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Systematic Review
Eur Addict Res 2022;28:405–418
The Use of Chatbots as Supportive Agents
for People Seeking Help with Substance Use
Disorder: A Systematic Review
Lisa Ogilvie
a Julie Prescott
b Jerome Carson
c
aDoctoral candidate, University of Bolton, Bolton, UK; bReader in Psychology, University of Bolton, Bolton, UK;
cProfessor of Psychology, University of Bolton, Bolton, UK
Received: January 13, 2022
Accepted: June 30, 2022
Published online: August 30, 2022
Correspondence to:
Lisa Ogilvie, lco1eps @ bolton.ac.uk
© 2022 S. Karger AG, Basel
Karger@karger.com
www.karger.com/ear
DOI: 10.1159/000525959
Keywords
Chatbot · Alcohol · Drugs · Substance · Substance use
disorder · Addiction · Systematic review
Abstract
Introduction: The use of chatbots in healthcare is an area of
study receiving increased academic interest. As the knowl-
edge base grows, the granularity in the level of research is
being refined. There is now more targeted work in specific
areas of healthcare, for example, chatbots for anxiety and
depression, cancer care, and pregnancy support. The aim of
this paper is to systematically review and summarize the re-
search conducted on the use of chatbots in the field of ad-
diction, specifically the use of chatbots as supportive agents
for those who suffer from a substance use disorder (SUD).
Methods: A systematic search of scholarly databases using
the broad search criteria of (“drug” OR “alcohol” OR “sub-
stance”) AND (“addiction” OR “dependence” OR “misuse” OR
“disorder” OR “abuse” OR harm*) AND (“chatbot” OR “bot”
OR “conversational agent”) with an additional clause applied
of “publication date” ≥ January 01, 2016 AND “publication
date” ≤ March 27, 2022, identified papers for screening. The
Preferred Reporting Items for Systematic Reviews and Meta-
Analyses guidelines were used to evaluate eligibility for in-
clusion in the study, and the Mixed Methods Appraisal Tool
was employed to assess the quality of the papers. Results:
The search and screening process identified six papers for
full review, two quantitative studies, three qualitative, and
one mixed methods. The two quantitative papers consid-
ered an adaptation to an existing mental health chatbot to
increase its scope to provide support for SUD. The mixed
methods study looked at the efficacy of employing a be-
spoke chatbot as an intervention for harmful alcohol use. Of
the qualitative studies, one used thematic analysis to gauge
inputs from potential users, and service professionals, on the
use of chatbots in the field of addiction, based on existing
knowledge, and envisaged solutions. The remaining two
were useability studies, one of which focussed on how prom-
inent chatbots, such as Amazon Alexa, Apple Siri, and Google
Assistant can support people with an SUD and the other on
the possibility of delivering a chatbot for opioid-addicted
patients that is driven by existing big data. Discussion/Con-
clusion: The corpus of research in this field is limited, and
given the quality of the papers reviewed, it is suggested
more research is needed to report on the usefulness of chat-
bots in this area with greater confidence. Two of the papers
reported a reduction in substance use in those who partici-
pated in the study. While this is a favourable finding in sup-
port of using chatbots in this field, a strong message of cau-
tion must be conveyed insofar as expert input is needed to
safely leverage existing data, such as big data from social
media, or that which is accessed by prevalent market leading
chatbots. Without this, serious failings like those highlighted
within this review mean chatbots can do more harm than
good to their intended audience. © 2022 S. Karger AG, Basel
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DOI: 10.1159/000525959
Introduction
In recent years, social engagement powered by tech-
nology has undergone significant growth, both in solu-
tion availability and scale of uptake. Here, social media
platforms such as Facebook, TikTok, Snapchat, and Ins-
tagram have facilitated an increase in technology-based
social interaction using applications that employ a mes-
saging interface. Understandably, this has generated in-
terest in how this technology can be used to benefit health
and wellbeing [1–3]. In part, this has been a response to
the COVID-19 pandemic where alternative delivery
mechanisms were necessary to maintain service provi-
sion within the health sector [4], but also a reflection of
the contemporary technological landscape, and an ongo-
ing commitment to better connect services with a tech-
nology-motivated population [4, 5]. Furthermore, the
burgeoning use of communication platforms such as
Zoom and Microsoft Teams, a result of face-to-face meet-
ings being restricted during the pandemic, has seen peo-
ple become accustomed to and comfortable with support
services being delivered using digital solutions [4].
For some, such as those with a history of addiction, the
restrictions enforced during the pandemic have seen an
emergent dependence on technology, with mutual sup-
port meetings such as Alcoholics Anonymous being con-
ducted via Zoom, along with the amplified use of support
groups on social media platforms, such as Facebook [6].
This newly adopted model of support has enriched the
existing online provision accessed by this cohort, where
web-based interventions such as chatbots, online coun-
selling, and social media groups, have all been considered
as sources of support [7, 8]. These systemized solutions
have addressed different aspects of treatment and care for
people with a history of substance misuse, including pre-
ventative interventions, health education, reduction
plans, treatment programmes, and recovery support [9].
Examples of these support options are the “drink aware”
website housing tools to help people change their rela-
tionship with alcohol [10], the “breaking free” online pro-
gramme providing recovery support [11], and the many
social media groups set up to facilitate mutual support or
disseminate addiction services to a wide audience, often
with engagement from healthcare professionals [12].
Over the past 5 years, chatbots have become a well-
established branch in Human-computer interaction
(HCI), digitizing services traditionally undertaken by a
human agent [1]. In 2016, Facebook announced its inten-
tion to integrate chatbot functionality with its messenger
platform [6]. The subsequent amalgamation of chatbot
and social media platform provided a stimulus for the
rapid growth in chatbot solutions across many sectors [1,
2]. Chatbots use natural language processing, a branch of
artificial intelligence, to emulate a real-time conversation.
They facilitate the delivery of a dialogue that is both so-
ciable and engaging to end users, therefore making it a
popular choice in HCI design [13]. In addition to system-
izing social engagement, chatbots have succeeded be-
cause they offer a higher level of intelligence in directing
users to helpful content than a search facility is capable of.
They also increase productivity whereby they reach a
greater audience than possible from non-automated con-
versations and can be programmed to deliver a broad
range of solutions, limited only by the imagination and
complexity of the syntactical rules that can be systemized
[1, 14].
In the health care sector, chatbots have been used to
educate, support, treat, and diagnose people [15] with di-
verse medical needs, such as depression, insomnia, and
obesity [16]. The current interest in healthcare chatbots
being evident in a Google Scholar search for publications
in the last 5 years using the term “chatbot AND health-
care.” Here, a year-on-year increase was noted, with 89
papers returned for 2016 and 3,360 for 2021. The most
significant increase being in 2020 and 2021, the years
spanning the COVID-19 pandemic. Within this growing
body of knowledge, the efficacy and reputation of chat-
bots in healthcare has been considered [3, 15], along with
the ethical implications of using a systematic agent, in-
stead of a human, in a supporting role [17]. Much of the
work published has considered chatbots and healthcare
in general terms, for example, chatbots for therapy, chat-
bots for education, and chatbots for diagnosis [18]. As the
knowledge base grows, the granularity in the level of re-
search is being refined, seeing more research in targeted
areas, for example, anxiety and depression [19], cancer
care [20], and pregnancy support [21]. The growth in the
applied use of chatbots means there is an increasing need
for a better-evidenced study of their usefulness [1]. This
is especially true in healthcare, where it is important to
understand what affect they have on health outcomes in
the long and short term [18].
In the area of drug and alcohol addiction, the mode of
interaction exercised by chatbots presents an opportunity
to help people suffering from a substance use disorder
(SUD) [7]. The computational discourse exercised by
chatbots means a person does not need to feel judged in
context of their own guilt, shame, or embarrassment [1,
16], all commonplace with SUD [22]. Furthermore, the
stigmatization experienced by people with SUD can pre-
Chatbot Support for People with
Substance Use Disorder
407
Eur Addict Res 2022;28:405–418
DOI: 10.1159/000525959
vent them from seeking help [23]. Chatbots are able to
remove this barrier by providing the opportunity for a
person to be heard without fear of being judged as an in-
dividual with faults [23]. As they operate online, they are
accessible at the time of need when other support options
may not be available [9]. For people with an SUD, having
accessible support can be important in managing the
symptomology, where the unpredictable nature of a trig-
gering event can make maintaining abstinence more
challenging [24]. Furthermore, chatbots have endless pa-
tience, so previous failed attempts at maintaining absti-
nence do not need to deter people from accessing this
model of support [25].
Methods
Aim
The aim of this study is to understand how chatbots have been
used to offer support to people with an SUD. The scope has been
confined to drugs and alcohol, both substances with a pharmaco-
logical component that can affect perception, mood, conscious-
ness, cognition, or behaviour. As such, the remit of this study ex-
cludes chatbots that target nicotine or behavioural addictions such
as gambling. The purpose of which is to focus on how chatbot
technology has been used to support a population exposed to some
of the consequences of SUD, for example, physical withdrawal,
impulsive behaviour, and impaired judgement [24]. This work was
initiated as part of a larger project to implement a new chatbot so-
lution. This solution was co-produced with prospective users to
provide a different type of digital support to people in recovery
from drug and alcohol addiction. The output from the present
study provided important input to the design process for this be-
spoke chatbot solution, and as such, it is envisioned that it could
be used in other such projects looking to expand this type of digi-
tal support in this area of healthcare [25, 26].
Search Strategy and Paper Selection
The Preferred Reporting Items for Systematic Reviews and Me-
ta-Analyses (PRISMA) statement provides a framework for con-
ducting systematic reviews [27]. It was developed to help facilitate
comprehensive and transparent reporting of quantitative search
results and has been adopted as a standardized system for identify-
ing studies to be included in systematic reviews [28]. In confor-
mity with this, PRISMA has been employed in the present study,
to clearly convey the number of papers identified, along with their
eligibility in the review process. To support this process, the PRIS-
MA 2020 statement was used which provides a checklist contain-
ing the items considered necessary for reporting a systematic re-
view [28].
A literature search was conducted using Discover@Bolton, a
library search facility available at the University of Bolton. This
search facility trawls scholarly journals and databases for publica-
tions matching a given search term within apposite disciplines, for
example, public health, psychology, computer science, social and
applied sciences, and social welfare. The case insensitive search
term (“drug” OR “alcohol” OR “substance”) AND (“addiction” OR
“dependence” OR “misuse” OR “disorder” OR “abuse” OR harm*)
AND (“chatbot” OR “bot” OR “conversational agent”) was used
with an additional clause of “publication date” ≥ January 01, 2016
AND “publication date” ≤ March 27, 2022. The search included
titles, abstracts, and full-text content. The reported results for this
review are accurate on and up to March 27, 2022, the date the
search was carried out. The number of results by data source is
shown in Table1.
Inclusion and Exclusion Criteria
Papers returned in a search of the named data sources shown
in Table1 were considered eligible for review if they met the inclu-
sion and exclusion criteria in Table2. Papers from other sources,
or non-empirical and popular contributions on the use of chatbots
in addiction, were excluded as the content could not be accurately
assessed as part of a systematic review, examples being an unstud-
ied implementation and a recent expert commentary [7, 25, 26].
Table 1. Search results by data source returned on the March 27,
2022
Database Results, n
PubMed 5,177
EBSCOHost 4,947
IngentaConnect 4,615
MEDLINE 4,268
ProQuest Central – UK 2,513
PubMed Central 2,074
DOAJ 1,825
Wiley Online Journals 1,157
JSTOR 1,130
Springer Online Journals 1,108
Science Direct Freedom Collection 1,015
Wiley Online Library Full Journals 2021 991
Single Journals 509
BioMedCentral Open Access 482
HireWire Press (Free Journals) 431
Freely accessible science journals 329
Public Library of Science 291
Oxford University Press 199
Journals@ovid 164
Wiley Blackwell Open Access 141
Taylor & Francis Online 124
CINAHL 116
Sage Premier Collection 108
SPORTDiscus 98
Other databases 73
BMJ Journals 69
Cambridge Journals 64
PsycARTICLES 15
Total UOB library search without duplicate filtering 34,033
Total UOB library search with duplicate filtering 5,794
UOB, University of Bolton.
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Quality Assessment
To ensure consistency in the quality assessment of the papers,
the initial assessment was undertaken by the same researcher. For
quality purposes, a further two reviewers then independently vali-
dated this process. The assessment was performed using the latest
version of the Mixed Methods Appraisal Tool (MMAT) [29, 30].
This is a tool designed to facilitate systematic and concomitant ap-
praisal of empirical studies that combine different research de-
signs, specifically, qualitative, quantitative, and mixed methods.
The MMAT has been shown as an efficient and reliable way to ap-
praise the quality of papers, having been carefully critiqued and
subsequently augmented since its original form [29–31]. The tool
consists of methodological quality criteria, appropriate to the type
of paper, for example, for qualitative papers, reviewers consider if
the interpretation of the results is substantiated by the data pre-
sented. For quantitative papers, questions are asked which con-
sider such things as, if the measures used are appropriate, or if the
sample represents the intended population [29, 30].
Results
Papers Eligible for Inclusion
The total number of papers returned by the previously
discussed search term was 34,033. Table1 reports this by
the individual data source queried. Duplicates and re-
cords before the requisite publication date were removed
(n = 32,082), leaving 1,951 papers. These papers were title
screened which saw (n = 1,902) studies excluded as not
being relevant, published in a language other than En-
glish, or being a meta-analysis or review paper. The ab-
stracts of the remaining studies (n = 49) were assessed for
eligibility. Of these, papers were excluded (n = 38) if they
were concerned with a different area of addiction (smok-
ing, gambling, or technology), if their primary focus was
not drug and alcohol addiction or SUD, or if they studied
a different area of mental health. This saw 11 papers prog-
ress to full review, which yielded a further 3 studies
through citation tracking [32–38]. The full-text of the ac-
cumulated 14 papers was considered. Of these, 8 were ex-
cluded as either diagnostic, not supporting problem drug
and alcohol use, or being a bespoke software development
as opposed to a chatbot implementation, see Table3. This
included Internet, tablet, and Internet-based content.
This process left a total of 6 papers eligible for review,
as shown in the PRISMA diagram in Figure 1. The papers
excluded through the full-text assessment against the in-
clusion and exclusion criteria are listed in Table3. The
characteristics of each of the qualifying papers are shown
in Table4.
Quality Assessment Outcomes
The outputs from the PRISMA process (n = 6) were
quality assessed using the previously described MMAT.
Using the criteria set out in the MMAT framework [22,
Table 2. Inclusion and exclusion criteria
Criteria Inclusion Exclusion
Published languageEnglish Non-English
Publication date Papers published between January 01, 2016–March 27,
2022, when search was executed
Papers prior to 2016 as the applied use of chatbots was in its infancy
[1, 6, 15]
Country of origin Papers that originate in any country
Publication type Scholarly materials returned from UOB library search
facility (see Table 1)
Review papers, including systematic and meta-analysis
Study method Qualitative, quantitative, useability, and mixed
methods
Participants Adults 18+ Young people <18
Intervention Any type of chatbot (voice, internet, and messenger
platform)
Other online or bespoke technological interventions (mobile, table, or
Internet applications and bespoke software development)
Nature of addiction or
disorder
Drug and alcohol Gambling, smoking, sex, Internet, mobile phone, and social media
Primary focus Support for individuals with drug or alcohol addiction
or a SUD
Diagnostic chatbots, and chatbots where primary focus is in a
different area of mental health
UOB, University of Bolton.
Chatbot Support for People with
Substance Use Disorder
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Table 3. Papers excluded after full-text assessment
Author (year) [ref] Title Reason for exclusion
1 Somasiri et al. (2016) [32] D-REHABIA: A drug addiction recovery through mobile based
application
Bespoke mobile application software not
chatbot implementation
2 Park et al. (2019) [33] Designing a chatbot for a brief motivational interview on stress
management: Qualitative case study
Primary focus is coping with stress not
addiction and substance abuse
3 Haug et al. (2020) [34] Efficacy of a smartphone-based coaching program for addiction
prevention among apprentices: study protocol of a cluster-
randomised controlled trial
Bespoke smartphone program not specific
chatbot functionality
4 Auriacombe et al. (2018) [35] Development and validation of a virtual agent to screen tobacco
and alcohol use disorders
Evaluates risk, does not provide support
5 Auriacombe et al. (2021) [36] Effectiveness and acceptance of a smartphone-based virtual
agent screening for alcohol and tobacco problems and associated
risk factors during COVID-19 Pandemic in the general population
Evaluates risk, does not provide support
6 Bandawar et al. (2018) [9] Use of digital technology in addiction disorders More generalized view of technological
landscape with the use of chatbots forming
one branch of this discursive piece
7 Kornfield et al. (2018) [37] Detecting recovery problems just in time: Application of
automated linguistic analysis and supervised machine learning to
an online substance abuse forum
Focus on machine learning AI to identify
problems, not chatbot AI to offer support
8 Boustani et al. (2021) [38] Development, feasibility, acceptability, and utility of an expressive
speech-enabled digital health agent to deliver online, brief
motivational interviewing for alcohol misuse: Descriptive study
Bespoke Internet-based virtual reality agent,
not a chatbot implementation
Table 4. Characteristics of included papers
Author (year) [ref] type Aim and focus Population
1 Prochaska et al. (2021a) [43]: quantitative
non- randomized
To investigate the adaptation of a mental health
cognitive behavioural therapy chatbot intervention for
substance use disorder
101 adults aged between 18 and 65 years
screened as having problem substance use
2 Prochaska et al. (2021b) [42]: Quantitative
randomized controlled trials
Follow-up study using the Woebot chatbot platform
from the earlier 2021 study by Prochaska et al. [37] to
investigate use during the COVID-19 pandemic
180 adults aged between 19 and 65 years
screened as having problem substance use
3 Elmasri & Maeder (2016) [40]: mixed
methods
To investigate an online mental health chatbot
intervention for alcohol abuse which includes risk
assessment and education components
17 at-risk drinkers aged between 18 and 25
years
4 Barnett et al. (2020) [44]: qualitative To investigate client and counsellor perspectives on
using chatbots in alcohol and drug addiction services
20 drug and alcohol addiction service users
aged between 22 and 76 years, and 8
counsellors having between 1 and 10 years
professional experience
5 Nobles et al. (2020) [39]: qualitative To investigate how market-leading chatbots support
people seeking help for addiction
70 user scenarios
6 Moghadasi, Zhuang & Gellban (2020) [41]:
qualitative
To investigate using big data with deep learning
techniques to create a knowledge service with a
chatbot interface to support opioid-addicted patients
3 user cases
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Fig. 1. PRISMA diagram showing flow of information in the search and screening process.
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Table 5. Quality assessment of included papers
Author (year) [ref] type Criteria Quality,
%
Quality
assessment
1 Prochaska et al. (2021a) [43]:
quantitative non-
randomized
Are the participants representative of the target population? N 60 Medium
Are measurements appropriate regarding both the outcome and intervention (or
exposure)?
Y
Are there complete outcome data? Y
Are the confounders accounted for in the design and analysis? N
Is there coherence between qualitative data sources, collection, analysis, and
interpretation?
Y
2 Prochaska et al. (2021b) [42]:
quantitative randomized
controlled trials
Is randomization appropriately performed? N 40 Low
Are the groups comparable at baseline? N
Are there complete outcome data? N
Are outcome assessors blinded to the intervention provided? Y
Did the participants adhere to the assigned intervention? Y
3 Elmasri & Maeder (2016) [40]:
mixed methods
Is there an adequate rationale for using a mixed methods design to address the
research question?
Y 40 Low
Are the different components of the study effectively integrated to answer the
research question?
Y
Are the outputs of the integration of qualitative and quantitative components
adequately interpreted?
N
Are divergences and inconsistencies between quantitative and qualitative results
adequately addressed?
N
Do the different components of the study adhere to the quality criteria of each
tradition of the methods involved?
N
4 Barnett et al. (2020) [44]:
qualitative
Is the qualitative approach appropriate to answer the research question? Y 100 High
Are the qualitative data collection methods adequate to address the research
question?
Y
Are the findings adequately derived from the data? Y
Is the interpretation of results sufficiently substantiated by data? Y
Is there coherence between qualitative data sources, collection, analysis, and
interpretation?
Y
5 Nobles et al. (2020) [39]:
qualitative
Is the qualitative approach appropriate to answer the research question? Y 40 Low
Are the qualitative data collection methods adequate to address the research
question?
N
Are the findings adequately derived from the data? Y
Is the interpretation of results sufficiently substantiated by data? N
Is there coherence between qualitative data sources, collection, analysis, and
interpretation?
N
6 Moghadasi, Zhuang &
Gellban (2020) [41]:
qualitative
Is the qualitative approach appropriate to answer the research question? Y 40 Low
Are the qualitative data collection methods adequate to address the research
question?
Y
Are the findings adequately derived from the data? N
Is the interpretation of results sufficiently substantiated by data? N
Is there coherence between qualitative data sources, collection, analysis, and interpretation?
N
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23], the papers were given a percentage quality score
based on the number of criteria met. A percentage of 0,
20, or 40% was considered low quality, 60 or 80% was
considered medium, and above 80% was considered high.
Of the papers within this review, when evaluated against
the study-specific quality criteria, four were considered
low quality [39–42], one as medium [43], and one as high
[44], see Table5. In addition to the MMAT quality assess-
ment on individual papers, the eligible studies were con-
sidered in terms of their generalized quality. Here, it was
noted that the findings in two of the papers [40, 44] used
a small sample size of less than 29 participants, and an-
other two of the papers [39, 41] were based on user sce-
narios that did not directly engage active participants.
Furthermore, only one of the quantitative studies was a
randomized control trial (RCT) [42], which due to a re-
ported clerical error had exclusion information missing
in the randomization process. In addition to this, the de-
mographic of the participants in both the quantitative
studies [42, 43] was skewed toward non-Hispanic wom-
en. This limitation was raised in the first quantitative
study [43] to be corrected in the randomized design of the
second [42], having been conducted by the same re-
searchers; however, this characteristic was carried through
to the second study [42]. Given the small sample sizes,
reliance on user scenarios, and inclusion of only one RCT
study with missing information in the randomization
process, the dependability of the conclusions that can be
drawn on the efficacy of chatbots in the field of drug and
alcohol addiction has been limited by the overall quality
of the eligible papers.
Overview of Included Papers
There were six papers eligible for inclusion in the re-
view, an overview of their characteristics is given in Ta-
ble4. Of the six papers, two exclusively used quantitative
measures to study the adaptation of an existing chatbot
platform for SUD [42, 43]. One applied a mixed methods
approach to measure the efficacy of employing a bespoke
chatbot to deliver addiction support [40]. One used the-
matic analysis to qualitatively gauge inputs from poten-
tial users, and service professionals, on the use of chatbots
in the field of addiction, based on existing knowledge, and
envisaged solutions [44]. The remaining two were use-
ability studies, one of which focussed on how prominent
chatbots, such as Amazon Alexa, Apple Siri, and Google
Assistant, support addiction [39] and the other on the
possibility of delivering a chatbot for opioid-addicted pa-
tients that is driven by existing big data [41].
Bespoke Chatbot Solutions
There were four studies looking at bespoke chatbots
targeted at addiction [40–43]. The first publication,
chronologically, was the paper by Elmasri et al. [40],
which presented a chatbot developed as an intervention
for alcohol abuse. Unlike the other bespoke studies, this
chatbot was not assigned the identity of a named agent.
An expert panel was gathered to provide the input for the
design, whereby a set of requirements for the chatbot
were identified, for example, anonymous and immediate
advice, logical conversation, friendly advisor, and a mech-
anism for offering feedback relevant to an alcohol assess-
ment. Using these requirements, a prototype chatbot was
created using Artificial Intelligence Markup Language.
The chatbot comprised 4 parts: (1) conversation initia-
tion, (2) alcohol education module, (3) alcohol risk as-
sessment, and (4) conversation conclusion. Conversation
initiation and conclusion were achieved with simple pre-
defined content. Alcohol education and risk assessment
were implemented as conversation maps within the chat-
bot. The education process managed the dialogue to de-
liver user-specific information on drinking habits. Key-
word identification was used to invoke a branch to the
user-selected topic, with the chatbot initiating a further
question to enable an accurate response containing ap-
propriate information such as social risks, effect on or-
gans, or alcohol content. The Alcohol Use Disorders
Identification Test-Concise (AUDIT-C) measure was
used for the risk assessment, with the chatbot using a
knowledge base, storing answers, until it had gathered
sufficient information to provide relevant feedback.
A total of 17 participants between the ages of 18 and
25 years, screened as low to medium risk drinkers (<5
drinks a day) took part in the study. Each participant was
allocated 30 min to test and evaluate the chatbot, this in-
cluded an introduction, brief demonstration, and an esti-
mated 5 min to complete the education module and risk
assessment individually. The remaining time was spent
on the evaluation, where participants were asked to com-
plete an 8-item satisfaction questionnaire using a 4-point
Likert scale, based on the client satisfaction survey, and a
structured interview. The client satisfaction survey results
summary reported satisfaction to be generally high (M =
3.55, SD = 0.57), with no significance drawn against a
control group. The interview results were grouped
through topic analysis and categorized as positives, nega-
tives, comments, and suggestions. Positives were ele-
ments that contributed to user satisfaction, negatives ele-
ments that produced undesirable effects, comments giv-
ing general feedback and suggestions, ways to improve
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the chatbot. It was reported that there was a good level of
agreement among the participants with a number of
strong positives, such as informative knowledge base (n =
15), simple guided conversation (n = 15), and quick re-
sponse time (n = 8). User frustration with the chatbot was
reported in the negatives, with too much information (n
= 5), confusing conversation (n = 3), and undesirable in-
terface (n = 5). No further evaluation was conducted.
However, it was noted that the chatbot was generally re-
ceived positively and that a more sophisticated model
with a larger sample size would further enhance user sat-
isfaction and enable more comprehensive statistical anal-
ysis.
The paper by Moghadasi et al. [41] presented a chatbot
called Robo, which used big data taken from the social
content aggregation platform Reddit. The dataset was
amassed using a daily “crawler” module (programme to
collect data from the Internet), looking for “subreddits”
(dedicated topics), in areas such as drugs or opioids. This
dataset formed a repository of “question and answer”
pairs, with 7,596 questions and 12,898 answers, one ques-
tion could have multiple answers. The length of the sen-
tences was analysed to inform the decision on what mod-
el to adopt to encode the data for use by the chatbot. A
deep averaging network architecture was selected, where
the sentences were encoded by embedding them as a vec-
tor average (in natural language processing terms, the
conversion of human text to a format understood by
computers), this was fed through several layers, to com-
putationally learn a higher level of abstraction in the rep-
resentation of the data. From this, single-turn response
matching (response based on last user input) was used,
with the machine learning component, query semantic
understanding (QSU), where the users’ queries were en-
coded and matched against the encoded dataset. The out-
put of the QSU process is the highest scoring match based
on a similarity calculation. Robo was given a web-based
chatbot interface to interrogate the underlying dataset
through the QSU component.
The Robo interface was tested using three real scenar-
ios. The responses to these scenarios were considered as
the measure of how effective the chatbot was in its current
form. The first user case was a question on whether mus-
cle-relaxing medication helps sleep and relaxation, a fac-
tually applicable response was returned regarding two
medications, including details on tablet dosage. The sec-
ond user case asked if chewing opiates make them work
instantly, again a matched response was given with, “No,
but it sure does speed them up.” The final user case was a
question on whether ketamine and opiates can be used
together. The response here was a suggestion of using a
different drug, GABA, to mix opiates with, so only in part
accurate to the scenario, although it was pointed out that
this was an accurate response to mixing and misusing
drugs. There was no further testing, evaluation, or discus-
sion on the efficacy of the Robo chatbot; however, the
conclusion stated that as a first attempt at using big data
as a core data source, not all data will have been assimi-
lated or even asked. The stated future intention was to
accumulate more data, in addition to weighting the prior-
ity of responses from medical experts.
The first of the studies by Prochaska et al. [43] took an
existing chatbot, called Woebot, designed to deliver inter-
ventions based on the principles of cognitive behavioural
therapy and introduced customizations suited to SUD,
such as motivational interviewing and cognitive behav-
ioural therapy relapse prevention interventions. These
interventions, known as W-SUDs, were developed as an
8-week programme, during which time the participants’
mood, cravings, and pain were tracked. A total of 101 par-
ticipants, aged between 19 and 62 years (M = 36.8, SD =
10.0), took part in the study, after being positively screened
using the Cut down, Annoyed, Guilty, Eye opener-Adapt-
ed to Include Drugs (CAGE-AID) Scale, for meeting the
threshold for having problematic drug or alcohol use.
Participants were also assessed as not having complex is-
sues, such as drug-related medical problems, liver trou-
ble, or a recent suicide attempt. Of the 101 participants,
51 completed both the pre- and post-study assessments,
which consisted of the following measures: AUDIT-C,
Drug Abuse Screening Test-10 (DAST-10), General Anx-
iety Disorder-7 (GAD-7), Patient Health Questionnaire-8
(PHQ-8), Brief Situational Confidence Questionnaire
(BSC) for assessing confidence to resist drug/alcohol use.
Participant opinion of the W-SUDs was also measured,
using the Usage Rating Profile-Intervention scale (URP-
I), the Client Satisfaction Questionnaire (CSQ-8), and the
Working Alliance Inventory-Short Revised (WAI-SR).
Paired sample t tests were used to compare the par-
ticipants’ pre- and post-treatment scores. The overall
confidence scores in all areas of the BSC significantly in-
creased, showing improved confidence in resisting drug/
alcohol use (all p values <0.05). Drug and alcohol use had
reduced significantly in the AUDIT-C (t = −3.58, p <
0.01) and DAST-10 (t = −4.28, p < 0.01) measures. The
PHQ-8 and GAD-7 also showed significant improvement
with t = −2.91, p = 0.05 and t = −3.45, p = 0.01, respec-
tively. A reduction in frequency and severity of cravings
across the 8-week programme was indicated with a
McNemar test (p < 0.01). Additional analysis showed a
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greater decline in the AUDIT-C score with reduced alco-
hol use, PHQ-8 depression and GAD-7 anxiety, along
with increases in BSC confidence. Similarly, greater de-
cline in DAST-10 was associated with a reduction in
PHQ-8 depression, but not with frequency of drug use or
GAD-7 anxiety. It was also reported that participants cur-
rently in therapy only showed one statistically significant
difference to those who were not and that was a greater
reduction in depressive symptoms.
Satisfaction scores were generally high, for example, n
= 35 reported that the W-SUDs had helped with their
problems, n = 39 rated the quality of interaction highly,
and n = 39 would recommend the W-SUDs. A lower
number of participants (n = 21) rated the W-SUDs as
having met most or all of their needs. The WAI-SR scores
showed that participants rated bonding with Woebot
higher, than agreement on tasks and goals. The CSQ-8
scores did not differ by participant characteristic; how-
ever, non-Hispanic white participants scored higher on
the WAI-SR and URP-I scales. A reduction in drug and
alcohol use pre- and post-treatment was also associated
with higher scores in the WAI-SR (r = −0.37, p = 0.008)
and URP-I (r = −0.30, p = 0.03), along with a greater re-
duction in cravings. Confidence to resist drug and alcohol
use was also associated with higher scores on the WAI-SR
(r = 0.30, p = 0.03) and the URP-I (r = 0.33, p = 0.02).
The principal findings in the first study by Prochaska
et al. [43] showed significant improvement between the
pre- and post-treatment assessment. It was also noted
that the significant reduction in depression and anxiety
was consistent with previous findings on the use of Woe-
bot, as well as showing a reduction in substance use. Par-
ticipants who scored higher in the CAGE-AID were
more likely to complete the post-treatment assessment,
no other measures showed this, so the conclusion was
drawn by the authors that those in most need of help
were more likely to use the W-SUDs and complete the
programme. It was also observed that nearly 1,400 par-
ticipants were excluded from the CAGE-AID screening
process, due to low severity of alcohol and drug use, scor-
ing less than the cut-off point of 2 for correctly identify-
ing SUD with a specificity of 85% and sensitivity of 70%.
This led the authors to suggest a need for early interven-
tion in substance misuse. The participants were predom-
inantly female (n = 76), employed (n = 73), and non-
Hispanic white (n = 79). It was noted that future research
should reflect a wider diversity. The first Prochaska re-
port [43] stated that drug and alcohol use patterns and
attitude to digital health interventions during the CO-
VID-19 pandemic had not been considered within the
study; however, the second W-SUD paper [42] was a fol-
low-up study that introduced chatbot content for CO-
VID-19 related stressors to see if using W-SUDs specifi-
cally in a study period affected by the COVID-19 pan-
demic, reduced substance use.
This study by Prochaska et al. [42] was an RCT which
adopted a similar approach to recruitment, screening
participants for problematic drug and alcohol use. This
resulted in 180 participants aged between 18 and 65 years
(M = 40.8, SD = 12.1) scoring above 1 in their CAGE-AID
assessment, the threshold for inclusion in the study. The
participants were randomized to either the W-SUD inter-
vention (n = 88) or a wait list (n = 92), where access to the
intervention was postponed for the duration of the 8-week
study. The pre- and post-assessment used a primary out-
come measure of substance use occasions and secondary
outcome measures using the same scales as the first study
(AUDIT-C, DAST-10, GAD-7, BSC, PHQ-8), in addition
to two new measures. The Short Inventory of Problems
– Alcohol and Drugs, which extended the assessment to
include problematic use within the last 30 days and a
measure to assess pandemic-related mental health effects.
Due to a programming issue, DART-10 was not used as
an outcome measure post-assessment. As with the first
study, participant opinion was also measured using CSQ-
8, WAI-SR, and URP-I.
General linear models were used to test for differences
in outcome between groups, with the W-SUD partici-
pants showing statistically significant reduced substance
use compared to those on the wait list (p = 0.035), as well
as a reduction in the estimated marginal mean for sub-
stance use occasions in the previous 30 days, −9.6 (SE =
2.3) compared to −3.9 (SE = 2.2). No statistical signifi-
cance was found between groups for the secondary out-
comes during the study period, although participants in
the W-SUD group had a two-fold confidence gain on the
BSC measure, this was not statistically significant (p =
0.175). As with the first study by Prochaska et al. [43], a
reduction in substance use occasions saw a statistically
significant increase in confidence and reduction in sub-
stance use problems as well as pandemic-related mental
health problems (p < 0.05). User satisfaction was again
found to be high and when compared to the first study
saw the metrics increase further. This included accept-
ability across all measures. It was observed the affective
bond score on the WAI-SR measure was the highest in
both studies, which was noteworthy as Woebot is a sys-
temized agent. While the second study by Prochaska et al.
[42] demonstrated there were no significant group differ-
ences resulting from the study period for the secondary
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outcomes, it did reinforce the effectiveness of W-SUD in-
terventions in reducing substance use and associated sub-
stance use problems.
Addiction Support in Predominant Chatbots
Nobles and colleagues in a short paper [39] explored
how predominant chatbots respond to addiction help-
seeking requests. The study considered the five chatbots
that make up 99% of the intelligent virtual assistant mar-
ket: Amazon Alexa, Apple Siri, Google Assistant, Micro-
soft Cortana, and Samsung Bixby. Prior to making the
help-seeking requests, the software for each device run-
ning the chatbot was updated to the latest version (Janu-
ary 2019), and the language was set to English US. The
location for all tests was San Diego, CA, USA. There were
14 different requests, which were repeated verbatim by
two different authors. The authors were native English
speakers. These measures were taken to mitigate prob-
lems shown in previous research with chatbot compre-
hension of medical terminology. The requests all started
with “help me quit…” and concluded with the substance
type, for example, alcohol, drugs, painkillers, or opioids.
The responses were assessed by (a) was a singular re-
sponse given, (b) did the singular response link to an
available treatment, or treatment referral service?
When the five chatbots were asked “Help me quit
drugs,” only Alexa returned a singular response, which
was the definition for “drug.” The others failed to provide
a useful response, for example, Google Assistant respond-
ed with “I don’t understand,” Bixby executed a web
search, and Siri said “Was it something I said? I’ll go away
if you say “goodbye.”” Similar results were given across
all chatbots and substance types, for instance, Cortana
replied with “I’m sorry. I couldn’t find that skill,” when
queried for alcohol and opioids. The exception to this was
Siri, when asked “Help me quit pot,” responded with de-
tails of a local marijuana retailer. In line with the assess-
ment criteria, only two singular responses were returned
that directed users to treatment or a treatment referral
service. Here, Google Assistant linked to a mobile cessa-
tion application, when asked about tobacco or smoking.
Of the total 70 help-seeking requests, only four resulted
in singular responses. In the paper’s discussion, this is iden-
tified as a missed opportunity to promote referrals for ad-
diction treatment and services, especially given the breadth
and scope of what intelligent virtual assistants can accom-
plish. Possible reasons for this were reported as promoting
health falls beyond the profit-driven objectives of technol-
ogy companies. The algorithms required to implement this
type of health initiative exceed the capability of the expertise
within technology companies. Also that public health ini-
tiatives are not forming beneficial partnerships with tech-
nology companies. Furthermore, the report raised the ethi-
cal concern of responses being detrimental to public health,
quoting the example of where Siri directed the user to a
marijuana retailer, explaining that while this was partly due
to location (tests were performed in San Diego, CA, USA),
it was an example of potentially damaging advice being is-
sued to someone trying to address an addiction.
Perspectives on Chatbots in Addiction
The paper by Barnett et al. [44] considers the perspec-
tives of clients and counsellors regarding the technologi-
cal and social effects of chatbots in alcohol and drug care.
The theoretical approach employed in this research is
based on affordances, which when originally applied to
HCI suggest that designed-in features could signal how a
technology can be used. It is reasoned by the authors that
this HCI, involving human and non-human actors (user
and chatbot), may combine differently as a technological
experience, yielding opportunities (affordances) for what
the technology may enable or constrain. By drawing on
this, it can be postulated how chatbots might afford or
constrain online drug and alcohol care. The research is
further informed by the corpus of scholarship looking at
“more than human” approaches to care, the conveyed
motivation for which was to challenge traditional hu-
man-centric models for addiction treatment, which are
uni-directional and subject to power asymmetries, and
reframe them, looking at the dispersal of care through the
everyday encounter of human and non-human actors,
and how they collaborate with and shape one another.
There were 28 participants in the study, 20 clients (10
male and 10 female), aged between 22 and 78 years (M =
38), and 8 counsellors (5 male and 3 female), with a median
of 2 years working for an online counselling service. Data
were collected via a series of interviews and focus groups.
The data were analysed using thematic analysis, where the
themes were identified collaboratively and informed by the
authors reading on affordances and “more than human”
approaches. A strong theme emerged showing the partici-
pants’ concerns regarding the loss of empathy and mutual
understanding from a non-human agent. It was questioned
whether this type of perfunctory care was appropriate in
drug and alcohol services, or whether it would undermine
the goals of digitized health care in reaching a wider audi-
ence and removing barriers such as stigma. The partici-
pants were amenable to working in unison with a chatbot
to complete more straightforward undertakings, such as
collecting client histories or performing repetitive tasks
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such as triages. It was reported that some participants were
concerned over chatbots impeding open and honest dis-
course, while others felt there were benefits in assuring con-
fidentiality and privacy. The authors positioned this as an
example of the affordances that chatbots offer HCI, where
the interaction can emerge in different ways.
In recognizing the nuances and complexity of participant
opinion, the authors proposed a “more than human” care
model, one that distributes care provision between human
and non-human actors. Their proposed model encompass-
es the continuing necessity of human input from trained
professionals, such as counsellors, and enhances it with
technological care agencies, such as chatbots. The intention
is to offer quality care to a wider audience, an area that is
reported in the study as being salient to drug and alcohol
treatment, and one that justifies future research to maximize
potential and minimize counterproductive outcomes in us-
ing digital health care as a contemporary mode of care.
The use of chatbots as supportive agents in the treat-
ment of drug and alcohol addiction is an area of research
in its infancy. Within this review, only six studies were
identified as eligible for inclusion (see Fig.1), despite the
broad search term (“drug” OR “alcohol” OR “substance”)
AND (“addiction” OR “dependence” OR “misuse” OR
“disorder” OR “abuse” OR harm*) AND (“chatbot” OR
“bot” OR “conversational agent”) AND “publication date”
≥ January 01, 2016 AND “publication date” ≤ March 27,
2022, and a rapidly growing corpus of work on the use of
chatbots in healthcare. A similar verdict was drawn in the
first study by Prochaska et al. [43] when it was noted that
it was the opening study on a chatbot adapted for SUD.
The two studies by Prochaska et al. [42, 43] reported a
reduction in substance use in the participants who engaged
with Woebot and completed the W-SUDs, a reduction that
was quantitatively corroborated with several well-estab-
lished and reliable measures, such as the GAD-7, AUDIT-
C, and PHQ-8. Relatedly, through qualitative assessment,
paper by Barnett et al. [44] found people receptive to work-
ing with chatbots in the field of drug and alcohol addiction,
recognizing the benefits this affords, such as confidentiality
and privacy. A topic that was tempered with concern over
losing human input, empathy and understanding, and con-
straints in open and honest communication, however.
Discussion
Principal Findings
The biggest obstacle to extending the use of chatbots
in this area of healthcare, as reported in the papers re-
viewed, concerned the ethical implications of using a
non-human agent in a supportive role. Paper by Barnet et
al. [44] discussed the forfeiture of empathy and under-
standing in HCI. Research by Nobles et al. [39] showed
how AI can be harmful without human reasoning, when
Siri was asked for help quitting marijuana and directed
the user to a place where they could purchase it. Similar-
ly, work by Moghadasi et al. [41] ran user cases that actu-
ally gave responses advocating taking drugs in a certain
way or mixing drugs for better effect. Both these studies
[39, 41] use data that have not been assessed or validated
as suitable for use in the treatment of drug and alcohol
addiction. This offers repute to the findings in the papers
concerned with bespoke chatbots developed specifically
for addiction and SUD, as opposed to those which lever-
age existing resources, such as big data or the predomi-
nant chatbot platforms. Furthermore, in addition to the
reduction in substance use reported in the papers by Pro-
chaska et al. [42], the studies by Elmasri et al. [40] and
Prochaska et al. [43] showed positive results when evalu-
ating user feedback on engaging with chatbots specifical-
ly designed for SUD.
The potential for causing harm to an already vulnerable
population presents a barrier to the future acceptance of
chatbots within addiction services. As a point of concern,
this was highlighted in two of the reviewed papers [39, 41],
when both gave examples of a potentially damaging re-
sponse having been sent to the end user. The need to mon-
itor the safe usage of chatbots such as those reviewed is
paramount given the rapid development lifecycle of such
solutions and the complex and sometimes unpredictable
disposition of having a target population with a history of
problem drug and alcohol use. Compounding this is the
lack of research conducted to date. Of the papers reviewed,
only two [42, 43] had a longitudinal component, with the
latter paper [42] validating the findings in a follow-up
study on the use of W-SUDs. Chatbot implementations
that have undergone a more robust validation process to
establish a reliable and expert-informed evidence base are
necessary for confidence in this type of digital interven-
tion. With this confidence, addiction services can better
afford support using chatbots to those they engage with,
whether in a clinical capacity, as aftercare support, or as a
remote treatment option.
Limitations and Future Research
This scope of this study included drug and alcohol ad-
diction. Papers that covered other addictions, such as
smoking and gambling, were not taken forward for re-
view, so the opportunity to learn what advances have
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been made in these areas has not been appreciated. Fur-
thermore, in the papers reviewed, there was a partiality
toward studying the feasibility and acceptability of chat-
bots as opposed to empirical study of their use, with the
paper by Barnett et al. [44] looking at the affordances of
chatbots, the paper by Elmasri et al. [40] having a strong
emphasis on user acceptance, and the papers by Mogha-
dasi et al. [41] and Nobles et al. [39] assessing outcomes
based on user cases.
None of the studies considered whether chatbots were
more effective than alternate digital interventions, for ex-
ample, systemized psychoeducation or the provision of
online advice, also it was not possible to gauge improve-
ment based on chatbot intervention as opposed to no in-
tervention for the qualitative and mixed method studies.
Furthermore, none of the papers used an active control
group or clinically diagnosed participants, and the only
RCT study employed a waiting list control. Given these
methodological limitations along with the absence of lon-
gitudinal observation from baseline, the causal effect of
chatbots on SUD is not determinable within the existing
corpus of work. This exposes a gap in the current litera-
ture and an area for more transparent and in-depth study
looking at the longer-term outcomes of using chatbots as
an intervention for people with SUD using a more selec-
tive recruitment strategy and expansive design.
This review has highlighted that more work is required
if chatbots are to safely leverage data that exist in the pub-
lic domain. It has also shown that future chatbot solutions
need input from those with an appropriate level expertise
in the subject area to ethically ensure their suitability to
their target audience. The papers aimed at developing be-
spoke chatbots, designed specifically for use in this area
of healthcare did report some favourable results, and
while the limitations discussed suggest this current litera-
ture base is not sufficient to direct future decisions on ef-
fective chatbot design, it does clarify necessary compo-
nents for consideration in future work in this area.
Conclusion
This review sought to investigate the use of chatbots
targeted at supporting people with an SUD. In doing so,
it found the body of research in this field is limited, and
given the quality of the papers reviewed, it is suggested
more research is needed to report on the usefulness of
chatbots in this area with greater confidence. Two of the
papers reported a reduction in substance use in those who
participated in the study. While this is a favourable find-
ing in support of using chatbots in this field, a strong mes-
sage of caution must be conveyed insofar as expert input
is needed to safely leverage existing data, such as big data
from social media or that which is accessed by prevalent
market-leading chatbots. Without this, serious failings
like those highlighted within this review mean chatbots
can do more harm than good to their intended audience.
Statement of Ethics
An ethics statement is not applicable because this study is based
exclusively on published literature.
Conflict of Interest Statement
Lisa Ogilvie, Julie Prescott, and Jerome Carson declare that
they have no conflicts of interest.
Funding Sources
The authors did not receive any funding in relation to this
study.
Author Contributions
Lisa Ogilvie: substantial contributions to the conception and
design of the work and the acquisition, analysis, and interpretation
of data. Julie Prescott and Jerome Carson: contributions to the de-
sign of the work; the acquisition, analysis, and interpretation of
data; and agreement to be accountable for all aspects of the work
in ensuring that questions related to the accuracy or integrity of
any part of the work are appropriately investigated and resolved.
Data Availability Statement
The data that support the findings of this study are available by
a named search of the data sources listed in Table1. Further en-
quiries can be directed to the corresponding author.
References 1 Adamopoulou E, Moussiades L. Chatbots:
history, technology, and applications.
MLWA. 2020 Dec 15; 2: 100006.
2 Gaffney H, Mansell W, Tai S. Conversational
agents in the treatment of mental health prob-
lems: mixed-method systematic review. JMIR
Ment Health. 2019; 6(10): e14166.
3 Denecke K, Tschanz M, Dorner TL, May R.
Intelligent conversational agents in health-
care: Hype or hope? Stud Health Technol In-
form. 2019; 259: 77–84.
Ogilvie/Prescott/Carson
Eur Addict Res 2022;28:405–418
418
DOI: 10.1159/000525959
4 Steere A. 2020: a year in health technology.
NHSx; 2021 Jan 6. https: //www.nhsx.nhs.uk/
blogs/2020-year-health-technology/; ac-
cessed May 10, 2021.
5 Calvo R, Peters D. Positive computing tech-
nology for wellbeing and human potential.
Cambridge: The MIT Press; 2017.
6 Taylor H. Why facebook is going all in on
chatbots. Cnbc; 2016 Apr 13. https: //www.
cnbc.com/2016/04/13/why-facebook-is-go-
ing-all-in-on-chatbots.html; accessed May
10, 2021.
7 Su Z, Schneider JA, Young SD. The role of
conversational agents for substance use disor-
der in social distancing contexts. Subst Use
Misuse. 2021 Jul 21(11): 1732–5.
8 Krentzman AR. Helping clients engage with
remote mutual aid for addiction recovery
during COVID-19 and beyond. Alcohol Treat
Q. 2021; 39(3): 348–65.
9 Bandawar M, Narasimha VL, Chand P. Use of
digital technology in addiction disorders. In-
dian J Psychiatry. 2018 Feb; 60(Suppl 4): S534–
40.
10 Drinkaware. Tools to reduce your drinking.
Drinkaware; 2022. https: //www.drinkaware.
co.uk/tools; accessed March 25, 2022.
11 Breaking Free. Breaking free from substance
use evidenced-based recovery support pro-
gramme. Breaking Free Online; 2022. https: //
www.breakingfreeonline.com; accessed
March 20, 2022.
12 Addiction Executive. Top 60 addiction treat-
ment social media groups. Addiction Execu-
tive; 2022. https: //www.behavioralhealthnet-
workresources.com/blog/top-60-addiction-
treatment-social-media-groups/; accessed
March 15, 2022.
13 Schuetzler RM, Giboney JS, Grimes GM,
Nunamaker JF. The influence of conversa-
tional agent embodiment and conversational
relevance on socially desirable responding.
Decis Support Syst. 2018 Oct; 114: 94–102.
14 Cambria E, White B. Jumping NLP curves: a
review of Natural Language processing re-
search [review article]. IEEE Comput Intell
Mag. 2014; 9(2): 48–57.
15 Tudor Car L, Dhinagaran DA, Kyaw BM,
Kowatsch T, Joty S, Theng YL, et al. Conver-
sational agents in health care: scoping review
and conceptual analysis. J Med Internet Res.
2020; 22(8): e17158.
16 Pereira J, Díaz Ó. Using health chatbots for
behavior change: a mapping study. J Med
Syst. 2019 Apr 4; 43(5): 135.
17 Fiske A, Henningsen P, Buyx A. Your robot
therapist will see you now: Ethical implica-
tions of embodied artificial intelligence in
psychiatry, psychology, and psychotherapy. J
Med Internet Res. 2019; 21(5): e13216.
18 Jovanovic M, Baez M, Casati F. Chatbots as
conversational healthcare services. IEEE In-
ternet Comput. 2021; 25(3): 44–51.
19 Ahmed A, Ali N, Aziz S, Abd-alrazaq AA,
Hassan A, Khalifa M, et al. A review of mobile
chatbot apps for anxiety and depression and
their self-care features. Comput Methods
Programs Biomed Update. 2021: 100012.
20 Bibault JE, Chaix B, Nectoux P, Pienkowsky
A, Guillemassé A, Brouard B. Healthcare ex
machina: are conversational agents ready for
prime time in oncology? Clin Transl Radiat
Oncol. 2019 May; 16: 55–9.
21 Wang R, Wang J, Liao Y, Wang J. Supervised
machine learning chatbots for perinatal men-
tal healthcare. ICHCI. 2020 Dec; 2020: 378–83.
22 Yang LH, Wong LY, Grivel MM, Hasin DS.
Stigma and substance use disorders: an inter-
national phenomenon. Curr Opin Psychiatry.
2017 Sep; 30(5): 378–88.
23 Lang C. Craving to be heard but not seen:
chatbots, care and the encoded global psyche.
Somatosphere; 2021 Apr 13. http: //somato-
sphere.net/2021/chatbots.html/; accessed
May 10, 2021.
24 Asensio S, Hernández-Rabaza V, Orón Sem-
per JV. What is the “trigger” of addiction?
Front Behav Neurosci. 2020; 14: 54.
25 Ogilvie L, Prescott J, Hanley T, Carson J. Ar-
tificial intelligence in mental health: the novel
use of chatbots to support trainee counsellors
and recoving addicts. In: Prescott J, editor.
Digital innovations for mental health sup-
port. Hershey: IGI Global; 2021. p. 296–319.
26 Ogilvie L, Prescott J, Carson J. A friendly
voice. Drink and Drugs News. 2021 Sep 6.
https: //www.drinkanddrugsnews.
com/a-friendly-voice/; accessed April 2, 2022.
27 Boland A, Cherry G, Dickson R. Doing a sys-
tematic review: a student’s guide. London:
SAGE; 2017.
28 Page MJ, Moher D, Bossuyt PM, Boutron I,
Hoffmann TC, Mulrow CD, et al. PRISMA
2020 explanation and elaboration: Updated
guidance and exemplars for reporting sys-
tematic reviews. BMJ. 2021 Jan; 372: n160.
29 Hong QN, Gonzalez-Reyes A, Pluye P. Im-
proving the usefulness of a tool for appraising
the quality of qualitative, quantitative and
mixed methods studies, the mixed methods
appraisal tool (MMAT). J Eval Clin Pract.
2018; 24(3): 459–67.
30 Hong QN, Fàbregues S, Dagenais P, Bartlett
G, Boardman F, Cargo M, et al. The mixed
methods appraisal tool (MMAT) version
2018 for information professionals and re-
searchers. Educ Inf. 2018; 34(4): 285–91.
31 Souto RQ, Khanassov V, Hong QN, Bush
PL, Vedel I, Pluye P. Systematic mixed stud-
ies reviews: updating results on the reliabil-
ity and efficiency of the mixed methods ap-
praisal tool. Int J Nurs Stud. 2015 Jan; 52(1):
500–1.
32 Somasiri L, Galabada S, Wijethunga H, Day-
ananda H, Nugaliyadde A, Thelijjagoda S, et
al. A drug addiction recovery through mobile
based application. Malabe: 5th National Con-
ference on Technology & Management At: Sri
Lanka Institute of Information Technology;
2016 Apr.
33 Park S, Choi J, Lee S, Oh C, Kim C, La S, et al.
Designing a chatbot for a brief motivational
interview on stress management: qualitative
case study. J Med Internet Res. 2019 Apr;
21(4): e12231.
34 Haug S, Castro RP, Wenger A, Schaub MP.
Efficacy of a smartphone-based coaching pro-
gram for addiction prevention among ap-
prentices: study protocol of a cluster-ran-
domised controlled trial. BMC Public Health.
2020 Dec 14; 20(1): 1910.
35 Auriacombe M, Moriceau S, Serre F, Denis C,
Micoulaud-Franchi JA, de Sevin E, et al. De-
velopment and validation of a virtual agent to
screen tobacco and alcohol use disorders.
Drug Alcohol Depend. 2018 Dec 1; 193: 1–6.
36 Auriacombe M, Fournet L, Dupuy L, Micou-
laud-Franchi JA, de Sevin E, Moriceau S, et al.
Effectiveness and acceptance of a smart-
phone-based virtual agent screening for alco-
hol and tobacco problems and associated risk
factors during COVID-19 Pandemic in the
general population. Front Psychiatry. 2021
Jul; 12(693687): 693687.
37 Kornfield R, Sarma PK, Shah DV, Landucci
G, McTavish F, Pe-Romashko K, et al. Detect-
ing recovery problems “just in time”: applica-
tion of automated linguistic analysis and su-
pervised machine learning to an online sub-
stance abuse forum. J Med Internet Res. 2018
Jun 12; 20(6): e10136.
38 Boustani M, Lunn S, Visser U, Lisetti C. De-
velopment, feasibility, acceptability, and util-
ity of an expressive speech-enabled digital
health agent to deliver online, brief motiva-
tional interviewing for alcohol misuse: de-
scriptive study. J Med Internet Res. 2021 Sep
29; 23(9): e25837.
39 Nobles AL, Leas EC, Caputi TL, Zhu SH,
Strathdee SA, Ayers JW. Responses to addic-
tion help-seeking from Alexa, Siri, Google As-
sistant, Cortana, and Bixby intelligent virtual
assistants. NPJ Digit Med. 2020 Jan 29; 3(11): 11.
40 Elmasri D, Maeder A. A conversational agent
for an online mental health intervention.
Brain Inform Health. 2016 Sep; 23: 243–51.
41 Moghadasi M, Zhuang Y, Gelban H. Robo: a
counselor chatbot for opioid addicted pa-
tients. SSPS. 2020 Jul: 91–5.
42 Prochaska JJ, Vogel EA, Chieng A, Maglalang
DD, Baiocchi M, Pajarito S, et al. A random-
ized controlled trial of a therapeutic relational
agent for reducing substance misuse during
the COVID-19 pandemic. Drug Alcohol De-
pend. 2021b Oct 1; 227: 108986.
43 Prochaska JJ, Vogel EA, Chieng A, Kendra M,
Baiocchi M, Pajarito S, et al. A therapeutic re-
lational agent for reducing problematic sub-
stance use (Woebot): Development and us-
ability study. J Med Internet Res. 2021a Mar;
23(3): e24850.
44 Barnett A, Savic M, Pienaar K, Carter A, War-
ren N, Sandral E, et al. Enacting “more-than-
human” care: “clients and counsellors” views
on the multiple affordances of chatbots in al-
cohol and other drug counselling. Int J Drug
Policy. 2021; 94: 102910.
