Post-stroke depression, anxiety, and stress symptoms and their associated factors: A cross-sectional study

Almhdawi, Khader A; Alazrai, Alza; Kanaan, Saddam F; Shyyab, Awni A.; Oteir, Alaa O; Mansour, Zaid M.; Jaber, Hanan

doi:10.1080/09602011.2020.1760893

Cited by 57 publications

(61 citation statements)

References 59 publications

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“…The literature has shown that self-reported short sleep duration was significantly correlated with PSA (22), sleep loss, and experimental sleep deprivation are linked to increased negative emotions (such as depression, madness, and nervousness) (23), and treatment of insomnia helps relieve the symptoms of anxiety (24). In line with previous studies, we also found that short sleep duration (<6 h) significantly increased the risk of PSA at 3 months compared with sleep duration of >7 h.…”

Section: Sleep Duration (H)supporting

confidence: 90%

Impact of Sleep Duration on Depression and Anxiety After Acute Ischemic Stroke

et al. 2021

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Background: Abnormal sleep duration predicts depression and anxiety. We seek to evaluate the impact of sleep duration before stroke on the occurrence of depression and anxiety at 3 months after acute ischemic stroke (AIS).Methods: Nationally representative samples from the Third China National Stroke Registry were used to examine cognition and sleep impairment after AIS (CNSR-III-ICONS). Based on baseline sleep duration before onset of stroke as measured by using the Pittsburgh Sleep Quality Index (PSQI), 1,446 patients were divided into four groups: >7, 6–7, 5–6, and <5 h of sleep. Patients were followed up with the General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) for 3 months. Poststroke anxiety (PSA) was defined as GAD-7 of ≥5 and poststroke depression (PSD) as PHQ-9 of ≥5. The association of sleep duration with PSA and PSD was evaluated using multivariable logistic regression.Results: The incidences of PSA and PSD were 11.2 and 17.6% at 3 months, respectively. Compared to a sleep duration of >7 h, 5–6 h, and <5 h of sleep were identified as risk factors of PSA [odds ratio (OR), 1.95; 95% confidence interval (CI), 1.24–3.07; P < 0.01 and OR, 3.41; 95% CI, 1.94–6.04; P < 0.01) and PSD (OR, 1.47; 95% CI, 1.00–2.17; P = 0.04 and OR, 3.05; 95% CI, 1.85–5.02; P < 0.01), while 6–7 h of sleep was associated with neither PSA (OR, 1.09; 95% CI, 0.71–1.67; P = 0.68) nor PSD (OR, 0.92; 95% CI, 0.64–1.30; P = 0.64). In interaction analysis, the impact of sleep duration on PSA and PSD was not affected by gender (P = 0.68 and P = 0.29, respectively).Conclusions: Sleep duration of shorter than 6 h was predictive of anxiety and depression after ischemic stroke.

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Section: Sleep Duration (H)supporting

confidence: 90%

Impact of Sleep Duration on Depression and Anxiety After Acute Ischemic Stroke

et al. 2021

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“…This study also revealed that patients with hypertension or diabetes mellitus are more prone to have PSA. A cross-sectional study also found that chronic physical diseases is an identified factor significantly associated with post-stroke mental health (Almhdawi et al, 2020). Interestingly, our study found that the level of HDL-C is independent protective factors for PSA.…”

Section: Discussionsupporting

confidence: 63%

Post-stroke Anxiety Analysis via Machine Learning Methods

Wang

Zhao

Miao

et al. 2021

Front. Aging Neurosci.

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Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.

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“…Depression, anxiety, and stress are particularly common and persistent psychological problems following a stroke, with a high risk of relapse, even after a long period of remission [ 11 ]. Approximately 22%-40% of stroke survivors are affected by depression symptoms, 9.4%-36.7% by an anxiety disorder, and around 31% by poststroke stress [ 12 ]. Multiple studies have also shown that due to unsatisfactory sequelae and irreversible prognosis, patients with stroke always experience higher levels of emotional distress and ineffective coping than the general population, which poses adverse effects on the rehabilitation process and indirectly affects the patients’ quality of life (QoL) [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of Virtual Reality-Based Therapies on Cognition and Mental Health of Stroke Patients: Systematic Review and Meta-analysis

Zhang¹,

Fu²,

Lu³

et al. 2021

J Med Internet Res

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Background Stroke remains one of the major chronic illnesses worldwide that health care organizations will need to address for the next several decades. Individuals poststroke are subject to levels of cognitive impairment and mental health problems. Virtual reality (VR)-based therapies are new technologies used for cognitive rehabilitation and the management of psychological outcomes. Objective This study performed a meta-analysis to evaluate the effects of VR-based therapies on cognitive function and mental health in patients with stroke. Methods A comprehensive database search was performed using PubMed, MEDLINE (Ovid), Embase, Cochrane Library, and APA PsycINFO databases for randomized controlled trials (RCTs) that studied the effects of VR on patients with stroke. We included trials published up to April 15, 2021, that fulfilled our inclusion and exclusion criteria. The literature was screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.3 software. Results A total of 894 patients from 23 RCTs were included in our meta-analysis. Compared to traditional rehabilitation therapies, the executive function (standard mean difference [SMD]=0.88, 95% confidence interval [CI]=0.06-1.70, P=.03), memory (SMD=1.44, 95% CI=0.21-2.68, P=.02), and visuospatial function (SMD=0.78, 95% CI=0.23-1.33, P=.006) significantly improved among patients after VR intervention. However, there were no significant differences observed in global cognitive function, attention, verbal fluency, depression, and the quality of life (QoL). Conclusions The findings of our meta-analysis showed that VR-based therapies are efficacious in improving executive function, memory, and visuospatial function in patients with stroke. For global cognitive function, attention, verbal fluency, depression, and the QoL, further research is required. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42021252788; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252788

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Post-stroke depression, anxiety, and stress symptoms and their associated factors: A cross-sectional study

Cited by 57 publications

References 59 publications

Impact of Sleep Duration on Depression and Anxiety After Acute Ischemic Stroke

Impact of Sleep Duration on Depression and Anxiety After Acute Ischemic Stroke

Post-stroke Anxiety Analysis via Machine Learning Methods

Impact of Virtual Reality-Based Therapies on Cognition and Mental Health of Stroke Patients: Systematic Review and Meta-analysis

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