Behavior Change Research and Theory. Psychological and Technological Perspectives

210 Pages·2016·4.5 MB·English

by  Linda Little, Elizabeth Sillence and Adam Joinson (Eds.)

BEHAVIOR
CHANGE
RESEARCH
AND THEORY
PSYCHOLOGICAL AND
TECHNOLOGICAL PERSPECTIVES
Edited by
Linda LittLe
PaCT Lab, Department of Psychology, Faculty of Health and Life Sciences
Northumbria University, United Kingdom
eLizabeth SiLLence
PaCT Lab, Department of Psychology, Faculty of Health and Life Sciences
Northumbria University, United Kingdom
adam JoinSon
School of Management, University of Bath, Bath, United Kingdom
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List of Contributors
C. Abraham Psychology Applied to Health (PAtH), University of Exeter Medical
School, University of Exeter, Exeter, United Kingdom
T. Alahäivälä Department of Information Processing Science, University of
Oulu, Oulu, Finland
P. Briggs Northumbria University, Newcastle upon Tyne, United Kingdom
R. Comber Open Lab, School of Computing Science, Newcastle University,
Newcastle upon Tyne, United Kingdom
L.A. Condon School of Medicine, Nottingham University, Nottingham, United
Kingdom
R. Cooke Aston University, Birmingham, United Kingdom
N.S. Coulson School of Medicine, Nottingham University, Nottingham, United
Kingdom
L. Coventry Northumbria University, Newcastle upon Tyne, United Kingdom
S. Denford Psychology Applied to Health (PAtH), University of Exeter Medical
School, University of Exeter, Exeter, United Kingdom
A. Fielden School of Psychology, Newcastle University, Newcastle upon Tyne,
United Kingdom
M. Harjumaa VTT Technical Research Center of Finland, Oulu, Finland
D. Jeske Edinburgh Napier University, Edinburgh, United Kingdom
A. Joinson School of Management, University of Bath, Bath, United Kingdom
P. Karppinen Oulu Advanced Research on Software and Information Systems,
Department of Information Processing Science, University of Oulu, Oulu,
Finland
S. Langrial Oulu Advanced Research on Software and Information Systems,
Department of Information Processing Science, University of Oulu, Oulu,
Finland; Sur University College, Sur, Sultanate of Oman
T. Lehto Oulu Advanced Research on Software and Information Systems,
Department of Information Processing Science, University of Oulu, Oulu,
Finland
L. Little PaCT Lab, Department of Psychology, Faculty of Health and Life
Sciences, Northumbria University, Newcastle, United Kingdom
M. Oduor Department of Information Processing Science, University of Oulu,
Oulu, Finland
H. Oinas-Kukkonen Oulu Advanced Research on Software and Information
Systems, Oulu, Finland
ix
x List of Contributors
L. Piwek School of Management, University of Bath, Bath, United Kingdom
B. Schüz University of Tasmania, Hobart, TAS, Australia
N. Schüz University of Tasmania, Hobart, TAS, Australia
E. Sillence PaCT Lab, Department of Psychology, Faculty of Health and Life
Sciences, Northumbria University, Newcastle, United Kingdom
A. Thieme Open Lab, School of Computing Science, Newcastle University,
Newcastle upon Tyne, United Kingdom
G.M. van Koningsbruggen Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands
I N T R O D U C T I O N
Digital Behavior Change
E. Sillence*, L. Little*, A. Joinson**
*PaCT Lab, Department of Psychology, Faculty of Health and Life
Sciences, Northumbria University, Newcastle, United Kingdom;
**School of Management, University of Bath, Bath, United Kingdom
DIGITAL BEHAVIOR CHANGE
This book is concerned with the planning, implementation, and evalu-
ation of digital behavior change interventions. We define digital behav-
ior change as a behavior change that makes use of technology to either
(1) promote the delivery of the intervention, (2) enhance the environment
through which the intervention occurs, or (3) encourage specific patterns
of interaction that underpin the intervention.
BACKGROUND
Organizations, researchers, and professionals are increasingly interest-
ed in behavior change with the focus spreading beyond health behaviors
to encompass issues such as energy consumption and security (Bell, Toth,
Little, & Smith 2015; Blythe, Coventry, & Little, 2015). While the motiva-
tion for changing behavior is usually apparent and well documented, un-
derstanding how to bring about change is less clear cut. Psychologists, for
example, utilize a wide range of behavior change techniques. Indeed a re-
view of the literature by Michie, Johnston, Francis, Hardeman, and Eccles
(2008) identified at least 137 individual techniques and concluded that a
“one-size-fits-all” approach cannot be adopted within behavior change
research. As Bell et al. (2015) suggest techniques should be selected based
on an understanding of the specific antecedents of the behavior that is
to be changed, and the approach tailored for this. Attention must also be
given to the target population and their ability to access, understand, and
process the information presented.
xi
xii INTRODUCTION
Behavior change techniques need to be evaluated in order to develop
a clear scientific approach that is driven by theory and practice. Develop-
ing a clear behavior change science will in turn evidence the determinants
of behavior best to target in interventions. Interventions that are driven
by theoretical underpinning, evaluated, planned, and designed will create
approaches that are cost effective and have long-term success in changing
behavior.
Many researchers now recognize the role technology can play in shap-
ing behavior change techniques (Lehto & Oinas-Kukkonen, 2015). For ex-
ample, smartphones are easy to use and afford the opportunity to collect
and record real-time behavioral data through mobile applications. Web-
based delivery of intervention programs offers significant advantages
in terms of resources and distribution (Griffiths, Lindenmeyer, Powell,
Lowe, & Thorogood, 2006). The extent to which the technology plays a
facilitating rather than a driving role in behavior change is, however, im-
portant (Patel, Asch, & Volpp, 2015) as is the notion that any use of tech-
nology in this context needs to focus on the end users themselves and be
person-centered rather than technologically dictated (Yardley, Morrison,
Bradbury, & Muller, 2015).
The chapters in this book attempt to capture the essence of “digital be-
havior change.” The overarching goal of digital behavior change is align-
ment, put simply, that is the mapping of the target behavior with the af-
fordances and opportunities provided by the technology. This process of
identifying the behavior and possible technologies is one that has to be
closely coupled. Rather than allowing the technology to determine the in-
tervention, we need to begin with a strong sense of what it is that needs to
change, and how technology supports that change through both the affor-
dances of the technology, and the underpinning behavior change mecha-
nism. Once the behavior has been identified it is possible to start thinking
about the potential for technology. It may be that there is no appropriate
and useful role for technology within any given context and the aim of
this book is not to suggest that simply adding technology to any behavior
change intervention will always be a good idea. Far from it, we suggest
that successful digital behavior change requires the same detailed plan-
ning as specified in Abraham and Denford in Chapter 1. However, we also
suggest that to maximize the return on a technology investment, interven-
tions need to carefully consider how the technology used might support
(or indeed work against) the underpinning change mechanisms. The goal
of the remaining chapters in the book support this process by considering
not only how technology can be used, but also why and when a specific
technology might be best implemented as part of an intervention.
This book aims to offer practical guidance as to how to plan, imple-
ment, and evaluate behavior change interventions. The book encapsulates
a number of different perspectives on behavior change including those
INTRODUCTION xiii
from psychology, design, and software engineering and introduces key
models and processes in relation to health and well-being, security, and
environmental issues. Importantly, the theme of technology runs through-
out the book, with authors highlighting current technology use, near fu-
ture use, and suggesting directions for future work. Here we gain valuable
insights into the ways in which technology can underpin the delivery of
the intervention and assist with data recording and collection.
The book has been divided into three main areas: planning, interven-
tion, and evaluation. This was done consciously to aid the reader in navi-
gating to the most appropriate section. However, we are very aware of the
importance of integrating all elements of a behavior change program, a
point reiterated by the authors of Chapter 1. To these ends Chapters 1–4
discuss approaches to planning a behavior change intervention taking into
account high level principles applicable across a range of contexts through
to a number of specific worked examples of planning or takeaway guide-
lines and frameworks that could be used or adapted as appropriate.
In Chapter 1, Abraham and Denford outline the steps that can guide op-
timal planning of interventions to change behavior and guide the reader
through an “intervention mapping approach.” Chapter 2 presents a clear ex-
ample of planning in action as it relates to a health intervention to optimize
healthy dietary and exercise behaviors in adolescents. Chapters 3 and 4 take
more of a computing science and design approach to planning interven-
tions. Chapter 3 introduces the idea of persuasive system design approach
(PSD) and outlines a number of software patterns as guides to developing
interventions. Chapter 4 presents a planning framework for designing mo-
bile applications for intervention. In the implementation section of the book
we focus on three individual level interventions. In Chapter 5 the authors
introduce self-affirmation and detail the literature on this psychological
level intervention particularly within a health setting. In Chapter 6 the au-
thors compare and contrast two different approaches to intervention within
a security setting. Choice architecture and Protection Motivation Theory are
reviewed in terms of guiding specific interventions in the security domain.
While in Chapter 7, Piwek and Joinson take a more technological approach
to intervention and focus on the use of wearable and mobile data collection
and recording tools for facilitating behavior change.
Evaluation case studies in Chapters 8 and 9 provide a detailed ac-
count of how three digital behavior change interventions were evaluated.
Health, energy consumption, and recycling behaviors are all considered
and attention is paid to both the outcomes and the process of the inter-
vention. The evaluation chapters highlight the different roles that tech-
nology can play in terms of delivering the intervention and collecting the
resulting data. Finally the editors provide some concluding thoughts in
which they propose a method for the alignment of behavioral change
goals and the technology available.
xiv INTRODUCTION
References
Bell, B., Toth, N., Little, L., & Smith, M. A. (2015). Planning to save the planet: using an online
intervention based on implementation intentions to change teen energy-saving behav-
iour. Environment & Behavior. Advance online publication.
Blythe, J. M., Coventry, L., & Little, L. (2015). Unpacking security policy compliance: the
motivators and barriers of employees’ security behaviors. In Symposium on Usable Privacy
and Security (SOUPS), Vol. 22.
Griffiths, F., Lindenmeyer, A., Powell, J., Lowe, P., & Thorogood, M. (2006). Why are health
care interventions delivered over the internet? A systematic review of the published lit-
erature. Journal of Medical Internet Research, 8, e10.
Lehto, T., & Oinas-Kukkonen, H. (2015). Explaining and predicting perceived effectiveness
and use continuance intention of a behaviour change support system for weight loss.
Behaviour and Information Technology, 34(2), 176–189.
Michie, S., Johnston, M., Francis, J., Hardeman, W., & Eccles, M. (2008). From theory to inter-
vention: mapping theoretically derived behavioural determinants to behaviour change
techniques. Applied Psychology, 57(4), 660–680.
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers,
of health behavior change. JAMA, 313(5), 459–460.
Yardley, L., Morrison, L., Bradbury, K., & Muller, I. (2015). The person-based approach to
intervention development: application to digital health-related behavior change inter-
ventions. Journal of Medical Internet Research, 17(1), e30.
C H A P T E R
1
Planning Interventions
to Change Behavior
C. Abraham, S. Denford
Psychology Applied to Health (PAtH), University of Exeter
Medical School, University of Exeter, Exeter, United Kingdom
OVERVIEW
In this chapter, we highlight the need for behavior change, and its po-
tential impact on health and health expenditure. We discuss how change
can be initiated, facilitated, and maintained, and introduce the “NUDGE”
framework—a strategy tried and tested by both UK and US governments.
We then use the Intervention Mapping (IM) approach to take the reader
through the processes involved in optimal intervention design and evalu-
ation. Drawing on the Information, Motivation, Behavioral skills (IMB)
model, we introduce reflective and impulsive behavioral determinants
that underpin behavior and behavior change. We end this chapter by
explaining and emphasizing the need for evaluation of behavior change
interventions in order to develop a science of behavior change.
INTRODUCTION
Population-level behavior change research has become especially topi-
cal because of the recognition that patterns of behavior shared across pop-
ulations determine our health and well-being. More than 40 years ago,
the Alameda County study of health-related behavior patterns followed
7000 people over 10 years and showed that sleep, exercise, alcohol con-
sumption, and eating habits predicted mortality (Belloc & Breslow, 1972).
Similarly, following 4886 individuals, Kvaavik, Batty, Ursin, Huxley, and
Gale (2010) found that those who smoked, consumed less than 3 portions
Behavior Change Research and Theory. http://dx.doi.org/10.1016/B978-0-12-802690-8.00002-5
Copyright © 2017 Elsevier Inc. All rights reserved.
1
2 1. PlannIng InterventIons to Change BehavIor
of fruits and vegetables daily, did less than 2 h of physical activity per
week; and consumed more than 14 units of alcohol had an all-cause mor-
tality risk equivalent to being 12 years older than those that did none of
the above. Increasingly, eating and physical activity patterns are problem-
atic across international populations (World Health Organization, 2015).
In England, for example, more than 60% of the population is overweight
or obese which, in turn, is associated with a range of health problems,
including type 2 diabetes, coronary heart disease, hypertension, osteo-
arthritis, and particular cancers (Guh et al., 2009) that reduce life expec-
tancy (Flegal, Kit, Orpana, & Graubard, 2013; Whitlock et al., 2009). This
greatly increases health service costs. In the United Kingdom, for exam-
ple, weight-related health problems are estimated to cost an additional £5
billion annually (Department of Health, 2013). Engaging people in their
own self-management and behavior change is critical to health service
management and to governance more generally.
CHANGE IS POSSIBLE
For most people, our reprogrammable brains (Maslin & Christensen,
2007), consisting of approximately 86 billion neurons has the capacity,
over time, to rewrite the processes that direct what we think, what we
feel, and what we do (Herculano-Houzel, 2009). We try new things and
form new relationships. As we practice new behavioral routines over time
we automatically reprogram our perceptions, our feelings, our thoughts,
and our skills. So activities that at one time seem difficult or strange can
become easy and everyday over time. The challenge for scientists working
on behavior change is to model, explain, and then harness the cognitive,
emotional, and behavioral dynamism of our regulatory control sys-
tems (Reynolds & Branscombe, 2015) and then to develop interventions
that help people accelerate and consolidate behavior change processes
(Denford et al., 2015).
We know too that interventions to change behavior patterns across
groups can be effective. Reductions in smoking and unsafe sexual be-
havior, increases in physical activity, healthy diets, self-care, and health
screening have all been observed following particular interventions
(Denford, Taylor, Campbell, & Greaves, 2013; Greaves et al., 2011).
The National Institute of Health and Care Excellence (NICE) of United
Kingdom commissioned a review of reviews that included data on
103 systematic reviews of interventions targeting one of six behaviors
(cigarette smoking, alcohol consumption, physical activity, healthy eat-
ing, drug use, and sexual risk taking) and, although the degree of ef-
fectiveness varied between populations and intervention characteristics,
overall, interventions were found to be successful in changing behavior