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Original Paper
Assessing Mood With the Identifying Depression Early in
Adolescence Chatbot (IDEABot): Development and
Implementation Study
Anna Viduani1,2, BA, MSc; Victor Cosenza3, BA; Helen L Fisher4,5, PhD; Claudia Buchweitz1,2, BA, MSc; Jader
Piccin1,2, MD, MSc; Rivka Pereira1,2, BA; Brandon A Kohrt6, MD, PhD; Valeria Mondelli7, MD, PhD; Alastair van
Heerden8, PhD; Ricardo Matsumura Araújo3*, PhD; Christian Kieling1,2*, MD, PhD
1Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
2Child and Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
3Center for Technological Advancement, Universidade Federal de Pelotas, Pelotas, Brazil
4Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United
Kingdom
5Economic and Social Research Council Centre for Society and Mental Health, King’s College London, London, United Kingdom
6Division of Global Mental Health, Department of Psychiatry, School of Medicine and Health Sciences, The George Washington University, Washington,
DC, United States
7Department of Psychological Medicine, Institute of Psychiatry, Psychology, King’s College London, London, United Kingdom
8Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
*these authors contributed equally
Corresponding Author:
Christian Kieling, MD, PhD
Department of Psychiatry
Universidade Federal do Rio Grande do Sul
Rua Ramiro Barcelos, 2400
Porto Alegre, 90035003
Brazil
Phone: 55 5133085624
Email: ckieling@ufrgs.com
Abstract
Background: Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances
provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on
their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment.
Objective: This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence
Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents’mood.
Methods: The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying
Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of
self-reported structured items or questionnaires and audio responses. The development explored WhatsApp’s default features,
such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot
supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings
Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female
participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of
the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo
study.
Results: The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that
were subsequently implemented. The IDEABot’s final version follows a structured script with the choice of answer based on
exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and
132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with
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good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively),
low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high
compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of
14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively).
Conclusions: The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows
a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden
of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and
potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and
development.
(JMIR Hum Factors 2023;10:e44388) doi: 10.2196/44388
KEYWORDS
depression; adolescent; ambulatory assessment; chatbot; smartphone; digital mental health; mobile phone
Introduction
Background
The challenges and limitations of the current tools of mental
health assessment—mostly performed using standardized
scales—have increased the interest in alternative monitoring
tools. Traditional assessment often fails to incorporate the
dynamic nature of psychological constructs and other relevant
clinical features [1] and is not capable of capturing prognostic
and therapeutic differences among patients [2] as well as the
personalized aspects that are essential to address mental health
issues.
Over recent decades, technology has created an opportunity to
expand data collection and analysis beyond clinical and research
facilities and centers, with flexibility to create participative,
2-way communication applications that can be easily adapted
and used in everyday settings for a variety of target populations
[3]. Considering the central role of language in the diagnosis
and assessment of mental health, a shift toward a technology
focused on conversational aspects may be key to systematizing
natural language domains that are not currently explored in
clinical settings [4].
In this sense, we propose that using chatbots—digital systems
that rely on a conversational interaction that mimics human
conversation [5]—may be an alternative to using traditional
assessment methods. Chatbots are capable of capturing real-time
accounts of events (ie, at the moment the event is being
experienced) [6] and thus may further our current understanding
of time- and context-contingent associations among activities,
moods, and experiences [7]. Primarily, it has been theorized
that chatbots both facilitate disclosure [8,9] and provide an
opportunity to collect real-time information on mood and
behavior in real-world settings with lower perceived burden for
participants and researchers, increasing ecological validity,
minimizing recall biases [10], and taking advantage of
human-like conversation features to assess psychological
constructs (such as depression) in a scalable, systematic fashion
that is not possible with the usual application of instruments
and scales.
One important advantage of chatbots is that they may be
integrated into existing applications that are routinely used by
the general public and designed as affordable, potentially
scalable tools, following a frugal innovation model [11]. In
addition, chatbots could be explored to reduce barriers that
typically prevent identification of mental health disorders
among, and help-seeking by, young people, a group especially
susceptible to these conditions [12]. Given the scarcity of
resources allocated to mental health care, particularly in
middle-income countries such as Brazil, the development of
frugal chatbot apps is a promising alternative.
Objectives
Chatbots have been used in mental health research for purposes
such as therapy, training, and screening [13,14]. Nevertheless,
most studies on user-chatbot interactions have focused on adults
[15], although adolescents are often more familiar with
smartphones than other populations [16]. Thus, exploring the
feasibility of using chatbots to collect data on adolescent mood
and behavior in an ecological fashion may be a promising
avenue of inquiry. We hypothesize that, by leveraging already
existing technologies, chatbots are a feasible, viable form of
monitoring changes in mood and symptoms over time in
adolescent populations. Moreover, we believe that their use
lessens participant burden, possibly augmenting sustained
engagement with the tool.
Therefore, we aimed to develop a chatbot tool to collect real-life
data on mood and behavior from adolescents using text and
audio messages. Here, we present the development and
feasibility pilot of and initial results obtained with the
implementation of the WhatsApp-based Identifying Depression
Early in Adolescence Chatbot (IDEABot).
Methods
Study Setting: Identifying Depression Early in
Adolescence Risk Stratified Cohort
The IDEABot was developed as part of the Identifying
Depression Early in Adolescence Risk Stratified Cohort
(IDEA-RiSCo) study [17]. The IDEA-RiSCo study includes
150 Brazilian adolescents (n=75, 50% female participants and
n=75, 50% male participants) aged 14 to 16 years at baseline,
stratified into 3 groups: low risk for developing depression
(50/150, 33.3%), high risk for developing depression (50/150,
33.3%), and experiencing a current untreated major depressive
episode (50/150, 33.3%). Participants were selected for each
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group using the Identifying Depression Early in Adolescence
Risk Score (IDEA-RS), an empirically generated algorithm
developed to estimate the individual-level probability of a
unipolar depressive episode 3 years after initial assessment
[17-19]. Additional details on procedures used in the
IDEA-RiSCo study are described elsewhere [17].
Rationale and Feasibility Pilot
The IDEABot was developed to collect data from Brazilian
adolescents via WhatsApp (Meta) [11]. In 2019, WhatsApp was
reported to have been used at least once every hour by 81% of
Brazilians [20]. Moreover, among adolescents from public state
schools in the city of Porto Alegre, Rio Grande do Sul, Brazil
(the population from which the IDEA-RiSCo sample was
derived), WhatsApp was the most popular web-based platform,
used at least once a day by 90% of the sample [21].
The IDEABot was devised to collect daily data on current mood
via both structured items or questionnaires and free audio
reporting of the aspects of daily life considered by participants
(Multimedia Appendix 1). An interdisciplinary team was
engaged in the project, including mental health practitioners
(psychiatrists and psychologists), computer scientists, and
writers. The prototype version of the IDEABot was designed
and implemented in Brazilian Portuguese using inputs from the
research team, followed by a feasibility pilot that generated a
round of adjustments.
For the feasibility pilot, 6 adolescents were invited to test a
prototype version of the IDEABot and comment on their user
experience. They tested the chatbot system for 5 days, during
which they answered the Short Mood and Feelings
Questionnaire (sMFQ) and participated in 2 days of brief audio
recordings. All features and possible response modes were
tested. After test completion, the adolescents participated in an
individual interview and a focus group discussion, conducted
on the web by 2 researchers (AV and CK).
The interviews focused on the overall experience, feasibility,
and acceptability of using the IDEABot (including concerns
about data safety and privacy). In addition, the adolescents were
engaged in jointly exploring and proposing improvements and
solutions for perceived problems. In the focus group, anchored
vignettes were used [22] to explore participants’perceptions of
the chatbot (Multimedia Appendix 2).
Implementation of the Final Version of the IDEABot
After the pilot test, the final version of the IDEABot was
generated and subsequently implemented in the second- and
third-year follow-ups of the IDEA-RiSCo study [17]. On the
basis of a review of the literature, the following usability
indicators [23] were evaluated to define successful
implementation [24,25]: (1) acceptance (ie, the proportion of
participants who were invited to take part in the IDEABot data
collection and agreed to use the tool); (2) initial attrition (ie,
failure to further engage in the protocol after agreeing to
participate in the data collection and complete the initial steps);
and (3) compliance, defined as the proportion of days on which
participants generated at least 1 data point over the 15 days of
data collection.
Socioeconomic status was also assessed with data collected at
baseline using the Brazilian Criterion of Economic Classification
[26], along with administration of a 9-item questionnaire on the
frequency of the participants’ use of 8 social media platforms,
including the frequency of WhatsApp use [21,27]. Responses
were aggregated into 3 strata (1=never, 2=several times/week,
and 3=several times/day or constantly).
Categorical and numerical variables were compared using the
chi-square and Mann-Whitney Utests, respectively. In addition,
the Spearman correlation coefficient was used to verify
correlations among continuous variables. All analyses were
performed using SPSS software (version 26.0; IBM Corp).
Ethics Approval
The development and research use of the IDEABot was
approved by the Hospital de Clínicas de Porto Alegre ethics
committee (50473015.9.0000.5327).
Informed Consent and Participation
All adolescents and caregivers provided written assent or
consent to participate in each stage of data collection and were
given the opportunity to withdraw assent or consent at any time.
For participants aged >18 years, written consent was obtained
directly. If participants wished to stop receiving messages from
the chatbot before the completion of the 15-day trial, they were
instructed to contact a research team member. In addition,
participants were instructed to use the WhatsApp delete button
if they preferred to delete sent messages or audio files. Along
with the research team’s explanation on the functioning of the
IDEABot, the chatbot’s first interaction with the user explicitly
stated the nature of the exchange that would take place.
Participants were thus aware that the audio recordings were not
listened immediately and that the chatbot was not a channel for
seeking help. Participants were provided with an additional
telephone number and instructed to contact a team member (a
board-certified psychiatrist) in case they were actively seeking
information related to mental health issues. Furthermore,
participants received information regarding the national helpline
for health and safety emergencies. Following Brazilian
legislation, participants did not receive financial incentives for
taking part in the study but were offered compensation for
mobile internet data use during their participation.
Results
Results of the Feasibility Pilot
Six adolescents (n=4, 67% male participants and n=2, 33%
female participants; n=1, 25% of the 4 male participants had
lived experience of depression, as did n=1, 50% of the 2 female
participants) aged 16 to 18 years participated in the feasibility
pilot. They were selected by convenience among the group of
adolescents who had already participated in other projects
conducted by our research team. Despite their heterogeneous
socioeconomic backgrounds, all had a smartphone with internet
access. Parental consent was obtained for all underage
participants (those aged <18 years). As most of the participants
(5/6, 83%) had already participated in other stages of the
research, they were familiar with the investigators and knew
about the IDEA-RiSCo objectives and procedures. The
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interviews lasted 20 to 30 minutes, whereas the duration of the
focus group was 50 minutes.
Overall, participants considered the IDEABot easy to use and
enjoyable. All 6 adolescents completed at least 4 (80%) of the
5 interactions and sent an average of 54.5 (range 2-97) seconds
of audio recordings per day. The adolescents expressed that
directed questions (such as those asking about their daily
routine) were easier to answer than more open questions (such
as the initial request for participants to introduce themselves).
In addition, the adolescents considered the prompts that targeted
the collection of at least 1 minute of audio recordings over the
day to be adequate.
Overall, they perceived the burden of integrating the chatbot
into their daily routine as low. In fact, they highlighted a positive
effect of talking about their daily lives:
It was a good experience...I felt I was talking about
my things to someone—it even sounded like there was
someone there wanting to know how my day was.
Sometimes you spend your day without anyone asking
you that. But the chatbot asked. [Female participant,
aged 17 years]
Regarding the sMFQ, the adolescents found that some of the
instructions provided by the chatbot were unclear and made
suggestions on how to fix these issues. It asked participants to
answer the sMFQ using the numbers 0, 1, or 2. The adolescents
suggested further anchoring of these responses (eg, through
reminders of the meaning of each number during the completion
of the questionnaire). The instructions were adjusted accordingly
after these difficulties and possible solutions were explored with
the adolescents. In the final version, an explanation of each
possible choice of answer was provided (0=no, 1=sometimes,
and 2=yes) before the participants were asked to complete each
item of the sMFQ, using, for example, the statement “I feel sad
today.” In addition, a short reminder of the meaning of each
numeric answer (0, 1, or 2) was added after each chatbot prompt.
An important adjustment made possible by the feasibility pilot
was as follows: the adolescents tended to respond to the
chatbot’s final interaction by either thanking it or sending an
emoji. In the chatbot’s initial programming, this was interpreted
as an unsolicited interaction to which the IDEABot responded
by requesting an audio message to explain what the participant
had said. This chatbot response would often confuse the
adolescents. To avoid this, we developed a content-based rule:
if participants responded with a predefined set of words (“ok,”
“see you,” “thank you,” or variations), this was interpreted as
a conversation closure, and the chatbot’s probe would not be
triggered.
Another aspect that required changing was suggested by the
adolescents in relation to the schedule of interactions. The
adolescents argued that they would most likely be at school or
asleep at 10:30 AM and therefore would probably not feel
comfortable responding to the questions owing to their current
environment (especially if they were at school). The adolescents
then suggested that the first interaction of the day be moved
from 10:30 AM to 1:30 PM, which was implemented in the
final version of the IDEABot.
Implementation of the IDEABot
Development of the IDEABot
The IDEABot was successfully developed to perform prescripted
interactions requesting audio and text responses from
participants to the questions it posed. The chatbot questions and
responses were expressed only in text format, regardless of the
format of user input. The IDEABot was also designed to delay
answers proportionally to the length of the text being sent to
users to simulate a more natural typed conversation. Using a
rule-based approach, four types of interactions were developed:
(1) mood ratings, (2) emoji mood ratings, (3) brief audio
recordings, and (4) questionnaire answers (Multimedia Appendix
3).
As a first step to activate the chatbot, users were required to
send a WhatsApp text message (any content was acceptable) to
the chatbot’s mobile number. To ensure both the standardization
of instructions given to users and clarity regarding the nature
of the conversation, as well as to prevent misconceptions (such
as participants believing that the chatbot is a real person or that
the audio recordings would be listened immediately), the first
interaction with the chatbot was designed to review overall
functionality. This initial interaction was named day 0 and
covered the routines that users should expect over the subsequent
14 days and how they were supposed to respond. Because of
the IDEABot’s nature and objective, data generated on day 0
will be excluded from future analyses.
The chatbot follows a time-contingent sampling for each
participant. In this sense, it is designed to initiate interactions
at fixed times: every day, beginning at 1:30 PM, participants
receive a message asking whether they are available to answer
the scheduled questions. They may answer immediately after
the first message prompt or use a snooze function to schedule
a reminder for a later time in the day (the IDEABot allows
snoozing until 3 AM the next day). If participants ignore the
first prompt, additional messages are sent at 3-hour intervals.
Participants have until 6 AM the following day to respond to
the questions of each daily cycle. If the interaction is not
completed, at 10 AM the following day, the chatbot informs
the participant that the daily cycle will end without completion
and that a new daily cycle will begin, also providing the time
when the next message would be sent. In addition to scheduled
interactions, participants are also given the option to send
unprompted audio recordings throughout the day (Figure 1).
The chatbot’s schedule is divided into five interaction modes:
(1) introduction (the first interaction with users), (2) audio
questions, (3) administration of a version of the sMFQ, (4) other
messages, and (5) the snooze function (Multimedia Appendix
3). On 7 (47%) of the 15 days, IDEABot asks broad questions
about daily life, social interactions, and preferences (Textbox
1), and participants are invited to answer through audio
recordings. The goal is to collect at least 1 minute of audio
recordings per day from each participant. If the answers
provided by participants to the 2 daily questions do not add up
to 1 minute in duration, the chatbot asks 2 standard follow-up
questions, encouraging the participant to say more. If after the
first follow-up question (“Thank you for sending this audio!
Tell us a little bit more about it, [participant]!”), the total
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duration of the audio recording still does not reach 1 minute,
the chatbot sends the second question (“It would be very
important if you could tell us a little more, okay?”). Regarding
this last question, participants can choose whether to send
another audio recording (typing “yes” or “no” before sending
the audio recording). One example is provided in Figure 2.
On the 7 days without audio prompts, participants are asked to
complete the sMFQ [28,29]. The 13 questions of the sMFQ
cover the current day (instead of the last 2 weeks as in the
original sMFQ; Multimedia Appendix 4). Participants are
instructed to type 0, 1, or 2 to answer each question, and they
have the option to correct their answers (for relevant aspects of
the processing of the collected data and analyses, refer to
Multimedia Appendix 5 [30-36]).
Figure 1. Overview of the functioning of the Identifying Depression Early in Adolescence Chatbot over the period of a day.
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Textbox 1. Questions (question 1 [Q1] and question 2 [Q2]) or prompts for audio responses requested by the Identifying Depression Early in Adolescence
Chatbot (the original questions are in Brazilian Portuguese).
Day 1
•Q1. Can you introduce yourself?
•Q2. What have you done today? Is your day going according to your usual routine?
Day 3
•Q1. Are you at home?
•[If the response is “yes”] What are you doing? Is someone else around?
•[If the response is “no”] Who do you live with? Do you get along with the people you live with?
•Q2. Can you tell me more about your house? Do you like living there?
Day 5
•Q1. Did you go outside today at all, [participant]? Do you spend more time inside, or do you sometimes go out? When you’re out, what do you
normally do?
•Q2. And how’s your neighborhood? Are there nice things around?
Day 7
•Q1. Today I want to know about your favorite story. What is it? You can choose a movie, a series, a book...whatever you want!
•Q2. And why is this your favorite story, [participant]?
Day 9
•Q1. Do you use your mobile phone a lot, [participant]? What are your favorite things to do on the mobile phone?
•Q2. And how much time do you think you spend on the internet each day? Do you use the internet mostly during the day or at night? Why?
Day 11
•Q1. Not counting the audio recordings you send here [grinning face with sweat emoji], who do you talk to about things that happen in your life?
How’s your relationship with this person?
•Q2. And why do you trust this person?
Day 13
•Q1. It’s been almost 2 weeks since we started talking, [participant]! How did you feel about answering these questions?
•Q2. And how have you been in these last 2 weeks? Has anything different happened?
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Figure 2. Example of the interaction with users of the Identifying Depression Early in Adolescence Chatbot: day 2.
Initial Results of a Full-Sample Implementation of the
IDEABot
The IDEABot was first implemented as part of the IDEA-RiSCo
second-year follow-up assessment, which took place between
August 1, 2020, and January 31, 2022. It was subsequently also
used in the third-year follow-up of the IDEA-RiSCo sample,
which occurred between August 1, 2021, and September 30,
2022.
To explain the chatbot’s functioning and features to participants,
an animated video (Multimedia Appendix 6) was developed by
the research team, providing a comprehensive overview of the
research process. It reminded participants about the previous
waves of data collection and the overall research goal, as well
as presented the various steps of data collection that they could
engage in (including the IDEABot). In addition, the video
provided information regarding data confidentiality, including
end-to-end encryption by WhatsApp for all chats, and the
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measures taken by the research team to ensure data protection.
After the video was sent, if participants agreed to use the
IDEABot, a research team member sent a link that directed
users to initiate the interaction.
For the second- and third-year follow-up assessments, 9.7%
(11/113) and 11.5% (14/122) of the adolescents, respectively,
did not have a smartphone and agreed to receive a device from
the study team to enable data collection completion. All other
participants used their own smartphones and already had
WhatsApp installed. In terms of technical challenges
experienced during the IDEABot implementation, we recorded
6 and 14 occurrences or technical malfunctions in the second-
and third-year follow-up assessments, respectively.
In the second-year follow-up, there were 5 issues with the
integration with WhatsApp’s application programming interface
(API; September 11 and 15, 2020; December 11, 2020; April
4, 2021; and June 15, 2021) and 1 instance in which WhatsApp
was offline around the world owing to an instability in Meta’s
servers (October 4, 2021) [37]. All issues were resolved within
24 hours, but the interactions of 6.2% (7/113) of the participants
were affected directly. As result, these participants lost 8
interaction days in total. In addition, in the second-year
follow-up, there were 3 instances in which the chatbot’s
malfunctioning prevented participants from completing the
scheduled interactions. In all cases, participants repeated the
interaction days affected. Finally, there was 1 occasion on which
a participant was not able to complete the day’s interaction
owing to a problem with telephone billing, which was later
resolved.
In the third-year follow-up, there were 12 issues with the
integration with WhatsApp’s API (March 18 and 19, 2022;
April 5 and 20, 2022; May 5, 18, and 20, 2022; June 14 and 26,
2022; July 8, 2022; and August 16 and 28, 2022), as well as 2
instances in which chatbot was unable to access the internet
(October 10, 2021, and February 18, 2022). In addition, the
instance in which WhatsApp was offline worldwide (October
4, 2021) also affected the third-year follow-up. Only 1
occurrence was not resolved within 24 hours (March 18 and 19,
2022), owing to the API’s instability. Interactions were affected
for 33.6% (41/122) of the participants, resulting in a loss of 16
occasions in which these participants could have completed the
day’s interaction. The greatest number of occurrences were
mostly caused by the changes in WhatsApp Web, the web-based
interface for WhatsApp required for running the API.
In the second-year follow-up, 140 adolescents took part in some
aspects of data collection and were therefore eligible to use the
IDEABot. Of the 140 adolescents, 113 (80.7%) agreed to use
the IDEABot and completed the initial interaction. Of these 113
participants, 1 (0.9%) interacted with the chatbot only on the
first interaction. The 112 adolescents who continued interacting
with the chatbot engaged on average 12.8 (SD 3.5) of the 14
possible days, corresponding to a compliance rate of 91.4%.
The snooze function was used 609 times, resulting in 331
completed interactions. In addition, participants sent on average
65 (SD 37.7) seconds of audio recordings per day, resulting in
an average of 7.6 (SD 4.3) minutes of audio recordings per
participant.
For the third-year follow-up, 132 adolescents took part in some
aspects of data collection and were therefore eligible to use the
IDEABot. Of the 132 adolescents, 122 (92.4%) agreed to use
the IDEABot and completed the initial interaction. Of these 122
participants, 1 (0.8%) interacted with the chatbot only on the
first interaction. The 121 adolescents who continued interacting
with the chatbot engaged on average 10.57 (SD 3.4) of the 14
possible days, corresponding to a compliance rate of 75.5%.
The snooze function was used 569 times, resulting in 258
completed interactions. In addition, participants sent an average
of 69.2 (SD 66.1) seconds of audio recordings per day, resulting
in an average total of 8.1 (SD 7.8) minutes of audio recordings
per participant.
No significant association between socioeconomic status and
the number of days of interaction with the IDEABot was found
(P.88); the number of days on which responses were recorded
also did not differ when participants were stratified according
to the pattern of previous WhatsApp use (ie, never, several
times/week, or several times/day; P.98) or by sex (male or
female; P.66).
Discussion
Principal Findings
This study outlines the development, feasibility pilot, and initial
results obtained with the implementation of a chatbot to support
mood assessment in adolescents. Although chatbots are
becoming increasingly more common in health care settings
[38], few studies have provided detailed analyses and empirical
discussions of specific design elements and development
techniques [39]. In this sense, we believe that reporting the
development and implementation of the IDEABot is a novel
and relevant contribution, especially given the overall good
acceptance for using the tool, low attrition, and high compliance
in terms of the proportion of responses in relation to the total
number of elicited prompts.
To the best of our knowledge, the IDEABot is the first chatbot
specifically tailored to aid multimodal research data collection
with adolescent populations. Our decision to use an existing
platform made it possible to design, develop, and implement
the IDEABot in a way that directly addresses the constraints
that the use of new mobile apps may pose to research teams and
users, in addition to saving development and adjustment time.
The IDEABot runs on any smartphone with WhatsApp,
regardless of operating system, as long as internet connectivity
is available. The IDEABot thus qualifies as a frugal innovation:
it is significantly cheaper than other alternatives (such as the
development of a new stand-alone app); it has proven sufficient
for the proposed level of data collection; and by using it, we
were able to reach participants who would otherwise remain
underrepresented [11]. Moreover, the proposed approach to data
collection is highly flexible and could potentially leverage all
forms of interactions available on WhatsApp, including
photographs and video recordings.
The initial administration of the IDEABot indicates engagement
rates of >80%, with more than half of the participants (59/113,
52.2% and 52/122, 42.6% for second- and third-year follow-up
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use, respectively) completing all 15 days of collection. In
ecological momentary assessment studies (ie, studies that are
designed to collect individual data at several time points), 80%
has been proposed as an indicator of adequate compliance [40].
Although compliance tends to vary in ecological momentary
assessment studies (also depending on the number of measures
made over time) [41], we believe that the rate obtained with the
IDEABot matches the expected rates in similar studies and is
adequate, considering the target population and that no financial
or other direct incentive was used.
In this sense, we believe that repurposing an already ubiquitous
tool in the life of adolescents to collect research data can
increase overall engagement as well as diminish the perceived
burden of data collection. Moreover, we highlight the
importance of youth participation in the creation, adaptation,
and implementation of the IDEABot. A chatbot’s personality,
interaction flow, conversation length, and dialogue structure
are important aspects and can influence user satisfaction [39].
In the case of the IDEABot, all these aspects were created and
tailored with the aid of a group of adolescents, who were active
in pointing out any strangeness or discomfort and were ready
to brainstorm solutions. Thus, not only was the final chatbot
tailored to collect relevant research data, but it was also pleasant
in terms of appearance and the manner of interaction with
adolescents themselves, which can greatly decrease the burden
of research participation.
All things considered, the IDEABot still has important
limitations that need to be addressed. Despite good engagement
rates among Brazilian adolescents, the IDEABot is a basic
chatbot that uses a rule-based approach. Although this gives the
researchers optimal control over conversation flow and topics,
the limited response range may decrease usability by adolescents
(who may, for example, become frustrated with repeated error
messages) [42]. In addition, as a WhatsApp-based chatbot, the
IDEABot is susceptible to changes in policies and bugs affecting
the platform. In this sense, the usability of the IDEABot
becomes heavily linked to WhatsApp as a commercial product,
and researchers have no control over policies such as data
security and other features. The instance in which WhatsApp
was offline worldwide preventing data collection is also an
indication of the bot’s susceptibility to the platform’s
functioning, which may hinder its applicability.
Furthermore, although the chatbot’s user-oriented design may
contribute to higher self-disclosure [43], privacy concerns
regarding the use of the data are a relevant topic. WhatsApp
policies include “end-to-end encryption” [44], and the IDEABot
also stores information (audio recordings and conversation logs)
on secure encrypted servers with additional anonymization of
sensitive information in reports. However, all conversation logs
and sent audio files remain accessible to other users in the
mobile phone or any other devices that may be used to connect
to WhatsApp (such as WhatsApp Web). Local backups may
also store this information in user’s mobile phones, creating the
risk of confidentiality breaches that cannot be controlled by the
research team.
Another important aspect is the chatbot’s response to serious
health concerns. As the IDEABot often queries participants on
mood and daily events, we might expect sensitive information
to be disclosed at the moment when distressing events occur.
However, the IDEABot’s rule-based approach may not be
suitable for fully and effectively responding to these events. In
our project, mitigation efforts included full disclosure that audio
messages would not be listened to immediately by the research
team and that the IDEABot was not equipped to deal with mental
health emergencies. Participants were also provided with the
national emergency service hotline number for acute cases, and
they were also able to call a research team psychiatrist in case
of significant distress during the data collection process.
However, this particular safety measure was never used by
participants during the data collection process in either follow-up
wave.
Also important is the susceptibility of the interface to technical
error, such as bugs in the chatbot response routine (it does not
respond, or it provides responses that do not fit the conversation
context). As people may anthropomorphize chatbots [43],
perceiving them as having a mind with intention, consciousness,
and goals [45], these instances may generate negative feelings
or distress responses, with a potential negative impact on
participants who could become attached to the chatbot [46], or
even hinder retention and continuous use. For the IDEABot,
preventive measures include continuous function supervision
by both humans and software monitoring the integration with
WhatsApp’s API. In addition, using the platform as a medium
for data collection also gives researchers little control over the
quality of the data while they are being collected. This can be
critical, for example, during data analysis, in which the selection,
extraction, and assessment of acoustic features are dependent
on the quality of the audio files and the data obtained [30]. This
highlights the need for further research to explore the data
collected as well as the techniques that are best suited for
collecting and analyzing the data.
Therefore, the IDEABot presents limitations that may be
considered inherent to the methods chosen. However, its
development was guided by the principle of user transparency,
and challenges regarding privacy and adverse incidents have
been, and continue to be, closely and continuously assessed
throughout development, implementation, and use. In addition,
we believe that, as a tool, the IDEABot supports stakeholder
values [47]. Nonetheless, the ethical considerations involving
chatbot use will change with time and technical development,
and continuous reassessment is vital to address any resulting
ethical concerns that may arise.
Conclusions
The IDEABot is a novel WhatsApp chatbot developed to aid
intensive longitudinal collection of mood data among
adolescents. The collection of audio recordings and information
on mood and behavior throughout 15 days may enable analyses
of adolescents’ data that would otherwise not be possible. The
completion rate shows that the IDEABot was able to collect
information in a manner that is attuned to the adolescents’lives.
In this sense, the use of sequenced audio recordings may be
considered similar to an audio diary, capturing much of the
sense making and representation of experiences at different time
points [48].
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It is worth noting that the choice for a multimodal data collection
approach that combines audio recordings of prompted speech,
daily information on mood, and traditional assessment methods
(such as questionnaires) sheds light on aspects of
depression—such as the temporal evolution of
symptomatology—that have only recently become a focus of
research and are also rapidly advancing. Thus, the IDEABot
generates a rich database that combines different types of input
information that can be compared and triangulated.
The IDEABot is a frugal innovation and therefore has a goal to
meet the basic needs of a population that would otherwise
remain underserved [11]; a strength of the IDEABot is its
reliance on an available ubiquitous medium as a way to reach
a population that is still underrepresented in research [49,50].
However, adaptability is key, and thus we chose to use a simple
rule-based approach, allowing the IDEABot to be easily
implemented, both technically and economically. As a result,
the IDEABot is a feasible tool for data collection that can be
adapted, tested, and implemented in different settings and for
different purposes.
Another strength of the IDEABot is its capability for intensive
data collection over extended periods within a longitudinal
3-year research project with a careful phenotypic
characterization of the sample, including multiple informants.
Such intensive and momentary data collection can elucidate
aspects of the overall trajectory of different groups of
individuals, such as those taking part in the IDEA-RiSCo study.
This group approach can be useful for monitoring change and
fluctuations in mood and to address the overall trajectories of
different groups over time. In addition, periods of intensive data
collection in individual participants may capture unique changes
or symptom fluctuation patterns that would not otherwise be
detected [7], contributing important information regarding
symptom connectivity and centrality over time. The contrast
between group and idiographic findings provides a further level
of information not usually available in traditional research
designs. In this sense, in addition to furthering our understanding
of individual and group trajectories, the characterization of the
sample also provides an opportunity to further explore the
patterns of chatbot-assisted data collection.
In summary, the initial apps of the IDEABot were successful.
The IDEABot seems to be a feasible, potentially scalable tool
to collect data that can further our understanding of how mood
changes and develops over time among adolescents.
Acknowledgments
The authors are extremely grateful to the schools and individuals who participated in this study and to all members of the Identifying
Depression Early in Adolescence (IDEA) team for their dedication, hard work, and insights. This project was supported by the
Royal Academy of Engineering under the Frontiers Follow-On Funding scheme (FF\1920\1\61). The original IDEA project was
funded by an MQ Brighter Futures grant (MQBF/1 IDEA). Additional support was provided by the UK Medical Research Council
(MC_PC_MR/R019460/1) and the Academy of Medical Sciences (GCRFNG_100281) under the Global Challenges Research
Fund. This work was also supported by research grants from Brazilian public funding agencies Conselho Nacional de
Desenvolvimento Científico e Tecnológico (477129/2012-9 and 445828/2014-5), Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (62/2014), and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (17/2551-0001009-4). CK
is a Conselho Nacional de Desenvolvimento Científico e Tecnológico researcher and an Academy of Medical Sciences Newton
Advanced Fellow. CK and BAK are supported by the US National Institute of Mental Health (R21MH124072). HLF was partly
supported by the Economic and Social Research Council Centre for Society and Mental Health at King’s College London
(ES/S012567/1). VM was supported by the National Institute for Health and Care Research Maudsley Biomedical Research
Centre hosted by South London and Maudsley NHS Foundation Trust and King’s College London and MQ funding (MQBF/4).
The views expressed are those of the authors and not necessarily those of the funders, the National Health Service, the National
Institute for Health and Care Research, the Department of Health and Social Care, the Economic and Social Research Council,
or King’s College London.
Conflicts of Interest
VM has received research funding from Johnson & Johnson, but the research described in this paper is unrelated to this funding.
All other authors declare no other conflicts of interest.
Multimedia Appendix 1
Technical aspects of the development of the Identifying Depression Early in Adolescence Chatbot (IDEABot).
[DOCX File , 14 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Anchoring vignettes.
[DOCX File , 2390 KB-Multimedia Appendix 2]
Multimedia Appendix 3
The types of interactions users can have with the Identifying Depression Early in Adolescence Chatbot (IDEABot).
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[DOCX File , 15 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Chatbot script for the Short Mood and Feelings Questionnaire instructions.
[DOCX File , 13 KB-Multimedia Appendix 4]
Multimedia Appendix 5
Processing and analysis of data and questionnaires.
[DOCX File , 15 KB-Multimedia Appendix 5]
Multimedia Appendix 6
Animated video.
[MP4 File (MP4 Video), 78470 KB-Multimedia Appendix 6]
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Abbreviations
API: application programming interface
IDEABot: Identifying Depression Early in Adolescence Chatbot
IDEA-RiSCo: Identifying Depression Early in Adolescence Risk Stratified Cohort
IDEA-RS: Identifying Depression Early in Adolescence Risk Score
sMFQ: Short Mood and Feelings Questionnaire
Edited by Y Quintana; submitted 24.11.22; peer-reviewed by B Chaudhry, R Pine; comments to author 19.02.23; revised version
received 03.04.23; accepted 02.05.23; published 07.08.23
Please cite as:
Viduani A, Cosenza V, Fisher HL, Buchweitz C, Piccin J, Pereira R, Kohrt BA, Mondelli V, van Heerden A, Araújo RM, Kieling C
Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study
JMIR Hum Factors 2023;10:e44388
URL: https://humanfactors.jmir.org/2023/1/e44388
doi: 10.2196/44388
PMID:
©Anna Viduani, Victor Cosenza, Helen L Fisher, Claudia Buchweitz, Jader Piccin, Rivka Pereira, Brandon A Kohrt, Valeria
Mondelli, Alastair van Heerden, Ricardo Matsumura Araújo, Christian Kieling. Originally published in JMIR Human Factors
(https://humanfactors.jmir.org), 07.08.2023. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic
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