Developing an EMA app


Since the 1980s, in an effort to address the limitations associated with traditional quantitative methods, there has been a growing interest in developing innovative approaches to assessing psychological phenomena in real-time, or within daily life.  Such methods are called ecological momentary assessment (EMA).  The earliest form of EMA was the daily diary.  However, as mobile electronic technologies advance new approaches to EMA have become possible.


Up to this point, EMA has relied on cell phone calls, text messages, and handheld computers (PDAs), each of which come with inherent limitations that have constrained its use.  On the one hand, the use of cell phone calls and text messages requires the use of cell phone plans.  Further, the coding of this data can be cumbersome and, as a result, be a delimiting factor.  One the other hand, the introduction of PDAs into daily life is intrusive, and requires participants to become familiar with and carry a distinct device.  Additionally, this approach is costly, requires programming expertise, is constrained by software limitations, and requires researchers to personally give, or send, these devices to each participant and familiarize them with how to use it.


In order to solve problems associated with current EMA approaches, our lab has developed a dynamic EMA system that taps into smartphone application (“app”) technology: the iHabit app.  This app system is designed to be flexible so that it can be programmed to (a) generate data pertaining to enumerable research questions.  It can also be programmed to (b) provide prompts that break into the busyness of the day, helping individuals be more reflective and intentional about their thought patterns and behavior (see Self-monitoring and personal change below).  With the widespread use of smart phones, the iHabit app makes EMA more practical for multiple purposes, and in multiple contexts, thereby potentially expanding the domain of a more acute and powerful approach to psychological science.


Studying patterns in human life


We are interested in measuring psychological aspects of human life—experiences, states and activities—as they occur and change within the context of everyday life.  We are equally interested in detecting how psychological changes correlate with other occurrences in daily life.  Neither of these aims can be accomplished by laboratory experiments (which take place in an artificial context) or questionnaires (which depend upon retrospective and generalized responses and, usually, sample participants only once).  We have, therefore, developed an approach to EMA that allows the measurement of dynamic micro-processes (processes that are not stable but may, nevertheless, occur in distinct situations), and allows the measurement of intra-subject variability (changes within participants).  Here are a couple of examples to illustrate how the iHabit app can be used for these purposes:


Example 1: We have recently performed an EMA study that examined self-efficacy amongst undergraduate freshman.  We found that self-efficacy should not necessarily be thought of as a stable construct.  Rather, at the beginning of the semester students tended to vary greatly in the degree to which they thought they were able to succeed academically whereas as the semester progressed their self-efficacy became more stabilized.  This dynamic pattern, wherein self-efficacy varies more when one is a novice, may be a reoccurring pattern in various contexts—school, sports training, work, relationships, etc.  If so, since self-efficacy is known to promote optimal performance (Bandura, 1982), these findings would have implications for how we should structure programs, as well as social and work environments, in order to promote growth, development, and success.


Example 2: We have used the iHabit app to measure intra-subject variability in bedtime and wake-time amongst college students and have made some novel observations.  We found that those with higher fluctuations in bedtime, but not wake-time, perceived themselves to be good students, as knowing how to write papers, and had positive views about their position in life and their academic abilities.  We theorize that this might be because those who perceive themselves as in a good position in life and in their academic progress are more likely to stay up later when necessary to meet demands but do not alter their wake-time.  Interestingly, we further found that those with higher fluctuations in wake-time, but not bedtime, felt anxious about personal relationships, did not feel like they had close relationships, and did not feel connected to their institution.


Ultimately, we are interested in measuring psychological aspects of human life as they occur and change within everyday life—and we are interested in developing better ways to do so—in order to gain a more complete understanding of ourselves; and to uncover patterns and tendencies that promote human growth and flourishing.


Self-monitoring and personal change


In the way that technological advances allow for new approaches to EMA, these advances, likewise, allow new ways to intervene within our daily lives; that is, it allows for ecological momentary intervention (EMI).  It has long been known that peoples’ awareness, behavior and experience are altered when they are knowingly being assessed, or even when they are monitoring themselves.  This phenomenon is called “reactivity”.  Reactivity is enhanced when assessment occurs repeatedly and in close proximity to the modified behavior and cognitive patterns (Shiffman 2009).  This makes EMI particularly effective in promoting positive behavioral and cognitive change as it can be used to repeatedly draw attention to the focal behavior, or thought pattern, close to when it occurs, and in the context within which it occurs.


Within the last several years EMI has been effectively used to promote change in individuals who are actively seeking change.  For example, it has been used to help with smoking cessation (Shiffman 2008; Berkman 2011; Ferguson 2011), facilitate emotional regulation (Bylsma 2011), and facilitate prevention behavior amongst HIV-infected individuals (Cook 2010; MacDonnell 2011).  However, up to this point EMI has predominately relied on cell phone calls, text messages and PDAs, and thus have been limited in scope.  Using the iHabit app, we have been able to test the viability of using EMI to promote positive behavioral and cognitive change in a wider demographic, and in individuals who are not expressly seeking change, or are even made aware that using the app may promote change.


We have used the iHabit app to ask college students about how they spend their time (Runyan et al. 2013).  What we found was that participants who used the app (the “iHabit group”) indicated wasting more time than those who had not.  We, further, found that the amount of time the iHabit group wasted negatively correlated with end-of-semester GPA (r=-.49), at a strength higher than ACT scores (r=.42).  No correlation was found amongst those who did not use the app (r=.15).  This provides indication that the iHabit group became, not only more aware of the time they wasted, but more accurate at assessing wasted time as a result of using the app.  To add to these findings, at the end of the study, 77.7% of the iHabit group reported that using the app made them more aware of how they spent their time, and 44.4% reported changing their behavioral patterns as a result of using the app.  Several lines of evidence indicate that these self-reports are, to a significant degree, accurate; specifically—we found that these self-reports correlate with several behavioral patterns (i.e., amount of time spent study, amount of time spend using electronics) only after having already used the app.


The above provides indication that, not only can the iHabit app be used to uncover patterns and tendencies that promote human growth and flourishing, it can also be used to help promote growth and flourishing in peoples’ lives.  And this is our lab’s ultimate aim in seeking to understand ourselves better.

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