Skip to main content
SpringerLink
Log in

Identifying the Effects of COVID-19 on Psychological Well-Being Through Unsupervised Clustering for Mixed Data

  • Conference paper
  • First Online:
Proceedings of Sixth International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 235))

Abstract

The COVID-19 pandemic has a strong worldwide impact on not only the health and economic sectors but also the (socio-)psychological functioning of individuals. Since psychological health is an important protective factor to prevent diseases, it is crucial to identify individuals with increased vulnerability during the crisis. 275 adults participated in a German online survey from April until August 2020 which investigated health-related, social, behavioral, and psychological effects of the COVID-19 pandemic. We here introduce an unsupervised clustering approach suitable for mixed data types combining the Gower distance with the Partitioning Around Medoids (PAM) algorithm k-Medoids. We were able to identify three clusters differing significantly in subjects’ well-being, psychological distress, and current financial and occupational concerns. The clusters also differed in age with younger persons reporting greater financial and occupational concerns, increased anxiety, higher psychological distress, and reduced subjective well-being. Features with the strongest impact on the clustering were examined using a wrapping method and the feature importance implemented in the random forest. Particularly, answers regarding financial and occupational concern, psychological distress, and current well-being were decisive for the assignment to a cluster. In summation, the clustering approach can identify persons with weakened psychological protective factors allowing them to provide tailored recommendations for preventive actions based on the cluster affiliation, e.g., via a web application.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Valtorta NK, Kanaan M, Gilbody S, Ronzi S, Hanratty B (2016) Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies. Heart 102:1009–1016

    Article  Google Scholar 

  2. Kühne S, Kroh M, Liebig S, Zinn S (2020) The need for household panel surveys in times of crisis: the case of SOEP-CoV. Surv Res Methods 14:195–203

    Google Scholar 

  3. COSMO Germany: German COVID-19 Snapshot MOnitoring (COSMO Germany). PsychArchives Preprints. https://www.psycharchives.org/handle/20.500.12034/2398 (2020)

  4. Pierce M, Hope H, Ford T, Hatch S, Hotopf M, John A, Kontopantelis E, Webb R, Wessely S, McManus S, Abel KM (2020) Mental health before and during the covid-19 pandemic: a longitudinal probability sample survey of the UK population. Lancet Psychiatry 7:883–892

    Article  Google Scholar 

  5. Panchal N, Kamal R, Orgera K, Cox C, Garfield R, Hamel L, Munana C, Chidambaram P (2020) The implications of COVID-19 for mental health and substance use. Kaiser Familiy Foundation

    Google Scholar 

  6. Li S, Wang Y, Xue J, Zhao N, Zhu T (2020) The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. Int J Environ Res Public Health 17

    Google Scholar 

  7. Jha IP, Awasthi R, Kumar A, Kumar V, Sethi T (2020) Learning the mental health impact of COVID-19 in the United States with explainable artificial intelligence. medRxiv

    Google Scholar 

  8. Clatworthy J, Buick D, Hankins M, Weinman J, Horne R (2005) The use and reporting of cluster analysis in health psychology: a review. Br J Health Psychol 10:329–358

    Article  Google Scholar 

  9. Romesburg HC (2004) Cluster analysis for researchers. Lulu Press, S.l.

    Google Scholar 

  10. Nagpal A, Jatain A, Gaur D (2013) Review based on data clustering algorithms. In: 2013 IEEE conference on information & communication technologies (ICT 2013). Thuckalay, Tamil Nadu, India, 11–13 April 2013. IEEE, Piscataway, NJ, pp 298–303

    Google Scholar 

  11. van de Velden M, Iodice D'Enza A, Markos A (2019) Distance‐based clustering of mixed data. WIREs Comput Stat 11, e1456

    Google Scholar 

  12. Ahmad A, Khan SS (2019) Survey of state-of-the-art mixed data clustering algorithms. IEEE Access 7:31883–31902

    Article  Google Scholar 

  13. Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27:857–871

    Article  Google Scholar 

  14. Grané A, Salini S, Verdolini E (2020) Robust multivariate analysis for mixed-type data: novel algorithm and its practical application in socio-economic research. Socio-Econ Plan Sci

    Google Scholar 

  15. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  16. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  17. Park H-S, Jun C-H (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341

    Article  Google Scholar 

  18. Madhulatha TS (2011) Comparison between K-means and K-medoids clustering algorithms. In: Wyld DC, Wozniak M (eds) Advances in computing and information technology. First international conference; proceedings, vol 198. Springer, Berlin, pp 472–481

    Google Scholar 

  19. Budiaji W, Leisch F (2019) Simple K-medoids partitioning algorithm for mixed variable data. Algorithms 12:177

    Article  Google Scholar 

  20. Foss AH, Markatou M, Ray B (2019) Distance metrics and clustering methods for mixed-type data. Int Stat Rev 87:80–109

    Article  MathSciNet  Google Scholar 

  21. Kroenke K, Spitzer RL, Williams JBW, Löwe B (2009) An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics 50:613–621

    Google Scholar 

  22. Kroenke K, Spitzer RL, Williams JBW, Monahan PO, Löwe B (2007) Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med 146:317–325

    Article  Google Scholar 

  23. Löwe B, Kroenke K, Gräfe K (2005) Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res 58:163–171

    Article  Google Scholar 

  24. Ludwig K, Graf von der Schulenburg J-M, Greiner, W (2018) German value set for the EQ-5D-5L. Pharm Econ 36, 663–674

    Google Scholar 

  25. Janssen MF, Szende A, Cabases J, Ramos-Goñi JM, Vilagut G, König HH (2019) Population norms for the EQ-5D-3L: a cross-country analysis of population surveys for 20 countries. Eur J Health Econ: HEPAC 20:205–216

    Article  Google Scholar 

  26. Huber MB, Reitmeir P, Vogelmann M, Leidl R (2016) EQ-5D-5L in the general german population: comparison and evaluation of three yearly cross-section surveys. Int J Environ Res Public Health 13:343

    Article  Google Scholar 

  27. Yuan C, Yang H (2019) Research on K-value selection method of k-means clustering algorithm. J-Multidiscip Sci J 2:226–235

    Google Scholar 

  28. Cumming G, Finch S (2005) Inference by eye: confidence intervals and how to read pictures of data. Am Psychol 60:170–180

    Article  Google Scholar 

  29. Raschka S (2018) MLxtend: providing machine learning and data science utilities and extensions to python’s scientific computing stack. JOSS 3:638

    Article  Google Scholar 

  30. Cabases J, Szende A, Janssen B (2014) Self-reported population health: an international perspective based on EQ-5D. Springer

    Google Scholar 

  31. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res, 1157–1182

    Google Scholar 

  32. Mannor S, Jin X, Han J, Zhang X (2010) K-Medoids Clustering. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, US, Boston, MA, pp 564–565

    Google Scholar 

  33. Janssen D, Lingelbach K, Piechnik D, Gado S, Maurer P, Eichler M, Knopf D, Hentschel L, Schuler M, Sernatinger D, Peissner M (2021) WIBCE—a web application helping people to reflect their infection risk and psychological well-being and act accordingly during the COVID-19 pandemic. In: Proceedings of the 12th international conference on applied human factors and ergonomics and the affiliated conferences, New York, USA

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Human Factors and Technology Management (IAT), University of Stuttgart, Stuttgart, Germany

    Katharina Lingelbach

  2. Fraunhofer IAO, Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany

    Sabrina Gado, Doris Janssen, Daniela Piechnik & Matthias Peissner

  3. National Center for Tumor Diseases, University Hospital Dresden, Dresden, Germany

    Martin Eichler, Leopold Hentschel & Markus Schuler

  4. Seracom GmbH, Stuttgart, Germany

    Dennis Knopf & Daniel Sernatinger

Authors
  1. Katharina Lingelbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabrina Gado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Doris Janssen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniela Piechnik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Eichler
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dennis Knopf
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Leopold Hentschel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Markus Schuler
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Sernatinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Matthias Peissner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katharina Lingelbach .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lingelbach, K. et al. (2022). Identifying the Effects of COVID-19 on Psychological Well-Being Through Unsupervised Clustering for Mixed Data. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_81

Download citation

Publish with us

Policies and ethics

Search

Navigation