Valero-Ramon Zoe, Ibanez-Sanchez Gema, Martinez-Millana Antonio, Fernandez-Llatas Carlos
ITACA-SABIEN, Universitat Politècnica de València, 46022 Valencia, Spain.
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden.
Sensors (Basel). 2025 Mar 27;25(7):2097. doi: 10.3390/s25072097.
Recent advancements in wearable devices have significantly enhanced remote patient monitoring, enabling healthcare professionals to evaluate conditions within home settings. While electronic health records (EHRs) offer extensive clinical data, they often lack crucial contextual information about patients' daily lives and symptoms. By integrating continuous self-reported outcomes related to vulnerability, anxiety, and depression from older adult cancer survivors with objective data from wearables, we can develop personalised risk models that address time-varying risk factors in cancer care. Our study combines real-world data from wearable devices with self-reported information, employing process mining techniques to analyse dynamic risk models for vulnerability and anxiety. Unlike traditional static assessments, this approach recognises that risk factors evolve. Collaborating with healthcare professionals, we analysed data from the LifeChamps study to create two dynamic risk models. This collaborative effort revealed how activity and sleep patterns influence self-reported vulnerability and anxiety among participants. It underscored the potential of wearable sensors and artificial intelligence techniques for deeper analysis and understanding, making us all part of a larger effort in cancer care. Overall, patients with prolonged sedentary activity had a higher risk of vulnerability, while those with highly dynamic sleep patterns were more likely to report anxiety and depression. Prostate-metastatic patients showed an increased risk of vulnerability compared to other cancer types.
可穿戴设备的最新进展显著增强了远程患者监测能力，使医疗保健专业人员能够在家庭环境中评估病情。虽然电子健康记录（EHRs）提供了广泛的临床数据，但它们往往缺乏有关患者日常生活和症状的关键背景信息。通过将老年癌症幸存者与脆弱性、焦虑和抑郁相关的持续自我报告结果与可穿戴设备的客观数据相结合，我们可以开发个性化风险模型，以应对癌症护理中随时间变化的风险因素。我们的研究将可穿戴设备的实际数据与自我报告信息相结合，采用过程挖掘技术来分析脆弱性和焦虑的动态风险模型。与传统的静态评估不同，这种方法认识到风险因素是不断变化的。我们与医疗保健专业人员合作，分析了来自LifeChamps研究的数据，以创建两个动态风险模型。这项合作努力揭示了活动和睡眠模式如何影响参与者自我报告的脆弱性和焦虑。它强调了可穿戴传感器和人工智能技术进行更深入分析和理解的潜力，使我们所有人都参与到癌症护理的更大努力中。总体而言，久坐不动时间较长的患者脆弱性风险较高，而睡眠模式高度动态的患者更有可能报告焦虑和抑郁。与其他癌症类型相比，前列腺转移性患者的脆弱性风险增加。
