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

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

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

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

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

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

Empath

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

AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use

Zhang, T., Chung, T. A., Dey, A. K., & Bae, S. W. (2025) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc64332.2025.11118599 · Cited by 6

Understanding Digital Wellbeing Through Smartphone Usage Intentions and Regrettable Patterns

Islambouli, R., Ingram, S., & Gillet, D. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00061 · Cited by 8

Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Bae, S. W., Chung, T. A., Zhang, T., Dey, A. K., & Islam, R. (2024) · JMIR AI · doi:10.2196/52270 · Cited by 6

EA^2: Energy Efficient Adaptive Active Learning for Smart Wearables

Alikhani, H., Wang, Z., Kanduri, A., Liljeberg, P., Rahmani, A. M., & Dutt, N. D. (2024) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/3665314.3670840 · Cited by 6

Feasibility, Acceptability, and Preliminary Outcomes of a Mobile Adaptation of a Relational Savoring Intervention to Prevent Loneliness in College Students: Mixed Methods Pilot Study

Nguyen, B., Lai, J., Qureshi, H., Marcotullio, C., Labbaf, S., Wang, Y., Jafarlou, S., Dutt, N. D., Rahmani, A. M., & Borelli, J. L. (2025) · JMIR Formative Research · doi:10.2196/70528 · Cited by 7

Vision-based happiness inference

Sarsenbayeva, Z., Ferreira, D., van Berkel, N., Luo, C., Vaisanen, M., Kostakos, V., & Gonçalves, J. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3124438 · Cited by 6

Recognising Intrinsic Motivation using Smartphone Trajectories

Ibrahim, A., Clinch, S., & Harper, S. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102650 · Cited by 8

Collecting and analyzing smartphone sensor data for health

Drake, J. A., Schulz, K. W., Bukowski, R., & Gaither, K. (2021) · Practice and Experience in Advanced Research Computing · doi:10.1145/3437359.3465599 · Cited by 6

Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers

Lukan, J., Bolliger, L., Clays, E., Mayora, O., Osmani, V., & Luštrek, M. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3479367 · Cited by 6

Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating

Vega, J., Bell, B. T., Taylor, C. S., Xie, J., Ng, H., Honary, M., & McNaney, R. (2022) · JMIR Mental Health · doi:10.2196/32146 · Cited by 7

Two Birds with One Phone: The Role of Mobile Use in the Daily Practices of Remote Information Work

Swain, V. D., Williams, S., Fourney, A., & Iqbal, S. T. (2022) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3533406.3533416 · Cited by 6

Mood Measurement on Smartphones

Torkamaan, H. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580864 · Cited by 6

Personalizing Context-Aware Access Control on Mobile Platforms

Das, P. K., Joshi, A., & Finin, T. (2017) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2017.00025 · Cited by 6

Modelling Contexts for Interactions in Dynamic Open-World Scenarios

Böck, R., & Wrede, B. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914656 · Cited by 6

AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI

Singh, P., Gupta, A., Kumar, M. J., & Singh, P. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749519 · Cited by 9

LINC: a framework for maintaining high-quality passive data in digital phenotyping studies

Calvert, E., Lane, E., Flathers, M., & Torous, J. B. (2026) · Scientific Reports · doi:10.1038/s41598-026-41435-0 · Cited by 5

Sensitivity analysis of driving event classification using smartphone motion data: case of classifier type, sensor bundling, and data acquisition rate

Sarteshnizi, I. T., Khomeini, F. T., Khedri, B., & Samimi, A. (2022) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2022.2140048 · Cited by 5

Designing an Intervention against Occupational Stress Based on Ubiquitous Stress and Context Detection

Luštrek, M., Lukan, J., Bolliger, L., Lauwerier, E., & Clays, E. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3611326 · Cited by 5

DataSentry: Building Missing Data Management System for In-the-Wild Mobile Sensor Data Collection through Multi-Year Iterative Design Approach

Jung, Y., Law, H. Y., Lee, H. S., Lee, J., Lee, B., & Lee, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713314 · Cited by 5

Intelligent Monitoring of Affective Factors Underlying Sport Performance by Means of Wearable and Mobile Technology

Bailón, C., Damas, M., Pomares, H., Sanabria, D., Perakakis, P., Goicoechea, C., & Baños, O. (2018) · International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) · doi:10.3390/proceedings2191202 · Cited by 5

Scalable real-time health data sensing and analysis enabling collaborative care delivery

Dimitriadis, I., Mavroudopoulos, I., Kyrama, S., Toliopoulos, T., Gounaris, A., Vakali, A. I., Billis, A. S., & Bamidis, P. D. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00891-y · Cited by 5

Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report

Single, M., Bruhin, L. C., Schütz, N., Naef, A. C., Hegi, H., Reuse, P., Schindler, K. A., Krack, P., Wiest, R., Chan, A., et al. (2023) · JMIR Formative Research · doi:10.2196/43092 · Cited by 6

Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models

Liu, T., Giorgi, S., Tao, X., Guntuku, S. C., Bellew, D., Curtis, B. L., & Ungar, L. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22226 · Cited by 5

Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize Surveys

Velykoivanenko, L., Niksirat, K. S., Teofanovic, S., Chapuis, B., Mazurek, M. L., & Huguenin, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642572 · Cited by 5

PupilSense: Detection of Depressive Episodes through Pupillary Response in the Wild

Islam, R., & Bae, S. W. (2024) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc61795.2024.10652166 · Cited by 8

Adolescents’ Self-Regulation of Social Media Use During the Beginning of the COVID-19 Pandemic: An Idiographic Approach

Dreier, M. J., Low, C. A., Fedor, J., Durica, K. C., & Hamilton, J. L. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00465-z · Cited by 11

Measuring group dynamics in an elementary school setting using mobile devices

van Berkel, N., Сергей, К., Hosio, S. J., Malmberg, J., Järvenoja, H., & Kostakos, V. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971596 · Cited by 5

Indoor light scavenging on smartphones

Ferreira, D., Schuss, C., Luo, C., Gonçalves, J., Kostakos, V., & Rahkonen, T. E. (2016) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3012709.3017603 · Cited by 5

Informing Caregivers Through an Assistive Tool: An Investigation of Elderly Care Metrics

Klakegg, S., van Berkel, N., Visuri, A., Luo, C., Gonçalves, J., Hosio, S. J., Huttunen, H.-L., & Ferreira, D. (2017) · Electronic workshops in computing · doi:10.14236/ewic/hci2017.46 · Cited by 5

Designing a Mobile Behavior Sampling Tool for Spatial Analytics

Konomi, S., & Sasao, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91131-1_7 · Cited by 5

Formal representation of ambulatory assessment protocols in HTML5 for human readability and computer execution

Batalas, N., Khan, V.-J., Franzen, M., Markopoulos, P., & Rot, M. A. H. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1148-y · Cited by 5

MEEC: First Workshop on Momentary Emotion Elicitation and Capture

Ali, A. E., Perusquía-Hernández, M., Denman, P., Abdelrahman, Y., Hassib, M. E., Meschtscherjakov, A., Ferreira, D., & Henze, N. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3375175 · Cited by 5

CAPP: A Context-Aware Proof of Presence for Crowdsensing Incentives

Handaja, N., & Lagesse, B. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156232 · Cited by 5

Intentions of First Year University Business Students to Use Smartphones as Learning Tools in Botswana: Issues and Challenges.

Matyokurehwa, K., Rudhumbu, N., & Mlambo, C. (2020) · The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies) · Cited by 5

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

Chen, Q., Leelaprachakul, P., Landers, M., Low, C. A., Dey, A. K., & Doryab, A. (2021) · Sensors · doi:10.3390/s21227510 · Cited by 6

Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities

Knauer, J., Terhorst, Y., Philippi, P., Kallinger, S. M., Eiler, S., Kilian, R., Waldmann, T., Moshagen, M., Bader, M., & Baumeister, H. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-061259 · Cited by 6

Effect of Context on Smartphone Users’ Typing Performance in the Wild

Akpınar, E., Yeşilada, Y., & Karagöz, P. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3577013 · Cited by 5

Feasibility and acceptability of mobile methods to assess home and neighborhood environments related to adolescent sleep

Mayne, S. L., DiFiore, G., Hannan, C., Nwokeji, U., Tam, V. W., Filograna, C., Martin, T., South, E. C., Mitchell, J. A., Glanz, K., et al. (2023) · Sleep Health · doi:10.1016/j.sleh.2023.01.014 · Cited by 5

Context-dependent privacy and security management on mobile devices

Das, P. K. (2017) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m2est2-ru9v · Cited by 5

Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity

Haus, M., Ding, A. Y., & Ott, J. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156105 · Cited by 5

Review of Open-Source AI Tools for Healthcare IT Infrastructures

AL-Kharusi, Y., Khan, A., Mughal, M. R., & Bait‐Suwailam, M. M. (2025) · IEEE Access · doi:10.1109/access.2025.3599912 · Cited by 4

Temporal variations of depressive symptoms in patients with bipolar, borderline personality, and major depressive disorder: an ecological momentary assessment study

Martikkala, A., Baryshnikov, I., Granroth‐Wilding, H. M. V., Heikkilä, R., Riihimäki, K., Saleva, O., Holmén, J., Darst, R. K., Rosenström, T. H., Aledavood, T., et al. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.09.054 · Cited by 4

Emotion-Adaptive Large Language Model–Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability

Zhang, T., Bae, S. W., Chung, T., & Dey, A. K. (2026) · JMIR Human Factors · doi:10.2196/89005 · Cited by 3

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) · BMC Psychology · doi:10.1186/s40359-024-01836-0 · Cited by 5

Evaluating the Privacy Valuation of Personal Data on Smartphones

Fan, L., Zhang, S., Kong, Y., Yi, X., Wang, Y., Xu, X., Yu, C., Li, H., & Shi, Y. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3678509 · Cited by 4

Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing

Bardram, J. E. (2022) · Sensors · doi:10.3390/s22072813 · Cited by 4

Leveraging Large Language Models for Generating Mobile Sensing Strategies in Human Behavior Modeling

Gao, N., Yu, Z., Xu, Y., Yu, C., Wang, Y., Salim, F. D., & Shi, Y. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678423 · Cited by 4

Unmasking Nuances Affecting Loneliness: Using Digital Behavioural Markers to Understand Social and Emotional Loneliness in College Students

Qirtas, M. M., Zafeiridi, E., White, E. B., & Pesch, D. (2025) · Sensors · doi:10.3390/s25061903 · Cited by 8

Energy-efficient prediction of smartphone unlocking

Luo, C., Visuri, A., Klakegg, S., van Berkel, N., Sarsenbayeva, Z., Möttönen, A., Gonçalves, J., Anagnostopoulos, T., Ferreira, D., Flores, H., et al. (2018) · Personal and Ubiquitous Computing · doi:10.1007/s00779-018-01190-0 · Cited by 5

FutureWare: Designing a Middleware for Anticipatory Mobile Computing

Mehrotra, A., Pejović, V., & Musolesi, M. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2943554 · Cited by 4

Ecological Momentary Assessment Tools: Lessons Learned from an HCI Perspective

Crovari, P., Catania, F., Spitale, M., & Garzotto, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49059-1_28 · Cited by 4

Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study

Ibrahim, A., Zhang, H., Clinch, S., Poliakoff, E., Parsia, B., & Harper, S. (2021) · JMIR Formative Research · doi:10.2196/23461 · Cited by 4

Crowd Sensing and Living Lab Outdoor Experimentation Made Easy

Pournaras, E., Ghulam, A. N., Kunz, R., & Hänggli, R. (2021) · IEEE Pervasive Computing · doi:10.1109/mprv.2021.3116466 · Cited by 4

MiMoSense: An Open Crowdsensing Platform for Micro-Mobility

Han, Z., Nguyen, H. D., Aoki, S., Nishiyama, Y., & Sezaki, K. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564524 · Cited by 4

Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression

Asare, K. O., Visuri, A., Vega, J., & Ferreira, D. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06368-8_9 · Cited by 6

Open-Source Data Collection for Activity Studies at Scale

Hoelzemann, A., Pithan, J. S., & Laerhoven, K. V. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-0361-8_2 · Cited by 4

Following Topics Across All Apps and Media Formats: Mobile Keyword Tracking as a Privacy-Friendly Data Source in Mobile Media Research

Krieter, P., Zerrer, P., Puschmann, C., & Geise, S. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664879 · Cited by 4

Influencing and Measuring Behaviour in Crowdsourced Activities

Gould, S. J. J., Cox, A. L., & Brumby, D. P. (2018) · Human-computer interaction series · doi:10.1007/978-3-319-73374-6_7 · Cited by 4

EgoSENSE: A Framework for Context-Aware Mobile Applications Development

Milic, E. M., & Stojanovic, D. H. (2017) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.1203 · Cited by 4

An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data

Bonaquist, A., Grehan, M., Haines, O., Keogh, J., Mullick, T., Singh, N., Shaaban, S., Radović, A., & Doryab, A. (2021) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds52267.2021.9483755 · Cited by 4

Cross-Platform Ecological Momentary Assessment App (JTrack-EMA+): Development and Usability Study

Far, M. S., Fischer, J. M., Senge, S., Rathmakers, R., Meißner, T., Schneble, D., Narava, M., Eickhoff, S. B., & Dukart, J. (2024) · Journal of Medical Internet Research · doi:10.2196/51689 · Cited by 3

Network dynamics between digital media use and depressive symptomatology: A computational phenotyping approach using ecological momentary assessment

Zhan, Y., & Wang, Y. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116896 · Cited by 4

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study

Yan, R., Liu, X., Dutcher, J. M., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, J. D., Creswell, K. G., Mankoff, J. C., Dey, A. K., et al. (2024) · JMIR AI · doi:10.2196/47194 · Cited by 3

Detecting Longitudinal Trends between Passively Collected Phone Use and Anxiety among College Students

Gyorda, J. A., Lekkas, D., & Jacobson, N. C. (2024) · Digital Biomarkers · doi:10.1159/000540546 · Cited by 5

InCense IoT: A Collective Sensing System for Behavior Data in Shared Spaces

Ramos-Monteon, J., Castro, L. A., Rodríguez, L.-F., & Baños, O. (2018) · International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) · doi:10.3390/proceedings2191206 · Cited by 3

Human Data Model

Mäkitalo, N., Flores-Martin, D., Flores, H., Lagerspetz, E., Christophe, F., Ihantola, P., Babazadeh, M., Hui, P., Murillo, J. M., Tarkoma, S., et al. (2020) · ACM Transactions on Computing for Healthcare · doi:10.1145/3402524 · Cited by 3

Ubiquitous computing for person-environment research: Opportunities, considerations, and future directions

Vaid, S. S., Abdullah, S., Thomaz, E., & Harari, G. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00020-x · Cited by 3

Contact duration: Intricacies of human mobility

Tonetto, L., Adikari, M., Mohan, N., Ding, A. Y., & Ott, J. (2022) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100196 · Cited by 3

Extracting behavioural features from smartphone notifications

Ibrahim, A., Clinch, S., & Harper, S. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2145996 · Cited by 3

Tracking social behaviour with smartphones in people with Parkinson's: a longitudinal study

Zhang, H., Parsia, B., Poliakoff, E., & Harper, S. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2243521 · Cited by 3

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

Mullick, T., Shaaban, S., Radović, A., & Doryab, A. (2024) · JMIR AI · doi:10.2196/47805 · Cited by 4

Using Passive Sensing to Identify Depression

Zafeiridi, E., Qirtas, M. M., White, E. B., & Pesch, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73741-1_9 · Cited by 3

An MDD‐based method for building context‐aware applications with high reusability

Paspallis, N. (2019) · Journal of Software Evolution and Process · doi:10.1002/smr.2200 · Cited by 3

Couplable Components for Data Processing in Mobile Sensing Campaigns

Maya-Zapata, D., Félix, I. R., Castro, L. A., Rodríguez, L.-F., & Domitsu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67585-5_31 · Cited by 3

Enabling remote assessment of cognitive behaviour through mobile experience sampling

Wohlfahrt-Laymann, J., Hermens, H. J., Villalonga, C., Vollenbroek-Hutten, M. M. R., & Baños, O. (2018) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2018.8480310 · Cited by 3

End-User Development in Social Psychology Research: Factors for Adoption

Rough, D. J., & Quigley, A. J. (2018) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2018.8506573 · Cited by 3

A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour

Konsolakis, K., Hermens, H. J., & Baños, O. (2019) · International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI) · doi:10.3390/proceedings2019031043 · Cited by 3

The role of cognition in mediating the relationship between media use and health in a media saturated world

Bediou, B., & Wac, K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818872-9.00190-4 · Cited by 3

Towards a Toolkit for Free Living Wearable Development

Rothrock, B., Curtiss, A., Bai, J., & Hester, J. D. (2022) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3544793.3560399 · Cited by 2

No relationships between self-reported Instagram use or type of use and mental well-being: A study using a nationally representative online sample of UK adults

Roberts, S. G. B., Malcolm, C., McCarty, K., & Pollet, T. V. (2024) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2024-1-3 · Cited by 2

Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data

Xu, L., Nishiyama, Y., Tsubouchi, K., & Sezaki, K. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3680050 · Cited by 2

A Methodology and a Platform for High-quality Rich Personal Data Collection

Kayongo, I., Malcotti, L. J., Zhao, H., & Giunchiglia, F. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554251333615 · Cited by 2

Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study

Beikzadeh, M., Holloway, I. W., Kärkkäinen, K., Hong, C., Cascalheira, C. J., Wu, E. S. C., Boka, C., Avendaño, A., Yonko, E. A., & Sarrafzadeh, M. (2025) · Online Journal of Public Health Informatics · doi:10.2196/68013 · Cited by 2

Separating Instantaneous and Delayed Battery-Drain Effects for Smartphone Remaining Runtime Prediction

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DAIMON: Designing AI-Augmented Research Dashboards to Enable Novel Human-AI Collaborative Workflows in Longitudinal Sensing Studies

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Towards continuous mental state detection in everyday settings: investigating between-subjects variations in a longitudinal study

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A Real-Time System for Monitoring and Managing Neuropsychiatric Symptoms in Dementia Patients

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Effects-Driven Participatory Design: Learning from Sampling Interruptions

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Identifying Symptoms Using Technology

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A service-oriented context-awareness reasoning framework and its implementation

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Towards Multi-Device Context Aware Systems for Elders Well-being

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Calmify

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A Testbed for Intelligent Control of Traffic Lights at Pedestrian Crossings on a Road

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CamTest: A laboratory testbed for camera-based mobile sensing applications

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Capturing contextual morality

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Context-Aware Transfer of Task-Based IoT Service Settings

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Estimating symptoms caused by CIPN using mobile and wearable devices

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

Ferreira, D. (2013) · Cited by 32

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

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An Extensible Toolkit for Context-Aware Mobile Applications

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Research In Mobile Context-aware Applications

Cheverst, K. (2007)

Mobile Context Toolbox

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