Evaluating intervention components in the SmokeFree Baby smartphone app to aid smoking cessation in pregnancy

Abstract

Background: Optimizing digital behaviour change interventions presents major methodological challenges. Factorial designs enable the evaluation of multiple intervention components simultaneously. This presentation will describe a factorial screening experiment aimed at identifying the optimum combination of intervention components in a smartphone app to help pregnant smokers stop smoking or cut down. Methods: The SmokeFree Baby app was designed around five experimental modules (identity change, stress management, health information, promoting use of face-to-face support, behavioural substitution), each in a ‘minimal’ and ‘intensive’ version. A full factorial design was used. Participants were randomly allocated to one of 32 (2x2x2x2x2) experimental groups. Participants were pregnant, age 18 and over, and smoked cigarettes daily or at least once a week. Self-reported smoking status was assessed at each login. The target sample size for the study is 400 (recruitment is ongoing), which will provide sufficient power to assess the additive effects of each module. Findings: Between October 2014 and February 2016, 286 participants were randomised. 72% (n=206) of participants selected complete cessation and 28% (n=80) cutting down as their behaviour change goal. Participants logged in 3.1 days on average (SD=22.2) after initiating behaviour change, with 39.4% (n=112) logged in at least once. The mean number of smokefree days was 1.73 (SD=17.75) with 16.2% of participants (n=46) registered at least one day. Discussion: Mobile technologies allow for a relatively straightforward implementation of multiple experimental conditions to evaluate the effects of intervention components using factorial designs. However, engagement with a digital intervention may be a potential barrier.