Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

Although the falls in elderly people lead to serious health consequences, the economic burden is underestimated. The aim of this study was to calculate the medical costs of fall consequences in elderly women. Women aged 65 years and older were interviewed by phone recording the consequences and healthcare procedures related to every fall sustained during the previous 12 months. The healthcare costs were estimated by calculating the sum of costs for all self-reported contacts with medical care providers: ambulance, emergency department, visits to family doctor and other specialists, hospitalisations, and rehabilitation. The study population consisted of 878 community-dwelling women (mean age 72.2±4.8 years). Falls were reported by 310 (35.3%) women; one in three of them had fallen twice or more. Of all women who fell, 280 (90.3%) reported their fall resulted in an injury, and 77 (15.3%) falls led to bone fractures. Fear of falling was reported by 72.9% of women. Fall-related medical care was provided to 135 women (43.5% of those fallen), and 18 (5.8%) subjects were hospitalised, mostly for the fracture. The mean estimated healthcare cost was 254 EUR per patient receiving fall-related medical care, and 116 EUR per women fallen. The highest mean cost (1289 EUR) was estimated in falls resulted in hip fracture; the lowest (135 EUR), in nonfracture injury. The data on the self-reported consequences of falls in elderly women showed a significant number of fall-related injuries and a high cost of healthcare.

This paper presents a finite element method (FEM)-based fracture risk assessment in patient-specific osteoporotic lumbar vertebra L1. The influence of osteoporosis is defined by variation of parameters such as thickness of the cortical shell, the bone volume–total volume ratio (BV/TV), and the trabecular bone score (TBS). The mechanical behaviour of bone is defined using the Ramberg–Osgood material model. This study involves the static and nonlinear dynamic calculations of von Mises stresses and follows statistical processing of the obtained results in order to develop the patient-specific vertebra reliability. In addition, different scenarios of parameters show that the reliability of the proposed model of human vertebra highly decreases with low levels of BV/TV and is critical due to the thinner cortical bone, suggesting high trauma risk by reason of osteoporosis.

Background and Objectives: It is thought that muscle and bone interact only on a biomechanical level, however, some research is now emerging that links bone and muscle on a cellular level. The aim of this study was to explore associations between physical function, muscle mass and bone density in community-dwelling elderly men with sarcopenia. A secondary goal was to analyze if muscle morphology was associated with bone density and physical functioning. Materials and Methods: Body composition was measured by dual-energy X-ray absorptiometry (DXA). Bone density was evaluated according to WHO criteria. Sarcopenia was diagnosed according to European Working Group on Sarcopenia in Older People (EWGSOP) criteria: low muscle mass and low muscle strength or low physical performance. Microbiopsy of musculus vastus lateralis was performed with a disposable muscle microbiopsy system. The perimeter and cross-sectional area of muscle fibers were calculated using image analysis software in whole slide images; type of fibers and their distribution were evaluated as well. Results: A total of 151 men, 60 years or older were included in this study. Mean age of the subjects was 72.9 ± 8.02 years. Sarcopenia was diagnosed in 45 (29.8%) men. Multiple significant correlations were found between bone mineral density, lean mass, appendicular lean mass, arm and leg lean mass, gait speed, balance test and handgrip strength in sarcopenic men. Lean mass was associated with femoral neck BMD (bone mineral density; r = 0.418, p = 0.006) and handgrip strength (r = 0.553, p < 0.001). In the sarcopenia group, 25 muscle biopsies were examined. In 9 sarcopenic men with T-scores equal or below −2.5, the muscle fiber area had a significant correlation with the balance test (r = 0.73, p = 0.025). Conclusions: In men with sarcopenia, low lean muscle mass was associated with low femoral neck BMD and low muscle strength. In sarcopenic men with osteoporosis, low muscle fiber area was associated with low scores in a balance test.

Frailty is a common syndrome in the elderly, marked by an increased risk of negative health outcomes such as falls, disability and death. It is important to detect frailty early and accurately to apply timely interventions that can improve health results in older adults. Traditional evaluation methods often depend on subjective evaluations and clinical opinions, which might lack consistency. This research uses deep learning to classify frailty from spectrograms based on IMU data collected during gait analysis. The study retrospectively analyzed an existing IMU dataset. Gait data were categorized into Frail, PreFrail, and NoFrail groups based on clinical criteria. Six IMUs were placed on lower extremity segments to collect motion data during walking activities. The raw signals from accelerometers and gyroscopes were converted into time–frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. Minimal preprocessing did not improve subject-wise performance, suggesting that the raw time–frequency representation retains the most salient gait cues. These findings suggest that wearable sensor technology combined with deep learning provides a robust, objective tool for frailty assessment, offering potential for clinical and remote health monitoring applications.