Use of Ecological Momentary Assessment and Intervention in Treatment With Adults

EMA methods have been used for several decades. EMA was based on the experience-sampling method developed by Czikszentmihalyi and Larson (3), whereby researchers prompt participants at random intervals throughout the day to gain an understanding of the ebb and flow of daily life. Prior to the advent of smartphones, EMA was typically used with mobile pagers, landline phones, and automated-response interfaces.

EMA generally involves prompting respondents to answer 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 behavior 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 conceptualization of clinical issues, such as substance use disorders (4), depression (5), and self-injury (6), it has not seen wide uptake in clinical settings.

EMA predates the advent of widespread use of personal handheld, wireless devices (e.g., smartphones and tablets), and these devices have made the use of EMA easier for researchers and participants. In research settings, EMA has been used as a method of collecting a rich set of data, because it allows for the analysis of patterns occurring within and across days. In contrast, many self-report questionnaires permit a single data point reflecting a month’s worth of data.

It is difficult to ascertain whether EMA has seen widespread use by clinicians outside of a research context, although the method bears some similarity to routine self-monitoring employed by many cognitive-behavioral clinicians. (A key difference is that the “momentary” aspect of EMA implies that the respondent is contacted by the clinician and prompted to provide data at some schedule or at random times, rather than using a self-directed process of logging a behavior at regular intervals.) EMA may be useful in conducting 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).

A pilot study of individuals with bipolar disorder suggests that people with mental illness can be expected to respond as frequently to EMA prompts as healthy controls (8). EMA has been used a research tool with samples reflecting a range of severity, including individuals with schizophrenia (9). Taken together, these studies suggest that data collection is feasible across a number of clinically important populations.

Although EMA does not generally include any form of intervention, some studies have reported that participants showed improvement over time on the variables that were being assessed. For example, a study of combat veterans with posttraumatic stress disorder (PTSD) and problematic alcohol use showed reduced PTSD severity and reduced alcohol use following 28 days of participation in EMA. During the EMA intervention, they were prompted four times per day to report on their PTSD, alcohol use, mood, coping, and self-efficacy (10).

One study of adolescents and young adults with mild to moderate depressive symptoms found that when respondents were prompted to monitor their depression, anxiety, and stress reactions at random intervals, they showed increased emotional awareness and reduced depressive symptoms. A control group of participants who only monitored their behavior (but not their emotions) did not show these same changes (11). Findings from this study support the idea that increased awareness, in particular emotional self-awareness, is an important mechanism through which EMA can improve psychological processes. Likewise, a study of college students who self-monitored time allocation to academics, exercise, and other activities using EMA showed increased awareness of how they spent their time (12). These findings suggest that EMA monitoring alone may promote change in respondents’ symptoms, in addition to providing detailed data to the clinician or researcher. (For a thorough analysis of EMA, we direct the reader to a review by Shiffman and colleagues) (13). Nevertheless, no research has investigated for whom symptom monitoring alone engenders therapeutic gain.

With the advent of smartphones and similarly connected devices, the door has opened for EMA to become more interactive and more interventional. An outgrowth of EMA, EMI goes a step further and provides intervention remotely in respondents’ natural environments. In EMI, the researcher, clinician, or designer can program an app to obtain 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). A subset of EMI, “just-in-time” adaptive interventions, includes remotely delivered interventions that are personally tailored and implemented on the basis of an individual’s context (with the GPS feature of the mobile device), psychophysiology, or responses to questions (14). A majority of studies of EMI have been conducted in the area of behavioral health, addressing issues such as smoking and weight concerns. Several studies have also reported on the use of EMI for mood and anxiety disorders.

This summary does not reflect an exhaustive review. Instead, we aim to describe the efficacy of these interventions, characterize how EMI has been deployed in research studies, and elaborate on possible implications for clinical practice. Unfortunately, research has not yet determined precisely under what conditions EMI can be most effective. Therefore, to best serve mental health professionals who are new to ecological assessment and intervention, we provide a description of some examples of the use of EMI in research. We urge readers to consider these findings and use their clinical judgment to speculate as to when and how EMI might best be implemented.

Several studies have investigated EMI methods to address mood and anxiety disorders. These programs use notification features to prompt participants to practice skills, engage in exposure activities, and provide encouragement. Recent reviews by Schueller and colleagues (14) and Versluis and colleagues (15) provide a concise overview of the use of EMI for mental health.

The former review was focused on studies of EMI for depression and anxiety, whereas the latter review was broader in scope (14, 15). 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. Although many self-help apps essentially offer some form of EMI without the assistance of human support, research indicates that these effects may be enhanced with professional or expert assistance.

For generalized anxiety disorder in particular, two different EMI treatments have been developed and tested in research settings. The Worry Outcome Journal asks participants to write about their worries on paper when prompted by text messages several times per day for 10 days (16). In a randomized trial of the Worry Outcome Journal, participants logged the content of their worry at that particular moment in time and answered a series of questions related to the degree of distress, the amount of time spent thinking about that particular worry, how likely they felt it was that the feared outcome would happen, and how likely it was that “a person would logically conclude the worried outcome would occur if they were thinking as realistically as possible” (16, p. 832).

At postintervention, the Worry Outcome Journal was more effective than the control condition, which asked participants to use a “thought log” that tracked general thoughts (not specific to worry) and did not prompt analysis of the content of the thoughts. A marginally significant advantage for the Worry Outcome Journal was maintained at the 20-day follow-up. Results suggest that EMI may be used effectively to record and also challenge anxious thoughts. Furthermore, the high rate of completed prompts (all participants in the study completed at least 80%) may suggest that novel aspects of EMI can be useful even with highly anxious clients who might otherwise allow their worry to interfere with self-initiated homework completion.

EMI has also been used in a computer-assisted group therapy treatment for generalized anxiety disorder (17). This intervention used software called the Stress Manager. The Stress Manager collected baseline information from the respondent for a two-week period, prompting the client five times during each day. After this baseline period, participants initiated in-person group therapy, and the Stress Manager app prompted them with material relevant to the content of the intervention. This computer-assisted version of the therapy took place over six weeks and was compared with six- and 12-week versions of in-person cognitive-behavioral group therapy without computer assistance.

The computer-assisted intervention showed a statistically significant advantage over the six-week in-person therapy at posttreatment and was equivalent (as predicted) to the 12-week in-person therapy at posttreatment. Although the finding was not statistically significant, the authors pointed out that the group receiving the computer-assisted six-week treatment showed a larger proportion of participants demonstrating reliable change across all follow-up points, relative to the two comparison conditions. The authors of the study suggested that posttreatment benefits might have been the result of increased homework compliance (as a result of mobile prompts), increased generalizability of treatment (because of in-the-moment instructions), or an increased sense of safety (not otherwise provided by paper handouts).

In a similar vein, Kenardy and colleagues (18) compared different delivery methods of cognitive-behavioral therapy (CBT) for panic disorder in an international, multicenter randomized trial. Six sessions of CBT plus a computer-assisted momentary support were compared with six sessions of CBT without the computer, 12 sessions of CBT without the computer, and a wait-list group. The computer assistance included five daily alarm signals on a handheld electronic device referred to as a “personal digital assistant,” which prompted participants to complete self-statements, practice breathing control, and engage in exposure activities. Although all treatment conditions were significantly better than the wait-list condition, there were no differences between the computer-assisted six-session CBT therapy and the noncomputer-assisted therapy sessions.

Similarly, a group of patients diagnosed with social phobia were provided with a program that reminded them of the therapeutic strategies taught during group sessions. The program also collected debriefing information after exposure exercises, which a therapist then reviewed. Results showed that an eight-session computer-assisted therapy was equally as effective as a 12-session standard CBT treatment protocol (19). Although the computer-assisted therapy group experienced more positive thoughts at posttreatment than the standard CBT group, this finding was diminished by the six-week follow-up time point. Given the findings of this and the previous two studies, it may be that the effect of EMI for anxiety disorders is overshadowed in instances when intensive in-person therapies are used. At present, applications of EMI-assisted CBT for anxiety do not appear to offer therapeutic advantages over traditional approaches, but novel methods of integrating EMI into standard protocols may provide fruitful avenues for future investigation.

Several EMI approaches have been developed to target depression and to incorporate EMI into CBT (20) as well as acceptance and commitment therapy (21). The most common approach has been to include CBT elements of self-monitoring and behavioral activation in an EMI delivery format. Generally, as reviewed by Schueller et al. (14), EMIs for depression show small to medium effects within participants on depressive symptoms.

One unique approach by Burns et al. (20) followed eight participants who utilized a program that used GPS, ambient light, recent calls, and several other functions to predict mood, emotions, motivational states, activities, and environmental context. This information was incorporated into a feedback graph that the participant could review and that provided tools to teach patients rudimentary behavioral activation concepts. Study clinicians provided telephone calls and e-mails to participants to maintain program adherence. Results from this small open trial showed that self-reported depressive symptoms significantly improved over eight weeks of use. This suggests that when used in conjunction with a therapist or provider, EMI is feasible, functionally reliable, and acceptable among patients with depression. Future research to determine its efficacy compared with other treatment interventions is needed.

With respect to bipolar disorder, EMI has been used to reduce depressive symptoms (22) and to increase medication adherence (23). Depp and colleagues (22) compared 10 weeks of paper-and-pencil mood monitoring with an EMI treatment that included personalized coping strategies among 82 individuals diagnosed with bipolar disorder. Those who were in the EMI condition showed significant reductions in depressive symptoms in the short term compared with participants in the control condition, although these gains were no longer evident by 24 weeks. There were also no differences at any time point between groups in terms of manic symptoms or functional impairment.

Wenze and colleagues (23) conducted an uncontrolled pilot study of an EMI to promote medication adherence over a two-week period. They found evidence of feasibility and acceptability as well as efficacy for medication adherence and depressive symptoms in their sample of 14 individuals with bipolar disorder. Thus, research on EMI for bipolar disorder suggest that EMI is feasible and acceptable. However, preliminary evidence may hint that EMI is better suited to managing medication adherence and symptoms of depression than it is to improving symptoms of mania. Still, efficacy trials are needed to answer more fine-tuned questions of when and for whom EMI is most efficacious among those with bipolar disorder.

Taken together, findings from the literature investigating EMI as an adjunctive or stand-alone treatment for anxiety and mood disorders are promising but currently limited. Although most research suggests that EMI combined with other forms of in-person therapy is feasible, acceptable, and effective, it is uncertain whether EMI provides a clear advantage over other forms of treatment when deployed in its current form. There is a dearth of literature exploring mechanisms of EMI or dismantling specific components’ efficacy, and the questions of how, for whom, and when to use EMI are still widely unanswered. The current body of research provides a strong foundation for understanding EMI as a potentially therapeutic resource for anxiety and depression, but more rigorous research investigating its comparative efficacy is needed in the next several years.

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 triggers 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).

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).

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.

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. Research 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 integrated 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 effective therapies.