Do automated digital health behaviour change interventions have a positive effect on self-efficacy? A systematic review and meta-analysis.

Self-efficacy is an important determinant of health behaviour. Digital interventions are a potentially acceptable and cost-effective way of delivering programmes of health behaviour change at scale. Whether behaviour change interventions work to increase self-efficacy in this context is unknown. This systematic review and meta-analysis sought to identify whether automated digital interventions are associated with positive changes in self-efficacy amongst non-clinical populations for five major health behaviours, and which BCTs are associated with that change. A systematic literature search identified 20 studies (n = 5624) that assessed changes in self-efficacy and were included in a random-effects meta-analysis. Interventions targeted: healthy eating (k = 4), physical activity (k = 9), sexual behaviour (k = 3) and smoking (k = 4). No interventions targeting alcohol use were identified. Overall, interventions had a small, positive effect on self-efficacy (g¯=0.190,CI[0.078;0.303]). The effect of interventions on self-efficacy did not differ as a function of health behaviour type (Q-between = 7.3704, p = .061, df = 3). Inclusion of the BCT 'information about social and environmental consequences' had a small, negative effect on self-efficacy (Δg¯=-0.297,Q=7.072,p=.008). Whilst this review indicates that digital interventions can be used to change self-efficacy, which techniques work best in this context is not clear.

Factors influencing application of behavioural science evidence by public health decision-makers and practitioners, and implications for practice.

The National Institute of Health and Care Excellence (NICE) in the UK recommends behavioural science evidence underpins public health improvement services. In practice, level of implementation varies. This study is the first to explore factors affecting use of behaviour-specific evidence by public health decision-makers and practitioners for design and delivery of health improvement services. Twenty semi-structured interviews were conducted, along with a review of the commissioning cycle with public health decision-makers and practitioners across a range of health improvement fields (e.g. weight management). Interviews were informed and analysed using the Theoretical Domains Framework (TDF). Limited comprehension of behaviour change, challenges identifying specific behaviour change strategies and translating research into practice were prevalent. Local authority processes encouraged uptake of evidence to justify solutions as opposed to evidence-driven decision-making. Some decision-makers perceived research evidence may stifle innovation and overwhelm practitioners. Potential facilitators of research use included: ensuring uptake and implementation of evidence is compulsory within commissioning and its potential to show value for money. A strong belief in local evidence and achieving outcomes were identified as barriers to research evidence uptake. Social and environmental challenges included cultural, political, and workload pressures and journal article accessibility. Embedding behavioural science systematically into public health practice requires changes throughout the public health system; from priorities set by national public health leaders to the way in which relevant evidence is disseminated. Framing factors affecting use of behavioural science evidence using the TDF is helpful for identifying the range of interventions and support needed to affect change.