Does deidentification of data from wearable devices give us a false sense of security? A systematic review

Chikwetu, Lucy; Yu, Min; Woldetensae, Melat K; Bell, Diarra; Goldenholz, Daniel M.; Dunn, Jessilyn

doi:10.1016/s2589-7500(22)00234-5

Cited by 23 publications

(10 citation statements)

References 37 publications

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“…With respect to structure measures, the interoperability of digital therapeutics (ie, the ability to communicate with local health informatics systems, such as electronic health records) was the most often emphasized requirement from a design viewpoint [ 1 , 4 , 13 , 15 , 17 , 19 , 44 , 45 , 54 , 57 , 61 , 66 , 80 , 82 , 103 , 106 , 107 , 110 , 111 , 124 , 147 , 189 - 192 , 197 , 199 , 200 , 202 , 222 , 224 , 230 , 231 , 246 , 264 - 266 , 270 , 273 ]. However, it is important that digital therapeutics remain compliant with local privacy and security legislation and prevent direct-to-consumer advertisements through the application [ 4 , 47 - 49 , 51 , 57 , 93 , 115 , 125 , 138 , 187 , 211 , 219 , 220 , 224 ], as well as identification through secondary data sharing [ 79 , 91 , 93 , 138 , 217 ]. The hardware and software requirements [ 63 , 91 , 163 , 194 , ...…”

Section: Resultsmentioning

confidence: 99%

“…Finally, digital therapeutics can aid in the generation and analysis of meaningful real-world (health) data through their integration with electronic health records [ 5 , 136 , 231 , 273 ], enable the use of cloud-based diagnostics and (big data) analyses [ 48 , 72 , 223 ], and be continuously developed and improved through iterative user feedback [ 201 ]. However, privacy-conscious data-sharing platforms (eg, PhysioNet and the US National Institute of Health All of Us research program) are vital for minimizing the risk of reidentification [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

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Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

van Kessel,

Roman-Urrestarazu,

Anderson

et al. 2023

J Med Internet Res

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Background Digital therapeutics are patient-facing digital health interventions that can significantly alter the health care landscape. Despite digital therapeutics being used to successfully treat a range of conditions, their uptake in health systems remains limited. Understanding the full spectrum of uptake factors is essential to identify ways in which policy makers and providers can facilitate the adoption of effective digital therapeutics within a health system, as well as the steps developers can take to assist in the deployment of products. Objective In this review, we aimed to map the most frequently discussed factors that determine the integration of digital therapeutics into health systems and practical use of digital therapeutics by patients and professionals. Methods A scoping review was conducted in MEDLINE, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar. Relevant data were extracted and synthesized using a thematic analysis. Results We identified 35,541 academic and 221 gray literature reports, with 244 (0.69%) included in the review, covering 35 countries. Overall, 85 factors that can impact the uptake of digital therapeutics were extracted and pooled into 5 categories: policy and system, patient characteristics, properties of digital therapeutics, characteristics of health professionals, and outcomes. The need for a regulatory framework for digital therapeutics was the most stated factor at the policy level. Demographic characteristics formed the most iterated patient-related factor, whereas digital literacy was considered the most important factor for health professionals. Among the properties of digital therapeutics, their interoperability across the broader health system was most emphasized. Finally, the ability to expand access to health care was the most frequently stated outcome measure. Conclusions The map of factors developed in this review offers a multistakeholder approach to recognizing the uptake factors of digital therapeutics in the health care pathway and provides an analytical tool for policy makers to assess their health system’s readiness for digital therapeutics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

van Kessel,

Roman-Urrestarazu,

Anderson

et al. 2023

J Med Internet Res

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show abstract

“…Data privacy and deidentification are challenges that remain largely unaddressed for wearable device data. Recent works further demonstrate how it might be possible to re-identify individuals using de-identified wearable device data [ 38 ]. Furthermore, as of 2024, these data fall under the category of “personal health data” in the EU [ 39 ] and US [ 40 ], and these data are subject to regulations that vary by jurisdiction.…”

Section: Discussionmentioning

confidence: 99%

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

Kasl,

Keeler Bruce,

Hartogensis

et al. 2024

Sensors

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Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

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“…xx There are notable concerns about the extent to which data can be truly deidentified, especially as larger amounts of open data continue to become available [67][68][69].…”

Section: 13mentioning

confidence: 99%

Consensus recommendations for measuring the impact of contraception on the menstrual cycle in contraceptive clinical trials

Mackenzie,

Chung,

Hoppes

et al. 2024

Preprint

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ObjectiveWe sought to develop consensus recommendations for improved measurement of contraceptive-induced menstrual changes (CIMCs) in contraceptive clinical trials to build upon previous standardization efforts.Study DesignWe undertook a virtual consensus-building process using a modified Delphi approach, incorporating aspects of Nominal Group Technique and the Jandhyala method. The process consisted of four half-day meetings, developing recommendations within five topical working groups, a series of consensus questionnaires to assess recommendation agreement on a 4-point Likert-like scale, and opportunities for input and feedback throughout the process. Recommendations required at least 75% consensus to be included.ResultsOur interdisciplinary group—from 26 organizations and 13 countries in five global regions across academia, nonprofit research organizations, the pharmaceutical industry, and funding agencies— developed 44 consensus recommendations. These included recommendations on standardization, patient-reported outcome measure development aligned with current regulatory guidance, trial design, protocol development, participant recruitment, data collection, data analysis, and areas for exploratory research. Seven recommendations were deemed a priority by over 85% of our group.ConclusionUsing a formal consensus-building process, we reached high levels of agreement around recommendations for more standardized and patient-centered measurement of CIMCs in contraceptive clinical trials, now and in the future.Implications statementConsensus recommendations on measuring bleeding changes and related outcomes in contraceptive clinical trials can improve reporting of standardized, patient-centered outcomes in future product labeling. These improvements can enable providers to offer more relevant counseling on contraceptives and permit greater comparability and data synthesis across trials to inform clinical guidance.

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Does deidentification of data from wearable devices give us a false sense of security? A systematic review

Cited by 23 publications

References 37 publications

Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

Consensus recommendations for measuring the impact of contraception on the menstrual cycle in contraceptive clinical trials

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