The present study consists of two two-armed randomized controlled trials between experimental and waitlist control groups. It aims to evaluate the effectiveness of conversational chatbot in improving mental health literacy, uptake of self-care behaviors, and mental well-being, compared to the waitlist control, and the effectiveness of daily notification on adherence. This study will provide important findings for the future development and implementation of chatbots in mental health, which may increase public access to immediate mental health support. It is hypothesized that participants in the experimental condition will show (H1) better mental health literacy (H2) better improvement in self-care and self-efficacy in mental well-being, and (H3) better mental well-being, compared with participants in the control condition. Also, it is hypothesized that participants with daily reminders will show (H4) a better adherence rate in using chatbot compared with participants without daily reminders
Background In Hong Kong, insufficient resources in the current public health system cause a long waiting time. Mental health services provided by the public health system mainly rely on traditional one-to-one face-to-face sessions. In the past 12 months, there were 48,520 new bookings in public psychiatry outpatient clinics and the longest waiting time was 94 weeks. Priority is always given to people with more severe mental health issues, which causes long waiting time for people with mild mental health symptoms. Untreated mental health issues can be escalated to more severe symptoms. Thus, in addition to treating mental illness, preventing common mental health issues and fostering mental health self-care in the general population are crucial to promote public mental health and reduce illness burden in the society. The Hong Kong Mental Morbidity Study found 1 in 7 individuals in Hong Kong has either depression, anxiety, or a mix of the two disorders; however, only a quarter of them sought professional help. Rather than resorting to mental health professionals for face-to-face service to treat common mental health concerns, digital technology provides a highly scalable and accessible means through which individuals can access mental health resources for self-care. Among these tools, conversational agent is one of the viable options. It has been applied in health care industries to cater to different health needs, including providing timely information and supporting mental health disorders. Healthcare conversational agents were found to be effective in reducing depression and anxiety symptoms and had higher engagement rate compared with standard industry metrics. Chatbot is a type of conversational agent. It is a rule-based computer algorithm that conducts an automatic conversation with people based on predefined instructions. Based on a self-guided approach, users can search for topics that they are interested in and engage with pre-designed computer algorithms at the convenience of their own space and time without the constraints of specialized care. Applying chatbots in mental health self-care provides an opportunity for individuals to directly learn about relevant mental health-related knowledge and tips as well as practice self-care exercises at anytime, anywhere. The Present Study The present study aims to evaluate the effectiveness of conversational chatbot in improving mental health literacy, uptake of self-care behaviors, and mental well-being, compared to the waitlist control, and the effectiveness of daily notification on adherence. This study will provide important findings for the future development and implementation of chatbots in mental health, which may increase public access to immediate mental health support. It is hypothesized that participants in the experimental condition will show (H1) better mental health literacy (H2) better improvement in self-care and self-efficacy in mental well-being, and (H3) better mental well-being, compared with participants in the control condition. Also, it is hypothesized that participants with daily reminders will show (H4) a better adherence rate in using chatbot compared with participants without daily reminders Participants Participants will be recruited through (1) advertising at popular online networking platforms (e.g., Facebook and Instagram), mass mailing at investigator's institutions, and snowball sampling. Procedure Upon completing the screening and pre-evaluation questionnaire, participants will be randomly assigned to the experimental group or waitlist control group, with and without notifications based on computer-generated random digits. They will complete 2 more sets of questionnaires, including a post-evaluation 11 days after group allocation, and a follow-up questionnaire 21 days after group allocation. In the experimental group, participants will go through one assigned chatbot each day for 10 days, with the sequence of the assigned chatbot randomized. The chatbot contents are developed by clinical psychologists and well-being promotion officers. Content includes relationships, stress, value, emotion, and positive psychology. Each chatbot can only be assessed on the day of distribution. The access link will expire upon completion and the day after distribution to prevent repeat and delay in completion. Experimental group participants can freely access all chatbots after the completion of post-evaluation and before the follow-up questionnaire is sent to them. In the waitlist control group, participants are to refrain from using the chatbot until they finished the follow-up questionnaire. All participants will be able to access the chatbot materials in an online platform after they have completed the research.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Enrollment
293
The chatbot contents are developed by Clinical psychologists and well-being promotion officers. Content includes relationships, stress, value, emotion, and positive psychology.
Diversity and Well-being Lab, CUHK
Shatin, N.T., Hong Kong
Self-Care Self-efficacy - Strategies Used by People to Promote Health (SUPPH)
The scale is reliable, with a Cronbach's alpha of internal consistency of 0.93 . It includes 29 items to measure self-care self-efficacy using a 6-point scale from 1 (very little confidence) to 5 (quite a lot of confidence). High scores reflect better self-care self-efficacy. The SUPPH includes three subscales, stress reduction, decision-making, and positive attitudes. This study only includes items on stress reduction and positive attitudes. Cronbach's alpha of stress reduction and positive attitudes were 0.92 and 0.92 respectively.
Time frame: Day 10
Self-Care Self-efficacy - Strategies Used by People to Promote Health (SUPPH)
The scale is reliable, with a Cronbach's alpha of internal consistency of 0.93 . It includes 29 items to measure self-care self-efficacy using a 6-point scale from 1 (very little confidence) to 5 (quite a lot of confidence). High scores reflect better self-care self-efficacy. The SUPPH includes three subscales, stress reduction, decision-making, and positive attitudes. This study only includes items on stress reduction and positive attitudes. Cronbach's alpha of stress reduction and positive attitudes were 0.92 and 0.92 respectively.
Time frame: Day 20
Self-Care - Self-Care Behavior Inventory
It includes 19 items to measure self-care behavior using a 5-point scale from 1 (very little) to 5 (quite a lot). Internal consistency if the scale was 0.83. One item regarding medication is removed, while two items related to time spent on things that respondents enjoy and feel interested in and time spent alone were added.
Time frame: Day 10
Self-Care - Self-Care Behavior Inventory
It includes 19 items to measure self-care behavior using a 5-point scale from 1 (very little) to 5 (quite a lot). Internal consistency if the scale was 0.83. One item regarding medication is removed, while two items related to time spent on things that respondents enjoy and feel interested in and time spent alone were added.
Time frame: Day 20
Mental Health Literacy
Sixteen items were developed to measure the knowledge of various aspects to do with mental health. Items are rated on a 7-points scale from 1 (strongly disagree) to 7 (strongly agree). Items are designed according to the content of chatbots and a well-developed Mental Health Literacy Scale.
Time frame: Day 10
Mental Health Literacy
Sixteen items were developed to measure the knowledge of various aspects to do with mental health. Items are rated on a 7-points scale from 1 (strongly disagree) to 7 (strongly agree). Items are designed according to the content of chatbots and a well-developed Mental Health Literacy Scale.
Time frame: Day 20
Depressive symptoms - Patient Health Questionnaire (PHQ-9)
Patient Health Questionnaire. It includes 9 items to assess the extent to which respondents are bothered by depression-related symptoms using a 4-point scale from 0 (not at all) to 3 (nearly every day). PHQ-9 has been validated and used widely in the general population for screening and measuring depression severity. Scores of 5, 10, 15, and 20 denote mild, moderate, moderately severe, and severe levels of depression respectively (range: 0-27). PHQ-9 has a sensitivity of 0.88 and a specificity of 0.88 in detecting major depressive disorder (MDD) at a cut-off of 10. The internal consistency reliability of the Chinese version of the scale was 0.86.
Time frame: Day 10 and 20
Anxiety symptoms - Generalized Anxiety Disorder Assessment (GAD-7)
It is a 7-item scale to assess the extent to which respondents are bothered by anxiety-related symptoms using a 4-point scale from 0 (not at all) to 3 (nearly every day). Scores of 5, 10, and 15 denote the mild, moderate, and severe levels of anxiety respectively (range: 0-21). At a cut-off of 10, GAD-7 has sensitivity of 0.89 and specificity of 0.82 in detecting generalized anxiety disorder (GAD). The internal consistency of the Chinese version was 0.93.
Time frame: Day 10 and 20
Mindfulness - Mindful Awareness Attention Scale (MASS)
It is a 15-item scale to measure individual differences in mindful states during the previous week using a 6-point scale from 1 (almost always) to 6 (almost never). Higher scores reflect higher levels of dispositional mindfulness. The internal consistency among general adults was 0.87.
Time frame: Day 10 and 20
Well-being - PERMA-Profiler (PERMA)
It includes 23 items to assess respondents' well-being using an 11-point scale from 0 (never/terrible/not at all) to 10 (always/excellent/completely). It includes domains in positive emotion, engagement, relationship, meaning, accomplishment, negative emotion, physical health, loneliness, and overall well-being. PERMA is reliable (Cronbach's alpha = 0.94) and has been validated in different populations such as Australian, Italian, Malaysian, and Turkish.
Time frame: Day 10 and 20
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Behavioral Intention - a subscale in the E-therapy Attitude and Process Questionnaire (eTAP)
It includes 3 items to measure participants' intention in using online psychological intervention on a 7-point scale from 1 (strongly disagree) to 7 (strongly agree). The subscale showed excellent reliability (Cronbach's alpha = 0.94).
Time frame: Day 10 and 20
Attitude towards chatbot
Nine items were developed to measure attitudes toward the ability of chatbots in improving mental well-being. It is measured by a 7-points scale from 1 (strongly disagree) to 7 (strongly agree).
Time frame: Day 10 and 20
Credibility and Expectancy. The Credibility and Expectancy Questionnaire (CEQ)
It measures treatment expectancy and rationale credibility for use in clinical outcomes. Six items are rated from 0% to 100%. The first section of the questionnaire is about thoughts toward online self-care intervention and the second part is about feelings toward online self-care intervention. The internal consistency of the scale was 0.81.
Time frame: Day 10 and 20
Usability
Twenty-four items were developed to measure the usability of the chatbot, including satisfaction, engagement, functions, favorite elements, and useful topics of chatbots.
Time frame: Day 10 and 20
Usage
Chatbot usage, including time spent on chatbots, number of conversations engaged with the chatbot, completion of exercises, behavioral change, attitudinal shifts, condition improvements, and the level of knowledge transfer, will be extracted from the back-end system.
Time frame: Day 10 and 20