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• Published: 29 October 2020

Wearable sensor data and self-reported symptoms for COVID-19 detection

• Giorgio Quer   ORCID: orcid.org/0000-0003-2208-7912 1   na1 ,
• Jennifer M. Radin   ORCID: orcid.org/0000-0003-3843-0842 1   na1 ,
• Matteo Gadaleta   ORCID: orcid.org/0000-0001-6470-6537 1   na1 ,
• Katie Baca-Motes 1 ,
• Lauren Ariniello   ORCID: orcid.org/0000-0002-6005-8824 1 ,
• Edward Ramos 1 , 2 ,
• Vik Kheterpal   ORCID: orcid.org/0000-0002-4752-4229 2 ,
• Eric J. Topol   ORCID: orcid.org/0000-0002-1478-4729 1 &
• Steven R. Steinhubl   ORCID: orcid.org/0000-0002-9256-7914 1  

Nature Medicine volume  27 ,  pages 73–77 ( 2021 ) Cite this article

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• Disease prevention
• Public health
• Risk factors
• Signs and symptoms

Traditional screening for COVID-19 typically includes survey questions about symptoms and travel history, as well as temperature measurements. Here, we explore whether personal sensor data collected over time may help identify subtle changes indicating an infection, such as in patients with COVID-19. We have developed a smartphone app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results, from individuals in the United States, and have assessed whether symptom and sensor data can differentiate COVID-19 positive versus negative cases in symptomatic individuals. We enrolled 30,529 participants between 25 March and 7 June 2020, of whom 3,811 reported symptoms. Of these symptomatic individuals, 54 reported testing positive and 279 negative for COVID-19. We found that a combination of symptom and sensor data resulted in an area under the curve (AUC) of 0.80 (interquartile range (IQR): 0.73–0.86) for discriminating between symptomatic individuals who were positive or negative for COVID-19, a performance that is significantly better ( P  < 0.01) than a model 1 that considers symptoms alone (AUC = 0.71; IQR: 0.63–0.79). Such continuous, passively captured data may be complementary to virus testing, which is generally a one-off or infrequent sampling assay.

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Owing to the current lack of fast and reliable testing, one of the greatest challenges for preventing transmission of SARS-CoV-2 is the ability to quickly identify, trace and isolate cases before they can further spread the infection to susceptible individuals. As regions across the United States start implementing measures to reopen businesses, schools and other activities, many rely on current screening practices for COVID-19, which typically include a combination of symptom and travel-related survey questions and temperature measurements. However, this method is likely to miss pre-symptomatic or asymptomatic cases, which make up ~40–45% of those infected with SARS-CoV-2, and who can still be infectious 1 , 2 . An elevated temperature (>100 °F (>37.8 °C)) is not as common as frequently believed, being present in only 12% of individuals who tested positive for COVID-19 3 and just 31% of patients hospitalized with COVID-19 (at the time of admission) 4 .

Smartwatches and activity trackers, which are now worn by one in five Americans 5 , can improve our ability to objectively characterize each individual’s unique baseline for resting heart rate 6 , sleep 7 and activity and can therefore be used to identify subtle changes in that user’s data that may indicate that they are coming down with a viral illness. Previous research from our group has shown that this method, when aggregated at the population level, can significantly improve real-time predictions for influenza-like illness 8 . Consequently, we created a prospective app-based research platform, called DETECT (Digital Engagement and Tracking for Early Control and Treatment), where individuals can share their sensor data, self-reported symptoms, diagnoses and electronic health record data with the aim of improving our ability to identify and track individual- and population-level viral illnesses, including COVID-19.

A previously reported study that captured symptom data in over 18,000 SARS-CoV-2-tested individuals via a smartphone-based app found that symptoms were able to help distinguish between individuals with and without COVID-19 1 . The aim of this study is to investigate if the addition of individual changes in sensor data to symptom data can be used to improve our ability to identify COVID-19-positive versus COVID-19-negative cases among participants who self-reported symptoms.

Between 25 March and 7 June 2020, our research study enrolled 30,529 individuals, with representation from every state in the United States. Among the consented individuals, 62.0% are female and 12.8% are 65 or more years old. Of the participants, 78.4% connected their Fitbit devices to the study app, 31.2% connected the data from the Apple HealthKit, while 8.1% connected data from Google Fit (note that an individual can connect to multiple platforms). In addition, 3,811 reported at least one symptom (12.5%); of those, 54 also reported testing positive for COVID-19 and 279 reported testing negative. The numbers of days per different data type and data aggregator system are reported in Table 1 , while the symptoms distribution for symptomatic individuals tested for COVID-19, or not tested, is shown in Fig. 1 .

Participants who reported at least one symptom were divided into three groups: participants who tested negative for COVID-19 or positive for COVID-19 and participants who were not tested. The frequencies of the indicated symptoms in each of these three groups are shown. P values of a two-sided Fisher’s exact test applied to COVID-19-positive (54 individual subjects) and COVID-19-negative (279 individual subjects) participants are reported. Symptoms with a significant difference ( P  < 0.05) are marked with an asterisk.

A minority of symptomatic participants (30.3%) who tested for COVID-19 had a resting heart rate (RHR) greater than two standard deviations above the average baseline value during symptoms. The change in RHR on its own (Table 1 ) did not allow significant discrimination between COVID-19-positive and COVID-19-negative participants using the RHRMetric (area under the curve (AUC) of 0.52 (interquartile range (IQR): 0.41–0.64)) (Fig. 2a ).

a – f , Receiver operating characteristic curves (ROCs) for the discrimination between COVID-19-positive (54 individuals) and COVID-19-negative (279 individuals) cases based on the available data: RHR data ( a ); sleep data ( b ); activity data ( c ); all available sensor data ( d ); symptoms only ( e ); symptoms with sensor data ( f ). Models are based on a single decision threshold. Median values and 95% confidence intervals (CIs) for sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) are reported, considering the point on the ROC with the highest average value of sensitivity and specificity. Error bars represent 95% CIs. P values from a one-sided Mann–Whitney U test are reported.

Sleep and activity did show a significant difference among the two groups (Table 1 ), with an AUC of 0.68 (0.57–0.79) for the SleepMetric (Fig. 2b ) and 0.69 (0.61–0.77) for the ActivityMetric (Fig. 2c ), supporting that the sleep and activity of COVID-19 positive participants were impacted significantly more than COVID-19-negative participants. Sleep and activity are slightly correlated, with a negative correlation coefficient of −0.28, P  < 0.01.

To evaluate the contribution of all the data types commonly available through personal devices, we combined the RHR, sleep and activity metrics in a single metric (SensorMetric, Fig. 2d ). This improved the overall performance from the three sensor metrics to an AUC of 0.72 (0.64–0.80).

We also considered a model based only on self-reported symptoms (SymptomMetric, Fig. 2e ), along with age and sex. With respect to the previously published model 1 , we measure a slightly lower AUC of 0.71 (0.63–0.79).

When participant-reported symptoms and sensor metrics are jointly considered in the analysis (OverallMetric, Fig. 2f ), the achieved performance was significantly improved ( P  < 0.01) relative to either alone, with an AUC of 0.80 (0.73–0.86).

Our results show that individual changes in physiological measures captured by most smartwatches and activity trackers are able to significantly improve the distinction between symptomatic individuals with and without a diagnosis of COVID-19 beyond symptoms alone. Although encouraging, these results are based on a relatively small sample of participants.

This work builds on our earlier retrospective analysis demonstrating the potential for consumer sensors to identify individuals with influenza-like illness, which has subsequently been replicated in a similar analysis of over 1.3 million wearable users in China for predicting COVID-19 8 , 9 . In response to the COVID-19 pandemic, a number of prospective studies, led by device manufacturers and/or academic institutions, including DETECT, have accelerated deployment to allow interested individuals to voluntarily share their sensor and clinical data to help address the global crisis 10 , 11 , 12 , 13 , 14 . The largest of these efforts, Corona-Datenspende, was developed by the Robert Koch Institut in Germany and has enrolled over 500,000 volunteers 15 .

As different individuals experience a wide range of symptomatic and biological responses to infection with SARS-CoV-2, it is likely that their measurable physiological changes will also vary 16 , 17 , 18 . For that reason, it is possible that biometric changes may be more valuable in identifying those at highest risk for decompensation rather than just a dichotomous distinction in infection status. Because of the limited testing in the United States, especially early in the spread of the COVID-19 pandemic, individuals with more severe symptoms may have been more likely to be tested. In fact, the majority of symptomatic participants in our study did not undergo testing. However, using the optimal tradeoff of sensitivity and specificity on the ROC, we would predict that, of the 3,478 symptomatic participants who did not undergo diagnostic testing, 1,061 would have tested positive. Consequently, the ability to differentiate between COVID-19-positive and COVID-19-negative cases based on symptoms and sensor data may change over time as testing increases, and as other upper respiratory illnesses such as seasonal influenza increase this fall.

The early identification of symptomatic and pre-symptomatic infected individuals would be especially valuable as transmission is common and people may potentially be even more infectious during this period 19 , 20 , 21 . Even when individuals have no symptoms, there is evidence that the majority have lung injury (according to computed tomography (CT) scans), and a large number have abnormalities in inflammatory markers, blood cell counts and liver enzymes 18 , 22 , 23 , 24 . As the depth and diversity of data types from personal sensors continue to expand—such as heart rate variability (HRV), respiratory rate, temperature, oxygen saturation and even continuous blood pressure, cardiac output and systemic vascular resistance—the ability to detect subtle individual changes in response to early infectious insults will potentially improve and enable the identification of individuals without symptoms.

In the past, the normality of a specific biometric parameter, such as RHR, duration of nightly sleep or daily activity, was based on population norms. For example, a normal RHR is generally considered anything between ~60–100 b.p.m. However, recent work looking at individual daily RHRs over two years found that each person has a relatively consistent RHR, for them, that fluctuates by a median of only 3 b.p.m. weekly 6 . On the other hand, what would be considered a normal RHR for an individual can vary by as much as 70 b.p.m. (between 40 and 109 b.p.m.) between individuals. The potential value in identifying important changes in an individual’s RHR as an early marker for COVID-19 infection is suggested by the description of 5,700 patients hospitalized with COVID-19 4 : at the time of admission, a greater percentage of individuals had a heart rate of >100 b.p.m. (43.1%) than had a fever (30.7%). Similarly, work in primate models of other viral and bacterial infections found that a significant increase in heart rate can be detected ~2 days before a fever 25 .

Just as individuals have heart rate patterns that are unique to them, the same is true for sleep patterns. Although population norms for sleep duration have been defined by one-time survey data 26 , longitudinal analysis of daily sleep over several years supports much greater variation in what is normal for a specific individual 7 . Recognizing what is normal for an individual enables much earlier detection of deviations from that normal.

A strategy of test, trace and isolate has played a central role in helping control the spread of COVID-19. However, testing comes with many challenges, including the enormous logistical and cost hurdles of recurrently testing asymptomatic individuals. In addition, testing in a population with very low prevalence can lead to a high proportion of false positive cases. A refined predictive model, based on personal sensors, could enable an early, individualized testing strategy to improve performance and lower costs. Early testing may make the use of a contact tracing app more effective by identifying positive cases in advance and allowing for early isolation.

DETECT (and similar studies) also represent the transitioning of research from a dependence on brick and mortar research centers to a remote, direct-to-participant approach now possible through a range of digital technologies, including an ever expanding collection of sensors, applications of machine learning to massive datasets, and the ubiquitous connectivity that enables rapid two-way communications 24/7 27 , 28 . The promise of digital technologies is that their evolution will continue to bring us closer to identifying the best combination of measures and associated algorithms that identify infection with SARS-CoV-2 or other pathogens. However, it is equally critical to develop and continuously improve on an engaging digital platform that provides value to participants and researchers. This has proven to be extremely challenging, with a recent analysis of eight different digital research programs involving 100,000 participants having a median duration of retention of only 5.5 days 29 . Digital trials such as DETECT also do come with unique challenges to assure privacy and security, which can only be dealt with by effectively informing participants before consent, storing the data with the appropriate level of security and providing access to the data only for research purposes 30 . App-based contact tracing, which is not part of DETECT, is an especially sensitive and ethically complicated use of digital technology that can be used to address the pandemic 31 .

Our analyses are dependent entirely on participant-reported symptoms and testing results, as well as the biometric data from their personal devices. Although this is not consistent with the historically more common direct collection of information in a controlled laboratory setting or via electronic health records, previous work has confirmed their value and their accuracy beyond data routinely captured during routine care 32 , 33 , 34 . Additionally, individuals owning a smartwatch or activity tracker and having access to COVID-19 diagnostic testing are unlikely to be representative of the general population and may exclude those most affected by COVID. Although a recent survey found no racial or ethnic variation in smartwatch or activity tracker usage (23%, 26% and 21% for Black, Hispanic and White individuals, respectively), the lowest percentage of users were identified in those with the lowest annual earnings (12%), the lowest educational attainment (15%) and in those over age 50 (17%) 5 . In the future, if the value of wearable devices to improve individual health is confirmed, this gap in usage will need to be proactively addressed to assure health equity. The decreasing cost of these devices, some now less than US$35, will help decrease the financial barriers to accomplishing this. Finally, in the early version of the DETECT app we were not able to track the duration or trajectory of individual symptoms, care received and eventual outcomes.

These results suggest that sensor data can incrementally improve symptom-only-based models to differentiate between COVID-19-positive and COVID-19-negative symptomatic individuals, with the potential to enhance our ability to identify a cluster before more spread occurs. Such a passive monitoring strategy may be complementary to virus testing, which is generally a one-off, or infrequent, sampling assay.

Study population

Any person living in the United States over the age of 18 years old is eligible to participate in the DETECT study by downloading the iOS or Android research app, MyDataHelps. After consenting into the study, participants are asked to share their personal device data (including historical data collected prior to enrollment), report symptoms and diagnostic test results, and connect their electronic health records. Participants can opt to share as much or as little data as they like. Data can be pulled in via direct application programming interface (API) with Fitbit devices, and any device connected through Apple HealthKit or Google Fit data aggregators. Participants were recruited via the study website ( www.detectstudy.org ), media reports and outreach from our partners at Fitbit, Walgreens, CVS/Aetna and others.

Ethical considerations

The protocol for this study was reviewed and approved by the Scripps Office for the Protection of Research Subjects (IRB 20–7531). All individuals participating in the study provided informed consent electronically.

Statistical analysis

Only participants with self-reported symptoms and COVID-19 test results were considered in this analysis. For each participant, two sets of data were extracted: the baseline data, which included signals spanning from 21 to 7 days before the reported start date of symptoms, and the test data, which included signals beginning at the first date of symptoms to seven days after symptoms. Three types of data were considered from personal sensors: daily resting heart rate (DailyRHR), sleep duration in minutes (DailySleep) and activity based on daily total step count (DailyActivity). The daily resting heart rate is calculated by the specific device 35 . The total amount of sleep for a given day was based on the total period of sleep between 12 noon of the current day to 12 noon of the next day. When multiple devices from the same individual provided the same information, Fitbit device data were prioritized, for consistency. Overlapping data were combined minute by minute, before aggregating for the whole day.

A single baseline value per individual was extracted for each data type by considering the median value over the individual’s baseline data. This value is representative of a participant’s ‘normal’ before the reported symptoms. The baseline value was compared to the test data as follows:

Values were normalized to have a unitary IQR using normalization parameters calculated on all data recorded. For all these metrics, values close to zero indicate small variations from baseline values. This allows us to focus on intra-individual changes, which are minimally affected by the inter-individual variability due to the specific sensor’s hardware and estimation algorithms. For the metric based on symptoms only, we adapted the results from the study by Menni et al. 1 to our available data:

The multivariate logistic regression model from Menni et al. combined symptoms, age and gender to predict an infection. The parameters were optimized by the authors on a large dataset including over 2 million people, 18,401 of which had undergone a COVID-19 test.

A simple manual metric aggregation strategy without optimization was used to enable a clear understanding of the benefits provided when data from multiple sources were considered together. The aggregated metrics were

The main outcomes are ROC curves for each of the proposed metrics. The curves are obtained by considering a binary classification task between participants self-reported as COVID-19-positive and COVID-19-negative. The models are based on a single decision threshold, which is directly compared to the metric values, with the aim of minimizing overfitting issues while providing a fair comparison. Confidence intervals, reported with a confidence level of 95%, are estimated using a bootstrap method by repeatedly sampling the dataset with replacement. The sampling is performed in a stratified manner; that is, the balance of the classes is maintained over all experiments. Values for sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) were also calculated (Fig. 2 ). SE and SP are defined as the fraction of positive and negative individuals correctly classified, respectively, while PPV and NPV are the fraction of individuals predicted as positive and negative that are correctly classified, respectively. These values are based on the point in the ROC with the optimal tradeoff between sensitivity and specificity, which may vary depending on the shape of the curve. For each metric analyzed, we applied the one-sided Mann–Whitney U test with the alternate hypothesis that the underlying model of the positive class is stochastically greater than the negative class. All statistical tests were evaluated using the Python package scipy version 1.5.2. The comparison metric to assess the overall performance was the AUC of the ROC.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All interested investigators will be allowed access to the analysis dataset following registration and pledging to not re-identify individuals or share the data with a third party. All data inquiries should be addressed to the corresponding author.

Menni, C. et al. Real-time tracking of self-reported symptoms toÿ predict potential COVID-19. Nat. Med 26 , 1037–1040 (2020).

Article   CAS   Google Scholar  

Oran, D. P. & Topol, E. J. Prevalence of asymptomatic SARS-CoV-2 infection. Ann. Intern. Med. https://doi.org/10.7326/M20-3012 (2020).

New COVID-19 Test Data (Color Genomics, 2020); https://www.color.com/new-covid-19-test-data-majority-of-people-who-test-positive-for-covid-19-have-mild-symptoms-or-are-asymptomatic

Richardson, S. et al. Presenting characteristics, comorbidities and outcomes among 5,700 patients hospitalized with COVID-19 in the New York City area. JAMA 323 , 2052–2059 (2020).

Vogels, E. A. About One-in-Five Americans Use a Smart Watch or Fitness Tracker (Pew Research Center, 2020); https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/

Quer, G., Gouda, P., Galarnyk, M., Topol, E. J. & Steinhubl, S. R. Inter- and intraindividual variability in daily resting heart rate and its associations with age, sex, sleep, BMI and time of year: retrospective, longitudinal cohort study of 92,457 adults. PLoS ONE 15 , e0227709 (2020).

Jaiswal, S. J. et al. Association of sleep duration and variability with body mass index: Sleep measurements in a large US population of wearable sensor users. JAMA Intern. Med. https://doi.org/10.1001/jamainternmed.2020.2834 (2020).

Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit. Health 2 , e85–e93 (2020).

Article   Google Scholar  

Zhu, G. et al. Learning from large-scale wearable device data for predicting epidemics trend of COVID-19. Discrete Dynamics Nat. Soc. 2020 , 6152041 (2020).

Google Scholar  

Mishra, T. et al. Early detection of COVID-19 using a smartwatch. Preprint at medRxiv https://doi.org/10.1101/2020.07.06.20147512 (2020).

Natarajan, A., Su, H.-W. & Heneghan, C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. Preprint at https://doi.org/10.1101/2020.08.14.20175265 (2020).

Evidation Health and BARDA Partner on Early Warning System for COVID-19 (Evidation, 2020); https://evidation.com/news/evidationhealthandbardapartner/

Tempredict Study (Oura Health, 2020); https://ouraring.com/ucsf-tempredict-study

Covidentify (Duke University, 2020); https://covidentify.covid19.duke.edu/

Corona Datenspende (Robert Koch Institut, 2020); https://corona-datenspende.de/science/en

Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182 , 59–72 (2020).

Sharma, R., Agarwal, M., Gupta, M., Somendra, S. & Saxena, S. K. in Coronavirus Disease 2019 ( COVID-19 ) (ed. Saxena, S.) 55–70 (Springer, 2020).

Tabata, S. et al. Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: a retrospective analysis. Lancet Infect. Dis. 20 , 1043–1050 (2020).

Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368 , eabb6936 (2020).

Chau, N. V. V. et al. The natural history and transmission potential of asymptomatic SARS-CoV-2 infection. Clin. Infect. Dis. https://doi.org/10.1093/cid/ciaa711 (2020).

Jing, Q. L. et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect. Dis. 20 , 1141–1150 (2020).

Meng, H. et al. CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China. J. Infect. 81 , e33–e39 (2020).

Inui, S. et al. Chest CT findings in cases from the cruise ship ‘ Diamond Princess ’ with coronavirus disease 2019 (COVID-19). Radiol. Cardiothorac. Imaging 2 , e200110 (2020).

Long, Q. X. et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat. Med 26 , 1200–1204 (2020).

Milechin, L. et al. Detecting pathogen exposure during the non-symptomatic incubation period using physiological data. Preprint at bioRxiv https://doi.org/10.1101/218818 (2017).

Sleep and Sleep Disorders (Center for Disease Control and Prevention, 2020); https://www.cdc.gov/sleep/data_statistics.html

Steinhubl, S. R., Wolff-Hughes, D. L., Nilsen, W., Iturriaga, E. & Califf, R. M. Digital clinical trials: creating a vision for the future. NPJ Digit. Med. 2 , 126 (2019).

Steinhubl, S. R., McGovern, P., Dylan, J. & Topol, E. J. The digitised clinical trial. Lancet 390 , 2135 (2017).

Pratap, A. et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit. Med. 3 , 21 (2020).

Coravos, A. et al. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. NPJ Digit. Med. 3 , 37 (2020).

Bradford, L. R., Aboy, M. & Liddell, K. COVID-19 contact tracing Apps: a stress test for privacy, the GDPR and data protection regimes. J. Law Biosci. https://doi.org/10.1093/jlb/lsaa034 (2020).

Rivera, S. C. et al. The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis. Health Qual. Life Outcomes 17 , 156 (2019).

Basch, E. et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA 318 , 197–198 (2017).

Bell, S. K. et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw. Open 3 , e205867 (2020).

Heneghan, C., Venkatraman, S. & Russell, A. Investigation of an estimate of daily resting heart rate using a consumer wearable device. Preprint at medRxiv https://doi.org/10.1101/19008771 (2019).

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Acknowledgements

This work was funded by grant no. UL1TR002550 from the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health (NIH; E.J.T. and S.R.S.). We thank N. Dalton for his support of DETECT. We also thank D. Oran, T. Peters, R. Kamyar and C. Nowak for their contributions to DETECT.

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These authors contributed equally: Giorgio Quer, Jennifer M. Radin, Matteo Gadaleta.

Authors and Affiliations

Scripps Research Translational Institute, La Jolla, CA, USA

Giorgio Quer, Jennifer M. Radin, Matteo Gadaleta, Katie Baca-Motes, Lauren Ariniello, Edward Ramos, Eric J. Topol & Steven R. Steinhubl

CareEvolution, Ann Arbor, MI, USA

Edward Ramos & Vik Kheterpal

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Contributions

G.Q., J.M.R., K.B.-M., V.K., E.J.T. and S.R.S. made substantial contributions to the study conception and design. K.B.-M., L.A., E.R., V.K. and S.R.S. made substantial contributions to the acquisition of data. G.Q. and M.G. conducted statistical analysis. G.Q., J.M.R., M.G. and S.R.S. made substantial contributions to the interpretation of data. G.Q., J.M.R. and S.R.S. drafted the first version of the manuscript. G.Q., J.M.R., M.G., K.B.-M., L.A., E.R., V.K., E.J.T. and S.R.S. contributed to critical revisions and approved the final version of the manuscript. G.Q., J.M.R. and S.R.S. take responsibility for the integrity of the work.

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Correspondence to Giorgio Quer .

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S.R.S. reports grants from Janssen and personal fees from Otsuka and Livongo, outside the submitted work. The other authors declare no competing interests.

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Quer, G., Radin, J.M., Gadaleta, M. et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med 27 , 73–77 (2021). https://doi.org/10.1038/s41591-020-1123-x

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Case Study: Case Study: Why Are You Wearing That? (Wearable Technology)

Case study: why are you wearing that, the problem.

The corporate strategy and competitive insights group at a growing apparel company was interested in learning more about the technology developments and competitive landscape of the wearable technology market. The team had not been able to locate individuals who had relevant experience and were free from external conflicts which would prevent them from consulting on the topic. With a rapidly-approaching Board Meeting at which the group was expected to report on the outlook for the industry over the next decade, they were running out of time to gather the insights they needed.

Critical Issues

Conflict Free  |   Immediately Available   |  Comprehensive Knowledge

The Solution

Maven launched a Telephone Consultation inquiry to quickly qualify individuals for wearable technology expertise, immediate availability, and absence of conflicts. Within four hours of initiating the request, the customer began conducting qualitative interviews with relevant individuals in order to identify someone who could provide a detailed report of the current industry landscape. After six interviews the customer selected a candidate who began conducting their own research and reporting back to the customer on their findings. In less than 3 days, the Maven delivered a detailed report laying that the customer presented at the Board meeting.

“This is not how we planned on using Maven, but after learning that this option was available to us and because of our tight timeframe, we trusted the approach and Maven delivered.” – VP of Corporate Strategy

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Airbus soars with wearables

• What Accenture Did
• People and Culture
• Value Delivered
• Related Capabilities

In today’s highly competitive aerospace and defense market, it is essential for companies like Airbus to optimize their productivity. Moreover, employee experience, safety and satisfaction are key success factors in the war for talent that is raging.

Building an Airbus aircraft involves complex manufacturing processes consisting of thousands of moving parts. Speed and accuracy are critical to business and competitive advantage. Improvements in both would have high impact on Airbus’ bottom line.

Airbus wanted to help operators reduce the complexity of assembling cabin seats and decrease the time required to complete this task.

"Wearable technology can be a game changer in taking the industry to where it is going on a massive scale: to an all-digital mindset, all the time." — JOHN SCHMIDT , Global Lead – Accenture Aerospace and Defense

What Accenture did

Accenture and Airbus collaborated to develop a state of the art application for wearables in aerospace and defense – digitally enabled, industrial grade smart glasses to improve the accuracy and reduce the complexity of cabin furnishing.

Working in a start-up mode which enabled rapid iterations by both companies’ professionals, Accenture and Airbus delivered this initiative in less than a month and used smart glasses to improve accuracy and reduce the time required to complete the cabin seat marking process.

Using contextual marking instructions, the smart glasses display all required information for an operator to help mark the floor faster and reduce errors to zero. In addition, the eyewear technology, implemented by Accenture, also offers interactivity by granting access to features including barcode scanning, data retrieval from the cloud, voice command and augmented reality. Using this innovation technology, all aircrafts’ seat locations can be marked down to the last millimeter and checked for accuracy and quality.

This is the first case of industrialized usage of wearable technology on the final assembly line for a major aircraft manufacturer.

People and culture

What if hands-free wearable technology could be used in airplane manufacturing to increase workforce productivity and engagement, reduce the risk of errors, and improve worker safety in a complex assembly environment?

Wearable technology is indeed a relevant solution for a manufacturing environment and provides instant access to critical information, enabling improved productivity and increased operator satisfaction while reducing training requirements.

Using contextual marking instructions, the smart glasses display all required information for an operator to help mark the floor faster and reduce errors to zero.

In addition, the eyewear technology, implemented by Accenture, also offers interactivity by granting access to features including barcode scanning, data retrieval from the cloud, voice command and augmented reality. Using this innovation technology, all aircrafts’ seat locations can be marked down to the last millimeter and checked for accuracy and quality.

"At Airbus, innovation is a pervasive new mindset that can be applied to the daily jobs of all employees if they use the technology and methods at hand." — JULIO JUAN PRIETO , Managing Director – Accenture Aerospace and Defense

Value delivered

Driving financial performance is a top priority for aerospace and defense companies. Accenture and Airbus demonstrated that wearable technology can bring breakthrough innovation by providing information at speed while leaving the operators hands free.

The results are impressive…the overall productivity for the cabin seat marking process per aircraft was improved 500%, error rate reduced to zero, and marking operations have been significantly accelerated. In addition, training requirements are reduced as operators receive data from the smart glasses in real time without needing to rely on a manual.

Since March 2016, the technology has been industrialized for the Airbus 330 long rage aircraft seat marking and installation process. Additional use cases are currently under development with Airbus, using similar technologies, which contribute to the digitalization of the A330 shopfloor.

Increased flexibility with reduced training needs.

Improved ergonomics and increased operator satisfaction.

Error rate reduced to zero.

500% increased productivity.

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The Airbus Group is a global pioneer in aeronautics, space and defense-related services, creating cutting-edge technology.

With over 20 years of continuous innovation, the A330 is the most modern, profitable and reliable aircraft family on the market, providing a tailored solution for every market today and for the future. As the newest member of the A330 family, the A330neo is a truly modern aircraft that shares the values of Airbus product line: unrivalledcost-efficiency for airlines and superior comfort for passengers.

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Aerospace and defense, industrial equipment consulting.

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Stanford Medicine scientists hope to use data from wearable devices to predict illness, including COVID-19

Researchers from Stanford Medicine and their collaborators aim to predict the onset of viral infection through data provided by wearable technology. What they need now are participants.

April 14, 2020 - By Hanae Armitage

The study is collecting data from five different brands of   wearable device, including a variety of smartwatches. Myriam B/Shutterstock.com

Stanford Medicine researchers and their collaborators, Fitbit and Scripps Research , are launching a new effort that aims to detect early signs of viral infection through data from smartwatches and other wearable devices.

By using wearable devices to measure things such as heart rate and skin temperature, which are known to elevate when the body is fighting off an infection, the team seeks to train a series of algorithms that indicates when your immune system is acting up. 

If the algorithms succeed, the team hopes they could help curb the spread of viral infections, such as  COVID-19 .

 “Smartwatches and other wearables make many, many measurements per day — at least 250,000, which is what makes them such powerful monitoring devices,” said  Michael Snyder , PhD, professor and chair of genetics at the  Stanford School of Medicine . “My lab wants to harness that data and see if we can identify who’s becoming ill as early as possible — potentially before they even know they’re sick.”

Snyder, who holds the Stanford W. Ascherman, MD, FACS, Professorship in Genetics, and his team are recruiting participants for the study through his lab’s  Personal Health Dashboard . Fitbit, a company that makes wearable devices, will assist in that effort by raising awareness of the study with its users and offering them the option to participate. In addition, Fitbit plans to donate 1,000 smartwatches to Snyder’s research. As part of this collaboration, scientists at Scripps Research will also work with Fitbit to try to track how infection spreads in a community.

Once the algorithms are developed and verified, Snyder said, they could help people keep tabs on their health. Devices with an algorithm could alert users when their heart rate, skin temperature or some other part of their physiology signals that their body is fighting an infection. When people come down with a cold or flu, there’s usually a period just before symptoms set in when they wonder if they’re actually getting sick. Even during that time, without heavy symptoms, a sick individual often can still spread the virus. “You might wonder, ‘Are these sniffles allergies, or am I getting sick?’ These algorithms could help people determine if they should stay home in case their body is fighting off an infection,” Snyder said.

Watching for signs

Snyder’s research will be based on an algorithm that he and former postdoctoral scholar  Xiao Li , PhD, now an assistant professor in the  Center for RNA Science and Therapeutics  at Case Western Reserve University, created in 2017. The algorithm showed that it was possible to detect infection using data — specifically, data from a change in heart rate — from a smartwatch. Snyder’s study showed that specific patterns of heart rate variation can indicate illness, sometimes even while the individual is asymptomatic. Li is also a collaborating principal investigator in the current study.

“Smartwatches and other wearables make many, many measurements per day — at least 250,000, which is what makes them such powerful monitoring devices,” Michael Snyder said. Paul Sakuma

For this study, Snyder is collecting data from five different brands of wearable device, including a smart ring and a variety of smartwatches. Each participant will also fill out surveys that keep track of their health status. Snyder and his team will create five new algorithms — one for each of the different wearables — to potentially detect when someone is getting sick. How quickly they can develop and verify the algorithms will depend on the number of participants who sign up for the study, Snyder said.

Although he’s hopeful that these algorithms will be able to successfully flag a specific change in heart rate linked to viral infection, Snyder also foresees some kinks to work out. “It’s possible that the algorithms could detect an elevated heart rate, but the user could be watching a scary movie or participating in some other activity that naturally elevates heart rate,” he said. “An alert isn’t a direct diagnosis, and it will be important for folks to be able to contextualize their situation and use some common sense.” Snyder also adds that even as his team works to develop algorithms that can flag illness, the next step is to investigate whether those signals can be sorted to be able to differentiate between viruses.

The study is an example of Stanford Medicine’s focus on  precision health , the goal of which is to anticipate and prevent disease in the healthy and precisely diagnose and treat disease in the ill.

“I feel confident based on our former study that we’ll be able to detect some signal of infection based off of the wearables’ data,” Snyder said. “And I’m hopeful that as our study picks up, we may even have the granularity to anticipate the severity of viral infection based on smart device data. This tool may end up being a plus for both diagnosis and for prognosis.”

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Hope amid crisis

Psychiatry’s new frontiers

COVID-19 pandemic changed attitudes toward wearables

• Wearable Technology

The COVID-19 pandemic significantly increased interest in wearable health-monitoring devices among low-income Hispanic and Latine adults living in the U.S., a new Northwestern University study has found.

While the pandemic highlighted the need for regular health monitoring, these groups often lack access to affordable health care and sometimes distrust existing health systems. Wearables, therefore, could provide a reliable, at-home alternative to traditional in-clinic health monitoring.

But, although interest has increased, several barriers remain that prevent these groups from adopting wearable technologies. According to the researchers, tech companies historically have designed current wearable devices with affluent, predominantly white users in mind.

“Current designers do not consider the needs of low-income people of color regarding usability, accessibility and affordability,” said Northwestern’s Stefany Cruz , who led the study. “If this trend continues, it will worsen digital and health inequities. In this study, we want to bring attention to existing health disparities and how wearable devices expand that gap. Wearables have the potential to fill the gap eventually, but we’re not there yet. We need to build devices that are more inclusive, and the design process should consider the context and culture of individuals from marginalized communities.”

The study was published today (May 8) in the Journal of Medical Internet Research.

A personal connection

Cruz is a Ph.D. candidate in computer engineering at Northwestern’s McCormick School of Engineering . In her engineering work, Cruz is particularly interested in building equitable, efficient and intelligent wearable systems for groups historically excluded from the design process.

Cruz’s own experiences as a child of Salvadoran immigrants inspired her to embark on this new study. Growing up in East Los Angeles, Cruz was often sick, and her family did not have health insurance. After suffering a bout of strep throat, she watched her family struggle to pay the medical bills — an experience that sparked her interest in developing new technologies with a focus on health.

“That set up the whole trajectory of what I want to pursue in the computer engineering field,” Cruz said. “Because I witnessed the severe lack of access to health care, I want to build technologies from the ground up that can help support and uplift my community.”

Assembling participants

Although Cruz planned the study before pandemic hit, she noticed that COVID-19 changed the role of wearables in society. Once used mostly for counting steps and motivating people to move through the day, wearable devices now began playing a bigger role in health monitoring. These devices could track vital physiological signals, including blood oxygen levels. Low blood oxygen levels often have no symptoms until organs are irreparably damaged. But wearables could detect early warning signs, prompting a person to head to the hospital sooner — before it’s too late.

It was easy for Cruz to see how this technology could help her community. But why weren’t people taking advantage of these devices?

To understand perceptions of wearables and identify the barriers to adoption, Cruz assembled a small group of low-income Hispanic and Latine adults in Chicago and Los Angeles. Participants met the low-income criteria if their income levels fell at or below the low-income threshold according to their county’s Department of Housing and Community Development.

After establishing a focus group, Cruz held two rounds of in-depth interviews between December 2021 and March 2022. In the first interviews, Cruz noticed that multiple participants made connections between COVID-19 and wearable devices. So, then she conducted a second round of interviews with more emphasis on using wearables for health monitoring. In these conversations, Cruz explored the participants’ opinions regarding wearable technology for health, their community’s perception of wearables and the features they would like to see in future wearables. She also asked participants about their access to Wi-Fi and other resource constraints.

Uncovering an overwhelming interest

Throughout the interviews, Cruz consistently found that the COVID-19 pandemic strongly influenced perceptions of wearable electronics. Participants who felt apathetic before the pandemic expressed a significantly increased interest in wearables for personal health monitoring and management.

About two-thirds of the participants in the study lost a close family member to COVID-19. Several of the participants also contracted COVID-19 before the vaccine and other treatments became available. These experiences made them realize how useful wearable health-monitoring tools can be.

“I guess the one thing that scares me that I never even thought of until I got COVID were my oxygen levels,” one participant said. “Like, am I at normal levels? Is that an issue that I need to kind of think about?”

“One thing I noticed, especially with COVID right now which is…the timing of getting all your vitals measured can actually save somebody's life,” another participant said. “So, I think that's a very important thing. Like oxygen levels to be measured.”

Alternative to in-clinic care

Participants also discussed difficulties when trying to access health care and how wearables could potentially compensate for the lack of local resources. Specifically, some participants shared how their neighborhood hospitals had closed, forcing community members to seek care at small, overcrowded clinics.

“It's overly populated. Even if you make an appointment, you're there all day,” one Los Angeles-based participant said. “Whatever time you go, whatever day you go, it's always crowded, because it's one of the very few [clinics] that accepts Medi-Cal. So low-income communities, they don't have the resources; it's always crowded.”

One participant highlighted that community members' lack of trust in doctors, coupled with high medical expenses, posed barriers to seeking medical treatment.

“Hispanic people don't go to the doctor because they don't believe in the doctor,” the participant said. “They think the doctors are gonna kill them and then they're poor, so they can't pay for the doctor. So, like if [a wearable] could do basic [vital] tests that would be great.”

Community-driven design

As a part of the interview process, Cruz asked participants what features and functions they desired in wearable devices. Cruz noted that oftentimes technologies designed for low-income groups do not take the intended users’ needs into account.

“If we are the ones that are supposed to wear the devices, then it makes sense to ask our opinions of how they can be incorporated into our daily lives,” she said.

In addition to wanting health monitoring capabilities (for heart rate, oxygen levels, blood pressure and more), the participants also desired enhanced affordability, control over the captured health data and increased durability. For wearables to be most effective, users must wear them continuously to capture consistent health data. This is where durability becomes a critical factor.

“I do think that it has to be very durable because the purpose is [for] low-income communities,” one participant said. “They don't have money to replace it. We just don't have comfy jobs. A lot of us work more physically demanding jobs. Some of us are plumbers, some are construction workers, some of us are gardeners. Some of us run a business and like that business involves pots and pans like we're restaurant workers. If [the device] breaks, they're just gonna say ‘oops’ and throw it away…If it is more durable that’s one of the biggest keys to wearing it.”

‘My community suffered a lot’

Although many people have moved on from the pandemic and resumed normal lives, Cruz said her community is still reeling. Cruz lost several family members to COVID-19 and hopes that designing more inclusive technologies can prevent future suffering.

“During COVID-19, my community suffered a lot,” Cruz said. “Some people have been able to brush it off and move on, but some of us are still scarred. We lost family members that probably would still be alive if they weren’t infected. Many people have long-COVID symptoms, which wearables also could help monitor. As these technologies get better at sensing vital signals, they also should become more inclusive.”

The title of the paper is “Perceptions of wearable health tools post-COVID-19 in low-income Latine communities.” Northwestern co-authors include Maia Jacobs , who is the Lisa Wissner-Slivka and Benjamin Slivka Professor of Computer Science at McCormick, and students Claire Lu and Mara Ulloa. Cruz is advised by study co-author Josiah Hester, who was an assistant professor computer engineering at Northwestern when the research launched. Now, Hester is an associate professor of interactive computing and computer science at Georgia Tech.

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Ethical Considerations of Wearable Technologies in Human Research

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 have been commercialized and cleared for medical usage. The successful translation of current proof-of-concept prototypes will require 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.

Table of Content

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 have been commercialized and cleared for medical usage. The successful translation of current proof-of-concept prototypes will require extensive in-human testing. This essay presents relevant ethical concerns in early-stage human research from a researcher’s perspective.

1. Introduction

Driven by the promise of revolutionizing healthcare, the field of wearable technology has evolved rapidly into a broad, multidisciplinary topic in the past few years. Advances in microfabrication of silicon electronics and the development of soft electronic materials have enabled the seamless integration of sensing technologies with skin. [ 1 ] A plethora of studies have expanded the capability to access and analyze biofluids for broader applications of continuous disease monitoring. [ 2 , 3 ] The development of low-energy, self-powered systems makes continuous and autonomous operation for extended times possible. [ 4 ]

At the same time, commercial wearable technologies have also expanded from consumer health wearables towards wearable medical technology as fitness tracker giants like Apple Watch and Fitbit received FDA clearance for their ECG features. Accelerated by the shortage of medical resources and the need for telemedicine tools amid the pandemic, FDA also granted Emergency Use Authorizations (EUA) to several remote or wearable patient monitoring devices such as VitalPatch and VSMS ECG Patch (G Medical) to aid the remote monitoring of patients. [ 5 ]

The forced adoption of telemedicine during the extended lockdown period and the recent breakthrough in wearable technology will fuel the shift of the healthcare paradigm to virtual and voluntary at-home monitoring and diagnosis of diseases in a foreseeable future. Still, only a handful of wearable technologies have been successfully commercialized and adopted for clinical decision-making currently. [ 6 ] Solutions proposed at the bench side to address on-body operational challenges of wearable technologies will eventually need to be validated in humans and clinical studies before their translation into practice.

Similar to all emerging technologies, the lack of an overarching framework to guide wearable technology researchers in practice poses a barrier to the recruitment of subjects and the design of proper human research to collect meaningful data. Undoubtedly, wearable research involving human participants is guided by the three major principles of the Belmont Report, namely, respect of persons, beneficence, and justice. Researchers could also learn and draw parallels from past experiences on clinical trials involving new medical technologies when considering whether a study is ethical. For instance, Emanuel et al. proposed seven key evaluation requirements: (1) scientific/societal value of the research; (2) scientific validity; (3) fair subject selection; (4) risk-benefit analysis; (5) involvement of Institutional Review Board; (6) informed consent and (7) respect for participants. [ 7 ] While these broad frameworks apply to human research in general; wearable technology poses unique challenges beyond past case studies of medical technologies. The vast amount of multimodal, real-time data collected during human research instigate a new set of concerns on data privacy and security. The multidisciplinary nature of the field also makes the identification of a particular set of principles or a use case for ethical guidance difficult. Ethical considerations for the development or application of wearable technology for generic fitness tracking may differ from those for medical-grade wearable technology. Although Institutional Review Boards (IRBs) are the major stakeholder in protecting the rights and welfare of human subjects, IRB members may fall short of covering all ethical issues revolved around a new wearable technology due to the lack of experience and expertise. [ 8 ] Wearable researchers, on the other hand, are more familiar with a new technology and the potential risks involved. Therefore, the research community also shares the onus of identifying and addressing ethical concerns of human research and safeguarding the welfare of participants.

In this essay, we briefly discuss ethical considerations and challenges specific to the wearable research community with close reference to the current technological advancements and their potential applications. In their course of experimental design and subject recruitment, wearable researchers could play a role in addressing various ethical considerations, including reliability and validity of a device, risk assessment, subject selection and exclusion, data privacy and security as well as informed consent ( Figure 1 ). While this essay is by no means an exhaustive discussion of all potential ethical concerns, we hope to provide better insight for investigators in various domains and different stages of wearable technology development.

Ethical considerations and challenges of using new wearable technologies in human research.

2. Reliability and Validity

To resolve challenges faced by conventional wearable systems such as the mechanical mismatch between the skin and rigid electronics during motion, increasing efforts have been invested in the synthesis of novel stretchable materials and their integration in skin-interfaced wearable sensors wearable and mountable devices. [ 1 , 9 ] Soft material innovation and smart structural engineering in the past decade have enabled the development of epidermal sensing systems for monitoring physical activities and physiological signals, such as pressure, skin temperature, pulse oximetry, as well as chemical and biochemical analytes in biofluids such as sweat, saliva, and tear. [ 10 ] In the meantime, the dynamic working environment that a wearable physical or chemical sensor faces during on-body operation still introduces additional complexity and uncertainty into the real-time collection of accurate physiological information. For example, skin temperature sensors that rely on electrical behavior changes of the materials against temperature can easily be influenced by the mechanical strain. [ 11 ] Skin temperature variation inadvertently affects the performance of potentiometric sensors and enzymatic sensors. [ 12 , 13 ] In addition to motion artifacts, photoplethysmography (PPG) based wearable sensors may have reduced accuracy in subjects with darker tones. [ 14 , 15 ] Although various soft epidermal systems under research have demonstrated the intimate and unobtrusive integration of such system on the skin, [ 16 ] the technological limitations of visible light-based PPG are seldom discussed and assessed in both commercially available rigid substrate wearable devices and soft electronics research. Many factors present on the skin may affect the absorption of light differently; darker skin tones, tattoos, the presence of arm hair, sweat, body mass could all influence PPG accuracy and compromise PPG-related health outcome analysis.

Inaccurate data collected during human research due to insufficient device validation is ineffective at best. These data could also potentially exert unintended harm if they are incorporated in closed-loop body computing systems and result in incorrect health conclusions or trigger unintended intervention to the physiological environment. [ 17 ] Therefore, the onus is on researchers engaged in developing novel sensing strategies on-the-skin to account for the dynamic changes in environmental and operational factors during human research and validate the veracity of a newly developed sensor against potential influences. One common strategy adopted by several research groups is the cross-validation of sensor response with laboratory gold standard ( Figure 2a and ​ andb b ). [ 18 – 21 ] Others cross-reference the data collected on-body with those collected ex vivo to identify any potential interference caused by the on-body operation. [ 22 ] Recently, various in-situ calibration mechanisms have also been introduced to account for the dynamic changes and improve sensor accuracy. [ 13 , 23 , 24 ] In conjunction with ex-situ and in-vitro validation of the sensor, many investigators of wearable chemical sensors may also opt to evaluate the relationship/correlation between serum and biomarkers present in alternative biofluid source, considering the potential influences from biofluid secretion rate and mechanism. [ 25 , 26 ] It is important to recognize that even if the results may not lead forward the translation of a technology (i.e., in the case of a weak or insignificant correlation), these studies still contain important information for the entire research community to evaluate the clinical significance of certain biomarkers and steer the research focus in a different direction. The appropriate and responsible reporting of validation data, as well as disclosure of uncertainty, are not only essential to ensure that results from human research are of scientific and societal significance but also the safety of participants.

Examples of investigational wearable technologies in human studies (a-d), commercial digital therapeutics (e), and conceptualized closed loop systems (f). a) Cross validation of a wearable sweat uric acid sensor’s responses in raw sweat samples using HPLC analysis. Reproduced with permission. [ 30 ] Copyright 2020 Springer Nature. b) Performance comparison of epidermal electroencephalography (EEG) electrodes (E1 and E2) and conventional EEG cup (Pz) electrodes in recording P3 event-related potential values. Reproduced with permission. [ 21 ] Copyright 2019 Springer Nature. c) Biocompatibility test of conventional polymeric films and a newly developed gas-permeable nanomesh conductors: participants reported on feelings of discomfort based on a visual analogue scale (VAS) of 0–10 while the films were attached to the skin for a week. Reproduced with permission. [ 31 ] Copyright 2017 Springer Nature. d) Glucose monitoring by reverse iontophoresis using GlucoWatch. Reproduced with permission. [ 70 ] Copyright 2001 Elsevier. e) Illustration of a wearable sensor-augmented insulin pump which measures interstitial glucose levels and calculates appropriate insulin to be delivered in real-time and photograph of Medtronic’s MiniMed Paradigm REAL-Time which combines insulin delivery with continuous glucose monitoring (CGM) system. Reproduced with permission. [ 71 ] Copyright 2017 Elsevier. f) An integrated insulin delivery system consists of sweat glucose sensors and a thermoresponsive microneedle for diabetes therapy. Reproduced with permission. [ 41 ] Copyright 2016 Springer Nature.

In addition to the common reliability and accuracy issues faced by new sensing technologies, a unique challenge to wearable sensing devices is participants’ constant access to the sensing data. False positives as a result of inaccurate sensor reading may cause unnecessary anxiety, and the nature of wearable devices with frequent measurements and accessible data may exacerbate this emotional stress and confusion. For wearable sensing devices that target for day-to-day usage/evaluation in participants, efforts should also be devoted towards identifying the right way and appropriate frequency of presenting accurate data to the participants.

3. Risk Assessment

Although ‘non-invasiveness’ has been one of the key driving forces for the development of wearable devices for biomarkers monitoring; researchers should not overlook any physical or chemical risks associated with the operation of wearable technology in human research. Common risks associated with the on-body evaluation of wearable technologies include skin irritation, electrical shock, radiation exposure, chemical exposure and infection.

Often, epidermal devices built on conventional polymeric substrates, such as polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), and polyimide (PI), are not gas permeable. [ 27 , 28 ] In some use case scenario this property is leveraged to prevent evaporation of sweat and facilitate the retention of volatile organic components within the skin device interface; [ 29 , 30 ] on the other hand, this may also lead to skin irritation and introduce discomfort when such devices are worn for a long time. Sometimes, other choices of breathable, inflammation-free design of epidermal electronics may be available for longer-term human study ( Figure 2c ). [ 27 , 31 ] Researchers should take skin irritation and the length of study into consideration when designing human studies to minimize the risk and discomfort of participants.

Mountable devices like smart mouth guard, [ 32 ] earpieces [ 24 ] or glasses [ 33 ] warrant a closer examination of potential hazards due to chemical exposure because they are placed close to body cavities with weaker barriers of defense even though they are still considered “non-invasive” by many. In the case of a mouth guard, not only is the sensor/electrode exposed to the oral cavity but also other electronic components such as the printed circuit board (PCB). [ 34 ] The biocompatibility of individual components should be considered because even minute details like the choice of PCB solder may lead to accidental ingestion of toxic heavy metal (e.g. lead) during human studies. Additional precautions should be taken to encapsulate potential harmful components or replacing components with more biocompatible alternatives before researchers embark on device evaluation in human studies.

Soft electronics that are designed for direct contact with the ocular cavity [ 35 , 36 ] and open wounds [ 37 , 38 ] are typically associated with more risks when evaluated in vivo. In addition to biocompatibility and device design ergonomics concerns, an important factor to consider in order to meet the principle of nonmaleficence is the sterilization of devices to minimize risks of infection. [ 39 , 40 ] Sensible steps to take before human research include the in vitro cytotoxicity screening of materials and the testing in preclinical animal models. [ 41 – 43 ] In these two cases, ethical considerations relevant to animal research and the choice of animal models with modest translational distance (characterized by the number and size of inferential leaps from animals to humans [ 44 ] ) are important.

Wearable transdermal sensors in the form of microneedles are minimally invasive because of the small dimensions of the needles. Although reports show that recovery of skin barrier function can be as fast as a few hours after micropore creation, [ 45 ] the application of wearable transdermal device introduces additional risks of infections as unclosed microchannels may promote microcirculation of bacteria. [ 46 ] Standard operation protocols that ensure the implementation of good clinical practice prior to the application of microneedle patches are essential in minimizing the influx of exogenous microbiomes from surroundings. Confounding factors such as random movements, natural variations in skin texture, manual application pressure may introduce additional compression or shear stress that could potentially result in the failure and fracture of hollow microneedles. Moreover, microneedle materials or residual chemicals from microneedle processing methods could introduce additional risks of skin irritation. Various mechanical and biophysical characterization methods could be conducted in vitro and in vivo to evaluate potential hazards and assess the safety (skin irritation) of new devices. [ 47 , 48 ]

In addition to performing sensing and monitoring tasks, many wearable technologies developed in the lab also involve certain intervention capabilities where built-in actuators are triggered to deliver electrical/thermal stimulation or, in some cases, active drug components. GlucoWatch’s reverse iontophoresis (RI) might be the earliest demonstration of such types of intervention to facilitate the access and concentration of biofluids or biomarkers ( Figure 2d ). [ 49 ] RI applies a mild current between two electrodes to induce ion migration across the skin and extracts interstitial fluids due to electro-osmotic flow. One reason for the later retraction of this device from the market is the reported skin irritations due to the application of current. [ 50 ] Similarly, skin irritation is also associated with the long-term operation of epidermal iontophoretic devices that rely on the application of mild current to deliver sweat-stimulating drugs to trigger the local secretion of sweat under sedentary conditions. [ 2 , 51 ] Risks of skin irritation due to electrical shock and chemical build-up can be controlled and minimized by reducing current density, the time of application, appropriate buffering recipe and switching of cathode/anode to maintain local pH. [ 52 ] Other examples of intervention technologies are most commonly found in next-generation closed-loop systems where continuous monitoring of biomarkers is coupled with actuators that can be triggered when the level of a biomarker fluctuates beyond desirable levels. [ 17 , 41 ] In addition to performing and disclosing electrical safety risk assessment, researchers should also consider biochemical risks such as allergic reaction when an intervention technology is designed to deliver active drug components to subjects. Extra caution should be taken to address potential drug interaction when the subjects are taking additional medications.

While all wearable devices with wireless communication capabilities expose subjects to radiofrequency radiation, devices employing high-power communication technologies such as Wi-Fi to transfer large datasets are more susceptible to radiation risks. Although high-power devices like smartphones are generally regulated by specific absorption rate (SAR) testing and there is currently no clear evidence on the risks of low-level radiation; [ 53 ] wearable devices are clearly placed in closer proximity to the human body for longer periods of time. Risks associated with chronic exposure to low-intensity radiation are currently unknown. In addition, researchers should also be cautious of the cumulative effects of low-intensity radiation by operating multiple high-power wearable/portable devices in parallel. [ 54 ]

4. Fair subject selection and exclusion

Human research studies in this emerging field mostly fall into the category of first-in-human (FIH) or early-stage human trials. Experiments are designed based on information from limited literature sources or animal studies that predict a participant’s safety can be adequately protected with certain assumptions. Along with the objectives of scientific validity and societal value, experimental designs of human trials should clearly identify risks of harm to the subjects and outline all possible precautionary or intervention steps during the study to minimize risks and prevent harm. Selecting subjects who can make well-informed choices about research participation and from whom scientifically relevant data with minimal risks is a critical step.

Apt and fair subject selection may pose considerable challenges for FIH trials. For wearable medical technologies targeted at various vulnerable populations (patients with specific disease conditions), substantially more risks are involved as compared to the participation of healthy subjects. The evaluation of wearable sweat sensors typically requires subjects to perform mid- to high-intensity physical exercise. Human studies dealing with the non-invasive monitoring and management of chronic diseases such as metabolic syndrome or diabetes may require the recruitment of subjects with pre-existing medical conditions. Subjects who are physically inactive may find typical cycle ergometer exercise protocol designed for sweat collection (e.g. timed trial with constant workload or graded workload) more physically demanding. Potential risks and exercise-induced emergencies (e.g. bronchoconstriction, anaphylaxis, heat-illness) should be identified with appropriate standard operating procedures outlined prior to the recruitment of subjects to safeguard vulnerable populations.

Human studies may also aim to intentionally trigger a transient physiological or psychological abnormality in subjects (e.g. stress [ 55 ] and fatigue [ 56 ] experiments). Under the oversight of IRB, researchers are responsible for weighing the potential scientific value against the susceptibility to risk for certain groups of individuals (e.g. pregnant women, students) and determining the appropriate exclusion criteria of a study. As the ultimate goal of most wearable technologies is to monitor or diagnose a user’s health conditions, researchers may occasionally encounter incidental findings (e.g. abnormalities in the data collected from a participant) in the course of human research. A detailed framework for addressing and managing incidental findings during human research can be found elsewhere. [ 57 ]

Investigators should also make concerted efforts in recruiting individuals of various backgrounds in order to conduct scientifically and ethically sound research. The main goal of early-stage human research in wearable technology is to validate and translate a novel technological breakthrough to a viable prototype that could potentially benefit the largest population. Therefore, potential risks/benefits and device validity should be evaluated across different groups to minimize subject selection biases or inadvertent exclusion-by-design.

Wearable exoskeletons that are designed to restore or enhance human strength and agility hold great promise in rehabilitation. However, the device size and weight of wearable exoskeletons impose certain weight, height restrictions on the user/subject. [ 58 ] Commercial exoskeleton providers tend to impose rigid inclusion criteria from a cost perspective by investing on one-size-fits-all prototypes. As a result, children and individuals who are obese (which could be common for disabled individuals with sedentary lifestyles) may be denied access to such technologies due to exclusion by design. Women from certain ethnic groups with lower average height also tend to be “underweight” based on the user selection criteria of most commercial exoskeletons. Wearable exoskeleton research could potentially tackle this discrimination in marginalized communities by understanding and reflecting on the exclusion criteria and improve the inclusivity of a device from the design stage. Researchers share the responsibility to identify potential biases and dismantle any disparities caused by an inappropriate device or human study designs from the start. Incomplete metrics obtained in validation studies that lack diversity may also cause unintended consequences by reinforcing existing disparities in healthcare. [ 59 ]

5. Data Privacy and Security

The integration of a plethora of sensors on soft epidermal systems has enabled the passive collection of temporal information of a wide range of behavioral and biometric data. Real-time, continuous transmission of the information collected to other devices or cloud storage for post-processing can be achieved with various wireless communication technologies such as Near Field Communication (NFC), Bluetooth, Zigbee, and Wi-Fi. [ 14 ] Information collected and transmitted through current wearable technologies ranges from a simple heartbeat to the geographical location of a user and his medical conditions. While data sharing presents its unique advantage to personalized and adaptive health interventions, the vast amount of private identifiable information associated with human research raises serious concern over the privacy and data confidentiality of participants. A recent survey on digital consumer health revealed that the use of consumer health wearable devices has decreased from 33% in 2018 to just 18% in 2019. [ 60 ] Participants of human studies involving pervasive sensing technologies also cited data privacy and confidentiality as a major concern. [ 61 ] Therefore, investigators need to think ahead of research and incorporate ethical and regulatory considerations of data privacy and security early in the research design.

At times, data anonymization via distortion or removal of identifying features is introduced in research protocol to protect personal data. However, the effectiveness of such approaches against personal identity theft is still questionable. [ 62 ] Depending on the nature of the human study (population-level or personalized medicine), requirements on the extent of personal information gathered may differ. Controversies over COVID-19 tracing with mobile health and wearable technologies manifest the risks and potential conflicts associated with personal data in large scale data-rich human research. The decentralized contact tracing app promoted by Google and Apple allows anonymized pairing between infected people and their close contacts on their phones; on the other hand, the centralized tracing method traces contacts with a health authority-owned database by collecting personal information with mobile phone apps, wearable dongle or other surveillance methods. Although advocates of centralized tracing cited epidemiological benefits as health authorities can monitor the disease’s spread, concerns over intensive surveillance and intrusion of privacy stalled the adoption of centralized tracing in many countries. [ 63 ] Some biometric information collected with wearable technology may fall in the grey zone when it comes to regulatory compliance of data protection laws like General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA). While the ethical, legal, and social concerns in data-driven human studies may require collaborative efforts from IRB-related stakeholders, security experts and legal and regulatory expertise to outline case-specific data management and storage protocols, [ 61 ] on an individual research level, investigators can also address this trust deficit crisis by being forthcoming with how data is collected and used.

6. Informed consent

Informed consent is an ethical, regulatory, and legal requirement in human research that allows researchers to communicate the potential benefits and risks of the study to the participants. However, an informed consent document can be lengthy and contain technical jargons that are hard for potential research participants to comprehend. To practice respect for persons and to minimize information asymmetry, the information about the human study must be conveyed in a simple language to ensure adequate understanding. 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. Another important point to take note of and clarify in the informed consent for the wearable research community is the issue of data ownership and secondary use of data. In addition to the sensor and wireless communication technology development, a sizeable number of studies focus on software development and data analysis through machine learning. [ 66 , 67 ] Research groups with limited hardware development expertise may opt for commercially available consumer health or medical health wearable devices to collect large scale human data. [ 68 ] In such cases, end-user licensing agreement of the commercial device may complicate the issue of data ownership and usage. For example, Fitbit users may be unintentionally sharing their information with third parties when they sign up for an account. [ 69 ] Researchers should inform participants of potential secondary usage of data as stated in the privacy policy documents of commercial devices.

While both medical grade and consumer health grade wearable technologies are available on the market, the fine distinctions between these two device categories tend to cause confusion among the general public. A user’s misconception over the information collected by a wellness device may also be exacerbated by commercial advertisements’ choice of wording and the implied benefits. Therefore, an informed consent should explicitly state if the purpose of the device under evaluation is to diagnose or treat a medical condition (which constitute as a medical device) or to collect information to avoid participants’ confusion and over trust of a device and its data.

7. Summary and perspective

To date, much effort has been invested in the development and prototyping of soft electronics and robust sensing technologies at the bench side. Moving forward, current wearable technologies will need to demonstrate their validity and utility in clinical or point-of-care settings with larger scale human data from longitudinal and cohort studies. As current epidermal sensing technologies mature, they are expected to integrate into more complex closed-loop systems that allow autonomous intervention for therapeutic purposes to achieve the ultimate goal of personalized disease management. Although there have been commercial products that are capable of closing the loop in disease management such as Medtronic’s sensor-augmented insulin pump therapy for diabetes management ( Figure 2e ); these systems are based on rigid electronics with minimally invasive monitoring techniques. Future advances in biomaterials and flexible electronics will drive the evolution of such closed-loop systems into smaller, more conformal, hassle-free prototypes that can find applications in a broader audience. For example, an integrated drug delivery system consists of graphene-based multipixel biosensors for noninvasive sweat glucose monitoring and a thermoresponsive microneedle patch (triggered by elevated glucose level) for insulin therapy was proposed ( Figure 2f ). [ 41 ] Still, wearable closed-loop sensor-augmented drug delivery system is in its infancy. Such prototypes have yet to be validated rigorously in vivo . In addition to a multitude of technological bottlenecks in reliable sensor reading, energy harvesting, communication, and closed-loop algorithm, challenges such as therapy effectiveness, reliability and safety will need to be answered with large-scale and in-depth animal and/or human studies.

Despite the exponential growth of the field in the past decade, we are only at the beginning of harnessing wearable technology for performance enhancement and health management. As the field progresses, more innovative solutions to current technical challenges may become available; at the same time, these technologies may also bring about unforeseen ethical concerns during human research. We believe the active engagement of the research community in the ethical discussions and protection of human welfare is instrumental in facilitating successful early-stage human trials. Clear and close communication with research oversight bodies ensures that knowledge held by the researchers can be formalized and transferred to independent regulatory oversights and close the gap between current regulatory guidelines and the rapidly evolving research landscape. The medical community’s acceptance of these non-invasive technologies and their subsequent translation to a broader audience will require the concerted efforts of the research community to conduct scientifically and ethically sound in-human validation and extensive investigation on the clinical relevance of data collected with wearable technologies.

Acknowledgements

This project was supported by the National Institutes of Health grant 5R21NR018271, the Translational Research Institute for Space Health through NASA NNX16AO69A, NASA Cooperative Agreement 80NSSC20M0167, High Impact Pilot Research Award T31IP1666 from Tobacco Related-Disease Research Program, and the Rothenberg Innovation Initiative program at California Institute of Technology.

Biographies

Jiaobing Tu received her BEng in Materials Science and Engineering from Imperial College London. She joined Dr. Wei Gao’s research group in 2018 and is currently pursuing her PhD degree in Medical Engineering at the California Institute of Technology. Her research interests include wearable electronics and biosensors.

Wei Gao is currently an Assistant Professor of Medical Engineering at the California Institute of Technology. He received his PhD in Chemical Engineering from the University of California, San Diego in 2014. He worked as a postdoctoral fellow in Electrical Engineering and Computer Sciences at the University of California, Berkeley between 2014 and 2017. His current research interests include wearable biosensors, robotics, flexible electronics, and nanomedicine.

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COVID-19 Pandemic Changed Attitudes toward Wearables

Low-income hispanic/latine individuals more receptive to health-monitoring tech.

The Problem

The pandemic highlighted a need for regular health monitoring, but there are  several barriers remaining that prevent low-income Hispanic and Latine adults from adopting wearable technologies.

By studying this problem, researchers are hoping to  bring attention to existing health disparities and how wearable devices expand that gap. The study found that the pandemic significantly increased interest in wearable health-monitoring devices among low-income Hispanic and Latine adults living in the U.S.

Why it Matters

Wearables could provide an alternative to in-clinic monitoring for people without access to affordable health care and/or for people who distrust health systems. 

PhD candidate Stefany Cruz, Professor Maia Jacobs

The COVID-19 pandemic significantly increased interest in wearable health-monitoring devices among low-income Hispanic and Latine adults living in the US, a new Northwestern Engineering study has found.

While the pandemic highlighted the need for regular health monitoring, these groups often lack access to affordable health care and sometimes distrust existing health systems. Wearables, therefore, could provide a reliable, at-home alternative to traditional in-clinic health monitoring. 

But, although interest has increased, several barriers remain that prevent these groups from adopting wearable technologies. According to the researchers, tech companies historically have designed current wearable devices with affluent, predominantly white users in mind.

“Current designers do not consider the needs of low-income people of color regarding usability, accessibility and affordability,” said Northwestern’s  Stefany Cruz , who led the study. “If this trend continues, it will worsen digital and health inequities. In this study, we want to bring attention to existing health disparities and how wearable devices expand that gap. Wearables have the potential to fill the gap eventually, but we’re not there yet. We need to build devices that are more inclusive, and the design process should consider the context and culture of individuals from marginalized communities.”

The  study was published May 8  in the Journal of Medical Internet Research .

A personal connection

Cruz is a PhD candidate in computer engineering at the McCormick School of Engineering. In her engineering work, Cruz is particularly interested in building equitable, efficient, and intelligent wearable systems for groups historically excluded from the design process.

Cruz’s own experiences as a child of Salvadoran immigrants inspired her to embark on this new study. Growing up in East Los Angeles, Cruz was often sick, and her family did not have health insurance. After suffering a bout of strep throat, she watched her family struggle to pay the medical bills — an experience that sparked her interest in developing new technologies with a focus on health.

“That set up the whole trajectory of what I want to pursue in the computer engineering field,” Cruz said. “Because I witnessed the severe lack of access to health care, I want to build technologies from the ground up that can help support and uplift my community.”

Assembling participants

Although Cruz planned the study before pandemic hit, she noticed that COVID-19 changed the role of wearables in society. Once used mostly for counting steps and motivating people to move through the day, wearable devices now began playing a bigger role in health monitoring. These devices could track vital physiological signals, including blood oxygen levels. Low blood oxygen levels often have no symptoms until organs are irreparably damaged. But wearables could detect early warning signs, prompting a person to head to the hospital sooner — before it’s too late.

It was easy for Cruz to see how this technology could help her community. But why weren’t people taking advantage of these devices?

Because I witnessed the severe lack of access to health care, I want to build technologies from the ground up that can help support and uplift my community.

Stefany Cruz Computer Engineering PhD Candidate

To understand perceptions of wearables and identify the barriers to adoption, Cruz assembled a small group of low-income Hispanic and Latine adults in Chicago and Los Angeles. Participants met the low-income criteria if their income levels fell at or below the low-income threshold according to their county’s Department of Housing and Community Development.

After establishing a focus group, Cruz held two rounds of in-depth interviews between December 2021 and March 2022. In the first interviews, Cruz noticed that multiple participants made connections between COVID-19 and wearable devices. So, then she conducted a second round of interviews with more emphasis on using wearables for health monitoring. In these conversations, Cruz explored the participants’ opinions regarding wearable technology for health, their community’s perception of wearables and the features they would like to see in future wearables. She also asked participants about their access to Wi-Fi and other resource constraints.

Uncovering an overwhelming interest

Throughout the interviews, Cruz consistently found that the COVID-19 pandemic strongly influenced perceptions of wearable electronics. Participants who felt apathetic before the pandemic expressed a significantly increased interest in wearables for personal health monitoring and management. 

About two-thirds of the participants in the study lost a close family member to COVID-19. Several of the participants also contracted COVID-19 before the vaccine and other treatments became available. These experiences made them realize how useful wearable health-monitoring tools can be.

“I guess the one thing that scares me that I never even thought of until I got COVID were my oxygen levels,” one participant said. “Like, am I at normal levels? Is that an issue that I need to kind of think about?”

“One thing I noticed, especially with COVID right now which is…the timing of getting all your vitals measured can actually save somebody's life,” another participant said. “So, I think that's a very important thing. Like oxygen levels to be measured.”

Alternative to in-clinic care

Participants also discussed difficulties when trying to access health care and how wearables could potentially compensate for the lack of local resources. Specifically, some participants shared how their neighborhood hospitals had closed, forcing community members to seek care at small, overcrowded clinics.

“It's overly populated. Even if you make an appointment, you're there all day,” one Los Angeles-based participant said. “Whatever time you go, whatever day you go, it's always crowded, because it's one of the very few [clinics] that accepts Medi-Cal. So low-income communities, they don't have the resources; it's always crowded.”

One participant highlighted that community members' lack of trust in doctors, coupled with high medical expenses, posed barriers to seeking medical treatment.

“Hispanic people don't go to the doctor because they don't believe in the doctor,” the participant said. “They think the doctors are gonna kill them and then they're poor, so they can't pay for the doctor. So, like if [a wearable] could do basic [vital] tests that would be great.”

Community-driven design

As a part of the interview process, Cruz asked participants what features and functions they desired in wearable devices. Cruz noted that oftentimes technologies designed for low-income groups do not take the intended users’ needs into account.

“If we are the ones that are supposed to wear the devices, then it makes sense to ask our opinions of how they can be incorporated into our daily lives,” she said. 

In addition to wanting health monitoring capabilities (for heart rate, oxygen levels, blood pressure, and more), the participants also desired enhanced affordability, control over the captured health data and increased durability. For wearables to be most effective, users must wear them continuously to capture consistent health data. This is where durability becomes a critical factor.

As these technologies get better at sensing vital signals, they also should become more inclusive. Stefany Cruz

“I do think that it has to be very durable because the purpose is [for] low-income communities,” one participant said. “They don't have money to replace it. We just don't have comfy jobs. A lot of us work more physically demanding jobs. Some of us are plumbers, some are construction workers, some of us are gardeners. Some of us run a business and like that business involves pots and pans like we're restaurant workers. If [the device] breaks, they're just gonna say ‘oops’ and throw it away…If it is more durable that’s one of the biggest keys to wearing it.”

‘My community suffered a lot’

Although many people have moved on from the pandemic and resumed normal lives, Cruz said her community is still reeling. Cruz lost several family members to COVID-19 and hopes that designing more inclusive technologies can prevent future suffering. 

“During COVID-19, my community suffered a lot,” Cruz said. “Some people have been able to brush it off and move on, but some of us are still scarred. We lost family members that probably would still be alive if they weren’t infected. Many people have long-COVID symptoms, which wearables also could help monitor. As these technologies get better at sensing vital signals, they also should become more inclusive.”

The title of the paper is “Perceptions of Wearable Health Tools Post-COVID-19 in Low-Income Latine Communities.” Northwestern co-authors include  Maia Jacobs , who is the Lisa Wissner-Slivka and Benjamin Slivka Professor of Computer Science at McCormick, and students Claire Lu and Mara Ulloa. Cruz is advised by study co-author Josiah Hester, who was an assistant professor computer engineering at Northwestern when the research launched. Now, Hester is an associate professor of interactive computing and computer science at Georgia Tech.

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What can we lea…

What can we learn from wearable tech marketing strategies?

The article at a glance.

Apple’s launch of the VisionPro headset highlights the 4 Ps of marketing – product, price, place and promotion – while a Cambridge Judge alumnus has taken a different approach to wearable technology.

Category: AI and technology Insight Marketing

Successful companies regularly launch new products, as existing markets become saturated and firms seek new avenues for growth. But how do companies introduce their new products in terms of promotion, pricing and other key marketing metrics? 

Vincent Mak, Professor of Marketing and Decision Sciences at Cambridge Judge Business School, has given this topic plenty of thought in line with various theories on product launches and promotion – and the launch earlier this year in the US of Apple’s new VisionPro mixed reality headset provides an opportunity to explore this issue: while Apple frequently launches new versions of its popular iPhone, iPad and other products, the VisionPro marks Apple’s first new product category since the Apple Watch debuted in 2015. 

A very different approach to Apple’s has been pursued by Bobak Tavangar, an MPhil in Management graduate (MPhil 2012) of Cambridge Judge and the co-founder and CEO of Brilliant Labs, a Singapore-based company that is making AI-powered glasses, called Frame, that are powered by artificial intelligence (AI). 

Both these approaches were examined in recent editions of the Balance Sheet podcast hosted by Conrad Chua, Executive Director of the Cambridge MBA programme at Cambridge Judge Business School – in this article we cover excerpts from these podcasts with Vincent Mak and Bobak Tavangar.

Pricing wearable tech

Before launching a new product, marketing professionals have for a long time focused on the 4 Ps: 

These are also known in marketing as the ‘marketing mix’.  

In the case of the VisionPro, Apple has pitched the VisionPro as mixed reality or ‘spatial computing’ rather than a virtual reality device that amounts to a 3D camera. Its promotional adverts say the device is “familiar yet groundbreaking”. The price is hefty, with a cost of $3.499 (it is not yet available in the UK) clearly marking it as a high-end product, and for Apple the physical place of sale is its sleek Apple-branded stores. 

Brilliant Lab’s Frame, in contrast, is priced at $349, and the company says that it is unusual in that it’s a “hardware start-up” whose business model is based around its open-source philosophy aimed at attracting developers and others to the concept.

Vincent Mak on marketing new products 

Some edited excerpts from the podcast with Vincent Mak of Cambridge Judge: 

Is it a good idea for luxury brands to showcase the price when marketing a new product?  

The price actually makes you think about money. So, if you want to market your product as a high-class brand, as a brand that will convey high value and quality, you’d want people to think of the product as a more high-level product, and to think about values, life transformation, and being cool. In the case of Apple, they really have to give out the price point: it seems they are not making a huge fuss about it but are more concentrated on the cutting-edge gadget with great specifications.  

Why do we have prices like $3.499, instead of a round number like $3.500?  

The non-round number price is based on consumer behaviour research, because it might be useful for people on the margin who really have the final doubts about whether to buy it or not. Usually, the number nine effect is more impactful on non-premium products, where you want to mass sell. But maybe Apple already has its eyes on the mass-selling aspect, that VisionPro might not be that premium at some point in time.

What is the interplay between product strategy and product marketing?  

In terms of new product pricing, there are 2 strategies.  

• One is the penetration pricing strategy in which you really price it cheap in order to get as large an installed customer base as possible to exploit the network effect.
• The other one is probably what Apple is trying to do, which is skim pricing that aims to attract the most staunch believers. It is these believers who really want to use cutting-edge gadgets and then spread the word if they think it’s good. Apple has a fan community who would really go to the ends of the world in order to spread the word. The company would then want to capture the buzz, to create a sense of being part of the community – and what they do next is probably to reduce the price and release new versions that are more common and consumer friendly. 

How can some products break the rules on promotion where a great product  builds up with little or no advertising?  

A zero-advertising budget could be one of the options. I think the best way to promote your product is not to advertise, but to do it by word of mouth. That means you should not promote the product yourself but get other people to do this. There is this slightly mythic rule about the one central market research question you should ask in a survey if people don’t have more time for you, and that one question is about how likely is it that the consumer will recommend your product to other people. Doing things that create this may cost you very little, if you do them well. For example, you could run events where you just meet with some of your fans very casually, or just try to work on your social media where people talk about you.  

Is Apple’s VisionPro going to be a success like iPhone, or do you think it will maybe flop like Google Glass?  

If you look at a product like the iPod, it didn’t necessarily fizzle out but sort of morphed into something else, the iPhone. So, this could be another product that will be absorbed into something else after a few years. 

Bobak Tavangar on Brilliant Labs’ AI glasses 

Some excerpts from the podcast with Bobak Tavangar of Brilliant Labs.

What is the core essence behind Frame? 

Frame is as light and as thin as a pair of glasses that people wear every single day. There is a little charging dock that powers the battery, and there is little bit of text in the display – a see-through holographic display. Frame interfaces with a cloud-based AI agent: ours is called Noa, and its job is to understand what a user’s question might be and then go to any range of large language models for the answer. The idea is that it leaves you hands free. 

Your idea for Frame is quite different from the kind of vision that you see on Meta Oculus or Apple VisionPro. Why did you decide to go down this path versus what those guys are doing? 

Take the Apple VisionPro, it’s a VR device. And I think that’s a market in and of itself. It has its own set of use cases, its own set of technical challenges and dynamics. And Apple’s clearly chosen that as the beachhead for them. So, the key difference is that we are very open and ecosystem focused; Meta has that all closed off, so what you do with it is really only limited to what Meta themselves have launched or allow you to do with it. 

You emphasise that you have kept Frame open source. Please explain the reasoning behind this? 

We’ve kept it totally open source, because we want to empower a world of developers that are really sinking their teeth into this new creative medium of generative AI to begin tinkering and exploring what’s possible. Since we announced about 2 months ago, we’ve had developers coming out of the woodwork – anyone from large manufacturing, to agriculture, to industrial companies that are looking to train a model. And then, of course, there’s a lot of folks on the startup side of things, AI companies, building a proprietary model that they want embodied on the end user: maybe they’re serving doctors and EMTs (emergency medical technicians) in a medical setting, or maybe they’re serving teachers in the education setting, or lawyers who are poring through reams of documentation. 

Are there other advantages to your open source approach? 

We think about this 3 ways.  

• The first is that to have technology be inspectable, it should be modifiable.  
• The second is that we just think that a lot of people have some really cool ideas around generative AI.  
• The third reason, and this is more long-term strategic thinking, is that our bet is that the genius and the value is in the data – it not in the model, and it’s not in the hardware design. 

You mentioned that will.i.am, the Black Eyed Peas musician and design/fashion icon, has tried Frame. I have to ask, what does he think? 

He looks great in them. He looks very professorial and he absolutely loved it. I’m wearing his glasses. And I don’t think they look half as good as on me as Frame does on him, but that’s okay. I don’t think that was the point. 

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Vincent mak.

Professor of Marketing & Decision Sciences

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