Comparing the Effectiveness of AI Chatbot with That of Telephone Hotline

NCT06621134 | Completed DMC

• 2 other identifiers: UW21-344Collaborative Research Fund
• Study Type: observational
• Target: 48
• Locations: 1 country
• Sites: 1

Brief Summary

The COVID-19 pandemic has significantly impacted the wellbeing of people in Hong Kong, leading to social distancing policies and changes in healthcare service utilization. School closures and remote work have increased stress levels for parents and children. Vulnerable populations, such as low-income families and children with special needs, are at higher risk of maltreatment and mental health issues. Parental burnout has become a concern as parents juggle work, childcare, and education responsibilities. There is a need for research on the physical and mental health effects of COVID-19 on families and the potential role of AI in addressing these challenges. AI, particularly chatbots, can provide accessible healthcare information and support, aiding in early diagnosis and treatment. AI chatbots offer timely responses, accurate information, and continuous availability, making them valuable tools for remote health assistance. While AI chatbots are not without limitations, further research can help integrate them more effectively into healthcare services.

Conditions

Telephone Hotlines; AI Chatbot

Keywords

Artificial Intelligence; AI chatbot

Outcome Measures

Primary Outcomes (2)

• General Anxiety Disorder (GAD - 7)

  The Generalized Anxiety Disorder 7-item (GAD-7) questionnaire is a self-reported screening tool used to assess the severity of generalized anxiety disorder symptoms in adults. It consists of seven questions that ask about various symptoms commonly associated with generalized anxiety disorder, such as feeling nervous, anxious, or on edge.General Anxiety Disorder (GAD - 7)

  1 week within Pre-test, 1 week within Post-test

• Patient Health Questionnaire-9

  The Patient Health Questionnaire-9 (PHQ-9) is a self-administered questionnaire used to screen for and assess the severity of depression in patients. It consists of nine questions based on the criteria for diagnosing major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Patients are asked to rate how often they have experienced certain symptoms of depression over the past two weeks, with response options ranging from \"not at all\" to \"nearly every day.\" The total score on the PHQ-9 can help healthcare providers determine the presence and severity of depression in a patient, as well as monitor their response to treatment over time.

  1 week within Pre-test, 1 week within Post-test

Secondary Outcomes (1)

• Service Satisfaction Survey

  1 week within Pre-test, 1 week within Post-test

Study Arms (2)

Control Group

Participants will be asked to consent to randomization on their first access to our system. Users ask questions covered by the question bank and specific questions not covered by the question through a telephone hotline.

Other: Telephone hotline

Intervention Group

Participants will be required to provide consent for randomization when they first access our system. Users can ask questions covered by the question bank, as well as specific questions not covered by the bank, through an AI chatbox.

Other: AI Chatbot

Interventions

AI Chatbot OTHER

Participants will be required to provide consent for randomization when they first access our system. Users can ask questions covered by the question bank, as well as specific questions not covered by the bank, through an AI chatbox. The aim is to understand the significant difference between using AI chatbots and telephone hotlines to assist parents, as well as the effectiveness of AI chatbots compared to telephone hotlines.

Intervention Group

Telephone hotline OTHER

Participants will be asked to consent to randomization on their first access to our system. Users ask questions covered by the question bank and specific questions not covered by the question through a telephone hotline.

Control Group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

No specific population

You may qualify if:

• Subjects who give consent to participate in the study.

You may not qualify if:

• Subjects who do not give consent to participate in the study.

Sponsors & Collaborators

• The University of Hong Kong: lead

Study Sites (1)

Department of Paediatrics and Adolescent Medicine, The University of Hong Kong

Hong Kong, 0000, Hong Kong

Location

Related Publications (8)

• Tso WWY, Wong RS, Tung KTS, Rao N, Fu KW, Yam JCS, Chua GT, Chen EYH, Lee TMC, Chan SKW, Wong WHS, Xiong X, Chui CS, Li X, Wong K, Leung C, Tsang SKM, Chan GCF, Tam PKH, Chan KL, Kwan MYW, Ho MHK, Chow CB, Wong ICK, Lp P. Vulnerability and resilience in children during the COVID-19 pandemic. Eur Child Adolesc Psychiatry. 2022 Jan;31(1):161-176. doi: 10.1007/s00787-020-01680-8. Epub 2020 Nov 17.

  PMID: 33205284 BACKGROUND

• Russell BS, Hutchison M, Tambling R, Tomkunas AJ, Horton AL. Initial Challenges of Caregiving During COVID-19: Caregiver Burden, Mental Health, and the Parent-Child Relationship. Child Psychiatry Hum Dev. 2020 Oct;51(5):671-682. doi: 10.1007/s10578-020-01037-x.

  PMID: 32749568 BACKGROUND

• Naseem M, Akhund R, Arshad H, Ibrahim MT. Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review. J Prim Care Community Health. 2020 Jan-Dec;11:2150132720963634. doi: 10.1177/2150132720963634.

  PMID: 32996368 BACKGROUND

• Lee J. Mental health effects of school closures during COVID-19. Lancet Child Adolesc Health. 2020 Jun;4(6):421. doi: 10.1016/S2352-4642(20)30109-7. Epub 2020 Apr 14. No abstract available.

  PMID: 32302537 BACKGROUND

• Kretzschmar K, Tyroll H, Pavarini G, Manzini A, Singh I; NeurOx Young People's Advisory Group. Can Your Phone Be Your Therapist? Young People's Ethical Perspectives on the Use of Fully Automated Conversational Agents (Chatbots) in Mental Health Support. Biomed Inform Insights. 2019 Mar 5;11:1178222619829083. doi: 10.1177/1178222619829083. eCollection 2019.

  PMID: 30858710 BACKGROUND

• Garrido S, Millington C, Cheers D, Boydell K, Schubert E, Meade T, Nguyen QV. What Works and What Doesn't Work? A Systematic Review of Digital Mental Health Interventions for Depression and Anxiety in Young People. Front Psychiatry. 2019 Nov 13;10:759. doi: 10.3389/fpsyt.2019.00759. eCollection 2019.

  PMID: 31798468 BACKGROUND

• Cluver L, Lachman JM, Sherr L, Wessels I, Krug E, Rakotomalala S, Blight S, Hillis S, Bachman G, Green O, Butchart A, Tomlinson M, Ward CL, Doubt J, McDonald K. Parenting in a time of COVID-19. Lancet. 2020 Apr 11;395(10231):e64. doi: 10.1016/S0140-6736(20)30736-4. Epub 2020 Mar 25. No abstract available.

  PMID: 32220657 BACKGROUND

• Chew AMK, Ong R, Lei HH, Rajendram M, K V G, Verma SK, Fung DSS, Leong JJ, Gunasekeran DV. Digital Health Solutions for Mental Health Disorders During COVID-19. Front Psychiatry. 2020 Sep 9;11:582007. doi: 10.3389/fpsyt.2020.582007. eCollection 2020. No abstract available.

  PMID: 33033487 BACKGROUND

Related Links

• Nature
• Global economic observer
• HKSAR Government

MeSH Terms

Interventions

Hotlines

Intervention Hierarchy (Ancestors)

Emergency Medical Services; Health Services; Health Care Facilities Workforce and Services

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: September 16, 2024
• First Posted: October 1, 2024
• Study Start: September 3, 2021
• Primary Completion: September 4, 2024
• Study Completion: September 5, 2024
• Last Updated: October 1, 2024

Record last verified: 2024-09

Locations

HK (1)