According to Arthur Stone, who is one of the most prolific researchers and authors of EMA methodology, EMA refers to the "repeated collection of real-time data on participants' momentary states in the natural environment," and that "the key elements of EMA are real-time collection of data about momentary states, collected in the natural environment, with multiple repeated assessments over time." (Stone, Shiffman, Atienza, Nebeling, 2010). Let's briefly look at each of these elements.
Real-time data collection: Data are captured about individuals as life happens.
This means that instead of asking people how they felt or what they did in the past (which is much more frequently the case in health research), we capture how they feel as they're feeling it, and what they're doing, as they're doing it.
If a doctor asks you how much pain you've been having over the past month, you will likely try to recall how high your pain got and what proportion of the time your pain was at this level. This process puts a great burden on memory, which a tremendous volume of research has shown is fallible and subject to numerous biases that can shift the reality that we remember quite far from the reality that actually occurred.
EMA avoids this reliance on memory altogether by capturing data about mental and physical states at the time those states are occurring.
Momentary states: What's happening in and to individuals at brief slices of time (i.e., moments)
Imagine an assembly line making computers. To ensure quality, a robot is programmed to pluck individual computers from the line at random intervals and runs tests on them. You can think of each computer as a moment and the results of the tests as the data regarding the state of the system (the assembly line). That is data are collected on the behavior of the system by measuring the state of the computers at moments in time. Notice that not every computer (i.e., moment) is tested. Sure, testing every computer would provide an extremely detailed picture of the system but it would also pose a very heavy burden to do so. Therefore computers (moments) are sampled from among all possible computers (moments).
In the same way, EMA samples moments in peoples' lives. The data that are collected -- the variables -- depends on the research questions. Participants can be asked to rate how they currently feel on various emotions (happy, excited, frustrated, etc.), about the location and intensity of their physical pain, and their current level of physical capability on some scale, say, from 0 (not at all) to 10 (very much). Now, increasingly capable yet inexpensive devices are appearing that enable the measurement of a host of physiological parameters such as heart rate, skin conductance (for sympathetic arousal), heart rate variability, blood pressure, electromyography, and more. Things get particularly exciting when the relationship between self-report measures are correlated against objective physiological measures (but that's a topic beyond the scope of this article).
Natural environment: Data capture occurs as people go about their lives.
This is where the "ecological" in EMA comes in. Instead of bringing people into a lab, doing something to them, and then measuring their responses, we follow people out "in the wild" in their "natural habitats", where all the complexities of life and all the forces acting on people are preserved.
As an example, for my doctoral dissertation I investigated the effects of social disconnectedness on physical pain. In one of my studies, healthy undergrads were invited into the lab where they were exposed to a social interaction with a partner who was in fact a collaborator of the researchers posing as another participant. For some participants (the lucky ones), the partner was very warm and friendly, whereas for other participants, the partner was cool and aloof. Physical pain sensitivity was measured both before and after this social exchange. Everything was scripted and tightly controlled. But the problem is that uses contrived experiences in a relatively safe setting (a university lab) that are isolated from all the wonderful complexity of life. And this should make us wonder whether the results we obtain in such experiments apply beyond the walls of the lab, which is really the place where we hope the results apply because after all, life doesn't happen in a lab, it happens "out there". EMA techniques and technologies allow us to get "out there".
Repeated assessments over time: Data are captured multiple times daily over many days.
In most experimental studies, measurements are taken once. Participants roll in, measures are taken, then participants roll back out. In pre-post studies, measures are taken twice. In follow-up studies, measures may be taken three or four times. These studies are typically referred to, collectively, as repeated measures (RM) studies. EMA should not be confused with RM studies. EMA studies typically involve multiple assessments per day, repeated over a number of days. This dense sampling provides a level of temporal resolution that permits the examination of how dynamic processes unfold over time. For example, we can look at whether certain physiological parameters (arousal) immediately precedes anxious thoughts, or whether increasing levels of pain vs. steadily high pain levels lead to differences in psychological wellbeing.
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