Objective: To determine the prevalence of sarcopenia in a population of obese older women and to assess the effect of a diet moderately rich in proteins on lean mass in sarcopenic obese older women. Materials and methods: A total of 1,030 females, >65 years old, body mass index >30 kg/m², were investigated about their nutritional status. Muscle mass (MM) was estimated according to the Janssen equation (MM =0.401× height²/resistance measured at 50 kHz +3.825× sex -0.071× age +5.102). Sarcopenia was defined according to the MM index, MM/height² (kg/m²), as two standard deviations lower than the obesity-derived cutoff score (7.3 kg/m²). A food-frequency questionnaire was used to measure participants’ usual food intake during the previous 3 months. Moreover, a group of sarcopenic obese older women (n=104) was divided in two subgroups: the first (normal protein intake [NPI], n=50) administered with a hypocaloric diet (0.8 g/kg desirable body weight/day of proteins), and the second treated with a hypocaloric diet containing 1.2 g/kg desirable body weight/day of proteins (high protein intake [HPI], n=54), for 3 months. Dietary ingestion was estimated according to a daily food diary, self-administered, and three reports of nonconsecutive 24-hour recall every month during the follow-up.
Results: The 104 women were classified as sarcopenic. After dieting, significant reductions in body mass index were detected (NPI 30.7±1.3 vs 32.0±2.3 kg/m², HPI 30.26±0.90 vs 31.05±2.90 kg/m²; P<0.01 vs baseline). The MM index presented significant variations in the NPI as well as in the HPI sarcopenic group (NPI 6.98±0.1 vs 7.10±0.2 kg/m², HPI 7.13±0.4 vs 6.96±0.1 kg/m²; P<0.01 vs baseline). Conclusion: A diet moderately rich in proteins was able to preserve MM in sarcopenic women. Therefore, adequate protein intake could contribute to the prevention of lean-mass loss associated with weight reduction in obese older people.

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

We propose an approach based on time reversibility analysis to characterize the cardiovascular regulation and its nonlinearities as derived from 24 hours Holter recordings of heart period variability in a healthy population ( n =12, age: median=43 years, range=34–55 years) and in a pathological group of age-matched chronic heart failure (CHF) patients ( n =13, primarily in NYHA class II, age: median=37 years, range=33–56 years, ejection fraction: median=25%, range=13–30%). Two indices capable of detecting nonlinear irreversible dynamics according to different strategies of phase-space reconstruction (i.e. a fixed two-dimensional phase-space reconstruction and an optimal selection of the embedding dimension, respectively) are tested and compared with a more traditional nonlinear index based on local nonlinear prediction. Results showed that nonlinear dynamics owing to time irreversibility at short time scales are significantly present during daytime in healthy subjects, more frequently present in the CHF population and less frequently during night-time in both groups, thus suggesting their link with a dominant sympathetic regulation and/or with a vagal withdrawal. On the contrary, nonlinear dynamics owing to time irreversibility at longer, dominant time scales were insignificantly present in both groups. During daytime in the healthy population, irreversibility was mostly due to the presence of asymmetric patterns characterized by bradycardic runs shorter than tachycardic ones. Nonlinear dynamics produced by mechanisms different from those inducing temporal irreversibility were significantly detectable in both groups and more frequently during night-time. The present study proposes a method to distinguish different types of nonlinearities and assess their contribution over different temporal scales. Results confirm the usefulness of this method even when applied in uncontrolled experimental conditions such as those during 24 hours Holter recordings.

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

Frameworks for human activity recognition (HAR) can be applied in the clinical environment for monitoring patients’ motor and functional abilities either remotely or within a rehabilitation program. Deep Learning (DL) models can be exploited to perform HAR by means of raw data, thus avoiding time-demanding feature engineering operations. Most works targeting HAR with DL-based architectures have tested the workflow performance on data related to a separate execution of the tasks. Hence, a paucity in the literature has been found with regard to frameworks aimed at recognizing continuously executed motor actions. In this article, the authors present the design, development, and testing of a DL-based workflow targeting continuous human activity recognition (CHAR). The model was trained on the data recorded from ten healthy subjects and tested on eight different subjects. Despite the limited sample size, the authors claim the capability of the proposed framework to accurately classify motor actions within a feasible time, thus making it potentially useful in a clinical scenario.