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Experience sampling method

The experience sampling method (ESM) is an intensive longitudinal research technique that captures real-time data on individuals' thoughts, feelings, behaviors, and contextual experiences in their natural environments through repeated, brief self-reports prompted at random or semi-random intervals throughout the day.^[1] Originally developed in the late 1970s and early 1980s by psychologists Mihaly Csikszentmihalyi and Reed Larson to study the structure of everyday subjective experiences, ESM minimizes retrospective recall biases by assessing momentary states as they occur, often using signaling devices such as pagers, watches, or modern smartphone applications.^[2]^[3] This method provides a valid and reliable means to describe variations in mental processes, enabling researchers to examine both within-person fluctuations and between-person differences over time. ESM has evolved from early pen-and-paper or electronic pager-based protocols to digital platforms that facilitate automated prompting and data collection, enhancing feasibility for large-scale studies.^[1] Key principles include ecological validity—studying phenomena in real-world settings—and the use of multilevel statistical models to analyze temporal data, such as multilevel/mixed-effects modeling for nested observations.^[1] Applications span multiple fields, particularly psychology and mental health research, where it has been employed to investigate daily mood variability, social interactions, symptom dynamics in disorders like depression and psychosis, and optimal experience states such as "flow."^[1]^[3] For instance, in mental health, ESM reveals how affective variability predicts depressive recurrence and supports ecological momentary interventions for real-time therapeutic adjustments.^[1] Among its advantages, ESM offers high ecological validity by embedding assessments in participants' daily lives, reduces memory distortions compared to traditional surveys, and allows for the detection of dynamic processes like emotional reactivity to stressors.^[1]^[3] However, challenges include participant burden from frequent prompts, potential measurement reactivity where reporting alters natural behavior, non-compliance leading to missing data, and the need for advanced analytical tools to handle intensive datasets.^[1]^[3] Despite these, ongoing technological advancements continue to broaden ESM's utility in personalized medicine and behavioral science, with recent developments as of 2025 including advanced statistical approaches like non-linear models and survival analysis, as well as expanded applications in areas such as dementia and physical activity research.^[1]^[4]

History and Development

Origins

The experience sampling method (ESM) was developed in the early 1970s at the University of Chicago's Committee on Human Development, primarily through the doctoral work of Suzanne Prescott, a graduate student, with significant assistance from psychologist Mihaly Csikszentmihalyi and fellow graduate student Reed Larson.^[5] This collaboration emerged as part of broader efforts to study human consciousness and subjective well-being in naturalistic settings, marking a shift from retrospective self-reports to real-time data capture.^[6] The method's foundational pilot study on adolescents was conducted in 1975, building on Csikszentmihalyi's earlier 1976 application to adult workers.^[6] The inspiration for ESM drew from emerging pager technology, which dispatchers used to receive real-time alerts and maintain immediate responsiveness in dynamic environments.^[5] Csikszentmihalyi and his colleagues recognized the potential of this technology to interrupt participants' daily routines at random intervals, prompting them to record their immediate thoughts, feelings, and activities without the distortions of memory recall.^[5] This approach was initially applied to investigate adolescents' daily lives, focusing on their engagement in activities and experiences of flow states—moments of deep immersion and optimal challenge-skill balance. The first major publication on ESM appeared in 1977, detailing a study co-authored by Csikszentmihalyi, Larson, and Prescott, which analyzed the ecology of adolescent activities and emotional states using data from 25 high school students signaled eight times daily over a week. This work laid the groundwork for Csikszentmihalyi's subsequent research on optimal experience and happiness throughout the late 1970s and 1980s, including explorations of mood variability and well-being in everyday contexts.^[6] Early implementations relied on pen-and-paper diaries paired with random signaling devices, such as pre-programmed wristwatches or rudimentary beepers, to ensure unobtrusive and ecologically valid sampling.^[5] Over the following decades, ESM transitioned from these analog tools to digital platforms, enabling broader scalability in research.^[5]

Evolution and Technological Advances

The experience sampling method (ESM) initially relied on analog technologies such as wristwatch alarms and simple beepers paired with paper self-report forms during the 1980s, which often resulted in inconsistent prompting due to limited precision in timing and participant compliance.^[7] By the 1990s, the adoption of electronic pagers marked a significant advancement, allowing researchers to deliver more accurate random signals to participants' devices, thereby improving the temporal reliability of data capture while still using paper booklets for responses.^[8] This shift from basic beepers to programmable pagers enabled broader application in field studies, reducing reliance on manual timing and enhancing the method's feasibility for real-time ecological assessments. The early 2000s saw the integration of smartphones and mobile applications, transforming ESM by automating both prompting and data entry through custom software developed for platforms like iOS and Android. These digital tools facilitated immediate electronic responses, geolocation tagging, and multimedia inputs, minimizing errors associated with paper-based methods and enabling context-aware sampling.^[9] A pivotal contribution to this era was the 2007 publication of Experience Sampling Method: Measuring the Quality of Everyday Life by Joel M. Hektner, Jennifer A. Schmidt, and Mihaly Csikszentmihalyi, which synthesized theoretical foundations and practical guidelines, standardizing protocols for study design, implementation, and validation across diverse applications.^[10] As of 2025, ESM has evolved further with AI-driven adaptive prompting, where algorithms dynamically adjust sampling schedules based on real-time participant data to optimize engagement and relevance, as demonstrated in just-in-time adaptive interventions (JITAIs) for mental health monitoring.^[11] Concurrently, integration with wearables such as smartwatches and fitness trackers allows for passive physiological data collection— including heart rate and activity levels—complementing self-reported ESM entries to provide multimodal insights without increasing participant burden.^[12] These advancements, supported by platforms like Apple HealthKit and Android Health Connect, enhance ESM's scalability and predictive accuracy in longitudinal research.^[13]

Methodology

Sampling Protocols

The experience sampling method (ESM) employs three primary sampling protocols to prompt participants for real-time data collection, each tailored to capture different aspects of daily experiences while minimizing participant burden and maximizing ecological validity. Signal-contingent sampling involves random or semi-random prompts delivered at fixed intervals throughout the day, such as 8-10 times daily, to obtain a representative snapshot of ongoing activities and states without reliance on participant memory.^[14] Event-contingent sampling triggers reports immediately following specific predefined occurrences, like mood shifts or social interactions, allowing researchers to focus on rare or targeted events as they unfold.^[15] Interval-contingent sampling requires reports at predetermined fixed times, such as every three hours or once daily at set periods (e.g., morning and evening), which suits studies examining predictable rhythms or less variable phenomena.^[14] Design considerations for these protocols emphasize balancing comprehensive coverage of experiences with participant feasibility. Prompt frequency typically ranges from 5-10 signals per day in signal-contingent designs to avoid fatigue while ensuring sufficient data points to detect fluctuations, with study durations commonly spanning 1-2 weeks to capture a full cycle of daily life without excessive dropout.^[15] In event-contingent approaches, frequency depends on the base rate of the target event, necessitating clear operational definitions to prevent under- or over-reporting.^[14] Overall, protocol selection hinges on the research question, such as the need to sample infrequent events versus routine states. To enhance ecological validity, randomization techniques are integral, particularly in signal-contingent protocols, where algorithms generate prompts at variable intervals within participant-defined awake hours (e.g., 9 a.m. to 11 p.m.) to prevent clustering during sleep, work, or other constrained periods.^[14] These methods, evolved from early pager-based signaling to modern smartphone applications, ensure prompts occur across diverse contexts without predictability.^[15] Compliance monitoring is crucial for reliable data, with strategies including automated reminders via devices, participant training on response expectations, and incentives such as monetary compensation to foster engagement.^[14] Target response rates of 70-80% are common in signal-contingent studies using electronic tools, though interval-contingent designs can achieve higher rates (over 90%) due to their predictability and lower frequency.^[15] Researchers often track compliance through built-in device logs and adjust protocols iteratively based on pilot testing to maintain these thresholds.^[14]

Data Collection and Reporting

In the experience sampling method (ESM), participants record their immediate experiences through structured reporting formats designed to capture momentary states with minimal intrusion. These formats typically include short questionnaires assessing affective dimensions, such as positive and negative mood on Likert scales (e.g., 1-7 intensity ratings), alongside open-ended or multiple-choice items for current thoughts, ongoing behaviors or activities, contextual factors like location and social setting, and physiological sensations such as energy levels or arousal.^[16]^[17] The evolution of ESM tools has progressed from paper-based diaries in the 1980s, where participants manually noted responses in booklets triggered by pagers, to digital interfaces that reduce participant burden and enhance compliance. Early implementations relied on wristwatches or pagers signaling participants to complete paper forms, but these were prone to retrospective filling and logistical challenges; subsequent advancements incorporated electronic diaries, short message service (SMS) prompts, dedicated mobile applications like Google's PACO for customizable surveys, and voice-response systems via interactive voice response (IVR) technology for hands-free input.^[16]^[18] To maintain brevity and feasibility, ESM reporting is standardized around 7-10 questions per prompt, designed to take no more than 1-2 minutes to complete, allowing for frequent sampling without overwhelming participants. This structure ensures focused, reliable data entry while accommodating variations across studies, such as branching logic in apps to tailor follow-up items based on initial responses.^[16] Protocols emphasize immediacy to capture experiences in near real-time and mitigate recall bias, requiring responses within 15-30 minutes of a random signal prompt. This window balances ecological validity with practical compliance, as electronic tools automatically timestamp entries to verify adherence.^[19]

Data Analysis Techniques

Experience sampling method (ESM) data exhibit a nested structure, with multiple observations per individual across time, necessitating multilevel modeling—also known as hierarchical linear modeling—to disentangle within-person (momentary or state-level) variance from between-person (trait-level) differences. This approach accounts for the interdependence of repeated measures within participants, avoiding biased standard errors that arise from treating data as independent. A basic multilevel model for ESM outcomes can be expressed as:

Y_{ij} = \beta_0 + \beta_1 X_{ij} + u_{0j} + e_{ij}

where Y_{ij} is the outcome for occasion i nested within person j, \beta_0 is the overall intercept, \beta_1 is the within-person slope relating predictor X_{ij} to Y_{ij}, u_{0j} captures between-person random intercepts, and e_{ij} represents within-person residuals. To examine temporal dependencies, such as autocorrelation in mood or affect fluctuations, time-series analysis techniques are applied, including autoregressive integrated moving average (ARIMA) models that model trends, seasonality, and serial correlations in intensive longitudinal data. ARIMA models help identify patterns like lagged effects in emotional responses, enabling forecasts of future states based on prior observations within an individual's time series.^[20] Aggregation methods summarize raw ESM data into person-level metrics, such as means (e.g., average daily positive affect across prompts) or slopes (e.g., rate of change in stress over the study period), while variability indices like the standard deviation of experiences capture intra-individual fluctuations. These aggregates facilitate between-person comparisons and reduce data complexity for broader analyses, though they may obscure dynamic processes if over-relied upon. Contemporary analysis leverages software like R packages lme4 and nlme for multilevel modeling, Mplus for advanced time-series and latent variable approaches, and machine learning algorithms—such as random forests or neural networks—for detecting nonlinear patterns in large-scale ESM datasets as of 2025. Machine learning enhances predictive accuracy over traditional models in high-dimensional data, particularly for classifying outcomes like mental health states from multimodal features.^[21]^[22]^[23]

Applications

In Psychological Research

The experience sampling method (ESM) has been instrumental in psychological research for examining flow and optimal experience, particularly through the pioneering work of Mihaly Csikszentmihalyi in the 1980s. In a study involving 78 adult workers tracked over one week using ESM, Csikszentmihalyi found that moments of flow—characterized by a balance between perceived challenges and personal skills—were associated with higher levels of momentary happiness and engagement compared to other daily activities.^[24] This approach revealed that optimal experiences occur frequently in everyday contexts, such as work or leisure, when individuals are fully immersed in tasks that match their abilities, thereby contributing to overall psychological well-being.^[6] ESM has also advanced the study of affect dynamics in non-clinical populations, capturing real-time fluctuations in emotions that traditional retrospective methods often overlook. Research using ESM has documented diurnal mood rhythms, showing that positive affect is typically lowest in the early morning and increases toward the evening in healthy individuals, influenced by daily routines and stressors.^[25] For instance, studies have demonstrated heightened reactivity to acute stressors, such as interpersonal conflicts, leading to temporary spikes in negative affect that resolve within hours, underscoring the transient nature of emotional responses in everyday life. In investigations of social interactions, mindfulness, and well-being, ESM provides insights into how momentary practices influence emotional states. A study employing ESM over seven days linked trait gratitude to elevated levels of daily positive emotions, with participants reporting greater abundance and satisfaction during social encounters when gratitude was salient.^[26] Similarly, ESM research on mindfulness has shown that brief, in-the-moment awareness practices during social interactions enhance well-being by reducing rumination and fostering positive relational dynamics.^[27] These findings highlight ESM's role in tracking how social contexts modulate mindfulness and emotional positivity in real time.^[28] A key insight from ESM in psychological research is the low correlation between trait self-reports and momentary states, emphasizing the impact of situational factors over stable personality characteristics. Meta-analyses of ESM studies indicate that aggregated state measures of traits, such as extraversion, correlate modestly with global self-reports (typically r = 0.20 to 0.60), revealing that daily experiences drive much of the variance in behavior and affect.^[29] This discrepancy underscores situational influences, as individuals exhibit trait-like consistency only when averaging across many moments, but show substantial variability in specific contexts.^[15]

In Clinical Practice

The experience sampling method (ESM) has been widely adopted in clinical practice for real-time monitoring of symptoms in mental health disorders, providing clinicians with dynamic insights into patients' daily experiences that complement traditional retrospective assessments. In depression, ESM enables the tracking of momentary affective states and anhedonia, allowing for the identification of patterns in emotional fluctuations that inform treatment adjustments. For instance, studies have shown that ESM self-monitoring can enhance emotion differentiation and reduce depressive symptoms by capturing positive affect in real-time contexts. Similarly, in anxiety disorders, ESM facilitates the assessment of transient worry and physiological arousal during daily activities, supporting early intervention in clinical settings. For schizophrenia, ESM is particularly valuable for monitoring paranoia and anhedonia in psychotic patients, revealing how these symptoms manifest in naturalistic environments and their impact on social functioning. Research using ESM has demonstrated its utility in quantifying consummatory pleasure deficits and social anhedonia, which are core features of the disorder, thereby aiding in personalized symptom management.^[30] ESM integrates seamlessly into cognitive behavioral therapy (CBT) through mobile applications that prompt users for momentary mood logging and homework compliance, fostering greater self-awareness and adherence to therapeutic goals. Apps employing ESM deliver tailored feedback on daily behaviors and thoughts, helping patients challenge maladaptive patterns in real-time, such as during exposure exercises for anxiety. This approach has been shown to improve treatment outcomes by bridging session-based insights with everyday experiences, particularly in adolescent populations where digital tools enhance engagement. In clinical trials, ESM serves as a sensitive outcome measure for evaluating interventions, including the effects of antipsychotics on daily functioning, with applications dating back to the early 2000s. Trials have utilized ESM to assess changes in delusional distress and psychotic exacerbations following antipsychotic treatment switches, demonstrating improvements in emotional reactivity and social engagement without relying solely on static scales. For example, ESM has captured the real-time benefits of dose reductions or novel antipsychotics on subjective well-being and negative symptoms, providing ecological validity to trial results. Specific ESM protocols in clinical practice often involve tailored prompts adapted from established tools, such as real-time versions of the Patient Health Questionnaire-9 (PHQ-9) items, to measure symptom severity with high temporal precision. These adaptations have demonstrated strong psychometric properties, including reliability in detecting momentary depressive states. Additionally, idiographic ESM approaches emphasize personalized assessments, where prompts are customized to individual symptom profiles, enabling clinicians to derive patient-specific models for targeted interventions in mental health care. This method supports self-insight and adaptive treatment planning, as evidenced by its role in generating dynamic networks of symptoms for individualized therapy.

In Other Fields

In education, the experience sampling method (ESM) has been employed to investigate student engagement and attention in real-time during school days, particularly by prompting reports on boredom in classroom settings. For instance, a study of 537 eleventh-grade students used ESM over two weeks during math classes to capture state-level boredom and coping strategies, revealing that cognitive-approach coping (e.g., reframing task importance) was associated with lower boredom levels and higher engagement, while avoidance behaviors like chatting exacerbated disengagement.^[31] Similarly, research involving 437 secondary students and 17 teachers applied ESM with post-class prompts to examine how perceived teacher boredom influences student boredom and motivation, finding that students' detection of teacher disinterest increased their own in-class boredom by up to 20% and reduced motivational outcomes.^[32] A 2025 ESM study with 111 ninth-grade students further demonstrated that unsatisfied psychological needs, such as for mental stimulation and autonomy, predicted 80.8% of within-person variance in real-time classroom boredom, with boys reporting higher levels than girls during language lessons.^[33] In organizational psychology, ESM facilitates the assessment of workplace stress, productivity, and job satisfaction through repeated employee sampling in natural work environments. A systematic mapping of 82,798 participants across 46 studies highlighted ESM's role in detecting daily stress via mobile prompts, showing that real-time reports of emotional labor and recovery needs correlated with reduced productivity and lower job satisfaction, with regression analyses explaining up to 41% of variance in performance outcomes.^[34] Another analysis in organizational sciences used ESM to track intraindividual fluctuations, finding that daily negative affect from stress diminished task productivity by linking it to episodic emotional states, while positive mood episodes enhanced satisfaction and output in dynamic models.^[35] These applications underscore ESM's utility in revealing how momentary stressors, such as noise perceptions, mediate between environmental factors and employee well-being, with studies reporting 8-14 day sampling periods yielding reliable within-person insights.^[36] In neuroscience and health research, ESM is often combined with physiological measures like heart rate variability (HRV) to study emotion regulation in everyday contexts. A physiologically triggered ESM approach with 46 participants over 14 days integrated electrocardiogram data to prompt self-reports during cardiac changes, identifying multiple within-person physiological clusters (mean 4.76 per person) that varied with affective states but lacked fixed emotion-specific patterns, supporting context-dependent emotion regulation dynamics.^[37] In a seven-day ambulatory study of 26 young adults, continuous ECG alongside ESM prompts revealed that higher HR and sympathetic HRV (LF/HF ratio) predicted increased positive affect (β = 0.14-0.19), particularly enthusiasm, during inactive upright postures, informing models of autonomic influences on daily emotion regulation without between-person effects.^[38] Such integrations highlight ESM's value in capturing real-time autonomic responses, like reduced HRV during emotional inertia, to understand regulatory processes beyond self-reports alone.^[39] As of 2025, emerging applications of ESM include consumer behavior tracking in marketing, where it captures real-time responses to leisure and purchase contexts for deeper insights into motivations. A study applying ESM in leisure research with 45 participants over 32 days used event- and signal-based mobile prompts integrated with wearables, achieving 80% compliance and revealing significant differences in subjective well-being (F = 15.672, p < 0.001) tied to consumption experiences, enabling marketers to analyze dynamic consumer attitudes and behaviors.^[40] In environmental psychology, ESM assesses nature exposure effects on well-being through momentary urban sampling. A 2025 study in Japan with 306 adults collected 900 ESM reports via app prompts, finding that perceived nature elements like greenery boosted momentary well-being (coefficient 0.64), while long-term well-being linked to restorative greenspaces and reduced stress from urban vibrancy.^[41] Additionally, integrating ESM into environmental research tracks pollution and temperature impacts on mood in real-time, as shown in studies linking air exposure to stress variations and heat to affective declines, promoting pro-environmental behaviors via self-regulatory resource conservation.^[42]

Advantages and Limitations

Advantages

The experience sampling method (ESM) minimizes retrospective bias by capturing participants' experiences in real time, or in vivo, as they occur in natural settings, thereby avoiding the distortions inherent in memory recall or aggregated retrospective reports. This approach yields more accurate data on momentary thoughts, feelings, and behaviors compared to traditional self-report methods. Furthermore, by conducting assessments outside controlled laboratory environments, ESM enhances ecological validity, ensuring that findings reflect authentic, everyday contexts rather than artificial simulations. ESM excels at examining intra-individual variability and dynamic psychological processes, allowing researchers to track how fluctuating internal states interact with external contexts across time. For instance, it reveals how immediate environmental factors, such as social interactions or daily routines, influence momentary emotions or cognitions within the same person, uncovering patterns that cross-sectional or between-person designs might overlook. This granularity supports a deeper understanding of temporal dynamics, such as the ebb and flow of mood in response to situational triggers. The method's high temporal resolution facilitates stronger causal inferences by enabling the sequencing of events, where immediate antecedents can be directly linked to subsequent outcomes, such as acute stressors precipitating real-time physiological or emotional responses. Brief sampling protocols, like random prompts via mobile devices, underpin this real-time capture without disrupting participants' routines. With the advent of digital tools such as smartphone applications, ESM has become cost-effective for studying large samples, generating rich longitudinal datasets over extended periods while requiring minimal ongoing researcher intervention. This scalability allows for intensive, repeated assessments across diverse populations without the logistical burdens of in-person monitoring, making it accessible for broad-scale investigations.

Limitations

One significant limitation of the experience sampling method (ESM) is the substantial burden it places on participants, which can lead to fatigue, dropout, and reduced compliance over time. In signal-contingent protocols involving frequent prompts, such as more than eight times per day, average response rates often hover around 70%, but they can drop as low as 50% in longer studies spanning two weeks or more, particularly when using portable devices like PDAs. Recent studies (as of 2025) indicate that indirect protocols, triggered by events like smartphone use, can improve compliance by up to 1.7 times compared to direct prompts, though they may increase response latency and perceived burden. This burden arises from the repeated interruptions to daily activities, contributing to selection biases where only highly motivated or conscientious individuals persist, potentially skewing results toward non-representative samples.^[14]^[43] Reactivity effects represent another potential drawback, as the act of prompting and self-reporting can alter participants' natural behaviors and experiences. For instance, repeated assessments may heighten self-awareness, inflate emotional reactivity, or encourage postponement of activities to respond, thereby distorting the ecological validity of the data. Some studies have shown that ESM can induce changes in within-person variability, affect levels, and even emotional awareness over the course of the study, with evidence of decreasing positive affect and shifts in emotion regulation patterns, particularly in vulnerable populations like adolescents with self-harm histories. However, a 2025 systematic analysis found no significant reactivity effects on self-reported states such as positive or negative affect across various protocols, though behavioral changes like increased smartphone use were observed.^[44]^[45]^[43]^[46] Sampling biases further compromise the method's representativeness, as certain activities are systematically underrepresented due to protocol restrictions designed for feasibility and safety. Prompts are typically scheduled only during waking hours, leading to under-sampling of sleep-related experiences, while logical non-responses occur during such periods without introducing mood-related bias. Similarly, prompts may be restricted or ignored during high-risk activities like driving to avoid distractions, resulting in gaps in data for those contexts and potential overemphasis on more accessible daily routines.^[45]^[14] Data quality in ESM is also affected by missing responses and inherent measurement errors in self-reports, necessitating advanced handling techniques. Missing data, often comprising 20-30% in intensive protocols, stem from non-compliance or situational factors and can bias analyses if not addressed, though they are frequently missing at random or logically (e.g., during sleep). Self-reports, despite reducing recall bias, remain susceptible to social desirability influences, where participants may adjust responses to align with perceived expectations, introducing systematic error. To mitigate these issues, researchers often employ imputation methods such as multiple imputation or full information maximum likelihood estimation, which preserve data structure in multilevel models but require careful validation to avoid further distortion.^[47]^[48]^[49]

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