The ethics of sensor technology use in clinical research

Ulrich, Connie M.; Demiris, George; Kennedy, Rosemary; Rothwell, Erin

doi:10.1016/j.outlook.2020.04.011

Cited by 17 publications

(16 citation statements)

References 18 publications

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“…Additional methods such as video and in‐person demonstration may facilitate comprehension during the consent process. [ 64 ] Adaptions of the informed consent may be necessary to account for varying degrees of educational literacy, cognitive ability, and clinical status in potential participants. [ 65 ] In an informed consent document, potential risks and the purpose of the study should be clearly communicated for participants to make informed decision.…”

Section: Informed Consentmentioning

confidence: 99%

Ethical Considerations of Wearable Technologies in Human Research

Gao

2021

Adv Healthcare Materials

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Wearable technologies hold great promise for disease diagnosis and patient care. Despite the flourishing research activities in this field, only a handful of wearable devices are commercialized and cleared for medical usage. The successful translation of current proof‐of‐concept prototypes requires extensive in‐human testing. There is a lag between current standards and operation protocols to guide the responsible and ethical conduct of researchers in such in‐human studies and the rapid development of the field. This essay presents relevant ethical concerns in early‐stage human research from a researcher's perspective.

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Section: Informed Consentmentioning

confidence: 99%

Ethical Considerations of Wearable Technologies in Human Research

Gao

2021

Adv Healthcare Materials

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“…Further, it can be easily forgotten by consumers that continual data collection is occurring and periodic re-consenting may be necessary with the use of sensor technology, as has been suggested for the use of wearable devices for research purposes [3]. In the case of minors, where parents/guardians are required to provide consent, it is even more imperative that both the type of data and the amount of data are explicitly explained as well as issues pertaining to data ownership and third-party usage, as discussed further below.…”

Section: Informed Consentmentioning

confidence: 99%

“…Use of such devices for research purposes and ethical concerns and suggestions have been discussed before [2,3] and are not the focus of this paper, while a very recent publication discusses the ethical challenges in using ambient sensors in health-care settings [Martinez-Martin et al, 2021-4]. We will review and discuss the use of commercially available devices by minors in the community, with a focus on the ethical aspects arising from such everyday use; and more specifically the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used in children without medical advice.…”

Section: Introductionmentioning

confidence: 99%

Ethical Issues Concerning the Use of Data from Commercially Available Wearable Sensors in Children

Panayiotou¹,

Protopapadakis²

2021

Preprint

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Wearable and mobile technology has advanced in leaps and bounds in the last decade with technological advances creating a role from enhancing healthy living to monitoring and treating disease. However, the discussion about the ethical use of such commercial technology in the community, especially in minors, is lacking behind. In this paper, we first summarize the major ethical concerns that arise from the usage of commercially available wearable technology in children, with a focus on smart watches, highlighting issues around the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used without medical advice. Then through a relevant thought experiment we move on to outline some further ethical concerns that are connected to the use of wearables by minors, to wit the issue of informed consent in the case of minors, forcing them to live in the spotlight, and compromising their right to an open future. We conclude with the view that mitigating potential pitfalls and enhancing the benefits of wearable technology especially for minors requires brave and comprehensive moral debates.

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“…This is evident from cases involving access to credit, employment opportunities, and health care services in the United States [ 14 ]. (Even ostensibly inclusive advertisements highlighting employment and housing opportunities on Facebook have been susceptible to this bias [ 15 ] This may be even more pronounced with regard to clinical research and vulnerable populations [ 16 – 18 ].) There are also compounding and marginalizing effects on youth of color [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting age and gender from network telemetry: Implications for privacy and impact on policy

Kuang

Pobbathi²,

Mansury³

et al. 2022

PLoS ONE

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The systematic monitoring of private communications through the use of information technology pervades the digital age. One result of this is the potential availability of vast amount of data tracking the characteristics of mobile network users. Such data is becoming increasingly accessible for commercial use, while the accessibility of such data raises questions about the degree to which personal information can be protected. Existing regulations may require the removal of personally-identifiable information (PII) from datasets before they can be processed, but research now suggests that powerful machine learning classification methods are capable of targeting individuals for personalized marketing purposes, even in the absence of PII. This study aims to demonstrate how machine learning methods can be deployed to extract demographic characteristics. Specifically, we investigate whether key demographics—gender and age—of mobile users can be accurately identified by third parties using deep learning techniques based solely on observations of the user’s interactions within the network. Using an anonymized dataset from a Latin American country, we show the relative ease by which PII in terms of the age and gender demographics can be inferred; specifically, our neural networks model generates an estimate for gender with an accuracy rate of 67%, outperforming decision tree, random forest, and gradient boosting models by a significant margin. Neural networks achieve an even higher accuracy rate of 78% in predicting the subscriber age. These results suggest the need for a more robust regulatory framework governing the collection of personal data to safeguard users from predatory practices motivated by fraudulent intentions, prejudices, or consumer manipulation. We discuss in particular how advances in machine learning have chiseled away a number of General Data Protection Regulation (GDPR) articles designed to protect consumers from the imminent threat of privacy violations.

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The ethics of sensor technology use in clinical research

Cited by 17 publications

References 18 publications

Ethical Considerations of Wearable Technologies in Human Research

Ethical Considerations of Wearable Technologies in Human Research

Ethical Issues Concerning the Use of Data from Commercially Available Wearable Sensors in Children

Predicting age and gender from network telemetry: Implications for privacy and impact on policy

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