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.

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.