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

Kroenke, K., & Spitzer, R. L. (2002) · Psychiatric Annals · doi:10.3928/0048-5713-20020901-06 · Cited by 5,898 · cited 4×

A Practical Guide to Support Vector Classication

Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056

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

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365

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

Buisman, H., Gomez-rodriguez, M., & Hassanpour, S. (1993) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(93)90083-n · Cited by 1,212

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 59

Intelligent Notification Systems

Mehrotra, A., & Musolesi, M. (2020) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00965ed1v01y201911mpc014 · Cited by 64

Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches

Rout, A., Nitoslawski, S. A., Ladle, A., & Galpern, P. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101705 · Cited by 54

Generalization and Personalization of Mobile Sensing-Based Mood Inference Models

Meegahapola, L., Droz, W., Kun, P., de Götzen, A., Nutakki, C., Diwakar, S., Correa, S. R., Song, D., Xu, H., Bidoglia, M., et al. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569483 · Cited by 67

Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection

Yang, M., Ngai, E. C. ‐., Hu, X., Hu, B., Liu, J., Erol Gelenbe, A., & Leung, V. C. M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3542324 · Cited by 56

Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States

Mehrotra, A., & Musolesi, M. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3264937 · Cited by 57

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study

DeMasi, O., Feygin, S., Dembo, A., Aguilera, A., & Recht, B. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7820 · Cited by 56

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 54

Using digital phenotyping to understand health-related outcomes: A scoping review

Lee, K., Lee, T. C., Yefimova, M., Kumar, S., Puga, F., Azuero, A., Kamal, A. H., Bakitas, M. A., Wright, A. A., Demiris, G., et al. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105061 · Cited by 54

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples

Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51

Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students

Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51

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

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 51

Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Matteo, D. D., Fotinos, K., Lokuge, S., Mason, G. E., Sternat, T., Katzman, M. A., & Rose, J. (2021) · Journal of Medical Internet Research · doi:10.2196/28918 · Cited by 48

Adding a smartphone app to internet-based self-help for social anxiety: A randomized controlled trial

Boettcher, J., Magnusson, K., Marklund, A., Berglund, E., Blomdahl, R., Braun, U., Delin, L., Lundén, C., Sjöblom, K., Sommer, D., et al. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.052 · Cited by 47

Ridesharing car detection by transfer learning

Wang, L., Xu, G., Ma, X., Zhang, D., & Yang, Q. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.12.008 · Cited by 48

Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing

Wang, W., Mirjafari, S., Harari, G. M., Ben‐Zeev, D., Brian, R., Choudhury, T., Hauser, M., Kane, J. M., Masaba, K., Nepal, S. K., et al. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376855 · Cited by 47

Digital Footprints of Sensation Seeking

Schoedel, R., Au, Q., Völkel, S. T., Lehmann, F., Becker, D., Bühner, M., Bischl, B., Hußmann, H., & Stachl, C. (2018) · Zeitschrift für Psychologie · doi:10.1027/2151-2604/a000342 · Cited by 46

Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia: Feasibility Case Series

Thorpe, J. R., Forchhammer, B. H., & Maier, A. M. (2019) · JMIR Formative Research · doi:10.2196/12346 · Cited by 45

Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia

Tseng, V. W.-S., Sano, A., Ben-Zeev, D., Brian, R., Campbell, A. T., Hauser, M., Kane, J. M., Scherer, E. A., Wang, R., Wang, W., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-71689-1 · Cited by 44

Daytime Data and LSTM can Forecast Tomorrow’s Stress, Health, and Happiness

Umematsu, T., Sano, A., & Picard, R. W. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856862 · Cited by 46

Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data

Rashid, H., Mendu, S., Daniel, K. E., Beltzer, M., Teachman, B. A., Boukhechba, M., & Barnes, L. E. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3411823 · Cited by 49

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

Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors

Barlacchi, G., Perentis, C., Mehrotra, A., Musolesi, M., & Lepri, B. (2017) · EPJ Data Science · doi:10.1140/epjds/s13688-017-0124-6 · Cited by 43

StressMon

Zakaria, C., Balan, R. K., & Lee, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359139 · Cited by 43

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

Matteo, D. D., Fotinos, K., Lokuge, S., Yu, J., Sternat, T., Katzman, M. A., & Rose, J. (2020) · JMIR Formative Research · doi:10.2196/18751 · Cited by 45

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Sun, S., Folarin, A., Zhang, Y., Cummins, N., Garcia‐Dias, R., Stewart, C., Ranjan, Y., Rashid, Z., Conde, P., Laiou, P., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/45233 · Cited by 48

Person-Centered Predictions of Psychological Constructs with Social Media Contextualized by Multimodal Sensing

Saha, K., Grover, T., Mattingly, S. M., Swain, V. D., Gupta, P., Martinez, G. J., Robles-Granda, P., Mark, G. J., Striegel, A., & Choudhury, M. D. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448117 · Cited by 45

Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure

Ware, S., Yue, C., Morillo, R., Lü, J., Shang, C., Kamath, J., Bamis, A., Bi, J., Russell, A. C., & Wang, B. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287073 · Cited by 41

Prediction of stress levels with LSTM and passive mobile sensors

Acikmese, Y., & Alptekin, S. E. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.09.221 · Cited by 44

Passive mobile sensing and psychological traits for large scale mood prediction

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329213 · Cited by 40

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 42

Use of In-Game Rewards to Motivate Daily Self-Report Compliance: Randomized Controlled Trial

Taylor, S., Ferguson, C., Peng, F., Schoeneich, M., & Picard, R. W. (2018) · Journal of Medical Internet Research · doi:10.2196/11683 · Cited by 40

Big Five personality traits predict daily spatial behavior: Evidence from smartphone data

Ai, P., Liu, Y., & Zhao, X. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.04.027 · Cited by 40

The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones

Laiou, P., Kaliukhovich, D. A., Folarin, A., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Sun, S., Zhang, Y., Matcham, F., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/28095 · Cited by 41

Multi-Task Learning for Randomized Controlled Trials

Dai, R., Kannampallil, T., Zhang, J., Lv, N., Ma, J., & Lu, C. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534591 · Cited by 40

Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones

Yue, C., Ware, S., Morillo, R., Lu, J., Shang, C., Bi, J., Kamath, J., Russell, A., Bamis, A., & Wang, B. (2020) · Smart Health · doi:10.1016/j.smhl.2020.100137 · Cited by 27

Prediction of Mental Health Treatment Adherence using Machine Learning Algorithms

Bajaj, M., Rawat, P., Diksha, Vats, S., Sharma, V., & Gopal, L. (2023) · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) · doi:10.1109/cictn57981.2023.10141520 · Cited by 37

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. N. N. (2023) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000469 · Cited by 37

Semantic Gap in Predicting Mental Wellbeing through Passive Sensing

Swain, V. D., Chen, V., Mishra, S., Mattingly, S. M., Abowd, G. D., & Choudhury, M. D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502037 · Cited by 35

Modeling Personality vs. Modeling Personalidad

Khwaja, M. K., Vaid, S. S., Zannone, S., Harari, G. M., Faisal, A. A., & Matic, A. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351246 · Cited by 35

Daily mobility patterns in patients with bipolar disorder and healthy individuals

Faurholt‐Jepsen, M., Busk, J., Vinberg, M., Christensen, E. M., Þórarinsdóttir, H., Frost, M. M., Bardram, J. E., & Kessing, L. V. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.09.087 · Cited by 38

COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing

Nepal, S. K., Wang, W., Vojdanovski, V., Huckins, J. F., DaSilva, A. W., Meyer, M. L., & Campbell, A. T. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502043 · Cited by 40

Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol

Bilal, A.-M., Fransson, E., Bränn, E., Eriksson, A., Zhong, M., Gidén, K., Elofsson, U. O. E., Axfors, C., Skalkidou, A., & Papadopoulos, F. C. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-059033 · Cited by 37

First-Gen Lens

Wang, W., Nepal, S. K., Huckins, J. F., Hernandez, L., Vojdanovski, V., Mack, D. L., Plomp, J., Pillai, A., Obuchi, M., DaSilva, A. W., et al. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3543194 · Cited by 46

Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability

He-Yueya, J., Buck, B., Campbell, A. T., Choudhury, T., Kane, J. M., Ben‐Zeev, D., & Althoff, T. (2020) · Schizophrenia · doi:10.1038/s41537-020-00123-2 · Cited by 33

K-EmoPhone: A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Kang, S., Choi, W., Park, C. Y., Cha, N., Kim, A., Khandoker, A. H., Hadjileontiadis, L. J., Kim, H., Jeong, Y., & Lee, U. (2023) · Scientific Data · doi:10.1038/s41597-023-02248-2 · Cited by 33

Fusing Location Data for Depression Prediction

Yue, C., Ware, S., Morillo, R., Lü, J., Shang, C., Bi, J., Kamath, J., Russell, A. C., Bamis, A., & Wang, B. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2872569 · Cited by 32

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Thorpe, J. R., Forchhammer, B. H., & Maier, A. M. (2019) · JMIR mhealth and uhealth · doi:10.2196/12013 · Cited by 32

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

Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30

Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330730 · Cited by 30

Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Kadirvelu, B., Bel, T. B., Freccero, A., Simplicio, M. C. D., Nicholls, D., & Faisal, A. A. (2025) · Journal of Medical Internet Research · doi:10.2196/72501 · Cited by 32

Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study

Goodspeed, R., Yan, X. “., Hardÿ, J., Vydiswaran, V. G. V., Berrocal, V. J., Clarke, P. J., Romero, D. M., Gomez-Lopez, I., & Veinot, T. C. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9771 · Cited by 30

Smartphone Sensing for the Well-Being of Young Adults: A Review

Meegahapola, L., & Gática-Pérez, D. (2020) · IEEE Access · doi:10.1109/access.2020.3045935 · Cited by 34

Predicting Brain Functional Connectivity Using Mobile Sensing

Obuchi, M., Huckins, J. F., Wang, W., daSilva, A., Rogers, C., Murphy, E., Hedlund, E., Holtzheimer, P., Mirjafari, S., & Campbell, A. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3381001 · Cited by 29

Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns

Mulvaney, S. A., Vaala, S., Hood, K. K., Lybarger, C., Carroll, R., Williams, L., Schmidt, D. C., Johnson, K., Dietrich, M. S., & Laffel, L. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0064 · Cited by 29

Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings

Crivellari, A., & Beinat, E. (2019) · Applied Sciences · doi:10.3390/app9142861 · Cited by 30

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

A Systematic Review of Location Data for Depression Prediction

Shin, J., & Bae, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20115984 · Cited by 31

Personalized versus Generic Mood Prediction Models in Bipolar Disorder

Constantinides, M., Busk, J., Matic, A., Faurholt‐Jepsen, M., Kessing, L. V., & Bardram, J. E. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3267536 · Cited by 29

A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing

DeMasi, O., & Recht, B. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3125609 · Cited by 28

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts

Wu, C., Fritz, H., Bastami, S., Maestre, J. P., Thomaz, E., Julien, C., Castelli, D. M., de Barbaro, K., Bearman, S. K., Harari, G. M., et al. (2021) · GigaScience · doi:10.1093/gigascience/giab044 · Cited by 28

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Meegahapola, L., Labhart, F., Phan, T.-T., & Gatica-Perez, D. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478126 · Cited by 33

Detecting Job Promotion in Information Workers Using Mobile Sensing

Nepal, S. K., Mirjafari, S., Martinez, G. J., Audia, P. G., Striegel, A., & Campbell, A. T. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3414118 · Cited by 29

Approaches for tailoring between-session mental health therapy activities

Oewel, B., Areán, P. A., & Agapie, E. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642856 · Cited by 33

Where You Go Matters

Baron, B., & Musolesi, M. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3432699 · Cited by 27

Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study

Park, J., Arunachalam, R., Silenzio, V., & Singh, V. K. (2022) · JMIR Formative Research · doi:10.2196/34366 · Cited by 27

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Teles, A. S., Rocha, A., Silva, F., Lopes, J. C., O’Sullivan, D., van de Ven, P. W. J., & Endler, M. (2017) · Sensors · doi:10.3390/s17010127 · Cited by 26

Wearable Social Sensing: Content-Based Processing Methodology and Implementation

Gu, J. J., Gao, B., Chen, Y., Jiang, L., Gao, Z., Ma, X., Ma, Y., Woo, W. L., & Jin, J. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2754289 · Cited by 26

Daily estimates of clinical severity of symptoms in bipolar disorder from smartphone-based self-assessments

Busk, J., Faurholt‐Jepsen, M., Frost, M. M., Bardram, J. E., Kessing, L. V., & Winther, O. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00867-6 · Cited by 26

The Empathetic Car

Liu, S., Koch, K., Zhou, Z., Föll, S., He, X., Menke, T., Fleisch, E., & Wortmann, F. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478078 · Cited by 27

The Mobile Territorial Lab: a multilayered and dynamic view on parents’ daily lives

Centellegher, S., Nadai, M. D., Caraviello, M., Leonardi, C., Vescovi, M., Ramadian, Y., Oliver, N., Pianesi, F., Pentland, A., Antonelli, F., et al. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0064-6 · Cited by 25

Emotion Detection through Smartphone's Accelerometer and Gyroscope Sensors

Piskioulis, O., Tzafilkou, K., & Economides, A. A. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450613.3456822 · Cited by 25

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Kim, S., & Lee, K. (2021) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s339412 · Cited by 26

Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing

Lee, H., Kang, S., & Lee, U. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534623 · Cited by 25

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

Assi, K. C., Meegahapola, L., Droz, W., Kun, P., GÖTZEN, A. D., Bidoglia, M., Stares, S., GASKELL, G. D., Chagnaa, A., Ganbold, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581190 · Cited by 25

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Braund, T. A., Zin, M. T., Boonstra, T. W., Wong, Q. J. J., Larsen, M. E., Christensen, H. M., Tillman, G., & O’Dea, B. (2022) · JMIR Mental Health · doi:10.2196/35549 · Cited by 28

DepreST-CAT

Tlachac, M., Flores, R., Reisch, M., Houskeeper, K., & Rundensteiner, E. A. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534596 · Cited by 24

The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health

Kruzan, K. P., Ng, A., Stiles‐Shields, C., Lattie, E. G., Mohr, D. C., & Reddy, M. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581209 · Cited by 24

Designing Dashboard for Campus Stakeholders to Support College Student Mental Health

Yoo, D. W., & Choudhury, M. D. (2019) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329200 · Cited by 27

The CARP Mobile Sensing Framework -- A Cross-platform, Reactive, Programming Framework and Runtime Environment for Digital PhenotypingPreprint

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