揭开可穿戴设备的神秘面纱:探索生物识别应用和生命体征及行为影响的全球格局。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-06-11 DOI:10.1186/s13040-024-00368-y
Carolina Del-Valle-Soto, Ramon A Briseño, Leonardo J Valdivia, Juan Arturo Nolazco-Flores
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引用次数: 0

摘要

能够记录大脑和周围神经系统活动的神经科学技术的发展推动了认知科学的研究。最近的技术进步为诱导行为改变提供了新的可能性,特别是通过经济有效的互联网干预。然而,实验室设备数量的限制阻碍了将结果推广到现实生活中。物联网(IoT)设备(如配备传感器和微型芯片的可穿戴设备)的出现开创了行为改变技术的新时代。包括智能手表、电子纹身等在内的可穿戴设备将得到大规模应用,预计未来五年的年增长率将达到 55%。这些设备可提供个性化指导,从而提高生产力和效率,特别是在工业生产领域。此外,医疗保健领域对可穿戴设备的需求也非常大,全球超过 80% 的消费者愿意使用可穿戴设备进行健康监测。本研究探讨了可穿戴设备的主要生物识别应用及其对用户健康的影响,重点是物联网设备促进的行为改变技术的整合。可穿戴设备通过提供实时反馈、个性化干预和游戏化,彻底改变了健康监测。它们通过提供即时反馈、量身定制的建议和游戏化体验来鼓励积极的行为改变,从而持续改善健康状况。此外,可穿戴设备还能与数字平台无缝集成,通过社会支持和连接增强其影响力。然而,为了维护用户的信任,必须解决隐私和数据安全问题。随着技术的不断进步,完善物联网设备的设计和功能对于促进行为改变和改善健康状况至关重要。本研究旨在调查可穿戴设备促进行为改变技术对个人健康结果的影响,以及可穿戴设备在促进更健康生活方式中的作用。
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Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact.

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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