{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:57:05Z","timestamp":1782842225972,"version":"3.54.5"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"\n Background<\/jats:title>\n Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed data from a nationally representative survey of US adults about their COVID-19\u2013related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters\u2019 friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/38633","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T10:17:37Z","timestamp":1679480257000},"page":"e38633","source":"Crossref","is-referenced-by-count":4,"title":["Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7082-4836","authenticated-orcid":false,"given":"Kevin E","family":"Cevasco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2321-1575","authenticated-orcid":false,"given":"Amira A","family":"Roess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm7003a3"},{"key":"ref2","unstructured":"Staffing Up: workforce levels needed to provide basic public health services for all Americansde Beaumont Foundation2021102022-12-18https:\/\/debeaumont.org\/wp-content\/uploads\/2021\/10\/Staffing-Up-FINAL.pdf"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S2468-2667(20)30157-2"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-020-01578-0"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/0960085x.2020.1784046"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.00106"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1080\/14737167.2021.1891883"},{"key":"ref8","unstructured":"mHealth app developer economics 2016: the current status and trends of the mHealth app marketResearch2Guidance20162022-12-18https:\/\/research2guidance.com\/product\/mhealth-app-developer-economics-2016\/"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.8832"},{"key":"ref10","unstructured":"Postmarket surveillance under Section 522 of the Federal Food, Drug, and Cosmetic ActUS Food and Drug Administration20222022-12-18https:\/\/www.fda.gov\/media\/81015\/download"},{"key":"ref11","unstructured":"Digital health policies and public health solutions for COVID-19US Food and Drug Administration202204282022-12-18https:\/\/www.fda.gov\/medical-devices\/coronavirus-covid-19-and-medical-devices\/digital-health-policies-and-public-health-solutions-covid-19"},{"key":"ref12","unstructured":"Developing a software precertification program: a working model v1US Food and Drug Administration2019012022-12-18https:\/\/www.fda.gov\/media\/119722\/download"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocab041"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1017\/S026646231800051X"},{"key":"ref15","unstructured":"App Advisor: An American Psychiatric Association Initiative. 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