The experience sampling method has countless applications, broadly classified into four categories. These four categories are individual differences, natural history, temporal sequences, and contextual associations.
The ecological momentary assessment data is aggregated to measure the individual subject’s response over the specified period of time. For instance, in the case of the pain experienced by the patient, the data could be gathered before and after the intervention to quantify the subject’s quality of life. The aggregated ESM data is projected to provide reliable (because of aggregation) and valid (because of the absence of recall bias, representative sampling, and ecological validity) assessments.
To elaborate on natural history, ecological momentary assessment measures are analyzed for trajectories over time. The time factor serves as an independent variable, whereas the subject’s intrinsic variation over time is taken as the dependent variable. For instance, McCarthy et al. 2006 demonstrated the trends of various withdrawal symptoms experienced by the ex-smokers after quitting. The ESM data revealed that some symptoms, although intense in the early phase gradually faded away with time, while others increased and lingered, and still others increased only progressively over time. These trajectories declined the widely held beliefs about the progression of the withdrawal syndrome and were linked with differences in treatment outcomes. Therefore, the basic descriptive evidence about the natural history of the symptoms over time can pave the path for a better understanding of clinical disorders and consequences.
The longitudinal nature of experience sampling method data is employed to probe events or experiences in the closest time possible, whether to document antecedents or outcomes of events or behaviors or to examine the cascades of events. In these assessments, the sequence of events is the main focus.
Curry and Marlatt 1987 hypothesized that the psychological response to lapses plays an integral part in the development of relapse. Later, Shiffman et al. 1997b investigated smoking cessation assessed smokers’ effect and self-efficacy before and after lapses to smoking, and their effects on consequent development toward relapse. Marlatt’s hypothesis was validated by Shiffman et al. 1997b by comparing assessments before and after lapses, stating that lapses would result in increased negative affect and decreased self-efficacy. However, later comparisons with EMA data depicted that retrospective reports of relapse episodes were erroneous and biased. The subjects recalled their mood as worse than it actually had been, and those who started smoking again at the time of recall exaggerated the demoralizing nature of the initial lapse. Therefore, a valid and robust understanding of behavior could be accomplished with prospective assessments of the flow of behavior and experience.
These cases represent the utilization of EMA data to assess hypotheses with respect to the dynamic connections among procedures over time. Data provided by EMA studies might be compared to a motion picture, in which dynamic correlations emerge over time, whereas worldwide or recall-based assessments are analogous to a still photograph, a solitary static preview of time. By providing temporal resolution, experience sampling methods enable investigators to scrutinize sequences of events and experiences, and empower them to analyze and break down the cascade of events in specific periods of time for better understanding.
Human behaviors are intricate; therefore, insight into micro-processes can give a better understanding of the overall process. Many theories of psychopathology and treatment focus on how the disease process unfolds over time. In addition, splitting the events into micro-processes can help develop more efficacious interventions. The ability of the Experience Sampling methods to focus on dynamic processes and situational influences is potentially the most stellar contribution in the field of clinical psychology.
Contextual association studies usually investigate the association between two or more events or experiences occurring simultaneously. Although the data is collected longitudinally, the analysis of contextual association is cross-sectional, and it focuses on the co-occurrence of events or experiences rather than their sequence; timeframe is not represented explicitly. For instance, Myin-Germeys et al. 2001 probed emotions associated with stressful events to scrutinize a diathesis-stress model of schizophrenia. They concluded that susceptibility to schizophrenia would be reflected as excessive emotional outbursts accompanying stress.
Schizophrenics, their first-degree relatives (who are hereditarily susceptible), and controls were surveyed 10 times daily about distressing occasions and moods. An examination of individual differences in average demonstrated that schizophrenics reported more negative effects and more stressful occasions, whereas susceptible people and controls did not vary. The contextual association between the stressor and mood uncovered that the first-degree relatives responded more unequivocally than did controls. This contextual association was helpful in determining the genetic predisposition due to schizophrenia.
Understanding the momentary cross-sectional relationship between various aspects of experience has, likewise, been essential for foundational investigations of the structure of behavior and experience. To address whether positive and negative feelings are inverses or are autonomous dimensions and can be experienced simultaneously: Feldman-Barrett and Russell 1998 utilized EMA data to address the contention that albeit one could be both happy and distressed over some period, but in a specific moment, these opposite forces cannot co-exist together.
Although most contextual association studies are conducted between different variables within the same individual, a fascinating variation discussed the impact of one individual on the other in a relationship (Bolger et al. 2005). In accordance with the same line of thought, Larson et al. 1994 instructed the couples to track their experience in parallel and record how the mood of each affected the other. They concluded that a husband’s mood when he comes home from work greatly influences his wife’s mood, but not vice versa.
Applications of Experience Sampling Method in Treatment and Intervention
The experience sampling method/ecological momentary assessment can also help in designing effective treatment and intervention plans.
Applications in Treatment
Besides the applications in the research data, the EMA studies can also be employed for ongoing assessment during treatment. A properly structured EMA data can provide revealing opportunities for the treatment plan. As change is expected during treatment, ongoing assessments can prove to be informative. EMA data can also capture the processes and mediators of psychotherapy-induced change.
Kramer et al. 2014 demonstrated that the supplemental use of ecological momentary assessment along with the standard antidepressant treatment might prove to be an effective tool. They concluded that the EMA data complement the anti-depressive treatment significantly, and EMA-derived positive feedback was correlated with the linear decrease in HDRS depressive symptoms over time that lingered until the previous follow-up six months later.
Applications in Intervention
The implementation of EMA methods in real-time interventions can revolutionize clinical treatment plans. Newman et al. 2003 discussed a variety of electronic assessment methods for the treatment of psychological disorders. Earlier, Newman et al. 1997 reported that a brief Electronic momentary assessment for panic disorder was comparable in efficacy to a longer therapist-administered treatment. This depicts the incremental benefits of EMA intervention. Momentary interventions have also been evaluated in addictive disorders (Riley et al. 2002) and eating disorders (Norton et al. 2003).
The idea of delivering intervention immediately on the spot can address behaviors at crucial moments in a patient’s life. The individual patient’s history may prove to be helpful in designing effective interventions for others. Additionally, the screening of patients over time could be beneficial in making predictive algorithms. For instance, by noticing the increasing stress levels, and intervening in the early phase before the symptoms get worse.