Effects of the Inerventions method on gross motor function in children with spastic cerebral palsy

Aim of the study To investigate the effect of the Inerventions method on gross motor function in children with spastic cerebral palsy (CP). Clinical rationale for the study The Inerventions method is the type of transcutaneous electrical nerve stimulation (TENS) delivered through a full-body garment (Mollii suit) that aims to prompt reciprocal inhibition via the antagonist to reduce spasticity in selected muscle groups. Although Mollii is approved by the European Union as a medical device, independent clinical tests have not yet been performed. Materials and methods 16 children with spastic CP, aged 4.7 ± 1.3 were recruited and then willingly assigned to the Inerventions method (n = 8) and control groups (n = 8). In the Inerventions method group, TENS was applied 1 h per session, 3 days weekly for 3 weeks. Children of the control group received functional exercises program for the same duration, frequency and length. Outcome measures included the Gross Motor Function Measure, passive range of motion (PROM), the Modified Tardieu Scale, and the Timed Up and Go test. Results While both groups experienced improvements in gross motor function and mobility, the difference in improvement between children treated with the TENS and physiotherapy did not reach statistical significance. No change occurred in PROM and spasticity in either group following the interventions. Conclusions There is no superior efficacy of the Inerventions method compared to conventional physiotherapy

Photoplethysmography-Based Continuous Systolic Blood Pressure Estimation Method for Low Processing Power Wearable Devices

Regardless of age, it is always important to detect deviations in long-term blood pressure from normal levels. Continuous monitoring of blood pressure throughout the day is even more important for elderly people with cardiovascular diseases or a high risk of stroke. The traditional cuff-based method for blood pressure measurements is not suitable for continuous real-time applications and is very uncomfortable. To address this problem, continuous blood pressure measurement methods based on photoplethysmogram (PPG) have been developed. However, these methods use specialized high-performance hardware and sensors, which are not available for common users. This paper proposes the continuous systolic blood pressure (SBP) estimation method based on PPG pulse wave steepness for low processing power wearable devices and evaluates its suitability using the commercially available CMS50FW Pulse Oximeter. The SBP estimation is done based on the PPG pulse wave steepness (rising edge angle) because it is highly correlated with systolic blood pressure. The SBP estimation based on this single feature allows us to significantly reduce the amount of data processed and avoid errors, due to PPG pulse wave amplitude changes resulting from physiological or external factors. The experimental evaluation shows that the proposed SBP estimation method allows the use of off-the-shelf wearable PPG measurement devices with a low sampling rate (up to 60 Hz) and low resolution (up to 8-bit) for precise SBP measurements (mean difference MD = −0.043 and standard deviation SD = 6.79). In contrast, the known methods for continuous SBP estimation are based on equipment with a much higher sampling rate and better resolution characteristics

Explainable Artificial Intelligence-Based Decision Support System for Assessing the Nutrition-Related Geriatric Syndromes

The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system’s prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician