WV Gets Active
The City of Wolverhampton (CWC) has one of the highest levels of inactivity in the country; 35.8% of residents do less than 30 minutes of exercise per week, compared to the national average of 27.1% (City of Wolverhampton Council, n.d.). For vulnerable populations and those with a higher body mass index (BMI) the COVID-19 pandemic resulted in increased levels of inactivity. These groups have been exercising less or leaving their house less, despite guidance during lockdowns encouraging residents to exercise for at least one hour per day (Office for Health Improvement and Disparities, 2022).
Additionally, the Covid-19 pandemic has had a more serious impact on people with higher body mass index (BMI’s; Sattar & Valabhji, 2021). This is due to individuals with higher BMIs having a lower lung capacity making it difficult for them to breathe which is exacerbated with COVID-19 (Sattar & Valabhji, 2021).
Therefore, there was a clear argument to address and tackle inactivity in Wolverhampton through a sustainable behaviour change intervention. The Council were successful in securing funding from the Local Government Association (LGA) Behavioural Insights Programme to deliver a physical activity (PA) intervention to support Wolverhampton residents in low socio-economic areas in line with behaviour change principles.
The City of Wolverhampton Council in partnership with Active Black Country commissioned The Behaviouralist following a competitive tender process.
Aim: To increase physical activity levels in Wolverhampton to at least 30 minutes a week for low socio-economic and inactive residents across the city, through a sustainable behaviour change intervention.
Scoping phase: Alongside a literature review, semi structured interviews were conducted with local stakeholders and subject experts to understand some of the barriers to PA in the target population. From these, several drivers and barriers to exercising were identified and used to inform the design of the programme, drawing on two behaviour change models (the COM-B model; Michie, Van Stralen & West, 2011, and the B=MAP model; Fogg, 2019).
Following on from the scoping phase, a randomised controlled trial (RCT) was designed to increase the activity levels of Wolverhampton residents. The intervention included a treatment group which consisted of a 6-week intervention. This intervention was designed to build habit and was delivered via an existing app on the market titled ‘MoveSpring’. MoveSpring is a fitness app that syncs with smartphones or wearable devices and tracks step counts and physical activity levels. It is highly customisable, enabling the team to design and deliver a programme that featured: daily and weekly step count targets; local Wolverhampton walks and content; motivational messages; performance feedback; a chat function and technical support. The app was also branded with WV Gets Active and partners logos.
The intervention was based on the following behavioural science principles using concepts such as goal setting, commitment, social comparison, and feedback.
- B=MAP (Fogg, 2019) – 'Tiny habits' stresses the importance of repetitive small steps going towards building a habit. This was central to using the 'Stick to it' challenge on the MoveSpring app. This challenge was designed to help build exercise habits through daily step targets that had incrementally increased across the duration of the programme.
- Construal-Level Theory of Psychological Distance (Trope & Liberman 2010) – shaped some of the support messages sent to participants on a weekly level. For example showing the positive impact exercise was having on their health and wellbeing.
- Theory of planned behaviour (Ajzen, 1991) – used to develop messaging, especially focusing on the subjective norms that have been shown to be important in physical activity. This was also used to help form and shape the construction of the groups and social norms built through the chat function, leaderboard and group composition.
- COM-B Model (Michie, Van Stralen & West, 2011) – The COM-B model was used as a diagnostic tool to understand how the project has performed. For example, drawing on the COM-B model (which states that Capability, Opportunity and Motivation need to be present for a Behaviour to happen), the findings were linked to it by investigating the barriers to participant behaviour change and opportunities to improve the programme (see Figure 1).
The control group were asked to download the MoveSpring app to capture their physical activity levels but were not given access to the programme content. The use of a control condition was adopted to allow for an evaluation of the impact of WV Gets Active using an RCT design.
Participants were asked to download the app, report their baseline physical activity levels and wellbeing scores through the Warwick, Edinburgh Mental Wellbeing Scale (WEMWBS, Tennant, Hiller, Fishwick, Platt, Jospeh, Weich & Stewart-Brown, 2007). WEMWBS was used to track participants’ subjective wellbeing across the programme. This scale focuses on the feelings and functioning of an individual’s wellbeing over the past two weeks.
If participants had fitness devices, they were able to sync their devices with the app. Participants who did not have fitness devices were still able to take part in the programme through manually inputting their step count onto the app. The app included nudges to remind participants to be active and increase their step counts. It also offered localised routes for participants.
Branding for the programme was created and developed with the name ‘WV Gets Active. The programme name was based on Council owned leisure centres – WV Active. A webpage was created (www.wolverhampton.gov.uk/wvgetsactive) and further communications consisted of recruiting participants through press releases, local radio, adult carers bulletin, community champions network, food banks and through faith leaders. Council social media accounts were used to promote the programme with Facebook posts being boosted to target the deprived wards of Wolverhampton. The majority of residents were recruited from social media, particularly Facebook.
Participants were recruited over two cohorts due to the high interest in the programme. A total of 593 participants signed up via the WV Gets Active website, 384 participants in cohort 1 and 209 participants in cohort 2. After the sign-up period closed, participants were screened out on the basis of not living in Wolverhampton (based on postcode) and doing more than one day of physical activity already. For cohort 1, this qualified 222 participants who were randomly assigned to either the control or treatment group. For cohort 2 after screening out there were 190 participants (see figure 2).
Social media was the most effective channel of recruitment. Most of the participants were recruited through social media from both cohort 1 (70.3%) and cohort 2 (88.2%). The cohorts recruited a heavy skew of female participants (over 85%) with the majority being in the 35-54 year old age bracket (60%).
Unfortunately, due to high participant dropout the programme could not robustly be evaluated using the proposed RCT design.
Power calculations highlighted a sample size of at least 144 was required, splitting the participants between Group 1 (94) and Group 2 - Control (50). For cohort 1, 222 participants were recruited onto the programme but only 88 participants went on to sync their data; of those 88, 37 went on to complete the 6-week programme. For cohort 2, 190 participants were recruited initially, with 99 syncing the app or manually uploading their data and 25 competing the 6-week programme.
Although the programme could not be robustly evaluated using the RCT design, significant insights and learnings were gained from the programme. As a result, a blended approach to evaluate the programme was utilised, combining the objective data captured through MoveSpring and qualitative data collected through surveys and focus group discussions.
For a small group of previously inactive residents, WV Gets Active supported them to make significant behaviour changes and to build a walking habit. This group reached their daily step target and increased their daily step count from 3,000-4,000 to over 7,000-8,000 by the end of the programme over both cohorts. Based on qualitative insights, the profile of those who completed the programme appear to be self-motivated participants who mainly engaged with the daily feedback and stuck to their goal of completing the programme.
There were no noticeable differences in outcomes based on where participants lived. WV Gets Active focused on recruiting participants from wards with high levels of social deprivation. There were no observed differences in terms of engagement or drop-out between participants who were recruited from these wards and those from other areas.
The type of smartphone and fitness tracker participants used was important to engage and form habits. Participants could sync the MoveSpring app to a range of fitness trackers through a smartphone or alternatively enter their step count data manually. The type and whether participants used a fitness tracker was important in terms of engagement, programme completion and step count level. Those who manually entered their data dropped out quickly. This is unsurprising as the manual entry, despite prompts, required additional effort on a daily basis. In addition, it was observed that certain types of smartphones (e.g., Huawei) prevented participants from taking part in the programme as they were unable to sync with the MoveSpring app. Conversely, participants who synced using a fitness device (Garmin, Fitbit or Withings) showed higher engagement, fewer dropouts and also a higher level of daily step count.
Further to this, the programme demonstrated that participants underestimated their physical activity levels. The data captured through MoveSpring enabled us to compare participants’ step count prior to the start of the programme with their stated self-reported levels of exercise. For Cohort 1, those who stated they had exercised for zero days in the previous week had a higher step than those who reported one day of exercise. It was evident that residents were already active although they reported that they weren’t.
Moreover, the results demonstrated that participants had more positive feelings and thoughts as the programme progressed. There was a clear increasing trend amongst participants who took the survey at baseline, after the programme and 6 weeks after the programme. Encouragingly, such effects were observed to persist (and even improve over time) for the few participants who took the survey 6 weeks after the programme had ended.
This was the first City wide app based physical activity intervention in Wolverhampton underpinned by behavioural science. A strength of the programme is that it targeted residents from deprived areas across the city and changed behaviour of a cohort who were not active.
The programme recruited participants with a strong skew towards women, mainly in the 35-54 age bracket and predominantly white. This is useful to know, as inactive women of this age are traditionally underrepresented in sport and have been highlighted as a priority group by Sport England (Sport England, 2016).
For a minority of participants, WV Gets Active succeeded in building an exercise habit. From a cursory review this group appeared to be relatively self-motivated and persevered through the programme, benefiting from the daily feedback provided through the app. This observation suggests a future line of research to understand the profile of those who dropped out versus those that persisted with the programme
However, limitations of the programme should be addressed to evaluate the success of the programme. The app used within the programme was an existing app already on the market. However, this app was not downloadable on some platforms such as Huawei making it impossible for participants with a Huawei smartphone to take part.
There are considerable learnings from the programme. It highlights the importance of bridging the intention gap by engaging sign ups immediately. The programme also highlighted the difficulty in forming new exercise habits and that creating lasting behaviour change requires motivation and commitment. The internal and external motivation has to outweigh the barriers otherwise people will drop out. There is no ‘one size fits all’ and some form of triaging whereby individuals are assigned to groups based on their motivational needs would have been beneficial. Fitness apps provide new opportunities and challenges which demonstrates how the pandemic has shifted the world to online resources. However, a number of considerations in welcoming this digital world includes digital exclusions with individuals not having access to a smartphone or the cost of data which reduces access.
Central to a behavioural science approach is using an evidence-based approach in evaluating the impact of interventions. This, however, can be challenging. WV Gets Active looked to evaluate the impact of the programme through a randomised controlled trial (RCT) as an evidence-based approach. With hindsight adopting a blended evaluation methodology from the start of the project may have been more appropriate. This would entail combining qualitative and quantitative approaches to capture learnings and insight. The reason to adopt this approach is because new approaches and methodologies were trialled for the first time. This includes the use of an app, alongside collecting objective physical activity data and recognising the dynamic and challenging context of delivering the project during COVID-19.
Further work on reducing intention-gap for residents who want to increase their physical activity levels is required. Work in showing how quick it is to increase levels i.e. gardening is recommended in order to support residents to be physically active daily. The findings from this project will feed into the Office of Health, Improvement and Disparities (OHID)’s Health Incentives pilot study which Wolverhampton is contributing to. This study will aim to increase physical activity and improve healthy eating through using an app and receiving rewards.
- Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
- Fogg, B. J. (2019). Tiny habits: The small changes that change everything. Eamon Dolan Books.
- Michie, S., Van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation science, 6(1), 1-12.
- Office for Health Improvement and Disparities (2022). Wider impacts of covid-19 on health (WICH) monitoring tool.
- City of Wolverhampton Council (n.d.). Public Health Annual Report (2018-19). Health in the City of Wolverhampton.
- Sattar, N., & Valabhji, J. (2021). Obesity as a risk factor for severe COVID-19: Summary of the best evidence and implications for health care. Current Obesity Reports, 10(3), 282-289.
- Sport England (2016). New strategy to tackle inactivity.
- Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., ... & Stewart-Brown, S. (2007). The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation. Health and Quality of life Outcomes, 5(1), 1-13.
- Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological review, 117(2), 440.