mail-Based Interventions

With 82% of the British population using e-mails (Statista, 2018c), e-mail newsletters can be a useful, practical and cheap channel to deliver behavioural change interventions to diverse populations. Indeed, previous research suggests that e-mail-based interventions can be effective in generating behavioural change. However, to date, few studies used a more comprehensive and theory-based approach, in which several behavioural change techniques were used in a systematic way, as was the case in this project.

The majority of interventions I was able to identify (through a Scopus search in which I looked for publications that used the words “behavioural”, “intervention” and “e-mail” or “newsletter” in the title, abstract or as

a keyword) used e-mail in a supportive role, in combination with other techniques or channels (e.g. Brendryen et al., 2017; Dudziński et al., 2016; Griffin et al., 2018; Houston et al., 2015; Hutchesson et al., 2016; Levenson et al., 2016; Limaye et al., 2017; Ngamruengphong et al., 2015; Schweier et al., 2018; Skolarus et al., 2017; van Dijk et al., 2017; Young et al., 2017; Young et al., 2018; Zwickert et al., 2016). The studies that used only e-mails typically relied on one of three BCTs: feedback (Carrico & Riemer, 2011; Chambliss et al., 2011; Dennis & Horn, 2014; Emeakaroha et al., 2014; Kramer & Kowatsch, 2017; Leung et al., 2017; Moreira, Oskrochi & Foxcroft, 2012; Taverez et al., 2017), reminders (e.g. Abel et al., 2015; Bradley et al., 2017; Greaney et al., 2012; Murphy & DuPietro, 2012; Petrella et al., 2017; Robertson, 2016; Robinson et al., 2014) or information provision/education (Kattlemann et al., 2014; Kothe & Mullan, 2014; Morgan, Mackinnon & Jorm, 2013; Plotnikoff et al., 2010; Poddar et al., 2012; Schneider et al., 2015).

Several other publications reported e-mail-only-based interventions that used other BCTs. Four of these studies compared the impact of generic

versus personalized e-mails, showing that personalised messages could have a greater impact on increasing physical activity than generic ones (Hageman, Walker & Pullen, 2005; Short et al., 2014; 2015; Walker et al., 2010; 2011; Yates et al., 2012). Other publications described interventions that did not rely on a specific methodology, framework or theory, and which had a mixed effect. For example, Gunter et al. (2017) aimed to reduce the number

of returns of newly adopted dogs to a shelter. They sent e-mails to new dog owners (who adopted their pets) with advice on dog behaviour and human activity, as well as invitations to join weekly dog walks. People from the intervention group were not significantly more active than those in control group, nor were they less likely to return their dogs. Leone et al. (2016) used e-mails to promote cancer screening and physical activity among an urban African American population. They sent out a series of three e-mails, over a six-month period, addressing cancer screening and physical activity behaviours but were unable to influence either of the outcome variables. Block et al. (2008) designed an email-based intervention to increase physical activity, reduce added sugar, saturated and trans fats consumption and to increase fruit and vegetable consumption. Their intervention consisted of an assessment, followed by feedback, weekly goal-setting, tips, reminders and promotion of social support and was successful in changing the target behaviours. Finally, Simons-Morton et al. (2005) designed an intervention, in which families with teenage children who had recently received their drivers’ licenses, received newsletters with persuasive messages about high-risk teenage driving and a parent–teenager driving agreement. Patents who received these newsletters reported stricter limits on teen driving, at 12 months, compared to parents from a control group, who received standard information on driver safety.

One reason for the mixed effectiveness of the aforementioned interventions can be the fact that they did not rely on theory nor used a systematic approach to selecting the right behavioural change techniques. Previous research suggests that theory-based interventions are more effective than ones that rely on intuition (Abraham et al., 2009; Albarracin et al., 2005; Noar & Zimmerman, 2005). Indeed, the e-mail-based interventions that did use a theory-informed approach, while few, were all effective in

generating a behavioural change. For example, Parrott et al., (2008) designed a three-week study in which e-mails were developed using Theory of Planned Behaviour (TPB; Ajzen, 1985, 1991; Ajzen & Madden, 1986). Research

participants first filled out a self-reported TPB measure. Next, they received positively or negatively framed e-mails, delivered every other day over the period of two weeks. More frequent exercise behaviour was reported by the positive-frame group, as compared to the negative-frame group and a control group. Similarly, Blake et al., (2017) showed that TPB-based e-mails had a greater impact on increasing physical activity than text messages; and Kothe, Mullan and Butow (2012) were able to increase fruit and vegetable consumption among Australian students through an e-mail-delivered intervention, develop based on TBP. Two more theory-informed

interventions, one using messages based on a habit framework (Rompotis, Grove & Byrne, 2014) and one that used a cognitive behavioural therapy approach (Trockel et al., 2011) proved to be effective in changing health- related behaviours as well.

The key learning that comes from this literature review is that while e-mails are ubiquitous and simple to use, so far there have been few attempts at designing a theory- and evidence-based approach to applying behavioural insights to this form of communication, with the aim of changing behaviours. While several studies did rely on theory, each of these studies used only one theory (mostly Theory of Planned Behaviour), implying there is a need for a more comprehensive and methodical approach. This is where the

Behavioural Change Wheel can be of help, as this framework integrates 33 different theories and guides a choice architect how to easily use them in practice. Therefore, the objective of this study was to design an e-mail-based intervention with the help of the BCW, to outline a methodology of how to approach such a task and, importantly, to report results. To my knowledge, these objectives make this study the first one to use the BCW in the context of non-health decision-making and in e-mails. It is also one of the first studies to follow the framework in its entirety and to report results of such an intervention.

Methodology

This study followed the BCW approach to behavioural change

intervention development, as described by Michie, Atkins and West (2014) and in Chapter 3. First, I identified the target behaviour (Step 1) and

designed a survey, based on the theoretical domains framework, to identify key mediators of the selected target behaviour (Step 2). Next, I conducted the survey (Study 1) and, based on results and the BCW methodology, identified the best behavioural change techniques to use in the intervention (Steps 3 to 7). I then designed and conducted the intervention (Step 8/Study 2), in which respondents were asked to post pictures on LitterGram. Finally, I conducted a follow-up survey (Step 9/Study 3), to evaluate which of the BCTs used in the intervention had the biggest impact on behaviour and evaluated the effectiveness of the intervention (Step 10).

Table 24

LitterGram research project/BCW steps.

Step Description

1 Problem definition Define the problem in behavioural terms; select and specify target behaviour

2

Theoretical domains framework questionnaire development and

distribution

Develop a list of statements relating to 14 domains; set up a survey; distribute the link to the survey to LitterGram users via a newsletter

3 Key domains (Study 1) Identify key behavioural mediators of the selected target behaviour

4 Identify intervention functions Match the identified domains with the BCW interventions functions

5 Identify policy domains Match interventions functions with policy domains 6 Identify behavioural change

techniques

Select and develop BCTs to be used in the intervention

7 Identify mode of delivery Select mode of delivery for the chosen BCTs 8 Intervention (Study 2) Conduct the intervention (main experiment) 9 BCT evaluation (Study 3) Evaluate the impact of individual BCTs on

behaviour change

Step 1: Problem Definition

The first step of the Behavioural Change Wheel is to define a problem in behavioural terms and to select and specify a target behaviour. The target behaviour was defined as follows in this study:

• Who needs to perform the behaviour? LitterGram users, who are subscribed to LitterGram newsletter.

• What do they need to do differently to achieve the desired change? They need to post at least three pictures of litter on LitterGram every week4_{of the intervention. By “litter” I mean any of the eight}

categories listed in the LitterGram App, i.e. litter, fly tipping,

roadworks mess, filthy or broken sign, potholes, dog mess, overfilled bin, graffiti.

• When do they need to do it? Anytime they see litter.

• Where do they need to do it? On LitterGram.

• How often do they need to do it? At least three times a week during the six-week intervention period.

• With whom do they need to do it? Alone.

Step 2: The Theoretical Domains Framework Questionnaire