Prevalence of depression and its associated factors among stroke survivors in Saudi Arabia

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.

Heart rate variability biofeedback enhances cognitive, motor, psychological, and autonomic functions in post-stroke rehabilitation

Post-stroke patients often experience psychological distress and autonomic nervous system (ANS) dysregulation, impacting their well-being. This study evaluated the effectiveness of heart rate variability (HRV) biofeedback on cognitive, motor, psychological, and ANS functions in sixty-two ischemic stroke patients (43 males, mean age = 60.1) at a Medical Center in southern Taiwan. To prevent interaction, we allocated patients to the HRV biofeedback or control (usual care) group based on their assigned rehabilitation days, with 31 patients in each group. Assessments conducted at baseline, three, and six months included the Montreal Cognitive Assessment (MoCA), Fugl-Meyer Assessment for Upper Extremities (FMA-UE), Perceived Stress Scale, Hospital Anxiety and Depression Scales (HADS), and HRV indices. Mixed-effect models were used to analyze Group by Time interactions. The results revealed significant interactions across all functions. At 3 months, significant improvements in the HRV biofeedback group were observed only in MoCA, FMA-UE, and HADS-depression scores compared to the control group. By 6 months, all measured outcomes demonstrated significant improvements in the biofeedback group relative to the control group. These results suggest that HRV biofeedback may be an effective complementary intervention in post-stroke rehabilitation, warranting further validation.

Health-Related Quality of Life Among Patients With Stroke: A Cross-Sectional Study

PURPOSE

To assess levels and predictive factors of health-related quality of life (HRQOL) among stroke patients.

METHODS

The study employed a cross-sectional predictive correlational design. Levels of HRQOL were assessed using the Stroke-Specific Quality of Life (SS-QOL) scale, and the Hospital Anxiety and Depression Scale was employed to assess psychological aspects among 209 Saudi stroke patients. The analysis included demographic and medical variables to comprehensively explore influencing factors.

RESULTS

A two-step hierarchical multiple regression analysis was performed. The overall SS-QOL summary score (49 items) showed a mean score of 94.4 (SD = 8.1), indicating poor functioning. Nine predictor variables were found to significantly predict HRQOL levels, including age (β = -0.212, p ≤ .001), female (β = -5.33, p ≤ .001), unmarried (β = 2.48, p ≤ .001), low gross monthly income (GMI) (β = -9.02, p ≤ .001), medium GMI (β = -8.36, p ≤ .001), having a medical history of hypertension (β = 2.7, p ≤ .01), time since stroke (β = 3.26 p ≤ .001), and being a probable case of anxiety (β = -4.29, p ≤ .001) and/or depression (β = -2.75, p ≤ .001). These variables collectively explained ~76% of the variance in HRQOL scores (adjusted R2 = .762, F (16,192) = 42.6, p ≤ .001).

CONCLUSIONS

Stroke patients exhibited poor HRQOL levels influenced by various factors. Clinicians should consider these predictors and intervene early to enhance HRQOL among patients at risk, emphasizing the importance of optimizing patient outcomes.