A behaviour change intervention is only as good as the behaviour change techniques (BCTs) used. Irreducible, observable, measurable, and replicable, BCTs are the active components in an intervention. Previously, researchers did not share a standardised vocabulary across interventions; for example, a ‘goal setting’ BCT could be interpreted differently from one intervention to another. This use of inconsistent, non-standardised vocabulary and duplicate definitions was problematic and did not conform with the advice from the Consolidated Standard of Reporting Trends (CONSORT). This requires researchers report the exact particulars of how an intervention was conducted, as effective interventions cannot otherwise be replicated (Wood et al., 2015). Furthermore, due to the inconsistencies, interventions were not seen as a reliable scientific study (Abraham & Michie, 2008; Michie et al., 2011; Michie et al., 2013; Wood et al., 2015).
Graham and Michie (2008) aimed to develop and test a theory-linked BCT taxonomy. Initially a 26 BCT taxonomy was devised and applied to healthy eating and physical activity interventions, reflecting a range of theoretical accounts of behavioural change (Abraham & Michie, 2008). Michie et.al. (2011) further aimed to refine this 26-item behaviour change taxonomy. This evolved into the CALO-RE taxonomy; a 40-item taxonomy which was more clearly labelled, comprehensive and well specified. It has been developed into other taxonomies for different behavioural pathogens including a taxonomy for smoking intervention, consisting of 43 BCTs (Michie et al., 2011). Before arriving at the current BCTv1 taxonomy, there were approximately seven other taxonomies for BCTs (Wood et al., 2015). Michie et.al (2013) continued to work toward a universal approach where there is one taxonomy suitable for use in behaviour change interventions regardless of the behavioural pathogen being addressed, taking away the need for multiple taxonomies (Michie et al., 2013;Wood et al., 2015).
This essay will consider the effectiveness of BCTs used in interventions to reduce sedentary behaviour (SB)/ increase physical activity (PA). Any time an individual is sitting or lying down, they are engaging in SB, which commonly occurs when watching TV, video gaming or computer use, driving and reading (The Sedentary Behaviour Research Network (SBRN), 2018). The more time spent in SB the higher the health risks. The World Health Organisation (WHO) lists health risks resulting from SB as the leading risk factor for global mortality and the key risk factor for noncommunicable diseases (NCD) i.e. cancer, diabetes and cardiovascular disease (World Health Organisation (WHO), 2018). One in four adults, worldwide, are not active enough according to WHO. One way to reduce SB is to increase PA. WHO define PA as ‘any bodily movement produced by skeletal muscles that requires energy expenditure’ (World Health Organisation, 2018). There are significant health benefits from increasing PA to the WHO’s recommended 150 minutes of moderate to vigorous intensity PA per week, which include reducing the risk of NCDs (WHO, 2018). Research continues to provide evidence for reducing SB by increasing PA. Short, James and Plotnikoff (2013) observational research during 70 health outcome trials found regular PA in breast cancer survivors may be a protective factor against poor survival outcomes. This was regarding less of the stage they were at (Short, James, & Plotnikoff, 2013). Lang, McNeil, Tremblay, and Saunders (2015) found by breaking up time spent sitting by doing some light intensity activity, improved insulin sensitivity in overweight/obese adults by 25% (Lang et al., 2015). Reducing SB also helps improve mental health, wellbeing and cognitive function (Boulton, Horne, & Todd, 2017).
There have been many behaviour change interventions aimed at increasing PA. Paul et al., (2017, developed an intervention to increase PA which used a smartphone app. This counted the number of steps per day using the phones inbuilt accelerometer. BCTs from the BCT v1 taxonomy (Michie et al., 2013) were embedded into the app which included action planning and self-monitoring, along with several others such as goal setting, social support, social reward, feedback on behaviour and monitoring of behaviour by others without feedback. The app was designed to encourage participants to be more active by trying to reach 10,000 steps per day within a group of 4 who were represented as fish in the app. Each member of the group could see the others progress and non-incentive rewards were given with steps taken. Self-monitoring was established using the app counting the steps. Action planning was allowed for as the app contained a personal planner prompting users to make specific plans on how they were going to reach their target each day (Paul et al., 2017).
The results from this study show the mean step count increased from 9773 to 10773 steps. While this was a mean increase of 14% it was not statistically significant (p=0.077). However, participants reported they felt using the app had helped them feel better both physically and psychologically (Paul et al., 2017). The self-monitoring aspect of the app increased awareness of activity levels amongst participants, however they reported after a time they got used to knowing how many steps each activity took, therefore didn’t feel the need to check the app as often. While the app usage waned, participants learned to self-monitor without relying on technology telling them what they had done. Only one participant used the app’s personal planner, other participants used other ways of planning step target reaching activities into their day. While the app’s resources were not used to their full potential, participants awareness was raised to the point they were actively utilising other methods of self-monitoring and action planning.
Previous research using the CALO-RE taxonomy (Michie et al., 2011) provides evidence towards using a refined taxonomy of BCTs to develop and deliver interventions. This was another step-based PA intervention in people previous diagnosed with cancer and their carer’s. The intervention was underpinned in the Social Cognitive Theory (SCT) which argues self-efficacy directly affects behaviour and indirectly impacts outcome expectations and goals (Bandura, 1994). Participants were provided with sealed pedometers and self-monitoring was exercised by recording the length of time worn and occasions of other activities such as swimming and cycling on a concurrent log sheet. They were also asked to record thoughts before, during and after exercise to help with any barriers and improve commitment. Action planning was incorporated into the setting of SMART (specific, measurable, achievable, realistic, timely) goals (Stacey, James, Chapman, & Lubans, 2016). The results show the mean daily steps increased from a baseline of 8815 to 10849 at 8 weeks, dropping to 10307 at 20 weeks. These results were statistically significant (p<0.05). While action planning was incorporated within SMART, it was not clear from the study whether this contributed to the increase in PA. Likewise, this was also true of the self-monitoring aspect (Stacey et al., 2016). While it is possible these BCTs have made an impact separately or more effective used together along with other BCTs such as goal setting, this study has not clearly reported this.
A further review of the literature found BCTs are not always effective in interventions. Biddle et al., (2015) looked at reducing SB in young adults at risk of Type-2 Diabetes Mellitus (T2DM). A randomised controlled trial (RCT) was carried out over 12 months and included a 3-hour group-based structured education workshop, use of a self-monitoring tool and a follow up motivational telephone call. The intervention group failed to show any significant reduction in SB compared with the control group (Biddle et al., 2015). A similar study had previously reported contrasting results where PA was increased significantly in adults aged over 64, with the intervention group receiving a 3-hour educational workshop and self-monitoring with a pedometer (Yates, Davies, Gorley, Bull, & Khunti, 2009). This study was also underpinned in SCT utilizing self-efficacy, self-regulatory skills and targeting barriers (Yates, Davies, Gorely, Bull, & Khunti, 2008) . Biddle et al (2015) use of multiple BCTs was vague and not clearly reported, therefore it is not known whether others were used or whether the lack of BCTs impacted the outcome.
The taxonomies are not the only way to apply relevant BCTs to interventions. Persuasive technology can be used to spur behaviour change. This is described as ‘any interactive computing system designed to change people’s attitudes and behaviours’ (Bartlett, Webb, & Hawley, 2017). There are persuasive design principles which are organised to include techniques used to design persuasive systems which include primary task support, dialogue support, social support and credibility support (Oinas-kukkonen & Harjum, 2009). Using a web-based computer-tailored intervention, De Cocker, De Bourdeaudhuij, Cardon, and Vandelanotte, (2016) conducted a 3-group RCT (computer-tailored advice, generic advice and no advice control) to reduce sitting time in the workplace. The study was grounded in self-determination theory, theory of planned behaviour (TPB) and self-regulation theory, utilising concepts such as social support, self-efficacy and stages of change. Self-regulatory skills were supported with goal setting activates, self-monitoring tools, action planning, skill building, email reminders and booster sessions and chosen as effective in previous behaviour change interventions. These were not chosen in line with taxonomies, however the results were in favour of the computer-tailored advice group where there was a significant increase in activity compared to the generic and no advice groups using this method of behaviour change (De Cocker et al., 2016)
A review of the literature shows that BCTs are effective however, detailed reporting of this is necessary. The interventions need to be clearly underpinned in theory. These studies have been underpinned in theory whether it is TPB, SCT or other. The BCT v1 taxonomy is relatively new, however previous studies using taxonomies such as CALO-re have been effective in providing clear descriptions of BCTs. The current BCTv1 taxonomy provides a one size fits all tool which is a convenient replacement for the multiple taxonomies preceding it. A lot rests on how the BCTs are reported within the intervention, none of the studies using BCT taxonomies were distinct when it came to the impact the BCTs had on the intervention, either in isolation or collectively. Other interventions which use different ways of applying techniques such as web-based computer-tailoring and persuasive Currently, there is not any concrete evidence that one way is better than and another in successful behaviour change intervention, and reporting of effective BCTs needs to be clearer for subsequent interventions to be replicated successfully.
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