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 2,943

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

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

A framework for developing mobile, context-aware applications

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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

Factors influencing quality of experience of commonly used mobile applications

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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

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 168

Momento

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Empath

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Understanding Health and Behavioral Trends of Successful Students Through Machine Learning Models

Kim, A., Nikseresht, F., Dutcher, J. M., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswel, K., Creswell, D. J., Dey, A. K., Mankoff, J. C., et al. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-74009-2_66 · Cited by 2

MEEC: Second Workshop on Momentary Emotion Elicitation and Capture

Ali, A. E., Perusquía-Hernández, M., Hassib, M. E., Abdelrahman, Y., & Newn, J. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3441351 · Cited by 2

Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Liang, S., Wu, D., & Dong, J. (2022) · Sustainability · doi:10.3390/su142012992 · Cited by 2

AWARE-TEXT: An Android Package for Mobile Phone Based Text Collection and On-Device Processing

Giorgi, S., Sherman, G. T., Bellew, D., Guntuku, S. C., Ungar, L., & Curtis, B. L. (2023) · Workshop for NLP Open Source Software (NLP-OSS) · doi:10.18653/v1/2023.nlposs-1.12 · Cited by 2

ColEmo: A Flexible Open Source Software Interface for Collecting Emotion Data

Rahmani, M. H., Berkvens, R., & Weyn, M. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388161 · Cited by 2

Enhancing Account Recovery with Location-based Dynamic Questions

Yamaguchi, S., Gomi, H., & Uehara, T. (2023) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c60940.2023.00061 · Cited by 2

Decentralized, distributed and fault-tolerant context recognition architectures for smart cities: A systematic mapping

do Nascimento, L. V., & de Oliveira, J. P. M. (2021) · SBC Reviews on Computer Science · doi:10.5753/reviews.2021.1805 · Cited by 2

An Android Wear OS Framework for Sensor Data and Network Interfaces

Berzati, B., Ippisch, A., & Graffi, K. (2018) · IEEE Conference on Local Computer Networks Workshops (LCN Workshops) · doi:10.1109/lcnw.2018.8628494 · Cited by 2

JTrack: A Digital Biomarker Platform for Remote Monitoring in Neurological and Psychiatric DiseasesPreprint

Far, M. S., Stolz, M., Fischer, J. M., Eickhoff, S. B., & Dukart, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10091 · Cited by 2

Trial Monitor: Scaffolding personalised Web dashboards for Human–Computer Interaction field trials

Ribeiro, J., Lima, P., & Nunes, F. (2021) · SoftwareX · doi:10.1016/j.softx.2021.100883 · Cited by 2

JuTrack: A Digital Biomarker Platform for Remote Monitoring in Neuropsychiatric and Psychiatric Diseases (Preprint)Preprint

Far, M. S., Stolz, M., Fischer, J. M., Eickhoff, S. B., & Dukart, J. (2021) · doi:10.2196/preprints.27476 · Cited by 2

Daily Life Methods in Adolescence and Emerging Adulthood Studies in Croatia, Serbia, and Slovenia: A Scoping Review

Šutić, L., & Novak, M. (2023) · Youth · doi:10.3390/youth3040072 · Cited by 1

Data Protection—Issues and Perspectives

Gupta, I. (2024) · Springer eBooks · doi:10.1007/978-981-99-6778-0_4 · Cited by 1

Participant Engagement and Data Quality: Lessons Learned from a Mental Wellness Crowdsensing Study

Zhang, E., Trujillo, R., & Poellabauer, C. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711043 · Cited by 1

Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study

Ren, Y., Mulukutla, R., Mankoff, J. C., & Dey, A. K. (2025) · JMIR Formative Research · doi:10.2196/78657 · Cited by 2

Predicting The Severity of Anxiety in Adolescents Through Passively Sensed Behaviors

Zhang, E., Clark, M., & Doryab, A. (2025) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3721389 · Cited by 1

Multimodal Sleep Measurement and Alignment Analysis in Outpatients With Major Depressive Episode: Observational Study

Mahir, A., Luong, N., Baryshnikov, I., Martikkala, A., Isometsä, E. T., & Aledavood, T. (2025) · JMIR mhealth and uhealth · doi:10.2196/82465 · Cited by 1

Biobehavioral Rhythms in Everyday Life: Data and Models for Capturing Cyclic Behavior in Naturalistic Settings

Zhao, C., Cardei, M. A., Clark, M., Yan, R., & Doryab, A. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770698 · Cited by 1

Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults

Boka, C., Yonko, E. A., Beikzadeh, M., Kärkkäinen, K., Hong, C., Sarrafzadeh, M., & Holloway, I. W. (2026) · Prevention Science · doi:10.1007/s11121-025-01872-1 · Cited by 1

An open-source platform for multimodal digital trace data collection from smartphones

Kim, I., Boffa, J., Cho, M.-J., Conroy, D. E., Kline, N., Haber, N., Robinson, T. N., Reeves, B. B., & Ram, N. (2026) · Nature Health · doi:10.1038/s44360-026-00072-7 · Cited by 1

Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping

Ikäheimonen, A., Luong, N., Baryshnikov, I., John, T., Martikkala, A., Isometsä, E. T., & Aledavood, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-48625-w · Cited by 2

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

Towards context of quality in mobile sensing campaigns

Castro, L. A., Gutiérrez, L. J., Rodríguez, L.-F., & Ruiz, E. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3124431 · Cited by 1

Music at Your Fingertips: Designing Mobile Interaction Interfaces for Runners

Stigberg, S. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91797-9_53 · Cited by 1

Exploring the Interplay of Context and Interaction in the Field

Schröder, S., Hirschl, J., & Reichl, P. (2018) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2018.8463361 · Cited by 1

Context-Aware Coproduction: Implications for Recommendation Algorithms

Chen, J., Doryab, A., Hanrahan, B. V., Yousfi, A., Beck, J., Wang, X., Bellotti, V. M. E., Dey, A. K., & Carroll, J. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-15742-5_54 · Cited by 1

Teaching Ubiquitous Computing Using Simulations Based on Smartphone Sensors

Peixoto, M. J. P., Duarte, P., ARAÚJO, P. T., PINTO, P. I. C., Sarmento, W. W. F., Trinta, F. A. M., & Viana, W. (2020) · Informatics in Education · doi:10.15388/infedu.2020.07 · Cited by 1

TYDR: Track Your Daily Routine

Beierle, F. (2021) · T-labs series in telecommunication services · doi:10.1007/978-3-030-68840-0_4 · Cited by 1

The Impact of Smartphone Usage on Circadian Cycles: A Case Study with Wearable Ring

Peltonen, E., Sharmila, P., Kuosmanen, E., Koskimäki, H., & Visuri, A. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767352 · Cited by 1

OpenDPMH: A Framework for Developing Mobile Sensing Applications of Digital Phenotyping

Mendes, J. P. M., Silva, F., Cardoso, A., de Moura, I. R., Coutinho, L., Viana, D., Endler, M., & Teles, A. S. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00216 · Cited by 1

Police Interventions as a Context-aware System. A Case of a Contextual Data Modelling

Klimek, R. (2022) · Proceedings of the International Conference on Information Systems Development · doi:10.62036/isd.2022.34 · Cited by 1

User Verification System using Location-based Dynamic Questions for Account Recovery

Yamaguchi, S., Gomi, H., & Uehara, T. (2024) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw63631.2024.00006 · Cited by 1

Capturing Real-Time Behavior of Post Stroke Survivors using Mobile Technologies

Hettiarachchi, P., Senadheera, I., Nawaratne, R., Haslam, B. S., Alahakoon, D., & Carey, L. M. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613559 · Cited by 1

The Bidirectional Relationship between Emotional Change and Physical Movement Activities: An Analysis Using Propensity Score Matching Methods

Wu, Z., Nishiyama, Y., & Shibuya, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825918 · Cited by 1

Understanding User Behavior in the Wild Using Smartphones

Teng, S., D’Alfonso, S., & Kostakos, V. (2024) · Springer eBooks · doi:10.1007/978-3-319-27648-9_109-1 · Cited by 2

Mobile Phone Usage Cycles

Williamson, J., Williamson, J. H., Kostakos, V., Hamilton, K., & Green, J. B. A. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2892402 · Cited by 1

Online?

Ferreira, D., Flores, H., Vandenbroucke, K., & Visuri, A. (2016) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3012709.3016060 · Cited by 3

S3

Asare, K. O., Leikanger, T., Schuss, C., Klakegg, S., Visuri, A., & Ferreira, D. (2018) · International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI Adjunct) · doi:10.1145/3236112.3236155 · Cited by 1

Building a Crowdsensing Platform Based on Spatio-Temporal Fencing

Miyagawa, N., Tsuchimoto, R., Suzaki, S., & Kaji, K. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-94822-1_52 · Cited by 1

Senbay

Nishiyama, Y., Dey, A. K., Ferreira, D., Yonezawa, T., & Nakazawa, J. (2018) · International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI Adjunct) · doi:10.1145/3236112.3236154 · Cited by 1

CoRhythMo: A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data StreamsPreprint

Yan, R., Liu, X., Dutcher, J. M., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, D. J., Creswell, K. G., Mankoff, J. C., Dey, A. K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.244020 · Cited by 1

Nation-wide Mood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor DataPreprint

Sasaki, W., Kawane, H., Miyahara, S., Tsubouchi, K., & Okoshi, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05537 · Cited by 1

Mobile Monitoring of Mood (MoMo-Mood) Pilot: A Longitudinal, Multi-Sensor Digital Phenotyping Study of Patients with Major Depressive Disorder and Healthy ControlsPreprint

Triana, A. M., Martikkala, A., Baryshnikov, I., Heikkilä, R., Alakörkkö, T., Darst, R. K., Ekelund, J., Isometsä, E. T., & Aledavood, T. (2020) · medRxiv · doi:10.1101/2020.11.02.20222919 · Cited by 1

Multi-Modal Sleep Measurement and Alignment Analysis in Outpatients with Major Depressive EpisodePreprint

Mahir, A., Luong, N., Baryshnikov, I., Martikkala, A., Isometsä, E. T., & Aledavood, T. (2025) · medRxiv · doi:10.1101/2025.04.29.25326308 · Cited by 1

Research on Social Context Acquisition and Reasoning Techniques for Online Social Networks

Bakhsh, B. (2017) · Tuwhera (Auckland University of Technology)

AndroCon: An Android-Based Context-Aware Middleware Framework

Yu, J., Sheng, Q. Z., Adeleye, O., & Wang, C. (2018) · EAI Endorsed Transactions on Context-aware Systems and Applications · doi:10.4108/eai.14-3-2018.154338

AndroCon

Yu, J., Sheng, Q. Z., Yan, W. Q., Wang, C., & Adeleye, O. (2017) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3144457.3144509

Technology and Aging: Ubiquitous Sensing Technology for Aging Research

Favela, J., & Castro, L. A. (2018) · Springer eBooks · doi:10.1007/978-3-319-95387-8_13

Verifying nondeterministic processes driven by broadcasts on Android

Luo, C., Ma, X., Tian, Y., Gonçalves, J., Velloso, E., & Kostakos, V. (2019) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec.2019.8729296

Capturing Subjective Time as Context and It's Applications (poster)

Yonezawa, T., Nishiyama, Y., Hiroi, K., & Kawaguchi, N. (2019) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3328719

Towards Approximate Mobile ComputingPreprint

Pejović, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08972

ESPIM: um modelo para guiar o desenvolvimento de sistemas de intervenção a distância

Cunha, B. C. R. (2019) · doi:10.11606/t.55.2019.tde-29082019-090151

Definición de contexto por medio de ontologías como mejora de servicios y aplicaciones en dispositivos móviles

Cabrera, V. D. A. (2019) · doi:10.35376/10324/39289

A Systematic Overview and Comparative Analysis of Service Discovery Protocols and Middlewares

Awan, J. H., Naseem, U., Khalid, S., & Waheed, N. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110633

Can Smartphone Co-locations Detect Friendship? It Depends How You Model ItPreprint

Malik, M. M., Doryab, A., Merrill, M., Pfeffer, J., & Dey, A. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02919

Trading energy for accuracy in mobile interruptiblity inference

Cuculoski, A., & Pejović, V. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414429

Streaming and Learning the Personal ContextPreprint

Giunchiglia, F., Britez, M. D. R., Bontempelli, A., & Li, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.08234

Location Finder Mobile Application Using Android and Google SpreadSheets

Olufemi, A. N., & Şah, M. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3637-0_51

A Comparative Study of CIPN Symptom Estimation Methods Based on Machine Learning

Hamanaka, S., Sasaki, W., Okoshi, T., Nakazawa, J., Yagasaki, K., & Komatsu, H. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3479316

Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case Study

Thorpe, D., Fouyaxis, J., Lipschitz, J. M., Nielson, A., Li, W., Murphy, S. A., & Bidargaddi, N. P. (2022) · JMIR Formative Research · doi:10.2196/29988

Passive Social Sensing with Smartphone: A Systematic Review (Preprint)Preprint

Zhang, H., Ibrahim, A., Parsia, B., Poliakoff, E., & Harper, S. (2020) · doi:10.2196/preprints.20539

Related Work

Beierle, F. (2021) · T-labs series in telecommunication services · doi:10.1007/978-3-030-68840-0_3

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

Low, C. A., Dey, A. K., Ferreira, D., Kamarck, T. W., Sun, W., Bae, S. W., & Doryab, A. (2017) · doi:10.2196/preprints.9046

LV-Linker: Supporting Fine-grained User Interaction Analyses by Linking Smartphone Log and Recorded Video Data

Lee, H., Lee, S., Koh, Y., & Lee, U. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526114.3558714

Large-Scale Estimation and Analysis of Web Users' Mood from Web Search Query and Mobile Sensor Data

Sasaki, W., Hamanaka, S., Miyahara, S., Tsubouchi, K., Nakazawa, J., & Okoshi, T. (2023) · Big Data · doi:10.1089/big.2022.0211

Specification of Quality of Context Requirements for Digital Phenotyping Applications

Laurindo, L. E. C., de Moura, I. R., Coutinho, L., & Silva, F. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-34586-9_43

Police Interventions as a Context-Aware System. A Case of a Contextual Data Modelling

Klimek, R. (2023) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-32418-5_8

Forecasting Smartphone Application Chains: an App-Rank Based Approach

Masi, A. D., & Wac, K. (2023) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3626705.3627802

Visual Characterization of Gathered Data for Digital Phenotyping

Ceja, J. M. O., Arenas, A. G., Romero, C. D. G., Rodríguez, S. R., & Luna, G. L. M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-54235-0_19

Machine-learning to promote better practices of low-cost wearable devices in tremor diseases

Duque, J. D. L. (2023) · doi:10.5821/dissertation-2117-404064

Digitale Phänotypisierung – Integration alltagsnah erhobener Daten in die Einzelfallbeurteilung

Garatva, P., & Baumeister, H. (2024) · Springer eBooks · doi:10.1007/978-3-662-64801-8_143-1

Data Collection Through Wearable Medical Devices for Mobile Health

Manna, M., Mondal, L., Manna, A. S., & Naskar, A. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2762-3.ch003

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary AssessmentsPreprint

Xia, Z., Chikersal, P., Venkatesh, S., Walker, E., Dey, A. K., & Goel, M. (2024) · medRxiv · doi:10.1101/2024.11.02.24316647

Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Young People Using Mobile Phone Data: Development and Validation Study (Preprint)Preprint

Beikzadeh, M., Holloway, I. W., Kärkkäinen, K., Hong, C., Cascalheira, C. J., Wu, E. S. C., Boka, C., Avandaño, A., & Sarrafzadeh, M. (2024) · doi:10.2196/preprints.68013

Application progress of Ecological Momentary Assessment technology in health management platform (Preprint)Preprint

Zhang, X., 吴, 觅., 王, 佳., 傅, 若., & 潘, 红. (2025) · doi:10.2196/preprints.70868

Digitale Phänotypisierung – Integration alltagsnah erhobener Daten in die Einzelfallbeurteilung

Garatva, P., & Baumeister, H. (2025) · Springer eBooks · doi:10.1007/978-3-662-64797-4_143

Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-awareness In Computing

Okoshi, T., Gao, Z., Tan, Y. Z., Karasawa, T., Miki, T., Sasaki, W., & Balan, R. K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713638

Stanford Screenomics: An Open-source Platform for Unobtrusive Multimodal Digital Trace Data Collection from Android SmartphonesPreprint

Kim, I., Boffa, J., Cho, M.-J., Conroy, D. E., Kline, N., Haber, N., Robinson, T. N., Reeves, B. B., & Ram, N. (2025) · medRxiv · doi:10.1101/2025.06.24.25329707

StudyAlign: A Software System for Conducting Web-Based User Studies with Functional Interactive Prototypes

Lehmann, F., & Buschek, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3733053

Comparative analysis of Mobile Crowdsensing and Ecological Momentary Assessment systems

Stach, M., Ramović, A., Reichert, M., & Pryss, R. C. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103315 · Cited by 1

From prediction to explanation: Using screen text to understand smartphone use and user behaviour

Teng, S., Jia, H. G., D’Alfonso, S., & Kostakos, V. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103878

Cognitive performance measurements and the impact of sleep quality using wearable and mobile sensors

Visuri, A., Koskimäki, H., van Berkel, N., Alorwu, A., Peltonen, E., Abdullah, S., & Hosio, S. J. (2025) · Personal and Ubiquitous Computing · doi:10.1007/s00779-025-01847-7 · Cited by 4

Evaluating the Potential of Data-Driven Surveys for Fitness-Tracking Research

Velykoivanenko, L., Niksirat, K. S., Abdrabo, A., & Huguenin, K. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770665

Additional file 2 of Centering and flourishing: an online intervention study assessing the effects of a Christian contemplative practice on stress-reduction and human flourishing

Rohde, J. S., Goldy, S. P., Graziosi, M., Ferguson, M. A., Thrul, J., & Yaden, D. B. (2024) · Figshare · doi:10.6084/m9.figshare.26741585

Additional file 1 of Centering and flourishing: an online intervention study assessing the effects of a Christian contemplative practice on stress-reduction and human flourishing

Rohde, J. S., Goldy, S. P., Graziosi, M., Ferguson, M. A., Thrul, J., & Yaden, D. B. (2024) · Figshare · doi:10.6084/m9.figshare.26741582

Contact Logger: Measuring Everyday Intergroup Contact Experiences in Near-TimePreprint

Koschate, M., Keil, T. F., & Levine, M. (2020) · OSF Preprints (OSF Preprints)

A Framework for Enhancing Account Recovery Using Dynamically Generated Location-based Questions

Yamaguchi, S., Gomi, H., & Uehara, T. (2025) · Journal of Information Processing · doi:10.2197/ipsjjip.33.631

Academic Performance Patterns Detection Using Digital Phenotyping

Gil, J. G., Ceja, J. M. O., Arenas, A. G., & Luna, G. L. M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10731-2_19

Predicting Craving-Related Emotions Among Opioid Use Disorder Patients: Preliminary Results

King, Z., Setiadi, Z., Hamdan, L., Ahmed, H., Lamichhane, B., Sabharwal, A., Salas, R., Moukaddam, N. J., & Sano, A. (2025) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn66969.2025.11337847

Examining the Temporal and Directional Dynamics of Depression Through Digital Social Traces: A Dual-Structural Equation Modeling Approach (Preprint)Preprint

Ahmed, E. W., Peterson, A., & Singh, V. K. (2026) · doi:10.2196/preprints.94021

Plateforme adaptative pour le suivi de l’état de santé de patients mobiles

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