Using SMS text messaging to assess moderators of smoking reduction: Validating a new tool for ecological measurement of health behaviors

Abstract

Objective: Understanding the psychological processes that contribute to smoking reduction will yield population health benefits. Negative mood may moderate smoking lapse during cessation, but this relationship has been difficult to measure in ongoing daily experience. We used a novel form of ecological momentary assessment to test a self-control model of negative mood and craving leading to smoking lapse.

Design: We validated short message service (SMS) text as a user-friendly and low-cost option for ecologically measuring real-time health behaviors. We sent text messages to cigarette smokers attempting to quit eight times daily for the first 21 days of cessation (N-obs = 3,811).

Main outcome measures: Approximately every two hours, we assessed cigarette count, mood, and cravings, and examined between- and within-day patterns and time-lagged relationships among these variables. Exhaled carbon monoxide was assessed pre- and posttreatment.

Results: Negative mood and craving predicted smoking two hours later, but craving mediated the mood-smoking relationship. Also, this mediation relationship predicted smoking over the next two, but not four, hours.

Conclusion: Results clarify conflicting previous findings on the relation between affect and smoking, validate a new low-cost and user-friendly method for collecting fine-grained health behavior assessments, and emphasize the importance of rapid, real-time measurement of smoking moderators.

(c) 2011 APA, all rights reserved

Figures

Figure 1

The natural-log relationship between EMA-calculated smoking on Day 21 and exhaled CO at the endpoint assessment approximately one week later. The logarithmic relationship is significant (F1,24= 8.00, p < .01), and corresponds to a significant linear correlation between log-transformed EMA-calculated smoking and CO (r = .50, p < .01).

Figure 2

Plot of mean smoking (in number of cigarettes) and mean cravings (from 0–4) at each time point across days. Error bars represent 95% confidence interval.

Figure 3

The relationship between prior mood (A) and prior cravings (B) on concurrent smoking, controlling for the quadratic trend within days, the linear effect between days, and baseline nicotine dependence. (A) Mood at time i − 1 predicting smoking at time i, controlling for time i mood, is a significant predictor of smoking (log-expectation γ = −.05, t(476) = 2.84, p < .01). A one-point decrease on the 5-point mood scale (i.e., more negative mood) related to 4.5% increase in smoking at the following time point. (B) Craving at time i − 1 predicting smoking at time i, controlling for time i craving, is a significant predictor of smoking (log-expectation γ = .20, t(476) = 8.78, p < .01). A one-point increase on the 5-point craving scale (i.e., higher cravings) related to 22% increase in smoking at the following time point.

Figure 4

Craving mediates the within-day relationship between negative mood and smoking. Negative mood relates to increased smoking at the following time point (Figure 4a), as does craving (Figure 4b). Negative mood is also associated with craving concurrently (γ = −.30, t(476) = 4.10, p < .01). When entered simultaneously, prior craving significantly relates to smoking (log-expectation γ = .23, t(476) = 9.47, p < .01) but prior mood does not (p ns), suggesting full mediation (Sobel’s z = 3.79, p < .01).