Frédéric Chorin

Background: Although dual-energy X-ray absorptiometry (DXA) is the reference method for measuring appendicular skeletal muscle mass (ASMM) and diagnosing sarcopenia, seated bioelectrical impedance analysis (BIA) may represent a practical alternative. As its validity remains still insufficiently documented in specific populations, the objective is to evaluate the agreement between seated BIA and DXA, and examine associations between phase angle (PhA) and physical performance in old adults. Methods: Fifty-nine old adults (75 ± 7 years) underwent DXA and seated BIA (Biody XpertZM3). ASMM was estimated using the Kyle and Sergi equations. Agreement with DXA was assessed using Lin’s concordance correlation coefficient (CCC), Bland–Altman analysis, root mean square error (RMSE) and mean absolute error (MAE). Associations between PhA, handgrip strength, and five-times sit-to-stand (5-STS) performance were evaluated. Results: Both equations showed good agreement with DXA, but the Sergi equation performed better, with higher concordance (CCC = 0.942 vs. 0.898), lower bias (0.44 vs. 1.11 kg), and lower RMSE (1.33 vs. 1.96 kg). PhA correlated positively with handgrip strength (r = 0.315, p = 0.015) but was not associated with 5-STS. Conclusions: Seated BIA provides clinically acceptable estimates of ASMM in older adults. The Sergi equation demonstrated superior accuracy, supporting population-specific equations. PhA may provide additional information on muscle quality and sarcopenia.

Objective: Sarcopenia is a progressive muscular disease. The European Working Group on Sarcopenia in Older People revised protocols to include the F-A-C-S procedure. Sarcopenia severity is assessed by physical performance tests. However, these tests can be influenced by various factors. Ultrasound offers a more objective method for measuring muscle mass and assessing sarcopenia severity. This study aims to determine reliable ultrasound measurements for objectively evaluating sarcopenia severity in older adults. Design: A prospective study was conducted, assessing 200 patients over 75, with sarcopenia identified via SARC-F questionnaire, grip strength, and chair stand test. Ultrasound measured muscle thickness in the rectus femoris and other muscles. Comparative analyses divided patients into nonsevere sarcopenia and severe sarcopenia groups, with receiver operating characteristic curves and logistic regression to evaluate ultrasound measurements’ discriminative power. Results: Muscle thickness, particularly of the rectus femoris, significantly correlated with sarcopenia severity. Ultrasound measurements accurately discriminated sarcopenia severity with an area under the receiver operating characteristic curve (AUC) of 0.629. Logistic regression confirmed rectus femoris thickness as a predictive measure for severe sarcopenia. Conclusions: Ultrasound measurement of rectus femoris muscle thickness is a promising tool for diagnosing sarcopenia severity, enhancing early identification and improving management strategies for the elderly.

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.

Background: Falling is an important public health issue because of its prevalence and severe consequences. Evaluating muscle performance is important when assessing fall risk. The study aimed to identify factors [namely muscle capacity (strength, quality, and power) and spatio-temporal gait attributes] that best discriminate between fallers and non-fallers in older adults. The hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. Methods: 184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83 years. We compared body composition, mean grip strength, spatio-temporal parameters, and muscle capacity of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip and power were also controlled by fat-free mass. We performed univariate analysis, logistic regression, and ROC curves. Results: The falling patients had lower muscle quality, muscle mass-controlled power, and mean weighted handgrip than the non-faller. Results showing that lower muscle quality increases fall risk (effect size = 0.891). Logistic regression confirmed muscle quality as a significant predictor (p < .001, OR = 0.82, CI [0.74; 0.89]). ROC curves demonstrated muscle quality as the most predictive factor of falling (AUC = 0.794). Conclusion: This retrospective study showed that muscle quality is the best predictor of fall risk, above spatial and temporal gait parameters. Our results underscore muscle quality as a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the aging population.

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Background: As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted. Objective: On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities. Methods: A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group. Results: The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51). Conclusions: To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances.

To test a novel instrumented knee brace intended for use as a rehabilitation system, based on inertial measurement units (IMU) to monitor home-based exercises, the device was compared to the gold standard of motion analysis. The purpose was to validate a new calibration method through functional tasks and assessed the value of adding magnetometers for motion analysis. Thirteen healthy young adults performed a 60-second gait test at a comfortable walking speed on a treadmill. Knee kinematics were captured simultaneously, using the instrumented knee brace and an optoelectronic camera system (OCS). The intraclass correlation coefficient (ICC) showed excellent reliability for the three axes of rotation with and without magnetometers, with values ranging between 0.900 and 0.972. Pearson’s r coefficient showed good to excellent correlation for the three axes, with the root mean square error (RMSE) under 3° with the IMUs and slightly higher with the magnetometers. The instrumented knee brace obtained certain clinical parameters, as did the OCS. The instrumented knee brace seems to be a valid tool to assess ambulatory knee kinematics, with an RMSE of <3°, which is sufficient for clinical interpretations. Indeed, this portable system can obtain certain clinical parameters just as well as the gold standard of motion analysis. However, the addition of magnetometers showed no significant advantage in terms of enhancing accuracy.

Multidisciplinary supportive care, integrating the dimensions of exercise alongside oncological treatments, is now regarded as a new paradigm to improve patient survival and quality of life. Its impact is important on the factors that control tumor development, such as the immune system, inflammation, tissue perfusion, hypoxia, insulin resistance, metabolism, glucocorticoid levels, and cachexia. An increasing amount of research has been published in the last years on the effects of physical activity within the framework of oncology, marking the appearance of a new medical field, commonly known as “exercise oncology”. This emerging research field is trying to determine the biological mechanisms by which, aerobic exercise affects the incidence of cancer, the progression and/or the appearance of metastases. We propose an overview of the current state of the art physical exercise interventions in the management of cancer patients, including a pragmatic perspective with tips for routine practice. We then develop the emerging mechanistic views about physical exercise and their potential clinical applications. Moving toward a more personalized, integrated, patient-centered, and multidisciplinary management, by trying to understand the different interactions between the cancer and the host, as well as the impact of the disease and the treatments on the different organs, this seems to be the most promising method to improve the care of cancer patients.

BACKGROUND As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted. OBJECTIVE On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities. METHODS A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group. RESULTS The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51). CONCLUSIONS To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances.

The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.