Fatigue 7 years post-stroke: Predictors and correlated features

Profiling 92 circulating neurobiological proteins identifies novel candidate biomarkers of long-term cognitive outcome after ischemic stroke

The biological underpinnings of post-stroke cognitive function are largely unknown, and protein investigations can point towards important pathways for further study. We profiled plasma levels of 91 neurology-related proteins (Olink Neurology panel) and serum Neurofilament light chain (NfL) levels in 205 cases in the Sahlgrenska Academy Study on Ischemic Stroke. Blood was sampled in the acute and convalescent (3 months post-stroke) phase. Cognitive outcome was evaluated by the Barrow Neurological Institute Screen for Higher Cerebral Functions 7 years post-stroke. In linear regression models, 6 and 5 proteins in the acute and convalescent phase, respectively, were univariably associated with cognitive outcome at False Discovery Rate (FDR) < 0.05, and 9 and 8 at p < 0.05 after adjustment for age, sex, education and sampling day (model 1) and/or additional adjustment for stroke severity (model 2). Of these, 15 proteins contributed with information in multi-protein models on at least one time-point. These included brain-expressed proteoglycans (NCAN, BCAN, GPC5, SPOCK1); contactin-5 (CNTN5); metabolic enzymes (HAGH, NMNAT1); cluster of differentiation (CD)-proteins (SIGLEC1, CLEC10A, CD200R1); GDNF family receptor alpha-1 (GFR-alpha-1); brorin (VWC2); beta-nerve growth factor (beta-NGF); myostatin (GDF-8); and NfL. We identified novel candidate protein biomarkers of post-stroke cognitive outcome that likely reflect different biological processes, warranting further exploration.

Prevalence, Trajectory, and Predictors of Poststroke Fatigue in Older Adults

OBJECTIVE

To explore the prevalence, trajectories, and predictors of poststroke fatigue in older adults after a first ischemic stroke.

DESIGN

A longitudinal observational cohort study.

SETTING

Two hospitals.

PARTICIPANTS

A total of 381 patients aged ≥65 years with their first ischemic stroke were included. The mean (standard deviation) age was 71.1 (4.27) years, with 96 patients (25.2%) being women and 285 (74.8%) being men.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Patients were assessed using the Fatigue Severity Scale at admission, 3 months, and 12 months. Growth mixture models were used to identify distinct fatigue trajectories, and baseline variables were analyzed to determine their association with these trajectories.

RESULTS

The prevalence of clinical fatigue was 39.11%, 33.33%, and 22.31% at admission, 3 months, and 12 months, respectively. Five distinct fatigue trajectories were identified: persistently low fatigue (class 1, 49.1%), persistently high fatigue (class 2, 21.5%), initial high but early decreasing fatigue (class 3, 15.0%), initial high but late decreasing fatigue (class 4, 8.7%), and increasing-then-decreasing fatigue (class 5, 5.8%). Multinomial logistic regression analysis revealed that several factors were significantly associated with high and persistent fatigue (class 2), including older age, lower social support, decreased physical activity, higher depression and anxiety scores, cognitive impairment, and greater stroke severity.

CONCLUSIONS

These findings indicate significant variability in the progression of fatigue among stroke survivors. Further research is necessary to determine the outcomes linked to these fatigue trajectory subgroups and to identify the most effective treatment strategies tailored to each specific subgroup.

Machine Learning-Based Model for Prediction of Early Post-Stroke Fatigue in Patients With Stroke: A Longitudinal Study

BackgroundPost-stroke fatigue, as one of the long-lasting physical and mental symptoms accompanying stroke survivors, will seriously affect the daily living ability and quality of life of stroke patients.ObjectiveThe aim of this study was to develop machine learning (ML) algorithms to predict early post-stroke fatigue among patients with stroke.MethodsA longitudinal study of 702 patients with stroke followed for 3 months. Twenty-three clinical features were obtained from medical records and questionnaires before discharge. Early post-stroke fatigue was assessed using the Fatigue Severity Scale. The dataset was randomly divided into a training group (70%) and an internal validation group (30%), applied oversampling, 10-fold cross-validation, and grid search to optimize the hyperparameter. Feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Sixteen ML algorithms were performed to predict early post-stroke fatigue in this study. Accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and brier score were used to evaluate the models performance.ResultsAmong the 16 ML algorithms, the Bagging model was the optimal model for predicting early post-stroke fatigue in patients with stroke (AUC = 0.8479, accuracy = 0.7518, precision = 0.5741, recall = 0.7209, F1 score = 0.6392, brier score = 0.1490). The feature selection based on LASSO revealed that risk factors for early post-stroke fatigue in patients with stroke included anxiety, sleep, social support, family care, pain, depression, neural-functional defect, quit/no drinking, balance function, type of stroke, sex, heart disease, smoking, and hemiplegia.ConclusionsIn this study, the Bagging model proved to be effective in predicting early post-stroke fatigue.

Factors associated with post-stroke fatigue among stroke survivors: a cross-sectional study

Background

Post-stroke fatigue (PSF) is a common complication experienced by stroke survivors. These individuals often confront psychological challenges such as depression and anxiety, along with significant obstacles like reduced quality of life (QoL) and limitations in activities of daily living (ADLs). Such challenges can profoundly affect their overall recovery and well-being. Despite its prevalence, the associated factors contributing to PSF remain poorly understood. This study aims to primarily investigate these associated factors, while also examining the interrelationships among PSF, depression level, QoL, and ADLs, highlighting the need for a better understanding of these complex interactions.

Methods

This cross-sectional study involved 271 stroke survivors and was conducted at the Department of Neurology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, China, from September 2023 to January 2024. Participants independently completed the Fatigue Severity Scale (FSS), Patient Health Questionnaire-9 (PHQ-9), and the Short Version of the Stroke-Specific Quality of Life Scale (SV-SS-QoL) as part of a convenience sampling method, while medical professionals assessed the Barthel Index (BI) using the same sampling framework. Multivariable linear regression analyses were employed to determine the factors associated with the persistence of PSF.

Results

The mean FSS score was 35.04 ± 11.60, while the average score for the SV-SS-QoL was 34.28 ± 9.51, and the BI score averaged 77.79 ± 25.90. Approximately 45.8% of participants (n = 124) experienced PSF. The mean score on the PHQ-9 was 7.63 ± 6.13. A significant negative correlation was identified between fatigue and both QoL and ADLs (P < 0.01). Furthermore, multiple linear regression analyses revealed that age, gender, income level, depression level, QoL, and ADLs were associated factors of fatigue severity among stroke survivors, all showing statistically significant differences (P < 0.01).

Conclusion

This study emphasizes the critical factors associated with PSF and highlights the necessity of developing targeted interventions, such as depression management, QoL enhancement, and ADLs restoration, to alleviate fatigue in stroke survivors. Future research should focus on evaluating the effectiveness of these interventions to optimize rehabilitation outcomes and improve survivors' overall recovery and well-being.

Prevalence, Trajectory, and Factors Associated With Patient-Reported Nonmotor Outcomes After Stroke: A Systematic Review and Meta-Analysis

Importance

The evidence on nonmotor outcomes after acute ischemic stroke and intracerebral hemorrhage (ICH) is limited and domain-specific. The prevalence, natural history, and factors associated with nonmotor outcomes across multiple domains are unclear.

Objective

To synthesize the published data and to identify the prevalence, natural history, and factors associated with multidomain nonmotor outcome burden in patients with stroke.

Data Sources

A search of PubMed, MEDLINE, EMBASE, and PsycINFO databases between January 1999 to June 2023 was supplemented by search of bibliographies of the key articles.

Study Selection

The analysis included prospective cohort studies that reported nonmotor outcomes across 10 domains: anxiety, depression, fatigue, sleep disturbance, social participation, pain, bladder dysfunction, bowel dysfunction (constipation and fecal incontinence), and sexual dysfunction assessed by patient-reported scales.

Data Extraction and Synthesis

Two reviewers independently assessed studies, extracting baseline data, nonmotor prevalence, and follow-up information. Pooled prevalence of nonmotor outcomes was estimated using random-effects models. Meta-regression models were used to analyze natural history and factors associated with nonmotor outcomes. Subgroup analysis was used to assess prevalence by symptom description within each nonmotor domain. Publication bias and study quality were assessed using funnel plots and the Newcastle-Ottawa scale.

Results

A total of 279 prospective cohort studies met the inclusion criteria (117 440 participants with stroke; median [IQR] age, 65 [59-70] years; 209 of 279 studies with more male than female participants) with a nonmotor outcome follow-up period ranging from 30 days to 10 years after stroke. The most prevalent adverse nonmotor outcomes by pooled prevalence were sleep disturbance (59.9%; 95% CI, 53.9%-63.9%), sexual dysfunction (59.8%; 95% CI, 50.0%-69.5%), constipation (58.2%; 95% CI, 53.9%-62.6%), reduced social participation (56.5%; 95% CI, 52.1%-60.8%), bladder dysfunction (45.9%; 95% CI, 38.0%-53.8%), and fatigue (45.2%; 95% CI, 40.7%-49.5%). Meta-regression analysis showed no significant improvement over time for most nonmotor outcomes, except pain (coefficient = -11.0%; P = .05) and sexual dysfunction (coefficient = -24.1%; P < .001). The heterogeneity ranged between 52% and 98% across all studies. The common factors associated with adverse nonmotor outcomes were female sex, studies with mixed stroke cohort (ischemic stroke or ICH), and older age.

Conclusions and Relevance

Patient-reported nonmotor outcomes were common after stroke. Sexual dysfunction, sleep disturbance, constipation, reduced social participation, bladder dysfunction, and fatigue were most prevalent. These adverse outcomes often persisted over time, especially in women, older adults, and those in studies with mixed stroke cohorts.

Post-stroke fatigue - a multidimensional problem or a cluster of disorders? A case for phenotyping post-stroke fatigue

Post-stroke fatigue is a chronic problem with significant impact on morbidity and mortality, which urgently needs effective treatments. The last decade has seen a considerable increase in interest in understanding the pathophysiology of fatigue and developing treatments. In this review, following a summary of theoretical frameworks to understand chronic fatigue, I make a case for why phenotyping fatigue is a necessary step to fully understand pathophysiology, which in turn is essential for the development of robust treatments. I then appraise current post-stroke fatigue literature with the view of identifying post-stroke fatigue phenotypes.

The doctor-patient perception mismatch: Improving approaches to assessing outcomes after ischemic stroke treated with reperfusion therapy

The long-term effects of ischemic stroke on cognition and mental health are not reflected in traditional outcome metrics, like the modified Rankin Scale (mRS) for functional independence. Consequently, this may lead to mismatches in perceptions of overall recovery, despite otherwise qualifying as having good functional outcomes (mRS 0-2). In our multicenter, multinational analysis, we aim to describe the prevalence of, and factors associated with, patient-reported cognitive impairment despite achieving good functional outcomes. Acute ischemic stroke patients at Cooper University Hospital (2021-2024) and Hospital Vall d'Hebron in Barcelona, Spain (2020-2021) treated with reperfusion therapy and achieved 90-day mRS 0-2 were surveyed with the previously-validated PROMIS Global-10 scale for physical health (PROMIS-PH) and mental health (PROMIS-MH). The primary outcome was the rate of fair or poor PROMIS-MH scores (≤ 11). Univariable and multivariable linear regressions for PROMIS-MH scores were performed. Of 157, 90-day mRS 0-2 patients, the mean age was 68 (standard deviation 15) years, and 61 % were male. Fair or poor PROMIS-MH scores were reported in 43 % of patients. Clinical factors independently associated with PROMIS-MH scores in a multivariable linear regression include: sex, tobacco use, PROMIS-PH score, and National Institutes of Health Stroke Scale at 3-day follow-up. Despite achieving favorable post-stroke mRS, there is a high prevalence of patient-reported cognitive impairment, underscoring an important gap in post-stroke care. The emphasis in post-stroke care should extend beyond the scope of traditional metrics, and should encompass evaluations and interventions targeting additional domains significant to overall patient recovery, especially patient-reported cognitive symptoms.

Relationships of post-stroke fatigue with mobility, recovery, performance, and participation-related outcomes: a systematic review and meta-analysis

Background

Effective post-stroke mobility, recovery, performance, and participation are key goals for stroke survivors. However, these outcomes may be hindered by post-stroke fatigue (PSF), which can affect numerous aspects of post-stroke mobility, recovery, performance, functioning, community participation, and return to work. This review aimed to assess the scientific evidence on the relationship between PSF and mobility function, functional recovery, functional performance, and participation-related outcomes among stroke survivors.

Method

A comprehensive search of Cochrane Central, PubMed, Embase, and Web of Science (WoS) databases was conducted from inception to December 2023. Observational, cross-sectional, and longitudinal studies were included. The methodological quality of the included studies was assessed using the National Institute of Health's quality assessment tool, while the risk of bias was assessed using the Quality in Prognostic Studies tool. A total of 28 studies (n = 2,495 participants, 1,626 men, mean age ranging from 52.5 ± 9.5 to 71.1 ± 9.9 years) were included. The data analysis was conducted using narrative and quantitative synthesis. Fixed and random effects meta-analyses were conducted to explore the relationships between PSF and relevant outcomes.

Results

Chronic PSF was found to have significant negative correlations with mobility (meta r = -0.106, p < 0.001), balance performance (meta r = -0.172; 95%; p = 0.004), and quality of life (meta r = -0.647; p < 0.001). It also showed significant positive correlations with stroke impairment (meta r = 0.144, p < 0.001) and disability (meta r = 0.480, p < 0.001). Additionally, exertion/acute PSF had significantly negative correlations with walking economy (meta r = -0.627, p < 0.001) and walking endurance (meta r = -0.421, p = 0.022). The certainty of evidence was deemed moderate for these relationships.

Conclusion

Our findings indicate that higher levels of PSF are associated with poorer mobility, balance, and participation, as well as greater disability and stroke impairment. Future studies, especially prospective longitudinal and randomized controlled trials, are warranted to substantiate our findings.

Systematic review registration

PROSPERO, identifier: CRD42023492045.

[Analysis of the Mediating Effect of Fatigue Between Social Support and Depression in Hospitalized Patients With Ischemic Stroke]

Objective

To explore the mediating effect of fatigue between social support and depression in hospitalized patients with ischemic stroke, so as to provide reference for improving post-stroke depression.

Methods

A total of 142 hospitalized patients with ischemic stroke were enrolled and investigation was conducted with self-rating depression scale (SDS), social support rating scale (SSRS), and fatigue severity scale (FSS). Spearman's correlation analysis was used to analyze the correlation between the variables. A model for the mediating effect between the variables was established by using the AMOS 23.0 software to analyze the mediating effect of fatigue between social support and depression. The Bootstrap method was used to test the significance of intermediary effect.

Results

The scores for SDS, SSRS, and FSS in the hospitalized ischemic stroke patients enrolled were 48.96±9.09, 31.34±8.35, and 30.70±13.99, respectively. Spearman's correlation analysis showed that depression was positively correlated with fatigue and negatively correlated with social support. In addition, fatigue was negatively correlated with social support. Analysis of the mediating effect model established that fatigue played a mediating role between social support and depression in patients with ischemic stroke, with the mediating effect value being -0.170 and the mediating effect accounting for 90.0% of the total effect.

Conclusion

The effect of social support on depression in hospitalized patients with ischemic stroke is mainly achieved by affecting the sense of fatigue. Health workers should pay attention to the severity of fatigue of patients and reduce their sense of fatigue as much as possible, which will help enhance social support for the patients and reduce their depression.

Prevalence and Predictive factors of Post-Stroke Depression in Patients with Acute Cerebral Infarction

Objective

Acute cerebral infarction (ACI) has a high mortality and disability, which brings a heavy burden to the medical and health system. This study aims to discover the clinical prevalence of post-stroke depression (PSD) in patients with ACI, explore the predictive factors leading to this complication, and provide more evidence for better identification of PSD in clinic.

Methods

From April 2021 to April 2023, this retrospective study selected 166 ACI patients as the research subjects, collected clinical symptoms and laboratory indicators at baseline, and observed the prevalence of PSD using the Hamilton depression scale 17 and the diagnostic and statistical manual of mental disorders. Multiple logistic regression analysis was adopted to explore the predictive factors of PSD in patients with ACI.

Results

The total incidence of PSD was 35.54% in 166 patients with ACI. The score of National Institute of Health Stroke Scale (NIHSS), the score of daily life ability scale (ADL), and homocysteine (Hcy) level in the PSD group were higher than non-PSD group (P NIHSS < .001, P ADL < .001, P Hcy = .001). Multiple logistic regression analysis showed that high Hcy levels, NIHSS scores, and ADL scores were independent risk factors for PSD (P Hcy =.038, P NIHSS =.002, P ADL <.001). The receiver operating characteristic (ROC) curve showed that areas under curve (AUC) = 0.894, standard errora = 0.025, progressive significanceb <.001, 95% CI = 0.845-0.943, cut-off value = 0.520, sensitivity = 91.60%, specificity = 74.60%, and Hosmer-Lemeshow goodness-of-fit test P = .246, suggesting that ROC curve has a certain clinical predictive efficacy.

Conclusion

The prevalence of early PSD in patients with ACI is relatively high. Homocysteine levels, NIHSS scores and ADL scores may be independent risk factors for PSD, and targeted clinical intervention should be implemented for the above factors.

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study

OBJECTIVE

This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.

DESIGN

A prospective multicenter cohort-study with 18-month follow-up.

SETTING

Outpatient clinics at 3 university hospitals and 2 local hospitals.

PARTICIPANTS

474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke.

RESULTS

The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI: 0.50, 0.86), a specificity of 0.74 (95% CI: 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI: 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI: 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI: 0.83, 0.96).

CONCLUSIONS

Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings.

Factors associated with fatigue among people who have returned to work after stroke: an exploratory study

OBJECTIVE

To explore the associations between fatigue impact and (a) personal and stroke-related characteristics, (b) functional impairments and (c) work-related factors among individuals who have returned to work after stroke.

DESIGN

A cross-sectional exploratory study.

SUBJECTS

87 working stroke survivors.

METHODS

This study comprises data from a postal survey targeting work ability and perceived stroke-related consequences 1 year after stroke. Fatigue was evaluated using the Fatigue Severity Scale (FSS). Factors associated with having fatigue (FSS total score ≥ 4) were identified using univariable and multivariable logistic regression analyses. Three domain-specific multivariable models and 1 final combined model were created.

RESULTS

Fatigue was reported by 43% of the participants. Several factors representing all the investigated domains were associated with fatigue. In the final combined regression model, self-perceived low cognitive functioning, low decision control at work and high quantitative job demands had the strongest independent effects on the odds of having fatigue.

CONCLUSION

Among people who were working 1 year after stroke, fatigue was associated with both personal and stroke-related characteristics as well as functional impairments and work-related factors. This highlights the complex nature of post-stroke fatigue. Fatigue management interventions should have a comprehensive approach and also consider the work environment.

Post-Stroke Neuropsychiatric Complications: Types, Pathogenesis, and Therapeutic Intervention

Almost all stroke survivors suffer physical disabilities and neuropsychiatric disturbances, which can be briefly divided into post-stroke neurological diseases and post-stroke psychiatric disorders. The former type mainly includes post-stroke pain, post-stroke epilepsy, and post-stroke dementia while the latter one includes post-stroke depression, post-stroke anxiety, post-stroke apathy and post-stroke fatigue. Multiple risk factors are related to these post-stroke neuropsychiatric complications, such as age, gender, lifestyle, stroke type, medication, lesion location, and comorbidities. Recent studies have revealed several critical mechanisms underlying these complications, namely inflammatory response, dysregulation of the hypothalamic pituitary adrenal axis, cholinergic dysfunction, reduced level of 5-hydroxytryptamine, glutamate-mediated excitotoxicity and mitochondrial dysfunction. Moreover, clinical efforts have successfully given birth to many practical pharmaceutic strategies, such as anti-inflammatory medications, acetylcholinesterase inhibitors, and selective serotonin reuptake inhibitors, as well as diverse rehabilitative modalities to help patients physically and mentally. However, the efficacy of these interventions is still under debate. Further investigations into these post-stroke neuropsychiatric complications, from both basic and clinical perspectives, are urgent for the development of effective treatment strategies.

The impact of post-stroke fatigue on work and other everyday life activities for the working age population - a registry-based cohort study

INTRODUCTION

Life after stroke is a comprehensive area that involves engagement in meaningful everyday activities, including work, and can be adversely affected by post-stroke fatigue. This study investigates post-stroke fatigue, its development over time, and its impact on return to work and other everyday life activities. In addition, we investigated whether post-stroke fatigue could predict functioning in everyday life activities one year after stroke.

MATERIAL AND METHODS

This prospective registry-based study includes 2850 working age (18 - 63 years) patients registered in the Swedish Stroke Register (Riksstroke) during year 2017 and 2018. Post-stroke fatigue and everyday activities were analyzed 3- and 12-months post-stroke.

RESULTS

The mean age of the included participants was 54 years and the majority, 65%, were men. Three months post-stroke, 43% self-reported fatigue, at 12-months the proportion increased to 48%. About 90% of the patients were independent in basic ADL at 3-month. Dependence in complex activities one year post-stroke was significantly associated with fatigue. Not experiencing fatigue one year after stroke could predict positive functioning in everyday activities, increasing the chance of returning to work (OR = 3.7) and pre-stroke life and everyday activities (OR = 5.7).

CONCLUSION

Post-stroke fatigue is a common persistent disability that negatively impacts complex activities; therefore, fatigue needs to be acknowledged and addressed long term after discharge.

Association of post-stroke fatigue with physical activity and physical fitness: A systematic review and meta-analysis

BACKGROUND

It has been hypothesized that post-stroke fatigue (PSF) is associated with reduced physical activity (PA) and impaired physical fitness (fitness). Understanding associations between PSF and PA, and/or fitness could help guide the development of targeted exercise interventions to treat PSF.

AIMS

Our systematic review and meta-analysis aimed to investigate PSF's associations with PA and fitness.

SUMMARY OF REVIEW

Following a registered protocol, we included studies with cross-sectional or prospective observational designs, published in English or a Scandinavian language, which reported an association of PSF with PA and/or fitness in adult stroke survivors. We searched MEDLINE, Embase, AMED, CINAHL, PsycINFO, ClinicalTrials.gov, and World Health Organization's International Clinical Trials Registry Platform from inception to November 30, 2022. Risk of bias was assessed using Quality in Prognosis Studies. Thirty-two unique studies (total n = 4721 participants, 55% male), and three study protocols were included. We used random-effects meta-analysis to pool data for PA and fitness outcomes, and vote-counting of direction of association to synthesize data that could not be meta-analyzed. We found moderate-certainty evidence of a weak association between higher PSF and impaired fitness (meta r = -0.24; 95% confidence interval (CI) = -0.33, -0.15; n = 905, 7 studies), and very low-certainty evidence of no association between PSF and PA (meta r = -0.09; 95% CI = -0.34, 0.161; n = 430, 3 studies). Vote-counting showed a higher proportion of studies with associations between higher PSF and impaired fitness (pˆ = 0.83; 95% CI = 0.44, 0.97; p = 0.22, n = 298, 6 studies), and with associations between higher PSF and lower PA (pˆ = 0.75; 95% CI = 0.51, 0.90; p = 0.08, n = 2566, 16 studies). Very low- to moderate-certainty evidence reflects small study sample sizes, high risk of bias, and inconsistent results.

CONCLUSIONS

The meta-analysis showed moderate-certainty evidence of an association between higher PSF and impaired fitness. These results indicate that fitness might protect against PSF. Larger prospective studies and randomized controlled trials evaluating the effect of exercise on PSF are needed to confirm these findings.

Fatigue in stroke survivors: a 5-year follow-up of the Fall study of Gothenburg

Longer term knowledge of post-stroke fatigue (PSF) is limited. Our aim was to describe the prevalence of, and to identify baseline predictors associated with, PSF 5 years after stroke. We undertook a follow-up of stroke survivors from the 504 consecutively recruited participants in the observational "The Fall Study of Gothenburg", conducted between 2014 and 2016. The dependent variable, PSF, was assessed using the Swedish version of the Fatigue Assessment Scale (S-FAS) and defined as having a S-FAS score ≥ 24. The S-FAS questionnaire was mailed to potential participants in August 2020. The independent variables, previously obtained from medical records, included age; sex; comorbidities; stroke severity; hospital length of stay; body mass index (BMI); number of medications and lifestyle factors at index stroke. To identify predictors of PSF, univariable and multivariable logistic regression analyses were performed. Of the 305 eligible participants, 119 (39%) responded with complete S-FAS. Mean age at index stroke was 71 (SD 10.4) years and 41% were female. After a mean of 4.9 years after stroke, the prevalence of PSF was 52%. Among those with PSF, almost two thirds were classified as having both physical and mental PSF. In the multivariable analysis, only high BMI predicted PSF with an odds ratio of 1.25 (95% CI 1.11-1.41, p < 0.01). In conclusion, half of the participants reported PSF 5 years after index stroke and higher body mass index was identified as a predictor. The findings from this study are important for healthcare professionals, for planning health-related efforts and rehabilitation of stroke survivors.ClinicalTrials.gov, Identifier NCT02264470.

Unraveling the interplay between daily life fatigue and physical activity after subarachnoid hemorrhage: an ecological momentary assessment and accelerometry study

BACKGROUND

Fatigue is one of the most commonly reported symptoms after subarachnoid hemorrhage (SAH) and is indirectly associated with physical activity (PA). Associations between fatigue and PA are primarily examined based on conventional measures (i.e. a single fatigue score or average PA levels), thereby assuming that fatigue and PA do not fluctuate over time. However, levels of fatigue and PA may not be stable and may interrelate dynamically in daily life. Insight in direct relationships between fatigue and PA in daily life, could add to the development of personalized rehabilitation strategies. Therefore we aimed to examine bidirectional relationships between momentary fatigue and PA in people with SAH.

METHODS

People (n = 38) with SAH who suffer from chronic fatigue were included in an observational study using Ecological Momentary Assessment (EMA) and accelerometry. Momentary fatigue was assessed on a scale from 1 to 7 (no to extreme fatigue), assessed with 10-11 prompts per day for 7 consecutive days using EMA with a mobile phone. PA was continuously measured during this 7-day period with a thigh-worn Activ8 accelerometer and expressed as total minutes of standing, walking, running and cycling in a period of 45 min before and after a momentary fatigue prompt. Multilevel mixed model analyses including random effects were conducted.

RESULTS

Mean age was 53.2 years (SD = 13.4), 58% female, and mean time post SAH onset was 9.5 months (SD = 2.1). Multilevel analyses with only time effects to predict fatigue and PA revealed that fatigue significantly (p < 0.001) increased over the day and PA significantly (p < 0.001) decreased. In addition, more PA was significantly associated with higher subsequent fatigue (β = 0.004, p < 0.05) and higher fatigue was significantly associated with less subsequent PA (β=-0.736, p < 0.05). Moreover, these associations significantly differed between participants (p < 0.001).

CONCLUSIONS

By combining EMA measures of fatigue with accelerometer-based PA we found that fatigue and PA are bidirectionally associated. In addition, these associations differ among participants. Given these different bidirectional associations, rehabilitation aimed at reducing fatigue should comprise personalized strategies to improve both fatigue and PA simultaneously, for example by combining exercise therapy with cognitive behavioral and/or energy management therapy.

FIND Stroke Recovery Study (FIND): rationale and protocol for a longitudinal observational cohort study of trajectories of recovery and biomarkers poststroke

INTRODUCTION

Comprehensive studies mapping domain-specific trajectories of recovery after stroke and biomarkers reflecting these processes are scarce. We, therefore, initiated an exploratory prospective observational study of stroke cases with repeated evaluation, the FIND Stroke Recovery Study. We aim to capture trajectories of recovery from different impairments, including cognition, in combination with broad profiling of blood and imaging biomarkers of the recovery.

METHODS AND ANALYSIS

We recruit individuals with first-ever stroke at the stroke unit at the Sahlgrenska University Hospital, Sweden, to FIND. The inclusion started early 2018 and we aim to enrol minimum 500 patients. Neurological and cognitive impairments across multiple domains are assessed using validated clinical assessment methods, advanced neuroimaging is performed and blood samples for biomarker measuring (protein, RNA and DNA) at inclusion and follow-up visits at 3 months, 6 months, 1 year, 2 years and 5 years poststroke. At baseline and at each follow-up visit, we also register clinical variables known to influence outcomes such as prestroke functioning, stroke severity, acute interventions, rehabilitation, other treatments, socioeconomic status, infections (including COVID-19) and other comorbidities. Recurrent stroke and other major vascular events are identified continuously in national registers.

ETHICS AND DISSEMINATION

FIND composes a unique stroke cohort with detailed phenotyping, repetitive assessments of outcomes across multiple neurological and cognitive domains and patient-reported outcomes as well as blood and imaging biomarker profiling. Ethical approval for the FIND study has been obtained from the Regional Ethics Review Board in Gothenburg and the Swedish Ethics Review Board. The results of this exploratory study will provide novel data on the time course of recovery and biomarkers after stroke. The description of this protocol will inform the stroke research community of our ongoing study and facilitate comparisons with other data sets.

TRIAL REGISTRATION NUMBER

The protocol is registered at http://www.

CLINICALTRIALS

gov, Study ID: NCT05708807.

Is Cardiorespiratory Fitness Independently Associated with Fatigue in Patients with Transient Ischemic Attack or Minor Stroke?

Fatigue is a common complaint and a disabling symptom among patients following transient ischemic attack (TIA) or minor stroke. In patients with stroke, decreased cardiorespiratory fitness (CRF) is believed to be related to increased severity of post-stroke fatigue (PSF). However, this association between PSF and CRF in patients with TIA or minor stroke has been less investigated, and currently there is no proven treatment for PSF. We aimed to determine the association between PSF and CRF in patients with TIA or minor stroke and to find out whether this association was distorted by confounders. A cross-sectional association study was conducted among a total of 119 patients with TIA or minor stroke. PSF was measured by the Fatigue Severity Scale (FSS) and CRF was quantified by maximal exercise capacity (V̇O2max). The FSS showed a significant association with V̇O2max (ß = −0.061, SE: 0.022; p = 0.007). This association was confounded by anxiety (ß = −0.044, SE: 0.020; p = 0.028) and depression (ß = −0.030, SE: 0.022; p = 0.177) as measured by the subscales of the Hospital Anxiety and Depression Scale (HADS). After controlling for HADS scores on depression and anxiety, the univariate relationship between V̇O2max and FSS was no longer significant. These results suggest that the association between PSF and CRF in patients with TIA or minor stroke is weak and significantly confounded by the factors of depression and anxiety.