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Objectives: This study aimed at identifying distinct quitting trajectories over 29 days after an unassisted smoking ces- sation attempt by ecological momentary assessment (EMA). In order to validate these trajectories we tested if they predict smoking frequency up to six months later. Methods: EMA via mobile phones was used to collect real time data on smoking (yes/no) after an unassisted quit attempt over 29 days. Smoking frequency one, three and six months after the quit attempt was assessed with online questionnaires. Latent class growth modeling was used to analyze the data of 230 self-quitters. Results: Four different quitting trajectories emerged: quitter (43.9%), late quitter (11.3%), returner (17%) and persistent smoker (27.8%). The quitting trajectories predicted smoking frequency one, three and six months after the quit attempt (all p < 0.001). Conclusions: Outcome after a smoking cessation attempt is better described by four distinct trajectories instead of a binary variable for abstinence or relapse. In line with the relapse model by Marlatt and Gordon, late quitter may have learned how to cope with lapses during one month after the quitting attempt. This group would have been allocated to the relapse group in traditional outcome studies.

The definition of relapse depends on the underlying disorder model [

The relapse model of Marlatt and Gordon has a different approach. In this model, relapse is seen as a transitional process (process-oriented view). The appearance of a lapse is viewed as a fork in the road and defined as the initial use of substance or a violation of a self-imposed rule. One of the two paths leads to the former problem level (relapse or total collapse), the other path is a continuation in the positive change [

To assess the relapse process multiple time points are needed. Several research groups published studies using the Ecological Momentary Assessment (EMA) to study the relapse process [10-13].

Distinct patterns of the relapse process were identified in two studies [14,15]. Furthermore, another study, which focused on the cigarette use reduction process found three distinct trajectories [

Based on the relapse model and prior evidence, we assume that more than two classes (abstinent/relapsed) exist among self-quitters in their relapse process. Our primary aim of this study is to identify trajectories of the relapse process in self-quitters. Furthermore we want to assess if these trajectories predict smoking frequency one, three and six months after the quit attempt.

Participants aged 20 - 40 years were recruited through newspapers, magazines, radio, the Internet, Facebook, our study homepage, mailing lists, and flyers. The smoking prevalence among 20 - 24-year-olds is the highest and declines afterwards [

The data collection for the EMA combined timeand event sampling strategies. Participants used their mobile phones and were prompted to complete a short questionnaire at three random times a day. In situations where participants experienced an intense urge to smoke or smoked they were directed to download a short questionnaire (event sampling). The ambulant measurement took place over 29 days.

A total of N = 269 study participants who made a quit attempt were included. Of these 269 self-quitters, only those 230 self-quitters who had answered text messages at least for 3 days during 29 days were included into the current analysis. This is the minimum number of measurement points to estimate linear growth curves. The average number of answered text messages per participant was 20.92 (range 3 to 29).

The majority of the participants were men (76%), with a mean age of 28.0 years (SD = 5.57). Most had a Swiss citizenship (91.7%), were not married (83.9%), had experienced a previous quit attempt (83.5%) and had a low to very low nicotine dependency score (M = 3.7, SD = 1.8) (67.9%).

At baseline (T1), study participants completed an online questionnaire containing sociodemographic variables including gender, age, nationality, marital status, and smoking history such as previous quit attempt, and nicotine dependency.

In the ecological momentary assessment after the quit attempt one variable assessed the outcome variable. Have you smoked since the last text message? The response was either yes or no. If participants smoked in one of the three measurement points, then participants were given the value smoking (yes) at that day.

One, three and six months after the smoking cessation attempt, participants reported the frequency of smoking in the last month. Response categories were 1 = “never”, 2 = “one to three times a month”, 3 = “one to six times a week”, and 4 = “daily”.

To identify discrete classes with similar patterns in smoking over 29 days, Latent Class Growth Model with binary outcome (LCGM) was used (Mplus 6.1.1.) [

To choose the number of classes, every model fit was evaluated using different criteria. The BootstrappedLikelihood Ratio Test (BLRT), the Bayesian Information Criterion (BIC), the Entropy, the average posterior probabilities for class membership, the greater drop from one to the other model in the model fit indices, and the number of participants in each class were taken into account.

Regression analysis was used to test whether class membership of the quitting trajectories predicts the frequency of smoking one, three and six months after the quit attempt.

Of the 230 participants 18.7% (n = 43) did not smoke over 29 days of EMA. More males (14.4%) than females (4.3%) did not smoke. The majority (61.3%) of the 230 participants did not smoke on the first day after quitting; these were 18.3% female and 43% male compared to 14.8% female and 23.9% male who smoked.

A two-, three-, fourand five-class model was tested. Based on the goodness-of-fit indices, the theoretical background, the interpretability and the size of the classes, we preferred the four-class model as the best fitting model. As presented in

The quitting trajectories did not differ in terms of gender, marital status, nationality, previous quit attempt, and nicotine dependency (all p > 0.10). Only age resulted in a significant effect on quitting trajectories, F(3, 226) = 5.57, p < 0.01. Quitters (M = 26.4, SD = 4.7) were younger than persistent smokers (M = 29.5, SD = 6.0) and late quitters (M = 29.9, SD = 6.2).

Regression analysis^{1} showed that the trajectories significantly predicted smoking frequency one month, (β = .83, p < 0.001) three (β = .64, p < 0.001) and six months (β = .48, p < 0.001) after the quit attempt. The returners smoked less frequently than persistent smokers (one month: β = –.57, p < 0.001; three month: β = –.25, p < 0.05; six month: β = –.21, p = 0.09). Late quitters smoked more than quitters (one month: t1: β = .56, p < 0.001; three month: β = .21, p < 0.05; six month: β = .16, p = 0.14).