Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This paper introduces time-varying effect models (TVEM) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describes unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and post- cessation period.

With technological advances, collection of intensive longitudinal data (ILD), such as ecological momentary assessments, becomes more widespread in prevention science. In ILD studies, researchers are often interested in the effects of time-varying covariates (TVCs) on a time-varying outcome to discover correlates and triggers of target behaviors (e.g., how momentary changes in affect relate to momentary smoking urges). Traditional analytical methods, however, impose important constraints, assuming a constant effect of the TVC on the outcome. In the current paper, we describe a time-varying effect model (TVEM) and its applications to data collected as part of a smoking-cessation study. Differentiating between groups of short-term successful quitters (N=207) and relapsers (N=40), we examine the effects of momentary negative affect and abstinence self-efficacy on the intensity of smoking urges in each subgroup in the 2 weeks following a quit attempt. Successful quitters demonstrated a rapid reduction in smoking urges over time, a gradual decoupling of the association between negative affect and smoking urges, and a consistently strong negative effect of self-efficacy on smoking urges. In comparison, relapsers exhibited a high level of smoking urges throughout the post-quit period, a time-varying and, generally, weak effect of self-efficacy on smoking urges, and a gradual reduction in the strength of the association between negative affect and smoking urges. Implications of these findings are discussed. The TVEM is made available to applied prevention researchers through a SAS macro.

Objective Cancer patients are advised to quit smoking to reduce treatment complications and future cancer risk. This study's main objective was to evaluate the efficacy of a novel, pre-surgical cessation intervention in newly diagnosed cancer patients scheduled for surgical hospitalization. Methods We conducted a parallel-arm, randomized controlled trial comparing the efficacy of our hospital-based, tobacco cessation “best practices” treatment model (BP; cessation counseling and nicotine replacement therapy) with BP enhanced by a behavioral tapering regimen (scheduled reduced smoking; BP+SRS) administered by a handheld computer before hospitalization for surgery. Cessation outcomes were short (hospital admission and three months) and longer-term (6 months) biochemically-verified smoking abstinence. We hypothesized that BP+SRS would be superior to BP alone. One hundred eighty-five smokers were enrolled. Results Overall, 7-day-point prevalence, confirmed abstinence rates at six months for BP alone (32%) and BP+SRS (32%) were high; however, no main effect of treatment was observed. Patients who were older and diagnosed with lung cancer were more likely to quit smoking. Conclusions Compared to best practices for treating tobacco dependence, a pre-surgical, scheduled reduced smoking intervention did not improve abstinence rates among newly diagnosed cancer patients.

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Purpose To quantify the prevalence of biomechanical deficit patterns associated with ACL injury risk and their inter-connections in a large cohort of female athletes during an unanticipated cutting task. Methods High school female athletes (N=721) performed an unanticipated cutting task in the biomechanics laboratory. Trunk and lower extremity 3D kinetics and kinematics were measured and entered into a latent profile analysis model. Results Approximately 40% of female athletes demonstrated no biomechanical deficits and were categorized into the low risk group. The second most prevalent profile (24%) demonstrated a combination of high quadriceps and leg dominance deficits and was labeled as quadriceps-leg. The third most prevalent profile (22%) demonstrated a combination of trunk and leg dominance deficits and to lesser extent ligament dominance deficits, and was labeled as trunk-leg-ligament. Finally, the fourth profile (14%) demonstrated very high ligament dominance deficits only and it was labeled as ligament dominance profile. Conclusions This is the first study to identify the most common biomechanical profiles associated with ACL injury during a cutting task in a large cohort of female athletes. Approximately 60% of female athletes belong to one of the high-risk profiles. With the exception of the ligament dominance profile, the current analysis indicates that risk profiles consist of a combination of biomechanical deficits. The findings provide important insight into the prevalence of biomechanical deficits and future directions for the development of injury prevention programs. The findings can be used to guide the development of quick and easy tests that accurately categorize athletes into one of the profiles and subsequently prescribe tailored injury prevention programs that will be more effective and efficient than the current generic ones.

Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see Tan, Shiyko, Li, Li, & Dierker, 2012) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, inter-individual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semi-parametric regression modeling, in order to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs.

This study examined whether the deÞnition and use of the word "bully" would result in lower self-reports of bullying behavior by providing students with one of three versions of a self-report measure with: (a) no reference to the word bully or its deÞnition, (b) the deÞnition of the word bully followed by use of the word in each item, or (c) the deÞnition of the word bully and no further mention of the word bully in the item stems. Participants (N = 114) completed surveys, and statistical comparisons examined the impact of the word bully on reports of bullying behavior. Analyses indicated that respondents provided with a deÞnition of and repeated exposure to the word bully reported signiÞcantly less bullying behavior than those who were not exposed to the word or its deÞnition. C 2009 Wiley Periodicals, Inc.

introduction: Ecological momentary assessments (EMA) are increasingly used in studies of smoking behavior. Through EMA, examination of lagged relationships is particularly useful for establishing a temporal order of events and for identifying types and timing of risk factors. The time-varying effect model (TVEM) handles EMA data challenges and addresses unique questions about the time-varying effects.