System for Tracking and Evaluating Performance (Step-App®): validation and clinical application of a mobile telemonitoring system in patients with knee and hip total arthroplasty. A prospective cohort study.

BACKGROUND: Technological advances and digital solutions have been proposed to overcome barriers to sustainable rehabilitation programs in patients with musculoskeletal disorders. However, to date, standardized telemonitoring systems able to precisely assess physical performance and functioning are still lacking. AIM: To validate a new mobile telemonitoring system, named System for Tracking and Evaluating Performance (Step-App®), to evaluate physical performance in patients undergone knee and hip total arthroplasty. DESIGN: Prospective cohort study. METHODS: A consecutive series of older adults with knee and hip total arthroplasty participated in a comprehensive rehabilitation program. The Step-App®, a mobile telemonitoring system, was used to remotely monitor the effects of rehabilitation, and the outcomes were assessed before (T0) and after the rehabilitation treatment (T1). The primary outcomes were the 6-Minute Walk Test (6MWT), the 10-Meter Walk Test (10MWT), and the 30-Second Sit-To-Stand Test (30SST). RESULTS: Out of 42 patients assessed, 25 older patients were included in the present study. The correlation analysis between the Step-App® measurements and the traditional in-person assessments demonstrated a strong positive correlation for the 6MWT (T0: r2=0.9981, P<0.0001; T1: r2=0.9981, P<0.0001), 10MWT (T0: r2=0.9423, P<0.0001; T1: r2=0.8634, P<0.0001), and 30SST (T0: r2=1, P<0.0001; T1: r2=1, P<0.0001). The agreement analysis, using Bland-Altman plots, showed a good agreement between the Step-App® measurements and the in-person assessments. CONCLUSIONS: Therefore, we might conclude that Step-App® could be considered as a validated mobile telemonitoring system for remote assessment that might have a role in telemonitoring personalized rehabilitation programs for knee and hip replacement patients. CLINICAL REHABILITATION IMPACT: Our findings might guide clinicians in remote monitoring of physical performance in patients with musculoskeletal conditions, providing new insight into tailored telerehabilitation programs.

NRTS: A Client-Server Architecture for Supporting Education in a Neonatal Resuscitation Simulation Scenario.

In this paper, we describe Neonatal Resuscitation Training Simulator (NRTS), an Android mobile app designed to support medical experts to input, transmit and record data during a High-Fidelity Simulation course for neonatal resuscitation. This mobile app allows one to automatically send all the recorded data from the Neonatal Intensive Care Unit (NICU) of Casale Monferrato Children's Hospital, (Italy) to a server in the cloud managed by the University of Piemonte Orientale (Italy). The medical instructor can then view statistics on simulation exercises, that may be used during the debriefing phase for the evaluation of multidisciplinary teams involved in the simulation scenarios.

Gait Monitoring and Analysis: A Mathematical Approach.

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson's, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson's disease by monitoring their gait due to wearable devices that can accurately detect a person's movements. In our study, about 50 people were involved in the trial (20 with Parkinson's disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the "gait quality" based on the measure of entropy obtained by applying the Fourier transform.

The prevention of falls in patients with Parkinson's disease with in-home monitoring using a wearable system: a pilot study protocol.

BACKGROUND: Parkinson's disease (PD) is a chronic, progressive neurodegenerative condition that gradually worsens motor function and leads to postural instability and, eventually, falls. Several factors may influence the frequency of future falls, such as slowness, freezing of gait, loss of balance, and mobility problems, cognitive impairments, and the number of previous falls. The TED bracelet is an advanced technological wearable device able to predict falls. AIMS: This principal aim is to investigate the feasibility of a full-scale research project that uses the TED bracelet to identify whether individuals with PD are at risk of falling. METHODS: This study will involve a pilot prospective observational study design; the subjects will include 26 patients suffering from mild PD and 26 others with no PD and no gait problems. Data will be collected from the TED bracelet and then compared to a paper-based fall diary. The enrolled participants will have a scheduled outpatient evaluation to collect both clinical and instrumental data as well as biological samples. DISCUSSION: This pilot study could then be implemented in a larger form to further evaluate the effectiveness of the TED device. Finally, it will help further develop gait monitoring systems for people with Parkinson's disease and other neurodegenerative diseases that can affect physical function and mobility, such as dementia and Alzheimer's. CONCLUSIONS: Preventing falls and their complications could lead to major advancements in the quality of home care for patients with PD, which would significantly impact the quality of life of both these patients and their caregivers.

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review.

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.