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Question: What is the behavioral risk factor target of treatment and how much
improvement in it is needed to move the clinical endpoint? The behavioral target
of treatment should have a known association to a clinical endpoint; that is, it should
be a behavioral risk factor for a chronic disease. If it is not an established risk factor,
then preliminary work showing such an association, usually accomplished with epi-
demiologic studies, would be indicated before setting sights on a clinical trial.
A trickier task is identifying the level of change on the behavioral risk factor that is
needed to move the clinical endpoint. This is a categorical, not a continuous, under-
taking because improvement that is statistically signicant on continuous scale may
be an inadequate level of improvement to move a clinical endpoint. A target for the
level of behavior change needed to reduce clinical risk is key to evaluating the suc-
cess of a behavioral treatment in early developmental studies. Clinically signicant
cutpoints exist for established behavioral risk factors such as smoking, weight, alco-
hol/drug abuse, fat intake, sodium intake, physical inactivity, depression, and low
social support. For example, weight loss of ≥5% of body weight in obese adults
with cardiovascular disease risk factors has been shown to result in clinically mean-
ingful cardiovascular and metabolic benets [66]. However, in the case of novel
behavioral risk factors where such an evidence base may not exist, observational
studies examining covariation between the behavioral factor and a relevant bio-
medical risk factor would be justied. For example, a cross-sectional study of the
covariation between different levels of a measure of stress and a cardio- metabolic
biomarker such as blood pressure would be an important way to determine the cut-
point on a stress scale that is associated with a normal blood pressure.
(See Chapter 5: Clinical Signicance.)
Question: What components, strategies for change, and level of implementa-
tion are needed to improve the behavioral risk factor? Promising strategies for
change are informed by understanding controlling antecedent conditions which can
include biological, psychophysiological, psychological, neuropsychological, behav-
ioral, social, cultural, environmental, and policy-level factors. Informant interviews
and focus groups can elicit beliefs about factors that affect behavior. But watching
people unobtrusively make choices as they go about their day in their naturalistic
settings may be an even more powerful approach. Human-centered design [57–59]
aims to make the healthy choice the easy choice by rst watching people behave in
their natural settings and then manipulating some aspect of that naturalistic context
in the service of changing a behavior. Involvement of the consumer in the design
and testing of a product was developed and rened in the eld of marketing [67, 68]
and then adapted to the eld of behavioral medicine to foster user-centered strate-
gies which place a premium on strength, acceptability, and feasibility [23, 69]. New
applications of this approach have been made possible by advances in the technolo-
gies for connecting with people throughout their day, including ecological momen-
tary assessment [70] and “just-in-time” adaptive interventions [71]. These
methodologies facilitate the identication of treatment components, strategies for
change, and critical time points for treatment delivery and outcome assessment.
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The very early phases of treatment development feature intensive studies of small
numbers of highly selected subjects. There has been renewed interest in small N
studies, and recent investigations have shown their versatility in use, range of
designs (e.g., time series, ABA, and multiple baseline), and analytic techniques
[60–64]. A series of small, early phase, easy to implement studies which inform the
design of a treatment is the essence of Stevens’ concept of “evidentiary studies”
[16]. An example would be a naturalistic observational study of physical activity in
children before and after their teacher was given specialized training.
Milestone. The milestone for progression is the existence of a “roadmap” for
the forthcoming program of research. This takes the form of a quantied pathway
by which a new behavioral treatment is hypothesized to improve a clinical endpoint.
(See Chapter 4: Hypothesized Pathway and Bias.)
Rene forStrength andEfciency: Phase Ib
Purpose.
As healthcare resources tighten, treatments that are both efcacious
and efcient become increasingly desirable. This can be a problem of particular
concern for behavioral treatments which may be strong but overly complex, or sim-
ple but too weak. The central goal of the Rene phase is to streamline a promising
but complex treatment to promote efciency while at the same time retaining suf-
cient strength to accomplish a clinically signicant improvement on a behavioral
risk factor
Common Questions and Selected Methods. Table 3.6 summarizes the
goals, milestones, common questions, and selected methods that are common in the
Rene phase. These types of studies are relatively common in applied behavioral
science. The general format is to study dimensions of the treatment such as the
optimal dose, mode, or agent of delivery, the minimum number of components
needed, and adaptations to the treatment to maximize efcacy in subgroups or for
non-response. In some areas, such as adapting treatment to heterogeneous response,
the methodology is well-established [72]. In others, such as determining the opti-
mum dose (i.e., the duration, frequency, and amount of treatment needed), a meth-
odological literature is emerging [73]. If a treatment is well-established but there is
interest in applying it to a new population, setting, or mode of delivery, renement
studies are ideal.
Several of the questions in Table 3.6 deserve special comment because they are
important, but understudied, in renement studies.
Question: What is the optimum timing of intervention? Finding a time to inter-
vene when the subject is most likely to be receptive maximizes the ability of the
treatment to be received and acted upon. For example, a peak learning moment can
occur when a patient has “a new lease on life” after receiving a coronary artery stent
that unblocks an artery. Learning moments occur during daily life when faced with
repeating cues that trigger habits which, when summed over a lifetime, inuence
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health. “Just-in-time” adaptive interventions make use of mobile technology to
deliver interventions when the need arises [71]. With mobile sensing technology, it
is possible to track individuals as they go throughout their day, identify environmen-
tal cues that trigger unwanted behaviors, intervene in real time, and replace old
habits with new ones in real time [71]. An excellent example is provided by
Gustafson and colleagues [80] who track the location of young addicts who have
just emerged from rehabilitation and, when they approach the place where their
addicted friends congregate, deliver a series of smartphone interceptions that bolster
the will to pass the temptation by.
Question: What is the optimum timing of outcome assessment? The sustain-
ability of effects of behavioral treatments is a question at the forefront of behavioral
medicine. The “checkmark” phenomenon in which short-term change is followed
by a longer-term return to baseline is the rule rather than the exception. The problem
is that treatments that produce powerful short-term effects which are not sustained
Table 3.6 Rene for strength and efciency (Phase Ib)
Central Goal Common Questions Selected Methods Milestone
Rene treatment
to preserve
strength while
maximizing
efciency
What is the optimum
– Number of
components?
– Dose?
– Duration?
– Mode of delivery?
What is the optimum
timing of delivery?
What is the optimum
timing of assessment?
How can acceptability
be maximized to reduce
dropouts and improve
adherence?
How can heterogeneity
in response be reduced?
What is the impact of
treatment on the
complex system within
which it is embedded?
•
Factorial, fractional
factorial designs [
74, 75]
•
Experiments [76]
•
Bayesian estimation [77,
78]
•
Single-case designs
[60–64, 79]
•
“Just-in-time” adaptive
interventions [71, 80–82]
•
Single-case designs
[60–64, 79]
• Time series designs [83]
•
Randomized comparative
effectiveness designs [84]
• Qualitative studies [69, 85]
• Human-centered design
[57–59]
•
Personalized behavioral
treatments [86–88]
• SMART designs [72, 89]
• Adaptive interventions
[
90, 91]
• Signal detection [92]
•
Modeling complex
systems [93–95]
Investigator
satisfaction that
treatment package
is complete and a
xed protocol is
now ready for
preliminary testing
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over time are unlikely to be powerful enough to affect chronic disease endpoints
which accumulate over long periods of time. Long-term studies of small numbers of
subjects to describe patterns of change during and after an intervention are well
suited to providing insight into this important question. Figure3.2 portrays an array
of effects that could occur after an intervention (I) is concluded. Analytic proce-
dures drawn from engineering can quantify these patterns over time to determine
the appropriate timing for outcome assessment [96]. More importantly for the treat-
ment developer, understanding that the effects of treatment are either short-term,
delayed, or variable can encourage interest in rening for the purpose of producing
more enduring effects. Single-case designs [60–64] and time series designs [83]
offer low-cost options for studying evolution of effects over long periods of time in
small numbers of subjects.
Question: How can heterogeneity in response be reduced? Identication of
moderators of treatment is an aim for a renement study, not a conrmatory clinical
trial. A signicant moderator indicates that treatment worked better for one sub-
Fig. 3.2 Varieties of intervention (I) effects
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group than it did for another. This jeopardizes efcacy in a clinical trial which is
usually powered on the assumption that on average all treated participants will
respond and, at best, considers only the effect of dropouts, not non-adherers, in the
determination of sample size. If moderator effects are identied in renement stud-
ies, they provide the basis for tailoring treatment to improve subgroup response.
Readiness for a conrmatory Phase II or Phase III behavioral efcacy trial is sig-
naled when treatment has been optimized for all important subgroups. The methods
for studying heterogeneity in response to treatment are developing rapidly. Signal
detection methods can identify subgroups for whom tailoring of treatment could
optimize outcomes [92]. Adaptive designs provide the methodology for identifying
decision rules for potentiating treatment in the face of non-response, giving rise to
adaptive treatments which go beyond a “one-size-ts-all” approach to behavioral
treatments [72].
Milestone. The essential milestone for moving to Phase II Preliminary Testing
is the satisfaction of the investigative team that it has a xed treatment package that
needs no further renement. This treatment includes essential components, is safe
and acceptable to the target population, and offers promise for producing a clini-
cally signicant benet on a behavioral risk factor. A shift from Phase I Design to
Phase II Preliminary Testing is a shift from focusing on dimensions of the treatment
to focusing on the impact of the treatment on outcomes
Preliminary Testing: Phase II
If the long-term goal is to identify behavioral treatments that can improve a chronic
disease outcome, it would be a big leap to jump from treatment renement studies
with intermediate outcomes to conducting a large Phase III efcacy trial. Some
preliminary testing could help ease this transition before making such a leap.
Proof ofConcept: Phase IIa
Purpose.
The essential purpose of
a proof-of-concept study is to provide
an initial exploration of the plausibility
that a xed treatment can improve a
clinically signicant endpoint. The
term is drawn from the eld of engi-
neering where innovations are accelerated by repeated investments of small seed
monies into the development of prototypes to assess potential for effectiveness.
Although such small preliminary looks are widely used in engineering, business,
security systems, software development, and drug development, they are relatively
uncommon in applied behavioral science. This is unfortunate because these “quick
and dirty” preliminary looks at the potential potency of a treatment can save costs
and foster innovation through renement. Indeed, if the end game is to improve a
Proof of Concept: Rationale
A comparator is useless when a
behavioral treatment is too weak to
warrant any comparison.
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chronic disease endpoint, it is a waste of time and resources to conduct a random-
ized study of a treatment that is too weak to affect such an endpoint. A comparator
is useless when a treatment is too weak to warrant any comparison.
A common design for a proof-of-concept study is a small, quasi-experimental, pre-
post comparison where the outcome is whether or not a clinically signicant goal
was met. Sample size calculations are unnecessary because no statistical hypothesis
testing is conducted. Proof-of-concept studies are not intended to provide solid
“proof” that the intervention works. It is acceptable for threats to internal validity to
be present. Small select samples limit generalizability of results. Absence of a com-
parator leaves unanswered such questions as whether participants would get better
over time on their own. Clinical, rather than statistical, tests are used to judge results,
raising the possibility that any changes were due to chance alone.
But the goal of this design is not to answer any of these questions. It is simply to
determine whether or not there is any merit in going forward with a more rigorous
randomized study to answer them. Since the resources needed to conduct these
small studies are minimal, they provide an ideal bridge between early studies aimed
at designing and rening a treatment and rigorous testing using randomized designs.
Common Questions and Selected Methods. Table 3.7 summarizes the
goal, question, methods, and milestone for proof-of-concept studies. The key goal
is to determine whether or not it is plausible that a treatment can achieve a clinically
signicant improvement in a behavioral risk factor target of treatment or a surrogate
clinical outcome.
Table 3.7 Proof of concept (Phase IIa)
Central
Goal Common Questions Selected Methods Milestones
Clinically
signicant
signal
In a small, highly selected
sample, is it plausible that a xed
treatment package can produce a
clinically signicant
improvement in a behavioral risk
factor or clinical endpoint?
• Quasi- experimental
treatment-only
pre-post design [97]
• N-of-1 time series
[
98].
Clinical
signicance
justies
proceeding to a
randomized trial
Question: In a small, selected sample, can this treatment produce a clinically
signicant improvement on a behavioral or biomedical risk factor? There are four
basic elements of a proof-of-concept test: (1) a pretreatment baseline; (2) a post-
treatment follow-up; (3) a clinically signicant cutpoint on the behavioral or clinical
target of treatment; and (4) a small number of accessible subjects. In quasi-experi-
mental, within-subjects designs, subjects act as their own controls in a pre-post
treatment comparison. The sample size can be small since clinical, not statistical,
benet is sought, thus obviating the need for sample size calculations. The sample
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can be selected from accessible subjects, rather than representative subjects, because
this initial test will determine only whether the treatment merits further, more rigor-
ous testing. The focus is placed only on the treatment and whether it can produce
clinically signicant change. The question of why any change might have occurred
(e.g., the passage of time; non-specic attention, etc.) is dealt with in subsequent
randomized designs.
Figure 3.3 provides an
example. The aim of this
study was to determine if it
was plausible that a smart-
phone intervention could
produce clinically mean-
ingful adherence to asthma
controller medication in
inner-city teens with
asthma [99]. The study was
supported by a very small
institutional grant permit-
ting the assessment of only
four subjects in a pre-post,
quasi-experimental design. The milestone for judging success was provided by clin-
ical guidelines in allergy and immunology, which indicate that ≥70% adherence to
controller medication for asthma is needed to achieve a clinical benet. It can be
seen that compared to a run-in baseline of only 40% adherence, the introduction of
the smartphone treatment was associated with a clinically signicant benet of
>70% adherence.
This small study can be criticized on a variety of fronts. Perhaps this dramatic ben-
et was an historical trend that had nothing to do with the actual treatment under
investigation. This was not a representative sample. It may have been a highly-
selected and highly-motivated sample, unlike most inner-city teens. Because there
was no statistical analysis, it is unclear whether the results could have occurred by
chance alone. Because there was no control for attention, results may be due to non-
specic attention. These are legitimate questions that need to be answered. But the
point here is that this small study increased interest in answering them. Under fur-
ther investigation, the treatment might work again, but maybe it would not. To
answer this question, a larger, more diverse sample using a randomized design is
needed. Alternatively, had this intervention not demonstrated a clinically signicant
effect, it would be a waste of time and money to test it in a larger randomized trial
without further renement.
Studies such as this one can be embedded into treatment development or career
development grants and can serve as part of preliminary studies that justify support
for a randomized study. The study presented in Fig.3.3 provided the basis for receipt
of an NIH career development K-award to the investigator.
Fig. 3.3 Adherence to drug therapy during run-in and
after introduction of treatment, in four subjects
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Milestone. If a behavioral treatment achieves clinically signicant benet on a
behavioral risk factor, this provides the milestone for moving forward for more
preliminary testing of this xed protocol. If this milestone was approached, but not
achieved, all that may be needed might be small adjustments to justify forward
movement to randomized pilot testing. If the clinical criterion was not even
approached, return to earlier developmental studies (Phase I) or a decision to aban-
don the treatment may be indicated.
Feasibility andPilot Studies: Phase IIb
The term “pilot study” has come to be a vague term, meaning a variety of different
things ranging from a miniature efcacy trial to a feasibility study. The importance of
preparatory work before undertaking a Phase II or Phase III efcacy trial makes it
imperative to reduce confusion and promote clarity. Thus, a chapter in this book has
been devoted to this important topic. (See Chapter 7: Feasibility and Pilot Studies.)
Phase II Efcacy Trials: Phase IIc
Purpose
The ORBIT model in Fig.3.1 presents an important revision to the
original model [15]. It now includes Phase II behavioral efcacy trials among the
activities in the Preliminary Testing phase. The Phase II behavioral trial is the con-
comitant of the Phase II drug trial. On the drug side, the aim is to determine if the
drug has its intended biologic effect on a surrogate endpoint [100]. On the behavioral
side, the aim is to determine if the behavioral intervention has its intended effect on
a behavioral risk factor, biologic mediator, or relevant surrogate endpoint.
Common Questions and Selected Methods. It is easiest to understand
ORBIT Phase II efcacy trials by comparing them to Phase I renement studies
and Phase III efcacy trials (Table 3.8)
Table 3.8 Comparison of three types of randomized behavioral studies
Phase I
Renement
Phase II
Efcacy
Phase III
Efcacy
Aim Relative value of various
treatment elements
Efcacy in reducing
behavioral or biological
risk
Efcacy in improving a
denitive clinical outcome
Outcome Behavioral risk factor
or biological mediator
Behavioral risk factor,
biological mediator, or
surrogate endpoint
Chronic condition or
disease
Comparator Compares different
treatment elements
Controls for non-specic
factors or compares to
existing treatments
Compares to existing
practice
Function Optimization of
treatment to enhance
efciency while
preserving efcacy
Prerequisite for Phase
III trial
Prerequisite for
effectiveness trial or
introduction into clinical
practice
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Renement studies focus on treatment. Efcacy trials focus on outcome. Renement
studies aim to identify optimum elements of treatment by comparing such things as
different components, modes of administration, durations, and intensities. Phase II
efcacy trials aim to determine whether a behavioral treatment can produce a clini-
cally signicant improvement in behavioral or biomedical risk, and whether or not
it is better than non-specic factors or other existing treatments. Phase III efcacy
trials aim to determine whether or not a behavioral treatment can produce a clini-
cally signicant improvement in a chronic disease endpoint and whether or not it is
better than some current standard of care.
A Phase II efcacy trial can function as a prerequisite for a Phase III trial or an end
unto itself. It can be an end unto itself when improvement in a behavioral risk factor
has such a strong and consistent association with improvement in a chronic disease
that a Phase III trial is unnecessary. A good example of this is in the area of smok-
ing. Since the association between smoking and lung cancer is irrefutable, a behav-
ioral trial showing that a reduction in smoking reduces lung cancer incidence is
unnecessary. A more pressing need may be to identify treatments that can produce
sustained smoking cessation. Here, the state of the science has obviated the need to
document the value of a behavioral treatment on a chronic disease and moved
toward the need for more powerful behavioral treatments. If, instead, interest was
in testing the value of smoking cessation on some other chronic disease where
associations were less well established, a Phase III efcacy trial would be needed.
Phase II and Phase III behavioral efcacy trials face similar challenges in such
areas as bias from unblinded designs, choice of comparators, and protection from
confounding. Thus, the methods proposed in various chapters of this book pertain to
both types of trials. Where there are differences, they are noted.
Although Phase I renement studies, Phase II efcacy trials, and Phase III efcacy
trials all share a randomized design, the ORBIT model makes a point of distinguish-
ing among them. This is to encourage progressive translational science that is char-
acterized by a progression of phase-specic questions linked to a progression of
phase-specic methods, and a push toward testing in Phase III trials with chronic
disease endpoints rather than settling with the Phase II trial in the belief that the
work has been done and the case has been made. The fundamental promise that
behavioral treatments can help to reduce the burden of chronic diseases remains
largely unfullled [101]. To make this case, convincing evidence from Phase III
trials that shows that chronic disease endpoints are improved by a behavioral treat-
ment is needed.
Milestones. When a Phase III trial is the end game, support from a Phase II
trial is a milestone. Here, the Phase II trial would be conceptualized as a prelimi-
nary study showing that the behavioral treatment is strong enough to reduce behav-
ioral risk to a level needed to improve a chronic disease endpoint. When a Phase II
trial is the end game, effectiveness or implementation studies would be reasonable
next steps.
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SELECTED EXPERIENCE
FROMABEHAVIORAL TRIAL
Scientic Progression intheDiabetes Prevention Program
Scientic progression in developing a behavioral treatment is a key to success. An
excellent example of this is the development of the Diabetes Prevention Program
(DPP) [55, 56], one of the most successful Phase III behavioral efcacy trials to
date. The DPP showed that lifestyle was 30% better than the best drug available and
60% better than placebo in preventing conversion to diabetes in patients who were
insulin resistant. This lifestyle treatment was developed progressively and system-
atically over 40years. Table3.9 reconstructs this development using the basic ele-
ments of the ORBIT model.
Extensive development such as that described in Table3.9 would be a daunting task
for any single investigator. But it is evident that the DPP was based upon the work
of a wide range of multidisciplinary investigators who were, at best, loosely con-
nected. To borrow the analogy of building a house, the director of the DPP lifestyle
treatment, Rena Wing, served in the role of “architect.” She and her team created the
“design” for the “house” relying upon a large number of “contractors” to inform
such specialty tasks as “painting, plumbing, and electrical wiring.” Becoming an
“architect” and drawing on the skills of “contractors” makes treatment development
less daunting for any one individual.
RECOMMENDATIONS
The healthcare crisis demands a cut, not a shift, in healthcare costs. This presents an
opportunity for behavioral treatments, which have the potential to cut costs by tar-
geting fundamental behavioral roots of chronic diseases. But to do this, behavioral
treatments must be well-developed, tested with rigorous methods, successful in
improving clinically signicant endpoints, and visible in publications in top medi-
cal journals and clinical practice guidelines. Funding agencies, publication prac-
tices, and investigators can help to achieve this end.
Funding Agencies
More support for behavioral treatment development is needed. By way of compari-
son, industry spends approximately 7years developing a drug and $1.3 billion to
bring it to market [8]. The NIH, a major funder for behavioral treatment develop-
Recommendations
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