As an intervention strategy, digital media for behavior change uses all features of digital devices to communicate and create environmental cues and incentives (i.e., following behavioral economics) that encourage the adoption of new behaviors and the maintenance of existing ones. These approaches may take place on social media, mobile phone apps, chatbots, text, and social messaging (e.g., WhatsApp), as well as many specific modalities of information that can be communicated within them, such as video, memes, website links, and graphic images, among others. Digital interventions have the flexibility to be based on tailored, individual communication and on group-level communication, such as in a Facebook group, enabling group interaction and social support. Peer-to-peer interventions (where the intervention is essentially delivered by the participants) are also possible ^[3][12].

Digital platforms such as social media have the inherent feature of interactivity and the potential to engage participants and populations in the context of their social networks, thus building a sense of identification and connection with the intervention. This can be done through ‘gamification’ features, such as virtual incentives and rewards and through social role modeling by individuals that are appealing and aspirational for the audience. Digital platforms, like social media, provide the opportunity for participant co-creation (i.e., content co-generated by users and investigators).

As a data collection and research strategy, digital media may be used in many ways. Social media provides large quantities of available data on registered users (e.g., Facebook analytics) that can be used to identify potential study participants (e.g., individuals who, based on Facebook data, are likely to fit a specific socio-demographic profile, such as young male Latinos). Apps such as Facebook Messenger and other ‘chat’ functions, may be automated to run surveys through ‘chatbots’ that deliver questions through individual messages with pre-defined response options for study participants. Such technologies can be used to design randomized controlled trials ^[4][14]. Additionally, intervention research may be conducted using the combination of social media technology for remarketing (i.e., delivery of specific content, such as advertising, to an individual user based on previous online activity, such as viewing social media content) and chatbot data collection. Therefore, participants may be recruited into a research study using Facebook advertising (e.g., to join a research study), the data are then collected using chatbot technology, and then the same participants may be exposed to an intervention using remarketing technology, and subsequent data may be collected. This creates the potential for social media based randomized experimental studies to evaluate online behavior change interventions. Behavior change interventions that rely on social networks for their success are hypothesized to have greater impact, and to generate greater interactivity and feedback, than interventions that rely on changes in individual behavior, due to the amplifying effects of social support and social participation ^[5][6][15,16]. Indeed, social network researchers have measured the impact of interventions beyond those immediately exposed to an intervention. Thus, in addition to the effect of treatment-on-the-treated, social network researchers have measured the effect of treatment on the untreated (e.g., for healthy weight management and weight loss) ^[7][17].

2. Methods of Digital Health for Behavior Change

Digital health for behavior change employs both intervention and research methodologies. In the context of behavior change interventions, there are three main methods: (1) digital media interventions aimed at behavior change, (2) formative research using digital media aimed at aiding in the design of interventions, and (3) digital media used to conduct outcome and impact evaluations. These are methods for conducting digital media practice and research.

2.1. Methods to Deliver Digital Media Campaigns for Behavior Change

On social media (the predominant digital communication channel), interventions can be delivered in a variety of ways. Social media (e.g., Facebook, Twitter) can be used as a digital environment for serving ads that can be microtargeted to the individual characteristics of users. Additionally, social media accounts with many followers (e.g., influencers) have been used to promote behavior change messaging with their followers. Social media platforms can also serve as a platform for group-based interventions aimed at behavior change. Several large RCTs have tested the efficacy of randomizing people into private social media groups and then using the groups as the settings for providing education content to users in the form of group posts, including stimulating engagement among users (e.g., polls, questions, conversations) and social support among group members ^[8][9][10][20,21,22]. These can be supplemented with direct messages to individual users providing additional individualized support or information. Increasingly, there is also an interest in shaping the content of existing groups and pages on social media by having lay health workers either join such groups and post health-promoting content or reach out to group and page administrators requesting them to post such content. These strategies have potential to create a sense of widespread support for and adoption of specific behaviors. A kind of bandwagon effect may result, leading to behavior change. Theoretically, the effect of such social media campaigns may be to promote a social norm in support of specific health behaviors, such as COVID-19 vaccination, healthy eating and physical activity, or avoidance of nicotine consumption ^[11][19]. The following case studies illustrate interventions and research that investigate this hypothesized effect of social media campaigns.

2.2. Digital Media Methods in Formative Research

Another important research opportunity and capability while using digital media is to conduct formative research, or research aimed at helping to design campaigns for behavior change. One example of such efforts was a formative study conducted by Agha and colleagues in Nigeria in 2021. The study applied a behavioral lens to understand drivers of COVID-19 vaccination uptake among healthcare workers (HCWs) in Nigeria. The study used data from an online survey of Nigerian HCWs ages 18 and older conducted in July 2021. Analyses examined the predictors of getting two doses of a COVID-19 vaccine. One-third of HCWs reported that they had gotten two doses of a COVID-19 vaccine. Motivation and ability were powerful predictors of being fully vaccinated: HCWs with high motivation and high ability had a 15-times higher odds ratio of being fully vaccinated. However, only 27% of HCWs had high motivation and high ability. This was primarily because the ability to get vaccinated was quite low among HCWs: only 32% of HCWs reported that it was very easy to get a COVID-19 vaccination. By comparison, motivation was relatively high: 69% of HCWs reported that a COVID-19 vaccine was very important for their health. Much of the recent literature coming out of Nigeria and other LMICs focuses on increasing motivation to get a COVID-19 vaccination. Findings highlight the urgency of making it easier for HCWs to get COVID-19 vaccinations.

2.3. Digital Media Methods for Evaluation Research

Finally, the outcomes and impact of digital media interventions for behavior change may be evaluated using digital media and social media platforms. The previously mentioned example of virtual lab is a state-of-the-art example in the social media domain. The power of social media to first identity individual users who appear, based on publicly available data, to fit a specific demographic, behavioral, or lifestyle profile (e.g., being a health care provider in a specific country of a certain age range) allows for targeting of recruitment efforts. Facebook advertising, for example, may be used to reach a specified population based on Facebook proprietary data, and then those individuals may be invited through the ads to join a research study. Upon initial expression of interest through the clicking on a link to a survey, additional screening may be done (e.g., through eligibility questions) to confirm study inclusion or exclusion, followed by informed consent. Such studies may follow multiple designs, including observational, quasi-experimental (e.g., examining the effects of exposure to social media messages on outcomes of interest, such as vaccine hesitancy or vaccination) and randomized controlled studies. In social media, the Facebook Messenger (DM) app is one relatively simple way to deliver surveys, as individual questionnaire items may be delivered as chats DMs in sequence to allow a participant to complete a survey wave. Such surveys may be followed by randomization to study condition to receive a social media and/or other treatment aimed at promoting behavior change and/or other outcomes (e.g., changes in social norms or intentions). Because the participants have provided contact data through the social media platform (e.g., through DM), they may be recontacted for longitudinal data collection. In this way, bespoke panels may be created to run studies. Additionally, surveys, as individual questionnaire items, can be delivered with text messaging and apps, offering real-time assessments (i.e., ecological momentary assessments, EMA) that can monitor time sensitive symptoms or conditions such as urges and cravings related to addiction. An important advantage of this methodology and technology is that it is relatively low cost ^[12][23]. Large scale data collection may be conducted with relatively low marginal costs of initial survey programming and data management. Incentives may be provided through such modes as electronic gift cards or mobile phone use credits, the latter being highly valuable in many low- and middle-income countries (LMIC) where pay-by-use mobile phone plans are common.

3. Digital Media for Behavior Change

The field of digital media for behavior change is growing rapidly, but research is still in its infancy. While a recent paper found some 3300 digital media publications related to behavior change, only a fraction of these provided evidence for the effectiveness of a digital intervention in changing behavior. Many studies monitor the digital media landscape, such as “digital listening” projects, and studies of large social media datasets (infodemiology), which are critically important and advance the understanding of the digital landscape. However, rigorous intervention studies on the effectiveness of digital media in actually changing behavior, specifically in public health, remain relatively sparse ^[13][41]. The field has tremendous room for growth in the coming years. Behavior change can take time, and the potential for regression to earlier states is well-known ^[14][42]. Future research should include longitudinal follow-up to assess the long-term effect of social media behavioral interventions. Additionally, there was a lack of evidence on the effectiveness of theories of change in social media interventions, and future research should focus on testing the processes of change. Given the relative dearth of rigorously evaluated digital media interventions for behavior change, more formative research evaluating the feasibility, appropriateness, and acceptability of specific types of projects is needed. A more rigorous application of the principles of program evaluation will help develop targeted, effective digital media interventions. In particular, digital media interventions need to explore the full range of functionality of digital devices and their near-constant role in personal self-management and day-to-day living to maximize opportunities for behavior change. One area that deserves further attention is the potential for multi-factorial studies that examine the effects of adding and subtracting features of digital devices (e.g., an intervention with and without an app, with and without social media interactivity, etc.) on behavior change. More elaborated research designs that examine how to optimize delivery of digital interventions using the full range of functions of devices such as mobile phones and tablets is needed. One of the strengths of social media interventions is that objective dosage and exposure data from analytics are available. However, some studies have reported that their social media efforts were effective without clearly reporting quantitative data (e.g., clicks, shares, views, etc.) on social media use [1]. Future research should examine the characteristics of engagement exposure to evaluate dose-response effects—i.e., to determine whether more exposure or exposure of a specific type is associated with successful behavior change. This is important in order to be able to objectively attribute intervention effects to observed behavior changes and build the evidence base in the field. Another important dimension of digital media for behavior change is health literacy. Digital media represent another dimension of health literacy: the ability to successfully use, navigate, and obtain benefit from health-related information on digital devices ^[15][43]. Research should focus on how to make digital media behavior change interventions more sensitive to health literacy needs, and on how to improve the extent to which participants can successfully consume and use digital health information. Finally, future experimental research should rigorously examine the effects of variable levels of engagement with, and frequency and intensity of exposure to, multiple forms of digital media for behavior change. In particular, longitudinal studies that follow participants over extended periods of time are needed to evaluate more distal outcomes (e.g., beyond immediate content recognition, engagement, and short-term attitudinal outcomes. Studies should also investigate dose-response effects by increasing the number of digital ad exposures over an extended period of time and evaluating varying dose-response curves for different campaign outcomes ^[14][42]. Adding greater levels of digital exposure would allow a study to plot possible threshold and drop-off effects of exposure. Such research will inform the understanding of the impact of varying levels of digital ad exposure on longer-term outcomes.