David G. Schlundt, Ph.D.
Associate Professor of Psychology
CRC Research Skills
January 20, 2011
NIH party line on translation research
Problems with the party line
Reductionism in modern science
Problems with reductionism
Complex systems science as an alternative
Problems with complex systems science
Examining the obesity epidemic as a real-life
exemplar
 Integrating scientific approaches
 Implications for basic and applied research on
obesity
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Problem: basic research findings take years or
decades to find their way into evidence-based
practice
 Problem: Landmark clinical trials take years or
decades to find their way into evidence-based
practice
 Problem: The investment in basic research has not
resulted in a corresponding improvement in health
care delivery
 Goal: Translate the discoveries of basic scientific
research into population level gains in health

New Pathways to Discovery - unravel the
complexity of biologic systems and their regulation
 Research Teams of the Future – break down the
barriers to interdisciplinary and transdisciplinary
research
 Re-engineering the Clinical Research Enterprise –
bring more scientists into clinical research
 Solution: Clinical Science Translation Awards (CTSA)
– infrastructure to support clinical and translation
research at academic institutions

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T1 – from bench to bedside
 Taking basic biological sciences and using them to create useful
diagnostic tests, drugs, and therapies

T2 – from bedside to community
 Moving clinical research findings into evidence-based practice
and looking at the impact on the public’s health

These definitions:
 Were created by the basic scientists who run the NIH research
enterprise
 Imagine a one-way flow of knowledge from basic research to
improved health care
 Over simplify what is a complicated problem (how to improve
human health)
The amount of resources at the NIH continues to be
disproportionately allocated for basic research
 The basic scientists in charge have underestimated the
difficulty and amount of time required to plan and execute
translation research studies
 The clinical relevance of basic research findings is
overestimated
 Translation research proposals are too often reviewed by
basic scientists who review translation studies using their basic
research framework
 Much greater improvement in population health could be
achieved by improving current health care delivery – based
standards of care that are not implemented
 Much greater improvement in population health could be
achieved through health care reform

There are assumptions and frameworks behind the
practice of science that drive the questions, the
methodologies, and the development of new knowledge
 Philosophical Reductionism

 Offshoot of materialist philosophy
 Idea that one science (biology) can be reduced to the principals of
another science (chemistry)
 Drive to find the most basic explanation
 There is potentially a single, underlying physical science that explains
everything
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Methodological Reductionism
 The best scientific explanations come from breaking problems into
their most fundamental elements
 Goal of science is to identify, isolate, and study basic causal
mechanisms
 Approach is to create experiments in which only one parameter is
allowed to vary so that its causal effect can be isolated
 Goal is to develop mechanistic explanations
Much “basic” research follows a reductionist
framework in biological and behavioral sciences
 Reductionism

 Leads to increasing specialization
 Leads to problems being broken down into ever smaller problems
 Leads to a rapidly expanding base of knowledge in which the pieces
are largely disconnected from each other
 Leads to new technologies and methodologies for achieving tighter
and tighter control of ever smaller processes
Even when the rationale for the research is an important
clinical problem (e.g., diabetes, depression,
schizophrenia), the research itself ends up isolating only a
small piece of the problem and studying it out of context

Reductionism is not the most efficient way to improve
the physical and mental health of populations of human
beings
 Most “breakthroughs” in basic health and neuroscience
do not lead to new diagnostic or treatment approaches
 The overspecialization of disciplines makes it difficult for
any one scientist to pull together enough basic knowledge
to create meaningful new diagnostics or interventions
 Funding of basic science does not encourage
interdisciplinary or transdisciplinary cooperation needed
to create clinical applications

In reductionism, causality moves one way from low
order phenomenon to higher order phenomenon
 Ignores the possibility of complex higher order systems
exerting a causal influence on more basic lower order
systems
 Biogenetic determinism moves explanation of social and
behavioral problems to the genes

 Individual rather than social conditions or economic inequities is
responsible for problems
 However, the individual is not responsible, the genes are responsible
 Many modern individuals have a sense of helplessness due to a naive
reductionism (obesity and depression good examples)
Much effort is put towards finding new drugs that will
solve social/interpersonal/emotional/economic/political
problems

Holism – systems cannot be understood by taking
them apart
 Emergent Properties – as components associate
into systems, new properties of the systems emerge
which cannot be predicted from the properties of the
components (e.g., hydrogen + oxygen  water)
 Complex systems science – systems form
hierarchies of increasing complexity and exhibit
adaptive behavior at each level of analysis

 Homeostasis
 Feedback loops
 Cross-level linkages
http://necsi.org/projects/mclemens/cs_char.gif
Goals of science are the same (understanding,
prediction, and control) but the methods are different
 Requires different frameworks and methodologies
which are not as well developed as experimental
reductionism
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Mathematical simulations
Complex statistical modeling
Nonlinear models
Multilevel models
Evaluation of real-world interventions
It becomes difficult to make reassuring cause and effect
statements; Scientists are forced to live with uncertainty.
 It becomes difficult to create unambiguous mechanistic
explanations
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The United States and other developed countries
are experiencing an epidemic of obesity
 Why is this happening?
 What can be done to reverse the trends?
 Problem is so serious that life expectancies may
begin to decline by the middle of the 21st century

Obesity Trends* Among U.S. Adults
BRFSS, 1985
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1986
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1987
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4”
person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1988
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1989
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1990
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1991
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1992
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1993
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1994
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1995
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1996
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1997
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1998
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 1999
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2000
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
≥20%
Source: Behavioral Risk Factor Surveillance System, CDC.
Obesity Trends* Among U.S. Adults
BRFSS, 2001
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
≥25%
Obesity Trends* Among U.S. Adults
BRFSS, 2002
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
≥25%
Obesity Trends* Among U.S. Adults
BRFSS, 2003
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
≥25%
Obesity Trends* Among U.S. Adults
BRFSS, 2004
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
≥25%
Obesity Trends* Among U.S. Adults
BRFSS, 2005
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2006
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2007
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2008
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2009
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2010
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data
<10%
10%–14%
15%–19%
20%–24%
Source: Behavioral Risk Factor Surveillance System, CDC.
25%–29%
≥30%
What are some possible explanations?
Is there a single cause we need to be looking for?
If there are multiple causes, how do we study them?
Are the causes additive or synergistic?
Do the causes cascade across levels of analysis (e.g.,
macroeconomic factors influencing individual
behaviors)?
 Does our framework (reductionism versus complex
systems science) make a difference in how we
approach these problems?
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Cascade of Causal Influences
Complex Systems Science
Environment
Antecedents
Past
Biobehavioral
Self
Present
Reductionist Science
Causal Nexus
Self
Cascade of Causal Influences
Consequences
Future
The question is not which approach is the best
approach, but which is the best for solving a specific
problem
 Reductionism does not automatically lead to
translation research
 Complex systems science may have much more
translation potential
 Complex systems science requires interdisciplinary
research, different methodological approaches, and
the abandonment of simple one-cause explanations

Addresses problems in clinical care and population
health
 Evidence-based (based on best science available)
 Involves transfer of knowledge and or methods
across disciplinary boundaries
 Requires consideration of context (target is
imbedded in real-world systems)
 Coalitions and partnerships
 Engagement of communities
 Moves away from trying to find a single causal
factor and towards

Familiar example of complex systems approach to improve chronic disease
management

Personalized medicine?
 Matching drugs to genes
 How about matching treatment to other systems that are influencing
health
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Family
Neighborhood
Work setting
Psychology (cognition and emotion)
Health services research?
 Are there gains to be had from adopting complex systems framework?
 Need viable alternatives to the clinical trial

Implementation science?
 Can methods such as continuous quality improvement become
scientific tools for answering questions about improving clinical care
and population health
 What other methods can be adapted?