Article

Harnessing AI in Anxiety Management: A Chatbot-Based

Intervention for Personalized Mental Health Support
Alexia Manole 1 , Răzvan Cârciumaru 2, * , Rodica Brînzas, 3 and Felicia Manole 4

1 Faculty of Medicine, University of Oradea, 410087 Oradea, Romania; manole.alexia@student.uoradea.ro

2 Doctoral School of Sociology, Faculty of Social and Human Sciences, University of Oradea,
410087 Oradea, Romania
3 Independent Researcher, 031046 Bucharest, Romania; brinzas.rodica@gmail.com
4 ENT Department, Faculty of Medicine, University of Oradea, 410087 Oradea, Romania; fmanole@uoradea.ro
* Correspondence: carciumaru.razvandacian@student.uoradea.ro or carciumaru.razvand@gmail.com

Abstract: Anxiety disorders represent one of the most widespread mental health challenges globally,
yet access to traditional therapeutic interventions remains constrained, particularly in resource-
limited settings. This study evaluated the effectiveness of an AI-powered chatbot, developed using
ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral therapy
(CBT) techniques. Fifty participants with mild to moderate anxiety symptoms engaged with the
chatbot over two observational phases, each lasting seven days. The chatbot delivered personalized
interventions, including mindfulness exercises, cognitive restructuring, and breathing techniques,
and was accessible 24/7 to provide real-time support during emotional distress. The findings revealed
a significant reduction in anxiety symptoms in both phases, with an average improvement of 21.15%
in Phase 1 and 20.42% in Phase 2. Enhanced engagement in Phase 2 suggested the potential for
sustained usability and familiarity with the chatbot’s functions. While participants reported high
satisfaction with the accessibility and personalization of the chatbot, its inability to replicate human
empathy underscored the importance of integrating AI tools with human oversight for optimal
outcomes. This study highlights the potential of AI-driven interventions as valuable complements to
traditional therapy, providing scalable and accessible mental health support, particularly in regions
with limited access to professional services.
Citation: Manole, A.; Cârciumaru, R.;
Brînzas, , R.; Manole, F. Harnessing Keywords: AI; anxiety treatment; ChatGPT; cognitive-behavioral therapy; mental health; chatbot;
AI in Anxiety Management: A digital health; personalized therapy; empathy in AI
Chatbot-Based Intervention for
Personalized Mental Health Support.
Information 2024, 15, 768. https://
doi.org/10.3390/info15120768 1. Introduction
Academic Editor: Neil Vaughan Anxiety disorders rank among the most prevalent mental health conditions globally,
affecting approximately 284 million individuals annually, as reported by the World Health
Received: 19 October 2024
Organization (WHO). These disorders significantly impair quality of life, manifesting
Revised: 20 November 2024
through persistent worry, physical tension, and difficulties in daily functioning. Despite
Accepted: 29 November 2024
their widespread prevalence, access to effective treatments remains limited, particularly
Published: 2 December 2024
in low-resource regions and developing countries. This disparity highlights the urgent
need for scalable, accessible solutions that can complement traditional mental health
interventions and bridge the treatment gap [1–3].
Copyright: © 2024 by the authors. Mental health conditions, including anxiety, also pose significant economic burdens.
Licensee MDPI, Basel, Switzerland. The costs associated with mental health care extend beyond the affected individuals, placing
This article is an open access article strain on healthcare systems and societies worldwide. For instance, research indicates that
distributed under the terms and older adults often face higher healthcare expenses due to comorbidities and intensive
conditions of the Creative Commons care needs, further exacerbating financial pressures [4–6]. Addressing these economic
Attribution (CC BY) license (https:// challenges necessitates innovative approaches to reduce costs while improving accessibility
creativecommons.org/licenses/by/ and effectiveness.
4.0/).

Information 2024, 15, 768. https://doi.org/10.3390/info15120768 https://www.mdpi.com/journal/information

Information 2024, 15, 768 2 of 26

In this context, artificial intelligence (AI) has emerged as a promising avenue for
augmenting psychological therapies. AI-based systems, particularly those leveraging
natural language processing (NLP) models such as ChatGPT, have shown their potential
as tools for delivering psychological support. These systems can provide personalized,
rapid interventions and operate continuously without being restricted by the availability of
human therapists. The COVID-19 pandemic underscored the relevance of such solutions,
as many individuals turned to digital platforms to meet their mental health needs when
in-person care was unavailable [7–9].
Among the therapeutic approaches for anxiety disorders, cognitive-behavioral therapy
(CBT) is widely recognized for its effectiveness. However, access to trained CBT specialists
is limited, making it challenging for many individuals to receive appropriate care [10,11].
AI-powered chatbots have begun to simulate CBT principles, offering tools and techniques
that empower users to manage anxiety. For example, chatbots like Wysa and Youper
integrate mindfulness exercises, cognitive restructuring, and relaxation techniques to help
users navigate stress and anxiety in daily life [12,13].
Despite these advancements, AI applications in therapy face significant limitations.
Chief among these is the absence of human empathy, a cornerstone of the therapeutic
relationship [14]. Trust and emotional support, which are integral to successful treatment
outcomes, cannot be fully replicated by an algorithm. While AI systems can simulate
human-like dialogue and provide tailored responses, the depth of the emotional connection
achieved in a therapist–patient relationship remains beyond their reach [15,16].
This study seeks to address these challenges by developing and evaluating an AI-based
chatbot algorithm grounded in ChatGPT, designed to deliver personalized interventions
for anxiety management. By integrating CBT principles with advanced prompt engineering
techniques, the proposed system aims to provide tailored mental health support [17].
This research investigates the effectiveness of the chatbot in reducing anxiety symptoms,
examines user engagement, and evaluates its potential to bridge gaps in traditional mental
health care.
The primary objective of this study is to assess the role of AI in augmenting anxiety
treatment, particularly in contexts where traditional therapies are inaccessible [18]. By
highlighting its strengths and limitations, this research aims to contribute to the growing
field of AI-driven mental health interventions, paving the way for future improvements that
address ethical considerations and meet users’ emotional needs comprehensively [19,20].

2. Critical Literature Review

2.1. Introduction to the Literature Review
Anxiety disorders rank among the most prevalent mental health conditions globally,
with an estimated 284 million people affected annually. As healthcare systems struggle
to meet the demand for traditional therapeutic interventions, particularly in resource-
constrained regions, the development of scalable and accessible solutions has become
imperative. Artificial intelligence (AI) represents a promising avenue for augmenting men-
tal health interventions, especially through its ability to deliver continuous, personalized
support. This section critically reviews the existing literature on AI-driven mental health
solutions, identifies gaps, and lays the foundation for the hypotheses of the current study.

2.2. AI in Mental Health Interventions

The integration of AI into mental health care has gained significant traction, particu-
larly through the use of chatbots powered by natural language processing (NLP). These
tools have shown promise in replicating elements of cognitive-behavioral therapy (CBT),
such as cognitive restructuring and mindfulness techniques. Studies by Fulmer et al. (2018),
Kretzschmar et al. (2019) and Graham et al. (2019) demonstrate the potential of chatbots
like Wysa, Youper, Joy, and Woebot to reduce symptoms of anxiety and depression in
diverse populations [18,21,22]. These studies emphasize the utility of chatbots in provid-
Information 2024, 15, 768 3 of 26

ing immediate support during crises, thereby filling critical gaps in the accessibility and
availability of human therapists.
Recent studies highlight the growing role of AI in broader mental health applications.
For example, Fitzpatrick et al. (2017) explored how chatbot interventions might enhance
user engagement through advanced personalization, while research by Li et al. (2023)
demonstrated the feasibility of integrating physiological data, such as heart rate variability,
into chatbot frameworks to refine therapeutic recommendations [23,24]. These advance-
ments suggest that chatbots are not merely supplementary tools but integral components
of comprehensive mental health strategies.
However, while promising, the current literature also reveals several persistent limita-
tions that hinder the full potential of chatbot-based interventions [25,26].

2.3. Gaps in Existing Research

While the efficacy of AI in mental health care is well documented, the persisting gaps
are as follows:
• Short Study Durations: Most of the existing research evaluates chatbot efficacy over
periods of two to four weeks, limiting insights into sustained improvements. The
current study, while initially following a seven-day intervention period, addresses this
limitation by incorporating a second observation phase after two months, aiming to
evaluate the durability of therapeutic effects over time.
• Limited Sample Diversity: Participant demographics in prior studies often skew
toward younger, technologically adept populations, leaving gaps in understanding
how chatbots perform across age, gender, and socio-economic groups. The current
study seeks to broaden the scope by including participants from diverse backgrounds.
• Focus on Psychological Symptoms Alone: While many studies concentrate solely on
psychological outcomes, few integrate physiological markers, such as nutrition or
stress-related biomarkers, into their frameworks. This gap neglects the holistic nature
of mental health, which encompasses both psychological and physical well-being. Our
study uniquely incorporates insights into nutritional deficiencies and physiological
indicators, expanding the scope of chatbot utility.
• Insufficient Examination of Personalization: Although personalization is a frequently
cited advantage of AI interventions, its direct impact on outcomes remains underex-
plored. This study evaluates the role of tailored recommendations in sustaining user
engagement and improving therapeutic efficacy.
• Lack of Conceptual Models: Few studies provide a theoretical framework that ex-
plicitly links chatbot interventions to specific psychological or behavioral outcomes.
The current study addresses this by grounding its approach in established CBT prin-
ciples and providing a conceptual model to illustrate the relationship between user
engagement, intervention personalization, and mental health outcomes.

2.4. Proposed Hypotheses

Building on the identified gaps, this study advances the following hypotheses:

• Hypothesis 1: AI-based chatbots significantly reduce anxiety symptoms within a short-term

intervention (seven days), with measurable improvement in anxiety scores across diverse
demographic groups.
Justification: Existing research validates short-term efficacy; this study aims to extend
these findings to a more heterogeneous population.
• Hypothesis 2: Long-term re-engagement with the chatbot (two months after initial use)
demonstrates sustained or improved anxiety management.
Justification: Incorporating a second observation phase addresses the common limita-
tion of short study durations.
• Hypothesis 3: Personalized feedback and contextualized recommendations enhance user
engagement and efficacy.
Information 2024, 15, 768 4 of 26

Justification: Personalization has been recognized as a critical factor but is rarely

quantified; this study investigates its impact on user outcomes.
• Hypothesis 4: Integrating physiological insights (e.g., nutritional recommendations) into
chatbot interventions leads to superior mental health outcomes compared to standard psycho-
logical support alone.
Justification: Addressing both physical and psychological health acknowledges the
interdependence of these dimensions in managing anxiety.
By addressing these hypotheses, this study seeks to fill critical gaps in the literature, offer-
ing a nuanced understanding of how AI-driven chatbots can be optimized for mental health
care. The findings aim to inform future research and development in this rapidly evolving
field, providing a foundation for more effective and comprehensive AI-based interventions.

2.5. Study Objectives

This study aimed to assess the effectiveness of an AI-powered chatbot, developed us-
ing ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral
therapy (CBT) techniques. Specifically, the study sought to perform the following:
• Evaluate the immediate impact of chatbot interactions on anxiety symptoms during a
seven-day period (Phase 1).
• Investigate the chatbot’s capacity for long-term retention and reusability of CBT
techniques over an additional seven-day period conducted two months later (Phase 2).
• Analyze engagement metrics, such as interaction frequency and duration, and their
relationship with anxiety symptom reduction.
• Compare the chatbot’s effectiveness and user experience to similar AI-based mental
health interventions.

3. Materials and Methods

To evaluate the effectiveness of a chatbot based on ChatGPT in treating anxiety disor-
ders, we developed a personalized algorithm that utilizes prompt engineering techniques
to generate responses tailored to each individual user. Prompt engineering refers to the
process of configuring questions and response scenarios so that the language model pro-
vides relevant and accurate results based on the data provided by the user [27]. This aspect
is crucial to ensure that the algorithm does not provide generic responses but is capable of
adapting to the unique needs and emotional states of each individual [11,28].

3.1. Conceptual Model

This study is guided by a conceptual model (Figure 1) that illustrates the relationships
between key variables involved in the evaluation of the chatbot’s effectiveness in managing
anxiety symptoms. The model emphasizes the interplay between user engagement, the
personalized interventions delivered by the chatbot, and the observed changes in anxiety
levels over two phases of observation.
The model hypothesizes the following:
• Engagement Metrics: Frequent and sustained interaction with the chatbot positively cor-
relates with reductions in anxiety symptoms. Engagement metrics, such as interaction
frequency and duration, serve as critical predictors of user outcomes.
• Personalized Interventions: The tailored use of cognitive-behavioral therapy (CBT)
techniques, mindfulness exercises, and cognitive restructuring delivered through the
chatbot mediates the relationship between engagement and anxiety reduction.
• Outcome Measures: Improvements in anxiety levels, measured across two phases,
are expected to demonstrate the chatbot’s long-term applicability and potential for
sustained mental health support.
By structuring the study around these variables and their relationships, the conceptual
model provides a foundation for analyzing the chatbot’s effectiveness. The relationships
 • Personalized Interventions: The tailored use of cognitive-behavioral therapy (CBT)
Information 2024, 15, 768
techniques, mindfulness exercises, and cognitive restructuring delivered through the
5 of 26
chatbot mediates the relationship between engagement and anxiety reduction.
• Outcome Measures: Improvements in anxiety levels, measured across two phases, are
expected to demonstrate the chatbot’s long-term applicability and potential for
depicted in Figure 1 help clarify the hypothesized pathways of influence and serve as a
sustained mental health support.
framework for the statistical analyses detailed in subsequent sections.

Figure 1. Conceptual model of chatbot effectiveness.

Figure 1. Conceptual model of chatbot effectiveness.
3.2. Study Design
By structuring the study around these variables and their relationships, the
This study aimed to evaluate the efficacy of a personalized AI-based chatbot, utilizing
conceptual
ChatGPT, in model provides
assisting a foundation
individuals for analyzing
with anxiety disorders.theThe
chatbot’s effectiveness.
research employed a Thetwo-
relationships depicted in Figure 1 help clarify the hypothesized
phase observational design, each phase spanning a period of seven days. pathways of influence and
A total of
serve as a framework
50 participants, for the statistical
all experiencing mild toanalyses
moderatedetailed
anxiety in subsequent
symptoms, sections.
were selected through
a rigorous screening process based on their scores on the Beck Anxiety Inventory (BAI) and
3.2.
theStudy Design Anxiety Disorder Scale (GAD-7).
Generalized
This study aimed to evaluate the efficacy of a personalized AI-based chatbot, utilizing
• Phase 1: Participants were instructed to interact with the chatbot as needed over a seven-
ChatGPT, in assisting individuals with anxiety disorders. The research employed a two-
day period, with the system being accessible 24/7. During this phase, the chatbot
phase observational design, each phase spanning a period of seven days. A total of 50
delivered real-time support and interventions tailored to individual user inputs. It
participants, all experiencing mild to moderate anxiety symptoms, were selected through
employed evidence-based cognitive-behavioral therapy (CBT) techniques, including
a rigorous screening
mindfulness processguided
exercises, based on their scores
breathing on the
strategies, Beck
and Anxietyrestructuring.
cognitive Inventory (BAI)
and
• the Generalized
Phase Anxiety Disorder
2: Approximately Scaleafter
two months (GAD-7).
completing Phase 1, the same cohort of
• Phase 1: Participants
participants were instructed
was re-invited to engageto withinteract with the
the chatbot forchatbot
anotheras needed over
seven-day a
period.
seven-day
This phase period,
aimed towith the
assess thesystem being
chatbot’s accessible
capacity 24/7. During
for long-term thisitsphase,
retention, the
reusability,
chatbot delivered
and sustained real-timeinsupport
effectiveness reducingand interventions
anxiety symptoms.tailored to individual
Comparative analysesuser
were
inputs. It employed
conducted between evidence-based
the two phases to cognitive-behavioral
evaluate the progressiontherapyover(CBT)
time,techniques,
retention of
including
techniques,mindfulness
and engagement exercises, guided breathing strategies, and cognitive
differences.
restructuring.
The dual-phase study design provided a comprehensive framework to analyze both
•the immediate
Phase 2: Approximately
and enduringtwo months
effects after completing
of chatbot interventions Phase 1, the symptoms.
on anxiety same cohortThis of
participants was re-invited to engage with the chatbot for another seven-day
structure also enabled a detailed examination of changes in user interaction patterns and period.
This phase
the potential aimed of
scalability tothe
assess
chatbottheaschatbot’s
a long-term capacity
mentalfor long-term
health supportretention,
tool. its
reusability, and sustained effectiveness in reducing anxiety symptoms.
The initial duration of 7 days was chosen to assess the feasibility and immediate Comparative
analyses were
effectiveness of theconducted between theThis
chatbot intervention. twotimephases to allowed
frame evaluate participants
the progression over
to engage
time, retention of techniques, and engagement differences.
with the chatbot in a structured manner and provided sufficient data to evaluate short-term
improvements in anxiety
The dual-phase studysymptoms. Recognizing
design provided the limitations
a comprehensive of this duration,
framework a second
to analyze both
phase
the was conducted
immediate two months
and enduring effectslater, replicating
of chatbot the seven-day
interventions usage period
on anxiety to analyze
symptoms. This
long-term effects and sustained usability.

3.3. Algorithm Configuration

The development of the chatbot algorithm was centered on ChatGPT, an advanced
natural language processing (NLP) model capable of generating dynamic and contextually
relevant responses that emulate human interaction [29–31]. The configuration process
Information 2024, 15, 768 6 of 26

focused on ensuring that the chatbot could provide personalized, evidence-based inter-
ventions tailored to individual user needs. Key elements of the algorithm’s configuration
included the following:
• Prompt Engineering: A personalized and adaptive flow of questions and responses was
designed to align with the user’s emotional state and specific anxiety symptoms [27].
This process ensured that the chatbot delivered relevant and precise responses rather
than generic advice. Through iterative refinement, the prompts were optimized to
enable the chatbot to effectively guide users through techniques such as breathing
exercises, cognitive restructuring, and mindfulness practices [32].
• Integration of Clinical Scales: The chatbot incorporated validated clinical tools, including
the Beck Anxiety Inventory (BAI) [33,34] and the Generalized Anxiety Disorder Scale
(GAD-7) [35]. These scales were used to assess participants’ baseline anxiety levels,
monitor changes throughout the study, and guide the chatbot’s interactions [36]. By
leveraging these tools, the chatbot could measure the severity of anxiety symptoms
and adjust its responses to better address the user’s needs [37].
• Behavioral Customization: The algorithm was designed to dynamically adjust its recom-
mendations based on participant inputs. For instance, users who reported high anxiety
related to a specific trigger were guided through tailored cognitive-behavioral therapy
(CBT) techniques, such as cognitive restructuring [38,39]. Similarly, participants ex-
periencing physical symptoms of anxiety, such as muscle tension or rapid breathing,
received targeted interventions like progressive muscle relaxation or diaphragmatic
breathing exercises [17]. This level of customization enhanced the chatbot’s ability to
provide meaningful and effective support.
The combination of these elements ensured that the chatbot offered an individualized,
high-quality mental health support experience. By integrating prompt engineering, vali-
dated clinical scales, and behaviorally tailored interventions, the chatbot was positioned as
a scalable and adaptable tool for managing anxiety symptoms.

3.4. Chatbot Architecture and Interaction Flow

To address the needs of participants with anxiety, the chatbot was designed to deliver
evidence-based interventions grounded in principles of cognitive-behavioral therapy (CBT).
Leveraging the capabilities of ChatGPT, the system employed advanced natural language
processing (NLP) to provide tailored support in real-time. The architecture of the chatbot
and its decision-making process are illustrated in Figure 2.

3.4.1. Initial Engagement and Input Collection

When a participant initiated an interaction with the chatbot, they were prompted to
describe their current emotional state or anxiety-related symptoms. Questions included
prompts such as the following: “How are you feeling right now?” or “Are you experiencing
physical symptoms like a racing heart or difficulty breathing?” These inputs allowed the chatbot
to establish a baseline understanding of the user’s mental state.

3.4.2. Input Analysis and Symptom Categorization

The chatbot analyzed participant responses using NLP algorithms to identify emo-
tional cues and symptom patterns. Key elements such as emotional tone, frequency of
anxiety indicators, and physical symptoms were extracted and categorized. This anal-
ysis informed the chatbot’s subsequent recommendations and ensured relevance to the
participant’s needs.

3.4.3. Decision-Making and Intervention Selection

Based on the analysis, the chatbot employed a rule-based system integrated with CBT
principles to determine the most suitable intervention. For instance, as follows:
• Participants expressing intense emotional distress were guided through mindfulness
exercises or diaphragmatic breathing techniques to promote immediate relaxation.
 Information 2024, 15, 768 7 of 26

• Users reporting persistent negative thoughts received cognitive restructuring prompts

Information 2024, 15, x FOR PEER REVIEW 7 of 28
to challenge maladaptive thinking patterns.
• Physical symptoms, such as muscle tension, triggered recommendations for progres-
sive muscle relaxation exercises.

Chatbotarchitecture.
Figure2.2.Chatbot
Figure architecture.

3.4.4. Intervention Delivery

3.4.1. Initial Engagement and Input Collection
The selected intervention was communicated to the participant through clear, struc-
When
tured a participant
instructions. initiatedaan
For example, interactionexercise
mindfulness with themight
chatbot, theythe
involve were prompted
following: to
“Close
describe their current emotional state or anxiety-related symptoms. Questions
your eyes and focus on your breath. Inhale deeply for a count of four, hold your breath for four included
prompts suchexhale
seconds, and as theforfollowing:
four seconds.“How are this
Repeat you cycle
feeling
fiveright now?” or “Are you experiencing
times.”
physical symptoms like a racing heart or difficulty breathing?”
To provide further clarity on the chatbot’s interaction design These inputs allowed
and the chatbot
therapeutic frame-
towork,
establish a baseline understanding of the user’s mental state.
examples of chatbot prompts have been included in Appendix A. These examples
illustrate how the chatbot delivers personalized interventions, such as mindfulness exer-
3.4.2.
cises,Input Analysis
cognitive and Symptom
restructuring Categorization
techniques, and breathing instructions, tailored to the user’s
The chatbot
emotional state andanalyzed
input. The participant responsestheusing
prompts exemplify NLP
system’s algorithms
ability to simulateto aidentify
support-
emotional cues and
ive, human-like symptom patterns.
conversational style whileKey elements
adhering to such as emotional
evidence-based tone, frequency
cognitive-behavioral
oftherapy
anxietyprinciples.
indicators, and physical symptoms were extracted and categorized. This
analysis informed the chatbot’s subsequent recommendations and ensured relevance to
3.4.5.
the Feedback needs.
participant’s and Adaptive Learning
Participants were encouraged to provide feedback on the effectiveness of each inter-
3.4.3. Decision-Making
vention. Responses such andasIntervention
“I feel calmer”Selection
or “This didn’t work for me” were logged to refine
future
Based on the analysis, the chatbot employed the
interactions. This feedback loop enabled chatbot tosystem
a rule-based adapt integrated
its recommendations
with CBT
over time,toimproving
principles determinethe thepersonalization of interventions.
most suitable intervention. For instance, as follows:
• 3.4.6.
Participants
Continuousexpressing intense
Monitoring emotional
and Data distress were guided through mindfulness
Logging
exercises or diaphragmatic breathing techniques to promote immediate relaxation.
The chatbot maintained a record of participant interactions, including frequency,
• Users reporting persistent negative thoughts received cognitive restructuring
duration, and the type of interventions delivered. These data facilitated both real-time
prompts to challenge maladaptive thinking patterns.
support and the subsequent analysis of engagement patterns, contributing to the study’s
• evaluation
Physicalmetrics.
symptoms, such as muscle tension, triggered recommendations for
progressive muscle
The interaction relaxation
flow exercises.
highlighted the chatbot’s ability to mimic human-like therapeu-
tic guidance while maintaining scalability and accessibility. Figure 3 provides a visual
3.4.4. Intervention Delivery
The selected intervention was communicated to the participant through clear,
structured instructions. For example, a mindfulness exercise might involve the following:
 3.4.6. Continuous Monitoring and Data Logging
The chatbot maintained a record of participant interactions, including frequency,
duration, and the type of interventions delivered. These data facilitated both real-time
support and the subsequent analysis of engagement patterns, contributing to the study’s
Information 2024, 15, 768 8 of 26
evaluation metrics.
The interaction flow highlighted the chatbot’s ability to mimic human-like
therapeutic guidance while maintaining scalability and accessibility. Figure 3 provides a
representation of this process,
visual representation of thisoutlining
process, the decision-making
outlining pathways and
the decision-making intervention
pathways and
delivery framework.
intervention delivery framework.

Figure 3. The process of continuous monitoring and data logging for an AI-powered chatbot used in
anxiety management.

3.5. Participant Selection

Participants were recruited on a voluntary basis and screened according to predefined
inclusion and exclusion criteria to ensure the study’s relevance and reliability. The selection
criteria were as follows (Table 1):
• Inclusion Criteria:
# Participants experiencing mild to moderate anxiety symptoms, as measured
by validated tools such as the Beck Anxiety Inventory (BAI) or Generalized
Anxiety Disorder Scale (GAD-7).
# A willingness to participate in both phases of the study.
• Exclusion Criteria:
# Individuals undergoing ongoing psychotherapy or taking medication for anxi-
ety at the time of the study.
# Severe anxiety or other mental health conditions requiring immediate profes-
sional intervention.
Upon selection, demographic information, including age, gender, and educational
background, was collected to ensure a diverse participant pool. This demographic data
was analyzed to confirm that the sample represented a range of individuals and was not
limited to a specific subgroup.
The demographic composition of the participants is summarized in Table 2, which
highlights key characteristics such as mean age, gender distribution, and baseline anxiety
Information 2024, 15, 768 9 of 26

levels. This diversity ensured that the findings of the study could be generalized across
a broader population, while also allowing for subgroup analyses to explore variations in
responses to the chatbot intervention.

Table 1. Inclusion and exclusion criteria.

Criteria Description
Inclusion Mild to moderate anxiety symptoms (BAI or GAD-7)
Willingness to participate in the study
Exclusion Ongoing psychotherapy or medication for anxiety
Severe anxiety or other mental health conditions

Table 2. Participant demographics and baseline characteristics.

Characteristic Value
Sample Size 50
Mean Age (SD) 36.9 (±6.65) years
Gender Distribution 52% Male, 48% Female
Baseline Anxiety Level (BAI) Mean: 70.16 (±3.02); Range: 65–75
Employment Status 70% Employed, 30% Unemployed
Education Level 40% Higher Education, 60% Secondary Education

3.6. Data Collection

A robust data collection framework was implemented to capture both quantitative
and qualitative insights regarding the chatbot’s effectiveness in managing anxiety symp-
toms. Data was collected systematically throughout both phases of the study, allowing for
comprehensive analysis.

3.6.1. Quantitative Data Collection

• Daily Assessments: Participants completed self-administered questionnaires at the
beginning of each day and immediately after each chatbot interaction [40]. These
questionnaires were designed to measure the following:
# Anxiety Levels: Assessed using validated scales, such as BAI and GAD-7, to
track changes over time.
# Sleep Quality: Questions addressing sleep disturbances, onset latency, and
overall restfulness.
# Effectiveness of CBT Techniques: Evaluated the perceived usefulness of mindful-
ness exercises, breathing techniques, and cognitive restructuring provided by
the chatbot.
# Emotional State and Stress Management: Captured participants’ perceived ability
to manage stress and emotional challenges on a daily basis.
• Interaction Metrics: The chatbot recorded the interaction frequency and duration for
each participant, daily. These metrics allowed for an analysis of user engagement and
its correlation with anxiety reduction. Specific data points included the following:
# Total interaction time per day.
# Frequency of interactions within a 24 h period.
# Trends in engagement over the seven days of each phase.

3.6.2. Qualitative Data Collection

• Participant Feedback: Qualitative feedback was gathered at the end of each phase to
better understand participants’ experiences with the chatbot [41,42]. Feedback focused
on the following:
# The perceived utility of the chatbot in managing anxiety symptoms.
Information 2024, 15, 768 10 of 26

# The relevance and personalization of responses provided by the chatbot.

# Suggestions for improvement in functionality and interaction quality.

3.6.3. Data Recording and Management

All data were anonymized and securely stored to ensure participant confidentiality.
The collected data were organized into datasets for statistical analysis, enabling compar-
isons between phases and identifying trends in anxiety reduction, user engagement, and
effectiveness of CBT techniques.
By combining objective measures with subjective feedback, the study provided a
holistic understanding of the chatbot’s impact on the participants’ mental health [43].

3.7. Statistical Analysis

The statistical analysis aimed to evaluate the effectiveness of the chatbot in reducing
anxiety symptoms and its sustained impact over two observation phases. Quantitative
data analysis focused on changes in anxiety scores, while qualitative feedback provided
additional insights into participant experiences.

3.7.1. Primary Outcomes

• Anxiety Score Changes: The primary outcomes were the reductions in anxiety scores
from the beginning to the end of each phase, as well as the improvement percentages
across both phases.

3.7.2. Secondary Outcomes

• Engagement Metrics: Interaction frequency and duration were analyzed to identify
patterns of engagement and their relationship with anxiety reduction.
• Qualitative Feedback: Participant feedback was reviewed to evaluate the perceived
utility and relevance of the chatbot.

3.7.3. Statistical Methods

• Phase 1 Analysis:
# Paired t-tests were conducted to compare anxiety scores between Day 1 and
Day 7, assessing short-term improvements during Phase 1.
# Results were reported with means, standard deviations, and confidence intervals.
• Phase 2 Analysis:
# Paired t-tests were applied to anxiety scores in Phase 2, comparing Day 1 and
Day 7 to assess long-term improvements.
# Comparisons between Phases 1 and 2 were performed to evaluate the chatbot’s
sustained impact over time, specifically examining differences in mean anxiety
scores and improvement percentages.
• Validation Tests:
# Tests for Normality: Shapiro–Wilk tests were applied to ensure that anxiety score
differences were normally distributed, a key assumption for parametric analyses.
# Non-Parametric Validation: For robustness, the Wilcoxon Signed-Rank Test was
conducted to validate the paired t-test results, especially for data that deviated
from normality.

3.7.4. Analytical Tools

• All analyses were conducted using SPSS, ensuring rigorous statistical standards.
• Graphical representations, including box plots and line charts, were generated to
visualize changes in anxiety scores and engagement metrics over time.
This multi-layered approach to statistical analysis ensured both the validity and
reliability of the study findings, allowing for a comprehensive evaluation of the chatbot’s
effectiveness in managing anxiety symptoms.
Information 2024, 15, 768 11 of 26

3.8. Ethical Considerations

This study adhered to established ethical guidelines for research involving human
participants. Key aspects included the following:
• Informed Consent: Verbal informed consent was obtained from all participants before
their inclusion in the study. Participants were provided with detailed information
about the study’s objectives, procedures, and their right to withdraw at any time
without repercussions. A sample of the consent form is included in the Appendix B.
• Minimal Risk Nature: The study was observational and involved no medical interventions
or sensitive data collection. The chatbot’s role was limited to providing general psycho-
logical support and did not replace professional medical or therapeutic interventions.
• Ethical Approval: In line with international guidelines for observational studies, formal
ethical approval was deemed unnecessary. The study did not pose risks to partici-
pants’ physical or psychological well-being and followed ethical research practices for
minimal-risk studies.
• Data Privacy: The participants’ data were anonymized and stored securely. No identifi-
able personal information was collected, ensuring participant confidentiality through-
out the research process.
This approach ensured that the study maintained ethical integrity while respecting
the rights and well-being of all participants.

4. Results
4.1. Statistical Analysis
The primary objective of this study was to evaluate the effectiveness of the chatbot in
reducing anxiety symptoms across two observational phases. Statistical methods, including
paired t-tests and Wilcoxon Signed-Rank Tests, were employed to assess changes in anxiety
scores and their relationship with user engagement metrics. (Table 3).

Table 3. Statistical summary of anxiety improvement percentages and engagement metrics.

Average Daily
Improvement Mean Anxiety Score
Phase Interaction Time
Percentage (%) Reduction (SD)
(minutes) (SD)
Significant (t(49) = 10.24,
Phase 1 21.15 (±2.32) 19.55 (±3.2)
p < 0.001)
Sustained (t(49) = 8.47,
Phase 2 20.42 (±2.96) 24.15 (±4.5)
p < 0.001)

4.1.1. Phase 1 Findings

• Reduction in Anxiety Symptoms: A paired t-test comparing anxiety scores from Day 1 to
Day 7 of Phase 1 showed a statistically significant reduction (t(49) = 10.24, p < 0.001).
The average improvement percentage for Phase 1 was 21.15% (SD = 2.32). This
highlights the chatbot’s short-term effectiveness in alleviating anxiety symptoms, as
seen in Figure 2, which details individual improvement trends across participants.
• Engagement Metrics: The analysis of interaction frequency revealed variability in
participants’ engagement with the chatbot. While higher interaction frequencies were
observed among participants reporting greater symptom relief, statistical analyses did
not confirm a direct correlation. This suggests that while engagement may play a role
in outcomes, additional factors likely contribute to the observed improvements.
• Responses to CBT Techniques: Participants responded variably to the cognitive-behavioral
therapy (CBT) techniques offered by the chatbot. Mindfulness exercises and guided
breathing were particularly effective in improving participants’ emotional states.
Information 2024, 15, 768 12 of 26

4.1.2. Phase 2 Findings

Two months after Phase 1, participants were invited to engage with the chatbot for
another seven-day period to assess long-term retention and reusability.
• Sustained Improvement: A paired t-test comparing Phase 1 and Phase 2 mean anxiety
scores revealed sustained improvements (t(49) = 8.47, p < 0.001). The average improve-
ment percentage in Phase 2 was slightly lower, at 20.42% (SD = 2.96), indicating that
while the benefits of the intervention were retained, there was a marginal decrease
compared to Phase 1.
• Reusability and Consistency: Participants demonstrated longer average daily interaction
times in Phase 2 (M = 24.15 min, SD = 4.5) compared to Phase 1 (M = 19.55 min,
SD = 3.2). This suggests greater familiarity and comfort with the chatbot during the
second phase.

4.1.3. Variability and Individual Trends

Figure 4 illustrates the improvement percentages for each participant across the two
phases, highlighting individual variability. While most participants demonstrated improve-
Information 2024, 15, x FOR PEER REVIEW
ment, a few outliers exhibited either minimal or no change. These results underscore13 ofthe
28
importance of tailoring interventions to individual needs.

Individual Improvement Trends Across Two Phases

30.00
Improvement Percentage (%)

25.00

20.00

15.00

10.00

5.00

0.00
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Participant Number

Improvement Percentage (Phase 1) Improvement Percentage (Phase 2)

Figure 4. Individual improvement trends across two phases.

Figure 4. Individual improvement trends across two phases.

4.1.4. Validation and Robustness

ensure the
To ensure the reliability
reliability of
of the
the results,
results, normality
normality checks
checks were performed using the
Shapiro–Wilk test, confirming that the data were were suitable
suitable for
for parametric
parametric tests.
tests. Wilcoxon
Signed-Rank
Signed-Rank TestsTestsfurther
furthervalidated
validatedthethe
paired t-testt-test
paired findings, demonstrating
findings, robustrobust
demonstrating statis-
tical outcomes
statistical acrossacross
outcomes both phases (Table(Table
both phases 4). 4).
This table highlights the key statistical tests conducted to validate the results across
Tablephases
both 4. Validation
of theand robustness—statistical
study. It demonstrates thetestsconsistency
summary. and robustness of the findings
while accounting for minor deviations from normality.
Statistical Test Phase Test Statistic (Z/t) p-Value Result
Data slightly
Shapiro–Wilk
Phase 1 W = 0.939 0.012 deviates from
Test (Normality)
normality
Data slightly
Phase 2 W = 0.945 0.014 deviates from
normality
Phase 1 (Day 1 vs. Significant reduction
Paired t-Test t(49) = 10.24 <0.001
Day 7) in anxiety
Phase 2 (Mean Phase Sustained
t(49) = 8.47 <0.001
1 vs. Mean Phase 2) improvement
Wilcoxon Signed- Robust validation of
Information 2024, 15, 768 13 of 26

Table 4. Validation and robustness—statistical tests summary.

Test Statistic
Statistical Test Phase p-Value Result
(Z/t)
Data slightly
Shapiro–Wilk
Phase 1 W = 0.939 0.012 deviates from
Test (Normality)
normality
Data slightly
Phase 2 W = 0.945 0.014 deviates from
normality
Significant
Phase 1 (Day 1 vs.
Paired t-Test t(49) = 10.24 <0.001 reduction in
Day 7)
anxiety
Phase 2 (Mean Phase 1 Sustained
t(49) = 8.47 <0.001
vs. Mean Phase 2) improvement
Wilcoxon Robust
Signed-Rank Phase 1 Z = −6.160 <0.001 validation of
Test t-test
Robust
Phase 2 Z = −5.930 <0.001 validation of
t-test

4.2. Anxiety Score Progression

The progression of anxiety scores over the two phases highlights the effectiveness of
the AI-based chatbot in reducing symptoms of anxiety. Statistically significant reductions
were observed in both phases, supported by paired t-tests and additional robustness checks.
These findings underscore the potential of personalized AI interventions in managing
anxiety disorders (Table 5).

Table 5. Summary of anxiety scores across phases.

Baseline
Final Anxiety Mean Anxiety Improvement
Phase Anxiety Score
Score (Day 7) Score (7 Days) Percentage (%)
(Day 1)
Phase 1 70.36 ± 2.99 55.52 ± 3.58 62.81 ± 3.28 21.15
Phase 2 57.00 ± 3.28 45.20 ± 2.76 50.92 ± 2.80 20.42

A paired t-test comparing anxiety scores from Day 1 to Day 7 in Phase 1 revealed a
statistically significant reduction in anxiety levels. The mean difference was 14.84 ± 1.36
(t(49) = 77.12, p < 0.001), with a 95% confidence interval ranging from 14.45 to 15.23. This
finding confirms that the chatbot intervention contributed to a substantial improvement in
anxiety symptoms over the initial seven-day period.
Similarly, in Phase 2, a paired t-test demonstrated a significant reduction in anxiety
scores, with a mean difference of 11.80 ± 1.97 (t(49) = 42.37, p < 0.001). The 95% confidence
interval ranged from 11.24 to 12.36, further validating the chatbot’s effectiveness during
the second phase of observation.
To evaluate the sustained impact of the chatbot intervention, a paired t-test was
conducted to compare the mean anxiety scores across the two phases. The analysis showed
a significant mean difference of 11.89 ± 2.36 (t(49) = 35.61, p < 0.001), indicating that
improvements observed during Phase 1 were largely retained in Phase 2. These results
suggest that participants were able to apply techniques learned during the first phase
effectively over the long term.
Figure 5 visualizes the baseline (Day 1) and final (Day 7) anxiety scores for both phases.
The graph illustrates a consistent decline in anxiety symptoms across the seven days in each
 showed a significant mean difference of 11.89 ± 2.36 (t(49) = 35.61, p < 0.001), indicating
that improvements observed during Phase 1 were largely retained in Phase 2. These
results suggest that participants were able to apply techniques learned during the first
phase effectively over the long term.
Information 2024, 15, 768 14 both
of 26
Figure 5 visualizes the baseline (Day 1) and final (Day 7) anxiety scores for
phases. The graph illustrates a consistent decline in anxiety symptoms across the seven
days in each phase, with Phase 2 demonstrating a slightly steeper reduction. These results
phase, with
indicate Phase
that the 2chatbot
demonstrating a slightly
was effective in steeper reduction.
supporting These results
both immediate andindicate that
long-term
the chatbot was effective
anxiety management. in supporting both immediate and long-term anxiety management.

Progression of Anxiety Scores Across Phases

80

Mean Anxiety Score 60

40

20

0
Day 1 Observation Days Day 7
Phase 1 Phase 2

Figure5.5.Progression
Figure Progressionof
ofanxiety
anxietyscores
scoresacross
acrossphases.
phases.

4.3.
4.3. Engagement
Engagement and and Personalization
Personalization
The
Thechatbot
chatbotdemonstrated
demonstrated robust
robustengagement
engagement levels,
levels, especially
especially during
during emotionally
emotionally
challenging
challenging moments. Participants utilized the chatbot consistently throughout both
moments. Participants utilized the chatbot consistently throughout both
phases,
phases,with
with engagement
engagement metrics highlighting
metrics a shiftatowards
highlighting increasedincreased
shift towards interaction duration
interaction
in Phase 2.
duration in This
Phasesuggests that familiarity
2. This suggests with thewith
that familiarity system
the and confidence
system in its utility
and confidence in its
contributed to higher levels of usage over time.
utility contributed to higher levels of usage over time.
Table
Table66presents
presentsthetheclassification ofof
classification participants into
participants engagement
into engagement categories based
categories on
based
their daily average interaction time with the chatbot. The comparison between
on their daily average interaction time with the chatbot. The comparison between Phases Phases 1 and 2
indicates
1 and 2 an increaseaninincrease
indicates engagement during the second
in engagement during phase, with more
the second phase,participants
with more
spending 20–25 min daily on average. This shift may reflect enhanced familiarity with the
participants spending 20–25 min daily on average. This shift may reflect enhanced
chatbot’s functionality or increased reliance on the system for anxiety management.

Table 6. Classification of participants into engagement categories.

Daily Interaction Time Frequency Frequency

% Phase 1 % Phase 2
Interval (min) Phase 1 Phase 2
0–15 1 2% 3 6%
15–20 30 60% 26 52%
20–25 19 38% 21 42%
25–30 0 0% 0 0%
Over 30 0 0% 0 0%

This shift in engagement metrics highlights the chatbot’s adaptability and its ability
to maintain relevance across both immediate and long-term interventions. The increased
duration of interactions in Phase 2 also reflects participants’ trust in the system as a reliable
tool for managing anxiety symptoms.
The continuous availability of the chatbot and its personalized approach likely con-
tributed to these engagement patterns, demonstrating its potential as an accessible and
effective mental health support system.
The results highlight that most participants engaged with the chatbot for 15–20 min
daily during Phase 1, whereas Phase 2 demonstrated a slight increase in interaction time,
with 42% of participants moving to the 20–25 min category. This increased engagement may
correlate with higher familiarity or improved utility of the chatbot as perceived by users.
Additionally, Figures 6 and 7 illustrate the daily interaction times for all 50 participants
across the seven days of Phases 1 and 2, respectively. These visualizations provide a detailed
 The results highlight that most participants engaged with the chatbot for 15–20 min
daily during Phase 1, whereas Phase 2 demonstrated a slight increase in interaction time,
with 42% of participants moving to the 20–25 min category. This increased engagement
may correlate with higher familiarity or improved utility of the chatbot as perceived by
Information 2024, 15, 768 users. 15 of 26
Additionally, Figures 6 and 7 illustrate the daily interaction times for all 50
participants across the seven days of Phases 1 and 2, respectively. These visualizations
view of usage
provide patterns,
a detailed view showcasing individual
of usage patterns, variability
showcasing as well as
individual general trends.
variability In
as well as
Phase 1,trends.
general interaction times1,were
In Phase relatively
interaction stable,
times werewith most participants
relatively stable, withengaging between
most participants
15 and 25 min
engaging daily.15
between The
andvariability
25 min in interaction
daily. time indicates
The variability differencestime
in interaction in individual
indicates
needs and preferences.
differences in individual needs and preferences.

Daily Interaction Time - Phase 1

35.00
Daily Interaction Time

30.00
25.00
20.00
15.00
10.00
5.00
0.00
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Participant Number

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Information 2024, 15, x FOR PEER REVIEW 17 of 28
Figure
Figure 6.
6. Daily
Daily interaction
interaction time—Phase
time—Phase 1.
1.

Daily Interaction Time - Phase 2

40.00
Daily Interaction Time

35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
Participant Number

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Figure 7. Daily interaction time—Phase 2.

Figure 7. Daily interaction time—Phase 2.

In Phase
Phase 2,2,there
there
waswas a noticeable
a noticeable increase
increase in interaction
in interaction times, times, with several
with several partici-
participants engaging for longer durations compared to Phase 1. This suggests
pants engaging for longer durations compared to Phase 1. This suggests that participants that
participants became more comfortable with the chatbot and relied on it more
became more comfortable with the chatbot and relied on it more frequently for support. frequently
for support.
These findings align with the summary provided in Table 6, further supporting the
idea These findings
that the align
chatbot’s with the summary
accessibility provided
and personalized in Table 6, further
interventions supporting
encouraged the
sustained
idea that the across
engagement chatbot’s
bothaccessibility
immediateand andpersonalized interventions encouraged sustained
long-term use.
engagement across both immediate and long-term use.
4.4. Participant Feedback
4.4. Participant Feedback
The feedback provided by participants underscored the chatbot’s effectiveness in
supporting anxietyprovided
The feedback management and highlighted
by participants several aspects
underscored of the user
the chatbot’s experience.
effectiveness in
supporting anxiety management and highlighted several aspects of the user experience.
Overall, participants consistently rated the chatbot as a valuable tool, particularly for its
accessibility and personalized responses. The chatbot’s availability around the clock was
frequently mentioned as a critical feature, as it allowed users to access support whenever
needed, including during moments of acute emotional distress. Personalized responses
Information 2024, 15, 768 16 of 26

Overall, participants consistently rated the chatbot as a valuable tool, particularly for its
accessibility and personalized responses. The chatbot’s availability around the clock was
frequently mentioned as a critical feature, as it allowed users to access support whenever
needed, including during moments of acute emotional distress. Personalized responses
that were tailored to the specific needs of participants further enhanced the perceived utility
of the intervention.
One of the strengths of this chatbot was its ability to maintain a high level of en-
gagement among participants throughout the study. Users frequently interacted with the
chatbot, and their feedback indicated that they felt supported and guided by the responses
provided, even in the absence of direct interaction with a human therapist. This increased
frequency of interactions was facilitated by the chatbot’s continuous accessibility, as it was
available 24/7. Participants could access the tool at any time of the day, particularly during
moments of emotional crisis when human support might not be available.
Furthermore, we assessed the participants’ responses to various therapeutic techniques
offered by the chatbot, such as cognitive restructuring and relaxation exercises [44]. The
aim was to determine which of these techniques had the greatest impact on the participants’
emotional state. The results indicated that the use of mindfulness techniques and guided
breathing significantly contributed to improving overall well-being.
Participants expressed high satisfaction with the mindfulness exercises and guided
breathing techniques offered by the chatbot. These features were often described as im-
mediately effective in alleviating anxiety symptoms and were particularly appreciated by
users who reported high levels of stress during the study period. The ability of the chatbot
to integrate evidence-based therapeutic techniques into its interactions was a key factor in
its positive reception.
Despite the overwhelmingly favorable feedback, some participants suggested areas for
improvement. A common recommendation was the refinement of the chatbot’s conversa-
tional tone to better emulate human empathy. While the chatbot provided valuable support,
its inability to replicate the emotional connection characteristic of human therapists was
noted as a limitation. This feedback aligns with broader critiques of AI-based interventions
in mental health, where the lack of emotional resonance remains a challenge.
Quantitative measures of participant satisfaction further validated the qualitative
feedback. Using a 5-point Likert scale (1 = very dissatisfied, 5 = very satisfied), participants
reported an average satisfaction rating of 4.44 (SD = 0.577), indicating a high level of
approval for the chatbot’s functionality and effectiveness (Table 7).

Table 7. Participant satisfaction ratings.

Std.
Measure N Minimum Maximum Mean
Deviation
Satisfaction Rating
50 3 5 4.44 0.577
(1–5 Scale)

These results demonstrate the chatbot’s significant potential as a scalable and accessible
tool for managing anxiety. While participants expressed satisfaction with the intervention,
their constructive feedback highlights opportunities for further refinement, particularly in
enhancing the chatbot’s ability to simulate empathetic interactions.

5. Discussion
The findings of this study provide valuable insights into the potential of AI-driven
chatbots as supplementary tools for managing anxiety. The statistically significant reduc-
tions in anxiety scores across both phases underscore the effectiveness of the chatbot in
delivering immediate and sustained improvements in emotional well-being. Additionally,
the increased engagement and retention observed in Phase 2 suggest that familiarity with
the system enhances its utility over time.
Information 2024, 15, 768 17 of 26

One of the key strengths of this study lies in the chatbot’s ability to employ evidence-
based cognitive-behavioral therapy (CBT) techniques, such as mindfulness, guided breath-
ing, and cognitive restructuring, in a personalized and scalable manner. Participants
consistently reported satisfaction with these interventions, particularly their accessibility
during moments of emotional distress. These results align with the existing literature that
highlights the potential of AI in providing scalable mental health solutions, particularly in
contexts where access to human therapists is limited.
The variability in individual responses highlights the importance of personalization in
AI-based mental health interventions. While some participants reported significant reduc-
tions in physical symptoms, such as palpitations and muscle tension, others emphasized
cognitive improvements, such as greater clarity of thought and better sleep quality. This
diversity of outcomes reflects the complex nature of anxiety and the necessity for adaptable
therapeutic strategies.
AI has the capability to collect and analyze behavioral data, such as daily physical
activity, sleep patterns, and social interactions. These insights can provide a comprehensive
overview of the user’s mental state, aiding in the adjustment of therapeutic suggestions to
meet specific needs. For instance, an individual with an active daily routine may benefit
from recommendations that emphasize maintaining this lifestyle for anxiety management,
while someone exhibiting signs of sedentarism may need encouragement to enhance their
physical activity and reconnect with their social environment.
By incorporating these techniques, the chatbot successfully equipped users with tools
that they could directly apply in their daily lives, helping them better manage their anxiety
symptoms. For example, during moments of emotional crisis, users received suggestions
for breathing techniques that helped them quickly alleviate physical stress symptoms,
such as palpitations or feelings of panic. Similarly, cognitive restructuring was utilized to
assist users in reanalyzing negative thoughts and replacing them with more realistic and
constructive alternatives.
To illustrate this concept, consider two distinct cases:
• Active Individual: A user with a daily active schedule may receive suggestions for
mindfulness techniques integrated into their exercise routine. For example, the chatbot
might recommend breathing exercises or meditation to be performed before or after
workouts, maximizing both physical and mental benefits while keeping sessions brief
to fit the user’s schedule.
• Sedentary Individual: In contrast, a user showing signs of sedentarism may benefit
from an intervention plan that includes small steps to improve their physical activity.
The chatbot could suggest simple stretching exercises to be done at home or in the
office and periodically remind them to stand and move, thereby helping reduce anxiety
symptoms through improved circulation and endorphin release.
The personalization of interventions does not stop there. The chatbot can continue
to monitor user responses to various suggested techniques, constantly adjusting them
based on the feedback received. By employing personalized strategies, the chatbot becomes
capable of offering not only general advice but also specific interventions tailored to each
user. This level of customization is essential for maintaining the relevance and effectiveness
of the chatbot in psychological counseling sessions.
Furthermore, the chatbot’s ability to monitor user responses over multiple sessions
allows for continuous adjustment and improvement of recommendations based on user
feedback. This can be accomplished through periodic questionnaires or quick assessments
after each session to evaluate the effectiveness of the techniques employed. For instance, if
a user reports that a particular breathing technique was ineffective, the chatbot can offer
alternatives such as meditation exercises or muscle relaxation techniques.
Participants reported that the chatbot provided practical techniques they could im-
mediately apply to manage their anxiety. This sense of control over their mental state
was essential in enhancing feelings of safety and autonomy. The chatbot’s responses were
Information 2024, 15, 768 18 of 26

viewed as a continuous guide, assisting users in developing personalized strategies to cope

with stress and anxiety, particularly during challenging times.
While the chatbot could not replicate the deep emotional connection that a human ther-
apist provides, participants nonetheless experienced a high level of support. Many noted
that frequent interactions with the chatbot gave them the feeling of receiving assistance and
useful advice in a manner that enabled them to manage their symptoms proactively. Even
in the absence of a therapist’s physical presence, the chatbot offered consistent support,
contributing to the development of effective self-help skills.
An important aspect of the study was the chatbot’s effectiveness during emotional
crises. Participants reported that the chatbot provided appropriate and relevant solutions
when they faced acute anxiety episodes. This ability to deliver rapid and specific inter-
ventions in emergency situations significantly contributed to reducing stress levels and
improved participants’ sense of control over their emotional reactions.
Although the lack of direct human interaction may be considered a disadvantage, the
majority of participants indicated that the chatbot was a valuable tool in managing their
anxiety, especially in critical situations. The personalized responses, tailored to each user’s
evolving condition, contributed to a sense of support and guidance, offering an effective
and accessible solution for daily mental health management.

5.1. Comparison with Similar Studies on Chatbots for Mental Health

The findings of this study align with and extend existing research on the role of chat-
bots in mental health interventions [45,46]. Previous studies, such as those by
Fitzpatrick et al. (2017), demonstrated the potential of chatbots to deliver effective cognitive-
behavioral therapy (CBT) for individuals experiencing symptoms of depression and anx-
iety [23]. Similarly, research by Inkster et al. (2023) highlighted the ability of AI-driven
systems to improve user engagement and provide accessible therapeutic support [47].
In the present study, the observed reduction in anxiety symptoms—averaging 21.15% in
Phase 1 and 20.42% in Phase 2—is consistent with the findings of other chatbot-based
interventions, which report symptom reductions ranging from 15% to 30%.
The findings of our study align with the growing body of research highlighting the
potential of AI-powered chatbots in supporting mental health interventions. For instance,
Perplexity, another AI-driven chatbot, has been evaluated in studies focusing on its ability
to manage anxiety and depression symptoms [48–50]. These studies emphasize Perplex-
ity’s strengths in delivering personalized therapeutic suggestions and maintaining high
user engagement through natural language processing. However, our chatbot, developed
using ChatGPT, demonstrated several unique contributions. Specifically, it integrates
evidence-based cognitive-behavioral therapy (CBT) techniques, such as mindfulness exer-
cises, breathing strategies, and cognitive restructuring, in a manner that tailors responses
to individual emotional states.
Unlike Perplexity, which primarily relies on general-purpose natural language models,
our chatbot’s design incorporates prompt engineering informed by clinically validated
anxiety assessment tools, such as the Beck Anxiety Inventory (BAI) and Generalized
Anxiety Disorder Scale (GAD-7). This distinction enhances its ability to provide targeted
interventions based on specific anxiety symptoms reported by users. While both chatbots
share limitations, such as the lack of human empathy, our study highlights the importance
of integrating structured therapeutic frameworks into chatbot design to maximize their
impact on mental health outcomes. These comparisons underscore the broader potential
of AI in mental health while also emphasizing the need for future research to refine and
optimize such tools.
What differentiates this study is the inclusion of a dual-phase observational design,
enabling the evaluation of both short-term and sustained effects of chatbot usage. While
existing studies often focus on single-session or short-duration interventions, this research
demonstrated that the familiarity gained in Phase 1 contributed to the enhanced engage-
ment and symptom reduction in Phase 2. Additionally, the higher average interaction time
Information 2024, 15, 768 19 of 26

in Phase 2 suggests that repeated exposure to the chatbot may foster a deeper integration
of therapeutic techniques.
Despite these similarities, the results also highlight certain challenges noted in the
literature. For instance, studies such as Gaffney et al. (2019) emphasize the limitations of
chatbots in replicating human empathy—a finding echoed by the qualitative feedback in
this study [51]. Addressing this limitation remains a critical area for future development,
particularly in enhancing the chatbot’s conversational tone and emotional responsiveness.
By situating the findings within the broader context of chatbot-based mental health
interventions, this study underscores the potential of AI as a scalable, accessible tool for
managing anxiety, while also identifying avenues for further refinement and application.

5.2. Limitations, Individual Variability, and Future Research

While the findings demonstrate that the chatbot significantly reduced anxiety symp-
toms for the majority of participants, individual responses varied considerably. Some
participants reported more pronounced improvements in physical symptoms, such as
reduced palpitations and muscle tension, while others highlighted cognitive enhancements,
including greater clarity of thought and improved sleep quality. These variations under-
score the importance of personalization in mental health interventions, particularly when
addressing the diverse needs of users.
One methodological limitation of this study is the relatively small sample size (N = 50)
and the short duration of each observational phase (seven days). While these parameters
were sufficient to capture immediate and short-term effects, they may not fully reflect the
long-term impact or scalability of the chatbot intervention. Future research should aim to
include larger and more diverse populations, as well as extend the observational periods,
to provide a more comprehensive understanding of the chatbot’s efficacy.
Additionally, the absence of a control group represents another limitation. Without
a comparative framework, it is difficult to isolate the effects of the chatbot from external
factors that may have influenced participants’ anxiety levels. Integrating a control group in
future studies would allow for more robust assessments and a clearer understanding of the
chatbot’s unique contributions to anxiety management.
Another significant limitation lies in the chatbot’s inability to replicate human empathy,
a critical element in traditional therapeutic relationships. The emotional connection between
a therapist and a patient, characterized by trust and empathy, remains a cornerstone of
effective mental health interventions. While the chatbot effectively employed evidence-
based cognitive-behavioral therapy (CBT) techniques, its lack of emotional resonance may
limit its effectiveness in certain cases. This limitation highlights the potential benefits of
hybrid models that combine AI systems with human oversight, ensuring that users receive
both the accessibility of AI and the emotional support of a human therapist.
Studies in the literature support these findings, highlighting that while AI can provide
significant benefits in mental health through accessibility and personalization, it cannot
fully substitute for human interaction. The therapeutic relationship, characterized by
empathy, understanding, and emotional support, is essential for the long-term success
of treatment. This underscores the importance of intuition and the therapist’s ability to
respond to the emotional subtleties of patients.
Within the therapeutic relationship, empathy serves not only as a supportive element
but also as a catalyst for change. Human therapists are capable of perceiving the nuances
and subtleties of nonverbal communication, providing emotional feedback, and adapting
interventions based on the patient’s emotional state. This adaptability is crucial, particularly
when addressing complex issues related to anxiety, depression, and trauma. In contrast,
chatbots may offer predefined responses but lack the capacity to react with the same degree
of sensitivity and human understanding.
Despite these limitations, the chatbot’s accessibility and scalability present promising
opportunities for expanding mental health support, particularly in underserved popula-
tions. The findings from this study indicate that personalized AI-driven interventions can
Information 2024, 15, 768 20 of 26

play a valuable role in complementing traditional therapies. However, further refinement

of the chatbot’s algorithms is necessary to address individual variability more effectively,
particularly in tailoring responses to specific user needs.
This personalized approach, supported by AI technology, has the potential to trans-
form how mental health interventions are delivered. Customization not only enhances the
effectiveness of interventions but also increases accessibility, adapting to the specific needs
of each individual. In the future, the use of AI technologies in this context could open
new avenues for mental health treatments, facilitating the necessary access and support for
those facing anxiety and other emotional disorders.
In conclusion, while the study provides compelling evidence of the chatbot’s potential
in managing anxiety, the limitations outlined above should guide the design of future
research to enhance the robustness, generalizability, and ethical implementation of AI-
based mental health interventions.
In addition to addressing these limitations, future research should explore the long-
term effects of repeated chatbot usage and its potential integration into broader mental
health care frameworks. Extending the observational period and examining the impact of
the chatbot in diverse cultural and demographic contexts could further elucidate its utility.
Furthermore, advancements in natural language processing could enhance the chatbot’s
conversational tone, making it more engaging and empathetic.

6. Conclusions
This study highlights the significant potential of artificial intelligence (AI)-driven
technologies in supporting mental health, particularly in the management of anxiety
disorders. Findings indicate that a chatbot powered by ChatGPT effectively reduced
anxiety symptoms, with improvements averaging 21.15% in Phase 1 and 20.42% in Phase 2.
These results underscore the value of AI as a complementary tool in mental health care,
particularly in contexts where access to human therapists is limited. The dual-phase design
further demonstrated the chatbot’s capacity to provide both immediate relief and sustained
benefits, reinforcing its utility as a long-term support mechanism.
While AI cannot replicate the empathy and nuanced clinical judgment of a human ther-
apist, chatbots like the one studied here can play a crucial role in delivering accessible and
personalized psychological support. By integrating evidence-based cognitive-behavioral
therapy (CBT) techniques such as mindfulness exercises, guided breathing strategies, and
cognitive restructuring, the chatbot empowered participants to develop self-help skills and
better manage their anxiety. Its ability to provide real-time support represents a key advan-
tage in addressing the urgent needs of individuals during moments of emotional distress.
The accessibility of AI-driven solutions is particularly relevant for individuals facing
barriers such as financial limitations or stigma associated with seeking mental health care.
By offering 24/7 support, chatbots can bridge gaps in traditional care systems, providing
immediate assistance when human support may not be available. This scalability and
constant availability make AI-based interventions a practical addition to the mental health
care landscape.
However, the study also identified important limitations. The chatbot’s lack of human
empathy remains a significant challenge, as many participants expressed a desire for deeper
emotional connection during interactions. While the chatbot effectively facilitated stress
management techniques and offered helpful resources, the absence of genuine empathy
could limit its effectiveness for certain users. Addressing this limitation will require
advancements in AI to enhance the conversational tone and simulate empathetic responses.
Future research should focus on integrating AI with human oversight to address
limitations such as the absence of empathy and ethical concerns related to data privacy.
A hybrid model, combining the strengths of AI with the expertise of human therapists,
may provide optimal outcomes. For example, chatbots could function as supplementary
tools within traditional therapeutic frameworks, allowing therapists to leverage chatbot-
generated insights to tailor interventions to individual needs.
Information 2024, 15, 768 21 of 26

In addition to its role in mental health support, the AI-based chatbot possesses the
potential to identify early signs of various health issues, including nutritional deficiencies
and other physiological conditions that may contribute to anxiety and related psychological
disorders. By analyzing user input and behavioral data, the chatbot can formulate informed
dietary recommendations, such as the inclusion of essential vitamins and minerals that
could alleviate symptoms.
For instance, research underscores the significance of vitamin D in the aging pro-
cess [52–54] and its broader implications for overall health [55,56]. Adequate levels of
vitamin D are crucial not only for physical well-being but also for cognitive function, with
deficiencies potentially exacerbating mental health issues. Furthermore, studies highlight
the role of food biochemistry in the prevention of nervous system diseases, emphasizing
the pivotal contribution that proper nutrition makes to mental health management [57–59].
The chatbot can leverage this knowledge to conduct a more in-depth physiological analysis
of users, offering insights into necessary dietary adjustments. For example, by monitoring
symptoms related to mood and energy levels, the chatbot might suggest dietary modifi-
cations aimed at addressing potential deficiencies in vitamins such as B12 or magnesium,
both known to influence mental health outcomes.
Beyond its nutritional guidance capabilities, the chatbot can detect early signals
of other health issues that may exacerbate anxiety [60,61]. Medical conditions such as
cervicofacial vascular anomalies [62]—where abnormal blood vessels can cause physical
discomfort or concern—have been shown to heighten emotional distress if left unmanaged.
Research indicates that early detection and intervention are crucial for mitigating the effects
of such anomalies [63]. By monitoring physiological data and identifying early warning
signs of potential anomalies, the chatbot could provide timely alerts, prompting users to
seek further medical evaluation before the condition worsens.
The chatbot’s capability to tailor recommendations based on user data enhances its
functionality as a comprehensive health support tool. It can combine insights from users’
behavioral patterns, physical symptoms, and medical knowledge to offer personalized
recommendations, whether they involve adjusting dietary intake or flagging early signs
of medical conditions like vascular anomalies. This holistic approach not only empowers
users to take charge of their health but also underscores the potential of AI to bridge the
gap between mental health, physical health, and nutrition. In this way, the chatbot becomes
a valuable resource for managing both emotional well-being and detecting underlying
health conditions that may contribute to psychological distress.
In conclusion, AI presents a promising opportunity for enhancing mental health care
and expanding access to effective treatments for anxiety. While challenges and limitations
persist, the integration of AI technologies with human support has the potential to yield
significant and long-lasting outcomes. It is crucial that the development of these technolo-
gies be approached with careful consideration of ethical standards, data privacy, and the
specific needs of users. By doing so, we can ensure that individuals facing mental health
challenges have access to the assistance they require, thereby improving overall well-being
and ensuring equitable access to care.

Author Contributions: A.M.: Data curation, formal analysis, writing—review and editing; R.C.:
conceptualization, methodology, investigation, writing—original draft preparation; R.B.: resources,
writing—review and editing; F.M.: supervision, project administration, validation. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Ethical review and approval were waived for this study,
since the research was observational and non-invasive, involving interactions with a chatbot, and
was classified as minimal risk. According to research guidelines, such low-risk studies do not require
formal ethical approval, particularly when no sensitive or personal data are collected.
Information 2024, 15, 768 22 of 26

Informed Consent Statement: Informed consent (verbal consent) was obtained from all the partici-
pants involved in the study. The verbal consent obtained aligns with ethical standards for this type
of research.
Data Availability Statement: The raw data supporting the findings of this study are available upon
request from the corresponding author, subject to ethical restrictions. The original contributions
presented in this study are included in the article, and any further inquiries can be directed to the
corresponding author. The authors confirm that they did not use Artificial Intelligence (AI) tools
for the initial drafting of this article. However, ChatGPT-4 was employed for specific tasks such
as formatting, copyediting, and improving readability in certain sections, including the abstract
and conclusion. ChatGPT-4 was also utilized to ensure coherence between sections and assist in
identifying potential limitations of the work. The authors retained full control throughout the
process, carefully reviewing all AI-generated suggestions to maintain the accuracy and integrity
of the manuscript. The data input for the study and all revisions were provided and managed by
the authors. Any contributions from ChatGPT-4 were thoroughly evaluated to ensure they did not
introduce unintended content or inaccuracies. This disclosure reflects the authors’ commitment to
transparency in the use of AI tools, which is becoming increasingly relevant in academic research.
The authors advocate for responsible use of AI technologies and believe that acknowledging their
role in the writing process contributes to the ethical evolution of scholarly work.
Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A
Appendix A.1. Example of Chatbot Prompts
To illustrate the adaptability and personalized nature of the AI-based chatbot, a
variety of prompt examples are provided in this appendix. These prompts demonstrate
how the chatbot responds to different user inputs related to anxiety symptoms, offering
tailored interventions based on cognitive-behavioral therapy (CBT) techniques, mindfulness
practices, and emotional support. The examples below cover a range of scenarios, from
managing negative thoughts to coping with physical symptoms of anxiety, showcasing the
chatbot’s capacity to deliver targeted, real-time guidance to users.
1. Dealing with Negative Thoughts (Cognitive Restructuring)
- User Input: “I keep thinking I’m going to fail at everything I try”.
- Chatbot Response:
“It sounds like you’re struggling with some negative thoughts about yourself.
Let’s challenge that.
Can you think of a time when you succeeded at something, even if it was some-
thing small?
How did that make you feel? Sometimes focusing on past successes can help us
reframe our current thoughts”.
2. Physical Symptoms of Anxiety (Breathing Techniques)
- User Input: “My chest feels tight and I’m struggling to breathe”.
- Chatbot Response:
“That sounds really uncomfortable. Let’s try to help your body relax. Try this
breathing technique:
Breathe in deeply through your nose for 4 s, hold for 7 s, and exhale slowly
through your mouth for 8 s.
Do this three times, and let me know how you’re feeling afterward”.
3. Sleep Disruption Due to Anxiety (Sleep Hygiene)
- User Input: “I can’t sleep, my mind won’t stop racing”.
- Chatbot Response:
“I’m sorry to hear you’re having trouble sleeping. Let’s try a few things to calm
your mind.
Can you try putting your phone away for a few minutes, dimming the lights, and
doing a quick body scan exercise?
Information 2024, 15, 768 23 of 26

Start from your feet and work your way up, noticing each part of your body and
releasing any tension you might be holding”.
4. Social Anxiety (Exposure Therapy Techniques)
- User Input: “I feel overwhelmed at social events and want to leave immediately”.
- Chatbot Response:
“Social situations can be tough. One thing that might help is breaking it down
into smaller, manageable steps.
For example, try staying for just five more minutes, and focus on one small
conversation.
What’s the worst that could happen? Often, we imagine outcomes worse than reality.”
5. Feeling Overwhelmed (Grounding Techniques)
- User Input: “Everything feels too much. I don’t know where to start”.
- Chatbot Response:
“When things feel overwhelming, it can help to focus on the present moment.
Try this grounding exercise: Look around the room and name 5 things you
can see,
4 things you can feel, 3 things you can hear, 2 things you can smell, and 1 thing
you can taste.
This will help you anchor yourself in the present”.
6. Panic Attack (Crisis Intervention)
- User Input: “I feel like I’m losing control. My heart is racing and I can’t focus”.
- Chatbot Response:
“It sounds like you’re having a panic attack. Let’s focus on your breathing:
Inhale slowly through your nose for 3 s, hold for 3 s, and exhale gently through
your mouth for 5 s.
Focus only on your breath. I’m here with you, let’s do this together until you feel
more in control”.
7. Self-Esteem and Self-Worth (Positive Affirmations)
- User Input: “I don’t feel like I’m good enough”.
- Chatbot Response:
“I understand those feelings can be overwhelming, but they don’t define you.
Let’s try repeating a few affirmations: ‘I am capable,’ ‘I am worthy of love and
respect,’ ‘I am doing my best.’
You are more than your negative thoughts”.
8. Worrying About the Future (Mindfulness and Acceptance)
- User Input: “I keep worrying about things that haven’t happened yet”.
- Chatbot Response:
“It’s normal to feel anxious about the future, but let’s focus on what’s within your
control right now.
Can you take a moment to sit in a quiet space and pay attention to your breathing?
Focus on what’s happening around you in the present moment. Let’s bring your
attention back to the here and now”.
9. Procrastination (Behavioral Activation)
- User Input: “I can’t seem to start anything. I feel paralyzed”.
- Chatbot Response:
“When tasks seem overwhelming, breaking them into smaller steps can help.
Let’s start with something simple: What’s the first, tiniest step you can take?
Even if it’s just writing down one word or setting a timer for 5 min to work on it”.
10. Grief and Loss (Emotional Support)
- User Input: “I can’t stop thinking about the person I lost”.
Information 2024, 15, 768 24 of 26

- Chatbot Response:
“Grief can be incredibly heavy. It’s important to allow yourself to feel those
emotions, but also to take care of yourself.
Would it help to talk about your favorite memory with them? Sometimes reflect-
ing on positive moments can bring comfort, even in difficult times”.

Appendix B
Appendix B.1. Informed Consent Script (Verbal Consent)
Purpose of the Study: You are invited to participate in a study testing an AI-based
chatbot designed for anxiety management. This study aims to improve mental health
support by offering personalized feedback through a chatbot interface.
Participation Details: Your participation is entirely voluntary. You will interact with
the chatbot over the course of 7 days, during which your responses will be monitored. No
personal identifying information will be collected.
Risks and Benefits: There are no known risks associated with your participation in this
study. The chatbot’s interactions are based on general mental health strategies, and you
may withdraw from the study at any time.
Confidentiality: The data collected will be used solely for research purposes and will
remain anonymous.
Consent Statement: By agreeing to participate, you confirm that you understand the
study’s purpose, procedures, and risks and agree to take part voluntarily.
Participant’s Agreement: Do you agree to participate in this study?

References
1. Cate, A.; Troy, B. The Role of AI in Mental Health: Benefits, Risks, & Ethical Considerations. Available online: https://www.
choosingtherapy.com/ai-and-mental-health/ (accessed on 13 October 2024).
2. Casu, M.; Triscari, S.; Battiato, S.; Guarnera, L.; Caponnetto, P. AI Chatbots for Mental Health: A Scoping Review of Effectiveness,
Feasibility, and Applications. Appl. Sci. 2024, 14, 5889. [CrossRef]
3. Bancsik, K.; Ilea, C.D.N.; Daina, M.D.; Bancsik, R.; S, uteu, C.L.; Bîrsan, S.D.; Manole, F.; Daina, L.G. Comparative Analysis of
Patient Satisfaction Surveys—A Crucial Role in Raising the Standard of Healthcare Services. Healthcare 2023, 11, 2878. [CrossRef]
[PubMed]
4. Karlsson, M.; Iversen, T.; Øien, H. Aging and Healthcare Costs. In Oxford Research Encyclopedia of Economics and Finance; Oxford
University Press: Oxford, UK, 2018; p. 54. [CrossRef]
5. De Meijer, C.; Wouterse, B.; Polder, J.; Koopmanschap, M. The effect of population aging on health expenditure growth: A critical
review. Eur. J. Ageing 2013, 10, 353–361. [CrossRef] [PubMed]
6. Manole, F.; Marian, P.; Mekeres, G.M.; Voiţă-Mekereş, F. Systematic review of the effect of aging on health costs. Arch. Pharm.
Pract. 2023, 14, 58–61. [CrossRef]
7. Sutton, J. Revolutionizing AI Therapy: The Impact on Mental Health Care. Available online: https://positivepsychology.com/ai-
therapy/ (accessed on 13 October 2024).
8. Kasula, B.Y. Ethical Considerations in the Adoption of Artificial Intelligence for Mental Health Diagnosis. Int. J. Creat. Res.
Comput. Technol. Des. 2023, 5, 1–7.
9. Bancsik, K.; Ilea, C.D.N.; Daina, M.D.; Bancsik, R.; S, uteu, C.L.; Bîrsan, S.D.; Manole, F.; Daina, L.G. Patient-Perceived Quality
Assessment in Orthopedics and Traumatology Departments during COVID-19 Pandemic. Healthcare 2024, 12, 879. [CrossRef]
10. Knapp, P.; Beck, A.T. Cognitive therapy: Foundations, conceptual models, applications and research. Braz. J. Psychiatry 2008, 30,
54–64. [CrossRef]
11. Nicoară, N.D.; Marian, P.; Petris, , A.O.; Delcea, C.; Manole, F. A review of the role of cognitive-behavioral therapy on anxiety
disorders of children and adolescents. Pharmacophore 2023, 14, 35–39. [CrossRef]
12. Gao, J.; Garsole, P.; Agarwal, R.; Liu, S. AI Test Modeling and Analysis for Intelligent Chatbot Mobile App-A Case Study on Wysa.
In Proceedings of the 2024 IEEE International Conference on Artificial Intelligence Testing (AITest), Shanghai, China, 15–18 July
2024; pp. 132–141.
13. Mehta, A.; Niles, A.N.; Vargas, J.H.; Marafon, T.; Couto, D.D.; Gross, J.J. Acceptability and effectiveness of artificial intelligence
therapy for anxiety and depression (Youper): Longitudinal observational study. J. Med. Internet Res. 2021, 23, e26771. [CrossRef]
14. Alanzi, T.; Almahdi, R.; Alghanim, D.; Almusmili, L.; Saleh, A.; Alanazi, S.; Alshobaki, K.; Attar, R.; Al Qunais, A.; Alzahrani, H.;
et al. Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management. Cureus
2023, 15, e49724. [CrossRef]
15. Tsaryk, V.Y.; Bychkova, D.; Klishch, S. Competitive analysis of existing software applications for improving mental health. Syst.
Technol. 2024, 4, 166–179.
Information 2024, 15, 768 25 of 26

16. Gillis, K.; Sills, D. The Impact of AI in the Mental Health Field. Available online: https://www.psychologytoday.com/us/blog/
invisible-bruises/202407/the-impact-of-ai-in-the-mental-health-field (accessed on 13 October 2024).
17. Patil, R.; Heston, T.F.; Bhuse, V. Prompt Engineering in Healthcare. Electronics 2024, 13, 2961. [CrossRef]
18. Fulmer, R.; Joerin, A.; Gentile, B.; Lakerink, L.; Rauws, M. Using psychological artificial intelligence (Tess) to relieve symptoms of
depression and anxiety: Randomized controlled trial. JMIR Ment. Health 2018, 5, e9782. [CrossRef] [PubMed]
19. Alowais, S.A.; Alghamdi, S.S.; Alsuhebany, N.; Alqahtani, T.; Alshaya, A.I.; Almohareb, S.N.; Aldairem, A.; Alrashed, M.;
Bin Saleh, K.; Badreldin, H.A. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med. Educ.
2023, 23, 689. [CrossRef] [PubMed]
20. Kelly, C.J.; Karthikesalingam, A.; Suleyman, M.; Corrado, G.; King, D. Key challenges for delivering clinical impact with artificial
intelligence. BMC Med. 2019, 17, 195. [CrossRef] [PubMed]
21. Graham, S.; Depp, C.; Lee, E.E.; Nebeker, C.; Tu, X.; Kim, H.-C.; Jeste, D.V. Artificial intelligence for mental health and mental
illnesses: An overview. Curr. Psychiatry Rep. 2019, 21, 26. [CrossRef]
22. Kretzschmar, K.; Tyroll, H.; Pavarini, G.; Manzini, A.; Singh, I.; NeurOx Young People’s Advisory Group. Can your phone be
your therapist? Young people’s ethical perspectives on the use of fully automated conversational agents (chatbots) in mental
health support. Biomed. Inform. Insights 2019, 11, 1178222619829083. [CrossRef] [PubMed]
23. Fitzpatrick, K.K.; Darcy, A.; Vierhile, M. Delivering cognitive behavior therapy to young adults with symptoms of depression and
anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment. Health 2017, 4, e7785.
[CrossRef]
24. Li, H.; Zhang, R.; Lee, Y.-C.; Kraut, R.E.; Mohr, D.C. Systematic review and meta-analysis of AI-based conversational agents for
promoting mental health and well-being. Digit. Med. 2023, 6, 236. [CrossRef]
25. Ray, A.; Bhardwaj, A.; Malik, Y.K.; Singh, S.; Gupta, R. Artificial intelligence and Psychiatry: An overview. Asian J. Psychiatry
2022, 70, 103021. [CrossRef]
26. Cheng, S.W.; Chang, C.W.; Chang, W.J.; Wang, H.W.; Liang, C.S.; Kishimoto, T.; Chang, J.P.C.; Kuo, J.S.; Su, K.P. The now and
future of ChatGPT and GPT in psychiatry. Psychiatry Clin. Neurosci. 2023, 77, 592–596. [CrossRef] [PubMed]
27. Priyadarshana, Y.; Senanayake, A.; Liang, Z.; Piumarta, I. Prompt engineering for digital mental health: A short review. Front.
Digit. Health 2024, 6, 1410947. [CrossRef] [PubMed]
28. Wang, L.; Chen, X.; Deng, X.; Wen, H.; You, M.; Liu, W.; Li, Q.; Li, J. Prompt engineering in consistency and reliability with the
evidence-based guideline for LLMs. NPJ Digit. Med. 2024, 7, 41. [CrossRef] [PubMed]
29. Le Glaz, A.; Haralambous, Y.; Kim-Dufor, D.-H.; Lenca, P.; Billot, R.; Ryan, T.C.; Marsh, J.; Devylder, J.; Walter, M.; Berrouiguet, S.
Machine learning and natural language processing in mental health: Systematic review. J. Med. Internet Res. 2021, 23, e15708.
[CrossRef] [PubMed]
30. Tang, J.; Shang, Y. Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment.
arXiv 2024, arXiv:2408.01614.
31. Alanzi, T.M.; Alharthi, A.; Alrumman, S.; Abanmi, S.; Jumah, A.; Alansari, H.; Alharthi, T.; Alibrahim, A.; Algethami, A.;
Aburass, M. ChatGPT as a psychotherapist for anxiety disorders: An empirical study with anxiety patients. Nutr. Health Policy
2024, 7, 02601060241281906. [CrossRef]
32. Nemesure, M.D.; Heinz, M.V.; Huang, R.; Jacobson, N.C. Predictive modeling of depression and anxiety using electronic health
records and a novel machine learning approach with artificial intelligence. Sci Rep 2021, 11, 1980. [CrossRef]
33. Kaviani, H.; Mousavi, A.S. Psychometric properties of the Persian version of Beck Anxiety Inventory (BAI). Tehran Univ. Med. J.
2008, 66, 136–140.
34. Steer, R.A.; Ranieri, W.F.; Beck, A.T.; Clark, D.A. Further evidence for the validity of the beck anxiety inventory with psychiatric
outpatients. J. Anxiety Disord. 1993, 7, 195–205. [CrossRef]
35. Löwe, B.; Decker, O.; Müller, S.; Brähler, E.; Schellberg, D.; Herzog, W.; Herzberg, P.Y. Validation and standardization of the
Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med. Care 2008, 46, 266–274. [CrossRef]
36. Yun-Jeong, K. Early Diagnosis of anxiety Disorder Using Artificial Intelligence. Int. J. Adv. Cult. Technol. 2024, 12, 242–248.
37. Jacobson, N.C.; Feng, B. Digital phenotyping of generalized anxiety disorder: Using artificial intelligence to accurately predict
symptom severity using wearable sensors in daily life. Transl. Psychiatry 2022, 12, 336. [CrossRef] [PubMed]
38. Tzavela, E.C.; Mitskidou, P.; Mertika, A.; Stalikas, A.; Kasvikis, Y. Treatment engagement in the early phase of cognitive-behavior
therapy for panic disorder: A grounded theory analysis of patient experience. Psychother. Res. 2018, 28, 842–860. [CrossRef]
[PubMed]
39. Spitzer, R.L.; Kroenke, K.; Williams, J.B.; Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch.
Intern. Med. 2006, 166, 1092–1097. [CrossRef] [PubMed]
40. Hamilton, N.; Freche, R.; Zhang, Y.; Zeller, G.; Carroll, I. Test anxiety and poor sleep: A vicious cycle. Int. J. Behav. Med. 2021, 28,
250–258. [CrossRef]
41. Danieli, M.; Ciulli, T.; Mousavi, S.M.; Silvestri, G.; Barbato, S.; Di Natale, L.; Riccardi, G. Assessing the impact of conversational
artificial intelligence in the treatment of stress and anxiety in aging adults: Randomized controlled trial. JMIR Ment. Health
2022, 9, e38067. [CrossRef]
42. Liu, B.; Sundar, S.S. Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychol.
Behav. Soc. Netw. 2018, 21, 625–636. [CrossRef] [PubMed]
Information 2024, 15, 768 26 of 26

43. Liao, T.; Yan, B. Are You Feeling Happy? The Effect of Emotions on People’s Interaction Experience Toward Empathetic Chatbots.
In Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering
Conference, St. Louis, MO, USA, 14–17 August 2022; p. 11.
44. Creswell, J.D. Mindfulness interventions. Annu. Rev. Psychol. 2017, 68, 491–516. [CrossRef]
45. Rahsepar Meadi, M.; Bernstein, J.S.; Batelaan, N.; van Balkom, A.J.; Metselaar, S. Does a lack of emotions make chatbots unfit to
be psychotherapists? Bioethics 2024, 38, 503–510. [CrossRef] [PubMed]
46. Seitz, L. Artificial empathy in healthcare chatbots: Does it feel authentic? Comput. Hum. Behav. Artif. Hum. 2024, 2, 100067.
[CrossRef]
47. Inkster, B.; Kadaba, M.; Subramanian, V. Understanding the impact of an AI-enabled conversational agent mobile app on users’
mental health and wellbeing with a self-reported maternal event: A mixed method real-world data mHealth study. Front. Glob.
Women’s Health 2023, 4, 1084302. [CrossRef] [PubMed]
48. Divino, F. From Meditation to Techno-Mindfulness: On the Medicalization of Contemplative Practices and Future Prospects.
Histories 2024, 4, 125–143. [CrossRef]
49. Nazi, Z.A.; Peng, W. Large language models in healthcare and medical domain: A review. Informatics 2024, 11, 57. [CrossRef]
50. Wen, B.; Norel, R.; Liu, J.; Stappenbeck, T.; Zulkernine, F.; Chen, H. Leveraging Large Language Models for Patient Engagement:
The Power of Conversational AI in Digital Health. arXiv 2024, arXiv:2406.13659.
51. Gaffney, H.; Mansell, W.; Tai, S. Conversational agents in the treatment of mental health problems: Mixed-method systematic
review. JMIR Ment. Health 2019, 6, e14166. [CrossRef] [PubMed]
52. Fantini, C.; Corinaldesi, C.; Lenzi, A.; Migliaccio, S.; Crescioli, C. Vitamin D as a Shield against Aging. Int. J. Mol. Sci. 2023, 24, 4546.
[CrossRef]
53. Trifan, D.F.; Tirla, A.G.; Mos, C.; Danciu, A.; Bodog, F.; Manole, F.; Ghitea, T.C. Involvement of Vitamin D3 in the Aging Process
According to Sex. Cosmetics 2023, 10, 114. [CrossRef]
54. Tuohimaa, P. Vitamin D and aging. J. Steroid Biochem. Mol. Biol. 2009, 114, 78–84. [CrossRef]
55. Bocheva, G.; Slominski, R.M.; Slominski, A.T. The impact of vitamin D on skin aging. Int. J. Mol. Sci. 2021, 22, 9097. [CrossRef]
56. Trifan, D.F.; Tirla, A.G.; Moldovan, A.F.; Mos, , C.; Bodog, F.; Maghiar, T.T.; Manole, F.; Ghitea, T.C. Can vitamin D levels alter the
effectiveness of short-term facelift interventions? Healthcare 2023, 11, 1490. [CrossRef]
57. Voiţă-Mekereş, F.; Manole, F.; Voiţă, I.B.; Marian, P. The Role of Food Biochemistry in the Control and Prevention of Nervous
System Diseases. J. Biochem. Technol. 2023, 14, 112–116. [CrossRef]
58. Bourre, J.-M. Effects of nutrients (in food) on the structure and function of the nervous system: Update on dietary requirements
for brain. Part 1: Micronutrients. J. Nutr. Health Aging 2006, 10, 377. [PubMed]
59. Pogačnik, L.; Ota, A.; Poklar Ulrih, N. An overview of crucial dietary substances and their modes of action for prevention of
neurodegenerative diseases. Cells 2020, 9, 576. [CrossRef] [PubMed]
60. Thomas, A.J.; Kalaria, R.N.; O’Brien, T.J. Depression and vascular disease: What is the relationship? J. Affect. Disord. 2004, 79,
81–95. [CrossRef] [PubMed]
61. Roy-Byrne, P.P.; Davidson, K.W.; Kessler, R.C.; Asmundson, G.J.; Goodwin, R.D.; Kubzansky, L.; Lydiard, R.B.; Massie, M.J.;
Katon, W.; Laden, S.K. Anxiety disorders and comorbid medical illness. Gen. Hosp. Psychiatry 2008, 30, 208–225. [CrossRef]
[PubMed]
62. Kim, T.H.; Choi, J.W.; Jeong, W.S. Current concepts of vascular anomalies. Arch. Craniofacial Surg. 2023, 24, 145. [CrossRef]
[PubMed]
63. Vrinceanu, D.; Dumitru, M.; Marinescu, A.; Dorobat, B.; Palade, O.D.; Manole, F.; Muresian, H.; Popa-Cherecheanu, M.; Ciornei,
C.M. New Insights into Cervicofacial Vascular Anomalies. J. Clin. Med. 2024, 13, 3515. [CrossRef] [PubMed]

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