Presentation on theme: "ROBIN MERMELSTEIN, PH.D. INSTITUTE FOR HEALTH RESEARCH AND POLICY AND PSYCHOLOGY DEPARTMENT UNIVERSITY OF ILLINOIS AT CHICAGO Opportunities and Challenges."— Presentation transcript:
ROBIN MERMELSTEIN, PH.D. INSTITUTE FOR HEALTH RESEARCH AND POLICY AND PSYCHOLOGY DEPARTMENT UNIVERSITY OF ILLINOIS AT CHICAGO Opportunities and Challenges in Using Ecological Momentary Assessments with Adolescent Smokers SRNT Pre-Conference Workshop March, 2012
Overview More than a decade of progress with EMA and adolescent smoking Advanced the field in the in-depth understanding of the phenomenon of adolescent smoking Many lessons learned about how to design and implement studies Examples of what we’ve learned from combined EMA and novel methods Focus on types of questions can address: concepts, not numbers Design and methodological considerations in collecting EMA data with adolescents New questions and challenges to address
EMA Study with Adolescents Data from large NCI-funded program project study of adolescents starting in 9 th and 10 th grade, oversampled for smoking experience Subset (461) participated in EMA study with four, week long measurement waves (baseline, 6-, 15-, and 24-months) Additional EMA waves at 5 and 6 years Participants responded to random prompts and event-recorded smoking and nonsmoking episodes (decisions not to smoke; can’t smoke times)
What We’ve Learned Context and subjective experience surrounding smoking among adolescents Teasing apart within- and between-subject effects to understand mood-smoking relationships Considering the question of mood regulation from the perspective of variability and stability Dynamic, reciprocal, longitudinal within-subject relationships between mood and smoking
Context of Adolescent Smoking Table 1. Selected Location of Events Over Time (% of responses) Location RandomSmokeDecide NotCan’t Smoke Bsl24 MoBsl24 MoBsl24 MoBsl24 Mo Home School Friend house In Car Outside- Public Work
Distinction Between Within-Person and Between-Person Variability Need to differentiate within-person causal mechanisms from between–person data Between-subjects question: Are individuals who have higher levels of negative affect more likely to smoke? Within-subjects questions: Do individuals smoke when their level of negative affect increases? Does smoking improve an adolescent’s mood? Whether smoking relieves negative affect is a within-person question Thus, analytic models need to disentangle between and within-person effects. EMA data well-suited for distinguishing between between- and within-person effects.
Mean Levels of Mood vs Variability Most research has focused on examining changes in mean levels of mood with smoking However, affect regulation inherently implies the modulation of variability in mood as well – but largely neglected Variation in mood and mood changes may be particularly important in helping to explain the development of tolerance Examining individual variability may enhance our ability to predict changes in smoking above and beyond what can be achieved by examining mean levels alone.
Individual Differences May be individual differences in the extent to which adolescents’ mood vary, and the extent to which they vary with smoking Identification of moderators may help in the prediction of escalation
Examining Mood, Variance, and Individual Differences Use EMA to examine teen smokers’ real time reports of moods during smoking and random times to examine: The degree to which mood changes with smoking Whether smoking level moderates any smoking-associated changes in mood Whether smoking level influences smoking-related changes in mood variation Do “heavier” smokers experience greater mood stabilization when smoking than do “lighter smokers”
Hypotheses Hedeker, Mermelstein, Berbaum, & Campbell (2009) examined the hypotheses that: Mood variability would decrease during smoking, compared to random times Mood variability would decrease as smoking level increased May be an early sign of the development of tolerance In essence, Does smoking serve as a mood stabilizer?
Analytic Approach Linear mixed model approach with relatively novel feature Modeling of the variances of the random subject effects, allowing for the influence of covariates Usually these are assumed to be homogenous across subjects Allows for inclusion of both within- and between-subject effects
Analytic Model Contrasts smoking events relative to random prompts Includes the subject’s proportion of events that were smoking events (relative to total) as covariate Within subject effects – how a subject’s mood differs between random and smoke events, controlling for proportion of smoke events Variances associated with random effects also modeled in terms of covariates Interaction term for smoke level
Positive Mood Random vs Post Smoke These effects are within subjects, not between subjects. Controlling for proportion Of smoking events subject makes, positive mood significantly different, p<.0001, When making a smoking report, relative to random.
Negative Mood Random vs Post Smoke These effects are within subjects, not between subjects. Controlling for proportion Of smoking events subject makes, negative mood significantly different, p<.0001, When making a smoking report, relative to random.
Between Subjects Mood Variation Simpler model rejected (one that assumes subject homogeneity) in favor of one that shows strong evidence of subject heterogeneity in mood changes between smoking and random events In other words, changes in mood associated with smoking vary considerably from subject to subject.
Changes in Mood Variation with Smoking Between subjects mood variation is reduced under smoke reports, relative to Random, for both positive and negative moods.
Smoking Level and Positive Mood Variance Smoking level significantly affects the variance associated with the random-smoke change in positive affect Estimate = -.337, p<.02 Increased smoking level is associated with a reduced degree of change in positive mood relative to random That is, positive mood response to smoking is significantly less in more frequent smokers
Smoking Level and Negative Mood Variance Similar effects as with positive mood Significant interaction effect Estimate = -.446, p <.004 Increased smoking level is associated with a diminished degree of change in negative mood for smoking events, relative to random
Summary Overall, following smoking, adolescents experienced higher positive mood and lower negative mood than they did at random, nonsmoking times. However, analyses also indicated an increased consistency of subjective mood responses as smoking experience increased and a diminishing of mood change as smoking level increased. Found strong evidence that between-subjects mood variance (for both positive and negative mood) was reduced following smoking, relative to random times Significant interaction with smoking level At low levels of smoking, there was considerable heterogeneity between subjects in mood responses from random to smoking times But responses to smoking became far more consistent (Stable) for adolescents who smoked more Results suggest an increased consistency in mood responses for adolescents who smoke more.
Examining Dynamic and Reciprocal Relationships Between Smoking and Mood Using longitudinal EMA data on smoking and mood in adolescents, address questions: Does negative affect predict smoking escalation among a sample of adolescents who are experimenting (intermittent smoking) with cigarette smoking? Does the escalation in smoking then lead to reductions in negative affect?
Analytic Approach Used location scale models (Hedeker, Mermelstein, & Demirtas, 2008) at each time point to obtain both means and estimated variances for positive and negative affect Derive estimates of smoking rate over time for each person 7 day rate; proportion of smoking events NLMIXED with 2-level random trend probit model run to obtain estimates of intercept and slope for smoking level over time Proportion smoke adjusts for relative amount of smoking events recorded compared to all events (random plus others) Mixed effects model approach used to examine effects of smoking rates on both the intercept and slope for negative affect over time Also examined with joint growth analysis of smoking and negative affect
Dynamic Changes in Mood and Smoking As smoking increases over time, does negative affect decrease? (slope-slope correlation) r = -.13, p =.06 YES, as smoking rate increases, overall level of daily negative affect decreases. Effect slightly stronger for girls (r = -. 17) than for boys (r = -.11)
Summary Among adolescents who are smoking at relatively low levels, daily levels of negative affect and smoking rates are dynamically linked High initial levels of negative affect are associated with increased smoking over a 2 year period As smoking increased over time, negative affect decreased No strong gender differences in relationship between smoking and change in negative affect
Methodological Considerations in EMA in Adolescents Design Measurement Data Quality and Handling Real time recordings Devices
Methodological Considerations in EMA and Adolescents Design Considerations Sample Age or developmental stage Composition in terms of smoking level; experience Representativeness for what? Sample Size and Power What matters? Between or within subject effects? Over time? Types of responses (smoking/random/”wanting to” /decisions not to smoke) – events and non events
Methodological Considerations Design Considerations Frequency of assessments What is “EMA” or other daily recordings Random vs event recordings Number of days Schedule of assessments within day Longitudinal considerations Patterns of smoking
Methodological Considerations Measurement Issues: What to Assess and What Goes on/into EMA Scale or item development Construct clarity and purity Full scales; established scales; item representativeness Longitudinal developmental issues and construct/measurement variance
Methodological Considerations Data Quality Training on device use Compliance enhancement and feedback Managing Data and Data Usage How will you use the questions? E.g., activity items
Methodological Considerations How “real time” should devices be? When is “real time” data collection enhanced by “real time” feedback/monitoring? Do you need to transmit data in real time? What other “real time” data are recorded? Issues of data transmission and data security
Methodological Considerations Device selection Programming Portability Ease of use and contexts of use Features to enhance responding
Future Considerations: Methods Conceptual and analytic challenges of handling missing data in EMA Time series and sequencing of events E.g., build up of background events, then trigger or precipitating event Flexible assessment schedules Dynamic scheduling depending on behavior Power analysis
Future Considerations: Interventions Ecological Momentary Interventions Are our analytic methods up to the challenge?
Acknowledgements Funding from the National Cancer Institute Grant #P01 CA98262 Collaborators Don Hedeker Kathi Diviak Siu Chi Wong John O’Keefe