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. Author manuscript; available in PMC: 2026 Jun 23.
Published in final edited form as: Exp Clin Psychopharmacol. 2026 Jun;34(3):217–223. doi: 10.1037/pha0000841

Innovations in Health Behavior Research: Current Status and Strategic Future Directions

Nioud Mulugeta Gebru 1, Samuel F Acuff 2
PMCID: PMC13286044  NIHMSID: NIHMS2143423  PMID: 42313640

Abstract

Given the public health burden associated with health behaviors such as substance use, significant efforts are directed towards developing effective interventions to facilitate health behavior change. As digital technologies continue to advance at accelerating pace and integrate into people’s daily lives, researchers have a unique and significant opportunity to leverage these tools to advance theory and practice. Emerging tools, novel intervention targets, and technologies have great potential to improve health behavior change research to unburden individuals and communities impacted by substance use and related behaviors, though much work remains to be done to fully realize this potential. This paper examines the current state of the health behavior change research highlighting recent innovations facilitated by digital transformations. Looking ahead, potential challenges and opportunities are discussed, along with a call for health behavior change researchers to become intentional interdisciplinary collaborators and innovation brokers towards the development of effective theory-informed digital tools and to maximize the field’s impact on public health outcomes.

Keywords: substance use, health behavior change, digital health

Introduction

There is considerable evidence that human behavior drives public health outcomes. Nearly two-thirds of global mortality is due to noncommunicable diseases (Gakidou et al., 2017), most of which are driven by three health behaviors: substance use, poor diet, and sedentary lifestyle. Scientific and technological advancements have resulted in significant strides in improving persistent public health and health outcomes, in part by facilitating behavior changes that lead to healthier and longer lives. One quintessential success of public health efforts in the 20th century is the dramatic reductions in tobacco use that were achieved in the second half of the century: in 1965, 42.4% of U.S. adults used tobacco whereas only 23.5% used tobacco in 1998, and only 11.6% in 2021 (American Lung Association, 2024). Yet, much work remains as behaviors that impact health, such as substance use and obesity, still persist and carry a high burden for affected individuals and communities. In 2024, about 48.5 million individuals in the U.S. aged 12 or older met criteria for a past-year substance use disorder (SAMHSA, 2025). Additionally, more than 2 in 5 American adults, representing over 100 million people, met criteria for obesity, and rates continue to grow over time (CDC, 2025).

The pressing behavioral health problems of our age have been largely resistant to our efforts, and there is a critical need to find innovative solutions with the novel technologies available to health behavior researchers, afforded by advancements of the 21st century. The technological landscape has changed dramatically since the early 90s, and novel technologies continue to emerge at a rapid pace. Although many of these technologies introduce challenges, they also introduce novel opportunities for public health. In this perspective, we highlight the critical nature of innovation in the “war of attrition” against the major behavioral health problems of the current age. We first provide examples of the innovations of the last few decades that have already changed research and practice. Next, we outline pockets of health behavior science where there is substantive potential for innovation, including recent technological advances that may drive innovation in each area. These first two aims focus on four areas of innovation: technological innovation, innovation in theory and method, innovation in user engagement and dissemination, and leveraging changes in the zeitgeist for innovative solutions. Finally, we provide a summary of challenges with innovation for scientists, and the opportunities that we are likely to see in the coming years.

Recent technological innovations to enhance health research and practice

Technology has already revolutionized the way providers and researchers communicate with patients and research participants, opening doors for when, where, how, and with whom interactions occur, enabling greater flexibility in the timing, location, methods, and participants involved health behavior research. Historically, health behavior research has been conducted within laboratory settings on university campuses or in hospitals. Similarly, interventions and treatments have also largely been implemented in person. Although in-person research and practice have advantages, including greater control of the environment and variables of interest, it also limits who can access treatments and prevents targeting key mechanisms and behaviors in real time, potentially impeding therapeutic gains from reaching individuals at the right time in their natural environments on a recurring basis.

The introduction of mobile devices (e.g., phones, activity trackers, smartwatches) has resulted in the proliferation of ambulatory assessments broadly, and ecological momentary assessment more specifically, to measure behavior on more discrete timescales (e.g., daily, throughout the day) than was previously possible. This has led to increasing complexity in our understanding of behavioral processes underlying health outcomes, and novel debates about previously widely-accepted psychological principles (Dora et al., 2023). In an example of demand driven innovation, these new technology-based methods of more fine-grained data collection have created a need for more complex statistical modeling approaches capable of handling multilevel data and the disaggregation of between- and within-person variance. This has been, in part, facilitated by technological advancements in other disciplines (e.g., biostatistics, computer programming), including the introduction of Rstudio, which has provided greater widespread access to statistical approaches for researchers. Advances in computational tools have also transformed statistical analytical approaches (e.g., Bayesian methods) by providing flexible and reproducible frameworks for data management, visualization, and analyses and allowing more complicated modeling approaches. In the past few years, artificial intelligence (AI) has emerged as a potentially groundbreaking tool for both research and practice. Machine learning has also emerged as a large-scale data collection tool; efforts which have produced datasets with millions of data points and provides novel opportunities for identifying trends in big data that are simply too complex for the analytic tools that were previously available.

Beyond mobile technologies, technology has revolutionized data collection through advances in participant accessibility. Expanded use of online survey platforms (e.g., Mturk, Qualtrics) has enabled improved large-scale data collection efficiency, increased reach to diverse populations, and made it feasible to ask questions that were previously too costly or logistically challenging. Video communication technology, which has been widely available for decades but only recently became widespread during the COVID-19 pandemic, also provides an opportunity to recruit participants that may have previously been unlikely to participate, which may serve to increase the generalizability of research findings.

From a treatment and intervention perspective, video technology offers new opportunities to reach underserved people outside of population centers, such as those living in rural areas. Ambulatory methods also provide a new approach to intervention through just-in-time adaptive interventions (JITAI; Perski et al., 2022). Given that most health behaviors are optimally targeted in the environment in real time, JITAIs can prompt behavior change during critical moments to help facilitate more robust intervention that is not possible through traditional (e.g., weekly therapy) formats.

Technology continues to advance at an unprecedented pace, necessitating continuous learning and translation to practice to effectively impact public health outcomes. Methodologies that are currently considered “state of the science” will soon be overtaken by novel developments. Generative AI, with a particular focus on large language models (LLM), has become a part of everyday life, particularly since the first release of ChatGPT (Chatterji et al., 2025). More than half of U.S. adults have used LLMs (Imagining the Digital Future Center, 2025). If the promises of AI are achieved over the ensuing decades, the potential payoffs could be enormous, with potential gains in psychometric measurement and diagnosis, screening and brief intervention dissemination, and improved research workflows that simplify complex processes. However, the rapid emergence of AI has introduced critical practical, clinical, and ethical challenges that will be difficult to overcome. LLMs require vast training data, and thus ultimately use other people’s work to create responses, a challenge for intellectual property law. Further, the environmental impact of LLMs must be balanced against the potential gains.

Despite these challenges, the rest of society pushes forward, with or without researchers and practitioners. Mental health chatbots and companion AI have proliferated, including for smoking cessation (Andrew, 2025), and their use will continue to grow, as it appears to address unmet needs. Companion AIs have emerged in part as people’s attempt to create connection in the context of a sprawling loneliness epidemic (Murthy, 2023) that remains inadequately addressed. Further, 34% of American adults report they would be more comfortable sharing their mental health concerns with a confidential AI chatbot than with a therapist (YouGov, 2024). Even among those who do value traditional therapy, it is infeasible that to meet the need: there are 345 qualified providers per 100,000 people in the United States (U.S. Department of Health and Human Services, 2024). If only 20% of people were to require mental health services, each qualified provider would need to carry a caseload of 58 patients. Meanwhile, while many clinicians and scientists debate the viability of chatbots as therapists, industry entities are creating products, and millions of people are using them without input from our community. Researchers and practitioners should work towards bringing our voices to the forefront of the movement, rather than commenting from the side.

Innovations in methods and theoretical frameworks to solve novel problems

Innovations in health behavior research need not only be driven by capitalizing on the latest technological advancements but can also be enhanced by deliberate application of established approaches, methods, and frameworks to novel contexts. Continuous learning, fostering interdisciplinary collaborations, and innovations will ensure significant progress in health behavior research. Behavioral economics provides a compelling, recent example. Behavioral economics first came about in the second half of the 20th century when researchers combined psychological principles with economic perspectives to understand human health behaviors, including substance use. Its impact in understanding human decision-making has been far-reaching and recognized across disciplines, including recent Nobel Prizes in Economic Sciences to Daniel Kahneman (2002) and Richard Thaler (2017) for their foundational contributions in integrating economics with psychology, highlighting what can be achieved when cross-disciplinary translation is done intentionally. Now, insights gleaned from behavioral economic principles permeate health research, including areas of substance use (Acuff et al., 2023; Bickel et al., 2014; Gebru et al., 2023; Madden et al., 1997; Martínez-Loredo et al., 2021), sexual health (Gebru et al., 2024; Johnson et al., 2021), obesity (Carr et al., 2011), and vaccine hesitation (Hursh et al., 2020), guiding research ranging from effective interventions for substance use to public policy and governance. This kind of intentional translation (i.e., integrating) exemplified in deliberately breaking down silos between psychology and economics disciplines has had a near transformative impact on the science of health behavior change.

Other examples can be found throughout psychological science. Behaviorism emerged from the study of physiological reflexes. Bronfenbrenner’s social ecological model is in part an application of systems theory from biology to human behavior and development. Evolutionary biology helps explain why seemingly some risky behaviors persist: in part because of their historical, evolutionary benefits, which now play out in environments where the behavior is considered maladaptive (i.e., the mismatch hypothesis; Ellis et al., 2012; Li et al., 2018). The emergence of computers introduced a novel metaphor for human cognition and has led to computational modeling, such as values-based decision making (Berkman et al., 2017; Field et al., 2020). Building on this success, to advance the field of health behavior research, identifying how time-tested methods, frameworks, and theories are being applied in one discipline (e.g., substance use) can solve emerging challenges in another (e.g., food sciences), and vice versa. To this end, increasing open channels of communication across disciplines and breaking down silos (e.g., increased prioritization and funding for translational sciences) will be a critical part of unlocking the full potential of technological advancements to help support healthy lives. This will help ensure that innovation is an active ingredient of our scientific process in health behavior research rather than a series of chance breakthroughs.

Innovations in user engagement

Traditional scientific approaches that follow a linear trajectory of research, publication, and then adaptation by users and providers are often insufficient in part because they fail to effectively engage end-users and findings often fail to reach practitioners and communities in a timely manner. Thus, rethinking how we engage potential end-users and how we disseminate novel findings to maximize their potential effectiveness and sustained adoption is equally important in advancing health behavior research.

A central goal of health behavior research is to promote the adoption of health-enhancing behaviors (e.g., medication adherence) or to reduce behaviors that contribute to disease risk (e.g., substance use). To be effective, interventions not only need to be supported by theory and empirical evidence but must also be designed to align with the needs, goals, and lived experiences of the individuals and communities they are intended to serve. Indeed, given that the success of interventions in producing lasting change is determined by user engagement, obtaining stakeholders input early, and throughout the development process, is not only recommended but critical. This principle is known as human-centered design, in yet another instance of deliberate integration of cross-disciplinary fields between psychology, computer science, and engineering (Carroll, 1997). Substantial evidence now demonstrates that designing products, including technology-based interventions, in ways that align with human cognitive and behavioral tendencies will enhance the likelihood of adoption and sustained use (Stiles-Shields et al., 2022; Vial et al., 2022; Yardley et al., 2016). These insights are of particular importance in health behavior science, where interventions often demonstrate limited real-life effectiveness not necessarily because they lack theoretical rigor or behavioral insight but because they are not aligned with user needs or fail to meaningfully engage key stakeholders, impeding implementation.

Human-centered and user-centered design principles are now increasingly being applied in health behavior research, often through co-development of interventions with users and iterative prototyping. Our advancing technologies offer new opportunities to extend these approaches to support optimal personal and public health outcomes. Remote participation and asynchronous methods have the potential to enhance equity by finding ways to include individuals and communities that are historically underrepresented in research and underserved in practice. Digital platforms such as Dynamicare (Hammond et al., 2021) and Motiv8 (Dallery et al., 2021) provide excellent examples of how digital tools can expand access to an effective intervention (e.g., contingency management) by enabling remote biochemical verification and automating incentive delivery, reducing logistical barriers to real-world implementation. When applied together with rigorous design principles and testing along with an eye towards inclusion and equity, these technologies offer unprecedented opportunities to not only optimize intervention effectiveness, but to also increase accessibility and to facilitate the timely adoption of advancements in behavioral sciences into lasting practice. Importantly, this should be done not only for novel technologies and interventions but also through the process of developing and psychometrically testing novel measures, in addition to survey and experiment development.

Yet, as our tools and methods evolve, it is important to acknowledge that the broader research enterprise, and the academic research system in particular, rewards scientific contributions and innovation, rather than dissemination. Scientists are incentivized to develop and test new ideas, which lead to grant funding and citations, and ultimately promotions and enhanced scientific reputations. There is not a substantive incentive mechanism for dissemination and implementation, which is often considered a job for clinicians, policy makers, or industry. One solution and potential area of innovation is to create frameworks that incentivize dissemination and implementation. Efforts toward recognizing implementation milestones (e.g., demonstrating real-world adoption) as scholarly contributions, incentivizing development of scalable adoptable strategies, and rewarding engagement with community stakeholders can streamline the pathway from innovation to real-world impact in public health outcomes.

Riding the wave of the zeitgeist to enhance innovation

Innovation materializes not only out of novel technology, but also due to shifting sociocultural zeitgeists that introduce new problems worth solving. Over the last three decades, cellphone, internet, and social media use have become widespread, and have transformed how we communicate, consume information, and engage with health-related content. These technological and cultural shifts have coincided with changes in substance use trends, including a sustained opioid epidemic (Kolodny et al., 2015), declining perceptions of cannabis harms (and subsequently legalization; (Acuff & Strickland, 2025; Chiu et al., 2022; Levy et al., 2021), and increased perception of alcohol harms (Gallup, 2024). Each of these examples has resulted in waves of novel research. Gambling has also emerged as a growing area of importance, particularly with changes in sports betting laws, as prevalence of gambling disorder is predicted to increase at an alarming rate (Tran et al., 2024). Relatedly, there has been persistent increased concern about the harms of internet use, which has led to a great deal of academic focus, alongside the proposal to identify online gaming disorder as a potential psychiatric disorder to be considered in the next iteration of the DSM. Social media, in particular, plays a dual role: it can amplify public health messaging while also serving as a conduit for misinformation (e.g., anti-vaccine campaigns, inaccurate substance use perceptions and guidance). Social media is the primary form of news for most young people, and videos and influencers with popularity on these sites have a significant impact on the beliefs and behaviors of the general population, including trends like the sober curious movement promoting temporary abstinence challenges (e.g., Dry January; Alcohol Change UK, 2022) and a growing market for non-alcoholic beverages (Bainbridge, 2021; De-Loyde et al., 2024). By closely tracking and studying these rapidly evolving phenomena, researchers can leverage societal trends and technological tools to design timely and impactful public health interventions.

How can paying attention to our changing world help us to innovate, and what novel trends will guide research in the next few decades? Whether or not we agree that it is safe to use LLMs to target health behaviors, the train has left the station, and scientists in industry (e.g., Google) are developing agents that will likely become widespread (Heydari et al., 2025). It is critical for academic scientists and practitioners to be involved in the evaluation of such technology as it is disseminated to the public. GLP-1s have rapidly gained momentum as treatments for obesity (Drucker, 2024), and there are currently several clinical trials evaluating GLP-1 efficacy for alcohol use disorder, among other health behaviors. Certainly GLP-1s will continue to be an area of interest over at least the next decade and perhaps beyond (Volkow & Xu, 2025), depending upon their efficacy and side effect profile. In terms of drug trends, cannabis use continues to rise, there has been a marked uptick in methamphetamine and opioid co-use since 2018 (Strickland et al., 2019), and Kratom has gained more public awareness (Eggleston et al., 2019; K. E. Smith et al., 2021, 2022). Further, there has been a push to legalize psilocybin treatment centers across the United States (Smith & Appelbaum, 2021). These changing trends will impact the focus of research in the next few decades, assuming they continue.

Because trends change and technology continues to advance, the potential impact of research can be time sensitive. A critical area where innovation is desperately needed is our alert systems for detecting emerging and shifting trends. Systems like the National Drug Early Warning System in the United States are critical for identifying emerging trends (Cottler et al., 2020), which allows for rapid research to be conducted on emerging issues to develop urgent response. U.S. policies are actively shaping how such early-warning and other public health surveillance systems operate, increasing or reducing their capabilities with shifting political priorities. Yet, these systems remain limited in scope and speed compared to how some trends are evolving, necessitating innovations for more agile data-driven surveillance and detection systems that can withstand shifts, political and otherwise.

Looking ahead: Challenges and opportunities

A major challenge facing scientists today is diminishing financial resources to conduct research. Annual inflation has increased at an average rate of approximately 2.5% since the year 2000. The minimum salary for a postdoctoral fellow was $26,916 in 2000, which has risen to $62,652 in 2025. The maximum salary for a NIH-funded investigator was $141,300 in 2000, which has increased to $225,700 in 2025. During the same period, the annual direct costs for a NIH R01 grant has remained the same at $500,000. According to an inflation calculator, $500,000 in 2000 is worth approximately $940,000 in 2025. In other words, as costs have increased dramatically over the past quarter century, the purchasing power of grants has almost halved. Particularly during a period of stunning technological growth, where state-of-the-science technology can be nearly cost prohibitive, how can scientists in the United States continue to conduct groundbreaking research and simultaneously disseminate?

Connecting with industry may be one solution to this problem, particularly as it pertains to dissemination of scientific findings. It is a reality in our country that scientific findings must have either stable government funding or a path to market to make a broad impact on populations where it might count. Disseminating a scientific finding from the laboratory to the general population requires a significant amount of funding and sustained engagement that is typically only possible through the federal government or through industry, with industry often taking the lead. This is common in pharmacotherapeutics. The average cost of research and development to bring a new drug to market is approximately $172 million (Sertkaya et al., 2024), which is simply not possible for an academic research team. With regard to digital therapeutics, however, sometimes the cart comes before the horse: small businesses may sometimes sprout prematurely around interesting and elegant, but untested, scientific concepts to take advantage of an emerging business opportunity. Applications to the Food and Drug Administration seeking approval for digital therapeutics devices (e.g., mental health mobile apps, electronic health records, and extended reality) have increased in recent years (Liang et al., 2025), typically led by business and industry. This poses an additional challenge for behavioral scientists. While there are many trustworthy industry partners firmly grounded in the scientific method, industry has a different goal than academia. How do we partner with industry, perhaps the most viable way to disseminate findings to the general population, while remaining firmly anchored to scientific integrity? These are some of challenges that we must solve if we hope to make continued impacts on health behavior in the coming decades.

In this article, we have highlighted the critical nature of innovation in driving forth novel scientific progress for the betterment of the human condition. However, we would be remiss not to caution against an overemphasis on innovation as the driving criterion for evaluating scientific contribution. Just because an idea is new does not mean it is useful; further, just because an idea, concept, or method is old does not mean that it lacks utility. In fact, the most valuable scientific contributions should likely increase in usefulness as it ages. Once a novel idea or method has been established, it should begin the long progress of making its impact through dissemination. The novel NIH Simplified Peer Review Framework combines significance and innovation under “Importance of the Research”, which should go a long way toward protecting against an overreliance on innovation. As a scientific community, we should continue to balance significance and innovation as we evaluate the impact of individual research contributions.

Taken together, faced with the current landscape of public health problems related to human health behavior, recent technological advancements offer unprecedented opportunities to tackle these persistent societal problems in new ways. Researchers are now faced with considering the best ways to integrate established methods, theories, and frameworks with new ways of reaching people, collecting data, and delivering interventions. Focusing on problems related to substance use, technological advancements will allow us to ask increasingly fine-grained questions. Whereas before we could only deliver interventions in limited settings or with limited frequency (e.g., in-person therapy sessions weekly or biweekly), we can now broaden our horizons and ask more fine grained questions, for instance, to test whether certain interventions are more effective if they are delivered on certain days (e.g., weekdays vs weekends), and even more specifically (e.g., morning vs afternoon vs evening). We can also broaden our horizons to think about how to structure our interventions (e.g., technology focused only or using tech-based interventions as an adjunct to in-person support) and test the types of interventions that can be delivered (e.g., using written feedback such as text messaging and chatbots vs video). As we navigate this uncharted territory, new challenges will arise - including ethical considerations, keeping large amounts of data safe from malicious actors, and how best to work with industry - balancing innovation with rigorous science. As noted, for technology-based assessment and intervention methods to be maximally useful, they must be accessible and acceptable for the intended end-users, and thus, accurate measurement of how people are interacting and engaging with the technologies will be a critical piece of determining the utility of these technology-based approaches. As digital therapeutics become more widely acceptable, there will be a growing need to measure more fine-grained aspects of digital content, including the amount of intervention doses (e.g., one text message vs several messages), frequency of use (e.g., is daily texting needed vs texting on weekends), duration of use, timing of delivery (e.g., morning vs evening), and user engagement patterns (e.g., whether participants actively respond, ignore, or revisit messages later). These will likely look different depending on the technology and the desired behavior change (e.g., reducing drinking vs smoking). For us to achieve meaningful, scalable, and equitable impact in digital health interventions, we must be guided by our defining principles of user-centered design, behavioral theory, and implementation feasibility. Without these as anchors, we might risk building tools that are innovative in form but may prove ineffective, or even inaccessible and unusable, for individuals and communities most impacted by substance use and related health behavior problems.

Public health significance statement:

This perspective highlights emerging tools, novel intervention targets, and technologies in health behavior research, emphasizing their potential to advance public health outcomes. Intended to inform researchers and decision-makers shaping future health initiatives, the perspective underscores opportunities to improve prevention and intervention efforts while looking ahead to potential challenges.

Disclosures and Acknowledgements

This research was supported in part by grant R00 AA031443 (Gebru) and by the Center for AIDS Research at Emory University (P30 AI050409). NIH had no role in the study design, collection, analysis, or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

All authors have contributed to the manuscript and have read and approved this submission.

Authors have no conflicts of interest.

This work was supported by NIH (R00 AA031443; Gebru) and by the Center for AIDS Research at Emory University (P30 AI050409). All authors declare no conflicts of interest. Ideas presented in this manuscript have not been previously disseminated.

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