The prediction of behavioral intention to use online mental health interventions - PubMed (original) (raw)

doi: 10.1002/pchj.333. Epub 2020 Jan 19.

Affiliations

The prediction of behavioral intention to use online mental health interventions

Krittipat Chuenphitthayavut et al. Psych J. 2020 Jun.

Abstract

This cross-sectional study addresses two important contributions to understanding behavioral intention to use online mental health interventions: (a) to investigate the proposed behavioral intention model in predicting the use of online mental health interventions, and (b) to compare the proposed behavioral intention model in predicting the use of online mental health interventions between Chinese and Thai patients and between healthy individuals and those with mental health disorders. The samples included 250 Chinese respondents and 251 Thai respondents. Data were collected using Likert questionnaires with a reliability of 0.75 to 0.95 and analyzed via confirmatory factor analysis and multigroup structural equation modeling. The structural model of behavioral intention to use online mental health intervention was shown the significant acceptances on indices in overall group. However, the result did not enhance the previous literature review according to the proposed behavioral intention model. Most latent variables in this study did not show an influence on behavioral intention to use online mental health services. Individual characteristics, particularly mental health literacy, only significantly predicted behavioral intention to use online mental health interventions. However, significant influences in some observed variables were detected, such as informational support and emotional support in social support, including media and the general public in social influence. Comparing the multigroup model, no difference in the causal relationship model between Chinese and Thai patients was found, but a difference between healthy individuals and those with mental health disorders, especially individual characteristics to behavioral intention path and social support to attitude toward use path, was found. Results highlight the benefits of further development and patient education of online mental health services in the future.

Keywords: depression; digital cognitive behavior therapy (CBT); e-mental health services; suicide ideation; telemedicine.

© 2020 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

PubMed Disclaimer

Similar articles

Cited by

References

    1. Allport, G. (1935). Attitudes. In C. Murchison (Ed.), A handbook of social psychology (pp. 789-844). Worcester, MA: Clark University Press.
    1. Agarwal, R., & Prasad, J. A. (1998). Conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
    1. Ajzen, I. (2012). The theory of planned behavior. In A. M. V. L. Paul, W. K. Arie, & T. Higgins (Eds.), Handbook of theories of social psychology (pp. 438-459). Thousand Oaks, CA: SAGE Publications Inc. https://doi.org/10.4135/9781446249215.n22
    1. Alzahrani, A. I., Mahmud, I., Ramayah, T., Alfarraj, O., & Alalwan, N. (2016). Extending the theory of planned behavior (TPB) to explain online game playing among Malaysian undergraduate students. Telematics and Informatics. https://doi.org/10.1016/j.tele.2016.07.001
    1. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411

MeSH terms

Grants and funding

LinkOut - more resources