Last month, I attended the Annual Convention of the Association for Psychological Sciences in New York City. One of the conference highlights was a workshop by Dr. Tamlin Conner of the University of Otago in New Zealand. During the two-hour session, Dr. Conner offered a great overview of Experience Sampling Method—what we refer to as Ecological Momentary Assessment (EMA)—and provided practical advice for those new to this methodology. While the session covered a lot of information, here are a few takeaways for those interested in learning more about EMA strategies:
1. Methods for studying daily life go by a variety of names.
Some of the most common names include experience sampling, ecological momentary assessment, and ambulatory assessment. Experience Sampling Methods (ESM) originated in the 1970s, when researchers used pagers to randomly alert participants to report on their momentary experiences or subjective states. An excellent resource on this methodology is Csikszentmihalyi and Larson’s 1987 article or a book by Hektner et al. (2006). Ecological Momentary Assessment (EMA) was borne out of the behavioral medicine field and is often used interchangeably with ESM. However, EMA tends to encompass a broader set of data collection methods that range from daily diaries focused on psychological states to real-time physical assessment (e.g., the use of accelerometers to measure movement or sensors to monitor heart rate). For an excellent overview of EMA, see Shiffman, Stone & Hufford (2008). Finally, Ambulatory Assessment tends to be used by academic researchers who focus on the repeated assessment of physical variables in the context of everyday life.
2. Keep it Simple.
Dr. Conner suggests choosing “the least technical solution to answer your research question.” I think she’s right. And, from my perspective, simplicity should be evaluated in three ways. First and foremost, it’s important to determine whether the EMA system is simple for participants to use. Can they easily navigate the assessment interface or does it confuse or tax them? Second, from the researcher’s perspective, does the platform make it easy to schedule notifications and link questions or sets of questions to those notifications? We spent months thinking through these issues with the LifeData system, as we recognized that researchers needed an intuitive interface that would allow them to easily build EMA protocols. Finally, the platform should provide a simple way to access participant data. A fellow researcher at the conferences shared with me the challenges she faced in accessing data gathered on a custom EMA system developed by computer scientists at her university. Having wasted too much of my life cleaning and restructuring data, I have spent a lot of time with our development team trying to design an intuitive way to select and download data into a format that is ready for analysis. However, as Dr. Conner also noted, simplicity must be balanced with scalability.
3. Keep it Scalable.
Scalability refers to a platform’s ability to serve your research needs in the long run, as your need for additional features grows. While basic EMA protocols can be delivered through several available systems, watch out for functional limitations that won’t allow you to carry out that next study you’re dreaming about. You don’t want to invest in a system that won’t be able to handle larger, more complex studies in the future. One of the reasons we launched LifeData was because of some of the studies we hoped to conduct in the future, and our recognition that existing systems wouldn’t allow us to run those studies.
4. Select your technology based on your population.
Dr. Conner emphasized that understanding the kind of technology a study population uses is essential. To date, many researchers have relied on text messaging because of its widespread availability and participants’ familiarity with it. While text messaging systems cover a wide range of users, there are inherent limitations of this method, relative to app-based approaches. At LifeData, we anticipate that the rapid adoption of smartphones will soon make app-based EMA the gold standard for studying daily life. A recent study found that over 80% of 18-29 year-olds own a smartphone so we think it’s only a matter of time before nearly everyone is using them. But, again, going back to Dr. Conner’s point, it’s important to understand that there are trade-offs here in terms of reach, functionality, and backend data management.
Check in next week for part two of this blog.
To learn about the LifeData experience sampling system, click here.