Telephone HotlinesAI chatbotNCT06621134
Comparing the Effectiveness of AI Chatbot with That of Telephone Hotline
An observational study of Telephone Hotlines and AI Chatbot, run by The University of Hong Kong at 1 site in Hong Kong.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Telephone Hotlines, AI Chatbot
- What is being tested?
- AI Chatbot and Telephone hotline
- How many people
- 48 participants
- Where
- 1 siteHong Kong
- Study dates
- 3 Sep 2021to 5 Sep 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: No specific population
Who may be able to join
- Subjects who give consent to participate in the study.
Who cannot join
- Subjects who do not give consent to participate in the study.
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site
Department of Paediatrics and Adolescent Medicine, The University of Hong Kong
Hong Kong, 0000
About this study
Official title: Comparing the Effectiveness of an AI Chatbot with That of a Telephone Hotline for Answering COVID-19 Related Issues
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.
Read the detailed description
The COVID-19 pandemic has had an unprecedented impact on the wellbeing of people in Hong Kong since the outbreak in December 2019. The Government has adopted social distancing policies to minimise the risk of infection. These include but are not limited to; school closure, remote working, and the prohibition of group-gatherings. These anti-infection measures have led to a change in pattern in the use of healthcare services and help-seeking activities. Studies have also shown that a dearth of socialisation leads to higher stress levels for both parents and children.
As school closure and remote work measures continue, both children and parents are under great pressure. UNESCO (2020) reported that over 1.58 billion children and youth in 200 countries were affected by school closure, as of mid-April 2020. Although the long-term effect of COVID-19 on children's and parents' mental health is unknown, cases of child abuse, neglect and exploitation have increased in the face of such unprecedented times. Low-income families or families with children with special education needs (SEN) are prone to children being maltreated and/or having mental health crisis . Parents who work from home are facing challenges of fulfilling a triple role: work, childcare and homecare. Worse still, children's lack of learning interests and motivation adds extra burden on parents as they take up the role of teachers. Parents are inclined to experience parental burnout, which is characterised by mental and physical exhaustion, with a feeling of hopelessness. Therefore it is clear there are strong societal needs for COVID-19 physical and mental health research. It is imperative to prevent potential and mitigate existing problems regarding parent-child relationship, parental stress and family functioning caused by COVID-19.
Consequently, exploring more easily accessible and efficient ways of dealing with potential and existing health problems (both physically and mentally) should be a priority. Artificial Intelligence (AI) in healthcare services has the potential to reduce the workload of healthcare workers by answering frequently asked questions through the AI system all from the comfort of the subject's home. Considering the potentially detrimental effect of COVID-19 on both children and parents it is important to fill the research gap as to how AI may serve as a platform for help-seeking, particularly during times of social distancing.
AI has been widely adopted in healthcare services in the past decade. The use of chatbots, in particular, has enhanced public engagement in health service all from the comfort of the subject's home. AI chatbots utilised natural language processing (NLP) to facilitate interaction with users in conversations, making appropriate medical advice accessible to the public. Intelligent algorithms in AI enables early diagnosis of disease and offers treatment techniques to those who may otherwise have been diagnosed too late. For instance, the U.S. Centres for Disease Control and Prevention (CDC) has launched a chatbot named Clara to help users access information on potential symptoms of coronavirus and help enable them to make decisions about the need to seek medical care). This is especially useful as it identifies high-risks groups in need of medical attention by triaging patients according to their symptoms, therefore reducing hospital visits for minor cases. It also provides support to family members of high-risk groups as to what measures can be taken to prevent infection and ways to relieve pressure in taking care of patients within their family.
AI chatbots merit attention in its prompt response to users' questions as it provides a service around the clock. In addition, answers provided by AI are considered more accurate than that of search engines, subject to the proficiency of data mining methods. These features are of significance as users are able to seek psycho-medical advice while practising social distancing, without face-to-face appointments with clinicians.
AI chatbots may serve as a self-help tool for gaining insights in dealing with both mental and physical conditions but it is far from perfection. The hope is that this study can contribute to making AI chatbots an integrated part of the health care service.
- Conditions
- Telephone Hotlines, AI Chatbot
- Being tested
- AI Chatbot, Telephone hotline
- Keywords
- Artificial Intelligence, AI chatbot
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI ChatbotOther
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.
Telephone hotlineOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: AI Chatbot |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 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.
Measured: 1 week within Pre-test, 1 week within Post-test
Secondary outcomes (1)
Service Satisfaction Survey
A Service Satisfaction Survey is a method used by organizations to collect feedback from customers or clients regarding their satisfaction with the services provided. These surveys typically include questions that assess various aspects of the service experience, such as quality, responsiveness, professionalism, and overall satisfaction. The feedback gathered from these surveys can help organizations identify areas for improvement and make informed decisions to enhance their services.
Measured: 1 week within Pre-test, 1 week within Post-test
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Oct 2024
- Study start3 Sep 2021
- Primary completion4 Sep 2024
- Study completion5 Sep 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Telephone HotlinesStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 48 participants (actual)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dr. Patrick Ip, The University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06621134
- Sponsor's protocol ID
- UW21-344
- Other IDs
- Collaborative Research Fund
- Acronym
- AI chatbot
- Status last verified
- Sep 2024
- First submitted
- 16 Sep 2024
- Last updated
- 1 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 33205284Russell 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.
PubMed 32749568Naseem 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.
PubMed 32996368Lee 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.
PubMed 32302537Kretzschmar 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.
PubMed 30858710Garrido 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.
PubMed 31798468Cluver 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.
PubMed 32220657Chew 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.
PubMed 33033487Chen C, Lam KT, Yip KM, So HK, Lum TYS, Wong ICK, Yam JC, Chui CSL, Ip P. Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial. JMIR Hum Factors. 2025 Mar 6;12:e65785. doi: 10.2196/65785.
PubMed 40048637
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparing the Effectiveness of an AI Chatbot with That of a Telephone Hotline for Answering COVID-19 Related Issues
- ClinicalTrials.gov ID
- NCT06621134
- Acronym / sponsor ID
- AI chatbot / UW21-344
- Conditions
- Telephone Hotlines, AI Chatbot
- Keywords
- Artificial Intelligence, AI chatbot
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 48 participants (actual)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Study start
- 3 Sep 2021 (actual)
- Primary completion
- 4 Sep 2024 (actual)
- Study completion
- 5 Sep 2024 (actual)
- First posted
- 1 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The COVID-19 pandemic has had an unprecedented impact on the wellbeing of people in Hong Kong since the outbreak in December 2019. The Government has adopted social distancing policies to minimise the risk of infection. These include but are not limited to; school closure, remote working, and the prohibition of group-gatherings. These anti-infection measures have led to a change in pattern in the use of healthcare services and help-seeking activities. Studies have also shown that a dearth of socialisation leads to higher stress levels for both parents and children.
As school closure and remote work measures continue, both children and parents are under great pressure. UNESCO (2020) reported that over 1.58 billion children and youth in 200 countries were affected by school closure, as of mid-April 2020. Although the long-term effect of COVID-19 on children's and parents' mental health is unknown, cases of child abuse, neglect and exploitation have increased in the face of such unprecedented times. Low-income families or families with children with special education needs (SEN) are prone to children being maltreated and/or having mental health crisis . Parents who work from home are facing challenges of fulfilling a triple role: work, childcare and homecare. Worse still, children's lack of learning interests and motivation adds extra burden on parents as they take up the role of teachers. Parents are inclined to experience parental burnout, which is characterised by mental and physical exhaustion, with a feeling of hopelessness. Therefore it is clear there are strong societal needs for COVID-19 physical and mental health research. It is imperative to prevent potential and mitigate existing problems regarding parent-child relationship, parental stress and family functioning caused by COVID-19.
Consequently, exploring more easily accessible and efficient ways of dealing with potential and existing health problems (both physically and mentally) should be a priority. Artificial Intelligence (AI) in healthcare services has the potential to reduce the workload of healthcare workers by answering frequently asked questions through the AI system all from the comfort of the subject's home. Considering the potentially detrimental effect of COVID-19 on both children and parents it is important to fill the research gap as to how AI may serve as a platform for help-seeking, particularly during times of social distancing.
AI has been widely adopted in healthcare services in the past decade. The use of chatbots, in particular, has enhanced public engagement in health service all from the comfort of the subject's home. AI chatbots utilised natural language processing (NLP) to facilitate interaction with users in conversations, making appropriate medical advice accessible to the public. Intelligent algorithms in AI enables early diagnosis of disease and offers treatment techniques to those who may otherwise have been diagnosed too late. For instance, the U.S. Centres for Disease Control and Prevention (CDC) has launched a chatbot named Clara to help users access information on potential symptoms of coronavirus and help enable them to make decisions about the need to seek medical care). This is especially useful as it identifies high-risks groups in need of medical attention by triaging patients according to their symptoms, therefore reducing hospital visits for minor cases. It also provides support to family members of high-risk groups as to what measures can be taken to prevent infection and ways to relieve pressure in taking care of patients within their family.
AI chatbots merit attention in its prompt response to users' questions as it provides a service around the clock. In addition, answers provided by AI are considered more accurate than that of search engines, subject to the proficiency of data mining methods. These features are of significance as users are able to seek psycho-medical advice while practising social distancing, without face-to-face appointments with clinicians.
AI chatbots may serve as a self-help tool for gaining insights in dealing with both mental and physical conditions but it is far from perfection. The hope is that this study can contribute to making AI chatbots an integrated part of the health care service.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control Group | – | Other: Telephone hotlineParticipants 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. |
| Intervention Group | – | Other: AI ChatbotParticipants 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- No specific population
Key inclusion criteria (1 listed)
- Subjects who give consent to participate in the study.
Key exclusion criteria (1 listed)
- Subjects who do not give consent to participate in the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI ChatbotParticipants 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. | Other | Intervention Group |
| Telephone hotlineParticipants 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 | Control Group |
Primary outcome
General Anxiety Disorder (GAD - 7)
Time frame: 1 week within Pre-test, 1 week within Post-test
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)
Patient Health Questionnaire-9
Time frame: 1 week within Pre-test, 1 week within Post-test
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 secondary outcome
Service Satisfaction Survey
Time frame: 1 week within Pre-test, 1 week within Post-test
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2024.
- Background: PubMed 33205284Tso 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.
- Background: PubMed 32749568Russell 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.
- Background: PubMed 32996368Naseem 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.
- Background: PubMed 32302537Lee 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.
- Background: PubMed 30858710Kretzschmar 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.
- Background: PubMed 31798468Garrido 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.
- Background: PubMed 32220657Cluver 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.
- Background: PubMed 33033487Chew 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.
- Derived from this study: PubMed 40048637Chen C, Lam KT, Yip KM, So HK, Lum TYS, Wong ICK, Yam JC, Chui CSL, Ip P. Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial. JMIR Hum Factors. 2025 Mar 6;12:e65785. doi: 10.2196/65785.
- NatureLink provided by the sponsor.
- Global economic observerLink provided by the sponsor.
- HKSAR GovernmentLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Telephone Hotlines and AI Chatbot, run by The University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
General Anxiety Disorder (GAD - 7) (time frame: 1 week within Pre-test, 1 week within Post-test). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06621134, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2024.