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Sweat is a biomarker-rich fluid with potential for continuous patient monitoring via wearable devices. However, biomarker concentrations vary with the sweat rate per gland, posing a challenge for sweat sensing. To address this, we propose an algorithm to compute both the number of active sweat glands and their individual sweat rates. We developed models of sweat glands and a discrete sweat-sensing device to sense sweat volume. Our algorithm estimates the number of active glands by decomposing the signal into patterns generated by the individual sweat glands, allowing for the calculation of individual sweat rates. We assessed the algorithm's accuracy using synthetic datasets for varying physiological parameters (sweat rate and number of active sweat glands) and device layouts. The results show that device layout significantly affects accuracy, with error rates below 0.2% for low and medium sweat rates (below 0.2 nL min−1 per gland). However, the method is not suitable for high sweat rates. The suitable sweat rate range can be adapted to different needs through the choice of device. Based on our findings, we provide recommendations for optimal device layouts to improve accuracy in estimating active sweat glands. This is the first study to focus on estimating the sweat rate per gland, which essential for accurate biomarker concentration estimation and advancing sweat sensing towards clinical applications. [ABSTRACT FROM AUTHOR]

Cardiac surgery patients infrequently mobilize during their hospital stay. It is unclear for patients why mobilization is important, and exact progress of mobilization activities is not available. The aim of this study was to select and evaluate accelerometers for objective qualification of in-hospital mobilization after cardiac surgery. Six static and dynamic patient activities were defined to measure patient mobilization during the postoperative hospital stay. Device requirements were formulated, and the available devices reviewed. A triaxial accelerometer (AX3, Axivity) was selected for a clinical pilot in a heart surgery ward and placed on both the upper arm and upper leg. An artificial neural network algorithm was applied to classify lying in bed, sitting in a chair, standing, walking, cycling on an exercise bike, and walking the stairs. The primary endpoint was the daily amount of each activity performed between 7 a.m. and 11 p.m. The secondary endpoints were length of intensive care unit stay and surgical ward stay. A subgroup analysis for male and female patients was planned. In total, 29 patients were classified after cardiac surgery with an intensive care unit stay of 1 (1 to 2) night and surgical ward stay of 5 (3 to 6) nights. Patients spent 41 (20 to 62) min less time in bed for each consecutive hospital day, as determined by a mixed-model analysis (p < 0.001). Standing, walking, and walking the stairs increased during the hospital stay. No differences between men (n = 22) and women (n = 7) were observed for all endpoints in this study. The approach presented in this study is applicable for measuring all six activities and for monitoring postoperative recovery of cardiac surgery patients. A next step is to provide feedback to patients and healthcare professionals, to speed up recovery. [ABSTRACT FROM AUTHOR]

Study Objectives: While various wearable EEG devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy subjects. A major barrier for applying automated wearable EEG sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as strategy to overcome limited data availability and optimize automated single-channel EEG sleep staging in people with sleep disorders., Methods: We acquired 52 single-channel frontopolar headband EEG recordings from a heterogeneous sleep-disordered population with concurrent PSG. We compared three model training strategies: 'pre-training' (i.e., training on a larger dataset of 901 conventional PSGs), 'training-from-scratch' (i.e., training on wearable headband recordings), and 'fine-tuning' (i.e., training on conventional PSGs, followed by training on headband recordings). Performance was evaluated on all headband recordings using 10-fold cross-validation., Results: Highest performance for 5-stage classification was achieved with fine-tuning (κ = .778), significantly higher than with pre-training (κ = .769) and with training-from-scratch (κ = .733). No significant differences or systematic biases were observed with clinically relevant sleep parameters derived from PSG. All sleep disorder categories showed comparable performance., Conclusions: This study emphasizes the importance of leveraging larger available datasets through deep transfer learning to optimize performance with limited data availability. Findings indicate strong similarity in data characteristics between conventional PSG and headband recordings. Altogether, results suggest the combination of the headband, classification model, and training methodology can be viable for sleep monitoring in the heterogeneous clinical population., (© 2024 American Academy of Sleep Medicine.)

Objective. Wearable devices that measure vital signals using photoplethysmography are becoming more commonplace. To reduce battery consumption, computational complexity, memory footprint or transmission bandwidth, companies of commercial wearable technologies are often looking to minimize the sampling frequency of the measured vital signals. One such vital signal of interest is the pulse arrival time (PAT), which is an indicator of blood pressure. To leverage this non-invasive and non-intrusive measurement data for use in clinical decision making, the accuracy of obtained PAT-parameters needs to increase in lower sampling frequency recordings. The aim of this paper is to develop a new strategy to estimate PAT at sampling frequencies up to 25 Hertz. Approach. The method applies template matching to leverage the random nature of sampling time and expected change in the PAT. Main results. The algorithm was tested on a publicly available dataset from 22 healthy volunteers, under sitting, walking and running conditions. The method significantly reduces both the mean and the standard deviation of the error when going to lower sampling frequencies by an average of 16.6% and 20.2%, respectively. Looking only at the sitting position, this reduction is even larger, increasing to an average of 22.2% and 48.8%, respectively. Significance. This new method shows promise in allowing more accurate estimation of PAT even in lower frequency recordings., (Creative Commons Attribution license.)

Background: Malnutrition, a very common condition in the elderly, is known to increase their vulnerability to adverse health events. This study aimed to estimate the prevalence of malnutrition in the over 75-yr-old community-dwelling population in the "Chiavarese" Local Health Unit district (North West Italy)., Methods: The short version of the Mini Nutritional Assessment (MNA-SF) was used by General Practitioners (GP) as a screening tool to investigate the nutritional status of elderly people (1039 subjects). The study was conducted in 2012-2013 in the Local Health Unit of Chiavari (Liguria Region), Italy. The malnutrition was subsequently confirmed by means of biochemical parameters. Subjects at risk of malnutrition or malnourished (n=22) received personalized dietary counseling by the GP., Results: The MNA-SF recognized 21% of the elderly people being at risk of malnutrition and biochemical tests confirmed a malnutrition prevalence of 3.5%. The dietary counseling improved the MNA-SF score and biochemical parameters, but the difference was statistically significant only for the MNA-SF score ( P =0.00613)., Conclusion: Malnutrition can be evaluated with a simple tool such as the MNA-SF, recognized at the earliest stage and successfully treated by the GP., Competing Interests: Conflict of interest The authors declare that there is no conflict of interest.

Background: Few systematic studies describe rehabilitation trainings for upper limb in diplegic children with cerebral palsy (CP), who - especially once grown up - are often not considered as a target for rehabilitation interventions., Aim: In this pilot study, we describe the details and the effectiveness of an intensive, technology assisted intervention for upper limb., Setting: The treatment combines the utilization of Armeo® Spring with a training focused on hand/finger fluency and dexterity in a pre-post treatment experimental design., Population: Participants were ten school-aged children (mean age 11.2) with bilateral CP and diplegia, attending mainstream schools., Methods: Participants underwent 40 therapy sessions in four weeks. Armeo® Spring measures, standardized motor and perceptual outcome indexes, as well as everyday life indicators were utilized to assess the effect of the intervention., Results: Upper limb coordination, fluency and quality of movements mainly of hands and fingers significantly improved, with a good transferability to everyday life also in areas not specifically trained, such as self-care abilities and mobility. Probably due to the visual feedback provided by the virtual reality setting (which was all in one the context, the incentive and the product of activities), perceptual abilities significantly improved, too., Conclusions: Our study suggests the importance of intervention on upper limb even in milder CP diplegic forms and in relatively grown-up children. The possibility of modification at least partially relies on learning processes that are active all along development and benefit from stimulation., Clinical Rehabilitation Impact: Though further studies with control groups and follow-up perspective are needed to confirm, new technologies offer interesting possibilities to be integrated into new evidence-based rehabilitation models.