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This study aimed at investigating the effectiveness of a robotic fall prevention program on knowledge, exercises, balance, and incidence of falls among elderly in senior housings. This is a quasi-experimental study. Sixty-four elderly in two senior housings in Bangkok with Barthel Index scale ≥12, who had either at least one fall experience in the past 12 months and/or had Timed Up and Go (TUG) test ≥20 seconds were recruited and purposively assigned to the intervention group (received a small robot-installed fall prevention software, personal coaching, and handbook, n=32) and control group (received only handbook, n=32). Outcomes were knowledge score evaluated by structured questionnaire through face-to-face interviews, number of exercises measured by self-recorded diary, and balance score assessed by TUG and Berg Balance Scale (BBS). The incidence of falls was assessed by face-to-face interviews. Both groups were assessed at baseline, 3rd, and 6th month after the intervention. There was a statistically significant improvement in knowledge mean score at 6th month in both the groups. However, the intervention group showed faster increase in knowledge mean score than the control group at 3rd month ( <0.01). The intervention group showed a statistically significant higher number of exercises than the control group at 3rd and 6th month ( <0.05). There was no statistically significant difference on TUG and BBS mean scores between the two groups at baseline, 3rd, and 6th month. However, the intervention group showed a statistically significant improvement in TUG and BBS at 6th month post-intervention ( <0.01). There was one fall reported in the control group. The robotic fall prevention program increased knowledge on fall prevention and promoted exercises and balance among elderly in senior housings.

Background Various outcome measures are used for the assessment of balance and mobility in patients with stroke. The purpose of the present study was to examine test-retest reliability, construct validity, and responsiveness of the Timed Up and Go Test (TUG), Berg Balance Scale (BBS), and Dynamic Gait Index (DGI) for measuring balance in patients with chronic stroke. Methods Fifty-six patients (39 male and 17 female) with chronic stroke participated in this study. A senior physical therapist assessed the test-retest reliability and validity of three scales, including the DGI, TUG, and BBS over two testing sessions. In addition, the third assessment of each scale was taken at the time of discharge to determine the responsiveness of the three outcome measures. Results The reliability of the TUG (intraclass correlation coefficient [ICC 2,1 ] = 0.98), DGI (ICC 2,1  = 0.98) and BBS (ICC 2,1  = 0.99) were excellent. The standard error of measurement (SEM) of the TUG, DGI, and BBS were 1.16, 0.71, and 0.98, respectively. The minimal detectable change (MDC) of the TUG, DGI, and BBS were 3.2, 1.9, and 2.7, respectively. There was a significant correlation found between the DGI and BBS (first reading [r] = 0.75; second reading [r] = 0.77), TUG and BBS (first reading [r] = −.52; second reading [r] = −.53), and the TUG and DGI (first reading [r] = 0.45; second reading [r] = 0.48), respectively. Conclusions The test-retest reliability of the TUG, BBS, and DGI was excellent. The DGI demonstrated slightly better responsiveness than TUG and BBS. However, the small sample size of this study limits the validity of the results.

Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk and determining their need for participation in fall prevention programs. We present an automated and efficient system for fall risk assessment based on a multi-depth camera human motion tracking system, which captures patients performing the well-known and validated Berg Balance Scale (BBS). Trained machine learning classifiers predict the patient’s 14 scores of the BBS by extracting spatio-temporal features from the captured human motion records. Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality and accuracy of the BBS assessment. The reduced battery, termed Efficient-BBS (E-BBS), can be performed by physiotherapists in a traditional setting or deployed using our automated system, allowing an efficient and effective BBS evaluation. We report on a pilot study, run in a major hospital, including accuracy and statistical evaluations. We show the accuracy and confidence levels of the E-BBS, as well as the average number of BBS tasks required to reach the accuracy thresholds. The trained E-BBS system was shown to reduce the number of tasks in the BBS test by approximately 50% while maintaining 97% accuracy. The presented approach enables a wide screening of individuals for fall risk in a manner that does not require significant time or resources from the medical community. Furthermore, the technology and machine learning algorithms can be implemented on other batteries of medical tests and evaluations.

Objective A growing body of studies has examined the effect of exercise on balance function in stroke patients, with conflicting findings. This study aimed to investigate the effect of exercise on balance function in stroke patients and to determine the optimal exercise prescription for stroke patients. Methods We conducted an extensive search across various databases, including PubMed, Web of Science, EBSCO, Cochrane, and Scopus. The search was conducted until March 11th, 2024. Data were pooled using the weighted mean difference (WMD) and 95% confidence interval. Results Twenty-nine studies fulfilled the inclusion criteria. Exercise significantly improved Berg balance scale (BBS, WMD, 5.24, P  < 0.00001) and timed up and go test (TUG, WMD, − 2.91, P  < 0.00001) in stroke patients. Subgroup analyses showed that aerobic exercise (WMD, 6.71, P  = 0.003), exercise conducted ≥ 8 weeks (WMD, 6.43, P  < 0.00001), > 3 times per week (WMD, 6.18, P  < 0.00001), ≥ 60 min per session (WMD, 6.40, P  < 0.0001), and ≥ 180 min per week (WMD, 7.49, P  < 0.00001) were more effective in improving BBS. Conclusions Exercise improved balance function in stroke patients, and aerobic exercise might be the most effective intervention. To improve balance function, this meta-analysis provides clinicians with evidence to recommend that stroke patients participate in a minimum of 8 weeks of exercise at least 3 times per week for more than 60 min per session, with a goal of 180 min per week being achieved by increasing the frequency of exercise.

Balance impairment following stroke is a leading cause of disability and falls. Hemoglobin (Hb) affects systemic and cerebral oxygen delivery and may influence neuromuscular function and post-stroke balance, but evidence from large multicenter clinical samples is limited. We investigated the association between hemoglobin concentration and balance performance in a Chinese multicenter cross-sectional study of stroke patients. We studied 2,006 neuroimaging-confirmed stroke patients from 26 hospitals. Balance impairment was defined as BBS ≤ 40. Admission Hb (g/dL) was analyzed per 1 g/dL and by tertiles (<12.6 g/dL, 12.6-14.0 g/dL, ≥14.0 g/dL). Multivariable logistic regression with sequential adjustment, restricted cubic splines, and prespecified subgroup and sensitivity analyses evaluated associations. Balance impairment occurred in 70.5% (1,414/2,006). Each 1 g/dL higher Hb was associated with lower odds of impairment in unadjusted (OR 0.83, 95% CI 0.78-0.87;  < 0.001) and fully adjusted models (OR 0.89, 95% CI 0.83-0.96;  = 0.002). This association remained robust after comprehensive adjustment for demographic, lifestyle, comorbidity, stroke characteristics, and lesion location factors. Compared with the lowest tertile, adjusted ORs were 0.72 (95% CI 0.53-0.99;  = 0.042) for the middle tertile and 0.62 (95% CI 0.45-0.85;  = 0.003) for the highest tertile. Spline analyses suggested a broadly linear inverse association; results were consistent across subgroups and sensitivity checks. Higher admission hemoglobin was independently associated with better balance after stroke. Prospective studies should test whether Hb optimization improves rehabilitation outcomes.

IntroductionThe Berg Balance Scale, possibly the most widely used balance-related measure, has gained popularity in clinical trials. It provides information about patients’ balance-related abilities and can be used to assess improvement or worsening after rehabilitation. The aim of this study is to determine the cut-off value of the Berg Balance Scale for the fall risk in patients with multiple sclerosis (MS).MethodsThis study was designed as a prospective descriptive trial, and 186 patients with MS were included. Fall history was recorded by interview; balance was assessed using the Berg Balance Scale (BBS).ResultsThe mean ages of 96 patients with a fall history within the previous month and 90 patients without a fall history were 35.98 ± 8.58 and 35.71 ± 9.33 years, respectively. The mean value of the BBS score of the faller group was 49.44 ± 5.43 while 52.36 ± 3.53 in non-faller group. The cut-off value of the BBS for fall risk in patients with MS was determined as 50.50 points.ConclusionsFor patient safety and the success of rehabilitation, it is crucial to evaluate the risk of falling in patients with MS, one of the neurological patient groups where complaints about falling are most prevalent. The results showed that BBS is a sensitive and specific measure for identifying in patients with MS at risk of falling.

A comprehensive analysis integrating kinematic, kinetic, and electromyographic data to evaluate balance impairments in patients with stroke is lacking. We investigated balance disparities in patients with balance impairment following stroke using principal component analysis (PCA). The complete waveforms of lower-limb-joint angles, centre of pressure, and muscle activity in 43 stroke patients during four Berg Balance Scale (BBS) standing balance tasks were analysed. Multiple regression analysis using principal components (PCs) was conducted to predict BBS scores. Thirteen patients had balance impairments (BBS score < 45). Significant differences in bilateral standing PCs were observed between patients with and without balance impairments during the standing balance tasks ( p  < 0.2). The strongest predictor of BBS score was the performance of the paretic leg during quiet standing with open eyes ( p  < 0.01). Key contributors to balance impairment included bilateral sagittal plane ankle and pelvic joint angles, bilateral vertical ground response forces, and paretic plantar-flexor activation across all standing tasks. These findings highlight that postural control of the paretic limb is a key determinant of balance ability, with distinct balance strategies observed across ability levels. Additionally, PCA effectively quantified balance impairments, revealing significant associations with Fugl-Meyer lower extremity, ankle joint range of motion, and strength. These results emphasize the role of sagittal plane postural control and plantar-flexor activation in stability and suggest that PCA may be a valuable tool for developing targeted rehabilitation strategies.

Mediolateral balance control during walking is a challenging task in post-stroke hemiparetic individuals. To detect and treat dynamic balance disorders, it is important to assess balance using reliable methods. The Berg Balance Scale (BBS), Dynamic Gait Index (DGI), margin-of-stability (MoS), and peak-to-peak range of angular-momentum (H) are some of the most commonly used measures to assess dynamic balance and fall risk in clinical and laboratory settings. However, it is not clear if these measures lead to similar conclusions. Thus, the purpose of this study was to assess dynamic balance in post-stroke hemiparetic individuals using BBS, DGI, MoS and the range of H and determine if these measure are correlated. BBS and DGI were collected from 19 individuals post-stroke. Additionally, kinematic and kinetic data were collected while the same individuals walked at their self-selected speed. MoS and the range of H were calculated in the mediolateral direction for each participant. Correlation analyses revealed moderate associations between all measures. Overall, a higher range of angular-momentum was associated with a higher MoS, wider step width and lower BBS and DGI scores, indicating poor balance control. Further, only the MoS from the paretic foot placement, but not the nonparetic foot, correlated with the other balance measures. Although moderate correlations existed between all the balance measures, these findings do not necessarily advocate the use of a single measure as each test may assess different constructs of dynamic balance. These findings have important implications for the use and interpretation of dynamic balance assessments.

Fall rates are increasing among the aging population and even higher falls rates have been reported in populations with neurological impairments. The Berg Balance Scale is often used to assess balance in older adults and has been validated for use in people with stroke, traumatic brain injury, and Parkinson’s disease. While the Berg Balance Scale (BBS) has been found to be predictive of the length of rehabilitation stay following stroke, a recent review concluded the BBS lacked predictive validity for fall risk. Conversely, sophisticated measures assessing center of mass (COM) displacement have shown to be predictive of falls risk. However, calculating COM displacement is difficult to measure outside a laboratory. Accordingly, we sought to validate COM displacement measurements derived from an HTC Vive tracker secured to the pelvis by comparing it to COM derived from ‘gold’ standard laboratory-based full-body motion capture. Results showed that RMS between the COM calculated from HTC Vive tracker and full body motion capture agree with an average error rate of 2.1 ± 2.6 cm. Therefore, we conclude measurement of COM displacement using an HTC Vive tracker placed on the pelvis is reasonably representative of laboratory-based measurement of COM displacement.

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20–70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.