Biopsychosocial vs. Biomedical Model

P E R S P E C T I V E S

The SPUR Model: A Framework for Considering Patient Behavior

This article was published in the following Dove Press journal: Patient Preference and Adherence

Kevin Dolgin

Observia, Paris, France

Background: Medication nonadherence is a global problem that requires urgent attention.

Roughly half of all drugs that are prescribed for chronic treatments are not taken by the patients in

question. Initiatives designed to support patients and help them modify their behavior are

enhanced by personalization, and a number of profiling tools exist to help customize such

interventions. Most of these tools were originally designed as paper-based questionnaires, but

the growth of digital adherence technologies (DATs) illuminate the need for the development of

digital profiling systems that can interact with fully automated patient interfaces.

Objective: The objective of this study was to examine existing frameworks from medicine,

psychology, sociology, consumer behavior, and economics to elaborate a comprehensive, quanti-

tative profiling approach that can be used to drive the customization of patient support initiatives.

Results: Building primarily on IcekAjzen’s Theory of Planned Behavior (TPB), the Health Belief

Model (HBM) was used to inform the beliefs about behavior posited in the TPB, while incorporat-

ing established factors regarding self-efficacy in the “control” elements of the TPB and selected

social and psychological factors in the other constituents of themodel. The resulting SPUR (Social,

Psychological, Usage, Rational) framework represents a holistic, profiling tool with detailed,

quantitative outputs that describe a patient’s behavioral risks and the drivers of that risk.

Conclusion: An interactive, digital questionnaire built around SPUR represents

a potentially useful tool for those desirous of building interactive digital support programs

for patients with chronic diseases.

Keywords: adherence, compliance, health beliefs, chronic diseases, review

Introduction According to the World Health Organization (WHO),1 poor adherence to treatment

of chronic diseases is a worldwide problem of “striking magnitude” and the burden

of poor adherence is growing worldwide as the prevalence of chronic disease

increases. The WHO goes on to point out that the consequences of poor adherence

to long-term chronic therapies are both poor health outcomes and increased health-

care costs. In 2012, global avoidable cost due to non-adherence was estimated at

$269 billion.2 The impact of non-adherence led the WHO to agree with Hayne’s

contention that “increasing the effectiveness of adherence interventions may have

a far greater impact on the health of the population than any improvement in

specific medical treatments”.3

The WHO estimates that roughly 50% of medications prescribed for chronic

diseases are actually taken.1 Even in life-threatening cases adherence rates can be

much lower than expected, with adherence rates measured as low as 77.3% in post-

transplant immunosuppressant drugs4 and 71% for oral oncology drugs.5 A 2012meta-

Correspondence: Kevin Dolgin Observia, 16 Rue Brancion, Paris 75015, France Tel +33 1 81 80 24 50 Email [email protected]

Patient Preference and Adherence Dovepress open access to scientific and medical research

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http://doi.org/10.2147/PPA.S237778

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analysis of adherence in drugs that prevent cardiovascular

disease found an adherence rate of 57% inmore than 370,000

patients.6 Over the past several years, stakeholders in the

healthcare world have intensified their efforts to both under-

stand this issue and to put into place patient support programs

that will help address non-adherence. Addressing non-

adherence through targeting intervention is one of the few

topics on which everyone is in agreement: patients certainly

benefit from increased support and payers would very much

like to reduce overall costs by enhancing adherence to those

drug treatments which they have decided are beneficial;

health-care professionals would like to ensure that the treat-

ments they prescribe are being followed and the pharmaceu-

tical industry benefits by increased sales of their products.

Physicians have traditionally been poor at determining

patient adherence. As early as 1978, Roth et al7 found that

physicians overestimated their patients’ adherence by 400%,

and that the patients too overestimated their own adherence.

In 2010, Copher et al8 found that physicians overestimated

the number of adherent patients by over 60% and in the

following year Trindade et al9 found similar overestimation

of the adherence rates of IBD patients. In 2016, Clyne et al10

found a weak correlation between physician estimates of

their patients’ adherence and objective measures, as well as

a systematic bias among prescribers to assume that their

patients are more adherent than the norm.

Given their difficulty in perceiving the problem, relying

on health-care professionals alone to address patient non-

adherence can lead to suboptimal outcomes.11 Furthermore,

decisions about whether or not to take medication are typi-

cally made outside of a health-care institution, when the

patient is not in direct contact with health-care professionals.

Many tools have been provided to health-care profes-

sionals to help prescribers more accurately assess adher-

ence. The most widely used of these is the 4-question

Morisky Medication Adherence Scale (MMAS 4).12 This

tool has been and continues to be of great use to health-

care professionals, but it and other tools like it typically

require extra time and effort from often busy professionals.

Both physicians and patients often cite a lack of time

during visits, and surveys such as 2007’s Global Asthma

Physician and Patient Survey13 underscore the need for

more time spent on education and coaching.14 At the same

time, a recent survey carried out in the United States

indicates that only 11% of patients and 14% of physicians

feel that doctors have the time they need to provide excel-

lent care.15 Faced with the need to balance ease of use and

thoroughness of analysis, behavioral profiling tools such as

the MMAS-4 (and the later, 8-question MMAS-8) must

sacrifice the latter to ensure the former. As such, existing

tools have been criticized as being too restrictive to offer

a basis for highly tailored behavioral interventions.16

The availability of digital solutions (Digital Adherence

Technologies: DAT) provides effective new means of iden-

tifying patients at risk of non-adherence and promoting

behavioral change while minimizing demands on physi-

cian time.17–19 However, this technology by its nature

lacks the personalization that can be provided by

a trained human during an interpersonal exchange. This

gives rise to a need for more flexible and personalized

digital support that takes into account each individual’s

behavioral drivers and triggers without the need for human

analysis. Tailored DATs have great potential to support

patients effectively without undue demands on physician

time and with much lower costs than traditional telephone-

based programs, as demonstrated by a 2018 review of the

literature on such technologies with tuberculosis patients20

as well as a 2019 study with hypertensive patients in the

UK.21 These promising approaches warrant further devel-

opment, including the design of DAT-friendly profiling

tools. Such tools would be digital in nature, thorough in

their quantification of the drivers of adherence, predictive

of actual adherence behavior and easily incorporated into

DATs.

The tools that are typically used to determine indivi-

dual patient risk and behavioral needs were designed to be

used by humans and do not incorporate the kind of con-

tinuous and detailed mathematical principles that can inter-

act effectively with digital support programs. For example,

most online retailers, such as Amazon.com, use Bayesian

product recommendation engines such as that described in

US patent 8.255.263 B2.22 Digital patient support pro-

grams could likewise benefit from a similar high degree

of customization, yet they need the detailed, quantifiable

personal profiling that drives them. As pointed out by

Prochaska, Redding, and Evers, “. . . most [health behavior

frameworks] have not even developed constructs that are

subject to such mathematical principles.”23 We believe that

the SPUR (Social, Psychological, Usage, Rational) frame-

work, built on existing behavioral frameworks, can fill this

gap by allowing detailed quantitative measures of estab-

lished adherence behavioral drivers, determined through

an interactive digital questionnaire. Such a questionnaire,

designed from the start to be administered in a digital

setting, can provide engaging intermediate feedback to

patients while providing the kind of driver-specific

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measures that can serve as the foundation for personalized

digital interaction. In order to achieve this in a valid way,

such a tool must be built on solid theoretical frameworks.

Theoretical Frameworks A number of frameworks are often cited when referring to

patient adherence decision-making. Among the first of

these was the Health Belief Model (HBM),24 first postu-

lated over fifty years ago. This model has been verified in

more recent studies,25 but it does not address the non-

cognitive components of patient behavior, such as medica-

tion costs. Icek Ajzen’s Theory of Planned Behavior

(TPB), and its predecessor the Theory of Reasoned

Action do address these factors and have also often been

applied to healthcare decision-making,26 as has the more

recent derivative of the TPB, the Integrated Behavior

Model.27 Prochaska and Clemente’s Transtheoretical

Model (TTM) has also been a staple model, as has social

cognition theory.28,29 On a more psychological level,

Gérard Reach has postulated elements of relationship to

authority (reactance), and he and others have borrowed

from behavioral economics concepts regarding the ability

to project into the future as determinants of adherence

behavior.30

Outside of medical or even strictly behavioral aca-

demic domains, the field of consumer behavior has gener-

ated interesting frameworks for addressing adherence

problems. Notably, issues of identity, described in the

concept of the extended self, as researched by Russel

Belk,31 provides insights into how individuals view con-

sumption as an extension of self-identity. This has an

impact both on the acceptance of their disease (as

described by Graffigna and Barello 201832) and on their

acceptance of the prescribed treatment.

Patient Support Programs (PSPs) built using these and

other models have proven useful as public health initia-

tives at a population level.33 These models have been used

to build patient-specific support, and they are also often

used to profile patients and provide guidance to health-care

professionals who can then offer tailored advice, educa-

tion, and coaching. Some models have been specifically

designed to profile patients, either in terms of their risk of

non-adherence or the reasons behind this risk. One of the

more widely used frameworks to investigate and affect

patient behavior is the Patient Activation Measure

(PAM).34 The goal of PAM is to measure the degree to

which patients have been “activated” to engage with their

own health; and PAM scores have been strongly correlated

to outcomes.35,36 The Patient Health Engagement model32

purports to go beyond what the authors see as

a “passivizing approach” to patients’ care to examine the

“meaning and lived experiences” of patients, as well as

their emotional and psychological make-up.

While all of these models have demonstrated their

value both at the level of individual prescribers (or provi-

ders) as well as in the design of programs at population

level, none were designed to provide a multi-dimensional

behavioral assessment model that can be used to profile

individuals and interface with a digital agent, such as

a DAT driven by artificial intelligence. What is needed is

a comprehensive model of patient behavior that can both

accurately predict adherence and identify modifiable dri-

vers of health behavior that deliver useful input to digital

coaching and support systems.

In sum, the imperative is to build a model and an

associated profiling tool that successfully meet the follow-

ing criteria: 1) accurately predicts patient adherence to

medication; 2) identifies actionable drivers of adherence

behavior; 3) can be used in the absence of human inter-

pretation so as to inform the customization of purely

digital support programs; and 4) is based on accepted

behavioral models drawn from different domains. Such

a model can then be used both to further investigate the

non-adherence phenomenon at the population level as well

as to provide tailored support to individual patients. By

combining established behavioral models with digital

questionnaire technology, a “grand unified” model can be

created to serve as the basis for the digital questionnaire.

The Proposed Model: SPUR The core of the SPUR framework is Ajzen’s Theory of

Planned Behavior (TPB). This theory, as seen in Figure 1,

is a well-established approach to consider complex deci-

sion-making. It has been successfully and widely applied

to general healthcare behavior,37 and a smaller number of

studies have shown its utility in predicting medication

adherence,38,39 although its use has been criticized in this

context for being simplistic, too rational in nature, not

taking into account psychological factors such as identity,

and for not having demonstrated impact when applied to

health behavior.40 McEachan et al’s41 2010 meta-analysis

of the use of the TPB determined that the model was

predictive of health behavior and psychological constructs

such as behavioral intent. Using Rothman and Salovey’s

classification of health behaviors into those that prevent,

detect, and cure health problems,42 McEachan et al

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determined that the type of behavior was important in

assessing the applicability of the TPB and particularly, in

the relative importance of its constituents. This was rein-

forced in McEachan et al’s subsequent 2016 meta-

analysis.43 In both of these studies, however, the authors

underlined that no satisfactory study had been made of the

applicability of the TPB in cases of curative behaviors

such as adherence to medication for chronic disease.

The lack of studies examining the TPB in curative

behavior as well as the identified weaknesses of the

approach led us to believe that augmenting the TPB with

health-specific frameworks focusing on curative behavior

can both enhance its applicability in this domain while

further elaborating on it to provide quantifiable outputs

that can then be used to drive algorithms in digital support

programs for chronic disease management.

The TPB describes complex behavior as a function of

attitudes about the behavior (driven by beliefs about the

behavior and tempered by other psychological factors),

subjective norms, and perceived control. Each of these is

weighted to reflect its importance for each individual.

Drawing on the TPB, the SPUR model is focused

specifically on chronic patients’ adherence behavior and

includes elements of ancillary frameworks as mentioned

above.

Specifically, SPUR includes concepts from the HBM to

inform the cognitive elements of attitude formation within

the TPB by detailing the perceived understanding of the

patient and their beliefs about the prescribed behavior.

Commonly cited practical reasons for non-adherence, such

as financial difficulties, complexity of the treatment, etc.1

can inform the control elements of the TPB, typically

referred to as self-efficacy in health behavior, and afore-

mentioned behavioral factors such as reactance and dis-

counting of future benefits can address how beliefs about

behavior combine with the other elements to generate

attitudes. The resulting model is specific to behaviors

regarding chronic diseases and takes into account

a number of established frameworks that have been demon-

strated to influence adherence behavior, generating the fol-

lowing enhanced framework (Figure 2).

The model is called “SPUR”, since the different ele-

ments fall into four major domains: Social, Psychological,

Usage and Rational that are discussed below. The manner

in which these four domains relate to the TPB can be seen

in Figure 3.

Social Factors Social elements in the case of medical adherence have also

been studied at length. Most of the work in this area has

focussed on perceived social support as a factor in patient

adherence and has shown greater support improves adher-

ence. This has been considered in general44 as well as in

specific cases such as diabetes by Gu, et al45 as well as

Shallcross et al46 in epilepsy and Kim, et al47 in HIV.

Social factors have been considered both with respect

to the impact of those close to the patient and the role of

society as a whole (e.g. the influence of socio-cultural

expectations). This latter definition corresponds more clo-

sely to the TPB’s construct of social norms and can be

clearly seen when considering the changing cultural

acceptability of smoking in many Western societies and

its impact on the prevalence of smoking. Likewise, the

impact of social norms on general health-related behavior

has been well considered (see, for example, Baer, Stacy,

and Larimer48), although the direct impact of norms has

been poorly studied in relation to actual medication

adherence.

Psychological Factors Many psychological constructs have been examined with

respect to health behavior in cases of chronic disease.

Mental illness itself, such as depression, has long been

linked to non-adherence49–51 and treatment with anti-

depression medication has been shown to increase adher-

ence with depressed HIV patients.52 In SPUR, we have

avoided examining the impact of mental disorders such as

depression, bi-polar or Post-traumatic stress disorder, as

they merit specific treatment beyond the type of behavioral

support that is the focus of our research. We have therefore

focussed on three compelling and well-documented non-

clinical psychological factors: self-concept, reactance and

the discounting of future values.

Beliefs about behavior

Beliefs about social norms

Attitude about behavior

Subjective norms Action

Beliefs about control

Perceived control

Figure 1 The theory of planned behavior.

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Beliefs about behavior

Beliefs about social norms

Attitude about behavior

Subjective norms Action

Beliefs about control

Perceived control

Forgetfulness Financial Capability

Reactance

Identity

Discounting

Perceived Susceptibility

Perceived Seriousness

Perceived Benefits

Perceived Barriers

Perceived Threat

Outcome Expectations

Figure 2 The concepts that constitute SPUR.

Beliefs about behavior

Beliefs about social norms

Attitude about behavior

Subjective norms Action

Beliefs about control

Perceived control

Forgetfulness Financial Capability

Reactance

Identity

Discounting

Perceived Susceptibility

Perceived Seriousness

Perceived Benefits

Perceived Barriers

Perceived Threat

Outcome Expectations

Rational

Psychological

Usage

Social

Figure 3 The SPUR framework in detail.

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The Concept of the Self In the case of medical adherence, there is a latent tendency

to deny the existence or extent of the illness despite

a cognitive understanding of the facts.53,54 While denial

has been linked to well-being and general health54,55 the

role of denial of personal traits and circumstances in

behavior modification has not been adequately examined.

There is some indication that denial is directly correlated

to non-adherence, particularly in the case of mental illness.

Greenhouse et al29 demonstrated that coping behaviors

associated with denial were inversely correlated with

adherence in bipolar disorder56 and Aldebot and

Weisman de Mamani57 observed that denial led to lower

adherence rates in schizophrenia. Outside of mental ill-

nesses, denial has been identified as a significant contri-

butor to non-adherence in pathologies ranging from

cardiovascular disease58 to HIV.59

We thus hypothesize that questions of denial in the

case of health are driven by conflicts between the pre-

existing self-concept and the new identification with the

disease state, e.g. as a “diabetic”, “cancer patient”, “asth-

matic”, etc. In order to examine the concept of the self

with respect to the consumption of medication, we turn to

consumer behavior and notably, Russel Belk. Belk consid-

ered the effect of possessions on the “extended self” and

the impact of consumption decisions.31,60,61 We believe

that similar concepts can help elucidate a model to under-

stand the impact of medication consumption on identity

and therefore its impact on adherence. Specifically, does

the person accept their illness and their status as “patient”?

We hypothesize that denial of this attribute – equated to

a refusal to incorporate it into their sense of identity – is

detrimental to adherence.

In order to better examine this potential disconnect, it

is useful to begin by considering the patient’s perceived

identity and their concept of self, both from the point of

view of the individual (i.e. Belk) and his or her relations to

others and the community, i.e. Kashima.62 This becomes

particularly relevant for individuals who express a degree

of denial with respect to their disease. Kortte and

Wegener53 have considered specifically denial of illness

among patients across a number of pathologies and have

investigated the psychological literature on the subject,

ranging from Freud to modern thinkers. However, they

have not considered the impact of denial on either sense

of self directly, nor on consumer behavior, such as

adherence.

Reactance Reactance is the psychological tendency to resist authority.

A number of studies have examined the impact of reactance

on patient adherence. Fogarty and Young63 did not find an

expected correlation between the use of an authoritative tone

by physicians and patient behavior, however, it did lead the

authors to conclude that the underlying degree of psycholo-

gical reactance of the patient was a factor in their adherence

behavior. Gérard Reach’s analysis of diabetic patients’

adherence35 bears this out, demonstrating a strong correlation

between “obedient” behavior (wearing a seatbelt in the back

seat of a car) and adherence, leading him to postulate that

there are two elements to adherence behavior, a “passive”

(i.e. driven by deference to authority) and a “motivational”

element. In 2011, De La Cuevas et al49 determined that

reactance was a stronger driver of adherence behavior than

self-efficacy in patients suffering from depression.

Discounting and Prospect Theory Researchers in the domain of behavioral economics have

demonstrated the degree to which our asymmetrical valu-

ing of losses over gains affects behavior.64 A number of

researchers have investigated these factors with respect to

adherence behavior.

Zhao et al37 demonstrated a significant difference in

the impact of differential messaging on intended health

behavior across people with different levels of focus on

future outcomes,65 in which patients with high scores in

Strathman’s Consideration of Future Consequences scale

were significantly more sensitive to messages about future

consequences than were subjects with lower scores, who

were more likely to adhere when presented with messages

concerning short-term benefits.

Lebeau et al,66 building on the work of Gérard Reach30

discovered a direct correlation between the discounting of

future gains in type 2 diabetes patients and their measure

of glycated hemoglobin (HbA1c).

Usage The TPB’s inclusion of perceived control as an important

behavioral driver leads to a consideration of what exactly

this represents in adherence to treatment for chronic dis-

ease. This idea of self-efficacy has been closely studied

and indeed, many patient support initiatives focus on it

exclusively. Issues of control that affect adherence include

non-access to medication due to physical constraints;

financial difficulty;67 difficulty with self-administration,

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which can be the result of a multitude of causes including

anxiety regarding self-injection;68 and forgetfulness.

Rational Factors The HBM is the most widely and the longest used “expec-

tancy-value” approach to health behavior. The fundamen-

tal behavioral choice is presented as a comparison of the

outcome expectations of health behavior as compared to

the perceived threat of not adopting that behavior. The

former is further subdivided into the perceived benefits

and the perceived threats of the behavior and further sub-

divided into the perceived gravity of the disease coupled

with the perceived susceptibility of the subject to these

nefarious consequences. While the HBM lacks the psycho-

logical, social and practical elements that have since been

studied for their impact on health behavior, it does provide

a good breakdown of the purely rational thought processes

that can inform beliefs about health behavior that can then

be incorporated into the TPB.

Conclusions and Next Steps By building on the Theory of Planned Behavior, incorporat-

ing behavioral frameworks that are relevant to behavior in

the case of medication adherence for patients with chronic

diseases, the SPUR framework represents a coherent

approach to build a quantifiable questionnaire that can be

used to profile patients for adherence risk while identifying

the drivers behind that risk. The inherently hierarchical struc-

ture of the framework, in which four major sets of drivers:

social, psychological, utilitarian and rational are further bro-

ken down in accordance with relevant theories should allow

both for assessment of an individual’s behavioral risk as well

as analysis of the salient sources of that risk.

The next step will be to build a questionnaire based on

the SPUR framework and test its predictive validity. We

are in the process of doing exactly that and will be carry-

ing out such a study across a range of pathologies in the

near future. It is our hope that SPUR will prove to be

a valuable tool for health-care professionals to determine

the behavioral risks of each patient and construct support

services for them that correspond to their personal beha-

vioral drivers.

Abbreviations DAT, Digital Adherence Technologies; HBM, Health Belief

Model; PAM, Patient Activation Measure; PSP, Patient

Support Program, SPUR, Social, Psychological, Usage,

Rational; TPB, Theory of Planned Behavior; TTM,

Transtheoretical Model; WHO, World Health Organization.

Acknowledgments The author would like to thank John D. Piette, Reem

Kayyali, Marie-Eve Laporte and Benoit Arnould for their

input and critical review of the manuscript; Lea Kombargi

for her support in preparing the manuscript; Béatrice

Tugaut for her support in submitting the manuscript.

Author Contributions The author contributed to data analysis, drafting or revising

the article, gave final approval of the version to be published,

and agrees to be accountable for all aspects of the work.

Ethics Approval and Informed Consent This study did not require any ethics approval.

Funding This study was funded by Observia.

Disclosure Kevin Dolgin is an employee of Observia. The author

reports no other conflicts of interest in this work.

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