Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Opportunities and challenges in the collection and analysis of digital phenotyping data


The broad adoption and use of smartphones has led to fundamentally new opportunities for capturing social, behavioral, and cognitive phenotypes in free-living settings, outside of research laboratories and clinics. Predicated on the use of existing personal devices rather than the introduction of additional instrumentation, smartphone-based digital phenotyping presents us with several opportunities and challenges in data collection and data analysis. These two aspects are strongly coupled, because decisions about what data to collect and how to collect it constrain what statistical analyses can be carried out, now and years later, and therefore ultimately determine what scientific, clinical, and public health questions may be asked and answered. Digital phenotyping combines the excitement of fast-paced technologies, smartphones, cloud computing and machine learning, with deep mathematical and statistical questions, and it does this in the service of a better understanding our own behavior in ways that are objective, scalable, and reproducible. We will discuss some fundamental aspects of collection and analysis of digital phenotyping data, which takes us on a brief tour of several important scientific and technological concepts, from the open-source paradigm to computational complexity, with some unexpected insights provided by fields as varied as zoology and quantum mechanics.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Mobile Fact Sheet Pew Research Center. 2019.

  2. 2.

    Torous J, Kiang MV, Lorme J, Onnela JP. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health. 2016;3:e16.

    Article  PubMed Central  Google Scholar 

  3. 3.

    Onnela JP, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacol: Off Publ Am Coll Neuropsychopharmacol. 2016;41:1691–6.

    Article  Google Scholar 

  4. 4.

    Kumar M. Quantum: Einstein, Bohr, and the great debate about the nature of reality. WW Norton & Company, New York, NY, 2008.

  5. 5.

    Delude CM. Deep phenotyping: the details of disease. Nature. 2015;527:S14–5.

    CAS  Article  Google Scholar 

  6. 6.

    Onnela J-P, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, et al. Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci. 2007;104:7332–6.

    CAS  Article  Google Scholar 

  7. 7.

    Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela J-P. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacol: Off Publ Am Coll Neuropsychopharmacol. 2018;43:1660.

    Article  Google Scholar 

  8. 8.

    Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 2020.

  9. 9.

    Freedman DA. Ecological inference and the ecological fallacy. Int Encycl Soc Behav Sci. 1999;6:1–7.

    Google Scholar 

  10. 10.

    Chan Y-FY, Wang P, Rogers L, Tignor N, Zweig M, Hershman SG, et al. The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit. Nat Biotechnol. 2017;35:354.

    CAS  Article  PubMed Central  Google Scholar 

  11. 11.

    Baker J, Barrick E, Eichi HR, Barnett I, Ongur D, Onnela J-P, et al. F242. Intensive longitudinal assessment of mania and psychosis using commonly available technologies. Biol Psychiatry. 2018;83:S333.

    Article  Google Scholar 

  12. 12.

    Yang WE, Spaulding EM, Lumelsky D, Hung G, Huynh PP, Knowles K, et al. Strategies for the successful implementation of a novel iPhone Loaner System (iShare) in mHealth interventions: prospective study. JMIR mHealth uHealth. 2019;7:e16391.

    Article  PubMed Central  Google Scholar 

  13. 13.

    Reports: Childwise.

  14. 14.

    media P. Most children own mobile phone by age of seven, study finds The Guardian. 2020.

  15. 15.

    Kiang MV, Chen J, Krieger N, O Buckee C, Onnela J-P. Working paper: human factors and missing data in digital phenotyping. 2020.

  16. 16.

    Lupač P. Digital divide research’, beyond the digital divide: contextualizing the information society. Emerald Publishing Limited, Bingley, UK, 2018.

  17. 17.

    Noah B, Keller MS, Mosadeghi S, Stein L, Johl S, Delshad S, et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digital Med. 2018;1:1–12.

    Article  Google Scholar 

  18. 18.

    Dorsch AK, Thomas S, Xu X, Kaiser W, Dobkin BH. SIRRACT: an international randomized clinical trial of activity feedback during inpatient stroke rehabilitation enabled by wireless sensing. Neurorehabilitation Neural Repair. 2015;29:407–15.

    Article  PubMed Central  Google Scholar 

  19. 19.

    Logan AG, Irvine MJ, McIsaac WJ, Tisler A, Rossos PG, Easty A, et al. Effect of home blood pressure telemonitoring with self-care support on uncontrolled systolic hypertension in diabetics. Hypertension. 2012;60:51–7.

    CAS  Article  Google Scholar 

  20. 20.

    Klasnja P, Consolvo S, Pratt W, editors. How to evaluate technologies for health behavior change in HCI research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2011.

  21. 21.

    Shih PC, Han K, Poole ES, Rosson MB, Carroll JM. Use and adoption challenges of wearable activity trackers. In Conference 2015 Proceedings. 2015.

  22. 22.

    Partners E. Inside wearables: how the science of human behavior change offers the secret to long-term engagement. 2014., accessed May 2020.

  23. 23.

    Gartner. User survey analysis: wearables need to be more useful. 2016., accessed May 2020.

  24. 24.

    Khan WZ, Xiang Y, Aalsalem MY, Arshad Q. Mobile phone sensing systems: a survey. IEEE Commun Surv Tutor. 2012;15:402–27.

    Article  Google Scholar 

  25. 25.

    EmotionSense. 2020.

  26. 26.

    Miluzzo E, Cornelius CT, Ramaswamy A, Choudhury T, Liu Z, Campbell AT, editors. Darwin phones: the evolution of sensing and inference on mobile phones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services; 2010.

  27. 27.

    Mobile phone based sensing software Wikipedia. 2020. accessed May 2020.

  28. 28.

    The average ‘good’ indie game makes just $25,000 in its first year on sale, says Grey Alien’s Birkett. 2018.

  29. 29.

    Overview. 2018. accessed May 2020.

  30. 30.

    Timed Walk. 2018. accessed May 2020.

  31. 31.

    CMSensorRecorder Apple Developer. 2020. accessed May 2020.

  32. 32.

    iPhone users spend $101 every month on tech purchases, nearly double of android users, according to a survey conducted by Slickdeals PR Newswire. 2018.

  33. 33.

    Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets. Nat Rev Drug Disco. 2011;10:712.

    CAS  Article  Google Scholar 

  34. 34.

    Begley CG, Ellis LM. Raise standards for preclinical cancer research. Nature. 2012;483:531–3.

    CAS  Article  Google Scholar 

  35. 35.

    Freedman LP, Cockburn IM, Simcoe TS. The economics of reproducibility in preclinical research. PLoS Biol. 2015;13:e1002165.

    Article  PubMed Central  Google Scholar 

  36. 36.

    Koo BM, Vizer LM. Mobile technology for cognitive assessment of older adults: a scoping review. Innov Aging. 2019;3:igy038.

    Article  PubMed Central  Google Scholar 

  37. 37.

    Dhand A, Lang CE, Luke DA, Kim A, Li K, McCafferty L, et al. Social network mapping and functional recovery within 6 months of ischemic stroke. Neurorehabilitation Neural Repair. 2019;33:922–32.

    Article  PubMed Central  Google Scholar 

  38. 38.

    Hudson K, Lifton R, Patrick-Lake B. The precision medicine initiative cohort program—Building a Research Foundation for 21st Century Medicine. National Institutes of Health, 2015., accessed May 2020.

  39. 39.

    Notice of information: NIMH high-priority areas for research on digital health technology to advance assessment, detection, prevention, treatment, and delivery of services for mental health conditions. National Institute of Mental Health; 2018., accessed May 2020.

  40. 40.

    National Advisory Mental Health Council (NAMHC). 2020. accessed May 2020.

  41. 41.

    Health TNIoM. Research Domain Criteria (RDoC). 2020. accessed May 2020.

  42. 42.

    Reis, H. T. (2012). Why researchers should think “real-world”: A conceptual rationale. In (Eds. M. R. Mehl & T. S. Conner), Handbook of research methods for studying daily life (p. 3–21). The Guilford Press.

  43. 43.

    Koster EH, De Raedt R, Leyman L, De Lissnyder E. Mood-congruent attention and memory bias in dysphoria: exploring the coherence among information-processing biases. Behav Res Ther. 2010;48:219–25.

    Article  Google Scholar 

  44. 44.

    Myin-Germeys I, Klippel A, Steinhart H, Reininghaus U. Ecological momentary interventions in psychiatry. Curr Opin Psychiatry. 2016;29:258–63.

    Article  Google Scholar 

  45. 45.

    Solhan MB, Trull TJ, Jahng S, Wood PK. Clinical assessment of affective instability: comparing EMA indices, questionnaire reports, and retrospective recall. Psychol Assess. 2009;21:425.

    Article  PubMed Central  Google Scholar 

  46. 46.

    Bos FM, Schoevers RA, aan het Rot M. Experience sampling and ecological momentary assessment studies in psychopharmacology: a systematic review. Eur Neuropsychopharmacol 2015;25:1853–64.

    CAS  Article  Google Scholar 

  47. 47.

    aan het Rot M, Hogenelst K, Schoevers RA. Mood disorders in everyday life: a systematic review of experience sampling and ecological momentary assessment studies. Clin Psychol Rev. 2012;32:510–23.

    Article  Google Scholar 

  48. 48.

    Oorschot M, Kwapil T, Delespaul P, Myin-Germeys I. Momentary assessment research in psychosis. Psychol Assess 2009;21:498.

    Article  Google Scholar 

  49. 49.

    Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21:486.

    Article  PubMed Central  Google Scholar 

  50. 50.

    Walz LC, Nauta MH, aan het Rot M. Experience sampling and ecological momentary assessment for studying the daily lives of patients with anxiety disorders: a systematic review. J Anxiety Disord. 2014;28:925–37.

    Article  Google Scholar 

  51. 51.

    Haedt-Matt AA, Keel PK. Revisiting the affect regulation model of binge eating: a meta-analysis of studies using ecological momentary assessment. Psychol Bull. 2011;137:660.

    Article  PubMed Central  Google Scholar 

  52. 52.

    Linux servers under attack for a decade. 2020.

  53. 53.

    Schryen G. Security of open source and closed source software: an empirical comparison of published vulnerabilities. In Proceedings of the AMCIS 2009. 2009:387.

  54. 54.

    Brown DD, Kays R, Wikelski M, Wilson R, Klimley AP. Observing the unwatchable through acceleration logging of animal behavior. Anim Biotelemetry. 2013;1:20.

    Article  Google Scholar 

  55. 55.

    Barback J, Onnela JP. Working paper: accelerometry-based algorithm for smartphone proximity classification. 2020.

  56. 56.

    Barnett I, Onnela J-P. Inferring mobility measures from GPS traces with missing data. Biostatistics. 2020;21:e98–e112.

    Google Scholar 

  57. 57.

    Python Copyright. 2020. accessed May 2020.

  58. 58.

    Jin HR. Think big: the need for patent rights in the era of big data and machine learning. NYU J Intell Prop Ent L. 2017;7:78.

    Google Scholar 

  59. 59.

    Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bull. 2017;143:187.

    Article  PubMed Central  Google Scholar 

  60. 60.

    Friedman LM, Furberg C, DeMets DL, Reboussin DM, Granger CB. Fundamentals of clinical trials. Springer, Cham, Heidelberg, New York, Dordrecht, London, 2010.

  61. 61.

    Lillie EO, Patay B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Med. 2011;8:161–73.

    Article  Google Scholar 

  62. 62.

    Barlow DH, Nock MK, Hersen M. Single case experimental designs: strategies for studying behavior for change. Pearson, 2008.

  63. 63.

    Guyatt GH, Heyting A, Jaeschke R, Keller J, Adachi JD, Roberts RS. N of 1 randomized trials for investigating new drugs. Controlled Clin Trials. 1990;11:88–100.

    CAS  Article  Google Scholar 

  64. 64.

    Topol EJ. Transforming medicine via digital innovation. Sci Transl Med. 2010;2:16cm4–cm4.

    PubMed Central  Google Scholar 

Download references


I am grateful to my past and present students, postdocs, mentees, mentors, collaborators, and staff for all their hard work, energy, and enthusiasm as we’ve tackled challenges in the collection and analysis of digital phenotyping data.

Author information




JPO wrote this article.

Corresponding author

Correspondence to Jukka-Pekka Onnela.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Onnela, JP. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacol. 46, 45–54 (2021).

Download citation


Quick links