早期发现和治疗认知衰退的新机遇:坚持治疗的挑战和以人为本的智能技术的前景

BMC digital health Pub Date : 2023-01-01 Epub Date: 2023-02-14 DOI:10.1186/s44247-023-00008-1
Zhe He, Michael Dieciuc, Dawn Carr, Shayok Chakraborty, Ankita Singh, Ibukun E Fowe, Shenghao Zhang, Mia Liza A Lustria, Antonio Terracciano, Neil Charness, Walter R Boot
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引用次数: 0

摘要

早期发现与年龄相关的认知衰退具有变革性的潜力,可以通过确定临床试验的相关参与者来推进对认知障碍的科学理解和可能的治疗。此外,一旦开发出有效的治疗方法,早期发现也是早期干预的关键。早期发现认知能力下降的新方法,例如通过移动应用程序进行评估,可能需要频繁的家庭测试,这可能会给依从性带来挑战。而且,一旦检测到下降,治疗可能需要经常进行行为和/或生活方式干预(例如,认知训练),这在坚持方面提出了自己的挑战。我们讨论了早期检测和治疗认知衰退的最新方法,与这些方法相关的依从性挑战,以及智能和以人为本的技术解决依从性挑战的承诺。具体来说,我们重点介绍了作为以人为本的技术(APPT)坚持促进项目的一部分进行的先前和正在进行的工作,以及完成的工作将如何有助于设计和开发及时,量身定制的智能提醒系统,该系统可以推断参与者的背景和动机。以及正在进行的工作如何构建一个包含动态机器学习算法的提醒系统,该系统能够预测和预防依从性失效。APPT活动和研究结果不仅将对认知评估和培训产生影响,还将对技术介导的依从性支持系统产生影响,以促进体育锻炼、营养、药物管理、远程医疗和社会联系,具有广泛改善老年人参与、健康和福祉的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies.

Early detection of age-related cognitive decline has transformative potential to advance the scientific understanding of cognitive impairments and possible treatments by identifying relevant participants for clinical trials. Furthermore, early detection is also key to early intervention once effective treatments have been developed. Novel approaches to the early detection of cognitive decline, for example through assessments administered via mobile apps, may require frequent home testing which can present adherence challenges. And, once decline has been detected, treatment might require frequent engagement with behavioral and/or lifestyle interventions (e.g., cognitive training), which present their own challenges with respect to adherence. We discuss state-of-the-art approaches to the early detection and treatment of cognitive decline, adherence challenges associated with these approaches, and the promise of smart and person-centered technologies to tackle adherence challenges. Specifically, we highlight prior and ongoing work conducted as part of the Adherence Promotion with Person-centered Technology (APPT) project, and how completed work will contribute to the design and development of a just-in-time, tailored, smart reminder system that infers participants' contexts and motivations, and how ongoing work might build toward a reminder system that incorporates dynamic machine learning algorithms capable of predicting and preventing adherence lapses before they happen. APPT activities and findings will have implications not just for cognitive assessment and training, but for technology-mediated adherence-support systems to facilitate physical exercise, nutrition, medication management, telehealth, and social connectivity, with the potential to broadly improve the engagement, health, and well-being of older adults.

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