ROBIN MERMELSTEIN, PH.D.INSTITUTE FOR HEALTH RESEARCH AND POLICY AND PSYCHOLOGY DEPARTMENTUNIVERSITY OF ILLINOIS AT CHICAGO

Opportunities and Challenges in Using Ecological Momentary Assessments with Adolescent Smokers

SRNT Pre-Conference WorkshopMarch, 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 9th and 10th 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)

LocationRandom Smoke Decide Not Can’t Smoke

Bsl 24 Mo Bsl 24 Mo Bsl 24 Mo Bsl 24 MoHome 49.9 52.2 29.5 34.6 25.3 38.9 30.3 23.6School 28.0 22.1 7.3 4.4 18.3 13.0 45.2 48.9Friend house

5.8 6.7 14.6 9.7 15.7 16.3 3.3 2.2

In Car 4.6 6.5 13.8 28.5 9.0 9.2 5.9 6.2Outside-Public

1.8 1.2 19.6 7.9 11.9 6.7 2.3 4.0

Work 0.9 3.2 0.8 5.8 1.2 2.9 1.3 6.7

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 MoodRandom vs Post Smoke

6.4

6.5

6.6

6.7

6.8

6.9

7

7.1

7.2

Random Smoke

Mea

n P

osi

tive

Mo

od

These effects are within subjects, not between subjects. Controlling for proportionOf smoking events subject makes, positive mood significantly different, p<.0001, When making a smoking report, relative to random.

Negative MoodRandom vs Post Smoke

3.33.353.4

3.453.5

3.553.6

3.653.7

3.753.8

Random Smoke

These effects are within subjects, not between subjects. Controlling for proportionOf 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

0

0.5

1

1.5

2

2.5

PositiveMood

NegativeMood

Bet

wee

n S

ub

ject

s V

aria

nce

Random

Smoke

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