Predicting anxiety state using smartphone-based passive sensing
Open accessAbstract
This study predicts the change of stress levels using real-world and online behavioral features extracted from smartphone log information. Previous studies of stress detection using smartphone data focused on a single feature and did not consider all features simultaneously. We propose a method to extract a co-occurring combination of a user's real-world and online behavioral features by converting raw sensor data into categorical features. We conducted an experiment in which the State Trait Anxiety Inventory (STAI) was used to assess the anxiety-related stress levels of 20 healthy participants. The participants installed a log-collecting application on their smartphones and answered the STAI questions once a day for one month. The proposed method showed an F-score of 74.2%, which is 4.0% higher than the F-score of previous studies (70.2%) that used single non-combined features. The results demonstrate that anxiety-related stress levels can be predicted using combined features extracted from smartphone log data.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Passive data collected through smartphones can detect daily ratings of depression and anxiety as well as aspects of well-being.
“Although to our knowledge, no work to date has investigated these data streams using machine learning in the context of an MBI, passive data collected through smartphones can detect daily ratings of depression137 and anxiety138 as well as aspects of well-being139.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [137]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
(1970) · Cited by 24,983 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104 · see citing works
The Role of Actigraphy in the Study of Sleep and Circadian Rhythms
Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., & Pollak, C. P. (2003) · Sleep · doi:10.1093/sleep/26.3.342 · Cited by 2,751 · see citing works
Using Pedometers to Increase Physical Activity and Improve Health: A Systematic Review
Bravata, D. M., Smith-Spangler, C., Sundaram, V., Gienger, A. L., Lin, N., Lewis, R., Stave, C. D., Olkin, I., & Sirard, J. R. (2007) · JAMA · doi:10.1001/jama.298.19.2296 · Cited by 2,064 · see citing works
Mental health problems and help-seeking behavior among college students
Hunt, J., & Eisenberg, D. (2010) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2009.08.008 · Cited by 1,949 · see citing works
mHealth for mental health: Integrating smartphone technology in behavioral healthcare.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011) · Professional Psychology: Research and Practice · doi:10.1037/a0024485 · Cited by 809 · see citing works
Association between physical activity and mental disorders among adults in the United States
Goodwin, R. D. (2003) · Preventive Medicine · doi:10.1016/s0091-7435(03)00042-2 · Cited by 675 · see citing works
Mobile Phone Sensing Systems: A Survey
Khan, W. Z., Xiang, Y., Aalsalem, M. Y., & Arshad, Q. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.031412.00077 · Cited by 556 · see citing works
Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review
Roig, J. B., Gilson, N. D., Puig-Ribera, A. M., Contreras-Espinosa, R. S., & Trost, S. G. (2014) · Sports Medicine · doi:10.1007/s40279-014-0142-5 · Cited by 554 · see citing works
Seeing everyone else’s highlight reels: how Facebook usage is linked to depressive symptoms.
Steers, M.-L. N., Wickham, R. E., & Acitelli, L. K. (2014) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.2014.33.8.701 · Cited by 535 · see citing works
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gática-Pérez, D., & Choudhury, T. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370270 · Cited by 511 · see citing works
Can Mobile Health Technologies Transform Health Care?
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2013) · JAMA · doi:10.1001/jama.2013.281078 · Cited by 496 · see citing works
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
Clustering of time-series subsequences is meaningless: implications for previous and future research
Keogh, E., & Lin, J. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-004-0172-7 · Cited by 467 · see citing works
Accelerometer-based transportation mode detection on smartphones
Hemminki, S., Nurmi, P., & Tarkoma, S. (2013) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2517351.2517367 · Cited by 436 · see citing works
Health at hand: A systematic review of smart watch uses for health and wellness
Reeder, B., & David, A. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.09.001 · Cited by 412 · see citing works
Systematic review of smartphone-based passive sensing for health and wellbeing
Cornet, V. P., & Holden, R. J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.12.008 · Cited by 406 · see citing works
Physiological, cognitive and behavioral aspects of social anxiety
Beidel, D. C., Turner, S. M., & Dancu, C. V. (1985) · Behaviour Research and Therapy · doi:10.1016/0005-7967(85)90019-1 · Cited by 357 · see citing works
Mental health: there’s an app for that
Anthes, E. (2016) · Nature · doi:10.1038/532020a · Cited by 333 · see citing works
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Dey, A. K., Chu, H.-H., & Hayes, G. R. (2012) · doi:10.1145/2370216 · Cited by 332 · see citing works
What Characteristics of Primary Anxiety Disorders Predict Subsequent Major Depressive Disorder?
Bittner, A., Goodwin, R. D., Wittchen, H.-U., Beesdo, K., Höfler, M., & Lieb, R. (2004) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v65n0505 · Cited by 272 · see citing works
Neuroticism and low self-esteem as risk factors for psychosis
A, L. K. M., Janssen, I., Bak, M., Bijl, R. V., de Graaf, R., & van Os, J. (2002) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s127-002-8207-y · Cited by 259 · see citing works
Social sensing for epidemiological behavior change
Madan, A., Cebrian, M., Lazer, D., & Pentland, A. (2010) · Proceedings of the 12th ACM international conference on Ubiquitous computing · doi:10.1145/1864349.1864394 · Cited by 219 · see citing works
Risk Factors of Psychosis: Identifying Vulnerable Populations Premorbidly
Olin, S.-C. S., & Mednick, S. A. (1996) · Schizophrenia Bulletin · doi:10.1093/schbul/22.2.223 · Cited by 188 · see citing works
Psychophysiological differences between subgroups of social phobia
Hofmann, S. G., Newman, M. G., Ehlers, A., & Roth, W. T. (1995) · Journal of Abnormal Psychology · doi:10.1037/0021-843x.104.1.224 · Cited by 169 · see citing works
Psychophysiological differences between subgroups of social phobia.
Hofmann, S. G., Newman, M. G., Ehlers, A., & Roth, W. T. (1995) · Journal of Abnormal Psychology · doi:10.1037//0021-843x.104.1.224 · Cited by 169 · see citing works
Proceedings of the 12th ACM international conference on Ubiquitous computing
Bardram, J. E., Langheinrich, M., Truong, K. N., & Nixon, P. (2010) · doi:10.1145/1864349 · Cited by 156 · see citing works
Turner, S. M., Beidel, D. C., & Larkin, K. T. (1986) · Journal of Consulting and Clinical Psychology · doi:10.1037//0022-006x.54.4.523 · Cited by 155 · see citing works
Kawakami, N., & Tsutsumi, A. (2015) · Journal of Occupational Health · doi:10.1539/joh.15-0001-er · Cited by 155 · see citing works
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works
Turner, S. M., Beidel, D. C., & Larkin, K. T. (1986) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.54.4.523 · Cited by 117 · see citing works
Can smartphones detect stress-related changes in the behaviour of individuals?
Bauer, G., & Lukowicz, P. (2012) · 2012 IEEE International Conference on Pervasive Computing and Communications Workshops · doi:10.1109/percomw.2012.6197525 · Cited by 111 · see citing works
Facebook behaviors associated with diurnal cortisol in adolescents: Is befriending stressful?
Morin-Major, J. K., Marin, M.-F., Durand, N., Wan, N., Juster, R.-P., & Lupien, S. J. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2015.10.005 · Cited by 90 · see citing works
Vineetha, R., Pai, K. M., Vengal, M., Gopalakrishna, K., & Narayanakurup, D. (2014) · Journal of Clinical and Experimental Dentistry · doi:10.4317/jced.51355 · Cited by 80 · see citing works
Proceedings of the 6th Augmented Human International Conference
(2015) · doi:10.1145/2735711 · Cited by 42 · see citing works
Canzian, L., & Musolesi, M. (2015) · Cited by 39 · see citing works
StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones
Lu, H., Frauendorfer, D., Gática-Pérez, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., & Choudhury, T. (2012) · Cited by 39 · see citing works
An, K. O., Jang, J. Y., & Kim, J. (2015) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.237.279 · Cited by 37 · see citing works
Kim, M., Lee, Y. J., & Ahn, R. S. (2010) · Yonsei Medical Journal · doi:10.3349/ymj.2010.51.2.212 · Cited by 33 · see citing works
Docherty, N. M., St-Hilaire, A., Aakre, J. M., Seghers, J. P., McCleery, A., & Divilbiss, M. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbp123 · Cited by 32 · see citing works
Albers, D. J., Elhadad, N., Claassen, J., Perotte, R., Goldstein, A., & Hripcsak, G. M. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.01.004 · Cited by 25 · see citing works
International Conference on Pervasive Computing and Communications Workshops
Degeler, V., & Lazovik, A. (2012) · The Institute of Electrical and Electronics Engineers · Cited by 13 · see citing works
(2016) · Nature · doi:10.1038/532006a · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Stress detection using ECG and EMG signals: A comprehensive study
Pourmohammadi, S., & Maleki, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105482 · Cited by 214 · see citing works
Pramanik, P. K. D., Sinhababu, N., Mukherjee, B., Padmanaban, S., Maity, A., Upadhyaya, B. K., Holm‐Nielsen, J. B., & Choudhury, P. (2019) · IEEE Access · doi:10.1109/access.2019.2958684 · Cited by 201 · see citing works
Jacobson, N. C., & Bhattacharya, S. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104013 · Cited by 92 · see citing works
de Moura, I. R., Teles, A. S., Viana, D., Marques, J., Coutinho, L., & Silva, F. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104278 · Cited by 91 · see citing works
Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing
Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2977059 · Cited by 78 · see citing works
“ing from depression [8]–[10], anxiety [11], autism spectrum disorder [12], post-traumatic stress disorder [13], and suicidal ideation [14].”
BackgroundA common factors perspective on mindfulness-based interventions
Goldberg, S. B. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00090-8 · Cited by 75 · see citing works
Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review
Choi, A., Ooi, A., & Lottridge, D. M. (2023) · JMIR mhealth and uhealth · doi:10.2196/40689 · Cited by 71 · see citing works
“Exercise duration was positively correlated with changes in anxiety [36] and depressive symptoms [48].”
BackgroundMethodInfluentialJacobson, N. C., Lekkas, D., Huang, R., & Thomas, N. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.12.086 · Cited by 70 · see citing works
“The results are especially notable because they suggest an ability to predict change in long-term GAD and PD symptoms, whereas previous studies have leveraged predictive models within more restrictive, cross-sectional paradigms (Fukazawa et al., 2019; Sano et al., 2013).”
ResultUeafuea, K., Boonnag, C., Sudhawiyangkul, T., Leelaarporn, P., Gulistan, A., Chen, W., Mukhopadhyay, S. C., Wilaiprasitporn, T., & Piyayotai, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3046259 · Cited by 72 · see citing works
“considered environmental, real-world, and online behavioral features to predict the stress level alteration of 20 young adults [84].”
BackgroundPrasad, T. R., Dhadwal, A. S., Kumar, P., & Sainaba, P. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12315-2 · Cited by 61 · see citing works
“[20] collected raw sensor data from mobile phone, such as brightness, acceleration, rotation/orientation, and application usage.”
BackgroundCastro, D., Gysi, D. M., Ferreira, F., Ferreira‐Santos, F., & Ferreira, T. B. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297058 · Cited by 51 · see citing works
“However, with passive data [76–80], future studies can surpass the temporal dynamics problem that emerge from ESM and EMA methods since passive data can be collect continuously.”
BackgroundA review of smartphone point‐of‐care adapter design
Alawsi, T., & Mahdi, Z. F. (2019) · Engineering Reports · doi:10.1002/eng2.12039 · Cited by 46 · see citing works
Aalbers, G., Hendrickson, A. T., Abeele, M. M. P. V., & Keijsers, L. (2022) · JMIR mHealth and uHealth · doi:10.2196/37469 · Cited by 30 · see citing works
“As asking individuals to consistently self-monitor and self-report stress over extended periods is a difficult, costly, and time-intensive procedure [4], researchers have started developing algorithms to unobtrusively detect stress from passively logged data, such as smartphone app use log data [5].”
BackgroundLekkas, D., & Jacobson, N. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-89768-2 · Cited by 38 · see citing works
Kolenik, T. (2022) · Internet of things · doi:10.1007/978-3-030-91181-2_7 · Cited by 34 · see citing works
Baumgartner, S. E., Sumter, S. R., Petkevič, V., & Wiradhany, W. (2022) · Social Science Computer Review · doi:10.1177/08944393211071068 · Cited by 32 · see citing works
“As a result, for every individual that takes part in social scienti fi c research, there is potentially already an abundance of data available (e.g., Fukazawa et al., 2019; Montag et al., 2020; Trifan et al., 2019).”
BackgroundAssociations Between Social Media Use and Anxiety Among Adolescents: A Systematic Review Study
Kerr, B. R., Garimella, A., Pillarisetti, L., Charlly, N., Sullivan, K., & Moreno, M. A. (2024) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2024.09.003 · Cited by 46 · see citing works
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 · see citing works
“In [27], researchers also report on the use of smartphones to predict changes in anxiety as measured by the State-Trait Anxiety Inventory [90] with an F1 score of 0.742.”
MethodInfluentialPassive social sensing with smartphones: a systematic review
Zhang, H., Ibrahim, A., Parsia, B., Poliakoff, E., & Harper, S. (2022) · Computing · doi:10.1007/s00607-022-01112-2 · Cited by 29 · see citing works
“…utilised by the reviewed studies were the support vector machine (SVM) [25, 30, 42, 51, 52, 57, 63], random forest [25, 31, 37, 38, 57, 58, 60, 65, 66], regression analysis [21, 24, 35, 37, 60], AdaBoost [37, 38, 65], Naive Bayes [37, 57, 65], neural networks [50, 62], Bayes net [37, 65],…”
BackgroundMethodInfluentialBeames, J. R., Han, J., Shvetcov, A., Zheng, W. Y., Slade, A., Dabash, O., Rosenberg, J., O’Dea, B., Kasthurirathne, S. N., Hoon, L., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35472 · Cited by 34 · see citing works
“For example, magnitude of acceleration in one study was defined in terms of several descriptive features (e.g., mean, minimum) [38], as the lowest mean of acceleration at night-time in another study [41], and as specific speeds travelled in another study [58].”
Patel, J., Hung, C., & Katapally, T. R. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116277 · Cited by 29 · see citing works
“…United States (N = 9) (Currey and Torous, 2023, Wang et al., 2018, Thakur and Roy, 2021, Jafarlou et al., 2023, Langholm et al., 2023, Sano et al., 2018, Shah et al., 2021, Xu et al., 2019, Yue et al., 2023), Japan (N = 1) (Fukazawa et al., 2019), and the Netherlands (N = 1) (Aalbers et al., 2023).”
InfluentialThe Use of Sensors to Detect Anxiety for In-the-Moment Intervention: Scoping Review
Dobson, R., Li, L. L., Garner, K., Tane, T., McCool, J. P., & Whittaker, R. (2023) · JMIR Mental Health · doi:10.2196/42611 · Cited by 27 · see citing works
Linardon, J., Chen, K., Gajjar, S., Eadara, A., Wang, S., Flathers, M., Burns, J., & Torous, J. B. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116483 · Cited by 32 · see citing works
Lee, Y. W., Choi, J. W., & Shin, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115658 · Cited by 27 · see citing works
Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information
Jeon, T., Bae, H. B., Lee, Y.-J., Jang, S., & Lee, S. (2021) · Sensors · doi:10.3390/s21227498 · Cited by 26 · see citing works
Choudhary, S., Thomas, N., Alshamrani, S., Srinivasan, G., Ellenberger, J., Nawaz, U., & Cohen, R. (2022) · JMIR Medical Informatics · doi:10.2196/38943 · Cited by 23 · see citing works
“Along with the accuracy levels, sensitivity and specificity results for the GAD-7 binary model are also higher than studies done by Nemesure et al [47] and Fukazawa et al [48], which used binary classification for prediction of anxiety.”
ResultMulti-modal interactive fusion method for detecting teenagers’ psychological stress
Zhang, H., Cao, L., Feng, L., & Yang, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103427 · Cited by 22 · see citing works
Ometov, A., Mezina, A., Lin, H.-C., Arponen, O., Bürget, R., & Nurmi, J. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00200-0 · Cited by 22 · see citing works
“The use of smartphone sensors for unobtrusive monitoring and passive sensing can assist in achieving this goal [81, 82].”
Woll, S., Birkenmaier, D., Biri, G. I., Nissen, R., Lutz, L., Schroth, M., Ebner‐Priemer, U., & Giurgiu, M. (2025) · JMIR mhealth and uhealth · doi:10.2196/59660 · Cited by 19 · see citing works
Machine learning approaches to anxiety detection: trends, model evaluation, and future directions
Taskynbayeva, M., & Gutoreva, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1630047 · Cited by 18 · see citing works
Ganguly, C., Nayak, S., & Gupta, A. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91196-2.00016-8 · Cited by 13 · see citing works
Gui, D.-Y., Dai, Y., Zheng, Z., & Liu, S. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.998017 · Cited by 16 · see citing works
“Previous research has shown that anxiety-related stress levels can be predicted using the combined features extracted from smartphone log data (Fukazawa et al., 2019).”
BackgroundPiccin, J., Viduani, A. C., Buchweitz, C., Pereira, R. B., Zimerman, A., Amando, G. R., Cosenza, V., Ferreira, L. Z., McMahon, N. A. G., de Melo, R. F., et al. (2023) · JAACAP Open · doi:10.1016/j.jaacop.2023.11.002 · Cited by 14 · see citing works
“61 It has been theorized that low-level sensor data may correlate with depression severity in young adults, 62 and behavioral data have been shown to predict symptoms of depression and post-traumatic stress disorder.”
Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective
Fukazawa, Y., Yamamoto, N., Hamatani, T., Ochiai, K., Uchiyama, A., & Ohta, K. (2020) · Journal of Information Processing · doi:10.2197/ipsjjip.28.16 · Cited by 12 · see citing works
“Feature ACT-5, physical orientation, which is the angle of the display to the ground, has been calculated using smartphone rotation sensor data [69].”
BackgroundMethodInfluentialSelf-citationArtificial Intelligence based Early Detection and Timely Diagnosis of Mental Illness - A Review
Singh, P., Srinivas, K. K., Peddi, A., Shabarinath, B. B., Neelima, I., & Kandala, A. B. (2022) · International Mobile and Embedded Technology Conference (MECON) · doi:10.1109/mecon53876.2022.9752219 · Cited by 10 · see citing works
A survey of autonomous monitoring systems in mental health
Gopalakrishnan, A., Gururajan, R., Zhou, X., Venkataraman, R., Chan, K. C., & Higgins, N. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1527 · Cited by 11 · see citing works
“Using passive sensing data, the article demonstrates how to accurately identify people who suffer from high levels of anxiety (Fukazawa et al., 2019).”
BackgroundAlexiadis, A., Tsanas, A., Shtika, L., Efopoulos, V., Votis, K., Tzovaras, D. K., & Triantafyllidis, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3350201 · Cited by 9 · see citing works
“Machine learning methods, which harness data generated through mobile and sensing devices, have already shown their capability to predict disease exacerbations and health status deterioration, e.g. exacerbation in chronic obstructive pulmonary disease [15], anxiety [16] and cardiovascular risk [17].”
Anxiety Detection Leveraging Mobile Passive Sensing
Levine, L., Gwak, M., Kärkkäinen, K., Fazeli, S., Zadeh, B., Peris, T. S., Young, A. S., & Sarrafzadeh, M. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-64991-3_15 · Cited by 9 · see citing works
“present several challenges, some of which have already been noted, which can result in limited accuracy owing to differences in behavioral patterns across users, and the indirect manner of detection [18]. We present a system for the remote monitoring of mental health symptoms, their fluctuation, and their attendant dis…”
BackgroundA multimodal stress detection dataset with facial expressions and physiological signals
Hosseini, M., Sohrab, F., Gottumukkala, R. N., Bhupatiraju, R. T., Katragadda, S., Raitoharju, J., Iosifidis, A., & Gabbouj, M. (2025) · Scientific Data · doi:10.1038/s41597-025-05812-0 · Cited by 24 · see citing works
Detection of Anxiety in Psychiatric Patients using Physiological Signals
Desai, U., Shetty, A. D., Thanmay, M., Abu Suhel, A., Nekar, G., & Shashikanth, B. (2022) · IEEE India Conference · doi:10.1109/indicon56171.2022.10040142 · Cited by 8 · see citing works
Prenatal depression level prediction using ensemble based deep learning model
Goapalakrishnan, A., Zhou, X., Venkataraman, R., Gururajan, R., Chan, K. C., Zhu, G., & Higgins, N. (2025) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2024.12.002 · Cited by 8 · see citing works
Paromita, P., Mundnich, K., Nadarajan, A., Booth, B. M., Narayanan, S. S., & Chaspari, T. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1195795 · Cited by 8 · see citing works
“Apart from collecting physiological data, smartphones have been also used to collect other behavioral metrics, such as user feedback data, overall phone activity, speech, location, and physical movement, which play a key part in detecting stress (47–49), anxiety (50), negative affect (51), unusual activities (52), and…”
MethodIntroducing MDPSD, a Multimodal Dataset for Psychological Stress Detection
Chen, W., Zheng, S., & Sun, X. N. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-0705-9_5 · Cited by 8 · see citing works
Adolescents’ daily social media use and mood during the COVID-19 lockdown period
Dreier, M. J., Boyd, S. I., Jorgensen, S. L., Merai, R., Fedor, J., Durica, K. C., Low, C. A., & Hamilton, J. L. (2024) · Current Research in Ecological and Social Psychology · doi:10.1016/j.cresp.2024.100196 · Cited by 8 · see citing works
Lim, S., Kim, C., Cho, B. H., Choi, S., Lee, H., & Jang, D. P. (2023) · Biomedical Engineering Letters · doi:10.1007/s13534-023-00337-0 · Cited by 7 · see citing works
“Fukazawa et al., on the other hand, harnessed multimodal mobile passive data, encompassing factors like ambient illuminance levels, acceleration, geo-location, app and browser usage, and email activity [ 10].”
BackgroundSmartphone-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 6 · see citing works
“However, we found that a substantial number, about 30.5% (18/59) of the studies, did not engage with ML models beyond the perfunctory [38,45,53,56,66,69,77,80,81, 86,100,101,103,107,108,110,112,118].”
InfluentialFloor plan optimization for indoor environment based on multimodal data
Kang, S.-J., & Kim, S. K. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03952-9 · Cited by 6 · see citing works
“Fukazawa et al. [25] proposed an experiment using the state-trait anxiety inventory (STAI) to evaluate the anxiety-related stress levels of 20 participants.”
MethodVega, 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 6 · see citing works
Daniel, K. E., Mendu, S., Baglione, A. N., Cai, L., Teachman, B. A., Barnes, L. E., & Boukhechba, M. (2021) · Anxiety Stress & Coping · doi:10.1080/10615806.2021.1959916 · Cited by 5 · see citing works
Estimating Sense of Agency from Behavioral Logs: Toward a Just-in-Time Adaptive Intervention System
Togawa, R., Legaspi, R., Nishimura, Y., Miyamoto, A., Yang, B., Sugisaki, E., Ikeda, K., Kobayashi, N., & Naruse, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-58226-4_21 · Cited by 5 · see citing works
Transforming fatigue assessment: Smartphone-based system with digitized motor skill tests
Valla, E., Toose, A.-J., Nõmm, S., & Toomela, A. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105152 · Cited by 6 · see citing works
Technological Advances in Clinical Assessment
Heinz, M. V., Thomas, N., Nguyen, N. D., Griffin, T. Z., & Jacobson, N. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00171-0 · Cited by 5 · see citing works
SeSaMe: A Framework to Simulate Self-Reported Ground Truth for Mental Health Sensing Studies
Choube, A., Swain, V. D., & Mishra, V. (2024) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii63134.2024.00031 · Cited by 5 · see citing works
Theory of Mind in Artificial Intelligence Applications
Garcia-Lopez, A. (2023) · Logic, argumentation & reasoning · doi:10.1007/978-3-031-46742-4_23 · Cited by 5 · see citing works
Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log
Yao, W., Kaminishi, K., Yamamoto, N., Hamatani, T., Yamada, Y., Kawada, T., Hiyama, S., Okimura, T., Terasawa, Y., Maeda, T., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.780566 · Cited by 3 · see citing works
“Fukazawa et al. (19) estimated the change in users’ anxiety based on STAI with 0.742 F-score using smartphone logs of experimental participants.”
BackgroundMethodInfluentialSelf-citationLarrazabal, M. A., Wang, Z., Rucker, M., Toner, E. R., Boukhechba, M., Teachman, B. A., & Barnes, L. E. (2025) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp65954.2025.00074 · Cited by 3 · see citing works
“…the present work offers insight into how biobehavioral features predict fluctuations in anxiety over brief time scales (a few minutes ), offering a much more granular view than much existing work (which detects anxiety as it occurs on a daily or weekly level [23, 32, 42]); though see also [31].”
InfluentialOutlier Behavior Detection for Indoor Environment Based on t-SNE Clustering
Kang, S.-J., & Kim, S. K. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016828 · Cited by 3 · see citing works
“[20] presented a technique that can detect a co-occurring combination of a user’s real-world and online behavioral features that convert raw sensor data into categorical features.”
BackgroundPsikoterapide Yapay Zeka ve Chatbot: Etik Kurallar ve Sınırlılıklar
Erdoğdu, M., Yıldızeli, S., & Mazman, I. (2024) · Türkiye Sağlık Enstitüleri Başkanlığı Dergisi · doi:10.5578/tusebdergisi.20240121 · Cited by 2 · see citing works
A review of multimodal depression detection methods using smartphone usage and audio-visual clues
Prasad, T. R., & Kumar, P. (2026) · Iran Journal of Computer Science · doi:10.1007/s42044-025-00359-0 · Cited by 2 · see citing works
“Fukazawa et al. in [133] studied application usage logs and smartphone sensor features on a daily basis and found them important in predicting anxiety disorders.”
Behavior analysis method for indoor environment based on app usage mining
Kang, S.-J., & Kim, S. K. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-020-03532-3 · Cited by 2 · see citing works
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 · see citing works
“…have been bountiful studies aiming at automated estimation of human internal context related to health (e.g. stress [13], depression [3], anxiety [7], etc.) by using smartphone, the estimation of human cognitive performance is still under explored, and to date, only a few studies have been…”
BackgroundSelf-citationDecision Level Fusion Using Hybrid Classifier for Mental Disease Classification
Ahmad, M., Wahid, N., Hamid, R. A., Sadiq, S., Mehmood, A., & Choi, G. S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026077 · Cited by 5 · see citing works
Girousse, E., & Vuillerme, N. (2022) · JMIR Research Protocols · doi:10.2196/38785 · Cited by 2 · see citing works
“Three articles studied depression only [42,66,67], 1 studied anxiety only [65], and 4 included both depression and anxiety [61-64].”
BackgroundMethodInfluentialDeep Learning Approaches for Detecting Psychological Instability: An Evaluation of Performance
Majety, T. T., Sravan, K., Avinash, B. M., Tokala, S., Enduri, M. K., & Anamalamudi, S. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307238 · Cited by 2 · see citing works
“The World Health Organization (WHO) reported that greater than 90% of suicides are due to mental disorders [13] and it also considers mental illness to be a global challenge and going to be an economic problem that can be severe in the future.”
BackgroundZhao, 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 · see citing works
Lin, H.-C., Ometov, A., Arponen, O., Nikunen, K., & Nurmi, J. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102948 · Cited by 1 · see citing works
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 · see citing works
Self-citationBehavior-based Outlier Detection for Indoor Environment
Kang, S.-J., & Kim, S. K. (2020) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci51800.2020.00135
“Fukazawa et al. [7] proposed a method to extract a co-occurring combination of a user’s real-world and online behavioral features by converting raw sensor data into categorical features.”
MethodmHealth: O uso de smartphones para aplicações biomédicas
Wurzel, P. M., & Evald, P. J. D. D. O. (2021) · Disciplinarum Scientia | Naturais e Tecnológicas · doi:10.37779/nt.v22i1.3686
GameDepot: A Visual Analytics System for Mobile Game Performance Testing
Jang, D., & Jo, J. (2023) · IEEE Access · doi:10.1109/access.2023.3301775
“used in healthcare [7], [8], [9], [10].”
Apps e Jovens Adultos: Contributos para um Mapeamento de Práticas Mediadas
Antunes, E. (2024) · UMinho Editora/CECS eBooks · doi:10.21814/uminho.ed.82
Poleac, G. (2024) · UMinho Editora/CECS eBooks · doi:10.21814/uminho.ed.82.6
Davis-Stewart, T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4820875
Synthetic Mobility Feature Generation for Mental Health Prediction using Diffusion Models
Suzuki, M., Kodaka, M., & Fukazawa, Y. (2024) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat62293.2024.00022
Self-citationMultimodality Fusion for Depression Detection Using Audio, Video and Text CuesPreprint
Prasad, T. R., Kumar, P. U. P., & Vaidyanathan, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5338501
Rethinking epistemic injustice in psychiatric digital phenotyping from the perspective of ignorance
Yang, J. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2025-110858
Weingarden, H. M., Holstein, V., Jonathan, G., Armey, M. F., Onnela, J.-P. “., & Wilhelm, S. (2025) · Behavior Therapy · doi:10.1016/j.beth.2025.09.001
Valla, E., Väli, L., Nõmm, S., & Toomela, A. (2025) · Cognition Technology & Work · doi:10.1007/s10111-025-00829-2
“…of smartphone sensors in monitoring physical and cognitive abilities, offering new avenues in health and well-being monitoring in free-living environments (Guidoux et al. 2014; Kay et al. 2011 ; Antosik-Wójcińska et al. 2020; Kheirkhahan et al. 2019; Fukazawa et al. 2019; Cornet and Holden 2018).”
Estimation of Human Internal States and Application of Diffusion Models
Kohjima, M., & Nambu, Y. (2026) · Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaieti magajin/B-plus/Denshi Jouhou Tsuushin Gakkai Tsuushin Sosaeti magajin · doi:10.1587/bplus.19.305
Fukazawa, Y., & Kodaka, M. (2026) · New Generation Computing · doi:10.1007/s00354-026-00319-7
“Withthe widespreadadoptionofsmartphonesandwearabledevices,therehasbeensignificant progressinpredictingmentalhealthstatesusingdatacollectedfromthesedevices [8,9].”
Self-citationGorbaniuk, O., Szwed, M., Mazur, M., Boreczek, W., & Buglińska, J. (2026) · Roczniki Psychologiczne · doi:10.18290/rpsych2025.0016
“…revision should take into account modern multilevel approaches in psy-chometrics, especially when the aim is to distinguish within-person variability from between-person differences (Nezlek, 2020), as well as technological possibilities that make diary studies more feasible (Fukazawa et al., 2019).”
- Authors
- Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., & Ota, J.
- Affiliations
- Research into Artifacts, Center for Engineering, The University of Tokyo (JP)
- Keio University (JP)
- National Center of Neurology and Psychiatry (JP)
- Corresponding author
- Fukazawa, Yusuke
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 93
- Pages
- 103151
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2921304398
- Cited by
- 81
- References
- 48
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 14 · 2023: 10 · 2022: 16 · 2021: 11 · 2020: 9 · 2019: 3
- FWCI
- 11.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (JP26120001, JP26120006)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- stress detection, behavioral characteristics, State-Trait Anxiety Inventory, feature combination
- MeSH terms
- Smartphone, Anxiety, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
