Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study
Open accessPublished version of a preprint
Abstract
BACKGROUND: Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. OBJECTIVE: The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. METHODS: Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). RESULTS: =.34, P=.001. Multiple significant variables were demonstrated for each measure. CONCLUSIONS: Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Passively collected keystroke metadata may be used to predict state changes found in bipolar disorder.
“Furthermore, passively collected keystroke metadata may also be used to predict state changes found in BD176.”
From Circadian rhythm disruption and mental health (2020) · cites this paper as [176]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835 · see citing works
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817 · see citing works
Nakagawa, S., & Schielzeth, H. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00261.x · Cited by 10,356 · see citing works
A Rating Scale for Mania: Reliability, Validity and Sensitivity
Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978) · British Journal of Psychiatry · doi:10.1192/bjp.133.5.429 · Cited by 8,744 · see citing works
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
piecewiseSEM : Piecewise structural equation modelling in r for ecology, evolution, and systematics
Lefcheck, J. S. (2015) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12512 · Cited by 4,893 · see citing works
Estimating the true global burden of mental illness
Vigo, D., Thornicroft, G., & Atun, R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00505-2 · Cited by 2,494 · see citing works
Cognitive Function Across Manic or Hypomanic, Depressed, and Euthymic States in Bipolar Disorder
Martínez‐Arán, A., Vieta, E., Reinares, M., Colom, F., Torrent, C., Sánchez‐Moreno, J., Benabarre, A., Goikolea, J. M., Comes, M., & Salamero, M. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.2.262 · Cited by 1,106 · 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
Measuring Skewness: A Forgotten Statistic?
Doane, D. P., & Seward, L. W. (2011) · Journal of Statistics Education · doi:10.1080/10691898.2011.11889611 · Cited by 769 · 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
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015) · Nature Biotechnology · doi:10.1038/nbt.3223 · Cited by 425 · see citing works
(2012) · PubMed · Cited by 317 · see citing works
Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5505 · Cited by 192 · see citing works
Smartphone data as objective measures of bipolar disorder symptoms
Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.03.009 · Cited by 185 · see citing works
Effect of symptoms on executive function in bipolar illness
Dixon, T., Kravariti, E., Frith, C., Murray, R. M., & McGuire, P. (2003) · Psychological Medicine · doi:10.1017/s0033291703001570 · Cited by 172 · see citing works
Bauhoff, S. (2011) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-011-0069-3 · Cited by 144 · see citing works
Towards unobtrusive emotion recognition for affective social communication
Lee, H., Choi, Y. S., Lee, S., & Park, I. P. (2012) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2012.6181098 · Cited by 136 · see citing works
(2000) · American Psychiatric Association Publishing eBooks · doi:10.1176/appi.books.9780890420249.dsm-iv-tr · Cited by 131 · see citing works
A review of emotion recognition methods based on keystroke dynamics and mouse movements
Kolakowska, A. (2013) · 2013 6th International Conference on Human System Interactions (HSI) · doi:10.1109/hsi.2013.6577879 · Cited by 130 · see citing works
Intermediate: Cognitive phenotypes in bipolar disorder
Langenecker, S. A., Saunders, E. F. H., Kade, A. M., Ransom, M. T., & McInnis, M. G. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2009.08.018 · Cited by 108 · see citing works
Self-citationBipolar disorder: disease burden.
Sajatovic, M. (2005) · PubMed · Cited by 90 · see citing works
Kuntsche, E. N., & Labhart, F. (2012) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000137 · Cited by 88 · see citing works
Associating internet usage with depressive behavior among college students
Katikalapudi, R., Chellappan, S., Montgomery, F., Wunsch, D., & Lutzen, K. (2012) · IEEE Technology and Society Magazine · doi:10.1109/mts.2012.2225462 · Cited by 81 · see citing works
Differential executive functioning performance by phase of bipolar disorder
Ryan, K. A., Vederman, A. C., McFadden, E. M., Weldon, A. L., Kamali, M., Langenecker, S. A., & McInnis, M. G. (2012) · Bipolar Disorders · doi:10.1111/j.1399-5618.2012.01032.x · Cited by 76 · see citing works
Self-citationCohort Profile: The Heinz C. Prechter Longitudinal Study of Bipolar Disorder
McInnis, M. G., Assari, S., Kamali, M., Ryan, K. A., Langenecker, S. A., Saunders, E. F. H., Versha, K., Evans, S. J., O’Shea, K. S., Provost, E. M., et al. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx229 · Cited by 66 · see citing works
Self-citationUsing Personal Cell Phones for Ecological Momentary Assessment
Kuntsche, E. N., & Labhart, F. (2013) · European Psychologist · doi:10.1027/1016-9040/a000127 · Cited by 65 · see citing works
Psychometric properties of a computerized mobile phone method for assessing mood in daily life
Courvoisier, D. S., Eid, M., Lischetzke, T., & Schreiber, W. H. (2010) · Emotion · doi:10.1037/a0017813 · Cited by 56 · see citing works
Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2017) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3098279.3098564 · Cited by 47 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [14]Fox J Weisberg S An R Companion to Applied Regression Internet. Second Edition 2011 Thousand Oaks, CA Sage
- [17]De Chourdry M Gamon M Counts S Horvitz E Predicting Depression via Social Media 2013 International Conference on Weblogs and Social Media July 8-11, 2013 Cambridge, MA
- [21]Huang A Similarity Measures for Text Document Clustering 2008 New Zealand Computer Science Research Student Conference 2008 2008 New Zealand
- [22]Singhal A Modern information retrieval: A brief overview IEEE Data Eng Bull 2001 24 4 43
- [34]Ghosh S Ganguly N Mitra B De P TapSense: Combining Self-Report Patterns and Typing Characteristics for Smartphone based Emotion Detection 2017 International Conference on Human-Computer Interaction with Mobile Devices and Services 2017 Vienna, Austria
Cited by
Sorted by citations
Circadian rhythm disruption and mental health
Walker, W. H., Walton, J. C., DeVries, A. C., & Nelson, R. J. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0694-0 · Cited by 1,030 · see citing works
Piau, A., Wild, K., Mattek, N., & Kaye, J. (2019) · Journal of Medical Internet Research · doi:10.2196/12785 · Cited by 252 · see citing works
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., Berdun, J., et al. (2019) · JMIR Mental Health · doi:10.2196/mental.9819 · Cited by 148 · see citing works
Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139 · see citing works
The foundation and architecture of precision medicine in neurology and psychiatry
Hampel, H. J., Gao, P., Cummings, J. L., Toschi, N., Thompson, P. M., Hu, Y., Cho, M., & Vergallo, A. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2022.12.004 · Cited by 149 · see citing works
Opoku 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
Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health
Oudin, A., Maatoug, R., Bourla, A., Ferreri, F., Bonnot, O., Millet, B., Schoeller, F., Mouchabac, S., & Adrien, V. (2023) · Journal of Medical Internet Research · doi:10.2196/44502 · Cited by 140 · see citing works
Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study
Martinez‐Martin, N., Greely, H. T., & Cho, M. K. (2021) · JMIR mhealth and uhealth · doi:10.2196/27343 · Cited by 122 · see citing works
Birnbaum, M., Ernala, S. K., Rizvi, A., Arenare, E., Meter, A. V., de Choudhury, M., & Kane, J. M. (2019) · npj Schizophrenia · doi:10.1038/s41537-019-0085-9 · Cited by 96 · see citing works
Touchscreen typing pattern analysis for remote detection of the depressive tendency
Mastoras, R.-E., Iakovakis, D., Hadjidimitriou, S., Charisis, V. S., Kassie, S. A., Alsaadi, T. M., Khandoker, A. H., & Hadjileontiadis, L. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-50002-9 · Cited by 99 · see citing works
Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study
Schueller, S. M., Neary, M., Lai, J., & Epstein, D. A. (2021) · JMIR Mental Health · doi:10.2196/29368 · Cited by 102 · see citing works
Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders
Aledavood, T., Torous, J. B., Triana, A. M., Naslund, J. A., Onnela, J.-P. “., & Keshavan, M. S. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1043-y · Cited by 91 · see citing works
Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 82 · see citing works
Digital Mental Health: The Answer to the Global Mental Health Crisis?
Rudd, B. N., & Beidas, R. S. (2020) · JMIR Mental Health · doi:10.2196/18472 · Cited by 81 · see citing works
Antosik-Wójcińska, A. Z., Dominiak, M., Chojnacka, M., Kaczmarek‐Majer, K., Opara, K. R., Radziszewska, W., Olwert, A., & Święcicki, Ł. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104131 · Cited by 82 · see citing works
Vesel, C., Rashidisabet, H., Zulueta, J., Stange, J. P., Duffecy, J. L., Hussain, F., Piscitello, A., Bark, J. S., Langenecker, S. A., Young, S., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa057 · Cited by 74 · see citing works
Self-citationZarate, D., Stavropoulos, V., Ball, M., Collier, G. D. S., & Jacobson, N. C. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04013-y · Cited by 79 · see citing works
Hilty, D. M., Armstrong, C. M., Luxton, D. D., Gentry, M. T., & Krupinski, E. A. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00199-2 · Cited by 68 · see citing works
Integrating digital phenotyping in clinical characterization of individuals with mood disorders
Brietzke, E. M., Hawken, E. R., Idzikowski, M. A., Pong, J., Kennedy, S. H., & Soares, C. N. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.07.009 · Cited by 65 · see citing works
Alfalahi, H., Khandoker, A. H., Chowdhury, N., Iakovakis, D., Dias, S. B., Chaudhuri, К. R., & Hadjileontiadis, L. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-11865-7 · Cited by 71 · see citing works
The relationship between text message sentiment and self-reported depression
Liu, T., Meyerhoff, J., Eichstaedt, J. C., Karr, C. J., Kaiser, S. M., Kording, K. P., Mohr, D. C., & Ungar, L. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.12.048 · Cited by 67 · see citing works
Lam, K., Meijer, K. A., Loonstra, F. C., Coerver, E. M. E., Twose, J., Redeman, E. G. M., Moraal, B., Barkhof, F., de Groot, V., Uitdehaag, B. M. J., et al. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520968797 · Cited by 64 · see citing works
Bidmon, S., Elshiewy, O., Terlutter, R., & Boztuğ, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/13830 · Cited by 54 · see citing works
Portable technologies for digital phenotyping of bipolar disorder: A systematic review
Saccaro, L. F., Amatori, G., Cappelli, A., Mazziotti, R. M., Dell’Osso, L., & Rutigliano, G. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.052 · Cited by 58 · see citing works
Vega, J., Li, M., Aguillera, K., Goel, N., Joshi, E., Khandekar, K., Durica, K. C., Kunta, A. R., & Low, C. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.769823 · Cited by 54 · see citing works
A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder
Til, K. V., McInnis, M. G., & Cochran, A. L. (2019) · Bipolar Disorders · doi:10.1111/bdi.12849 · Cited by 48 · see citing works
Self-citationOrsolini, L., Fiorani, M., & Volpe, U. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21207684 · Cited by 47 · see citing works
Stamatis, C. A., Meyerhoff, J., Meng, Y., Lin, Z. C. C., Cho, Y. M., Liu, T., Karr, C. J., Liu, T., Curtis, B. L., Ungar, L., et al. (2024) · npj Mental Health Research · doi:10.1038/s44184-023-00041-y · Cited by 50 · see citing works
Beierle, F., Schobel, J., Vogel, C., Allgaier, J., Mulansky, L., Haug, F., Haug, J., Schlee, W., Holfelder, M., Stach, M., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18147395 · Cited by 49 · see citing works
Addressing anhedonia to increase depression treatment engagement
Khazanov, G. K., Forbes, C. N., Dunn, B. D., & Thase, M. E. (2021) · British Journal of Clinical Psychology · doi:10.1111/bjc.12335 · Cited by 44 · see citing works
Wearables in Schizophrenia: Update on Current and Future Clinical Applications
Fonseka, L. N., & Woo, B. K. P. (2022) · JMIR mhealth and uhealth · doi:10.2196/35600 · Cited by 44 · see citing works
Digital phenotype of mood disorders: A conceptual and critical review
Maatoug, R., Oudin, A., Adrien, V., Saudreau, B., Bonnot, O., Millet, B., Ferreri, F., Mouchabac, S., & Bourla, A. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.895860 · Cited by 43 · see citing works
Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery
(2021) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2021/871032021 · Cited by 40 · see citing works
Ortiz, A., Maslej, M. M., Husain, M. I., Daskalakis, Z. J., & Mulsant, B. H. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.140 · Cited by 41 · see citing works
Assche, E. V., Ramos‐Quiroga, J. A., Pariante, C. M., Sforzini, L., Young, A. H., Flossbach, Y., Gold, S. M., Hoogendijk, W. J. G., Baune, B. T., & Maron, E. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.05.007 · Cited by 39 · see citing works
AWARE-Light: a smartphone tool for experience sampling and digital phenotyping
van Berkel, N., D’Alfonso, S., Susanto, R., Ferreira, D., & Kostakos, V. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01697-7 · Cited by 39 · see citing works
Using ambulatory assessment to measure dynamic risk processes in affective disorders
Stange, J. P., Kleiman, E. M., Mermelstein, R. J., & Trull, T. J. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.08.060 · Cited by 38 · see citing works
Jayakumar, P., Lin, E., Galea, V. P., Mathew, A. J., Panda, N., Vetter, I., & Haynes, A. B. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040282 · Cited by 41 · see citing works
Big Data From Small Devices: The Future of Smartphones in Oncology
Purswani, J., Dicker, A. P., Champ, C. E., Cantor, M., & Ohri, N. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.05.008 · Cited by 37 · see citing works
The crossroads of digital phenotyping
Davidson, B. I. (2020) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2020.11.009 · Cited by 37 · see citing works
Liu, G., Henson, P., Keshavan, M. S., Onnela, J.-P. “., & Torous, J. B. (2019) · Schizophrenia Research Cognition · doi:10.1016/j.scog.2019.100144 · Cited by 36 · see citing works
Bilal, A.-M., Fransson, E., Bränn, E., Eriksson, A., Zhong, M., Gidén, K., Elofsson, U. O. E., Axfors, C., Skalkidou, A., & Papadopoulos, F. C. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-059033 · Cited by 34 · see citing works
Zhang, D., Lim, J., Zhou, L., & Dahl, A. A. (2021) · JMIR Mental Health · doi:10.2196/31633 · Cited by 35 · see citing works
Funkhouser, C. J., Trivedi, E., Li, L. Y., Helgren, F., Zhang, E., Sritharan, A., Cherner, R., Pagliaccio, D., Durham, K., Kyler, M., et al. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13931 · Cited by 32 · see citing works
Birnbaum, M., Kulkarni, P., Meter, A. V., Chen, V., Rizvi, A., Arenare, E., de Choudhury, M., & Kane, J. (2020) · JMIR Mental Health · doi:10.2196/19348 · Cited by 30 · see citing works
Naturalistic smartphone keyboard typing reflects processing speed and executive function
Ross, M. K., Demos, A. P., Zulueta, J., Piscitello, A., Langenecker, S. A., McInnis, M. G., Ajilore, O., Nelson, P. C., Ryan, K. A., & Leow, A. (2021) · Brain and Behavior · doi:10.1002/brb3.2363 · Cited by 30 · see citing works
Self-citationAssociations between smartphone keystroke dynamics and cognition in MS
Chen, M. H., Leow, A., Ross, M. K., DeLuca, J., Chiaravalloti, N. D., Costa, S. L., Genova, H. M., Weber, E., Hussain, F., & Demos, A. P. (2022) · Digital Health · doi:10.1177/20552076221143234 · Cited by 30 · see citing works
Self-citationDistant mood monitoring for depressive and bipolar disorders: a systematic review
van der Watt, A. S. J., Odendaal, W., Louw, K.-A., & Seedat, S. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-02782-y · Cited by 27 · see citing works
Artificial Intelligence and the Future of Psychiatry
Allen, S. E. (2020) · IEEE Pulse · doi:10.1109/mpuls.2020.2993657 · Cited by 27 · see citing works
Digital phenotyping in psychiatry: A scoping review
Chia, A. Z. R., & Zhang, M. W. (2022) · Technology and Health Care · doi:10.3233/thc-213648 · Cited by 26 · see citing works
Bennett, C. C., Ross, M. K., Baek, E., Kim, D., & Leow, A. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101598 · Cited by 26 · see citing works
Self-citationHackett, K., & Giovannetti, T. (2022) · JMIR Aging · doi:10.2196/38130 · Cited by 26 · see citing works
Hackett, K., Xu, S., McKniff, M., Paglia, L., Barnett, I., & Giovannetti, T. (2024) · JMIR Human Factors · doi:10.2196/59974 · Cited by 25 · see citing works
Ambulatory Assessment Methods to Examine Momentary State-Based Predictors of Opioid Use Behaviors
Burgess-Hull, A. J., & Epstein, D. H. (2021) · Current Addiction Reports · doi:10.1007/s40429-020-00351-7 · Cited by 24 · see citing works
Use of Artificial Intelligence in Clinical Neurology
Hillis, J. M., & Bizzo, B. C. (2022) · Seminars in Neurology · doi:10.1055/s-0041-1742180 · Cited by 23 · see citing works
Digital phenotyping in molecular psychiatry—a missed opportunity?
Montag, C., & Quintana, D. S. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01795-1 · Cited by 23 · see citing works
Lam, K., Twose, J., Lissenberg-Witte, B. I., Licitra, G., Meijer, K. A., Uitdehaag, B. M. J., de Groot, V., & Killestein, J. (2022) · Journal of Medical Internet Research · doi:10.2196/37614 · Cited by 23 · see citing works
Chen, M. H., Cherian, C., Elenjickal, K., Rafizadeh, C. M., Ross, M. K., Leow, A., & DeLuca, J. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1049686 · Cited by 22 · see citing works
Self-citationNguyen, T. M., Leow, A., & Ajilore, O. (2023) · Brain Sciences · doi:10.3390/brainsci13060959 · Cited by 22 · see citing works
Self-citationEthical Dimensions of Digital Phenotyping Within the Context of Mental Healthcare
D’Alfonso, S., Coghlan, S., Schmidt, S., & Mangelsdorf, S. N. (2024) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-024-00423-9 · Cited by 22 · see citing works
Radhakrishnan, K., Kim, M. T., Burgermaster, M., Brown, R. A., Xie, B., Bray, M. S., & Fournier, C. A. (2020) · Nursing Outlook · doi:10.1016/j.outlook.2020.03.007 · Cited by 22 · see citing works
Digital Phenotyping in Child and Adolescent Psychiatry: A Perspective
Nisenson, M., Lin, V., & Gansner, M. (2021) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000310 · Cited by 21 · see citing works
The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review
Block, V. J., Bove, R. M., & Nourbakhsh, B. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.878313 · Cited by 21 · see citing works
The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective
Potier, R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01218 · Cited by 22 · see citing works
Amin, R., Schreynemackers, S., Oppenheimer, H., Petrovic, M., Hegerl, U., & Reich, H. (2025) · Journal of Medical Internet Research · doi:10.2196/57418 · Cited by 24 · see citing works
Mouchabac, S., Adrien, V., Falala-Séchet, C., Bonnot, O., Maatoug, R., Millet, B., Peretti, C.-S., Bourla, A., & Ferreri, F. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.622506 · Cited by 19 · see citing works
Vlisides‐Henry, R. D., Gao, M. M., Thomas, L. R., Kaliush, P. R., Conradt, E. D., & Crowell, S. E. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.618442 · Cited by 18 · see citing works
Bennett, C. C., Ross, M. K., Baek, E., Kim, D., & Leow, A. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00741-3 · Cited by 18 · see citing works
Self-citationA Large-scale Study on the Risks of the HTML5 WebAPI for Mobile Sensor-based Attacks
Marcantoni, F., Diamantaris, M., Ioannidis, S., & Polakis, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313539 · Cited by 18 · see citing works
Digital phenotyping for migraine: A game-changer for research and management
Petrušić, I. (2025) · Cephalalgia · doi:10.1177/03331024251363568 · Cited by 17 · see citing works
Rashid, N. A. A., Martanto, W., Yang, Z., Wang, X., Heaukulani, C., Vouk, N., Buddhika, T., Wei, Y., Verma, S. K., Tang, C. Y. Z., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-046552 · Cited by 17 · see citing works
Bennett, C. C., Stanojević, Č., Šabanović, S., Piatt, J. A., & Kim, S. (2021) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3472307.3484670 · Cited by 17 · see citing works
Caporusso, E., Melillo, A., Perrottelli, A., Giuliani, L., Marzocchi, F. F., Pezzella, P., & Giordano, G. M. (2025) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2025.1543005 · Cited by 18 · see citing works
A systems biology approach to the digital behaviorome
Rashidisabet, H., Thomas, P. J., Ajilore, O., Zulueta, J., Moore, R. C., & Leow, A. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.003 · Cited by 16 · see citing works
Self-citationHoeijmakers, A., Licitra, G., Meijer, K. A., Lam, K., Molenaar, P. C. G., Strijbis, E. M. M., & Killestein, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-28990-6 · Cited by 15 · see citing works
Braund, T. A., O’Dea, B., Bal, D., Maston, K., Larsen, M. E., Werner‐Seidler, A., Tillman, G., & Christensen, H. M. (2023) · JMIR Mental Health · doi:10.2196/44986 · Cited by 16 · see citing works
Ortiz, A., Park, Y., Gonzalez‐Torres, C., Alda, M., Blumberger, D. M., Burnett, R., Husain, M. I., Sanches, M., & Mulsant, B. H. (2023) · International Journal of Bipolar Disorders · doi:10.1186/s40345-023-00297-5 · Cited by 15 · see citing works
Smartphone keyboard dynamics predict affect in suicidal ideation
Knol, L., Nagpal, A., Leaning, I. E., Idda, E., Hussain, F., Ning, E., Eisenlohr-Moul, T. A., Beckmann, C. F., Marquand, A. F., & Leow, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01048-1 · Cited by 15 · see citing works
Self-citationCuesta, S. A., Sánchez-García, Á., Conejero, J. A., Sáez, C., Rivero-Rodriguez, A., & Garcia-Gomez, J. M. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16030461 · Cited by 15 · see citing works
Ross, M. K., Tulabandhula, T., Bennett, C. C., Baek, E., Kim, D., Hussain, F., Demos, A. P., Ning, E., Langenecker, S. A., Ajilore, O., et al. (2023) · Sensors · doi:10.3390/s23031585 · Cited by 15 · see citing works
Self-citationNing, E., Cladek, A. T., Ross, M. K., Kabir, S., Barve, A., Kennelly, E., Hussain, F., Duffecy, J. L., Langenecker, S. A., Nguyen, T. M., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580906 · Cited by 15 · see citing works
Self-citationDigitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?
Baumeister, H., Garatva, P., Pryss, R. C., Ropinski, T., & Montag, C. (2022) · Psychologische Rundschau · doi:10.1026/0033-3042/a000609 · Cited by 15 · see citing works
The Seven Deadly Sins of the HTML5 WebAPI
Diamantaris, M., Marcantoni, F., Ioannidis, S., & Polakis, J. (2020) · ACM Transactions on Privacy and Security · doi:10.1145/3403947 · Cited by 14 · see citing works
Comparison of In-home Robotic Companion Pet Use in South Korea and the United States: A Case Study
Bennett, C. C., Stanojević, Č., Kim, S., Lee, J.-J., Yu, J., Oh, J., Šabanović, S., & Piatt, J. A. (2022) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob52689.2022.9925468 · Cited by 13 · see citing works
Mobile Acceptance and Commitment Therapy in Bipolar Disorder: Microrandomized Trial
Cochran, A. L., Maronge, J. M., Victory, A. M., Hoel, S., McInnis, M. G., & Thomas, E. B. K. (2023) · JMIR Mental Health · doi:10.2196/43164 · Cited by 13 · see citing works
Self-citationThe comfort of adolescent patients and their parents with mobile sensing and digital phenotyping
Orr, M., MacLeod, L., Bagnell, A. L., McGrath, P., Wozney, L., & Meier, S. M. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107603 · Cited by 13 · see citing works
Huang, D., Emedom-Nnamdi, P., Onnela, J.-P. “., & Meter, A. V. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000883 · Cited by 12 · see citing works
Campbell, L. M., Tang, B., Watson, W.-M., Higgins, M., Cherner, M., Henry, B. L., & Moore, R. C. (2020) · Cannabis · doi:10.26828/cannabis.2020.02.005 · Cited by 12 · see citing works
Real-Time Monitoring: A Key Element in Personalized Health and Precision Health
Zulueta, J., Leow, A., & Ajilore, O. (2020) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20190042 · Cited by 11 · see citing works
Self-citationLearning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data
Liang, P. P., Liu, T., Cai, A., Muszyński, M., Ishii, R., Allen, N., Auerbach, R. P., Brent, D. A., Salakhutdinov, R., & Morency, L. (2021) · doi:10.18653/v1/2021.acl-long.322 · Cited by 12 · see citing works
Yu, J., Chiu, C., Wang, Y., Dzubur, E., Lu, W., & Hoffman, J. E. (2021) · Journal of Medical Internet Research · doi:10.2196/27709 · Cited by 11 · see citing works
Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study
Nguyen, V. C., Lu, N. Y., Kane, J. M., Birnbaum, M. L., & Choudhury, M. D. (2022) · JMIR Mental Health · doi:10.2196/39747 · Cited by 10 · see citing works
Digitalization in mental health care. New opportunities for specialists and patients
Незнанов, Н. Г., & Vasileva, A. V. (2023) · National Health Care (Russia) · doi:10.47093/2713-069x.2023.4.2.15-24 · Cited by 11 · see citing works
Wu, C., Hsu, J.-H., Liou, C.-R., Su, H.-Y., Lin, E. C., & Chen, P. S. (2023) · IEEE Access · doi:10.1109/access.2023.3328342 · Cited by 12 · see citing works
A digital self‐report survey of mood for bipolar disorder
Gruichich, T. S., Gomez, J. C. D., Zayas‐Cabán, G., McInnis, M. G., & Cochran, A. L. (2021) · Bipolar Disorders · doi:10.1111/bdi.13058 · Cited by 11 · see citing works
Self-citationHirdes, J. P., Morris, J. N., Perlman, C. M., Saari, M., Betini, G. S., Franco, M., van Hout, H. P. J., Stewart, S. L., & Ferris, J. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.787463 · Cited by 11 · see citing works
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
Liu, Q., Ning, E., Ross, M. K., Cladek, A. T., Kabir, S., Barve, A., Kennelly, E., Hussain, F., Duffecy, J. L., Langenecker, S. A., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/51269 · Cited by 10 · see citing works
Self-citationOn the Need for Digital Phenotyping to Obtain Insights into Mental States in the COVID-19 Pandemic
Montag, C., Dagum, P., & Elhai, J. D. (2020) · Digital Psychology · doi:10.24989/dp.v1i2.1857 · Cited by 10 · see citing works
- Authors
- Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., McInnis, M., Ajilore, O., Nelson, P. C., Ryan, K., & Leow, A.
- Affiliations
- University of Illinois Chicago (US)
- University of Michigan (US)
- Corresponding author
- Leow, Alex
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-07-20
- Volume
- 20
- Issue
- 7
- Pages
- e241
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6076371
- OpenAlex
- W2805638371
- Cited by
- 233
- References
- 28
- Citations by year
- 2026: 20 · 2025: 17 · 2024: 11 · 2023: 30 · 2022: 35 · 2021: 74 · 2020: 29 · 2019: 16 · 2018: 1
- FWCI
- 39.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Robert Wood Johnson Foundation
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- digital phenotyping, mood disorders, bipolar disorder, depression, mania, passive data collection, Hamilton Depression Rating Scale, Young Mania Rating Scale, accelerometer data, smartphone usage patterns
- MeSH terms
- Female, Humans, Male, Middle Aged, Phenotype, Cohort Studies, Mood Disorders, Cell Phone
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
