Rule-Based Chatbot for Early Self-Depression Indication: A Promising Approach

Wan Nurhayati Wan Ab.Rahman - Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
Nurul Munirah Abdul Hamid - Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.1628

Abstract


Depression is a prevalent mental health condition worldwide, often characterized by persistent sadness, loss of interest or pleasure, and feelings of worthlessness. Depression is the leading cause of mental health issues worldwide, and it is becoming more severe without self-awareness, early screening, and further medication. Early detection and intervention are critical in mitigating its adverse effects. Leveraging advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), chatbots have emerged as potential tools for early depression indication. Chatbots are beneficial tools in the mental health domain, such as in assisting mental health risk users. This paper presents the development of a rule-based chatbot aimed at detecting early signs of depression through conversational interactions by screening symptoms of depression. Predefined rules are developed to ensure the assessment can generate reliable results. The rule-based chatbot is developed to assist in depression indication assessment for mental health-risk individuals at an early stage and provide the risky patient with appropriate support and resources. The chatbot assessment has adopted the Depression Anxiety and Stress Scale 21 (DASS21) instrument. Based on the System Usability Scale (SUS) results, the rule-based chatbot has been accepted by all 30 respondents with good acceptance of an average SUS score of 77.2. Thus, the outcome of this chatbot can be utilized as a professional platform to encourage self-disclosure of mental depression indications for users, and it can be beneficial as the initial reference before recommending further action before the earlier help-seeking.

Keywords


Depression; chatbot; early indication; rule-based chatbot; predefined rules; self-depression indication

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References


World Health Organization (WHO): Depressive disorder (depression), 2023, [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/depression current September 2023)

Ministry of Health Malaysia (MOH): NCDs – Non-communicable diseases: Risk factors and other health problems, National Health and Morbidity Survey 2019, Technical Report, vol. 1, Institute for Public Health, National Institute of Health, 2020, ISBN: e978-967-18159-2-2, [Online]. Available: https://iku.gov.my/images/IKU/Document/REPORT/NHMS2019/Report_NHMS2019-NCD_v2.pdf (current September 2023)

B. Lew, K. Kolves, D. Lester, W. S. Chen, N. Ibrahim, N. R. Khamal, F. Mustapha, C. M. H. Chan, N. Ibrahim, C. S. Siau, and L. F. Chan, “Looking into recent suicide rates and trends in Malaysia: A comparative analysis,” Frontiers in Psychiatry, vol. 12, no. 770252, doi: 10.3389/fpsyt.2021.770252, 2022

A. Maddock, “The relationships between stress, burnout, mental health and well-being in social workers,” The British Journal of Social Network, vol. 54, issue 2, pp. 668-686, doi: 10.1093/bjsw/bcad232, 2023

R. A. Wajdi, R. Irniza, S. I. Syarifah Norkhadijah, and Z. A. Emilia, “Return to work (RTW) challenges and mental health issues among unemployed persons in Selangor,” Malaysian Journal of Medicine and Health Sciences, eISSN: 2636-9346, vol. 10, pp. 161-168, 2023

B. Agyapong, G.O. Donkor, L. Burback, and Y. Wei,”Stress, burnout, anxiety, and depression among teachers: A scoping review,” International Journal Environment Research and Public Health, vol. 19, issue 17, doi: 10.3390/ijerph191710706, 2022

N. H. Rusli, S. M. Anua, A. T. Endarti, W. S. H. WanMohdAmin, and N. Mazlan, “Occupational stress among academicians between two selected universities in Malaysia and Indonesia during the covid-19 pandemic: A comparative study,” National Public Health Journal, vol. 18, issue 1, pp. 105-111, doi: 10.21109/kesmas.v18isp1.7052, 2023

E. Unguren, N. Onur, H. Demirel, and O. A. Tekin, “The effect of job stress on burnout and turnover intention: The moderating Effects of job security and financial dependency,” vol. 14, no. 4, doi: 10.3390/bs14040322, 2024

E. L. Y. Loh, J. Z. E. Ean, H. M. Kim, M. W. Loo, and S. Y. Wong, “Factors affecting the subjective financial well-being of emerging adults in Malaysia,” (Doctoral dissertation, UTAR), 2021

T.D. Hill, T.R. Leppard, M.V. Miller, A.P. Davis, and V.R. Zapata, “Pandemic job separation and psychological distress: Modeling chains of adversity,” Journal of Social Currents, vol. 10, issue 5, pp. 465-499, doi: 10.1177/23294965231183420, 2023, [Online]. Available: https://journals.sagepub.com/doi/pdf/10.1177/23294965231183420

O. Han, “Factors influencing work-related depression, anxiety and stress among Malaysian fresh graduates,” Asian Journal of Research in Education and Social Sciences, vol. 5, no. 3, pp. 184-198, 2023, [Online]. Available: https://myjms.mohe.gov.my/index.php/ajress/article/view/24250/13270 (current November 2023)

World Health Organization (WHO): COVID-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide, WHO News Release, 2022, [Online]. Available: https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide (current November 2024)

Z. Khawaja, and J.C.B. Pipon, “Your robot therapist is not your therapist: Understanding the role of AI-powered mental health chatbots,” Frontiers Digital Health, vol. 5, doi:10.3389/fdgth.2023.1278186, 2023

E. Bendig, B. Erb, L. Schulze-Thuesing, and H. Baumeister, “The next generation: Chatbots in clinical psychology and psychotherapy to foster mental health–a scoping review,” Verhaltenstherapie Journal, vol. 32, pp. 64, 2022

A. S. Islind, M. Oskarsdottir, S. A. Smith, and E. S. Arnardottir, “The friendly chatbot: Revealing why people use chatbots through a study of user experience of conversational agents,” In the 14th Scandinavian Conference on Information Systems, pp. 1-16, AIS Electronic Library, 2023, [Online]. Available: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1005&context=scis2023

The top 12 healthcare chatbots: The medical futurist, 2021, [Online]. Available: https://medicalfuturist.com/top-12-health-chatbots (current May 2021)

G. Park, J. Chung, and S. Lee, “Effects of AI chatbot emotional disclosure on user satisfaction and reuse intention for mental health counselling: A serial mediation model,” Current Psychology, vol. 42, pp. 28663-28673, doi: 10.1007/s12144-022-03932-z, 2023

C. Sweeney, C. Potts, E. Ennis, R. Bond, M. D. Mulvenna, S. O’neill, M. Malcolm, L. Kuosmanen, C. Kostenius, A. Vakaloudis, G. Mcconvey, R. Turkington, D. Hanna, H. Nieminen, A. K. Vartiainen, A. Roberstson, and M. F. Mctear, “Can chatbots help support a person’s mental health? Perceptions and views from mental healthcare professionals and experts,” ACM Transactions on Computing Healthcare, vol. 2, no. 3, article 25, pp. 1-15, doi: 10.1145/3453175, 2021

H. Chin, H. Song, G. Baek, M. Shin, C. Jung, M. Cha, J. Choi, and C. Cha, “The potential of chatbots for emotional support and promoting mental well-being in different cultures: Mixed methods study,” Journal Medical Internet Research, 25:e51712, doi: 10.2196/51712, 2023

R. Suraya, Lebih 120,000 panggilan isu kesihatan mental diterima. MyMetro, 2021, [Online]. Available: https://www.hmetro.com.my/mutakhir/2021/05/708501/lebih-120000-panggilan-isu-kesihatan-mental-diterima (current September 2023)

J. Tang and T. Zhang, “Causes of the male-female ratio of depression based on the psychosocial factors,” Frontiers in Psychology, vol. 13, doi: 10.3389/fpsyg.2022.1052702, 2022

F. Dorani, D. Bijlenga, A. T. F. Beekman, E. J. W. V. Someren, and J. J. S. Kooij, “Prevalence of hormone-related mood disorder symptoms in women with ADHD,” Journal of Psychiatric Research, vol. 133, pp.10-15, doi: 10.1016/j.jpsychires.2020.12.005, 2021

A. Thiyagarajan, T. G. James, and R. R. Marzo, “Psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) among Malaysians during covid-19: A methodology study,” Humanities and Social Sciences Communications, vol. 9, no. 220, doi: 10.1057/s41599-022-01229-x, 2022

W. Chen, K. Peng, M. Gao, Z. Meng, L. Wang, and Y. Liao, “Chinese left-behind and non-left-behind children: An exploratory structural equation modelling approach,” BMC Public Health, vol. 24, no. 1660, doi: 10.1186/s12889-024-19160-y, 2024

R. H. Kim, Y. C. Paulino, and Y. Kawabata, “Validating constructs of the Depression, Anxiety, and Stress Scale-21 and exploring health indicators to predict the psychological outcomes of students enrolled in the Pacific Islands cohort of college students,” International Journal Environment Research Health, vol. 21, no. 4, doi: 10.3390/ijerph21040509, 2024

M. G. Masuri, K. A. M. Isa, J. Santhanam, M. S. M. Aris, and I. Ismail, “The development of environment & occupation likelihood scale on workers mental health status,” Environment-Behaviour Proceedings Journal, vol. 6, no. 18, pp. 163-173, 2021

K. Pau, A. Ahmad, H. Y. Tang, A. J. Jusoh, A. Perveen and K. K. Tat, “Mental health and wellbeing of secondary school teachers in Malaysia,” International Journal of Learning, Teaching and Education Research, vol. 21, no. 6, pp. 50-70, doi: 10.26803/ijlter.21.6.4, 2022

E. H. Kim, A. Coumar, W. B. Lober, and Y. Kim, “Addressing mental health epidemic among university students via web-based, self-screening, and referral system: a preliminary study,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 301-307, 2011

L. Costantini, C. Pasquarella, A. Odone, M. E. Colucci, A. Costanza, G. Serafini, and A. Amerio, “Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review,” Journal of Affective Disorders, vol. 279, no. 52, pp. 473-483, 2021

Z. F. Negeri, B. Levis, Y. Sun, C. He, A. Krishnan, Y. Wu, P. M. Bhandari, D. Neupane, E. Brehaut, A. Benedetti, and B. D. Thombs, “Accuracy of the Patient Health Questionnaire-9 for screening to detect major depression: Updated systematic review and individual participant data meta-analysis,” BMJ, vol. 375, no. 2183, doi: 10.1136/bmj.n2183, 2021

E. H. Seo, S. G. Kim, J. H. Lee, C. C. Dickey, M. Y. Kim, R. K. Ghumman, and H. J. Yoon, “Psychosocial factors influencing quality of life among medical students,” Psychiatry Investig, vol. 20, no. 11, pp. 1077-1085, e-ISSN: 1976-3026, doi: 10.30773/pi-2023-0161, 2023

G. S. Joo, D. M. O. Devan, C. S. Qi, and S. S. Patil, “Association between depression, anxiety, stress and perceived quality of life in a Malaysian B40 urban community during the covid-19 lockdown: A cross-sectional study [version 1; peer review: awaiting peer review],” F1000Research, vol. 10, no. 693, doi: 10.12688/f1000research.51924.1, 2021

L. Pirzadi, K. Vakilian, F. F. Azar, and E. Norouzi, “Effect of solution-focused counseling on depression and quality of life in women under mastectomy: A randomized clinical trial,” The Open Public Health Journal, vol. 16, doi: 10.2174/0118749445259180230921113303, 2023

A. Yee, N. A. M. Hodori, Y. Z. Tung, P. L. Ooi, S. A. B. A. Latif, H. M. Isa, and S. B. Tan, “Depression level and coping responses toward the movement control order and its impact on quality of life in the Malaysian community during the COVID-19 pandemic: a web-based cross-sectional study,” Annals of general psychiatry, vol. 20, no. 1, pp. 1-9, 2021

C. He, B. Levis, K. E. Riehm, N. Saadat, A. W. Levis, M. Azar, and A. Benedetti, “The accuracy of the Patient Health Questionnaire-9 algorithm for screening to detect major depression: an individual participant data meta-analysis,” Psychotherapy and psychosomatics, vol. 89, no. 1, pp. 25-37, 2020

M. H. Asgarabad, F. Etesam, P. S. Yegaei, Z. Vahabi, N. A. Saneh, F. Fathi, F. Ghosi, and N. Wiium, “Construct validity of the General Health Questionnaire (GHQ-12) in patients with covid-19 and its demographic and medical correlates,” Frontiers in Psychology, vol. 14, doi: 10.3389/fpsyg.2023.1132154, 2023

R. Musa and R. Maskat, “Psychometric properties of Depression Anxiety Stress Scale 21-item (DASS-21) Malay version among a big sample population,” Mediterranean Journal of Clinical Psychology, vol. 8, no. 1, 2020

S. T. Chong, M. S. Mohamad, and A. C. Er, “The mental health development in Malaysia: History, current issue and future development,” Asian Social Science, vol. 9, no. 6, pp. 1, 2013

C. Oliveira, A. Pereira, P. Vagos, C. Nobrega, J. Goncalves, and B. Afonso, “Effectiveness of mobile app-based psychological interventions for college students: A systematic review of the literature,” Frontiers in Psychology, vol. 12, doi: 10.3389/fpsyg.2021.647606, 2021

F. Khademian, A. Aslani, and P. Bastani, “The effects of mobile apps on stress, anxiety, and depression: overview of systematic reviews,” International journal of technology assessment in health care, vol. 37, no. 1, 2021

F. Diano, L. S. Sica, and M. Ponticorvo, “Empower psychotherapy with mhealth apps: The design of safer, an emotion regulation application,” vol. 14, issue 6, doi:10.3390/info14060308, 2023

B. Doty, A. Grzenda, S. Hwang, S. Godar, D. Gruttadaro, K. A. Hauge, B. Sherman, and D. E. Clarke, “An ecological study of a universal employee depression awareness and stigma reduction intervention: Right direction,” Frontiers in Psychology, vol. 12, doi: 10.3389/fpsyt.2021.581876, 2021

Q. Ma, Y. Shi, W. Zhao, H. Zhang, D. Tan, C. Ji, and L. Liu, “Effectiveness of internet-based self-help interventions for depression in adolescents and young adults: A systematic review and meta-analysis,” BMC Psychiatry, vol. 24, no. 604, 2024

J. L. Yue, W. Yan, Y. K. Sun, K. Yuan, S. Z. Su, Y. Han, and L. Lu, “Mental health services for infectious disease outbreaks including COVID-19: a rapid systematic review,” Psychological Medicine, vol. 50, no. 15, pp. 2498-2513, 2020

A. Yadav, “Usefulness and benefits of telemedicine in psychiatry: A review of literature,” Journal of Pharmaceutical Negative Results, vol. 13, no. 9, pp. 2821-2824, doi: 10.47750/pnr.2022.13.S09.341, 2022

R. Raut, S. Kolambe, P. Borkar, A. Shingade, and M. Gulhane, “Depression therapy chat-bot using natural language processing,” ISSN: 2147-6799, International Journal of Intelligent Systems and Applications in Engineering, vol. 12, pp. 181-187, 2023 [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/3569/2175 (current November 2024)

K. Anttila, M. Anttila, and M. Välimäki, “A web-based adolescent depression support system: feedback and implications for the future,” Informatics for Health and Social Care, vol. 45, no. 2, pp. 111-129, 2020

T. Donker, K. Petrie, J. Proudfoot, J. Clarke, M. R. Birch, and H. Christensen, “Smartphones for smarter delivery of mental health programs: a systematic review,” Journal of medical Internet research, vol. 15, no. 11, e2791, 2013

P. Suta, X. Lan, B. Wu, P. Mongkolnam, and J. Chan, “An overview of machine learning in chatbots,” International Journal Mech Engineer Robotics Res, vol. 9, no. 4, pp. 502-510, 2020

Counselling Unit Universiti Putra Malaysia (UPM): Program saringan kesejahteraan diri UPM, 2022 [Online]. Available: https://script.google.com/a/macros/upm.edu.my/s/AKfycbzhDNuPNh7Wc4Um6q73G4tbADvhc9LJIgOCL2AFNg1FS0yEtNuFYMVyTWohojegsOh1ag/exec (current November 2023)

I. Hungerbuehler, K. Daley, K. Cavanagh, H. G. Claro, and M. Kapps, “Chatbot-based assessment of employees’ mental health: Design process and pilot implementation,” JMIR Formative Research, vol. 5, no. 4, e21678, 2021

A. Abd-Alrazaq, Z. Safi, M. Alajlani, J. Warren, M. Househ, and K. Denecke, “Technical metrics used to evaluate health care chatbots: scoping review,” Journal of medical Internet research, vol. 22, no. 6, e18301, 2020

D. Peras, “Chatbot evaluation metrics,” Economic and Social Development: Book of Proceedings, pp. 89-97, 2018

K. Boyd, C. Potts, R. Bond, M. Mulvenna, T. Broderick, C. Burns, A. Bickerdike, M. Mctear, C. Kostenius, A. Vakaloudis, I. Dhanapala, E. Ennis, and F. Booth, “Usability testing and trust analysis of a mental health and wellbeing chatbot,” In the 33rd European Conference on Cognitive Ergonomics, Germany, October 2022, doi: 10.1145/3552327.3552348

S. Limpanopparat, E. Gibson, and A. Harris, “User engagement, attitudes, and the effectiveness of chatbots as a mental health intervention: A systematic review,” Journal of Computers in Human Behavior: Artificial Humans, vol. 2, issue 2, doi:10.1016/j.chbah.2024.100081, 2024

R. Ren, M. Zapata, J. W. Castro, O. Dieste, and S. T. Acuña, “Experimentation for chatbot usability evaluation: A secondary study,” IEEE Access, vol. 10, pp. 12430-12464, 2022

M.P. Folker, S.L. Nielsen, and M.A. Craggs, “Harnessing the potential of technology for mental healthcare,” Human Matters, vol. 21, no. 3, 2021

E. M. Boucher, N. R. Harake, H. E. Ward, S. E. Stoeckl,J. Vargas, J. Minkel, A. C. Parks, and R. Zilca, “Artificially intelligent chatbots in digital mental health interventions: A review,” Expert Review of Medical Devices, vol. 18, issue sup1: Digital Health, pp. 37-49,2021. doi: 10.1080/17434440.2021.2013200.

H. Liu, H. Peng, X. Song, C. Xu, and M. Zhang, “Using AI chatbots to provide self-help depression interventions for university students: A randomized trial of effectiveness,” Internet Interventions, vol. 27, doi: 10.1016/j.invent.2022.100495, 2022

E. Adamopouloua and L. Moussiades, An overview of chatbot technology, In IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer, Cham, pp. 373-383, 2020

G. Caldarini, S. Jaf, and K. McGarry, “A Literature Survey of Recent Advances in Chatbots,” Jounal of Information, vol. 13, no. 1, doi: 10.3390/info13010041, 2022

L. Gkinko, and A. Elbanna, “Hope, tolerance and empathy: Employees emotions when using an AI-enabled chatbot in digitalised workplace,” Information Technology & People, vol. 35, no. 6, pp. 1714-1743, doi: 10.1108/ITP-04-2021-0328, 2022

A. E. Lee, N. Ju, and K. H. Lee, “Service chatbot: Co-citation and big data analysis toward a review and research agenda,” Technological Forecasting and Social Change, vol. 194, doi: 10.1016/j.techfore.2023.122722, 2023

C. Chakraborty, S. Pal, M. Bhattacharya, S. Dash, and S. S. Lee, “Overview of chatbots with special emphasis on artificial intelligence-enabled chatGPT in medical science,” Frontiers in Artificial Intelliegence, vol. 6, no. 1237704, doi: 10.3389/frai.2023.1237704, 2023

J. Brooke, “System Usability Scale (SUS): A retrospective,” Journal of Usability Studies, vol.8, no. 2, pp. 29-40, 2013