Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

Pratap, Abhishek; Neto, Elias Chaibub; Snyder, Phil; Stepnowsky, Carl; Elhadad, Noémie; Grant, Daniel; Mohebbi, Matthew H.; Mooney, Sean D.; Suver, Christine; Wilbanks, John; Mangravite, Lara M.; Heagerty, Patrick J.; Areán, Patricia A; Omberg, Larsson

doi:10.1038/s41746-020-0224-8

Cited by 299 publications

(331 citation statements)

References 77 publications

(79 reference statements)

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“…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 .…”

Section: Discussionmentioning

confidence: 99%

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

Quer

Radin

Gadaleta

et al. 2020

Nat Med

387

368

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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. 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 ele...

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Section: Discussionmentioning

confidence: 99%

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

Quer

Radin

Gadaleta

et al. 2020

Nat Med

387

368

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“…Recent software platforms like Research-Kit 14 and ResearchStack 15 facilitate the use of mobile technology to recruit and consent patients into studies 16 . The first wave of app-based studies have shown that patients can provide valuable information, with the appropriate recruitment and retention strategies 17 , to advance our understanding of disorders over time, generating new insights about diseases 18,19 and overall health 20,21 . This work contributes to the emerging area of research on digital phenotyping from patient-generated health data, specifically from data collected through smartphone applications 13,22 . Digital phenotyping aims at the automatic characterization of a patient's phenotype using electronic data.…”

Section: Introductionmentioning

confidence: 99%

Learning endometriosis phenotypes from patient-generated data

2020

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Endometriosis is a systemic and chronic condition in women of childbearing age, yet a highly enigmatic disease with unresolved questions: there are no known biomarkers, nor established clinical stages. We here investigate the use of patient-generated health data and data-driven phenotyping to characterize endometriosis patient subtypes, based on their reported signs and symptoms. We aim at unsupervised learning of endometriosis phenotypes using self-tracking data from personal smartphones. We leverage data from an observational research study of over 4000 women with endometriosis that track their condition over more than 2 years. We extend a classical mixed-membership model to accommodate the idiosyncrasies of the data at hand, i.e., the multimodality and uncertainty of the self-tracked variables. The proposed method, by jointly modeling a wide range of observations (i.e., participant symptoms, quality of life, treatments), identifies clinically relevant endometriosis subtypes. Experiments show that our method is robust to different hyperparameter choices and the biases of self-tracking data (e.g., the wide variations in tracking frequency among participants). With this work, we show the promise of unsupervised learning of endometriosis subtypes from self-tracked data, as learned phenotypes align well with what is already known about the disease, but also suggest new clinically actionable findings. More generally, we argue that a continued research effort on unsupervised phenotyping methods with patient-generated health data via new mobile and digital technologies will have significant impact on the study of enigmatic diseases in particular, and health in general.

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“…The present study also surfaces challenges in the use of wearable technology for longitudinal monitoring. Recent research has shown that participant retention ( 56 ) and compliance ( 57 ), i.e., the amount of time the wearable sensor patch is worn continuously, remains a challenge. Of the 112 enrolled participants, only 22 (19.6%) wore the patch for at least 24 h across all three sessions.…”

Section: Discussionmentioning

confidence: 99%