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Background: Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. Objective: The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). Methods: We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method’s conceptual model, support to 8 real case studies, and postdesign interviews. Results: The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists’ target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. Conclusions: The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology–based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.

Fear of missing out, known colloquially as FOMO, appears to be a common experience, and has recently become part of the vernacular, receiving frequent mentions in the popular media. The present paper provides a multi-method empirical examination of FOMO. In a first study, experience sampling was used to assess FOMO experiences among college freshmen. Nightly diaries and end-of-semester measures provided data on the short and long-term consequences of experiencing FOMO. Results showed that students experience FOMO frequently, particularly later in the day and later in the week, and while doing a required task like studying or working. More frequent experiences of FOMO were associated with negative outcomes both daily and over the course of the semester, including increasing negative affect, fatigue, stress, physical symptoms, and decreased sleep. A second experimental study investigated FOMO on a conceptual level, distinguishing FOMO from general self-regulation and exploring its links with social media.

OBJECTIVE Real-time assessment of gastrointestinal (GI) symptoms in irritable bowel syndrome (IBS) using the experience sampling method (ESM) is suggested as a more appropriate approach than currently used end-of-day or end-of-week reports. This psychometric evaluation study assesses the validity and reliability of a previously developed ESM-based patient-reported outcome measure (PROM) for real-time GI symptom assessment in IBS. METHODS This multicenter validation study included 230 Rome IV IBS patients (80% female; mean age 41.2 years) in three European countries. Patients completed the electronic ESM-PROM (up to ten random moments daily, with a weekly minimum completion rate of 33%) and an end-of-day symptom diary for seven consecutive days. End-of-week questionnaires (GSRS-IBS, IBS-SSS, PHQ-9, and GAD-7) were completed at the end of the 7-day period. RESULTS The ESM assessment had a mean completion rate of 71%. Strong and significant correlations (0.651-0.956) with moderately-to-good consistency (ICCs 0.580-0.779) were observed between ESM and end-of-day scores. However, end-of-day scores were significantly higher (Δ0.790-1.758, p<0.001) than mean daily ESM scores. Differences with end-of-week scores were more pronounced, with weaker correlations (Pearson's r 0.393-0.802). ESM-PROM exhibited moderate-to-good internal consistency (Cronbach’s α 0.585-0.887) across five symptom domains. First and second half-week scores demonstrated good-to-excellent consistency (ICCs 0.871-0.958). CONCLUSIONS Psychometric evaluation demonstrated strong validity and reliability of the ESM-PROM for real-time GI symptom assessment in IBS. In addition, the ESM-PROM provides a precise and reliable ascertainment of individual symptom pattern and trigger interactions, without the bias of peak reporting when compared to retrospective methods. This highlights its potential as a valuable tool for personalized healthcare in monitoring disease course and treatment response in IBS patients.

Abstract Schizophrenia is a major cause of disability worldwide. As new treatments for functioning are tested, the need grows to demonstrate real-world functioning gains. Ecological momentary assessment (EMA) may provide a more ecologically valid measure of functioning. In this study, smartphone-based EMA was used to signal participants with schizophrenia (N = 100) and controls (N = 71) 7 times a day for 7 days to respond to brief questionnaires about social interactions and functioning behaviors. Excellent adherence was found, with both groups completing an average of 85% of surveys and only 3% of participants with schizophrenia excluded for poor adherence. Four-week test–retest reliability was high (r = .83 for total productive behaviors). Relative to controls, participants with schizophrenia reported significantly less total productive activity (d = 1.2), fewer social interactions (d = 0.3), more nonproductive behaviors (d = 1.0; watching TV, resting), and more time at home (d = 0.8). Within the schizophrenia group, participants living independently showed better functioning on EMA relative to participants in supported housing (d = 0.8) and participants engaged in vocational activities showed better functioning than individuals not engaged in vocational activities (d = 0.55). Modest correlations were found between EMA and an in-lab self-report measure of functioning activities performed in the community, but not between EMA and measures of functional capacity or potential. This study demonstrated the feasibility, sensitivity reliability, and validity of EMA methods to assess functioning in schizophrenia. EMA provides a much-needed measure of what individuals with schizophrenia are actually doing in real-world contexts. These results also suggest that there may be important disjunctions between indices of abilities and actual real-world functioning.

It remains poorly understood how negative symptoms are experienced in the daily lives of individuals in the early stages of psychosis. We aimed to investigate whether altered affective experience, anhedonia, social anhedonia, and asociality were more pronounced in individuals with an at-risk mental state for psychosis (ARMS) and individuals with first-episode psychosis (FEP) than in controls. We used the experience sampling methodology (ESM) to assess negative symptoms, as they occurred in the daily life of 51 individuals with FEP and 46 ARMS, compared with 53 controls. Multilevel linear regression analyses showed no overall evidence for a blunting of affective experience. There was some evidence for anhedonia in FEP but not in ARMS, as shown by a smaller increase of positive affect (BΔat-risk v. FEP = 0.08, p = 0.006) as the pleasantness of activities increased. Against our expectations, no evidence was found for greater social anhedonia in any group. FEP were more often alone (57%) than ARMS (38%) and controls (35%) but appraisals of the social situation did not point to asociality. Overall, altered affective experience, anhedonia, social anhedonia and asociality seem to play less of a role in the daily life of individuals in the early stages of psychosis than previously assumed. With the experience of affect and pleasure in daily life being largely intact, changing social situations and appraisals thereof should be further investigated to prevent development or deterioration of negative symptoms.

This manuscript presents a novel geofencing method in behavioral research. Geofencing, built upon geolocation technology, constitutes virtual fences around specific locations. Every time a participant crosses the virtual border around the geofenced area, an event can be triggered on a smartphone, e.g., the participant may be asked to complete a survey. The geofencing method can alleviate the problems of constant location tracking, such as recording sensitive geolocation information and battery drain. In scenarios where locations for geofencing are determined by participants (e.g., home, workplace), no location data need to be transferred to the researcher, so this method can ensure privacy and anonymity. Given the widespread use of smartphones and mobile Internet, geofencing has become a feasible tool in studying human behavior and cognition outside of the laboratory. The method can help advance theoretical and applied psychological science at a new frontier of context-aware research. At the same time, there is a lack of guidance on how and when geofencing can be applied in research. This manuscript aims to fill the gap and ease the adoption of the geofencing method. We describe the current challenges and implementations in geofencing and present three empirical studies in which we evaluated the geofencing method using the Samply application, a tool for mobile experience sampling research. The studies show that sensitivity and precision of geofencing were affected by the type of event, location radius, environment, operating system, and user behavior. Potential implications and recommendations for behavioral research are discussed.

Engagement has been recognized as one of the most important factors of learning and achievement in academic settings. Research on engagement has been gearing toward a “person-in-context” orientation, where both personal characteristics and contextual features in relation to students’ engagement are considered. This orientation allows a more in-depth understanding of how a person embedded within a context engages in a task, and it pays particular attention to the interactions between the person and contextual features. Engagement in context is situational, longitudinal, and multi-dimensional. This in-situ orientation requires a research methodology that is embedded in and responsive to the context where learning occurs. In this paper, we provide a conceptual synthesis of research on academic engagement in proposing a framework of engagement in context. We introduce the affordances of Experience Sampling Methodology (ESM) and provide a review of current technologies in supporting ESM. In addition, we provide example cases of examining engagement using ESM and technology. In these cases, we discuss details about how ESM combines with technologies and statistical approaches in providing insights to educational research, theory, and practice.

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression.

In current research, emotions in language use situations are often examined only at their starting and ending points, akin to observing the beginning and end of a wave, while neglecting their complex fluctuations in between. To fully comprehend the dynamics of emotions in language use situations, it is essential to delve into their intricate unfolding throughout their progression. This is particularly critical in the context of Second Language Acquisition (SLA), where emotional dynamics can significantly influence learning outcomes and proficiency. Drawing on existing empirical research and theories, we propose a novel interpretation rooted in complex dynamic systems theory (CDST) to elucidate the dynamic nature of emotions in language use situations. Furthermore, we suggest methodologies for capturing the complete dynamics of emotional and language behaviours, including an analysis of their dynamic interrelationships. By embracing a dynamic perspective, we can advance our understanding of interplay between emotions and language behaviours from epistemological theory to methodology and analysis, paving the way for future research in this field.

Galvanized with the availability of sophisticated statistical techniques and large datasets, network medicine has emerged as an active area of investigation. Following this trend, network methods have been utilized to understand the interplay between symptoms of mental disorders. This realistic approach that may provide an improved framework into understanding mental conditions and underlying mechanisms is certainly to be welcomed. However, we have noticed that symptom network studies tend to lose sight of the fundamentals, overlook major limitations embedded in study designs, and make inferences that are difficult to justify with current findings. There is concern that disregarding these flaws may halt the progress of the network approach in psychiatry. Therefore, in this paper, we first attempt to identify the pitfalls: (1) a reductionist understanding of medicine and psychiatry, thereby inadvertently reintroducing the dichotomy of medicine (lung cancer) and psychiatry (depression), (2) a shortsighted view of signs and symptoms, (3) overlooking the limitations of available datasets based on scales with embedded latent class structures, (4) overestimating the importance of the current findings beyond what is supported by the study design. By addressing current issues, the hope is to navigate this rapidly growing field to a more methodologically sound and reproducible path that will contribute to our understanding of mental disorders and its underlying mechanisms.