Ecological Momentary Assessment

Ecological Momentary Assessment in Health Behavior Research

Ecological Momentary Assessment (EMA) or Experience Sampling has been used in psychology research since the 1940s, emerging from a recognition of the accuracy limitations of recall. Our memories are systematically biased by emotional intensity, priming and psychological states, drawing into question the validity of retrospective autobiographical reports. EMA offers a method for capturing time-varying subjective experiences close to when they happen, which greatly reduces concerns of response biases and memory distortions.

More Accurate — Context-Aware

Ecological Momentary Assessment (EMA) aims to capture more accurate self-reports by asking people about their experiences closer to the time and the context in which they occur. The context could be the external environment, or the participants’ physical or internal state.

The mEMA app is context-aware. It taps into the sensors on phones (e.g. GPS, light, sound, proximity, motion, environmental data) and to those from wearable devices (e.g. heart rate, HRV, GSR, altitude, UV exposure, etc.) in order to deliver a survey at just the right moment. Furthermore, mEMA allows users to capture photo, video, and audio files, and to submit them as a survey response. This provides researchers with secondary contextual information that the user themselves may not have considered important.  All of this data is then sent to the platform for further analysis. Additionally, the mEMA platform is integrated with third-party databases, providing added data regarding local weather at the time any assessment was taken.

This method of data capture is much more accurate than the traditional method. By providing us with vast amounts of information regarding context, rather than relying on notoriously biased retrospective self-reports, EMA has been found to outperform pencil-and-paper data collection methods.

Captures Dynamic Processes

EMA allows for more frequent sampling (often multiple times a day) so that time-series analysis can be performed. This provides a deeper understanding of the processes at work rather than static snapshots from distant timeframes. Research has found that health behaviors, emotional experiences and strategies for dealing with stress fluctuate significantly throughout the day, and across different moment-by-moment contexts. Some suggest that the larger portion of this variability can be accounted for by changes in the situation and not the person (Hoppmann & Gerstof, 2013). EMA allows us to delve deeper into intra-individual variability as a valid developmental process. Furthermore, EMA methods allow for long-term data capture, providing insights into events that may happen less frequently and would be difficult to replicate in a lab.

Captures Interpersonal Dynamics

EMA can also be used to understand interpersonal processes. Our daily experiences are often linked with that of those closest to us, which is not easily captured through traditional methods. For instance, EMA studies in which each spouse provides timestamped self-reports throughout the day can help to illuminate these more complex dynamics.

History of EMA

Early EMA studies used paper-based daily diaries, usually asking participants to record their own behavior only once a day. With the advent of pagers, researchers were able to design signal-driven sampling studies by “beeping” participants throughout the day, signaling them to record data at that moment. This method, popularized by Czikszentmihalyi and colleagues, became known as the Experience Sampling Method. It aimed to capture participants’ subjective experience in the moment. As the technology developed, so too did the methodology.

With the introduction of handheld (palm-top) computers, many digital daily diary studies were carried out in a diverse range of health behavior fields. Additionally, ambulatory physiological monitoring has been included in some EMA protocols to capture biometrics such as EDA, heart rate and movement.

Emerging Technology

In 2015, 64% of American adults owned a Smartphone, a powerful tool for collecting both self-reported and passive data from either in-phone, external or wearable sensors. Bluetooth and WiFi allow for us to collect data from a variety of sources in the participant’s environment or from their physical body, combine it with their own perception of their experience, and to then deliver the entire data package to researchers anywhere. This widely available technology has the ability to revolutionize the way psychologists, therapists, physicians, and behavioral health researchers understand people.

The rapid development of wearable and in-home sensors now allows for integrated health-monitoring solutions to be more easily created, and for just-in-time interventions to be made more readily available. These technological developments allow the capture of objective measures, (e.g. activity, heart rate, etc.) alongside self-report data, alleviating concerns of socially desirable responding.

Current EMA methods are being employed for a wide variety of areas, including eating behaviors, drug and alcohol use, sexual behavior, emotional and overall well-being, and medical and psychiatric disorders.


mEMA by ilumivu

ilumivu’s EMA package represents the next generation of tools for EMA researchers by providing:

  • Data capture directly from participants’ own phones (iOS or Android), eliminating the need for a second device
  • Data stored locally on phone until within cell or WiFi range. Data is then automatically pushed to secure central server for high data security.
  • Integration of biometric (electrodermal activity, heart rate, or actigraphy) and self-reported data
  • Reduction in software development costs by accessing the Survey Editor yourself. No programming experience required to add and edit your questionnaires. We understand that during the pilot phase, there will be multiple iterations of your questionnaire, and we don’t believe you should pay extra to refine your questions in response to user participation and feedback. This is why we built the tools that enable you to edit your survey questions without input from ilumivu staff.
  • Reduction in cost of data transcription and transcription errors. All data is automatically sent to a central database to be viewed in real-time.

New to EMA? Download the Mobile EMA Guide.