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Alcohol abuse led to 5.3% of all deaths and 5.1% of all disability-adjusted life years globally in 2016, representing a heavier public health burden than diabetes, tuberculosis or HIV/AIDS (as documented in the World Health Organization (WHO) Global Status Report on Alcohol and Health). The increasing consumption of alcohol for a few decades has led to a higher risk of cirrhosis, cancers, hypertension, and cardiovascular and cerebrovascular diseases. Strengthening of the prevention and treatment of alcohol abuse has been incorporated in the Sustainable Development Goals (SDG3) by the United Nations.
Strong evidence from a meta-analysis demonstrated the efficacy of screening and brief intervention (SBI) in reducing weekly alcohol consumption. Although SBI is known to be effective in reducing alcohol consumption in at-risk drinkers, barriers to implementing SBI have been an issue. A systematic review identified that common barriers to the routine delivery of SBI by doctors and nurses included a lack of alcohol-related knowledge, time, confidence, ability, and incentive to intervene; worrying about offending patients; and SBI being an uncomfortable and frustrating task.
To scale up behavioural change interventions in primary care for expanding the scalability and reachability, artificial intelligence (AI) and AI-chatbots have been increasingly used in recent years. A systematic review showed that chatbots for mental health counselling were effective and safe. Other reviews also reported that chatbots might improve physical activity, diet, and weight management and oncology care. However, having searched PubMed and the Cochrane Library, there was no a randomised controlled trial on the use of an AI-chatbot for alcohol reduction.
Aim:
To adapt a self-developed SBI chatbot and conduct a proof-of-concept evaluation on its preliminary effectiveness and usability in reducing alcohol consumption after 4 weeks for at-risk working-age adults by using a randomised, open label, two-arm, parallel-group controlled trial.
Objectives of this project are:
Hypotheses Hypothesis 1 (Primary outcome): Participants receiving chatbot-delivered SBI (intervention group) will have a higher reduction in weekly alcohol consumption (grams/week) than those in the waitlist control group at 4-week follow-up.
Hypothesis 2 (Secondary outcome): The intervention group will have a lower AUDIT score than the control group at 4-week follow-up.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention group | Experimental | Participants in the intervention group will engage with a chatbot-delivered screening and brief intervention (SBI) over a 30-minute period on the chatbot webpage. The SBI chatbot involves screening individuals for alcohol consumption by AUDIT before recruitment and then providing a brief intervention comprising personalised feedback. Its delivery is fully automated via a smartphone app. |
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| Waitlist control group | Experimental | All participants in the waitlist control will receive the SBI chatbot 4 weeks later. The intervention has the same content for both intervention and waitlist control groups. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| chatbot-delivered screening and brief intervention | Behavioral |
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| Measure | Description | Time Frame |
|---|---|---|
| Alcohol consumption in grams per week | The primary outcome is the amount of alcohol consumed per week (grams/week) at the 4-week follow-up, which is recommended as the gold standard for determining effectiveness in many alcohol trials. The data collected for computing the alcohol consumption comprise type of alcohol (e.g. wine, beer, liquor, etc.) and units (e.g. 1 glass of 250ml, 1 can of 330 ml, etc.) consumed in the past week, which will be computed according to a formula with standard alcohol (unit per week) x 10 gram. | 4-week follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| AUDIT scores | AUDIT has 10 items and the total scores range from 0 to 40. Scores of 1 to 7 suggest low-risk alcohol consumption, 8 to 15 suggest hazardous or harmful alcohol. | 4-week follow-up |
| The 15-item Bot Usability Scale |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| School of Nursing | Hong Kong | Hong Kong Island | 000 | Hong Kong |
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| ID | Term |
|---|---|
| D003419 | Crisis Intervention |
| ID | Term |
|---|---|
| D011613 | Psychotherapy |
| D004191 | Behavioral Disciplines and Activities |
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Blinding of the study participants will not be possible. However, as the questionnaires will be completed via an online platform instead of instant messaging app (i.e. WhatsApp), the outcome assessor will be blinded during data collection.
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It was designed to evaluate chatbot usability in 5 domains: perceived accessibility to the chatbot function, perceived quality of chatbot functions, perceived quality of conversation and information provided, perceived privacy and security, and time response. Participants will rate each item on a 5-point Likert scale (1=strongly disagree to 5=strongly agree).
| 4-week follow-up |
| Economic evaluation | An EQ-5D-5L utility score will be estimated using the Hong Kong EQ-5D-5L value set. The EQ-5D-5L is a 25-item self-reported health questionnaire plus a visual analogue scale to describe how good or bad your health is today. | 4-week follow-up |