Usability

The study's objective is to conduct a Phase II randomized controlled trial examining the preliminary efficacy of the VoiceLove app compared to usual care on maternal postpartum depression in mothers with infants admitted to the Neonatal Intensive Care Unit (NICU). Primary aim: Assess the effects of VoiceLove on maternal postpartum depression, measured by the Edinburgh Postnatal Depression Scale (EPDS). The estimates from this study will be used for a future definitive Phase III trial. Secondary aim: Assess feasibility, acceptability, and patterns of communication and engagement among mothers, partners, and NICU clinicians during the NICU hospitalization, measured through app usage metrics, satisfaction surveys, and qualitative interviews. Additionally, we will evaluate effects of infant length of stay.

The aim of this study is to improve the instructions for use and to evaluate the usability of a novel first-void urine collection device.

Frailty is not related exclusively to age, but represents a state of increased biological and clinical vulnerability characterized by reduced functional reserve and greater susceptibility to adverse events, even in response to minor stress factors. In patients with complex conditions, such as severe acquired brain injuries, early detection and prompt treatment of clinical deterioration are essential to reduce the risk of acute readmission, complications, and in-hospital mortality. Many of these events are potentially predictable and preventable, given the presence of appropriate monitoring systems. Advanced care models, including early warning systems, have been developed to support healthcare professionals in identifying patients at risk by detecting significant changes in physiological parameters. In recent years, technological innovations-particularly in the fields of robotics and telemedicine-have created new opportunities to enhance clinical monitoring, enabling continuous data collection and more timely, proactive interventions. Social assistive robots are emerging as promising tools in healthcare settings. These systems can support clinicians and nurses in routine monitoring activities, help reduce workload, and facilitate more efficient and personalized care. By integrating robotic assistance with digital health platforms, it is possible to improve the quality and responsiveness of patient management, especially in high-complexity environments such as neurorehabilitation units. This study focuses on the use of an innovative robotic telemedicine system based on the integration of the MOVER-L prototype with the ROB.IN.CARE platform, promoted by Item Oxygen (Bari, Italy). The system is designed to continuously monitor patients' clinical conditions, collecting vital parameters and identifying early signs of deterioration. It supports healthcare professionals by providing real-time data and alerts, enabling earlier and more targeted interventions. The study is a low-intensity interventional clinical trial conducted in a Neurorehabilitation Unit. Patients with varying levels of clinical severity, classified according to the Glasgow Coma Scale, are enrolled and divided into two groups: a control group receiving standard care and an experimental group monitored using the robotic system in addition to usual care. The primary objective of the study is to evaluate the feasibility, usability, safety, and reliability of this robotic telemedicine solution in a real clinical setting. In addition, the study assesses its acceptability among both patients and healthcare professionals. These aspects are measured using validated tools, including the System Usability Scale (SUS) and the Health and Safety Executive Stress Scale (HSE), to understand the impact of the technology on user experience and workload. The integrated MOVER-L/ROB.IN.CARE system combines robotic assistance with continuous monitoring of vital signs and advanced software architecture. The platform incorporates predictive algorithms designed to stratify clinical risk and assess patient severity. It utilizes established clinical scoring systems, such as the Modified Early Warning Score (MEWS) for early detection of clinical deterioration, the quick Sequential Organ Failure Assessment (qSOFA) for sepsis risk evaluation, and the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for the assessment of acute kidney injury. By enabling early identification of clinical worsening, the system aims to improve the timeliness and effectiveness of healthcare interventions, optimize clinical workflow, and reduce the risk of adverse events and complications. Ultimately, this study explores whether robotic telemedicine can represent a safe, feasible, and valuable tool to enhance the management of fragile patients in neurorehabilitation settings.

Clinical rounds in the intensive care unit (ICU) involve substantial manual documentation. Retrieving the correct protocol text and structuring notes at the bedside is time-consuming and may contribute to variation in documentation quality. Modern artificial intelligence (AI) can help structure existing information and automate protocol look-ups within a restricted, manually selected document set. The tool evaluated in this study acts as an AI-based informational assistant for clinicians. It (1) pre-populates a standardized physical-exam and daily-rounds format, (2) prepares a concise ICU course/overview using predefined formatting, and (3) retrieves relevant passages from protocols to enable rapid consistency checks by the clinician. The AI-based informational assistant does not provide treatment recommendations or patient-specific advice; all outputs require clinician verification and clinical responsibility remains with the physician.

The purpose of this 32-week study is to use an innovative experimental design known as SMART (Sequential Multiple Assignment Randomized Trial), which will allow us to determine the best way to sequence the delivery of teleexercise (referred to as an adaptive intervention), combined with predictive analytics on participant adherence in a stepped program of physical activity interventions. All 257 participants will have access to a library of recorded video exercise content, and a weekly wellness article. Some participants will receive health coaching calls (1st randomization). Analytic data will be used to determine which participants are responding or not responding to the intervention. Participants not responding after 4 weeks will receive either live one-on-one or group exercise training (2nd randomization). After 8 weeks, the participant will receive only pre-recorded exercise content and articles for a 24-week maintenance phase (weeks 9-32). The study outcomes are: The effectiveness of the adaptive interventions; Exploring mediating and moderating variables; Sensitivity analysis of the predictive analytics.

The principal aim of this study is to evaluate the usability of the Hope-D gestational and 6 months postpartum weight management program. We will evaluate the usability, functionality, and acceptability of the developed Hope-D weight management program that includes digital pathway for weight management, general or individual weight management plan and individual or group coaching for weight management from pregnancy to 6 months postpartum.

This is a small pilot feasibility and acceptability study of implementing a Technology-Enhanced Gender Support (TEGS) platform with parents and caregivers. Parents and caregivers who consent to participate will complete an electronic survey at baseline. They will then receive access to the TEGS platform and complete follow up surveys 1 mo and 3 mo after. Each participant will also be given the option to participate in an exit interview.