Experience sampling method

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The experience sampling method (ESM),[1] also referred to as a daily diary method, or ecological momentary assessment (EMA), is an intensive longitudinal research methodology that involves asking participants to report on their thoughts, feelings, behaviors, and/or environment on multiple occasions over time.[2] Participants report on their thoughts, feelings, behaviors, and/or environment in the moment (right then, not later; right there, not elsewhere) or shortly thereafter.[3] Participants can be given a journal with many identical pages. Each page can have a psychometric scale, open-ended questions, or anything else used to assess their condition in that place and time. ESM studies can also operate fully automatized on portable electronic devices or via the internet.[4] The experience sampling method was developed by Larson and Csikszentmihalyi.[5]


There are different ways to signal participants when to take notes in their journal or complete a questionnaire,[6] like using preprogrammed stopwatches. An observer can have an identically programmed stopwatch, so the observer can record specific events as the participants are recording their feelings or other behaviors. It is best to avoid letting subjects know in advance when they will record their feelings, so they can't anticipate the event, and will just be "acting naturally" when they stop and take notes on their current condition. Conversely, some statistical techniques require roughly equidistant time intervals, which has the limitation that assessments can be anticipated. Validity in these studies comes from repetition, so you can look for patterns, like participants reporting greater happiness right after meals. For instance, Stieger and Reips[7] were able to replicate and refine past research about the dynamics of well-being fluctuations during the day (low in the morning, high in the evening) and over the course of a week (low just before the beginning of the week, highest near the end of the week).[8] These correlations can then be tested by other means for cause and effect, such as vector autoregression,[9] since ESM just shows correlation. Moreover, by using the experience sampling method different research questions can be analyzed regarding the use of mobile devices in research. Following on from this, Stieger and colleagues[10] used the experience sampling method to show that smartphones can be used to transfer computer-based tasks (CBTs) from the lab to the field.

Some authors also use the term experience sampling to encompass passive data derived from sources such as smartphones, wearable sensors, the Internet of Things, email and social media that do not require explicit input from participants.[11] These methods can be advantageous as they impose less demand on participants improving compliance and allowing data to be collected for much longer periods, are less likely to change the behaviour being studied and allow data to be sampled at much higher rates and with greater precision. Many research questions can benefit from both active and passive forms of experience sampling.

Software and related tools[edit]

The first mobile device application that could be used as a tool for Experience Sampling Method was the ESP Package (dating to the late 1990s). This had limited functionality in that it is designed for older iOS Palm devices and had limited scheduling capabilities. It no longer works on modern mobile devices.[12] iHabit was the first smartphone mobile application designed for Experience Sampling. It was developed in 2011 and used in a study published by PLOS ONE in 2013.[13] In 2015, it was superseded by the LifeData system, which was used in a study published by JAMA Pediatrics in 2016.[14] This system has subsequently been used in numerous studies. The PIEL Survey app (first version 2012) is a free app available in iOS[15] and Android [16] versions and has since been used in more than 12 academic publications. It can be used for scheduled, random and on-demand surveys. Unlike many platforms, no server is required as data is saved on the device and emailed to the researcher or else retrieved by file sharing.[17] Other early smartphone platforms for ESM include SurveySignal

[18] and Ilumivu (developed in 2012), MetricWire (developed in 2013), m-Path, Instant Survey, Movisens, and Aware (Open Source). The largest ESM study was achieved through PSYT's Mappiness App,[19] PSYT’s apps collect data through ESM as well as reporting the data back to users to enable real-time visualisation and tracking of variables. Several other commercial and open source systems are currently available to help researchers run ESM studies,[20] including BeepMe,[21] and Expimetrics.[22] Physiqual enables researchers to gather and integrate data from commercially available sensors and service providers to use them in ESM,[23] including Fitbit and Google Fit. As of 2014, Movisens have developed the ability to trigger sampling forms from physiological data such as actigraphy and ECG.[24] unforgettable.me provide a platform for both active and passive experience sampling that allows the integration of some 400 data sources.

m-Path, launched in 2020, is a freemium tool that is optimized for clinical practice, therefore clinical research protocols implemented in m-Path can be readily made available to a wider community of practitioners. Moreover, studies protocols can be easily personalized for each participant.

In 2020, the AthenaCX platform (beta) was launched by an Irish based startup. AthenaCX's platform enables researchers to easily create and distribute experience sampling studies which can also be integrated with wearable devices; giving researchers access to health data along with their study. The powerful software enables researchers to trigger specific questionnaires which are dependent on a participant's gathered health/activity data. The platform has a central focus on the BYOD (Bring Your Own Device) process. The app is readily available from the Google and Apple App Stores so participants can get fully up and running within platform in a matter of minutes.

With context-sensitive experience sampling, researchers can trigger questions based on app usage or location: "You just used Instagram for 30min. How do you feel?" "You just left a coffee shop. How much did you pay?" This solution is offered by the German company Murmuras.

One consumer market example is Mood Patterns, a mood tracking app available for Android.

In 2021, the open-source platform Samply [25] was developed by the research group iScience at the University of Konstanz in Germany. In general, Samply supports experience sampling, ambulatory assessment and diary studies. Samply enables researchers to access the complete interface via a web browser and to manage their present studies. In addition, it allows researchers to easily schedule, customize and send notifications linking to online surveys or experiments created in any Internet-based service or software (e.g., Google Forms, SurveyMonkey, Qualtrics, WEXTOR, lab.js). The flexible schedule builder enables the creation of a customized notification schedule, which can be randomized for each participant. The Samply Research mobile application is free for both researchers and participants and is available at the Google Play or the App Store. The mobile app logs the history of notifications, and at the same time preserves participants’ anonymity, as the personal login and application use data are stored separately from the response data. Shevchenko and colleagues[25] demonstrated via two empirical studies the app’s functionality and usability.

ESM in clinical practice[edit]

Increasingly, ESM is being tested as a clinical monitoring tool in psychiatric and psychological treatments. Patients then use ESM to monitor themselves for several weeks or months and discuss feedback based on their ESM data with their clinician. Patients and clinicians are enthusiastic about the clinical use of ESM.[26] Qualitative studies suggest ESM may increase insight and awareness, help personalize treatments, and improve communication between patient and clinician.[27][28] ESM may be viewed as an improved form of registration and monitoring already often used in psychiatric treatments, and may therefore be an excellent fit. Randomized controlled trials so far show mixed evidence for the efficacy of ESM in improving symptoms and functioning in patients with depression,[29][30] although many more trials in diverse clinical populations are currently underway.[31]

Several tools are being developed to aid clinicians in using personalized ESM diaries in treatment such as PETRA and m-Path. PETRA[32] is a Dutch tool with which patients and clinicians can construct a personalized ESM diary and examine personalized feedback together. PETRA is developed in collaboration with patients and clinicians and integrated in electronic personal health records (PHR) to facilitate easy access. m-Path[33] is a freely accessible flexible platform to facilitate real-time monitoring as well as real-life interventions. Practitioners are able to create new questionnaires and interventions from scratch or can use existing templates shared by the community.

See also[edit]


  1. ^ Sather T (November 2014). "Experience Sampling Method". ASHA Journals Academy. Retrieved 2021-03-21.
  2. ^ Bolger N, Laurenceau JP (2013). Intensive longitudinal thods: An introduction to diary and experience sampling research. New York, N.Y.: Guilford Press.
  3. ^ Csikszentmihalyi M (July 2014). Validity and Reliability of the Experience-Sampling Method. New York: Springer. p. 322. ISBN 978-94-017-9087-1.
  4. ^ Krieke LV, Jeronimus BF, Blaauw FJ, Wanders RB, Emerencia AC, Schenk HM, et al. (June 2016). "HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths". International Journal of Methods in Psychiatric Research. 25 (2): 123–44. doi:10.1002/mpr.1495. PMC 6877205. PMID 26395198.
  5. ^ Larson R, Csikszentmihalyi M (1983). "The experience sampling method". New Directions for Methodology of Social and Behavioral Science. 15: 41–56.
  6. ^ Hektner JM, Schmidt JA, Csikszentmihalyi M, eds. (2007). Experience sampling method : measuring the quality of everyday life. Thousand Oaks, Calif.: Sage Publications. ISBN 978-1-4129-2557-0.
  7. ^ Stieger, S., & Reips, U. D. (2019). Well-being, smartphone sensors, and data from open-access databases: A mobile experience sampling study. Field Methods, 31(3), 277-291. doi:10.1177/1525822X18824281
  8. ^ Akay, A., & Martinsson, P. (2009). Sundays are blue: Aren't they? The day-of-the-week effect on subjective well-being and socio-economic status. IZA Discussion Paper No. 4563. https://ssrn.com/abstract=1506315 (accessed May 19, 2021).
  9. ^ van der Krieke L, Blaauw FJ, Emerencia AC, Schenk HM, Slaets JP, Bos EH, et al. (2016). "Temporal Dynamics of Health and Well-Being: A Crowdsourcing Approach to Momentary Assessments and Automated Generation of Personalized Feedback". Psychosomatic Medicine. 79 (2): 213–223. doi:10.1097/PSY.0000000000000378. PMID 27551988. S2CID 10955232.
  10. ^ Stieger, S., Lewetz, D., & Reips, U. D. (2018). Can smartphones be used to bring computer-based tasks from the lab to the field? A mobile experience-sampling method study about the pace of life. Behavior research methods, 50(6), 2267-2275.
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  19. ^ MacKerron G, Mourato S (October 2013). "Happiness is greater in natural environments" (PDF). Global Environmental Change. 23 (5): 992–1000. doi:10.1016/j.gloenvcha.2013.03.010. Archived from the original (PDF) on 2016-07-05.
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