Behaviour change techniques in cardiovascular disease smartphone apps to improve physical activity and sedentary behaviour: Systematic review and meta-regression

Abstract

Background: Smartphone apps are increasingly used to deliver physical activity and sedentary behaviour interventions for people with cardiovascular disease. However, the active components of these interventions which aim to change behaviours are unclear.

Aims: To identify behaviour change techniques used in smartphone app interventions for improving physical activity and sedentary behaviour in people with cardiovascular disease. Secondly, to investigate the association of the identified techniques on improving these behaviours.

Methods: Six databases (Medline, CINAHL Plus, Cochrane Library, SCOPUS, Sports Discus, EMBASE) were searched from 2007 to October 2020. Eligible studies used a smartphone app intervention for people with cardiovascular disease and reported a physical activity and/or sedentary behaviour outcome. The behaviour change techniques used within the apps for physical activity and/or sedentary behaviour were coded using the Behaviour Change Technique Taxonomy (v1). The association of behaviour change techniques on physical activity outcomes were explored through meta-regression.

Results: Forty behaviour change techniques were identified across the 19 included app-based interventions. Only two studies reported the behaviour change techniques used to target sedentary behaviour change. The most frequently used techniques for sedentary behaviour and physical activity were habit reversal and self-monitoring of behaviour respectively. In univariable analyses, action planning (β =0.42, 90%CrI 0.07-0.78) and graded tasks (β =0.33, 90%CrI -0.04-0.67) each had medium positive associations with increasing physical activity. Participants in interventions that used either self-monitoring outcome(s) of behaviour (i.e. outcomes other than physical activity) (β = - 0.47, 90%CrI -0.79--0.16), biofeedback (β = - 0.47, 90%CrI -0.81--0.15) and information about health consequences (β = - 0.42, 90%CrI -0.74--0.07) as behaviour change techniques, appeared to do less physical activity. In the multivariable model, these predictors were not clearly removed from zero.

Conclusion: The behaviour change techniques action planning and graded tasks are good candidates for causal testing in future experimental smartphone app designs.

Keywords: Action planning; Bayesian meta-analysis; Hypertension; Lifestyle modification; Stroke; mHealth.

Fig. 1

PRISMA flow diagram demonstrating the flow of studies through the review

Fig. 2

Univariable model distribution plot of behaviour change techniques. In this plot, the points are the estimated medians, the heavy bars are the 50% credible intervals and the light bars are the 90% credible intervals. The behaviour change techniques are listed based on behaviour change technique category

Fig. 3

Multivariable model distribution plot of behaviour change techniques. In this plot, the points are the estimated medians, the heavy bars are the 50% credible intervals and the light bars are the 90% credible intervals. The behaviour change techniques are listed based on behaviour change technique category