Evidence map›Paper›PMID 32961527›Full record

ArticleJournal of medical Internet research2020

Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study.

Amichai Perlman, Alina Vodonos Zilberg, Peter Bak, Michael Dreyfuss, Maya Leventer-Roberts, Yael Vurembrand, Howard E Jeffries, Eyal Fisher, Yael Steuerman, Yinat Namir and 2 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Amichai Perlman *K Health Inc, New York, NY, United States.ORCID 0000-0001-6034-7995
Alina Vodonos Zilberg *K Health Inc, New York, NY, United States.ORCID 0000-0002-7397-4784
Peter BakK Health Inc, New York, NY, United States.ORCID 0000-0002-2639-3012
Michael DreyfussK Health Inc, New York, NY, United States.ORCID 0000-0003-0122-3321
Maya Leventer-RobertsK Health Inc, New York, NY, United States.ORCID 0000-0001-5797-0786
Yael VurembrandK Health Inc, New York, NY, United States.ORCID 0000-0001-5079-4679
Howard E JeffriesK Health Inc, New York, NY, United States.ORCID 0000-0003-3640-7761
Eyal FisherK Health Inc, New York, NY, United States.ORCID 0000-0003-3546-6153
Yael SteuermanK Health Inc, New York, NY, United States.ORCID 0000-0002-5310-7974
Yinat NamirK Health Inc, New York, NY, United States.ORCID 0000-0002-3492-344X
Yaara GoldschmidtK Health Inc, New York, NY, United States.ORCID 0000-0002-1294-870X
Daniel SouroujonK Health Inc, New York, NY, United States.ORCID 0000-0002-7769-8836

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice.

objectiveThe aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19-related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools.

methodsThis study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)-driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19-related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician.

resultsDuring the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department.

conclusionsOur results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

Indexed as

Clinical Laboratory TechniquesAdultArtificial IntelligenceBetacoronavirusCoronavirus InfectionsCOVID-19COVID-19 TestingFemaleHumansMalePandemicsPneumonia, ViralReferral and ConsultationRetrospective StudiesSARS-CoV-2Self Reportcohort studyCOVID-19digital healthonline toolremote careself-reportedsymptomsymptom checkertelemedicine

Identifiers

PMID32961527
PMCPMC7609191

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.