Remote illness detection faces a trust barrier
Authors: Benjamin L Smarr
Journal: The Lancet Digital Health (2024)

On average, wearable devices can detect when a person is becoming unwell with an acute respiratory illness.1Alyafei K Ahmed R Abir FF Chowdhury MEH Naji KK A comprehensive review of COVID-19 detection techniques: from laboratory systems to wearable devices.Comput Biol Med. 2022; 149106070Crossref PubMed Scopus (15) Google Scholar There is real opportunity for health systems to become dynamic and responsive moment to moment for very large and dispersed populations through integration of diverse emergent data sources and artificial intelligence (AI). But what differentiates clear signs of illness for one person, or one group of people, might differ substantially in another. This is important because health services are rightly wary of adopting algorithm-based tools when there is a real danger that the performance of the algorithms will bias results against minoritised populations,2Johnson I, McMahon C, Schöning J, Hecht BJ. The effect of population and 'structural' biases on social media-based algorithms: a case study in geolocation inference across the urban–rural spectrum. 2017 CHI Conference on Human Factors in Computing Systems; May 2, 2017 (1154–66).Google Scholar, 3Obermeyer Z Powers B Vogeli C Mullainathan S Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453Crossref PubMed Scopus (2301) Google Scholar, 4Ledford H Millions of black people affected by racial bias in health-care algorithms.Nature. 2019; 574: 608-609Crossref PubMed Scopus (98) Google Scholar as happens when an algorithm fails to generalise outside of the context in which it was trained.5Futoma J Simons M Panch T Doshi-Velez F Celi LA The myth of generalisability in clinical research and machine learning in health care.Lancet Digit Health. 2020; 2: e489-e492Summary Full Text Full Text PDF PubMed Google Scholar This is a frequently occurring problem.2Johnson I, McMahon C, Schöning J, Hecht BJ. The effect of population and 'structural' biases on social media-based algorithms: a case study in geolocation inference across the urban–rural spectrum. 2017 CHI Conference on Human Factors in Computing Systems; May 2, 2017 (1154–66).Google Scholar, 3Obermeyer Z Powers B Vogeli C Mullainathan S Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453Crossref PubMed Scopus (2301) Google Scholar, 4Ledford H Millions of black people affected by racial bias in health-care algorithms.Nature. 2019; 574: 608-609Crossref PubMed Scopus (98) Google Scholar, 5Futoma J Simons M Panch T Doshi-Velez F Celi LA The myth of generalisability in clinical research and machine learning in health care.Lancet Digit Health. 2020; 2: e489-e492Summary Full Text Full Text PDF PubMed Google Scholar, 6Goetz L Seedat N Vandersluis R van der Schaar M Generalization—a key challenge for responsible AI in patient-facing clinical applications.NPJ Digit Med. 2024; 7: 126Crossref PubMed Scopus (2) Google Scholar, 7Larrazabal AJ Nieto N Peterson V Milone DH Ferrante E Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.Proc Natl Acad Sci USA. 2020; 117: 12592-12594Crossref PubMed Scopus (300) Google Scholar People are diverse and complicated, as is their environment (natural and societal), so it might be a long time before algorithms—rote procedures by definition, even when they are large models—are highly accurate for all people from different backgrounds. However, people are also often inaccurate, and medicine remains very difficult for humans too. This is why using algorithms as decision support tools might be the path forward for the foreseeable future. The study by Giorgio Quer and colleagues8Quer G Coughlin E Villacian J et al.Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.Lancet Digit Health. 2024; 6: e546-e554Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar in The Lancet Digital Health shows the feasibility of using algorithms as decision support tools even in diverse populations. In so doing, they also identified many challenges to be overcome before the dream of remote, digitally enabled public health can be realised equitably and safely. Briefly, Quer and colleagues recruited 450 patients from the DETECT study.9Quer G Radin JM Gadaleta M et al.Wearable sensor data and self-reported symptoms for COVID-19 detection.Nat Med. 2021; 27: 73-77Crossref PubMed Scopus (286) Google Scholar Enrolment intentionally included a large proportion of participants historically under-represented in medical research (including race or ethnicity not White and non-Hispanic, gender other than male or female, and age older than 65 years), and who have historically been the most affected by algorithmic and health biases. Participants were randomly assigned into three groups. All participants received a wearable device and an app to report symptoms of acute respiratory infection. Groups 1 and 2 also received at-home tests for infection. The app alerted users based on their data when to take one of the at-home tests. Group 1 could be alerted based on deviations in wearable data or due to reporting symptoms, and group 2 were alerted only due to reported symptoms. Group 3 served as the control and did not receive alerts or at-home test kits. It is important to note that the study was not focused on demonstrating the detection accuracy of an algorithm based on wearable sensor data. Prizing precise physiological insights from complex and diverse physiological signals, without recourse to context (ie, what is happening that is causing changes in the body) is a brand new field, and a great deal of research will be needed before such health signal processing is robust. Without participation from a lot of (diverse) people, there is no way to find out how to differentiate, for example, changes in heart rate and activity due to alcohol, illness, stress, or healthy exercise. The results of Quer and colleagues' study are therefore encouraging. 300 (81%) of 372 participants used their wearable device throughout the whole study period,8Quer G Coughlin E Villacian J et al.Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.Lancet Digit Health. 2024; 6: e546-e554Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar which reflects very high adherence.10Shiba SK Temple CA Krasnoff J et al.Assessing adherence to multi-modal Oura ring wearables from COVID-19 detection among healthcare workers.Cureus. 2023; 15e45362PubMed Google Scholar In the context of expanding medical support to people outside of a clinic, this adherence level suggests that people are interested in supporting these at-home monitoring efforts. Symptom reporting rates were also high, but it is not possible to know if some symptoms went unreported. Both intervention groups received alerts, and across both groups, 61 (52%) of 118 individuals who were alerted responded by taking their at-home test.8Quer G Coughlin E Villacian J et al.Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.Lancet Digit Health. 2024; 6: e546-e554Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar Interestingly, only six (23%) of 26 participants took the at-home test when the alert was triggered by wearable device data instead of symptom report.8Quer G Coughlin E Villacian J et al.Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.Lancet Digit Health. 2024; 6: e546-e554Summary Full Text Full Text PDF PubMed Scopus (1) Google Scholar These results reinforce the point that technology is only one part of digital health solutions. Algorithms will only be useful if people from diverse backgrounds trust them and want to use them. Quer and colleagues correctly point out that the future of medicine will depend on collaboration between engineering, basic biomedical science, and a lot of social science and diverse representation to ensure not only appropriateness, but also adoption of new solutions by diverse communities. This is the first prospective trial to show that there is substantial willingness by diverse groups to participate in this kind of research. It should encourage collaborative efforts to realise this opportunity to develop inclusive, participatory, data-driven health systems. I declare grants or contracts from Medical Technology Enterprise Consortium, the US Department of Defense, the #StartSmall Foundation, and Oura Health. I have also received remuneration for consulting work from, and have a financial interest in, Oura Ring but I declare no other non-financial competing interests. I am listed as co-inventor on patent applications as follows: 17/357,922, filed June 24, 2021, entitled Illness Detection Based on Temperature Data, status is pending; PCT/US21/39260, filed June 25, 2021, entitled Illness Detection Based on Temperature Data, status is expired; and 17/357,930, filed June 24, 2021, entitled Health Monitoring Platform for Illness Detection, status is pending. These patents were all filed as of July, 2021 by Oura Health on behalf of University of California San Diego. All applications cover the use of wearable device data to detect illness onset. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Government. Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trialIn this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation. Full-Text PDF Open Access