Artificial Intelligence-Assisted Internet-Based Mindfulness Intervention for Emotional Distress: Protocol for a Randomized Controlled Trial on Long-Term Outcomes and Cost-Effectiveness
Phase: NA
Conditions: Subclinical Depression, Subclinical Anxious Adults, Subclinical Emotional Distress
Sponsor: Xinghua Liu

PROTOCOL COVER PAGE
Official Title
Artificial Intelligence–Assisted Internet-Based Mindfulness Intervention for
Emotional Distress: Protocol for a Randomized Controlled Trial on Long-Term
Outcomes and Cost-Effectiveness
NCT Number
Not yet assigned
Document Date
April 15, 2026

Artificial Intelligence–Assisted Internet-Based Mindfulness Intervention for Emotional Distress:
Protocol for a Randomized Controlled Trial on Long-Term Outcomes and Cost-Effectiveness
Short title: AI-Assisted Mindfulness Intervention in Emotional Distress
Abstract
Background: Common emotional disorders, such as anxiety and depression, have become major
contributors to the global burden of disease. However, current mental health service systems still tend
to emphasize treatment over prevention, and there remains a lack of long-term, scalable, and
economically evaluated digital interventions for individuals with subclinical emotional distress.
Internet-based Mindfulness Intervention for Emotional Distress (iMIED) offers an accessible, scalable,
and low-cost approach for early intervention, but its long-term outcomes and cost-effectiveness have
yet to be systematically established. Objective: This study aims to evaluate the long-term outcomes,
cost-effectiveness, and cost-utility of an artificial intelligence-assisted internet-based mindfulness
intervention (iMIED) among individuals with subclinical emotional distress through a two-year
randomized controlled trial. It also seeks to explore its potential psychological mechanisms, differential
preventive effects, and feasibility and acceptability in real-world settings. Methods: This study is an
assessor-blinded, parallel-group, two-arm randomized controlled trial using a superiority framework.
Eligible participants aged 18-65 years will be randomly assigned in a 1:1 ratio to either the iMIED
intervention group or the control group. The intervention group will receive a 49-day AI-assisted online
mindfulness self-help intervention, while the control group will have access to usual mental health
resources. The primary outcomes are anxiety and depressive symptoms, assessed using the Generalized
Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9), respectively. Secondary
outcomes include asymptomatic remission, reliable deterioration, healthcare resource utilization,
quality of life, cost-effectiveness, cost-utility, resilience, mindfulness, perceived stress, life satisfaction,
sleep status, and overall anxiety and depression severity and impairment. Exploratory outcomes include
engaging in life, distress tolerance, excessive emotional behavior, cognitive flexibility, AI interaction
behavioral indicators, and adverse events. Assessments will be conducted at baseline, Weeks 3, 5, and
8, and at 3, 6, 12, 18, and 24 months post-intervention. Conclusion: This study will provide evidence
on the long-term efficacy and health economic value of digital early intervention for individuals with
subclinical emotional distress, and will offer both theoretical and practical support for the

implementation of AI-assisted psychological interventions in public health systems.
Keywords: artificial intelligence-assisted intervention; internet-based mindfulness intervention;
emotional distress; subclinical anxiety and depression; randomized controlled trial; long-term
effectiveness; cost-effectiveness; digital mental health; prevention; mindfulness intervention
1. Background
The prevalence of common mental disorders, including anxiety and depression, has continued to rise
and has become one of the leading contributors to the global burden of disease . However, current
intervention research and clinical practice in the field of emotional disorders remain markedly skewed
toward treatment rather than prevention, with substantial resources concentrated on individuals who
have already developed moderate-to-severe clinical conditions. This tendency has been documented in
multiple authoritative studies and policy reports. The World Health Organization (WHO), in its 2022
World Mental Health Report, explicitly noted that nearly 50% of individuals with mental disorders
worldwide do not receive timely intervention at the early stages of illness, resulting in worsening
symptoms and greater treatment complexity . Such delayed intervention not only leads to poorer
prognosis for affected individuals but also imposes a substantial economic burden on society.
In its 2023 strategic plan, the U.S. National Institute of Mental Health (NIMH) explicitly shifted its
research focus upstream, emphasizing the need to strengthen research on quantifiable risk states and
prodromal phases in order to advance truly preventive psychiatry . This strategic reorientation reflects a
growing recognition of the limitations of current clinical models. Against this backdrop, early
intervention strategies targeting subclinical or high-risk populations have become especially important.
For example, a large network meta-analysis published in The Lancet in 2019 demonstrated that
preventive psychological interventions delivered in schools and other community settings significantly
reduced the risk of future anxiety and depression among children and adolescents . This study provides
strong evidence supporting the advancement of the intervention window to earlier stages.
Emerging evidence further suggests that early intervention is not only clinically effective but also
highly cost-effective. An investment case analysis of adolescent mental health in England showed that
preventive and early intervention measures for individuals aged 10–19 years yielded substantially

higher health and economic returns on investment than treatment strategies targeting already affected
populations . Evidence from specific high-risk populations further reinforces this conclusion. For
instance, a 22-year prospective cohort study, the Dutch Bipolar Offspring Study, found that offspring
of individuals with bipolar disorder already exhibited significant psychopathological risk during
adolescence, underscoring the importance of early identification and intervention within a critical
window of opportunity . Similarly, the large RADAR cohort study published in The Lancet Psychiatry
demonstrated that cognitive behavioral intervention among community individuals with only mild
depressive symptoms (PHQ-9 scores of 5–9) reduced the subsequent risk of developing major
depression by 38% (hazard ratio (HR) = 0.62, 95% confidence interval (CI) 0.49–0.78) . Collectively,
these findings point to a central conclusion: while the existing intervention model centered on
moderate-to-severe clinical populations remains necessary, it is insufficient to address the growing
global burden of emotional disorders. A strategic shift of research and clinical resources toward
prevention and early intervention, particularly for individuals with subclinical symptoms and other
high-risk groups, is a key pathway to improving population mental health and reducing long-term
socioeconomic costs. This paradigm shift from disease treatment to health promotion and risk
prevention has increasingly been regarded as a core direction for the future development of mental
health services .
Subclinical populations generally refer to individuals with scores of ≥5 on the Patient Health
Questionnaire-9 (PHQ-9) or the Generalized Anxiety Disorder-7 (GAD-7), but who do not yet meet
formal diagnostic criteria. This group represents a critical window for intervention and has several
advantages. First, treatment responsiveness appears to be better: subclinical cases receiving preventive
cognitive behavioral therapy (CBT) show significantly faster initial reductions in overall symptoms and
anxiety scale scores than clinical cases . Second, intervention effects can be substantial: a web-based
self-help intervention for subthreshold depression, such as GET.ON Mood Enhancer, demonstrated a
large between-group effect size after treatment (d = 0.69), with significant effects maintained at
6-month follow-up . Third, early improvement predicts better prognosis: among individuals with mild
major depression, minor depression, or subsyndromal depression, early symptom improvement predicts
final treatment outcomes . Fourth, low-intensity intervention is feasible: an eHealth platform that
stratified management based on prognostic severity significantly improved PHQ-9 scores at 3 months,

demonstrating the short-term feasibility and benefits of low-intensity interventions for people with mild
symptoms .
More importantly, early intervention may effectively prevent the progression of subclinical symptoms
into full clinical disorders. Longitudinal evidence suggests that late-adolescent subthreshold depression
predicts an elevated risk of major depression and suicidal behavior by age 25, indicating that early
intervention may avert illness progression . Randomized controlled trials provide additional support.
The internet-based prevention program CATCH-IT for adolescents with subthreshold depression or a
history of depression showed that adolescents with subthreshold depression might experience fewer
depressive episodes . Likewise, a multicenter randomized trial of a proactive eHealth intervention for
individuals with subthreshold depression reported beneficial outcomes at 2-year follow-up , offering
direct evidence for the long-term benefits of early intervention.
Against this backdrop, digital mental health interventions, particularly internet-based Mindfulness
Intervention for Emotional Distress (iMIED), provide an accessible, scalable, and low-cost solution for
early intervention among subclinical populations. Digital interventions can overcome geographical,
financial, and stigma-related barriers associated with traditional face-to-face treatment, thereby making
large-scale prevention feasible . Existing evidence suggests that iMIED yields significant
improvements in anxiety, depression, stress, and sleep outcomes .
Nevertheless, several key gaps remain in the current literature. First, long-term follow-up data are
severely lacking. Although short-term findings are promising, evidence regarding the long-term
stability of digital interventions in subclinical populations remains scarce, and many trials report
follow-up outcomes only up to 6 or 12 months . For example, the ICare Prevent study found
small-to-moderate short-term reductions in subclinical anxiety and depression symptoms, but no
average long-term effect at 12 months . Randomized controlled trials with follow-up periods of 2 years
or longer are exceedingly rare, limiting evaluation of the durability of digital interventions.
Second, cost-effectiveness evidence is extremely limited. To date, only a small number of studies have
conducted economic evaluations in specific areas such as post-traumatic stress disorder , whereas
cost-effectiveness evidence for individuals with subclinical emotional distress remains largely absent.
Given the finite nature of public health resources, clarifying the economic value of digital interventions

is essential for policy decision-making and large-scale implementation.
In light of these issues, the present study proposes a 2-year randomized controlled trial to
systematically evaluate the long-term effectiveness and cost-effectiveness of iMIED in individuals with
subclinical emotional distress, defined as PHQ-9 or GAD-7 scores ≥5. Specifically, the study aims to:
(1) fill the gap in long-term follow-up evidence for digital mental health interventions; (2) provide the
first cost-effectiveness analysis for individuals with subclinical emotional distress; and (3) generate
high-quality evidence for preventive strategies centered on early identification and early treatment,
thereby advancing the intervention window for emotional disorders, reducing disease burden, and
improving public health outcomes.
2. Objectives
2.1 Primary Objective
The primary objective of this study is to evaluate, in a 2-year randomized controlled trial (RCT), the
preventive effects of an AI-supported mindfulness self-help intervention (iMIED) in individuals with
subclinical emotional distress, with particular focus on its effectiveness in reducing anxiety and
depressive symptoms. Specifically, the study will compare the trajectories of anxiety and depressive
symptoms over follow-up between participants receiving iMIED and those in the control group, and
will further assess the potential preventive value of this intervention in subclinical populations. In this
way, the study seeks to clarify the public health value of digital mindfulness intervention in reducing
the risk of emotional disorders. The primary outcomes to be compared between the iMIED group and
the control group are:
Anxiety symptom severity, measured by the Generalized Anxiety Disorder-7 (GAD-7)
Depressive symptom severity, measured by the Patient Health Questionnaire-9 (PHQ-9)
2.2 Secondary Objectives
Building upon the evaluation of iMIED in reducing the occurrence and progression of anxiety and
depressive symptoms, the study further aims to assess its cost-effectiveness and cost-utility.
Specifically, from a health economics perspective, this study will compare the iMIED group and the
control group with respect to mental health service utilization, healthcare resource consumption, and

improvements in quality of life, and will calculate the Incremental Cost-Effectiveness Ratio (ICER) to
evaluate the economic feasibility of the intervention within public health systems.
In addition, this study will explore the potential effects of iMIED on other important dimensions of
mental health, including sleep quality, perceived stress, resilience, and life satisfaction, as secondary
outcome indicators, in order to provide a more comprehensive assessment of its potential benefits for
overall psychological well-being.
2.3 Exploratory Objectives
In addition to the primary and secondary objectives, the present study includes several exploratory aims
to further deepen understanding of the mechanisms and target populations for iMIED.
First, the study will explore the psychological mechanisms underlying iMIED. Specifically, it will test
whether engaging in life, distress tolerance, cognitive flexibility, and excessive emotional behavior
mediate intervention effects, thereby examining the theoretical mechanisms proposed by the
Psychopathology Diamond Model of emotional distress.
Second, to examine whether the preventive effects of iMIED differ across risk levels, the study will
compare intervention effects between a higher-risk group and a lower-risk group, defined as PHQ-9
≥10 or GAD-7 ≥10 versus PHQ-9 <10 and GAD-7 <10, respectively, in order to evaluate its potential
role within the prevention framework.
Third, the study will explore the contribution of AI-assisted support in digital psychological
intervention, including the relationships between chatbot interaction frequency, intensity of
personalized feedback, and intervention effects, in order to evaluate the potential added value of AI in
enhancing digital mental health intervention outcomes.
Fourth, the study will assess the feasibility and acceptability of iMIED in real-world settings, including
adherence, completion rate, usage frequency, and participant satisfaction, to determine its scalability
for large-population dissemination.
Fifth, subgroup analyses will be conducted to examine whether intervention effects differ across
participants with different sociodemographic characteristics.

3. Methods
3.1 Study Design
This study is an assessor-blinded, parallel-group, two-arm randomized controlled trial (RCT) using a
superiority framework to evaluate the long-term outcomes and cost-effectiveness of an AI-assisted
mindfulness self-help intervention (iMIED) for the prevention of emotional distress. Specifically, the
study will compare iMIED plus access to usual mental health resources with usual mental health
resources alone in reducing the onset and progression of anxiety and depressive symptoms. Participants
will be randomly allocated in a 1:1 ratio to either the iMIED intervention group or the control group.
A parallel-group design will be adopted. The intervention period will last 8 weeks, with assessments
conducted at baseline, Week 3, Week 5, and Week 8, followed by post-intervention follow-up
assessments at 3, 6, 12, 18, and 24 months, in order to evaluate both short-term and long-term effects.
The trial will employ assessor blinding, meaning that the researchers responsible for outcome
evaluation and data analysis will remain unaware of group allocation, thereby reducing assessment
bias.
This study also has the characteristics of a pragmatic randomized controlled trial, as the intervention
will be implemented in real-world settings to enhance the external validity of the findings in public
health practice. The study will not only assess the effects of the intervention on anxiety and depressive
symptoms, but will also examine, from a preventive perspective, rates of reliable deterioration and
clinically asymptomatic remission.
Furthermore, the study will incorporate health economic methods to evaluate the cost-effectiveness and
cost-utility of iMIED within mental health service systems, thereby exploring the potential for
large-scale dissemination of digital psychological intervention.
The design, conduct, and reporting of this protocol will follow the Standard Protocol Items:
Recommendations for Interventional Trials (SPIRIT) guidelines , and will comply with reporting
standards for randomized controlled trials.
3.2 Participants

The inclusion and exclusion criteria are as follows.
Inclusion criteria
Aged 18 to 65 years
PHQ-9 or GAD-7 score ≥5
Able to use a smartphone or other internet-enabled electronic device and possessing sufficient digital
literacy to participate in the online intervention and complete follow-up assessments
Willing to participate and able to provide informed consent
Exclusion criteria
Current psychotic disorder or bipolar disorder
Current organic mental disorder, pervasive developmental disorder, severe cognitive impairment, or
substance use disorder
Current suicide risk, defined as a score of ≥2 on Item 9 of the PHQ-9
Previous participation in a structured 8-week mindfulness course or the 49-day self-guided course
developed by the Peking University Mindfulness Laboratory
3.3 Study Procedures
Participants will be publicly recruited through internet platforms. Recruitment information will be
posted on social media, mental health-related websites, and online communities (e.g., Xiaohongshu and
WeChat public accounts), together with a study registration link. All study procedures, including
screening, informed consent, randomization, intervention delivery, and follow-up assessments, will be
completed via an online research platform.
As shown in Figure 1, potential participants will first enter the study registration page through the
recruitment link and read an introduction to the study, the eligibility criteria, and the study procedures.
After understanding the study objectives, procedures, and potential risks and benefits, participants will
be required to sign an electronic informed consent form online before proceeding to the screening
process.

Participants will then complete a structured online screening questionnaire, which includes
demographic information, mental health screening measures (e.g., GAD-7 and PHQ-9), and self-report
items relevant to the inclusion and exclusion criteria. The system will automatically determine
eligibility based on pre-specified rules. Individuals who meet the inclusion criteria and none of the
exclusion criteria will be formally enrolled; those who do not meet eligibility criteria will automatically
exit the study process.
After initial screening, eligible participants will enter the baseline assessment stage. Baseline measures
will include anxiety symptoms (GAD-7), depressive symptoms (PHQ-9), and other relevant
psychological indicators via self-report questionnaires. All questionnaires will be completed online and
automatically recorded. Upon completion of the baseline assessment, the study platform will use a
computer-generated random sequence to allocate participants in a 1:1 ratio to either the iMIED
intervention group or the waitlist control group. Following randomization, participants will enter the
intervention phase. The intervention group will receive the AI-assisted internet-based Mindfulness
Intervention for Emotional Distress (iMIED) according to the study protocol; the control group will not
receive this intervention during the trial period, but their autonomous use of usual mental health
resources, including but not limited to medication and psychotherapy, will not be restricted during the
waiting period.
Throughout the study, participants in both groups will complete online questionnaire assessments
according to the study schedule. Self-report measures will be administered at baseline, Week 3, Week 5,
Week 8, and at 3, 6, 12, 18, and 24 months after the intervention, in order to evaluate short-term and
long-term outcomes. Participants will receive reminders via system notifications or email at each
assessment point. All data will be collected and stored on the secure online platform Wenjuanxing.
After the intervention group completes the 24-month follow-up, the waitlist group will have the
opportunity to receive the same intervention.
Figure 1. Consolidated Standards of Reporting Trials (CONSORT) 2025 Flow Diagram
Flow diagram of the progress through the phases of a randomized trial of two groups (i.e., enrollment,
intervention allocation, follow-up, and data analysis)

3.4 Randomization and Blinding
3.4.1 Randomization
This study will use simple randomization to allocate participants in a 1:1 ratio to the iMIED
Assessed for eligibility (n= )
Excluded (n= )
Not meeting inclusion criteria
(n= )
Randomised (n= )
Enrollment
Allocated to intervention (n= )
Received allocated intervention (n= )
Did not receive allocated intervention (give
Allocation
Follow-Up
Allocated to intervention (n= )
Received allocated intervention (n= )
Did not receive allocated intervention (give
Analysed for primary outcome (n= )
Excluded from analysis (give reasons)
Discontinued intervention (give reasons) (n= )
Lost to follow-up for primary outcome (give
Analysis
Discontinued intervention (give reasons) (n= )
Lost to follow-up for primary outcome (give
Analysed for primary outcome (n= )
Excluded from analysis (give reasons)

intervention group or the control group. The random sequence will be generated in advance by an
independent researcher who will not be involved in participant recruitment, screening, intervention
delivery, or outcome assessment, thereby ensuring the independence and objectivity of the
randomization process.
Once participants complete the baseline assessment, the study platform will automatically assign them
according to the pre-generated randomization sequence. Members of the research team will not have
access to the sequence prior to allocation and will not be able to predict future assignments, thereby
ensuring allocation concealment. Group assignment will only be released by the system when
participants enter the intervention phase and will then be communicated to the relevant research
personnel.
3.4.2 Blinding
Because this is a digital psychological intervention study, full blinding of participants and intervention
providers is not feasible. Therefore, blinding procedures will be implemented primarily at the stages of
data collection and statistical analysis to minimize potential bias.
(1) Assessor blinding
All assessments will be conducted by independent assessors who are not involved in screening,
intervention delivery, or randomization, and who remain unaware of participants’ group assignments.
Prior to each assessment, participants will be instructed not to disclose their group allocation to the
assessors, and assessors will be instructed not to ask participants about their group assignment. To
systematically evaluate the quality of blinding, after each assessment the assessor will complete a
blinding integrity check form, indicating whether they believe the participant belongs to the
intervention group, the control group, or whether they are uncertain. This form will be collected after
each assessment to quantify the integrity of blinding.
(2) Blinded data analysis
Primary outcome analyses will be conducted using coded group labels. Statistical analysts will remain
unaware of the actual group assignments until the primary analyses have been completed.
(3) Documentation of unblinding events

Routine unblinding will not be performed. If a serious adverse event (SAE) occurs during the trial and
the principal investigator determines that knowledge of a participant’s group assignment is clinically
necessary for safety management, single-case unblinding may be undertaken. All such unblinding
events will be documented with respect to time, reason, and involved personnel, and will not affect
blinding procedures for other participants.
3.5 Intervention
The Internet-based Mindfulness Intervention for Emotional Distress (iMIED) was developed on the
basis of the Mindfulness Intervention for Emotional Distress (MIED) created by Xinghua Liu’s
research group. It is an evidence-based intervention designed for individuals experiencing emotional
distress, and previous short-term studies have demonstrated its beneficial effects in alleviating
emotional distress and accelerating symptom improvement in emotional disorders .
Delivered via the internet, the intervention is grounded in the Psychopathology Diamond Model
proposed by Xinghua Liu’s team . Based on advanced large language models, including Tencent
Hunyuan and DeepSeek R1, and supported by an official MIED knowledge base comprising the
“Mindfulness Practice” WeChat public account, supervision question-and-answer (Q&A) records, and
other official materials produced by Xinghua Liu and colleagues, the team developed an AI assistant
called the “MIED Mindfulness Assistant,” along with a WeChat self-help mini-program. Together,
these tools support participants’ learning and practice over a 49-day continuous training period .
iMIED is theoretically grounded in an integrated biopsychosocial framework, with the
Psychopathology Diamond Model serving as the core intervention framework. According to this model,
the onset and maintenance of emotional distress are closely associated with four key psychological
processes: insufficient engaging in life, low distress tolerance, excessive emotional behavior, and poor
cognitive flexibility. In response to these mechanisms, MIED proposes four corresponding intervention
strategies: increasing engaging in life, regulating distress tolerance, modifying emotional behavior, and
enhancing cognitive flexibility, thereby facilitating improvement in emotional distress . See Figure 2.

Figure 2. Psychopathological Model and Intervention Strategies of MIED
In practical implementation, the iMIED intervention lasts 49 days, with approximately 30 minutes of
learning and practice each day. This includes around 15 minutes of psychoeducational video content
and accompanying written learning materials, as well as approximately 15 minutes of audio-guided
mindfulness practice. After each session, participants are required to record their daily practice
experiences and reflections in the mini-program using a standardized check-in template. In addition,
the accompanying AI support system, the “MIED Assistant,” can respond to participants’ questions in
real time during practice, providing necessary clarification and guidance to help them better understand
and complete the exercises. The detailed content of the 49-day check-in program is provided in
Appendix 1.
iMIED has strong potential for clinical application. Its intervention duration is relatively brief
(typically 49 days), onset of benefit is relatively rapid, and its four-dimensional pathological
mechanisms derived from the biopsychosocial model correspond directly to four intervention strategies.
This one-to-one correspondence makes the intervention target clear and closely links etiological
understanding with operational techniques, thereby holding promise for more precise intervention and
reduced relapse risk.
4. Outcomes

4.1 Primary Outcomes
The primary outcomes are changes in anxiety and depressive symptom severity, assessed using the
Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9),
respectively. Both scales are widely used self-report instruments with good reliability and validity for
assessing the severity of anxiety and depressive symptoms over the past two weeks. The primary
analysis will compare between-group differences in changes in PHQ-9 and GAD-7 scores across the
intervention and follow-up periods .
4.2 Secondary Outcomes
(1) Categorical clinical outcomes
In addition to continuous outcomes, a series of categorical clinical outcome indicators will be
constructed to assess the effects of the intervention in terms of clinical improvement, symptom
recovery, and potential adverse outcomes. These indicators will be derived in accordance with
conventional outcome reporting standards and will be based on changes in PHQ-9 and GAD-7 scores.
I. Asymptomatic remission
A more stringent outcome, asymptomatic remission, will be defined as PHQ-9 <5 and GAD-7 <5.
These thresholds correspond to the minimal symptom range on the two scales and indicate the virtual
absence of clinically meaningful emotional distress .
II. Reliable deterioration
To evaluate potential adverse effects of the intervention, symptom deterioration indicators will also be
calculated. Reliable deterioration will be defined as an increase exceeding the reliable change threshold,
namely PHQ-9 increase ≥6 points or GAD-7 increase ≥4 points. These indicators will be calculated
separately for PHQ-9 and GAD-7 .
Secondary outcomes will also include the economic value of iMIED in public health systems, including
both cost-effectiveness and cost-utility. Specifically, the study will comprehensively evaluate the
economic value of iMIED relative to the control group by integrating intervention costs, healthcare
resource utilization, and changes in quality of life.

(2) Healthcare resource utilization
Healthcare resource utilization will be assessed using the Treatment Inventory of Costs in Patients with
Psychiatric Disorders (TIC-P). TIC-P is a widely used health economic instrument in mental health
research for assessing psychiatric healthcare use and societal costs, including service utilization,
medication use, and productivity losses . In this study, TIC-P will be used to record healthcare resource
consumption during the trial, including outpatient visits, medication costs, and other healthcare-related
expenditures. To capture healthcare utilization more comprehensively, a self-developed healthcare
utilization questionnaire will also be administered.
(3) Health-related quality of life
Quality of life will be assessed using the EuroQol 5-Dimension 5-Level (EQ-5D-5L). This is one of the
most widely used instruments in health economic evaluation for measuring health-related quality of life
and includes five dimensions: mobility, self-care, usual activities, pain/discomfort, and
anxiety/depression, each with five response levels . The health utility values derived from EQ-5D-5L
will be used to calculate Quality-Adjusted Life Years (QALYs) for cost-utility analysis .
(4) Incremental cost-effectiveness ratio
Based on intervention costs, healthcare resource utilization, and changes in quality of life, the
incremental cost-effectiveness ratio (ICER) of iMIED relative to the control condition will be
calculated. ICER will be derived by comparing cost differences and effect differences between groups
and will be used to evaluate both cost-effectiveness and cost-utility within the public health system .
Sensitivity analyses will also be conducted to assess the robustness of the economic results .
(5) Other psychological health indicators
To further explore the potential impact of iMIED on broader dimensions of mental health, the study
will assess a number of additional indicators, including resilience, perceived stress, life satisfaction,
and sleep status. These variables will be analyzed as continuous outcomes.
A. Resilience
Resilience will be measured using the Connor-Davidson Resilience Scale (CD-RISC), one of the most
widely used instruments for assessing the ability to cope with stress and adversity, with good reliability

and validity .
B. Perceived stress
Perceived stress will be measured using the Perceived Stress Scale (PSS), a widely used instrument that
assesses individuals’ subjective appraisal of stress over a recent period .
C. Life satisfaction
Life satisfaction will be measured using the Satisfaction With Life Scale (SWLS), which assesses
individuals’ cognitive evaluation of their overall life circumstances and has sound psychometric
properties .
D. Sleep status
Sleep status will be assessed using the Insomnia Severity Index (ISI), a brief 7-item self-report scale
widely used in clinical and digital health research to assess the severity of insomnia symptoms and
their impact on daily functioning .
(6) Related symptoms and functional impairment
In addition, to more comprehensively evaluate the effects of iMIED on emotional distress-related
symptoms and functional impairment, this study will include the Overall Anxiety Severity and
Impairment Scale (OASIS) and the Overall Depression Severity and Impairment Scale (ODSIS) as
secondary outcome measures. The OASIS is a 5-item self-report scale that assesses the frequency and
severity of anxiety symptoms, avoidance behavior, and related functional impairment over the past
week. Total scores range from 0 to 20, with higher scores indicating greater overall anxiety severity
and associated functional impairment. The scale has demonstrated good reliability and validity . The
ODSIS is likewise a 5-item self-report scale that assesses the frequency and severity of depressive
symptoms, avoidance behavior, and their impact on social and daily functioning over the past week.
Total scores range from 0 to 20, with higher scores indicating greater overall depression severity and
functional impairment. It has shown good psychometric properties in both clinical and non-clinical
samples . In this study, both the OASIS and ODSIS will be analyzed as continuous variables to further
assess the effects of the intervention on anxiety- and depression-related functional impairment and
overall symptom burden.

(7) Mindfulness
Mindfulness will be assessed using the Five Facet Mindfulness Questionnaire (FFMQ). The FFMQ
measures trait mindfulness across five dimensions: observing, describing, acting with awareness,
nonjudging of inner experience, and nonreactivity to inner experience. It is one of the most widely used
instruments in mindfulness research and has demonstrated good reliability and validity . In this study,
the total FFMQ score and its subscale scores will be analyzed as exploratory continuous variables.
4.3 Exploratory Outcomes
Exploratory outcomes are intended to further clarify the mechanisms of action of iMIED, its suitable
target populations, and the role of AI support in the intervention.
(1) Psychological mechanism variables
To test the theoretical mechanisms proposed by the Psychopathology Diamond Model of emotional
distress, the following psychological variables will be measured:
A. Engaging in life
The Engaging in Life Scale (ELS) will be used to assess the degree to which individuals engage in key
life domains such as work, study, social interaction, and sleep, and is an important tool for evaluating
levels of engaging in life .
B. Distress tolerance
Distress tolerance will be assessed using the Distress Tolerance Scale (DTS), which measures
individuals’ capacity to tolerate negative emotional experiences and their regulatory responses to
emotional distress . In addition, the Discomfort Intolerance Scale (DIS) will be used to assess tolerance
of unpleasant bodily sensations, which has been widely applied in anxiety disorder and emotion
regulation research .
C. Excessive emotional behavior
The Excessive Emotional Behavior Scale (EEBS) will be used to assess excessive emotion-driven and
avoidant behaviors under negative affective states, thereby providing a more comprehensive
assessment of this psychological process . In addition, the Brief Experiential Avoidance Questionnaire

(BEAQ) will be used to further assess experiential avoidance as a core process .
D. Cognitive flexibility
Cognitive flexibility will be measured using the Cognitive Flexibility Inventory (CFI), which assesses
individuals’ ability to shift cognitive frameworks, generate alternative explanations, and adopt adaptive
thinking strategies under stress .
These psychological variables will be incorporated into statistical models as potential mediators, and
mediation analyses will be used to examine their role in the pathway linking iMIED to improvements
in anxiety and depressive symptoms.
(2) AI interaction indicators
To evaluate the role of AI support in the intervention, the following behavioral data will be recorded:
number of interactions with the AI assistant, number of personalized feedback responses, and daily
duration of use. Associations between these variables and intervention outcomes will then be analyzed.
(3) Feasibility and acceptability indicators
To assess the scalability of iMIED in real-world settings, the following indicators will be measured:
intervention completion rate, average usage frequency, participant adherence (proportion of exercises
completed), and user satisfaction. Satisfaction will be measured using the Client Satisfaction
Questionnaire (CSQ-8), an 8-item instrument assessing participants’ overall satisfaction with the
intervention service .
(4) Adverse Events
To evaluate the safety of the intervention, this study will record adverse events (AEs) and serious
adverse events (SAEs) occurring during the study period. These events will be defined, documented,
and reported in accordance with international clinical trial guidelines, and will be descriptively
analyzed with respect to the time of occurrence, severity, relationship to the intervention, and outcomes,
in order to comprehensively assess the safety and acceptability of iMIED .
4.4 Sample Size Calculation

Sample size estimation for this study is based primarily on the primary outcomes, namely
between-group differences in changes in anxiety symptoms (GAD-7) and depressive symptoms
(PHQ-9) over repeated follow-up assessments. Because the study uses a longitudinal
repeated-measures design and plans to analyze group, time, and group × time interaction effects using
linear mixed-effects models (LMMs), sample size estimation was conducted with reference to
repeated-measures designs .
Based on prior internet-based psychological and mindfulness intervention studies, digital mindfulness
interventions typically produce small-to-moderate effects on anxiety and depressive symptoms . To
ensure a conservative estimate, the group × time interaction effect size was assumed to be Cohen’s f =
0.12. A two-sided significance level of α = 0.05 and statistical power of 0.80 were specified. The study
includes 9 measurement time points: baseline, Week 3, Week 5, Week 8, 3 months, 6 months, 12
months, 18 months, and 24 months. Correlations between repeated measurements were assumed to be
approximately 0.50. Based on these parameters, G*Power 3.1 estimated that a minimum sample size of
approximately 306 participants would be required for the primary outcome analysis.
Given that the follow-up period extends to 24 months and internet-based intervention studies
commonly experience attrition and declining adherence, an attrition rate of 40% was further
incorporated into the sample size estimation . After adjustment, the required total sample size was
approximately 510 participants. Taking both feasibility and statistical requirements into account, the
study plans to enroll a final total of 550 participants, who will be randomized 1:1 to the iMIED
intervention group and the control group, with approximately 275 participants per group.
4.5 Data Management
All questionnaire data will be collected online via the Wenjuanxing platform, which participants can
access using mobile devices, and stored on its encrypted servers. Informed consent will be signed
online through the same platform before the baseline assessment begins, and the relevant documents
will be stored in the platform backend and accessible only to authorized research personnel.
The following additional data management procedures will be implemented:

(1) Data coding and de-identification
All personally identifiable information, such as names, telephone numbers, WeChat accounts, and
other contact information, will be used solely for screening, study communication, and follow-up
arrangements and will not be included in statistical analyses. During data analysis, only a randomly
generated unique study ID will be retained. All study variables will be standardized according to a
pre-defined data dictionary (e.g., PHQ9_total, GAD7_item3).
(2) Data quality control
Exported raw data will be checked by an independent research assistant for completeness and logical
consistency, including screening for anomalies such as completion times shorter than 120 seconds or
duplicate IP addresses. Any records flagged as suspicious will undergo secondary review.
(3) Data security and access management
A hierarchical data access management system will be used. A data manager designated by the
principal investigator (PI) will be responsible for overall access control and authorization. Each
batch-specific research assistant will have access only to the data for their assigned batch. All study
data will be stored on encrypted servers and protected by password authentication; unauthorized
personnel will not be able to access any raw or processed study data.
(4) Data version control
After data cleaning is completed, the dataset will be frozen and assigned a version number to ensure
that all subsequent statistical analyses are performed on a fixed and traceable dataset version.
(5) Data retention and backup
All study data will be retained after trial completion in accordance with the minimum retention period
required by national regulations. Automatic server backup mechanisms will be used to prevent data
loss. Once the retention period has expired, all data will be destroyed centrally by the data manager.
4.6 Strategies to Improve Adherence to Study Assessments

Several measures will be adopted to improve adherence to study assessments.
First, before each assessment time point begins, research assistants will send multiple reminder
messages to participants via the project WeChat group, for example, at the start of the assessment
period, 3 days before the deadline, and 1 day before the deadline. If participants fail to complete the
assessment on time, research assistants will contact them privately to understand the situation and
provide necessary support.
Second, participants will receive compensation ranging from RMB 5 to RMB 40 for each completed
assessment, to compensate them for their time and effort. If all assessments are completed as scheduled,
participants may receive up to RMB 180 in total compensation.
4.7 Study Oversight and Management
This study compares an AI-assisted online mindfulness intervention for emotional distress (MIED)
with a control condition in which participants have access to usual care resources. Participants in the
control group may freely receive any form of treatment as usual (TAU), including pharmacotherapy,
psychotherapy, or other interventions. The research team will impose no restrictions on whether
treatment is received or on the type of treatment chosen, and will not participate in treatment decisions.
As this is a low-risk, non-invasive psychological intervention study, no independent Data Monitoring
Committee (DMC) will be established. Consequently, no DMC-related issues regarding membership
composition, independence, or conflicts of interest arise.
No interim analysis will be conducted, and no formal stopping rules are specified. No unblinded
interim results will be generated or disclosed during the trial. If a serious adverse event potentially
related to the psychological intervention occurs, the principal investigator will promptly report it to the
ethics committee and consult with the committee regarding whether the trial should be paused or
terminated. Medical problems unrelated to the study, such as adverse reactions to routine
pharmacological treatment, will be managed according to usual clinical practice.
After trial completion, participants in the intervention group will no longer have continued access to
the digital intervention program, but may continue to use usual care services. Participants in the usual

care group may voluntarily choose to receive the same mindfulness intervention after the trial ends.
5. Statistical Analysis
5.1 Overall Analytical Strategy
All statistical analyses will follow the intention-to-treat (ITT) principle, meaning that all randomized
participants will be included in the final analyses regardless of whether they fully completed the
intervention. This approach preserves the initial between-group balance achieved by randomization and
reduces selection bias due to attrition or non-adherence . Continuous variables will be summarized as
means and standard deviations or medians and interquartile ranges, while categorical variables will be
presented as frequencies and percentages. Baseline characteristics will be compared descriptively
between groups to assess post-randomization comparability, in accordance with reporting standards for
randomized controlled trials .
Missing data will be handled under the Missing At Random (MAR) assumption (Little & Rubin, 2019).
For the primary analyses, linear mixed-effects models (LMMs) will be used. These models rely on
maximum likelihood estimation, can yield unbiased estimates in the presence of partially missing data,
and make full use of all available observations . In addition, multiple imputation will be applied as a
sensitivity analysis, and results before and after imputation will be compared to assess robustness . All
statistical tests will be two-sided, with statistical significance set at p < 0.05.
5.2 Primary Outcome Analysis
The primary outcomes are longitudinal changes in anxiety symptoms (GAD-7) and depressive
symptoms (PHQ-9). Continuous outcomes such as changes in GAD-7 and PHQ-9 scores will be
analyzed using linear mixed-effects models. The models will include group (iMIED intervention vs.
control), time (baseline, Week 3, Week 5, Week 8, 3 months, 6 months, 12 months, 18 months, and 24
months), and the group × time interaction as fixed effects, with participant included as a random effect
(random intercept) to account for the correlation structure of repeated measurements.

The primary parameter of interest will be the group × time interaction, which will indicate whether
symptom trajectories differ significantly between the intervention and control groups over follow-up.
Model estimates, 95% confidence intervals, and p values will be reported. Where necessary, models
will be adjusted for potential confounders such as age, sex, and baseline symptom severity.
5.3 Secondary Outcome Analysis
In addition to continuous outcomes, a series of categorical clinical outcomes will be constructed to
assess symptom recovery and potential adverse outcomes. These indicators will be derived according
to conventional outcome reporting standards and based on changes in PHQ-9 and GAD-7 scores.
(1) Asymptomatic remission
Asymptomatic remission will be defined as PHQ-9 <5 and GAD-7 <5, corresponding to the minimal
symptom range on both scales and indicating the virtual absence of clinically meaningful emotional
distress .
(2) Reliable deterioration
To assess potential adverse effects of the intervention, reliable deterioration will be calculated and
defined as symptom increase beyond the reliable change threshold: PHQ-9 increase ≥6 points or
GAD-7 increase ≥4 points .
All categorical outcomes will be reported as n/N (%), where the denominator is defined as all
randomized participants who entered the study (the ITT population). This approach follows the
demonstrated recovery principle, under which participants are considered not recovered unless there is
explicit follow-up evidence showing recovery; this includes participants lost to follow-up.
Between-group differences in categorical outcomes will be analyzed using logistic regression models,
with odds ratios (ORs) and 95% confidence intervals reported. Models will be adjusted a priori for
baseline depression severity, baseline antidepressant use, and other relevant covariates. Sensitivity
analyses will be conducted to examine the robustness of findings under different missing-data handling
approaches and outcome definitions.

(3) Cost-effectiveness analysis
Cost-effectiveness analysis will be conducted from a societal perspective and will comprehensively
evaluate both direct and indirect costs. Direct costs will include psychotherapy, psychiatric outpatient
visits, pharmacological treatment, and other healthcare resource utilization; indirect costs will primarily
include productivity loss associated with emotional distress. These data will be collected using the
TIC-P and a self-developed healthcare utilization questionnaire . Total costs will be estimated using a
bottom-up approach based on individual-level resource consumption data.
The health economic evaluation will include both cost-effectiveness analysis (CEA) and cost-utility
analysis (CUA). In the CEA, the main effectiveness outcome will be asymptomatic remission at
24-month follow-up, defined as PHQ-9 <5 and GAD-7 <5. By comparing the differences in costs and
asymptomatic remission between the intervention group and the control group, the incremental
cost-effectiveness ratio (ICER) will be calculated as ICER = ΔCost / ΔAsymptomatic Remission Rate,
representing the additional cost required for each additional participant achieving asymptomatic
remission.
In the CUA, health utility values will be obtained using the EQ-5D-5L with the Chinese value set.
Linear interpolation will be used to estimate changes in health utility over the follow-up period, and
QALYs will be calculated for each participant. The incremental cost-utility ratio (ICER = ΔCost /
ΔQALY) will then be calculated to evaluate the cost-utility of the intervention in terms of health gains.
To assess uncertainty and robustness, 5,000 non-parametric bootstrap replications will be used to
construct cost-effectiveness planes and cost-effectiveness acceptability curves (CEACs). If necessary,
Seemingly Unrelated Regression (SUR) models will be used to jointly model costs and QALYs while
adjusting for covariates such as age, sex, and baseline symptom severity. All costs and utilities will be
discounted in accordance with health economic evaluation standards (e.g., annual discount rate of 3%),
and final results will be reported from the societal perspective.
5.4 Additional Analyses

(1) Subgroup analyses
To explore potential heterogeneity in intervention effects across different populations, pre-specified
exploratory subgroup analyses will be conducted. Subgroup variables will include baseline symptom
severity (e.g., PHQ-9 and GAD-7 scores), demographic characteristics (e.g., sex and age strata), and
adherence-related digital intervention factors (e.g., number of days using the mini-program). All
subgroup analyses will be conducted by including a group × subgroup interaction term in the main
model. Because these analyses are exploratory, the results will be used for hypothesis generation only
and not as the basis for primary inference.
(2) Mechanism analyses
To explore potential mechanisms of AI-assisted online mindfulness intervention, exploratory analyses
will be conducted on several mechanism-related variables.
First, the longitudinal trajectories of mechanism variables such as ELS, DTS, DIS, EEBS, BEAQ, CFI,
and FFMQ will be analyzed to examine the effects of the intervention on key psychological processes.
Second, mediation analyses will be conducted using structural equation modeling (SEM) and related
methods to evaluate potential mediating pathways linking intervention group to clinical outcomes such
as PHQ-9 and GAD-7 through mechanism variables.
Third, moderation analyses will be conducted to test whether baseline variables, such as baseline
FFMQ scores, influence the magnitude of intervention effects.
In addition, the study will explore the associative structure between mechanism variables and symptom
indicators. To this end, longitudinal network analysis methods, including but not limited to
cross-lagged panel networks, network intervention analysis, and time-varying longitudinal network
models, will be used to analyze the temporal dynamic interactions between mechanism variables and
clinical symptoms from a systems science perspective. By estimating temporal networks and
contemporaneous networks, the study aims to identify core bridge nodes within the network. Network
Comparison Tests (NCTs) based on permutation procedures will also be used to compare differences
between the intervention and control groups in network structure, connectivity strength, and patterns of
evolution, so as to explore the dynamic restructuring effects of the intervention on the

symptom-mechanism system.
All mechanism-related analyses are exploratory in nature and are intended primarily to generate
hypotheses for future research rather than serve as the basis for primary inference.
5.5 Harms
All adverse events (AEs) and serious adverse events (SAEs) will be summarized descriptively by event
type and frequency. If event counts permit, generalized estimating equations (GEE) or logistic
regression models will be used to compare group differences in the incidence of SAEs.
5.6 Missing Data Handling
Primary outcome analyses using linear mixed-effects models (LMMs) or Cox proportional hazards
models, as well as secondary outcome analyses using linear mixed-effects models (LMMs) and
generalized linear mixed models (GLMMs), will be conducted using likelihood-based estimation
methods. Under the MAR assumption, these models can directly use all available observed data to
yield unbiased estimates. Therefore, the primary analyses will not involve explicit imputation of the
raw data.
For analyses involving multivariable joint modeling, such as health economic outcomes or mediation
analyses, multiple imputation will be used to handle cross-sectional missingness. The imputation model
will include group, time, baseline scores, and demographic variables, and 20 imputed datasets will be
generated. Results will be pooled according to Rubin’s rules.
In addition to per-protocol (PP) analyses as sensitivity analyses, if the missingness rate for the primary
outcomes exceeds 10%, missing-data sensitivity analyses will also be conducted. Specifically,
assumption-driven multiple imputation will be performed by applying different offsets to missing
values in the intervention group to simulate unfavorable scenarios in which dropouts derive less benefit
than predicted by the model. The offset will then be gradually increased until the study conclusions
reverse, that is, a tipping-point analysis will be performed. The plausibility of such offsets in clinical
reality will then be assessed in order to examine the robustness of the study conclusions under Missing

Not at Random (MNAR) assumptions.
6. Ethics
6.1 Research Ethics Approval
The study will not commence until ethical approval has been obtained from the relevant ethics
committee. All study activities will comply with the Declaration of Helsinki and the principles of the
International Council for Harmonisation Good Clinical Practice (ICH-GCP) guideline.
If substantial amendments to the study protocol are made, they will be resubmitted for ethics approval.
Information in the trial registry will also be updated in a timely manner. If relevant modifications affect
participants’ rights or interests, participants will be informed appropriately.
6.2 Additional Consent Provisions for Ancillary Studies
This study will not collect biological samples. If ancillary studies related to the trial are conducted in
the future, only de-identified study data will be used, and such studies will require additional ethics
approval.
Participants may choose whether they consent to the use of their anonymized data for future related
research. This decision will not affect their right to participate in the present trial or the treatment they
receive.
7. Patient and Public Involvement
Although no formal Patient and Public Involvement (PPI) committee was established for this study,
several small-scale feasibility and preliminary effectiveness tests were conducted during the
preparatory phase in order to gather feedback from the target population regarding the digital
recruitment process, online course experience, and questionnaire burden.
Based on this feedback, adjustments were made to the presentation format of the WeChat

mini-program content, the pacing of check-in reminders, follow-up frequency, and questionnaire length,
with the aim of improving acceptability and adherence.
During the trial, the research team will continue collecting participant feedback on intervention
experience, technical usability, and potential adverse events in order to further evaluate feasibility and
safety. Study findings will be made publicly available after publication, but patients and members of
the public will not be directly involved in protocol design, data interpretation, or manuscript
preparation.
8. Trial Registration
This trial will be prospectively registered on ClinicalTrials.gov, and the registration number will be
added to the protocol and related documents once obtained. Registration will be completed before
enrollment of the first participant.
9. Protocol and Statistical Analysis Plan
The trial protocol, including the statistical analysis plan (SAP), will be made publicly available in
open-access form on the journal website following publication of the protocol paper. Any substantial
updates to the protocol or SAP thereafter will also be archived on ClinicalTrials.gov.
10. Data Sharing
Because the study involves sensitive mental health data from participants and is subject to ethical
restrictions, there are currently no plans to publicly share individual-level data.
For reasonable academic requests, researchers may contact the corresponding author. Such requests
will be jointly reviewed by the study team and the ethics committee, and, where appropriate, data
summaries or statistical support may be provided.

11. Funding and Support
This study has not received funding from any commercial organization, healthcare company, or
business entity related to digital mindfulness products.
12. Conflicts of Interest
All investigators declare that they have no financial conflicts of interest. The intervention content
(MIED) and digital platform used in this study were independently developed by the research team and
are not associated with any commercial interests.
13. Dissemination Policy
The research team plans to disseminate study findings to participants, healthcare professionals, and the
public through the following channels:
(1)
Updating trial results on ClinicalTrials.gov
(2)
Publishing the main study findings and long-term follow-up results in peer-reviewed journals
(3)
Providing participants with a plain-language summary of the main findings
(4)
Reporting study findings at academic conferences, clinical training events, and professional
meetings related to mental health
14. Discussion
The present study proposes a 24-month assessor-blinded, parallel-group, two-arm randomized
controlled trial to systematically evaluate the long-term effectiveness, cost-effectiveness, and feasibility
of an AI-assisted internet-based Mindfulness Intervention for Emotional Distress (iMIED) in
individuals with subclinical emotional distress. This study has clear practical relevance. Anxiety and
depression have become major contributors to the global burden of disease, while existing mental
health service systems remain largely characterized by a pattern of emphasizing treatment over
prevention. The WHO World Mental Health Report (2022) and subsequent commentaries have

emphasized that service priorities should be shifted further upstream toward health promotion, early
identification, and preventive intervention, particularly through the development of simple, low-cost,
and scalable community-based and digital intervention models .
By focusing on individuals with PHQ-9 or GAD-7 scores ≥5, the present study addresses a population
of substantial preventive importance. Recent individual participant data meta-analysis has shown that
psychological interventions for subthreshold depression can significantly reduce the subsequent risk of
major depressive disorder, supporting the view that the subclinical stage constitutes a critical and
modifiable intervention window . Against this background, the study is not limited to mean symptom
changes, but also incorporates more clinically interpretable categorical outcomes, including
asymptomatic remission and reliable deterioration. This approach helps evaluate the value of the
intervention from the perspective of whether it produces meaningful clinical change and is thus more
closely aligned with the needs of public health and service decision-making.
Another important contribution of this study lies in its attempt to address two longstanding evidence
gaps in digital mental health intervention research: insufficient evidence on long-term efficacy and
insufficient evidence from health economic evaluation. In recent years, multiple high-quality reviews
and meta-analyses have shown that digital mental health interventions are generally capable of
improving depression and anxiety symptoms, but effect sizes are typically small to moderate and vary
according to study population, intervention format, guidance intensity, and control condition. At the
same time, important limitations remain regarding long-term follow-up, real-world implementation,
and overall evidence quality . For mindfulness-based apps specifically, the latest meta-analysis
likewise indicates some short-term effects on anxiety and depression, yet high-quality trials with
extended follow-up remain relatively scarce . Accordingly, the present study includes a 24-month
follow-up period and incorporates both cost-effectiveness and cost-utility analyses into the a priori
design, thereby offering a more complete evidence base for evaluating whether digital mindfulness
intervention is not only effective but also worthy of wider dissemination.
From a design perspective, the study has several notable strengths. First, it adopts a strongly pragmatic
design, with the control group permitted to access usual mental health resources freely. This feature
brings the results closer to real-world service conditions and facilitates evaluation of the incremental
value of iMIED within existing public health systems. Second, the study simultaneously includes

continuous outcomes, categorical clinical outcomes, economic indicators, implementation indicators,
and mechanism variables, thereby allowing a comprehensive evaluation of the intervention from the
perspectives of overall symptom change, clinically meaningful improvement, resource allocation
efficiency, real-world scalability, and potential mechanisms of action. Third, the study embeds
AI-assisted support into a self-guided mindfulness intervention. In theory, such support may improve
engagement and adherence by providing timely feedback, personalized explanations, and a greater
sense of accompaniment during practice. Existing studies suggest that certain interactive design
features in digital mental health interventions, especially those involving chatbot technologies, may be
associated with greater symptom improvement; however, evidence remains insufficient regarding
which forms of personalized support are genuinely effective. Therefore, the present study’s tracking of
AI interaction frequency, intensity of personalized feedback, and usage duration may also provide
useful evidence for future optimization of digital interventions .
Nonetheless, several limitations should be acknowledged. First, as with most digital mental health
intervention trials, full blinding of participants and the intervention process is not possible, and
expectancy effects and reporting bias may therefore remain. Second, the study relies primarily on
self-report measures to assess anxiety, depression, and related psychological mechanisms. Although
PHQ-9 and GAD-7 have strong psychometric properties, they cannot fully substitute for structured
clinical interviews. Third, the control group is permitted unrestricted access to usual care resources.
While this enhances external validity, it may also increase heterogeneity in treatment exposure and
thereby attenuate between-group differences. Fourth, digital mental health interventions commonly
face challenges related to fluctuating engagement, declining adherence, and long-term attrition, and
recent reviews have identified engagement and retention as critical bottlenecks in the real-world
dissemination of digital interventions . Although the present study seeks to mitigate these issues
through systematic reminders, financial compensation, behavioral usage tracking, and sensitivity
analyses, findings should still be interpreted with caution.
At the health economic level, this study also has potential value. Existing reviews suggest that internet-
and mobile-based interventions for depression and anxiety generally show promising cost-effectiveness,
but conclusions remain influenced by differences in evaluation frameworks, cost definitions, control

conditions, and target populations. In particular, evidence remains limited for digital mental health
promotion and prevention programs targeting non-clinical or subclinical populations . By conducting
both cost-effectiveness and cost-utility analyses from a societal perspective and using standard methods
such as QALYs, ICERs, bootstrap resampling, and cost-effectiveness acceptability curves, the present
study is expected to generate relatively high-quality local evidence regarding the resource allocation
value of digital mindfulness intervention in the Chinese context. If iMIED is shown to produce stable
clinical benefits and improved quality of life at a relatively low incremental cost, its dissemination
across universities, communities, internet platforms, and stepped-care mental health service systems
may carry considerable practical significance.
Overall, the present study will evaluate the long-term efficacy, mechanism pathways, implementation
feasibility, and economic value of an AI-assisted digital mindfulness intervention in individuals with
subclinical emotional distress through an extended follow-up period, a strongly pragmatic design, and a
comprehensive outcome framework. Regardless of whether the results support the superiority of
iMIED over the control condition, the study will provide important evidence for early intervention
strategies targeting subclinical emotional distress, the optimization of digital mental health services,
and the standardized development of AI-assisted psychological intervention. In the context of the
current transformation in mental health care from disease treatment toward risk prevention and health
promotion, the study has clear methodological and public health significance.
Appendix 1. 49-Day iMIED Check-in Schedule
Day
Theme
Mindfulness Practice
Homework
Week 1
1
Course Introduction and
Goal Setting
Awareness of Breathing
(Short)
1. Identify situations involving distress
avoidance 2. Choose an appropriate goal
2
Motivation and
Commitment
Awareness of Breathing
(Short)
1. Complete the decision worksheet 2.
Complete the coping plan for difficulties
3. Prepare 3 raisins for tomorrow
3
Mindful Eating of
Raisins
Mindful Eating of
Raisins
1. Set a fixed practice time for this week
2. Engaging in life: mindful
toothbrushing
4
Formal Practice 1: Body Body Scan
1. First body scan practice 2. Engaging in

Day
Theme
Mindfulness Practice
Homework
Scan
life: mindful face washing
5
Core Elements of
Mindfulness:
Awareness and
Acceptance
Body Scan
Engaging in life: mindful dressing
6
Nine Attitudes of
Mindfulness Practice
Body Scan
1. Mindfulness attitudes2 . Engaging in life:
mindful copying/writing
7
Mindful Communication Body Scan
Engaging in life: mindful communication
Week 2
8
Formal Practice 2:
Awareness of Breathing
Awareness of Breathing
1. First awareness of breathing practice 2.
Set a practice time 3. Engaging in life:
others
9
Understanding the
Function of Emotions
Body Scan
1. Reflection question
10
Deconstructing the Three
Components of Emotion Awareness of Breathing
1. Nine-dot diagram
11
Thoughts: Judgments and
Interpretations of Events Body Scan
1. Reflection question: understanding
thoughts
12
Feelings: Physical and
Psychological
Experiences at the Time
Awareness of Breathing
1. Reflection question: understanding
feelings
13
Behavior: Reactions at
the Time
Body Scan
1. Reflection question: understanding
behavior
14
Emotion Log
Awareness of Breathing
1. Complete one emotion log
Week 3
15
Mindfulness Practice 3:
Mindful Stretching
Mindful Stretching
1. Practice experience, insights, or
questions 2. Emotion log
16
Sources of Emotional
Distress: The
Biopsychosocial Systems
Model
Mindful Awareness of
Breathing
1. Practice experience, insights, or
questions 2. Emotion log3. Reflect on the
sources of your own emotional distress
17
The MIED Diamond
Model
Mindful Stretching
1. Practice experience, insights, or
questions 2. Emotion log3. Assess your
current condition from four dimensions
18
Engaging in Life
Mindful Awareness of
Breathing
1. Practice experience, insights, or
questions 2. Emotion log
19
Regulating Distress
Tolerance
Mindful Stretching
1. Practice experience, insights, or
questions 2. Emotion log
20
Regulating Emotional
Mindful Awareness of
1. Practice experience, insights, or

Day
Theme
Mindfulness Practice
Homework
Behaviors
Breathing
questions 2. Emotion log
21
Enhancing Cognitive
Flexibility
Mindful Stretching
1. Practice experience, insights, or
questions 2. Emotion log 3. Weekly
summary
Week 4
22
Setting Challenging
Tasks
Choose one of breathing,
body scan, or stretching +
Three-Step Breathing
Space
1. Set a fixed practice time for this week
2. Set a challenging task
23
Increasing Tolerance for
Uncomfortable
Sensations
Choose one of breathing,
body scan, or stretching
1. Reflect on what bodily sensations you
find difficult to tolerate
24
Interoceptive Exposure:
Rapid Breathing Exercise
Choose one of breathing,
body scan, or stretching
1. Rapid breathing exercise
25
Midterm Summary
Choose one of breathing,
body scan, or stretching
1. Midterm summary 2. Evaluate your
progress across these four dimensions and
reflect on the areas in which you have
improved since the start of the course 3.
Rapid breathing exercise
26
Choosing an
Interoceptive Exposure
Exercise That Suits You
Choose one of breathing,
body scan, or stretching
1. Find an exercise that can evoke
sensations you find difficult to tolerate 2.
Review completion of the challenging task
27
Increasing Tolerance for
Negative Emotional
Experiences
Choose one of breathing,
body scan, or stretching
1. Interoceptive exposure practice 2.
Review completion of the challenging task
28
Loving-Kindness
Meditation
Choose one of breathing,
body scan, or stretching +
Loving-Kindness
Meditation
1. After today’s mindfulness practice,
complete the loving-kindness meditation
practice 2. Review completion of the
challenging task 3. Weekly summary
Week 5
29
Challenging Task 2
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Set this week’s challenging task
30
Mindful Walking
Mindful Walking +
Loving-Kindness
Meditation
1. Do your first mindful walking practice in
this course 2. Interoceptive exposure
practice
31
Identifying Excessive
Emotional Behaviors
Choose one of standing
(or seated) stretching or
walking +
1. Reflect on whether you have excessive
emotional behaviors

Day
Theme
Mindfulness Practice
Homework
Loving-Kindness
Meditation
32
A Closer Look at the
Three-Step Breathing
Space
Choose one of awareness
of breathing or walking +
Three-Step Breathing
Space
1. Make good use of the Three-Step
Breathing Space 2. Interoceptive exposure
practice
33
Revisiting the Regulation
of Emotional Behaviors
Choose one of body scan
or walking + Three-Step
Breathing Space
1. Review completion of the challenging
task 2. Reflect on what excessive emotional
behaviors you may have and how action,
especially actions that engage you in life,
can gradually reduce them
34
Example of Regulating
Emotional Behaviors:
Coping with
Procrastination
Choose one of breathing,
walking, or stretching +
Three-Step Breathing
Space
1. Identify any excessive procrastination
behaviors you may have and think of ways
to address them 2. Review completion of
the challenging task 3. Interoceptive
exposure practice
35
Facing More
Distress-Avoidant
Situations
Choose one of breathing,
walking, or stretching +
Three-Step Breathing
Space
1. Review completion of the challenging
task 2. Weekly summary
Week 6
36
Challenging Task 3
Mindful Walking +
Three-Step Breathing
Space
1. Set this week’s challenging task 2. Make
good use of the Three-Step Breathing Space
37
Learning to Identify
Thoughts
Mindful Walking +
Three-Step Breathing
Space
1. Learn to distinguish between our
thoughts and objective facts 2. Make good
use of the Three-Step Breathing Space
38
Treating Thoughts as
Thoughts
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Practice mindful awareness of breathing
or mindful walking to improve the ability to
see thoughts as thoughts 2. Make good use
of the Three-Step Breathing Space 3.
Integrate mindfulness more into daily life
and work
39
The Scope of Acceptance
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Understand the scope of acceptance in
the MIED course 2. Integrate mindfulness
more into daily life and work 3. Make good
use of the Three-Step Breathing Space
40
Identifying Thinking
Traps
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Review completion of the challenging
task 2. Identify thinking traps

Day
Theme
Mindfulness Practice
Homework
41
Cognitive Reappraisal
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Review completion of the challenging
task 2. Reevaluate your thoughts
42
Enhancing Cognitive
Flexibility and Engaging
in Life
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Review completion of the challenging
task 2. Weekly summary
Week 7
43
Challenging Task 4
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Set this week’s challenging task
44
Facing Difficult
Situations Directly
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Review completion of the challenging
task
45
Reviewing What Has
Been Learned
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Reflect on your understanding and
application of the course content/practices
46
Summarizing Gains and
Their Sources
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Look back on the journey so far
2. Understand the sources of your gains
47
Setting New Goals
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Task: future goals
48
Tips for Sustaining
Mindfulness Practice
Choose one of breathing,
stretching, or body scan +
Loving-Kindness
Meditation
1. Write down your personal mindfulness
toolkit for persistence
49
The End Is Also the
Beginning
Choose one of breathing,
stretching, or body scan +
Three-Step Breathing
Space
1. Task: mindfulness integrated into daily
life