AWARE: Mobile Context Instrumentation Framework

A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications

Dey, A. K., Abowd, G. D., & Salber, D. (2001) · Human-Computer Interaction · doi:10.1207/s15327051hci16234_02 · Cited by 2,958 · see citing works

Sensing meets mobile social networks

Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., & Campbell, A. T. (2008) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1460412.1460445 · Cited by 1,033 · see citing works

ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications

Raento, M., Oulasvirta, A., Petit, R., & Toivonen, H. T. T. (2005) · IEEE Pervasive Computing · doi:10.1109/mprv.2005.29 · Cited by 483 · see citing works

A framework for developing mobile, context-aware applications

Biegel, G., & Cahill, V. (2004) · doi:10.1109/percom.2004.1276875 · Cited by 309 · see citing works

Understanding Human-Smartphone Concerns: A Study of Battery Life

Ferreira, D., Dey, A. K., & Kostakos, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21726-5_2 · Cited by 240 · see citing works

Factors influencing quality of experience of commonly used mobile applications

İckin, S., Wac, K., Fiedler, M., Janowski, L., Hong, J.-H., & Dey, A. K. (2012) · IEEE Communications Magazine · doi:10.1109/mcom.2012.6178833 · Cited by 225 · see citing works

Getting closer

Dey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J.-H., & Ramos, J. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030135 · Cited by 197 · see citing works

Contextual experience sampling of mobile application micro-usage

Ferreira, D., Gonçalves, J., Kostakos, V., Barkhuus, L., & Dey, A. K. (2014) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2628363.2628367 · Cited by 160 · see citing works

Momento

Carter, S., Mankoff, J. C., & Heer, J. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240644 · Cited by 103 · see citing works

Empath

Dickerson, R. F., Gorlin, E. I., & Stankovic, J. A. (2011) · doi:10.1145/2077546.2077552 · Cited by 103 · see citing works

Supporting context-aware mobile applications: an infrastructure approach

Sinderen, M. J. V., van Halteren, A., Wegdam, M., Meeuwissen, H. B., & Eertink, E. H. (2006) · IEEE Communications Magazine · doi:10.1109/mcom.2006.1705985 · Cited by 92 · see citing works

SystemSens

Falaki, H., Mahajan, R., & Estrin, D. L. (2011) · doi:10.1145/1999916.1999923 · Cited by 73 · see citing works

Utilising context ontology in mobile device application personalisation

Korpipää, P., Häkkilä, J., Kela, J., Ronkainen, S., & Känsälä, I. (2004) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/1052380.1052399 · Cited by 67 · see citing works

Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application

Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., & Campbell, A. T. (2008) · Cited by 65 · see citing works

Revisiting human-battery interaction with an interactive battery interface

Ferreira, D., Ferreira, E., Gonçalves, J., Kostakos, V., & Dey, A. K. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493465 · Cited by 64 · see citing works

Mobile cloud storage

Vandenbroucke, K., Ferreira, D., Gonçalves, J., Kostakos, V., & Moor, K. D. (2014) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2628363.2628386 · Cited by 19 · see citing works

Getting closer: an empirical investigation of the proximity of user to their smart phones

Dey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J.-H., & Ramos, J. (2011) · Archive ouverte UNIGE (University of Geneva) · Cited by 15 · see citing works

Momento: Support for Situated Ubicomp Experimentation

Carter, S., Mankoff, J., & Heer, J. (2018) · doi:10.1184/r1/6470270 · Cited by 8 · see citing works

The Experience Sampling Method on Mobile Devices

van Berkel, N., Ferreira, D., & Kostakos, V. (2018) · ACM Computing Surveys · doi:10.1145/3123988 · Cited by 432 · see citing works

Stress-Related Biosocial Mechanisms of Discrimination and African American Health Inequities

Goosby, B. J., Cheadle, J. E., & Mitchell, C. M. (2018) · Annual Review of Sociology · doi:10.1146/annurev-soc-060116-053403 · Cited by 340 · see citing works

Smartphone-Based Ecological Momentary Assessment of Well-Being: A Systematic Review and Recommendations for Future Studies

de Vries, L. P., Baselmans, B. M. L., & Bartels, M. (2020) · Journal of Happiness Studies · doi:10.1007/s10902-020-00324-7 · Cited by 253 · see citing works

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.625247 · Cited by 261 · see citing works

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

Ranjan, Y., Rashid, Z., Stewart, C., Kerz, M., Begale, M. J., Verbeeck, D., Boettcher, S., Conde, P., Hyve, T., Dobson, R., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/11734 · Cited by 229 · see citing works

Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement.

Wright, A. G. C., & Zimmermann, J. (2019) · Psychological Assessment · doi:10.1037/pas0000685 · Cited by 204 · see citing works

The Design of Ecological Momentary Assessment Technologies

Doherty, K., Balaskas, A., & Doherty, G. J. (2020) · Interacting with Computers · doi:10.1093/iwcomp/iwaa019 · Cited by 187 · see citing works

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Doryab, A., Villalba, D. K., Chikersal, P., Dutcher, J. M., Tumminia, M. J., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J. C., Creswell, J. D., et al. (2019) · JMIR mhealth and uhealth · doi:10.2196/13209 · Cited by 162 · see citing works

Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches

Khoo, L. S., McNaney, R., Lim, M. K., & McNaney, R. (2024) · Sensors · doi:10.3390/s24020348 · Cited by 178 · see citing works

Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions

Bae, S. W., Chung, T. A., Ferreira, D., Dey, A. K., & Suffoletto, B. P. (2017) · Addictive Behaviors · doi:10.1016/j.addbeh.2017.11.039 · Cited by 151 · see citing works

Digital Emotion Regulation

Wadley, G., Smith, W., Koval, P., & Gross, J. J. (2020) · Current Directions in Psychological Science · doi:10.1177/0963721420920592 · Cited by 150 · see citing works

Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study

Servia-Rodríguez, S., Rachuri, K. K., Mascolo, C., Rentfrow, P. J., Lathia, N., & Sandstrom, G. M. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052618 · Cited by 134 · see citing works

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

Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing

Chikersal, P., Doryab, A., Tumminia, M. J., Villalba, D. K., Dutcher, J. M., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J. C., Creswell, J. D., et al. (2021) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3422821 · Cited by 144 · see citing works

Digital Inclusion in Older Adults: A Comparison Between Face-to-Face and Blended Digital Literacy Workshops

Martínez-Alcalá, C. I., Rosales-Lagarde, A., Alonso-Lavernia, M. D. L. Á., Ramírez-Salvador, J. Á., Jiménez-Rodríguez, B., Cepeda-Rebollar, R. M., López‐Noguerola, J. S., Díaz, M. L. B., & Agis-Juárez, R. A. (2018) · Frontiers in ICT · doi:10.3389/fict.2018.00021 · Cited by 134 · see citing works

Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students

Xu, X., Chikersal, P., Doryab, A., Villalba, D. K., Dutcher, J. M., Tumminia, M. J., Althoff, T., Cohen, S., Creswell, K. G., Creswell, J. D., et al. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351274 · Cited by 125 · see citing works

OmniTrack

Kim, Y.-H., Jeon, J. H., Lee, B., Choe, E. K., & Seo, J. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130930 · Cited by 119 · see citing works

Sensus

Xiong, H., Huang, Y., Barnes, L. E., & Gerber, M. S. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971711 · Cited by 116 · see citing works

GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling

Xu, X., Liu, X., Zhang, H., Wang, W., Nepal, S., Sefidgar, Y., Seo, W., Kuehn, K. S., Huckins, J. F., Morris, M. E., et al. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3569485 · Cited by 96 · see citing works

Effect of experience sampling schedules on response rate and recall accuracy of objective self-reports

van Berkel, N., Gonçalves, J., Lovén, L., Ferreira, D., Hosio, S. J., & Kostakos, V. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.12.002 · Cited by 96 · see citing works

Student Perspectives on Digital Phenotyping: The Acceptability of Using Smartphone Data to Assess Mental Health

Rooksby, J., Morrison, A., & Murray-Rust, D. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300655 · Cited by 87 · see citing works

The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing

Lind, M. N., Byrne, M. L., Wicks, G., Smidt, A. M., & Allen, N. B. (2018) · JMIR Mental Health · doi:10.2196/10334 · Cited by 84 · see citing works

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Mendes, J. P. M., de Moura, I. R., van de Ven, P. W. J., Viana, D., Silva, F., Coutinho, L., Teixeira, S. S., Rodrigues, J. J. P. C., & Teles, A. S. (2021) · Journal of Medical Internet Research · doi:10.2196/28735 · Cited by 85 · see citing works

Intelligent positive computing with mobile, wearable, and IoT devices: Literature review and research directions

Lee, U., Han, K., Cho, H., Chung, K., Hong, H., Lee, S.-J., Noh, Y., Park, S., & Carroll, J. M. (2018) · Ad Hoc Networks · doi:10.1016/j.adhoc.2018.08.021 · Cited by 85 · see citing works

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors

Bae, S. W., Ferreira, D., Suffoletto, B. P., Puyana, J. C., Kurtz, R., Chung, T. A., & Dey, A. K. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3090051 · Cited by 84 · see citing works

Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments

Saha, K., Chan, L., De Barbaro, K., Abowd, G. D., & De Choudhury, M. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130960 · Cited by 77 · see citing works

mobileQ: A free user-friendly application for collecting experience sampling data

Meers, K., Dejonckheere, E., Kalokerinos, E. K., Rummens, K., & Kuppens, P. (2020) · Behavior Research Methods · doi:10.3758/s13428-019-01330-1 · Cited by 82 · see citing works

The Lang‐Track‐App: Open‐Source Tools for Implementing the Experience Sampling Method in Second Language Acquisition Research

Arndt, H. L., Granfeldt, J., & Gullberg, M. (2022) · Language Learning · doi:10.1111/lang.12555 · Cited by 30 · see citing works

Overcoming compliance bias in self-report studies: A cross-study analysis

van Berkel, N., Gonçalves, J., Hosio, S. J., Sarsenbayeva, Z., Velloso, E., & Kostakos, V. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.10.003 · Cited by 69 · see citing works

A Systematic Assessment of Smartphone Usage Gaps

van Berkel, N., Luo, C., Anagnostopoulos, T., Ferreira, D., Gonçalves, J., Hosio, S. J., & Kostakos, V. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858348 · Cited by 70 · see citing works

Mobile manifestations of alertness

Murnane, E. L., Abdullah, S., Matthews, M. L., Kay, M., Kientz, J. A., Choudhury, T., Gay, G. K., & Cosley, D. (2016) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2935334.2935383 · Cited by 65 · see citing works

Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire

Matteo, D. D., Fine, A., Fotinos, K., Rose, J., & Katzman, M. A. (2018) · JMIR Mental Health · doi:10.2196/mental.9539 · Cited by 68 · see citing works

Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis

Sarsenbayeva, Z., Marini, G., van Berkel, N., Luo, C., Jiang, W., Yang, K., Wadley, G., Dingler, T., Kostakos, V., & Gonçalves, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376163 · Cited by 67 · see citing works

Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study

Low, C. A., Dey, A. K., Ferreira, D., Kamarck, T. W., Sun, W., Bae, S. W., & Doryab, A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.9046 · Cited by 66 · see citing works

Understanding the Challenges of Mobile Phone Usage Data

Church, K., Ferreira, D., Banović, N., & Lyons, K. (2015) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/2785830.2785891 · Cited by 65 · see citing works

Leveraging Collaborative-Filtering for Personalized Behavior Modeling

Xu, X., Chikersal, P., Dutcher, J. M., Sefidgar, Y. S., Seo, W., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, K. G., Creswell, J. D., et al. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448107 · Cited by 65 · see citing works

User profiling from their use of smartphone applications: A survey

Zhao, S., Li, S., Ramos, J., Luo, Z., Jiang, Z., Dey, A. K., & Pan, G. (2019) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2019.101052 · Cited by 63 · see citing works

Supporting Social Interactions with an Expressive Heart Rate Sharing Application

Liu, F., Dabbish, L., & Kaufman, G. F. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130943 · Cited by 60 · see citing works

“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality

Kheirinejad, S., Visuri, A., Ferreira, D., & Hosio, S. J. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01694-w · Cited by 67 · see citing works

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

Vega, J., Li, M., Aguillera, K., Goel, N., Joshi, E., Khandekar, K., Durica, K. C., Kunta, A. R., & Low, C. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.769823 · Cited by 54 · see citing works

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

Orzikulova, A., Han, X. L., Li, Z., Yan, Y., Wang, Y., Shi, Y., Ghassemi, M., Lee, S.-J., Dey, A. K., & Xu, X. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642747 · Cited by 55 · see citing works

Nightly sleep duration predicts grade point average in the first year of college

Creswell, J. D., Tumminia, M. J., Price, S., Sefidgar, Y., Cohen, S., Ren, Y., Brown, J., Dey, A. K., Dutcher, J. M., Villalba, D., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209123120 · Cited by 53 · see citing works

What data are smartphone users willing to share with researchers?

Beierle, F., Tran, V. T., Allemand, M., Neff, P. K. A., Schlee, W., Probst, T., Zimmermann, J., & Pryss, R. C. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01355-6 · Cited by 53 · see citing works

Relationship between Problematic Smartphone Use, Sleep Quality and Bedtime Procrastination: A Mediation Analysis

Correa-Iriarte, S., Hidalgo-Fuentes, S., & Vilar, M. M. (2023) · Behavioral Sciences · doi:10.3390/bs13100839 · Cited by 56 · see citing works

Securacy

Ferreira, D., Kostakos, V., Beresford, A. R., Lindqvist, J., & Dey, A. K. (2015) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/2766498.2766506 · Cited by 51 · see citing works

Context Data Categories and Privacy Model for Mobile Data Collection Apps

Beierle, F., Tran, V. T., Allemand, M., Neff, P., Schlee, W., Probst, T., Pryss, R., & Zimmermann, J. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.139 · Cited by 50 · see citing works

People Like Me

Feustel, C., Aggarwal, S., Lee, B., & Wilcox, L. G. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264917 · Cited by 49 · see citing works

Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications

Kumar, D., Jeuris, S., Bardram, J. E., & Dragoni, N. (2020) · ACM Transactions on Computing for Healthcare · doi:10.1145/3422158 · Cited by 47 · see citing works

A Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring

Elfouly, T., & Alouani, A. T. (2025) · Electronics · doi:10.3390/electronics14173443 · Cited by 56 · see citing works

Can Programming Frameworks Bring Smartphones into the Mainstream of Psychological Science?

Piwek, L., & Ellis, D. A. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01252 · Cited by 46 · see citing works

mCerebrum

Hossain, S. M., Hnat, T. W., Saleheen, N., Nasrin, N. J., Noor, J., Ho, B.-J., Condie, T., Srivastava, M. B., & Kumar, S. (2017) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3131672.3131694 · Cited by 46 · see citing works

Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Beierle, F., Schobel, J., Vogel, C., Allgaier, J., Mulansky, L., Haug, F., Haug, J., Schlee, W., Holfelder, M., Stach, M., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18147395 · Cited by 49 · see citing works

On Curating Multimodal Sensory Data for Health and Wellness Platforms

Amin, M. B., Baños, O., Khan, W. A., Bilal, H. M., Gong, J., Bui, D.-M., Cho, S., Hussain, S., Ali, T., Akhtar, U., et al. (2016) · Sensors · doi:10.3390/s16070980 · Cited by 45 · see citing works

Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype

Aledavood, T., Triana, A. M., Alakörkkö, T., Kaski, K. K., Saramäki, J., Isometsä, E. T., & Darst, R. K. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6919 · Cited by 43 · see citing works

Monetary Assessment of Battery Life on Smartphones

Hosio, S. J., Ferreira, D., Gonçalves, J., van Berkel, N., Luo, C., Ahmed, M., Flores, H., & Kostakos, V. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858285 · Cited by 42 · see citing works

Students' Experiences with Ecological Momentary Assessment Tools to Report on Emotional Well-being

Chan, L., Swain, V. D., Kelley, C., de Barbaro, K., Abowd, G. D., & Wilcox, L. G. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191735 · Cited by 42 · see citing works

When phones get personal: Predicting Big Five personality traits from application usage

Peltonen, E., Sharmila, P., Asare, K. O., Visuri, A., Lagerspetz, E., & Ferreira, D. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101269 · Cited by 41 · see citing works

TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction

Xu, X., Zou, T., Han, X. L., Li, Y., Wang, R., Yuan, T., Wang, Y., Shi, Y., Mankoff, J. C., & Dey, A. K. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517476 · Cited by 40 · see citing works

AWARE-Light: a smartphone tool for experience sampling and digital phenotyping

van Berkel, N., D’Alfonso, S., Susanto, R., Ferreira, D., & Kostakos, V. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01697-7 · Cited by 39 · see citing works

Back to Analogue

Vega, J., Couth, S., Poliakoff, E., Kotz, S. A., Sullivan, M., Jay, C., Vigo, M., & Harper, S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173648 · Cited by 39 · see citing works

Relationship between daily rated depression symptom severity and the retrospective self-report on PHQ-9: A prospective ecological momentary assessment study on 80 psychiatric outpatients

Baryshnikov, I., Aledavood, T., Rosenström, T. H., Heikkilä, R., Darst, R. K., Riihimäki, K., Saleva, O., Ekelund, J., & Isometsä, E. T. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.12.127 · Cited by 36 · see citing works

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

Lee, U., Jung, G., Ma, E.-Y., Kim, J. S., Kim, H., Alikhanov, J., Noh, Y., & Kim, H. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123015 · Cited by 40 · see citing works

Personalized mood prediction from patterns of behavior collected with smartphones

Balliu, B., Douglas, C., Seok, D., Shenhav, L., Wu, Y., Chatzopoulou, D., Kaiser, W., Chen, V., Kim, J. S., Deverasetty, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01035-6 · Cited by 37 · see citing works

The crossroads of digital phenotyping

Davidson, B. I. (2020) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2020.11.009 · Cited by 37 · see citing works

Open-source smartphone app and tools for measuring, quantifying, and visualizing technology use

Geyer, K., Ellis, D. A., Shaw, H., & Davidson, B. I. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01585-7 · Cited by 23 · see citing works

SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation

Sels, L., Homan, S., Ries, A., Santhanam, P., Scheerer, H., Colla, M., Vetter, S., Seifritz, E., Galatzer‐Levy, I. R., Kowatsch, T., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.554811 · Cited by 32 · see citing works

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni, P., Kirkham, R., & Mcnaney, R. (2022) · Italian National Conference on Sensors · doi:10.3390/s22103893 · Cited by 33 · see citing works

Evolving the Ecosystem of Personal Behavioral Data

Wiese, J., Das, S., Hong, J., & Zimmerman, J. (2017) · Human-Computer Interaction · doi:10.1080/07370024.2017.1295857 · Cited by 35 · see citing works

Understanding the potential of human–machine crowdsourcing for weather data

Niforatos, E., Vourvopoulos, A., & Langheinrich, M. (2016) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2016.10.002 · Cited by 35 · see citing works

Influence of Topic Familiarity and Prompt Specificity on Citation Fabrication in Mental Health Research Using Large Language Models: Experimental Study

Linardon, J., Jarman, H. K., McClure, Z., Anderson, C., Liu, C., & Messer, M. (2025) · JMIR Mental Health · doi:10.2196/80371 · Cited by 40 · see citing works

Modelling smartphone usage

Kostakos, V., Ferreira, D., Gonçalves, J., & Hosio, S. J. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971669 · Cited by 34 · see citing works

Health Multimedia

Nag, N., Pandey, V., & Jain, R. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3080545 · Cited by 34 · see citing works

Personal Mobile Messaging in Context

Buschek, D., Hassib, M. E., & Alt, F. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3201404 · Cited by 34 · see citing works

A Survey of Context Simulation for Testing Mobile Context-Aware Applications

Luo, C., Gonçalves, J., Velloso, E., & Kostakos, V. (2020) · ACM Computing Surveys · doi:10.1145/3372788 · Cited by 34 · see citing works

PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps

Li, Y., Chen, F., Li, T. J.-J., Guo, Y., Huang, G., Fredrikson, M., Agarwal, Y., & Hong, J. I. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130941 · Cited by 39 · see citing works

From Multimedia Logs to Personal Chronicles

Oh, H., & Jain, R. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123375 · Cited by 33 · see citing works

Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders

Baumeister, H., Bauereiss, N., Zarski, A.-C., Braun, L., Buntrock, C., Hoherz, C., Idrees, A. R., Kraft, R., Meyer, P., Nguyen, T. B. D., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.660534 · Cited by 30 · see citing works

Al-light

Matsui, H., Hashizume, T., & Yatani, K. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264936 · Cited by 32 · see citing works

Psychiatry Outpatients’ Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study

Rieger, A., Gaines, A. N., Barnett, I., Baldassano, C. F., Gibbons, M. B. C., & Crits-Christoph, P. F. (2019) · JMIR Formative Research · doi:10.2196/14329 · Cited by 32 · see citing works

Contact Logger: Measuring everyday intergroup contact experiences in near-time

Keil, T. F., Koschate, M., & Levine, M. (2020) · Behavior Research Methods · doi:10.3758/s13428-019-01335-w · Cited by 36 · see citing works

Passively-sensed Behavioral Correlates of Discrimination Events in College Students

Sefidgar, Y. S., Seo, W., Kuehn, K. S., Althoff, T., Browning, A., Riskin, E. A., Nurius, P. S., Dey, A. K., & Mankoff, J. C. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359216 · Cited by 29 · see citing works

IOS Crowd–Sensing Won’t Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform

Nishiyama, Y., Ferreira, D., Eigen, Y., Sasaki, W., Okoshi, T., Nakazawa, J., Dey, A. K., & Sezaki, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50344-4_17 · Cited by 30 · see citing works

Requirements and Operational Guidelines for Secure and Sustainable Digital Phenotyping: Design and Development Study

Jagesar, R. R., Vorstman, J. A. S., & Kas, M. J. (2021) · Journal of Medical Internet Research · doi:10.2196/20996 · Cited by 30 · see citing works

Passive social sensing with smartphones: a systematic review

Zhang, H., Ibrahim, A., Parsia, B., Poliakoff, E., & Harper, S. (2022) · Computing · doi:10.1007/s00607-022-01112-2 · Cited by 29 · see citing works

FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Islam, R., & Bae, S. W. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3676505 · Cited by 29 · see citing works

A simple location-tracking app for psychological research

Geyer, K., Ellis, D. A., & Piwek, L. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1164-y · Cited by 29 · see citing works

Gamified Participatory Sensing in Tourism: An Experimental Study of the Effects on Tourist Behavior and Satisfaction

Kawanaka, S., Matsuda, Y., Suwa, H., Fujimoto, M., Arakawa, Y., & Yasumoto, K. (2020) · Smart Cities · doi:10.3390/smartcities3030037 · Cited by 29 · see citing works

Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study

Low, C. A., Li, M., Vega, J., Durica, K. C., Ferreira, D., Tam, V., Hogg, M. E., Zeh, H. J., Doryab, A., & Dey, A. K. (2021) · JMIR Cancer · doi:10.2196/27975 · Cited by 29 · see citing works

Putting human behavior predictability in context

Zhang, W., Shen, Q., Teso, S., Lepri, B., Passerini, A., Bison, I., & Giunchiglia, F. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00299-2 · Cited by 32 · see citing works

Predicting interruptibility for manual data collection: a cluster-based user model

Visuri, A., van Berkel, N., Luo, C., Goncalves, J., Ferreira, D., & Kostakos, V. (2017) · Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services · doi:10.1145/3098279.3098532 · Cited by 28 · see citing works

mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study

Wen, H., Sobolev, M., Vitale, R., Kizer, J., Pollak, J., Muench, F. J., & Estrin, D. L. (2021) · JMIR Mental Health · doi:10.2196/25019 · Cited by 28 · see citing works

Menthal

Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971591 · Cited by 28 · see citing works

Protocol of the STRess at Work (STRAW) Project: How to Disentangle Day-to-Day Occupational Stress among Academics Based on EMA, Physiological Data, and Smartphone Sensor and Usage Data

Bolliger, L., Lukan, J., Luštrek, M., Bacquer, D. D., & Clays, E. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17238835 · Cited by 27 · see citing works

Emotion trajectories in smartphone use: Towards recognizing emotion regulation in-the-wild

Tag, B., Sarsenbayeva, Z., Cox, A. L., Wadley, G., Gonçalves, J., & Kostakos, V. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102872 · Cited by 27 · see citing works

A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment

Senaratne, H., Oviatt, S. L., Ellis, K., & Melvin, G. A. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3556980 · Cited by 28 · see citing works

Towards A Framework for Mobile Behavior Change Research

Okeke, F., Sobolev, M., & Estrin, D. L. (2018) · Technology, Mind, and Society Conference (TechMindSociety) · doi:10.1145/3183654.3183706 · Cited by 27 · see citing works

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

González‐Pérez, A., Matey-Sanz, M., Granell, C., Díaz-Sanahuja, L., Bretón-López, J. M., & Casteleyn, S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104359 · Cited by 30 · see citing works

Environmental exposure assessment using indoor/outdoor detection on smartphones

Anagnostopoulos, T., Garcia, J. C., Gonçalves, J., Ferreira, D., Hosio, S. J., & Kostakos, V. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1028-y · Cited by 24 · see citing works

Smart Sensors for Health Research and Improvement

Garatva, P., Terhorst, Y., Meßner, E.-M., Karlen, W., Pryss, R. C., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_23 · Cited by 26 · see citing works

Survey of Federated Learning Models for Spatial-Temporal Mobility Applications

Belal, Y., Mokhtar, S. B., Haddadi, H., Wang, J., & Mashhadi, A. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3666089 · Cited by 26 · see citing works

AWARE : a mobile context instrumentation middleware to collaboratively understand human behavior

Ferreira, D. (2013) · Cited by 32 · see citing works

Mobile Context Toolbox: An Extensible Context Framework for the Maemo Platform

Stopczynski, A., Larsen, J. E., & Skomail, L. (2010)

An Extensible Toolkit for Context-Aware Mobile Applications

Clayton, B., Hull, R., Melamed, T. J., & Hawkes, R. (2009) · International Symposium on Wearable Computers · doi:10.1109/iswc.2009.42

Research In Mobile Context-aware Applications

Cheverst, K. (2007)

Mobile Context Toolbox

Larsen, J. E., & Jensen, K. L. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04471-7_15 · Cited by 7 · see citing works