A N NA L S O F FA M I LY M E D I C I N E ✦ W W W. A N N FA.docx

A N NA L S O F FA M I LY M E D I C I N E ✦ W W W. A
N N FA M M E D . O R G ✦ VO L . 2 , N O. 6 ✦ N OV E M
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The Biopsychosocial Model 25 Years Later:
Principles, Practice, and Scientifi c Inquiry
ABSTRACT
The biopsychosocial model is both a philosophy of clinical care
and a practical
clinical guide. Philosophically, it is a way of understanding how
suffering, disease,
and illness are affected by multiple levels of organization, from
the societal to the
molecular. At the practical level, it is a way of understanding
the patient’s subjec-
tive experience as an essential contributor to accurate diagnosis,
health outcomes,
and humane care. In this article, we defend the biopsychosocial
model as a nec-
essary contribution to the scientifi c clinical method, while
suggesting 3 clarifi ca-
tions: (1) the relationship between mental and physical aspects
of health is com-

plex—subjective experience depends on but is not reducible to
laws of physiology;
(2) models of circular causality must be tempered by linear
approximations when
considering treatment options; and (3) promoting a more
participatory clinician-
patient relationship is in keeping with current Western cultural
tendencies, but may
not be universally accepted. We propose a biopsychosocial-
oriented clinical prac-
tice whose pillars include (1) self-awareness; (2) active
cultivation of trust; (3) an
emotional style characterized by empathic curiosity; (4) self-
calibration as a way to
reduce bias; (5) educating the emotions to assist with diagnosis
and forming thera-
peutic relationships; (6) using informed intuition; and (7)
communicating clinical
evidence to foster dialogue, not just the mechanical application
of protocol. In con-
clusion, the value of the biopsychosocial model has not been in
the discovery of
new scientifi c laws, as the term “new paradigm” would suggest,
but rather in guid-
ing parsimonious application of medical knowledge to the needs
of each patient.
Ann Fam Med 2004;2:576-582. DOI: 10.1370/afm.245.
GEORGE ENGEL’S LEGACY
T
he late George Engel believed that to understand and respond
adequately to patients’ suffering—and to give them a sense of
being
understood—clinicians must attend simultaneously to the

biologi-
cal, psychological, and social dimensions of illness. He offered
a holistic
alternative to the prevailing biomedical model that had
dominated indus-
trialized societies since the mid-20th century.1 His new model
came to be
known as the biopsychosocial model. He formulated his model
at a time
when science itself was evolving from an exclusively analytic,
reductionis-
tic, and specialized endeavor to become more contextual and
cross-disci-
plinary.2-4 Engel did not deny that the mainstream of
biomedical research
had fostered important advances in medicine, but he criticized
its exces-
sively narrow (biomedical) focus for leading clinicians to
regard patients
as objects and for ignoring the possibility that the subjective
experience of
the patient was amenable to scientifi c study. Engel championed
his ideas
not only as a scientifi c proposal, but also as a fundamental
ideology that
tried to reverse the dehumanization of medicine and
disempowerment of
patients (Table 1). His model struck a resonant chord with those
sectors of
the medical profession that wished to bring more empathy and
compassion
into medical practice.
In this article we critically examine and update 3 areas in which
the

biopsychosocial model was offered as a “new medical
paradigm”5,6: (1) a
Francesc Borrell-Carrió, MD1
Anthony L. Suchman MD2,3
Ronald M. Epstein MD4
1Department of Medicine, University of
Barcelona, CAP Cornellà, Catalonian
Institute of Health (ICS), Cornellà de
Llobregat, Spain
2Relationship Centered Health Care,
Rochester, NY
3Department of Medicine, University of
Rochester School of Medicine and Dentistry,
Rochester, NY
4Department of Family Medicine,
University of Rochester School of Medicine
and Dentistry, Rochester, NY
CORRESPONDING AUTHOR
Francesc Borrell-Carrió, MD
Department of Medicine
University of Barcelona
CAP Cornellà, Catalonian Institute of
Health (ICS)
C/Bellaterra 39
08940 Cornellà de Llobregat, Spain
[email protected]

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B I O P SY C H O S O C I A L M O D E L 2 5 Y E A RS L
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world view that would include the patient’s subjective
experience alongside objective biomedical data, (2) a
model of causation that would be more comprehensive
and naturalistic than simple linear reductionist models,
and (3) a perspective on the patient-clinician relation-
ship that would accord more power to the patient in
the clinical process and transform the patient’s role
from passive object of investigation to the subject and
protagonist of the clinical act. We will also explore the
interface between the biopsychosocial model and evi-
dence-based medicine.
DUALISM, REDUCTIONISM,
AND THE DETACHED OBSERVER
In advancing the biopsychosocial model, Engel was
responding to 3 main strands in medical thinking that
he believed were responsible for dehumanizing care.
First, he criticized the dualistic nature of the biomedi-
cal model, with its separation of body and mind (which
is popularly, but perhaps inaccurately, traced to Des-

cartes).7,8 This conceptualization (further discussed in
the supplemental appendix, available online at http://
www.annfammed.org/cgi/content/full/2/6/576/
DC1) included an implicit privileging of the
former as more “real” and therefore more worthy
of a scientifi c clinician’s attention. Engel rejected this
view for encouraging physicians to maintain a strict
separation between the body-as-machine and the nar-
rative biography and emotions of the person—to focus
on the disease to the exclusion of the person who
was suffering—without building bridges between the
two realms. His research in psychosomatics pointed
toward a more integrative view, showing that fear, rage,
neglect, and attachment had physiologic and develop-
mental effects on the whole organism.
Second, Engel criticized the excessively materialis-
tic and reductionistic orientation
of medical thinking. According
to these principles, anything that
could not be objectively verifi ed
and explained at the level of cel-
lular and molecular processes was
ignored or devalued. The main
focus of this criticism—a cold,
impersonal, technical, biomedi-
cally-oriented style of clinical
practice—may not have been
so much a matter of underlying
philosophy, but discomfort with
practice that neglected the human
dimension of suffering. His semi-
nal 1980 article on the clinical

application of the biopsychoso-
cial model5 examines the case of a man with chest pain
whose arrhythmia was precipitated by a lack of caring
on the part of his treating physician.
The third element was the infl uence of the observer
on the observed. Engel understood that one cannot
understand a system from the inside without disturbing
the system in some way; in other words, in the human
dimension, as in the world of particle physics, one can-
not assume a stance of pure objectivity. In that way,
Engel provided a rationale for including the human
dimension of the physician and the patient as a legiti-
mate focus for scientifi c study.
Engel’s perspective is contrasted with a so-called
monistic or reductionistic view, in which all phenom-
ena could be reduced to smaller parts and understood
as molecular interactions. Nor did he endorse a holis-
tic-energetic view, many of whose adherents espouse
a biopsychosocial philosophy; these views hold that
all physical phenomena are ephemeral and control-
lable by the manipulation of healing energies. Rather,
in embracing Systems Theory,2 Engel recognized that
mental and social phenomena depended upon but
could not necessarily be reduced to (ie, explained in
terms of) more basic physical phenomena given our
current state of knowledge. He endorsed what would
now be considered a complexity view,9 in which differ-
ent levels of the biopsychosocial hierarchy could inter-
act, but the rules of interaction might not be directly
derived from the rules of the higher and lower rungs
of the biopsychosocial ladder. Rather, they would be
considered emergent properties that would be highly
dependent on the persons involved and the initial con-

ditions with which they were presented, much as large
weather patterns can depend on initial conditions and
small infl uences.9 This perspective has guided decades
of research seeking to elucidate the nature of these
interactions.
Table 1. Engel’s Critique of Biomedicine
1. A biochemical alteration does not translate directly into an
illness. The appearance of illness
results from the interaction of diverse causal factors, including
those at the molecular, individ-
ual, and social levels. And the converse, psychological
alterations may, under certain circum-
stances, manifest as illnesses or forms of suffering that
constitute health problems, including,
at times, biochemical correlates
2. The presence of a biological derangement does not shed light
on the meaning of the symp-
toms to the patient, nor does it necessarily infer the attitudes
and skills that the clinician must
have to gather information and process it well
3. Psychosocial variables are more important determinants of
susceptibility, severity, and course of
illness than had been previously appreciated by those who
maintain a biomedical view of illness
4. Adopting a sick role is not necessarily associated with the
presence of a biological derangement
5. The success of the most biological of treatments is infl
uenced by psychosocial factors, for
example, the so-called placebo effect

6. The patient-clinician relationship infl uences medical
outcomes, even if only because of its infl u-
ence on adherence to a chosen treatment
7. Unlike inanimate subjects of scientifi c scrutiny, patients are
profoundly infl uenced by the way in
which they are studied, and the scientists engaged in the study
are infl uenced by their subjects
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COMPLEXITY SCIENCE: CIRCULAR
AND STRUCTURAL CAUSALITY
Engel objected to a linear cause-effect model to
describe clinical phenomena. Clinical reality is far more
complex. For example, although genetics may have
a role in causing schizophrenia, no clinician would
ignore the sociologic factors that might unleash or con-
tain the manifestations of the illness.
Complexity and Causality
Few morbid conditions could be interpreted as being
of the nature “one microbe, one illness”; rather, there
are usually multiple interacting causes and contributing
factors. Thus, obesity leads to both diabetes and arthri-
tis; both obesity and arthritis limit exercise capacity,

adversely affecting blood pressure and cholesterol lev-
els; and all of the above, except perhaps arthritis, con-
tribute to both stroke and coronary artery disease. Some
of the effects (depression after a heart attack or stroke)
can then become causal (greater likelihood of a second
similar event). Similar observations can be made about
predictors of relapse in schizophrenia. These obser-
vations set the stage for models of circular causality,
which describes how a series of feedback loops sustain
a specifi c pattern of behavior over time.10-13 Complex-
ity science is an attempt to understand these complex
recursive and emergent properties of systems14,15 and to
fi nd interrelated proximal causes that might be changed
with the right set of interventions (family support and
medications for schizophrenia; depression screening and
cholesterol level reduction after a heart attack).
Structural Causality
In contrast to the circular view, structural causality
describes a hierarchy of unidirectional cause-effect
relationships—necessary causes, precipitants, sustaining
forces, and associated events.16 For instance, a neces-
sary cause for tuberculosis is a mycobacterium, precipi-
tants can be a low body temperature, and a sustaining
force a low caloric intake. Complexity science can
facilitate understanding of a clinical situation, but most
of the time a structural model is what guides practical
action. For example, if we think that Mr. J is hyperten-
sive because he consumes too much salt, has a stress-
ful job, poor social supports, and an overresponsible
personality type, following a circular causal model,
possibly all of these factors are truly contributory to his
high blood pressure. But, when we suggest to him that
he take an antihypertensive medication, or that he con-
sume less salt, or that he take a stress-reduction course,
or that he see a psychotherapist to reduce his sense of

guilt, we are creating an implicit hierarchy of causes:
Which cause has the greatest likely contribution to his
high blood pressure? Which would be most responsive
to our actions? What is the added value of this action,
after having done others? Which strategy will give the
greatest result with the least harm and with the least
expenditure of resources?
Interpretations, Language, and Causality
Causal attributions have the power to create reality and
transform the patient’s view of his/her own world.17 A
physician who listens well might agree when a patient
worries that a family argument precipitated a myo-
cardial infarction; although this interpretation may
have meaning to the patient, it is inadequate as a total
explanation of why the patient suffered a myocardial
infarction. The attribution of causality can be used to
blame the patient for his or her illness (“If only he had
not smoked so much.…”), and also may have the power
of suggestion and might actually worsen the patient’s
condition (“Every time there is a fi ght, your dizziness
worsens, don’t you see?”).
TOWARD A RELATIONSHIP-CENTERED
MODEL
Power and Emotions in the Clinical Relationship
Patient-centered, relationship-centered, and client-cen-
tered approaches18-24 propose that arriving at a correct
biomedical diagnosis is only part of the clinician’s task;
they also insist on interpreting illness and health from
an intersubjective perspective by giving the patient
space to articulate his or her concerns, fi nding out
about the patient’s expectations, and exhorting the
health professional to show the patient a human face.
These approaches represent movement toward an egali-

tarian relationship in which the clinician is aware of
and careful with his or her use of power.
This “dialogic” model suggests that the reality of
each person is not just interpreted by the physician,
but actually created and recreated through dialogue25-31;
individual identities are constructed in and maintained
through social interaction.32 The physician’s task is to
come to some shared understanding of the patient’s
narrative with the patient. Such understanding does not
imply uncritical acceptance of whatever the patient
believes or hypothesizes, but neither does it allow for
the uncritical negation of the patient’s perspective, as
so frequently occurs, for example, when patients com-
plain of symptoms that physicians cannot explain.33,34
The patient’s story is simultaneously a statement about
the patient’s life, the here-and-now enactment of his
life trajectory, and data upon which to formulate a
diagnosis and treatment plan.
Underlying the analysis of power in the clinical
relationship is the issue of how the clinician handles the
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strong emotions that characterize everyday practice. On
the one hand, there is a reactive clinical style, in which

the clinician reacts swiftly to expressions of hostility or
distrust with denial or suppression. In contrast, a proac-
tive clinical style, characterized by a mindful openness to
experience, might lead the clinician to accept the patient’s
expressions with aplomb, using the negative feelings to
strengthen the patient-clinician relationship.35 The clini-
cian must acknowledge and then transcend the tendency
to label patients as “those with whom I get along well”
or “diffi cult patients.” By removing this set of judgments,
true empathy can devolve from a sense of solidarity with
the patient and respect for his or her humanity, leading
to tolerance and understanding.18 Thus, in addition to the
moral imperative to treat the patient as a person, there is
a corresponding imperative for the physician to care for
and deepen knowledge of himself or herself.35,36 Without
a suffi cient degree of self-understanding, it is easy for the
physician to confuse empathy with the projection of his
or her needs onto the patient.
Implications for Autonomy
Most patients desire more information from their
physicians, fewer desire direct participation in clinical
decisions, and very few want to make important deci-
sions without the physician’s advice and consultation
with their family members.37-40 This does not mean that
patients wish to be passive, even the seriously ill and the
elderly.41 In some cases, however, clinicians unwittingly
impose autonomy on patients.19,42,43 Making a reluctant
patient assume too much of the burden of knowledge
about an illness and decision making, without the advice
from the physician and support from his or her family,
can leave the patient feeling abandoned and deprived
of the physician’s judgment and expertise.42 The ideal,
then, might be “autonomy in relation”—an informed
choice supported by a caring relationship.19 The clini-
cian can offer the patient the option of autonomy41

while considering the possibility that the patient might
not want to know the whole truth and wish to exercise
the right to delegate decisions to family members.40,44
The Social Milieu
There is an ecological dimension of each encounter—it
is not just between patient and physician, but rather an
expression of social norms.45 Sometimes clinicians face
a dilemma: can or should a private clinical relationship
between patient and physician be a vehicle for social
transformation? Or, should the relationship honor and
conform to the cultural norms of patients?19 Our view is
that adaptation normally should occur before transfor-
mation—the physician must fi rst understand and accom-
modate to the patient’s values and cultural norms before
trying to effect change. Otherwise, the relationship
becomes a political battleground and the focus of a pro-
cess to which the patient has not consented and may not
desire. This debate, however, becomes much more diffi -
cult in situations in which patients have suffered abuse—
for example domestic violence or victims of torture.46
In those cases, not trying to remedy the social injustices
that resulted in the patient seeking care may interfere
with the formation of a trusting relationship. The physi-
cian may be tempted to effect a social transformation in
these cases, for example, to advise the patient to leave an
abusive situation, even though the patient may state that
she only wants care for the bruises. Premature advice
may interfere with enabling the patient to be the agent
of change, however. Stopping short of attempting to
transform social relationships until the patient has given
consent should not be interpreted as indifference to,
acceptance of, or complicity in such situations; rather, it

should be viewed as a prudent course of action that will
ultimately be validating and empowering.
Caring, Paternalism, and Empathy
Taking Engel’s view, perhaps it is not paternalism that is
the problem but practicing as a cold technician rather
than a caring healer.47,48 The physician who sees his or
her role as nothing more than a technical adviser can
regard empathy as a useless effort that has no infl uence
on clinical decisions, or, worse, a set of linguistic tricks
to get the patient to comply with treatment. Because
it is entirely possible to advocate for shared decision
making without challenging the notion of the cold
technician, we propose to move the emphasis to an
approach that emphasizes human warmth, understand-
ing, generosity, and caring.
THE BIOPSYCHOSOCIAL MODEL
AND RELATIONSHIP-CENTERED CARE
The practical application of the biopsychosocial model,
which we will call biopsychosocially oriented clinical prac-
tice does not necessarily evolve from the constructs of
interactional dualism or circular causality. Rather, it may
be that the content and emotions that constitute the
clinician’s relationship with the patient are the funda-
mental principles of biopsychosocial-oriented clinical
practice, which then inform the manner in which the
physician exercises his or her power. The models of
relationship that have tended to appear in the medical
literature, with a few notable exceptions,19 have perhaps
focused too much on an analysis of power and too little
on the underlying emotional climate of the clinical
relationship. For this reason, we suggest a reformulation
of some of the basic principles of the biopsychosocial
model according to the emotional tone that engraves the
relationship with such characteristics as caring, trustwor-

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thiness, and openness.49,50 Some principles of biopsycho-
social-oriented clinical practice are outlined below.
Calibrating the Physician
The biopsychosocial model calls for expanding the
number and types of habits to be consciously learned
and objectively monitored to maintain the centrality of
the patient.51 The physician is in some ways like a musi-
cal instrument that needs to be calibrated, tuned, and
adjusted to perform adequately.36 The physician’s skills
should be judged on their ability to produce greater
health or to relieve the patient’s suffering—whether they
include creating an adequate emotional tone, gather-
ing an accurate history, or distinguishing between what
the patient needs and what the patient says he or she
wants. In that regard, a clinical skill includes the ethical
mandate not only to fi nd out what concerns the patient,
but to bring the physician’s agenda to the table and infl u-
ence the patient’s behavior. Sometimes doing so may
include uncovering psychosocial correlates of otherwise
unexplained somatic symptoms (such as ongoing abuse
or alcoholism) to break the cycle of medicalization and
iatrogenesis.33 To abandon this obligation, in our view, is
breaking an implicit social contract between physicians

and society. This deliberative and sometimes frankly
physician-centered approach has its perils, however.
The physician must be capable of an ongoing self-audit
simply because his or her performance is never the same
from moment to moment. Weick and Sutcliffe52 regard
this constant vigilance as a fundamental requirement for
professions that require high reliability in the face of
unexpected events. Mindfulness—the habits of attentive
observation, critical curiosity, informed fl exibility, and
presence—underlies the physician’s ability to self-moni-
tor, be vigilant, and respond with compassion.35,53,54
Creating Trust
The expert clinician considers explicitly, as a core skill,
the achievement in the encounter of an emotional tone
conducive to a therapeutic relationship. For that reason,
all consultations might be judged on the basis of cordial-
ity, optimism, genuineness, and good humor. By receiv-
ing a hostile patient with respect,55 it clarifi es for the cli-
nician that the patient’s emotions are the patient’s—and
not the physician’s—and also sets the stage for the
patient to refl ect as well. Similarly, the physician must
know how to recognize and when to express his or her
own emotions, sometimes setting limits and boundaries
in the interest of preserving a functional relationship.
Cultivating Curiosity
The next step in the application of clinical evidence
to medical care is the cultivation of curiosity. Thus,
cultivated naïvete56 might be considered one of the
fundamental habits characteristic of expert practitioners.
Another aspect of this emotional tone is an empathic
curiosity about the patient as person. Empathic curiosity
allows the clinician to maintain an open mind and not
to consider that any case is ever closed. If the patient

does not surprise us today, perhaps he or she will
tomorrow. We have described this capacity using the
term, beginner’s mind.35,57 It is the capacity for expecting
the unexpected, just as if the physician were another cli-
nician seeing the patient for the fi rst time. There is also
an ethical component of this emotional tone—there are
no “good” or “bad” patients, nor are there “interesting”
and “boring” diseases. Patients should not have to legiti-
mize their suffering by describing illnesses that make
the clinician feel comfortable or confi dent.58
Recognizing Bias
The grounding of medical decisions based on scientifi c
evidence while also integrating the clinician’s professional
experience is now a well-accepted tenet of the founders
of the evidence-based medicine movement.59 The method
for incorporation of experience, however, has been less
well described than the method for judging the quality of
scientifi c evidence. For example, clinicians should learn
how their decisions might be biased by the race and sex
of the patient, among other factors,51 and also the ten-
dency to close the case prematurely to rid oneself of the
burden of attempting to solve complex problems.60
Educating the Emotions
There are methods for emotional education, just as
there are for learning new knowledge and skills.35
Tolerance of uncertainty, for example, is amenable to
observation and calibration—making decisions in the
absence of complete information is a characteristic of
an expert practitioner, in contrast to the technician
who views his role as simply following protocols.
Using Informed Intuition
The role of intuition is central. Just as Polanyi and

Schön maintain that professional competence is based
in tacit, rather than explicit, knowledge,61,62 expertise
often is manifest in insights that are diffi cult to track
on a strictly cognitive level. If a clinician, encountering
a situation in which he normally would use a particu-
lar treatment, has the intuition, for a reason that has
not yet become clear, that treatment might not be the
best for this particular patient, we suggest, rather than
considering it a feeling from nowhere that might be dis-
carded, perhaps the intuition can later be traced to a set
of concrete observations about the patient that were not
easy for the clinician to describe at the time. Because
these observations often are manifest only when cases
are reviewed after the fact does not diminish the ethical
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obligation that the clinician use all of his or her capa-
bilities, not only those which can be readily explained.
Communicating Clinical Evidence
Evidence should be communicated in terms the patient
can understand, in small digestible pieces, at a rate
at which it can be assimilated. Information overload
may have two effects—reduction in comprehension
and increasing the emotional distance between physi-
cian and patient. Communication of clinical evidence

should foster understanding, not simply answers.63
FURTHER DEVELOPMENT OF
THE BIOPSYCHOSOCIAL MODEL
George Engel formulated the biopsychosocial model as
a dynamic, interactional, but dualistic view of human
experience in which there is mutual infl uence of mind
and body. We add to that model the need to balance a
circular model of causality with the need to make linear
approximations (especially in planning treatments) and
the need to change the clinician’s stance from objective
detachment to refl ective participation, thus infusing
care with greater warmth and caring. The biopsycho-
social model was not so much …
Explaining fatigue in multiple sclerosis: cross-validation
of a biopsychosocial model
Melloney L. M. Wijenberg1,3 • Sven Z. Stapert1,3 • Sebastian
Köhler2 • Yvonne Bol3
Received: December 14, 2015 / Accepted: May 20, 2016 /
Published online: May 28, 2016
� Springer Science+Business Media New York 2016
Abstract Fatigue is a common and disabling symptom in
patients with multiple sclerosis (MS), but its pathogenesis is
still poorly understood and consequently evidence-based
treatment options are limited. Bol et al. (J Behav Med

33(5):355–363, 2010) suggested a new model, which explains
fatigue in MS from a biopsychosocial perspective, including
cognitive-behavioral factors. For purposes of generalization to
clinical practice, cross-validation of this model in another
sample of 218 patients with MS was performed using structural
equation modeling. Path analysis indicated a close and ade-
quate global fit (RMSEA = 0.053 and CFI = 0.992). The
cross-validated model indicates a significant role for disease
severity, depression and a fear-avoidance cycle in explaining
MS-related fatigue. Modifiable factors, such as depression and
catastrophizing thoughts, propose targets for treatment options.
Our findings are in line with recent evidence for the effec-
tiveness of a new generation of cognitive behavioral therapy,
including acceptance and mindfulness-based interventions,
and provide a theoretical framework for treating fatigue in MS.
Keywords Multiple sclerosis � Fatigue � Catastrophizing �
Physical disability � Structural equation modelling �
Biopsychosocial model
Introduction

Multiple sclerosis (MS) is characterized by a chronic
inflammation of the central nervous system, which results
in demyelination and atrophy, but has an unknown patho-
genesis and an unpredictable course. It is one of the most
common neurological disorder in young adults (Compston
& Coles, 2008) with a prevalence of 0.9 per 1000 (Hirtz
et al., 2007). Patients with MS report a variety of physical
and neuropsychiatric symptoms, with fatigue being the
most frequent and disabling symptom reported: 80–92 %
of patients with MS report fatigue, and 40–69 % rate
fatigue as their most disabling symptom (Brañas et al.,
2000; Giovannoni, 2006; Minden et al., 2006). Fatigue is a
major reason for decreased societal participation and is also
related to disability and poor quality of life.
Unfortunately, the multifactorial pathogenesis of fatigue
in MS is not completely understood, and evidence-based
treatment options remain scarce (Asano et al., 2014; Bol
et al., 2009; Kos et al., 2008; Pucci et al., 2007). Bol et al.

(2010) examined its multifactorial pathogenesis by fitting a
biomedical and a cognitive behavioral model in a sample
of 262 patients with MS using structural equation mod-
elling (SEM). Results showed that both models poorly
explained fatigue in MS, and based on previous research
and the results of their SEM analyses, they formulated a
new model. This final model was an integration of the first
two models, including both biomedical and cognitive-be-
havioral factors, and can be considered as the fatigue
equivalent of the fear-avoidance model of chronic muscu-
loskeletal pain (Crombez et al., 2012; Vlaeyen et al., 1995).
In this integrated model, catastrophizing about fatigue has
a central role: being fueled by depression, it mediated the
relationship between fatigue and fatigue related fear and
avoidance behavior (Bol et al., 2010).
& Yvonne Bol
[email protected]
1
Faculty of Psychology and Neuroscience, Maastricht
University, Maastricht, The Netherlands

2
Faculty of Health, Medicine and Life Sciences, School for
Mental Health and Neuroscience, Maastricht University,
Maastricht, The Netherlands
3
Department of Medical Psychology/Academic MS Center
Limburg, Zuyderland Medical Center, PO Box 5500,
6130 MB Sittard-Geleen, The Netherlands
123
J Behav Med (2016) 39:815–822
DOI 10.1007/s10865-016-9749-3
http://crossmark.crossref.org/dialog/?doi=10.1007/s10865-016-
9749-3&domain=pdf
http://crossmark.crossref.org/dialog/?doi=10.1007/s10865-016-
9749-3&domain=pdf
Catastrophizing about fatigue is defined as a fearful
interpretation of the meaning of fatigue by exaggerated
negative thinking, magnification of symptoms, and help-
lessness (e.g. ‘fatigue is terrible and I think it can never
improve’ or ‘when I feel tired, there is nothing I can do to

decrease its intensity’) (Lukkahatai & Saligan, 2013). If
fatigue is erroneously interpreted as a sign of pathology
over which one has little or no control, this could gradually
extend to a fear and avoidance of physical activities and
subsequently decreased physical abilities. According to the
fear-avoidance model, this would then lead to an increase
in fatigue concluding its cyclic pattern. Lukkahatai and
Saligan (2013) showed in their systematic review a con-
sistent strong positive correlation between catastrophizing
and fatigue severity in several clinical conditions that share
fatigue as one of their core symptoms, such as multiple
sclerosis, chronic fatigue syndrome, fibromyalgia and
cancer.
Besides the role of catastrophizing and fear-avoidance
behavior, previous research has shown a significant asso-
ciation between depression and fatigue in patients with MS,
independent of physical disability (Bakshi et al., 2000).
With regard to the direction of influence, a longitudinal

study of Patrick et al. (2009), including 2768 patients with
MS, showed that depression was one of the most important
predictors of fatigue at 1-year follow-up. With regard to
disease severity, Hadjimichael et al. (2008) showed a sig-
nificant positive correlation between disease severity and
fatigue in patients with MS, explaining that more physical
disability and neurological impairment are associated with
higher levels of fatigue.
This biopsychosocial model of Bol et al. (2010) inte-
grates these individual observations in a single model of
fatigue in MS, however cross-validation is necessary to
make a valid generalization and application to everyday
clinical practice possible. In the present study, we
hypothesize that the associations between fatigue, depres-
sion, catastrophizing and disease severity described by the
biopsychosocial model will explain fatigue in another large
group of MS patients. This cross-validation is important for
the understanding of the origin and perpetuating of fatigue

in patients with MS and will provide a theoretical frame-
work for treating fatigue in patients with MS.
Methods
Participants
Participants were recruited from hospital databases of the
department of Neurology of the Zuyderland Medical
Center in Sittard-Geleen, the Netherlands. A total of 621
Dutch-speaking patients with clinically definite MS
according to McDonald criteria (Polman et al., 2005), aged
between 18 and 65 years, were eligible for inclusion. Their
treating neurologist sent the initial letters to secure confi-
dentiality. A total of 403 patients were interested in par-
ticipating and responded (65 % response rate). These
patients were sent an information letter, an informed con-
sent and questionnaires. A total of 312 participants returned
the forms (77 % response rate). Questionnaires were filled
in between May 2011 and September 2011. Participants
who previously participated in the study of Bol et al.

(2010) (N = 86) were excluded. Informed consent was
obtained from all participants included in the study.
Patients did not receive any financial compensation for
their participation.
Measures
Basic demographic information
Age, gender, level of education, employment status, mar-
ital status and use of psychopharmacological drugs were
obtained by a demographic inventory filled in by the
patients. The level of education was based on the highest
completed level of education and divided into three cate-
gories: primary school (low level of education); junior
vocational training (middle level of education); senior
vocational training or academic training (high level of
education). Medical data, such as disease duration, disease
course, MS subtype and disease severity were collected
from the hospital databases.
Disease severity

Disease severity was assessed with the Expanded Disability
Status Scale (EDSS) (Kurtzke, 1983). This scale comprises
the evaluation of 8 functioning systems (pyramidal, cere-
bellar, brainstem, mental, bowel and bladder, visual-optic,
sensory and other). The EDSS score, based on the evalu-
ation of an experienced neurologist, ranges from 0 to 10,
where 0 indicates a normal neurological examination and
10 indicates death due to MS. Recent EDSS scores
(3 months) were extracted from the hospital database.
Physical disability
Physical disability was assessed with the physical dimen-
sion of the SF-36, a Dutch translation of the Short Form
Health Survey developed and validated by Aaronson et al.
(1998). Bol et al. (2010) showed a high reliability of this
measure in patients with MS. It consists out of four sub-
scales; physical functioning, role limitations due to physi-
cal health problems, bodily pain, and general health. Each
816 J Behav Med (2016) 39:815–822

123
standardized subscore of the physical dimension ranges
from 0 to 100, where a total score of 400 resembles optimal
physical health and no physical disability.
Fear avoidance
Fear avoidance was assessed with the fatigue version of the
Tampa Scale for Kinesiophobia (TSK-F) (Silver et al.,
2002), which is an adjusted version of the TSK for chronic
pain (Miller et al., 1991; Vlaeyen et al., 1995). Silver et al.
(2002) replaced in all 17 items the word ‘pain’ by the word
‘fatigue’ to make the questionnaire suitable for investiga-
tion of fatigue-related fear and avoidance behavior. The
score ranges from 17 to 68, where a higher score indicates
a higher level of fear-avoidance behavior. This instrument
is found to be valid (Silver et al., 2002) and reliable in
patients with MS (Bol et al., 2010; Silver et al., 2002).
Catastrophizing

Catastrophizing about fatigue was assessed with the Fati-
gue Catastrophizing Scale (FCS), which is an adapted
version of the Pain Catastrophizing Scale (PCS) (Sullivan
et al., 1995). Psychometric properties of the PCS are ade-
quate (Osman et al., 2000). The PCS consists out of 13
items measuring the self-reported frequency of catastro-
phizing thoughts about experienced pain. As with the TSK
adaptation, Bol et al. (2010) adapted all the PCS items by
replacing the word ‘pain’ by the word ‘fatigue’. Scoring
alternatives ranged from ‘strongly disagree’ to ‘strongly
agree’. As in the study of Bol et al. (2010), three MS-
related items were added (‘When I am tired, this is a signal
there is something wrong in my brain’, ‘When I am tired,
this is a warning for physical decline’, ‘When I am tired,
this is a sign that my MS is getting worse’). In total 16
items were administered and the score ranges from 0 to 64
with higher scores indicating higher intensity of catastro-
phizing. Bol et al. (2010) showed a high reliability of this

measure in patients with MS. In the current sample the
reliability was excellent (a = 0.94).
Fatigue
Fatigue was assessed with the Abbreviated Fatigue Ques-
tionnaire (AFQ), a valid and reliable instrument (Alberts
et al., 1997). Administration to patients with MS also
revealed its reliability (Bol et al., 2010). This questionnaire
is a selection of four items of the Checklist Individual
Strength (CIS-20) developed by Vercoulen et al. (1999).
Items are rated on a 7-point Likert scale with scoring
alternatives ranging from ‘Yes, that is true’ to ‘No, that is
not true’. The final score ranges from 4 till 28, with higher
scores indicating a higher severity of physical fatigue.
Depression
Depression was assessed with the subscale depression of
the Hospital Anxiety and Depression Scale (HADS) (Zig-
mond & Snaith, 1983), a valid and reliable screening
instrument for patients with MS (Honarmand & Feinstein,

2009). The total score ranges from 0 to 21 with a higher
score indicating a higher intensity of depression. Honar-
mand and Feinstein (2009) showed that patients with MS
with a score of 8 or higher are likely depressed.
Statistical analyses
Data analyses were performed using SPSS 22.0.0.0 for
Windows (SPSS Inc., Chicago, IL). If less than 25 % of the
items of questionnaires, or more than 50 % if a question-
naire consisted of four items, were missing, missing values
were imputed by the mean of the remaining non-missing
items of the scale (27 values across 24 participants).
Descriptive statistics were used to describe the sample. No
variable was significantly skewed (skewness -1 or [1)
nor were there any significant outliers (all cases were
within 1.5 interquartile ranges from the upper or lower
quartile). Cronbach’s alpha was used to test reliability of
all questionnaires. Relations between all variables were
analyzed by Pearson-correlations. An alpha level of .05

was used for all statistical tests.
Cross-validation was analyzed with structural equation
modeling in Mplus 7 (Muthén & Muthén, 1998–2012). The
biopsychosocial model of Bol et al. (2010) was specified in a
path analysis using manifest variables only (no measurement
model). Error terms were assumed to be uncorrelated and left
free. The Root Mean Square Error of Approximation
(RMSEA) was used as a global fit index, because parsimony
and sample size are taken into account. RMSEA represents
the lack of fit in comparison with a perfect fit and should
therefore be low. RMSEA values up to 0.05 indicate a close
fit, values between 0.05 and 0.08 indicate an acceptable fit,
values between 0.08 and 0.10 indicate a mediocre fit, and
those greater than 0.10 indicate a poor fit. Furthermore, the
comparative fit index (CFI) was used, because it represents
the relative improvement of the model in comparison with a
baseline model, usually a model in which all observed
variables are uncorrelated. Values larger than 0.95 indicate a

good fit and values between 0.90 and 0.95 indicate an
acceptable fit. Furthermore, the Chi square test of model fit,
Standardized Root Mean Square Residual (SRMR) and
Tucker–Lewis Index (TLI) were also reviewed as fit indexes.
A non-significant Chi square test of model fit indicates a
J Behav Med (2016) 39:815–822 817
123
good fit. SRMR values smaller than .08 indicate an accept-
able fit, whereas values smaller than 0.05 indicate a good fit.
TLI values higher than .90 are acceptable and values higher
than .95 represent a good fit. To control for possible nor-
mality assumption violation, a robust maximum likelihood
estimator for standard errors, also known as the ‘Huber
Sandwich Estimator’, was used (Huber, 1967). Modification
indices were inspected to consider further fine-tuning of the
model to the data-at-hand in an exploratory fashion. Finally,
direct and total effects of the significant variables were cal-

culated.
Results
Patient sample
A total of two participants were excluded due to too many
missing values ([25 % of items of questionnaires missing).
Finally, six participants were excluded due to a missing
value in the single exogenous variable, EDSS, which was
necessary for proper structural equation modeling (SEM)
analysis. This resulted in a final sample of 218 outpatients
(53 men, 165 women) with an average age of 48.0 years
(SD = 10.5, range 19–65). Most of them had a relapsing
remitting disease course (n = 153), while 43 patients had a
secondary progressive disease course and 21 patients had a
primary progressive disease course (1 missing value). The
mean disease duration was 8.8 years (SD = 7.5, range
0–30 years) with an average EDSS score of 3.6 (SD = 1.9,
range 0.5–8.0), which resembles a moderate disease
severity. Around 24 % of the sample showed high levels of
catastrophizing, using the cutoff score of 30 as suggested

by Sullivan et al. (1995) for patients with pain. Around
34 % of the sample showed high levels of fear avoidance,
using the cutoff score of 37 as suggested by Vlaeyen et al.
(1995) for patients with pain. See Table 1 for a summary of
all patient characteristics.
Reliability and correlations
Table 2 resembles means, standard deviations, ranges,
reliability indexes (Cronbach’s alphas) for all measures and
their intercorrelations (Pearson). All questionnaires had a
satisfactory internal consistency (range 0.69–0.94). All
intercorrelations were statistically significant (p 0.01)
with the strongest correlation between depression and
physical disability. Higher levels of depression were
associated with lower levels of physical ability (r = -0.58,
p 0.001). The weakest correlation was found between
disease severity and catastrophizing about fatigue
(r = 0.21, p 0.01).
Structural equation modeling analyses
Figure 1 shows the results of the path analysis of the new

model proposed by Bol et al. (2010). The RMSEA value
was 0.053 (90 % CI 0.000–0.112), which indicates an
acceptable fit. The SRMR, CFI and TLI value were
respectively 0.023, 0.992 and 0.979, indicating a good fit.
The Chi square test of model fit was non-significant
(p = 0.138) also indicating a good fit. Furthermore, all
hypothesized relationships were statistically significant.
The total explained variance of fatigue measured with the
AFQ was 44 %. All variables provided a significant con-
tribution to this explained variance. Both depression
(b = .27) and physical disability (b = -.45) were directly
associated with fatigue. There were no modification
indexes given, suggesting that no alternative specification
of relationships between the variables were identified
which could improve the model. We added a relationship
from disease severity to depression, due to its significance
in the second model postulated by Bol et al. (2010), but this
worsened the global fit of our model and was subsequently

removed. Moreover, we ran an additional post hoc analysis
to study the variance in fatigue explained by the fear
avoidance cycle. For this, we omitted the paths to and from
depression and disease severity (see Fig. 1) from the
model. This showed that physical disability, fear-avoid-
ance, catastrophizing and their underlying associations
explain 39 % of the variance in fatigue, compared with
Table 1 Patient characteristics (n = 218)
Variable Value
Gender % female (n) 76 (165)
Age in years [mean (SD)] 48.0 (10.5) range 19.6–65.6
Disease duration in years [mean
(SD)]
8.8 (7.5) range 0.1–30.2
Disease course
Relapsing remitting (%) 71
Secondary progressive (%) 20
Primary progressive (%) 9

Use of disease modifying drugs
(% yes, % no)
61/39
Use of psychopharmaca (% yes,
% no)
25/75
Level of education (% low, %
middle, % high)
24/37/39
Marital status (% partner, % no
partner)
82/28
Employment status (% working,
% not working)
32/68
818 J Behav Med (2016) 39:815–822
123

44 % of the total model. See Table 3 for an overview of the
standardized direct, indirect and total effects on fatigue.
Discussion
Due to the high prevalence of fatigue in patients with MS
and its disabling impact on everyday activities and quality
of life, understanding its pathogenesis and identifying its
modifiable contributing factors are crucial. Bol et al. (2010)
showed that neither a biomedical nor a cognitive-behav-
ioral model explained fatigue in 262 patients with MS, but
suggested a new biopsychosocial model integrating ele-
ments of the previously tested models, i.e. disease severity,
depression and fear-avoidance cycle. To generalize and
apply this model to everyday clinical practice, cross-vali-
dation of this integrated model in another sample was
needed. We hypothesized that the biopsychosocial model
of Bol et al. (2010) can explain fatigue in MS in another
large sample.
Table 2 Means, standard deviations (SD), ranges, Cronbach’s
alphas (a) and Pearson-correlations of all measures

Mean (SD) Range a 2 3 4 5 6
1. Disease severity (EDSS) 3.6 (1.9) 0.5–8 – .23** .21* .22**
.29** -.48**
2. Fatigue (AFQ) 19.7 (6.8) 4–28 0.90 – .55** .34** .54** -
.63**
3. Catastrophizing about fatigue (FCS) 19.9 (14.1) 0–56 0.94 –
– .58** .57** -.55**
4. Fatigue-related fear and avoidance (TSK-F) 34.3 (8.3) 20–68
0.73 – – – .41** -.42**
5. Depression (HADS-D) 6.0 (4.0) 0–17 0.82 – – – – -.58**
6. Physical disability (SF-physical) 208.5 (92.1) 25–400 0.69 –
– – – –
EDSS Expanded Disability Status Scale, AFQ Abbreviated
Fatigue Questionnaire, FCS Fatigue Catastrophizing Scale,
TSK-F Fatigue Version of
the Tampa Scale for Kinesiophobia, HADS-D depression
subscale of the Hospital Anxiety and Depression Scale, SF-
physical Physical scale of
the Short Form Health Survey
* p 0.01; ** p 0.001
Fig. 1 Path analysis of the
biopsychosocial model of fatigue
in multiple sclerosis (n = 218).

Note Values shown are
standardized regression
coefficients and based on cross-
sectional data. Light blue
variables and its relationships
represent the fear-avoidance cycle
within the model. Explained
variances are provided in
parentheses. Please note that the
scale of physical disability is
inverted. *p 0.05; **p 0.01;
***p 0.001 (Color
figure online)
J Behav Med (2016) 39:815–822 819
123
The SEM analyses presented in this study, explaining
fatigue in a new sample of 218 patients with MS, showed
good support of the biopsychosocial model of Bol et al.

(2010). Catastrophizing, depression, physical disability,
disease severity and fear avoidance all contribute signifi-
cantly to fatigue, either directly or indirectly. Comparing
the results to that of the original publication, the global fit
indices RMSEA and CFI even slightly improved respec-
tively from 0.085 towards 0.053 and from 0.983 towards
0.992. This implies an increase in fit from mediocre to
acceptable (RMSEA) or even good (CFI).
The biopsychosocial model indicates a significant role
for disease severity, depression and an adapted fear
avoidance model in explaining MS-related fatigue. This
integrated model partly overlaps with a recently formulated
model by Wu et al. (2015) explaining post-stroke fatigue.
They suggest also an integration of biological and psy-
chological variables, including depressive symptoms,
coping and behavioral factors. Also in stroke patients, an
intervention including CBT elements showed a long term
reduction in fatigue (Zedlitz et al., 2012). Moreover,

Zedlitz et al. (2012) stated that the addition of graded
activity to the cognitive elements, which focuses on
improvement of physical disability, resulted in a longer
endurance of the fatigue reducing effects.
Translating the biopsychosocial model of Bol et al.
(2010) to clinical practice in MS, the model indicates
several modifiable factors, such as the fatigue-enhancing
cycle of fear avoidance and depression, which form
important targets for interventions. Diagnosing and treating
depression could be a first step to treat MS related fatigue.
Depression is with a life-time prevalence of approximately
50 % very prevalent in MS and probably underdiagnosed
and untreated (Feinstein, 2011; Maier et al., 2015). When
depression is treated, for instance with cognitive behavioral
therapy (CBT) (Hind et al., 2014), it is likely that fatigue is
also reduced. Next, CBT focusing on changing catastro-
phizing thoughts about fatigue could help fatigued MS
patients (Knoop et al., 2011; Moss-Morris et al., 2012; van

Kessel et al., 2008). Knoop et al. (2011) concluded that
changes in thoughts about fatigue play a crucial role in
CBT for fatigue in MS. Hoogerwerf et al. (submitted)
showed that also the third generation CBT, Mindfulness
Based Cognitive Therapy (MBCT) is an effective inter-
vention for severely fatigued MS patients. Patients were
not only less fatigued after MBCT, but also less depressed
and less catastrophizing about fatigue. This suggests that
catastrophizing can be reduced not only by altering the
content of thoughts such as in regular CBT, but even by
disengaging from the maladaptive thoughts about fatigue.
There are several limitations to this study, which should
be taken into account when interpreting the results and
could be addressed in future studies. First of all, the design
is cross-sectional making it impossible to draw firm con-
clusions about causality and temporal relations in the dis-
ease process. More prospective and longitudinal studies are
needed to confirm the proposed causal relationships. Sec-

ondly, postal questionnaires were used which made us
unable to compare responders with non-responders. The
response rate was favorable (77 %), but lower in compar-
ison with Bol et al. (2010) (93 % response rate). A possible
explanation could be related to the fact that more ques-
tionnaires were included which demanded more time and
energy of the participants. As a result, we cannot exclude
the possibility of a selection bias. Thirdly, all data were
self-reported and are therefore sensitive to retrospective
bias and response styles. Fourthly, our main outcome
measure, the AFQ, is a questionnaire consisting out of four
items. Despite its sufficient validity and reliability, Hore-
mans et al. (2004) argued that the AFQ lacks precision at
the individual patient level. Future studies should include
fatigue questionnaires which are validated in MS patients,
such as the Fatigue Severity Scale or the Modified Fatigue
Impact Scale (Rietberg et al., 2010). Finally, other factors,
some even modifiable, such as sleep disorders, cognitive

impairments and maladaptive coping styles, were not
assessed and therefore lacking in the biopsychosocial
model. Their inclusion could increase the explained vari-
ance of the model due to their previously established
influences on fatigue in MS (Rabinowitz & Arnett, 2009;
Strober & Arnett, 2005; Ukueberuwa & Arnett, 2014).
Furthermore, the overall anxiety level and other distorted
Table 3 Standardized direct, indirect and total effects on fatigue
Variable Direct Indirect Total
Fear-avoidance (TSK-F) 0.000 0.103** 0.103**
Physical disability (SF-physical) -0.447*** -0.173*** -
0.620***
Depression (HADS-D) 0.274*** 0.024* 0.298***
Disease severity (EDSS) 0.000 0.288*** 0.288***
Catastrophizing (FCS) 0.000 0.054* 0.054*
TSK-F Fatigue Version of the Tampa Scale for Kinesiophobia,
SF-physical Physical scale of the Short Form Health Survey,
HADS-D depression
subscale of the Hospital Anxiety and Depression Scale, EDSS
Expanded Disability Status Scale, FCS Fatigue Catastrophizing

Scale
* p 0.05; ** p 0.01; *** p 0.001
820 J Behav Med (2016) 39:815–822
123
cognitive thinking habits besides catastrophizing, in which
elements of rumination, magnification and helplessness are
embedded (Sullivan et al., 1995), could possibly be another
useful addition for future studies due its modifiable char-
acter and insight in effective therapeutic elements.
Despite these limitations, this cross-validation of the
biopsychosocial model of Bol et al. (2010) forms an
important next step in explaining MS-related fatigue and
highlights a promising role for CBT. The integrated model
supports the clinical practice guidelines that both biological
and psychological factors should be taken into account
during the clinical assessment and treatment of fatigue in
MS (CBO, 2013; Van Kessel & Moss-Morris, 2006). It is

expected that development and evaluation of targeted
psychological interventions will help improving the
biopsychosocial model of MS related fatigue.
Acknowledgments We would like to thank all the patients who
took
part in this study; the therapists, psychological assistants and
MS
nurses of Zuyderland Medical Center; Dr. Myreen Moors for her
effort in gathering and monitoring the data acquisition; Prof.
Dr.
Raymond Hupperts for his kind cooperation and time
investment.
Compliance with ethical standards
Conflict of interest Melloney L. M. Wijenberg, Sven Z. Stapert,
Sebastian Köhler and Yvonne Bol declare that they do not have
any
conflict of interest.
Human and animal rights and Informed consent All procedures
were approved by and in accordance with the ethical standard of
the
medical ethics committee of Zuyderland Medical Center and
with the
1964 Helsinki declaration and its later amendments. Informed
consent

was obtained from all patients for being included in the study.
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(1997). ‘Verkorte vermoeidheidsvragenlijst’: een practisch
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247 Journal of Clinical Sleep Medicine, Vol. 12, No. 2, 2016
Study Objectives: Sleep and fatigue difficulties appear to be

highly prevalent among individuals with end-stage renal disease
and individuals who have
received a kidney transplant. While there is some evidence of
biopsychosocial factors predicting sleep disturbance in these
populations, previous studies
have relied on single time point retrospective measurements.
Methods: The study utilized a 2-week prospective measurement
approach, including one night of polysomnographic
measurement, nightly sleep diaries, and
self-report measures of health, sleep, and mood.
Results: The current study demonstrates that a number of
psychological and behavioral factors, including negative mood,
quality of life, napping, and
caffeine consumption, are related to sleep disturbance among
pre- and post-kidney transplant patients. This study also found
that many of these factors have
different relationships with sleep disturbance when comparing
pre- and post-kidney transplant patients.
Conclusions: These results suggest that such factors may be
worthwhile areas for intervention in treating the symptoms of
insomnia among pre- and post-
transplant recipients. A nuanced approach to understanding
sleep problems is likely warranted when conceptualizing
insomnia and developing a treatment plan.
Keywords: kidney transplantation, sleep disorders, insomnia
Citation: Williams JM, McCrae CS, Rodrigue JR, Patton PR. A
novel application of a biopsychosocial theory in the
understanding of disturbed sleep before
and after kidney transplantation. J Clin Sleep Med
2016;12(2):247–256.
I N T R O D U C T I O N
Sleep complaints are common among individuals with end-
stage renal disease (ESRD) and patients who have received
kidney transplantation (KTX).1–6 While on dialysis, patients

report that sleep disturbance is one of their most prominent
symptom complaints.1 Compared to dialysis, kidney transplan-
tation is considered the treatment of choice for ESRD due to
longer patient survival, fewer morbidities, and better quality
of life. Unfortunately, little is known about the relationship
between ESRD and sleep or the impact of KTX on that rela-
tionship. The research that does exist suggests that the rates
of common sleep disorders including insomnia (50% to 75%
v 9%), restless legs syndrome (30% to 80% v 5% to 15%), and
sleep apnea (~24%), are higher in ESRD than in the general
population, and ESRD patients are also at risk for more se-
vere sleep apnea.2–7 The rates of these disorders tend to de-
crease following KTX (expect apnea), but nonetheless remain
elevated compared to normative estimates.8 While consider-
able research has focused on predictors of sleep apnea and rest-
less legs syndrome (RLS), relatively little research has focused
on insomnia in these populations. Additionally, due to a reli-
ance on cross-sectional designs and retrospective assessment
of insomnia, previous research has been unable to provide
greater insights into sleep’s relationships with ESRD. Previous
research has been largely atheoretical and has examined in-
somnia in relative isolation without consideration of important
S C I E N T I F I C I N V E S T I G AT I O N S
A Novel Application of a Biopsychosocial Theory in the
Understanding of
Disturbed Sleep before and after Kidney Transplantation
Jacob M. Williams, PhD1; Christina S. McCrae, PhD2; James R.
Rodrigue, PhD3,4; Pamela R. Patton, PA, MSP5
1Department of Psychology/Neuropsychology, TIRR Memorial
Hermann, Houston, TX; 2Department of Health Psychology,
University of Missouri, Columbia, MO: 3Department of
Surgery, Beth Israel Deaconess Medical Center, Boston, MA;
4Department of Psychiatry, Harvard Medical School, Boston,

MA 5School of Physician Assistant Studies, University of
Florida, Gainesville, FL
pii: jc - 0 0 420 -14 ht t p: //dx.doi.org /10. 5 6 6 4 / jc sm. 5 49
4
biopsychosocial relationships that may be relevant in the con-
text of ESRD and KTX.
Biopsychosocial Correlates of Sleep and End-Stage
Renal Disease
There are several biopsychosocial factors which have been
found to be associated with ESRD including age, sex, medi-
cal comorbidity, psychological distress, quality of life, and
fatigue. These factors have also been found to be highly re-
lated to insomnia and other sleep disturances. Specifically,
older age and medical comorbidities are associated with
poorer sleep and poorer outcomes in ESRD patients.9 Also, in
the general population, men are more likely to develop sleep
BRIEF SUMMARY
Current Knowledge/Study Rationale: This study was conducted
in order to explore the biopsychosocial factors contributing to
sleep
disturbance among patients before and after kidney
transplantation.
Prior research indicates that sleep problems are extremely
common
among individuals with end stage renal disease both before and
after
kidney transplantation but has not provided an explanatory
model for
these sleep problems.
Study Impact: This study confirms the high rates of sleep
problems
found in prior research and identifies biopsychosocial factors

which
may contribute to sleep disturbance, particularly insomnia.
These
results provide evidence for specific factors which may be
useful
targets in the treatment of insomnia in these populations.
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248Journal of Clinical Sleep Medicine, Vol. 12, No. 2, 2016
JM Williams, CS McCrae, JR Rodrigue et al. Biopsychosocial
Research of Sleep before and after Kidney Transplantation
apnea and RLS than are women, while the reverse is true for
insomnia.7,10,11 Comorbidity rates between poor sleep, ESRD
and psychological distress, particularly anxiety and depres-
sion (~60% of dialysis patients), are also high.12,13 ESRD
patients who have elevated depressive symptoms report in-
creased difficulty falling asleep, staying asleep, waking too
early in the morning, and increased fatigue in the morning.14
Quality of life is often compromised among individuals suf-
fering from chronic health conditions such as insomnia and
ESRD.15,16 Fatigue, which can generally be defined as a per-
ceived lack of physical and/or mental energy that interferes
with usual or desired activities, is also associated with insom-
nia and ESRD.17,18

The Development of Insomnia
Research on insomnia in the context of ESRD and KTX has
been largely atheoretical, focusing instead on identifying
rates of sleep disorders and a limited number of biopsycho-
social correlates. While etiological models have aided the de-
velopment of treatments for RLS and sleep apnea, research
has yet to explore theoretically driven models of the process
by which insomnia develops and is maintained over time in
these patients. Such theory driven research is important for
identifying the mechanisms underlying insomnia and under-
standing how to effectively treat insomnia in the context of
ESRD and KTX.
According to Spielman’s 3Ps model, the course of chronic
insomnia includes predisposing conditions, precipitating cir-
cumstances, and perpetuating factors,19 which can be seen
in Figure 1.
Predisposing conditions alone are not sufficient to pro-
duce chronic insomnia but precede the onset of insomnia
and increase the likelihood for its occurrence and could
include age, sex, or comorbid medical conditions.7 For ex-
ample, predispositions to conditions known to reduce renal
functioning may serve as predisposing factors in the subse-
quent development of sleep problems. Additionally, previous
research has found increased rates of insomnia among older
adults, women, and individuals with comorbid conditions
suggesting that these variables are likely to act as predispos-
ing factors.7
Precipitating circumstances co-occur with the onset of
acute insomnia and might include stressful personal events
or rapid shifts in health which are likely related to increased
fatigue, changes in mood resulting in emotional arousal,
and decreased quality of life.20,21 Fatigue, common among

ESRD patients, often accompanies a reduction of daytime
activity and a perceived decline in quality of life. The com-
bination of reduced activity and increased fatigue can lead
to increased idle time in bed and is likely related to mood
disturbance.20,22
Insomnia is maintained by perpetuating factors, which may
include changes individuals make in their sleep/wake sched-
ules or daytime behaviors (e.g., stimulant use and napping)
as they attempt to compensate for sleeping poorly.20 Specifi-
cally, daytime naps may disrupt the sleep homeostat (drive for
sleep that increases the longer one is awake) by meeting some
of the sleep drive that typically builds during the day. Based
on qualitative reports, as dialysis patients experience increas-
ingly altered sleep patterns, including night time awakenings,
daytime naps often develop as a compensatory strategy.20 In-
dividuals experiencing significant fatigue and sleep problems
may utilize stimulant substances (caffeine or nicotine) as a
compensatory daytime strategy, which has adverse effects on
nighttime sleep.23
The development of chronic insomnia (lasting ≥ 6 months)
is often related to a combination of predisposing, precipitating,
and perpetuating factors that manifest themselves across bio-
psychosocial domains. The current study explores the role of
these three sets of factors among individuals at different stages
in the development of insomnia.
Application of the 3Ps Model in End Stage Renal
Disease
In a hypothesized patient scenario an individual with ESRD
has progressively declining kidney function which necessi-
tates dialysis to maintain adequate blood filtration. Prior to
this time, the individual experienced health problems causing
increased worry and predisposing them to nighttime sleeping
difficulties. Over time, emotional distress about their health

increases. While on dialysis, the individual experiences ane-
mia and a buildup of waste products in the blood resulting
in significant daytime fatigue. In response, they begin to en-
gage in increased napping and caffeine consumption to com-
pensate for their fatigue. The individual now develops acute
insomnia due to biological factors, changes in sleep related
compensatory behaviors, and continued worry and emotional
distress concerning their health. Over time, perpetuating
maladaptive compensatory behaviors become increasingly
influential and eventually supersede the impact of the predis-
posing and precipitating factors. The individual’s insomnia
progresses to a chronic stage. The individual is maintained
on dialysis until being matched for KTX. Following suc-
cessful transplantation, their kidney functioning returns to a
level that does not require dialysis. However, the individual
Figure 1—The 3Ps Model of the development of disturbed
sleep in end-stage renal disease.
Predisposing factors include age, gender, and medical
comorbidity.
Precipitating factors include fatigue, mood, and quality of life.
Perpetuating factors include napping and caffeine consumption.
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continues to experience insomnia due to their compensatory
perpetuating behaviors.
Much of the existing research on sleep disturbance in the
context of ESRD and KTX has utilized a single method of
measurement involving participants’ retrospective recall of
their sleep over a designated period of 30 or more days. Given
the highly variable nature of sleep and the potential for bias in
retrospective recall, more accurate characterization of sleep
disorders in these patients calls for research using prospective
assessment. As a result, this study is the first to allow for true
differential diagnosis of sleep disorders in pre- and post-KTX
patients. Second, it is the first to take a theoretical approach
to studying the impact of biopsychosocial factors (age, sex,
medical comorbidity, fatigue, mood, quality of life (QOL),
stimulant consumption, napping) on sleep in pre- and post-
KTX patients. These factors were chosen for inclusion, be-
cause: (1) previous research has shown they share observable
relationships with sleep, ESRD, and KTX; and (2) according
to Spielman’s model of insomnia,19 these factors represent the
predisposing, precipitating, and perpetuating factors that may
contribute to the development and maintenance of chronic
insomnia. It is hypothesized that these factors will have a
differential impact with predisposing and precipitating fac-
tors having a greater impact on the sleep of pre-KTX patients
and perpetuating factors having greater impact for post-KTX
patients.
M E T H O D S
Participants
The current study included a sample of adults who were on the
waiting list for a KTX (N = 25) and those that had received

a KTX (N = 30) at the University of Florida (UF) Transplant
Center and Nephrology Clinic. Of the 314 participants initially
approached about the study, 17.5% of the participants agreed
to participate. The most frequent reasons for declining partici-
pation were research site being too far (11%), insufficient time
to participate (14%), not interested in volunteering (48%), and
poor health (4%). For the pre-KTX group, participants were
(1) referred for kidney or kidney/pancreas transplant and (2)
had chronic kidney disease stages 3, 4, or 5. Among post-KTX,
participants had (1) received a kidney or kidney/pancreas
transplant, (2) were more than 3 months post-KTX, (3) had a
stable allograft with glomerular filtration rate (GFR) ≥ 40 mL/
min, and (4) were > 12 weeks after the treatment of any acute
rejection of the graft.
Procedures
Recruitment occurred during routine visits to the UF Trans-
plant Clinic. Patients were approached first by a member of the
Transplant Clinic staff. If individuals were interested in par-
ticipating, they were given additional information by a trained
research assistant. Potential participants were asked to provide
consent in a private examination room in the UF Sleep Re-
search Lab. Potential participants were given the opportunity
to read and sign the consent form during scheduled visits, or
to take the informed consent form home to consult with family
and friends before providing consent. The study protocol was
evaluated and approved by the UF Health Science Center Insti-
tutional Review Board.
Once informed consent was obtained, a graduate research
assistant or trained research assistant conducted a semi-struc-
tured clinical interview. Criteria were employed to rule out
severe, uncontrolled psychopathology (i.e., suicidal ideation/
intent, bipolar disorder, psychotic disorders, and dementia).
In addition, measures of depression (Beck Depression Inven-

tory-2) and anxiety (State Trait Anxiety Inventory) were ad-
ministered.24,25 The Mini-Mental State Examination (MMSE)
was used to screen for severe global impairment with exclusion
criteria including scores < 23 for individuals with a 9th grade
education level or higher or < 17 for those with less than a
9th grade level education.26 Participants were administered the
Kidney Disease Quality of Life Short Form to measure par-
ticipants’ perspective on their current functional health and
well-being.27
Participants who qualified completed multiple sleep mea-
sures over a 2-week period. Ambulatory polysomnographic
monitoring (PSG; Grass Technologies) was conducted in each
participant’s home for one night during the 2-week assessment
period to screen for physiological sleep disorders (e.g., apnea).
In addition, participants completed two weeks of sleep dair-
ies in order to confirm the diagnosis of insomnia. These sleep
measures have been recommended as standard assessments in
sleep research.28 Appropriate clinical referrals were provided
to participants with clinically significant sleep problems. Par-
ticipants were compensated $50 for participation.
Measures
Demographics and Health Survey
This survey consists of 13 items collecting information on de-
mographics (age, sex, race, education, work status, height, and
weight), sleep disorder symptoms, symptoms due to kidney dis-
ease, current medications, and other health information. Body
mass index (BMI) was calculated using the following formula:
(weight in pounds / [height in inches × height in inches]) × 703.
Participants were asked to report comorbid medical conditions
including heart attack, other heart problems, cancer, AIDS, hy-
pertension, neurological disorder (seizures, Parkinson disease),
breathing disorders (asthma, emphysema, allergies), urinary
problems (prostate problems), diabetes, pains (arthritis, back
pain, migraines), gastrointestinal disorders (stomach, irritable

bowels, ulcers, gastric reflux), and other medical conditions.
From these endorsements, a total number of comorbid condi-
tions reported was calculated.
Subjective Sleep Measure
Sleep diaries were completed each morning for 14 days and
provided subjective estimates of commonly reported sleep-
wake variables: (1) sleep onset latency (time from initial lights
out until sleep onset; SOL); (2) wake time after sleep onset
(time spent awake after initial sleep onset until last awakening;
WASO); and (3) total sleep time (computed by subtracting total
wake time from the time spent in bed; TST). In the current
study, SOL and WASO were combined to create a composite Do
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measure of total wake time (TWT). Sleep diaries have been
found to provide a reliable and valid index of insomnia symp-
toms and are essential in insomnia assessment.28
Sleep Related Compensatory Behaviors
Daily dairies were used to measure daily behavior known to be
disruptive to night time sleep including napping (total number
of minutes per day), and caffeine consumption (ounces of caf-
feinated beverages consumed per day). Average values of nap-

ping and caffeine consumption were used in the present study.
Physiological Sleep Measure (PSG)
The 25 Channel AURA Recording System (Grass Technolo-
gies) was used to conduct in-home overnight sleep monitor-
ing, consisting of 6 electroencephalography (EEG) measures,
2 electrooculography, and chin electromyography (EMG) ac-
cording to standard placements.29 Other channels included ox-
ygen saturation level, bilateral anterior tibialis EMG, heart rate,
thoracic strain gauge, and a nasal/oral thermistor. A single
night of PSG was collected during the 2 weeks of assessment.
Procedures for PSG training, data management, and scoring
were based on the published procedures of the Sleep Heart
Health Study.29 All studies were scored by a graduate research
assistant trained in PSG scoring up to a 0.8 concordance rate
with PSG technicians based on recommended scoring criteria
for staging sleep and identifying sleep disorders.30
Criteria for Diagnosing Sleep Disorders
Chronic Insomnia
Individuals were identified as having chronic insomnia based
on self-reported sleep over two weeks based on a SOL or
WASO > 30 min, a frequency ≥ 6 times over the two weeks,
and lasting > 6 months.28 In addition, individuals must report
significant distress and daytime impairments related to their
sleep problem. These criteria are consistent with research31 and
DSM-IV criteria for the diagnosis of insomnia.32
Obstructive Sleep Apnea
Obstructive sleep apnea was diagnosed according to research
and clinical recommendations.29,33 A diagnosis of obstructive
sleep apnea consists of both apneic (a complete cessation of
airflow) and hypopneic (a decrease in airflow volume) events.
Cessations of breathing occur with EEG-measured arousals
and decreases in oxygen saturation ≥ 3%. In order for hy-
popneic events to be considered clinically meaningful, EEG-

measured arousals must be associated with ≥ 30% reduction in
airflow and 4% oxygen desaturation or 50% reduction in air-
flow and 3% oxygen desaturation. The number of these events
per hour was calculated, and individuals having an apnea-
hypopnea index (AHI) > 10 events per hour were considered
positive for sleep apnea.
Restless Legs Syndrome
In accordance with NIH and research recommended diag-
nostic criteria, RLS was identified through report of (1) feel-
ings of creeping, crawling sensations that result in the urge
to move the limbs and (2) occur before bed or when at rest.10
Additionally, the participant had to report (3) relief of these
sensations with movement and (4) a greater intensity of these
sensations before bedtime and improvement in the morning.
Individuals needed to report all 4 symptoms in order to estab-
lish the presence of RLS.
Quality of Life
Kidney Disease Quality of Life Short Form (KDQOL) was
used to collect data on domains of QOL. The KDQOL is a
brief measure of physical and psychosocial functioning, both
generally and specific to kidney disease,27 with higher values
reflecting better QOL. This measure also includes the items on
the Short Form Health Survey (SF-36).34 The KDQOL and SF-
36 show good psychometric properties, and overall, the scale
have been found to be significantly related to other questions
about perceived health status, number of days in the hospital,
disability days, and overall health.27
Fatigue
Multidimensional Fatigue Symptom Inventory-Short Form is
an empirically developed measure of clinical fatigue which
includes 30 items that load onto 5 fatigue factors (general,
physical, mental, emotional, and vigor), with higher scores in-

dicating greater fatigue and has been found to be valid and
reliable (> 0.85).35 A single total fatigue score provided by the
measure was used as an estimate of fatigue.
Mood
Beck Depression Inventory-Second Edition (BDI-II) and State-
Trait Anxiety Inventory-State Form (STAI-Y) were used to
assess
current mood status at the end of the assessment period.24,25
The
BDI-II has been found to have adequate psychometric properties
among young and older adults and discriminate validity in sepa-
rating depressed and non-depressed individuals.36 The STAI-Y
has been found to be correlated with other measures of anxiety
and good internal consistency.25 In the interest of parsimony
and
based on prior research, in the current study, the 2 measures
(BDI-
II and STAI) were treated as measures of negative mood and
were
combined into one variable in final analyses by converting mea-
sure scores into Z scores and combining them.37
Statistical Analyses
Collected data were entered into IBM SPSS v20.0 statistical
analysis software and standard screening procedures were used
to identify missing or incomplete data. Data were assessed
for normality to ensure that statistical assumptions were met
within limits that allow for testing of the specified hypotheses.
In order to test the impact of group (pre- versus post-KTX) on
continuous variables, including demographic and medical fac-
tors, TWT, TST, comorbidty, fatigue, mood, QOL, napping, and
caffeine consumption, multiple ANOVAs were run. Categori-
cal demographic variables were analyzed using χ2 tests. Demo-
graphic and medical variables of interest included age, sex,

race,
education, BMI, comorbidities, and number of current medica-
tions. To test the fit for the Spielman 3P Model of chronic in-
somnia separate hierarchical regressions for pre-KTX and for
post-KTX were used to estimate the mean relationship between
TWT and TST as predicted by age, sex, comorbidity, QOL, Do
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fatigue, mood, napping, and caffeine consumption. In the first
block of these models, age, sex, and comorbidity were loaded.
In the second block, the QOL, fatigue, and mood were included.
In the third block, napping, and caffeine consumption were in-
cluded. Significant factors from the models computed for pre-
KTX and post-KTX were directly compared to determine the
relative importance of predictors in estimating TST and TWT
for the 2 groups. This comparison was completed by convert-
ing the derived β-weights for each predictor into semi-partial
correlations, which were then converted into z-scores and were
compared using a Fischer Z score transformation.
R E S U LT S
The total sample consisted of 25 pre-KTX and 30 post-KTX
patients. Table 1 provides a summary of demographic and

health characteristics of the 2 groups. The total sample was
56% female and had a mean age of 53.7 years (SD = 13.1). The
median time since kidney transpant for the post-KTX group
was 74 months (ranging from 6 to 322 months). In this sample,
5 pre-KTX and 6 post-KTX participants reported currently
using sleeping medication with no significant difference be-
tween groups is use of this medication. Seven pre-KTX par-
ticipants and 28 post-KTX participants reported currently
using immunosuppressant medication. Pre-KTX and post-
KTX participants were compared on demographic and medi-
cal variables and no significant group differences were found
(Table 1). There were trends toward greater TWT and lower
QOL among pre-KTX patients (Table 2). There were no sig-
nificant group differences on the other sleep related continu-
ous variables. Comparing rates of apnea, RLS, and insomnia
between the two groups found that pre-KTX patients had a
trend toward higher prevalence of RLS symptoms compared
Table 1—Mean demographic and health variables by kidney
transplant group.
Pre-Kidney Transplant
(n = 25)
Post-Kidney
Transplant (n = 30) df Test Statistic p
Age, mean (SD) 51.75 (13.73) 55.37 (12.51) 54 F = 1.02 0.39
Education, years, mean (SD) 13.63 (2.16) 14.47 (2.99) 54 F =
1.08 0.30
BMI, mean (SD) 29.36 (6.21) 30.69 (5.82) 54 F = 0.67 0.42
Race and ethnicity, % 3 χ2 = 3.15 0.37
Caucasian 48% 63.3%
African American 36% 23.3%
Hispanic or Latino 16% 10.1%

Asian American 0% 3.3%
Sex, % 64% Female 50% Female 1 χ2 = 1.09 0.41
Number of comorbid medical conditions, mean (SD) 3.28 (1.28)
3.83 (1.76) 54 F = 1.71 0.20
Number of current medications, mean (SD) 10.04 (4.92) 11.87
(5.35) 54 F = 1.66 0.20
Numbers represent mean values or percentages. SD, standard
deviation; BMI, body mass index, calculated using the formula:
(weight in pounds / [height
in inches × height in inches]) × 703.
Table 2—Mean outcome variables by kidney transplant group.
Pre-Kidney Transplant
(n = 25)
Post-Kidney
Transplant (n = 30) df Test Statistic p
TST, minutes, mean (SD) 469.65 (75.36) 444.91 (66.66) 53 F =
1.64 0.21
Male 462.72 (82.13) 432.16 (57.06)
Female 473.56 (73.79) 458.57 (75.02)
TWT, minutes, mean (SD) 64.28 (38.98) 47.36 (34.19) 53 F =
2.51 0.09
Male 29.21 (16.92) 44.90 (34.94)
Female 84.02 (33.54) 50.01 (34.49)
Total fatigue, score, mean (SD) 16.65 (18.14) 16.14 (22.33) 52
F = 0.01 0.96
Negative mood, z-score, mean (SD) −0.08 (0.72) −0.01 (.95) 51
F = 0.41 0.52
QOL, score, mean (SD) 34.03 (10.63) 39.89 (11.65) 53 F = 3.41

0.07
Caffiene, servings, mean (SD) 1.07 (1.40) 1.78 (1.79) 53 F =
2.53 0.12
Napping, minutes, mean (SD) 30.15 (21.82) 26.42 (27.18) 53 F
= 0.30 0.59
Sleep apnea, % 32.00% 33.33% 1 χ2 = 0.01 0.92
RLS, % 32.00% 13.30% 1 χ2 = 2.79 0.09
Insomnia, % 68.00% 48.30% 1 χ2 = 2.14 0.14
Numbers represent mean values or percentages. SD, standard
deviation; TST, total sleep time; TWT, total wake time; QOL,
quality of life; RLS, restless
legs syndrome.
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to post-KTX patients (Table 2). Average sleep onset latency
(pre-KTX = 33.1 min and post-KTX = 28.5 min) and wake af-
ter …

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