JMIR FORMATIVE RESEARCH

Toh et al

Original Paper

Efficacy of the Mental Health App Intellect to Reduce Stress:
Randomized Controlled Trial With a 1-Month Follow-up
Sean Han Yang Toh1, BA; Jessalin Hui Yan Tan2, BA; Feodora Roxanne Kosasih1, BA; Oliver Sündermann1, PhD
1Intellect Pte Ltd, Singapore, Singapore

2

Department of Psychology, National University of Singapore, Singapore, Singapore

Corresponding Author:
Oliver Sündermann, PhD
Intellect Pte Ltd
171 Tras St, #02-179 Union Building
Singapore, S079025
Singapore
Phone: 65 93571995
Email: oliver@intellect.co

Abstract
Background: Excessive stress is a major global health concern, particularly in young adults. Short skills-focused self-guided
interventions (SGIs) on smartphones are a scalable way to improve stress-coping skills at the population level.
Objective: In this randomized controlled trial, we aimed to examine the possible efficacy of a recently developed stress-coping
SGI (Intellect) in improving psychological distress, relative to an active control group and 2 potential moderators of this predicted
relationship (ie, psychological mindedness [PM] and coping self-efficacy [CSE]).
Methods: University students (N=321) were randomly assigned to either an 8-day SGI on stress-coping or an active control
group. Self-reported measures were obtained at baseline, after the intervention, and at the 1-month follow-up. The primary outcome
was psychological stress (Psychological Stress Measure-9). Secondary outcomes were anxiety (Generalized Anxiety Disorder-7)
and depressive symptoms (Patient Health Questionnaire-9). PM and CSE were assessed as potential moderators at baseline.
Results: The final sample (n=264) included 188 (71.2%) female, 66 (25%) male, 7 (2.7%) nonbinary, and 3 (1.1%) others
participants with a mean age of 22.5 (SD 5.41) years. The intervention group reported significantly lower perceived stress (partial
eta–squared [ηp2]=0.018; P=.03) and anxiety (ηp2=0.019; P=.03) levels after intervention relative to the active control group.
The effects on perceived stress levels remained statistically significant at the 1-month follow-up (ηp2=0.015; P=.05). Students
with the lowest CSE and highest PM experienced the fastest decline in perceived stress levels (β=6.37, 95% Cl 2.98-9.75).
Improvements in anxiety levels were not observed at 1-month follow-up. Similarly, no intervention effects were found for
depression levels at postintervention and follow-up periods.
Conclusions: This study provides evidence that the Intellect stress-coping SGI is effective in reducing perceived stress andanxiety levels among university students. Mobile health apps are brief, scalable, and can make important contributions to public
mental health.
Trial Registration: ClinicalTrials.gov NCT04978896; https://www.clinicaltrials.gov/ct2/show/NCT04978896(JMIR Form Res 2022;6(12):e40723) doi: 10.2196/40723
KEYWORDS
mobile health; mHealth; randomized controlled trial; RCT; self-guided interventions; cognitive behavioral therapy; CBT; stresscoping; stress management; university students; psychological mindedness; coping self-efficacy; mobile phone

Introduction
Background
Psychological stress occurs when one’s perceived demands
exceed one’s perceived capacity to cope [1,2]. Excessive andhttps://formative.jmir.org/2022/12/e40723

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prolonged stress can have multiple negative health effects,
including heightened risk of obesity [3], impaired working
memory [4,5], and cardiac arrest [6]. Adverse stress effects on
mental health are also common, including an increased risk ofdepression and anxiety [7], insomnia [8], and psychosis [9].
Excessive stress is widely recognized as a major health burden
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JMIR FORMATIVE RESEARCH[10]. Young adults and late adolescents are at particular risk ofexperiencing heightened stress levels owing to life transitions
from childhood to adulthood and taking on new responsibilities[11,12] or uncertainties relating to career prospects [13].
Equipping young adults with effective stress-coping skills is a
viable attempt to lower the public burden of excessive stress[9]. Digital tools such as mobile health (mHealth) apps are likely
going to play an increasingly important role in public mental
health [14].
Self-guided interventions (SGIs) on smartphones are easily
accessible, affordable, flexible, and convenient to use [15]. SGIs
can also promote well-being and prevent illness by equipping
individuals with the skills to address minor stressors before they
evolve into larger problems [16]. Various randomized controlled
trials (RCTs) have examined the efficacy of SGIs on mental
health outcomes [17], and meta-analyses have confirmed that
SGIs can improve symptoms of anxiety and depression acrossage groups [18,19]. A recent meta-analysis by Linardon et al[20] specifically found that SGIs were efficacious in reducing
stress levels. However, the effect sizes for most interventions
on stress (Hedges g=0.35), anxiety, and depression were rather
small (Hedges g=0.19-0.21) and inconsistent across studies[18,19]. Although many SGIs are freely available onsmartphones, only a few are supported by theoretical andempirical data [21]. In a review of 62 stress management SGIs[22], a quarter of them did not use any evidence-based stress
reduction strategies. These results correspond to a separate
review of 60 stress-coping SGIs whereby a third of them either
failed to provide any empirical strategies or delivered a different
strategy than that stated in its description [23]. In both reviews,
most of the remaining SGIs focused on momentary breathing
and mindfulness exercises, which produced short-term modest
effects on stress and anxiety levels. Only a minority of SGIs
involved evidence-based interventions such as cognitive
behavioral therapy (CBT) [21,23]. Indeed, the meta-analysis
by Linardon et al [20] found substantially larger effect sizes for
SGIs that involved CBT-based practices compared with waitlist
control group. These CBT-based effects on stress, anxiety, andmood were also superior to alternative interventions and could
be strengthened when combined with brief professional guidance
within the mHealth app. The same authors also concluded that
research on the overall efficacy and dissemination ofevidence-based SGIs is in its infancy because of the small
number of studies, especially those involving younger adults(ie, 5 studies). In addition, the meta-analysis by Linardon et al[20] highlighted methodological problems with RCTs involving
stress-coping SGIs, such as high risk of bias and lack offollow-up data.
Furthermore, very little is known about who benefits the mostfrom stress-coping SGIs [24]. Previous authors were unable toanalyze any moderators of web- and computer-based stressinterventions owing to the “lack and inconsistency of
information provided by the [reviewed] studies” [25]. To ourknowledge, only Coudray et al [26] evaluated moderators in asample of 920 college students and found that those with lower
perceived present control (ie, aspects of stressors perceived tobe controllable in the present) experienced significantly greater
reduction in stress after 1 to 3 weeks of web-based stress
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management interventions compared with those with higher
perceived present control. The authors concluded their findings
by encouraging similar research on moderators using diverse
samples. A potential moderator that may influence the efficacy
of CBT-based SGIs on mental health outcomes is coping
self-efficacy (CSE). CSE refers to one’s confidence in coping
strategies [27], particularly in times of hardship [28,29] andthreat [30]. Slightly different from perceived control, whichrefers to one’s perception of the availability of any effective
response, CSE describes one’s confidence in their ability toactually effect that response [31]. Some RCTs have found CSE
to be a mediator of the relationship between CBT-based SGIs
and improved stress, anxiety, and depression outcomes [32-34],
but researchers are less clear about CSE as a moderator. For
instance, individuals with higher perceived confidence in copingwith stressors were expected to maximize their gains from
CBT-based treatments [35], or similar to the findings on
perceived present control, it is also possible for individuals who
perceive that they need more help with coping have higher
motivation to benefit from using a CBT-based SGI, relative tothose with higher CSE. Another potential moderator of therelationship between CBT-based SGI and outcomes is
psychological mindedness (PM). PM refers to one’s
predisposition to be aware of, assess, and reflect on one’s mental
states and behaviors cognitively and emotionally [36,37].
Self-reflective practices within CBT were involved in improving
depression symptomatology in depressive disorders [38], anxiety
disorders [39], and social phobia [40], as it is thought to enhance
individuals’ abilities and frequency in self-monitoring andself-evaluating their cognitions, emotions, and behaviors.
Despite PM being central to the successful practice of CBT[41], very little is known about the differences in an individual’scapacity for self-reflection and insight that could moderate theefficacy of the modality [36,42]. Wiles et al [43], for example,
did not find PM to be a treatment moderator in CBT, although
they acknowledged that their small sample size was inadequate
for testing interaction effects. Some studies have recommended
higher initial levels of self-reflection and insight that may predict
faster responses to CBT as potential avenues for future research[36,42]. These authors speculated that metacognitive processes
such as higher PM might predispose an individual’s capacity
to appreciate and understand the cause-and-effect concepts of
CBT-based techniques. As CBT principles are often adopted
by evidence-based SGIs, it would be interesting to test how
initial levels of PM can influence CBT-based therapy outcomes,
as it would allow personalizing CBT-based SGI and optimizing
treatment outcomes.

Objectives
Using a randomized controlled design, we examined the possible
efficacy of a recently developed CBT-based stress-coping SGI
in the mHealth app (Intellect) on a sample of Singaporean
university students compared with an active control group. We
first hypothesized that participants in the intervention group(stress SGI) would report lower perceived stress than the active
control group after the intervention. Although the effects ofself-guided CBT interventions for stress coping are typicallysmall, we predicted possible gains for the intervention group to
be maintained at 1-month follow-up, in line with related RCTs
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that also found small effects present at follow-up assessments[44,45].
Second, we predicted that participants in the intervention groupwould report lower anxiety and depressive symptoms than theactive control group after the intervention. Third, we
hypothesized that users with higher levels of PM and CSE would
experience the greatest reduction in perceived stress levels. As
studies have shown that individuals who benefited most fromstress interventions typically also experienced greater
improvement in anxiety and depressive symptoms [46], we also
predicted that this moderation effect by CSE and PM also
applies to secondary outcomes.

Methods
Study Design
This study was an RCT with two groups: (1) an 8-day
stress-coping self-guided program (intervention group) and (2)an 8-day cooperation self-guided program (active control group).
A 2 × 3 mixed factorial experimental design was used withcondition (intervention vs active control) as a between-groupfactor and time of assessment (baseline vs postintervention vsToh et al
1-month follow-up) as a within-group factor. The primarydependent variable measured was perceived stress levels. The
secondary dependent variables were depression and anxiety
levels. The CSE and Psychological Mindedness Scale (PMS)
were also used for independent moderation analyses.

Recruitment and Study Participants
A total of 321 participants were recruited through the
Psychology Department’s Research Participation Programme
and the university’s research recruitment platform. Recruitment
posters comprising the study procedures, inclusion criteria, andreimbursement for participation were distributed on the web
and among the faculty. Of the 321 participants, 46 (14.3%)
participants were excluded owing to withdrawal and failing data
quality checks, whereas 3 (0.9%) participants were lost tofollow-up (Figure 1). Of the 321 participants, the final sample
of 264 (82.2%) participants was predominantly female (188/264,
71.2%), with a mean age of 22.45 (SD 5.41; range 18-59) years.
All the participants were undergraduate students who were able
to read and understand English. Elevated perceived stress was
not a requirement for inclusion. Participants also received either
course credits or monetary reimbursement for their participation.

Figure 1. CONSORT (Consolidated Standards of Reporting Trials) flowchart. SGI: self-guided intervention.

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Power Calculation
Most studies that investigated app-based stress-coping programs
found small to moderate effect sizes for the primary outcome
measures of stress, mood, and anxiety [47]. Subjecting a small
effect size of Cohen f=0.1, an α level of .05, and a power of 0.9
to G*Power revealed a minimum sample size of 214. Web-based
studies are prone to attrition, and in line with similar studies,
we expected 30% to 50% attrition rate [47,48] and consideredthat 10% of the questionnaire data may be inconsistent or invalid
as commonly found in web-based survey studies [49]. Thus,
we aimed to recruit 321 participants.

Procedure
Participants signed up for the study on the university’s
recruitment sites via a link that directed them to the web-based
survey platform hosted on Qualtrics (SAP America Inc). Upon
consenting to the web-based Participants Information Sheet,participants completed the primary outcome measure on
perceived stress (Psychological Stress Measure [PSM]; PSM-9),
secondary outcome measures on mental health (Patient Health
Questionnaire [PHQ]; PHQ-9 and Generalized Anxiety Disorder[GAD]; GAD-7), CSE (Coping Self-Efficacy Scale [CSES]),
PMS, and demographic information.
Next, participants were randomly assigned to either theintervention or active control condition using simple
randomization procedures through the Qualtrics software. They
were not informed about the conditions they were allocated toand the real nature of the study. Instead, they were informedthat the study would examine the efficacy of SGIs in promoting
well-being. They were then guided to download the mHealthapp Intellect on their personal smartphones from the Apple App
Store (Apple Inc) or Google Play Store (Google LLC).
According to their assigned conditions, a number code wasprovided to them to unlock the app. Participants in theintervention condition participated in the 8-day stress-coping
program, whereas those in the active control condition took partin the 8-day cooperation learning program. Both programs
involved fulfilling a series of tasks aimed at improving
well-being. This included content education and short daily
activities, averaging 5 minutes. To promote adherence to thehttps://formative.jmir.org/2022/12/e40723

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study, standardized daily reminders to complete the program
were sent via SMS text messages by the researcher toparticipants. Participants were only allowed to proceed onto thenext page of the app after they had completed the preceding
exercises. It was expected that every participant would complete
all the self-guided daily activities. All the participants were
instructed to refrain from using any other SGIs that affectwell-being other than the given SGI throughout the entire
duration of the study, lasting from the beginning until the endof the 1-month follow-up. This minimized the potential
confounding effects.
Upon completion of the 8-day program through technicalverification, participants received a survey link to complete theoutcome measures and the App Engagement Scale (AES). A
month after the completion of the SGI, participants were
provided with a survey link to complete the outcome measures.
Reimbursement was given upon completion of thepostintervention measures with either 3 course credits or
Singapore $10 (US $7.5) and after 1-month follow-up measureswith an additional 1 course credit or Singapore $5 (US $3.75).
University students intending to major in psychology wererequired to collect a minimum of 28 course credits over thecourse of an academic year. After the follow-up assessment,
participants in the active control group were also given accessto the stress-coping SGI.

Interventions
Stress-Coping ProgramThis was an 8-day program that provided psychoeducation on
the negative effects of stress and effective stress management
skills. Table 1 provides an overview of the 6 topics and content
covered. Guided by the principles of CBT, the program helped
users to identify and change unhelpful thought patterns andbehaviors related to stress. Participants engaged in a series ofdaily exercises involving reflection and mindfulness, and they
were guided to identify and type down their stressors, negative
thoughts associated with the stressors, and positive affirmations.
Participants were also taught breathing exercises and encouraged
to practice them (Table 1 presents an overview of the program).

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Table 1. Overview of stress-coping program.
Topic

Content

Topic 1: How stress affects the body••

Be able to identify personal stressors
Understand the difference between internal and external stressors

Topic 2: Saying “no” to burnout••

Understand the impact of stress on well-being
Understand and identify emotional, physiological, and behavioral signs and symptoms of stress

Topic 3: Learning to manage stress••In-depth introduction of stress
Understand that stress can be healthy if kept at manageable levels

Topic 4: Self-care of the mind

•
•
•
•

Understand that internal stressors are self-induced feelings and thoughts
Be able to identify negative thoughts that lead to stress
Develop skills to challenge negative thoughts
Develop skills to engage in positive affirmation

Topic 5: Self-care of the body

•
•
•
•

Understand that external stressors are events or situations caused by the environment
Understand the importance of prioritizing one’s own well-being in stressful situations that cannot be
controlled
Develop skills in communicating negative feelings in stressful situations
Be able to manage stress with the help of deep breathing exercises•

Recap of the entire learning path and key information in each learning session

Topic 6: Continuing daily self-care

Cooperation Learning Program
The 8-day program on cooperation functioned as the active
control group. This program provided psychoeducation through5 topics for participants to understand and improve collaborationand interpersonal relationships. Short quizzes andfeedback-giving exercises were included (Table 2 provides an
overview of the program). The duration of the cooperation SGI
matched the stress SGI in terms of time and effort required tocomplete the program.

Table 2. Overview of cooperation program.
Topic

Content

Topic 1: What is “cooperation”?

••

Be able to identify personal preferences to cooperation
Understand the differences between cooperation and conformity

Topic 2: Focusing on the bigger picture•••

Cultivate awareness of collective goals
Be able to identify possible strengths in a team member that can propel toward the collective goal
Be able to identify possible weaknesses in a team member that can hinder team efforts

Topic 3: Understanding group dynamics ••Topic 4: Building positive relationships

•
•
•
•
•

Topic 5: Learning to work together•

In-depth introduction of group dynamics
Be able to identify the correct scenarios that support or threaten group dynamics
Understand that healthy relationships, which strengthen group dynamics, are important drivers towardattaining team’s objectives
Learn 2 methods that can build empathy and trust to improve group relationships: support and feedback
Offering support: develop skills to manage tone, body language, and learn a set of encouraging words
Offering support: identify ways to empower team members’ strengths and to cover each other’s
weaknesses or blind spots
Providing feedback: in-depth introduction of the many ways one can provide constructive feedback
to team members
In-depth recap of the entire learning path and key information in each learning session

Measures
Primary Outcome Measure
PSM-9 [50,51] measures the affective, cognitive, behavioral,
and somatic components of psychological stress. This is a 9-item
self-report measure using an 8-point scale (ranging from 1=“Notat all” to 8=“Extremely”). Some examples of the items include“I feel calm” and “I feel a great weight on my shoulders.” As
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the midpoint of this scale is 4=“A bit,” an overall score of ≥36
on this measure would indicate that the overall sample is atelevated stress. Higher total scores reflect more stress symptoms.
The PSM-9 has an acceptable internal consistency with
Cronbach α ranging from .74 to .78 in this study.

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Secondary Outcome Measuresinvalid data were sequentially identified in 2 steps and removed.
First, submissions with an overall response time of <800 seconds
were flagged, as it indicated that participants sped through thequestionnaire and downloading the app, suggesting that they
may not have participated properly. The subsequent checks
confirmed whether the flagged responses were invalid. Next,
data with strings of identical responses (eg, selecting “agree”
to all items) for the entire scale of any of the self-reportmeasures were excluded. Subsequently, several attention checks
were used in the self-report scales [58]. For example, in the
PSM-9, items 1 and 6 were reverse-coded. A contradiction was
detected in the participants’ self-report if responses to these 2
items were congruent with the other items on the scale [51].
Similarly, in PMS, responses to items 5 and 23 should be similar
to each other but opposite to that of item 35 [37]. Contradictory
responses were considered invalid and were subsequently
removed. Importantly, removing these responses did not change
any outcomes significantly. All statistical analyses were
performed using IBM SPSS Statistics (version 25.0, IBM Corp).
Data were first visually inspected using scatter plots andhistograms to examine the distribution of the data and identify
significant outliers. Visual inspection of these data revealed anormal distribution within an acceptable range of skewness andkurtosis (all values between −1 and 1), in accordance with theguidelines of Kline [59]. In addition, 5 outliers (ie, participants
who reported data further than 2.5 SDs from the mean) were
removed before the analyses [60,61]. Independent 2-tailed ttests and chi-square tests were used to examine any baseline
differences of all demographic and dependent variables between
the intervention and control groups. Should group differencesof these variables emerge, they would be controlled for in thestatistical comparisons.

Patient Health Questionnaire
PHQ-9 [52,53] is a widely used 9-item measure of depression
symptoms. The PHQ-9 was included in this study as stresssymptoms often include low mood and depression. Items arescored on a 4-point scale (ranging from 0=“Not at all” to
3=“Nearly every day”), with higher scores indicating moredepressive symptoms. The total score of the PHQ-9 ranges from0 to 27, with scores of 5, 15, and 20 indicating the cutoff points
for mild, moderate, and severe depression, respectively [53].
The internal consistency of PHQ-9 in this study was very goodwith Cronbach α ranging from .86 to .88.
Generalized Anxiety Disorder
GAD-7 [54] is a 7-item self-report instrument that measures
symptoms of generalized anxiety. We included the GAD-7
because general anxiety is associated with heightened stresslevels. Similar to the PHQ-9, it uses a 4-point scale (ranging
from 0=“Not at all” to 3=“Nearly every day”), with higher scoresindicating more severe symptoms. Mild, moderate, and severe
anxiety were indicated by scores ranging from ≤9, 10-14, and15-21, respectively [55]. The internal reliability of the GAD-7
in this study was excellent with Cronbach α ranging from .90
to .91.
Coping Self-Efficacy Scale
CSES [28] is a 26-item scale that assesses perceived
self-efficacy for coping with threats and challenges. Items
include “Keep from feeling sad” and “Resist the impulse to act
hastily when under pressure.” An 11-point scale was used
(0=“Cannot do at all”; 5=“Moderately certain can do”;
10=“Certain can do”), with higher scores reflecting a stronger
belief in one’s coping abilities. In this study, CSES possessed
an excellent internal consistency of Cronbach α=.90.
Psychological Mindedness Scale
PMS [37] measures an individual’s ability to reflect on
psychological processes, emotional processing, and interpersonal
relationships. The PMS is a 45-item self-report instrument
consisting of items such as “I often find myself thinking about
what made me act in a certain way” and “I am sensitive to thechanges in my own feelings.” It uses a 4-point scale to score
items (1=“Strongly disagree”; 4=“Strongly agree”). Higher total
scores indicated higher levels of PM. The PMS produced a goodinternal consistency of Cronbach α=.84 in this study.
App Engagement Scale
The AES [56] assesses the degree of app engagement, which
was included to assess whether both groups were equallyengaged in the SGIs. This 7-item scale uses a 5-point Likert
scale (ranging from 1=“Strongly disagree” to 5=“Strongly
agree”). The scale had good internal reliability in this study,
with a Cronbach α=.85. App engagement was predictive ofpositive outcomes on measures of mood and anxiety [57].

Statistical Analyses
Data Screening
Incomplete responses were excluded from the study. Following
the multiple hurdles approach by Curran [58], the most likely
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Main Analyses
Analysis of covariance (ANCOVA) examined changes at
postintervention and follow-up periods between the intervention
and active control groups. ANCOVA is the recommended
analysis for inferential testing of intervention effects [57], as it
controls for baseline scores to ensure that group differences are
due to intervention effects [62]. The α level was set at P<.05.
Partial eta–squared (ηp2) was the effect size reported for
ANCOVA, whereas eta-squared (η2) was the effect size reported
for 2-tailed t tests and ANOVA. Guidelines by Cohen [63] foreta-squared were used, whereby 0.01 to 0.05 indicates small
effect, 0.06 to 0.13 indicates moderate effect, and ≥0.14 indicateslarge effect.

Moderation Analyses
Finally, double moderation analyses were conducted using
Hayes PROCESS (version 4.0, IBM Corp, macro Model 2;
Figure 2) [64]. A total of 6 separate models were conducted.
The first 3 models used the primary (ie, PSM-9) and secondary
outcome measures (ie, GAD-7 and PHQ-9) as dependent
variables after the intervention. The remaining 3 models used
the same outcome measures as dependent variables at follow-up.
Each model ran a multiple linear regression double moderation
analysis to examine the moderating effects of CSE and PM on
the relationship between condition (intervention and control)and PSM-9 or PHQ-9 or GAD-7 at postintervention or follow-up
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period. All analyses were conducted with the respective outcome
measures before the intervention as covariates. Each moderator
level was determined by the SD value of 1 from the mean. CIs

were set at 95%, with 5000 bootstrap iterations to assume
normality [65].

Figure 2. Hypothesized relationships with coping self-efficacy and psychological mindedness as independent moderators of the direct effect ofself-guided intervention on mental health outcomes.

Ethics Approval
Ethics approval to conduct the study was obtained from theInstitutional Review Board of the National University ofSingapore (NUS-IRB-2021-85). The study was preregisteredwith ClinicalTrials.gov (NCT04978896). All the participants
provided electronic consent before participating in the study.
Data collection took place in an entirely web-based setting, andall user data were deidentified before any analyses.

Results

any significant differences in age, sex, and baseline scores for
PSM-9, PHQ-9, GAD-7, CSES, and PMS (all P>.05) between
groups. Participants in both conditions did not show any
significant differences in their degree of engagement with their
SGIs (P>.05) as measured with the AES. The mean scores forthe whole sample’s characteristics were as follows (PSM-9:
mean 37.78, SD 9.58; PHQ-9: mean 7.07, SD 5.27; GAD-7:
mean 6.60, SD 4.57; CSES: mean 142.9, SD 42.2; and PMS:
mean 126.2, SD 10.5), indicating that the average student was
mildly distressed, depressed, and anxious.

Participants
The descriptive statistics for both groups are presented in Table
3. Independent 2-tailed t tests and chi-square tests did not findTable 3. Descriptive statistics for demographics and outcome variables by condition (n=264).
Variables

Intervention condition (n=135)

Active control condition (n=129)

P valueAge (years), mean (SD)22.88 (6.26)

22.00 (4.34).19

Sex, n (%)

a.50

Female94 (70.1)

94 (72.9)

Male

38 (27.7)

28 (21.7)

Nonbinary

3 (2.2)4 (3.1)Others0 (0)3 (2.3)

PSM-9a, mean (SD)38.5 (9.72)

37.0 (9.40).19

GAD-7b, mean (SD)6.81 (4.62)

6.37 (4.52).43

PHQ-9c, mean (SD)7.35 (5.11)

6.78 (5.44).38

CSESd, mean (SD)139.5 (45.1)

146.5 (38.9).18

PMSe, mean (SD)

125.6 (10.4)

126.9 (10.7).31

AESf, mean (SD)27.5 (3.30)

27.2 (3.68).56

PSM-9: Psychological Stress Measure-9.

b

GAD-7: Generalized Anxiety Disorder-7.

c

PHQ-9: Patient Health Questionnaire-9.

d

CSES: Coping Self-Efficacy Scale.

e

PMS: Psychological Mindedness Scale.

f

AES: App Engagement Scale (collected after intervention).

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Outcome Evaluations
The mean values of the outcome measures are listed in Table
4. The intervention group reported significantly lower perceived
stress levels at postintervention and follow-up periods comparedwith the active control group. The small effect sizes were
between 0.015 and 0.019. This indicated that the stress-coping
SGI was moderately more effective than the cooperation SGI(control) in reducing perceived stress over time. The intervention
group also reported significantly lower anxiety levels than theactive control group at postintervention period but not at
follow-up. No significant differences were observed between

the intervention and active control groups for depressive
symptoms at the 2 time points.
For the ANCOVA results (controlling for all baseline
measurements) at postintervention period, the time × groupinteraction exhibited significant differences for PSM-9(F1,261=7.16; P=.008) and GAD-7 (F1,261=5.86; P=.02) but not
for PHQ-9 (F1,261=1.65; P=.20). Only the interaction effect for
PSM-9 (F1,261=6.08; P=.01) remained significant at follow-up(GAD-7: F1,261=.796; P=.37; and PHQ-9: F1,261=3.24; P=.07).
Therefore, the main effects on psychological stress scoresindicated a significant intervention effect over time.

Table 4. Means (SDs), univariate F values, and effect sizes (ESs) for outcome variables at postintervention and 1-month follow-up.
VariableaBaseline

Postintervention

Intervention, Control,mean (SD)mean (SD)Intervention, Control,mean (SD)mean (SD)

Follow-up
F test(df)

P value

ESa

Intervention, Control,mean (SD)mean (SD)F test(df)

P valueES

PSM-9b38.5 (9.72)

37.0 (9.40) 32.5 (9.41)

33.7 (9.16) 4.82
.03 c
(1,261).018

33.7 (8.89)

34.8 (9.70) 3.89
.05
(1,261).015

GAD-7d

6.81 (4.62)

6.37 (4.52) 5.47 (3.94)

5.95 (4.47) 4.99
.03
(1,261).019

5.92 (4.33)

5.90 (4.22) 0.094 .76(1,261).000

PHQ-9e7.35 (5.11)

6.78 (5.44) 5.76 (4.29)

5.78 (4.36) 0.613 .43(1,261).002

5.73 (4.23)

6.06 (4.54) 1.80
.18
(1,261).007

ESs of 0.01=small, 0.06=moderate, 0.14=large [63].

bPSM-9: Psychological Stress Measure-9.

cItalicized values indicate significant P values at .05.

dGAD-7: Generalized Anxiety Disorder-7.

ePHQ-9: Patient Health Questionnaire-9.

At the postintervention period, the results revealed a significant
effect in the overall moderation model with PSM-9 as thedependent variable and CSES and PMS as moderators (R²=0.39;
F9,253=18.3; P<.001). Both interaction terms were significant
at postintervention period (CSES × condition: b=−0.0655,
SE=0.0231; t263=−2.83; P=.005; 95% Cl −0.111 to −0.020 and
PMS × condition: b=0.181, SE=0.0917; t263=1.97; P=.05; 95%
Cl 0.0002-0.362), indicating that these specific conditions must
be met for the intervention condition to predict lower
psychological stress [66]. The stress-coping SGI significantly
predicted lower scores on the PSM-9 at postintervention period
in participants who (1) experienced lower CSE and (2) had
moderate to high baseline PM (low CSE and moderate PM:
b=4.56, SE=1.30; t263=3.52; P<.001; 95% Cl 2.01-7.11 and low
CSE and high PM: b=6.37, SE=1.72; t263=3.71; P<.001; 95%
Cl 2.98-9.75). Similarly, the intervention condition significantly
predicted lower psychological stress at postintervention period

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for participants with (1) moderate CSE and (2) moderate to highbaseline PM (moderate CSE and moderate PM: b=1.91,
SE=0.920; t263=2.07; P=.04; 95% Cl 0.0953-3.72 and moderate
CSE and high PM: b=3.72, SE=1.27; t263=2.92; P=.004; 95%
Cl 1.21-6.22). The results are presented in Table 5. Finally,
regardless of the level of PMS, participants who began the
stress-coping SGI with high SCE did not experiencesignificantly lower psychological stress at postintervention
period. These moderation effects were not observed atfollow-up, even as the stress-coping SGI group reported
significantly lower perceived stress levels at postintervention
period (CSE × condition: b=−0.0415, SE=0.0241; t263=−1.72;
P=.09; 95% Cl −0.0891 to 0.0060 and PMS × condition:b=−0.0071, SE=0.0958; t263=−.0740; P=.94; 95% Cl −0.196 to
0.182). A visualization of these interactions is shown in Figure
3.

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Toh et al

Table 5. Intervention condition predicts lower psychological stress at each level of the moderators.

aCoping self-efficacy

Psychological mindedness

t test (df)β (SE; 95% CI)

P value

Low (–1 SD)

Low (–1 SD)

1.91 (262)2.75 (1.44; –0.090 to 5.60).06

Low (–1 SD)

Moderate

3.52 (262)

4.56 (1.30; 2.01 to 7.11)<.001 aLow (–1 SD)

High (+1 SD)

3.71 (262)

6.37 (1.72; 2.98 to 9.75)<.001

Moderate

Low (–1 SD)

0.075 (262).099 (1.32; –2.51 to 2.71).94

Moderate

Moderate

2.07 (262)

1.91 (0.920; 0.095 to 3.72).04

Moderate

High (+1 SD)

2.92 (262)

3.72 (1.27; 1.21 to 6.22).004

High (+1 SD)

Low (–1 SD)–1.44 (262)–2.59 (1.79; –6.11 to 0.940).15High (+1 SD)

Moderate–0.581 (262)–0.778 (1.34; –3.41 to 1.86).56

High (+1 SD)

High (+1 SD)

0.719 (262)1.03 (1.43; –1.79 to 3.86).47

Italicized values indicate significance.

Figure 3. Interactions between SGIs and moderators on psychological stress at postintervention. CSES: Coping Self-Efficacy Scale; PMS: Psychological
Mindedness Scale; PSM-9: Psychological Stress Measure; SGI: self-guided intervention. Mean of PMS is 126 (SD 10). Mean of CSES is 145 (SD 40).

Moderation analyses for the secondary measures were notsignificant. For postintervention and follow-up periods, theinteraction terms for both CSE and PM were not significant for
both anxiety and depressive outcomes. This means that even as
participants in the stress-coping intervention group experienced
significantly lower anxiety levels than active control participantsat postintervention period, no subgroup of individuals benefited
more than the others. Similarly, as there were no significant
main effects on anxiety and depressive symptoms at follow-up;
there was no moderation effect.

Discussion
Principal Findings
Overview
This study aimed to evaluate the possible efficacy of an 8-day
stress-coping SGI compared with an active control in improving
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perceived stress and well-being levels in a sample of Asianuniversity students. We also examined PM and CSE as factorsthat may identify people for whom the SGI worked best. Our
hypotheses were largely supported, with the intervention group
showing significantly greater improvement in perceived stresslevels at postintervention and 1-month follow-up periods. In
particular, students with low to medium CSE or medium to highPM benefited from significantly lower perceived stress levels
after the intervention than other participants. These moderation
effects were no longer observed at follow-up, indicating thatthese subgroups reaped the benefits of the stress-coping SGI
faster than others. There were no additional benefits forparticipants who began the stress SGI with high CSE. In
addition, the intervention group reported significantly lower
anxiety levels than the active control group after the intervention
without any moderation. The SGIs were also perceived to besatisfactory by both groups of participants. Participants wereequally likely to continue using the Intellect SGI.
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JMIR FORMATIVE RESEARCH

Psychological Stress
Our results extend the previous dearth of research that a
CBT-based stress-coping intervention delivered on amobile-based platform can be effective in reducing
psychological stress in Asian university students. Previous
studies that have compared CBT-based mobile interventions
with waitlist controls have shown similar results. Self-guided
CBT programs such as Calm [67], SMART-OP [68], andBioBase [69] previously helped decrease perceived stress inWestern college students using an RCT design. Calm,
consumer-based mindfulness meditation app that incorporates
various CBT techniques, enhanced concentration andpresent-moment awareness in students using daily mindfulness
meditations. This heightened awareness and focus, integrated
with an 8-week cognitive training course, and facilitated thedevelopment of balanced thinking in the face of stressors [67].
Likewise, student users of the 6-week SMART-OP intervention
had access to a variety of cognitive behavioral content, including
stress management psychoeducation and cognitive restructuring
exercises. These were further complemented with relaxation
skills (ie, focused breathing, guided muscle relaxation, andbiofeedback challenge) [68]. BioBase delivered a 4-week coursewith elements of CBT and self-compassion. The course focusedon recognizing stressors and increasing one’s perception ofcontrol to address them [69]. Comparably, Intellect’s stressintervention comprises very similar CBT-based content. In
addition, the stress-coping SGI within Intellect demonstrated
the efficacy of such content in reducing perceived stress levels
despite reduced exposure (ie, 5 minutes per day for 8 days). Our
small effect sizes were also consistent with the effect sizes
relative to active controls (g=0.09) presented in themeta-analyses of Linardon et al [20]. To address the lack offollow-up assessments in mental health mobile interventions[20,25], our study found that reduced perceived stress levels
were sustained at the 1-month follow-up. This was in line withthe few stress SGI studies that also conducted follow-up
assessments for up to 6 weeks [69,70].

Anxiety and Depression
Our second hypothesis was partially supported. The
stress-coping SGI was effective in reducing anxiety
symptomatology among college students only at
postintervention. Similarly, a multitude of stress-coping
mobile-based interventions have shown reductions in
self-reported anxiety [44,71-73] with similar effect sizes(Hedges g=0.07-0.29). However, the length of theseinterventions was longer, ranging minimally from 2 to 11 weeks[20]. These smartphone interventions used either
mindfulness-based practices only [71-73] or both mindfulness
and CBT [44]. Regardless of their theoretical orientations, theseinterventions used relaxation techniques, such as deep breathing
and mindful awareness, that are also present in the Intellectstress-coping SGI. These methods previously provided
short-term relief from anxiety symptoms [74,75]. Despite its
brief content, the Intellect stress-coping SGI reduced anxiety
among university students, possibly because of the use of similar
relaxation techniques. In contrast, the stress-coping SGI did not
reduce the depressive symptoms. This finding contradicted the
few CBT-based smartphone interventions reviewed by Linardon
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Toh et al
et al [20,76-78]. There are several possible reasons for the null
findings. First, in comparison with our stress-coping SGI, thecourse content within these CBT-based interventions ranged
from 2 weeks [77] to 2 months [78]. Hence, it is plausible that
their content was more exhaustive and also more specificallytailored to address the etiology and maintenance factors ofdepression [78]. The stress-coping SGI was specifically designed
to provide relief from perceived stress symptoms and did notincorporate more diverse content addressing depressive
symptoms. Consequently, it is plausible that its efficacy against
depressive symptoms would be limited. Second, the efficaciesof these interventions were demonstrated against waitlist control[77] or across multiple time points and without a control group[76]. Research has shown that treatment effects of CBTinterventions on depressive symptoms were significantly moredifficult to detect when there is an active control group [79]. It
is conceivable that the use of an active control may have
obscured the intervention effect in our study, given thatparticipants in both the intervention group and active control
group were significantly less depressed after the intervention(intervention: mean 5.74, SD 4.28; t134=−4.85; P<.001; andactive control: mean 5.78, SD 4.47; t128=−3.07; P<.01) relative
to baseline (intervention: mean 7.35, SD 5.11; and active
control: mean 6.78, SD 5.44). Considering that poor social
dynamics was a potential risk factor for depression in college
students [80], it is plausible that the active control group
improved group dynamics, which may have lowered depression
levels. This has been observed in previous studies [81].
However, this conclusion cannot be drawn, as we did notevaluate the efficacy of the cooperation SGI against a waitlist
control group. Future studies may shed further light on thishypothesis using a 3-arm RCT with a waitlist control group, aneutral active control group (eg, attention control), and thestress-coping SGI. Interestingly, a few similar interventions
have also detected anxiety effects but not depression [82] ordepression effects but no anxiety effects [47]. It remains possiblethat certain stress-focused strategies (ie, relaxation techniques)
were more effective in reducing anxiety than depressive
symptoms. There were no significant group differences inanxiety or depressive symptoms at the 1-month follow-up.
Sharing similar characteristics as our sample, previous RCTs
evaluating the efficacy of SGIs with nonclinical participants
experiencing mild baseline symptoms generally found small tono effects of app use after intervention [33,83-85]. Bakker andRickard [33] postulated that these participants may haveexperienced lower motivation to seriously engage with the app,thus reducing their chances of maintaining psychologicalbenefits.

Moderators
Our third hypothesis was partially supported. CSE and PM
emerged as significant moderators, such that students with thelowest CSE and highest PM faced the greatest reduction inperceived stress levels within the intervention condition at
postintervention. There were no benefits for participants withlow PM and high CSE. In addition, this moderation effect was
no longer observed at follow-up. The finding that perceived
stress levels improved relatively quicker within this group of
university students was not entirely surprising. Individuals withJMIR Form Res 2022 | vol. 6 | iss. 12 | e40723 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
a greater propensity for self-reflection and insight were more
likely to be aware and thus found it easier to look at theirstressors from a distance [36]. Coupled with a deeper motivation
to explore the cause of their stressors, these facilitated the useof self-guided CBT strategies to resolve them (eg, reappraisal)[86-88]. McCallum et al [89] also showed that higher levels of
PM in psychiatric outpatients were associated with morefavorable outcomes in short-term interpretive and supportive
therapies. In line with the increasing interest in the nonclinicaluse of CBT-based self-guided strategies, we found that
university students with higher PM reaped faster gains with a
CBT-based stress-coping intervention. The finding that
individuals with lower CSE also experienced faster gains may
be counterintuitive at first. In an RCT that compared theefficacies of 3 different CBT-based SGIs, Bakker et al [47]found that CSE was significantly increased in only 2 of them(ie, MoodKit and MoodMission). Further investigation,
however, showed that participants had much lower baseline
CSE levels in these 2 samples (152.00 and 154.46, respectively,
compared with 169.75 in MoodPrism; [56]). Bakker et al [47]did not find any significant group differences in baseline CSE
levels. Beyond CBT-based SGIs, other forms of SGIs have also
shown potential ceiling effects for CSE, such that a lack ofimprovement resulted in no mediation of better stress outcomes[90,91]. In comparison to those with high CSE, individuals withlower perceived confidence to destress successfully would
naturally be more motivated and invested in seeking additional
help [92]. The belief that one could still learn how to cope withstressors of emerging adulthood was also previously suggested
to enhance the effectiveness of stress management interventions[93]. Our results suggest that this may be the case. Future studies
may further evaluate CSE and PM as factors to optimize
treatment delivery for a nonclinical college student population.
The finding that CSE and PM did not moderate the efficacy ofthe stress-coping SGI on secondary outcomes meant that our
final hypothesis was not supported. These results were mostly
unsurprising, as there was no main effect of the intervention
condition on depression at postintervention and follow-up
periods and anxiety at follow-up. Other studies have shown thatdifferent forms of coping could be distinctly tailored to different
mental health outcomes [94]. For example, lower perceived
self-efficacy to problem-solving (problem-focused CSE) has
been shown to incline one’s need to find ways to destress [92].
Instead, lower perceived self-efficacy to regulate one’s own
emotions to the problem (emotion-focused CSE) may thenincline one’s own need to seek emotional help and reduce
anxiety. A probable reason that perceived stress levels weremoderated, but not anxiety and depression, is likely due to theheavy emphasis on problem-solving internal and external
stressors throughout the stress-coping SGI. Individuals with agreater capacity for self-reflection may have mostly reviewed
their thought processes and behaviors with regard to the sourcesof their stressors and adapted with the aim of resolving theseroot causes. This is similar to those who have lower CSE, asthey may channel most of their energy toward equipping new
skills to manage stress. As the stress-coping SGI in Intellectlacks sufficient emphasis on building emotional resources, we
encourage researchers to further evaluate the role of thesemoderators using alternative CBT-based SGIs.
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Toh et alStrengths and Limitations
The randomized controlled design allowed for causal
conclusions [95]. The active waitlist control group also
controlled for the effects of attention and other nonspecificfactors from the intervention effects, thus strengthening thevalidity of the results [96]. Finally, significant effects onperceived stress levels at the 1-month follow-up period showedthat the stress-coping SGI successfully improved university
students’ well-being over a short period. These directly
addressed the lack of follow-up measurements as stated in themost recent meta-analyses of mental health SGIs [20,25].
This study had several limitations. First, the actual amount ofeffort put in to complete the SGI was not controlled for. Dataon the duration that participants spent thinking about orpracticing the skills learned from the SGI beyond theintervention were not collected. It is plausible that the positiveeffects could also be explained by other variables that were notassessed during the intervention, such as better campus life ora less stressful curriculum. Second, the actual duration spenton the Intellect app was not collected or controlled for. This isa potentially serious concern, as low adherence and inconsistentengagement with SGIs were previously found to limit theirpositive effects [97]. However, efforts were made to ensure thatthe participants completed the app activities properly. Initially,
the participants were instructed to complete every daily activity
present in their respective SGIs during the intervention period.
Intellect SGIs also did not allow the participants to proceed to
the next page until they completed the preceding activities. We
encouraged maximum adherence through daily text reminders.
Importantly, every final participant was technically verified ashaving completed the activities in their SGI. We acknowledgedthat it is possible that the participants in the active control groupcompleted the activities in less time and with less care thanthose in the intervention group, which would threaten theinternal validity of our findings. However, both groups did not
differ on the subjective AES, which gives some confidence thatgroups did not differ in their motivation to engage with the SGIs
and the time they spent on them [98]. Nonetheless, future studies
should control for the time spent on the SGI and evaluate its
relationship with SGI outcomes. Third, subjective self-reports
are prone to retrospective recall biases [99]. This limits theaccuracy of our findings on perceived stress, anxiety, anddepressive symptoms given that they vary from time to time.
Therefore, our results should be interpreted with caution. Given
that the PSM-9 asked for self-reported psychological stress overthe last 4 to 5 days, we are less able to infer whether the
stress-coping SGI could ecologically reduce perceived stressamong university students without the implementation of
momentary assessments. To further strengthen the reliabilityof these findings, future studies can use daily diary methods toevaluate the real-time efficacy of mobile interventions on
university students’ well-being [100]. In place of self-reports,
recent studies have recommended behavioral or physiologicalmeasures to objectively assess stress symptoms [101]. Thismethod would also reduce the risk of recall bias and enhancethe validity of our findings. Fourth, the study design only
compared the intervention group with an active control group.
The absence of a waitlist control condition prevented the
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JMIR FORMATIVE RESEARCH
exclusion of the possibility of third variables contributing to
the findings, which can be addressed in future studies by
including a waitlist control alongside an active control. Fifth,
the absence of long-term follow-up assessments (eg, 3 or 6months) makes it difficult to deduce whether improvements canbe sustained over time. Therefore, our study could not evaluate
the possible long-term benefits on symptoms of chronic stress.
Finally, this study has limited external validity for the student
population at large, as our sample contained mainly female
students from Singapore with slightly elevated stress, anxiety,and depression levels. Therefore, our positive findings on
perceived stress and anxiety symptoms may be generalized to
the Western student population or clinical samples. Future
researchers may consider replicating our findings with a morediverse college sample while administering even longer-term
follow-up assessments.

Toh et alConclusions
In conclusion, this RCT found evidence for an 8-day
stress-coping SGI in improving perceived stress and anxiety
levels among Asian university students. The effects on perceived
stress levels were sustained at 1-month follow-up, but not foranxiety, thus giving some confidence that a brief, time-limited,
and CBT-based SGI can maintain its gains on perceived stress.
However, depressive symptoms did not decrease. Students withlower CSE and higher PM experienced reduced stress fasterthan other students in the CBT-based SGI intervention group.
The identification of these moderators can optimize the outcome
and treatment delivery of such stress-coping mobile apps. Ourfindings are useful, given the potential for scaling up such easily
accessible, and brief interventions. Several limitations werenoted, including the lack of ecological momentary assessments
to capture perceived stress more accurately, longer-term
follow-up measures to evaluate sustainability of gains, and morediverse samples to evaluate transferability of the findings.

Acknowledgments
This study was partly funded by Intellect Pte Ltd under a Research Collaboration Agreement with the National University ofSingapore.

Data Availability
Data supporting the findings of this study are available upon request from the corresponding author.

Conflicts of Interest
OS set up the Research Collaboration Agreement between the National University of Singapore and Intellect in 2020, and in
January 2022 joined Intellect Pte Ltd as their Clinical Director. The study design, data management, statistical analysis, interpretationof the data, and reporting of the study are independent of Intellect Pte Ltd.

Multimedia Appendix 1
CONSORT-EHEALTH Checklist (V 1.6.1).
[PDF File (Adobe PDF File), 1243 KB-Multimedia Appendix 1]

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Abbreviations
AES: App Engagement Scale
ANCOVA: analysis of covariance
CBT: cognitive behavioral therapy
CSE: coping self-efficacy
CSES: Coping Self-Efficacy Scale
GAD: Generalized Anxiety DisordermHealth: mobile health
PHQ: Patient Health Questionnaire
PM: psychological mindedness
PMS: Psychological Mindedness Scale
PSM: Psychological Stress Measure
RCT: randomized controlled trial
SGI: self-guided intervention

Edited by A Mavragani; submitted 04.07.22; peer-reviewed by D Hasson, E Kuhn; comments to author 08.08.22; revised version
received 20.08.22; accepted 24.11.22; published 14.12.22
Please cite as:Toh SHY, Tan JHY, Kosasih FR, Sündermann O
Efficacy of the Mental Health App Intellect to Reduce Stress: Randomized Controlled Trial With a 1-Month Follow-up
JMIR Form Res 2022;6(12):e40723
URL: https://formative.jmir.org/2022/12/e40723
doi: 10.2196/40723
PMID:©Sean Han Yang Toh, Jessalin Hui Yan Tan, Feodora Roxanne Kosasih, Oliver Sündermann. Originally published in JMIR
Formative Research (https://formative.jmir.org), 14.12.2022. This is an open-access article distributed under the terms of theCreative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The
complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright andlicense information must be included.

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