ResourcePublished: 04 March 2026Ian Kim ORCID: orcid.org/0000-0003-0818-36921,2,Jack Boffa1,Mujung Cho3,David E. Conroy4,Nathan Kline2,Nick Haber5,Thomas N. Robinson ORCID: orcid.org/0000-0002-2367-07742,6,Byron Reeves7 &…Nilàm Ram1,7 Nature Health (2026)Cite this articleSubjectsMedical researchSocial sciencesAbstractSmartphone-based digital trace data can offer powerful insights for identifying behavioural patterns and health risks. However, existing tools for comprehensive data collection lack scalability, customizability, transparency and accessibility. To address these gaps, we developed an open-source platform that enables in situ capture of multimodal digital traces from smartphones (for example, moment-by-moment capture of screenshots, application usage logs, interaction histories and phone sensor readings). The Stanford Screenomics Data Collection application allows researchers to tailor data types and quality, data transfer methods and upload cadence. The Dashboard application supports real-time monitoring of participants’ data provision, identification of data issues and automated reactive communications to participants. The platform’s back end uses a NoSQL database for secure, and Health Insurance Portability and Accountability Act-compliant storage. Using illustrative 24-h digital trace data, we demonstrate how the platform expands the range of possible digital phenotyping studies.This is a preview of subscription content, access via your institutionAccess optionsSubscribe to this journalReceive 12 digital issues and online access to articles118,99 € per yearonly 9,92 € per issueLearn moreBuy this articlePurchase on SpringerLinkInstant access to the full article PDF.39,95 €Prices may be subject to local taxes which are calculated during checkoutFig. 1: A chord diagram illustrating connections among multimodal digital trace data, traditional health measures and research domains.Fig. 2: General platform architecture and overall data flow.Data availabilityThe illustrative 24-h digital trace text-based data collected using the Stanford Screenomics platform are available in the Chapter 5 Data Management section of the GitHub repository at https://github.com/StanfordScreenomics/Platform/. The original screenshots are not available to underscore the privacy concerns surrounding their collection and use of screenshot data.Code availabilityThe source code for the Stanford Screenomics platform developed by I.K. and J.B. is available for public access at https://www.github.com/StanfordScreenomics/Platform/. The repository will be actively maintained, with periodic updates for bug fixes, compatibility with new Android versions and improvements to documentation. Researchers are encouraged to submit issues or feature requests via the GitHub repository, and contributions from the community will be reviewed and incorporated as appropriate.ReferencesNavarro, J. L. & Tudge, J. R. Technologizing Bronfenbrenner: neo-ecological theory. Curr. Psychol. 42, 19338–19354 (2023).Article Google Scholar Allen, K. A., Ryan, T., Gray, D. L., McInerney, D. M. & Waters, L. Social media use and social connectedness in adolescents: the positives and the potential pitfalls. Aust. Educ. Dev. Psychol. 31, 18–31 (2014).Article Google Scholar Pentland, A. Social Physics: how Good Ideas Spread-the Lessons From a New Science (Penguin, 2014).Audy Martínek, P., Caliandro, A. & Denegri-Knott, J. Digital practices tracing: studying consumer lurking in digital environments. J. Mark. Manag. 39, 244–274 (2023).Google Scholar Kunst, K. & Vatrapu, R. Understanding electronic word of behavior: conceptualization of the observable digital traces of consumers’ behaviors. Electron. Mark. 29, 323–336 (2019).Article Google Scholar Chan, M., Chen, H. -T. & Lee, F. L. Examining the roles of mobile and social media in political participation: a cross-national analysis of three Asian societies using a communication mediation approach. New Media Soc. 19, 2003–2021 (2017).Article Google Scholar Kosinski, M., Stillwell, D. & Graepel, T. Private traits and attributes are predictable from digital records of human behavior. Proc. Natl Acad. Sci. USA 110, 5802–5805 (2013).Article CAS PubMed PubMed Central Google Scholar Du, J., Hew, K. F. & Liu, L. What can online traces tell us about students’ self-regulated learning? A systematic review of online trace data analysis. Comput. Educ. 201, 104828 (2023).Article Google Scholar Lämsä, J. et al. Measuring secondary education students’ self-regulated learning processes with digital trace data. Learn. Individ. Differ. 118, 102625 (2025).Article Google Scholar Jensen, M., George, M. J., Russell, M. R. & Odgers, C. L. Young adolescents’ digital technology use and mental health symptoms: little evidence of longitudinal or daily linkages. Clin. Psychol. Sci. 7, 1416–1433 (2019).Article PubMed PubMed Central Google Scholar Orben, A. & Przybylski, A. K. The association between adolescent well-being and digital technology use. Nat. Human Behav. 3, 173–182 (2019).Article Google Scholar Amir, S., Dredze, M. & Ayers, J. W. Mental health surveillance over social media with digital cohorts. In Proc. 6th Workshop on Computational Linguistics and Clinical Psychology, 114–120 (2019).Kim, I. et al. E-Cigarette–related health beliefs expressed on Twitter within the US. AJPM Focus 2, 100067 (2023).Article PubMed PubMed Central Google Scholar Eagle, N. & Pentland, A. Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10, 255–268 (2006).Article Google Scholar Gordon, A. M. & Mendes, W. B. A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proc. Natl Acad. Sci. USA 118, e2105573118 (2021).Article CAS PubMed PubMed Central Google Scholar Lathia, N., Sandstrom, G. M., Mascolo, C. & Rentfrow, P. J. Happier people live more active lives: using smartphones to link happiness and physical activity. PLoS ONE 12, e0160589 (2017).Article PubMed PubMed Central Google Scholar Lee, K. et al. Using digital phenotyping to understand health-related outcomes: a scoping review. Int. J. Med. Inform. 174, 105061 (2023).Article PubMed Google Scholar Ohme, J. et al. Digital trace data collection for social media effects research: APIs, data donation, and (screen) tracking. Commun. Methods Meas. 18, 124–141 (2024).Article Google Scholar Kleinman, R. A. & Merkel, C. Digital contact tracing for COVID-19. CMAJ 192, E653–E656 (2020).Article CAS PubMed PubMed Central Google Scholar Mejova, Y. Digital epidemiology. In Handbook of Computational Social Science for Policy (eds Bertoni, E. et al.) 279–303 (Springer, 2022).Amez, S. & Baert, S. Smartphone use and academic performance: a literature review. Int. J. Educ. Res. 103, 101618 (2020).Article Google Scholar Nam, R. J. & Cha, C. B. Examining highly novel positive future thinking in suicidal and nonsuicidal adolescents. Arch. Suicide Res. 28, 1200–1214 (2024).Article PubMed Google Scholar Singh, M. K. K. & Samah, N. A. Impact of smartphone: a review on positive and negative effects on students. Asian Social Sci. 14, 83 (2018).Article Google Scholar Twenge, J. M. Have smartphones destroyed a generation? The Atlantic https://www.theatlantic.com/magazine/archive/2017/09/has-the-smartphone-destroyed-a-generation/534198/ (2017).Faust, A. M., Auerbeck, A., Lee, A. M., Kim, I. & Conroy, D. E. Passive sensing of smartphone use, physical activity and sedentary behavior among adolescents and young adults during the COVID-19 pandemic. J. Behav. Med. 47, 770–781 (2024).Yee, A. Z. et al. ScreenLife Capture: an open-source and user-friendly framework for collecting screenomes from Android smartphones. Behav. Res. Methods 55, 4068–4085 (2023).Article PubMed Google Scholar Ram, N. et al. Screenomics: a new approach for observing and studying individuals’ digital lives. J. Adolesc. Res. 35, 16–50 (2020).Article PubMed Google Scholar Reeves, B. et al. Screenomics: a framework to capture and analyze personal life experiences and the ways that technology shapes them. Hum. Comput. Interact. 36, 150–201 (2021).Article PubMed Google Scholar Cho, M. -J., Reeves, B., Robinson, T. N. & Ram, N. Media production on smartphones: analysis of the timing, content, and context of message production using real-world smartphone use data. Cyberpsychol. Behav. Soc. Netw. 26, 371–379 (2023).Article PubMed Google Scholar Lee, J., Hamilton, J. T., Ram, N., Roehrick, K. & Reeves, B. The psychology of poverty and life online: natural experiments on the effects of smartphone payday loan ads on psychological stress. Inform. Commun. Soc. 26, 2775–2796 (2023).Article Google Scholar Sun, X. et al. Connectedness and independence of young adults and parents in the digital world: observing smartphone interactions at multiple timescales using Screenomics. J. Soc. Pers. Relat. 40, 1126–1150 (2023).Article Google Scholar Jacobucci, R., Ammerman, B. & Ram, N. Examining passively collected smartphone-based data in the days prior to psychiatric hospitalization for a suicidal crisis: comparative case analysis. JMIR Form. Res. 8, e55999 (2024).Article PubMed PubMed Central Google Scholar Brinberg, M. et al. The idiosyncrasies of everyday digital lives: using the Human Screenome Project to study user behavior on smartphones. Comput. Hum. Behav. 114, 106570 (2021).Article Google Scholar Cerit, M. et al. Person-Specific analyses of smartphone use and mental health: intensive longitudinal study. JMIR Form. Res. 9, e59875 (2025).Article PubMed PubMed Central Google Scholar Ram, N., Haber, N., Robinson, T. N. & Reeves, B. Binding the person-specific approach to modern AI in the human screenome project: moving past generalizability to transferability. Multivariate Behav. Res. 59, 1211–1219 (2024).Article PubMed Google Scholar Rodrigues, A., Montague, K. & Guerreiro, T. Data donors: sharing knowledge for mobile accessibility. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 1–6 (2018).Antal, M., Bokor, Z. & Szabó, L. Z. Information revealed from scrolling interactions on mobile devices. Pattern Recogn. Lett. 56, 7–13 (2015).Article Google Scholar Zingaro, D., Savino, G. -L., Reichenbacher, T., Schöning, J. & Fabrikant, S. I. Tapping into mobile app use: what touch event data can reveal about active and passive in-app usage behaviour. Available at SSRN 4768783 (2024).Estrin, D. & Sim, I. Open mHealth architecture: an engine for health care innovation. Science 330, 759–760 (2010).Article CAS PubMed Google Scholar Ferreira, D., Kostakos, V. & Dey, A. K. AWARE: mobile context instrumentation framework. Front. ICT 2, 6 (2015).Article Google Scholar Jardine, J., Fisher, J. & Carrick, B. Apple’s ResearchKit: smart data collection for the smartphone era? J. R. Soc. Med. 108, 294–296 (2015).Article PubMed PubMed Central Google Scholar Lakshminarasimhappa, M. C. Web-based and smart mobile app for data collection: Kobo Toolbox/Kobo collect. J. Indian Libr. Assoc. 57, 72–79 (2022).Google Scholar Menaspà, P. Effortless activity tracking with Google Fit. Br. J. Sports Med. 49, 1598–1598 (2015).Article PubMed Google Scholar Onnela, J. -P. et al. Beiwe: a data collection platform for high-throughput digital phenotyping. J. Open Source Softw. 6, 3417 (2021).Article Google Scholar Wang, R. et al. StudentLife: using smartphones to assess mental health and academic performance of college students. In Mobile Health (eds Rehg, J. M. et al.) 7–33 (Springer, 2017).Freelon, D. On the interpretation of digital trace data in communication and social computing research. J. Broadcasting Electron. Media 58, 59–75 (2014).Article Google Scholar Huckvale, K., Venkatesh, S. & Christensen, H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digit. Med. 2, 88 (2019).Article PubMed PubMed Central Google Scholar Insel, T. R. Digital phenotyping: technology for a new science of behavior. JAMA 318, 1215–1216 (2017).Article PubMed Google Scholar Jungherr, A., Schoen, H., Posegga, O. & Jürgens, P. Digital trace data in the study of public opinion: an indicator of attention toward politics rather than political support. Soc. Sci. Comput. Rev. 35, 336–356 (2017).Article Google Scholar Liang, Y., Zheng, X. & Zeng, D. D. A survey on big data-driven digital phenotyping of mental health. Inf. Fusion 52, 290–307 (2019).Article Google Scholar Thylstrup, N. B. Data out of place: toxic traces and the politics of recycling. Big Data Soc. 6, 2053951719875479 (2019).Article Google Scholar Cheng, X., Fang, L., Yang, L. & Cui, S. Mobile big data: the fuel for data-driven wireless. IEEE Internet Things J. 4, 1489–1516 (2017).Article Google Scholar Maeckelberghe, E., Zdunek, K., Marceglia, S., Farsides, B. & Rigby, M. The ethical challenges of personalized digital health. Front. Med. 10, 1123863 (2023).Article Google Scholar Nebeker, C., Bartlett Ellis, R. J. & Torous, J. Development of a decision-making checklist tool to support technology selection in digital health research. Transl. Behav. Med. 10, 1004–1015 (2020).Article PubMed PubMed Central Google Scholar Nebeker, C., Gholami, M., Kareem, D. & Kim, E. Applying a digital health checklist and readability tools to improve informed consent for digital health research. Front. Digit. Health 3, 690901 (2021).Article PubMed PubMed Central Google Scholar Dunseath, S., Weibel, N., Bloss, C. S. & Nebeker, C. NIH support of mobile, imaging, pervasive sensing, social media and location tracking (MISST) research: laying the foundation to examine research ethics in the digital age. NPJ Digit. Med. 1, 20171 (2018).Article PubMed PubMed Central Google Scholar Nebeker, C., Torous, J. & Bartlett Ellis, R. J. Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med. 17, 137 (2019).Article PubMed PubMed Central Google Scholar Filkins, B. L. et al. Privacy and security in the era of digital health: what should translational researchers know and do about it? Am. J. Transl. Res. 8, 1560–1580 (2016).PubMed PubMed Central Google Scholar Watson, H. J. & Nations, C. Addressing the growing need for algorithmic transparency. Communic. Assoc. Inf. Syst. 45, 26 (2019).Google Scholar Wrzus, C. & Schoedel, R. Transparency and reproducibility in mobile sensing research. In Mobile Sensing in Psychology: Methods and Applications (eds Mehl, M. R. et al.) 53–77 (Guilford Press, 2023).Download referencesAcknowledgementsThis research was supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01 HL169601 (to T.N.R., B.R., N.H. and N.R.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.Author informationAuthors and AffiliationsDepartment of Psychology, Stanford University, Stanford, CA, USAIan Kim, Jack Boffa & Nilàm RamDepartment of Pediatrics, Stanford University, Stanford, CA, USAIan Kim, Nathan Kline & Thomas N. RobinsonResearch Center for Humanities and Social Sciences, Academia Sinica, Taipei City, TaiwanMujung ChoSchool of Kinesiology, University of Michigan, Ann Arbor, MI, USADavid E. ConroyGraduate School of Education, Stanford University, Stanford, CA, USANick HaberDepartment of Medicine, Stanford University, Stanford, CA, USAThomas N. RobinsonDepartment of Communication, Stanford University, Stanford, CA, USAByron Reeves & Nilàm RamAuthorsIan KimView author publicationsSearch author on:PubMed Google ScholarJack BoffaView author publicationsSearch author on:PubMed Google ScholarMujung ChoView author publicationsSearch author on:PubMed Google ScholarDavid E. ConroyView author publicationsSearch author on:PubMed Google ScholarNathan KlineView author publicationsSearch author on:PubMed Google ScholarNick HaberView author publicationsSearch author on:PubMed Google ScholarThomas N. RobinsonView author publicationsSearch author on:PubMed Google ScholarByron ReevesView author publicationsSearch author on:PubMed Google ScholarNilàm RamView author publicationsSearch author on:PubMed Google ScholarContributionsI.K. conceived and designed the work, conducted data acquisition and analysis, interpreted data, drafted the paper and performed critical revisions. I.K. and J.B. developed the software, with N.K. assisting in its testing. M.C. contributed to the conception and design of the work. D.E.C. contributed to data interpretation and critical revisions. T.N.R., B.R., N.H. and N.R. contributed to the conception and design, data interpretation and critical revisions, and secured funding. All authors reviewed and approved the final paper and consent to its publication.Corresponding authorCorrespondence to Ian Kim.Ethics declarationsCompeting interestsThe authors declare no competing interests.Peer reviewPeer review informationNature Health thanks Jakob Ohme and Andrew Yee for their contribution to the peer review of this work. Primary Handling Editor: Lorenzo Righetto, in collaboration with the Nature Health team.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationSupplementary Notes 1 and 2 and Fig. 1.Reporting SummaryRights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this article