National Institutes of Health Support of Digital Health Behavior Research

Abstract

The National Institutes of Health (NIH) has increasingly supported research in digital health technologies to advance research and deliver behavior change interventions. We highlight some of the research supported by the NIH in eHealth, mHealth, and social media as well as research resources supported by the NIH to accelerate research in this area. We also describe some of the challenges and opportunities in the digital health field and the need to balance the promise of these technologies with rigorous scientific evidence.

Keywords

eHealth health informatics health policy mHealth social media

The National Institutes of Health (NIH) has a long history of support for digital health research, including digital health behavior research. To illustrate this support, we searched NIH RePORTER (https://projectreporter.nih.gov/reporter.cfm), which performs text searches of the project title, abstract, and terms, to determine the number of competing grants awarded by fiscal year for three digital health platforms: eHealth, mHealth, and social media research. For eHealth, we used the search terms “eHealth” OR “electronic health” or (“internet” AND “health”). For mHealth, we used the search terms “mHealth” OR “cell phone” OR “smartphone” OR “personal digital assistant.” (Note that “mobile health” was not used because it includes a number of earlier grants that used transportation systems [e.g., vans, trucks] to deliver health care outside of the traditional healthcare system.) For social media, we used the search terms “social media” OR “Facebook” or “Twitter” (the latter two because these are the two primary social media platforms on which social media research has been performed to date).

As shown in Figure 1, funding for research that used the terms “internet,” “eHealth,” or “electronic health” began in the 1990s, corresponding with the rise of the World Wide Web, internet service providers, and graphic web browsers. In the 2000s, the NIH funded approximately 200 to 250 eHealth grants each year, and funding increased to 400 to 600 grants per year over the past decade when portable devices such as smartphones and expanded broadband infrastructure greatly increased internet access. Funding for mHealth research began slowly from 1998 through 2006, with research predominately on personal digital assistants and feature phones, but this funding increased substantially following the advent of smartphones and has continued to grow to 359 competing grants funded in fiscal year 2018. Social media research is a more recent phenomenon, beginning with competitive grant funding in 2009 and steadily increasing after 2015 when the number of grants funded doubled, with 153 competing grants in fiscal year 2018. Although a grant can be counted under one than one of these three categories, the increase in these digital health grants occurred during a period when the total number of NIH competitive grants awarded each year remained relatively flat (i.e., between 9,098 and 10,662 from 2001 to 2017).

Figure 1.

National Institutes of Health competitive grants funded by fiscal year for eHealth, mHealth, and social media research.

The digital health behavior research funded by the NIH is broad and diverse. In this commentary, we highlight the diversity of this research across behaviors, conditions, and digital platforms. Additionally, we describe some of the NIH initiatives, funding opportunity announcements (FOAs), and resources to facilitate the rigorous study of behavioral assessments and interventions delivered via digital technologies.

Internet and Web-Based Assessment and Intervention

NIH-funded research involving the use of the internet for the delivery of behavioral assessment and intervention has paralleled the platforms by which individuals access the internet. Early research was designed to be delivered primarily on desktop computers and was mostly static or noninteractive in nature. As internet access has expanded to portable and personal devices and as applications have become increasingly interactive and “smart,” the health behavior intervention field has followed. We have organized this article along eHealth, mHealth, and social media research efforts, but nearly all digital health research is “eHealth” in that nearly all applications now reside on internet servers and are accessed via the internet, agnostic to the device on which they are accessed.

Access to the internet via desktop and laptop computers provided the initial digital platform for the eHealth research grants funded by the NIH, beginning in the 1990s. Telemedicine was among the first uses of the internet, building on previous work using telephones as the primary platform for this research (Preston, Brown, & Hartley, 1992). The value of the internet for storing and distributing health information also became increasingly apparent in the 1990s (Lowe, Lomax, & Polonkey, 1996). As the internet became more accessible to the general population, the use of the internet for consumer health information and education grew (Eysenbach, 2000), along with concerns regarding the quality of this information (Cline & Haynes, 2001). Consumer internet health access radically changed health education, dissemination, and communication. Health information was no longer primarily shared among health professionals who served as conduits of this information to their patients and the public; health information became readily available to consumers without their health care providers serving as the intermediaries.

Health behavior researchers learned that the internet could be leveraged to deliver automated and adaptive interventions based on in-person intervention principles and strategies. These eHealth interventions began to be studied for a variety of health behaviors including diet and physical activity (Norman et al., 2007), pain (Cuijpers, van Straten, & Andersson, 2008), and cardiovascular disease management (Kuhl, Sears, & Conti, 2006). The NIH encouraged research on the use of the internet for automated and adaptive interventions. There have been 211 Funding Opportunity Announcements (FOAs) that include “eHealth” as a search term. Among the earliest was a National Institute of Mental Health FOA on “Information Technologies and the Internet in Health Services and Intervention Delivery” (PA-06-226). Among the most recent is a National Institute on Deafness and Other Communication Disorders FOA (PA-19-047) on “Advancing Research in Augmentative and Alternative Communication.”

As new platforms for delivering internet-based health behavior assessments and interventions such as smartphones emerged, research has shifted to these platforms. Although we have organized this commentary across three “platforms,” nearly all of the digital health research that the NIH funds is internet-based, including the mHealth and social media research sections that follow. The digital health field is becoming increasingly agnostic to platform and is providing multiplatform delivery of health behavior assessment and interventions. Platform “innovation” is secondary to the “goodness of fit” of the platform for the problem being addressed, the content provided, and the users and context in which it is being delivered. Indeed, in fiscal year 2018, the NIH funded a number of grants in which the internet intervention is still delivered primarily by desktops and laptops, including an eHealth intervention of childhood cancer caregivers (R03CA235002), a web-based pain intervention for pediatric pancreatitis (R01DK118752), and an internet intervention to improve the quality of ADHD (attention-deficit hyperactivity disorder) care delivered by pediatricians (R01MH118488).

mHealth and Sensor Assessment and Intervention

Nearly every American (95%) owns a cellphone, over three quarters (77%) own a smartphone, and mobile phones are increasingly the primary platform for accessing the internet (Pew Research Center, 2018a). The rapid growth of mobile phone use in the past decade has led to a corresponding growth of mobile health or mHealth research. As the mHealth field developed, the NIH supported work to encourage the use of methodologies suited to the research questions posed by mHealth (e.g., Kumar et al., 2013) and to support training institutes on the development and evaluation of mHealth (R25DA038167).

To provide a resource for rapid test-bed testing of mobile health applications, the NIH also supported the development of the Eureka research resource (U2CEB021881). Eureka provides investigators an accessible, nimble, and sustainable infrastructure to conduct efficient and cost-effective mHealth research using a cloud-based multitenant platform and a complementary web interface and iOS application for enrolling, retaining, and conducting studies with large cohorts.

Various institutes and centers of the NIH have developed initiatives to encourage rigorous mHealth research. NIH has issued more than 100 FOAs that include “mHealth” or “mobile health” in the FOA text. Among the earliest were FOAs encouraging the use of mHealth to increase HIV testing and follow-up treatment (PA-11-118) and to promote adherence to treatment for chronic diseases (PA-11-330). Over the past decade, NIH has issued mHealth FOAs to address health literacy (PAR-13-130), health disparities (RFA-MD-13-009), health care in lower and middle income countries (PAR-14-090), diabetes and obesity prevention (PAR-15-157), cancer prevention and control (PAR-16-278), mental health disorders (RFA-MH-17-606), and drug abuse (RFA-DA-18-010). As a nascent field, many of these FOAs utilized special emphasis panels (e.g., RFAs, PARs) to ensure adequate review expertise both in the content area and in mobile health technologies, but as the field has matured and mHealth research applications become more common, standing study sections are increasingly capable of reviewing mHealth grant applications.

mHealth applications have been used to intervene on a range of health behavior problems but with mixed outcomes. In numerous studies, text messaging has been found effective for smoking cessation (Scott-Sheldon et al., 2016). mHeath interventions to encourage physical activity and reduce sedentary behavior have shown only small to moderate effects (Direito, Carraca, Rawstorn, Whittaker, & Maddison, 2017). Similarly, mHealth interventions for weight management and healthy eating, including for specific medical indications such as diabetes, have shown only modest effects, suggesting that further innovation and optimization of these interventions are needed (McCarroll, Eyles, & Mhurchu, 2017; Wang, Xue, Huang, Huang, & Zhang, 2017). mHealth programs to facilitate self-management for a range of diseases also have shown modest and mixed effects to date. For example, self-management mHealth programs for cancer survivors show modest benefit for reducing pain and fatigue and limited effects on sleep or psychological distress (Hernandez Silva, Lawler, & Langbecker, 2019). mHealth is clearly a promising platform to provide adaptive health behavior interventions with reach and scalability, but the evidence remains mixed for some intervention applications.

One mHealth application with sufficient evidence to obtain U.S. Food & Drug Administration (FDA) approval is as an adjunct to substance abuse treatment. The 21st Century Cures Act clarified the FDA’s regulation of medical software (FDA, 2019), and there are now several examples of breakthrough digital technologies that have obtained FDA approval. A significant development in this area was the FDA approval of the reSET mobile app in 2017. ReSET—previously known as the Therapeutic Education System—is a mobile app approved for use in outpatient treatment for substance use disorders related to cocaine, other stimulants, cannabis, and alcohol. The mobile app delivers cognitive behavioral therapy and rewards users for continuing with therapy using various incentives, which can improve adherence. This treatment tool was created through NIDA’s (National Institute on Drug Abuse) Behavioral Therapy Development Program and validated through a major nationwide multisite trial conducted in the NIDA Clinical Trials Network program (Campbell et al., 2014). In the clinical trial, the 12-week abstinence rate from drugs and alcohol for users of the app (40%) was more than twice the abstinence rate for individuals who received standard care (18%). A new version of the app called reSET-O has been cleared by FDA with breakthrough designation and is intended for use as an adjunct to buprenorphine and standard treatment for patients with opioid use disorder (Pear Therapeutics, 2019).

Perhaps in part because smartphones include a wide array of sensors, the term “mHealth” often extends to wearable and home-based sensors. NIH supports the development, use, and evaluation of sensor technologies, whether integrated into smartphones or as separate wearable or home-based sensors, for biomedical and behavioral health research. Recently, the NIH’s Office of Behavioral and Social Sciences Research launched the Intensive Longitudinal Health Behaviors Network (RFA-OD-17-004; RFA-OD-17-005). The goal of the network is to collaboratively study factors that influence key health behaviors in the dynamic environment of individuals, using intensive longitudinal data collected from cutting-edge sensor technologies and big data analytic approaches. The network also will assess how study results can be leveraged to introduce innovations into long-standing behavioral theories to advance the field of theory-driven behavior change interventions. The knowledge gained will inform the development of personalized prevention strategies with the goal of reducing disease risk and optimizing health. The network includes a research coordinating center and seven U01 research projects. The research coordinating center will provide administrative research support for the network and be responsible for obtaining, accessing, and sharing digital footprint data (e.g., social media data, smartphone metadata) among the network projects and with the broader scientific community. The research projects will measure a wide variety of health behaviors including smoking cessation, substance abuse, self-harm and suicidal ideation, sleep, sedentary behavior, and physical activity.

Another ongoing project leveraging the use of sensor technology and analytics is the Collaborative Aging Research Using Technology (CART) initiative led by the National Institutes on Aging and its partners (https://www.ohsu.edu/xd/research/centers-institutes/orcatech/collaborative-aging-research-using-technology-cart/). Launched in 2016, the goals of the initiative are to develop a platform and create a research infrastructure to study the use of in-home sensors and other technologies to facilitate aging in place. The technologies used in CART also will be evaluated to determine their ability to detect meaningful changes in health indicators in older adults. Current sensors utilized in CART include wall and ceiling sensors, door sensors, digital scales, digital pillboxes, smart watches, and driving sensors.

There are a variety of funding announcements that aim to further technological advances in biomedical and behavioral health research. In addition to opportunities focusing on the technology development and evaluation lifecycle, there has been an increasing focus on training the behavioral workforce on the use of these technologies in health research and in the data analytic skills necessary to interpret the data from these devices. Specifically, OBSSR and partners released an announcement for a K18 Short-term Career Enhancement Award (PAR-18-881, PAR-18-882) for mobile and wireless health technology and data analytics. The program targets investigators who wish to expand their research capability in mobile and wireless health technology through cross-training behavioral and social science investigators and STEM (e.g., engineering, computer science, mathematics) scientists. Recently, OBSSR and partners also released an announcement for a T32 Institutional Research Training Program (RFA-OD-19-011) for pre-doctoral training in advanced data analytics for behavioral and social sciences research. The goal of this program is to develop and provide training in advanced analytics so that the future behavioral and social sciences workforce can interpret and analyze the dense and voluminous data collected by sensor technologies.

The application of mHealth and sensor technologies to change behavior continues to be a work in progress with further optimization and evaluation needed, but smartphones have become a powerful tool for event-based and location-based ecological momentary assessment (Kirchner & Shiffman, 2016), and various sensor technologies have provided the field with efficient and unobtrusive direct observations of behavior in context (Cornet & Holden, 2018). These technologies increasingly have become an integral part of health behavior research. For example, the use of mobile and other digital health technologies to assess behavior and its influences is increasing being used in longitudinal cohort studies like the Adolescent Brain and Cognitive Development (ABCD) study. ABCD is a comprehensive, nationwide study of the effects of substance use patterns on behavioral and brain development of adolescents. Data collection was launched in September 2016 with the objective of recruiting over 10,000 participants. Technology-enhanced tools are used including measurement of physical activity monitoring, SMS strategies to engage participants and measure key variables, and geocoding as part of a comprehensive battery of behavioral assessments, multimodal brain imaging, and bioassay data collection (Volkow et al., 2018).

Social Media Assessment and Intervention

Social media includes internet-based tools and platforms that allow individuals and communities to share ideas, personal messages and interests, images, videos, and other content online. Social media continues to proliferate. Currently, 7 out of 10 Americans use social media, and the predominant platforms include Facebook, Instagram, LinkedIn, and Twitter. In 2018, Facebook reported 2.23 billion monthly active users around the world (Statista Portal, 2018a). Trends in social media use indicate that while there is variability by gender and age in platforms of engagement, it is widely used, and use continues to rise across age groups, including older adults (Pew Research Center, 2018b).

At the NIH, a growing portfolio of research grants in social media span across health outcomes including HIV/AIDS, addictive behaviors, tobacco use, e-cigarettes, weight loss interventions, and mental health. Some of the major content areas of funded social media research across institutes include surveillance or “infodemiology” (Chew & Eysenbach, 2010) and big data analytics, development and delivery of health interventions and trials, research recruitment, and information dissemination, among others. Surveillance research in social media includes examination of exposure to advertising messages for tobacco and related promotions (Cavazos-Rehg, Krauss, Spitznagel, Grucza, & Bierut, 2013), and tracking epidemics like the flu (Sharpe, Hopkins, Cook, & Striley, 2016). Funded communication surveillance in social media has included determining the discourse around a particular health-related topic such as electronic cigarettes (or e-cigarettes; Lienemann, Unger, Cruz, & Chu, 2017). The FDA has introduced regulations that require FDA review for e-cigarette products, banning free samples and sales to minors, and requiring warning labels on certain products. In this context, surveillance of evolving themes and factors contributing to message popularity for e-cigarette chatter on social media platforms (e.g., on Twitter, the number of retweets, replies, votes, or endorsements), can inform the design of strategies to reduce diffusion of e-cigarette messages and also help identify particularly vulnerable population segments, like youth and young adults.

Worldwide, there are more than 336 million monthly active users of Twitter (Statista Portal, 2018b). This number underscores the potential reach for big data applications in social media to understand the “digital phenotype” of individuals by providing data that help us understand better health behaviors in naturalistic context. Social “big data” analyses use publicly available user data to assess organic observations of behavior to monitor and predict real-world public health problems. Building on past work that found a significant relationship between HIV-related tweets and county-level HIV cases (Ireland, Schwartz, Chen, Ungar, & Albarracín, 2015), one current NIH grant is developing a platform to collect social media data; identify, code, and label tweets that suggest HIV-related behaviors; and ultimately develop a tool that will allow health care workers to predict regional HIV incidence (5R01AI132030-02). Analytic approaches like these will allow for processing large quantities of social media data in real-time. Current challenges remain, including how to analyze the combination of visual, video, and text data across and within the platforms to better understand these social data.

Within the NIH portfolio, intervention studies have leveraged the strengths of social media platforms to build peer relationships through information exchange in real time and penetration of networks in hard to reach communities. For example, randomized controlled trials using social media include using online support groups (via Facebook) and participant engagement via twitter and text to deliver and test a smoking cessation intervention, Tweet2Quit (Pechmann, Delucchi, Lakon, & Prochaska, 2017; R01CA204356). This intervention found engagement of participants via social media extended prior research on peer-based social support for smoking cessation and doubled sustained abstinence (compared with control). This study has implications for the field to consider social media as a low-cost delivery of peer interventions. Another trial used peer leaders on Facebook to invite participants into closed Facebook groups to communicate messages about HIV testing and offer home-based HIV testing among at-risk African American and Latino MSM. The trial found that social networking sites like Facebook were an acceptable and an effective tool to promote HIV testing in an at-risk population (Young et al., 2013).

Because social media allows for collaboration with other users and bidirectional flow of information in real time, it has emerged as a potential channel to facilitate multidirectional communication between and among patient, advocate, researcher, and clinician communities.

Consequently, social media has emerged as a potential tool to enhance patient engagement and recruitment for trials. The advantage of social media is its reach given geographically diffuse populations, particularly for rare diseases outcomes (Comerford et al., 2018) and can reduce costs for recruitment while potentially increasing diversity.

In a recent Pew Report, 67% of Americans report that they now get at least some of their news from social media platforms (Pew Research Center, 2017). With declining trust in social institutions and news media (Newport, 2017), the use of social media to amplify polarization on health and other topics can inadvertently create echo-chambers that promote health misinformation and reduce public trust (Williams, McMurray, Kurz, & Lambert, 2015). Consequently, the challenge of “echo chamber” effects becomes pronounced as social media feeds are tailored to individual belief structures, reducing the likelihood of access to new information and potential innovations. Diffusion of innovations theory underscores this phenomenon in which trust and relationship capital may be high in closed networks but at the cost of information isolation (Rogers, 2003). A recent evaluation of twitter bots and trolls found that Russian trolls promoted discord in masquerading as users to erode public consensus on vaccination (Broniatowski et al., 2018). This work is particularly alarming as Vosoughi, Roy, and Aral (2018) have found that false news spreads faster and more broadly for all forms of news on Twitter than the truth. The next step to advance this work in health includes understanding how exposure in social media can affect health or behavioral outcomes as well as designing interventions to combat misinformation when determined harmful (Chou, Oh, & Klein, 2018).

Some important ethical and research challenges need to be acknowledged in this work, including privacy, confidentiality, consent, sampling, harm reduction, data security, and management (Hunter et al., 2018). Coming to consensus, however, remains a challenge as the field is growing rapidly in the research methods, questions, and changes in the privacy and availability of data on social media platforms (which are dictated by the companies, with limited transparency). Company user policies of these social media platforms are evolving, and many are still determining guidelines of permissible posting and ethical considerations following public controversies. As we move forward in the field, evolving research questions, methods, and tools will require transdisciplinary teams that include computer science expertise for data mining and analytics of complex social media data (considering, time, frequency, and source of the data), participant engagement and stakeholder perspectives to address ethics, behavioral and communication science to understand how networks influence behavior change and online behaviors, and better understanding from research review committees about the strengths, opportunities, and challenges of working with social media data.

Social media is a part of our public health and health care landscape, and its reach and impact will continue grow with time. Currently, NIH has 47 open funding announcements that include reference to social media. These span the range of mechanisms from R01s, R21s, R34s, K18s, P50, R41/R42, R43/R44 and are open for application at multiple different institutes. These mechanisms and topics illustrate the range of research questions and opportunities to explore digital health at NIH.

Summary

The rapid development and adoption of digital technologies by the general public has resulted in parallel development and evaluation of digital health technologies. The NIH has encouraged and supported rigorous research to evaluate the validity of these tools for health behavior assessment and the effectiveness of these tools for behavior change interventions.

Digital technologies hold considerable promise for assessing and changing health-related behaviors to improve health outcomes. Smartphones provide a ubiquitous platform for prospective, real-time assessment (Shiffman, Stone, & Hufford, 2008), and recent advances in sensor technologies provide automated remote “direct observation” of behaviors and health indicators (Cornet & Holden, 2018). In-person interventions, or at least some components of these interventions, can be automated, greatly extending reach and scalability while also delivering interventions in the context in which the behaviors occur and adapting to contexts (Nahum-Shani et al., 2018). Extensive use of digital technologies, such as text messaging and social media, provides a rich data repository of thoughts, behaviors, and social systems in the natural environment.

While there are many advantages of digital technologies for behavioral research and for improving health, there a number of challenges to address. Sustained engagement with health apps continues to be a problem that requires more research on engagement (Wagner et al., 2017). Adoption of these technologies are not uniform across subgroups and broadband access remains unevenly distributed (Pew Research Center, 2018c). Therefore, it is essential that the field address access and health and technology literacy challenges, particularly with underserved populations, to ensure that these digital health technologies reduce, not exacerbate, health disparities. Data privacy and security remains a concern, despite improved strategies for securely storing and sharing these often-sensitive data (Martinez-Perez, de la Torre-Diez, & Lopez-Coronado, 2015). The rapid pace of technology development does not match the slower pace of research, requiring novel and more rapid methods to evaluate health technologies (Riley, Glasgow, Etheredge, & Abernethy, 2013).

Digital health technologies hold considerable promise for advancing health education and health behavior research, and the NIH continues to be supportive of research in this rapidly evolving area. As research tools, these digital technologies provide the basis for a potential paradigmatic shift in how health behavior research is conducted. The intensive longitudinal nature of these data, coupled with the naturalistic behavioral surveillance capacities they provide, will transition the field from a data-poor to a data-rich science (Riley, 2016), driving changes in the measures, methods, theories, hypotheses, and data analytic approaches of the field. With these new insights into how behavior change is influenced within individuals over time, the field will be able to move beyond our current health behavior theories and better optimize digital interventions by improving not only the content but also the timing and adaptability of these interventions. With machine learning and other artificial intelligence approaches, we will be better able to learn from the implementation of these digital interventions and improve on them as they are being used, blurring the line between research and practice. As these technologies evolve along with their methods and analytics, the health behavior research community will need to keep pace with the technology to leverage new capabilities while remaining grounded in established empirical research on behavior change and in research methods that demonstrate the validity of these approaches.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

William T. Riley

Supplement Note

This article is part of the Health Education & Behavior supplement issue, “Advancing the Science and Translation of Digital Health Information and Communication Technology.” The printing and dissemination of the supplement was supported by the Office of Behavioral and Social Sciences Research, National Institutes of Health (Contract No. HHSN276201800167P). No federal funds were used in the development of these supplement manuscripts, and the views and findings expressed in them are those of the authors and are not meant to imply endorsement or reflect the views and policies of the U.S. Government. The entire supplement is available open access at .

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