Open Access
Supporting First Responders and Essential Workers During a Pandemic: Needs Assessment and Mixed-Methods Implementation Evaluation of a COVID-19 App-Based Intervention (Preprint)
Author(s) -
Stacie Vilendrer,
Alexis Amano,
Cati BrownJohnson,
Marissa Favet,
Nadia Safaeinili,
Jacqueline Villasenor,
Jonathan G. Shaw,
Attila Hertelendy,
Steven M. Asch,
Megan Mahoney
Publication year - 2021
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/26573
Subject(s) - snowball sampling , psychological intervention , mhealth , intervention (counseling) , pandemic , download , psychology , thematic analysis , social distance , medical education , medicine , covid-19 , qualitative research , nursing , computer science , world wide web , sociology , disease , pathology , infectious disease (medical specialty) , social science
Background The COVID-19 pandemic has created unprecedented challenges for first responders (eg, police, fire, and emergency medical services) and nonmedical essential workers (eg, workers in food, transportation, and other industries). Health systems may be uniquely suited to support these workers given their medical expertise, and mobile apps can reach local communities despite social distancing requirements. Formal evaluation of real-world mobile appbased interventions is lacking. Objective We aimed to evaluate the adoption, acceptability, and appropriateness of an academic medical centersponsored app-based intervention (COVID-19 Guide App) designed to support access of first responders and essential workers to COVID-19 information and testing services. We also sought to better understand the COVID-19related needs of these workers early in the pandemic. Methods To understand overall community adoption, views and download data of the COVID-19 Guide App were described. To understand the adoption, appropriateness, and acceptability of the app and the unmet needs of workers, semistructured qualitative interviews were conducted by telephone, by video, and in person with first responders and essential workers in the San Francisco Bay Area who were recruited through purposive, convenience, and snowball sampling. Interview transcripts and field notes were qualitatively analyzed and presented using an implementation outcomes framework. Results From its launch in April 2020 to September 2020, the app received 8262 views from unique devices and 6640 downloads (80.4% conversion rate, 0.61% adoption rate across the Bay Area). App acceptability was mixed among the 17 first responders interviewed and high among the 10 essential workers interviewed. Select themes included the need for personalized and accurate information, access to testing, and securing personal safety. First responders faced additional challenges related to interprofessional coordination and a culture of heroism that could both protect against and exacerbate health vulnerability. Conclusions First responders and essential workers both reported challenges related to obtaining accurate information, testing services, and other resources. A mobile app intervention has the potential to combat these challenges through the provision of disease-specific information and access to testing services but may be most effective if delivered as part of a larger ecosystem of support. Differentiated interventions that acknowledge and address the divergent needs between first responders and nonfirst responder essential workers may optimize acceptance and adoption.