Temporal validation of a clinical prediction rule for distinguishing locomotive syndromes in community-dwelling older adults: A cross-sectional study from the DETECt-L study.

Objectives: Clinical prediction rules are used to discriminate patients with locomotive syndrome and may enable early detection. This study aimed to validate the clinical predictive rules for locomotive syndrome in community-dwelling older adults. Methods: We assessed the clinical prediction rules for locomotive syndrome in a cross-sectional setting. The age, sex, and body mass index of participants were recorded. Five physical function tests-grip strength, single-leg standing time, timed up-and-go test, and preferred and maximum walking speeds-were measured as predictive factors. Three previously developed clinical prediction models for determining the severity of locomotive syndrome were assessed using a decision tree analysis. To assess validity, the sensitivity, specificity, likelihood ratio, and post-test probability of the clinical prediction rules were calculated using receiver operating characteristic curve analysis for each model. Results: Overall, 280 older adults were included (240 women; mean age, 74.8 ± 5.2 years), and 232 (82.9%), 68 (24.3%), and 28 (10.0%) participants had locomotive syndrome stages ≥ 1, ≥ 2, and = 3, respectively. The areas under the receiver operating characteristics curves were 0.701, 0.709, and 0.603, in models 1, 2, and 3, respectively. The accuracies of models 1 and 2 were moderate. Conclusions: These findings indicate that the models are reliable for community-dwelling older adults.

Estimation of Reference Values of Gait Spatiotemporal and Kinematic Parameters in the Lower Extremities and Trunk Using a Markerless Motion Capture System for Healthy Older Japanese Adults.

OBJECTIVE: This preliminary study aimed to explore the reference values of spatiotemporal and kinematic parameters in the lower extremities and trunk during gait for the healthy older adults. METHODS: Walking speed, stride length and time, cadence, walk ratio, and step width were calculated as spatiotemporal parameters of gait. Forward tilting of the trunk (FTT), hip flexion and extension, knee flexion and extension, and their laterality were measured as peak angles during one-gait cycle. The bootstrap method was conducted to estimate the 95% confidence interval (CI). RESULTS: This study included 334 healthy older adults (255 women). The following gait parameters were estimated with 95%CI: walking speed (95%CI 1.21-1.30), cadence (95%CI 116.35-121.20), walk ratio (95%CI 0.0055-0.0060), step width (95%CI 0.15-0.17), FTT (95%CI 1.91-4.19), hip flexion (95%CI 28.54-31.01), hip extension (95%CI 19.30-22.27), knee extension (95%CI 0.09-0.14), laterality of hip flexion (95%CI 1.31-2.02), laterality of hip extension (95%CI 1.32-1.97), laterality of knee flexion (95%CI 3.41-4.77), and laterality of knee extension (95%CI 0.07-0.13) in men, and walking speed (95%CI 1.28-1.34), walk ratio (95%CI 0.0050-0.0054), FTT (95%CI 2.54-3.73), hip flexion (95%CI 32.80-34.28), laterality of hip flexion (95%CI 1.65-2.05), laterality of hip extension (95%CI 2.06-2.57), and laterality of knee flexion (95%CI 3.04-3.89) in women. CONCLUSION: This study suggested provisional reference values of spatiotemporal and kinematic parameters in the lower extremities and trunk during gait for the healthy older adults.

Abnormal Gait Pattern Examination Screening for Physical Activity Level after One Year in Patients with Knee Osteoarthritis.

This study examined the relationship between abnormal gait pattern and physical activity level one year later in patients with knee osteoarthritis (KOA) and determined the clinical utility of the abnormal gait pattern examination. Initially, the patients' abnormal gait pattern was assessed using seven items, based on the scoring system reported in a previous study. The grading was based on a three-criteria system with 0: no abnormality, 1: moderately abnormal, and 2: severely abnormal. The patients were then classified into three groups according to physical activity level one year after gait pattern examination: low, intermediate, and high physical activity groups, respectively. Cut-off values for physical activity levels were calculated based on abnormal gait pattern examinations results. On follow-ups with 24 of the 46 subjects, age, abnormal gait pattern, and gait speed showed significant differences among the three groups according to the amount of physical activity. Effect size of abnormal gait pattern was higher than age and gait speed. Patients with KOA with physical activity < 2700 steps/day and <4400 steps/day at one year had abnormal gait pattern examination scores of ≥8 and ≥5, respectively. Abnormal gait pattern is associated with future physical activity. The results suggested that abnormal gait pattern examinations in patients with KOA could be used to screen for the possibility of physical activity being <4400 steps one year later.

Clinical prediction rule for locomotive syndrome in older adults: A decision tree model.

BACKGROUND: No previous studies have proposed a clinical prediction rule that analyzes the factors related to the severity of locomotive syndrome. This study developed and assessed a clinical prediction rule for the severity of locomotive syndrome in older adults. METHODS: A total of 186 patients were assessed using the locomotive syndrome risk test. Classification and regression tree methodologies were used to develop the clinical prediction rule. This study developed three prediction models based on the severity of the locomotive syndrome, of which Model 3 assessed the most severe condition. The following potential predictive factors were measured and entered into each model; single-leg standing time, grip strength, preferred and maximum walking time, and timed up and go test. RESULTS: The single-leg standing test (≤59.4 or >59.4 s) was the best single discriminator for Model 1. Among those with a single-leg standing time >59.4 s, the next best predictor was grip strength (≤37.8 or >37.8 kg). In Model 2, the single-leg standing test was also the best single discriminator (≤12.6 or >12.6 s). Among those with a single-leg standing time ≤12.6, the next best predictor was TUG (≤7.9 or >7.9 s). Additionally, among those with a single-leg standing time >12.6, the next best predictor was single-leg standing time (≤55.3 or >55.3 s). In Model 3, predictive value in Model 2 was the best single discriminator (0 or 1). Among those with 1, the next best predictor was maximum walking time (≤3.75 or >3.75 s). The area under the receiver operating characteristic curves of Models 1, 2, and 3 were 0.737, 0.763, and 0.704, respectively. CONCLUSIONS: A clinical prediction rule was developed to assess the accuracy of the models. These results can be used to screen older adults for suspected locomotive syndrome.

Effects of age and gender on spatiotemporal and kinematic gait parameters in older adults.

PURPOSE: Gait changes are more prominently observed in older adults than in young adults, especially in kinematics of lower extremities and trunk. These changes can result in incidental falls during gait, possibly leading to inability to perform activities of daily living independently. This study aimed to investigate the effect of gender and age on gait changes, such as spatiotemporal parameters and peak joint angles in lower extremities and trunk during gait. METHODS: A total of 387 participants (223 women) were included. The Microsoft Kinect V2 sensor was used to obtain the coordinate data of lower extremities and trunk during gait. The coordinate data obtained were processed using the software. Walking speed, stride length, stride time and cadence were calculated as spatiotemporal variables of walking. Forward trunk tilt angle (FTT), hip flexion and extension, and knee flexion and extension were measured as peak angles during one-gait cycle. Participants were categorized into five groups according to age by five years. Multivariate analysis of variance was performed to compare the spatiotemporal and kinematical data among groups. RESULTS: Significant differences among age groups were noted in terms of the walking speed and stride length. Significant differences were also observed in the FTT and hip extension angle. CONCLUSIONS: Increased gait changes, increased peak FTT and decreased peak hip extension angle were observed with an increase of age. These altered symptoms may contribute to the screening of older adults at risk of declined physical function at an early stage.