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首页> 外文期刊>Medical hypotheses >Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias
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Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias

机译：大数据与健康研究准确的数据：大规模的身体活动监控，智能手机，可穿戴设备以及无意识偏见的风险

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摘要

Fundamental to the advancement of scientific knowledge is unbiased, accurate and validated measurement techniques. Recent United Nations and landmark Nature publications highlight the global uptake of mobile technology and the staggering potential for big data to encourage people to be physically active and to influence health policy.However, concerns exist about inconsistencies in smartphone health apps. Big data has many benefits, but noisy data may lead to wrong conclusions. In reaction to the increasing availability of low quality data; we call for a rigorous debate into the validity of substituting big data for accurate data in health research.We evaluated the step counting accuracy of a smartphone app previously used by 717,527 people from 111 countries. Our new data (from 48 participants; aged 21–59?years; body mass index 17.7–33.5?kg/m2) revealed significant (15–66%) undercounting by Apple phones. In contrast to the generally positive performances of wearable devices for stereotypical treadmill like walking, we observed extraordinarily large (0–200% of steps taken) error ranges for both Android and Apple phones.Unconscious bias (developers’ perceptions of usual behaviour) may be embedded into many unvalidated smartphone apps. Consumer-grade wearable devices appear unsuitable to detect steps in people with slow, short or non-stereotypical gait patterns. Specifically, there is a risk of systematically undercounting the steps by obese people, females or people from different ethnic groups resulting in biases when reporting associations between physical inactivity and obesity. More research is required to develop smartphone apps suitable for all people of the heterogeneous global population.
机译：科学知识进步的基础是无偏见，准确和验证的测量技术。最近的联合国和地标自然出版物强调了移动技术的全球吸收以及大数据的惊人潜力，鼓励人们在物理上活跃并影响健康政策。然而，智能手机健康应用中的不一致存在担忧。大数据有很多好处，但嘈杂的数据可能会导致错误的结论。在反应不断增加的低质量数据的情况下;我们要求严格辩论争论替代大数据的有效性，以获得健康研究中的准确数据。我们评估了从111个国家的717,527人使用的智能手机应用程序的步骤计算精度。我们的新数据（来自48名参与者;年龄21-59岁？年龄;体重指数17.7-33.5？kg / m2）显示出由苹果手机润滑的重要（15-66％）。与陈规定型跑步机的可穿戴设备的一般积极性能相比，我们观察到非常大的（0-200％的步骤）错误的Android和苹果手机的错误范围（开发人员对通常行为的看法）可能是嵌入到许多未经验证的智能手机应用中。消费者级可穿戴设备似乎不适合检测具有缓慢，短期或非陈规定型步态图案的人们的步骤。具体而言，在报告物理不活动与肥胖之间的关联时，肥胖人群，女性或来自不同族裔的人，妇女或来自不同族裔的人们的步骤的风险有风险。需要更多的研究来开发适合所有人异质全球人口的智能手机应用程序。

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  • 来源
    《Medical hypotheses》 |2018年第2018期|共5页
  • 作者

    M.A. Brodie; E.M. Pliner; A. Ho; Kalina Li; Z. Chen; S.C. Gandevia; S.R. Lord;

  • 作者单位

    Falls Balance &

    Injury Research Centre Neuroscience Research Australia;

    Falls Balance &

    Injury Research Centre Neuroscience Research Australia;

    Graduate School of Biomedical Engineering University of New South Wales;

    Graduate School of Biomedical Engineering University of New South Wales;

    Graduate School of Biomedical Engineering University of New South Wales;

    Falls Balance &

    Injury Research Centre Neuroscience Research Australia;

    Falls Balance &

    Injury Research Centre Neuroscience Research Australia;

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  • 正文语种 eng
  • 中图分类 医药、卫生;
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