Although lateralization of the brain affects some specialized cortical functions, there are still limited data to address its influence on clinically important outcomes. This study aimed to reveal the prognostic variables that relate to functional recovery in stroke patients with a left-sided hemispheric lesion during 6 months of follow-up. Data from 167 left-sided and 183 right-sided hemispheric strokes were reviewed retrospectively. Outcomes in this study included walking capacity and functional recovery, assessed by the modified Rankin Scale (mRS). In order to obtain independent predictive variables, this study used the step-backward method of multivariable regression analysis of parameters. The final model demonstrated that motor function of the hemiparetic leg was the strongest independent predictor for both walking ability and functional recovery (risk ratio (RR) of 2.41, 95% CI: 1.61–3.60, and p < 0.001 and RR of 1.83, 95% CI: 1.03–3.26, and p = 0.04, resp.). Therefore, lateralization did not seem to be involved. Understanding predictable variables that are associated with recovery can guide the rehabilitation team in setting priority and appropriate treatment for stroke patients.
Background Most stroke survivors spent their lifetime with disability which not only affects the clients themselves and the family but also brings economic cost to the country. Therefore, this retrospective cohort study aimed to identify independent prognostic determinants associated with functional recovery in ischemic stroke within 6 months after onset. Methods Data from all first-onset ischemic stroke patients admitted to the acute stroke unit of the tertiary, university hospital were reviewed for 5 years consecutively. The functional outcome of the patients was recorded during 6-month follow-up by using the modified Rankin Scale (mRS). Baseline characteristics, motor assessment and all stroke-related variables were assessed during first week after stroke and 6-month follow-up. In order to derive clinical predictors, the backward stepwise multivariable risk regression analyses were used with the generalized linear model. Results The result revealed that in the 358 patients recruited into this study, 255 (71.2%) were in the functional recovery group (mRS score of 1 - 3) within 6 months after onset. The final model of multivariable risk regression analysis, with generalized linear model, demonstrated that the independent variables of functional recovery were leg score with a risk ratio (RR = 1.92, 95% confidence interval (CI): 1.14 - 3.21, P = 0.013), arm score (RR = 1.75, 95% CI: 1.02 - 3.01, P = 0.042) and age older than 75 years (RR = 1.36, 95% CI: 1.04 - 1.77, P = 0.025). Conclusions Achieving functional recovery during 6 months post stroke was related to age and motor improvement. With limited resources, continuity of rehabilitation training in the community system or allocation of caregiver training should be a part of discharge planning to promote recovery.
BackgroundThe measurements of body mass index (BMI) and percentage of body fat are used in many clinical situations. However, special tools are required to measure body fat. Many formulas are proposed for estimation but these use constant coefficients of age. Age spectrum might affect the predicted value of the body composition due to body component alterations, and the coefficient of age for body fat prediction might produce inconsistent results. The objective of this study was to identify variations of BMI and body fat across the age spectrum as well as compare results between BMI predicted body fat and bioelectrical impedance results on age.MethodsHealthy volunteers were recruited for this study. Body fat was measured by bioelectrical impedance. The age spectrum was divided into three groups (younger: 18–39.9; middle: 40–59.9; and older: ≥60 years). Comparison of body composition covariates including fat mass (FM), fat free mass (FFM), percentage FM (PFM), percentage FFM (PFFM), FM index (FMI) and FFM index (FFMI) in each weight status and age spectrum were analyzed. Multivariable linear regression coefficients were calculated. Coefficient alterations among age groups were tested to confirm the effect of the age spectrum on body composition covariates. Measured PFM and calculated PFM from previous formulas were compared in each quarter of the age spectrum.ResultsA total of 2324 volunteers were included in this study. The overall body composition and weight status, average body weight, height, BMI, FM, FFM, and its derivatives were significantly different among age groups. The coefficient of age altered the PFM differently between younger, middle, and older groups (0.07; P = 0.02 vs 0.13; P < 0.01 vs 0.26; P < 0.01; respectively). All coefficients of age alterations in all FM- and FFM-derived variables between each age spectrum were tested, demonstrating a significant difference between the younger (<60 years) and older (≥60 years) age groups, except the PFFM to BMI ratio (difference of PFM and FMI [95% confidence interval]: 17.8 [12.8–22.8], P < 0.01; and 4.58 [3.4–5.8], P < 0.01; respectively). The comparison between measured PFM and calculated PFM demonstrated a significant difference with increments of age.ConclusionThe relationship between body FM and BMI varies on the age spectrum. A calculated formula in older people might be distorted with the utilization of constant coefficients.
Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; p < 0.001 ). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89–0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.
Background: Elephants in Thailand have changed their roles from working in the logging industry to tourism over the past two decades. In 2020, there were approximately 2700 captive elephants participating in activities such as riding and trekking. During work hours, riding elephants carry one or two people in a saddle on the back with a mahout on the neck several hours a day and over varying terrain. A concern is that this form of riding can cause serious injuries to the musculoskeletal system, although to date there have been no empirical studies to determine the influence of weight carriage on kinematics in elephants. Methods: Eight Asian elephants from a camp in Chiang Mai Province, Thailand, aged between 21 and 41 years with a mean body mass of 3265 ± 140.2 kg, were evaluated under two conditions: walking at a normal speed without a saddle and with a 15% body mass load (saddle and two persons plus additional weights). Gait kinematics, including the maximal angles of fore- and hindlimb joints, were determined using a novel three-dimensional inertial measurement system with wireless sensors. Results: There were no statistical differences between movement angles and a range of motion of the fore- and hindlimbs, when an additional 15% of body mass was added. Conclusion: There is no evidence that carrying a 15% body mass load causes significant changes in elephant gait patterns. Thus, carrying two people in a saddle may have minimal effects on musculoskeletal function. More studies are needed to further test longer durations of riding on different types of terrain to develop appropriate working guidelines for captive elephants. Nevertheless, elephants appear capable of carrying significant amounts of weight on the back without showing signs of physical distress.
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