Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits
Abstract
Personality traits describe individual differences in patterns of thinking, feeling, and behaving ("between-person" variability). But individuals also show changes in their own patterns over time ("within-person" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.
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?
Research has shown that participant's behavior and sociodemographics may differ between Android platforms and other mobile platforms such as iOS.
“Research has shown that participant’s behavior and sociodemographics may differ between Android platforms and other mobile platforms such as iOS [44,95].”
Of 105 consenting students, 92 opted to be scanned, and 75 of the 92 scanned subjects met the mobile sensing data quality criteria of 18 hours per day for at least 14 days.
“of the 105 students consenting to be part of the NeuroSence study only 92 opted to be scanned at the start of the semester. In addition, only 75 of the 92 scanned subjects met the mobile sensing data quality criteria [100] of 18 hours of sensing data per day for a minimum of 14 days across the term.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [100]
Mobile sensing data quality criteria exclude data with less than 18 hours of data per day and less than 14 days of data during the term.
“Specifically, in terms of mobile sensing data we exclude data with less than 18 hours [100] of data per day and less than 14 days of data during the term.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [100]
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
(1996) · Cited by 20,179 · see citing works
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490 · see citing works
Binary Codes capable of currecting deletions, insertions, and reversals
Levenshtein, V. I. (1965) · Soviet physics. Doklady · Cited by 10,387 · see citing works
(1999) · Cited by 7,995 · see citing works
A working guide to boosted regression trees
Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484 · see citing works
The Elements of Statistical Learning
Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009) · Springer series in statistics · doi:10.1007/b94608 · Cited by 4,111 · see citing works
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 · see citing works
Self-citation(1961) · Journal of the Optical Society of America · doi:10.1364/josa.51.000248 · Cited by 2,288 · see citing works
Estimation of Error Rates in Discriminant Analysis
Lachenbruch, P. A., & Mickey, M. R. (1968) · Technometrics · doi:10.1080/00401706.1968.10490530 · Cited by 1,508 · see citing works
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116 · see citing works
Self-citationComputer-based personality judgments are more accurate than those made by humans
Youyou, W., Kosinski, M., & Stillwell, D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418680112 · Cited by 1,086 · see citing works
The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress
Lee, Y., Chang, C., Lin, Y., & Cheng, Z. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.10.047 · Cited by 1,012 · see citing works
Automatic personality assessment through social media language.
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000020 · Cited by 802 · see citing works
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 · see citing works
Mehl, M. R., Gosling, S. D., & Pennebaker, J. W. (2006) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.90.5.862 · Cited by 764 · see citing works
Fleeson, W., & Gallagher, P. (2009) · Journal of Personality and Social Psychology · doi:10.1037/a0016786 · Cited by 636 · see citing works
Predicting Personality from Twitter
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.33 · Cited by 613 · see citing works
Predicting personality with social media
Golbeck, J., Robles, C., & Turner, K. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979614 · Cited by 584 · see citing works
Personality and self reported mobile phone use
Butt, S., & Phillips, J. G. (2007) · Computers in Human Behavior · doi:10.1016/j.chb.2007.01.019 · Cited by 520 · 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
Self-citationOur Twitter Profiles, Our Selves: Predicting Personality with Twitter
Quercia, D., Kosiński, M., Stillwell, D. J., & Crowcroft, J. (2011) · International Conference on Social Computing · doi:10.1109/passat/socialcom.2011.26 · Cited by 483 · 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
Trait explanations in personality psychology
McCrae, R. R., & Costa, P. T. (1995) · European Journal of Personality · doi:10.1002/per.2410090402 · Cited by 426 · see citing works
BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing
Lane, N., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., & Campbell, A. (2011) · Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246161 · Cited by 409 · see citing works
Self-citationMining large-scale smartphone data for personality studies
Chittaranjan, G. T., Blom, J., & Gática-Pérez, D. (2011) · Personal and Ubiquitous Computing · doi:10.1007/s00779-011-0490-1 · Cited by 378 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Predicting Personality Using Novel Mobile Phone-Based Metrics
de Montjoye, Y.-A., Quoidbach, J., Robic, F., & Pentland, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_6 · Cited by 308 · see citing works
R -Squared Measures for Count Data Regression Models With Applications to Health-Care Utilization
Cameron, A. C., & Windmeijer, F. A. G. (1996) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1996.10524648 · Cited by 308 · see citing works
The Social Rhythm Metric An Instrument to Quantify the Daily Rhythms of Life
Monk, T. H., Flaherty, J. F., Frank, E., Hoskinson, K., & Kupfer, D. J. (1990) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-199002000-00007 · Cited by 295 · see citing works
Smartphone sensing methods for studying behavior in everyday life
Harari, G. M., Müller, S. R., Aung, M. H., & Rentfrow, P. J. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.018 · Cited by 248 · see citing works
Self-citationPassive and In-Situ assessment of mental and physical well-being using mobile sensors
Rabbi, M., Ali, S., Choudhury, T., & Berke, E. (2011) · Proceedings of the 13th international conference on Ubiquitous computing · doi:10.1145/2030112.2030164 · Cited by 243 · see citing works
Automatic detection of social rhythms in bipolar disorder
Abdullah, S., Matthews, M. L., Frank, E., Doherty, G. J., Gay, G. K., & Choudhury, T. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv200 · Cited by 225 · see citing works
R-Squared Measures for Count Data Regression Models with Applications to Health-Care Utilization
Cameron, A. C., & Windmeijer, F. A. G. (1996) · Journal of Business and Economic Statistics · doi:10.2307/1392433 · Cited by 223 · see citing works
SmartGPA: how smartphones can assess and predict academic performance of college students
Wang, R., Harari, G., Hao, P., Zhou, X., & Campbell, A. (2015) · Ubiquitous Computing · doi:10.1145/2750858.2804251 · Cited by 219 · see citing works
Self-citationDaily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654933 · Cited by 217 · see citing works
Kim, J.-H. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2016.11.004 · Cited by 196 · see citing works
Ben‐Zeev, D., Brian, R., Wang, R., Wang, W., Campbell, A. T., Aung, M. H., Merrill, M., Tseng, V. W.-S., Choudhury, T., Hauser, M., et al. (2017) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000243 · Cited by 185 · see citing works
Self-citationAmbulatory Assessment: An Innovative and Promising Approach for Clinical Psychology
Ebner-Priemer, U. W., & Trull, T. J. (2009) · European Psychologist · doi:10.1027/1016-9040.14.2.109 · Cited by 173 · see citing works
Staiano, J., Lepri, B., Aharony, N., Pianesi, F., Sebe, N., & Pentland, A. (2012) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2370216.2370266 · Cited by 171 · see citing works
Advancing Hand Gesture Recognition with High Resolution Electrical Impedance Tomography
Zhang, Y., Xiao, R., & Harrison, C. (2016) · doi:10.1145/2984511.2984574 · Cited by 146 · see citing works
Generalized Linear Mixed Models
McCulloch, C. E., & Neuhaus, J. M. (2012) · Encyclopedia of Environmetrics · doi:10.1002/9780470057339.vag009.pub2 · Cited by 133 · see citing works
Users of the main smartphone operating systems (iOS, Android) differ only little in personality
Götz, F. M., Stieger, S., & Reips, U.-D. (2017) · PLOS ONE · doi:10.1371/journal.pone.0176921 · Cited by 127 · see citing works
Abdullah, S., Matthews, M. L., Murnane, E. L., Gay, G. K., & Choudhury, T. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632100 · Cited by 125 · see citing works
A simple way to measure daily lifestyle regularity
Monk, T. H., Frank, E., Potts, J. M., & Kupfer, D. J. (2002) · Journal of Sleep Research · doi:10.1046/j.1365-2869.2002.00300.x · Cited by 121 · see citing works
Kanning, M. K., Ebner‐Priemer, U., & Schlicht, W. M. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00187 · Cited by 118 · see citing works
Objective assessment of depressive symptoms with machine learning and wearable sensors data
Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., & Picard, R. (2017) · 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2017.8273620 · Cited by 116 · see citing works
Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing
Wang, R., Wang, W., Aung, M. H., Ben‐Zeev, D., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., Scherer, E. A., et al. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130976 · Cited by 105 · see citing works
Self-citationHarari, G. M., Gosling, S. D., Wang, R., Chen, F., Chen, Z., & Campbell, A. T. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.10.027 · Cited by 95 · see citing works
Self-citationPersonality and Health: Progress and Problems in Psychosomatics
Wiebe, D., & Smith, T. W. (1997) · Elsevier eBooks · doi:10.1016/b978-012134645-4/50035-4 · Cited by 80 · see citing works
Generalized Linear Mixed Models
Gbur, E. E., Stroup, W. W., McCarter, K. S., Durham, S. L., Young, L. J., Christman, M. C., West, M., & Kramer, M. (2012) · ASSA, CSSA and SSSA · doi:10.2134/2012.generalized-linear-mixed-models.c5 · Cited by 80 · see citing works
(2006) · Choice Reviews Online · doi:10.5860/choice.43-6516 · Cited by 71 · see citing works
Towards a psychographic user model from mobile phone usage
de Oliveira, R. C., Karatzoglou, A., Cerezo, P. C., Armenta, A., & Oliver, N. (2011) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1979742.1979920 · Cited by 71 · see citing works
The Built-In Environment: The Role of Personality and Physical Activity
Rhodes, R. E. (2006) · Exercise and Sport Sciences Reviews · doi:10.1249/00003677-200604000-00008 · Cited by 59 · see citing works
Android and iOS users' differences concerning security and privacy
Benenson, Z., Gassmann, F., & Reinfelder, L. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2468502 · Cited by 52 · see citing works
A comparison of the characteristics of iOS and Android users of a smoking cessation app
Ubhi, H. K., Kotz, D., Michie, S., van Schayck, O. C. P., & West, R. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0455-z · Cited by 49 · see citing works
An Evaluation of Students’ Interest in and Compliance With Self-Tracking Methods
Harari, G. M., Müller, S. R., Mishra, V., Wang, R., Campbell, A. T., Rentfrow, P. J., & Gosling, S. D. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550617712033 · Cited by 39 · see citing works
Self-citationMorningness–eveningness and personality characteristics of young healthy adults
Antúnez, J. M., Navarro, J. F., & Adán, A. (2014) · Personality and Individual Differences · doi:10.1016/j.paid.2014.04.015 · Cited by 38 · see citing works
Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain
Aung, M. H., Alquaddoomi, F., Hsieh, C.-K., Rabbi, M., Yang, L., Pollak, J., Estrin, D. L., & Choudhury, T. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2565381 · Cited by 38 · see citing works
A Comparative Study of Android and iOS for Accessing Internet Streaming Services
Liu, Y., Li, F., Guo, L., Shen, B., & Chen, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36516-4_11 · Cited by 35 · see citing works
Modeling What Friendship Patterns on Facebook Reveal About Personality and Social Capital
Yong, L., Venkatanathan, J., Gonçalves, J., Karapanos, E., & Kostakos, V. (2014) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2617572 · Cited by 32 · see citing works
Morningness–eveningness, Big Five and the BIS/BAS inventory
Randler, C., Baumann, V. P., & Horzum, M. B. (2014) · Personality and Individual Differences · doi:10.1016/j.paid.2014.03.010 · Cited by 27 · see citing works
Network, personality and social capital
Venkatanathan, J., Karapanos, E., Kostakos, V., & Gonçalves, J. (2012) · ACM Web Science Conference (WebSci) · doi:10.1145/2380718.2380760 · Cited by 21 · see citing works
Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring
Mehrotra, A., & Musolesi, M. (2017) · doi:10.1145/3089341.3089342 · Cited by 19 · see citing works
McCormick, B. P., Frey, G. C., Lee, C.-T., Chun, S., Sibthorp, J., Gajić, T., Stamatović-Gajić, B., & Maksimovich, M. (2008) · International Journal of Social Psychiatry · doi:10.1177/0020764008091423 · Cited by 18 · see citing works
Estimation of english skill with a mobile eye tracker
Augereau, O., Kunze, K. S., Fujiyoshi, H., & Kise, K. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968275 · Cited by 15 · see citing works
Jiang, X., Chen, Y., Liu, J., Hayes, G. R., Hu, L., & Shen, J. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971447 · Cited by 11 · see citing works
Participants' compliance and experiences with self-tracking using a smartphone sensing app
Harari, G. M., Wang, W., Müller, S. R., Wang, R., & Campbell, A. T. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3123164 · Cited by 8 · see citing works
Self-citationAssessing personality through objective behavioral sensing
Wang, H., & Marsella, S. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273590 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Predicting personality from patterns of behavior collected with smartphones
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Völkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920484117 · Cited by 362 · see citing works
Self-citationPersonalized Models of Psychopathology
Wright, A. G. C., & Woods, W. C. (2020) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-102419-125032 · Cited by 281 · see citing works
Personality Research and Assessment in the Era of Machine Learning
Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S. S., Gosling, S. D., & Bühner, M. (2020) · European Journal of Personality · doi:10.1002/per.2257 · Cited by 158 · see citing works
Self-citationOpoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works
Personality computing: New frontiers in personality assessment
Phan, L., & Rauthmann, J. (2021) · Social & Personality Psychology Compass · doi:10.1111/spc3.12624 · Cited by 76 · see citing works
Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing
Mirjafari, S., Masaba, K., Grover, T., Wang, W., Audia, P. G., Campbell, A. T., Chawla, N. V., Swain, V. D., Choudhury, M. D., Dey, A. K., et al. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3328908 · Cited by 82 · see citing works
Self-citationGao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3411813 · Cited by 61 · see citing works
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Wang, Z., Xiong, H., Zhang, J., Yang, S., Boukhechba, M., Zhang, D., Barnes, L. E., & Dou, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3161046 · Cited by 62 · see citing works
Kawakami, A., Chowdhary, S., Iqbal, S. T., Liao, Q. V., Olteanu, A., Suh, J., & Saha, K. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610207 · Cited by 70 · see citing works
Personality Sensing for Theory Development and Assessment in the Digital Age
Harari, G. M., Vaid, S. S., Müller, S. R., Stachl, C., Marrero, Z., Schoedel, R., Bühner, M., & Gosling, S. D. (2020) · European Journal of Personality · doi:10.1002/per.2273 · Cited by 52 · see citing works
Self-citationPredicting Personality Traits From Physical Activity Intensity
Gao, N., Shao, W., & Salim, F. D. (2019) · Computer · doi:10.1109/mc.2019.2913751 · Cited by 50 · see citing works
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 · see citing works
Self-citationSwain, V. D., Saha, K., Rajvanshy, H., Sirigiri, A., Gregg, J. M., Lin, S., Martinez, G. J., Mattingly, S. M., Mirjafari, S., Mulukutla, R., et al. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3369828 · Cited by 46 · see citing works
Self-citationSaha, 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 43 · see citing works
When phones get personal: Predicting Big Five personality traits from application usage
Peltonen, E., Sharmila, P., Asare, K. O., Visuri, A., Lagerspetz, E., & Ferreira, D. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101269 · Cited by 41 · see citing works
How Are Personality States Associated with Smartphone Data?
Rüegger, D., Stieger, M., Nißen, M., Allemand, M., Fleisch, E., & Kowatsch, T. (2020) · European Journal of Personality · doi:10.1002/per.2309 · Cited by 36 · see citing works
Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes
Melumad, S., Hadi, R., Hildebrand, C., & Ward, A. F. (2020) · Customer Needs and Solutions · doi:10.1007/s40547-020-00107-4 · Cited by 36 · see citing works
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 · see citing works
Self-citationNepal, 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 34 · see citing works
Self-citationWang, 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 34 · see citing works
Self-citationHe-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 · see citing works
Self-citationLamichhane, B., Zhou, J., & Sano, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3265684 · Cited by 32 · see citing works
Marengo, D., Elhai, J. D., & Montag, C. (2023) · Journal of Personality · doi:10.1111/jopy.12817 · Cited by 31 · see citing works
n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the WildPreprint
Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04831 · Cited by 31 · see citing works
Langener, A. M., Stulp, G., Jacobson, N. C., Costanzo, A., Jagesar, R. R., Kas, M. J., & Bringmann, L. F. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231202677 · Cited by 29 · see citing works
Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing
Pieritz, S., Khwaja, M. K., Faisal, A. A., & Matic, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445523 · Cited by 30 · 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
Self-citationImputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior
Saha, K., Mulukutla, R., Nies, K., Robles-Granda, P., Sirigiri, A., Yoo, D. W., Audia, P. G., Campbell, A. T., Chawla, N. V., D’Mello, S. K., et al. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925479 · Cited by 29 · see citing works
Self-citationPutting human behavior predictability in context
Zhang, W., Shen, Q., Teso, S., Lepri, B., Passerini, A., Bison, I., & Giunchiglia, F. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00299-2 · Cited by 32 · see citing works
Investigating the Reliability of Self-report Data in the Wild: The Quest for Ground Truth
Gao, N., Rahaman, M. S., Shao, W., & Salim, F. D. (2021) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3460418.3479338 · Cited by 27 · see citing works
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 28 · see citing works
Self-citationMartinez, G. J., Mattingly, S. M., Robles-Granda, P., Saha, K., Sirigiri, A., Young, J., Chawla, N. V., Choudhury, M. D., D’Mello, S. K., Mark, G. J., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/22218 · Cited by 24 · see citing works
Detecting Smartwatch-Based Behavior Change in Response to a Multi-Domain Brain Health Intervention
Cook, D. J., Strickland, M., & Schmitter‐Edgecombe, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3508020 · Cited by 23 · see citing works
Hart, A., Reis, D., Prestele, E., & Jacobson, N. C. (2022) · Journal of Medical Internet Research · doi:10.2196/34015 · Cited by 22 · see citing works
Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connnected Wellness Devices
Tong, C., Craner, M. J., Vegreville, M., & Lane, N. D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351264 · Cited by 20 · see citing works
Wang, W., Xu, W., Chander, A., Nepal, S., Buck, B., Pakhomov, S., Cohen, T., Ben-Zeev, D., & Campbell, A. (2023) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3610890 · Cited by 19 · see citing works
Self-citationLücke, A. J., Quintus, M., Egloff, B., & Wrzus, C. (2020) · European Journal of Personality · doi:10.1177/0890207020962332 · Cited by 16 · see citing works
Smartphones within Psychological Science
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408 · Cited by 14 · see citing works
Guo, G., Zhang, H., Yao, L., Li, H., Xu, C., Li, Z., & Xu, W. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3494970 · Cited by 16 · see citing works
Danvers, A. F., Sbarra, D. A., & Mehl, M. R. (2020) · European Journal of Personality · doi:10.1002/per.2282 · Cited by 15 · see citing works
Federated Learning Framework for Mobile Sensing Apps in Mental Health
Suruliraj, B., & Orji, R. (2022) · SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health · doi:10.1109/segah54908.2022.9978600 · Cited by 14 · see citing works
Lai, J., Rahmani, A. M., Yunusova, A., Rivera, A. P., Labbaf, S., Hu, S., Dutt, N. D., Jain, R., & Borelli, J. L. (2021) · JMIR Formative Research · doi:10.2196/26186 · Cited by 14 · see citing works
Gao, S., Li, W., Song, L. J., Zhang, X., Lin, M., & Lu, S. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413591 · Cited by 14 · see citing works
Understanding practices and needs of researchers in human state modeling by passive mobile sensing
Xu, X., Mankoff, J. C., & Dey, A. K. (2021) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-021-00072-4 · Cited by 13 · see citing works
Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing
Vaid, S. S., & Harari, G. M. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_5 · Cited by 12 · see citing works
Self-citationARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms
Mansoor, H., Gerych, W., Alajaji, A. S., Buquicchio, L., Chandrasekaran, K., Agu, E., & Rundensteiner, E. A. (2021) · Visual Informatics · doi:10.1016/j.visinf.2021.07.001 · Cited by 11 · see citing works
Terhorst, Y., Meßner, E.-M., Asare, K. O., Montag, C., Kannen, C., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/55308 · Cited by 9 · see citing works
Analysis of Student Academic Performance Using Human-in-the-Loop Cyber-Physical Systems
Sinche, S., Lascano, P. H., Fernandes, J. M., Raposo, D., Silva, J. S., Rodrigues, A., Armando, N., & Boavida, F. (2020) · Telecom · doi:10.3390/telecom1010003 · Cited by 9 · see citing works
Wang, W., Wu, J., Nepal, S. K., DaSilva, A. W., Hedlund, E. L., Murphy, E. I., Rogers, C., & Huckins, J. F. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479888 · Cited by 9 · see citing works
Self-citationAmon, M. J., Mattingly, S. M., Necaise, A., Mark, G. J., Chawla, N. V., Dey, A. K., & D’Mello, S. K. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3514259 · Cited by 8 · see citing works
Dotti, D., Popa, M. C., & Asteriadis, S. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3338245 · Cited by 8 · see citing works
Khwaja, M. K., Vaid, S. S., Zannone, S., Harari, G. M., Faisal, A. A., & Matic, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04617 · Cited by 8 · see citing works
Self-citationKhwaja, M. K., & Matic, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29387-1_32 · Cited by 5 · see citing works
Xiang, Y., Li, S., & Zhang, P. (2021) · Fundamental Research · doi:10.1016/j.fmre.2021.11.006 · Cited by 8 · see citing works
Computational Personality Assessment - An Overview and PerspectivePreprint
Stachl, C., Boyd, R. L., Horstmann, K. T., Khambatta, P., Matz, S., & Harari, G. M. (2021) · doi:10.31234/osf.io/ck2bj · Cited by 7 · see citing works
Self-citationSaha, K., & Iqbal, S. T. (2023) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3596671.3598571 · Cited by 6 · see citing works
Twenty Years of Personality Computing: Threats, Challenges and Future Directions
Celli, F., Kartelj, A., Dordevič, M., Suhartono, D., Filipović, V., Milutinović, V. M., Spathoulas, G. P., Vinciarelli, A., Kosiński, M., & Lepri, B. (2026) · ACM Computing Surveys · doi:10.1145/3806009 · Cited by 12 · see citing works
Being the center of attention: A Person-Context CNN framework for Personality Recognition
Dotti, D., Popa, M. C., & Asteriadis, S. (2019) · Research Publications (Maastricht University) · doi:10.48550/arxiv.1910.06690 · 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
Measuring Self-Esteem with Passive Sensing
Morshed, M. B., Saha, K., Choudhury, M. D., Abowd, G. D., & Plötz, T. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3421952 · Cited by 6 · see citing works
Mobile Sensing Around the Globe: Considerations for Cross-Cultural ResearchPreprint
Phan, L. V., Modersitzki, N., Gloystein, K. K., & Müller, S. R. (2022) · doi:10.31234/osf.io/q8c7y · Cited by 6 · see citing works
Self-citationChoube, A., Bhattacharya, S., Majethia, R., Li, J., Swain, V. D., & Mishra, V. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3770647 · Cited by 6 · see citing works
Koh, Y., Lee, C., Joung, E., Lee, H., & Lee, U. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749485 · Cited by 5 · see citing works
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 · see citing works
Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study
Cao, Y., Rajendran, S., Sundararajan, P., Law, R., Bacon, S., Sumner, S. A., & Masuda, N. (2023) · Journal of Medical Internet Research · doi:10.2196/45171 · Cited by 4 · see citing works
Wilton, A. R., Saliba, C. T., Marrero‐Polanco, J., Sheffield, K. E., Wilkes, Q., Anacker, M., Croarkin, P. E., Chauhan, M., Dyrbye, L. N., Chesak, S. S., et al. (2025) · Clinical and Translational Science · doi:10.1111/cts.70155 · Cited by 3 · see citing works
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 · see citing works
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 · see citing works
Personality-Based Approaches to Health and Health Behavior Interventions
Juchem, C. M., Baumgardt, S., & Asselmann, E. (2024) · Springer eBooks · doi:10.1007/978-981-92-1290-3_13 · Cited by 2 · see citing works
INTOSIS: Interactive Observation of Smartphone Inferred Symptoms for In-The-Wild Data
Mansoor, H., Gerych, W., Buquicchio, L., Alajaji, A. S., Chandrasekaran, K., Agu, E., & Rundensteiner, E. A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378147 · Cited by 2 · see citing works
Personality and Individual Differences
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.006 · Cited by 2 · see citing works
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.003 · Cited by 2 · see citing works
MINDSET: A benchMarking suIte exploring seNsing Data for SElf sTates inference
Karagianni, C., Paraschou, E., Yfantidou, S., & Vakali, A. I. (2023) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa60987.2023.10302638 · Cited by 2 · see citing works
Personality Constructs Predictions Beyond FFM /Big5: A Digital Phenotyping‐Based Exploration
Hocherman, M., Mizrachi, Y., & Gal, H. C. (2025) · Journal of Personality · doi:10.1111/jopy.70006 · Cited by 1 · see citing works
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 · see citing works
Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data
Thomas, B. L., Holder, L. B., & Cook, D. J. (2022) · Methods of Information in Medicine · doi:10.1055/s-0042-1756649 · Cited by 1 · see citing works
Depression Detection from Network Behavior using Anomaly Detection and Feature Extraction Techniques
Li, L., Li, L. H., & Yang, L. (2023) · International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) · doi:10.1145/3616901.3616928 · Cited by 1 · see citing works
Investigating Self-supervised Learning for Predicting Stress and Stressors from Passive Sensing
Haresamudram, H., Suh, J., Hernandez, J., Butler, J., Chaudhry, A., Yang, L., Saha, K., & Czerwinski, M. P. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388191 · Cited by 1 · see citing works
A Survey of Passive Sensing for Workplace Wellbeing and Productivity
Nepal, S. K., Martinez, G. J., Pillai, A., Saha, K., Mirjafari, S., Swain, V. D., Xu, X., Audia, P. G., Choudhury, M. D., Dey, A. K., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93845-0_29 · Cited by 15 · see citing works
Self-citationSocial Interaction and Interpersonal Relationships
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.005 · Cited by 1 · see citing works
Investigating the Reliability of Self-report Survey in the Wild: The Quest for Ground TruthPreprint
Gao, N., Rahaman, M. S., Shao, W., & Salim, F. D. (2021) · arXiv (Cornell University)
Beierle, F. (2021) · T-labs series in telecommunication services · doi:10.1007/978-3-030-68840-0_3
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.008
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.001
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.004
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.002
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.009
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.007
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.010
Computer-Cognition Interfaces: Sensing and influencing mental processes with computer interactionPreprint
Mottelson, A. (2019) · doi:10.31237/osf.io/hg378
PIPS: Personality Insights and Prediction using Smartphone Non-Private Content
Bhattacharya, S., Yadav, G., Jha, A., Moorthy, V., Pushpalatha, M., Choudhary, S., & Kumar, S. (2024) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct62155.2024.10677019
Machine Learning for Prediction User Preferences Based on Personality Traits
Ketipov, R., Balabanov, T., Angelova, V., & Doukovska, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-80853-1_34
Towards Context-Aware Sensing and Personalized Interventions for Mental Health
Wang, Z. (2024) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw63320.2024.00018
Alchieri, L., Scocco, V., Abdalazim, N., Alecci, L., & Silvia Santini, B. (2025) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc64332.2025.11118532
Mitigating Interpersonal Variability in Machine Learning Models for Human Behavior Monitoring
Alecci, L. (2025) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3714394.3750544
Ng, M., Chi, Y., Cho, M.-J., Eichstaedt, J. C., Reeves, B. B., Robinson, T. N., & Ram, N. (2026) · Risk Analysis · doi:10.1111/risa.70235
董, 涛. (2026) · Advances in Psychology · doi:10.12677/ap.2026.164174
Personality trait prediction based on user behavior in mobile networks
Rostami, M. S., & Shahzadi, A. (2026) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-025-00200-4
Zhang, T., Varidel, M., Hickie, I. B., Ho, N., Turner, A., D’Alfonso, S., & Iorfino, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7120118
- Authors
- Wang, W., Harari, G. M., Wang, R., Müller, S. R., Mirjafari, S., Masaba, K., & Campbell, A. T.
- Affiliations
- Dartmouth College (US)
- Stanford University (US)
- University of Cambridge (GB)
- ORCID
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2018-09-18
- Volume
- 2
- Issue
- 3
- Pages
- 1-21
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2890085824
- Cited by
- 100
- References
- 72
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 19 · 2020: 31 · 2019: 11
- FWCI
- 18.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Funders
- National Science Foundation (BCS-1520288)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- within-person variability, personality traits, passive sensing, smartphone sensing, self-tracking, behavioral change over time, circadian rhythm, regularity index, physical activity, location data, personality prediction, openness, extraversion
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
