Experience-Sampling Method

The experience sampling method (ESM) is a way of studying everyday life. People report their thoughts, feelings and behaviour several times a day, in the moment and in their normal surroundings. Most studies use short questionnaires on a smartphone. The method shows how experience changes from moment to moment, which one questionnaire completed from memory cannot.

How Does an ESM Study Work?

ESM has a second name, ecological momentary assessment (EMA). The two names describe the same approach: repeated reports made in the moment, in daily life.

An Example: One Week of Stress and Cravings

A study of daily stress and alcohol cravings might work like this.

Each participant gets a smartphone app. For one week the app prompts them at random times, five times a day, between 5 p.m. and 10 p.m.

At each prompt the person rates their current stress and the strength of their craving. They also say whether they are alone or with others.

In the background, the app records the phone’s GPS location at each prompt. The researchers then know where the person was.

When the week ends, the researchers can ask several questions. How does stress change through the evening? Does company change cravings? Do some places go with stronger cravings?

The example is invented, but it shows the three parts of any ESM study: a device that prompts, a short questionnaire and a record of context.

Most studies run for several days or weeks. That makes ESM an intensive kind of longitudinal study, which follows the same people over time.

What the Questions Ask

Each prompt, often called a beep, signals the participant to answer a short questionnaire on the device.

The questions cover two things:

• Inner State: Current thoughts, feelings and behaviour, such as mood, stress, urges or social contact.
• Context: Where the person is, who they are with and what they are doing.

The format can be open-ended, closed-ended or a rating scale, depending on the study’s aims. Many studies use validated scales, which are questionnaires already tested for measuring things like mood, stress or social connectedness.

Researchers try to keep each questionnaire short. The aim is under five to seven minutes. They may shorten it, or send fewer prompts, if pilot participants find it too much.

What Sensors Add

Some studies also use sensors in smartphones or wearable devices. They collect passive data, which means the participant does not have to do anything.

Typical sensors are GPS, accelerometers, which measure movement, and microphones. They can capture location, movement, social interaction and bodily responses.

Sensor data give a richer picture of the context around an experience. They can also show objective signs that go with what participants report.

The Device

Smartphones are now the dominant tool for both signalling and collecting the answers. A participant’s own phone is the easiest case, because nobody has to learn a new system.

Not everyone has equal access to technology. Comfort with it varies too. Researchers should think about participants with visual or hearing impairments, different levels of technological skill and different preferences for entering answers.

Many apps let people mute or snooze a notification. That stops unwanted interruptions in sensitive moments and makes the study easier to live with.

Technology also fails. Notifications may not appear, calls may go unanswered, and systems may crash and lose data. Researchers need to check that the app works on participants’ phones, and to have a backup plan.

When Are Participants Prompted?

Researchers decide when to prompt. There are two basic designs, and many studies combine them.

Fixed and Random Prompts

In time-based sampling, prompts come at times chosen in advance, whatever the person is doing. The times can be fixed or random.

A fixed schedule might prompt every hour, or in the morning and evening. It suits experiences that change in a predictable way, such as mood across a working day.

A random schedule prompts at unpredictable times. It gives a more representative sample of the day. It also reduces recall bias, the distortion that comes from remembering an experience afterwards.

Time-stratified sampling is a middle route. The researcher splits the day into blocks, and the device picks a random time within each block.

Every part of the day is covered. The prompts stay unpredictable.

For example, a study might use a window from 8 p.m. to midnight. The window is split into two two-hour blocks.

The participant gets one prompt at a random time in each block, for seven days.

Published studies have used anything from a prompt every 30 minutes to one a day.

Frequent prompts, especially at awkward times, put a burden on participants, meaning extra effort and disruption. They can lead people to answer less often.

Event-Based and Mixed Designs

In event-based sampling, the participant completes a report each time a chosen event happens. The event might be outside the person, such as a conversation, or inside, such as a surge of anxiety.

A smoking study, for example, might ask for a report on every cigarette, with its time, place, mood and company.

The researchers must define the event precisely. In a smoking study, does a single puff count, or only a whole cigarette?

Events suit this design when they are clear-cut, noticeable and fairly frequent.

The weakness is that no one can check that every event was recorded. People may forget, or be unable to stop and record. The reports then lean towards memorable, intense events.

Hybrid designs combine approaches to capture both the flow of a day and rare events.

A study can add an evening diary to random prompts, to ask how the whole day went. Or it can add an event report after each social interaction that lasts beyond a set length.

Ecological momentary assessment covers each design in detail.

What Is the Evidence for ESM?

Four questions matter. Do answers given in the moment measure something real?

Are they more accurate than memory? Do people answer enough prompts? And does being asked change what is being measured?

Do Momentary Reports Work? (Csikszentmihalyi and Larson, 1987)

The founding test asked whether answers given in the moment could be trusted as measurements.

• Aim: To test whether answers given in the moment can be trusted as measurements: whether they are reliable, meaning repeatable, and valid, meaning they track something real.
• Method: Drawn from the authors’ own ESM research with adolescents and adults. Participants carried pagers and filled in short forms when signalled at random times. Scores were compared with physiological measures, one-off psychological tests and behavioural indices.
• Results: Reports from the same person were reasonably consistent across occasions. ESM scores correlated with the physiological, psychological-test and behavioural measures they were checked against.
• Conclusion: Momentary self-report is a defensible way to study the texture of everyday mental life, not just a convenient substitute for a questionnaire.

The method’s own originators ran this validation, using their own samples, which were heavy with adolescents and young adults.

Later researchers had to show that it held beyond those groups. The studies below do part of that work at much larger scale.

Are Momentary Reports More Accurate Than Memory? (Overton et al., 2022)

The usual claim is that answers given in the moment are more accurate than recall. That has not been tested directly.

• Aim: To ask whether momentary pain ratings are valid and reliable in people with persistent pain.
• Method: A systematic review and meta-analysis. 14 studies met the criteria, out of 3,270 found.
• Results: Momentary ratings correlated strongly with recalled pain intensity and interference, in the band the authors called good to excellent. The evidence was rated low or very low, and the included studies were judged doubtful or inadequate.
• Conclusion: Momentary ratings looked valid and reliable in persistent pain, at a preliminary level.

The review measured agreement between the two kinds of report. It did not show which is closer to what happened.

A strong link is what you would expect if both were valid. It is also what you would expect if recall were adequate for a stable quantity such as average pain.

Momentary reports carry error of their own.

One overview estimated the error in single-item momentary affect in two ways, using the same data (Tuerlinckx et al., 2026). The answers were 30 to 50 percent and 27 percent of the variability in responses.

So the accuracy advantage is plausible but not established. The case that experience is best captured where it happens does not depend on it.

How Many Prompts Get Answered? (Wrzus and Neubauer, 2023)

Compliance is the share of scheduled prompts that a participant answers. Wrzus and Neubauer (2023) measured it across the published literature.

• Aim: To find out how a study’s design and sample predict compliance and dropout.
• Method: A meta-analysis, which pools the results of many studies, of 477 articles (496 samples, 677,536 participants). The team coded each study’s length, number of daily prompts, incentives, sample and reported compliance.
• Results: The average study scheduled six prompts a day for seven days and got 79% compliance. Studies with more prompts a day tended to run for fewer days. The number of prompts did not predict compliance, but financial incentives did.
• Conclusion: Compliance can be managed by design. Payment is one of the few levers with a shown effect.

With nearly 500 studies, this is the strongest evidence on average compliance. It also challenges the idea that burden alone drives non-response. How many prompts were scheduled mattered less than whether people were paid.

The causes are less settled. A separate meta-analysis of mobile ESM in adults found 81.9% compliance (Williams et al., 2021). It agreed on the average.

In non-clinical data sets it linked compliance to the number of prompts a day and the number of items per prompt. It found no significant payment effect among its significant factors.

Compliance is lower in young people. A meta-analysis of 285 samples of children and adolescents found about 72% (Drexl et al., 2025).

Does Being Asked Change the Answers? (Cajita et al., 2023)

Reactivity means that measuring something changes it. Asking people to rate their mood several times a day might make them more self-aware, and that could alter their mood.

Three things make it more likely:

• Trying to Change: People are trying to change the behaviour being tracked.
• Asking Ahead: People are asked whether they intend to do something in the next hour.
• One Behaviour: Only one behaviour is tracked again and again, which heightens awareness of it.

Tracking several behaviours makes reactivity less likely. Anonymity, neutral wording and light schedules help too.

Cajita et al. (2023) tested for it in a 12-month weight-loss trial in which people completed, on average, more than a thousand random surveys. The number of surveys a person completed did not predict change in three measures of eating behaviour.

That kind of test is unusual. Of the 496 samples that Wrzus and Neubauer (2023) coded, 475 reported no test of reactivity at all.

So reactivity is a risk to design against. It is seldom checked, and it has been shown neither to be routine nor to be absent.

Strengths of ESM

The method has four main strengths:

1. Real-Time Reports: Reports rely less on memory. Retrospective reports are distorted by the shortcuts and current mood people use to reconstruct a period (Shiffman et al., 2008).
2. Ecological Validity: This is the extent to which findings apply to real life. ESM gathers data in daily life, not a laboratory.
3. Change Within a Person: Repeated reports show how one person’s experience moves over time, which a single questionnaire cannot.
4. Objective Context: Sensors can add measures such as location and movement to what people report.

These advantages are strongest against one-off retrospective questionnaires. A once-daily diary is also completed close to events and protects against much of the same recall bias, with less burden (Laurenceau & Bolger, 2013).

None of them guarantees accuracy.

Ecological validity concerns whether the setting and the cues are representative (Kihlstrom, 2021).

It does not by itself show that a momentary report is accurate. Nor does it show that prompting leaves the experience unchanged, or that the people who take part resemble everyone else.

Limitations of ESM

The method has six main limitations. Four of them are explained in more detail below the list.

1. Participant Burden: Long or frequent protocols make people less willing to take part (Smyth et al., 2021).
2. Missing Answers: If people skip prompts when the thing being studied is strongest, the data miss it.
3. Reactivity: Being asked may change what is measured, though one large study found no sign of it (Cajita et al., 2023).
4. Privacy Risks: Location and other sensor data can expose sensitive facts about a person (Rudolph et al., 2018).
5. Specialised Analysis: Answers from the same person are not independent, so ordinary tests do not fit and researchers need multilevel models.
6. Measurement Quality: There is no widely agreed way to build momentary scales, and many measures lack psychometric evidence (Tuerlinckx et al., 2026).

Burden Lowers Willingness

Burden is the effort and disruption that repeated prompts put on a participant. It can lower compliance and the quality of the data.

Smyth et al. (2021) tested design features directly. They gave prospective participants descriptions of possible studies and assigned them at random. The studies differed in length, prompt frequency, prompt length and pay.

Shorter, less frequent, shorter-worded and better-paid studies made people more willing to take part and more likely to say it would be easy.

Researchers reduce burden in several ways:

• Brief Questionnaires: Ideally under five to seven minutes.
• A Balanced Schedule: Five to eight prompts a day may suit many studies, though no firm guideline exists.
• Flexible Windows: Participants can adjust when prompts arrive.
• Clear Support: Clear instructions, check-ins during the study and feedback afterwards.

A light protocol can work well. Chirokoff et al. (2025) used a paid, seven-day protocol with five prompts a day to track medication adherence.

The patients had schizophrenia, depression or substance use disorders, and a healthy comparison group took part too. Compliance was over 90% in every group, well above the 79% average.

Missing Answers Can Bias Results

Participants miss prompts because they forget, are busy or have technical problems. The worry is not only less data. It is which data go missing.

Suppose the most stressed people are the most likely to skip a survey. The results would then underestimate the link between stress and other variables, because the data are missing where the effect is strongest.

Researchers reduce missing data by design. A simple, convenient system with clear instructions and reminders helps. So do a sensible schedule and payment.

They also use statistical methods that make use of every answer a person did give, instead of dropping anyone with gaps. Where the reasons for missing answers are unclear, firm conclusions should wait.

Privacy Risks Rise With Sensors

Reports from daily life can be sensitive. Sensors add to that. They collect data about a person’s day without a prompt each time.

Rudolph et al. (2018) interviewed people who use drugs and men who have sex with men about studies that tag momentary reports with location. The participants weighed the scientific value of location data against real fears about who might see it and what it could reveal.

Safeguards must fit the people and the topic. One generic consent template is not enough.

Informed consent should state the time commitment, the number of prompts and the disruption, and explain how data are stored, secured and made anonymous. Participants should know they can decline or reschedule prompts without penalty.

Analysis Needs Specialised Methods

Each person gives many answers. Answers from the same person are not independent of each other, and people give different numbers of answers.

Ordinary tests, such as ANOVA or simple regression, assume independence, so they do not fit. Researchers use multilevel models, which separate changes within one person over time from differences between people.

The split matters. A pattern found by comparing different people at one moment need not hold for how any one person changes over time (Molenaar, 2004; Sliwinski et al., 2010).

Studies on mobile devices also produce large, complex datasets. Researchers plan how to clean the data before the study starts.

They check for random responding, and they set rules in advance for dropping a participant, or a single moment, because of too few responses, technical errors or suspicious answers.

What Is ESM Used For?

Tracking Change in Therapy

ESM follows symptoms and treatment response as they unfold. Studies need not wait for the end of treatment.

Snippe et al. (2024) used daily-life data to find the order in which emotional, cognitive and behavioural gains emerge during depression treatment. Thewissen et al. (2008) tracked daily self-esteem and paranoia.

A systematic review of ESM in psychotherapy research treats it as an emerging standard for measuring outcomes. It also found that studies report the method unevenly (Mink et al., 2025).

Eating Disorders

Binge eating, purging and restrictive eating unfold within hours, which suits momentary sampling.

Racine et al. (2026) reviewed 185 studies. They asked what keeps eating-disorder symptoms going.

Negative feelings reliably came before binge eating and purging and predicted them. Positive feelings seemed more relevant to exercise. In some samples they mattered for restricting food.

Few of these studies tested the thinking and relationship factors that leading treatment models stress. So negative feelings are better evidenced than the rest.

A pooled analysis of 42 studies, with 1,745 participants, looked at emotion around binge eating (Borm et al., 2025). Negative emotion was higher before binge eating than before regular meals.

It affirmed distress as a trigger but challenged the idea that bingeing relieves distress.

Real-Time Help in Mobile Health

The same data can trigger help at the right moment. A just-in-time adaptive intervention is a mobile-health treatment that decides, from a person’s current state and context, whether, what and how to help (Nahum-Shani et al., 2018).

Ismail and Al Thani (2022) tested one that aimed to reduce sitting at work. It was practical to run and could shift short-term behaviour. As a single trial, though, it is weaker evidence than a meta-analysis.

Voice-Hearing and ADHD

Cusworth et al. (2025) asked 40 voice-hearers to answer 10 prompts a day for seven days.

Stressful events predicted hearing voices, both at the same moment and at the next prompt. Pleasurable events did not, and neither did the intensity of positive or negative emotion.

Completion was 63%. People who heard voices more often missed fewer prompts, so the missing answers were not random.

Ben-Dor Cohen et al. (2023) followed 122 young adults, 60 of them with ADHD, over five days of short thinking tasks, five times a day. How much their inhibitory control varied from moment to moment helped explain differences in emotion dysregulation, meaning difficulty managing emotions.

A single laboratory session could not show that variation. The design is correlational, so it cannot show cause.

Work and Development

Some projects repeat short bursts of ESM. In a measurement burst design, a burst of daily reports is repeated at intervals, such as every year.

It shows both the moment-to-moment texture of an experience, such as negative feelings, and how that texture changes over years (Zavala et al., 2026).

At work, Kim et al. (2018) used daily assessments to show that short breaks were associated with better performance afterwards. Overall work engagement changed the size of the effect.

How Does ESM Compare With Other Methods?

Questionnaires and Diaries

The most common alternative is a single questionnaire that asks people to sum up a period from memory. It is far less burdensome.

But people reconstruct the period using shortcuts and their current mood, so the report is distorted (Shiffman et al., 2008). That is the strongest argument for ESM.

A diary entry once or twice a day sits in between (Laurenceau & Bolger, 2013).

It is close enough in time to avoid most long-range recall bias, with a fraction of the burden. If the question is how a person’s whole day went, a diary can be the better choice.

Diaries can miss fast processes, though. Shin et al. (2022) compared 90-minute ESM data with once-daily diary data from similar clinical and control samples.

A measure of how strongly one moment’s emotions predict the next separated anxious and depressed people from controls in the ESM data but not in the diary data.

Sensors and Laboratories

Ambulatory assessment is the wider field (Fahrenberg et al., 2007; Trull & Ebner-Priemer, 2013). It also includes continuous recording of heart rate, movement or location that needs no response from the participant.

That avoids burden and reactivity for what it captures. But it cannot record anything that only the person can report, such as mood or meaning.

Laboratory experiments give the most control, because other variables can be held constant. The price is ecological validity.

Csikszentmihalyi and Larson (1987) argued that laboratory measures of mood and cognition do not reliably predict how the same processes behave where people live.

Where Did ESM Come From?

Two Names, One Approach

Csikszentmihalyi and Larson (1987) named the experience sampling method, which they used to study the patterning of everyday thought and mood.

Stone and Shiffman (1994) later named ecological momentary assessment for a family of methods already in scattered use in behavioural medicine.

Ambulatory assessment is a wider term (Fahrenberg et al., 2007). It also covers continuous recording from wearable sensors. Daily diary methods use a single end-of-day entry rather than several momentary ones (Laurenceau & Bolger, 2013).

The labels reflect different research traditions more than different techniques.

From Pagers and Paper to Smartphones

Early studies paired paper forms with a signal. In the validation work of Csikszentmihalyi and Larson (1987), participants carried pagers. They filled in short forms whenever they were signalled at random points in the day.

Programmed wristwatches could beep at random or fixed times.

By 1994 researchers were using written diaries, electronic diaries, telephone-based reports and physiological sensors for the same purpose (Stone & Shiffman, 1994).

Smartphones are now the dominant tool, which is why most studies today use a phone app.

The Naming Paper (Stone and Shiffman, 1994)

Stone and Shiffman (1994) reported no new data. They drew together studies of depression, social support, diet, work activity, drug use, stress, medication adherence and asthma. These had used diaries, electronic devices, phone reports and sensors.

They proposed one name with three defining features: reports made in the moment, in the person’s own setting, and repeated.

They argued that a shared name would let findings from these separate areas be compared and added up.

The paper is definitional, not empirical. So it cannot be judged on statistical rigour. Its lasting influence is that the name and the three features became the field’s standard vocabulary.

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