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.

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.

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.

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.

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.