ArticlePDF AvailableLiterature Review

Ecolocial Momentary Assessment

February 2008
Annual Review of Clinical Psychology 4(1):1-32

DOI:10.1146/annurev.clinpsy.3.022806.091415

Source
PubMed

Authors:

Saul Shiffman

University of Pittsburgh

Arthur A. Stone

University of Southern California

Michael Hufford

Pinney Associates

Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

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Ecological Momentary

Assessment

Saul Shiffman,

Arthur A. Stone,

and Michael R. Hufford

Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania

15260; email: Shiffman@pitt.edu

Psychiatry and Behavioral Sciences Department, State University of New York,

Stony Brook, Stony Brook, New York 11794-8790; email: Arthur.stone@sunysb.edu

Cypress Bioscience, Inc., San Diego, California 92121;

email: mhufford@cypressbio.com

Annu. Rev. Clin. Psychol. 2008. 4:1–32

First published online as a Review in Advance on

November 28, 2007

The Annual Review of Clinical Psychology is online

at http://clinpsy.annualreviews.org

This article’s doi:

10.1146/annurev.clinpsy.3.022806.091415

 2008 by Annual Reviews.

1548-5943/08/0427-0001$20.00

Key Words

diary, experience sampling, real-time data capture

Abstract

Assessment in clinical psychology typically relies on global retro-

spective self-reports collected at research or clinic visits, which are

limited by recall bias and are not well suited to address how behav-

ior changes over time and across contexts. Ecological momentary

assessment (EMA) involves repeated sampling of subjects’ current

behaviors and experiences in real time, in subjects’ natural envi-

ronments. EMA aims to minimize recall bias, maximize ecological

validity, and allow study of microprocesses that inﬂuence behavior in

real-world contexts. EMA studies assess particular events in subjects’

lives or assess subjects at periodic intervals, often by random time

sampling, using technologies ranging from written diaries and tele-

phones to electronic diaries and physiological sensors. We discuss

the rationale for EMA, EMA designs, methodological and practical

issues, and comparisons of EMA and recall data. EMA holds unique

promise to advance the science and practice of clinical psychology

by shedding light on the dynamics of behavior in real-world settings.

Annu. Rev. Clin. Psychol. 2008.4:1-32. Downloaded from arjournals.annualreviews.org

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Contents

INTRODUCTION................. 2

WHAT IS ECOLOGICAL

MOMENTARY

ASSESSMENT? ................. 3

Momentary, Real-Time

Assessment.................... 4

Real-World Data ................. 4

Repeated Assessment ............. 4

ECOLOGICAL MOMENTARY

ASSESSMENT SCOPE

AND HISTORY ................. 5

Historical Roots .................. 6

AUTOBIOGRAPHICAL MEMORY

AND LIMITATIONS

OF RECALL .................... 7

THE USES OF ECOLOGICAL

MOMENTARY

ASSESSMENT .................. 8

Individual Differences ............ 8

Natural History .................. 8

Contextual Associations........... 9

Temporal Sequences .............. 9

DATA COMPARING

RETROSPECTIVE AND

REAL-TIME REPORTS......... 10

Comparison of Aggregated

Recall-Based Data

andEMA..................... 10

Correlations Between

Disaggregated EMA

and Recall..................... 11

Construct Validity of Real-Time

Versus Recall Assessments ..... 12

ECOLOGICAL MOMENTARY

ASSESSMENT DESIGNS

AND APPROACHES ............ 13

Event-Based Monitoring.......... 14

Time-Based Designs.............. 14

Combination Designs ............ 15

Use of Recall in EMA ............ 16

Sampling Versus Coverage

Strategies ..................... 16

Daily Diaries: A Special Case

ofEMA....................... 17

USE OF ECOLOGICAL

MOMENTARY ASSESSMENT

IN TREATMENT ............... 17

For Assessment................... 17

For Intervention ................. 18

MEASUREMENT

CONSIDERATIONS FOR

ECOLOGICAL MOMENTARY

ASSESSMENT

SELF-REPORTS ................ 19

METHODOLOGICAL

CONSIDERATIONS IN

ECOLOGICAL MOMENTARY

ASSESSMENT STUDIES ....... 20

Reactivity ........................ 20

Compliance ...................... 20

SPECIAL POPULATIONS ......... 22

ANALYSIS OF EMA DATA ......... 22

PRACTICAL ISSUES IN

ECOLOGICAL MOMENTARY

ASSESSMENT .................. 23

EMA Hardware and Software ..... 23

Other Practical Considerations.... 24

CONCLUSION .................... 24

INTRODUCTION

Clinical psychologists, along with behavioral,

social, and health scientists and practitioners

of every stripe, are interested in people’s ev-

eryday real-world behavior. This interest is

perhaps especially marked for clinical psy-

chologists because psychopathology and its

functional impairments are expressed in real-

world settings: No one is diagnosed or treated

because of how they behave in a laboratory

or consulting room. Yet, behavior is seldom

studied, assessed, or observed as it unfolds in

the real world. Instead, both clinicians and re-

searchers rely on global, summary, or retro-

spective self-reports of behavior: We ask pa-

tients how often they experience anxiety, on

2 Shiffman

Stone

Hufford

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average, how many panic attacks they had dur-

ing the past week or month, how intense their

pain generally is during the day, or how de-

pressed their mood has been. Moreover, the

emphasis on global assessments can keep us

from seeing and studying dynamic changes

in behavior over time and across situations,

from appreciating how behavior varies, and is

governed, by context, and from understand-

ing cascades of behavior, or interactions with

others or with our environments that play out

as a sequence of events over time. Thus, our

frequent reliance on global, retrospective re-

ports seriously limits our ability to accurately

characterize, understand, and change behav-

ior in real-world settings and misses the dy-

namics of life as it is lived, day-to-day, hour by

hour. In this review, we discuss an alternative

to static retrospective reports—Ecological

Momentary Assessment (EMA, Stone &

Shiffman 1994), which allows subjects and pa-

tients to report repeatedly on their experi-

ences in real-time, in real-world settings, over

time and across contexts.

WHAT IS ECOLOGICAL

MOMENTARY ASSESSMENT?

EMA is not a single research method; it en-

compasses a range of methods and method-

ological traditions, which we discuss below.

However, to provide a quick sense of how

EMA can be used to inform our understand-

ing of clinical psychology, and as a reference

for its paradigmatic characteristics, we de-

scribe a prototypical EMA study. Our exam-

ple is a study of cigarette smoking cessation

and relapse (see Shiffman 2005). Smoking is a

good target for EMA, as it involves a behav-

ior with clearly discernible small-scale events.

Tracking experience over time allows the re-

search to track the process of quitting and re-

lapsing over time. In this study, smokers who

had recently quit were asked to monitor their

cigarette craving, nicotine withdrawal symp-

toms, mood, and activities over several weeks,

using palm-top computers as electronic di-

aries. Since episodes of smoking (“lapses”)

Ecological

Momentary

Assessment (EMA):

methods using

repeated collection

of real-time data on

subjects’ behavior

and experience in

their natural

environments

were of key interest, subjects were asked to

record any episodes of smoking as they hap-

pened, and were then prompted to complete

brief assessments of their craving, mood, and

activities during the episode. On top of this,

about ﬁve times each day, at random times, the

electronic diaries also prompted, or “beeped,”

subjects and administered a similar assess-

ment. These assessments captured not only

the events associated with lapses, but the ﬂow

of mood, behavior, and events in the hours

and days before and after lapses.

These EMA data, captured using the

electronic diaries, allowed the investiga-

tors to answer a variety of questions (see

Shiffman 2005), including: How much crav-

ing do smokers experience when they quit

smoking? (Surprisingly little, except in dis-

crete episodes; Shiffman et al. 1997a.) Does

craving intensity vary with individual charac-

teristics, such as nicotine dependence? (Yes;

Shiffman et al. 2004.) Are day-to-day changes

in craving intensity associated with variations

in subsequent risk of smoking? (Yes, espe-

cially craving experienced ﬁrst thing in the

morning; Shiffman et al. 1997a.) Does experi-

encing emotional distress predispose smokers

to lapse? (Yes, but only acute distress, over a

period of hours; Shiffman & Waters 2004.)

Do situational factors affect lapse risk? (Yes,

especially emotional distress, others smok-

ing, and alcohol consumption; Shiffman et al.

1996b.) Do lapses diminish self-efﬁcacy? (Yes;

Shiffman et al. 1997b.)

These are just some of the kinds of ques-

tions and answers that can be examined using

EMA. They illustrate the potential for EMA

data to address questions about individual dif-

ferences, about particular episodes or situa-

tions, about the unfolding of processes over

time, and about the interactions among these

factors. In this way, they illustrate both the

richness and the complexity of EMA data.

This study illustrates several key fea-

tures common to EMA approaches (Stone &

Shiffman 1994, Stone et al. 2007a):



Data are collected in real-world envi-

ronments, as subjects go about their

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Autobiographical

memory: memory

processes involved in

recalling one’s own

experience

lives. This is the “ecological” aspect

of EMA and allows generalization to

the subjects’ real lives, i.e., ecological

validity.



Assessments focus on subjects’ current

state; for example, self-reports ask about

current feelings (or very recent ones),

rather than asking for recall or summary

over long periods. This is the “momen-

tary” aspect of EMA and aims to avoid

the error and bias associated with retro-

spection.



Moments are strategically selected for

assessment, whether based on particu-

lar features of interest (e.g., occasions

when subjects smoked), by random sam-

pling (to characterize subjects’ experi-

ences through representative sampling),

or by other sampling schemes.



Subjects complete multiple assessments

over time, providing a picture of how

their experiences and behavior varies

over time and across situations.

This study is only illustrative. The par-

ticular design, assessment schedule, assess-

ment content, and even technology will vary

across studies, depending on the behavior re-

searchers are studying, their aims, and their

theoretical frameworks. But what EMA stud-

ies have in common is the collection of as-

sessments of subjects’ current or recent states,

sampled repeatedly over time, in their natural

environments. With this illustration as back-

ground, we now move to highlighting the key

aspects of EMA methods.

Momentary, Real-Time Assessment

EMA methods developed in part in response

to the limitations of retrospective recall. Al-

though we all feel conﬁdent in our own mem-

ories, research on autobiographical memory

teaches us that memory can be quite un-

reliable (Bradburn et al. 1987, Tourangeau

2000). Our recollections are not just inac-

curate: They are often systematically biased.

That is, the errors made in recalling informa-

tion are not just random noise; rather, they

change the data in systematic ways. For ex-

ample, people are more likely to retrieve neg-

atively valenced information when they are in

a negative mood, thus introducing substantial

bias (Clark & Teasdale 1982). Because the dy-

namics of recall are so important to justifying

and structuring EMA methods, these issues

are reviewed in more detail below.

Real-World Data

If one is interested in how subjects feel at

work, there is no point asking them how they

feel in the research clinic—or at home, for

that matter. EMA recognizes that many be-

haviors and experiences can be affected by

context. Therefore, in order for assessed ex-

perience or behavior to be representative, it

has to be sampled in the contexts in which it

naturally occurs. Stated more simply, EMA

emphasizes ecologically valid observations.

Because EMA data are collected in sub-

jects’ natural environments—in real life—

they should be generalizable to real-world,

real-life experience.

Repeated Assessment

EMA studies involve many repeated mea-

sures, covering various extents of time with

varying intensity of assessment. Some imple-

ment a dense schedule of assessment, assessing

subjects as often as every 30 minutes (Shapiro

et al. 2002) over a period of days. At the other

extreme, subjects may be assessed less fre-

quently (e.g., daily) over periods as long as a

year ( Jamison et al. 2001). Some EMA stud-

ies are primarily focused on using these many

measures to characterize the subject’s “typi-

cal” state, aggregating over the repeated as-

sessments to better characterize the subject’s

average state across situations. More often,

EMA studies use the temporal resolution af-

forded by multiple measures to focus on the

within-subject changes in behavior and expe-

rience over time and across contexts, address-

ing how symptoms vary over time or how sit-

uational antecedents inﬂuence behavior.

4 Shiffman

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Hufford

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In summary, EMA aims to assess the ebb

and ﬂow of experience and behavior over time,

capturing life as it is lived, moment to mo-

ment, hour to hour, day to day, as a way

of faithfully characterizing individuals and of

capturing the dynamics of experience and be-

havior over time and across settings.

ECOLOGICAL MOMENTARY

ASSESSMENT SCOPE

AND HISTORY

EMA is sometimes mistakenly associated nar-

rowly with particular designs, such as the

use of randomly scheduled prompts to col-

lect assessments, or with certain technolo-

gies, such as the use of palm-top comput-

ers. However, EMA is not a single method,

much less a particular technology, but rather

a collection of methods that share the charac-

teristics described above. EMA includes tra-

ditional diaries, whether they use paper and

pencil (Green et al. 2006), palm-top com-

puters (Shiffman et al. 1996b), or telephones

(Perrine et al. 1995). It encompasses inter-

personal interaction diaries (Reis & Wheeler

1991), ambulatory physiological monitoring

(Kop et al. 2001), and collection of medi-

cation compliance data by instrumented pill

bottles (Byerly et al. 2005). The technologies

differ, the targets of assessment differ, the

schedules of data collection differ, but all of

these methods focus on collecting data repeat-

edly, in close to real time, and in subjects’ nat-

ural environments. EMA aims to bring these

diverse methods under a common framework

in order to deﬁne higher-order methodologi-

cal principles, identify commonalities across

methods, and thus provide a framework in

which researchers and clinicians can select the

appropriate methods for their particular re-

search studies or interventions.

Although the term “EMA” was only coined

in 1994 (Stone & Shiffman 1994), EMA re-

search, as deﬁned here, has been an active area

for decades. A search using the terms “diary,”

“experience sampling,” and “ecological mo-

mentary assessment” yielded over 3000 cita-

tions in the past 25 years on PsychLit, and

196,000 “hits” on Google

Scholar. This in-

dicates a substantial volume of research activ-

ity using EMA methods.

The emergence of EMA as an important

research method is also indicated by a num-

ber of books and reviews that have appeared

on the topic, including books on EMA meth-

ods and ﬁndings by Stone et al. (2007b),

Hektner et al. (2007), and Fahrenberg &

Myrtek (2001); applications to mental health

by DeVries (1992); and discussions of data

analysis by Walls & Schafer (2006). Other

reviews include Wheeler & Reis (1991) on

sampling schemes for EMA methods, Scol-

lon et al. (2003) on the promise and chal-

lenges of EMA methods, Bolger et al. (2003)

on various uses of diary methods, and Pi-

asecki et al. (2007) on applications of EMA

methods to clinical treatment. A recent spe-

cial issue of the Journal of Personality (Tennen

et al. 2005) described several research pro-

grams applying EMA methods to issues rel-

evant to personality, clinical, and health psy-

chology, and several review papers discussed

their application to particular domains: Thiele

et al. (2002) reviewed the application of EMA

methods to clinical psychology, Moskowitz &

Young (2006) discussed applications to psy-

chopharmacology, and Beal & Weiss (2003) to

industrial psychology. The reader is referred

to these sources for more detail than can be

included here.

Finally, EMA methods are being used to

study a very wide range of behaviors, expe-

riences, and conditions. In a review of pub-

lished diary studies, Thiele and colleagues

(2002) found large groups of studies on pain,

mood, anxiety and anxiety disorders, eating,

sleep, gastrointestinal disorders, and alcohol

consumption. But even this is an incom-

plete list: EMA studies include studies of de-

pression, social support, initiate relationships,

diet, work activity and satisfaction, sexual

behavior, psychotherapy, drug use, allergies,

psychological stress, adverse effects of medi-

cations, self-esteem, and asthma, to name just

a few. Clinical disorders studied with EMA

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include addictive disorders, eating disorders,

anxiety disorders, depression, bipolar disor-

der, schizophrenia, sexual dysfunction, and

ADHD—in other words, the full range of

psychopathology. Beyond clinical syndromes

and symptoms, EMA is also widely used to

study basic adaptation processes relevant to

adjustment, such as coping, self-esteem, and

social support, as well as behaviors central to

health psychology and behavioral medicine,

such as coping with illness and treatment,

medication compliance, exercise, relaxation,

and safe sexual practices, among other behav-

iors. In sum, EMA methods are used in most

domains of concern to clinical psychology.

Historical Roots

EMA draws together several historical tradi-

tions, including diaries, self-monitoring, ex-

perience sampling, ambulatory monitoring,

and others. The oldest is the use of written

diaries for research (Verbrugge 1980), which

was systematically deployed in clinical re-

search in the 1940s. Self-monitoring of par-

ticular behaviors or experiences (Korotitsch

& Nelson-Gray 1999) has a long history in

research and particularly in behavioral treat-

ment. This includes simple counts of clinically

relevant events, but also collection of data

about their antecedents and contexts, which

provided data for “naturalistic functional anal-

ysis” (Schlundt et al. 1985). Related methods

included self-monitoring of particular targets;

a prominent example is the Rochester Inter-

action Record (Reis & Wheeler 1991), which

subjects used to record every social interac-

tion. Self-monitoring approaches often aimed

to capture all relevant events, and as a result

did not focus on sampling issues or collect data

outside of target events.

Other historical streams that fed into the

development of EMA focused on broad de-

scriptions of subjects’ behavior. One came

from the focus of the Kansas School of Eco-

logical Research (Barker 1978) on continuous

observation of behavior through the day in the

natural environment. Another derived from

the ethnographic method of describing indi-

viduals’ allocation of time, often to describe

differences among societies (Szalai 1966). A

variant of this within schools of manage-

ment and business examined the behavior of

workers in the workplace to understand how

they used their time and what activities they

engaged in.

More central to the development of mod-

ern EMA methods was the development

by Czikszentmihalyi and colleagues (DeVries

1992, Hektner et al. 2007) of the Experience

Sampling Method, demonstrating the inno-

vation of randomly sampling experience, ini-

tially using pagers to “beep” people at ran-

dom times to prompt them to complete diary

cards reporting their activity, mood, and/or

thoughts. The development of electronic di-

aries, based upon the emerging technology of

handheld computers, opened up new oppor-

tunities for more complex and sophisticated

EMA protocols that incorporated, combined,

and expanded upon the various approaches to

collecting EMA self-report data.

Enabled by technological developments,

ambulatory monitoring of cardiovascular

function, which became possible with the de-

velopment of wearable cardiac monitors, has

been used for several decades as a means of

understanding the link between experience

and cardiovascular health (Turner et al. 1994).

Ambulatory monitoring did not rely on self-

report (although it was often accompanied by

written diaries) and also enabled continuous

or near-continuous recording. Recent devel-

opments have expanded physiological moni-

toring to other parameters, such as galvanic

skin response, temperature, motion, and oth-

ers (Wilhelm et al. 2003). Often little atten-

tion was paid to sampling, in part because the

monitors could collect data almost continu-

ously (e.g., actigraph or heart-rate recording)

or at very high and stable frequencies. Some

physiological monitoring devices (e.g., mon-

itoring of blood-glucose, pulmonary func-

tion) require subjects to actively make pe-

riodic assessments, and thus raise many of

the sampling issues that arise with self-report.

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Technological developments have also en-

abled automated EMA assessment of behav-

iors (e.g., pill taking; Cramer et al. 1989) and

even of the physical environment (e.g., air

sampling; Saito et al. 2005).

These diverse traditions developed in dif-

ferent disciplinary contexts, often with dis-

tinct assessment targets. For example, the

Experience Sampling Method focused on sub-

jective states (Hektner et al. 2007 refer to it

as systematic phenomenology), whereas self-

monitoring focused on behaviors, and ambu-

latory monitoring focused on physiological

parameters. Moreover, because of their par-

ticular disciplinary histories and content foci,

these methods have been discussed in differ-

ent literatures, and investigators in one tradi-

tion often seem unaware of the others. Yet,

these methods share many goals, concerns,

and approaches. The object of EMA, which

attempts to encompass all of them, is to unify

these diverse approaches under a common

methodological framework.

AUTOBIOGRAPHICAL MEMORY

AND LIMITATIONS OF RECALL

A major motivation for EMA is to avoid the

pitfalls and limitations of reliance on autobio-

graphical memory. In this section, we expand

on the issues and concerns regarding autobi-

ographical memory, both to explain the ratio-

nale for EMA and to provide an essential foun-

dation to considerations of the uses of EMA

and speciﬁc EMA designs.

Research on autobiographical memory

(Bradburn et al. 1987) indicates that recall is

not just subject to random error but also is

fraught with systematic bias, which can dis-

tort recall even after relatively short inter-

vals. Modern cognitive science considers that

much of what we “recall” is actually recon-

struction, pieced together from fragmentary

inputs through the use of various heuristic

strategies. Many experiences are not retained

in memory, so often the information we are

asked to provide simply is not available for

direct retrieval. Experiences are particularly

likely to be encoded and retrieved if they are

emotionally salient or are unique; routine ex-

perience is less likely to be encoded and harder

to retrieve. Moreover, the process of retrieval

itself is subject to bias because the accessi-

bility of particular content in memory varies

with the subject’s mental state at the time of

retrieval.

Importantly, research inquiries usually ask

subjects not to just retrieve but also to aggre-

gate and summarize their experiences (e.g.,

“How intense was the pain, on average, to-

day?”). When trying to answer such questions,

subjects do not recall, enumerate, and then ag-

gregate their experience over time (Bradburn

et al. 1987). Rather, they use a variety of

heuristics to estimate the answer. The use of

cognitive heuristics and the processes of re-

trieval account for much of the bias in recall

data.

A key example of a biasing cognitive

heuristic is the “availability heuristic.” In its

original form, as described by Tversky &

Kahneman (1973), this refers to how peo-

ple make judgments about the frequency of

events. When deciding how often an event

(say, a ﬁght with a spouse) occurs, a person

tries to retrieve an example. If an example is

easy to think of (i.e., it is easily “available” in

memory), the event is considered to be fre-

quent. One can see how this heuristic would

work much of the time—rare events should be

harder to “ﬁnd” in memory. But one can also

see how biased it can be: if a ﬁght was partic-

ularly memorable because it was intense, if it

is easily recalled because it was recent, or be-

cause one was recently reminded of it (say, by

a previous question in the assessment), then

the frequency of ﬁghts will be overestimated.

Importantly, the process of memory re-

trieval is itself subject to bias by the person’s

context and mental state at the time of re-

call. It has been shown, for example, that sub-

jects in a negative mood more easily recall

negative information than positive (Kihlstrom

et al. 2000). Similarly, subjects who are in pain

ﬁnd it easy to remember past pain, but harder

to recall pain-free states; accordingly, it’s been

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Recall bias:

systematic errors in

data values (as

distinct from random

error) introduced by

processes of

autobiographical

memory

shown that subjects who are in pain at the time

of recall will overestimate their past pain (Eich

et al. 1985). This has profound implications

for research because it suggests that the sub-

ject’s state and situation at time of reporting

will inﬂuence what is reported.

A dramatic demonstration of the biases in

recall—and an indication of how quickly these

biases can set in—was reported by Redelmeier

et al. (2003). Summary ratings of pain by sub-

jects who had undergone a colonoscopy 20 to

30 minutes earlier were found to be unduly

inﬂuenced by the peak level of pain (presum-

ably because it was most salient) and the pain

intensity at the end of the procedure (most

recent). In other words, recall did not accu-

rately represent the average pain over the in-

terval, because it was based on a few of the

most memorable moments, essentially ignor-

ing most of the experience. This shows the

potential for bias even over short intervals.

Besides being distorted by the operation of

heuristic recall strategies, memory is also in-

ﬂuenced by what we know and believe rather

than actually recall. People unconsciously re-

organize their “memories” to make them ﬁt

a coherent script or theory of events or to

reconcile events with what transpired subse-

quently (Ross 1989). These biases are particu-

larly pernicious because they tend to produce

recalled patterns that are coherent and that

may conform to theoretical predictions, even

if they are false.

It is important to understand that these bi-

asing processes operate involuntarily and un-

consciously. They do not represent distortion

by uncooperative or defensive subjects—this

is simply the way memory operates. Research

inquiries often maximize the potential for bias

by asking about routine events, asking sub-

jects to summarize their experience, and so-

liciting recall in unusual settings (i.e., research

laboratories) and contexts (e.g., after being

asked other questions) that can bias recall.

Thus, heuristics that serve well enough to ad-

dress the demands placed on autobiographical

memory in everyday life can break down when

called upon to produce accurate research data.

THE USES OF ECOLOGICAL

MOMENTARY ASSESSMENT

EMA data are collected for a variety of pur-

poses (Bolger et al. 2003). We categorize these

into four classes: (a) characterizing individ-

ual differences, (b) describing natural history,

documenting temporal sequences. We illus-

trate each with an example.

Individual Differences

When used to characterize individual differ-

ences, EMA data are aggregated to obtain a

measure of the subject that is collapsed across

time (i.e., across multiple EMA measures); for

example, the average intensity of pain experi-

enced by a pain patient. As an extension of

this, aggregated EMA data might be used to

quantify subjects’ characteristics at two dif-

ferent time points; e.g., pain before and after

treatment administration. As estimates of sub-

ject characteristics, aggregated EMA data are

expected to provide assessments of individuals

that are more reliable (because of aggregation)

and more valid (because of avoidance of recall

bias, representative sampling, and ecological

validity). Of course, if the variable is very sta-

ble over time, if recall bias were not present,

and if contextual factors did not inﬂuence the

variable, then there would be no advantage in

using EMA.

Natural History

To describe natural history, EMA measures

are analyzed for trends over time. In this case,

the within-subject variation over time itself is

the focus, and time is the independent vari-

able, the X-axis in a graphical representation

of the data. For example, McCarthy et al.

(2006) documented the trajectories of various

withdrawal symptoms that smokers experi-

enced after quitting. The EMA data demon-

strated that some symptoms peaked immedi-

ately when smokers quit and then decreased

over time, while others increased and per-

sisted, and still others increased only gradually

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over time. These patterns contradicted widely

held notions about the course of the with-

drawal syndrome and were associated with

differences in treatment outcome. Basic de-

scriptive information about the natural his-

tory of symptoms over time can often be an

important foundation for understanding clin-

ical disorders and outcomes.

Contextual Associations

Studies that examine contextual associations

look at the association or interaction between

two (or more) phenomena that co-occur in

time. Analyses of contextual associations are

often cross-sectional, even when data are col-

lected longitudinally, in that they examine

the co-occurrence of events or experiences,

not their sequence. In these analyses, time

is not explicitly represented—it is more of a

stage against which the events of interest play

out. For example, Myin-Germeys et al. (2001)

examined emotions accompanying stressful

events as a way to test a diathesis-stress model

of schizophrenia. They postulated that vul-

nerability to schizophrenia would be reﬂected

in excess emotional responses accompanying

stress. Schizophrenics, their ﬁrst-degree rel-

atives (who are genetically vulnerable), and

normal controls were assessed 10 times daily

about stressful events and mood. An exam-

ination of individual differences in average

mood showed that the schizophrenics re-

ported more negative affect and more stress-

ful events, whereas vulnerable individuals and

normal controls did not differ. But a look at

stressor-mood associations revealed that the

ﬁrst-degree relatives reacted more strongly

than did controls. Thus, examination of the

association between stressors and mood at

particular moments was key to understanding

what vulnerabilities might be conferred by a

genetic predisposition to schizophrenia.

Understanding the momentary cross-

sectional associations between different as-

pects of experience has also been important

for foundational studies of the structure of

behavior and experience; for example, data

on the covariation of momentary emotions

have been central in the debate about whether

positive and negative emotions are polar op-

posites or are independent dimensions and

can be experienced simultaneously. Feldman-

Barrett & Russell (1998) used EMA data to

address the argument that although one could

be both happy and distressed over some in-

terval of time, in a particular moment, one

could be either happy or distressed but not

both.

Although most designs examine associa-

tions between different variables within the

same person, an interesting variation consid-

ers how one person in a relationship affects the

other (Bolger & Laurenceau 2005). For exam-

ple, Larson & Richards (1994) asked members

of families to track their experience in parallel

and examined how the mood of each affected

the other. They found, for example, that a hus-

band’s mood when he comes home from work

signiﬁcantly inﬂuences his wife’s mood, but

not vice versa.

Temporal Sequences

Finally, the longitudinal nature of EMA data is

used to explicitly examine temporal sequences

of events or experiences, to document an-

tecedents or consequences of events or be-

haviors, or to study cascades of events. In

these analyses, unlike those above, the order of

events or assessments is explicitly considered

and is a key focus. The previously cited study

of quitting smoking (Shiffman et al. 1997b) as-

sessed smokers’ affect and self-efﬁcacy before

and after lapses to smoking, and their effects

on subsequent progression toward relapse, to

test Marlatt’s theory (Curry et al. 1987) that

the psychological response to lapses is what

drives progression toward relapse. Compar-

ing assessments obtained before the lapse and

afterward conﬁrmed the theory’s hypothesis

that lapses would result in increased negative

affect and decreased self-efﬁcacy (Shiffman

et al. 1997b). Continued EMA monitoring,

however, contradicted the theory’s prediction

that increases in negative affect and decreases

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in self-efﬁcacy would predict the risk of

subsequent progression to another lapse or

relapse (Shiffman et al. 1996a). Importantly,

retrospective analyses had appeared to show

a relationship between initial responses to a

lapse and progression to relapse (Curry et al.

1987), but later comparisons with EMA data

(Shiffman et al. 1997c) showed that retrospec-

tive reports of lapse episodes were inaccurate

and biased: Subjects recalled their mood as

worse than it actually had been, and those who

had returned to smoking at the time of re-

call exaggerated how demoralizing the initial

lapse had been. Thus, prospective assessments

of the ﬂow of behavior and experience, and of

the antecedents and consequences of events,

can enable a more valid and more detailed un-

derstanding of behavior.

These examples illustrate the use of EMA

data to evaluate hypotheses regarding the

dynamic interactions among processes over

time. Data provided by EMA studies may be

likened to a movie, in which dynamic rela-

tionships emerge over time, whereas global or

recall measures are analogous to a still pho-

tograph, a single static snapshot of time. By

providing temporal resolution, EMA meth-

ods allow investigators to examine sequences

of events and experiences and enable them to

describe and analyze cascades of events and

interactions between events that shape behav-

ior over periods of minutes, hours, or days.

Insight into microprocesses—the interplay or

cascade of cognitive, affective, and behavioral

variables over short intervals of time—is par-

ticularly important because many theories of

psychopathology and treatment focus on how

these processes unfold over time. Evaluation

of microprocesses also facilitates development

of interventions because an understanding of

how affect, cognition, and behavior interact

and unfold over time helps identify leverage

points for timely, and at least potentially more

efﬁcacious, clinical interventions. The ability

to shed light on dynamic processes and situa-

tional inﬂuences is potentially the most criti-

cal contribution of EMA methods to clinical

psychology.

DATA COMPARING

RETROSPECTIVE AND

REAL-TIME REPORTS

Research suggests that recall measures, and

especially summary measures, are often biased

due to the use of mental heuristics to recall

information. This implies that there will be

discrepancies between EMA-based and recall-

based assessments of the same period. Rel-

atively few studies have examined such dis-

crepancies. In discussing this literature, we

distinguish comparison with aggregated EMA

measures from comparisons of disaggregated

time-speciﬁc estimates.

Comparison of Aggregated

Recall-Based Data and EMA

A number of studies have evaluated the rela-

tionship between EMA- and recall-based as-

sessments, comparing EMA assessments av-

eraged over some interval with recall-based

measures for the same interval. In some in-

stances, the two methods yield similar esti-

mates (Shrier et al. 2005), and in some other

cases, recall methods produce lower estimates

of intensity or frequency than do EMA meth-

ods (Carney et al. 1998, Litt et al. 2000). How-

ever, in many domains, recall-based assess-

ments tend to yield higher estimated levels

than diary ratings of the same target events:

That is, symptoms tend to be described as

more frequent, more intense, and longer last-

ing, sometimes dramatically so (Broderick

et al. 2006, Houtveen & Oei 2007, Shiffman

2007, Shiffman et al. 2006; see review in Van

den Brink et al. 2001). Behavior frequency is

also often overestimated in recall (Homma

et al. 2002, Shiffman & Paty 2003). This

phenomenon may be the result of the un-

due inﬂuence of more salient experiences in

recall: Intense pain is more salient than no

pain, headaches are more salient than non-

headaches, and so on, leading recall data to

overestimate clinical symptoms. An example

of this is the ﬁnding that subjects who had

the most intense headaches also overestimated

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headache frequency (Van den Brink et al.

2001).

Studies have also examined the correlation

between aggregated EMA and recall-based

data, which assesses whether the ranking of

individuals is similar across the two sources,

and can be high even when there are differ-

ences in the two means. Like the ﬁndings

on mean differences, the ﬁndings from cor-

relational studies are also variable. Studies of

drug use report good correspondence for fre-

quency or quantity used (Carney et al. 1998,

Shiffman & Paty 2003), but very poor cor-

respondence when characterizing situational

patterns of drug use, which involve more com-

plex judgments (Shiffman 1993, Todd et al.

2005). In one study of pain among chronic

pain patients ( J. Broderick, J. Schwartz, and

A.A. Stone, unpublished observations), corre-

lations in the 0.70s were seen between EMA

and recall-based measures of pain intensity,

whereas Van den Brink (2001) reported cor-

relations around 0.20 for headache frequency,

intensity, and duration.

Thus, ﬁndings on correspondence be-

tween aggregate EMA data and recall-based

estimates are inconsistent and variable. One

likely reason is that the magnitude and direc-

tion of recall bias can differ across subjects and

settings. Individual differences can affect re-

call accuracy. Feldman-Barrett (1997) found

that recalled distress was exaggerated among

more neurotic subjects and positive affect was

exaggerated among extraverted subjects, and

Van den Brink (2001) describes other individ-

ual differences that moderate recall bias. Be-

liefs can also moderate recall bias. McFarland

et al. (1989) found that women who believed

that their menstrual cycle inﬂuenced their

mood reported exaggerated negative mood in

retrospect, but only for menstrual days. In

contrast, women who did not believe men-

struation inﬂuenced mood did not show any

bias, either for menstrual or nonmenstrual

days.

Variation in the pattern of target symptoms

themselves can moderate bias. Stone et al.

(2005) showed that subjects whose pain was

relatively constant over time were able to es-

timate their pain more accurately since, in ef-

fect, they could easily know their typical pain

level or estimate accurately from any given

time point. In contrast, subjects whose pain

was variable demonstrated considerable bias.

Furthermore, Broderick et al. (2006) and oth-

ers have found that recalled pain was particu-

larly exaggerated when subjects were in pain at

the time of recall and understated when they

were not.

Thus, the validity of recall data likely varies

with characteristics of both the samples and

the setting. It is likely that the validity of re-

call is also inﬂuenced by the duration of the

recall interval and the variability, salience, and

uniqueness of what is being assessed: Some

kinds of end points may be well estimated,

whereas others are not. Better understand-

ing of these relationships could help estab-

lish when EMA is likely to add the most value

compared with aggregated recall measures.

Correlations Between Disaggregated

EMA and Recall

The studies reviewed above assess the cor-

relation between EMA- and recall-based es-

timates of experience when EMA data are

aggregated over time to broadly character-

ize person-level effects, and compared with

similar data based on global recall. How-

ever, EMA data are most often used not just

to characterize between-person differences,

but also to characterize within-person vari-

ations in experience over time, so that the

question is whether recall measures are able

to accurately reﬂect time-speciﬁc data. The

available evidence suggests that they cannot.

Even when global recall data correlate well

with aggregated EMA data, as in the stud-

ies cited above ( J. Broderick, J. Schwartz, and

A.A. Stone, unpublished observations; Carney

et al. 1998; Shiffman & Paty 2003), they

do not adequately capture time-varying data,

with correlations for recall of speciﬁc days

hovering in the range of 0.20–0.40. Moreover,

analyses show that the cross-day correlation

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also varies widely between subjects, with

some subjects actually showing negative cor-

relations between their recalled reports and

real-time reports of the same target events

(Carney et al. 1998, Searles et al. 2000).

In other words, even when recall appears

adequate to characterize aggregate experi-

ence, it is not typically adequate to charac-

terize day-to-day changes in cognitions and

behaviors of interest, which are typically the

focus of EMA research. This highlights one of

the unique contributions of EMA to the study

of processes that unfold over time.

Construct Validity of Real-Time

Versus Recall Assessments

In addition to comparisons of EMA-based and

aggregated recall-based assessments to each

other, comparisons can be made based on their

construct validity—in other words, their re-

lationships with other theoretically relevant

constructs. Several studies illustrate cases in

which EMA data show incremental validity

over and above retrospective or global as-

sessments of similar constructs. Kamarck and

colleagues (2007) directly contrasted the ef-

fect of job strain on cardiovascular outcomes

when strain was measured by EMA versus

global measures. Subjects ﬁlled out a stan-

dard global job strain questionnaire and used

an electronic diary to collect EMA data on

experienced strain every 45 minutes for six

days (data from the EMA was averaged to

create a single EMA summary variable). The

study’s outcome measure was a prospectively

assessed biological outcome: the progression

of blockage in the carotid artery (which corre-

lates with blockage of coronary arteries) over

the subsequent three years. EMA-based mea-

sures of job strain predicted progression of

arterial occlusion; however, traditional global

questionnaire measures of job strain did not.

Moreover, heart rate assessed by EMA in

the natural environment also independently

predicted progression of carotid blockage,

whereas heart rate measured in the lab did

not. This illustrates the potential of EMA

data to shed light on relationships that are

missed when relying on global retrospective

self-reports.

In a study of smoking, Shiffman and col-

leagues (2007) similarly compared global and

EMA-based measures, in this case, measures

of “negative-affect smoking”—the tendency

to smoke when distressed. The EMA-based

measure estimated negative-affect smoking

by comparing negative affect on smoking

and nonsmoking occasions; the global assess-

ment used standardized and validated ques-

tionnaires. Shiffman et al. tested the predic-

tion that smokers engaged in negative-affect

smoking would be more vulnerable to relapse

after they quit smoking. This was found to be

true for EMA-assessed negative-affect smok-

ing, but not for assessments based on standard

recall-based questionnaires.

The literature also suggests that EMA

measures may sometimes mirror the ﬁndings

of recall measures, but may capture the target

constructs with less noise and greater sensitiv-

ity. In a study of the efﬁcacy of analgesics in

the treatment of rheumatoid arthritis, Nived

and colleagues (1994) reported that diary data

differentiated active treatment from control

after only four weeks of treatment, whereas it

took 24 weeks for the differences to become

apparent in recall measures collected at clinic

visits. In a related vein, analyses have shown

that real-time diary-based methods resulted

in decreased error variance when capturing

events (McKenzie et al. 2004) and scaled rat-

ings (Pearson 2004), making such data more

sensitive to treatment effects.

In contrast to these studies favoring EMA

data, J. Broderick, J. Schwartz, and A.A.

Stone (unpublished observations) recently

found that EMA and recall-based measures of

pain were equally correlated with medication-

taking and social impairment. Importantly,

other studies have illustrated cases in which

recall measures were actually better predic-

tors of subsequent behavior than were EMA

data. These cases are illuminating. In a study

of bias in recall of pain, Kahneman and col-

leagues (Redelmeier et al. 2003) showed that

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participants who were randomized to un-

dergo a longer colonoscopy, with more overall

pain, but who experienced less intense pain

near the end of the procedure actually re-

called lower overall pain. A follow-up revealed

that subjects who had undergone the longer

procedure—which was experienced as more

painful in real time—were more likely to re-

turn for a repeat colonoscopy. In other words,

their future behavior was related to the ret-

rospective summary of pain, not the momen-

tary experience. Similarly, Oishi & Sullivan

(2006) found that break-up of dating cou-

ples was better predicted from their retro-

spective summaries of relationship dissatis-

faction than from their daily ratings. These

ﬁndings make sense, because people shape

their subsequent behavior—whether to return

for a colonoscopy, whether to proceed with

a relationship—by reference to their stored

summary memory of the experience, not the

actual experience at the time.

These examples illustrate a key point: Even

if they do not match momentary experience,

retrospective impressions or global beliefs can

exercise greater control over subsequent be-

havior, since they represent the information

that people use to make subsequent decisions.

In this sense, the momentary and retrospec-

tive reports represent different perspectives

on the same event. If one is interested in what

was experienced in the moment (e.g., to un-

derstand the physiological effects of the pain

or to evaluate the effects of an analgesic) or

wishes to understand what are the proximal

determinants of a speciﬁc event, then the mo-

mentary data are likely to provide a more valid

picture. But if one is interested in the person’s

impressions of an event or in the prediction

of future behavior, then the retrospective im-

pressions may well be better predictors than

EMA. This suggests that researchers evalu-

ating the validity and utility of EMA and re-

call data must consider whether they are con-

cerned with understanding the experience as

lived or with understanding subjects’ impres-

sions of those experiences. As we have argued

with regard to assessments of psychological

Event-based

sampling: a method

of data collection

whereby a recording

is made each time a

predeﬁned event

occurs

Time-based

sampling: a method

of data collection

whereby a recording

is solicited based on a

time schedule, often

based on random

time intervals

coping (Stone et al. 1998), confusion about

what is being assessed can misdirect theory

and research.

The discrepancy between momentary ex-

perience and global summary recall also sug-

gests that it would be worthwhile for research

to help us understand how momentary ex-

periences are integrated in the development

of global judgments. More research is also

needed on the circumstances in which global

or retrospective judgments are valid and cir-

cumstances in which EMA data are neces-

sary. In the interim, EMA is likely to provide

unique insights about processes that charac-

terize dynamics of behavior, cognition, and

affect over time.

ECOLOGICAL MOMENTARY

ASSESSMENT DESIGNS

AND APPROACHES

In this section, we describe and categorize

EMA designs, by which we mean the scheme

that dictates the scheduling, arrangement, and

temporal coverage of EMAs. In global assess-

ments such as personality questionnaires, the

researcher assumes that the assessment cap-

tures the subject’s entire experience in one fell

swoop, rendering it unimportant when the as-

sessment is made. In EMA, one assesses mo-

ments or periods of time, raising the issue of

how to ensure that the moments or periods

assessed are representative of the subject’s ex-

perience. In many cases, the assessments can

be conceptualized as a sample of the person’s

experience or behavior. Thus, designing an

EMA protocol can essentially amount to de-

signing a sampling scheme for moments in

an individual’s life. The most important inﬂu-

ence on the design must be the aims of the

study.

EMA sampling and assessment schemes

can be roughly divided into event-based

sampling and time-based sampling schemes

(Shiffman 2007, Wheeler & Reis 1991).

Event-based schemes do not aim to charac-

terize subjects’ entire experience, but rather

to focus on particular discrete events or

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episodes in subjects’ lives—e.g., headaches

(Niere & Jerak 2004) or drinking episodes

(Todd et al. 2005)—and organize the data col-

lection around these events. Time-based sam-

pling typically aims to characterize experience

more broadly and inclusively—e.g., observing

how mood varies over time—without a prede-

ﬁned focus on discrete events.

Event-Based Monitoring

In many disorders, the clinical and research

interest is in particular events or episodes, e.g.,

instances of drinking, violence, or panic at-

tacks. These cases lend themselves to event-

based monitoring, in which assessments are

triggered by the occurrence of a predeﬁned

event of interest to the investigator. For ex-

ample, subjects might be asked to complete

an assessment when they have a panic at-

tack (Taylor et al. 1990), engage in a so-

cial interaction lasting more than 10 minutes

(Reis & Wheeler 1991), or take a medica-

tion ( Jonasson et al. 1999). Typically, the sub-

jects themselves determine when the event has

occurred and initiate an assessment (though

some events can be automatically detected

by devices; see Kop et al. 2001). Such pro-

tocols require clear deﬁnitions of the event,

which can be surprisingly thorny to delin-

eate: If subjects are to make a record every

time they eat, does chewing gum count as

eating? Sometimes, target events are deﬁned

as episodic ﬂare-ups of otherwise continu-

ous experiences, for example, a pain episode

in which pain is experienced more intensely

(McCarberg 2007), or an episode of intense

cigarette craving (Shiffman et al. 1997a).

Deﬁning the algorithm for declaring an event

is particularly difﬁcult—and important—in

these cases.

If one only needs a record of events, for ex-

ample, to ascertain their frequency and time

distribution, subjects need only note that an

event has occurred, e.g., press a button on

a recording device. More often, investiga-

tors wish to collect data about the event:

its duration, intensity, antecedent mood, etc.

(Schlundt et al. 1985, Shiffman et al. 1996b).

If the events are too frequent, it may not be

realistic to assess each event, in which case

a subset of them can be sampled at random

(Shiffman et al. 2002).

An important limitation in the use of

event-based monitoring is that there is often

no way to independently assess or verify com-

pliance; i.e., there is no way to know whether

events occurred that were not entered or (less

likely) entries made for events that did not oc-

cur. In a few studies, diary entries have been

compared to records made by separate elec-

tronic devices (e.g., instrumented medication

dispensers); these have suggested that medica-

tion compliance is exaggerated in self-report

(see Hufford 2007). Event entries can some-

times also be roughly conﬁrmed by biochemi-

cal measures; e.g., Shiffman & Paty (2003) re-

ported that subjects’ electronic diary records

of cigarettes were consistent with biochemical

measures of smoking. In any case, event re-

ports are subject to error resulting from poor

compliance or falsiﬁcation.

Time-Based Designs

Some clinical phenomena—e.g., mood,

pain—vary continuously and are not easily

conceptualized in an episodic framework.

In some instances (e.g., actigraphy, heart

rate, skin conductance), the phenomenon

can be monitored continuously. In the more

typical case, where this is not possible, EMA

protocols rely on time-based sampling. There

are many varieties of time-based sampling

schemes, which vary in schedule, frequency,

and timing (see Delespaul 1995 for more

detail).

The frequency of time-based assessments

will determine the resolution the study will

have. The resolution needed depends on the

goal of the study, what is known about the be-

havior studied, and the theoretical framework

of the study.

A variety of time-based assessment sched-

ules are used in EMA. Some administer assess-

ment at ﬁxed intervals. This has been common

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in ambulatory monitoring of blood pressure

(e.g., every 30 to 45 minutes in Kamarck et al.

1998). Assessment at equal intervals allows

the time block to serve as the unit of analy-

sis and supports analyses that require evenly

spaced assessments, such as simple autocorre-

lation analysis and time series analysis. (Daily

diaries are a special case discussed below.)

Some studies have used somewhat irregu-

lar intervals, typically deﬁned by social pa-

rameters: For example, Hensley et al. (2003)

had subjects complete an asthma diary in the

morning and evening of each day. Because the

time blocks are vaguely deﬁned, they give sub-

jects considerable discretion in the timing of

assessments, and this has the potential to in-

troduce bias. For example, subjects might re-

member to complete their mood assessments

when they are reminded by extreme affect,

thus biasing the sample of mood.

An alternative to ﬁxed intervals is a vari-

able schedule, which usually administers as-

sessments at random times in order to achieve

a representative sampling of subjects’ state. A

variation is stratiﬁed random sampling, from

within strata deﬁned by blocks of time within

a day. For example, Afﬂeck and colleagues

(1998) assessed subjects once in each pre-

deﬁned time window in the morning, after-

noon, or evening, with assessments scheduled

at random within each interval. This guaran-

tees that the sample of assessments will evenly

sample time across the day and ensures (sub-

ject to missed assessments) that each time

block includes an assessment, which allows the

time block to serve as the unit of analysis.

Setting the frequency of assessments in-

cludes considerations of subject burden as well

as of how rapidly the target phenomenon is

expected to vary. Although assessing subjects

3 to 5 times per day is common, some studies

have succeeded with as many as 20 or more

assessments per day (Goldstein et al. 1992;

Kamarck et al. 1998, 2002, 2005, 2007). As-

sessment frequency may be varied to collect

more assessments at certain times of day if

those are of particular interest (Shiffman et al.

2000). Assessments should ideally be sched-

PDA: personal

digital assistant (a

handheld computer)

uled throughout the waking day. Some stud-

ies have limited assessment to a narrow range

of hours (e.g., 10am to 10pm in Kimhy et al.

2006), which misses early-morning and late-

night hours that may encompass important—

and substantially different—experiences and

behaviors.

When using time-based assessment sched-

ules, especially with variable intervals, EMA

studies require some method of signaling sub-

jects when an assessment is scheduled. This is

typically accomplished through the use of a

device—a beeper, phone, wristwatch, or per-

sonal digital assistant (PDA)—programmed

to signal the subjects at the appropriate times

(see Shiffman 2007 for a discussion of devices).

Thus, although events can be captured at

the subject’s initiative, continuous phenomena

typically have to be sampled using a suitable

time-based sampling scheme.

Combination Designs

Different sampling approaches can be fruit-

fully combined to test particular hypotheses.

When the researcher is interested in the cir-

cumstances that are associated with a target

event, it can be particularly helpful to com-

bine time- and event-based assessments in or-

der to provide a context for interpreting the

event data. Data on the situational context

of clinically meaningful events, such as panic

attacks (Margraf et al. 1987) or binge-eating

episodes (Engel et al. 2007) are hard to inter-

pret without comparison to base-rates (Paty

et al. 1992). For example, Greeno et al. (2000)

found depressed affect among bingers at the

time of a binge. However, this is difﬁcult to

interpret: Does it mean that negative moods

help trigger binges, or just that binge eaters

are generally unhappy, even when not binge-

ing? By comparing affect reported during a

binge with affect reported on random occa-

sions outside of binges, Greeno et al. (2000)

were able to establish that binges were specif-

ically associated with more distress. This de-

sign is modeled after the case-control design,

with the events as “cases” and the nonevent

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data collected by time-based methods consti-

tuting the “controls,” and is often referred to

as a case-crossover design (Maclure & Mittle-

man 2000).

In EMA designs combining event- and

time-based assessments, time-based assess-

ments can also document the antecedents or

sequelae of events. For example, Shiffman &

Waters (2004) used time-based data to show

that ex-smokers were experiencing escalating

levels of affective distress in the hours preced-

ing a smoking lapse (the event) and to show

that self-efﬁcacy decreased following a lapse,

but not after occasions when smokers success-

fully resisted a temptation to smoke (Shiffman

et al. 1997b). Time-based assessments can be

used to follow up on the sequelae of a recorded

event. For example, to test how quickly mi-

graine medications delivered pain relief and

how long the relief lasted, Sheftell and col-

leagues (2005) had subjects record the onset

of migraines and then scheduled a series of

assessments as follow-ups.

Combining different schedules of time-

based assessments can also be useful. For ex-

ample, Muraven et al. (2005) assessed social

drinking in subjects throughout the day but

also scheduled an assessment each morning to

ask about hangovers from the previous night’s

drinking. This illustrates how decisions about

design and scheduling of assessments need to

be driven by the research questions and by

knowledge about the natural history of the

target behavior.

Use of Recall in EMA

Although some EMA studies truly focus on

the moment, many assessments involve some

degree of retrospection. For example, Afﬂeck

and colleagues (1998) asked ﬁbromyalgia sub-

jects to report on their pain and fatigue over

the past 30 minutes. Event-based diaries of-

ten involve some retrospection if the entry

is made after the event: For example, studies

with the Rochester Interaction Diary (Reis &

Wheeler 1991) ask subjects about social in-

teractions they had just concluded. Thus, the

focus on momentary experience is not abso-

lute; it might more liberally be thought of as

a focus on recent experience. Of course, some

events and experiences are likely to be recalled

more accurately, particularly if they are un-

common and/or highly salient (e.g., a major

marital spat) or recent. However, since the lit-

erature shows that recall can be biased over

even a short interval, the researcher needs

to carefully consider the use of recall meth-

ods and the potential for bias even over short

intervals.

Sampling Versus Coverage Strategies

When subjects are assessed intermittently and

the focus is on their momentary state, the as-

sessments do not provide complete coverage

of their daily experience. That is, even though

we may have assessed their mood 12 times in a

day, and found them happy each time, it is pos-

sible that they experienced moments of mis-

ery in between the assessments. A sampling

strategy recognizes that only some moments

are assessed, but relies on the idea that, over

multiple days and multiple assessments, both

moments of happiness and moments of misery

will be sampled, and the mix will be represen-

tative of the subjects’ average mood.

In lieu of a sampling strategy, investiga-

tors sometimes adopt a coverage strategy,

which aims to cover every moment of the day.

For some kinds of objective data, continu-

ous measurement (e.g., actigraphy) is possi-

ble, enabling true coverage (e.g., Tulen et al.

2001). For self-report measures, continuous

assessment is not possible, so investigators

try to obtain coverage by having subjects re-

call and summarize the period between as-

sessments. This is sometimes implemented by

asking subjects several times a day to recall

and summarize their experience since the last

assessment, providing piecemeal coverage of

the entire day. For example, Van den Brink

et al. (2001) assessed pediatric headache pa-

tients four times each day, and at each as-

sessment, asked them to recall and character-

ize headaches experienced since the previous

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assessment. Besides being vulnerable to biases

introduced by recall, this strategy requires

subjects to accurately maintain a timeline of

experience and focus on experience since the

last assessment. Research suggests that peo-

ple are particularly poor at keeping track of

time and identifying when events took place

(Sudman & Bradburn 1973), suggesting that

this approach needs to be treated with cau-

tion. Coverage strategies may be useful when

the investigator needs an absolute estimate or

count of events; otherwise, sampling strate-

gies can yield representative estimates.

Daily Diaries: A Special Case of EMA

Daily (i.e., once-a-day) diaries are particularly

popular with researchers and clinicians be-

cause of their ease of administration and rela-

tively low subject burden. Within the frame-

work that we have been discussing, daily

diaries are ﬁxed-interval assessments with an

assessment frequency of once per day, em-

ploying a retrospective coverage strategy.

Even though daily diaries rely on recall of the

entire day and therefore are not momentary,

we consider them as falling into the family of

EMA designs because they are administered

repeatedly and provide a dynamic look at the

variables investigated, although with less res-

olution than that of within-day assessments.

Daily diaries bring some of the beneﬁts of

EMA in terms of illuminating process over

time, but they also carry with them some of the

limitations of retrospective assessments, ask-

ing participants to summarize the entire day’s

experience. This may introduce some bias be-

cause moods can vary substantially from one

moment to the next, and true recall is likely

to stretch the capabilities of autobiographi-

cal memory. Moreover, research on autobi-

ographical memory suggests that biases due

to heuristic recall strategies operate even over

short time frames. The typical scheduling of

diary completion at the end of the day may

itself become a source of bias, since this tends

to be a highly unrepresentative moment, when

subjects may be particularly fatigued, for ex-

ample, which could bias their recall of the day’s

experience. Accurate recall may be more likely

when subjects are asked to recall less frequent,

more notable and salient events, such as the

occurrence of a migraine headache. Compar-

isons of daily diary data to real-time data will

enable researchers to determine whether the

former is a reasonable proxy for the latter. Re-

search is needed to shed light on when ac-

curate recall is and is not likely. Thus, daily

diaries require a cautious approach.

Besides concern about retrospective bias,

daily assessment also provides limited tempo-

ral resolution in which to observe behavior;

that is, within-day processes may be critically

important for some phenomena. This was il-

lustrated in a study by Shiffman & Waters

(2004) on the antecedents of smoking lapses.

Daily diary data showed no increase in nega-

tive affect in the days leading up to a lapse and

no association between daily negative affect

and lapse risk. However, examination of hour-

by-hour affect data revealed a steep increase

in negative affect in the hours immediately

preceding lapse episodes. Thus, day-level data

may miss important sources of dynamic varia-

tion that drive behavior. However, when used

carefully, especially for highly memorable and

slow-moving targets, daily diaries can never-

theless be a tremendous asset for studying cer-

tain questions and are often an improvement

over relying on global retrospective recall.

USE OF ECOLOGICAL

MOMENTARY ASSESSMENT

IN TREATMENT

For Assessment

Although we have been addressing the use of

EMA for research data, it also seems natural

to use EMA for ongoing assessment during

treatment. The treatment context includes

many features that lend themselves to EMA

methods: The client is likely motivated to

devote energy to assessment, and there are

usually clear target behaviors or experiences

on which to focus, dictated by the client’s

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presenting problem, the nature of the pathol-

ogy, and/or the clinical formulation of the

case. EMA methods lend themselves to idio-

graphic n = 1 analysis; even formal statistical

testing can be done by using a single client’s

data to guide therapy and assess progress (e.g.,

Delespaul 1995). Properly structured EMA

data lend themselves to the sort of micro-

analysis of process that is often the subject of

therapeutic discussion, and that is seen as re-

vealing opportunities for intervention (“Let’s

ﬁgure out what led up to your anxiety attack

so we can understand it and think about how

to prevent it”). Because change is expected

during treatment, ongoing assessment can be

informative. EMA could also prove useful in

shedding light on processes and mediators of

psychotherapy-induced change.

EMA assessments are only beginning to

see application in clinical settings. Norton and

colleagues (2003) conducted a small outcome

study of CBT for binge eating, in which pa-

tients were randomized to use EMA-based

assessment or not. There were no signiﬁ-

cant differences in the primary outcome, but

there were strong trends favoring the pa-

tients with EMA, suggesting that a larger

study would have demonstrated reliable dif-

ferences. Protocols to make optimal use of

EMA in treatment have yet to be developed.

Widespread use of EMA in treatment will

also have to overcome both real and perceived

barriers of the technical and ﬁnancial bur-

den of these methods. We expect that EMA

methods will see increasing use in clinical set-

tings over the next decade (see Piasecki et al.

2007 for a review of EMA applied to clinical

assessment).

For Intervention

Although using EMA for clinical assessment

would be a minor extension of its use in

research, applying EMA methods directly

to intervention—that is, implementing real-

time in-the-moment interventions as sub-

jects go about their daily lives—may open

up more radical developments. Bridging the

gap between patients’ here-and-now experi-

ences and behaviors in the real world and the

removed, conﬁned, and limited contact with

the clinician in the consulting room has al-

ways been a major challenge for psychological

treatment. Only a few papers have described

momentary interventions, and fewer still have

evaluated them.

Newman et al. (2003) reviewed a vari-

ety of palm-top-assisted treatments for psy-

chological disorders. Newman et al. (1997)

reported that a brief palm-top-assisted mo-

mentary intervention for panic disorder was

equivalent in efﬁcacy to a longer therapist-

administered treatment. The control group

in this study also engaged in EMA and

may have beneﬁted from that, which high-

lights the importance of identifying the

incremental beneﬁts of EMA intervention

versus EMA. Momentary interventions have

also been described for eating disorders

(Norton et al. 2003) and addictive disorders

(Riley et al. 2002). Carter et al. (2007) describe

a particularly sophisticated and interactive

EMA-based treatment program for smoking

(still under evaluation) and present a concep-

tual foundation of EMA-based treatments.

At this stage, then, EMA-based momen-

tary intervention remains a promising, but

only partly proven, idea. However, its poten-

tial seems enormous. The idea of momentary

treatment, delivering intervention immedi-

ately on-the-spot and as needed in real-world

settings—a “therapist in your pocket”—holds

promise for addressing behavior at crucial

moments in the patient’s life. EMA-based

treatment can provide behavioral guidance or

other interventions (e.g., relaxation stimuli)

throughout a patient’s day as well as just at the

moment they are needed. By learning from

an individual’s history, algorithms could, for

example, tailor coping suggestions based on

what has worked before for this patient in this

situation. Furthermore, by observing the pa-

tient over time, predictive algorithms could

anticipate and respond to challenges before

they gain strength, for example, noting rising

stress levels and intervening (or contacting a

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counselor) before they result in maladaptive

behavior. In-the-moment interventions have

only begun to be explored, but they have the

potential to revolutionize clinical treatment.

MEASUREMENT

CONSIDERATIONS FOR

ECOLOGICAL MOMENTARY

ASSESSMENT SELF-REPORTS

When self-report assessments are used in

EMA, some special considerations are needed.

Many questionnaires were not designed for

assessing momentary states; the instructions

may have a longer recall time frame or no

time frame at all. Whether adapting an ex-

isting instrument for EMA or creating a new

one, it is important to consider whether the

item makes sense in the new time frame—i.e.,

momentary, hourly, etc.—both from the in-

vestigator’s conceptual perspective and from

the subject’s perspective. For the latter, cog-

nitive testing of proposed assessments (Willis

2005), as well as pilot testing of assessments

in the ﬁeld, is usually prudent.

An unusual aspect of EMA studies is that

subjects will typically encounter each assess-

ment many, many times. This requires that

assessments be well tuned: Something that is

just a small annoyance when seen once can

become a real irritant when encountered ﬁve

times a day for weeks.

Construction of EMA assessments may

also be inﬂuenced by the device or medium

used. For example, PDAs have limited screen

display space, requiring that items and re-

sponse options be reasonably succinct. When

interactive voice response systems are used,

the length, complexity, and number of op-

tions needs to be severely limited, keeping in

mind that subjects must listen to and remem-

ber all the response options and their corre-

sponding keypad numbers. Simply moving a

questionnaire from paper to a screen-based

electronic device should not be problematic:

A meta-analysis showed that paper-based and

screen-based questionnaires were equivalent

(Gwaltney et al. 2007).

Use of programmable electronic devices

to administer assessments provides oppor-

tunities to change the order in which as-

sessment items are administered. This can

be used to present items in random order

to counterbalance any effects of item se-

quencing (for free access to such software,

see http://www.experiencesampling.org/).

This programmability could be used to im-

plement computer-adaptive testing (Chang &

Reeve 2005), which provides efﬁcient assess-

ment of a construct with a minimum num-

ber of items, by dynamically changing which

items are administered to particular subjects

based on their responses to prior items.

Although most EMA studies collect struc-

tured quantitative data (using structured

scales to quantify responses), EMA meth-

ods can also be used to gather qualitative

data. Diaries can allow write-in responses, and

O’Connell et al. (2002) have supplemented

PDAs for quantitative assessment with tape

recorders for collecting qualitative narratives

for later coding. Automatically activated tape

recorders have also been used to collect qual-

itative data (i.e., conversations) without the

need for subject intervention (Mehl et al.

2001). Assessments of physiological parame-

ters usually bring with them a set of techni-

cal concerns speciﬁc to the system being mea-

sured and the method used.

Besides keeping in mind the special mea-

surement issues that apply to EMA, it is

equally important to recognize that many of

the measurement issues that apply to global

psychometric assessment also apply to EMA.

That is, it is important that assessments be re-

liable and valid; in EMA, reliability can some-

times be achieved through aggregation across

multiple assessments rather than across mul-

tiple items within a single assessment. The

meaning of EMA assessments has to be care-

fully considered; Schwarz (2007) has spec-

ulated that focusing self-report on immedi-

ate experience might shift the individual’s

focus to very small events at the cost of the

“big picture.” It is also important to keep

in mind that self-report data collected via

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EMA methods are still self-report data and

are subject to many of the limitations inher-

ent to self-report. Like any self-report data,

EMA data can be adversely inﬂuenced by the

effects of subjects’ psychopathology (Kessler

et al. 2000) and by deception or self-

deception.

METHODOLOGICAL

CONSIDERATIONS IN

ECOLOGICAL MOMENTARY

ASSESSMENT STUDIES

Reactivity

Reactivity is deﬁned as the potential for be-

havior or experience to be affected by the

act of assessing it. Behavioral studies of self-

monitoring, in which patients were asked to

monitor the behavior they were trying to

change, often demonstrated reduction in the

problem behavior due to monitoring alone—

so much so that self-monitoring came to be

considered a part of behavior-change treat-

ment, not just assessment. However, reactivity

is not universally observed. In particular, stud-

ies showed that reactivity emerged when sub-

jects were trying to change the target behavior

and when the behavior was recorded prior to

being executed (e.g., recording a meal before

eating it)—in other words, under conditions

that provide both the motivation and the op-

portunity to exert control over the behavior

(Korotitsch & Nelson-Gray 1999). This sug-

gests that EMA researchers need to be con-

cerned about reactivity under such conditions,

although there has not yet been a direct test

of this idea.

When such conditions are not present,

several studies of EMA ﬁnd little or no

evidence of reactive effects (Cruise et al.

1996; Hufford & Shields 2002; Hufford et al.

2002a,b; Hufford & Shiffman 2002; Litt

et al. 1998; Stone et al. 1998). In the most

controlled study of reactivity to EMA, Stone

and colleagues (2003a) assigned subjects with

pain syndromes to complete either no EMA

monitoring or sampling of their pain using

electronic diaries 3, 6, or 12 times daily. No

evidence was found that the pain ratings

were systematically reactive to the EMA

monitoring.

Besides traditional reactivity, one might

also be concerned that the burden of moni-

toring, including being interrupted by “beep-

ing” throughout the day, might cause distress

that would bias the data. Again, the evidence

suggests this is not typical. The Stone et al.

(2003a) study found no trend over time in pain

ratings and no systematic effect on pain rat-

ings of increased prompting, despite a delib-

erately aggressive prompting and assessment

schedule.

Given the earlier literature suggesting

that reactivity can occur, the literature does

not completely resolve when reactive effects

might or might not be observed. EMA re-

searchers should be alert to the potential for

reactivity while recognizing that little evi-

dence has been found to support the concern

that EMA engenders signiﬁcant reactivity.

Compliance

While EMA methods promise to capture

data closer in time to events and symptoms

of interest, they also place on subjects the

burden of making timely recordings as they

go about their daily lives. Missing assessments

have the potential to bias the obtained sample

of behavior and experience, especially if the

missing data are nonrandom. As a result,

EMA studies place a premium on obtaining

high levels of subject compliance with the

assessment protocol.

The methods used to collect diary data can

affect the investigator’s ability to obtain and

document compliance. Studies using paper di-

aries often report that about 90% of diary

cards are returned (Hufford & Shields 2002).

However, timely compliance is often inferred

from the date and time subjects have recorded

on the diary card. This opens the door for sub-

jects to hoard and backﬁll diaries (that is, to

complete entries, perhaps en masse, after the

fact) while seeming compliant.

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Concern about hoarding and backﬁlling

is heightened by the ﬁndings from studies

in which subjects completed written diaries

while also using electronically instrumented

devices (e.g., inhalers, blood glucose moni-

tors) that independently tracked the actual

time and date of events, such as medica-

tion taking or glucose monitoring. Across

eight studies (Hufford 2007), objective data

from the devices indicated that compliance

had actually been much lower than was in-

dicated by the written diaries: Many of the

written entries had been falsiﬁed. Moreover,

the written entries were systematically biased,

for example, showing better glycemic con-

trol than had actually been achieved (Mazze

et al. 1984). The written diary records un-

derstated problems of glucose control and ac-

centuated the concern about backﬁlling and

faked compliance. These concerns were fur-

ther accentuated by reports from paper di-

ary studies in which high proportions of sub-

jects later admitted to backﬁlling entries, even

when they had been electronically prompted

at the appropriate times and had recorded

those times on their diary cards (Litt et al.

1998, Moghaddam & Ferguson 2007).

To assess true compliance with written di-

aries, Stone et al. (2002) used an instrumented

paper diary (IPD), which was covertly instru-

mented with photosensors that recorded the

opening of the diary booklet—a prerequisite

to completing diary cards. Chronic pain sub-

jects were assigned to use either the IPD or

a PDA-based electronic diary to record their

pain at three scheduled times daily for three

weeks. Subjects were trained on the diary,

and steps were taken to encourage motivation

and compliance. The IPD allowed compar-

ison of reported compliance—based on the

time and date handwritten on returned diary

cards—and actual compliance—based on the

electronic records. Reported compliance was

90%, as in many other paper diary studies.

However, actual compliance was estimated at

11%. In other words, almost 90% of the sub-

mitted diary cards had been falsiﬁed. Evidence

indicated that the diaries had been hoarded

for days and backﬁlled. More disturbing still,

many subjects also showed evidence of ﬁll-

ing their paper diary cards in advance of the

stated time (Stone et al. 2003b), which ob-

viously renders the data invalid. Broderick

et al. (2003) subsequently showed that adding

an audible reminder to complete the diary

boosted compliance only modestly.

The other 40 pain sufferers in the study

by Stone et al. (Stone et al. 2002, 2003a,b;

Stone & Shiffman 2002) had been given an

electronic diary, which prompted for assess-

ments and did not allow entries outside the

designated time windows. These subjects ac-

tually did complete 94% of their assigned as-

sessments on time.

These compliance ﬁndings by Stone and

colleagues have stirred debate among diary

researchers (Green et al. 2006, Tennen et al.

2006), with critics noting, for example, that

various procedures (e.g., having subjects re-

turn diaries daily by mail, so that postmarks

provide some degree of time-stamping) might

improve timely compliance with written di-

aries. In any case, the Stone et al. study (Stone

et al. 2002, 2003a,b; Stone & Shiffman 1994,

2002), along with the prior literature docu-

menting falsiﬁed paper diary entries (Hufford

2007), raises critical questions about timely

compliance. Paper-and-pencil diaries are vul-

nerable to being falsiﬁed, and both forward-

and backﬁlling are possible and potentially

frequent, making veriﬁcation of timely com-

pliance both essential and challenging. Thus,

the burden of proof falls to the EMA inves-

tigator to establish, by whatever means, the

timeliness of recording.

The most common way to ensure and doc-

ument timely compliance is the use of elec-

tronic diaries, which tag each record with time

of entry and thus allow for detailed analy-

sis of compliance, particularly with interval-

or signal-contingent recordings. Many elec-

tronic diary studies document compliance

rates around 90% (Hufford & Shields 2002).

However, good compliance is not univer-

sal even with electronic diaries; rates as low

as 50% have been reported (e.g., Jamison

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et al. 2001), suggesting that the mere use

of electronic diary technology is not, by it-

self, a panacea. A variety of suggestions of

ways to enhance compliance with electronic

diary studies has been presented elsewhere

(Hufford & Shiffman 2003). As noted above,

compliance with event-oriented protocols is

difﬁcult to verify.

SPECIAL POPULATIONS

Because of the burdens they place on subjects,

EMA methods often raise concerns about ap-

plication to special populations. These con-

cerns are often magniﬁed when EMA stud-

ies employ electronic devices, such as PDAs

or cell phones, for data collection. A com-

mon concern is that elderly subjects will

be unable or unwilling to operate high-tech

devices. However, data on subject prefer-

ence for electronic versus more traditional

paper diaries do not support this assump-

tion. In a meta-analysis of patient prefer-

ence, older subjects are actually less likely

to prefer paper diaries (in Shiffman et al.

2007). Similarly, although there have been

concerns about the use of such technology

in low-education, low-socioeconomic-status

subjects, Finkelstein et al. (2000) demon-

strated good success with just such a sample.

Different concerns are sometimes raised

about the use of diaries with children. Sev-

eral studies have demonstrated that children

are able to use electronic diaries, and good

compliance and results have been seen with

children as young as 7 (see Van den Brink

et al. 2001). Of course, the use of EMA meth-

ods does not obviate, and may increase, some

more general issues about assessment of chil-

dren, such as comprehension of the question-

naires used.

Concerns also arise about whether some

clinical populations might be unable to per-

form in EMA studies precisely because of their

psychopathology. In this regard, it is notable

that several studies have been successfully

carried out among schizophrenics (Delespaul

1995, Myin-Germeys et al. 2001), and good

compliance using cell phones for EMA re-

porting has been reported in a sample of

homeless crack addicts (Freedman et al. 2006).

These examples suggest that, with appropriate

sensitivity, EMA methods and related tech-

nology may be useful in a wide array of patient

populations and that investigators should not

make decisions about feasibility without test-

ing their assumptions.

A subtler concern that is not addressed

by the performance of special populations

in EMA studies is that subjects who volun-

teer for and complete demanding EMA stud-

ies may not be representative. There have

been no reports of particular recruitment dif-

ﬁculties or notable deviations in EMA study

samples, but this has not been systematically

evaluated. It seems likely that personal and

environmental factors may discourage some

subjects’ participation in EMA studies. Oc-

cupational demands may preclude availabil-

ity for momentary assessment (consider sur-

geons and assembly line workers, although see

Goldstein et al. 1992). Also, some work en-

vironments may be so noisy as to preclude

hearing prompts or using a phone. Motor im-

pairments, impaired vision or hearing, and il-

literacy may preclude participation in EMA

(and in many non-EMA) studies. Finally, sub-

jects who are unfamiliar with or fearful of

technology may be put off by EMA studies

using high-tech devices. The limits of EMA

and their impact on studies should be contin-

ually evaluated.

ANALYSIS OF EMA DATA

EMA data usually consist of a large number of

observations from each subject, with the num-

ber and timing of observations often vary-

ing between subjects. EMA data do not lend

themselves to the basic approaches most clin-

ical psychologists learned in graduate-school

statistics classes, which require independent

observations. Nor do they ﬁt easily into tradi-

tional repeated-measures designs, which typi-

cally require the same number of observations

for each person and no missing data. However,

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a variety of methods—essentially extensions

of the regression framework most psycholo-

gists are familiar with—can handle such data.

Known variously as random effects models,

multilevel or hierarchical analysis, and gen-

eralized estimating equations, these methods

implement regression models while account-

ing for the interrelationships of observations

within subjects. Extensions of these methods

go beyond linear regression to cover logistic

regression and survival models, among oth-

ers. Detailed exposition about these methods

is beyond the scope of this review; the reader

is referred to Schwartz & Stone (1998, 2007)

for detailed and practical guidance on appli-

cations of these models to EMA data. The

bottom line is that EMA researchers can use

easily available software to apply the concep-

tual models they are familiar with from more

traditional between-subjects analyses to EMA

data. Besides these relatively familiar statisti-

cal models, a variety of novel approaches can

fruitfully be applied to EMA data (Walls &

Schafer 2006).

PRACTICAL ISSUES IN

ECOLOGICAL MOMENTARY

ASSESSMENT

The implementation of EMA places unique

demands on both researchers and subjects. A

detailed discussion of the practical aspects of

implementing EMA is beyond the scope of

this review. The interested reader is referred

to Hufford (2007). Here we touch only brieﬂy

on considerations of hardware and software,

and management of subjects and data.

EMA Hardware and Software

An investigator planning an EMA study faces

many practical issues and decisions. One key

decision is what type of mechanism to use to

capture data. Although EMA is most closely

associated with the use of palm-top comput-

ers (Hufford & Shields 2002), the method is

hardware agnostic and encompasses a range

of high- and low-tech devices, ranging from

paper and pencil to futuristic wireless sen-

sors (Intille 2007). The key factor to con-

sider in selecting a hardware platform is the

support provided for key functions, including

the ability to prompt subjects to complete as-

sessments, manage prompting schedules (in-

cluding randomization and contingencies),

present assessment content to subjects, man-

age assessment logic such as branching, ac-

cept and store subjects’ self-report data, time-

stamp entries to avoid faked compliance,

and/or directly collect physical or physio-

logical data (see Shiffman 2007). Investiga-

tors should also consider the suitability of a

particular device or medium to their subject

population: For example, a small screen or a

telephone system may be less suitable for an

elderly population with visual or hearing im-

pairments, respectively.

The cost of devices and of software devel-

opment is often an additional consideration.

Investigators should avoid being overly inﬂu-

enced by initial start-up costs and also should

consider downstream costs, such as the cost

of cell phone service or of data entry from pa-

per diaries. Unfortunately, high ﬁxed costs of

technological solutions can make it very hard

to initiate small studies or pilot studies.

EMA approaches using electronic devices

require software for basic system functional-

ity and for the speciﬁcs of the study proto-

col. The success of such studies depends on

correct and robust performance of software,

which can often be complex. Both freeware

and commercial versions of EMA software are

now readily available. Le et al. (2006) have re-

viewed a number of freely available programs,

and a number of commercial vendors also pro-

vide programming and support services for

EMA studies. These base systems have to be

conﬁgured or customized to the needs of a

particular study. Researchers should carefully

consider capabilities and limitations of various

software systems. For example, some systems

can only accommodate signaling during speci-

ﬁed periods of the day, which can bias the sam-

ple by, for example, omitting early-morning

and late-night events, which may be highly

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salient. Some programs may support event- or

signal-driven assessments, but not the combi-

nation of the two. In light of the availability

of software from multiple sources, investiga-

tors are well advised to use existing software or

services.

Other Practical Considerations

Investigators should also consider procedures

and resources related to managing study

subjects. Training of subjects on the protocol,

the assessment, and any device used is an es-

sential aspect of EMA studies. Data manage-

ment is an additional consideration in EMA

studies. EMA studies can produce volumi-

nous datasets with hundreds of thousands of

observations, possibly of diverse kinds (e.g.,

momentary, daily, events). Additional compli-

cations include the fact that the records are

ordered in time and the temporal relation-

ships need to be maintained, and there may be

nondata records that do not represent subject

assessments (e.g., records of missed assess-

ment prompts) that also have to be maintained

within the database. Data management facili-

ties for maintaining these databases and pro-

ducing analytical datasets are an essential re-

source for EMA studies and should be planned

into the study.

CONCLUSION

EMA studies help us appreciate the impor-

tance of change and context in everyday be-

havior and experience. The dynamic inﬂuence

of context is demonstrated not only for con-

structs that we might expect to be variable,

such as affect, craving, and pain, but also for

many constructs that are typically conceptu-

alized as stable trait-like characteristics, such

as extraversion (Fleeson 2001), self-esteem

(Kernis 2005), coping style (Schwartz et al.

1999), and self-efﬁcacy (Gwaltney et al. 2005).

EMA’s microscopic focus and temporal res-

olution is beginning to help us see the un-

derlying dynamics behind even trait-like con-

structs. EMA studies suggest that behavior

and experience are much more dynamic, and

much more inﬂuenced by its immediate con-

text, than we usually envision. Even individual

differences can sometimes be best understood

through analysis of within-person variation.

For example, differences in how persons react

to situational stimuli, such as stressors, are re-

lated to personality (Feldman-Barrett 1997),

psychopathology (Myin-Germeys et al. 2001),

and clinical outcomes (Kamarck et al. 2007),

and within-person variation in response may

sometimes be as important as the mean level

(Gwaltney et al. 2005, Kernis 2005). Despite

their limitations, EMA methods promise to

enhance our understanding of the dynamic

interactions between individuals and their

environments.

Clinical psychology—and psychology in

general—is prone to overestimate the role of

stable traits in determining behavior and to

underestimate the inﬂuence of the local set-

ting on behavior (Mischel 2004). Our behav-

ior in daily life is inﬂuenced not just by our

predispositions, but also by where we are and

whom we are with, by how we are feeling and

what situation we are in, by what has recently

happened and what we have done or felt in

the minutes and hours preceding the present

moment. One context can elicit dysfunctional

behavior while another elicits a healthier re-

sponse; one can fuel psychopathology or set

us on a course toward improvement. Under-

standing life as it is lived, up close, will help

us better understand both health and pathol-

ogy and help us see where there are oppor-

tunities to intervene on the side of health.

This requires methods that examine behav-

ior at the appropriate level of granularity:

“Research methods that examine this land-

scape from 10,000 feet cannot shed light on

how this landscape is shaped at ground level”

(Shiffman 2005, p. 1743). EMA methods pro-

vide an important tool to help clinical psy-

chology explore the dynamic nature of behav-

ior, thought, and feeling as they unfold over

time.

24 Shiffman

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SUMMARY POINTS

1. Autobiographical memory processes can introduce bias into retrospective self-reports,

which form the bulk of clinical assessments.

2. EMA is a collection of methods for obtaining repeated real-time assessments of sub-

jects’ behavior and experience in their natural environments, to minimize recall bias,

maximize ecological validity, and document variation over time.

3. EMA encompasses a variety of diary approaches and technologies used to collect data

on a schedule (e.g., daily diaries or assessments scheduled at random) or in response

to clinical events (e.g., symptom episodes or behaviors).

4. The collection of frequent assessments makes EMA studies well suited to study

microprocesses—i.e., how behavior and experience vary over time and across

changing contexts.

5. Although sometimes retrospective self-reports correspond well with those based on

aggregated EMA data, recall of behavior and experience over time does not correspond

well to real-time EMA assessments.

6. The application of EMA methods for assessment and intervention in treatment is

promising but is in its infancy.

7. EMA methods have great potential to advance the science and practice of clinical

psychology by providing more valid and more detailed data about real-world behavior

and experience.

FUTURE ISSUES

1. Further prospective longitudinal analyses of EMA data are necessary to shed light on

theoretically relevant microprocesses in studies of psychopathology and treatment.

2. Further improvements in technological tools for EMA studies would enhance ease of

use, reduce cost, and expand capabilities.

3. An improved understanding is needed of the relationship between recall and real-time

EMA data, the circumstances under which recall may be accurate, and the processes

by which persons develop retrospective accounts of their experience.

4. Models are needed for application of EMA methods to clinical assessment and

intervention.

ACKNOWLEDGMENTS

The authors are grateful to Julie Mickens for invaluable help in preparing this manuscript.

DISCLOSURE STATEMENT

S. Shiffman is a founder and Chief Science Ofﬁcer of invivodate, Inc., which provides electronic

diary software and services for research. A.A. Stone and M.R. Hufford own stock in invivodata,

Inc.

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Annual Review of

Clinical Psychology

Volume 4, 2008

Contents

Ecological Momentary Assessment

Saul Shiffman, Arthur A. Stone, and Michael R. Hufford ppppppppppppppppppppppppppppppp1

Modern Approaches to Conceptualizing and Measuring Human

Life Stress

Scott M. Monroe ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp 33

Pharmacotherapy of Mood Disorders

Michael E. Thase and Timothey Denko pppppppppppppppppppppppppppppppppppppppppppppppppp 53

The Empirical Status of Psychodynamic Therapies

Mary Beth Connolly Gibbons, Paul Crits-Christoph, and Bridget Hearon ppppppppppppp 93

Cost-Effective Early Childhood Development Programs from

Preschool to Third Grade

Arthur J. Reynolds and Judy A. Temple pppppppppppppppppppppppppppppppppppppppppppppppp109

Neuropsychological Rehabilitation

Barbara A. Wilson ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp141

Pediatric Bipolar Disorder

Ellen Leibenluft and Brendan A. Rich ppppppppppppppppppppppppppppppppppppppppppppppppppp163

Stress and the Hypothalamic Pituitary Adrenal Axis in the

Developmental Course of Schizophrenia

Elaine Walker, Vijay Mittal, and Kevin Tessner pppppppppppppppppppppppppppppppppppppppp189

Psychopathy as a Clinical and Empirical Construct

Robert D. Hare and Craig S. Neumann pppppppppppppppppppppppppppppppppppppppppppppppp217

The Behavioral Genetics of Personality Disorder

W. John Livesley and Kerry L. Jang pppppppppppppppppppppppppppppppppppppppppppppppppppp247

Disorders of Childhood and Adolescence: Gender and

Psychopathology

Carolyn Zahn-Waxler, Elizabeth A. Shirtcliff, and Kristine Marceau pppppppppppppppp275

vii

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AR339-FM ARI 5 March 2008 23:50

Should Binge Eating Disorder be Included in the DSM-V? A Critical

Review of the State of the Evidence

Ruth H. Striegel-Moore and Debra L. Franko ppppppppppppppppppppppppppppppppppppppppp305

Behavioral Disinhibition and the Development of Early-Onset

Addiction: Common and Speciﬁc Inﬂuences

William G. Iacono, Stephen M. Malone, and Matt McGue ppppppppppppppppppppppppppp325

Psychosocial and Biobehavioral Factors and Their Interplay

in Coronary Heart Disease

Redford B. Williams pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp

349

Stigma as Related to Mental Disorders

Stephen P. Hinshaw and Andrea Stier pppppppppppppppppppppppppppppppppppppppppppppppppp367

Indexes

Cumulative Index of Contributing Authors, Volumes 1–4 ppppppppppppppppppppppppppp395

Cumulative Index of Chapter Titles, Volumes 1–4 pppppppppppppppppppppppppppppppppppp397

Errata

An online log of corrections to Annual Review of Clinical Psychology chapters (if any)

may be found at http://clinpsy.AnnualReviews.org

viii Contents

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by Dr. David McKirnan on 10/08/08. For personal use only.

Article

Full-text available

Jan 2024
CLIN PSYCHOL PSYCHOT

Georgie Paulik

ABSTRACT Many trauma-affected voice hearers report comorbid posttraumatic stress and voices (auditory verbal hallucinations) that are directly (voices repeat phrases spoken by perpetrators) or indirectly (voice content and trauma are thematically similar) related to their trauma. Models of PTSD and positive symptoms of psychosis are insufficient in explaining the comorbidity between PTSD and voices, and interventions based on these models have limited effectiveness in treating voices. This study presents a model that generates novel research into the factors underlying trauma-related voices that may inform effective interventions. Maintaining factors from existing models of PTSD and positive symptoms were combined into an integrative model of trauma-related voices. Theorised relationships between factors were used to identify possible differential pathways to direct and indirect voices. An integrative, trauma-related voices (TRV) model was developed that identifies multiple causal pathways to trauma-related voices. A future research agenda is proposed to test novel hypotheses based on the integrative model. The TRV model is a practical tool for complex case conceptualisation and generates novel hypotheses that may inform more effective treatments. Future studies are needed to test elements of the TRV model and should recruit transdiagnostic samples with diverse posttraumatic stress and voice hearing symptoms.

A Randomized Controlled Trial of an Online Mindfulness Program for Adolescents at Risk for Internalizing Problems

Article

Full-text available

Dec 2024

Objective: The purpose of this randomized controlled trial was to test the effects of an online, coached mindfulness intervention on momentary negative affect (mNA) for youth with high levels of trait negative affectivity. Method: Participants were 111 youth ages 12 to 17 years old (M = 14.17, SD = 1.60). Youth self-identified as 68% female, 29% male, and 4.5% gender diverse; 54.55% identified as White; 31.82 reported being Hispanic/Latinx. Participants were selected for having high levels of trait negative affect and were randomized to receive either the mindfulness program or no intervention. We used ecological momentary assessment to measure stress and emotions and to derive measures of mNA comprised of stressor-independent and stressor-reactive negative affect. The ecological momentary assessment protocol involved participants completing a short survey/diary entry on Qualtrics four times per day for 5 days. Internalizing symptoms were assessed with the Patient Health Questionnaire–8, Generalized Anxiety Disorder–7, and the Screen for Child Anxiety Related Disorders. Results: The mindfulness intervention resulted in a significant reduction in stressor-reactive negative affect (t = 2.001, df = 96, p = .048; Cohen’s d = .40), but not stressor-independent mNA or overall mNA. Additionally, reductions in stressor-reactive negative affect significantly correlated with changes in internalizing symptomatology (standardized β = .26, p = .032). Conclusions: These results indicate that among youth with high levels of trait negative affectivity, a relatively affordable and accessible digital mindfulness program significantly reduced stressor-reactive negative affect. The absence of an effect on stressor-independent or overall average mNA suggests some specificity of the effects of mindfulness to stressor-reactive negative affect in an at-risk sample of adolescents.

Ecological Momentary Assessment of Momentary Associations Between Availability of Physical Activity Space and Physical Activity Opportunities Among Children from Rural, Urban, and Suburban Locales

Article

Full-text available

Nov 2024
Int J Environ Res Publ Health

Using Ecological Momentary Assessment (EMA), this study examined associations between momentary availability of physical activity (PA) space and accessibility of PA opportunities among 608 elementary and middle school students who were participating in an obesity prevention trial in one mid-Atlantic state in the U.S. Smartphones prompted EMA surveys at random times to assess children’s perceived availability of PA space and accessibility of PA opportunities during out-of-school time, three to seven times each day over seven days. Multilevel logistic regression, which accounted for multiple responses per student, examined within- and between-person relations as well as the moderating effects of locale. The participants (M age = 10.88 years) lived in suburban (64%), rural (23%), and urban locales (13%). PA space availability was associated with greater PA opportunity accessibility (within-person OR = 9.82, p < 0.001; between-person OR = 22.61, p < 0.001). Locale moderated within-person relationships (p < 0.001), indicating that urban students with space were unable to use it or could be active but were without space. These findings advance our knowledge of temporal and environmental aspects related to childhood PA across diverse locales and can be used by policymakers to make informed decisions to ensure the use of age-appropriate, high quality, and safe spaces, particularly for children in urban areas.

Using Ecological Momentary Assessment (EMA) to understand occupation from the perspective of the experiencing self: An illustrative example in workers with type 1 diabetes

Article

Dec 2024

Mood, Activity, and Instability in Bipolar Disorder and Unipolar Disorder—An Exploratory Post Hoc Study Using Digital Data

Article

Dec 2024
ACTA PSYCHIAT SCAND

Background Mood, activity, and instability in symptomatology hold significant roles in bipolar disorder (BD) and unipolar disorder (UD). The objectives were to examine disparities in these symptoms among patients with BD and UD. Methods Data from two studies including patients with BD and UD, respectively, were combined for exploratory analyses. Patients provided daily smartphone‐based evaluations of mood and activity/energy for a 6‐month period. A total of 47 patients with BD and 59 patients with UD were included in the analyses. The dataset contains more than 13,000 patient‐reported evaluations of mood and activity. Daily mood and activity instability measures were calculated using the root squared successive difference method. Results In linear mixed effect regression models adjusted for age, sex, and work status, there were statistically significant lower levels of activity in patients with BD as compared with patients with UD overall, during euthymic states and during depressive states ( B : −0.61, 95% CI: −0.98; −0.24, p = 0.001). There were no statistically significant differences in mood instability and activity instability between patients with BD and patients with UD overall, during euthymic states and during depressive states, when accounting for multiple testing ( p > 0.012). Limitations Analyses were exploratory and post hoc. Findings should be interpreted with caution. The sample size was modest. Conclusion Patients with BD presented with lower level of activity as compared with patients with UD. There were no differences in mood and activity instability between these groups. Future studies including larger sample sizes should investigate differences between BD and UD. Trial Registration: ClinicalTrials.gov identifier: NCT03033420

Application of Time Series Analysis in Emotion Fluctuation Research

Article

Jan 2024

Jiayi Zhao

Studying Everyday Variability in Gender Identity and Expression: An Ambulatory Assessment Perspective

Article

Nov 2024

No literature so far explores gender variability at the day‐to‐day, within‐person level, to what extent aspects of the social context impact this variability, and to what extent transgender, gender non‐conforming, and cisgender people experience this variability similarly or differently. This project explores the potential that ambulatory assessment methods hold for the empirical study of daily gender variability. A review of strengths and limitations of common ambulatory assessment methods is presented, and considerations for how to employ these methods for studying daily gender variability are discussed. Based on this review, and experience studying similar constructs using the methods herein described, we propose that self‐report‐based ambulatory assessments, such as daily diaries and/or experience sampling, may be particularly suited for capturing day‐to‐day variability in aspects of gender identity and that observational ambulatory assessments, such as the Electronically Activated Recorder or wearable cameras/sensors, may be particularly suited for capturing day‐to‐day variability in aspects of gender expression. When used in tandem, these methods can allow the study of daily gender variability jointly from the “inside out” perspective of the agent, and from the “outside in” perspective of the observer. In addition, mixed‐method qualitative elements such as open‐ended descriptions of momentary gender‐related experiences can be incorporated. Ambulatory assessment methods can help researchers better understand gender at the level of daily, within‐person variability.

Opening the contextual black box: a case for idiographic experience sampling of context for clinical applications

Article

Full-text available

Nov 2024
QUAL LIFE RES

The experience sampling method (ESM) is increasingly used as a clinical tool in mental health care. Currently, ESM studies pay relatively little attention to assessing contextual factors, such as a person’s experience and perception of events, activities, and social interactions. This has been referred to as the ‘contextual black box’. However, personalized context information is essential for applications in clinical settings to gain insight in triggering and maintaining factors of psychopathology. Typically, ESM context items are designed for nomothetic research questions, to capture broad factors that are shared across individuals, such as ‘unpleasant events’. We provide an overview of such nomothetic items. We argue that these items have limited clinical utility and describe idiographic alternatives to ESM context assessment to obtain more specific and personalized information about individual clients. Specifically, we present three existing idiographic ESM techniques to context assessment with clinical potential. First, we illustrate open-ended ESM items that prompt clients to fill in text, such as a description of a specific unpleasant event they experienced. Second, we describe personalized response options and self-learning items that ask clients to define personally relevant response categories, such as types of events the client finds unpleasant. Third, we describe personalized ESM items that client and clinician select or formulate together for concepts of interest. We discuss the advantages and disadvantages of these idiographic techniques. Additionally, we suggest future directions for clinical research aiming to address the ‘contextual black box’ and enhance the potential of ESM in mental health care.

Investigating the impact of COVID-19 related worries and loneliness on alcohol consumption: an ecological momentary assessment

Article

Full-text available

Nov 2024
EUR ARCH PSY CLIN N

Adverse alcohol consumption is a major public health concern, which might have been further increased by the COVID-19 pandemic. In this study we investigated the impact of a lockdown stage on the association between alcohol consumption, loneliness, and COVID-19-related worries. We used smartphone-based Ecological Momentary Assessment (EMA) conducted during the COVID-19 pandemic in Germany. We recruited 280 participants from the general population, who experienced at least mild loneliness and distress due to the COVID-19 pandemic. We assessed daily alcohol intake, loneliness, and COVID-19-related worries every evening for 7 consecutive days across a no-lockdown [8th August 2020–1st November 2020] and lockdown stage [2nd November 2020–11th March 2021]. We did not find that a lockdown stage, compared to a no-lockdown stage, is associated with increased alcohol consumption. We found that loneliness, previous day drinking, and COVID-19-related worries were not associated with increased, but with decreased alcohol consumption. Moreover, COVID-19-related worries were more negatively associated with alcohol consumption during a no-lockdown stage compared to a lockdown stage. We found that the effect of COVID-19 related worries on alcohol consumption is mediated by loneliness. Our study suggests that heightened levels of worry can decrease alcohol intake. This association can be explained by loneliness: individuals who worry more are lonelier and thus less likely to engage in social drinking. However, during a lockdown stage, the negative association between worrying and drinking diminishes.

An Ecological Momentary Assessment and Intervention Tool for Memory in Chronic Traumatic Brain Injury: Development and Usability of Memory Ecological Momentary Intervention

Article

Nov 2024

Background Memory and learning deficits are among the most impactful and longest-lasting symptoms experienced by people with chronic traumatic brain injury (TBI). Despite the persistence of post-TBI memory deficits and their implications for community reintegration, memory rehabilitation is restricted to short-term care within structured therapy sessions. Technology shows promise to extend memory rehabilitation into daily life and to increase the number and contextual diversity of learning opportunities. Ecological momentary assessment and intervention frameworks leverage mobile phone technology to assess and support individuals’ behaviors across contexts and have shown benefits in other chronic conditions. However, few studies have used regular outreach via text messaging for adults with chronic TBI, and none have done so to assess and support memory. Objective This study aimed to develop and test the usability of memory ecological momentary intervention (MEMI), a text message–based assessment and intervention tool for memory in daily life. MEMI is designed to introduce new information, cue retrieval of the information, and assess learning across time and contexts. We tested MEMI via an iterative, user-centered design process to ready it for a future trial. Methods We developed MEMI by leveraging automated text messages for prompts using a REDCap (Research Electronic Data Capture)/Twilio interface linking to the Gorilla web-based behavioral experimental platform. We recruited 14 adults with chronic, moderate-severe TBI from the Vanderbilt Brain Injury Patient Registry to participate in 3 rounds of usability testing: one round of ThinkAloud sessions using the platform and providing real-time feedback to an experimenter (n=4) and 2 rounds of real-world usability testing in which participants used MEMI in their daily lives for a week and provided feedback (n=5/round). We analyzed engagement and quantitative and qualitative user feedback to assess MEMI’s usability and acceptability. Results Participants were highly engaged with MEMI, completing an average of 11.8 out of 12 (98%) possible sessions. They rated MEMI as highly usable, with scores on the System Usability Scale across all rounds equivalent to an A+ on a standardized scale. In semistructured interviews, they stated that MEMI was simple and easy to use, that daily retrieval sessions were not burdensome, and that they perceived MEMI as helpful for memory. We identified a few small issues (eg, instruction wording) and made improvements between usability testing rounds. Conclusions Testing MEMI with adults with chronic TBI revealed that this technology is highly usable and favorably rated for this population. We incorporated feedback regarding users’ preferences and plan to test the efficacy of this tool in a future clinical trial.

Does Trait Coping Exist? A Momentary Assessment Approach to the Evaluation of Traits

Article

Full-text available

Aug 1999

This study investigated the extent to which momentary reports of coping are a traitlike phenomenon and the validity of a questionnaire assessment of coping style. Participants (N = 96) completed the questionnaire, then monitored stressful events and coping activities for 2 days using a hand-held computer that administered a brief assessment every 40 min. Momentary reports exhibited a sizable traitlike component: Individual differences accounted for 42% of the variance for 2 coping items and 15–30% of the variance for 15 items. The questionnaire assessment of coping style was a poor predictor of average momentary coping; the coping style measure and the aggregated momentary measure of trait coping shared 23% of their variance for religion and 0–12% for the 16 other coping measures. Self-report assessments of trait coping are poor measures of the trait component of momentary coping and very poor predictors of coping in specific situations.

A Day at a Time: Predicting Smoking Lapse From Daily Urge

Article

Full-text available

Feb 1997