Digital technology use during COVID‐19 pandemic: A rapid review

Abstract

The relationship between humans and digital technologies has been documented extensively in the past decades, but has yet to be reviewed through the lens of the current global pandemic crisis. This review synthesizes the rapidly growing literature on digital technology use during the current COVID‐19 pandemic. It addresses the following four topics: (1) the specific digital technologies that have been used, (2) the specific populations who have used these digital technologies, (3) the specific activities that individuals and groups have used these digital technologies, and (4) the specific effects of using these digital technologies on humans during the pandemic. The 281 empirical articles we have identified suggest that (1) 28 various forms of technologies have been used, ranging from computers to artificial intelligence, (2) 8 different populations of users are using these technologies, primarily medical professionals, (3) 32 generalized types of activities are involved, including providing health services remotely, analyzing data, and communicating, and (4) 35 various effects have been observed, such as improved patient outcomes, continued education, and decreased outbreak impact. Through this rapid review, we sketched an expansive, multilevel model of the current knowledge of how humans are using technology during the COVID‐19 pandemic. Major findings and future directions are discussed.

1 INTRODUCTION

COVID‐19 is an unprecedented, worldwide pandemic that has been compared to the Second World War, the Great Depression, and the 1918 Spanish Flu in terms of the impact on human behavior. To control the COVID‐19 pandemic, physical distancing, and quarantine measures were mandated. In an effort to meet this mandate while trying to maintain the status quo, various types of human behavior (e.g., shopping, learning, working, meeting, and entertaining) shifted from offline to online, resulting in an accelerated diffusion of emerging digital technologies among ordinary people, while the digital divide further increases between citizens with versus without access to the technologies. Consequently, unprecedented changes in both human behavior and emerging technology diffusion generate a new opportunity for our research community to study technology‐related behavior in the global crisis. Now, one basic question that needs to be answered is how much literature has been accumulated and how much is known about digital technology use during the COVID‐19 pandemic.

Despite the short time after COVID‐19 became a pandemic and the long article publication circle, a research team from University of Bologna of Italy has already released one review (Golinelli et al., 2020) as a form of preprint in medRxiv, a preprint server for health sciences and a free source for disseminating research on COVID‐19 before peer review. It is the earliest review we have found that synthesizes the research on the use of digital technologies during the COVID‐19 pandemic. In this rapid review, it focused on public health issues related to COVID‐19, identified 52 articles, reviewed 29 articles in detail, and found that digital technologies have been used for three medical activities: diagnosis, surveillance, and prevention. This review has not only provided an initial knowledge base in this area but also motivated further search and review of human behavior with emerging technologies. Just a few weeks ago, when revising the present review based on the received external reviews, a new review was published in Nature Medicine (Budd et al., 2020). This review also focused on public health responses, similar to the first review (Golinelli et al., 2020). It has reviewed four types of technology‐based public health activities (i.e., population surveillance, case identification, contact tracing, and evaluation of interventions), five types of digital technologies (i.e., mobile phones, large online datasets, connected devices, low‐cost computing resources, and machine learning and natural language processing), and two types of barriers in implementing technologies (individual‐based legal, ethical, and privacy concerns and institution‐based organizational and workforce concerns). This review represents the latest and most comprehensive published synthesis of the current knowledge of health behavior with emerging technologies.

The current review is our effort to expand and extend the first two reviews (Budd et al., 2020; Golinelli et al. 2020) in three ways: (1) searching multiple major databases, including Web of Science, Scopus, and Google Scholar rather than only PubMed and medRxiv, (2) reviewing the literature more broadly by examining human behavior rather than just health behavior, and (3) synthesizing the literature based on a theoretical model for analyzing four basic elements of technology behavior, that is, technologies, users, activities, and effects (Yan, 2017). In the text that follows, we will first summarize our literature search and synthesis method, then review the four areas of research, and conclude with a brief discussion on major findings and future directions.

2 METHODS

We chose rapid review as a fitted review approach in order to achieve our primary research goal of synthesizing the literature within a limited time (Tricco et al., 2017). For this review, we aimed to address four research questions; what kinds of digital technologies have been used? Who is using these digital technologies? How or in what way are these digital technologies used? What kinds of effects come from using these digital technologies?

2.1 Literature search

This rapid review of technology use in COVID‐19 on both the published literature and the grey literature. Our literature search consists of the following four steps shown in Figure 1.

[Image Omitted. See PDF.]

First, searches were conducted in Google Scholar, Web of Science, Scopus, and PubMed on May 11th, 2020, and updated on May 27th, 2020, to identify research literature that has used digital technology in healthcare, education, working, research, and other areas. A set of predetermined rules provides a basis for including or excluding certain studies. A priori inclusion criteria were the following: (1) any primary search will mainly focus on empirical studies and literature reviews, (2) the research aimed at technology use specifically during COVID‐19 pandemic (3) the search was limited to English articles only, and (4) the time‐restriction is from the first outbreak of the virus in late 2019 until now, with most research focusing on 2020.

Second, after an initial keyword search in the databases, references of relevant articles were searched manually for relevant studies. The initial search results consisted of 187 articles. Once a group of potential studies has been identified, we screened each of these articles to determine their relevance (Petticrew & Roberts, 2006).

Third, based on the four research questions presented above, the first two authors participated in the first level of coding to code the four elements for each article. Not every article included all four elements. Meanwhile, the third author continued to search the databases to expand the initial search pool. This expansion operated under the following Boolean search string: (“information technology”) OR “communication technology”) OR “digital technologies”) OR digital technology)) AND (([“coronavirus”] OR “covid 19”) OR covid‐19) in two areas, education (e.g., education, teaching, learning, and study) and business (e.g., job, office, work, and employment). By the end of May, the number of articles that we had found increased to 281.

Fourth, before continuing to complete the remaining coding, the first two authors coded each article in terms of the relevance to technology use during the pandemic. We independently rated the coding with “agree,” “somehow differently,” and “differently” to assess the reliability of the coding on relevance. The result showed that 99% of the codings were under the “agree” category. We discussed how and why the existing discrepancies occurred and reached an agreement on all the discrepancies before further coding.

2.2 Literature coding

We took three steps to further code and synthesize the identified articles into two additional levels. Coding levels involves organizing the identifiable characteristics present in each article into different categories that uniquely and systematically address the four research questions of this rapid review as shown in Figure 2. The results of this coding process are present in Tables 1–4. The steps for this coding are listed in the following sequence.

[Image Omitted. See PDF.] TABLE 1. Major technologies used in COVID‐19

Technology type	Healthcare (143)	Education (44)	Work (38)	Daily life (35)
Hardware	Computerized tomography machines (53)	Webcam‐enabled computers (8)	Mobile phones (6)	Drones (8)
	Mobile Devices (20)		Webcam‐enabled computers (2)	Mobile devices (4)
	Computers (17)	Mobile devices (3)		Computers (3)
	Robots (10)			Robots (4)
	Wearable devices (4)			Automated vehicles (2)
	Video devices (4)			Cameras (7)
	Sensors (3)
	Digital HIPAA‐compliant tools (2)
	3‐Dimensional machines (2)
Software	Video‐based communication platform (51)	Video‐based communication platform (12)	Tele‐work technology (39)	Social media (14)
			• Dataset technology	• Twitter
	• Zoom	• Zoom	• Email	• Instagram
	• Facetime	• WebEx	• Online survey	• Facebook
	• WhatsApp	• Google hangouts	• Google sheets	• YouTube
	• Facebook messenger	Online lectures (9)	• SPSS	Tele‐communication Tools (3)
	Computer or mobile applications (44)	• GitHub	Video‐based communication platform (29)	• WhatsApp
	• Google apps	• Blackboard		• Email
	• Online survey	• Coursera	• Zoom	Tracking and control software (9)
	• NHS attend anywhere	• VoiceThread	• Facebook messenger
	• Thoracic VCAR software	• Cloud classrooms	• WhatsApp	• Google trends
	• In touch health	• Google cardboard	• LINE	• Geographic information systems
	Information and dataset (23)
	• Information system			• SPSS
	• Dataset technology			• CRISPR
	• Electronic health records			• Trace together
	Social Media (17)
	• YouTube
	• Twitter
	• Facebook
	Email (14)
	Chest X‐ray (5)
Mix use	Artificial intelligence (33)	Artificial intelligence (5)	Artificial intelligence (11)	Artificial intelligence (16)
	Internet of things (4)	Virtual reality (1)	Internet of things (8)	Internet of things (4)
	Virtual reality (4)

TABLE 2.  Various user groups distribute in all walks of life

User type	Healthcare (147)	Education (53)	Work (46)	Others (24)
Providers	Medical professionals (55)	Education (17)	Researchers (36)	Government officials (18)
	• Radiologists	• College faculty members	Scientists (14)	Public health authorities (3)
	• Surgeons	• Trainees	Employees (14)	Policy makers (4)
	• Nurses	• Teaching assistants	Information experts (2)
	• Psychologists	• Obstetrics and gynecology
	• Urologists	• Health professions educators
	• Residents	• Tourism scholars
	• Healthcare workers
	• Emergency providers
	• X‐ray technicians
	• Hospital managers
	• Caregivers
	• Physical therapists
	• Medical librarians
	• Otolaryngologists
	• Ophthalmologists
	• Head and neck surgeons
	• Rheumatologists
Receivers	Patients (102)	Students (36)		General public (14)
	• Urological patients	• Medical schools students		• East Asians
	• Infected individuals	• College students		• Public transportation users
	• ICU patients	• Dental education students		• Rural areas residents
	• Cancer patients	• Anatomy students		• YouTube consumers
	• Orthopaedic patients	Adult learners (1)		• United States citizens
	• Total joint Arthroplasty patients			• Travelers
	• Elderly patients			• Europeans
	• Multiple sclerosis patients			• Smartphone users
	• Musculoskeletal patients			Susceptible populations (4)
	• Mental health patients
	• Diabetes patients
	• Critically III patients
	• Adults with Alzheimer's
	• Patients with oral diseases
	• Cirrhosis patients
	• Geriatric patients
	• Low‐risk patients

TABLE 3.  Activities with technology

Healthcare (132)	Education (25)	Daily use (95)	Telework (10)
• Providing health services (38)	• Transitioning from face‐to‐face to online learning (9)	• Tracking/Tracing (38)	• Communicating (8)
• Communicating (28)		• Analyzing date (34)	• Utilizing digital information (4)
• Monitoring patients (23)	• Communicating (8)	• Diagnosing virus (13)	• Exchanging virtual services (3)
• Diagnosing virus (16)	• Delivering instruction (6)	• Predicting/Forecasting (11)
• Consulting (12)	• Learning (6)	• Reporting cases (9)
• Imaging (12)	• Teaching (7)	• Identifying COVID‐19 characteristics (8)
• Assessing patients (11)	• Achieving educational goals (2)
• Detecting virus (11)	• Evaluating outcomes (2)	• Communicating (6)
• Screening (8)	• Training (2)	• Mapping virus (5)
		• Classifying findings (4)
		• Interacting socially (4)
		• Simulating (3)
		• Transporting goods and services (3)

TABLE 4.  Effects of technology use

Healthcare (129)	Education (23)	Daily use (105)	Telework (12)
• Improved patient outcomes (31)	• Continued education in context with safe distancing	• Decreased impact of outbreak (31)	• Altered personal and occupational lives (5)
• Delivered care at a distance (25)	• Delivered (a)synchronous online instruction (6)	• Improved COVID‐19 data quality (14)	• Ensured employees safety (4)
• Reduced spread of virus (23)	• Created virtual teaching and learning environment (5)	• Influenced policies (10)	• Delayed rise in COVID‐19 (3)
• Updated healthcare infrastructure (16)	• Offered pedagogically innovative approaches to facilitate learning (5)	• Created snapshot of current state of COVID‐19 (9)	• Created cybersecurity concerns (2)
• Supported healthcare systems during outbreak (15)	• Transformed medical education (4)	• Influenced privacy principles (9)	• Increased efficiency (2)
• Distinguished COVID‐19 from other diseases (14)	• Engaged and supported struggling students (4)	• Accelerated digitalization of economy and communities (8)	• Influenced housing and transportation risks (2)
• Protected medical providers/patients (14)		• Created highly efficient quarantining methods (8)	• Reduced some impact on economy (2)
• Produced datasets for analysis (9)		• Predicted outbreak (8)
• Lowered healthcare cost (6)		• Managed public perceptions (6)
• Increased productivity (6)		• Maintained social connections (5)
• Conserved PPE (5)
• Provided efficient documentation (5)

First, after completing all of the level one coding for the final batch of all 281 articles that were deemed relevant, we reviewed this sample and rated each article's level of importance to identify the major examples based on their quality and impact. The rating level ranged from 1 to 3. Level 3 represents the most important articles that may present the core idea of technology use during COVID‐19. The final decision was to choose major articles from Level 3 articles based on the citation rates, quality of the journal, and the importance of the authors. Only Level 3 articles were reserved for discussion and reference in this article.

Second, once importance ratings had been analyzed and sorted and the articles were coded by technology, user, activity, and effect, we began Level 2 coding. This consisted of sorting each article into subcategories that would translate into a table that would identify a more specific and detailed understanding of the Level 1 coding. This second layer of sorting was produced under the guidance of our research questions, specifically categorizing a synthesis of the “who” and “what” that this technology was being used for in each Level 1 category. Common themes discovered from a review of the articles were derived as the foundation for each Level 2 category. It was determined that for technology, the subcategories would be hardware, software, and mixed use. The users were categorized as providers and receivers. Both of these subcategories were then further sorted into categories identified as healthcare, education, daily use, and telework. For Level 2 coding for the activity and effect categories, the only subcategories needed for them were the healthcare, education, daily use, and telework. Initially, for all four categories, telework was lumped under the daily use subcategory. It was later determined that due to its importance, despite there being minimal literature, it needed to be separated into its own subcategory.

Third, with Level 2 coding in place, the researchers were able to take it another level further by synthesizing the Level 1 categories with the Level 2 subcategories to create a third level of coding. This Level 3 coding helped to generalize the Level 1 codes to a category in which we could compare them to analyze their frequency in the Level 2 subcategories. This helped us identify the most common forms of technology, users, activities, and effects.

3 RESULTS

3.1 The current knowledge about various technologies used during COVID‐19

There exists relatively extensive literature in this area, a total of 260 articles examining the varieties of digital technologies that have been used during the COVID‐19 pandemic. Based on the types of technologies, this section categorizes the technologies into hardware and software. There are approximately 15 types of hardware technologies and over 50 types of software technologies have been used to combat COVID‐19. Looking back at history, unlike the Spanish flu that occurred 100 years ago, COVID‐19 has rapidly spread to every inhabitable continent within weeks. Fortunately, the hardware and software technology used during the pandemic has greatly improved the health system's ability to detect, track, and contain people with suspected infection. Not only the use of hardware technology such as computerized tomography machine in the medical field, but also in the fields of education, work, and daily life, the technology represented by computers, smartphones, and video‐based communication platforms brings an unprecedented change to our lives. Table 1 highlights the most frequently used technologies that are categorized into Healthcare, Education, Work, and Daily Use to provide an overview during the pandemic.

3.1.1 Hardware

As shown in the first section of Table 1, the most commonly reported technology in healthcare services is the computerized tomography machine that has been widely used in early detection and diagnosis due to the unique symptoms of the coronavirus. According to Ai (2020), chest computerized tomography had higher sensitivity for the diagnosis of COVID‐19 as compared with initial reverse‐transcription polymerase chain reaction (RT‐PCR). By using chest computerized tomography machines and deep learning technology, the coronavirus disease can be detected and distinguished from community‐acquired pneumonia and other non‐pneumonic lung diseases. (Li et al., 2020). Other technologies were highlighted such as video‐based mobile devices, computers, and robots as unreplaceable in remotely monitoring and diagnosing during the pandemic. Most patients with COVID‐19 can be managed remotely with advice on symptomatic management and self‐isolation. Although such consultations can be done by telephone in many cases, the video provides additional visual cues and therapeutic presence (Greenhalgh, Wherton, et al., 2020). Table 1 shows that hardware technologies such as mobile devices and webcam‐enabled computers play a fundamental role to provide services and are broadly used in virtual education, remote work, and daily life. For instance, without webcam‐enabled computers or mobile devices, online conferencing applications such as Zoom and WebEx cannot be operated during COVID‐19.

3.1.2 Software

Compared with hardware technology, the number of software technologies is more considerable and more widely used. In the healthcare domain, the most prominent technology is the video‐based communication platforms, such as Zoom, Facetime, and WhatsApp. With other remote services such as the computer or mobile applications, information and dataset, social media, email, and chest x‐ray, for example, could be deployed to provide synchronous and asynchronous support both for patients with COVID‐19 and for those requiring other routine clinical services (Keesara et al., 2020). Not only in the healthcare domain, but also for education, work and daily life use, video‐based communication platforms, for example, Zoom, WebEx, Facebook Messenger, and Google Hangouts, either became the “teaching and working assistant” to prompt conferencing digitally safely and effectively or built bridges to keep the social interaction for daily life in this special time. For education, online lectures can be done using GitHub, Blackboard, Coursera, and so on, which provide platforms to continue knowledge distribution. One example includes teaching remotely by using a video‐based approach such as a program called VoiceThread to record short videos explaining the content of the class (Gewin, 2020). Besides email, online surveys, Google Sheets, and more, telework technologies utilize digital information to exchange virtual services at work. Moreover, social media including Twitter, Instagram, Facebook, and YouTube; systems and applications such as Google Trends, and Geographic Information Systems, work to help to track, locate, and analyze outbreaks in daily life.

3.1.3 Mix use

Besides hardware technology and software technology, there are about five types of major “cross using” technologies such as the Internet of Things, Artificial Intelligence, Computerized tomography, Virtual reality, and the Internet of Medical Things that combine hardware and software technologies to monitor, surveillance, detect and prevent the outbreak.

3.2 Various user groups in all walks of life

As shown in Table 2, relatively extensive literature has documented different users who have used these technologies during the COVID‐19 pandemic. The majority of the technology users distribute into four domains which are Healthcare, Education, Work, and Others. When talking about technology use, there are two types of users involved in—the providers and the receivers. For example, in the healthcare domain, medical professionals such as radiologists, surgeons, and nurses are the providers who use electronic technology to provide services, and patients with chronic disease and infected patients are the healthcare receivers. In education, work, and other domains, the providers and receivers may be teachers and students or employees and employers. Even if they have different uses for technology in their daily life, they are using the same type.

3.2.1 Healthcare

Medical professionals and all kinds of patients with multiple chronic conditions are undoubtedly the largest group of users of digital technology during the pandemic. Among them, the radiologists, surgeons, and nurses are active on the front lines to diagnose and treat patients. According to Ai (2020), the radiologists hold an important position to classify the chest computerized tomography as positive or negative for COVID‐19 and describe main computerized tomography features and lesion distribution. At the same time, patients with different chronic diseases are receiving the services and treatment from healthcare professionals through the use of technologies, especially, for those who have already been infected with the coronavirus. As Keesara et al. (2020) stated, vulnerable populations such as patients with multiple chronic conditions or immunosuppression will face the difficult choice between risking iatrogenic COVID‐19 exposure during a clinician visit and postponing needed care. Whether choosing a face‐to‐face visit, postponing a visit, or using virtual healthcare, patients have to face the inevitable use of technology such as computerized tomography machines and video‐based communication platforms to get instruction from a healthcare professional. For this reason, healthcare professionals and their patients are the largest group of users of technologies during COVID‐19.

3.2.2 Education

With the outbreak, a large population was forced to study remotely to follow the worldwide stay at home order. According to Table 2, most of the educators and students choose to use video‐based devices and platforms to continue their education. They are becoming the second largest group of digital technology users during the pandemic. Sun, Tang, & Zuo (2020) mentioned that teachers have to adapt the pace of online teaching and put greater effort into preparing for online courses, innovating, designing lessons, and patiently turn students from passive recipients to engaged learners. A specific example from Gewin (2020) is Leonardo Rolla, a mathematician, who also teaches math for two terms each year at New York University (NYU) of Shanghai in China. With technological help from colleagues, he developed a strategy to teach remotely for his advanced linear‐algebra class of 33 students from the other side of the world during COVID‐19. This example also reflects that educators are another group of professional workers that are involved with working remotely.

3.2.3 Work

As shown in Table 2, work professionals have become a very unique group of technology users during the pandemic. Researchers, scientists, and employees from all walks of life continue working remotely by using digital technologies during COVID‐19. Different from the healthcare and education domain, it is not obvious to identify the providers and receivers of technology used in telework. From the work perspective, most of the time, no matter what kind of technology is being used, the work professionals are acting as both providers and receivers. For example, an employee could get direction from their supervisor while also needing to report their works by using Zoom.

3.2.4 Others

Other than the two main groups of digital technology users above, public health authorities, government officials, and the general public are all involved in technology use. To face this worldwide outbreak, people from all over the world are getting familiar with the technology used in everyday life. Public health authorities and government officials either use the mobile‐based tracking technology to monitor the distribution of the epidemic or the big data technology to analyze outbreaks and deploy strategies. At the same time, the general public from all over the world is receiving information by using digital devices.

3.3 Activity

As shown in Table 3, the 262 articles that examine how or in what way digital technologies are being used during the COVID‐19 pandemic were sorted into four subcategories. The primary activity subcategories were sorted as Healthcare, Education, Daily use, and Telework. Each subcategory has a list of generalized activities and the number next to them indicates the number of articles that the activity was present in.

3.3.1 Healthcare

As previously mentioned, most of the technological activity that took place during the COVID‐19 pandemic was uniquely and distinctively reflective of the needs that different users had. In the healthcare field, the most commonly reported activities were providing health services remotely, communicating, and monitoring. Greenhalgh, Koh et al. (2020) even produced guidelines for how and when to provide specific kinds of healthcare related activities through technology during the COVID‐19 pandemic. Hollander & Carr (2020) reviewed more than 50 U.S. health systems and found that healthcare systems have greatly altered their use of technology as a response to the pandemic. The dominant applications of healthcare related digital technology in this review included virtually consulting and screening. Other common uses of technology in healthcare during the pandemic included diagnosing and imaging which Ai et al. (2020) outline in their analysis of chest CT scans. Their activity led to an impressive finding that COVID‐19 was 97% based on positive RT‐PCR results.

3.3.2 Education

The COVID‐19 pandemic forced education all around the world to operate remotely and much of the literature on digital technology used for education assessed the results of this dramatic shift. The majority of the activity present in this educational category includes teaching, learning, communicating, and transitioning from face‐to‐face to online. A couple of major articles included these activities and went a step further to analyze the outcomes of them. Sun, Tang, & Zuo (2020) reviewed statistics of responses to questionnaires collected among 39,854 students at Southeast University in China, ultimately assessing the effectiveness of these digital activities. They found that “around 50% of students believed that the planned teaching objectives were fully attained and 46% for objectives basically attained” (Sun, Tang, & Zuo, 2020). This suggests that the application of digital technology use in education could use further assessment to make improvements in its implementation. Gewin (2020) conducted an interview with NYU Shanghai Professor, Leonardo Rolla, to produce “five tips for moving teaching online as COVID‐19 takes hold.” Within these tips, recording and conferencing were key activities identified for delivering education online.

3.3.3 Daily use

In terms of daily use and digital technology, the most prominent activities according to Table 3 were tracing, analyzing data, predicting/forecasting, and diagnosing the virus. One major article that discusses the majority of the major activities listed in the table was Boulos & Geraghty (2020). In their article, they “offer pointers to, and describe, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world.” They found that the application of this digital tool “improves data transparency and helps authorities disseminate information” (Boulos & Geraghty, 2020). Naudé (2020) took a more expansive role in the analysis of reviewing how technology, specifically AI, is being used in various contexts to combat the pandemic. Through an early, rapid review and discussion of “this AI scramble”, Naude identified several activities in which AI is being used which include alerting, tracking, predicting, diagnosing, curing, and social controlling. Last, planning and modeling community outcomes were crucial for the control of the spread of the virus and this could not be done without tracking and reporting the outbreak. This is exactly what Dong, Du, & Gardner (2020) were able to accomplish by developing “an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualize and track reported cases of coronavirus disease 2019 (COVID‐19) in real time.”

3.3.4 Telework

One of the most prominent technological activities present during the COVID‐19 pandemic was telework. Our rapid review only collected 12 important articles that directly discussed telework during the pandemic and identified two major articles that address the expansive activities that are involved with working remotely (Belzunegui‐Eraso & Erro‐Garcés, 2020; Baert et al., 2020). Some of the activities mentioned in these articles include conferencing digitally, communicating, desktop sharing, utilizing digital information, and exchanging virtual services.

3.4 Effects

As shown in Table 4, 269 articles discuss the main effects that were produced from digital technology use during the COVID‐19 pandemic and were sorted into four subcategories. The primary activity subcategories are Healthcare, Education, Daily use, and Telework. Each subcategory has a list of generalized activities and the number next to them indicates the number of articles that the activity was present in. The reigning effects found in our rapid review are related to technology's capacity to reduce the transmission of the virus and alter how we operate to become remote. For example, Cai & Cai (2020) discussed the ways that VR + 5G were used at a university hospital in Zhejiang, China to “cut the infection rate of medical staff on the front line of the COVID‐19 epidemic.” Meanwhile, Wang (2020) reviewed the ways that Taiwan used digital technology to effectively respond as a society to the pandemic and compared it to their 2003 SARS experience. The results indicated that the digital solutions they implemented during COVID‐19 significantly protected and supported public health.

It is also worth noting that in an effort to produce effects such as “delivering care at a distance” and “continuing education in context with safe distancing” in Table 4, a further general effect of technology use during the pandemic was the transformation of turning previously offline tasks into online activities. While not all effects required this transformation and were previously being done digitally, there were a significant number of effects that would not have been achieved without this transformation.

3.4.1 Healthcare

Digital technology in healthcare during the COVID‐19 pandemic was both transformed and used to keep everyone as safe and healthy as possible. The prevailing effects included care being delivered at a distance, better patient outcomes, and a reduction in the spread of the virus. To provide insight into some of these effects, Wosik et al. (2020) used examples reported by U.S. healthcare organizations, including the authors, “to describe the role that telehealth has played in transforming healthcare delivery during the 3 phases of the U.S. COVID‐19 pandemic.” Their findings firmly suggest that “virtual care has arrived.” Similarly, to Dong, Du, & Gardner (2020), Schulz et al. (2020) built a tool on their computational health platform that served as an interactive dashboard that summarizes COVID data and presents it in a format suitable for analysis. This digital solution produced timely information about incident cases for healthcare organizations which increased efficiency for controlling the virus.

3.4.2 Education

With education being abruptly forced to be delivered remotely, its effects were quite reflective of the nature of this shift. In the activity section, Gewin (2020) and Sun, Tang, & Zuo's (2020) methods and findings were analyzed to better understand the application of digital technology in the world of education. However, the effects of these applications were reserved for discussion in this section as these were also the two major articles we were able to find in our rapid review that highlight the effects of the transition to online learning. Gewin (2020) was able to provide interpersonal insight into how educational technology during COVID‐19 engaged, identified, and supported struggling students whereas Sun, Tang, & Zuo (2020) exposed more of the structural impact that occurred from digital technology use during COVID‐19.

3.4.3 Daily use

Generally speaking, the dominating effects in our rapid review for every day users were the production of privacy concerns and information, as well as the implementation of policies that ensured safe standards that ultimately decreased the impact of the outbreak. In addition to those effects, our rapid review discovered a major article that used technology to expose general attitudes and perceptions among the public that resulted in an effect of digital technology use during the pandemic. Geldsetzer (2020) administered an online questionnaire to 3,000 adults in the US and 3,000 adults in the UK and found that “the general public in both the United States and the United Kingdom held several important misconceptions about COVID‐19.”

3.4.4 Telework

Due to the minimal literature present on the effects of telework, there is very little that can be discussed about the effects of telework during the COVID‐19 pandemic. As seen in Table 4, the primary effects included the alteration of the relationship between personal and professional lives and the ensured safety of employees. Baert et al. (2020) implemented an online survey to further examine “the perceived effects of the increased teleworking on other facets of the respondents' personal and professional lives during COVID‐19.” They found that telework “increased efficiency, reduced risk of burnout, and weakened ties with colleagues and employer” (Baert et al., 2020).

4 CONCLUSION

This rapid review provides an outline of the current knowledge of digital technology use during the COVID‐19 pandemic by synthesizing the existing literature in four areas: technologies, users, activities, and effects. It suggests the following major findings: (1) digital technologies that were represented by the computerized tomography machine, video‐based communication platform, and artificial intelligence have been broadly used in healthcare, education, work, and daily life domains during the COVID‐19 pandemic; (2) the main user groups of electronic technology are categorized to providers and receivers, mainly including doctors and patients, teachers and students, and the government and the general public. It is worth mentioning that work professionals who use telework to accomplish their tasks have both identities at the same time; (3) providing health services and communicating were the most frequent activities associated with technology in healthcare during the pandemic. The majority of the activity present in this educational category includes transitioning from face‐to‐face to online, communicating, and delivering instruction. In terms of daily use and digital technology, the most prominent activities were tracing, analyzing data, predicting/forecasting, and diagnosing the virus, and (4) digital solutions significantly protected and supported public health. It provided a better understanding of education and highlighted the transition to online learning. In the work and daily living domain, it very much blended personal and professional boundaries at the expense of decreasing the risk of burnout. Nearly half of the literature that was gathered and analyzed in this review focused primarily on the healthcare field which created considerable gaps. This is plausible considering the pandemic is health related and trying to understand both the biological consequences and how to best handle them was of great importance. These gaps can be addressed by expanding the scope of the coding by making it more specific to the unique characteristics of the literature and by collecting further literature on non‐healthcare related fields.

This review also suggests several critical future research directions to further understand technology use during the COVID‐19 pandemic in particular and during natural and social crises. First and foremost, immediate research is needed to study technological use during the mid‐ and post‐pandemic rather than during the initial phase of the pandemic. For example, it would be useful to examine technology use when schools reopen for the fall semester, in the second wave of the pandemic, potentially in the upcoming winter, and in the ending period of the pandemic to help individuals and societies deal with lasting impacts such as Post Traumatic Stress Disorder. Second, it is very important to discover and monitor the emergence of new and creative uses of digital technologies. For example, we should closely follow AI technologies for new strategies for tackling the pandemic. We also should continue observing existing ones, such as Zoom for distance learning or mobile apps for monitoring back to school and back to work, for effective and efficient use. Third, further efforts should be made to study, understand, and narrow various types of the digital divide and help more users, especially from developing countries, poor areas, and challenging groups, to access and use digital technologies. Fourth, researchers should design and conduct studies to examine various less‐studied but urgently needed sectors of societies such as nursing homes, public administration, law enforcement, or national defense rather than just in medicine, education, work, and daily life. Finally, future research should examine various effects of technology use, especially various negative effects, including misinformation, disinformation, cybersecurity, privacy, and terrorism.

Due to the short amount of time since COVID‐19 became a pandemic, the continuation of the pandemic, and the long article publication circle, this rapid review may have been limited in continuity. Future research is needed to address the following important topics: (1) What role does digital technology play in the continuous pandemic? (2) How far can digital technology reach in the mental health domain? (3) What are the risks of digital technology? and (4) What are other various domains to research to see a broader impact of COVID‐19?

During the pandemic, most studies focus on independently analyzing either the healthcare or education domain, without expanding or linking multiple fields together. For example, Keesara et al. (2020) only analyzed the digital technology use in the healthcare field, while Sun et al. (2020) discussed how digital technology affected the development of education in this crisis. In comparison, our results from multiple perspectives provides a more comprehensive overview of the use of digital technology during the pandemic. In addition to specifically listing the software and hardware technologies, users, activities, and effects, this review also systematically analyzes the importance of digital technology for pandemic prevention and control from the perspective of healthcare, education, work, and daily life.

Using the four‐element model and current literature, we can foresee several future research directions including: mid‐ and post‐pandemic use, new technologies and creative use, Zoom, effective, efficient and cost‐effective applications, monitoring apps for back to school and back to work, new users, access, digital divide, awareness, knowledge, new use in various sectors of society rather than just medical, education, and work, and new effects, especially negative ones such as misinformation, cybersecurity, and privacy.

ACKONWLEDGMENT

The only articles included in this reference section were the ones that were directly discussed in this article. A full list of the 281 articles that were used for this rapid review will be made available upon request.

CONFLICT OF INTEREST

The authors declare no potential conflict of interest.

Biographies

• Deedra Vargo is a graduate student in the Department of Educational and Counseling Psychology at University at Albany, State University of New York. She is also a Learning Management Software Support specialist and Financial Aid Coordinator at the Maria College of Albany. The area of research she is pursuing focuses on the dynamic and complex relations between emerging technologies and human behavior. Specifically, she is mainly concerned with increasing the understanding about the role that digital technology plays behind the psychology of education. Additionally, she has researched the psychology of cybersecurity. Her current research focuses on the effects and characteristics of online education. She is studying the development of instruments designed to measure learning management software skills and identifying how these skills correlate with psychological variables of interest. Her most recent project worked to identify qualitative factors of college level instructors and examined how they are associated with their ability to use Blackboard.

• Lin Zhu is a graduate student in Educational and Methodology Psychology at University at Albany. She holds her two Bachelor's degrees in Management and Informatics Sciences from both China and Japan. Lin Zhu is fluent in English, Japanese, and Mandarin Chinese. Prior to her study at the University at Albany, she used to work in the marketing department in companies in East Asia and the Middle East. Lin Zhu's current research interests include cross‐cultural and gender differences in human development studies. Recently, her research mainly focuses on the complex relationship between video games and psychological well‐being. Lin Zhu's current research interests include cross‐cultural and gender differences in human development studies. For future research directions, she may continue to expand the research field from video games to a variety of emerging technologies, from a psychological perspective to strategies of mediating the conflict between technology use and human development. Ultimately, her research would aim to use the positive side of technologies to help people gain knowledge, overcome psychological difficulties, and have a better self‐cognition.

• Briana Benwell is a graduate student at University at Albany, State University of New York. Her area of study is Special Education and Literacy and her undergraduate degree was Human Development with a concentration in special education and a minor in psychology. She has a background in Early Childhood Development from Schenectady County Community College. She has been working in childcare for the last 5 years, primarily at daycares, and has worked with children from birth through age 12. Currently, she works as a personal care provider for individuals with disabilities. Her future goal is to work as an elementary school teacher or a special education teacher. She is interested in studying the complexities of child psychology and how children learn. In 2019, she took Human Development with Dr. Yan at University at Albany and took on the role of research assistant for him, specializing in proofreading and editing manuscripts.

• Dr. Yan is Associate Professor of developmental psychology at University at Albany, State University of New York. His research mainly concerns dynamic and complex relations between emerging technologies and human behavior with the aim to address a basic question in the modern society: How do humans behave with technologies? He has been studying three technology‐based human behaviors: (1) computer behavior (e.g., how students learn to use computer software and how computer users develop Computer Vision Syndrome), (2) cyber behavior (e.g., how children understand the technical and social complexity of Internet and how Internet users make online decisions), and (3) mobile phone behavior (e.g., how school mobile phone policies impact learning and teaching and how mobile phone multitasking produces academic distraction). His current research focuses on cybersecurity judgment in particular and cybersecurity behavior in general (e.g., where the weakest links in cybersecurity are among ordinary cyber users). His recent books include Publishing journal articles (2020), Mobile Phone Behavior (2017), Encyclopedia of Mobile Phone Behavior (Volumes 1–3) (2015), and Encyclopedia of Cyber Behavior (Volumes 1–3) (2012).

Open Research

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

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