IRCCS INRCA, Ancona, Italy.
DigiRehab A/S, Viborg, Denmark.
Front Public Health. 2024 Mar 22;12:1293621. doi: 10.3389/fpubh.2024.1293621. eCollection 2024.
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.
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.
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.
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.: [https://clinicaltrials.gov/], identifier [NCT05846776].
跌倒在老年人中是一个全球性的重大健康问题。已经成功开展了多项基于家庭技术的物理康复计划,如在线 DigiRehab 平台。PRECISE 项目将个性化训练与基于人工智能的预测模型(AI-DSS 平台)相结合,用于评估跌倒风险。这个新系统称为 DigiRehab,将能够早期识别跌倒的重要危险因素,并提出个性化的身体训练计划来解决这些关键领域。
该研究将测试 DigiRehab 平台在家中生成个性化物理康复计划的可用性。将有 50 名老年人参与,其中 20 名测试测试版原型,30 名参与者测试更新版本。纳入标准为年龄≥65 岁,独立步行,跌倒风险(Tinetti 测试),Mini 精神状态检查≥24,居住在家,熟悉网络应用程序,有能力和意愿签署知情同意书。排除标准为临床状况不稳定,严重视力和/或听力障碍,日常生活活动严重受损和无主要照顾者。
筛选的第一部分包括一个包含 10 个问题的结构化问卷,涉及用户的限制,包括跌倒风险,第二部分包括 10 个身体测试,以评估功能状态。根据结果,该程序将帮助定义用户的个人资料,DSS 平台将根据该资料评估跌倒风险并设计在家中进行的个性化锻炼计划。初始筛选的所有措施都将重复,结果将用于优化预测算法,以便为最终版本准备工具。为了进行可用性评估,将使用系统可用性量表进行评估。干预结束后 12 周将在家中进行随访。还将发放一份半结构化满意度问卷,以验证该项目是否符合老年人及其家庭照顾者的需求。
我们期望 DigiRehab 平台规定的个性化训练可以帮助减少老年人的护理需求和护理负担。[https://clinicaltrials.gov/],标识符 [NCT05846776]。