Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias

Brodie, Matthew A.; Pliner, Erika M.; A, Ho; Li, Kalina; Chen, Zhixi; Gandevia, Simon C.; Lord, Stephen R.

doi:10.1016/j.mehy.2018.07.015

Cited by 51 publications

(39 citation statements)

References 36 publications

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“…Errors in detection of data are common owing to unconscious bias when developers apply a standard behavior to a heterogeneous population. As such, researchers have the responsibility to detect inconsistencies in health apps and wearables [24].…”

Section: Wearablesmentioning

confidence: 99%

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Alvarez-Perea¹,

Sánchez-García²,

R³

et al. 2019

J Investig Allergol Clin Immunol

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The emergence of new technology enables allergists and patients to compile data and receive feedback regarding key symptoms, risk behavior, and/or management. The term "eHealth" refers to a diverse group of tools that use computerized technologies to improve both the efficacy and the efficiency of the health care industry. eHealth comprises a variety of technologies, as follows: mobile devices (mHealth) in medical care, including electronic diaries, wearable sensors, and adherence monitoring; health informatics (eg, electronic health records, computerized physician order entry, clinical decision support); telemedicine, which is the use of information and communication technologies for the management of diseases and medical education; social media platforms, and the analysis of information acquired through these platforms using "big data" technologies. In this review, we summarize the latest findings on the use of eHealth technology and the relevance of eHealth to allergic conditions. ResumenLa aparición de nuevas tecnologías conlleva para los alergólogos y los pacientes la posibilidad de recopilar datos y recibir información directa sobre los síntomas clave de las enfermedades, los comportamientos de riesgo y/o su manejo. El término "eHealth", o salud electrónica, se refiere a un grupo diverso de herramientas que utilizan tecnologías informáticas para mejorar la eficacia y la eficiencia de la industria de la salud. La "eHealth" comprende varias tecnologías, como el uso de dispositivos móviles aplicados a la salud ("mHealth"), incluyendo diarios electrónicos, sensores ponibles o monitorización de la adherencia terapéutica; la informática biomédica (por ejemplo, la historia clínica electrónica, la prescripción electrónica o los sistemas de ayuda a las decisiones clínicas); la telemedicina, que es el uso de las tecnologías de la información y la comunicación para el manejo de enfermedades y de educación sanitaria; las plataformas de redes sociales, y el análisis de la información adquirida a través de estas plataformas, usando técnicas de "big data" o inteligencia de datos. En esta revisión, resumimos la evidencia que rodea al uso de tecnologías "eHealth" y su relevancia para las enfermedades alérgicas.

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Section: Wearablesmentioning

confidence: 99%

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Alvarez-Perea¹,

Sánchez-García²,

R³

et al. 2019

J Investig Allergol Clin Immunol

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“…Unconscious biases reflect carelessness more so than malfeasance [9]. Unconscious bias in research [10] is present in all forms of data capture in a litany of different patient populations.…”

Section: Unconscious Biasesmentioning

confidence: 99%

Post-randomization bias

Cook

Garcia

2020

Journal of Manual & Manipulative Therapy

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“…If the same features reveal all of the information, how will the personal traits not impact the extracted activities? For example, in [134], the authors show how noisy data in big data smartphone health applications may lead to misleading conclusion. The authors studied the accuracy of a step count app in Apple and Android smartphones.…”

Section: Accuracy Of the Extracted Informationmentioning

confidence: 99%

“…In [138], the authors conducted an experiment with the data harvested from more than 700k people from 111 countries to study countries' obesity situations. Other studies utilized 10-50 participants only [134,135].…”

Section: Accuracy Of the Extracted Informationmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

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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

Cited by 51 publications

References 36 publications

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Impact Of “eHealth” in Allergic Diseases and Allergic Patients

Post-randomization bias

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

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