Symptoms In Stroke Patients Research Articles

High-Frequency rTMS Broadly Ameliorates Working Memory and Cognitive Symptoms in Stroke Patients: A Randomized Controlled Trial

Objective To explore the efficacy and tolerability of high-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of post-stroke working memory (WM) impairment and its changes in brain function. Methods In the present randomized, double-blinded, sham-controlled design, 10 Hz rTMS was administered to the left dorsolateral prefrontal cortex (DLPFC) of patients with post-stroke WM impairment for 14 days. Measures included WM (primary outcome), comprehensive neuropsychological tests, and the functional near-infrared spectroscopy test. Patients were assessed at baseline, after the intervention (week 2), and 4 weeks after treatment cessation (week 6). Results Of 123 stroke patients, 82 finished the trial. The rTMS group showed more WM improvement at week 2 (t = 5.55, P < .001) and week 6 (t = 2.11, P = .045) than the sham group. Most of the neuropsychological test scores were markedly improved in the rTMS group. In particular, the rTMS group exhibited significantly higher oxygenated hemoglobin content and significantly stronger functional connectivity in the left DLPFC, right pre-motor cortex (PMC), and right superior parietal lobule (SPL) at weeks 2 and 6. Dropout rates were equal (18% [9/50 cases] in each group), and headaches were the most common side effect (rTMS: 36% [18/50 cases]; sham: 30% [15/50 cases]). Conclusions High-frequency rTMS was effective in improving post-stroke WM impairment, with good tolerability, and the efficacy lasted up to 4 weeks, which may be due to the activation of the left DLPFC, right PMC, and right SPL brain regions and their synergistic enhancement of neural remodeling.

Analysis of factors associated with depressive symptoms in stroke patients based on a national cross-sectional study

Post-stroke depression is commonly experienced by stroke survivors and has a significant negative impact on the physical, cognitive, and social functioning of those affected. This study aims to investigate the prevalence of depressive symptoms and their associated factors in Chinese stroke patients. Research samples were selected from the China Health and Retirement Longitudinal Study 2018 survey. Depression was evaluated using the 10-item Center for Epidemiological Studies Depression Scale, with a score ≥ 10 defined as depression. Univariate and multivariable analyses were performed to examine the associations of depressive symptoms with demographics, family relationships, health status, and lifestyle. A total of 963 stroke patients were included and 57.8% of them had depressive symptoms. Depressive symptoms were significantly associated with female sex (OR 1.762, 95% CI 1.235–2.514), lower education level (non-formal education: OR 2.148, 95% CI 1.235–3.737, primary to secondary school education: OR 1.964, 95% CI 1.272–3.033), dissatisfaction with spouse (OR 1.912, 95% CI 1.075–3.401), dissatisfaction with life (OR 1.779, 95% CI 1.080–2.931), dissatisfaction with health (OR 1.592, 95% CI 1.138–2.226), pain (OR 1.392, 95% CI 1.005–1.928) and abnormal sleep (OR 1.557, 95% CI 1.126–2.152). The findings suggest the need for regular depression screening and evaluation after a stroke, and that a well-functioning support system, effective health management, and lifestyle modifications could potentially improve the mental state of stroke patients.

Cerebral infraction prediction system using ECG and PPG bio-signal

Since stroke causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration. It is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, studies have mostly used image analysis such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to apply early in real time, but they also have limitations in terms of long testing times and high costs. This paper proposes a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using multimodal biosignals from Electrocardiogram (ECG) and Photoplethysmogram (PPG). As a result, the real-time prediction of stroke prognosis in elderly patients showed simultaneously high prediction accuracy and performance. Additionally, the CNN-LSTM model using raw data of ECG and PPG demonstrated a satisfactory prediction accuracy of 99.15%.

The Effects of Aromatherapy on Stroke Symptoms in Stroke Patients: A Systematic Review and Meta-Analysis

The Effects of Aromatherapy on Stroke Symptoms in Stroke Patients: A Systematic Review and Meta-Analysis

CNN and Rf based Early Detection of Brain Stroke Using Bio-Electrical Signals

The brain is a vital component of the body that is in control of involuntary and voluntary movements such as walking, memory, and vision. Nowadays, some of the most prevalent brain disorders include Alzheimer's disease, brain tumors, and epilepsy (paralysis or stroke). As a result, stroke has become a significant global health concern, with high rates of mortality and disability. Importantly, approximately two-thirds of all strokes occur in developing countries, highlighting the significant burden of this condition in these regions. Therefore, emphasizing the timely detection and appropriate treatment of brain tumors is crucial. Given the high potential for mortality or severe disability associated with stroke disease, prioritizing active primary prevention and early identification of prognostic symptoms is of paramount importance. Ischemic stroke and hemorrhagic stroke are the two primary classifications for stroke diseases. Each type calls for specific emergency treatments, such as the administration of thrombolytics or coagulants, tailored to their respective underlying mechanisms. However, to effectively manage stroke, it is crucial to promptly identify the precursor symptoms in real-time, as they can vary among individuals. Timely professional treatment within the appropriate treatment window is essential and should be provided by a medical institution. In contrast, prior research has primarily centered around the formulation of acute treatment strategies or clinical guidelines subsequent to the occurrence of a stroke, rather than giving sufficient attention to the early identification of prognostic symptoms. Specifically, recent research has extensively utilized image analysis techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), as a primary approach for detecting and predicting prognostic symptoms in stroke patients.&#x0D; Traditional methodologies not only encounter difficulties in achieving early real-time diagnosis but also exhibit limitations in terms of prolonged testing duration and high testing costs. In this study, we introduce a novel system that employs machine learning techniques to predict and semantically interpret prognostic symptoms of stroke. Our approach utilizes real-time measurement of multi-modal bio-signals, namely electrocardiogram (ECG) and photoplethysmography (PPG), with a specific focus on the elderly population.&#x0D; To facilitate real-time prediction of stroke disease during walking, we have developed a stroke disease prediction system that incorporates a hybrid ensemble architecture. This architecture synergistically combines Convolutional Neural Network (CNN) and Random Forest (RF) models, enabling accurate and timely prognostication of stroke disease. The suggested method prioritises the convenience of use of bio-signal sensors for the elderly by collecting bio-signals from three electrodes placed on the index finger. These signals include ECG and PPG, and they are obtained while the participants walk. The CNN-RF model delivers satisfactory prediction accuracy when using raw ECG and PPG data. F1-Score, Sensitivity, Specificity, and Accuracy were the performance parameters used to evaluated the model's performance.

Effects of Twins Therapy on Egocentric and Allocentric Neglect in Stroke Patients: A Feasibility Study.

(1) Background: Existing treatment methods for neglect are concentrated on egocentric neglect and may lead to various problems such as cost/space constraints and portability. Therefore, this study seeks to determine how a new treatment (also known as twins therapy, TT) for stroke patients can improve an existing problem associated with neglect. (2) Method: A pre/post-test control group research design was used and both groups continued to receive existing rehabilitation treatment, whilst TT intervention was only added to the experimental group. TT intervention was conducted for a total of 20 sessions (1 session for 30 min/day, 5 days/week, for 4 weeks). (3) Result: There was no significant difference in the manual function test (MFT) and the Korean version of the Modified Barthel Index (K-MBI) items (p > 0.05) before and after the TT intervention. However, the score and execution time of the apple cancellation test showed a significant reduction only in the experimental group (p < 0.05). (4) Conclusion: TT not only improved egocentric neglect, but also allocentric neglect symptoms in stroke patients.

Correlation of Fatigue with Cognition and Motor Performance among Stroke Patients

Introduction: Post-Stroke Fatigue (PSF) is the most common debilitating and persistent symptom after a stroke.The impact of fatigue increases during the first year of post-stroke. When most of the recovery has taken place,fatigue could remain an important problem with disabling consequences for everyday life. Understanding theeffect of PSF on cognition and mobility outcomes will help to better manage existing fatigue symptoms in strokepatients and develop strategies to optimize mobility and cognitive performance in patients with the chronic stageof stroke. With this background in mind, the present study aims at establishing a relationship between post-strokefatigue and cognitive and motor performance among persons with stroke.Aim: To correlate fatigue with cognition and motor performance in stroke patientsMaterials and Method: This is the pilot study where 15 post-stroke patients fulfilling the inclusion and exclusioncriteria were included by using the purposive sampling technique. Fatigue was assessed using Fatigue Scale forMotor and Cognitive Function. Motor Function was assessed by using Fugl Meyer Scale for upper and lowerextremity, Chedokes Arm and Hand Inventory, Lower Extremity Functional Scale, and Berg Balance Scale.Cognition was assessed by using the Montreal Cognitive Assessment Scale. Using the scores of these scales, fatiguewas correlated with motor and cognitive functions using Karl Pearson’s correlation test.Result: The study found a moderately significant correlation between Fatigue and Cognition (r=-0.605 p&lt;0.05) anda highly significant correlation between fatigue and motor performance (r=-0.804 p&lt;0.001).Conclusion: This study provides evidence that post-stroke fatigue has significant relationships with both motorperformance and cognitive performance. It is important to consider the influence of fatigue when planning anddelivering interventions for individuals with stroke.

Transcranial alternating brain stimulation at alpha frequency reduces hemispatial neglect symptoms in stroke patients

Background/ObjectiveNon-invasive brain stimulation techniques such as transcranial alternating current stimulation (tACS) may help alleviate attention deficits in stroke patients with hemispatial neglect by modulating oscillatory brain activity. We applied high-definition (HD)-tACS at alpha frequency over the contralesional hemisphere to support unilateral oscillatory alpha activity and correct for the pathologically altered attention bias in neglect patients. MethodsWe performed a within-subject, placebo-controlled study in which sixteen stroke patients with hemispatial neglect underwent 10 Hz (alpha) as well as sham (placebo) stimulation targeting the contralesional posterior parietal cortex. Attentional bias was measured with a computerized visual detection paradigm and two standard paper-and-pencil neglect tests. ResultsWe revealed a significant shift of attentional resources after alpha-HD-tACS, but not sham tACS, toward the ipsilateral and thus contralesional hemifield leading to a reduction in neglect symptoms, measured with a computerized visual detection paradigm and a widely used standard paper and pencil neglect tests. ConclusionsWe showed a significant alpha-HD-tACS-induced shift of attentional resources toward the contralesional hemifield, thus leading to a reduction in neglect symptoms. Importantly, HD-tACS effects persisted after the stimulation itself had ended. This tACS protocol, based on intrinsic oscillatory processes, may be an effective and well-tolerated treatment option for neglect.

Decision‐Making in Seeking Emergency Care for Stroke Symptoms

Background Previous studies have shown that rapid treatment for stroke, especially ischemic stroke, reduces mortality and disability. The focus has mainly been on reducing time from arrival at hospital to start of treatment. However, the main reason for delay is often time from symptom onset to arrival at hospital. This study therefore aimed to explore decision‐making processes after the onset of stroke symptoms in patients experiencing a first‐time stroke. Methods We included 36 patients aged 18 and older, all of whom were hospitalized with a first‐time stroke between October 2018 and April 2020. All patients were interviewed once within 4 weeks of symptom onset and before hospital discharge. Eligible patients were identified retrospectively through a targeted review of medical records. The data were collected and analyzed according to the grounded theory methodology. Results In total, 43 potential patients were identified and asked to participate. Overall, 36 patients were included in the study: 17 women (median age 77.0 years, interquartile range 17.5) and 19 men (median age 65.7 years, interquartile range 17.2). All interviewees felt fear, and this affected their decision to seek emergency care. The decision‐making processes were described by the core category of “Acting on fear.” The reason for feeling frightened determined the actions taken. The reasons were sorted into 3 main categories: (1) “seeking care”–recognized stroke symptoms and acted immediately; (2) “pending and reluctance”–suspected stroke but awaited to seek care; and (3) “seeking an explanation”–confused by symptoms. Conclusion We found that decision‐making when experiencing stroke symptoms was complex. All patients felt fear, which determined their actions. Some patients knew about stroke symptoms and acted immediately. Others suspected stroke but still chose to wait, whereas others were confused and tried to find answers. These results could contribute to form future awareness campaigns.

Factors influencing alertness to premonitory symptoms in stroke patients with pre-hospital delay.

The purpose was to explore the alertness of premonitory symptoms in stroke patients with prehospital delay, and to analyze the influencing factors. A cross-sectional study using the convenience sampling method was conducted in the neurology department of a general hospital between November 2018 and July 2019. A total of 352 stroke patients were participated in the survey. A hierarchical multiple regression was performed to analyze the factors related to the alertness of premonitory symptoms (0-9 scores) in stroke patients with prehospital delay. The alertness score was 6.53 ± 2.377. The lowest score of 0.55 ± 0.498 was for "Continuous yawning occurs continuously despite no tiredness or lack of sleep is okay, and need not be treated." The hierarchical regression results revealed that symptom onset, symptom change before admission, knowledge, social support were the influencing factors delaying the alertness of premonitory symptoms. Knowledge and support from friends could improve the alertness, while support from family and other support had a notable negative impact. Stroke patients need to be more alert toward premonitory symptoms. This alertness is related to stroke knowledge and social support. Nurses should formulate interventions andadvise stroke patients to improve their stroke knowledge and expand their social network.

Emerging Limb Rehabilitation Therapy After Post-stroke Motor Recovery.

Stroke, including hemorrhagic and ischemic stroke, refers to the blood supply disorder in the local brain tissue for various reasons (aneurysm, occlusion, etc.). It leads to regional brain circulation imbalance, neurological complications, limb motor dysfunction, aphasia, and depression. As the second-leading cause of death worldwide, stroke poses a significant threat to human life characterized by high mortality, disability, and recurrence. Therefore, the clinician has to care about the symptoms of stroke patients in the acute stage and formulate an effective postoperative rehabilitation plan to facilitate the recovery in patients. We summarize a novel application and update of the rehabilitation therapy in limb motor rehabilitation of stroke patients to provide a potential future stroke rehabilitation strategy.

Caregiver strain and fatigue are independent determinants of anxiety among patients early after stroke

ABSTRACT Anxiety and depression are common mental health disorders in stroke patients, and often co-occur. However, in contrast to depression, there is limited research about anxiety after stroke, although impact of anxiety can be substantial. Therefore, this cross-sectional observational study investigated determinants of anxiety after stroke and compared them with determinants of depression after stroke. Stroke surviving patients (n = 284) were seen six weeks after discharge from the hospital or rehabilitation setting. Of these, 238 patients answered all questions of the Hospital Anxiety and Depression Scale measuring anxiety (HADS-a) and 239 patients answered all questions measuring depression (HADS-d). Step-wise regression was used to identify independent determinants of anxiety and depression after stroke. Higher levels of anxiety were associated with a higher caregiver strain (p < 0.01) and more fatigue (p < 0.05). In contrast, higher levels of depression were associated with lower life satisfaction (p < 0.001) and less daily activities (p < 0.05), but also more fatigue (p < 0.001). Hence, our results show that both fatigue and caregiver strain are associated with higher levels of anxiety, but of these only fatigue is also associated with higher levels of depression. Practitioners should therefore ask for both anxiety and depression symptoms in stroke patients.

Depression in Stroke Survivors: Ten-Year Follow-Up. Determinants of the Natural Course of Depressive Symptoms in Stroke Survivors in the Netherlands: The SMART-Medea Study

ObjectivesStroke is the second most common cause of death and a major cause of disability. Besides the physical consequences, depressive symptoms are frequent in the aftermath after stroke. Every year, approximately 15 million stroke survivors worldwide are at risk of developing post-stroke depression. In this study we describe the natural course of depressive symptoms in stroke patients over a long-period of time post stroke and identify associated determinants. Materials and methodsFrom the Second Manifestations of ARTerial disease-Memory, depression and aging (SMART-Medea) study, an observational prospective cohort study, we selected patients with cerebrovascular disease, and used the biannually collected data of the Patient Health Questionnaire-9 for depressive symptoms. A score of ≥10 indicated the presence of depressive symptoms. A multinomial logistic regression analysis was used to identify prognostic determinants for courses of depressive symptoms after stroke. ResultsDuring a mean follow-up time of 7.9 years, 62% of the 172 participants was never depressed, 19% had a single episode and 19% had recurrent depressive symptoms. Physical function was associated with increased risk for single episode and recurrent depressive symptoms (OR=1.06 [1.01-1.11]). OR's for social, mental and (vascular) comorbidities variables were not significant. Participants’ physical function was only measured at baseline. Several relevant variables were not present in this dataset, including information about clinical events during follow-up. ConclusionNearly 40% of the participants are confronted with depressive symptoms on the long-term. Physical function plays a substantial part for stroke survivors in the development of these symptoms.

AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals

Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. First, it is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, prior studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, in recent studies, image analysis such as magnetic resonance imaging (MRI) or computed tomography (CT) has mostly been used to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to diagnose early in real-time, but they also have limitations in terms of a long test time and a high cost of testing. In this paper, we propose a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using the multi-modal bio-signals of electrocardiogram (ECG) and photoplethysmography (PPG) measured in real-time for the elderly. To predict stroke disease in real-time while walking, we designed and implemented a stroke disease prediction system with an ensemble structure that combines CNN and LSTM. The proposed system considers the convenience of wearing the bio-signal sensors for the elderly, and the bio-signals were collected at a sampling rate of 1,000Hz per second from the three electrodes of the ECG and the index finger for PPG while walking. According to the experimental results, C4.5 decision tree showed a prediction accuracy of 91.56% while RandomForest showed a prediction accuracy of 97.51% during walking by the elderly. In addition, the CNN-LSTM model using raw data of ECG and PPG showed satisfactory prediction accuracy of 99.15%. As a result, the real-time prediction of the elderly stroke patients simultaneously showed high prediction accuracy and performance.

Non-motor symptoms in stroke patients

Non-motor symptoms in stroke patients

Attention upturned: Bias toward and away from the affected side of the body and near space in a case of complex regional pain syndrome

People with Complex Regional Pain Syndrome (CRPS) following limb injury can show neuropsychological symptoms in the absence of observable brain pathologies. These can include sensory changes, distorted body representation, and inattention to their affected limb and its surrounding space, resembling post-stroke hemispatial neglect. The precise nature and mechanisms of these neuropsychological symptoms are unclear, however insights could be gained by testing for dissociations and associations that have been observed in stroke patients. Drawing from clinical and experimental methods for investigating spatial attention bias and related symptoms in stroke patients, we conducted a detailed investigation of neuropsychological symptoms in a woman with CRPS of her left arm who initially presented to us with pronounced inattention to her affected side. The patient showed visual and tactile neglect and extinction on her affected side on confrontation tests, but no attention deficits on “bedside” tests of neglect. On sensitive computer-based measures, attention biases were found in the patient's body and near space (in Temporal Order Judgements), but not far or imagined space (on the Greyscales task and Mental Number Line Bisection). Unique to the current literature, the patient showed a reversal in her Temporal Order Judgement bias across time, from inattention (first and second session) to hyperattention (third session) to her affected side. In contrast, pain and self-reported body representation distortion were similar across the three sessions. The patient had reduced central and peripheral visual acuity, however these deficits were near symmetrical and therefore could not explain her performance on the visual attention tasks. Given that spatial attention bias has been linked to imbalance in relative activation of the two cerebral hemispheres, we administered a Global-Local processing task to test for hemispheric asymmetry. This revealed no difference in global compared to local interference refuting any hemispheric imbalance. Instead, the patient showed impaired performance (compared to controls) on incongruent trials regardless of trial type, consistent with executive impairment. We conclude that spatial attention bias in CRPS can generalize across different sensory modalities and extend beyond the affected limb to the external space around it, independent of any low-level sensory disturbances. This bias is not necessarily directed away from the affected side or stable over time. People with CRPS can also demonstrate more generalized neuropsychological changes in sensory and executive functions. Our observations refute several existing theories about the mechanisms of attention bias in CRPS, and their relationship to pain, and have potential implications for treatment.

The effects of concurrent M1 anodal tDCS and physical therapy interventions on function of ankle muscles in patients with stroke: a randomized, double-blinded sham-controlled trial study

One of the most common symptoms in stroke patients is spasticity. The aims were to investigate the effects of anodal trans-cranial direct current stimulation (a-tDCS) over the affected primary motor cortex (M1) on ankle plantar flexor spasticity and dorsiflexor muscle activity in stroke patients. The design of this study was a randomized sham-controlled clinical trial. Thirty-two participants with stroke were randomly assigned to three groups (experimental, sham, control groups). Participants in the experimental and sham groups received 10-session 20-min M1 a-tDCS concurrent with physical therapy (PT), while the control group only received 10-session PT. All groups were instructed to perform home stretching exercises and balance training. Berg Balance Scale (BBS), Modified Ashworth Scale (MAS) of plantar flexors, and EMG activity of lateral gastrocnemius (LG) and tibialis anterior (TA) were recorded during active and passive ankle dorsiflexion immediately and 1 month after interventions. A significant reduction was shown in MAS and EMG activity of LG during dorsiflexion, immediately and 1 month after intervention in the M1 a-tDCS group (p <0.001). BBS also significantly increased only in the M1 a-tDCS group (p <0.001). In addition, EMG activity of TA during active dorsiflexion increased immediately and 1 month after intervention in the M1 a-tDCS group (p <0.001). However, in the sham and control groups, EMG activity of TA increased immediately (p<0.001), while this was not maintained 1 month after intervention (p >0.05). PT concurrent with M1 a-tDCS can significantly prime lasting effects of decreasing LG spasticity, increasing TA muscle activity, and also balance in stroke patients.

A Mixed-Method Study on Nurses’ Assessment of Post-Stroke Depressive Symptoms

Background: Stroke is the second leading cause of death in the world and the leading cause of death in China. Post-stroke depression is prevalent, affecting approximately one third of stroke survivors at any given time. However, the depressive symptoms of stroke patients are often overlooked in clinical practice. Objective: The aim is to investigate the knowledge, attitudes, and practice status of nurse clinicians on the assessment of post-stroke depressive symptoms and explore the influencing factors, so as to provide empirical evidence for clinical education related to Post-Stroke Depression (PSD) and the development of nursing clinical pathways for screening. Methods: A self-designed questionnaire is used to survey 298 nurse clinicians in Guangdong Province of China. 12 nurses and nurse managers among the study subjects are purposefully invited to participate in semistructured interviews for in-depth discussion about factors affecting the status of assessment. Results: The total scores of the survey of clinical nurses assessing poststroke depression are (84.76±13.255). The scores of knowledge, attitude, and practice of the survey are (25.08±8.975), (33.33±5.483), and (26.35±6.719) respectively. The results of multiple linear regression analysis show that predominate influencing factors of knowledge include position and whether appropriate training is offered by the employer. Nurses’ attitudes toward assessment are influenced by where they worke. Influencing factors for the practice of assessing PSD symptoms include nurses’ positions, participation in training, knowledge level, and attitudes. Conclusion: The total scores of nurse clinicians in assessing the symptoms of PSD are below average. The current practice needs to be improved.

The prevalence and course of neuropsychiatric symptoms in stroke patients impact functional recovery during in-hospital rehabilitation

ABSTRACT Background Neuropsychiatric symptoms are common after stroke and are distressing symptoms. They may persist after the acute event and negatively impact rehabilitation, functioning, and patients' quality of life. Objectives To elucidate the prevalence and course of neuropsychiatric symptoms in stroke patients during rehabilitation, we prospectively investigated the trajectory change of neuropsychiatric symptoms and the association between these symptoms and functional recovery. Methods This observational study enrolled 204 stroke patients consecutively admitted to the rehabilitation ward. We administered the Neuropsychiatric Inventory-Nursing Home Version (NPI-NH) for evaluating the neuropsychiatric symptoms (e.g., apathy and depression) and the Functional Independence Measure (FIM) and Montebello Rehabilitation Factor Score (MRFS) for functional recovery. Results The cumulative prevalence of neuropsychiatric symptoms was 48% during hospitalization; depression, anxiety, and sleep disturbances were most frequent. Binomial logistic regression analyses revealed that the factors associated with deterioration of neuropsychiatric symptoms during hospitalization included premorbid depression (odds ratio: 18.628, P = .001), the length of stay from onset to rehabilitation ward admission (odds ratio: 1.022, P = .012), motor FIM at admission (odds ratio: 0.950, P < .001), and NPI-NH at admission (odds ratio: 0.923, P = .011). Stepwise multiple regression analyses revealed that the NPI-NH at admission was associated with the MRFS (β = −0.128, P = .037). Conclusion Neuropsychiatric symptoms are common in stroke patients undergoing rehabilitation and have a substantially negative impact on rehabilitation outcomes. Stroke patients with prolonged acute-phase hospitalization and low functioning at admission must be monitored to detect any deterioration in the neuropsychiatric state.

The Effect of Structured Handicraft Program on the Improvement of Cognition and Depressive Symptoms in Stroke Patients: A Pilot Study

The Effect of Structured Handicraft Program on the Improvement of Cognition and Depressive Symptoms in Stroke Patients: A Pilot Study