ECE100: Intro to Engineering Design, Presentation No. 1csel.asu.edu/downloads/2009ACC/DERiveraPresentationfor2009ACC.pdf · interventions may be designed to address multiple disorders

Control Systems Engineering LaboratoryCSEL

Daniel E. Riveraa and Linda M. Collinsb

aControl Systems Engineering LaboratoryDepartment of Chemical Engineering

Arizona State University, Tempe, AZ [email protected]

http://www.fulton.asu.edu/~csel

bThe Methodology Center and Department of Human Development and Family Studies

Penn State University, State College, PA [email protected]

http://methodology.psu.edu

Engineering Control Approaches for OptimizingBehavioral Interventions

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mailto:[email protected]









Presentation Outline

• Brief overview of behavioral interventions

• Illustrations:

– Control-oriented analysis and design of a time-varying adaptive preventive intervention (Fast Track),

– Understanding cigarette smoking as a closed-loop dynamical system,– Dynamical systems perspective of the Theory of Planned Behavior in

an intervention to reduce excessive gestational weight gain.

• Some challenges and opportunities

• Summary and conclusions; acknowledgments.

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http://www.fulton.asu.edu/~csel/2009acc

http://www.fulton.asu.edu/~csel/2009acc.html

http://www.fulton.asu.edu/~csel/2009acc.html


Current Projects in Behavioral Health

• K25DA021173, “Control engineering approaches to adaptive interventions for fighting drug abuse,” Mentors: L.M. Collins (Penn State) and S.A. Murphy (Michigan).

• R21DA024266, “Dynamical systems and related engineering approaches to improving behavioral interventions,” NIH Roadmap Initiative Award on Facilitating Interdisciplinary Research Via Methodological and Technological Innovation in the Behavioral and Social Sciences, with L.M. Collins, Penn State, co-PI.

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Sponsors are NIH-NIDA (National Institute on Drug Abuse) and NIH-OBSSR (Office of Behavioral and Social Sciences Research)


Behavioral Interventions

• Behavioral interventions play an increasingly prominent role in the prevention and treatment of a wide number of public health disorders; these include drug and alcohol abuse, cancer, mental illness, obesity, HIV/AIDS, and cardiovascular health.

• Intervention components may be either pharmacological (e.g., naltrexone) or behavioral (e.g., motivational interviewing, cognitive behavioral therapy, relaxation exercises) in nature. Likewise, interventions may be designed to address multiple disorders that occur simultaneously (i.e., co-morbidities).

• Adaptive interventions (in contrast to fixed interventions) represent an important emerging paradigm for delivering behavioral interventions intended to address chronic, relapsing disorders.

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Adaptive Interventions(Collins, Murphy, and Bierman, Prevention Science, 5, No. 3, 2004)

• In an adaptive intervention, therapy is individualized by the use of decision rules that determine how the therapy level and type should vary according to measures of adherence, treatment burden and response (tailoring variables) collected during past treatment.

• An effective adaptive intervention may result in the following advantages over fixed interventions:

- Reduction of negative effects,- Reduction of inefficiency and waste,- Increased compliance,- Enhanced intervention potency

• Adaptive interventions are particularly useful as part of efforts to disseminate efficacious interventions in real-world settings.

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Fast Track Program (Conduct Problems Prevention Research Group, 1992, 1999)

• A multi-year program designed to prevent conduct disorders in at-risk children.

• Frequency of home-based counseling visits assigned to families based on the level of parental functioning.

• Parental function (the tailoring variable) is used to determine the frequency of home visits (the intervention dosage) according to the following decision rules:

- If parental function is “very poor” then the intervention dosage should correspond to weekly home visits,

- If parental function is “poor” then the intervention dosage should correspond to bi-weekly home visits,

- If parental function is “below threshold” then the intervention dosage should correspond to monthly home visits,

- If parental function is “at or above threshold” then the intervention dosage should correspond to no home visits.

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Parental Function Feedback Loop Block Diagram*(to decide on home visits for families with at-risk children)

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From Rivera, D.E., M.D. Pew, and L.M. Collins, “Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction,” Drug and Alcohol Dependence, Special Issue on

Adaptive Treatment Strategies, Vol. 88, Supplement 2, May 2007, Pages S31-S40.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T63-4MJS034-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=71fe359f500f8b10e59960b07383174b





Adaptive Interventions Block Diagram (Expanded Representation)

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r uecy

d

Pd

PC

Cf

nym

+-

+-

++

++

Pm

Moderators (e.g.,

gender,SES)

Controller Plant

Measured Parental Function

Parental

Function

Goal

Disturbances

(e.g., job loss,

stress)

Proximal

Intervention

Outcomes

Frequency of Counselor

Home Visits

Family Functioning

Questionnaire

Measurement

Unreliability


Parental function PF(t) is built up by providing an intervention I(t) (frequency of home visits), that is potentially subject to delay, and is depleted by potentially multiple disturbances (adding up to D(t)).

Parental Function - Counselor Home Visits Adaptive Intervention as a Production-Inventory Control Problem

PF (t + 1) = PF (t) + KI I(t! !)!D(t)

D(t) =m!

i=1

Di(t)

LT

CTL

Depletion

D(t) (Disturbance)

I(t) (Manipulated)

PF(t) (Controlled)

Decision Rules

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Parental Function Dynamics Modeling Challenges

• Quantitative models for supply mechanisms (how particular dosages of an intervention contribute to improving parental function) can be stochastic, nonlinear, and autoregressive in nature.

• Depletion mechanisms can be nonlinear, stochastic, and autoregressive as well; could be measured or unmeasured.

• The choice of review interval, how tailoring variables are defined and measured, and the selection of intervention dosage levels all play significant, important roles.

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• We are developing hybrid MPC algorithms for this class of problems, with particular interest in understanding the effectiveness of these control-oriented interventions on populations.

!6 0 6 12 18 24 30 36

0

20

40

60

80

100

% F

unct

ion

!6 0 6 12 18 24 30 36No Visit

Monthly

Bi!Weekly

Weekly

Time (month)

Inte

rven

tion

Dos

age

Parental Function - Counselor Home Visits Adaptive Intervention as a Production-Inventory Control Problem

−6 0 6 12 18 24 30 36

−50

0

50

050

100

time (Month)Gain Mismatch (%)

PF (%

)

−6 0 6 12 18 24 30 36

−50

0

50

No VisitMonthly

Bi−WeeklyWeekly

time (Month)Gain Mismatch (%)

Inte

rven

tion

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Control Systems Engineering LaboratoryCSEL R21 Grant Activities (to date)

• Modeling an intervention to prevent excessive weight gain during pregnancy (with D. Downs and L. Birch, Penn State).

• Dynamic modeling and analysis of a treatment intervention for alcohol abuse based on motivational interviewing (with J. Morgenstern, Columbia and J. McKay, Penn).

• Exploring dynamical systems and control engineering concepts applied to the Family Bereavement Program (FBP; with I. Sandler, T. Ayers, and F. Castro, ASU).

• Low-dose naltrexone interventions in fibromyalgia patients (J. Younger, Stanford School of Medicine).

• Understanding smoking behavior and cessation as a closed-loop dynamical system (with T. Walls, U of Rhode Island).

R21DA024266, “Dynamical systems and related engineering approaches to improving behavioral interventions,”

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Control Systems Engineering LaboratoryCSEL Cigarette Smoking Fluid Analogy

• Smoking can be viewed as a urge regulation feedback problem, where the decision to smoke (or frequency of smoking) is determined on the basis of a perceived excess in an urge “inventory,” beyond a desired setpoint or goal.

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dydt = Kd1 d1(t)!Ku u(t)!Kd2 d2(t)

Smoking

(Manipulated

Variable)

LT

Urge

(Controlled

Variable)Urge

Controller

y(t)

u(t)

Negative Affect (e.g., Mood, Anger, Irritation)

External Stimuli (e.g., Smoke, Others)

(Disturbance Variables)

Positive Affect

Physical Activity

(Disturbance Variables )

"Decision to

Smoke"d2(t)

d1(t)

LT: Level Transmitter

Control Systems Engineering LaboratoryCSEL Simulation “Thought Experiment”

• Frequency of smoking incidents diminishes urge, which increases in response to variation in irritation throughout the day.

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Controlled(Dependent)

Manipulated(Independent)

Disturbance(Exogeneous)

Control Systems Engineering LaboratoryCSEL Cigarette Smoking Feedback Loop

• A “block-diagram” signals and systems representation of how cigarette smoking regulates urge in smokers.

• We are interested in estimating these transfer functions from intensive data collection of all relevant variables, and using this information to “reverse-engineer” the feedback regulation mechanism involved.

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Setpoint 9i#s :i;otine <==e;t

4mitte> ?)"i)@*es

Pcu Pnc Pan

Pua

Pue

!o#e%&to%s *e.,.- ,en#e%- %&/e- S1S2

Control Systems Engineering LaboratoryCSEL Adaptive Interventions for

Smoking Cessation

• Adaptive interventions for smoking cessation can be conceptualized as additional feedback mechanisms that would ultimately replace smoking as a means to reduce urge.

Smoking

(Manipulated

Variable)

LT

Urge

(Controlled

Variable)Urge

Controller

y(t)

Negative Affect (e.g., Mood, Anger, Irritation)

External Stimuli (e.g., Smoke, Others)

(Disturbance Variables)

Positive Affect

(Disturbance Variable )

"Decision to

Smoke"

d2(t)

d1(t)

LT

Intervention

Decision

Rules

u1(t)u2(t)Medication, Therapy, Exercise

(Manipulated Variables )

dydt = Kd1 d1(t)−Ku1 u1(t)−Ku2 u2(t)−Kd2 d2(t)

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Dynamic Modeling of an Intervention for Excessive Gestational Weight Gain

• Excessive weight gain during pregnancy has negative health consequences for both mother and child.

• Intervention involves teaching healthy eating habits and increasing physical activity during pregnancy.

• Weight gain and loss has a mechanistic component based on energy balance.

• Understanding this intervention as a dynamical system involves the challenge of addressing the behavioral component as well as a mechanistic component.

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Theory of Planned Behavior (TPB, Ajzen, 1991)

• A Structural Equation Model (SEM) representation of this theory (not to be confused with a block diagram).

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!1 = "11#1 + "12#2 + $1

!2 = %21!1 + $2

!1 = w1

n1!

i=1

"i#i

!2 = w2

n2!

i=1

$i%i

Normative BeliefsBelief !

Motivation to comply

SubjectiveNorms

Intention(!1)

Behavior(!2)

Behavioral BeliefsBelief (!i) !Evaluation ofoutcome ("i)

AttitudeToward theBehavior

(!1)

w1

Control BeliefsBelief (!i) ×

Perceived Power ("i)

PerceivedBehavioral

Control(ξ2)

w2

!12

γ11

!21

ζ2ζ1


Dynamical Systems Model/Fluid Analogy for TPB

• The SEM model for TPB has been extended into a system of differential equations with a corresponding fluid analogy, drawn from supply chain management.

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w1

n1!

i=1

!i"i w2

n2!

i=1

! i " i

I20

Intention

Behavior

Attitude PBC

!11"1(t! #1) !12"2(t! #2)

(1! !12)"2(1! !11)"1

(1! !21)"1

!21"1(t! #3)

!2(t)

!1(t)

(!2)(!1)

(η1)

(!2)!2(t)

100%

0%

100%

0%

100%

0%

100%

0%

!1d"1

dt= w1

n1!

i=1

#i$i ! "1(t)

!2d"2

dt= w2

n2!

i=1

%i&i ! "2(t)

!3d'1

dt= (11"1(t! )1) + (12"2(t! )2)! '1(t) + *1(t)

!4d'2

dt= +21'1(t! )3)! '2(t) + *2(t).


Excessive Gestational Weight Gain Intervention Simulation

• Comprehensive simulation environment includes both behavioral and mechanistic components.

• Dynamical model can inform novel experimental designs aimed at understanding mechanisms of behavior change.

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0 20 40 60 80 100 120 140 160 180 200 220 240 260 28060

70

80

BM (k

g)

Body Mass (kg)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 28045

50

55

60

FFM

(kg)

Fat−free Mass (kg)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 28014

16

18

20

FM (k

g)

Fat−Mass(kg)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2801400

1500

1600

1700

1800

EI (k

cal)

Energy Intake (kcal)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 28090

100

110

PA (k

cal)

Physical Activity (kcal)

time (days)

InterventionNo Intervention

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800

5

10

Belie

fs (!

1, "1)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800

50

100

Attit

ude

(#1)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800

25

50

PBC

(#2)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800

25

50

Inte

ntio

n ($

1)

0 20 40 60 80 100 120 140 160 180 200 220 240 260 2800

25

time (days)

Beha

vior ($

2)

EI InterventionPA InterventionNo EI InterventionNo PA Intervention

Behavioral Mechanistic


Some Challenges and Opportunities

• Decision rules/controllers to deliver multi-component interventions that address co-morbidities, under constraints,

• Methods for obtaining “open-loop” models; this includes novel clinical trial mechanisms intended to inform adaptive interventions, and means to simplify complex simulation models into forms useful for adaptive intervention design and analysis.

• Simulation environments that enable understanding how the adaptive intervention can work in populations, and the proper role between the adaptive intervention and clinical judgement.

• Optimizing interventions from a multi-level perspective (e.g., genetic, individual, family, community, national, and global).

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From Glass, T.A. and M.J. McAtee (2006). “Behavioral science at the crossroads of public health: extending horizons, envisioning the future,” Social Science and Medicine, 62, 1650-1671.

Hierarchical Perspective on Behavioral Science

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Some Relevant Systems Technologies

• Model Predictive Control (to enable decision rules involving multiple tailoring variables and interventions with multiple components, with constraints),

• System identification (for modeling dynamic system behavior from data, developing novel alternatives to the randomized clinical trial, and simplifying complex simulation models),

• Data-centric estimation and control (to enable modeling and decision making without requiring fixed model structures),

• Comprehensive, hierarchical simulation environments to assess issues relating to control system robustness, integration of decision rules with clinical judgement, etc.

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Summary and Conclusions

• Time-varying adaptive interventions constitute closed-loop control systems applied to uncertain, nonlinear dynamical processes, and can therefore benefit from a control engineering perspective.

• Fluid analogies based on the principle of mass conservation are useful in establishing a modeling basis for diverse behavioral health phenomena.

• Illustrations taken from conduct disorder prevention (Fast Track), cigarette smoking, and obesity (excessive gestational weight gain) have been presented.

• Novel Model Predictive Control formulations relying on models obtained from data-centric system identification procedures represent a promising approach to improve behavioral interventions aimed at significant problems in public health.

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Acknowledgments

• Drs. Nareshkumer Nandola and J. Emeterio Navarro-Barrientos, Postdoctoral Research Associates, CSEL-ASU

• Susan D. Murphy, Ph.D., Department of Statistics, Department of Psychiatry, and Institute for Social Research, University of Michigan.

• Michael D. Pew, B.S.I.E., M.S.E., Industrial Engineering, Arizona State University.

• Felipe Castro, Laurie Chassin, David MacKinnon, Irwin Sandler, and Steve West, Prevention Research Center, Arizona State University.

Support from NIH-NIDA (National Institute on Drug Abuse) and NIH-OBSSR (Office of Behavioral and Social Sciences Research),

Grants K25DA021173 and R21DA024266

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Principal References

• Ajzen, I., “The theory of planned behavior,” Organizational behavior and human decision processes, 50, 2, pgs. 179-211, December 1991.

• Collins, L.M., S.A. Murphy, and K.L. Bierman, “A conceptual framework for adaptive preventive interventions,” Prevention Science, 5, No. 3, pgs. 185-196, Sept., 2004.

• Rivera, D.E., M.D. Pew, and L.M. Collins, “Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction,” Drug and Alcohol Dependence, Special Issue on Adaptive Treatment Strategies, Vol. 88, Supplement 2, May 2007, Pages S31-S40.

• Rivera, D.E., M.D. Pew, L.M. Collins, and S.A. Murphy, “Engineering control approaches for the design and analysis of adaptive, time-varying interventions,” Technical Report 05-73, The Methodology Center, Penn State University; available electronically from

http://methcenter.psu.edu/

or from the ASU-CSEL website:

http://www.fulton.asu.edu/~csel/

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http://www.springerlink.com/content/g1x28785052374j1/?p=00f8b858f8564e2194fedb5d67fafa9d&pi=3








http://methcenter.psu.edu/media/techreports/05-73.pdf




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Documents

ECE100: Intro to Engineering Design, Presentation No. 1csel.asu.edu/downloads/2009ACC/DERiveraPresentationfor2009ACC.pdf · interventions may be designed to address multiple disorders