Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot
Abstract
:1. Introduction
Research Questions and Hypotheses
2. Methods
2.1. Participants and Procedures
2.2. Measurements
2.3. Data Analysis
3. Results
3.1. Participant Characteristics
3.2. Motives for Depression-Related Digital Technology Use
3.3. Factors Influencing Intentions to Use MyMentalPocket
3.4. Factors Influencing Intentions to Perceived Usefulness
3.5. Factors Influencing Intentions to Parasocial Interactions
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- World Health Organization. The Impact of COVID-19 on Mental, Neurological and Substance Use Services: Results of a Rapid Assessment. 2020. Available online: https://www.who.int/publications/i/item/978924012455 (accessed on 6 October 2022).
- Organisation for Economic Co-Operation and Development. Tackling the Mental Health Impact of the COVID-19 Crisis: An Integrated, Whole-of-Society Response. 2021. Available online: https://www.oecd.org/coronavirus/policy-responses/tackling-the-mental-health-impact-of-the-covid-19-crisis-an-integrated-whole-of-society-response-0ccafa0b/ (accessed on 6 October 2022).
- World Health Organization. Depression. 2021. Available online: https://www.who.int/news-room/fact-sheets/detail/depression (accessed on 6 October 2022).
- Park, S.J.; Kim, S.Y.; Lee, E.-S.; Park, S. Associations among employment status, health behaviors, and mental health in a representative sample of South Koreans. Int. J. Environ. Res. Public Health 2020, 17, 2456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chang, S.M.; Hong, J.P.; Cho, M.J. Economic burden of depression in South Korea. Soc. Psychiatry Psychiatr. Epidemiol. 2012, 47, 683–689. [Google Scholar] [CrossRef] [PubMed]
- Zomer, E.; Rhee, Y.; Liew, D.; Ademi, Z. The health and productivity burden of depression in South Korea. Appl. Health Econ. Health Policy 2021, 19, 941–951. [Google Scholar] [CrossRef] [PubMed]
- Ranta, K. The Impact of User Acceptance in the Efficacy of Digital Therapeutics. Master’s Thesis, University of Jyväskylä, Jyväskylä, Finland, 2019. [Google Scholar]
- Leung, R.; Hastings, J.F.; Keefe, R.H.; Brownstein-Evans, C.; Chan, K.T.; Mullick, R. Building mobile apps for underrepresented mental health care consumers: A grounded theory approach. Soc. Work Ment. Health 2016, 14, 625–636. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lord, S.E.; Campbell, A.N.C.; Brunette, M.F.; Cubillos, L.; Bartels, S.M.; Torrey, W.C.; Olson, A.L.; Chapman, S.H.; Batsis, J.A.; Polsky, D.; et al. Workshop on implementation science and digital therapeutics for behavioral health. JMIR Ment. Health 2021, 8, e17662. [Google Scholar] [CrossRef] [PubMed]
- Saad, A.; Bruno, D.; Camara, B.; D’Agostino, J.; Bolea-Alamanac, B. Self-directed technology-based therapeutic methods for adult patients receiving mental health services: Systematic review. JMIR Ment. Health 2021, 8, e27404. [Google Scholar] [CrossRef]
- Beevers, C.G.; Pearson, R.; Hoffman, J.S.; Foulser, A.A.; Shumake, J.; Meyer, B. Effectiveness of an internet intervention (Deprexis) for depression in a united states adult sample: A parallel-group pragmatic randomized controlled trial. J. Consult. Clin. Psychol. 2017, 85, 367–380. [Google Scholar] [CrossRef]
- Economides, M.; Ranta, K.; Nazander, A.; Hilgert, O.; Goldin, P.R.; Raevuori, A.; Forman-Hoffman, V. Long-term outcomes of a therapist-supported, smartphone-based intervention for elevated symptoms of depression and anxiety: Quasiexperimental, pre-postintervention study. JMIR mHealth uHealth 2019, 7, e14284. [Google Scholar] [CrossRef]
- Stratton, E.; Lampit, A.; Choi, I.; Malmberg Gavelin, H.; Aji, M.; Taylor, J.; Calvo, R.A.; Harvey, S.B.; Glozier, N. Trends in effectiveness of organizational eHealth interventions in addressing employee mental health: Systematic review and meta-analysis. J. Med. Internet Res. 2022, 24, e37776. [Google Scholar] [CrossRef]
- Twomey, C.; O’Reilly, G.; Bültmann, O.; Meyer, B. Effectiveness of a tailored, integrative Internet intervention (deprexis) for depression: Updated meta-analysis. PLoS ONE 2020, 15, e0228100. [Google Scholar] [CrossRef]
- Li, J.; Theng, Y.-L.; Foo, S. Game-based digital interventions for depression therapy: A systematic review and meta-analysis. Cyberpsychol. Behav. Soc. Netw. 2014, 17, 519–527. [Google Scholar] [CrossRef] [Green Version]
- Twomey, C.; O’Reilly, G.; Byrne, M.; Bury, M.; White, A.; Kissane, S.; McMahon, A.; Clancy, N. A randomized controlled trial of the computerized CBT programme, MoodGYM, for public mental health service users waiting for interventions. Br. J. Clin. Psychol. 2014, 53, 433–450. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. Manag. Inf. Syst. 1989, 13, 319–339. [Google Scholar] [CrossRef] [Green Version]
- Davis, F.D.; Venkatesh, V. A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. Int. J. Hum. Comput. Stud. 1996, 45, 19–45. [Google Scholar] [CrossRef] [Green Version]
- Katz, E.; Blumler, J.G.; Gurevitch, M. Uses and gratifications research. Public Opin. Q. 1973, 37, 509–523. [Google Scholar] [CrossRef]
- Venkatesh, V.; Davis, F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
- Palos-Sanchez, P.; Saura, J.R.; Rios Martin, M.A.; Aguayo Camacho, M. Toward a better understanding of the intention to use mHealth apps: Exploratory study. JMIR mHealth uHealth 2021, 9, e27021. [Google Scholar] [CrossRef]
- Schueller, S.M.; Neary, M.; O’Loughlin, K.; Adkins, E.C. Discovery of and interest in health apps among those with mental health needs: Survey and focus group study. J. Med. Internet Res. 2018, 20, e10141. [Google Scholar] [CrossRef]
- Grindrod, K.A.; Li, M.; Gates, A. Evaluating user perceptions of mobile medication management applications with older adults: A usability study. JMIR mHealth uHealth 2014, 2, e11. [Google Scholar] [CrossRef]
- Cajita, M.I.; Hodgson, N.A.; Budhathoki, C.; Han, H.-R. Intention to use mHealth in older adults with heart failure. J. Cardiovasc. Nurs. 2017, 32, E1–E7. [Google Scholar] [CrossRef]
- Povey, J.; Mills, P.P.J.R.; Dingwall, K.M.; Lowell, A.; Singer, J.; Rotumah, D.; Bennett-Levy, J.; Nagel, T. Acceptability of mental health apps for Aboriginal and Torres Strait Islander Australians: A qualitative study. J. Med. Internet Res. 2016, 18, e65. [Google Scholar] [CrossRef] [PubMed]
- Chan, A.H.Y.; Honey, M.L.L. User perceptions of mobile digital apps for mental health: Acceptability and usability—An integrative review. J. Psychiatr. Ment. Health Nurs. 2022, 29, 147–168. [Google Scholar] [CrossRef] [PubMed]
- Keller, A.S.; Leikauf, J.E.; Holt-Gosselin, B.; Staveland, B.R.; Williams, L.M. Paying attention to attention in depression. Transl. Psychiatry 2019, 9, 279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zuckerman, H.; Pan, Z.; Park, C.; Brietzke, E.; Musial, N.; Shariq, A.S.; Iacobucci, M.; Yim, S.J.; Lui, L.M.W.; Rong, C.; et al. Recognition and treatment of cognitive dysfunction in major depressive disorder. Front. Psychiatry 2018, 9, 655. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lazard, A.J.; Watkins, I.; Mackert, M.S.; Xie, B.; Stephens, K.K.; Shalev, H. Design simplicity influences patient portal use: The role of aesthetic evaluations for technology acceptance. J. Am. Med. Inform. Assoc. JAMIA 2016, 23, e157–e161. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.; Lee, D.; Park, Y.; Lee, S.; Ha, T. Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles. Transp. Res. C: Emerg. Technol. 2019, 107, 411–422. [Google Scholar] [CrossRef]
- Liao, Y.K.; Wu, W.Y.; Le, T.Q.; Phung, T.T.T. The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: The moderating role of e-WOM. Sustainability 2022, 14, 815. [Google Scholar] [CrossRef]
- Rubin, A.M.; Haridakis, P.M.; Hullman, G.A.; Sun, S.; Chikombero, P.M.; Pornsakulvanich, V. Television exposure not predictive of terrorism fear. Newsp. Res. J. 2003, 24, 128–145. [Google Scholar] [CrossRef]
- Rubin, A.M.; Step, M.M. Viewing television talk shows. Commun. Res. Rep. 1997, 14, 106–115. [Google Scholar] [CrossRef]
- Rubin, A.M.; Perse, E.M. Audience activity and television news gratifications. Commun. Res. 1987, 14, 58–84. [Google Scholar] [CrossRef]
- Park, D.Y.; Goering, E.M. The health-related uses and gratifications of YouTube: Motive, cognitive involvement, online activity, and sense of empowerment. J. Consum. Health Internet 2016, 20, 52–70. [Google Scholar] [CrossRef] [Green Version]
- Meeks, K.; Peak, A.S.; Dreihaus, A. Depression, anxiety, and stress among students, faculty, and staff. J. Am. Coll. Health 2021. [Google Scholar] [CrossRef]
- Yzer, M.; Gilasevitch, J. Beliefs underlying stress reduction and depression help-seeking among college students: An elicitation study. J. Am. Coll. Health 2019, 67, 153–160. [Google Scholar] [CrossRef]
- Goldsmith, D.J. Communicating Social Support; Cambridge University Press: New York, NY, USA, 2004. [Google Scholar] [CrossRef]
- Kim, E.J.; Yu, J.H.; Kim, E.Y. Pathways linking mental health literacy to professional help-seeking intentions in Korean college students. J. Psychiatr. Ment. Health Nurs. 2020, 27, 393–405. [Google Scholar] [CrossRef]
- Joo, J.; Sang, Y. Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Comput. Hum. Behav. 2013, 29, 2512–2518. [Google Scholar] [CrossRef]
- Horton, D.; Richard Wohl, R. Mass communication and para-social interaction. Psychiatry 1956, 19, 215–229. [Google Scholar] [CrossRef]
- Rubin, A.M.; Perse, E.M. Audience activity and soap opera involvement a uses and effects investigation. Hum. Commun. Res. 1987, 14, 246–268. [Google Scholar] [CrossRef]
- Rubin, A.M.; Perse, E.M.; Powell, R.A. Loneliness, parasocial interaction, and local television news viewing. Hum. Commun. Res. 1985, 12, 155–180. [Google Scholar] [CrossRef]
- Sokolova, K.; Perez, C. You follow fitness influencers on YouTube. But do you actually exercise? How parasocial relationships, and watching fitness influencers, relate to intentions to exercise. J. Retail. Consum. Serv. 2021, 58, 102276. [Google Scholar] [CrossRef]
- Bernhold, Q.S.; Metzger, M. Older Adults’ Parasocial relationships with favorite television characters and depressive symptoms. Health Commun. 2018, 35, 168–179. [Google Scholar] [CrossRef]
- Matthews, E.B.; Savoy, M.; Paranjape, A.; Washington, D.; Hackney, T.; Galis, D.; Zisman-Ilani, Y. Shared decision making in primary care based depression treatment: Communication and decision-making preferences among an underserved patient population. Front. Psychiatry 2021, 12, 681165. [Google Scholar] [CrossRef] [PubMed]
- Do, R.; Park, J.-R.; Lee, S.-Y.; Cho, M.-J.; Kim, J.-S.; Shin, M.-S. Adolescents’ attitudes and intentions toward help-seeking and computer-based treatment for depression. Psychiatry Investig. 2019, 16, 728–736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peter, J.; Valkenburg, P.M.; Schouten, A.P. Characteristics and motives of adolescents talking with strangers on the Internet. Cyberpsychol. Behav. 2006, 9, 526–530. [Google Scholar] [CrossRef] [PubMed]
- Tsai, W.-H.S.; Liu, Y.; Chuan, C.-H. How chatbots’ social presence communication enhances consumer engagement: The mediating role of parasocial interaction and dialogue. J. Res. Interact. Mark. 2021, 15, 460–482. [Google Scholar] [CrossRef]
- Tavares, J.V.T.; Clark, L.; Furey, M.L.; Williams, G.B.; Sahakian, B.J.; Drevets, W.C. Neural basis of abnormal response to negative feedback in unmedicated mood disorders. NeuroImage 2008, 42, 1118–1126. [Google Scholar] [CrossRef] [Green Version]
- Whang, C.; Im, H. “I like your suggestion!” the role of humanlikeness and parasocial relationship on the website versus voice shopper’s perception of recommendations. Psychol. Mark. 2020, 38, 581–595. [Google Scholar] [CrossRef]
- Choi, S.; Kim, I.; Cha, K.; Suh, Y.-K.; Kim, K.-H. Travelers’ parasocial interactions in online travel communities. J. Travel Tour. Mark. 2019, 36, 888–904. [Google Scholar] [CrossRef]
- Blight, M.G.; Ruppel, E.K.; Schoenbauer, K.V. Sense of community on twitter and instagram: Exploring the roles of motives and parasocial relationships. Cyberpsychol. Behav. Soc. Netw. 2017, 20, 314–319. [Google Scholar] [CrossRef]
- Tsay, M.; Bodine, B.M. Exploring parasocial interaction in college students as a multidimensional construct: Do personality, interpersonal need, and television motive predict their relationships with media characters? Psychol. Pop. Media Cult. 2012, 1, 185–200. [Google Scholar] [CrossRef]
- Kim, H. Unpacking unboxing video-viewing motivations: The uses and gratifications perspective and the mediating role of parasocial interaction on purchase intent. J. Interact. Advert. 2020, 20, 196–208. [Google Scholar] [CrossRef]
- Zhao, C.; Shen, H.; Zhang, Y. The study on the impact of short video tourism vloggers at social media platform on online sharing intention. Front. Psychol. 2022, 13, 905002. [Google Scholar] [CrossRef]
- MyMentalPocket. Available online: https://mymentalpocket.com/ (accessed on 6 October 2022).
- Yonhap News Agency. S. Korea to Research Digital Treatment for Depression Amid Pandemic. 2021. Available online: https://en.yna.co.kr/view/AEN20210715001100320 (accessed on 6 October 2022).
- Papacharissi, Z.; Rubin, A.M. Predictors of internet use. J. Broadcast. Electron. Media 2000, 44, 175–196. [Google Scholar] [CrossRef]
- Rubin, A.M. An examination of television viewing motivations. Commun. Res. 1981, 8, 141–165. [Google Scholar] [CrossRef]
- Rubin, R.B.; Perse, E.M.; Barbato, C.A. Conceptualization and measurement of interpersonal communication motives. Hum. Commun. Res. 1988, 14, 602–628. [Google Scholar] [CrossRef]
- Hatcher, R.L.; Gillaspy, J.A. Development and validation of a revised short version of the working alliance inventory. Psychother. Res. 2006, 16, 12–25. [Google Scholar] [CrossRef]
- Yeum, J.-Y.; Hong, S.-C.; Jeong, J.-H.; Kim, T.-W.; Um, Y.-H.; Kim, S.-M.; Seo, H.-J. The reliability and validity of the Korean version of Working Alliance Inventory-Short Revised (WAI-SR-K). Anxiety Mood 2017, 13, 132–140. [Google Scholar] [CrossRef]
- Furneaux; Wade. An exploration of organizational level information systems discontinuance intentions. MIS Q. Manag. Inf. Syst. 2011, 35, 573. [Google Scholar] [CrossRef]
- Oliveira, T.; Thomas, M.; Baptista, G.; Campos, F. Mobile Payment: Understanding the determinants of customer adoption and intention to recommend the technology. Comput. Hum. Behav. 2016, 61, 404–414. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q.: Manag. Inf. Syst. 2003, 27, 425–478. [Google Scholar] [CrossRef]
- Torous, J.; Lipschitz, J.; Ng, M.; Firth, J. Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis. J. Affect. Disord. 2020, 263, 413–419. [Google Scholar] [CrossRef]
- Porter, C.E.; Donthu, N. Using the technology acceptance model to explain how attitudes determine internet usage: The role of perceived access barriers and demographics. J. Bus. Res. 2006, 59, 999–1007. [Google Scholar] [CrossRef]
- Zhong, Y.; Oh, S.; Moon, H.C. Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technol. Soc. 2021, 64, 101515. [Google Scholar] [CrossRef]
- An, J.-Y.; Seo, E.-R.; Lim, K.-H.; Shin, J.H.; Kim, J.B. Standardization of the Korean version of screening tool for depression (Patient Health Questionnaire-9, PHQ-9). J. Korean Soc. Biol. Ther. Psychiatry 2013, 19, 47–56. [Google Scholar] [CrossRef] [Green Version]
- Kroenke, K.; Spitzer, R.L.; Williams, J.B.W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef] [PubMed]
- Costantini, L.; Pasquarella, C.; Odone, A.; Colucci, M.E.; Costanza, A.; Serafini, G.; Aguglia, A.; Murri, M.B.; Brakoulias, V.; Amore, M.; et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review. J. Affect. Disord. 2021, 279, 473–483. [Google Scholar] [CrossRef]
- Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [Green Version]
- Dogruel, L.; Joeckel, S.; Bowman, N.D. The use and acceptance of new media entertainment technology by elderly users: Development of an expanded technology acceptance model. Behav. Inf. Technol. 2015, 34, 1052–1063. [Google Scholar] [CrossRef]
- Cohen, J.; Cohen, P.; West, S.G.; Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed.; Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, USA, 2003. [Google Scholar]
- Thompson, S.; O’Hair, H.D. Advice-giving and the management of uncertainty for cancer survivors. Health Commun. 2008, 23, 340–348. [Google Scholar] [CrossRef]
- Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, 3rd ed.; Guilford Publications: New York, NY, USA, 2022. [Google Scholar]
- Kerr, B.; D’Angelo, J.D.; Diaz-Caballero, A.; Moreno, M.A. College student problematic internet use and digital communication medium used with parents: Cross-sectional study. JMIR Pediatr. Parent. 2020, 3, e17165. [Google Scholar] [CrossRef]
- Litman, L.; Rosen, Z.; Spierer, D.; Weinberger-Litman, S.; Goldschein, A.; Robinson, J. Mobile exercise apps and increased leisure time exercise activity: A moderated mediation analysis of the role of self-efficacy and barriers. J. Med. Internet Res. 2015, 17, e4142. [Google Scholar] [CrossRef]
- Sousa, C.V.; Fernandez, A.; Hwang, J.; Lu, A.S. The effect of narrative on physical activity via immersion during active video game play in children: Mediation analysis. J. Med. Internet Res. 2020, 22, e17994. [Google Scholar] [CrossRef]
- Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
- Pitardi, V.; Wirtz, J.; Paluch, S.; Kunz, W.H. Service robots, agency and embarrassing service encounters. J. Serv. Manag. 2022, 33, 389–414. [Google Scholar] [CrossRef]
- Sun, H.; Gu, C. Understanding determinants of end-user’s continuance intention on fitness wearable technology. Int. J. Hum.-Comput. Interact. 2022. [Google Scholar] [CrossRef]
- Rubin, A.M. Television uses and gratifications: The interactions of viewing patterns and motivations. J. Broadcast. 1983, 27, 37–51. [Google Scholar] [CrossRef]
- Rubin, A.M. Uses of daytime television soap operas by college students. J. Broadcast. Electron. Media 1985, 29, 241–258. [Google Scholar] [CrossRef]
- Brinker, J.; Cheruvu, V.K. Social and emotional support as a protective factor against current depression among individuals with adverse childhood experiences. Prev. Med. Rep. 2017, 5, 127–133. [Google Scholar] [CrossRef]
- Cabassa, L.J.; Oh, H.; Humensky, J.L.; Unger, J.B.; Molina, G.B.; Baron, M. Comparing the impact on latinos of a depression brochure and an entertainment-education depression fotonovela. Psychiatr. Serv. 2015, 66, 313–316. [Google Scholar] [CrossRef] [Green Version]
- Gonzalez, F.; Benuto, L.T. ¡Yo no Estoy Loca! a behavioral health telenovela style entertainment education video: Increasing mental health literacy among Latinas. Community Ment. Health J. 2022, 58, 850–861. [Google Scholar] [CrossRef]
- Dixon De Silva, L.E. Entertainment Education for Depression in Latin Adults: Testing Mediators and Moderators of a Culture-Centric Narrative Intervention to Promote Help-Seeking Behavior. Ph.D. Dissertation, University of California, Los Angeles, CA, USA, 2021. [Google Scholar]
- Kim, Y.; Hong, S.; Choi, M. Effects of serious games on depression in older adults: Systematic review and meta-analysis of randomized controlled trials. J. Med. Internet Res. 2022, 24, e37753. [Google Scholar] [CrossRef]
- Ruiz, M.; Moreno, M.; Girela-Serrano, B.; Díaz-Oliván, I.; Muñoz, L.J.; González-Garrido, C.; Porras-Segovia, A. Winning the game against depression: A systematic review of video games for the treatment of depressive disorders. Curr. Psychiatry Rep. 2022, 24, 23–35. [Google Scholar] [CrossRef] [PubMed]
- Carroll, J.K.; Moorhead, A.; Bond, R.; LeBlanc, W.G.; Petrella, R.J.; Fiscella, K. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J. Med. Internet Res. 2017, 19, e125. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kontos, E.; Blake, K.D.; Chou, W.-Y.S.; Prestin, A. Predictors of eHealth usage: Insights on the digital divide from the Health Information National Trends Survey 2012. J. Med. Internet Res. 2014, 16, e172. [Google Scholar] [CrossRef] [PubMed]
- Tennant, B.; Stellefson, M.; Dodd, V.; Chaney, B.; Chaney, D.; Paige, S.; Alber, J. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J. Med. Internet Res. 2015, 17, e70. [Google Scholar] [CrossRef]
- Chen, B.; Liu, F.; Ding, S.; Ying, X.; Wang, L.; Wen, Y. Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BMC Psychiatry 2017, 17, 341. [Google Scholar] [CrossRef] [Green Version]
- Emslie, C.; Ridge, D.; Ziebland, S.; Hunt, K. Exploring men’s and women’s experiences of depression and engagement with health professionals: More similarities than differences? A qualitative interview study. BMC Fam. Pract. 2007, 8, 43. [Google Scholar] [CrossRef] [Green Version]
- Moradbakhti, L.; Schreibelmayr, S.; Mara, M. Do men have no need for “feminist” artificial intelligence? Agentic and gendered voice assistants in the light of basic psychological needs. Front. Psychol. 2022, 13, 855091. [Google Scholar] [CrossRef]
- Toader, D.-C.; Boca, G.; Toader, R.; Măcelaru, M.; Toader, C.; Ighian, D.; Rădulescu, A.T. The effect of social presence and chatbot errors on trust. Sustainability 2020, 12, 256. [Google Scholar] [CrossRef] [Green Version]
- Fuller-Tyszkiewicz, M.; Richardson, B.; Klein, B.; Skouteris, H.; Christensen, H.; Austin, D.; Castle, D.; Mihalopoulos, C.; O’Donnell, R.; Arulkadacham, L.; et al. A mobile app–based intervention for depression: End-user and expert usability testing study. JMIR Ment. Health 2018, 5, e54. [Google Scholar] [CrossRef] [Green Version]
- Goodwin, J.; Cummins, J.; Behan, L.; O’Brien, S.M. Development of a mental health smartphone app: Perspectives of mental health service users. J. Ment. Health 2016, 25, 434–440. [Google Scholar] [CrossRef]
- Park, D.Y.; Goering, E.M.; Head, K.J.; Bartlett Ellis, R.J. Implications for training on smartphone medication reminder app use by adults with chronic conditions: Pilot study applying the technology acceptance model. JMIR Form. Res. 2017, 1, e5. [Google Scholar] [CrossRef] [Green Version]
- Park, D.Y. A Theory-Based mHealth Intervention to Improve Medication Adherence by Adults with Chronic Conditions: Technology Acceptance Model-Based Smartphone Medication Reminder App Training Session. Ph.D. dissertation, Indiana University, Bloomington, IN, USA.
- Lee, S.; Jeon, Y.; Yoon, M.-S. Dual mediating effects of changes in daily life and anxiety on the relationship between occupation and depression in Korea during the COVID-19 pandemic. BMC Public Health 2022, 22, 1492. [Google Scholar] [CrossRef]
- Turvey, C.L.; Jogerst, G.; Kim, M.Y.; Frolova, E. Cultural differences in depression-related stigma in late-life: A comparison between the USA, Russia, and South Korea. Int. Psychogeriatr. 2012, 24, 1642–1647. [Google Scholar] [CrossRef]
- Park, K.H.; Kim, H.; Kim, J. Moderating effect of mindfulness on the influence of stress on depression according to the level of stress among university students in South Korea. Int. J. Environ. Res. Public Health 2020, 17, 6634. [Google Scholar] [CrossRef]
- Pflanz, S.E.; Ogle, A.D. Job stress, depression, work performance, and perceptions of supervisors in military personnel. Mil. Med. 2006, 171, 861–865. [Google Scholar] [CrossRef] [Green Version]
- Onyeaka, H.; Firth, J.; Enemuo, V.; Muoghalu, C.; Naslund, J.; Baiden, P.; Torous, J. Exploring the association between electronic wearable device use and levels of physical activity among individuals with depression and anxiety: A population level study. Front. Digit. Health 2021, 3, 707900. [Google Scholar] [CrossRef]
- Onyeaka, H.; Firth, J.; Kessler, R.C.; Lovell, K.; Torous, J. Use of smartphones, mobile apps and wearables for health promotion by people with anxiety or depression: An analysis of a nationally representative survey data. Psychiatry Res. 2021, 304, 114120. [Google Scholar] [CrossRef]
- Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behavior; Kuhl, J., Beckmann, J., Eds.; Springer-Verlag: Berlin/Heidelberg, Germany, 1985; pp. 11–39. [Google Scholar]
- Klein, R. Internet-based patient-physician electronic communication applications: Patient acceptance and trust. E-Serv. J. 2007, 5, 27–52. [Google Scholar] [CrossRef]
- Sicotte, C.; Taylor, L.; Tamblyn, R. Predicting the use of electronic prescribing among early adopters in primary care. Can. Fam. Physician 2013, 59, e312–e321. [Google Scholar]
- Amagai, S.; Pila, S.; Kaat, A.J.; Nowinski, C.J.; Gershon, R.C. Challenges in participant engagement and retention using mobile health apps: Literature review. J. Med. Internet Res. 2022, 24, e35120. [Google Scholar] [CrossRef]
- Daniore, P.; Nittas, V.; von Wyl, V. Enrollment and retention of participants in remote digital health studies: A scoping review and framework proposal. J. Med. Internet Res. 2022, 24, e39910. [Google Scholar] [CrossRef] [PubMed]
- Wang, T.; Wang, W.; Liang, J.; Nuo, M.; Wen, Q.; Wei, W.; Han, H.; Lei, J. Identifying major impact factors affecting the continuance intention of mHealth: A systematic review and multi-subgroup meta-analysis. Npj Digit. Med. 2022, 5, 145. [Google Scholar] [CrossRef] [PubMed]
Intentions to Use | |||
---|---|---|---|
Variable | Step 1 | Step 2 | Step 3 |
Gender | 0.14 * | 0.14 * | 0.03 |
Age | −0.19 ** | −0.17 ** | −0.02 |
Depression | 0.04 | 0.08 | 0.10 * |
Communication and emotional support motive | 0.14 | 0.06 | |
Information- and guidance-seeking motive | 0.15 * | −0.01 | |
Habitual entertainment-seeking motive | 0.14 | −0.01 | |
Escape motive | 0.01 | 0.02 | |
Perceived usefulness | 0.37 *** | ||
Perceived ease of use | 0.06 | ||
Parasocial interactions | 0.46 *** | ||
R2 | 0.06 | 0.14 | 0.66 |
∆R2 | 0.08 *** | 0.52 *** |
Perceived Usefulness | |||
---|---|---|---|
Variable | Step 1 | Step 2 | Step 3 |
Gender | 0.09 | 0.08 | −0.06 |
Age | −0.19 ** | −0.18 ** | −0.02 |
Depression | −0.06 | −0.03 | 0.00 |
Communication and emotional support motive | 0.05 | −0.01 | |
Information- and guidance-seeking motive | 0.21 ** | 0.09 | |
Habitual entertainment-seeking motive | 0.11 | −0.04 | |
Escape motive | 0.01 | 0.01 | |
Perceived ease of use | 0.22 *** | ||
Parasocial interactions | 0.64 *** | ||
R2 | 0.05 | 0.12 | 0.57 |
∆R2 | 0.07 ** | 0.45 *** |
Parasocial Interactions | ||
---|---|---|
Variable | Step 1 | Step 2 |
Gender | 0.19 ** | 0.18 ** |
Age | −0.18 ** | −0.16 ** |
Depression | −0.08 | −0.03 |
Communication and emotional support motive | 0.16 * | |
Information- and guidance-seeking motive | 0.14 * | |
Habitual entertainment-seeking motive | 0.22 ** | |
Escape motive | −0.04 | |
R2 | 0.08 | 0.18 |
∆R2 | 0.10 *** |
Pathways | Indirect Effect | Boot SE | Boot LLCI | Boot ULCI |
---|---|---|---|---|
PEOU → PU → IU | 0.38 | 0.06 | 0.26 | 0.50 |
CES → PSI → PU → IU | 0.07 | 0.02 | 0.03 | 0.12 |
IGS → PSI → PU → IU | 0.07 | 0.02 | 0.02 | 0.12 |
HES → PSI → PU → IU | 0.07 | 0.02 | 0.03 | 0.11 |
GEN → PSI → PU → IU | 0.09 | 0.03 | 0.03 | 0.17 |
AGE → PSI → PU → IU | –0.01 | 0.00 | –0.02 | –0.01 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Park, D.Y.; Kim, H. Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability 2023, 15, 872. https://doi.org/10.3390/su15010872
Park DY, Kim H. Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability. 2023; 15(1):872. https://doi.org/10.3390/su15010872
Chicago/Turabian StylePark, Daniel Y., and Hyungsook Kim. 2023. "Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot" Sustainability 15, no. 1: 872. https://doi.org/10.3390/su15010872
APA StylePark, D. Y., & Kim, H. (2023). Determinants of Intentions to Use Digital Mental Healthcare Content among University Students, Faculty, and Staff: Motivation, Perceived Usefulness, Perceived Ease of Use, and Parasocial Interaction with AI Chatbot. Sustainability, 15(1), 872. https://doi.org/10.3390/su15010872