Meanne is an inter-disciplinary scientist and entrepreneur with expertise that spans the areas of health psychology, affective neuroscience, neuroimmunology, digital health, and social health policy. The vision is to unmask trends across disciplines to understand stress biology and stratify social healthcare management for disadvantaged populations.
The research program aims to understand health disparities by characterizing bio-behavioral pathways between individuals and their communities. A multi-level, mixed methods approach examines how social factors at the neighborhood, family, and individual levels interact to predict health development across the lifespan, with a particular focus on childhood social disadvantage, stress, and its effects on emotional and neurobiological pathways relevant to the pathophysiology of age-related diseases.
Previous experience include launch of large-scale NIH R01 prospective studies with both healthy cohorts and clinical samples, digital health interventions, intensive longitudinal studies with eHealth/mobile data collection, design of community hubs to improve living conditions of disadvantaged populations, and adaptation of behavioral tasks for health studies.
Key technical skills include ecological momentary assessments, experience sampling, dyadic modeling, cloud-based data management, biomarker assessments, and semi-structured assessments for hybrid qualitative-quantitative data. Meanne also uses advanced analytical and data visualization approaches with R, Python, and Tableau.
Meanne runs a health technology consulting platform geared at cultivating community research projects and mobile health startup technology. Other translational experience include advocacy projects to engage relevant at-risk subgroups, ongoing partnerships with NGOs to reach a broad audience, and bridging across independent bureaus for an integrated interpretation of social trends to inform service systems.
In combination, models of risk and resilience are built to unmask patterns of health trajectories in various subgroups and families, coupled with technology harnessing, to optimize user-driven but evidence-based intervention approaches at the community level.