Mood Changes Associated with Smoking in Adolescents : An Application of a Mixed-Effects Location Scale Model for Longitudinal Ecological Momentary Assessment ( EMA ) Data

Mixed models are often used for analysis of Ecological Momentary Assessment (EMA) data. The error variance, which indicates the degree of within-subjects variation, is usually considered to be homogeneous across subjects. Modeling this variance can shed light on interesting hypotheses in psychological research. We describe how the within-subject variance can be modeled in terms of covariates to examine the stability/lability of mood within subjects, focusing on positive and negative mood and the degree to which these moods change as a function of smoking in adolescents. The data are drawn from an EMA study of adolescent smoking. Participants were 130 adolescents, either in 9th or 10th grade, who provided EMA mood reports following smoking events at two or more measurement waves. We focused on two mood outcomes: changes in the subject’s negative and positive affect, after minus before smoking, and several covariates: gender, wave, and smoking level. The results indicate that the variation in smoking-related mood changes were diminished as a person increased their smoking level. Thus, our analyses revealed an increased consistency of smoking-related mood changes as smoking level increased within a subject. Hedeker & Mermelstein 3 Introduction Modern data collection procedures, such as ecological momentary assessments (EMA; Smyth & Stone, 2003; Stone & Shiffman, 1994), experience sampling (Feldman Barrett & Barrett, 2001; Scollon, Kim-Prieto, & Diener, 2003), and diary methods (Bolger, Davis, & Rafaeli, 2001) yield relatively large numbers of subjects and observations per subject, and data from such designs are sometimes referred to as intensive longitudinal data (Walls & Schafer, 2006). Analysis of EMA data using mixed models (also known as multilevel or hierarchical linear models) is well-described by Schwartz and Stone (2007). Additionally, Moghaddam and Ferguson (2007) analyzed EMA data using mixed models to examine smoking-related changes in mood. These articles focus on the effects of covariates, either subject-varying or time-varying, on the EMA mean responses. Here we extend this approach by examining the degree to which covariates influence the within-subjects variation inherent in the EMA data. A few articles have described approaches for examining determinants of betweenand within-subjects variance from EMA studies. Penner, Shiffman, Paty, and Fritzsche (1994) used basic descriptive statistical methods to examine relations among within-subject variation in several mood variables. Jahng, Wood, and Trull (2008) described generalized mixed models to analyze within-subject differences in sequential EMA mood responses, specifically characterizing these as mean square successive differences. Hedeker, Mermelstein, Berbaum, and Campbell (2009) described a mixed model that included determinants of the betweensubjects variance, while Hedeker, Mermelstein, and Demirtas (2008) developed a model that additionally allowed determinants of the within-subjects variance plus a random subject scale effect. This model is referred to as a mixed-effects location scale model because subjects have both random location and scale effects. Models with random scale effects have been described in several articles where interest centers on variance modeling and/or accounting Hedeker & Mermelstein 4 for heterogeneous variation across individuals or clusters (Chinchilli, Esinhart, & Miller, 1995; Cleveland, Denby, & Liu, 2002; James, James, Venables, Dry, & Wiskich, 1994; Leckie, 2010; Lin, Raz, & Harlow, 1997; Myles, Price, Hunter, Day, & Duffy, 2003). In this chapter, we extend the mixed-effects location scale model to focus on the variation of mood change that is associated with smoking across measurement waves, and the degree to which subject and wave characteristics influence the variation in mood changes. Also, while Hedeker et al. (2008) only considered random subject intercepts for the one wave of EMA data, here we allow random subject time trends for the multiple waves of EMA data. We further consider a three-level model that treats observations nested within waves within subjects. To aid in making this class of models accessible, sample computer syntax is provided in the Appendix. Adolescent smoking, mood, and variability Many prominent models of cigarette smoking maintain that smoking is reinforcing, and that smoking can relieve negative affect (Kassel, Stround, & Paronis, 2003; Khantzian, 1997). Indeed, both adults and adolescents often claim that smoking is relaxing and reduces emotional distress (Chassin, Presson, Rose, & Sherman, 2007; Kassel & Hankin, 2006). However, although the relation between mood and smoking has received substantial empirical attention for adult smokers, much less is known about the acute changes in mood with smoking among adolescents. The present study, with its focus on real-time assessments of mood and smoking among adolescents, helps to shed light on this important topic. Although there is substantial consensus among both smokers and researchers that smoking helps to regulate affect, most of the empirical work investigating the smoking-mood relation has focused on the examination of changes in mean levels of mood with smoking. Surprisingly, although affect regulation inherently implies the modulation of variability in Hedeker & Mermelstein 5 mood as well, the examination of variability in mood and smoking has largely been neglected. As Hertzog and Nesselroade (2003) noted, describing mean levels of variables is not always adequate for examining key features of developmental change. Variation also conveys important information about the phenomenon of interest. In the case of adolescent smoking and the development of dependence, variation in mood and mood changes may help to explain more of the development of tolerance. Examining individual variability may enhance our ability to predict changes in smoking behavior above and beyond what can be achieved by examining mean information alone. Important, too, in the examination of mood and smoking, is the distinction between within-person and between-person variability. Kassel and colleagues (Kassel & Hankin, 2006; Kassel et al., 2003) have argued persuasively for the need to differentiate causal within-person mechanisms from between-person data. Whether smoking relieves negative affect is essentially a within-person question, and thus analytic models need to similarly differentiate between within-subject and between-subject effects. Much of the research on mood and smoking has also been limited to assessments of negative affect, while ignoring positive affect. This neglect is particularly problematic given the theoretical importance of differentiating between negative reinforcement models of smoking and positive reinforcement models, especially in the development of dependence among adolescents (Tiffany, Conklin, Shiffman, & Clayton, 2004). There is also considerable evidence to support the notion that positive and negative affect are distinct constructs, and not just opposite ends of a continuum (Watson & Tellegen, 1985; Watson, Wiese, Vaidya, & Tellegen, 1999). Thus, in the current study, we assessed both positive and negative affect. Finally, there may well be individual differences in the extent to which adolescents’ moods vary and whether these moods vary with smoking. Identifying potential moderator variables may also help in the prediction of smoking escalation among relatively novice Hedeker & Mermelstein 6 smokers. Indeed, in a previous paper (Hedeker et al., 2009) it was observed that adolescent smoking level was associated with variation in mood changes associated with smoking, diminishing this variance for both positive and negative affect. While this finding was noteworthy, it represents a between-subjects effect of smoking level, rather than addressing the point of whether variation in mood changes associated with smoking diminishes as a person increases their smoking level (a within-subjects effect). Here, we aim to assess this within-subjects effect by modeling the EMA data across several measurement waves as a subject changes their smoking level. We hypothesized that the between-subjects effect of smoking level, that we previously reported, would also be observed as a within-subjects effect. Namely, as adolescents increase their level of smoking across time, their variation in mood changes associated with smoking would diminish. Thus, following along the lines of the development of tolerance with dependence, we hypothesized that as smoking level or experience increased, mood responses to smoking would decrease, as would variability in overall mood. Methods Subjects The data are drawn from a natural history study of adolescent smoking. Participants included in this study were in 9th or 10th grade at baseline, 55.1% female, and reported on a screening questionnaire 6-8 weeks prior to baseline that they had smoked at least one cigarette in their lifetimes. The majority (57.6%) had smoked at least one cigarette in the past month at baseline. Written parental consent and student assent were required for participation. A total of 461 students completed the baseline measurement wave. Of these, 57% were white, 20% Hispanic, 16% black, and 7% of other race. The study utilized a multi-method approach to assess adolescents including self-report questionnaires, a week-long time/event sampling method via hand-held computers (EMA), Hedeker & Mermelstein 7 and detailed surveys. Adolescents carried the hand-held computers with them at all times during a data collection period of seven consecutive days and were trained both to respond to random prompts from the computers and to event record (initiate a data collection interview) in conjunction with smoking episodes. Random prompts and the self-initiated smoking records were mutually exclusive; no smoking occurred during random prompts. Questions concerned place, activity, companionship, mood, and o