Whole Person Modeling: a transdisciplinary approach to mental health research

Discover Mental Health · 3 authors, 6 centres

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TL;DR

This narrative review proposes 'Whole Person Modeling' as a conceptual framework for integrating biological, psychological, and environmental data in mental health research. The central result is the description of this integrative approach and its potential to improve understanding and clinical tools, while identifying key barriers to its implementation.

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This narrative review describes a proposed research framework called 'Whole Person Modeling' aimed at addressing the complexity of mental health by integrating data from biological, psychological, and environmental domains. The methods involve synthesizing the current landscape of mental health research, proposing a quantitative integrative framework, and exploring existing techniques. Central findings include the argument that this approach could enhance understanding of multilevel phenomena, improve diagnostic and prognostic tests, and test theoretical models, but faces significant barriers like interdisciplinary communication challenges and technical issues in model specification. Implications are for guiding future transdisciplinary research strategies in psychiatry.

STUDY TYPE

narrative_review

SPECIALTY

psychiatry

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AI pipeline

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ORIGINAL PAPER

PMC10449734

Whole Person Modeling: a transdisciplinary approach to mental health research

Discover Mental Health

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ABSTRACT

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

CITATION EXPORT

Daniel Felsky, Alyssa Cannitelli, Jon Pipitone. Whole Person Modeling: a transdisciplinary approach to mental health research. Discover Mental Health. (2023). https://doi.org/10.1007/s44192-023-00041-6

AUTHORS

3 · 6 CENTRES

Daniel Felsky, Alyssa Cannitelli, Jon Pipitone

1https://ror.org/03e71c577grid.155956.b0000 0000 8793 5925Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
2https://ror.org/03dbr7087grid.17063.330000 0001 2157 2938Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
3https://ror.org/03dbr7087grid.17063.330000 0001 2157 2938Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
4https://ror.org/03gp5b411grid.423198.50000 0004 0640 5156Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
5https://ror.org/02fa3aq29grid.25073.330000 0004 1936 8227Faculty of Medicine, McMaster University, Hamilton, ON Canada
6https://ror.org/02y72wh86grid.410356.50000 0004 1936 8331Department of Psychiatry, Queen’s University, Kingston, ON Canada