# Use of Ecological Momentary Assessment and Intervention in Treatment With Adults Meghan E. McDevitt-Murphy, Ph.D., Matthew T. Luciano, M.S., and Rebecca J. Zakarian, B.A. ![[Use of Ecological Momentary Assessment and Intervention in Treatment With Adults Meghan E. McDevitt-Murphy, Ph.D., Matthew T. Luciano, M.S., and Rebecca J. Zakarian, B.A.pdf]] #hope-s [[HOPES Project Index]] [[20211120 All about EMA and EMI]] - EMA generally involves prompting respondents to an- swer a series of questions about their behavior, thoughts, or emotions. This may include how often during a given time frame the behavior occurs, the context in which the behav- ior occurs, and any other accompanying information that the researcher wishes to obtain. EMA researchers may also be interested in asking contextual questions about a research participant’s moment-to-moment environment. Although EMA has a rich history in research to inform the concep- tualization of clinical issues, such as substance use disorders (4), depression (5), and self-injury (6), it has not seen wide uptake in clinical settings. Historical, contextual use of EMA - EMA may be useful in con- ducting a functional analysis, because the data can provide information about the antecedents and consequences of a given behavior in the context in which it occurs (7). EMI - an outgrowth of EMA, EMI goes a step further and provides intervention re- motely in respondents’ natural environments. In EMI, the researcher, clinician, or designer can program an app to ob- tain information from the respondent (participant, client, or patient) as well as prompt the respondent to engage in specific behaviors at opportunistic times. Typically, EMI is fully automated, and the clinician or researcher can view the data collected; however, the clinician or researcher does not interact with the respondent directly (thus, this is not a form of telehealth). [[List of EMA EMI SYSTEMS]] [[202201271457 EMI need to link with a human support]] - These reviews pointed out that several studies have shown that an important moderator of EMI success is the extent to which it is delivered with “human support,” in the form of a mental health professional who helps to guide the patient’s use of the EMI-based app. HEALTH BEHAVIOR CHANGE Health behavior change is a frequent target of EMI and EMA, with several empirically supported applications available across operating systems and platform. Such health behaviors include [[smoking cessation]] (24), [[weight management]] (25), and [[Physical Activity]] (26), among others (27, 28). Although behavior change is an increasingly studied area of interest for psychologists and psychiatrists, ==the use of EMI and EMA in this field has shown mixed results in addressing these problems==. EMI and EMA for smoking and nicotine use are among the most frequently studied, although results from open and randomized trials are unclear on how effectively these interventions function in practice. This is possibly due to different populations studied, differing intervention aims, and unique intervention components. One often-used practice in EMI is to provide specialized messages to respondents. For example, an open trial of college student smokers aiming to quit cigarette use included text-based coping messages timed to high-risk situations. Results showed that 43% of students reported a 24-hour quit attempt, and 34% had actually quit smoking after six weeks (29). Specialized messages can also include tailoring treatment content to be responsive to specific triggers to smoke. Hébert and colleagues (30), for example, found that EMI messages specifically addressing urge to smoke, cigarette availability, and stress-related urges reduced the salience of these trig- gers among treatment-seeking adult smokers. Conversely, general messages that provided nonspecific cessation advice or encouragement to maintain abstinence did not reduce specific trigger-related urges to smoke. Collectively, these findings suggest that the most effective EMI strategies are individualized and responsive to specific, personalized needs. Another intervention element used in EMIs for health behavior change is a scheduler function. In a controlled trial of smokers wishing to reduce cigarette use, participants were randomized equally into either a computerized scheduled gradual reduction (CSGR) group or a control group who received a paper manual on smoking reduction. Those in the CSGR group received a device that displayed the days left in the program and the hours and minutes remaining until the next prompted cigarette. Results indicated that there was no significant difference between the control group and CSGR group among cigarettes smoked, although the study might have been underpowered to detect a difference. However, both groups showed reductions in cigarette use overall (31). Other EMI approaches merge several therapeutic elements, including audio recordings and online modules, with text messages. Participants in one such program, Happy Ending, found significantly higher repeated point abstinence rates than participants who were given a self-help book (32). Therefore, developers of smoking-based EMI programs may find benefit in combining several different tools from which clients may choose on the basis of their individual needs. Weight management is another focus of behavioral health EMIs that has received empirical attention in the literature. Like EMIs that target smoking, interventions that target weight management often combine several distinct approaches. In an early example of a mobile ecological momentary approach for weight management, the developers created a “microcomputer” that reminded participants to report food intake every four hours. Feedback was then displayed for total calories reported for the day and remaining caloric intake limit for the day. This device also provided praise, instructions, and recommendations for healthy eating. The microcomputer was combined with weekly therapy appointments that helped participants set nutrition goals. Findings from this study showed that the microcomputer group outperformed the control group (who also met with a therapist and self-reported food consumption and exercise with a pen-and-paper method) on body weight and with respect to change in a composite score combining caloric intake and physical activity (33). [[Physical Activity]] EMIs have also targeted physical activity more directly. In one intervention, 37 healthy but underactive adults monitored their physical activity levels through a PDA that prompted them twice per day. This device then provided feedback, goal setting, and support. Compared with participants who used standard written physical activity materials, those who used the PDA reported significantly greater eight- week mean estimated caloric expenditure levels and minutes per week in activity. EMIs that allow clients an opportunity to check in and report event occurrences as they happen are an important aspect of programs aimed at weight management and health behavior change more broadly. This process of “event- contingent recording” (whereby a client can record an occurrence of a preidentified event) may be especially suited for health-related behavior change, because it involves tracking and responding to infrequent behaviors that could be missed with a signal-contingent design (a design in which notifications are sent at fixed or random intervals). CLINICAL IMPLICATIONS [[20211120 All about EMA and EMI]] Although additional, rigorous research is needed to understand when and how to maximize the efficacy of EMA and EMI, these techniques may be useful adjuncts for psychotherapy. In the framework of CBT, the notion of bringing the therapy material into the patient’s natural environment fits with the concept of generalization (or the application of learning outside of the context in which it initially occurs). The use of EMA may help to enhance the clinician’s understanding of the topography and function of a given behavior or thought pattern in the client’s usual environment and in the flow of his or her usual routine. This functional understanding may be further enhanced when data about location are collected either actively (respondent input) or passively (through the device’s GPS function). EMA also provides a window into the client’s life that is somewhat unfettered by the usual threats to the validity of autobiographical recall. Typically, when clinicians query about a patient’s week, the information that the client provides may be influenced by memory limitations or by the client’s current mood or cognitive state (13). Furthermore, systematic memory biases associated with depression or anxiety may further influence responding retrospectively (34). In other words, EMA offers a method for the client and therapist to collaboratively investigate behavior in context in real time, circumventing the problems of hindsight bias or inaccurate reporting that may result from retrospective recall. EMA can be used to develop a better understanding of the functional antecedents and correlates of a given behavior pattern that could, in turn, be used in planning interventions. EMI can take this a step farther: Therapists capitalizing on EMI technology have a rare opportunity to expand the context in which therapeutic learning can occur from the therapy room to the patient’s real-world environment. USING EMI AND EMA IN PRACTICE There is a disconnect between the realm of apps that have been used in research studies to implement EMA and EMI and those that are widely available. Many mental health apps can incorporate self-monitoring or generate prompts for specific behaviors, although these differ from EMA or EMI because the apps do not collect data that may be transmitted to a therapist. These apps are commercially available, generally through platform-specific app retailers (e.g., Apple’s App Store or Android’s Google Play), and most of these apps have not been subjected to empirical scrutiny. There are some professional resources that may help clinicians make decisions about which apps they could incorporate into their practice in a way that includes the features of EMA and EMI. The Anxiety and Depression Association of America reviewed and rated apps on their ease of use, effectiveness, personalization, degree of interaction, and evidence base (35). Additionally, the Trauma Psychology Division (division 56) and the Division of Media Psychology & Technology (division 46) of the American Psychological Association collaborated to publish a list of mental health apps rated on similar dimensions (36). A recent article aimed at practitioners reviewed commercially available apps and commented on the research evidence for each, which may additionally be a useful guide for practitioners (37). In terms of a specific recommendation, the Cognitive Behavioral Institute of Albuquerque offers a free app called iPromptU to deliver content consistent with CBT, including homework and self-monitoring. This material may be personalized by the therapist, and the app may also be used for EMA. There are no published empirical reports supporting the use of this app; however, it is customizable and could be used to incorporate some of the features of effective EMA and EMI apps. An important take-home message from the literature reviewed in this article is that EMI technology shows promise, but this potential is mostly likely to be realized when it is delivered in the context of (and integrated with) ongoing psychotherapy in a manner that is tailored to the individual client. In conclusion, technology has allowed for the development of tools that may be incorporated into the mobile technology that most people in the United States use daily. These tools (i.e., smartphone apps) have been used to collect information about behavior, thought, and emotion patterns and have been used as a form of self-monitoring. Increasingly, these apps have the capacity to provide microinterventions as an adjunct to psychotherapy. Re- search suggests that EMI material that is well integrated into ongoing psychotherapy may offer the better chance for success compared with EMI material that is not well in- tegrated into individualized therapy (14). Given the rapid pace at which technology develops, this is a ripe area and could lead to meaningful improvements in the reach of ef- fective therapies.