The Experience Sampling Method on Mobile Devices

Lecture Notes in Computer Science 1205

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The Impact of Incentives on Response Rates to Personal Interview Surveys: Role and Perceptions of Interviewers

Lynn, P. J. (2001) · International Journal for Quality in Health Care · doi:10.1093/ijpor/13.3.326 · Cited by 41 · see citing works

Interrupt Now or Inform Later?

Patil, S., Hoyle, R., Schlegel, R., Kapadia, A. C., & Lee, A. J. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702165 · Cited by 40 · see citing works

Reconexp

Khan, V.-J., Markopoulos, P., Eggen, B. H., IJsselsteijn, W. A., & de Ruyter, B. (2008) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/1409240.1409316 · Cited by 38 · see citing works

Sleep, mood and sociability in a healthy population

Moturu, S. T., Khayal, I., Aharony, N., Pan, W., & Pentland, A. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6091303 · Cited by 35 · see citing works

Reflection or action?

Patil, S., Schlegel, R., Kapadia, A. C., & Lee, A. J. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557121 · Cited by 34 · see citing works

Performing QoE-measurements in an actual 3G network

Ketykó, I., Moor, K. D., Joseph, W., Martens, L., & Marez, L. D. (2010) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/isbmsb.2010.5463132 · Cited by 34 · see citing works

EmoSnaps: a mobile application for emotion recall from facial expressions

Niforatos, E., & Karapanos, E. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0777-0 · Cited by 34 · see citing works

Supporting Opportunities for Context-Aware Social Matching

Mayer, J. M., Hiltz, S. R., Barkhuus, L., et al. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858175 · Cited by 34 · see citing works

Human Factors in the Design of Longitudinal Smartphone-based Wellness Surveys

Vhaduri, S., & Poellabauer, C. (2016) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2016.24 · Cited by 34 · see citing works

Relationship of Psychological and Physiological Variables in Long-Term Self-Monitored Data During Work Ability Rehabilitation Program

Pärkkä, J., Merilahti, J., Mattila, E., Malm, E.-J., Antila, K., Tuomisto, M. T., Saarinen, A., van Gils, M., & Korhonen, I. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2007078 · Cited by 32 · see citing works

Palm or Cell? Comparing Personal Digital Assistants and Cell Phones for Experience Sampling Research

Burgin, C. J., Silvia, P. J., Eddington, K. M., & Kwapil, T. R. (2012) · Social Science Computer Review · doi:10.1177/0894439312441577 · Cited by 31 · see citing works

Mobile experience sampling: Reaching the parts of Facebook other methods cannot reach

Abdesslem, F. B., Parris, I., & Henderson, T. (2010) · St Andrews Research Repository (St Andrews Research Repository) · Cited by 30 · see citing works

AudioSense: Enabling real-time evaluation of hearing aid technology in-situ

Hasan, S. S., Lai, F., Chipara, O., & Wu, Y.-H. (2013) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627783 · Cited by 30 · see citing works

Evaluating Auditory Contexts and Their Impacts on Hearing Aid Outcomes with Mobile Phones

Hasan, S. S., Chipara, O., Wu, Y.-H., & Aksan, N. S. (2014) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2014.254952 · Cited by 28 · see citing works

Predicting flow state in daily work through continuous sensing of motion rhythm

Ara, K., Sato, N., Tsuji, S., et al. (2009) · International Conference on Networked Sensing Systems · doi:10.1109/inss.2009.5409930 · Cited by 28 · see citing works

The curse of quantified-self

van Berkel, N., Luo, C., Ferreira, D., Gonçalves, J., & Kostakos, V. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2800946 · Cited by 27 · see citing works

A field study comparing approaches to collecting annotated activity data in real-world settings

Chang, Y.-J., Paruthi, G., & Newman, M. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807524 · Cited by 26 · see citing works

Knock, knock! who׳s there? Putting the user in control of managing interruptions

Grandhi, S. A., & Jones, Q. (2015) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2015.02.008 · Cited by 26 · see citing works

CASS-methods and tools for investigating higher education knowledge practices

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An Exploration into Activity-Informed Physical Advertising Using PEST

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Network Analysis of Ecological Momentary Assessment Data for Monitoring and Understanding Eating Behavior

Spanakis, G., Weiß, G., Boh, B., & Roefs, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-29175-8_5 · Cited by 23 · see citing works

Mobile questionnaires for user experience evaluation

Väätäjä, H., & Roto, V. (2010) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1753846.1753985 · Cited by 22 · see citing works

Anonymous smartphone data collection: factors influencing the users’ acceptance in mobile crowd sensing

Gustarini, M., Wac, K., & Dey, A. K. (2015) · Personal and Ubiquitous Computing · doi:10.1007/s00779-015-0898-0 · Cited by 22 · see citing works

Photographing information needs

Yue, Z., Litt, E., Cai, C. J., Stern, J., Baxter, K. K., Guan, Z., Sharma, N., & Zhang, G. (. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557192 · Cited by 21 · see citing works

School principals at their lonely work: Recording workday practices through ESM logs

López, V., Ahumada, L., Galdames, S., et al. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.07.014 · Cited by 21 · see citing works

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Ayzenberg, Y., & Picard, R. W. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2278213 · Cited by 20 · see citing works

Mobile cloud storage

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Privacy as a Tradeoff: Introducing the Notion of Privacy Calculus for Context-Aware Mobile Applications

Liu, Z., Shan, J., Bonazzi, R., & Pigneur, Y. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.138 · Cited by 19 · see citing works

An adaptive speed-call list algorithm and its evaluation with ESM

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Jeeves - A visual programming environment for mobile experience sampling

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SnapApp

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Pico-ing into the future of mobile projection and contexts

Wilson, M. L., Craggs, D., Robinson, S., Jones, M., & Brimble, K. (2011) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0376-2 · Cited by 17 · see citing works

Inferring player engagement in a pervasive experience

Fischer, J. E., & Benford, S. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518993 · Cited by 17 · see citing works

OSN mood tracking

Lee, J. A., Efstratiou, C., & Bai, L. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968304 · Cited by 17 · see citing works

Learning to recognise disruptive smartphone notifications

Smith, J., Lavygina, A., Ma, J., Russo, A., & Naranker Dulay, A. (2014) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2628363.2628404 · Cited by 16 · see citing works

Spatial subterfuge

Guha, S., & Wicker, S. B. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2804281 · Cited by 16 · see citing works

CircleSense: A pervasive computing system for recognizing social activities

Liang, G., Cao, J., & Zhu, W. (2013) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2013.6526733 · Cited by 16 · see citing works

The Kunming CalFit study: Modeling dietary behavioral patterns using smartphone data

Seto, E., Hua, J., Wu, L., Bestick, A., Shia, V., Eom, S., Han, J. J., Wang, M. D., & Li, Y. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945210 · Cited by 14 · see citing works

Development of a hand-held computer platform for real-time behavioral assessment of physicians and nurses

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On the Use of Personalization to Enhance Compliance in Experience Sampling

Markopoulos, P., Batalas, N., & Timmermans, A. A. A. (2015) · European Conference on Cognitive Ergonomics · doi:10.1145/2788412.2788427 · Cited by 13 · see citing works

Vital Analysis: Annotating sensed physiological signals with the stress levels of first responders in action

Gomes, P. D. S., Kaiseler, M., Queirós, C. M. L., et al. (2012) · doi:10.1109/embc.2012.6347530 · Cited by 13 · see citing works

Hybrid Cemetery Culture: Making Death Matter in Cultural Heritage Using Smart Mobile Technologies

Sabra, J. B., Andersen, H. J., & Rodil, K. (2015) · International Conference on Culture and Computing · doi:10.1109/culture.and.computing.2015.16 · Cited by 13 · see citing works

Mobile Context Awareness

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Should participants be given a mobile phone, or use their own? Effects of novelty vs utility

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Quantifying QoE of mobile video consumption in a real-life setting drawing on objective and subjective parameters

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On the mathematical modeling of the effect of treatment on human physical activity

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Mobile-Based Experience Sampling for Behaviour Research

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A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data

Nguyen, T., Gupta, S., Venkatesh, S., & Phung, D. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.352 · Cited by 11 · see citing works

Engaging the Periphery for Visual Communication on Mobile Phones

Cowan, L. G., Griswold, W. G., Barkhuus, L., & Hollan, J. D. (2010) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2010.184 · Cited by 11 · see citing works

Correlation of significant places with self-reported state of bipolar disorder patients

Sabatelli, M., Osmani, V., Mayora, O., Gruenerbl, A., & Lukowicz, P. (2014) · International Conference on Wireless Mobile Communication and Healthcare · doi:10.1109/mobihealth.2014.7015923 · Cited by 11 · see citing works

QoE-based energy reduction by controlling the 3g cellular data traffic on the smartphone

İckin, S., Wac, K., & Fiedler, M. (2013) · doi:10.1109/sseegn.2013.6705396 · Cited by 11 · see citing works

'Divert: mother-in-law'

Mihalic, K., & Tscheligi, M. (2007) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/1377999.1378016 · Cited by 11 · see citing works

How Well Can a User’s Location Privacy Preferences be Determined Without Using GPS Location Data?

Maekawa, T., Yamashita, N., & Sakurai, Y. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2335537 · Cited by 11 · see citing works

Ego-centric Graphlets for Personality and Affective States Recognition

Teso, S., Staiano, J., Lepri, B., Passerini, A., & Pianesi, F. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.132 · Cited by 10 · see citing works

Smartphone based experience sampling of stress-related events

Weppner, J., Lukowicz, P., Serino, S., Cipresso, P., Gaggioli, A., & Riva, G. (2013) · 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops · doi:10.4108/icst.pervasivehealth.2013.252358 · Cited by 9 · see citing works

Expanding the Locus of Control

Johansen, S. K., & Kanstrup, A. M. (2016) · Nordic Conference on Human-Computer Interaction · doi:10.1145/2971485.2971497 · Cited by 9 · see citing works

Using Serious Games to Train Adaptive Emotional Regulation Strategies

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Social Inference Risk Modeling in Mobile and Social Applications

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Nonparametric discovery of movement patterns from accelerometer signals

Nguyen, T., Gupta, S., Venkatesh, S., & Phung, D. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.11.003 · Cited by 8 · see citing works

An experience sampling study into awareness needs of busy families

Khan, V.-J., Markopoulos, P., & Eggen, B. H. (2009) · Conference on Human System Interactions · doi:10.1109/hsi.2009.5091002 · Cited by 5 · see citing works

Effect of MobileASL on communication among deaf users

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Correlation Between Self-Reported Mood States and Objectively Measured Social Interactions at Work: A Pilot Study

Maxhuni, A., Matic, A., Osmani, V., & Mayora, O. (2011) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246136 · Cited by 5 · see citing works

Network analysis of ecological momentary assessment data for monitoring and understanding eating behavior

Spanakis, G., Weiß, G., Boh, B., et al. (2016) · Multiagent System Technologies · Cited by 5 · see citing works

Assessing contextual mood in public transport

Costa, P. M., Pitt, J. V., Galvão, T., & Cunha, J. F. E. (2013) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2493190.2494429 · Cited by 4 · see citing works

Impact of experience sampling methods on tap pattern based emotion recognition

Ghosh, S., Chauhan, V., Ganguly, N., Mitra, B., & De, P. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2804396 · Cited by 4 · see citing works

Preventing Unwanted Social Inferences with Classification Tree Analysis

Motahari, S., Ziavras, S. G., & Jones, Q. (2009) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2009.15 · Cited by 4 · see citing works

Psychobehavioral validity of self-reported symptoms based on spontaneous physical activity

Kim, J., Nakamura, T., Kikuchi, H., & Yamamoto, Y. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319276 · Cited by 4 · see citing works

24-Hour ambulatory monitoring of complex physiological parameters with a wireless health system: Feasibility, user compliance and application

Rieger, A., Neubert, S., Behrendt, S., et al. (2012) · doi:10.1109/ssd.2012.6198122 · Cited by 3 · see citing works

Boosting Mobile Experience Sampling with Social Media

Tollmar, K., & Huang, C. (2015) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2785830.2785894 · Cited by 3 · see citing works

The effectiveness of centralised management for reducing wasted effort in participatory sensing

Linnap, M., & Rice, A. C. (2014) · IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) · doi:10.1109/percomw.2014.6815167 · Cited by 3 · see citing works

Extreme mediation

Faiola, A. J., & Srinivas, P. (2014) · doi:10.1145/2638728.2638741 · Cited by 3 · see citing works

In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones

Hasan, S. S., Brummet, R., Chipara, O., et al. (2015) · doi:10.1109/ichi.2015.101 · Cited by 3 · see citing works

Development of real-time biological data collection system using a cellular phone

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Understanding the Dynamic Relationships among Interpersonal Personality Characteristics, Loneliness, and Smart-Phone Use: Evidence from Experience Sampling

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Diary studies as a macro-ergonomic evaluation tool: Development of a shift diary and its application in ergonomic evaluations

Korunka, C., Prem, R., & Kubicek, B. (2012) · Southeast Asian Network of Ergonomics Societies Conference (SEANES) · doi:10.1109/seanes.2012.6299552 · Cited by 1 · see citing works

A Context-Sensitive Tool to Support Mobile Technology Acceptance Research

Pergler, E., Hable, R., Rico-Schmidt, E., et al. (2014) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2014.133 · Cited by 1 · see citing works

Ambulatory physiologic monitoring system supporting EMA with self-administered visual evoked potential recording at randomized intervals

Ellingson, R. M., & Oken, B. (2011) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/imtc.2011.5944091 · Cited by 1 · see citing works

Providing Patient Context to Mental Health Professionals Using Mobile Applications

van Berkel, N., Hosio, S. J., Durkee, T., Carli, V., Wasserman, D., & Kostakos, V. (2016) · doi:10.13140/rg.2.1.3793.1923 · Cited by 1 · see citing works

Ecological Momentary Assessment: A Meta-Analysis on Designs, Samples, and Compliance Across Research Fields

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Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works

What Could Possibly Go Wrong When Interacting with Proactive Smart Speakers? A Case Study Using an ESM Application

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Impact of social anxiety and social context on college students’ emotion regulation strategy use: An experience sampling study

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Passive mobile sensing and psychological traits for large scale mood prediction

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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

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Relationships between trait emotion dysregulation and emotional experiences in daily life: an experience sampling study

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How Anxious are You Right Now? Using Ecological Momentary Assessment to Evaluate the Effects of Cognitive Bias Modification for Social Threat Interpretations

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Optimizing Expectations via Mobile Apps: A New Approach for Examining and Enhancing Placebo Effects

Gruszka, P., Burger, C., & Jensen, M. P. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00365 · Cited by 15 · see citing works

Understanding How to Administer Voice Surveys through Smart Speakers

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Measuring Early Maladaptive Schemas in Daily Life

Baxendell, R. S., Schmitter, M., Spijker, J., Keijsers, G. P. J., Tendolkar, I., & Vrijsen, J. N. (2025) · Cognitive Therapy and Research · doi:10.1007/s10608-025-10574-5 · Cited by 8 · see citing works

“Virtual Surf Booth”: Assessment of a Novel Tool and Data Collection Process to Measure the Impact of a 6-Week Surf Programme on Mental Wellbeing

Gerami, A., Foster, C., & Murphy, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192416732 · Cited by 3 · see citing works

Review on Research of Marxism Popularity in 2008

Xia, T. (2009) · Journal of Bohai University

Analysis of a Sporting Event on a Social Network: True Popularity & Popularity Bond

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Higher education popularity:an inevitable alternative of higher education in new century

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Popularity: Does it magnify associations between popularity prioritization and the bullying and defending behavior of early adolescent boys and girls?

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On Phenomena of the Popularity and the Marketing of Literature

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