Advertisement

Smartphone Behavior Based Electronical Scale Validity Assessment Framework

  • Minqiang Yang
  • Jingsheng Tang
  • Longzhe Tang
  • Bin HuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)

Abstract

In the study, we developed a smartphone-based electronical scale validity assessment framework. 374 college students are recruited to fill in Beck Depression Inventory. A total of 544 filling of scales are collected, which may be filled accordingly or concealed. Via an electronical scale based WeChat applet and backend application, temporal and spatial behavioral data of subjects during the scale-filling process are collected. We established an assessment model of the validity of the scale-filling based on the behavior data with machine learning approaches. The result shows that smartphone behavior has significant features in the dimension of time and space under different motivations. The framework achieves an valuable assessment of the effectiveness of the scale, whose key indicators such as accuracy, sensitivity and precision are over 80% under multiple dimension behavior data classification. The framework has a good application prospect in the field of psychological screening.

Keywords

Validity assessment Smartphone WeChat applet Behavior data 

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China [Grant No. 61632014, No. 61627808, No. 61210010], in part by the National Basic Research Program of China (973 Program) under Grant 2014CB744600, in part by the Program of Beijing Municipal Science & Technology Commission under Grant Z171100000117005.

References

  1. 1.
    Schwarzer, R., Mueller, J., Greenglass, E.: Assessment of perceived general selfefficacy on the internet: data collection in cyberspace. Anxiety Stress Coping 12(2), 145–161 (1999)CrossRefGoogle Scholar
  2. 2.
    Statistical Report on Internet Development in China (2018)Google Scholar
  3. 3.
    Chen, J.Y., Zheng, H.T., Xiao, X., Sangaiah, A.K., Jiang, Y., Zhao, C.Z.: Tianji: implementation of an efficient tracking engine in the mobile Internet era. IEEE Access 5, 16592–16600 (2017)CrossRefGoogle Scholar
  4. 4.
    Harari, G.M., Müller, S.R., Aung, M.S., Rentfrow, P.J.: Smartphone sensing methods for studying behavior in everyday life. Curr. Opin. Behav. Sci. 18, 83–90 (2017)CrossRefGoogle Scholar
  5. 5.
    Boonstra, T.W., Nicholas, J., Wong, Q.J., Shaw, F., Townsend, S., Christensen, H.: Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions. J. Med. Internet Res. 20(7), e10131 (2018)CrossRefGoogle Scholar
  6. 6.
    Gong, J., et al.: Understanding behavioral dynamics of social anxiety among college students through smartphone sensors. Inf. Fusion 49, 57–68 (2019)CrossRefGoogle Scholar
  7. 7.
    Drummond, H.E., Ghosh, S., Ferguson, A., Brackenridge, D., Tiplady, B.: Electronic quality of life questionnaires: a comparison of pen-based electronic questionnaires with conventional paper in a gastrointestinal study. Qual. Life Res. 4(1), 21–26 (1995)CrossRefGoogle Scholar
  8. 8.
    Pouwer, F., Snoek, F.J., Van Der Ploeg, H.M., Heine, R.J., Brand, A.N.: A comparison of the standard and the computerized versions of the Well-being Questionnaire (WBQ) and the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Qual. Life Res. 7(1), 33–38 (1997)CrossRefGoogle Scholar
  9. 9.
    Velikova, G., et al.: Automated collection of quality-of-life data: a comparison of paper and computer touch-screen questionnaires. J. Clin. Oncol. 17(3), 998 (1999)CrossRefGoogle Scholar
  10. 10.
    Ryan, J.M., Corry, J.R., Attewell, R., Smithson, M.J.: A comparison of an electronic version of the SF-36 General Health Questionnaire to the standard paper version. Qual. Life Res. 11(1), 19–26 (2002)CrossRefGoogle Scholar
  11. 11.
    Carmines, E.G., Zeller, R.A.: Reliability and Validity Assessment, vol. 17. Sage Publications, Thousand Oaks (1979)CrossRefGoogle Scholar
  12. 12.
    Nieuwenhuijsen, K., De Boer, A.G.E.M., Verbeek, J.H.A.M., Blonk, R.W.B., Van Dijk, F.J.H.: The depression anxiety stress scales (DASS): detecting anxiety disorder and depression in employees absent from work because of mental health problems. Occup. Environ. Med. 60(Suppl 1), i77–i82 (2003)CrossRefGoogle Scholar
  13. 13.
    Guo, Y., Hu, X., Hu, B., Cheng, J., Zhou, M., Kwok, R.Y.: Mobile cyber physical systems: current challenges and future networking applications. IEEE Access 6, 12360–12368 (2017)CrossRefGoogle Scholar
  14. 14.
    Hu, X., et al.: Emotion-aware cognitive system in multi-channel cognitive radio ad hoc networks. IEEE Commun. Mag. 56(4), 180–187 (2018)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Al-Anazi, A., Gates, I.D.: A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng. Geol. 114(3-4), 267–277 (2010)CrossRefGoogle Scholar
  17. 17.
    Cover, T., Thomas, M., Peter, E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  18. 18.
    Zhang, B., Srihari, S.N.: Fast k-nearest neighbor classification using cluster based trees. IEEE Trans. Pattern Anal. Mach. Intell. 26(4), 525528 (2004)Google Scholar
  19. 19.
    Hart, P.: The condensed nearest neighbor rule (Corresp.). IEEE Trans. Inf. Theory 14(3), 515–516 (1968)CrossRefGoogle Scholar
  20. 20.
    Yu, X.-G., Yu, X.-P.: The research on an adaptive k-nearest neighbors classifier. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 1241–1246. IEEE (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minqiang Yang
    • 1
  • Jingsheng Tang
    • 2
  • Longzhe Tang
    • 2
  • Bin Hu
    • 1
    • 3
    • 4
    Email author
  1. 1.Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Life SciencesLanzhou UniversityLanzhouChina
  3. 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesBeijingChina
  4. 4.Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina

Personalised recommendations