Evidencing the behaviour change model underpinning a personalised and tailored app for low back pain

Purpose

Self-management of low back pain is supported by national guidance (Savigny 2016, Foster et al, 2018, ). Digital modalities have gained traction as a potentially effective way of delivering information and education. It is vital to evaluate how individual components of an intervention work, and how this influences the outcome and effectiveness of an intervention (Aalbers et al., 2011), in order to understand what makes people interact and act on health information delivered. The aim of this project was to use the Behaviour Change Wheel (BCW), a widely used model of Behaviour Change (Michie et al., 2011), to illustrate how content and functionality of an app was aligned with behavioural theory and behaviour change techniques.

Approach

The intervention evaluated was getUBetter – lower back pain. The app supports self-management from an acute phase of an injury or episode through to long-term condition. It is prescribed to patients from a GP practice either by face to face or self-referral. The BCW was used in conjunction with the Capability Opportunity Motivation – Behaviour (COM-B) model, a tool for understanding behaviour in the context in which it occurs. Three main stages included: 1) understanding the behaviour the intervention is attempting to change, 2) identifying which elements of the intervention might bring about the desired change in behaviour, and 3) describing intervention content and how this can be optimally implemented. The Persuasive Systems Design (PSD) Framework (Oinas-Kukkonen, H., & Harjumaa, M. (2009) was used to describe the functionality and delivery of content.

Outcomes

Results: This evaluation enabled the proposed mechanisms of action and theoretical foundations of intervention to be comprehensively described, highlighting the key techniques utilised to empower users to effectively self-manage their condition. The overall aim of the getUbetter self-management app is to empower individuals to self-manage their low back pain. This is done by providing knowledge, developing skills, identifying and developing support strategies, and by using persuasive language to develop positive beliefs about their capability to self-manage their condition. The content mapped to 23 behaviour change techniques in the BCTTV1 taxonomy, including: information about health consequences, instruction on how to perform a behaviour, social comparison, reducing negative emotions, and verbal persuasion about capability. Mapping to the PSD framework illustrated the use of a number of persuasive design principles, including: tailoring, personalisation, simulation, and reminders.

Conclusion(s): This process enabled the proposed mechanisms of action and theoretical foundations of a digital health behaviour change intervention to be comprehensively described, highlighting the key techniques utilised to empower users to effectively self-manage their condition. These findings provide guidance for the on-going evaluation of effectiveness (including quality of engagement) of the intervention, and highlights areas which might be strengthened in future iterations.

Cost and savings

This research was commissioned by getUBetter. It is part of the SBRI healthcare program which is led by NHS England and supported by the Academic Health Science Networks.

Implications

The new standard framework for digital health technology includes the evaluation of behaviour change (NHS 2020). There is limited evidence about how people engage with, and act upon information delivered via digital behaviour change interventions, especially in tools that span acute to long-term conditions. We have demonstrated that it is possible to evaluate the behavioural mechanisms of this DHT.

Top three learning points

No data available 

Funding acknowledgements

SBRI

Additional notes

There is a published paper which links to this work.

Berry A, McClellan C, Wanless B, Walsh N. Evidencing the logic model of behaviour change underpinning a personalised and tailored app for the self-management of musculoskeletal conditions. JMIR Formative Research. 13/01/2022:32669 

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