Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

The PHQ-9: Validity of a brief depression severity measure

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910

The validity of the Hospital Anxiety and Depression Scale

Bjelland, I., Dahl, A. A., Haug, T. T., & Neckelmann, D. (2002) · Journal of Psychosomatic Research · doi:10.1016/s0022-3999(01)00296-3 · Cited by 9,779

The PHQ-9: A New Depression Diagnostic and Severity Measure

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A Practical Guide to Support Vector Classication

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The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review

Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2010.03.006 · Cited by 4,057

Computational social science

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Limits of Predictability in Human Mobility

Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234

A survey of mobile phone sensing

Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292

The economic cost of brain disorders in Europe

Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H., & Jönsson, B. (2012) · European Journal of Neurology · doi:10.1111/j.1468-1331.2011.03590.x · Cited by 1,560

Department of Computer Science

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Machine Learning Prediction of TBI from Mobility, Gait and Balance Patterns

Patel, B. K., Srikanthan, S., Asani, F., & Agu, E. (2021) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase52844.2021.00010 · Cited by 7

Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review

Dumas, A., Hokayem, J., Goodman, G. R., Venkatasubramanian, K., & Chai, P. R. (2026) · Journal of Medical Internet Research · doi:10.2196/84146 · Cited by 8

Digital phenotyping for predicting relapse in psychiatric disorders: a systematic review of passive sensing approaches

Fang, S., & Chen, S.-H. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08157-z · Cited by 7

Trajectory test-train overlap in next-location prediction datasets

Luca, M., Pappalardo, L., Lepri, B., & Barlacchi, G. (2023) · Machine Learning · doi:10.1007/s10994-023-06386-x · Cited by 6

Beauty or Beast: Human Behavioral Insights and Learning Power of Federated Mobility Prediction

Esper, J. P., Viana, A. C., & Almeida, J. M. (2024) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3678717.3691323 · Cited by 8

Intelligent personal guidance of human behavior utilizing anticipatory models

Meurisch, C. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971355 · 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

The psychology of mobile technology and daily mobility.

Ross, M. Q., Müller, S. R., & Bayer, J. (2022) · American Psychological Association eBooks · doi:10.1037/0000290-005 · Cited by 6

Capturing Daily Student Life by Recognizing Complex Activities Using Smartphones

Meurisch, C., Gogel, A., Schmidt, B., Nolle, T., Janssen, F. W., Schweizer, I., & Mühlhäuser, M. (2017) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3144457.3144472 · Cited by 6

Internet of Things Applied to Mental Health: Concepts, Applications, and Perspectives

Teles, A. S., Barros, F., de Moura, I. R., Barbosa, A., Silva, F., Coutinho, L., & Teixeira, S. S. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-42934-8_4 · Cited by 6

Measuring the users and conversations of a vibrant online emotional support system

Calzarossa, M. C., Massari, L., Doran, D., Yelne, S., Trivedi, N., & Moriarty, G. (2016) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2016.7543898 · Cited by 6

Detecting Depression, Anxiety and Mental Stress in One Sequential Model with Multi-task Learning

Zhang, S., Tu, M., Yan, Y., Zhuang, Y., Ge, L., & Wei, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17902-0_14 · Cited by 6

Detecting Life Changes

Hsiao, J. C.-Y., & Dillahunt, T. R. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053099 · Cited by 6

Patient-Independent Schizophrenia Relapse Prediction Using Mobile Sensor Based Daily Behavioral Rhythm Changes

Lamichhane, B., Ben‐Zeev, D., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., Obuchi, M., Scherer, E. A., Walsh, M., Wang, R., et al. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-70569-5_2 · Cited by 6

Automated data collection methods

Lazar, J. K., Feng, J. H., & Hochheiser, H. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805390-4.00012-1 · Cited by 6

Mind and Body: The Complex Role of Social Resources in Understanding and Managing Depression in Older Adults

Yong, S., Hung, M.-W., Yuan, C. W., Chiu, C., Huang, M., & You, C.-W. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579507 · Cited by 5

Designing an Evidence-based Mental Health Intervention Alongside University Students

Williams, L., Pinto, M. D., Abrahim, H. L., Bhattacharya, A., Harrison, T., Burton, C. W., McIntyre, J. R. S., Negi, L. T., & Hayes, G. R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35572-1_13 · Cited by 5

Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening

Tlachac, M., Heinz, M. V., Reisch, M., & Ogden, S. S. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643554 · Cited by 5

Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement

Sahoo, S., Shende, C., Hossain, M. Z., Patel, P. A., Wang, X., Mahmud, M. I., Bi, J., Kamath, J., Russell, A. C., Song, D., et al. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00012 · Cited by 5

The Untapped Opportunity of Mobile Network Data for Mental Health

Matic, A., & Oliver, N. (2016) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/eai.16-5-2016.2263783 · Cited by 5

Smarticipation

Meurisch, C., Naeem, U., Azam, M. A., Janssen, F. W., Schmidt, B., & Mühlhäuser, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968436 · Cited by 5

Personality Is Revealed During Weekends: Towards Data Minimisation for Smartphone Based Personality Classification

Khwaja, M. K., & Matic, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29387-1_32 · Cited by 5

Personal productivity monitoring through smartphones

Khan, S. A., Farhan, A. A., Fahad, L. G., & Tahir, S. F. (2020) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-200567 · Cited by 5

Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing

Vaid, S. S., & Harari, G. M. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_6 · Cited by 5

A Review on Mood Assessment Using Smartphones

Sarsenbayeva, Z., Fleming, C., Tag, B., Withana, A. I., van Berkel, N., & McEwan, A. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42283-6_22 · Cited by 5

Speech analysis and depression

Shannon, T. T. E., Jingwen, A. D., & Lan, S. S. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820752 · Cited by 5

Understanding Skout users’ mobility patterns on a global scale: a data-driven study

Xie, R., Chen, Y., Lin, S., Zhang, T., Xiao, Y., & Wang, X. (2018) · World Wide Web · doi:10.1007/s11280-018-0551-8 · Cited by 5

Detecting and Monitoring Brain Disorders Using Smartphones and Machine LearningPreprint

Colbaugh, R., Glass, K., & Global, V. (2020) · medRxiv · doi:10.1101/2020.10.03.20206235 · Cited by 5

Measures of Behavior and Life Dynamics from Commonly Available GPS Data (DPLocate): Algorithm Development and ValidationPreprint

Rahimi-Eichi, H., Coombs, G., Onnela, J.-P. “., Baker, J. T., & Buckner, R. L. (2022) · medRxiv · doi:10.1101/2022.07.05.22277276 · Cited by 5

Mental Health and Mobile Communication Profiles of Crowdsourced Participants

Tlachac, M., & Heinz, M. V. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3436654 · Cited by 4

Anticipatory Computing for Human Behavioral Change Intervention: A Systematic Review

Lin, C., Zhao, G., Wu, Y. J., & Li, H. (2019) · IEEE Access · doi:10.1109/access.2019.2931835 · Cited by 4

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

Mental Health and Sensing

Tushar, A. K., Kabir, M. A., & Ahmed, S. I. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-54932-9_11 · Cited by 4

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

Sensing and Modeling Human Behavior Using Social Media and Mobile Data

Mehrotra, A., & Musolesi, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409548-9.09673-1 · Cited by 4

Challenges in supporting social practices around personal data for long-term mental health management

Kleunen, L. V., & Voida, S. (2019) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3341162.3346273 · Cited by 4

Emotion-aware Human–Robot Interaction and Social Robots

Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502407 · Cited by 4

AffectPro: Towards Constructing Affective Profile Combining Smartphone Typing Interaction and Emotion Self-reporting Pattern

Hari, S., Ajay, Sarcar, S., Sen, S., & Ghosh, S. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3556603 · Cited by 4

Deep Learning: A Primer for PsychologistsPreprint

Urban, C. J., & Gates, K. M. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/4q8na · Cited by 4

Predicting Depression Treatment Outcome Using Daily Step Count Sensory Data

Hossain, M. Z., Sahoo, S., Shende, C., Patel, P., Morillo, R., Pan, Z., Wang, X., Bi, J., Kamath, J., Russell, A. C., et al. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3777480 · Cited by 3

Disentangling the impacts of collective mobility of residents and non-residents on burglary levels

Chen, T., BOWERS, K. J., & Cheng, T. (2026) · Computational Urban Science · doi:10.1007/s43762-025-00234-5 · Cited by 3

Wearables for Well-Being: A Longitudinal in Situ Study of Smartwatches to Understand and Enhance Line-Level Housekeeping Work

Necaise, A., Mejia, C., Kider, J. T., Shoss, M. K., & Amon, M. J. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2313887 · Cited by 3

Predicting Next Useful Location with Context-Awareness: The State-of-the-Art

Nezhadettehad, A., Zaslavsky, A., Rakib, A., Shaikh, S. A., Loke, S. W., Huang, G., & Hassani, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744653 · Cited by 3

Designing adaptive passive personal mobile sensing methods using reinforcement learning framework

Cai, L., Barnes, L. E., & Boukhechba, M. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03432-1 · Cited by 3

Using personal smartphone location histories in public engagement: Locating a new campus amenity

Rout, A., & Galpern, P. (2018) · Applied Geography · doi:10.1016/j.apgeog.2018.09.003 · Cited by 4

Creating full individual-level location timelines from sparse social media data

Rehman, N. A., Relia, K., & Chunara, R. (2018) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3274895.3274982 · Cited by 3

A Mobile-Based Multimodal Framework for Pervasive Health Monitoring

Mastoras, R.-E., Triantafyllidis, A. K., Giakoumis, D., Kordonias, R.-K., Papaprodromou, A., Nalbadis, F., Segkouli, S., Lithoxoidou, E. E., Georgiadis, C., Paliokas, I., et al. (2021) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops51409.2021.9430967 · Cited by 3

Ubiquitous computing

Mao, S., Khalifa, Y., Zhang, Z., Shu, K., Suri, A., Bouzid, Z., & Sejdić, E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818914-6.00002-8 · Cited by 3

Online Hidden Conditional Random Fields to Recognize Activity-Driven Behavior Using Adaptive Resilient Gradient Learning

Shahi, A., Deng, J. D., & Woodford, B. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70087-8_54 · Cited by 3

An easy solution to use external sensors in Android: Experimental characterization

Bellagente, P., Crema, C., Depari, A., Ferrari, P., Flammini, A., Lenzi, G., Rinaldi, S., & Sisinni, E. (2018) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2018.8409818 · Cited by 3

From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity

Naghizade, E., Chan, J., & Tomko, M. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1730849 · Cited by 3

A Closer Look

Fan, L., & Gote, I. (2021) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3474717.3484219 · Cited by 3

Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable SensorsPreprint

Burghardt, K., Tavabi, N., Ferrara, E., Narayanan, S. S., & Lerman, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.01723 · Cited by 3

The Politics of Digital (Human) Rights

Wagner, B., Sanchez, A., Sekwenz, M.-T., Dideriksen, S., & Murray-Rust, D. (2023) · Oxford University Press eBooks · doi:10.1093/acrefore/9780190846626.013.694 · Cited by 3

Emotion Recognition in the Wild

Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502404 · Cited by 2

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study

Ahmed, M. S., Hasan, T., Islam, S., & Ahmed, N. (2024) · JMIR Research Protocols · doi:10.2196/51540 · Cited by 2

W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility Sensing

Atrey, A., Zakaria, C., Balan, R. K., & Shenoy, P. J. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637427 · Cited by 2

Mobility Patterns before, During and after the COVID-19 Pandemic in Singapore

Zhou, J., Tan, S. X., Yean, S., Schläpfer, M., & Lee, B. (2023) · International Conference on Traffic and Logistic Engineering (ICTLE) · doi:10.1109/ictle59670.2023.10508873 · Cited by 2

A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept

Wallace, M. L., Frank, E., McClung, C. A., Cote, S. E., Kendrick, J., Payne, S., Frost-Pineda, K., Leach, J., Matthews, M. J., Choudhury, T., et al. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00015-8 · Cited by 2

Smartphone-Based Psychological Sensing: A Large-Scale Study on the Impact of Extreme Isolation

Yuan, Z., Cao, Y., & Chen, Z. (2024) · Information Technology And Control · doi:10.5755/j01.itc.53.4.36565 · Cited by 2

Voice Recordings from Short Mobile Sessions versus Clinical Interviews for Mental Illness Screening: A Comparative Study with Deep Transfer Learning

Tlachac, M., Reisch, M., Shrestha, A., Flores, R., Toto, E., & Rundensteiner, E. A. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3716315 · Cited by 3

Toward Detecting Postpartum Depression Using Passive Mobile Sensing: Exploratory Analysis

Tang, J., Gu, X., Taya, A., Sezaki, K., & Nishiyama, Y. (2025) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3714394.3754405 · Cited by 2

MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings

Islam, R., Zhang, T., Bisen, P. S., & Bae, S. W. (2024) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn63547.2024.10780658 · Cited by 2

Evaluating Machine Learning Algorithms for Prediction of the Adverse Valence Index Based on the Photographic Affect Meter

Mikelsons, G., Mehrotra, A., Musolesi, M., & Shadbolt, N. (2019) · doi:10.1145/3325426.3329948 · Cited by 2

Learning Urban Nightlife Routines from Mobile Data

Pozo, A., Phan, T., & Gática-Pérez, D. (2020) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3428361.3428396 · Cited by 2

mRisk

Ullah, M. A., Chatterjee, S., Fagundes, C. P., Lam, C. Y., Nahum-Shani, I. B., Rehg, J. M., Wetter, D. W., & Kumar, S. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3550308 · Cited by 2

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

Berkemeier, L., Kamphuis, W., de Vries, H., Brouwer, A.-M., van Baardewijk, J. U., Schadd, M. P. D., Oldenhuis, H. K. E., Verdaasdonk, R. M., & Gemert-Pijnen, L. V. (2023) · AHFE international · doi:10.54941/ahfe1003013 · Cited by 2

Imputation Strategies for Longitudinal Behavioral Studies: Predicting Depression Using GLOBEM Datasets

Bhattacharya, S., Majethia, R., Choube, A., & Mishra, V. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678424 · Cited by 2

Smartphone Data Gathered Early in Depression Treatment Predicts Treatment Outcome

Sahoo, S., Hossain, M. Z., Shende, C., Patel, P., Wang, X., Bi, J., Kamath, J., Russell, A. C., Song, D., & Wang, B. (2025) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3721201.3721380 · Cited by 2

A Spatiotemporal Approach for Social Situation Recognition

Meurisch, C., Hussain, T., Gogel, A., Schmidt, B., Schweizer, I., & Mühlhäuser, M. (2015) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-29003-4_18 · Cited by 2

Identifying Symptoms Using Technology

Doryab, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-69638-6_11 · Cited by 2

Experimental analysis of cost-effective mobile sensing technologies for activity analytics in elderly care

Newcombe, L., Yang, P., Cater, C., Hanneghan, M., & Qi, J. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00238 · Cited by 2

Models of Minds

Merrill, N., & Chuang, J. C.-I. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3310427 · Cited by 2

Automated inference of cognitive performance by fusing multimodal information acquired by smartphone

Hamatani, T., Ochiai, K., Inagaki, A., Yamamoto, N., Fukazawa, Y., Kimoto, M., Kiriu, K., Kaminishi, K., Ota, J., Terasawa, Y., et al. (2019) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3341162.3346275 · Cited by 2

Emotion Recognition from Physiological Sensor Data to Support Self-regulated Learning

Yun, H., Fortenbacher, A., Helbig, R., Geißler, S., & Niels Pinkwart, C. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-58459-7_8 · Cited by 2

Detection of Social Identification in Workgroups from a Passively-sensed WiFi Infrastructure

Zakaria, C., Lee, Y., & Balan, R. K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449145 · Cited by 2

An Innovative Approach to Predict early Stage Depression using Mobile Network Usage

Dwivedi, Y. S., Semalty, G., & Moondra, A. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725669 · Cited by 2

Towards Future Health and Well-being: Bridging Behavior Modeling and Intervention

Xu, X. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526114.3558524 · Cited by 2

8th International Workshop on Mental Health and Well-being: Sensing and Intervention

Adler, D. A., Xu, X., Mishra, V., Sano, A., Kunchay, S., Abdullah, S., Bardram, J. E., Murnane, E. L., Choudhury, T., Musolesi, M., et al. (2023) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3594739.3605108 · Cited by 2

Towards Estimating Missing Emotion Self-reports Leveraging User Similarity: A Multi-task Learning Approach

Ghosh, S., Mandi, S., Sen, S., Mitra, B., & De, P. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642833 · Cited by 2

Recognizing whether a person is eating alone or has company by using wearable devices

Kiriu, K., Ochiai, K., Inagaki, A., Yamamoto, N., Fukazawa, Y., Kimoto, M., Okimura, T., Terasawa, Y., Maeda, T., & Ota, J. (2017) · International Conference on Mobile Computing and Ubiquitous Network (ICMU) · doi:10.23919/icmu.2017.8330088 · Cited by 2

Multi-Modal Data Collection for Measuring Health, Behavior, and Living Environment of Large-Scale Participant Cohorts: Conceptual Framework and Findings from DeploymentsPreprint

Wu, C., Fritz, H., Nagy, Z., Maestre, J. P., Thomaz, E., Julien, C., Castelli, D. M., de Barbaro, K., Harari, G. M., Craddock, R. C., et al. (2020) · arXiv (Cornell University) · doi:10.26153/tsw/10850 · Cited by 2

Depression Detection via Influence-based Relabeling for Resolving Training Set Noise

Tu, M., Zhao, Z., Wu, J., Lee, J., Lee, H., & Song, J. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945201 · Cited by 1

A decision integration strategy algorithm to detect the depression severity level using wearable and profile data

Hossain, M. N., & Yang, S. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-024-00187-8 · Cited by 1

Introduction to Applied Affective Computing

Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502400 · 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

The Reports of My Capabilities Are Greatly Exaggerated - Small LLMs for Depression Inference from Mobile Sensing Data

Kirovska, I., & Pejović, V. (2025) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3714394.3756240 · Cited by 1

From Detection to Forecasting: An Empirical Study of College Student Mental Health Using Smartphone Sensing

Gala, J. K., Sprigler, A., Malaviya, P. R., Shen, E., Zhao, Y., Ding, Y., & Shen, X. (2026) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00011 · Cited by 1

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

Using Sparse Digital Traces to Fill in Individual Level Mobility Timelines.

Rehman, N. A., Relia, K., & Chunara, R. (2017) · Cited by 1

Macroscopic Patterns in Sparse Location Data: Identifying Mobility Prototypes

Lüders, B. K., & Ruppel, P. (2018) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2018.00027 · 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

Fullproof: Towards the Detection of Impostor Syndrome Using Smartphone Sensors

Kim, J., Kim, Y., Jeong, Y., & Lee, Y. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3479333 · Cited by 1

Future of Affective Computing and Applied Affective Computing

Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502411 · Cited by 1

Estimating Mental Health Using Human-generated Big Data and Machine Learning

Fukazawa, Y. (2022) · The Brain & Neural Networks · doi:10.3902/jnns.29.78 · Cited by 1

Measuring Membership Privacy on Aggregate Location Time-Series

Pyrgelis, A., Troncoso, C., & Cristofaro, E. D. (2020) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3410048.3410091 · Cited by 1

COVID-19’s Impact on Geospatial Data: Ethics and Values

Seidl, D. E. (2022) · Global perspectives on health geography · doi:10.1007/978-3-031-11775-6_5 · Cited by 1

Data Processing Pipeline of Short-Term Depression Detection with Large-Scale Dataset

Lee, Y., Noh, Y., & Lee, U. (2023) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp57234.2023.00095 · Cited by 1

Mobility-Based Community Analysis for Early Detection of Complex Psychiatric Disorders

Thelagathoti, R. K., & Ali, H. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00034 · Cited by 1

AlarmCare: Exploring Mobile Alarm as a Supportive Communication Channel Between Peers

Kim, S. K., Kim, B., Kang, Y., Ko, M., & Kim, J. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3677594 · Cited by 1

From Mobile Phone Monitoring of Depressive States using GPS Traces Analysis to Data-Driven Behaviour Change Interventions

Canzian, L., & Musolesi, M. (2016) · Frontiers in Public Health · doi:10.3389/conf.fpubh.2016.01.00050

Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis

Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 86

Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction

Mehrotra, A., Hendley, R., & Musolesi, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968299 · Cited by 77

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782

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