Skip to main content
Original Article

Using Mobile Sensors to Study Personality Dynamics

Published Online:https://doi.org/10.1027/1015-5759/a000576

Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.

References

  • Aadland, E., & Ylvisåker, E. (2015). Reliability of the Actigraph GT3X+ accelerometer in adults under free-living conditions. PLoS One, 10, e0134606. https://doi.org/10.1371/journal.pone.0134606 First citation in articleCrossrefGoogle Scholar

  • Akther, S., Saleheen, N., Samiei, S. A., Shetty, V., Ertin, E., & Kumar, S. (2019). mORAL: An mHealth model for inferring oral hygiene behaviors in-the-wild using wrist-worn inertial sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3, 1. https://doi.org/10.1145/3314388 First citation in articleCrossrefGoogle Scholar

  • Baehr, E. K., Revelle, W., & Eastman, C. I. (2000). Individual differences in the phase and amplitude of the human circadian temperature rhythm: With an emphasis on morningness–eveningness. Journal of Sleep Research, 9, 117–127. https://doi.org/10.1046/j.1365-2869.2000.00196.x First citation in articleCrossrefGoogle Scholar

  • Barrick, M. R., Patton, G. K., & Haugland, S. N. (2000). Accuracy of interviewer judgments of job applicant personality traits. Personnel Psychology, 53, 925–951. https://doi.org/10.1111/j.1744-6570.2000.tb02424.x First citation in articleCrossrefGoogle Scholar

  • Beal, D. J. (2015). ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2, 383–407. https://doi.org/10.1146/annurev-orgpsych-032414-111335 First citation in articleCrossrefGoogle Scholar

  • Bleidorn, W., & Hopwood, C. J. (2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23(2), 190–203. https://doi.org/10.1177/1088868318772990 First citation in articleCrossrefGoogle Scholar

  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41, 15. https://doi.org/10.1145/1541880.1541882 First citation in articleCrossrefGoogle Scholar

  • Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal Ubiquitous Computing, 17, 433–450. https://doi.org/10.1007/s00779-011-0490-1 First citation in articleCrossrefGoogle Scholar

  • Connelly, B. S., & Ones, D. S. (2007). Combining conscientiousness scales: Can’t get enough of your trait, baby! Unpublished manuscript, University of Minnesota, Minneapolis, MN. First citation in articleGoogle Scholar

  • Connelly, B. S., & Ones, D. S. (2010). An other perspective on personality: Meta-analytic integration of observers’ accuracy and predictive validity. Psychological Bulletin, 136, 1092–1122. https://doi.org/10.1037/a0021212 First citation in articleCrossrefGoogle Scholar

  • Credé, M., Harms, P. D., Niehorster, S., & Gaye-Valentine, A. (2012). An evaluation of the consequences of using short measures of the Big Five personality traits. Journal of Personality and Social Psychology, 102, 874–888. https://doi.org/10.1037/a0027403 First citation in articleCrossrefGoogle Scholar

  • Dahlke, J. A., & Wiernik, B. M. (2019). Not restricted to selection research: Accounting for indirect range restriction in organizational research. Organizational Research Methods. Advance online publication. https://doi.org/10.1177/1094428119859398 First citation in articleCrossrefGoogle Scholar

  • Davies, S. E., Connelly, B. L., Ones, D. S., & Birkland, A. S. (2015). The general factor of personality: The “Big One”, a self-evaluative trait, or a methodological gnat that won’t go away? Personality and Individual Differences, 81, 13–22. https://doi.org/10.1016/j.paid.2015.01.006 First citation in articleCrossrefGoogle Scholar

  • de Montjoye, Y.-A., Quoidbach, J., Robic, F., & Pentland, A. (Sandy) (2013). Predicting personality using novel mobile phone-based metrics. In A. M. GreenbergW. G. KennedyN. D. BosEds., Social computing, behavioral-cultural modeling and prediction (pp. 48–55). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-37210-0_6 First citation in articleGoogle Scholar

  • Debusscher, J., Hofmans, J., & De Fruyt, F. (2016). From state neuroticism to momentary task performance: A person × situation approach. European Journal of Work and Organizational Psychology, 25, 89–104. https://doi.org/10.1080/1359432x.2014.983085 First citation in articleCrossrefGoogle Scholar

  • Debusscher, J., Hofmans, J., & De Fruyt, F. (2017). The multiple face(t)s of state conscientiousness: Predicting task performance and organizational citizenship behavior. Journal of Research in Personality, 69, 78–85. https://doi.org/10.1016/j.jrp.2016.06.009 First citation in articleCrossrefGoogle Scholar

  • Duffy, A., Keown‐Stoneman, C. D., Goodday, S. M., Saunders, K., Horrocks, J., Grof, P., … Geddes, J. (2019). Daily and weekly mood ratings using a remote capture method in high-risk offspring of bipolar parents: Compliance and symptom monitoring. Bipolar Disorders, 21, 159–167. https://doi.org/10.1111/bdi.12721 First citation in articleCrossrefGoogle Scholar

  • Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. The Annals of Statistics, 32, 407–499. https://doi.org/10.1214/009053604000000067 First citation in articleCrossrefGoogle Scholar

  • Evangelista, L. S., Berg, J., & Dracup, K. (2001). Relationship between psychosocial variables and compliance in patients with heart failure. Heart & Lung, 30, 294–301. https://doi.org/10.1067/mhl.2001.116011 First citation in articleCrossrefGoogle Scholar

  • Fleeson, W., & Gallagher, P. (2009). The implications of Big Five standing for the distribution of trait manifestation in behavior: Fifteen experience-sampling studies and a meta-analysis. Journal of Personality and Social Psychology, 97, 1097–1114. https://doi.org/10.1037/a0016786 First citation in articleCrossrefGoogle Scholar

  • Garaulet, M., Martinez-Nicolas, A., Ruiz, J. R., Konstabel, K., Labayen, I., González-Gross, M., … Ortega, F. B. (2017). Fragmentation of daily rhythms associates with obesity and cardiorespiratory fitness in adolescents: The HELENA study. Clinical Nutrition, 36, 1558–1566. https://doi.org/10.1016/j.clnu.2016.09.026 First citation in articleCrossrefGoogle Scholar

  • Ghahramani, Z., & Jordan, M. I. (1994). Supervised learning from incomplete data via an EM approach. In J. D. CowanG. TesauroJ. AlspectorEds., Advances in neural information processing systems (Vol. 6, pp. 120–127). San Diego, CA: Neural Information Processing Systems Foundation. Retrieved from https://papers.nips.cc/paper/767-supervised-learning-from-incomplete-data-via-an-em-approach.pdf First citation in articleGoogle Scholar

  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. Retrieved from http://www.jmlr.org/papers/v3/guyon03a.html First citation in articleGoogle Scholar

  • Harari, G. M., Müller, S. R., Aung, M. S., & Rentfrow, P. J. (2017). Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, 18, 83–90. https://doi.org/10.1016/j.cobeha.2017.07.018 First citation in articleCrossrefGoogle Scholar

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York, NY: Springer. Retrieved from https://web.stanford.edu/~hastie/ElemStatLearn/ First citation in articleCrossrefGoogle Scholar

  • Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22, 85–126. https://doi.org/10.1023/b:aire.0000045502.10941.a9 First citation in articleCrossrefGoogle Scholar

  • Hofstee, W. K. B., de Raad, B., & Goldberg, L. R. (1992). Integration of the Big Five and circumplex approaches to trait structure. Journal of Personality and Social Psychology, 63, 146–163. https://doi.org/10.1037/0022-3514.63.1.146 First citation in articleCrossrefGoogle Scholar

  • Hossain, S. M., Hnat, T., Saleheen, N., Nasrin, N. J., Noor, J., Ho, B.-J., … Kumar, S. (2017). mCerebrum: A mobile sensing software platform for development and validation of digital biomarkers and interventions, Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. https://doi.org/10.1145/3131672.3131694 First citation in articleGoogle Scholar

  • Kayhan, V. O., Chen, Z. (Chris), French, K. A., Allen, T. D., Salomon, K., & Watkins, A. (2018). How honest are the signals? A protocol for validating wearable sensors. Behavior Research Methods, 50, 57–83. https://doi.org/10.3758/s13428-017-1005-4 First citation in articleCrossrefGoogle Scholar

  • Kingma, D. P., Mohamed, S., Jimenez Rezende, D., & Welling, M. (2014). Semi-supervised learning with deep generative models. In Z. GhahramaniM. WellingC. CortesN. D. LawrenceK. Q. WeinbergerEds., Advances in neural information processing systems (Vol. 27, pp. 3581–3589). Retrieved from http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf First citation in articleGoogle Scholar

  • Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 14, 1137–1143. Retrieved from http://robotics.stanford.edu/~ronnyk/accEst.pdf First citation in articleGoogle Scholar

  • Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110, 5802–5805. https://doi.org/10.1073/pnas.1218772110 First citation in articleCrossrefGoogle Scholar

  • Kuncel, N. R., & Sackett, P. R. (2014). Resolving the assessment center construct validity problem (as we know it). Journal of Applied Psychology, 99, 38–47. https://doi.org/10.1037/a0034147 First citation in articleCrossrefGoogle Scholar

  • Liao, Y., Shonkoff, E. T., & Dunton, G. F. (2015). The acute relationships between affect, physical feeling states, and physical activity in daily life: A review of current evidence. Frontiers in Psychology, 6, 1975. https://doi.org/10.3389/fpsyg.2015.01975 First citation in articleCrossrefGoogle Scholar

  • Lievens, F., Lang, J. W. B., De Fruyt, F., Corstjens, J., Van de Vijver, M., & Bledow, R. (2018). The predictive power of people’s intraindividual variability across situations: Implementing whole trait theory in assessment. Journal of Applied Psychology, 103, 753–771. https://doi.org/10.1037/apl0000280 First citation in articleCrossrefGoogle Scholar

  • Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Hoboken, NJ: Wiley. https://doi.org/10.1002/9781119482260 First citation in articleGoogle Scholar

  • Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research, 30, 457–500. https://doi.org/10.1613/jair.2349 First citation in articleCrossrefGoogle Scholar

  • Matthews, M., Abdullah, S., Gay, G., & Choudhury, T. (2014). Tracking mental well-being: Balancing rich sensing and patient needs. Computer, 47, 36–43. https://doi.org/10.1109/mc.2014.107 First citation in articleCrossrefGoogle Scholar

  • Mõttus, R., Bates, T. C., Condon, D. C., Mroczek, D. K., & Revelle, W. R. (2017). Leveraging a more nuanced view of personality: Narrow characteristics predict and explain variance in life outcomes. In PsyArXiv. Preprint. https://doi.org/10.31234/osf.io/4q9gv First citation in articleGoogle Scholar

  • Mussel, P., Gatzka, T., & Hewig, J. (2018). Situational judgment tests as an alternative measure for personality assessment. European Journal of Psychological Assessment, 34, 328–335. https://doi.org/10.1027/1015-5759/a000346 First citation in articleLinkGoogle Scholar

  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw Hill. First citation in articleGoogle Scholar

  • Ode, S., Hilmert, C. J., Zielke, D. J., & Robinson, M. D. (2010). Neuroticism’s importance in understanding the daily life correlates of heart rate variability. Emotion, 10, 536–543. https://doi.org/10.1037/a0018698 First citation in articleCrossrefGoogle Scholar

  • Ones, D. S., Birkland, A. S., Dilchert, S., Ellis, B., Giordano, C., Kostal, J. W., … Srivastava, M. (2019a April). Using mobile sensors to model and predict typical job performance. In R. A. McCloyH. J. Kell (Chairs), Measurement: New methods for classic problems, classic methods for new problems. Symposium presented at the Society for Industrial and Organizational Psychology conference, National Harbor, MD. First citation in articleGoogle Scholar

  • Ones, D. S., Dilchert, S., Stanek, K. C., Mercado, B. K., & Wiernik, B. M. (Eds.) (2019b). Deploying unobtrusive, persistent mobile sensors to measure psychological constructs: Lessons from mPerf study. Manuscript in preparation, University of Minnesota, Minneapolis, MN. First citation in articleGoogle Scholar

  • Ones, D. S., Wiernik, B. M., Wilmot, M. P., & Kostal, J. W. (2016) Conceptual and methodological complexity of narrow trait measures in personality-outcome research: Better knowledge by partitioning variance from multiple latent traits and measurement artifacts. European Journal of Personality, 30, 319–321. https://doi.org/10.1002/per.2060 First citation in articleGoogle Scholar

  • Oswald, F. L., Schmitt, N., Kim, B. H., Ramsay, L. J., & Gillespie, M. A. (2004). Developing a biodata measure and situational judgment inventory as predictors of college student performance. Journal of Applied Psychology, 89, 187–207. https://doi.org/10.1037/0021-9010.89.2.187 First citation in articleCrossrefGoogle Scholar

  • Pace, V. L., & Brannick, M. T. (2010). How similar are personality scales of the “same” construct? A meta-analytic investigation. Personality and Individual Differences, 49, 669–676. https://doi.org/10.1016/j.paid.2010.06.014 First citation in articleCrossrefGoogle Scholar

  • Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., … Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108, 934–952. https://doi.org/10.1037/pspp0000020 First citation in articleCrossrefGoogle Scholar

  • Pennebaker, J. W., & Graybeal, A. (2001). Patterns of natural language use: Disclosure, personality, and social integration. Current Directions in Psychological Science, 10, 90–93. https://doi.org/10.1111/1467-8721.00123 First citation in articleCrossrefGoogle Scholar

  • Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter profiles, our selves: Predicting personality with Twitter (pp. 180–185). 2011 IEEE International Conference on Privacy, Security, Risk and Trust and 2011 IEEE International Conference on Social Computing. https://doi.org/10.1109/passat/socialcom.2011.26 First citation in articleGoogle Scholar

  • Rabbi, M., Ali, S., Choudhury, T., & Berke, E. (2011). Passive and in-situ assessment of mental and physical well-being using mobile sensors (pp. 385–394). Proceedings of the 13th International Conference on Ubiquitous Computing. https://doi.org/10.1145/2030112.2030164 First citation in articleGoogle Scholar

  • Rehg, J. M.Murphy, S. A.Kumar, S. (Eds.) (2017). Mobile health: Sensors, analytic methods, and applications. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-51394-2 First citation in articleCrossrefGoogle Scholar

  • Reynolds, P. A., Donaldson, A. N., Liossi, C., Newton, J. T., Donaldson, N. K., Arias, R., … Hosey, M. T. (2019). How families prepare their children for tooth extraction under general anaesthesia: Family and clinical predictors of non-compliance with a “serious game”. International Journal of Paediatric Dentistry, 29, 117–128. https://doi.org/10.1111/ipd.12450 First citation in articleGoogle Scholar

  • Roberts, B. W. (2018). A revised sociogenomic model of personality traits. Journal of Personality, 86, 23–35. https://doi.org/10.1111/jopy.12323 First citation in articleCrossrefGoogle Scholar

  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1, 27–42. https://doi.org/10.1177/2515245917745629 First citation in articleCrossrefGoogle Scholar

  • Rousseeuw, P. J., & Leroy, A. M. (1987). Robust regression and outlier detection. Hoboken, NJ: Wiley. https://doi.org/10.1002/0471725382 First citation in articleCrossrefGoogle Scholar

  • Sarker, H., Hovsepian, K., Chatterjee, S., Nahum-Shani, I., Murphy, S. A., Spring, B., … Kumar, S. (2017). From markers to interventions: The case of just-in-time stress intervention. In J. M. RehgS. A. MurphyS. KumarEds., Mobile health: Sensors, analytic methods, and applications (pp. 411–433). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-51394-2_21 First citation in articleGoogle Scholar

  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177. https://doi.org/10.1037/1082-989x.7.2.147 First citation in articleCrossrefGoogle Scholar

  • Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62, 529–540. https://doi.org/10.1037/0021-9010.62.5.529 First citation in articleCrossrefGoogle Scholar

  • Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113, 117–143. https://doi.org/10.1037/pspp0000096 First citation in articleCrossrefGoogle Scholar

  • Spielberger, C. D. (1983). State-trait anxiety inventory (STAI-AD) [Manual]. Palo Alto, CA: Consulting Psychologist Press. First citation in articleGoogle Scholar

  • Stanek, K. C., & Ones, D. S. (2018). Taxonomies and compendia of cognitive ability and personality measures relevant to industrial, work, and organizational psychology. In D. S. OnesN. AndersonC. ViswesvaranH. K. SinangilEds., The SAGE handbook of industrial, work and organizational psychology (2nd ed., Vol. 1, pp. 366–407). London, UK: SAGE. https://doi.org/10.4135/9781473914940.n14 First citation in articleGoogle Scholar

  • Tiainen, A.-M. K., Männistö, S., Lahti, M., Blomstedt, P. A., Lahti, J., Perälä, M.-M., … Eriksson, J. G. (2013). Personality and dietary intake: Findings in the Helsinki Birth Cohort Study. PLoS One, 8, e68284. https://doi.org/10.1371/journal.pone.0068284 First citation in articleCrossrefGoogle Scholar

  • Umaki, T. M., Umaki, M. R., & Cobb, C. M. (2012). The psychology of patient compliance: A focused review of the literature. Journal of Periodontology, 83, 395–400. https://doi.org/10.1902/jop.2011.110344 First citation in articleCrossrefGoogle Scholar

  • van Hees, V. T., Gorzelniak, L., León, E. C. D., Eder, M., Pias, M., Taherian, S., … Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One, 8, e61691. https://doi.org/10.1371/journal.pone.0061691 First citation in articleCrossrefGoogle Scholar

  • Van Iddekinge, C. H., Raymark, P. H., & Roth, P. L. (2005). Assessing personality with a structured employment interview: Construct-related validity and susceptibility to response inflation. Journal of Applied Psychology, 90, 536–552. https://doi.org/10.1037/0021-9010.90.3.536 First citation in articleCrossrefGoogle Scholar

  • Wainer, H. (1976). Estimating coefficients in linear models: It don’t make no nevermind. Psychological Bulletin, 83, 213–217. https://doi.org/10.1037/0033-2909.83.2.213 First citation in articleCrossrefGoogle Scholar

  • Watson, D., & Clark, L. A. (1999). The PANAS-X: Manual for the positive and negative affect schedule-expanded form. Iowa, IA: The University of Iowa. https://doi.org/10.17077/48vt-m4t2 First citation in articleCrossrefGoogle Scholar

  • Wiernik, B. M., & Dahlke, J. A. (2019). Obtaining unbiased results in meta-analysis: The importance of correcting for statistical artefacts. Advances in Methods and Practices in Psychological Science. Advance online publication. https://doi.org/10.1177/2515245919885611 First citation in articleGoogle Scholar

  • Wood, D., & Harms, P. D. (2016). On the TRAPs that make it dangerous to study personality with personality questionnaires. European Journal of Personality, 30, 327–328. https://doi.org/10.1002/per.2060 First citation in articleGoogle Scholar

  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122. https://doi.org/10.1177/1745691617693393 First citation in articleCrossrefGoogle Scholar

  • Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112, 1036–1040. https://doi.org/10.1073/pnas.1418680112 First citation in articleCrossrefGoogle Scholar

  • Youyou, W., Stillwell, D., Schwartz, H. A., & Kosinski, M. (2017). Birds of a feather do flock together: Behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychological Science, 28, 276–284. https://doi.org/10.1177/0956797616678187 First citation in articleCrossrefGoogle Scholar