# Presentation: 113470

## Ecological Momentary Assessment in Substance Abuse Research

- James W. Hopperjhopper@mclean.harvard.edu

- Behavioral Psychopharmacology Research LaboratoryMcLean Hospital and Harvard Medical School* Statistical and Data Analysis CenterHarvard School of Public Health

- Zhaohui Su*, Alison R. Looby, Elizabeth T. Ryan,David M. Penetar, Christopher M. Palmer, Scott E. Lukas

## Overview

- Ecological Momentary Assessment (EMA)
- General EMA research issues
- Our ‘minimalist’ EMA study
- Minimalist EMA strengths and limitations

## Ecological Momentary Assessment

- Collection of near-real-time data, in real-world environments of daily life
- Standard methods
- Participants carry handheld computers running ‘electronic diary’ software
- Computer-initiated random assessments
- User-initiated event recordings (e.g., smoking a cigarette) with assessments

## Standard EMA Methodology

- Relatively extensive assessments of...
- Setting: Location, alone or with others, target behavior allowed in setting, etc.
- Activity: Interacting, working, leisure, phone; recent food and substance consumption; etc.
- Mood, emotions, urges, craving, etc.

- See Shiffman et al., 1996, *J Consult Clin Psychol, 64,* 366

## Insights Yielded  by EMA: Example

- Nicotine patch, smoking lapse and relapse
- After initial lapse, staying on patch associated with _decreased_ likelihood of full relapse
- Largest effect in study
- Complete _opposite_ of current instructions to patch users, i.e., stop patch if smoke
- Progression from lapse to relapse not amenable to retrospective assessment, never previously assessed in outcome studies

- Shiffman et al., 2006, *J Consult Clin Psychol, 64,* 276

## General EMA Research Issues

- Training participants
- Piloting methods
- Monitoring data quality and completeness
- Managing and reducing extremely large data sets
- Appropriate descriptive and inferential analyses
- Reporting findings effectively in tables and figures
- Best methodology papers
- Stone & Shiffman, 1994, *Ann Behav Med, 16,* 199.
- Schwartz & Stone, 1998, *Health Psychol, 17,* 6.
- Stone & Shiffman, 2002, *Ann Behav Med, 24*, 236.

## My Experience with EMA

- Study designed by Scott Lukas
- _My role_: Oversee final stages of data collection and cleaning; work with statistician; write paper

## EMA Study of Polysubstance Use by Regular Ecstasy Users

- _Background_
- Retrospective studies have found high rates of polysubstance use among ecstasy users
- Popular, media-promoted belief: Ecstasy use is highly associated with, even causes, use of other illicit drugs
- Antidote to limited methods and speculation: EMA, especially in party and ‘rave’ situations

## EMA Study of Polysubstance Use by Regular Ecstasy Users

- _Research Goals / Questions_
- Assess patterns of ecstasy use and its relationship to other drug use in daily lives of regular ecstasy users
- Compare illicit drug use on nights when ecstasy was used vs. similar nights on which ecstasy was not used
- Assess patterns of craving for ecstasy
- Over hours preceding and following its use
- Over days of weeks ecstasy was used vs. not used

## ‘Minimalist’ EMA Approach

- ActiWatch®-Score Device

## ‘Minimalist’ EMA Approach

- Wrist actigraphy device, ActiWatch®-Score
- User-initiated data entry (drug use events)
- Device-prompted data entry (craving)
- Simpler and less obtrusive than standard EMA
- _Intent_: More acceptable to polysubstance users, especially in drug use settings
- Major limitation: No data on situations, activities, mood, etc.

## Sample

- Convenience, via net and newspaper ads
- 22 (of 34 enrolled) participants completed protocol and used ecstasy at least once during study
- Age 19 to 38, mean = 22.8 (13.5); 13 males
- Heavy recreational drug users
- 55% current regular smokers

## EMA Training for Participants

- Demonstrations, verbal instructions, practice in lab, and written instructions
- Enter numeric code each time used a drug (given small laminated card with codes)
- One or more ecstasy pills
- Individual drink or cigarette
- Marijuana use ‘session,’ cocaine ‘line’ or ‘rock’
- Single dose of other drugs (e.g., sedative, opiate)
- Respond (within 10s) to audible prompt, with 0-9 rating of current craving
- Write down mistakes (e.g., entered ‘4’ when meant ‘5’)

## EMA Procedures

- Wear device 24/7 for 6 weeks (except in water)
- Enter every drug use event, respond to every audible prompt with rating of current craving
- Daily diaries of drug use, wake and sleep times
- Phone check-ins over first 2-3 days, to ensure compliance with protocol and answer questions
- Return to lab every ~7 days to provide ActiWatch and daily diary data

## Pilot Work

- _Key issue: Hear and respond to audible prompts_
- Pilot participants wore device 1-3 weeks
- Calibration of prompt volume: Loudest possible without awakening participants or sleep partners
- 85% response rate during waking hours, i.e., good compliance rate found in other EMA studies

## Monitoring EMA Data Qualityand Completeness

- Question participant
- Evidence of ActiWatch malfunction?
- Responding to auditory prompts when awake?
- Any incorrect ActiWatch entries?
- Inspect ActiWatch data files immediately after downloading (every ~7 days)

## Data Quality and Completeness

- Across all participants, on 90% of study days data was entered into device, it was worn the entire day and did not malfunction
- Number of days participants’ entered data
- 10 participants, exactly 42 days/6 weeks
- 7 participants, > 42 days (max 47)
- 4 participants, 31-41 days
- 1 participant, 17 days

## Data Quality and Completeness

- Mean rates of responding to craving prompt
- 58% ecstasy use nights
- 67% ecstasy non-use nights
- Groupings of response rates
- 21 participants with >20%
- 18 with >40% rates
- 10 with >66%

## Managing and Reducing the Data

- EMA data sets are _huge_
- Data reduction methods depend on...
- Research questions and study design
- What is being described, compared
- Sampling frequencies (e.g., of prompts)
- Temporal aspects of phenomena studied (e.g., pharmacokinetics of substance)
- Statistics used for data analyses

## Reducing the Data

- Describe patterns of ecstasy use: hour and day bins
- Compare ecstasy use vs. ecstasy non-use nights
- Needed to define ‘night.’ Chose 5pm to 9am based on inspection of EMA data
- Needed non-use nights valid for comparison. Used Fridays and Saturdays, i.e., typical ‘party’ nights
- Craving analyses: hour and day bins
- Dependent on sampling rate of prompts
- 3-hour bins, entire days

## Reducing the Data

- _Pre_: Before using ecstasy
- _During_: When ecstasy taken to intoxication plateau
- _After_: ‘Coming down’ phase
- Multiple ecstasy uses: ‘after’ indexed to last use
- Requirements of statistics
- Need equal lengths for pre, during, and after periods
- Choice: 4-hour duration, based on likely (a) delay to ecstasy intoxication onset (40-90 mins) and (b) duration of intoxication plateau (~3 hrs after onset)

## Statistical Analyses

- Even descriptive statistics may not be simple, as they are inseparable from data reduction choices
- Inferential statistics require _high level_ expertise with _esoteric_ statistical methods
- Preliminary analyses are essential, to determine distributions, covariance structures, etc.
- Key complex data feature: Nesting of variables within temporal periods within participants

## Descriptive Findings

## Descriptive Findings

## Descriptive Findings

## Statistical Analyses: Data Features

- Difficult characteristic of non-ecstasy drug use data
- High between-subjects variability in number of times ecstasy was used during study
- Requires nesting drug use events not only within ecstasy use night, but within _participant_ so that heavy users aren’t driving results
- But this solution means very esoteric stats...

## Esoteric Stats

- “Logit link function and binomial error distribution of multivariate responses (i.e., before, during, and after periods) were specified.”
- “(For these and all GEE analyses described below, multiple covariance structures, including unstructured, autoregressive and exchangeable, yielded identical findings.)”

## Effectively Reporting Findings

- EMA provides real-time, real-life data – so one’s presentation of findings should reflect that...
- Related to events and rhythms of daily life
- Easily and intuitively grasped
- However, depending on the research questions and statistics, presentations of findings may...
- Be complex and detailed
- Demand close attention of readers

## Inferential Findings

- No drug was significantly more likely to be used on nights that ecstasy was used than on comparison Friday and Saturday nights.
- After correction for multiple tests, a trend for cocaine use more likely on ecstasy use than ecstasy non-use nights.

## Slide 30

## Interpretive Summary of Prior Table

## Findings unchanged by when analysis limited to 18 with response rates to craving prompt >40% or 10 with rate >66%

## Our Minimalist EMA Approach:Limitations and Challenges

- Our Minimalist EMA Approach:Limitations and Challenges

- That drug-use event record isn’t present may not mean that drug-use event didn’t occur
- Inherent limitation of _all_ real-time EMA event recording methodologies
- Could result in incorrect classification of drug use nights as non-use nights, thus bias results
- No data on whether compliance varies with time relative to ecstasy use or time of night
- Trend for lower rate of responding to craving prompt on ecstasy use nights does suggest some bias

## Our Minimalist EMA Approach:Limitations and Challenges

- Our Minimalist EMA Approach:Limitations and Challenges

- Low response rates to audible prompts
- Much lower than pilot testing rates ~85%
- Loud environments associated with drug use?
- When debriefed participants said volume was issue, but may not have acknowledged ignoring prompts
- Difficulty responding in 10s window allowed by device? Especially when active and/or intoxicated?
- _Possible solutions_: Vibration prompt (battery life); compensate participants based on response rate
- Issue _not_ addressed here: Concordance between EMA and daily diary data

## Conclusions I

- EMA is a powerful research methodology
- Data from real-life situations and activities
- Little to no retrospective self-report bias
- Challenges common to all EMA research
- Effective training of participants
- Monitoring data quality and completeness
- Appropriately reducing huge databases
- Complex and esoteric statistical issues

## Conclusions II

- Benefits of ‘minimalist’ EMA
- Minimal demands on participants’ time
- Easy, unobtrusive wrist watch-like form factor
- Limitations and challenges of ‘minimalist’ EMA
- No EMA data on situations, activities, mood, etc.
- Ensuring prompts are noticed and responded to
- Future directions
- Greater data capacity per event record/ assessment
- Option to postpone responses to prompts

## Ecological Momentary Assessment in Substance Abuse Research

- James W. Hopperjhopper@mclean.harvard.edu

- Behavioral Psychopharmacology Research LaboratoryMcLean Hospital and Harvard Medical School* Statistical and Data Analysis CenterHarvard School of Public Health

- Zhaohui Su*, Alison R. Looby, Elizabeth T. Ryan,David M. Penetar, Christopher M. Palmer, Scott E. Lukas