New and innovative opportunities for prevention science research, practice, and policy are emerging rapidly with advances in data science and technology. Collaboration across diverse disciplines and partners, including prevention scientists, community and practice partners, data scientists, methodologists, statisticians, engineers, computer programmers, and policymakers, is necessary to reveal the knowledge contained in complex data sets. Prevention scientists are poised to lead multidisciplinary, collaborative, and international teams that advance the development and application of data-analytic and technological innovations to benefit all individuals and communities. These innovations have the potential to improve the development, testing, implementation, and scale-up of effective prevention programs and services. For prevention science, the term “data science” includes “big data analytics” (i.e., advanced analytics for very large, intensively collected, and/or multimodal and integrated data sets) and artificial intelligence (AI; e.g., machine learning algorithms for prediction and/or clustering). Prevention scientists are leveraging data science techniques, and the technologies that make them possible, to advance and accelerate real-time data collection (e.g., ecological momentary assessment), analysis, prediction, and automation and to create interactive tools for tailored prevention and treatment.
Examples of Leveraging Data Science & Technology in Prevention Science
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Importantly, prevention scientists have a responsibility to critically evaluate both the potential benefits and unintended negative consequences to society of AI and related data and technologies. Overall, advances in AI and related data and technologies provide unprecedented opportunities to advance prevention science across diverse settings, including justice systems, schools, primary care, and treatment centers. However, the collection, analysis, interpretation, and quality control of data present challenges that require the development of new ethical paradigms guiding infrastructure, training, team science, and data sharing that prioritize equity.
Representative Ethical Challenges of AI in Prevention Science
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SPR is committed to ensuring that prevention science promotes a healthy and equitable society through research-informed and socially just programs, practices, and policies. This year’s conference theme complements the last four themes focused on equity and social justice: The Role of Prevention Science in Achieving Social Justice and Health Equity for All, Realizing the Power of Prevention Through Equitable Dissemination and Implementation Science, Addressing Racism and Disparities When Considering Biology and Context, Advancing Partnerships and Collaborative Approaches in Prevention Science. Technological tools (e.g., smartphones, ambulatory assessment devices, social media) expand our data collection and program/service design capabilities. Integrating advanced quantitative methods and data science strategies (e.g., generative AI, machine learning algorithms, natural language processing) expand the ability of prevention scientists to examine complex, intersecting effects of social and structural determinants of health, tailor program/service delivery, and accelerate health equity. However, as our opportunities grow, so do our ethical and practical challenges (e.g., algorithmic bias, data sharing, education and training) and the need to address equitably these challenges and their implications for individuals, families, communities, practices, and policies. Multidisciplinary partnerships, collaborative approaches, and ongoing, open dialogue are cornerstones of SPR’s continued focus on equity and imperative to address the challenges of the rapidly changing landscape of data and technology in prevention science. Submissions with diverse teams, particularly those including community, practice, or policy-making partners, are strongly encouraged.
2025 Special Conference Themes
Each year, SPR selects three special themes designed to highlight specific areas of research relevant to prevention science and the overall conference theme. The SPR Conference committee encourages basic, epidemiological, etiological, intervention, and dissemination and implementation research submissions across these special themes. Consistent with this year’s conference theme, Leveraging Data Science & Technology, the SPR Conference Committee encourages special conference theme submissions related to pressing needs and the role of prevention science in three areas:
Volumes of data on momentary and longer-term behaviors, real-time and historical locations, and biological processes can be collected efficiently and with limited intrusion. These data can then be integrated with community-based contextual data (e.g., regional socioeconomic deprivation, migration patterns, large-scale weather events), neurobiological and genetic data, and/or administrative data obtained from local, state, or national resources.
As new data collection modalities come online and need to be integrated with existing modalities, it is critical that high-quality, methodologically sound study designs, data integration techniques, and analysis approaches are developed and applied.
New opportunities for decision-making based on environmental context and based on information across multiple levels (e.g., family, peer, school, community) are available. For example, experimental designs to build adaptive interventions and just-in-time adaptive interventions (e.g., sequential, multiple-assignment, randomized trials, microrandomized trials) are in active development to help prevention scientists determine how to deliver intervention content.
There is increasing recognition of bias within data sets and the algorithms applied to them. Detecting and mitigating bias is a growing area of research critical to prevention science.
Despite concerns, AI-based content generation presents a growing opportunity to create and tailor program/service content and to return digestible take-home messages to individuals, families, schools, communities, and policymakers. Content generation combined with high-quality, user-friendly, attractive, and exciting data visualizations have the potential to revolutionize the way prevention scientists translate their work and increase its impact. Critically, a variety of audiences can be considered: individual data dashboards might be used to facilitate participant engagement or behavior change and community-level data dashboards or other visuals might be used to guide agencies and policymakers.
Each year, the SPR Conference committee encourages basic, epidemiological, etiological, intervention, and dissemination and implementation research submissions across key themes that promote advances in prevention research.
The SPR International Program and the Division of Epidemiology, Services, and Prevention Research of the National Institute on Drug Abuse (NIDA) will host their annual NIDA International SPR Poster Session. Posters should highlight research on the prevention of drug use, prevention of drug use in combination with alcohol use, or prevention of HIV/AIDS in the context of drug use or drug and alcohol use. See their separate call for poster abstracts at https://preventionresearch.org/2025-annual-meeting/call-for-poster-abstracts-nida-international-spr-poster-session/
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To submit to the NIDA International SPR Poster Session
Abstract submission guidelines for all presentation formats is available at
https://preventionresearch.org/2025-annual-meeting/abstract-submission-guidelines/
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