The Longshi Scale, a visual-based scale, is reliable and valid in activity assessment, but lacks cutoff definition corresponding to classical scales such as the modified Rankin Scale and Barthel Index. Therefore, this study aimed to investigate the relationships of the Longshi Scale with the modified Rankin Scale and Barthel Index and optimize cutoff scores of these scales in stroke outcomes assessment. This is a cross-sectional study. Stroke patients were measured concurrently by the Longshi scale, modified Rankin Scale and Barthel Index. Kruskal-Wallis test and Spearman correlation analysis were used to analyze the differences and associations among the three scales. The receiver operating characteristic curve was performed to determine the optimal cutoff scores. A total of 5475 stroke patients (67.3% ischemic) were included in this study. There are close relationships of the Longshi Scale with adjusted modified Rankin Scale and Barthel Index (r = -0.861, 0.922; p<0.001, <0.001; respectively). The activity levels assessed by adjusted modified Rankin Scale and Barthel Index among different Longshi scale grades were significantly different (χ²:4217.27, 4676.55; p<0.001, <0.001; respectively). The optimal cutoff scores were adjusted modified Rankin Scale 4, 3, 3, 3, 2 for Longshi scale grade 2 to 6 (sensitivity%: 96.12, 70.24, 89.10, 96.80, 86.23, specificity%: 72.72, 98.29, 93.81, 79.82, 92.89, respectively), and Barthel Index 15, 45, 60, 75, 80 for Longshi scale grade 2 to 6 (sensitivity%: 92.54, 89.28, 91.32, 90.30, 95.65, specificity%: 95.48, 89.51, 94.02, 90.41, 90.62, respectively). In conclusion, the classification of Longshi Scale is consistent with those of modified Rankin Scale and Barthel Index. We recommend the Longshi Scale as an effective supplement for modified Rankin Scale and Barthel Index in assessing the outcome in acute stroke patients.

To evaluate the effect of modified wheelchair arm-support to mitigate hemiplegic shoulder pain and reduce pain frequency in stroke patients. A single-blind randomized controlled trial using computer-generated simple randomization. Participants recruited from inpatients at the Guangdong Provincial Hospital of Chinese Medicine. A total of 120 patients with stroke were divided into two groups. All subjects underwent basic rehabilitation training and wheelchair assistance with eight weeks follow-up period. Patients in the treatment group additionally received modified wheelchair arm-support for at least 60 minutes a day, six days a week, for four weeks. Primary outcome was measured by the Visual Analogue Pain Scale or Numeric Pain Rating Scale. Secondary outcome was measured using the Upper Extremity Fugl-Meyer Assessment scale, Modified Barthel Index and Quality of Life Index. Measurements were made at 4 weeks and 12 weeks, following the intervention. Patients age from 21 to 83 years (mean ± SD = 62.41 ± 12.26). The average duration of disease was 1.9 ± 1.3 months. At four weeks, the median of pain intensity was higher in the control group (median, interquartile range = 3, 5.75 vs. 2, 3.75; P = 0.059). At 12 weeks, the median of pain intensity was higher in the control group (median, interquartile range = 3, 5.00 vs. 0, 1.00; P < 0.001). At 12 weeks, patients with shoulder pain were higher in the control group (6 vs. 1; P < 0.05). Using the modified wheelchair arm-support could lead to the mitigation of hemiplegic shoulder pain and reduction in pain incidence in stroke patients. It may also improve the patients’ quality of life.

Objectives. Handgrip strength (HS) is a risk factor of all‐cause mortality and cardiovascular diseases. However, the influencing factors and mechanisms contributing to this correlation remain unclear. Therefore, we aimed to explore factors related to HS and investigated the mechanism underlying its risk predictive value. Methods. This was a prospective, cross‐sectional study. One hundred forty‐five participants were recruited from December 2019 to November 2020. HS was measured using a hydraulic hand dynamometer and adjusted for body mass index (HS_BMI) and body surface area (HS_BSA). Body composition was assessed via bioimpedance spectroscopy. Physical fitness was measured using a cardiopulmonary exercise test system. Univariate, multiple linear regression analyses and receiver operator characteristic curve (ROC) were conducted to evaluate the associations between various participant characteristics and HS. Results. The average participant age was 21.68 ± 2.61 years (42.8% were male). We found positive correlations between HS_BMI/HS_BSA and VO_2max, VE_max, Load_max, and MET_max in both sexes (p < 0.05). Lean‐tissue, protein, total water, and inorganic salt percentages were positively correlated, and fat percentage was negatively correlated with HS_BMI in men and with HS_BMI and HS_BSA in women (p < 0.05). Multiple regression revealed that VO_2max was independently associated with HS_BSA in both sexes (β = 0.215, 0.173; 95%confidence interval [CI] = 0.032 − 0.398, 0.026‐0.321; p = 0.022, 0.022, respectively) and independently associated with HS_BMI in women (β = 0.016, 95%CI = 0.004 − 0.029, p = 0.011). ROC analysis showed that HS_BMI and HS_BSA can moderately identify normal VO_2max in men (area under curve [AUC] = 0.754, 0.769; p = 0.002, 0.001, respectively) and marginally identify normal VO_2max in women (AUC = 0.643, 0.635; p = 0.029, 0.042, respectively). Conclusions. BMI‐ and BSA‐adjusted HS could serve as indicators of physical health, and HS_BSA may moderately reflect cardiorespiratory fitness levels in healthy young adults, particularly in males. Clinical trials registry site and number: China Clinical Trial Center (ChiCTR1900028228).

Background: Many stroke survivors have multiple chronic diseases and complications coupled with various other factors which may affect their functional status. We aimed to investigate the factors associated with poor functional status in hospitalized patients with stroke in Shenzhen, China. Methods: In this cross-sectional study, four urban hospitals were selected using convenient sampling, and all stroke patients in these four hospitals were included using cluster sampling. The functional status of stroke survivors was evaluated using Longshi Scale. Explanatory variables (factors affecting functional status comprising age, sex, body mass index, smoking, alcohol consumption, complications, and chronic conditions) were collected. Ordinal logistic regression was used to examine which factors were associated with poor functional status. Results: Stroke survivors with poor functional status accounted for 72.14% and were categorised as the bedridden group based on Longshi scale, 21.67% of patients with moderate functional limitation were categorised as the domestic group, and 6.19% of the patients with mild functional restriction were categorised as the community group. The highest dependence scores were noted for feeding (73.39%), bowel and bladder management (69.74%) and entertainment (69.53%) among the bedridden group, and housework (74.29%) among the domestic group. In the adjusted model, the odds of poor functional status were higher among stroke patients with older age (odds ratio [OR] = 2.39, 95% CI: 1.55–3.80), female sex (OR = 1.73, 95% CI: 1.08–2.77), duration of stroke more than 12 months (OR = 1.94, 95% CI: 1.28–2.95), with pulmonary infection (OR = 10.91, 95% CI: 5.81–20.50), and with deep venous thrombosis (OR = 3.00, 95% CI: 1.28–7.04). Conclusions: Older adults (age ≥ 60 years) and women were more likely to exhibit poor functional status post-stroke. Pulmonary infection and deep venous thrombosis were related to an increased risk of being dependent on activities of daily living. Therefore, clinical and rehabilitation interventions aimed at preventing or treating these common complications should be addressed to deal with subsequent dysfunction post-stroke. Since all data were obtained in metropolitan areas where the economy is well developed, future studies should be conducted in rural areas and economically less developed cities.

Background: Neuroinflammatory responses are closely associated with poststroke prognosis severity. This study aimed to develop a predictive model, combining inflammation‐derived markers and clinical indicators, for distinguishing functional outcomes in patients with subacute ischemic stroke. Methods and Results: Based on activities of daily living assessments, ischemic stroke participants were categorized into groups with little effective (LE) recovery and obvious effective (OE) recovery. Initial biocandidates were identified by overlapping differentially expressed proteins from proteomics of clinical serum samples (5 LE, 5 OE, and 6 healthy controls) and differentially expressed genes from an RNA sequence of the ischemic cortex in middle cerebral artery occlusion mice (n=3). Multidimensional validations were conducted in ischemia–reperfusion models and a clinical cohort (15 LE, 11 OE, and 18 healthy controls). Models of robust biocandidates combined with clinical indicators were developed with machine learning in the training data set and prediction in another test data set (15 LE and 11 OE). We identified 194 differentially expressed proteins (LE versus healthy controls) and 174 differentially expressed proteins (OE versus healthy controls) in human serum, and 5121 differentially expressed genes (day 3) and 5906 differentially expressed genes (day 7) in middle cerebral artery occlusion mice cortex. Inflammation‐derived biomarkers TIMP1 (tissue inhibitor metalloproteinase‐1) and galactosidase‐binding protein LGLAS3 (galectin‐3) exhibited robust increases under ischemic injury in mice and humans. TIMP1 and LGALS3 coupled with clinical indicators (hemoglobin, low‐density lipoprotein cholesterol, and uric acid) were developed into a combined model for differentiating functional outcome with high accuracy (area under the curve, 0.8). Conclusions: The combined model is a valuable tool for evaluating prognostic outcomes, and the predictive factors can facilitate development of better treatment strategies.