Skip to main content
Springer Nature Link
Log in

Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression

  • Conference paper
  • First Online:
Wireless Mobile Communication and Healthcare (MobiHealth 2021)

Abstract

Research on mobile sensing for mental health monitoring has traditionally explored the correlation between smartphone and wearable data with self-reported mental health symptom severity assessments. The effectiveness of predictive techniques to monitor depression is limited, given the idiosyncratic nature of depression symptoms and the limited availability of objectively labelled depression sensor-driven behaviour. In this paper, we investigate the possibility of using unsupervised anomaly detection methods to monitor the fluctuations of mental health and its severity. Informed by literature, we created a mobile application that collects acknowledged data streams that can be indicative of depression. We recruited 11 participants for a 1-month field study. More specifically, we monitored participants’ mobility, overall smartphone interactions, and surrounding ambient noise. The participants provided three self-reports: Big five personality traits, sleep and depression. Our results suggest that digital markers, combined with anomaly detection methods are useful to flag changes in human behaviour over time; thus, enabling mobile just-in-time interventions for in-the-wild assistance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. I Acoustics: Comparitive Examples of Noise Levels—Industrial Noise Control, January 2020. https://www.industrialnoisecontrol.com/comparative-noise-examples.htm

  2. Adler, D.A., et al.: Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR Mhealth Uhealth 8(8), e19962 (2020). https://doi.org/10.2196/19962

  3. Barnett, I., Torous, J., Staples, P., Sandoval, L., Keshavan, M., Onnela, J.P.: Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 43(8), 1660 (2018). 10/gdrks3

    Google Scholar 

  4. Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. Arch. Gen. Psychiatry 4(6), 561–571 (1961). https://doi.org/10.1001/archpsyc.1961.01710120031004

    Article  Google Scholar 

  5. Ben-Zeev, D., Schueller, S.M., Begale, M., Duffecy, J., Kane, J.M., Mohr, D.C.: Strategies for mhealth research: lessons from 3 mobile intervention studies. Adm. Policy Mental Health Mental Health Serv. Res. 42(2), 157–167 (2015)

    Google Scholar 

  6. van Berkel, N., Ferreira, D., Kostakos, V.: The experience sampling method on mobile devices. ACM Comput. Surv. 50(6), 93:1–93:40 (2017). https://doi.org/10.1145/3123988. http://doi.acm.org/10.1145/3123988

  7. Beygelzimer, A., Kakadet, S., Langford, J., Arya, S., Mount, D., Li, S.: FNN: Fast Nearest Neighbor Search Algorithms and Applications, February 2019. https://CRAN.R-project.org/package=FNN

  8. Böhmer, M., Hecht, B., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with angry birds, Facebook and kindle: a large scale study on mobile application usage. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 47–56 (2011)

    Google Scholar 

  9. Bonful, H.A., Anum, A.: Sociodemographic correlates of depressive symptoms: a cross-sectional analytic study among healthy urban ghanaian women. BMC Public Health 19(1), 50 (2019)

    Article  Google Scholar 

  10. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: ACM SIGMOD Record, vol. 29, pp. 93–104. ACM (2000)

    Google Scholar 

  11. Charmaz, K., Belgrave, L., et al.: Qualitative interviewing and grounded theory analysis. In: The SAGE Handbook of Interview Research: The Complexity of the Craft, vol. 2, pp. 347–365 (2012)

    Google Scholar 

  12. Coravos, A., Khozin, S., Mandl, K.D.: Developing and adopting safe and effective digital biomarkers to improve patient outcomes. NPJ Digit. Med. 2(1), 1–5 (2019). https://doi.org/10.1038/s41746-019-0090-4

  13. Croux, C., Rousseeuw, P.J.: Time-efficient algorithms for two highly robust estimators of scale. In: Dodge, Y., Whittaker, J. (eds.) Computational Statistics, pp. 411–428. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-662-26811-7_58

  14. Dagum, P.: Digital biomarkers of cognitive function. NPJ Digit. Med. 1(1), 1–3 (2018). https://doi.org/10.1038/s41746-018-0018-4

  15. Dionisio, A., Menezes, R., Mendes, D.A.: Mutual information: a measure of dependency for nonlinear time series. Physica A 344(1–2), 326–329 (2004)

    Article  MathSciNet  Google Scholar 

  16. Dorsey, E.R., Papapetropoulos, S., Xiong, M., Kieburtz, K.: The first frontier: digital biomarkers for neurodegenerative disorders. Digit. Biomarkers 1(1), 6–13 (2017). https://doi.org/10.1159/000477383

  17. Elshawi, R., Al-Mallah, M.H., Sakr, S.: On the interpretability of machine learning-based model for predicting hypertension. BMC Med. Inform. Decis. Mak. 19(1), 146 (2019)

    Article  Google Scholar 

  18. Eric, G.: iForest: Isolation Forest Anomaly Detection, August 2019. https://rdrr.io/github/Zelazny7/isofor/man/iForest.html

  19. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)

    Google Scholar 

  20. Faurholt-Jepsen, M., et al.: Daily electronic self-monitoring in bipolar disorder using smartphones-the Monarca I trial: a randomized, placebo-controlled, single-blind, parallel group trial. Psychol. Med. 45(13), 2691–2704 (2015)

    Article  Google Scholar 

  21. Ferreira, D., Kostakos, V., Dey, A.K.: Aware: mobile context instrumentation framework. Front. ICT 2, 6 (2015)

    Article  Google Scholar 

  22. Ferreira, D., Kostakos, V., Schweizer, I.: Human sensors on the move. In: Loreto, V., et al. (eds.) Participatory Sensing, Opinions and Collective Awareness. UCS, pp. 9–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-25658-0_1

  23. Fraccaro, P., et al.: Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. J. Am. Med. Inform. Assoc. 26(11), 1412–1420 (2019). https://doi.org/10.1093/jamia/ocz043

    Article  Google Scholar 

  24. Fried, E.I., Nesse, R.M.: Depression is not a consistent syndrome: an investigation of unique symptom patterns in the star* d study. J. Affect. Disord. 172, 96–102 (2015). https://doi.org/10.1016/j.jad.2014.10.010

    Article  Google Scholar 

  25. Fried, E.I., Nesse, R.M.: Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 13(1), 72 (2015)

    Article  Google Scholar 

  26. Gerych, W., Agu, E., Rundensteiner, E.: Classifying depression in imbalanced datasets using an autoencoder-based anomaly detection approach. In: 2019 IEEE 13th International Conference on Semantic Computing (ICSC), pp. 124–127, January 2019. https://doi.org/10.1109/ICOSC.2019.8665535

  27. Goldberg, L.R.: The development of markers for the big-five factor structure. Psychol. Assess. 4, 26 (1992)

    Article  Google Scholar 

  28. Goldstein, M., Uchida, S.: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE 11(4), e0152173 (2016)

    Google Scholar 

  29. Google: Google Play, December 2019. https://play.google.com/store?hl=en%5FGB

  30. Google: Use of SMS or Call Log permission groups - Play Console Help, December 2019. https://support.google.com/googleplay/android-developer/answer/9047303?hl=en

  31. Greenberg, P.E., Fournier, A.A., Sisitsky, T., Pike, C.T., Kessler, R.C.: The economic burden of adults with major depressive disorder in the united states (2005 and 2010). J. Clin. Psychiatry 76(2), 155–162 (2015). https://doi.org/10.4088/JCP.14m09298

    Article  Google Scholar 

  32. Hamilton, M.: A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23(1), 56 (1960)

    Article  Google Scholar 

  33. Harari, G.M., Lane, N.D., Wang, R., Crosier, B.S., Campbell, A.T., Gosling, S.D.: Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 11(6), 838–854 (2016). https://doi.org/10.1177/1745691616650285

    Article  Google Scholar 

  34. Hemmerle, A.M., Herman, J.P., Seroogy, K.B.: Stress, depression and Parkinson’s disease. Exp. Neurol. 233(1), 79–86 (2012). https://doi.org/10.1016/j.expneurol.2011.09.035

    Article  Google Scholar 

  35. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65–70 (1979)

    Google Scholar 

  36. Holzer, A., Ondrus, J.: Mobile application market: a developer’s perspective. Telematics Inform. 28(1), 22–31 (2011)

    Article  Google Scholar 

  37. Hu, Y., Murray, W., Shan, Y.: RLOF: R Parallel Implementation of Local Outlier Factor (LOF), September 2015. https://CRAN.R-project.org/package=Rlof

  38. Huber, P.J.: Robust Statistics. Springer, Heidelberg (2011)

    Google Scholar 

  39. Huckvale, K., Venkatesh, S., Christensen, H.: Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digit. Med. 2(1), 1–11 (2019). https://doi.org/10.1038/s41746-019-0166-1

    Article  Google Scholar 

  40. Jacobson, N.C., Weingarden, H., Wilhelm, S.: Digital biomarkers of mood disorders and symptom change. NPJ Digit. Med. 2(1), 1–3 (2019). https://doi.org/10.1038/s41746-019-0078-0

    Article  Google Scholar 

  41. Jenkins.io: Jenkins and Android, January 2019. https://jenkins.io/solutions/android/index.html

  42. Klobas, J.E., McGill, T.J., Moghavvemi, S., Paramanathan, T.: Compulsive YouTube usage: a comparison of use motivation and personality effects. Comput. Hum. Behav. 87, 129–139 (2018)

    Article  Google Scholar 

  43. Kourtis, L.C., Regele, O.B., Wright, J.M., Jones, G.B.: Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity. NPJ Digit. Med. 2(1), 1–9 (2019). https://doi.org/10.1038/s41746-019-0084-2

    Article  Google Scholar 

  44. Kroenke, K., Spitzer, R.L., Williams, J.B.: The PHQ-9: validity of a brief depression severity measure. J. Gener. Internal Med. 16(9), 606–613 (2001). https://doi.org/10.1046/j.1525-1497.2001.016009606.x

    Article  Google Scholar 

  45. Lee, J., Lam, M., Chiu, C.: Clara: design of a new system for passive sensing of depression, stress and anxiety in the workplace. In: Cipresso, P., Serino, S., Villani, D. (eds.) MindCare 2019. LNICST, vol. 288, pp. 12–28. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25872-6_2

  46. Lépine, J.P., Briley, M.: The increasing burden of depression. Neuropsychiatr. Dis. Treat. 7(Suppl. 1), 3 (2011). https://doi.org/10.2147/NDT.S19617

  47. 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 

  48. Liao, Z., et al.: A visual analytics approach for detecting and understanding anomalous resident behaviors in smart healthcare. Appl. Sci. 7(3), 254 (2017)

    Article  Google Scholar 

  49. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data (TKDD) 6(1), 3 (2012)

    Google Scholar 

  50. Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Ambient. Intell. Humaniz. Comput. 3(3), 205–218 (2012)

    Article  Google Scholar 

  51. Macchia, A., et al.: Depression worsens outcomes in elderly patients with heart failure: an analysis of 48,117 patients in a community setting. Eur. J. Heart Fail. 10(7), 714–721 (2008)

    Article  Google Scholar 

  52. Madsen, J.H.: Connectivity-based Outlier Factor (COF) algorithm in DDoutlier: Distance & Density-Based Outlier Detection, May 2019. https://rdrr.io/cran/DDoutlier/man/COF.html

  53. Maechler, M., Rousseeuw, P., Croux, C., Todorov, V., Ruckstuhl, A., Salibian-Barrera, M.: Robustbase: Basic Robust Statistics, May 2019. https://CRAN.R-project.org/package=robustbase

  54. Mandryk, R.L., Birk, M.V.: The potential of game-based digital biomarkers for modeling mental health. JMIR Mental Health 6(4), e13485 (2019). https://doi.org/10.2196/13485

  55. Mastoras, R.E., et al.: Touchscreen typing pattern analysis for remote detection of the depressive tendency. Sci. Rep. 9(1), 1–12 (2019). https://doi.org/10.1038/s41598-019-50002-9

    Article  Google Scholar 

  56. Meister, S., Deiters, W., Becker, S.: Digital health and digital biomarkers - enabling value chains on health data. Curr. Dir. Biomed. Eng. 2(1), 577–581 (2016). https://doi.org/10.1515/cdbme-2016-0128

    Article  Google Scholar 

  57. Moshe, I., et al.: Predicting symptoms of depression and anxiety using smartphone and wearable data. Front. Psychiatry 12 (2021). https://doi.org/10.3389/fpsyt.2021.625247

  58. Norton, P.J.: Depression anxiety and stress scales (DASS-21): psychometric analysis across four racial groups. Anxiety Stress Coping 20(3), 253–265 (2007)

    Article  Google Scholar 

  59. Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., Ferreira, D.: Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: exploratory study. JMIR Mhealth Uhealth 9(7), e26540 (2021). https://doi.org/10.2196/26540

    Article  Google Scholar 

  60. Opoku Asare, K., Visuri, A., Ferreira, D.S.T.: Towards early detection of depression through smartphone sensing. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019 Adjunct, pp. 1158–1161. ACM, New York (2019). https://doi.org/10.1145/3341162.3347075

  61. Peltonen, E., Sharmila, P., Opoku Asare, K., Visuri, A., Lagerspetz, E., Ferreira, D.: When phones get personal: predicting big five personality traits from application usage. Pervasive Mob. Comput. 69, 101269 (2020)

    Google Scholar 

  62. van der Ploeg, T., Austin, P.C., Steyerberg, E.W.: Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med. Res. Methodol. 14(1), 137 (2014)

    Article  Google Scholar 

  63. Rodarte, C.: Pharmaceutical perspective: how digital biomarkers and contextual data will enable therapeutic environments. Digit. Biomarkers 1(1), 73–81 (2017). https://doi.org/10.1159/000479951

    Article  Google Scholar 

  64. Rohani, D.A., Faurholt-Jepsen, M., Kessing, L.V., Bardram, J.E.: Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review. JMIR Mhealth Uhealth 6(8), e165 (2018). https://doi.org/10.2196/mhealth.9691

  65. Saeb, S., Zhang, M., Kwasny, M., Karr, C.J., Kording, K., Mohr, D.C.: The relationship between clinical, momentary, and sensor-based assessment of depression. In: 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 229–232. IEEE (2015)

    Google Scholar 

  66. Schembre, S.M., et al.: Just-in-time feedback in diet and physical activity interventions: systematic review and practical design framework. J. Med. Internet Res. 20(3), e106 (2018)

    Google Scholar 

  67. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

    Article  MathSciNet  Google Scholar 

  68. Shmueli, G., Koppius, O.R.: Predictive analytics in information systems research. MIS Q. 35(3), 553–572 (2011). http://www.jstor.org/stable/23042796

  69. Sordo, M., Zeng, Q.: On sample size and classification accuracy: a performance comparison. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds.) ISBMDA 2005. LNCS, vol. 3745, pp. 193–201. Springer, Heidelberg (2005). https://doi.org/10.1007/11573067_20

  70. Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., Rentfrow, J.: Passive mobile sensing and psychological traits for large scale mood prediction. In: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2019, pp. 272–281. ACM, New York (2019). https://doi.org/10.1145/3329189.3329213

  71. Stachl, C., et al.: Predicting personality from patterns of behavior collected with smartphones. Proc. Natl. Acad. Sci. 117(30) (2020). https://doi.org/10.1073/pnas.1920484117

  72. Hausser, J., Strimmer, K.: Entropy: Estimation of Entropy, Mutual Information and Related Quantities, November 2014. https://CRAN.R-project.org/package=entropy

  73. Strober, L.B., Arnett, P.A.: Assessment of depression in three medically ill, elderly populations: Alzheimer’s disease, Parkinson’s disease, and stroke. Clin. Neuropsychol. 23(2), 205–230 (2009)

    Article  Google Scholar 

  74. Tang, J., Chen, Z., Fu, A.W., Cheung, D.W.: Enhancing effectiveness of outlier detections for low density patterns. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 535–548. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47887-6_53

  75. TENK: Guidelines for ethical review in human sciences. https://tenk.fi/en/advice-and-materials/guidelines-ethical-review-human-sciences

  76. Tseng, V.W.S., et al.: Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. Sci. Rep. 10(1), 1–17 (2020)

    Article  Google Scholar 

  77. Vega, J., Jay, C., Vigo, M., Harper, S.: Unobtrusive monitoring of Parkinson’s disease based on digital biomarkers of human behaviour. In: Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2017, pp. 351–352. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3132525.3134782

  78. Wagner, D.T., Rice, A., Beresford, A.R.: Device analyzer: large-scale mobile data collection. SIGMETRICS Perform. Eval. Rev. 41(4), 53–56 (2014). https://doi.org/10.1145/2627534.2627553

    Article  Google Scholar 

  79. Wang, R., et al.: Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(1), 43 (2018)

    Google Scholar 

  80. Wang, W., et al.: Sensing behavioral change over time: using within-person variability features from mobile sensing to predict personality traits. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(3), 1–21 (2018)

    Google Scholar 

  81. WHO: Depression, March 2018. https://www.who.int/news-room/fact-sheets/detail/depression

  82. Wright, B., Peters, E., Ettinger, U., Kuipers, E., Kumari, V.: Understanding noise stress-induced cognitive impairment in healthy adults and its implications for schizophrenia. Noise Health 16(70), 166–176 (2014)

    Article  Google Scholar 

Download references

Acknowledgment

The Me in the Wild study is supported by the Academy of Finland SENSATE (Grant Nos. 316253, 320089), 6Genesis Flagship (Grant No. 318927), and the Infotech Institute University of Oulu Emerging Project. We thank all the participants of the Me in the Wild study.

Author information

Authors and Affiliations

  1. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Kennedy Opoku Asare, Aku Visuri & Denzil Ferreira

  2. Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Julio Vega

Authors
  1. Kennedy Opoku Asare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aku Visuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julio Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Denzil Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kennedy Opoku Asare .

Editor information

Editors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  2. The University of Sydney, Sydney, NSW, Australia

    Abbas Jamalipour

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Opoku Asare, K., Visuri, A., Vega, J., Ferreira, D. (2022). Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06368-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06367-1

  • Online ISBN: 978-3-031-06368-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics