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Behavior Change Research and Theory. Psychological and Technological Perspectives PDF

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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 AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2017 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrange- ments with organizations such as the Copyright Clearance Center and the Copyright Licens- ing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and ex- perience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or edi- tors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-802690-8 For information on all Academic Press publications visit our website at https://www.elsevier.com/ Publisher: Nikki Levy Acquisition Editor: Emily Ekle Editorial Project Manager: Timothy Bennett Production Project Manager: Caroline Johnson Designer: Victoria Pearson Typeset by Thomson Digital 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

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