Within-Day Dynamics of Self-Efficacy and Smoking Attitudes: Ecological Momentary Assessment Study of Motivation to Quit and Forgoing Behavior.

Abstract

Motivation to quit smoking and decisions to smoke or forgo smoking vary throughout the day. However, little is known about how within-day patterns of psychological states such as self-efficacy and attitudes toward smoking relate to these determinants of smoking cessation attempts. Identifying these dynamic processes can inform the development of more precisely timed and tailored digital interventions. This study aimed to identify distinct within-day trajectories of self-efficacy for cutting down on cigarettes smoked and attitudes toward smoking, and to examine how these trajectories predicted end-of-day motivation to quit and same-day cigarette forgoing (ie, choosing not to smoke cigarettes that one would normally smoke). People who smoked at least 10 cigarettes a day at baseline (N=348, mean age 44.6, SD 12.1 years; n=212, 60.9% female) received smartphone surveys about 4-5 times a day after logging each cigarette, producing 15,614 surveys over 2561 days. Trajectories of self-efficacy and smoking attitudes were modeled at the person-day level using smooth functions, and 6 daily parameters of change (overall level, range of change, volatility, overall trend, acceleration of change, and trajectory shape [trend×acceleration]) were extracted. These parameters were then entered as predictors of (1) end-of-day motivation to quit (linear mixed models) and (2) whether participants forwent cigarettes that day (binomial generalized linear mixed models). Higher overall self-efficacy consistently predicted both greater end-of-day motivation and greater odds of forgoing. Upward trends and acceleration in self-efficacy further predicted greater odds of forgoing, indicating that days when confidence not only increased but did so quicker were most strongly associated with forgoing cigarettes that day. Less favorable attitudes toward smoking predicted greater motivation to quit and increased likelihood of forgoing cigarettes. Broader ranges of daily change in attitudes were linked with stronger motivation to quit and greater odds of forgoing, while more moment-to-moment volatility was associated with reduced odds of forgoing cigarettes that day. Dynamic features of self-efficacy and smoking attitudes, such as overall level, trend, and acceleration, were robust predictors of daily motivation to quit and cigarette forgoing. These findings highlight that the way self-efficacy and attitudes shift across the day is meaningful beyond their overall levels. Just-in-time adaptive interventions may be more effective if they monitor and respond to varying trajectory features rather than focusing on static states, supporting a shift toward dynamically aware intervention strategies in digital health.