Sara García-De-Villa

Background/Objectives: Conventional time-based sit-to-stand measures may not fully reflect lower-limb neuromuscular capacity. This study examined whether relative sit-to-stand (STS) power provides explanatory capacity compared with STS time in the functional assessment of older adults at risk of falls. Methods: A secondary cross-sectional analysis was conducted using data from the publicly available GAIT2CARE database. Participants had a documented history of falls, fear of falling, or stability problems. Absolute, relative, and allometrically scaled STS power were estimated from the five-repetition sit-to-stand test using validated equations. Associations between relative STS power, the Fried phenotype score, and conventional functional measures were examined using Spearman correlations, multiple linear regression, and ordinal logistic regression. Results: A total of 127 participants were included, of whom 111 completed the sit-to-stand test and had estimable STS power. Relative STS power was associated with lower Fried scores (ρ = −0.585), faster gait speed (ρ = 0.536), and better Timed Up and Go performance (ρ = −0.533; all p < 0.001). After adjustment for age and Timed Up and Go performance, relative STS power remained associated with lower Fried categories (B = −0.635; β = −0.450; p < 0.001). The model including relative STS power explained 39.6% of the variance in Fried score, compared with 33.1% for the corresponding model including STS time. Ordinal logistic regression confirmed the association between higher relative STS power and lower Fried categories (estimate = −1.247; p < 0.001). Conclusions: Relative STS power provides complementary information on lower-limb neuromuscular performance and shows greater explanatory capacity for Fried score than STS time when considered alongside age and Timed Up and Go performance. It may therefore complement, rather than replace, established functional measures in older adults at risk of falls.

Background/Objectives: Long-term effects of post-COVID-19 on several health outcomes remain unclear. We assessed PA and sedentary behaviour changes and explored behaviour-change factors twelve months post-COVID-19 in people with and without Long COVID. Methods: A prospective cohort study followed people treated for COVID-19 in different settings (home, hospital ward, intensive care unit) from twelve months to eighteen months post-COVID-19. Participants with and without Long COVID were identified. PA (Light PA-LPA, Moderate-to-Vigorous PA-MVPA, Steps·day⁻¹), sedentary time, functional capacity (six-minute walk test-6MWT), muscle strength (quadriceps maximal voluntary contraction-QMVC), dyspnoea (modified Medical Research Council scale-mMRC), fatigue, symptoms of anxiety and depression, and health-related quality of life-HRQoL were assessed. Results: Among 148 participants (58 ± 15 years, 54% male), 101 had Long COVID. All remained physically inactive. People with Long COVID significantly increased LPA (LPA_LongCOVID +28 [1; 55] min·day⁻¹; LPA_Controls +6 [−32; 45] min·day⁻¹), and decreased MVPA (MVPA_LongCOVID −4 [−7; −2] min·day⁻¹; MVPA_Controls −4 [−8; 1] min·day⁻¹) and sedentarism (Sedentarism_LongCOVID −47 [−89; −4] min·day⁻¹; Sedentarism_Controls −30 [−88; 28] min·day⁻¹). At eighteen months, higher proportions of individuals with Long COVID had impaired 6MWT (17% vs. 0%), reduced QMVC (25% vs. 6%), dyspnoea (24% vs. 0%), fatigue (67% vs. 13%), symptoms of anxiety (47% vs. 9%) and depression (26% vs. 0%) as well as poor HRQoL (50% vs. 6%). PA and sedentary behaviour changes at eighteen months were associated with dyspnoea and impaired QMVC at twelve months (LPA: mMRC ≥ 2: −41.56 [−129.30; 46.00] min·day⁻¹, Steps·day⁻¹: mMRC: −416.13 [−1223.83; 391.57]; QMVC ≤ 70% predicted: −1251.39 [−2661.69; 158.91], Sedentarism: mMRC ≥ 2: +47.21 [−90.57; 184.99] min·day⁻¹; 0.24 ≤ R² ≤ 0.32). Conclusions: PA and sedentary behaviour remain altered long after COVID-19, with people with Long COVID adjusting to fit lower PA levels, possibly driven by physical limitations and symptoms. Dyspnoea and muscle weakness may influence PA and sedentary behaviour.

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2—To identify variables that stratify participants according to frailty status and falls. 3—To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 ± 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies.

Falls are a severe problem in older adults, often resulting in severe consequences such as injuries or loss of consciousness. It is crucial to screen fall risk in order to prescribe appropriate therapies that can potentially prevent falls. Identifying individuals who have experienced falls in the past, commonly known as fallers, is used to evaluate fall risk, as a prior fall indicates a higher likelihood of future falls. The methods that have the most support from evidence are Gait Speed (GS) and Time Up and Go (TUG), which use specific cut-off values to evaluate the fall risk. There have been proposals for alternative methods that use wearable sensor technology to improve fall risk assessment. Although these technological alternatives are promising, further research is necessary to validate their use in clinical settings. In this study, we propose a method for identifying fallers based on a Support Vector Machine (SVM) classifier. The inputs for the classifier are the gait parameters obtained from a 30-minute walk recorded using an Inertial Measurement Unit (IMU) placed at the foot of patients. We validated our proposed method using a sample of 157 patients aged over 70 years. Our findings indicate significant differences (p< 0.05) in stride speed, clearance, angular velocity, acceleration, and coefficient of variability among steps between fallers and non-fallers. The proposed method demonstrates the its potential to classify fallers with an accuracy of $[79.6]{\%}$ , slightly outperforming the GS method which provides an accuracy of $[77.0]{\%}$ , and also overcomes its dependency on the cut-off speed to determine fallers. This method could be valuable in detecting fallers during long-term monitoring that does not require periodic evaluations in a clinical setting.

The GSTRIDE database contains information of the health status assessment of 163 elderly adults. We provide socio-demographic data, functional and frailty variables, and the outcomes from tests commonly performed for the evaluation of elder people. The database contains gait parameters estimated from the measurements of an Inertial Measurement Unit (IMU) placed on the foot of volunteers. These parameters include the total walking distance, the number of strides and multiple spatio-temporal gait parameters, such as stride length, stride time, speed, foot angles and clearance, among others. The main processed database is stored, apart from MS Excel, in CSV format to ensure their usability. The database is complemented with the raw IMU recordings in TXT format, in order to let researchers test other algorithms of gait analysis. We include the Python programming codes as a base to reproduce or modify them. The database stores data to study the frailty-related parameters that distinguish faller and non-faller populations, and analyze the gait-related parameters in the frail subjects, which are essential topics for the elderly.

Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out in eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work.

This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants’ age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.