 was one SD above the average (normal value) of the referenced population.

Health-related QoL (HRQoL) was assessed using the Stroke Impact Scale (SIS) version 3.0 (Duncan et al., 2003), PSD and PSA using Hospital Anxiety and Depression Scale (HADS) (Zigmond & Snaith, 1983). Subgroup comparison according to FSS scores was performed to identify possible association between PSF scores and age, sex, residual neurological deficit, functional outcome and HADS scores. The length and quality of sleep was assessed using the Neuro_QoL_Sleep scale (Cella et al., 2012).

Ethical Consideration

The FRAILTY study was approved by the Ethics Committee of University Hospital Olomouc and Palacký University (6/2021, n. NU22-09-00021). All enrolled patients gave signed informed consent, and patients were given explanation of the study’s purpose by researchers. All questionnaires were completed and returned without any identification information about participants. Sociodemographic and clinical data were recorded strictly anonymously without any possibility of retroactive identification. The data collection process followed regulations about the protection and security of personal data (GDPR).

Statistical Analysis

The Statistical Package for Social Sciences software version 23.0 (IBM Corp., Armonk, NY, USA) was used for the analysis with a significance level set at 0.05. The Kolmogorov–Smirnov test assessed normality of data distribution. The data were analyzed using descriptive and inferential statistics (Chi-squared and nonparametric tests: Mann–Whitney U Test, Kruskal–Wallis test, Fischer's Exact test, Bowker test, and Wilcoxon signed-rank test). The subgroup comparisons and logistic regression analysis (LRA) were performed to assess possible predictors of PSF. Given the exploratory design and the number of severe PSF events (n = 62), we restricted regression to univariate logistic models. A multivariable model including multiple intercorrelated predictors (e.g., SIS domains and HADS-A, HADS-D) was not prespecified and was avoided to reduce overfitting and unstable estimates. Post hoc pairwise comparisons were adjusted using the Bonferroni method; for 10 comparisons the adjusted significance level was α = 0.005.

Results

In total, 150 (54.0% males, mean age 51.2 ± 8.9 years) patients with IS were analyzed. Baseline clinical and descriptive characteristics of analyzed patients are shown in Table 1. Three months after IS, 118 (78.7%) patients had no residual neurological deficit (NIHSS 0) and 114 (76.0%) had mRS 0. Excellent clinical outcome (mRS 0–1) was present in 132 (88%) and good clinical outcome (mRS 0–2) in 144 (96.0%) patients 3 months after IS. Eighty-three (55.3%) patients reported presence of PSF after 3 months in FSS scale (categorial FSS mean score > 4) and 62 (41.3%) of them reported severe PSF (categorial FSS mean score > 5). Forty-six (30.7%) patients had NQFs T-scores above 50 points, indicating greater fatigue than the reference population average (T = 50), and thus the presence of PSF, consistent with the FSS where higher scores reflect more severe fatigue.

Table 1.

Selected Baseline Clinical and Descriptive Characteristics of All Analyzed Patients.

Parameter	Value
N, males (n, %)	150, 81, 54.0
Age (mean ± SD, years)	51.2 ± 8.9
Baseline NIHSS (median, range)	5 (2–23)
IS in anterior circulation (n, %)	124, 82.7
IV thrombolysis (n, %)	80, 53.3
MT (n, %)	46, 30.7
Arterial hypertension (n, %)	99, 66.0
Ischemic heart disease (n, %)	10, 6.7
Diabetes mellitus (n, %)	15, 10.0
Atrial fibrillation (n, %)	35, 23.3
Hyperlipidemia (n, %)	105, 70.0
Smoking (n, %)	50, 33.3
Marital status
Single (n, %)	16, 10.7
Married/couple (n, %)	97, 64.7
Widower/widow (n, %)	5, 3.3
Divorced (n, %)	32, 21.3
Employment
Currently employed (n, %)	135, 90.0
Unemployed (n, %)	15, 10.0
NIHSS 0 at 90 days after IS (n, %)	118, 78.7
NIHSS 1 at 90 days after IS (n, %)	10, 6.7
NIHSS 2 at 90 days after IS (n, %)	10, 6.7
NIHSS > 2 at 90 days after IS (n, %)	12, 8.0
mRS 0 at 90 days after IS (n, %)	114, 76.0
mRS 1 at 90 days after IS (n, %)	18, 12.0
mRS 2 at 90 days after IS (n, %)	12, 8.0
mRS > 2 at 90 days after IS (n, %)	6, 4.0
BI score at 90 days after IS (median, range)	100 (85–100)
HADS depression scores
Normal (≤ 7) (n, %)	120, 80.0
Mild (8–10) (n, %)	12, 8.0
Moderate (11–14) (n, %)	13, 8.6
Severe (15–21) (n, %)	5, 3.3
HADS anxiety scores
Normal (≤ 7) (n, %)	110, 73.3
Mild (8–10) (n, %)	21, 14.0
Moderate (11–14) (n, %)	14, 9.3
Severe (15–21) (n, %)	5, 3.3

BI = Barthel index; HADS = Hospital Anxiety and Depression Scale; IS = ischemic stroke; IV = intravenous; mRS = modified Rankin Scale; MT = mechanical thrombectomy; NIHSS = National Institutes of Health Stroke Scale; SD = standard deviation.

No difference was found in the FSS scores (total sum and mean value) as well as in categorial FSS (no fatigue, mild fatigue, and severe fatigue) between the baseline and after 3 months after IS (Table 2). The FSS scores remained unchanged in 66 (44.9%) patients, while 36 (24.5%) patients reported an improvement and 45 (30.6%) reported a worsening after 3 months. The NQFs “raw” and T-scores decreased significantly after 3 months (21 vs. 18, p=.001, and 48.4 vs. 45.6, p=.002) (Table 2).

Table 2.

Comparison of PSF at Baseline and at 3 Months After IS (FFS and NQF Scales).

Value	Baseline	At 3 Months	p
Total FSS score (median, mean ± SD)	4.0, 4.0 ± 1.7	4.2, 4.2 ± 1.8	.218
Mean FSS score (median, mean ± SD)	36.0, 35.8 ± 15.5	36.0, 37.9 ± 16.1	.143
FSS score < 4 (n, %)	72 (48.0)	65 (43.3)	.372
FSS score ≥ 4 ≤ 5	31 (20.7)	23 (15.3)	.392
FSS score > 5 (n, %)	46 (30.7)	62 (41.3)	.104
NQFs “raw” score (median, mean ± SD)	21.0, 21.0 ± 7.3	18.0, 19.1 ± 7.5	.001
NQFs T-score (median, mean ± SD)	48.4, 48.2 ± 8.1	45.6, 46.1 ± 8.5	.002

FFS = Fatigue Severity Scale; IS = ischemic stroke; NQF = Neuro_QoL_Fatigue scale; PSF= post-stroke fatigue; SD = standard deviation.

The comparison of categorial FSS scores showed no differences in sex, age, marital status, education level, regular physical exercise prior IS, length and quality of sleep, clinical and functional outcome after 3 months between the patients with no fatigue, mild/moderate fatigue and severe fatigue (Table 3). Patients with categorial FSS score > 5 (severe fatigue) had significantly lower scores in all SIS 3.0 domains except SIS domain “hand” after 3 months (Table 3). These patients had also higher scores in the HADS-D and HADS-A scales (Table 3).

Table 3.

Comparison of Selected Relevant Parameters According to a Level of PSF Presented After 3 Months.

Parameter	FSS < 4	FSS ≥ 4 ≤ 5	FSS > 5	p
n	67	21	62
Sex
Males (n, %)	32 (47.8)	12 (57.2)	36 (58.1)	.418
Females (n, %)	35 (52.2)	9 (42.8)	26 (41.9)
Age (median, range)	51 (32–64)	51.5 (29–64)	53.5 (18–64)	.679
Marital status
Married, with partner (n, %)	47 (70.1)	11 (52.4)	40 (64.5)	.251
Alone, widower/widow (n, %)	20 (29.9)	10 (47.6)	22 (35.5)
Education
Basic school (n, %)	5 (7.5)	0	2 (3.2)	.560
Secondary school (n, %)	52 (77.6)	17 (81.0)	49 (79.0)
University (n, %)	10 (14.9)	4 (19.0)	11 (17.8)
Regular physical exercise prior IS (n, %)	19 (31.7)	4 (19.4)	19 (31.7%)	.574
NIHSS at 3 M after IS (median, range)	0 (0–4)	0	0	.871
mRS at 3 M after IS (median, range)	0 (0–3)	0	0	.791
BI at 3 M after IS (median, range)	100 (85–100)	100	100	.972
SIS 3.0
Strength (median)	100	81.3	75.0	.045
Memory (median)	100	94.6	89.3	.006
Emotions (median)	77.8	80.6	72.2	.013
Communication (median)	100	100	92.9	.026
ADL (median)	100	100	97.5	.013
Mobility (median)	100	100	97.2	.027
Hand-F (median)	100	100	100	.655
Participation (median)	93.8	84.4	78.1	.015
NQoL Sleep “raw” score (median)	16.0	16.5	17.5	.337
NQoL Sleep T-score (median)	54.4	51.5	52.5	.337
HADS-A score (median, range)	4.0 (0–18.0)	3.0 (0–13.0)	5.5 (0–18.0)	.033
HADS-A total score (median, range)	1.0 (1.0–3.0)	1.0 (1.0–3.0)	1.0 (1.0–3.0)	.041
HADS-D score (median, range)	2.0 (0–15.0)	2.0 (0–13.0)	5.0 (0–21.0)	.0002
HADS-D total score (median, range)	1.0 (1.0–3.0)	1.0 (1.0–3.0)	1.0 (1.0–3.0)	.026

ADL = activities of daily living; BI = Barthel index; FSS = fatigue severity scale; HADS = Hospital Anxiety and Depression Scale; IS = ischemic stroke; mRS = modified Rankin Scale; NIHSS = National Institutes of Health Stroke Scale; NQF= Neuro_QoL_Fatigue; NQoL = neuroquality of life scale; PSF= post-stroke fatigue; SIS= Stroke Impact Scale.

Post hoc analysis of SIS domains using Mann–Whitney test with Bonferroni correction of significance for multiple comparison showed lower scores (lower QoL) in the patients with categorial FSS score > 5 in the following SIS domains: memory, emotions, communication, ADL (activity of daily living), mobility and participation, and higher scores in HADS-A and HADS-D scales in comparison to those with categorial FSS score < 4 (no fatigue). Post hoc between-group differences that remained significant after Bonferroni adjustment (α = 0.005 for 10 comparisons) are reported in Table 4.

Table 4.

Mann–Whitney Test With Bonferroni Correction of Significance for Multiple Comparison: Post Hoc Analysis of SIS Domains and HADS Scales, Which Differed Significantly in Kruskal–Wallis Test (Table 3).

Parameter	FSS < 4 vs. FSS 4–5	FSS < 4 vs. FSS > 5	FSS 4–5 vs. FSS > 5
SIS strength	0.148	0.098	1.000
SIS memory	1.000	0.007	0.169
SIS emotion	1.000	0.029	0.076
SIS communication	1.000	0.020	0.719
SIS ADL	1.000	0.020	0.170
SIS mobility	1.000	0.034	0.298
SIS participation	0.394	0.014	1.000
HADS-D score	1.000	0.0001	0.124
HADS-D total score	0.835	0.021	1.000
HADS-A score	1.000	0.052	0.201
HADS-A total score	1.000	0.049	0.492

ADL = activities of daily living; FSS = Fatigue Severity Scale; HADS = Hospital Anxiety and Depression Scale; SIS = stroke impact scale.

The univariate LRA showed the association between the lower scores of the following SIS domains: memory, emotions, communication, mobility and participation and the presence of severe PSF (FSS score > 5) after IS (Table 5). The higher scores in HADS-D and HADS-A scales were found as other predictors of severe PSF (Table 5).

Table 5.

Results of Univariate Logistic Regression Analysis for the Prediction of Presence of Severe PSF (FSS Score > 5).

Parameter	OR	95% CI	p
SIS strength	0.987	0.972–1.002	.091
SIS memory	0.968	0.947–0.990	.004
SIS emotion	0.966	0.945–0.987	.002
SIS communication	0.962	0.932–0.992	.014
SIS ADL	0.982	0.956–1.008	.175
SIS mobility	0.968	0.941–0.996	.027
SIS Hand-F	0.999	0.984–1.013	.843
SIS participation	0.983	0.969–0.997	.015
HADS-D score	1.178	1.079–1.287	.0003
HADS-D total score	1.983	1.192–3.301	.008
HADS-A score	1.111	1.027–1.202	.009
HADS-A total score	1.809	1.122–2.917	.015
NeuroQoL_Fatigue Raw score	1.082	1.031–1.135	.001
NeuroQoL_Fatigue T score	1.064	1.020–1.109	.004

ADL = activities of daily living; CI = confidence interval; FSS = Fatigue Severity Scale; HADS = Hospital Anxiety and Depression Scale; OR = odds ratio; PSF= post-stroke fatigue; SIS = stroke impact scale.

Discussion

In this study, 41.3% of patients reported the severe form of PSF at 3 months after IS despite excellent clinical outcome. Furthermore, the finding of the associations between PSF and SIS domains memory, emotions, communication, mobility, and social participation showed that PSF affected QoL after IS significantly. The found strong association between depression, anxiety, and PSF indicated the close relationship of PSF to psychosocial factors and cognition. On the other hand, no associations between PSF and age, sex and clinical outcome were observed in this study.

PSF is being currently considered an important sequel after IS, which may limit functional recovery and may also affect significantly QoL after stroke. PSF represents a crucial barrier for return back to work despite good functional outcome after IS (Donker-Cools et al., 2018; Hartke & Trierweiler, 2015; Schwarz et al., 2018). The prevalence of PSF may differ according to the time of its assessment (< 6 vs. ≥ 6 months after stroke onset), stroke type (ischemic vs. hemorrhagic/subarachnoid hemorrhage), and according to geographical location (East Asia vs. Europe) (Alghamdi et al., 2021; Cumming et al., 2016).

Lerdal et al. have shown the increased risk and rates of PSF in both younger and older patients after IS and suggested that the relationship between PSF and age may follow a U-shaped curve, with the highest rates of PSF in younger (< 60 years) and older (> 75 years) patients (Lerdal et al., 2013). In our study, PSF was present in 56.7% of patients in working age (18–65 years) at 3 months after the first-ever IS and the severe form of PSF was present in 41.3% of patients. However, the observed rates of PSF in our study set were in line with previous reports (Norlander et al., 2021; Vitturi et al., 2021; Vollertsen et al., 2023) the rate of severe form of PSF in our study was relatively higher (41.3%). The results of some previous studies indicated that younger patients and those with good physical recovery might be most disabled by PSF and would tend to rate the fatigue as a more severe symptom (Ingles et al., 1999; Staub & Bogousslavsky, 2001; Vitturi et al., 2021).

No associations between the residual neurological deficit and clinical outcome on one side and the occurrence and intensity of PSF on other side were found in our study (Table 3). Our results support previous reports that occurrence of PSF did not corelate with the stroke severity (Becker et al., 2015; Duncan et al., 2015; Radman et al., 2012). Furthermore, patients with excellent clinical outcome after IS are independent in all basic ADL, however, they can be limited due to PSF in some complex activities or in some abilities (e.g., memory, emotions, communication, and participation), which are not usually routinely assessed during clinical controls. Recently, no associations between the lesion location, size of ischemic damage and the presence of PSF were found in the study on 135 young patients with IS (Boot et al., 2022). Furthermore, no associations between PSF and brain network connectivity, which was assessed using MRI diffusion tensor imaging, were observed (Boot et al., 2022). These findings may also support the concept that pathophysiology of PSF is independent of stroke severity.

No association between the quality of sleep and sleep disturbances and the presence of PSF were observed in our patients (Table 3), however sleep disturbances (e.g., insomnia symptoms and especially, sleep-disordered breathing) are very frequently present after stroke and their relationship to PSF was well established (Becker et al., 2024; Ho et al., 2021; Luo et al., 2023). Sleep-disordered breathing (SDB) is the most frequent sleep disturbance and SDB affects more than 70% of patients with IS, mostly in a form of obstructive sleep apnea (OSA) (Chervin, 2000; Cowie et al., 2021; Seiler et al., 2019). The relationship between SDB and fatigue with lack of energy is well known and it can be improved with SDB treatment (Chervin, 2000; Chotinaiwattarakul et al., 2009; Cowie et al., 2021; Seiler et al., 2019). It has been recently shown that the rate of apneas and hypopneas per hour during sleep is well corelated with the severity of PSF in early post-stroke period (Becker et al., 2024). Thus, a screening of the PSF and quality of sleep may help to better identify SDB in patients with IS. It might be suggested that patients in our study were substantially younger and had less risk factors associated with SDB. A routine screening for sleep problems after IS (e.g., the Pittsburgh Sleep Quality Index, the Insomnia Severity Index, and pragmatic screening for sleep apnea) and nurse-delivered nonpharmacological management (sleep hygiene, stimulus control, and pacing) are warranted and further studies should incorporate objective, real-world sleep measures (e.g., actigraphy, heart-rate variability, and smartphone-based ecological momentary assessment) and evaluate whether improving sleep (e.g., cognitive behavioral therapy for insomnia) reduces fatigue burden after stroke (Ymer et al., 2025).

In our study, the strong association between the PSD and PSA and PSF was confirmed (Table 5). Currently, PSF may predispose the development of depression after IS and fatigue can be a symptom of depression, however treatment with antidepressants did not improve PSF (Chu et al., 2023). Despite previously found strong associations between PSF and depression (Boot et al., 2022; Snaphaan et al., 2011; Wu et al., 2015), PSF and depression are considered two different clinical entities, which should be distinguished, since not all patients with PSF have symptoms of depression or anxiety (Radman et al., 2012), and patients with PSF should not be treated empirically with antidepressants for their PSF, unless they have depression (Chu et al., 2023).

In our study, the lower scores of SIS domains memory, emotions, communication, mobility, and social participation were significantly associated with the presence of severe PSF (Table 3, 4). A very recently, a similar significant association between PSF and SIS domain memory was observed in a set of 107 stroke patients (Norlander et al., 2024), and the association between PSF and SIS domain emotions had a tendency toward significance (Norlander et al., 2024). These findings also support the concept of important role of cognitive functioning and emotional reactions for successful fatigue management, which requires multidisciplinary interventions. Moreover, memory and concentration difficulties are considered also symptoms of mental fatigue.

No association between PSF and sex, age, marital status and education level was found in our patients. Similar results were reported previously in 76 patients with minor stroke (Winward et al., 2009). On other hand, some previous reports showed that females suffered from a greater PSF after IS than males (Ingles et al., 1999; Norlander et al., 2021; Palm et al., 2017; Vollertsen et al., 2023; Zhang et al., 2021) and this difference persisted up to 7 years after IS (Pedersen et al., 2022). Recently, no associations between PSF and educational level and marital status were observed (Norlander et al., 2024).

Fatigue is considered one of the important factors, which may limit work ability and may be associated with work difficulties in the patients with neurological disorders, mostly in those with multiple sclerosis (Ponzio et al., 2024; Raggi et al., 2016). These work difficulties also occur in the patients after IS (Donker-Cools et al., 2018; Hartke & Trierweiler, 2015; Schwarz et al., 2018). In our study, the work ability and return back to work after IS were not included in the primary analysis, because 25% of patients had functional sequels after IS (mRS > 0), which might also affect the work ability.

Since dedicated tools for a reliable detection of PFS are available for clinical practice, there is still a lack of strong recommendations for management of PSF (Chu et al., 2023; Mead et al., 2023). How to manage PSF is one of the priorities of the Stroke Action Plan for Europe 2018–2030 (Norrving et al., 2018), and currently, multidisciplinary interventions for PSF treatment are being mostly discussed. Nevertheless, the current lack of objective clinical assessments and monitoring of fatigue may limit the further development of effective antifatigue treatments (VanDyk et al., 2023) and the concept of physical and mental fatigue underlines the need for objective fatigue assessments. It is hypothesized that physical fatigue impacts muscle activity, especially against gravidity and mental fatigue affects tasks requiring intersegmental coordination (e.g., sitting down) (Van Cutsem et al., 2017). The results of very recent study, which was focused on the objective assessments of fatigue in patients at home, showed for the first time that kinematic features of transition (sit-to stand, stand-to-sit, upper and lower limbs performance) were associated with fatigue. More associations were found for physical fatigue than mental one (Romijnders et al., 2025). These findings also highlight the potential of wearable-based assessment of fatigue for the use in home environment and bring new possibilities for the further research in this area (Romijnders et al., 2025). Such wearable-based and home-environment monitoring could be particularly useful for working-age stroke patients, allowing continuous assessment of fatigue patterns during everyday activities, including both home and workplace settings.

Strengths and Limitations of the Study

The fact that two dedicated scales for the evaluation of PSF were used might be a potential strength of our study. Another one might be the prospective study design with a well-defined and homogeneous study population. In our study, we focused on a detection of factors, which may have a relationship to PSF or contribute to PSF. On the other hand, our study has some other limitations. The monocentric design and the relatively small sample size represent the main limitations of our study. A potential bias cannot be excluded, as the patients self-reported the SIS and HADS questionnaires without a following verification of the provided data by their relatives or family members. Patients with concomitant severe systemic illnesses that could affect PSF or HRQoL were not enrolled. In addition, sleep was assessed with the Neuro_QoL Sleep scale only; we did not include objective measures or dedicated diagnostics for sleep-disordered breathing (e.g., polysomnography), which may underestimate sleep-related contributors to PSF. Furthermore, our cohort comprised working-age patients (18–65 years) assessed at a single time point (3 months), which may limit generalizability to other age groups and precludes causal inference about temporal dynamics of PSF. Finally, we restricted regression analyses to univariate models due to events-per-variable considerations and intercorrelation among candidate predictors; therefore, associations should not be interpreted as independent effects.

Implications and Recommendations

The present findings underscore the clinical importance of PSF as a prevalent and disabling consequence of IS in the working-aged patients. The associations with cognitive, emotional, communicative, and social dimensions of functioning highlight the need for multidimensional assessment and management of PSF in the clinical practice.

Routine screening for PSF using validated dedicated tools such as the FSS and NQFs should be incorporated into the post-stroke follow up. A biopsychosocial approach, including psychological evaluation and tailored supportive interventions, is warranted. In research perspective, further longitudinal studies are needed to explore the mechanisms of PSF and to validate multidisciplinary treatment strategies. The use of objective assessment tools, including wearable technologies, may offer new opportunities to monitor fatigue and evaluate its impact on long-term outcomes, including vocational reintegration and societal participation. In this context, nursing professionals play a pivotal role in the early detection and continuous monitoring of PSF, integrating systematic fatigue assessment into routine post-stroke care, providing patient and caregiver education on symptom recognition and self-management strategies, and facilitating access to appropriate multidisciplinary resources. Nursing practice should lead a standardized fatigue screening and provide individualized nursing care plans focused on nonpharmacological strategies (e.g., energy management and pacing, sleep hygiene, relaxation and breathing exercises, and adjustment of daily routines and environments), with clear escalation pathways to relevant professionals when red flags emerge.

Nurse-led clinics or telehealth follow ups can sustain longitudinal monitoring, reinforce ongoing patient/caregiver education, and support vocational reintegration through structured nonpharmacological interventions. Future studies should incorporate objective, real-world measures feasible in nursing practice (e.g., actigraphy, heart-rate variability, and smartphone-based assessment) to improve assessment accuracy and to test nurse-delivered, just-in-time supportive interventions. Such proactive nursing involvement is essential for optimizing HRQoL, supporting vocational reintegration, and enhancing the overall long-term recovery trajectory in this patient population.

Conclusions

PSF is a very frequent and important sequel after IS, which used to be underdiagnosed or considered a simple symptom of depression in the routine clinical practice. PSF may limit functional recovery, affect QoL after IS and represent a barrier for return back to work despite excellent functional outcome.

The severe form of PSF occurred in 41.3% of patients in working age despite excellent clinical outcome after three months post-stroke in our study. Our results showed significant associations between PSF and SIS domains memory, emotions, communication, mobility, and social participation at 3 months after IS. The strong association between depression, anxiety and PSF was also confirmed, and these findings indicate the close relationship of PSF to psychosocial factors and cognition. We suggest that PSF may be screened in all patients with IS during the routine clinical follow up after IS and with using of the dedicated scales.

Supplemental Material

sj-docx-1-son-10.1177_23779608251400226 - Supplemental material for Post-Stroke Fatigue in Patients at 3 Months After Ischemic Stroke: Analysis From the FRAILTY Study

Supplemental material, sj-docx-1-son-10.1177_23779608251400226 for Post-Stroke Fatigue in Patients at 3 Months After Ischemic Stroke: Analysis From the FRAILTY Study by Šárka Šaňáková, Elena Gurková, Daniela Bartoníčková, Lenka Štureková, David Franc, Jana Zapletalová, Petra Divišová and Daniel Šaňák in SAGE Open Nursing

Footnotes

ORCID iD

Šárka Šaňáková

Ethical Approval Statement

Study was approved by the Ethics Committee of University Hospital Olomouc and Palacký University (6/2021, n. NU22–09–00021).

Informed Consent Statement

All participants gave signed informed consent for participation.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Agentura Pro Zdravotnický Výzkum České Republiky (grant number NU22-09-00021).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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