Current clinical practice suggests that recovering the hand functionality lost or reduced by injuries, interventions and chronic diseases requires, beyond pharmacological treatments, a kinesiotherapic intervention. This form of rehabilitation consists of physical exercises adapted to the specific pathology. Its effectiveness is strongly dependent on the patient's adhesion to such a program. In this paper we present a novel device with remote monitoring capabilities expressly conceived for the needs of rheumatic patients. It comprises several sensorized tools and can be used either in an outpatient clinic for hand functional evaluation, connected to a PC, or afforded to the patient for home kinesiotherapic sessions. In the latter case, the device guides the patient in the rehabilitation session, transmitting the relevant statistics about his performance to a TCP/IP server exploiting a GSM/GPRS connection for deferred analysis. An approved clinical trial has been set up in Italy, involving 10 patients with Rheumatoid Arthritis and 10 with Systemic Sclerosis, enrolled for 12 weeks in a home rehabilitation program with the proposed device. Their evaluation has been performed with traditional methods but also with the proposed device. Subjective (hand algofunctional Dreiser's index) and objective (ROM, strength, dexterity) parameters showed a sustained improvement throughout the follow-up. The obtained results proved that the device is an effective and safe tool for assessing hand disability and monitoring kinesiotherapy exercise, portending the potential exploitability of such a methodology in clinical practice.
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http://dx.doi.org/10.1109/JTEHM.2014.2299274 | DOI Listing |
Curr Opin Oncol
January 2025
DIOPP, Gustave Roussy Cancer Campus, Villejuif, France.
Purpose Of Review: Monitoring the side effects of treatments in cancer patients is a key challenge in clinical practice, especially with the development of oral therapies.The impact on patients is multifaceted: morbidity or even life-threatening risks in the case of severe side effects; deterioration in quality of life and functional abilities; lower adherence to treatments; reduced dose intensity, which can affect the efficacy of therapies.
Recent Findings: The availability of digital tools for remote patient monitoring is transforming our ability to track these patients effectively.
Int J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Digit Health
January 2025
Ohad Cohen Endocrinology, Tel Hashomer, Israel.
Objective: The objective of this pilot study is to evaluate the feasibility of using an automatic weight management system to follow patients' response to weight reduction medications and to identify early deviations from weight trajectories.
Methods: The pilot study involved 11 participants using Semaglutide for weight management, monitored over a 12-month period. A cloud-based, Wi-Fi-enabled remote weight management system collected and analyzed daily weight data from smart scales.
Clin Kidney J
January 2025
Alio, Inc., Broomfield, CO, USA.
Anaemia is a prevalent complication in patients with end-stage kidney disease (ESKD) undergoing haemodialysis. This study evaluates the accuracy of the Alio SmartPatch™, a non-invasive remote monitoring device, in measuring haemoglobin (Hb) and haematocrit (Hct) levels in haemodialysis patients by comparing its results with standard blood-based laboratory methods. The results from 116 patients across multiple sites in the USA and the Kingdom of Jordan show that SmartPatch measurements align closely with standard blood-based laboratory methods, meeting clinically acceptable limits of agreement.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
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