@文章{信息:doi/10.2196/12785，作者=“Piau, Antoine和Wild, Katherine和Mattek, Nora和Kaye, Jeffrey”，标题=“现实生活中基于家庭的认知功能监测对轻度阿尔茨海默病的认知功能和临床护理的影响:系统评价”，期刊=“J Med Internet Res”，年=“2019”，月=“8”，日=“30”，卷=“21”，数=“8”，页数=“e12785”，关键词=“技术;阿尔茨海默病;认知障碍;老年痴呆症;老年人;数字生物标志物;数字表型出现;背景:在挑战痴呆症治疗进展的领域中，一直是对症状随时间变化的评估。数字生物标记物被定义为通过数字设备(如嵌入式环境传感器或可穿戴设备)收集和测量的客观、可量化、生理和行为数据。数字生物标记物提供了另一种评估方法，因为它们允许客观、生态有效和长期随访，并进行持续评估。 Despite the promise of a multitude of sensors and devices that can be applied, there are no agreed-upon standards for digital biomarkers, nor are there comprehensive evidence-based results for which digital biomarkers may be demonstrated to be most effective. Objective: In this review, we seek to answer the following questions: (1) What is the evidence for real-life, home-based use of technologies for early detection and follow-up of mild cognitive impairment (MCI) or dementia? And (2) What transformation might clinicians expect in their everyday practices? Methods: A systematic search was conducted in PubMed, Cochrane, and Scopus databases for papers published from inception to July 2018. We searched for studies examining the implementation of digital biomarker technologies for mild cognitive impairment or mild Alzheimer disease follow-up and detection in nonclinic, home-based settings. All studies that included the following were examined: community-dwelling older adults (aged 65 years or older); cognitively healthy participants or those presenting with cognitive decline, from subjective cognitive complaints to early Alzheimer disease; a focus on home-based evaluation for noninterventional follow-up; and remote diagnosis of cognitive deterioration. Results: An initial sample of 4811 English-language papers were retrieved. After screening and review, 26 studies were eligible for inclusion in the review. These studies ranged from 12 to 279 participants and lasted between 3 days to 3.6 years. Most common reasons for exclusion were as follows: inappropriate setting (eg, hospital setting), intervention (eg, drugs and rehabilitation), or population (eg, psychiatry and Parkinson disease). We summarized these studies into four groups, accounting for overlap and based on the proposed technological solutions, to extract relevant data: (1) data from dedicated embedded or passive sensors, (2) data from dedicated wearable sensors, (3) data from dedicated or purposive technological solutions (eg, games or surveys), and (4) data derived from use of nondedicated technological solutions (eg, computer mouse movements). Conclusions: Few publications dealt with home-based, real-life evaluations. Most technologies were far removed from everyday life experiences and were not mature enough for use under nonoptimal or uncontrolled conditions. Evidence available from embedded passive sensors represents the most relatively mature research area, suggesting that some of these solutions could be proposed to larger populations in the coming decade. The clinical and research communities would benefit from increasing attention to these technologies going forward. ", issn="1438-8871", doi="10.2196/12785", url="//www.mybigtv.com/2019/8/e12785/", url="https://doi.org/10.2196/12785", url="http://www.ncbi.nlm.nih.gov/pubmed/31471958" }