Preact to lower the risk of falling by customized rehabilitation across Europe: the feasibility study protocol of the PRECISE project in Italy.

Introduction: Falls are a major worldwide health problem in older people. Several physical rehabilitation programs with home-based technologies, such as the online DigiRehab platform, have been successfully delivered. The PRECISE project combines personalized training delivered through the application with an artificial intelligence-based predictive model (AI-DSS platform) for fall risk assessment. This new system, called DigiRehab, will enable early identification of significant risk factors for falling and propose an individualized physical training plan to attend to these critical areas. Methods: The study will test the usability of the DigiRehab platform in generating personalized physical rehabilitation programs at home. Fifty older adults participants will be involved, 20 of them testing the beta version prototype, and 30 participants testing the updated version afterwards. The inclusion criteria will be age ≥65, independent ambulation, fall risk (Tinetti test), Mini Mental State Examination ≥24, home residents, familiarity with web applications, ability and willingness to sign informed consent. Exclusion criteria will be unstable clinical condition, severe visual and/or hearing impairment, severe impairment in Activities of Daily Living and absence of primary caregiver. Discussion: The first part of the screening consists in a structured questionnaire of 10 questions regarding the user's limitations, including the risk of falling, while the second consists in 10 physical tests to assess the functional status. Based on the results, the program will help define the user's individual profile upon which the DSS platform will rate the risk of falling and design the personalized exercise program to be carried out at home. All measures from the initial screening will be repeated and the results will be used to optimize the predictive algorithms in order to prepare the tool in its final version. For the usability assessment, the System Usability Scale will be administered. The follow-up will take place after the 12-week intervention at home. A semi-structured satisfaction questionnaire will also be administered to verify whether the project will meet the needs of older adults and their family caregiver. Conclusion: We expect that personalized training prescribed by DigiRehab platform could help to reduce the need for care in older adults subjects and the care burden.Clinical trial registration: [https://clinicaltrials.gov/], identifier [NCT05846776].

An Open Platform for Seamless Sensor Support in Healthcare for the Internet of Things.

Population aging and increasing pressure on health systems are two issues that demand solutions. Involving and empowering citizens as active managers of their health represents a desirable shift from the current culture mainly focused on treatment of disease, to one also focused on continuous health management and well-being. Current developments in technological areas such as the Internet of Things (IoT), lead to new technological solutions that can aid this shift in the healthcare sector. This study presents the design, development, implementation and evaluation of a platform called Common Recognition and Identification Platform (CRIP), a part of the CareStore project, which aims at supporting caregivers and citizens to manage health routines in a seamless way. Specifically, the CRIP offers sensor-based support for seamless identification of users and health devices. A set of initial requirements was defined with a focus on usability limitations and current sensor technologies. The CRIP was designed and implemented using several technologies that enable seamless integration and interaction of sensors and people, namely Near Field Communication and fingerprint biometrics for identification and authentication, Bluetooth for communication with health devices and web services for wider integration with other platforms. Two CRIP prototypes were implemented and evaluated in laboratory during a period of eight months. The evaluations consisted of identifying users and devices, as well as seamlessly configure and acquire vital data from the last. Also, the entire Carestore platform was deployed in a nursing home where its usability was evaluated with caregivers. The evaluations helped assess that seamless identification of users and seamless configuration and communication with health devices is feasible and can help enable the IoT on healthcare applications. Therefore, the CRIP and similar platforms could be transformed into a valuable enabling technology for secure and reliable IoT deployments on the healthcare sector.