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      Intelligent risk prediction in public health using wearable device data

      editorial

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          Abstract

          The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.

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          “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health

          Jason Morgenstern, Laura Rosella, Mark J. Daley … (2021)
          Background Our objective was to determine the impacts of artificial intelligence (AI) on public health practice. Methods We used a fundamental qualitative descriptive study design, enrolling 15 experts in public health and AI from June 2018 until July 2019 who worked in North America and Asia. We conducted in-depth semi-structured interviews, iteratively coded the resulting transcripts, and analyzed the results thematically. Results We developed 137 codes, from which nine themes emerged. The themes included opportunities such as leveraging big data and improving interventions; barriers to adoption such as confusion regarding AI’s applicability, limited capacity, and poor data quality; and risks such as propagation of bias, exacerbation of inequity, hype, and poor regulation. Conclusions Experts are cautiously optimistic about AI’s impacts on public health practice, particularly for improving disease surveillance. However, they perceived substantial barriers, such as a lack of available expertise, and risks, including inadequate regulation. Therefore, investment and research into AI for public health practice would likely be beneficial. However, increased access to high-quality data, research and education regarding the limitations of AI, and development of rigorous regulation are necessary to realize these benefits. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-020-10030-x.
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            New Technology Add-On Payment (NTAP) for Viz LVO: a win for stroke care

            Ameer E Hassan (2020)
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              A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19

              Md. Shandhi, Peter Cho, Ali R. Roghanizad … (2022)
              Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7–11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model’s precision-recall curve (AUC-PR) by 38–50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.
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                Author and article information

                Contributors
                Marium M. Raza:
                ORCID: http://orcid.org/0000-0001-8050-9402
                mraza@hms.harvard.edu
                Journal
                Journal ID (nlm-ta): NPJ Digit Med
                Journal ID (iso-abbrev): NPJ Digit Med
                Title: NPJ Digital Medicine
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2398-6352
                Publication date (Electronic): 13 October 2022
                Publication date PMC-release: 13 October 2022
                Publication date Collection: 2022
                Volume: 5
                Electronic Location Identifier: 153
                Affiliations
                GRID grid.38142.3c, ISNI 000000041936754X, Harvard Medical School, ; Boston, MA USA
                Author information
                http://orcid.org/0000-0001-8050-9402
                http://orcid.org/0000-0002-1736-4367
                http://orcid.org/0000-0002-7517-2291
                Article
                Publisher ID: 701
                DOI: 10.1038/s41746-022-00701-x
                PMC ID: 9556285
                PubMed ID: 36229593
                SO-VID: ef314330-2bcd-4220-8099-c030239994c8
                Copyright © © The Author(s) 2022
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 19 September 2022
                Date accepted : 30 September 2022
                Categories
                Subject: Editorial
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                Keywords: preventive medicine,medical research
                Data availability:
                Keywords: preventive medicine, medical research

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