• Research Priority 1:
  Collaborative Science
• Research Priority 2:
  Innovative Scientific Investigation
• Research Priority 3:
  Implementation, Dissemination, and Equitable Impact

Research Priority 2: Innovative Scientific Investigation

Encouraging innovation and rigor in research measurement, experimental designs, and data analytics used in BSSR will accelerate scientific advances and support a more comprehensive and seamless integration of BSSR into the larger health research enterprise. Across BSSR disciplines, support is needed for the development and use of more advanced, multilevel, and harmonizable measurement approaches. OBSSR has pursued these goals through contributions to such projects as the PhenX COVID-19 Protocol Library, the NIH Toolbox, and the Disaster Research Response Resources Portal (DR2). We also have a decade-long history of supporting short courses focused on skills development in innovative and integrative methodologies and analytics.

OBSSR incentivizes investigators to incorporate novel research designs and explore cutting-edge analytic techniques to tackle the most pressing health questions. For example, OBSSR, along with several NIH IC partners, developed the Intensive Longitudinal Health Behaviors Network, a consortium of researchers who have worked to leverage technologies and modeling approaches toward better understanding of human behavior over the life course.

To promote equitable health impacts, OBSSR strives to ensure that research methodologies and paradigms are valid for use in all populations and are appropriately disseminated to all BSSR scientists. As an example, OBSSR has participated in the RADx Underserved Populations (RADx-UP) initiative since the beginning of the COVID-19 pandemic. Projects funded through RADx-UP focus on community-engaged participatory research to evaluate point-of-care testing methods in specific populations, areas, and settings. OBSSR also led the development of recommendations for common data elements to measure both behavioral uptake of screening opportunities and psychosocial outcomes associated with the pandemic.

OBSSR will continue to promote innovative, rigorous, and relevant research by implementing strategic priorities toward the following goals.

Goal 1: Refine BSSR Measurement

With multiple types of quantitative and qualitative measurement tools and strategies available, OBSSR encourages investigators to think deeply about how best to capture behavioral and social phenomena of interest.

Specifically, we will continue to promote and support research projects that advance:

• Novel precision measurement approaches to capture phenotypic characteristics that can explain differences in health and disease over time, help understand variation in response to interventions, and contribute to the development of personalized interventions
• Multilevel assessments of individuals, families, social networks, organizations, communities, and cultures, using a variety of data collection modalities
• Measures to capture changes in the environment, systems, and research structures that affect health in all populations
• Rigorous measurement approaches for studying SDOH
• Innovative technologies (e.g., wearables and mobile apps that are used for ecological momentary assessments) to advance the measurement of behavioral and social phenomena over time in real-world settings and to track complex behaviors and health indicators
• Common data elements and data harmonization to facilitate cross-study comparisons and meta-analyses
• Integration of BSSR measures into impactful NIH-wide initiatives, such as the All of Us Research Program, the Environmental influences on Child Health Outcomes (ECHO) Program, and the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN Initiative)

Goal 2: Promote Novel Experimental Designs

OBSSR is committed to expanding the repertoire of experimental designs available to and adopted by basic and applied BSSR investigators, as well as by integrated multidisciplinary teams. In addition to well-conducted, fully powered randomized control trials to test specific interventions, OBSSR recognizes the value of the following:

• Small-Scale Observational Investigations: OBSSR supports high-quality BSSR conducted with qualitative, small mixed-method, or even N-of-1 case studies.
• Large-Scale, Longitudinal, Observational Studies: OBSSR supports the use of robust sampling strategies and the use of both prospective and retrospective data sets to complement smaller experimental cross-sectional BSSR studies and strengthen causal inference.
• Natural Experiments: As emphasized by the COVID-19 pandemic, observational studies that assess the differential impacts of far-reaching events can be illuminating. OBSSR has developed the Time-Sensitive Opportunities for Health Research funding opportunity, through which we support rigorous study of research questions and data collection opportunities related to unexpected or time-sensitive events.
• Pragmatic Trials: Although they pose unique challenges, pragmatic clinical trials provide the opportunity to efficiently generate real-world evidence to inform service provision and medical decision-making.
• Supplemental Approaches in Epidemiological Studies: Incorporating mobile sensors and assessments for passive data collection into surveys can further the integration of social, behavioral, and biomedical research.
• Experimental Designs: Behavioral intervention development projects need to more systematically incorporate experimental designs that include hypothesized mechanisms or targets, test these hypotheses through direct measurement of changes in mechanisms or engagement of targets, and demonstrate that those changes are associated with behavioral outcomes.⁸ OBSSR’s support for such approaches is consistent with practices of multiple NIH ICs, the NIH Science of Behavior Change program, and the NIH Stage Model.^9,10,11,12

Goal 3: Advance Data Analytics

BSSR is advancing at an accelerated pace by incorporating innovative technologies and analytical techniques into health research. This effort includes collecting new forms of continuous, temporally dense behavioral and contextual data from wearable and environmental sensors and smartphone-based ecological momentary assessments. It also includes integrating multimodal data streams, such as social media, electronic health records, and administrative data.

Increasingly, BSSR is complementing traditional statistical analyses with novel computational tools and modeling approaches. These approaches include artificial intelligence methods, such as machine learning, natural language processing, large language models, text mining, data mining, data visualization, simulations, and predictive modeling.

OBSSR’s strategies to help advance, catalyze, and promote the use and development of computational and statistical tools and methods for BSSR, include support for:

• Ethical Applications of Artificial Intelligence for BSSR Researchers Using Internet, Commercial, and Administrative Records Data: This research includes social media platforms, internet data sources, crowdsourcing and citizen science data collections, retail purchasing tracking databases, and many other electronic administrative or commercial records (e.g., digital health care administrative data from patients, providers, and insurers).
• Training Efforts That Will Provide Tomorrow’s BSSR Researchers with Computational Skills and Rigorous Statistical Expertise to Properly Curate, Link, Mine, and Combine Data: Behavioral and social science trainees interested in large, internet-based data sets will require instruction in innovative computational and mathematical modeling approaches, techniques for data mining and harmonization, and new methods for dealing with unmeasured heterogeneity. OBSSR currently supports advanced data and analysis training for graduate students through its T32 training program.

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