1
|
Herbert C, Manabe YC, Filippaios A, Lin H, Wang B, Achenbach C, Kheterpal V, Hartin P, Suvarna T, Harman E, Stamegna P, Rao LV, Hafer N, Broach J, Luzuriaga K, Fitzgerald KA, McManus DD, Soni A. Differential Viral Dynamics by Sex and Body Mass Index During Acute SARS-CoV-2 Infection: Results from a Longitudinal Cohort Study. Clin Infect Dis 2023:ciad701. [PMID: 37972270 DOI: 10.1093/cid/ciad701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/25/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND There is evidence of an association of severe COVID-19 outcomes with increased body mass index (BMI) and male sex. However, few studies have examined the interaction between sex and BMI on SARS-CoV-2 viral dynamics. METHODS Participants conducted RT-PCR testing every 24-48 hours over a 15-day period. Sex and BMI were self-reported, and Ct values from E-gene were used to quantify viral load. Three distinct outcomes were examined using mixed effects generalized linear models, linear models, and logistic models, respectively: all Ct values (Model 1); nadir Ct value (model 2); and strongly detectable infection (at least one Ct value ≤28 during their infection) (Model 3). An interaction term between BMI and sex was included, and inverse logit transformations were applied to quantify the differences by BMI and sex using marginal predictions. RESULTS In total, 7,988 participants enrolled in this study, and 439 participants (Model 1) and 309 (Model 2 and 3) were eligible for these analyses. Among males, increasing BMI was associated with lower Ct values in a dose-response fashion. For participants with BMIs greater than 29, males had significantly lower Ct values and nadir Ct values than females. In total, 67.8% of males and 55.3% of females recorded a strongly detectable infection; increasing proportions of men had Ct values <28 with BMIs of 35 and 40. CONCLUSIONS We observed sex-based dimorphism in relation to BMI and COVID-19 viral load. Further investigation is needed to determine the cause, clinical impact, and transmission implications of this sex-differential effect of BMI on viral load.
Collapse
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Paul Hartin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Katherine A Fitzgerald
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| |
Collapse
|
2
|
Herbert C, Shi Q, Baek J, Wang B, Kheterpal V, Nowak C, Suvarna T, Singh A, Hartin P, Durnam B, Schrader S, Harman E, Gerber B, Barton B, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Hafer N, Broach J, Lin H, Heetderks W, McManus DD, Soni A. Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities. BMC Public Health 2023; 23:1848. [PMID: 37735647 PMCID: PMC10515232 DOI: 10.1186/s12889-023-16642-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.
Collapse
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - Paul Hartin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Giselle Corbie-Smith
- Department of Social Medicine, Department of Medicine, Center for Health Equity Research, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, MI, USA
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract With Kelly Services, Bethesda, MD, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
| |
Collapse
|
3
|
Golbus JR, Gupta K, Stevens R, Jeganathan VSE, Luff E, Shi J, Dempsey W, Boyden T, Mukherjee B, Kohnstamm S, Taralunga V, Kheterpal V, Murphy S, Klasnja P, Kheterpal S, Nallamothu BK. A randomized trial of a mobile health intervention to augment cardiac rehabilitation. NPJ Digit Med 2023; 6:173. [PMID: 37709933 PMCID: PMC10502072 DOI: 10.1038/s41746-023-00921-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, -7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.
Collapse
Affiliation(s)
- Jessica R Golbus
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
- Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), University of Michigan, Ann Arbor, MI, USA.
| | - Kashvi Gupta
- Department of Internal Medicine, University of Missouri Kansas City, Kansas City, MO, USA
| | - Rachel Stevens
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - V Swetha E Jeganathan
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Evan Luff
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jieru Shi
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Walter Dempsey
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Thomas Boyden
- Division of Cardiovascular Diseases, Department of Internal Medicine, Spectrum Health, Grand Rapids, MI, USA
| | | | - Sarah Kohnstamm
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Susan Murphy
- Departments of Statistics & Computer Science, Harvard University, Boston, MA, USA
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Brahmajee K Nallamothu
- Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), University of Michigan, Ann Arbor, MI, USA
- The Center for Clinical Management and Research, Ann Arbor VA Medical Center, Ann Arbor, MI, USA
| |
Collapse
|
4
|
Wang X, Pathiravasan CH, Zhang Y, Trinquart L, Borrelli B, Spartano NL, Lin H, Nowak C, Kheterpal V, Benjamin EJ, McManus DD, Murabito JM, Liu C. Association of Depressive Symptom Trajectory With Physical Activity Collected by mHealth Devices in the Electronic Framingham Heart Study: Cohort Study. JMIR Ment Health 2023; 10:e44529. [PMID: 37450333 PMCID: PMC10382951 DOI: 10.2196/44529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Few studies have examined the association between depressive symptom trajectories and physical activity collected by mobile health (mHealth) devices. OBJECTIVE We aimed to investigate if antecedent depressive symptom trajectories predict subsequent physical activity among participants in the electronic Framingham Heart Study (eFHS). METHODS We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (Center for Epidemiological Studies-Depression [CES-D] scores) and antidepressant medication use in eFHS participants who attended 3 Framingham Heart Study research exams over 14 years. At the third exam, eFHS participants were instructed to use a smartphone app for submitting physical activity index (PAI) surveys. In addition, they were provided with a study smartwatch to track their daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and physical activity including app-based PAI and smartwatch-collected step counts over a 1-year follow-up adjusting for age, sex, wear hour, BMI, smoking status, and other health variables. RESULTS We identified 3 depressive symptom trajectory groups from 722 eFHS participants (mean age 53, SD 8.5 years; n=432, 60% women). The low symptom group (n=570; mean follow-up 287, SD 109 days) consisted of participants with consistently low CES-D scores, and a small proportion reported antidepressant use. The moderate symptom group (n=71; mean follow-up 280, SD 118 days) included participants with intermediate CES-D scores, who showed the highest and increasing likelihood of reporting antidepressant use across 3 exams. The high symptom group (n=81; mean follow-up 252, SD 116 days) comprised participants with the highest CES-D scores, and the proportion of antidepressant use fell between the other 2 groups. Compared to the low symptom group, the high symptom group had decreased PAI (mean difference -1.09, 95% CI -2.16 to -0.01) and the moderate symptom group walked fewer daily steps (823 fewer, 95% CI -1421 to -226) during the 1-year follow-up. CONCLUSIONS Antecedent depressive symptoms or antidepressant medication use was associated with lower subsequent physical activity collected by mHealth devices in eFHS. Future investigation of interventions to improve mood including via mHealth technologies to help promote people's daily physical activity is needed.
Collapse
Affiliation(s)
- Xuzhi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States
| | - Belinda Borrelli
- Center for Behavioral Science Research, Boston University Henry M Goldman School of Dental Medicine, Boston, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - Emelia J Benjamin
- Section of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Joanne M Murabito
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Section of General Internal Medicine, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| |
Collapse
|
5
|
Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson ML, Manabe YC, Pandey S, Colubri A, O'Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study. Ann Intern Med 2023; 176:975-982. [PMID: 37399548 PMCID: PMC10321467 DOI: 10.7326/m23-0385] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. OBJECTIVE To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. DESIGN This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. SETTING Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. PARTICIPANTS Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. MEASUREMENTS The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. RESULTS Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. LIMITATION Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. CONCLUSION The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. PRIMARY FUNDING SOURCE National Institutes of Health RADx Tech program.
Collapse
Affiliation(s)
- Apurv Soni
- Program in Digital Medicine, Department of Medicine; Division of Health Systems Science, Department of Medicine; and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.S.)
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Honghuang Lin
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Yi Yan
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Biqi Wang
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Taylor Orwig
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Thejas Suvarna
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Summer Schrader
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Emma Harman
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Chris Nowak
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Vik Kheterpal
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Lokinendi V Rao
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Lisa Cashman
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Laura Gibson
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.G.)
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Ziyue Wang
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (Z.W.)
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Chad J Achenbach
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Robert L Murphy
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Matthew L Robinson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Shishir Pandey
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.C.)
| | - Laurel O'Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - Stephenie C Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine; Department of Population and Quantitative Health Sciences; and Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts (N.F.)
| | - Katherine L Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Kristian Roth
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Toby Lowe
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Timothy Stenzel
- Division of Microbiology, Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (T.S.)
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland (W.H.)
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - David D McManus
- Program in Digital Medicine, Division of Health Systems Science, and Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.D.M.)
| |
Collapse
|
6
|
Soni A, Herbert C, Pretz C, Stamegna P, Filippaios A, Shi Q, Suvarna T, Harman E, Schrader S, Nowak C, Schramm E, Kheterpal V, Behar S, Tarrant S, Ferranto J, Hafer N, Robinson M, Achenbach C, Murphy RL, Manabe YC, Gibson L, Barton B, O’Connor L, Fahey N, Orvek E, Lazar P, Ayturk D, Wong S, Zai A, Cashman L, Rao LV, Luzuriaga K, Lemon S, Blodgett A, Trippe E, Barcus M, Goldberg B, Roth K, Stenzel T, Heetderks W, Broach J, McManus D. Design and implementation of a digital site-less clinical study of serial rapid antigen testing to identify asymptomatic SARS-CoV-2 infection. J Clin Transl Sci 2023; 7:e120. [PMID: 37313378 PMCID: PMC10260333 DOI: 10.1017/cts.2023.540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/05/2023] [Accepted: 04/27/2023] [Indexed: 06/15/2023] Open
Abstract
Background Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Methods This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Conclusions The digital site-less approach employed in the "Test Us At Home" study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.
Collapse
Affiliation(s)
- Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | | | | | | | | | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Julia Ferranto
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Matthew Robinson
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robert L. Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yukari C. Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nisha Fahey
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Stephenie Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Allison Blodgett
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Elizabeth Trippe
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Mary Barcus
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Brittany Goldberg
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Kristian Roth
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Timothy Stenzel
- OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract with Kelly Services, Bethesda, MD, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| |
Collapse
|
7
|
Herbert C, Wang B, Lin H, Hafer N, Pretz C, Stamegna P, Tarrant S, Hartin P, Ferranto J, Behar S, Wright C, Orwig T, Suvarna T, Harman E, Schrader S, Nowak C, Kheterpal V, Orvek E, Wong S, Zai A, Barton B, Gerber B, Lemon SC, Filippaios A, D'Amore K, Gibson L, Greene S, Howard-Wilson S, Colubri A, Achenbach C, Murphy R, Heetderks W, Manabe YC, O'Connor L, Fahey N, Luzuriaga K, Broach J, McManus DD, Soni A. Performance of Rapid Antigen Tests Based on Symptom Onset and Close Contact Exposure: A secondary analysis from the Test Us At Home prospective cohort study. medRxiv 2023:2023.02.21.23286239. [PMID: 36865199 PMCID: PMC9980261 DOI: 10.1101/2023.02.21.23286239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Background The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on 'when to test'. Design Setting and Participants The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.
Collapse
|
8
|
Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson M, Manabe YC, Pandey S, Colubri A, Oâ Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection. medRxiv 2023:2022.08.05.22278466. [PMID: 35982680 PMCID: PMC9387089 DOI: 10.1101/2022.08.05.22278466] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance in screening for SARS-CoV-2 is not well established. We aimed to evaluate performance of Ag-RDT for detection of SARS-CoV-2 for symptomatic and asymptomatic participants. Methods Participants >2 years old across the United States enrolled in the study between October 2021 and February 2022. Participants completed Ag-RDT and molecular testing (RT-PCR) for SARS-CoV-2 every 48 hours for 15 days. This analysis was limited to participants who were asymptomatic and tested negative on their first day of study participation. Onset of infection was defined as the day of first positive RT-PCR result. Sensitivity of Ag-RDT was measured based on testing once, twice (after 48-hours), and thrice (after 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status. Results In total, 5,609 of 7,361 participants were eligible for this analysis. Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDT twice 48-hours apart resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Excluding singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower at 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with testing three times at 48-hour intervals. Discussion Performance of Ag-RDT was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours.
Collapse
|
9
|
Soni A, Herbert C, Pretz C, Stamegna P, Filippaios A, Shi Q, Suvarna T, Harman E, Schrader S, Nowak C, Schramm E, Kheterpal V, Behar S, Tarrant S, Ferranto J, Hafer N, Robinson M, Achenbach C, Murphy RL, Manabe YC, Gibson L, Barton B, O'Connor L, Fahey N, Orvek E, Lazar P, Ayturk D, Wong S, Zai A, Cashman L, Rao LV, Luzuriaga K, Lemon S, Blodgett A, Trippe E, Barcus M, Goldberg B, Roth K, Stenzel T, Heetderks W, Broach J, McManus D. Finding a Needle in a Haystack: Design and Implementation of a Digital Site-less Clinical Study of Serial Rapid Antigen Testing to Identify Asymptomatic SARS-CoV-2 Infection. medRxiv 2023:2022.08.04.22278274. [PMID: 35982663 PMCID: PMC9387154 DOI: 10.1101/2022.08.04.22278274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Rapid antigen tests (Ag-RDT) for SARS-CoV-2 with Emergency Use Authorization generally include a condition of authorization to evaluate the test's performance in asymptomatic individuals when used serially. Objective To describe a novel study design to generate regulatory-quality data to evaluate serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals. Design Prospective cohort study using a decentralized approach. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Setting Participants throughout the mainland United States were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Ag-RDTs were completed at home, and molecular comparators were shipped to a central laboratory. Participants Individuals over 2 years old from across the U.S. with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Measurements Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported. Key Results A total of 7,361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 U.S. states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Limitations New, complex workflows required significant operational and data team support. Conclusions: The digital site-less approach employed in the 'Test Us At Home' study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19, and can be adapted across research disciplines to optimize study enrollment and accessibility.
Collapse
|
10
|
Soni A, Herbert C, Filippaios A, Broach J, Colubri A, Fahey N, Woods K, Nanavati J, Wright C, Orwig T, Gilliam K, Kheterpal V, Suvarna T, Nowak C, Schrader S, Lin H, O'Connor L, Pretz C, Ayturk D, Orvek E, Flahive J, Lazar P, Shi Q, Achenbach C, Murphy R, Robinson M, Gibson L, Stamegna P, Hafer N, Luzuriaga K, Barton B, Heetderks W, Manabe YC, McManus D. Comparison of Rapid Antigen Tests' Performance Between Delta and Omicron Variants of SARS-CoV-2 : A Secondary Analysis From a Serial Home Self-testing Study. Ann Intern Med 2022; 175:1685-1692. [PMID: 36215709 PMCID: PMC9578286 DOI: 10.7326/m22-0760] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND It is important to document the performance of rapid antigen tests (Ag-RDTs) in detecting SARS-CoV-2 variants. OBJECTIVE To compare the performance of Ag-RDTs in detecting the Delta (B.1.617.2) and Omicron (B.1.1.529) variants of SARS-CoV-2. DESIGN Secondary analysis of a prospective cohort study that enrolled participants between 18 October 2021 and 24 January 2022. Participants did Ag-RDTs and collected samples for reverse transcriptase polymerase chain reaction (RT-PCR) testing every 48 hours for 15 days. SETTING The parent study enrolled participants throughout the mainland United States through a digital platform. All participants self-collected anterior nasal swabs for rapid antigen testing and RT-PCR testing. All Ag-RDTs were completed at home, whereas nasal swabs for RT-PCR were shipped to a central laboratory. PARTICIPANTS Of 7349 participants enrolled in the parent study, 5779 asymptomatic persons who tested negative for SARS-CoV-2 on day 1 of the study were eligible for this substudy. MEASUREMENTS Sensitivity of Ag-RDTs on the same day as the first positive (index) RT-PCR result and 48 hours after the first positive RT-PCR result. RESULTS A total of 207 participants were positive on RT-PCR (58 Delta, 149 Omicron). Differences in sensitivity between variants were not statistically significant (same day: Delta, 15.5% [95% CI, 6.2% to 24.8%] vs. Omicron, 22.1% [CI, 15.5% to 28.8%]; at 48 hours: Delta, 44.8% [CI, 32.0% to 57.6%] vs. Omicron, 49.7% [CI, 41.6% to 57.6%]). Among 109 participants who had RT-PCR-positive results for 48 hours, rapid antigen sensitivity did not differ significantly between Delta- and Omicron-infected participants (48-hour sensitivity: Delta, 81.5% [CI, 66.8% to 96.1%] vs. Omicron, 78.0% [CI, 69.1% to 87.0%]). Only 7.2% of the 69 participants with RT-PCR-positive results for shorter than 48 hours tested positive by Ag-RDT within 1 week; those with Delta infections remained consistently negative on Ag-RDTs. LIMITATION A testing frequency of 48 hours does not allow a finer temporal resolution of the analysis of test performance, and the results of Ag-RDTs are based on self-report. CONCLUSION The performance of Ag-RDTs in persons infected with the SARS-CoV-2 Omicron variant is not inferior to that in persons with Delta infections. Serial testing improved the sensitivity of Ag-RDTs for both variants. The performance of rapid antigen testing varies on the basis of duration of RT-PCR positivity. PRIMARY FUNDING SOURCE National Heart, Lung, and Blood Institute of the National Institutes of Health.
Collapse
Affiliation(s)
- Apurv Soni
- Program in Digital Medicine and Division of Clinical Informatics, Department of Medicine, and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.S.)
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (J.B., L.O.)
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.C.)
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine, Department of Population and Quantitative Health Sciences, and Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts (N.F.)
| | - Kelsey Woods
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Janvi Nanavati
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Taylor Orwig
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Karen Gilliam
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Vik Kheterpal
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | - Thejas Suvarna
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | - Chris Nowak
- CareEvolution, Ann Arbor, Michigan (V.K., T.S., C.N., S.S.)
| | | | - Honghuang Lin
- Program in Digital Medicine and Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L.)
| | - Laurel O'Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (J.B., L.O.)
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., A.F., K.W., J.N., C.W., T.O., K.G., C.P.)
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Julie Flahive
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, Department of Population and Quantitative Health Sciences, and University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (Q.S.)
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.A., R.M.)
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.A., R.M.)
| | - Matthew Robinson
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., Y.C.M.)
| | - Laura Gibson
- Department of Pediatrics and Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.G.)
| | - Pamela Stamegna
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (P.S., N.H.)
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts (P.S., N.H.)
| | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.)
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.A., E.O., J.F., P.L., B.B.)
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland (W.H.)
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.R., Y.C.M.)
| | - David McManus
- Program in Digital Medicine and Division of Cardiology, Department of Medicine, and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.M.)
| |
Collapse
|
11
|
Herbert C, Broach J, Heetderks W, Qashu F, Gibson L, Pretz C, Woods K, Kheterpal V, Suvarna T, Nowak C, Lazar P, Ayturk D, Barton B, Achenbach C, Murphy R, McManus D, Soni A. Feasibility of At-Home Serial Testing Using Over-the-Counter SARS-CoV-2 Tests With a Digital Smartphone App for Assistance: Longitudinal Cohort Study. JMIR Form Res 2022; 6:e35426. [PMID: 36041004 PMCID: PMC9580993 DOI: 10.2196/35426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The ongoing SARS-CoV-2 pandemic necessitates the development of accurate, rapid, and affordable diagnostics to help curb disease transmission, morbidity, and mortality. Rapid antigen tests are important tools for scaling up testing for SARS-CoV-2; however, little is known about individuals' use of rapid antigen tests at home and how to facilitate the user experience. OBJECTIVE This study aimed to describe the feasibility and acceptability of serial self-testing with rapid antigen tests for SARS-CoV-2, including need for assistance and the reliability of self-interpretation. METHODS A total of 206 adults in the United States with smartphones were enrolled in this single-arm feasibility study in February and March 2021. All participants were asked to self-test for COVID-19 at home using rapid antigen tests daily for 14 days and use a smartphone app for testing assistance and to report their results. The main outcomes were adherence to the testing schedule, the acceptability of testing and smartphone app experiences, and the reliability of participants versus study team's interpretation of test results. Descriptive statistics were used to report the acceptability, adherence, overall rating, and experience of using the at-home test and MyDataHelps app. The usability, acceptability, adherence, and quality of at-home testing were analyzed across different sociodemographic, age, and educational attainment groups. RESULTS Of the 206 enrolled participants, 189 (91.7%) and 159 (77.2%) completed testing and follow-up surveys, respectively. In total, 51.3% (97/189) of study participants were women, the average age was 40.7 years, 34.4% (65/189) were non-White, and 82% (155/189) had a bachelor's degree or higher. Most (n=133/206, 64.6%) participants showed high testing adherence, meaning they completed over 75% of the assigned tests. Participants' interpretations of test results demonstrated high agreement (2106/2130, 98.9%) with the study verified results, with a κ score of 0.29 (P<.001). Participants reported high satisfaction with self-testing and the smartphone app, with 98.7% (157/159) reporting that they would recommend the self-test and smartphone app to others. These results were consistent across age, race/ethnicity, and gender. CONCLUSIONS Participants' high adherence to the recommended testing schedule, significant reliability between participants and study staff's test interpretation, and the acceptability of the smartphone app and self-test indicate that self-tests for SARS-CoV-2 with a smartphone app for assistance and reporting is a highly feasible testing modality among a diverse population of adults in the United States.
Collapse
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,UMass Memorial Medical Center, Worcester, MA, United States
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, United States
| | - Felicia Qashu
- National Institutes of Health, Bethesda, MD, United States
| | - Laura Gibson
- Division of Infectious Disease and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kelsey Woods
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | | | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,Department of Cardiology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.,Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| |
Collapse
|
12
|
Ramirez AH, Sulieman L, Schlueter DJ, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman KN, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark CR, Cohn E, Ohno-Machado L, Schully SD, Ahmedani BK, Argos M, Cronin RM, O’Donnell C, Fouad M, Goldstein DB, Greenland P, Hebbring SJ, Karlson EW, Khatri P, Korf B, Smoller JW, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney SA, Gebo K, Denny JC, Carroll RJ, Glazer D, Harris PA, Hripcsak G, Philippakis A, Roden DM, Ahmedani B, Cole Johnson CD, Ahsan H, Antoine-LaVigne D, Singleton G, Anton-Culver H, Topol E, Baca-Motes K, Steinhubl S, Wade J, Begale M, Jain P, Sutherland S, Lewis B, Korf B, Behringer M, Gharavi AG, Goldstein DB, Hripcsak G, Bier L, Boerwinkle E, Brilliant MH, Murali N, Hebbring SJ, Farrar-Edwards D, Burnside E, Drezner MK, Taylor A, Channamsetty V, Montalvo W, Sharma Y, Chinea C, Jenks N, Cicek M, Thibodeau S, Holmes BW, Schlueter E, Collier E, Winkler J, Corcoran J, D’Addezio N, Daviglus M, Winn R, Wilkins C, Roden D, Denny J, Doheny K, Nickerson D, Eichler E, Jarvik G, Funk G, Philippakis A, Rehm H, Lennon N, Kathiresan S, Gabriel S, Gibbs R, Gil Rico EM, Glazer D, Grand J, Greenland P, Harris P, Shenkman E, Hogan WR, Igho-Pemu P, Pollan C, Jorge M, Okun S, Karlson EW, Smoller J, Murphy SN, Ross ME, Kaushal R, Winford E, Wallace F, Khatri P, Kheterpal V, Ojo A, Moreno FA, Kron I, Peterson R, Menon U, Lattimore PW, Leviner N, Obedin-Maliver J, Lunn M, Malik-Gagnon L, Mangravite L, Marallo A, Marroquin O, Visweswaran S, Reis S, Marshall G, McGovern P, Mignucci D, Moore J, Munoz F, Talavera G, O'Connor GT, O'Donnell C, Ohno-Machado L, Orr G, Randal F, Theodorou AA, Reiman E, Roxas-Murray M, Stark L, Tepp R, Zhou A, Topper S, Trousdale R, Tsao P, Weidman L, Weiss ST, Wellis D, Whittle J, Wilson A, Zuchner S, Zwick ME. The All of Us Research Program: Data quality, utility, and diversity. Patterns 2022; 3:100570. [PMID: 36033590 PMCID: PMC9403360 DOI: 10.1016/j.patter.2022.100570] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 03/30/2022] [Accepted: 07/14/2022] [Indexed: 11/05/2022]
Abstract
The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools. The All of Us Research Program has released data for over 315,000 participants Demonstration projects support the utility and validity of the All of Us dataset The cloud-based Researcher Workbench provides secure, low-cost compute power
The engagement of participants in the research process and broad availability of data to diverse researchers are essential elements in building precision medicine equitably available for all. The NIH has established the ambitious All of Us Research Program to build one of the most diverse health databases in history with tools to support research to improve human health. Here, we present the initial launch of the Researcher Workbench with data types including surveys, physical measurements, and electronic health record data with validation studies to support researcher use of this novel platform. Broad access for researchers to data like these is a critical step in returning value to participants seeking to support the advancement of precision medicine and improved health for all.
Collapse
|
13
|
Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnan B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O’Connor L, Broach J, Heetderks W, McManus D, Soni A. Use of a Digital Assistant to Report COVID-19 Rapid Antigen Self-test Results to Health Departments in 6 US Communities. JAMA Netw Open 2022; 5:e2228885. [PMID: 36018589 PMCID: PMC9419013 DOI: 10.1001/jamanetworkopen.2022.28885] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Widespread distribution of rapid antigen tests is integral to the US strategy to address COVID-19; however, it is estimated that few rapid antigen test results are reported to local departments of health. OBJECTIVE To characterize how often individuals in 6 communities throughout the United States used a digital assistant to log rapid antigen test results and report them to their local departments of health. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study is based on anonymously collected data from the beneficiaries of the Say Yes! Covid Test program, which distributed more than 3 000 000 rapid antigen tests at no cost to residents of 6 communities (Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; Ann Arbor and Ypsilanti, Michigan; and Chattanooga, Tennessee) between April and October 2021. A descriptive evaluation of beneficiary use of a digital assistant for logging and reporting their rapid antigen test results was performed. INTERVENTIONS Widespread community distribution of rapid antigen tests. MAIN OUTCOMES AND MEASURES Number and proportion of tests logged and reported to the local department of health through the digital assistant. RESULTS A total of 313 000 test kits were distributed, including 178 785 test kits that were ordered using the digital assistant. Among all distributed kits, 14 398 households (4.6%) used the digital assistant, but beneficiaries reported three-quarters of their rapid antigen test results to their state public health departments (30 965 tests reported of 41 465 total test results [75.0%]). The reporting behavior varied by community and was significantly higher among communities that were incentivized for reporting test results vs those that were not incentivized or partially incentivized (90.5% [95% CI, 89.9%-91.2%] vs 70.5%; [95% CI, 70.0%-71.0%]). In all communities, positive tests were less frequently reported than negative tests (60.4% [95% CI, 58.1%-62.8%] vs 75.5% [95% CI, 75.1%-76.0%]). CONCLUSIONS AND RELEVANCE These results suggest that application-based reporting with incentives may be associated with increased reporting of rapid tests for COVID-19. However, increasing the adoption of the digital assistant may be a critical first step.
Collapse
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | | | | | | | | | | | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Syed Naeem
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Kalibala
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | | | - Giselle Corbie-Smith
- Center for Health Equity Research, Department of Social Medicine, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, Michigan
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, via contract with Kelly Services, Bethesda, Maryland
| | - David McManus
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester
| |
Collapse
|
14
|
Soni A, Herbert C, Baek J, Shi Q, Marquez J, Harman E, Kheterpal V, Nowak C, Suvarna T, Lin H, Heetderks W, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Gerber BS, Hafer N, Barton B, Broach J, McManus D. Association of Mass Distribution of Rapid Antigen Tests and SARS-CoV-2 Prevalence: Results from NIH-CDC funded Say Yes! Covid Test program in Michigan. medRxiv 2022:2022.03.26.22272727. [PMID: 35411342 PMCID: PMC8996630 DOI: 10.1101/2022.03.26.22272727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Importance Wide-spread distribution of diagnostics is an integral part of the United States’ COVID-19 strategy; however, few studies have assessed the effectiveness of this intervention at reducing transmission of community COVID-19. Objective To assess the impact of the Say Yes! Covid Test (SYCT!) Michigan program, a population-based program that distributed 20,000 free rapid antigen tests within Ann Arbor and Ypsilanti, Michigan in June-August 2021, on community prevalence of SARS-CoV-2. Design This ecological study analyzed cases of SARS-CoV-2 from March to October 2021 reported to the Washtenaw County Health Department. Setting Washtenaw County, Michigan. Participants All residents of Washtenaw County. Interventions Community-wide distribution of 500,000 rapid antigen tests for SARS-CoV-2 to residents of Ann Arbor and Ypsilanti, Michigan. Each household was limited to one test kit containing 25 rapid antigen tests. Main Outcome and Measures Community prevalence of SARS-CoV-2, as measured through 7-day average cases, in Ann Arbor and Ypsilanti was compared to the rest of Washtenaw County. A generalized additive model was fitted with non-parametric trends for control and relative differences of trends in the pre-intervention, intervention, and post-intervention periods to compare intervention municipalities of Ann Arbor and Ypsilanti to the rest of Washtenaw County. Model results were used to calculate average cases prevented in the post-intervention period. Results In the post-intervention period, there were significantly lower standardized average cases in the intervention communities of Ann Arbor/Ypsilanti compared to the rest of Washtenaw County (p<0.001). The estimated standardized relative difference between Ann Arbor/Ypsilanti and the rest of Washtenaw County was -0.016 cases per day (95% CI: -0.020 to -0.013), implying that the intervention prevented 40 average cases per day two months into the post-intervention period if trends were consistent. Conclusions and Relevance Mass distribution of rapid antigen tests may be a useful mitigation strategy to combat community transmission of SARS-CoV-2, especially given the recent relaxation of social distancing and masking requirements.
Collapse
|
15
|
Herbert C, Shi Q, Kheterpal V, Nowak C, Suvarna T, Durnam B, Schrader S, Behar S, Naeem S, Tarrant S, Kalibala B, Singh A, Gerber B, Barton B, Lin H, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Baek J, Hafer N, Gibson L, O'Connor L, Broach J, Heetderks W, McManus D, Soni A. If you build it, will they use it? Use of a Digital Assistant for Self-Reporting of COVID-19 Rapid Antigen Test Results during Large Nationwide Community Testing Initiative. medRxiv 2022:2022.03.31.22273242. [PMID: 35411338 PMCID: PMC8996627 DOI: 10.1101/2022.03.31.22273242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Importance Wide-spread distribution of rapid-antigen tests is integral to the United States' strategy to address COVID-19; however, it is estimated that few rapid-antigen test results are reported to local departments of health. Objective To characterize how often individuals in six communities throughout the United States used a digital assistant to log rapid-antigen test results and report them to their local Department of Health. Design This prospective cohort study is based on anonymously collected data from the beneficiaries of The Say Yes! Covid Test program, which distributed 3,000,000 rapid antigen tests at no cost to residents of six communities between April and October 2021. We provide a descriptive evaluation of beneficiaries' use of digital assistant for logging and reporting their rapid antigen test results. Main Outcome and Measures Number and proportion of tests logged and reported to the Department of Health through the digital assistant. Results A total of 178,785 test kits were ordered by the digital assistant, and 14,398 households used the digital assistant to log 41,465 test results. Overall, a small proportion of beneficiaries used the digital assistant (8%), but over 75% of those who used it reported their rapid antigen test results to their state public health department. The reporting behavior varied between communities and was significantly different for communities that were incentivized for reporting test results (p < 0.001). In all communities, positive tests were less reported than negative tests (60.4% vs 75.5%; p<0.001). Conclusions and Relevance These results indicate that app-based reporting with incentives may be an effective way to increase reporting of rapid tests for COVID-19; however, increasing the adoption of the digital assistant is a critical first step.
Collapse
|
16
|
Herbert C, Kheterpal V, Suvarna T, Broach J, Marquez JL, Gerber B, Schrader S, Nowak C, Harman E, Heetderks W, Fahey N, Orvek E, Lazar P, Ferranto J, Noorishirazi K, Valpady S, Shi Q, Lin H, Marvel K, Gibson L, Barton B, Lemon S, Hafer N, McManus D, Soni A. Design and Preliminary Findings from Self-Testing for Our Protection from COVID-19 (STOP COVID-19): a prospective digital study of adherence to a risk-based testing protocol (Preprint). JMIR Form Res 2022; 6:e38113. [PMID: 35649180 PMCID: PMC9205422 DOI: 10.2196/38113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/11/2022] [Accepted: 05/29/2022] [Indexed: 01/15/2023] Open
Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - Emma Harman
- CareEvolution, Inc, Ann Arbor, MI, United States
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Kelly Services, Bethesda, MD, United States
| | - Nisha Fahey
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Julia Ferranto
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kamran Noorishirazi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Shivakumar Valpady
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kathryn Marvel
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Stephenie Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| |
Collapse
|
17
|
Soni A, Herbert C, Filippaios A, Broach J, Colubri A, Fahey N, Woods K, Nanavati J, Wright C, Orwig T, Gilliam K, Kheterpal V, Suvarna T, Nowak C, Schrader S, Lin H, O'Connor L, Pretz C, Ayturk D, Orvek E, Flahive J, Lazar P, Shi Q, Achenbach C, Murphy R, Robinson M, Gibson L, Stamegna P, Hafer N, Luzuriaga K, Barton B, Heetderks W, Manabe YC, McManus D. Comparison of Rapid Antigen Tests' Performance between Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) Variants of SARS-CoV-2: Secondary Analysis from a Serial Home Self-Testing Study. medRxiv 2022. [PMID: 35262091 DOI: 10.1101/2022.02.27.22271090] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background There is a need to understand the performance of rapid antigen tests (Ag-RDT) for detection of the Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) SARS-CoV-2 variants. Methods Participants without any symptoms were enrolled from October 18, 2021 to January 24, 2022 and performed Ag-RDT and RT-PCR tests every 48 hours for 15 days. This study represents a non-pre-specified analysis in which we sought to determine if sensitivity of Ag-RDT differed in participants with Delta compared to Omicron variant. Participants who were positive on RT-PCR on the first day of the testing period were excluded. Delta and Omicron variants were defined based on sequencing and date of first RT-PCR positive result (RT-PCR+). Comparison of Ag-RDT performance between the variants was based on sensitivity, defined as proportion of participants with Ag-RDT+ results in relation to their first RT-PCR+ result, for different duration of testing with rapid Ag-RDT. Subsample analysis was performed based on the result of participants' second RT-PCR test within 48 hours of the first RT-PCR+ test. Results From the 7,349 participants enrolled in the parent study, 5,506 met the eligibility criteria for this analysis. A total of 153 participants were RT-PCR+ (61 Delta, 92 Omicron); among this group, 36 (23.5%) tested Ag-RDT+ on the same day, and 84 (54.9%) tested Ag-RDT+ within 48 hours as first RT-PCR+. The differences in sensitivity between variants were not statistically significant (same-day: Delta 16.4% [95% CI: 8.2-28.1] vs Omicron 28.2% [95% CI: 19.4-38.6]; and 48-hours: Delta 45.9% [33.1-59.2] vs. Omicron 60.9% [50.1-70.9]). This trend continued among the 86 participants who had consecutive RT-PCR+ result (48-hour sensitivity: Delta 79.3% [60.3-92.1] vs. Omicron: 89.5% [78.5-96.0]). Conversely, the 38 participants who had an isolated RT-PCR+ remained consistently negative on Ag-RDT, regardless of the variant. Conclusions The performance of Ag-RDT is not inferior among individuals infected with the SARS-CoV-2 Omicron variant as compared to the Delta variant. The improvement in sensitivity of Ag-RDT noted with serial testing is consistent between Delta and Omicron variant. Performance of Ag-RDT varies based on duration of RT-PCR+ results and more studies are needed to understand the clinical and public health significance of individuals who are RT-PCR+ for less than 48 hours.
Collapse
|
18
|
Zhang Y, Pathiravasan CH, Hammond MM, Liu H, Lin H, Sardana M, Trinquart L, Borrelli B, Manders ES, Kornej J, Spartano NL, Nowak C, Kheterpal V, Benjamin EJ, McManus DD, Murabito JM, Liu C. Comparison of Daily Routines Between Middle-aged and Older Participants With and Those Without Diabetes in the Electronic Framingham Heart Study: Cohort Study. JMIR Diabetes 2022; 7:e29107. [PMID: 34994694 PMCID: PMC8783285 DOI: 10.2196/29107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 07/10/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background Daily routines (eg, physical activity and sleep patterns) are important for diabetes self-management. Traditional research methods are not optimal for documenting long-term daily routine patterns in participants with glycemic conditions. Mobile health offers an effective approach for collecting users’ long-term daily activities and analyzing their daily routine patterns in relation to diabetes status. Objective This study aims to understand how routines function in diabetes self-management. We evaluate the associations of daily routine variables derived from a smartwatch with diabetes status in the electronic Framingham Heart Study (eFHS). Methods The eFHS enrolled the Framingham Heart Study participants at health examination 3 between 2016 and 2019. At baseline, diabetes was defined as fasting blood glucose level ≥126 mg/dL or as a self-report of taking a glucose-lowering medication; prediabetes was defined as fasting blood glucose level of 100-125 mg/dL. Using smartwatch data, we calculated the average daily step counts and estimated the wake-up times and bedtimes for the eFHS participants on a given day. We compared the average daily step counts and the intraindividual variability of the wake-up times and bedtimes of the participants with diabetes and prediabetes with those of the referents who were neither diabetic nor prediabetic, adjusting for age, sex, and race or ethnicity. Results We included 796 participants (494/796, 62.1% women; mean age 52.8, SD 8.7 years) who wore a smartwatch for at least 10 hours/day and remained in the study for at least 30 days after enrollment. On average, participants with diabetes (41/796, 5.2%) took 1611 fewer daily steps (95% CI 863-2360; P<.001) and had 12 more minutes (95% CI 6-18; P<.001) in the variation of their estimated wake-up times, 6 more minutes (95% CI 2-9; P=.005) in the variation of their estimated bedtimes compared with the referents (546/796, 68.6%) without diabetes or prediabetes. Participants with prediabetes (209/796, 26.2%) also walked fewer daily steps (P=.04) and had a larger variation in their estimated wake-up times (P=.04) compared with the referents. Conclusions On average, participants with diabetes at baseline walked significantly fewer daily steps and had larger variations in their wake-up times and bedtimes than the referent group. These findings suggest that modifying the routines of participants with poor glycemic health may be an important approach to the self-management of diabetes. Future studies should be designed to improve the remote monitoring and self-management of diabetes.
Collapse
Affiliation(s)
- Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Michael M Hammond
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Hongshan Liu
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Mayank Sardana
- Cardiology Division, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Belinda Borrelli
- Center for Behavioral Science Research, Henry M. Goldman School of Dental Medicine, Boston University, Boston, MA, United States
| | - Emily S Manders
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Jelena Kornej
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, Boston, MA, United States
| | | | | | - Emelia J Benjamin
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Section of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.,Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States
| | - Joanne M Murabito
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| |
Collapse
|
19
|
Gadaleta M, Radin JM, Baca-Motes K, Ramos E, Kheterpal V, Topol EJ, Steinhubl SR, Quer G. Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms. NPJ Digit Med 2021; 4:166. [PMID: 34880366 PMCID: PMC8655005 DOI: 10.1038/s41746-021-00533-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/19/2021] [Indexed: 12/23/2022] Open
Abstract
Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.
Collapse
Affiliation(s)
- Matteo Gadaleta
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Jennifer M Radin
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Katie Baca-Motes
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Edward Ramos
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
- CareEvolution, 625N Main Street, Ann Arbor, MI, 48104, USA
| | - Vik Kheterpal
- CareEvolution, 625N Main Street, Ann Arbor, MI, 48104, USA
| | - Eric J Topol
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Steven R Steinhubl
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Giorgio Quer
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
| |
Collapse
|
20
|
Hammond MM, Zhang Y, Pathiravasan CH, Lin H, Sardana M, Trinquart L, Benjamin EJ, Borrelli B, Manders ES, Fusco K, Kornej J, Spartano NL, Kheterpal V, Nowak C, McManus DD, Liu C, Murabito JM. Relations between body mass index trajectories and habitual physical activity measured by smartwatch in the electronic cohort of the Framingham Heart Study: Cohort Study (Preprint). JMIR Cardio 2021; 6:e32348. [PMID: 35476038 PMCID: PMC9096636 DOI: 10.2196/32348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/14/2022] [Accepted: 03/14/2022] [Indexed: 12/11/2022] Open
Abstract
Background Objective Methods Results Conclusions
Collapse
Affiliation(s)
- Michael M Hammond
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Honghuang Lin
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States
| | - Mayank Sardana
- Cardiology Division, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Emelia J Benjamin
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Belinda Borrelli
- Department of Health Policy & Health Services Research, Henry M Goldman School of Dental Medicine, Boston University, Boston, MA, United States
| | - Emily S Manders
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Kelsey Fusco
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Jelena Kornej
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, Boston, MA, United States
| | | | | | - David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States
- Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Joanne M Murabito
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| |
Collapse
|
21
|
Sardana M, Lin H, Zhang Y, Liu C, Trinquart L, Benjamin EJ, Manders ES, Fusco K, Kornej J, Hammond MM, Spartano N, Pathiravasan CH, Kheterpal V, Nowak C, Borrelli B, Murabito JM, McManus DD. Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study. J Med Internet Res 2021; 23:e25591. [PMID: 34185019 PMCID: PMC8277303 DOI: 10.2196/25591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 02/22/2021] [Accepted: 03/16/2021] [Indexed: 01/18/2023] Open
Abstract
Background When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. Objective We aimed to study the association of habitual physical activity with home BP. Methods Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. Results We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. Conclusions In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.
Collapse
Affiliation(s)
- Mayank Sardana
- Department of Medicine, Division of Cardiology, University of California San Francisco, San Francisco, CA, United States
| | - Honghuang Lin
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Yuankai Zhang
- Boston University School of Public Health, Boston, MA, United States
| | - Chunyu Liu
- Boston University School of Public Health, Boston, MA, United States
| | - Ludovic Trinquart
- Boston University School of Public Health, Boston, MA, United States
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Boston University School of Public Health, Boston, MA, United States.,Framingham Heart Study, Framingham, MA, United States
| | | | - Kelsey Fusco
- Framingham Heart Study, Framingham, MA, United States
| | - Jelena Kornej
- Framingham Heart Study, Framingham, MA, United States
| | | | - Nicole Spartano
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | | | | | | | - Belinda Borrelli
- Henry M Goldman School of Dental Medicine, Center for Behavioral Science Research, Boston University, Boston, MA, United States
| | - Joanne M Murabito
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Framingham Heart Study, Framingham, MA, United States
| | - David D McManus
- Department of Medicine, UMass Medical School, Worcester, MA, United States
| |
Collapse
|
22
|
Pathiravasan CH, Zhang Y, Trinquart L, Benjamin EJ, Borrelli B, McManus DD, Kheterpal V, Lin H, Sardana M, Hammond MM, Spartano NL, Dunn AL, Schramm E, Nowak C, Manders ES, Liu H, Kornej J, Liu C, Murabito JM. Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study. J Med Internet Res 2021; 23:e24773. [PMID: 33470944 PMCID: PMC7857942 DOI: 10.2196/24773] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/15/2020] [Accepted: 12/19/2020] [Indexed: 01/25/2023] Open
Abstract
Background eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. Objective The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. Methods We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). Results Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). Conclusions We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.
Collapse
Affiliation(s)
| | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Emelia J Benjamin
- Section of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, United States.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Belinda Borrelli
- Center for Behavioral Science Research, Department of Health Policy & Health Services Research, Boston University Henry M Goldman School of Dental Medicine, Boston, MA, United States
| | - David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.,Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States
| | | | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Mayank Sardana
- Cardiology Division, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Michael M Hammond
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, Boston, MA, United States
| | - Amy L Dunn
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | | | | | - Emily S Manders
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Hongshan Liu
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Jelena Kornej
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Joanne M Murabito
- Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| |
Collapse
|
23
|
Lin H, Sardana M, Zhang Y, Liu C, Trinquart L, Benjamin EJ, Manders ES, Fusco K, Kornej J, Hammond MM, Spartano NL, Pathiravasan CH, Kheterpal V, Nowak C, Borrelli B, Murabito JM, McManus DD. Association of Habitual Physical Activity With Cardiovascular Disease Risk. Circ Res 2020; 127:1253-1260. [PMID: 32842915 DOI: 10.1161/circresaha.120.317578] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
RATIONALE A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD. OBJECTIVE To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults. METHODS AND RESULTS We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; P=0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women (P=1.3×10-26). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk (P=3.2×10-4). The association was attenuated but remained significant after further adjustment for body mass index (P=0.01). CONCLUSIONS In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.
Collapse
Affiliation(s)
- Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine (H.L.), Boston University School of Medicine, MA
| | - Mayank Sardana
- Cardiology Division, Department of Medicine, University of California San Francisco (M.S.)
| | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., C.L., L.T., C.H.P.)
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., C.L., L.T., C.H.P.)
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, MA (Y.Z., C.L., L.T., C.H.P.)
| | - Emelia J Benjamin
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
- Section of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, MA (E.J.B.)
| | - Emily S Manders
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
| | - Kelsey Fusco
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
| | - Jelena Kornej
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
| | - Michael M Hammond
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management (N.L.S.), Boston University School of Medicine, MA
| | | | | | | | - Belinda Borrelli
- Henry M. Goldman School of Dental Medicine, Center for Behavioral Science Research, Department of Health Policy & Health Services Research, Boston University, MA (B.B.)
| | - Joanne M Murabito
- Section of General Internal Medicine, Department of Medicine (J.M.M.), Boston University School of Medicine, MA
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, MA (H.L., E.J.B., E.S.M., K.F., J.K., M.M.H., J.M.M.)
| | - David D McManus
- Cardiology Division, Department of Medicine (D.D.M.), University of Massachusetts Medical School, Worcester
- Department of Quantitative Health Sciences (D.D.M.), University of Massachusetts Medical School, Worcester
| |
Collapse
|
24
|
Sardana M, Lin H, Trinquart L, Zhang Y, Liu C, Benjamin E, Manders E, Fusco K, Kornej J, Hammond M, Spartano N, Kheterpal V, Nowak C, Murabito J, McManus D. ASSOCIATION OF HABITUAL PHYSICAL ACTIVITY WITH HOME BLOOD PRESSURE: INSIGHTS FROM THE ELECTRONIC FRAMINGHAM HEART STUDY. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)30562-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
25
|
McManus DD, Trinquart L, Benjamin EJ, Manders ES, Fusco K, Jung LS, Spartano NL, Kheterpal V, Nowak C, Sardana M, Murabito JM. Design and Preliminary Findings From a New Electronic Cohort Embedded in the Framingham Heart Study. J Med Internet Res 2019; 21:e12143. [PMID: 30821691 PMCID: PMC6418484 DOI: 10.2196/12143] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/10/2019] [Accepted: 01/21/2019] [Indexed: 12/26/2022] Open
Abstract
Background New models of scalable population-based data collection that integrate digital and mobile health (mHealth) data are necessary. Objective The aim of this study was to describe a cardiovascular digital and mHealth electronic cohort (e-cohort) embedded in a traditional longitudinal cohort study, the Framingham Heart Study (FHS). Methods We invited eligible and consenting FHS Generation 3 and Omni participants to download the electronic Framingham Heart Study (eFHS) app onto their mobile phones and co-deployed a digital blood pressure (BP) cuff. Thereafter, participants were also offered a smartwatch (Apple Watch). Participants are invited to complete surveys through the eFHS app, to perform weekly BP measurements, and to wear the smartwatch daily. Results Up to July 2017, we enrolled 790 eFHS participants, representing 76% (790/1044) of potentially eligible FHS participants. eFHS participants were, on average, 53±8 years of age and 57% were women. A total of 85% (675/790) of eFHS participants completed all of the baseline survey and 59% (470/790) completed the 3-month survey. A total of 42% (241/573) and 76% (306/405) of eFHS participants adhered to weekly digital BP and heart rate (HR) uploads, respectively, over 12 weeks. Conclusions We have designed an e-cohort focused on identifying novel cardiovascular disease risk factors using a new smartphone app, a digital BP cuff, and a smartwatch. Despite minimal training and support, preliminary findings over a 3-month follow-up period show that uptake is high and adherence to periodic app-based surveys, weekly digital BP assessments, and smartwatch HR measures is acceptable.
Collapse
Affiliation(s)
- David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.,Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.,Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States
| | - Ludovic Trinquart
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Emelia J Benjamin
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Section of Preventive Medicine and Epidemiology and Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.,Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Emily S Manders
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Kelsey Fusco
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States
| | - Lindsey S Jung
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Nicole L Spartano
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University School of Medicine, Boston, MA, United States
| | | | | | - Mayank Sardana
- Cardiology Division, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Joanne M Murabito
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, United States.,Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| |
Collapse
|