JMIR FORMATIVE RESEARCH

Wang & Miller

Original Paper

Assessment and Disruption of Ruminative Episodes to Enhance
Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive
Interventions in Clinical Depression: Pilot Randomized Controlled
Trial
Liyuan Wang1, PhD; Lynn Miller2, PhD
1Children's Hospital, Los Angeles, Department of Adolescent and Young Adult, Los Angeles, CA, United States

2

University of Southern California, Annenberg School for Communication and Journalism, Los Angeles, CA, United States

Corresponding Author:
Liyuan Wang, PhD
Children's Hospital, Los Angeles
Department of Adolescent and Young Adult
3250 Wilshire Blvd
Suite 1202
Los Angeles, CA, 90027-0000
United States
Phone: 1 7654911022
Email: liwang@chla.usc.edu

Abstract
Background: A just-in-time adaptive intervention (JITAI) is “designed to address the dynamically changing needs of individuals
via the provision of the type or amount of support needed, at the right time when needed.” If and how rumination-focused cognitive
behavioral therapy (RFCBT), the gold standard, blocks emotional cascades underlying rumination is unclear. Furthermore,cognitive behavioral therapy has been successfully used as a mobile variant, but RFCBT has not been adapted for a mobile variant(mobile RFCBT [MRFCBT]) or for a JITAI variant.
Objective: This study aimed to pilot-test a fully automated JITAI leveraging RFCBT and ways to identify and block cascading
depressive rumination.
Methods: Patients in therapy for clinical depression were recruited for a randomized controlled trial (RCT). After consenting
to be part of the RCT, they were randomly assigned to either of the 2 mobile versions of the RFCBT conditions personalized tothe individual’s rumination timing patterns (JITAI-MRFCBT) or a no-treatment control condition through a double-blind procedure.
Although the initial design was to have a 3-armed trial with 2 JITAI conditions (a JITAI and a narrative JITAI condition), welater opted to collapse those 2 conditions into 1 JITAI condition because of the low number of participants. All participants wererecruited and participated through their smartphones, receiving 5 SMS text message reminders on each of the 35 days to self-reporttheir rumination-related symptoms (eg, rumination episodes and duration). In the JITAI-MRFCBT condition, they also received
treatment materials. The first 7 days provided a rumination baseline, and the last 7 days provided a postintervention rumination
value. In total, 42% (25/59) of volunteers were eligible and provided their phone numbers, 20% (5/25) of whom never replied to
the SMS text message reminding them to start the RCT. A total of 90% (18/20) of volunteers completed it (ie, finishing, asprespecified, 80% of the questionnaires and training tasks) and, therefore, were included in the analysis.
Results: Using independent 2-tailed t tests with bootstrapping, results showed that participants in the JITAI-MRFCBT condition,compared with those in the control condition, reported a greater reduction in counts of rumination episodes (mean −25.28, SD
14.50 vs mean 1.44, SD 4.12, P<.001) and greater reduced average time (minutes) spent in rumination (mean −21.53, SD 17.6
vs mean 1.47, SD 1.5; P=.04). Results also suggest that, compared with those in the control group, those in treatment reduced
ruminative carryover from one episode to the next.
Conclusions: The results suggest that JITAI-MRFCBT may reduce negative rumination by providing RFCBT just in time
following rumination, thereby blocking the next rumination episode using the same trigger. This study supports a subsequent,
full-scale JITAI and the importance of leveraging mobile smartphone technology with MRFCBT to curb depressive symptoms.
Trial Registration: ClinicalTrials.gov NCT04554706; https://clinicaltrials.gov/ct2/show/NCT04554706
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 1
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller(JMIR Form Res 2023;7:e37270) doi: 10.2196/37270
KEYWORDS
depressive rumination; mobile health; mHealth; just-in-time adaptive intervention; depression; mental health; mobile phone

Introduction
Background
Major depressive disorder (MDD) [1], or clinical depression,
is a mood disorder associated with prevalent sadness,
hopelessness, and anhedonia and often manifests symptoms
associated with cognitive impairment and social functioning
disability. A challenge underlying MDD is dysregulation of theemotional cascades underlying depressive rumination.
Rumination, or depressive rumination response, is a thinking
style defined as “the tendency to passively and repetitively focus
on the experience of negative moods, as well as their causes
and consequences” [2]. When rumination is driven by cascadesof negative emotions, one can become more depressed and use
more depressive rumination to cope with their depression [3].
Specifically, depressive rumination and the underlying emotionalcascades usually stem from problematic interpersonalcommunication [3,4]. Negative verbal and nonverbal
communicative patterns of depressed individuals can adversely
affect the nature of social interactions and strain social networks[5,6]. Social impairments such as inhibited communication withfamily members and friends and the use of negative self-focusedverbalizations have long been associated with negative
psychological states, including depression [7]. In interpersonal
relationships, depression-related patterns of rumination usually
precede a subsequent problematic interpersonal communication
episode, exacerbating levels of depression [7].
Cognitive behavioral therapy (CBT) has long been one of thechief promising face-to-face interventions in this domain [8].
Patients in CBT interventions learn ways to monitor and improvetheir psychological symptoms through a series of tasks such aslearning how to use diaries, emotion regulation techniques, andmindfulness tools [9]. CBT offers a wide range of strategies
that help patients maintain, trigger, and mitigate their depressive
symptoms [9,10]. A number of meta-analytical reviews haveshown that CBT has strong efficacy in mitigating depression
outcomes and recommend it as the gold standard frontline
treatment for depression [10-13].

Just-in-Time Adaptive Interventions Using
Rumination-Focused CBT to Reduce Depression
A number of meta-analytical reviews have suggested thatpeople’s everyday technologies (eg, smartphones and wearable
devices) can provide not only scalable but efficacious mental
health treatment [14-16]. Specifically, those reviews point to
the efficacy of CBT delivered via mobile phone (mobile CBT[MCBT]) through apps. MCBTs have the capacity to integrate“gold standard treatment (CBT)” to bolster and maintain
psychological resilience with scalable mobile alternatives [14].
However, MCBTs are not without their limitations. Although
MCBT apps can provide some treatment information, none ofthose apps currently have the functionality to further diagnoseand provide customized in-the-moment feedback based on a
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

given client’s specific symptoms [5]. This is due at least in partto a failure to typically use a customized within-person approachaffording intensive measurements (ie, ≥100 measures pervariable per person), which can be leveraged to track a given
participant’s progress within a given context and across different
situational contexts [17,18]. Therefore, there is a need forrumination-focused MCBTs (or mobile rumination-focused
CBTs [MRFCBTs]) that are more specifically designed toaddress rumination via a mobile device and effectively use a
within-person approach for personalized feedback to changes
in client state.
There are many reasons why an intensive within-subject
MRFCBT using a just-in-time adaptive intervention (JITAI)
approach—affording cues for personalized delivery ofin-the-moment diagnosis and feedback—matters. First, a given
client with depression does not respond with rumination acrossall contexts. Therefore, MCBTs without intensive within-subject
assessment offered by JITAIs could be perceived as
nonresponsive to the changing state of the client with depression,
which could undermine treatment effectiveness [19-22]. Second,an intensive within-person design could allow for the collectionof multimodal information using noninvasive sensors and active
self-reports. These rich data can enable computational
possibilities to predict and prevent risks when participants are
the most vulnerable. For example, ecological momentary
assessments (EMAs), which actively measure people’s
in-the-moment emotional reactivity to different stressful events
at various time points [23-25], have also been shown to bereliable predictors of subsequent depressive outcomes. Third,
it is unclear how rumination in one context for an individualwith depression carries over into other contexts, exacerbating
those effects as rumination within subjects is not evenassessed—let alone used for potential markers for personalized
feedback or interventions. Thus, current MCBT apps function
more like a pool of knowledge for active information seekers
rather than professional health care providers from which one
can seek personalized and responsive help. Fourth,
MRFCBT—with rumination treatment in mind [26]—has notbeen successfully tested, to our knowledge, in mobile form, letalone with JITAI features.
Clearly, there is a strong need for current MRFCBT apps tointegrate JITAI features (ie, algorithms), taking advantage ofmulti-time assessments (both active and passive) to detect
vulnerabilities and provide feedback at the exact moment ofneed [27]. To advance this science, this project used EMA,
where such moment-to-moment assessments in real time havebeen successfully applied in numerous health science domains[28]. That work suggests that JITAI designs could be translated
into MCBTs, including an MRFCBT. JITAIs tested byresearchers without “real-time” human clinicians—that havegiven participants feedback based on their self-reported
problems—have been found to be effective [5]. Therefore, thisproject was designed to test the efficacy of an MRFCBT withJMIR Form Res 2023 | vol. 7 | e37270 | p. 2
(page number not for citation purposes)

JMIR FORMATIVE RESEARCHJITAI features in reducing ruminative thoughts—a major
symptom of depression. To date, this has not been assessed.
Specifically, we hypothesized (hypothesis 1) that a
JITAI-delivered MRFCBT intervention would be more effective
than a no-treatment control group (assessing rumination but notproviding treatment) in reducing rumination over time.

The Link Between Rumination and Depression
JITAI design can target highly intrapersonal symptoms that areotherwise hard to track. In particular, one’s ruminative responses(ie, trait rumination) to their immediate social interactions cancontribute to one’s ruminative patterns and depression outcomes[29-31]. People with strong ruminative tendencies may findthemselves subconsciously revisiting and recurrently thinking
about some unpleasant interpersonal events. For example, after
experiencing an interpersonal disagreement, those who are highin ruminative response may find themselves more likely toengage in thoughts concerning “Why did this happen to me,”“What was that person thinking of me,” and “Why couldn’t I
handle this better.” Such ruminative processes usually leadindividuals to recurrently revisit negative experiences,
exacerbating negative affective states without producing
strategies to improve future communication patterns [32].
Theories of depression [30,31,33] from both the cognitive andinterpersonal or stress perspectives [4,34] have found thatrumination over unpleasant experiences is a major reason for
the increase in interpersonal conflict and the lack of relationshipsatisfaction, which subsequently leads to elevated depressive
outcomes. In various studies on depression, the aftermath ofdepressive rumination is usually documented from an
intrapersonal perspective. For example, using interviews,
Lyubomirsky et al [29] found that, when engaging in moreruminative thoughts, people are more likely to generate biased
interpretations of unpleasant events [29], and this usually elicits
negative autobiographical memories [29], which exacerbate andprolong depression [35-37]. Without intensive assessments of
one’s daily thought patterns, it would be impossible to track
this highly intrapersonal process in terms of when and how it
occurs, not to mention providing responsive treatment feedback
for those who are in great need. With JITAIs and formative
research on baseline rumination per participant, the occurrenceof a ruminative episode could more easily be targeted via
intensive measurements at multiple intervals.

Methods
Intervention System
The intervention system was created for a pilot study that, if
successful, might lead to a subsequent full-scale randomized
controlled clinical trial (RCT) nationally (ClinicalTrials.gov
identifier NCT04554706). As a pilot RCT that precedes alarge-scale trial, this project has some differences in sample
size and measurement described in the preregistered trial. To
be specific, this pilot is a 35-day–long, small-scale RCT with
2 between-subject conditions: a JITAI-MRFCBT condition(with personalized timing for participants’ own rumination
patterns) and a no-treatment control condition with datacollection to assess participant rumination but with no treatment.
As the goal of this project is to focus on a major symptom of
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller
depression, which is excessive negative rumination, we adaptedmaterials from rumination-focused CBT (RFCBT; materials on
RFCBT were obtained through subscription to Psychology Tools[38]. The efficacy of RFCBT has already been tested in previous
research [26]. MRFCBT includes activities, training exercises,and diaries that are designed to reduce ruminative episodes inpatients with clinical depression [26]. Therapists can takeadvantage of the diaries provided by patients to offer tailoredbehavioral training (eg, reframing emotions, attribution
perceptions, goal setting, and problem-solving) for patients.
The RFCBT program is ideal for adaptation to MRFCBT withJITAI features: (1) the diary and rating systems with which
therapists deliver tailored training sessions could be conveniently
designed as questionnaires for participants to self-rate anddocument their conditions (providing also a 1-week baseline
for personalized delivery, an adaptive feedback system sending
treatment messages as participants progress into treatment, anda 1-week postintervention assessment), (2) the JITAI system
can have the capacity to deliver automatic training sessions
tailored to the results of individual self-reports, and (3)participants can have access to training materials and messages
at the exact time of their requests. Therefore, 3 training material
sets (ie, problem-solving, conflict attribution, and emotion
regulation) from RFCBT were adapted into this JITAI condition.
The JITAI conditions asked participants to document theirruminative episodes for 7 days. After that, they received
intervention materials and training prompts for 3 weeks (21days). They were asked to document their ruminative episodes
for another week after completing the 3-week training materials(to compare the baseline experience with the one immediately
after the intervention). It is worth pointing out that, as originallyplanned for the full registered RCT, we designed 2 of the JITAI
conditions, one using the original adapted MRFCBT materials
directly and the other taking a narrative approach to delivery of
the MRFCBT. However, as we could not secure enoughparticipants for the 2 JITAI conditions and there were noapparent differences between them in the pattern of findings(n=4 in the JITAI-MRFCBT condition and n=5 in the JITAI
narrative RFCBT condition), we combined the 2 conditions as
the JITAI condition for this pilot study. The no-treatment control
condition did not have any training materials from the
MRFCBT. Participants in this condition were only asked to
document ruminative episodes (5 times a day) for 35 days.
Figure 1 is an illustration of the design of the JITAI system inthis intervention. We used EMA as the main assessment tool
to make decisions regarding which messages to deliver andwhen. EMA involved asking participants if they had experienced
stressful events in the past 3 hours and, if so, what the eventswere (dotted double-headed arrows). If the participant identified
the immediate presence of triggers of rumination episodes, thesystem prompted an inquiry regarding the nature of that trigger.
At such trigger points, participants were quite vulnerable interms of the probability of experiencing rumination episodeevents (dotted single-headed arrows). In addition, EMA
measures, along with the previous baseline daily activity
surveys, can determine whether participants are receptive totreatment at the moment of the rumination event (dotted
single-headed arrows). If participants were not engaged in otheractivities (eg, driving and walking), they received JITAIJMIR Form Res 2023 | vol. 7 | e37270 | p. 3
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
feedback tailored to the type of trigger in the form of support,
necessary problem-solving skills, motivation, and supportive
messages (solid lines in Figure 1). In the meantime, we provided

Wang & Miller
several sessions of RFCBT, adapted to (1) the screen size of amobile phone and (2) readable length, when participants were
receptive (dashed single-headed arrow).

Figure 1. Just-in-time adaptive intervention (JITAI) design process.

Participants signed up and consented to take part before they
were shown a link to the intervention website that asked
participants to enter their phone numbers to start the RCT. This
procedure was enabled by a programmable (through R; R
Foundation for Statistical Computing) survey platform, Formr[39]. Using this platform, participants’ responses at each time
were documented with a unique, anonymous identifier thatlinked to their phone number. The Formr platform wasprogrammed to send reminder SMS text messages 5 times aday. The SMS text messages contained the link for them todocument their symptoms. This pilot intervention did not havecointerventions.
The design of this RCT followed the necessary procedures thatare required for RFCBT. That is, to complete the RCT,
participants were asked to self-report and document theirruminative episodes every 3 hours (4-5 times a day). Each time,
if a participant documented their recent ruminative episode in
the past 3 hours, they were prompted to document the trigger,
immediate environments, duration, and emotional experiences

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

related to the current ruminative episode. The intervention
system was designed to be tailored to participants’ ruminative
patterns in three ways: (1) based on participants’ ruminative
episodes (triggers and immediate environments), participants
would receive a set of training materials that best targeted thetrigger of their ruminative episode (eg, problem-solving andinterpersonal conflicts); (2) participants could choose to review
their training materials at any time they wished as a means toprovide in-the-moment feedback; and (3) participants could
skip and withdraw from answering some of their documented
prompts at a time when they were otherwise engaged in anotheractivity. Therefore, each participant could have up to 175 times
within the 35 days of the intervention to report on theirruminative episodes.
To enhance our understanding of rumination and potential
interventions to reduce it, participants were encouraged to
document their ruminative episodes as many times as possible.
Figure 2 shows the RCT platforms on a mobile phone screen.

JMIR Form Res 2023 | vol. 7 | e37270 | p. 4
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

Figure 2. Randomized controlled trial platforms for participants to self-report and to receive information and feedbacks like the level of negative
emotions one has experienced, and cognitive behavioral therapy training materials tailored to their current rumination episode. No-treatment control
platforms for participants to self-report information (participants only report and document their rumination episodes.

Ethical Considerations
This study was approved by the institutional review board atthe
UniversityofSouthern
California(IRB;
UP-20-01073-AM002). The IRB that reviewed this workrequired that, for this trial involving diagnosed patients withdepression, researchers had to exclude patients who had nottaken medication or had not visited a therapist in the past 3
months—we did so. Therefore, all participants in the treatment
and control groups in this study were in active treatment forclinical depression. We explained to participants, both in the
recruitment message and the consent form, that (1) the study
was a double-blinded RCT to help reduce ruminative episodes
related to depression, (2) there was an equal chance of being
assigned to the no-treatment control and the JITAI condition,(3) this RCT was designed to test the effectiveness of mobile
phones as a potential mode of delivery for rumination-based
CBTs (MRFCBT) rather than to provide treatment for clinicaldepression, and (4) this RCT would only be delivered via mobile
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

phones. They were also informed of the name of the research
institution whose IRB approved the research. Participants werealso entered into a lottery with 1 in 3 odds of receiving a US
$50 gift card. A total of 59 volunteers agreed to be enrolled inthis RCT and participated in the screener survey on the web.

Recruitment
Participants in this study were recruited via a volunteeringwebsite originally developed using National Institutes of Healthfunds—ResearchMatch, a national web-based registry of>150,000 volunteers seeking to volunteer for new, potentiallydisorder-relevant studies. Upon approval from the IRB,
ResearchMatch allows researchers to access and contact
volunteers who are considering participating in research studies
or clinical trials [40,41]. ResearchMatch offers filters forresearchers to screen participants who may be eligible. It also
has a message system for researchers to contact potential
participants. We contacted potential participants who (1) wereaged ≥18 years; (2) had self-reported recently (within the past
JMIR Form Res 2023 | vol. 7 | e37270 | p. 5
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
12 months) having been given a diagnosis of clinical depression;(3) had concurrently self-reported that they had no diagnosis of
any other mental health disorder; (4) self-reported that they
could understand, read, and write in English; and (5) reported
that they had a smartphone with a data plan. Participants werealso asked to indicate their medication and specify their mostrecent visit to a therapist. These were our inclusion criteria.
Of the 59 volunteers, 25 (42%) were eligible and provided theirphone numbers. A total of 20% (5/25) of the volunteers neverreplied to the SMS text message reminding them to start the
RCT. Of the 20 participants who logged on to the RCT website

Wang & Miller
and initiated the RCT, 2 (10%) requested to quit the RCT during
the first week, and 18 (90%) completed the RCT (ie, finishing
80% of the questionnaires and training tasks). On average, those
18 participants answered 145.76 (SD 13.35) out of 175 survey
prompts, affording enough data points to conduct analysis on
intensive within-person changes. Data for the 10% (2/20) ofparticipants who left the RCT before the actual treatment(second week) were not included in the analysis. Table 1 shows
the demographic backgrounds and other information of that
90% (18/20) of participants. A total of 50% (9/18) of theparticipants were assigned to the JITAI condition, and the other
50% (9/18) were assigned to the no-treatment control condition.

Table 1. Participants’ demographics and diagnosis information (N=18).
Characteristics

Participants, n (%)

GenderMan

6 (33)Woman

12 (67)

Nonbinary0

Income (US $)<30,000

3 (17)

30,000-59,999

6 (33)

60,000-89,999

7 (39)≥90,000

2 (11)

Ethnicity
Non-Hispanic White

11 (61)

Hispanic

3 (17)

Black

2 (11)

Pacific Islander

0 (0)Native American

1 (6)Middle Eastern

1 (6)Asian American

0 (0)Depression diagnosis
Major depressive disorder

12 (67)

Persistent depressive disorder

1 (6)Psychotic depression

2 (11)“Situational” depression

1 (6)Mild depressive diagnosis

2 (11)

Double-blind assignment of participants was achieved through2 survey platforms: participants first filled in a consent form
and a screening survey on REDCap (Research Electronic Data
Capture; Vanderbilt University), and REDCap automaticallydirected those who were eligible to the RCT survey platform,
which randomly assigned participants to one of the 2 RCT
conditions. There was no way to link the screener and contact
information of REDCap to the RCT website. Therefore, neither
researchers nor participants had access to RCT assignment
information during the RCT. After participants completed the
RCT, the researchers could retrieve the no-treatment control
and treatment information per participant through website logs.
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

There were no significant differences in demographics anddiagnosis information by condition, as would be expected given
randomization to condition.
Might participants have ascertained the condition to which they
were assigned? This is possible. Unfortunately, we did not ask
participants at the conclusion of the study what condition they
thought they had been assigned to, and this should be added infuture research. However, participants were unlikely to ascertain
with any certainty the condition information. For example,
participants in one of the experimental conditions might havethought they were in an experimental condition (vs control).
JMIR Form Res 2023 | vol. 7 | e37270 | p. 6
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
However, it would have been hard to know if that experimental
condition was not actually a control condition against someother experimental intervention group. Participants in the control
condition also received message prompts every 3 hours to
document their rumination experiences and emotional states.
Therefore, participants who were assigned to the control group
might have thought that they were in an experimental group,that is, an experimental group in which there was frequentmonitoring of their rumination (eg, vs a no-monitoring control).
Indeed, before the beginning of participation, althoughparticipants were told that there was an “experimental” and a“control group,” the nature of these conditions was not specified.

Measurements
Overview
In addition to a set of demographic questions, we asked
participants to document daily their level of COVID-19 stressand the total amount of standard drinks that they had consumed.
To evaluate the ruminative experiences, participants also
indicated their responses on a battery of depression
measurements (see the following sections). After theintervention, participants were also asked to indicate their user
experiences (see the following sections).

Ruminative Response Scale
Rumination patterns for each individual (and the differences
therein) toward their depressive feelings were measured using
the Ruminative Response Scale [42]. The Ruminative Response
Scale consists of 22 items for assessing ruminative patterns andtheir dimensions (ie, depression, brooding, and reflection). Thisis a trait-like assessment of persistent rumination responses. In
this study, participants rated each of these items on a scale of
1 to 7 (1=not at all like me and 7=extremely like me), such as“I think about how alone I feel” or “what am I doing to deserve
this?” This measure exhibited excellent reliability in this study(Cronbach α=.95).

Ruminative Episode and Duration
To evaluate participants’ experiences of rumination, we asked
2 questions. First, in the past 3 hours, did you experience aruminative episode (by ruminative episode, we mean
experiencing a battery of negative thoughts that distracted fromyour current situation)? Second, if the participants answered“yes,” they were asked, “how long did this ruminative episodelast?”

State Depression Scale
The State Depression Scale (SDS) is a 3-item scale forparticipants to rate their in-the-moment level of depressedmood, anhedonia, and irritability on 7-point Likert scales
ranging from 1 (not at all) to 7 (very much) at the time of thealert. The SDS has already been tested and used in previous
EMA studies on rumination and depression [43]. This scale waspresented as a slider bar to fit the participants’ mobile phone
screen size. The Cronbach α for this scale has consistently beenhigh (ie, .8 to .9 range) each time it is used in research.

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller

Emotional Experiences
Participants’ in-the-moment emotions at the time of sampling
were measured using a pictorial self-report by the name of
Pick-A-Mood. This is essentially a 7-point Likert scalemeasuring a range of feelings such as tense, annoyed, calm, andrelaxed. Pick-A-Mood was designed specifically for mobile use[44], which serves as a better alternative than other emotion
measurements. The 8 emotional items were later combined into
a set of positive, neutral, and negative feelings. As in the casewith the SDS, the Cronbach α for this scale was consistently
high (ie, .8 to .9).

Statistical Analysis
Power
As the goal of this RCT involved evaluating changes inruminative episodes during the intervention, it follows therecommended design of an intensive within-person,
microrandomized trial. There are 2 issues pertaining to powerthat are important to differentiate. First, normally when
considering power for interventions, the authors calculate thenumber of participants needed for sufficient power (eg, 0.80)
to detect an effect given one’s design. Unfortunately, there iscurrently no concrete standard to calculate this type of sample
size for adequate power (or power given sample size) in suchtrials to formally test the effectiveness of the intervention versus
the control group per se [45]. There is a second type of power
consideration that is specific to Group Iterative Multiple Model
Estimation (GIMME). The issue is as follows: are there enoughdata points (eg, EMA events) within participants for the
researchers conducting such analyses to trust the rigor of theresults? That is, with GIMME, to have sufficient power to detecta small effect size when researchers are tracking individualchanges, each participant is required to document changes intheir behaviors of interest with sufficient time points(approximately n=60-75) [45]. Moreover, GIMME package
developers note that it usually takes 7 to 10 participants withineach comparison group to detect between-group differences[46,47]. In this study, of the 18 participants who completed thestudy, 9 (50%) were in the control condition and 9 (50%) werein the JITAI condition.

The GIMME Approach
Today’s technologies (eg, smartphone just-in-time interventions)can provide an enormous amount of intensive data over time
for a given person. At any given point in time when given eventsoccur (or not), an individual may have co-occurring affective
reactions, cognitions, and other states. In addition, each or noneof these phenomena in the moment (and their relationships with
one another) may predict reactions at the next moment in time(and the relationship among elements at that later point in time).
Often, these complex data are merely averaged over time orover chunks of time within a person, with considerable loss of
information as a given individual moves from moment tomoment and context to context, where variability in behavior
at the within-person level needs to be understood. In our work,for example, we would argue that a therapeutic client may
benefit more from a rumination intervention if given within a
few moments of a potentially causal event, cognition, or
JMIR Form Res 2023 | vol. 7 | e37270 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
emotional reaction that precipitated rumination than from anintervention that happens the next day or a week later outside
the current context. Such detailed co-occurrent andcross–time-lagged networks of information are critical fortherapists who would desire to know when to provide what
therapeutic message to optimize their effectiveness. To be able
to understand this intensive within-person idiographic level of
response and respond to it, we need newer statistical approaches.
The need for intensive within-person measurement has alreadybecome apparent in other areas of social science using intensive(every 3 hours) EMAs, such as in personality psychology. Overthe last 10 years—recognizing the importance of within-person
variability that cannot be accounted for based on between-person
measures (eg, Big Five trait measures)—the field has embraced
the importance of GIMME, which affords an intensive
within-person assessment over time. Beck and Jackson [48],publishing in the flagship journal of the field, the Journal of
Personality and Social Psychology, delineated this new approach
to idiographic personality models and how it identifies varying
patterns of behavior and behavior change by person by context
and lagging over time over 2 years. The method affords insightcompared with nomothetic approaches but also compared withother idiographic approaches that only look at phenomena at
one or relatively few points in time. GIMME affords anincredible tool for examining these sophisticated within-person
dynamics over context and time.
Although GIMME is a relatively new approach, it leverages
many existing statistical approaches that have been widely used
for many decades across science. For example, GIMME
leverages network science (and computer science) approaches
to implementing network theory. Although a full description ofthis approach is beyond the scope of this paper, the statisticalrationale for network analysis has been delineated in leading
journals in science, including in Nature (eg, see the work by
Whitfield [49]). In brief, network theory is a part of graph theory[50]—a network can be defined as a graph consisting of
attributes such as nodes (eg, in this case, rumination episodes
and affective states) or edges (associations between nodes).
Therefore, the edges in a network (as in GIMME) are essentiallycorrelation coefficients. GIMME allows us to examine therelationships (symmetrical or asymmetrical) among these
attributes in a network cotemporaneously as well as lagged overtime. GIMME takes advantage of the recent advances in
idiographic models and network science. Rather than using a
nomothetic multivariate time series analysis, GIMME uses a
network approach that highlights relationships among variables
both visually and quantitatively, representing both direct andindirect associations among them. Beck and Jackson [48]delineated how idiographic networks can answer questions
involving intraindividual change that are not easily addressedusing nomothetic models [51].
At the within level, GIMME uses an estimation analogous toautoregression. Using GIMME, one can model the subsequentscores (or occurrences) of an event using the previous scores(or occurrences). For example, in this study, we measured every
3 to 5 hours whether a participant experienced rumination ineach of the 35 days of the study period. We examined
contemporaneous and lagged relationships. Contemporaneous
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller
relationships estimate probabilistic within-person relationships
at the same time point. This is the tendency for ruminative
symptoms to occur at the same time. Lagged relationships
examine probabilistic within-person, cross–time-point (orcross-lagged) relationships. This is the tendency for ruminative
symptoms to follow one another across measurement conditions.
For example, in general, for ruminative behavior, if a rumination
episode happens at time 1, then there is a high possibility for asimilar rumination episode to happen later. Using thoseexperienced rumination episodes, one can use GIMME toconstruct a time series model of subsequent occurrences ofrumination episodes in the same way that one can construct atime series multilevel model, but one can construct a model thatis truly ideographic as there is no shrinkage of individual-level
estimates [52]. That is, GIMME can model the associations
between co-occurring scores (eg, symptoms or occurrences)both concurrently (0 lag) and at cross–time lags. In our analysis,
we also measured every 3 to 5 hours scores of affective reactionsof participants. Putting scores of affective reactions as
co-occurring variables with rumination, GIMME can generatea model to estimate both (1) the association between affective
reactions and rumination at the same time when they occur and(2) whether previous affective reactions could be predictive ofother variables in the model. In this study, we used GIMME toexamine whether previous affective states could be predictiveof the occurrence of subsequent rumination episodes. Thus,
GIMME [52,53] can reliably obtain the presence and directionof effects among variables collected at multiple (intensive) time
points, and it uses the full information maximum likelihood thatrigorously estimates the missing data points.
Furthermore, GIMME estimates both between- andwithin-person effects similar to a multilevel model, where there
is dependence between individual- and group-level estimates.
Specifically, GIMME can estimate both associations among
variables measured simultaneously and how variables measured
at a particular time point predict the effects and directions ofrelationships with variables collected at subsequent time points.
To do so, GIMME estimates those associations and directionsof effects through both unified structural equation modeling(uSEM) and network analysis. That is, in estimating a GIMME
model, the algorithm first uses uSEM, a structural vector
autoregressive model, to fit both the contemporaneous andlagged parameters. However, it does so in a way that is different
from a multilevel model that simply estimates “average”
relationships—GIMME keeps group-level paths that are
significant for at least 75% of individuals and gets rid of paths
that are not significant for at least 75% of the sample. GIMME
uses the selected path from uSEM to retain those group-level
paths [46,54].
GIMME models work well for considering the effects of anintervention—participants who reported experiencing arumination episode were immediately sent a JITAI message
tailored to the specific individual trigger of their rumination(eg, problem-solving skills or interpersonal skills). The GIMME
model, with its capacity to evaluate the significant changes inrumination episodes since their previous occurrences, canevaluate the extent to which tailored message feedback was
successful in helping participants take control of their condition
JMIR Form Res 2023 | vol. 7 | e37270 | p. 8
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Millerby showing whether there was a significant reduction inrumination episodes from their previous rumination occurrences.
After fitting each individual model, GIMME also fits a groupor subgroup-level model that looks at between-group effects.
GIMME analysis also produces network connections for eachparticipant and group (if there is a group-level effect). Network
graphs are produced by treating each variable of interest as anode and the associations (both contemporaneous and lagged)
between each variable as edges [46,54]. Analyses wereconducted using the GIMME package in R [46,54].

Results
Overview
Hypothesis 1 predicted stronger effects of the JITAI-MRFCBT
condition versus the control condition in reducing depressive
rumination. Table 2 shows each participant’s reported statistics(ie, total count and average duration) regarding ruminative
episodes (“in the past three hours, have you experienced anegative thinking episode?”). Table 2 is a summary of
rumination episodes and average minutes spent per rumination
episode per week throughout the 5-week period. Owing to thelimited sample size (each group had 9 participants), we usedbootstrapped independent 2-tailed t tests to compare differences
between the treatment and control conditions for the baseline
and postintervention weeks. In the baseline week (Table 2),
there were no significant differences in the counts of rumination
episodes (t16=−0.94; P=.93) and the average time spent
ruminating between the treatment and control groups (t16=−0.73;
P=.94). We also calculated reduction values. Reduction of
rumination episode counts and reduction in time spent on
rumination were created by subtracting rumination episodecounts in week 5 from those in week 1 per participant. Between
groups, using a series of independent t tests with bootstrapping,
results showed that participants in treatment (mean −25.28, SD
14.50) reported a significantly greater reduction in rumination
episode counts than those in the control condition (mean 1.44,
SD 4.12, 95% CI 14.25-33.67; P<.001; Cohen d=2.5). Similarly,
those in the treatment condition (mean −21.53, SD 17.6) showeda significant reduction in average minutes spent in ruminationcompared with those in the control condition (mean 1.47, SD
1.5, 95% CI 10.58-32.00; P=.04; Cohen d=1.84). Figure 3 also
shows participants’ counts of ruminative episodes and theaverage time they spent on each rumination episode per week.

Table 2. The count of rumination episodes and average duration (in minutes) of each ruminative episode each week.
Condition and partic- Week 1,
ipant ID
Na

Week 2,N

Week 3,N

Week 4,N

Week 5,N

Week 1,mean (SD)b

Week 2,mean (SD)

Week 3,mean(SD)

Week 4,mean(SD)

Week 5,mean (SD)Treatment
i_1

20

19

7

11

9.5

18.75

17.964

16.53

13.24

14.2308

i_2

5

1

0

0

0

15

15.6

10.01

5

0

i_3

25

7

4

3

2

113.571107.56

100.3

60.7

56.5

i_4

97

89

69

53

54

55.55

50.5

45.5

40.5

35.4

i_5

50

38

34

27

20

110

110.6

97.6

89.5

79

i_6

15

15

7

4

3

20.45

20.5

15.89

9.76

9.4

i_7

29

12

11

5

0

55.9

50.89

45.3

37.8

21.4

i_8

40

44

43

27

13

69

68.33

67.89

65.3

56.2

i_9

78

70

60

55

30

79.99

78.63

75.8

73.46

72.3

i_10

98

88

78

80

99

75

70

78.8

78.9

75.2

i_11

30

22

25

28

29

55

55.7

79.3

56.35

53.5

i_12

45

44

43

42

44

56.5

65.5

56.3

54.5

53.5

i_13

42

41

43

42

41

62.5

62.5

63.52

45.5

60.25

i_14

19

18

15

16

21

89

85.3

86.5

85.5

87

i_15

23

22

24

20

22

43

46.3

50.36

43.5

40.47

i_16

32

33

35

12

20

31.23

33.73

32.3

33.23

28

i_17

56

59

55

60

55

69.68

67.5

68.6

69.7

70

i_18

25

29

25

27

26

65.3

65.12

69.7

65.3

66.04

ControlaCount of ruminative episodes.

b

Average minutes spent on rumination.

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 9
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

Figure 3. Changes in count of ruminative episodes and average minutes per each ruminative episode 5 weeks.

Principal GIMME Results
GIMME provides a detailed look at the rumination reduction
found in this study. This is because traditional t tests could notprovide a rigorous estimation for RCT effects because of thesample size at the between-group level [51]. A GIMME analysis
was conducted to evaluate within-person changes, which
provided robust results in explaining the effects of this RCT.
We specified the analysis as a multiple group estimation thathttps://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

compares the differences between the JITAI MRFCBT andno-treatment control conditions. GIMME modeling firstestimates the association between each variable as β coefficientsat each individual level (if any). Table 3 shows a set of fit
indexes that indicate the model fit for each within-person change
model (18 individuals) using the uSEM approach. The root
mean square error of approximation (RMSEA) was evaluatedwith chi-square tests. In this case, an RMSEA of 0.01 is
considered an excellent fit [55]. A measure of comparative fit
JMIR Form Res 2023 | vol. 7 | e37270 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

is the comparative fit index (CFI); in this case, CFI=0.98. Huand Bentler [56] suggested that RMSEA values <0.06 and CFI
values >0.95 are indicative of a good fit. A more recent measureof fit to avoid model misspecification is the standardized root
mean square residual (SRMR) [57]. In this case, an SRMR of

0.06 indicates a good fit as it is <0.08. Our results show that agreat majority (15/18, 83%) of these models achieved excellent
fit. The estimation of group-level effects is set at 75%, meaningthat group-level analysis will not be conducted unless 75% of
the participants have shown an excellent fit [46,54].

Table 3. Within-individual fit indexes in Group Iterative Multiple Model Estimation (GIMME; waitlist control and just-in-time adaptive intervention[JITAI] comparison).a
Chi-square (df)

P value

RMSEAb

SRMRc

NFId

CFIe

individual_1

109.9389 (78)

.01

0.07

0.07

0.96

0.97

individual_2

101.2896 (77)

.03

0.06

0.07

0.96

0.97

individual_3

97.9413 (79)

.07

0.07

0.09

0.97

0.98

individual_4

94.4551 (79)

.11

0.05

0.06

0.98

0.99

individual_5

123.9945 (77)

<.001

0.07

0.05

0.95

0.97

individual_6

111.9069 (80)

.02

0.06

0.07

0.95

0.97

individual_7

114.0346 (79)

.36

0.02

0.06

1.00

1.00

individual_8

116.1622 (76)

.02

0.07

0.06

0.96

0.97

individual_9

118.2899 (74)

<.001

0.09

0.05

0.94

0.96

individual_10

120.4176 (79)

<.001

0.40

0.05

0.95

0.97

individual_11

122.5452 (76)

.04

0.30

0.05

0.94

0.97

individual_12

124.6729 (76)

.08

0.35

0.05

0.94

0.96

individual_13

126.8006 (76)

<.001

0.45

0.05

0.94

0.96

individual_14

128.9283 (75)

.02

0.52

0.05

0.94

0.96

individual_15

131.0559 (75)

.02

0.59

0.04

0.94

0.96

individual_16

95.5461 (77)

.01

0.05

0.06

0.98

0.99

individual_17

85.0285 (76)

.22

0.05

0.08

0.96

0.98

individual_18

146.7335 (77)

<.001

0.11

0.05

0.94

0.96

Condition and participant ID
JITAI condition

Control conditiona

Fit indexes provided by GIMME are subject to the same rule of thumb in structural equation modeling [46].

b

RMSEA: root mean square error of approximation.

c

SRMR: standardized root mean square residual.

d

NFI: normed fit index.

e

CFI: comparative fit index.

A sample of β correlations per participant (individual 16 in thecontrol condition and individual 7 in the JITAI condition)
showing predictive models based on a participant’s self-reported
rumination relationships can be found in Tables 4 and 5,
respectively (we did not provide the full estimation for all 18
participants to avoid repetition). The results (Table 4 forindividual 16) showed that the rumination episode significantly
predicted the occurrence of a subsequent rumination episodehttps://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX(β=.022; P=.02). Table 5 shows a model that describes anotherparticipant’s (JITAI condition, individual 7) self-reportedchanges during the intervention. This model also shows anacceptable model fit (Table 3), with RMSEA=0.02,
SRMR=0.06, and CFI=0.98. Interestingly, we see that, for thisparticipant, the rumination episode (episode) was not asignificant predictor of the occurrence of a subsequent
rumination episode (β=−.03; P=.75).

JMIR Form Res 2023 | vol. 7 | e37270 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

Table 4. Sample path analysis within individual 16 (control condition).a
Predictor
c

DVb
d

β

SE

z scoreP value

EpisodeEpisodelag

.022

0.09

2.43

.02

Durationminse

Durationminslag

.02

0.06

0.37

.71

StateRMf

StateRMlag

−.06

0.04

−1.41

.16

Neutralg

Neutrallag

−.11

0.10

−1.11

.27

Negativeh

Negativelag

.24

0.09

2.52

.01

Positive

Positivelag

.12

0.08

1.40

.16

Covidsti

Covidstlag

.91

0.02

52.88

<.001

Drinksj

Drinkslag

.86

0.03

33.58

<.001

StateRM

Episode

1.17

0.06

20.82

<.001

Durationmins

Episode

.78

0.04

19.50

<.001

EpisodeNeutral

−.40

0.08

−4.73

<.001

Negative

StateRM

.30

0.09

3.23

<.001

StateRM

Durationmins

−.35

0.07

−4.85

<.001

Positive

Negative

−.44

0.07

−5.87

<.001

a

Episode is the occurrence of a ruminative episode at each time; stateRM is the state depressive rumination level; and negative, positive, and neutralindicate participants’ emotional state.
b

DV: dependent variable.

c

Episode: the number of rumination episodes.

d

The affix -lag that comes after each variable (eg, episodelag or positivelag) indicates the subsequent scores and occurrences of the variable.

eDurationmins: minutes spent on a rumination episode.

fStateRM: level of rumination at the rumination episode.

g

Neutral: level of neutral affect in the past 3 to 5 hours.

hNegative: level of negative affect in the past hour.

i

Covidst: stress related to COVID-19.

jDrinks: drinking related to rumination.

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 12(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

Table 5. Sample path analysis within individual 7 (just-in-time adaptive intervention mobile rumination-focused cognitive behavioral therapy condition).a
Predictor
c

DVb
d

β

SE

z scoreP value

EpisodeEpisodelag

−.03

0.09

−0.32

.75

Durationminse

Durationminslag

.00

0.09

0.00

>.99

StateRMf

StateRMlag

.01

0.04

0.16

.87

Neutralg

Neutrallag

−.08

0.09

−0.94

.35

Negativeh

Negativelag

.18

0.08

2.23

.03

Positive

Positivelag

.03

0.11

0.23

.82

Covidsti

Covidstlag

.96

0.01

98.47

<.001

Drinksj

Drinkslag

.99

0.00

625.20<.001

StateRM

Episode

.73

0.05

14.79

<.001

Durationmins

Episode

.59

0.07

7.99

<.001

EpisodeNeutral

−.64

0.07

−9.78

<.001

Negative

Staterm

.03

0.12

0.26

.79

NeutralPositive

.60

0.07

8.55

<.001

StateRM

Neutral

−.26

0.06

−4.61

<.001

Negative

Neutral

−.64

0.11

−5.77

<.001

a

Episode is the occurrence of a ruminative episode at each time; stateRM is the state depressive rumination level; and negative, positive, and neutralindicate participants’ emotional state.
b

DV: dependent variable.

c

Episode: the number of rumination episodes.

d

The affix -lag that comes after each variable (eg, episodelag or positivelag) indicates the subsequent scores or occurrences of the variable.

eDurationmins: minutes spent on a rumination episode.

fStateRM: level of rumination at the rumination episode.

g

Neutral: level of neutral affect in the past 3 to 5 hours.

hNegative: level of negative affect in the past hour.

i

Covidst: stress related to COVID-19.

jDrinks: drinking related to rumination.

The GIMME results of comparisons both at the group (Figure4) and individual levels provide a detailed look at the networkof associations and effect carryover in time. In this analysis,shown in Figure 4, our model showed that, at the between-grouplevel, there are significant predictors that are responsible forthe occurrence of a subsequent ruminative episode. For example,
in the control condition, we found that factors such as rumination
episode, negative emotions, and duration of the rumination
episode were also estimated as significant predictors of asubsequent rumination episode. In Figure 4, for the

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JITAI-MRFCBT overall effects, it is noteworthy that the dashedlines found in the no-treatment control are absent, suggesting
little carryover from earlier rumination episodes and theirduration. The other noteworthy difference between these
conditions is the presence of a green line from positive toneutral. This can be interpreted as the presence in the momentof more positive affect that may be bolstering the user’s ability
to respond more productively in the face of the rumination
episode.

JMIR Form Res 2023 | vol. 7 | e37270 | p. 13(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

Figure 4. Between-group effects (no-treatment control vs just-in-time adaptive intervention [JITAI] mobile rumination-focused cognitive behavioral
therapy [MRFCBT]). Note: the graph on the left shows the associations of different variables within the no-treatment control, while the one on the rightshows the overall path models within the JITAI MRFCBT condition. Solid lines indicate a contemporaneous effect; dashed lines indicate a lagged
effect. Black lines indicate a group-level effect; green paths indicate an effect at a subgroup; gray paths indicate an individual effect.

The lack of dashed lines for participants in the treatment groupindicates that these participants did not seem to generate as
many subsequent cascading rumination episodes compared withthe control participants. This difference at the group level andthe individual level—and the detail with which we can “see”
these network processes over time—suggests that participantsin the JITAI condition did not bring their negative thoughts andprevious ruminative experiences into their next ruminative
episode. Indeed, the main goal of the JITAI-MRFCBT condition
was to train participants to make different attributions and focus
them on problem-solving when they experienced a particularruminative episode.

Discussion
Principal Findings
This pilot study compared a JITAI-MRFCBT condition with ano-treatment control condition in reducing rumination. To assess
rumination reduction, we compared the baseline (week 1) andthe postintervention week (week 5)—we found that bothruminative episodes and the average time participants spent on
those ruminative episodes decreased significantly in both groups,
but the reduction was significantly greater in the
JITAI-MRFCBT condition. Our results showed that participantsin the control condition demonstrated some time-lagged effects
such that experiencing ruminative episodes or negative emotions
at a time point predicted an increase in such experiences
subsequently. However, such lagged effects were not present
in the JITAI condition. That is, no dotted lines in the treatment
condition indicate lagged effects. This difference at the grouplevel shows that participants in the JITAI condition, comparedwith those in the control condition, did not bring their negative
thoughts and previous ruminative experiences into their next
ruminative episode. The treatment appeared to stop the “echochamber” of the previous negative experiences.

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

This study, in addition to affording a comparison between
treatment and control groups at both the group and the individuallevel, involved four innovations: (1) using MRFCBT as the“what” of the intervention and JITAI as the “when,” which has
promise for optimizing responsive and effective interventions
per client; (2) devising a “how” (in this case using participant
baseline measures of rumination and GIMME) to identify typicalbaseline patterns per participant when rumination (and change
in affect and cognitions) is likely; (3) using GIMME within
persons per client to better understand “why,” leveraging
smartphone technologies to deeply understand and predict, forthe first time, networks of cognitions and affect and patterns ofrumination and cascading rumination (and possible interpersonal
predictors) from one moment to the next; and (4) ability (viacombining these technologies) to afford identification,
understanding, and change in disruptive psychological patterns(eg, rumination) personalized to the user leveraging smartphone
technologies. In the following sections, we discuss each of these
innovations along with their implications for the literature on
rumination, depression, interpersonal communication, andtechniques and technologies (eg, GIMME, EMA, and JITAI).
We then discuss the limitations of this work and our overall
conclusions.

Comparison With Prior Work
The “What” and “When” of CBT Using JITAIs
CBT has been a “gold standard” for a diverse array of disorders.
The emergence of mobile phones as an alternative means todeliver face-to-face CBTs also speaks to an increasing need formental health services. Researchers have been working on phone
apps [58,59] to provide CBT (ie, MCBT) to those who are
depressed. Despite reports of positive user experiences [60],there is still a lack of symptom-focused MCBTs (eg, MCBTs
for depressive rumination). MCBTs to date, unfortunately, stilllack the level of interactive features needed to provide more

JMIR Form Res 2023 | vol. 7 | e37270 | p. 14(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
tailored feedback based on individual characteristics. This is
despite calls to address this need [60].
However, for many disorders, CBT delivered face-to-face is
apt to be less than optimally effective as clients cannot receive
the treatment at the optimal time. Even when included in a
mobile device, CBT is typically not responsive to the dynamics
of the client’s experience. Rumination provides a very good
example of these difficulties. If negative cognitions and affective
states are allowed to cascade, as mentioned earlier, thisexacerbates depression. Simply providing CBT materials people
can access even on a mobile device is likely not enough. A
scaffolding tool for responding at just the right moment withthe right therapeutic tools and messages for mitigating
rumination threats should be the intervention goal to which we
aspire. Although no previous study has tested mobile
phone–delivered JITAIs in reducing depressive rumination [26],the results of this study suggest that JITAIs could afford an
essential means to deliver more responsive CBT for ruminatingclients diagnosed with clinical levels of depression and that, indoing so, especially with intensive rumination measures andtreatments responsive to them, rumination could be reduced.
Interesting transdiagnostic CBT approaches are promisingtreatments for a broad range of emotional disorders but, inreviewing this work, only rumination and negative
metacognition significantly mediated client functioning [61].
These findings highlight the importance of mitigating the effects
of rumination for the effectiveness of CBT interventions, thus
highlighting the importance of this method and approach for“seeing” and reducing rumination.

The “How” of JITAIs for MCBTJITAIs focus on the “when” of intervention delivery [27].
However, knowing “when” to deliver a given intervention—the“what” (eg, MCBT component)—depends on what cues andindicators are available and should be used (eg, with one’s
smartphone) to make that determination. For example, JITAIs
are used in trying to inhibit an individual’s alcohol [62] or illicit
drug use [63,64], which need interventions to note a client’s
typical source of these substances (eg, local bars and locationof dealers). Knowing what environmental or personal cues might
initiate a problematic behavior (that the intervention is designed
to deter) provides the basis for the “how” (how JITAIs know
“when” to intervene). Some possibilities are through EMA andsensor technologies in the smartphone—these can potentiallyprovide the basis (ie, possible algorithms based on formative
research) for a JITAI to “know” “when” to deliver “what” tooptimize intervention goals. In this study, rumination had long
been identified as problematic in exacerbating depression[31,33,37]. The “how” for JITAI in this study became how can
we determine when a client (who ruminates and is diagnosed
with depression) is most likely to be ruminating? Our interim
step in ascertaining a relatively crude “how” was to first
determine how often individuals who were clinically depressed(and ruminating) ruminated, for example, how many times a
day did this occur? Across individuals in this population, there
was variability in these patterns. Therefore, “how” required amore personalized assessment (ie, a baseline) of the times ofday. The goal of RFCBT is to train participants to make different
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller
attributions and focus on problem-solving when they experiencea particular ruminative episode. The presumption is that, when
the next ruminative episode occurs, participants can immediately
become aware of their ruminative thoughts and adopt one of
the strategies offered to them by RFCBT. In this study, weachieved this goal through the week 1 baseline assessment. Thatis, we used the individualized information, which tells us thefrequency, duration, types, and high time of each client’s
rumination patterns, to create messages tailored to eachparticipant. For example, if the week 1 baseline information
told us that a participant had been experiencing more rumination
episodes elicited by interpersonal stress rather than personal
setbacks, this participant received more messages oninterpersonal problem-solving.
Our process of using baseline EMA reports for the “how” forestimating probable rumination times provides a model not only
for “rumination” estimates at the individual level but also forany behavior of interest we seek to change. That is, can we
identify in time and space when this behavior of interest is moreprobable per person of interest? Can we identify cues orindicators (and possible algorithms using smartphones andEMA) that anticipate the behavior of interest? Can we collectthat information at baseline to then use it in the intervention ofa personalized JITAI?

Sufficiently Fine-Grained Measurement for the“Why”: Blocking Rumination Cascades
The reported findings using GIMME within persons per client
provide insight into the “why” of ruminative cascades.
Identifying rumination, rumination cascades, and rumination
blocking is a known challenge [3,65]. The emotional cascade
model suggests that emotional dysregulation turns into
subsequent behavioral dysregulation through a process called
emotional cascades [66]. Emotional cascades are the feedback
loops between intense rumination and negative affect. Emotionalcascades occur mostly after a negative trigger such as unpleasantinterpersonal experiences, personal losses, and setbacks. As one
becomes more focused on the negative experiences, one canfind it hard to divert attention away from reliving the negative
event. This cycle is repeated as rumination and negative affectinteract, resulting in more dysregulated behaviors. However,“seeing” these networks of cognition and affect, especially inanything close to “real time,” has not been possible. This makes
it hard to understand what exactly is triggering in the situation
for this individual and how it is being framed by them. Suchinsights could provide targets for personalized interventions.
GIMME provides a method for examining at the individuallevel the associations between cognition and affect for theindividual at each time point and how these associations carryover and predict subsequent ruminative episodes. GIMME
methods, and sufficient time sampling using EMA, afford tools
to not only understand the client’s cognitive and affective
construals (eg, activating hurtful perceptions) of interpersonal
interactions but also “see” those with detail in a morefine-grained network than has been previously available. This
makes it possible for researchers, clients, and their therapists
to identify how these perceptions change and trigger rumination
events for specific individuals with depression from moment
JMIR Form Res 2023 | vol. 7 | e37270 | p. 15(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
to moment over time (and whether interventions designed toaddress them are proving effective).
Numerous empirical studies have highlighted the link between
ruminative process and behavioral dysregulation and called for
effective means of rumination disruption. However, researchsuggesting ways to effectively break this vicious cycle is scarce.
Blocking rumination is historically difficult to achieve [3,65].
An approach is to use “normal methods of distraction,” which
unfortunately “becomes ineffective” [65]. Part of the difficulty
may be that, historically, rumination has been assessed throughself-reports, often summarizing over days, weeks, or months.
As far as we know, there are no current effective measures ofrumination closer to “in the moment” in real time. The resultsof this study suggest that GIMME tools and personalizedbaselines can help us better “see” rumination in a morefine-grained way in each episode. This means that we can better“see” whether our interventions are effectively reducing theruminative links from one ruminative episode to the next.
“Seeing” in a fine-grained way when disruption of rumination
is occurring and why is a major advance.

Combining Intervention Design, Techniques, andTechnologies: Advancing Rumination Science
A major innovation in this pilot work is (1) putting these 3
innovations together, leveraging MRFCBT materials to afford
identification, understanding, and change in rumination patterns,
all while personalizing the intervention to the user (when tointervene with what), leveraging smartphone technologies, and(2) showing the promise of this JITAI-MRFCBT compared with
the no-treatment control group. JITAIs, as used in this study,
are an effective method to disrupt emotional cascades rather
than simply creating distraction—instead of using traditional
means of shifting attention (eg, physical sensation seeking),
participants can be provided with a scaffolding frame that helps
them reappraise their current emotions and alter attributions at
the exact moment of the emotional cascade.
In addition, this pilot trial evaluated the promise of a mobile
phone–delivered JITAI (with MRFCBT) as a delivery meansof reaching participants with an in-the-moment intervention[27,67]. The results of this project have shown that, in JITAIs,
participants ruminating can receive in-the-moment feedback on
their mobile phones. Third, this study provides a data-driven
explanation regarding the effects of JITAIs. That is, our GIMME
results suggest that, with messages timely tailored and sent toparticipants in the time of greatest need, participants in thetreatment condition showed a significant reduction in“next-time” rumination episodes. Results from the JITAI
conditions are promising for reducing the number of ruminative
episodes and lowering average rumination times. Therefore, theresults of this pilot RCT study indicate the viability of afull-scale RCT for reducing rumination in a population
diagnosed with clinical depression. Thus, this pilot serves anoverarching goal of identifying a viable approach that candeliver JITAIs focusing on depression outcomes and provides
the groundwork for subsequent clinical trials.
Rumination plays a significant role in a wide array of mental
health disorders and problematic behaviors. The focus in thisstudy was on MDD, but rumination also plays a significant role
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller
in other mental health disorders such as bipolar disorder [68],anxiety, and mood disorders [66,69]. Nolen-Hoeksema [30],noting the high comorbidity among many mental healthdisorders, conceptualized rumination as a transdiagnostic process
associated with not only depression and anxiety but also avariety of deleterious outcomes (ie, substance abuse, binge
eating, and self-injuring behaviors; see McLaughlin et al [66]).
Indeed, emerging literature provides evidence to support suchclaims [66,70,71], with McLaughlin and Nolen-Hoeksema [70]arguing for “the importance of targeting rumination intransdiagnostic treatment approaches for emotional disorders.”
As recently argued, “there is consistent evidence showing thata reduced use of cognitive reappraisal and an increased use ofnegative rumination are present across a number of disorders,
whereas increased levels of positive rumination appear to be
confined to bipolar disorder” [72]. Given the transdiagnostic
role of rumination across many disorders, the importance oftools to measure and understand it (and intervene with respect
to it) in a detailed way for individual clients across contexts andover time is critical and highly generalizable.

Limitations
However, the limitations of this RCT are apparent. The major
limitation is the self-reported nature of participants’ depression
diagnoses. Although we designed a series of questions such asasking participants to report their recent diagnosis and provide
names of their depression medications, we may still not be able
to fully generalize the efficacy of these interventions to allparticipants with clinical depression. Therefore, the results ofthis study are only limited to participants with self-reported
depression diagnoses. There are other less major limitations.
First, despite the small sample of participants, this analysis
could offer a robust pre- and postestimation of the RCT
following suggestions of a micro-RCT. However, this suggests
a need for a full-scale RCT with power that can speak to
between-group comparisons. Therefore, a subsequent RCT isneeded. Second, because of delays in the IRB process of >1
year, some exploratory research questions on how COVID-19
and drinking patterns at the time of COVID-19 (and the impact
on ruminative patterns) could not be examined in the timely
way originally planned. Among participants who provided daily
entries of their COVID-19–related stress, most (18/25, 72%)
indicated that they experienced zero or very low COVID-19
stress. Therefore, we did not detect any significant role of
COVID-19 and related drinking behaviors (shown in the
GIMME models). That is, the results of this study could only
be evaluated as a pilot for subsequent RCTs during futurewidespread events. Third, although this RCT showed someeffects of treatment in reducing ruminative episodes among
participants with clinical depression who were in therapy, it did
not examine the effect of the RCT on participants who weredepressed but who were not in therapy. Therefore, we do notknow if the RCT would be effective for patients who could notsecure a therapeutic relationship. Fourth, we do not knowwhether the treatment would be promising for patients who are
not clinically depressed. Therefore, we cannot generalize theefficacy of the RCT to a sample of participants who are notclinically depressed and in therapy. Finally, as part of this RCT,
we specifically designed materials for participants to reanalyze
JMIR Form Res 2023 | vol. 7 | e37270 | p. 16(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
their hurtful interpersonal feelings and the support they received;
these factors were designed as self-report diaries for participantsin line with previous CBT for rumination. However, as we could
not recruit enough participants to code these variables, we could
not evaluate perceived hurtfulness and social support as potential
mediators in the statistical analysis. Therefore, in subsequent
work, there is a need to design better methods and measures ofthese potential mediators.

Conclusions
This study is the first of its kind to focus on theepisode-to-episode measurement, both at the individual clientand group levels, of one specific outcome (depressive
rumination) underlying depression. This pilot shows how
promising this JITAI-MRFCBT approach is for identifying andblocking cascades of rumination in a timely way, providing
appropriate CBT at the critical time of its need. “Seeing” theindividual patient networks affords opportunities to better
understand and mitigate rumination—a critical psychologicalprocess that exacerbates not only depression but also a rangeof mental health disorders and adverse health outcomes. Indeed,as others have noted, rumination appears to be a transdiagnostic

Wang & Miller
process that provides insight across a large swath of disorders,making it particularly important to use tools such as thosedeveloped in this study to better understand these processes andmitigate their adverse effects—as doing so would appear tohave broad generalizability. It is worth pointing out that, in ourstudy, none of the users reported experiencing technicaldifficulties using their smartphones to access, navigate, andinteract with the RCT website. Therefore, it speaks to thefeasibility and scalability of delivering JITAI-MRFCBT without
technical difficulties. As the initial pilot intervention was
designed to reduce the complexity of installing apps andcomplicated interactive features, users were able to adhere to
the intervention with minimal technical support. Most
importantly, this design minimizes the requirements for specifictypes of smartphones. That is, a patient can have access to the
RCT if their phone has access to the internet and a browser. As
such, it is a promising approach in an era of increasingly
personalized interventions that provide feedback and scaffolding
for the client and their therapist and for researchers trying tobetter understand and mitigate adverse patterns of cognition,affect, and behavior across contexts and over time to advancewellness and this emerging field of rumination science.

Acknowledgments
This project was funded by the Annenberg School of Communication and Journalism Dissertation funds and summer researchfunds.

Authors' Contributions
LW is the corresponding author who worked on design, data collection, analysis, and writing of the manuscript. LCM also worked
on design, data collection, and writing of the manuscript.

Conflicts of Interest
None declared.

References
1.
2.

3.
4.
5.
6.

7.

8.

American Psychiatric Association. Desk Reference to the Diagnostic Criteria from DSM-5. Washington, DC, USA: American
Psychiatric Association Publishing; 2014.
Flynn M, Kecmanovic J, Alloy LB. An examination of integrated cognitive-interpersonal vulnerability to depression: therole of rumination, perceived social support, and interpersonal stress generation. Cognit Ther Res 2010 Oct;34(5):456-466[FREE Full text] [doi: 10.1007/s10608-010-9300-8] [Medline: 25429169]
Selby EA, Joiner Jr TE. Cascades of emotion: the emergence of borderline personality disorder from emotional and behavioral
dysregulation. Rev Gen Psychol 2009 Sep 01;13(3):219 [FREE Full text] [doi: 10.1037/a0015687] [Medline: 20161656]
Joiner Jr TE. A test of the hopelessness theory of depression in youth psychiatric inpatients. J Clin Child Psychol 2000
Jun;29(2):167-176. [doi: 10.1207/S15374424jccp2902_3] [Medline: 10802826]
Wang L, Miller LC. Just-in-the-Moment adaptive interventions (JITAI): a meta-analytical review. Health Commun 2020
Nov;35(12):1531-1544. [doi: 10.1080/10410236.2019.1652388] [Medline: 31488002]
Wang YG, Wang YQ, Chen SL, Zhu CY, Wang K. Theory of mind disability in major depression with or without psychotic
symptoms: a componential view. Psychiatry Res 2008 Nov 30;161(2):153-161. [doi: 10.1016/j.psychres.2007.07.018][Medline: 18926572]
Nalabandian T, Ireland M. Depressed individuals use negative self-focused language when recalling recent interactions
with close romantic partners but not family or friends. In: Proceedings of the 6th Workshop on Computational Linguistics
and Clinical Psychology. 2019 Presented at: CLPsych '19; June 6, 2019; Minneapolis, MN, USA p. 62-73. [doi:10.18653/v1/w19-3008]
Anxiety disorders | Quality standards. National Institutes for Health and Care Excellence. 2014 Feb 6. URL: https://www.
nice.org.uk/guidance/qs53 [accessed 2021-08-29]

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 17(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
9.

10.

11.

12.

13.

14.
15.

16.

17.
18.
19.

20.
21.

22.

23.

24.

25.

26.

27.

28.

29.

Reavell J, Hopkinson M, Clarkesmith D, Lane DA. Effectiveness of cognitive behavioral therapy for depression and anxiety
in patients with cardiovascular disease: a systematic review and meta-analysis. Psychosom Med 2018 Oct;80(8):742-753.
[doi: 10.1097/PSY.0000000000000626] [Medline: 30281027]
Kuhn E, Greene C, Hoffman J, Nguyen T, Wald L, Schmidt J, et al. Preliminary evaluation of PTSD Coach, a smartphone
app for post-traumatic stress symptoms. Mil Med 2014 Jan;179(1):12-18. [doi: 10.7205/MILMED-D-13-00271] [Medline:
24402979]
Horsch CH, Lancee J, Griffioen-Both F, Spruit S, Fitrianie S, Neerincx MA, et al. Mobile phone-delivered cognitive
behavioral therapy for insomnia: a randomized waitlist controlled trial. J Med Internet Res 2017 Apr 11;19(4):e70 [FREEFull text] [doi: 10.2196/jmir.6524] [Medline: 28400355]
Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, et al. Efficacy of self-guided Internet-based cognitive
behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. JAMA Psychiatry
2017 Apr 01;74(4):351-359. [doi: 10.1001/jamapsychiatry.2017.0044] [Medline: 28241179]
Sijbrandij M, Kunovski I, Cuijpers P. Effectiveness of Internet-delivered cognitive behavioral therapy for posttraumaticstress disorder: a systematic review and meta-analysis. Depress Anxiety 2016 Sep;33(9):783-791. [doi: 10.1002/da.22533][Medline: 27322710]
Chandrashekar P. Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental
health mobile apps. Mhealth 2018;4:6 [FREE Full text] [doi: 10.21037/mhealth.2018.03.02] [Medline: 29682510]
Chmitorz A, Kunzler A, Helmreich I, Tüscher O, Kalisch R, Kubiak T, et al. Intervention studies to foster resilience - asystematic review and proposal for a resilience framework in future intervention studies. Clin Psychol Rev 2018
Feb;59:78-100 [FREE Full text] [doi: 10.1016/j.cpr.2017.11.002] [Medline: 29167029]
Joyce S, Shand F, Tighe J, Laurent SJ, Bryant RA, Harvey SB. Road to resilience: a systematic review and meta-analysisof resilience training programmes and interventions. BMJ Open 2018 Jun 14;8(6):e017858 [FREE Full text] [doi:10.1136/bmjopen-2017-017858] [Medline: 29903782]
Stone A. Ecological momentary assessment and experience sampling. In: Vannette DL, Krosnick JA, editors. The Palgrave
Handbook of Survey Research. Cham, Switzerland: Palgrave Macmillan; 2018:563-571.
Stone AA, Shiffman S. Ecological momentary assessment (EMA) in behavioral medicine. Ann Behav Med
1994;16(3):199-202. [doi: 10.1093/abm/16.3.199]
Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time Adaptive Interventions(JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med2018 May 18;52(6):446-462 [FREE Full text] [doi: 10.1007/s12160-016-9830-8] [Medline: 27663578]
Pham Q, Wiljer D, Cafazzo JA. Beyond the randomized controlled trial: a review of alternatives in mHealth clinical trial
methods. JMIR Mhealth Uhealth 2016 Sep 09;4(3):e107 [FREE Full text] [doi: 10.2196/mhealth.5720] [Medline: 27613084]
Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, et al. Mobile health technology evaluation: the mHealthevidence workshop. Am J Prev Med 2013 Aug;45(2):228-236 [FREE Full text] [doi: 10.1016/j.amepre.2013.03.017][Medline: 23867031]
Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: an experimental designfor developing just-in-time adaptive interventions. Health Psychol 2015 Dec;34S:1220-1228 [FREE Full text] [doi:10.1037/hea0000305] [Medline: 26651463]
Connolly SL, Alloy LB. Rumination interacts with life stress to predict depressive symptoms: an ecological momentary
assessment study. Behav Res Ther 2017 Oct;97:86-95 [FREE Full text] [doi: 10.1016/j.brat.2017.07.006] [Medline:
28734979]
Hallensleben N, Glaesmer H, Forkmann T, Rath D, Strauss M, Kersting A, et al. Predicting suicidal ideation by interpersonalvariables, hopelessness and depression in real-time. An ecological momentary assessment study in psychiatric inpatientswith depression. Eur Psychiatry 2019 Feb;56:43-50. [doi: 10.1016/j.eurpsy.2018.11.003] [Medline: 30530103]
Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression prediction by using ecological momentary assessment, Actiwatch
data, and machine learning: observational study on older adults living alone. JMIR Mhealth Uhealth 2019 Oct 16;7(10):e14149[FREE Full text] [doi: 10.2196/14149] [Medline: 31621642]
Watkins ER, Mullan E, Wingrove J, Rimes K, Steiner H, Bathurst N, et al. Rumination-focused cognitive-behaviouraltherapy for residual depression: phase II randomised controlled trial. Br J Psychiatry 2011 Oct;199(4):317-322. [doi:10.1192/bjp.bp.110.090282] [Medline: 21778171]
Sharmin M, Raij S, Epstien D, Nahum-Shani I, Beck JG, Vhaduri S, et al. Visualization of time-series sensor data to informthe design of just-in-time adaptive stress interventions. Proc ACM Int Conf Ubiquitous Comput 2015 Sep;2015:505-516[FREE Full text] [doi: 10.1145/2750858.2807537] [Medline: 26539566]
Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and healthbehaviour treatments. Br J Health Psychol 2010 Feb;15(Pt 1):1-39 [FREE Full text] [doi: 10.1348/135910709X466063][Medline: 19646331]
Lyubomirsky S, Nolen-Hoeksema S. Effects of self-focused rumination on negative thinking and interpersonal problem
solving. J Pers Soc Psychol 1995 Jul;69(1):176-190. [doi: 10.1037//0022-3514.69.1.176] [Medline: 7643299]

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller

JMIR Form Res 2023 | vol. 7 | e37270 | p. 18(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
30.
31.
32.
33.

34.
35.
36.
37.

38.
39.

40.

41.

42.
43.
44.
45.
46.
47.
48.
49.

50.
51.
52.

53.

54.

Nolen-Hoeksema S. The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. J Abnorm
Psychol 2000 Aug;109(3):504-511. [Medline: 11016119]
Nolen-Hoeksema S, Wisco BE, Lyubomirsky S. Rethinking rumination. Perspect Psychol Sci 2008 Sep;3(5):400-424. [doi:10.1111/j.1745-6924.2008.00088.x] [Medline: 26158958]
Camper PM, Jacobson NS, Holtzworth-Munroe A, Schmaling KB. Causal attributions for interactional behaviors in married
couples. Cogn Ther Res 1988 Apr;12(2):195-209. [doi: 10.1007/bf01204931]
Michl LC, McLaughlin KA, Shepherd K, Nolen-Hoeksema S. Rumination as a mechanism linking stressful life events tosymptoms of depression and anxiety: longitudinal evidence in early adolescents and adults. J Abnorm Psychol 2013
May;122(2):339-352 [FREE Full text] [doi: 10.1037/a0031994] [Medline: 23713497]
Joiner Jr TE, Metalsky GI. A prospective test of an integrative interpersonal theory of depression: a naturalistic study ofcollege roommates. J Pers Soc Psychol 1995 Oct;69(4):778-788. [doi: 10.1037//0022-3514.69.4.778] [Medline: 7473031]
Johnson DP, Whisman MA. Gender differences in rumination: a meta-analysis. Pers Individ Dif 2013 Aug;55(4):367-374[FREE Full text] [doi: 10.1016/j.paid.2013.03.019] [Medline: 24089583]
Olatunji BO, Naragon-Gainey K, Wolitzky-Taylor KB. Specificity of rumination in anxiety and depression: a multimodal
meta-analysis. Clin Psychol Sci Pract 2013 Sep 16;20(3):225-257. [doi: 10.1111/cpsp.12037]
Rood L, Roelofs J, Bögels SM, Nolen-Hoeksema S, Schouten E. The influence of emotion-focused rumination and distraction
on depressive symptoms in non-clinical youth: a meta-analytic review. Clin Psychol Rev 2009 Nov;29(7):607-616. [doi:10.1016/j.cpr.2009.07.001] [Medline: 19682781]
Psychologytools. URL: https://www.psychologytools.com/ [accessed 2022-12-15]
Arslan RC, Walther MP, Tata CS. formr: a study framework allowing for automated feedback generation and complex
longitudinal experience-sampling studies using R. Behav Res Methods 2020 Feb;52(1):376-387 [FREE Full text] [doi:10.3758/s13428-019-01236-y] [Medline: 30937847]
Harris PA, Scott KW, Lebo L, Hassan N, Lightner C, Pulley J. ResearchMatch: a national registry to recruit volunteers forclinical research. Acad Med 2012 Jan;87(1):66-73 [FREE Full text] [doi: 10.1097/ACM.0b013e31823ab7d2] [Medline:
22104055]
Pulley JM, Jerome RN, Bernard GR, Olson EJ, Tan J, Wilkins CH, et al. Connecting the public with clinical trial options:
the ResearchMatch Trials Today tool. J Clin Transl Sci 2018 Aug;2(4):253-257 [FREE Full text] [doi: 10.1017/cts.2018.327][Medline: 30820361]
Nolen-Hoeksema S. Responses to depression and their effects on the duration of depressive episodes. J Abnorm Psychol
1991 Nov;100(4):569-582. [doi: 10.1037//0021-843x.100.4.569] [Medline: 1757671]
Moberly NJ, Dickson JM. Goal conflict, ambivalence and psychological distress: concurrent and longitudinal relationships.
Pers Individ Dif 2018 Jul;129:38-42. [doi: 10.1016/j.paid.2018.03.008]
Desmet PM, Vastenburg MH, Romero N. Mood measurement with Pick-A-Mood: review of current methods and designof a pictorial self-report scale. J Des Res 2016;14(3):241-279. [doi: 10.1504/jdr.2016.079751]
Ruwaard J, Kooistra L, Thong M. Ecological Momentary Assessment in Mental Health Research. EMA Research Manual.
2018 Nov 26. URL: https://jruwaard.github.io/aph_ema_handbook/ [accessed 2022-08-27]
Beltz AM, Gates KM. Network mapping with GIMME. Multivariate Behav Res 2017;52(6):789-804 [FREE Full text] [doi:10.1080/00273171.2017.1373014] [Medline: 29161187]
Lane ST, Gates KM. Automated selection of robust individual-level structural equation models for time series data. Struct
Equ Modeling 2017 May 01;24(5):768-782. [doi: 10.1080/10705511.2017.1309978]
Beck ED, Jackson JJ. Consistency and change in idiographic personality: a longitudinal ESM network study. J Pers Soc
Psychol 2020 May;118(5):1080-1100. [doi: 10.1037/pspp0000249] [Medline: 31120288]
Whitfield J. Group theory; What makes a successful team? John Whitfield looks at research that uses massive online
databases and network analysis to come up with some rules of thumb for productive collaborations. Nature 2008 Oct
9;455(7214):720-724.
Scott J, Carrington PJ. The SAGE Handbook of Social Network Analysis. Thousand Oaks, CA, USA: Sage Publications;
2011.
Piccirillo ML, Beck ED, Rodebaugh TL. A clinician’s primer for idiographic research: considerations and recommendations.
Behav Ther 2019 Sep;50(5):938-951. [doi: 10.1016/j.beth.2019.02.002] [Medline: 31422849]
Gates KM, Molenaar PC. Group search algorithm recovers effective connectivity maps for individuals in homogeneousand heterogeneous samples. Neuroimage 2012 Oct 15;63(1):310-319. [doi: 10.1016/j.neuroimage.2012.06.026] [Medline:
22732562]
Gates KM, Molenaar PC, Iyer SP, Nigg JT, Fair DA. Organizing heterogeneous samples using community detection of
GIMME-derived resting state functional networks. PLoS One 2014 Mar 18;9(3):e91322 [FREE Full text] [doi:10.1371/journal.pone.0091322] [Medline: 24642753]
Beltz AM, Moser JS, Zhu DC, Burt SA, Klump KL. Using person-specific neural networks to characterize heterogeneityin eating disorders: illustrative links between emotional eating and ovarian hormones. Int J Eat Disord 2018 Jul;51(7):730-740[FREE Full text] [doi: 10.1002/eat.22902] [Medline: 30132946]

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

Wang & Miller

JMIR Form Res 2023 | vol. 7 | e37270 | p. 19(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
55.
56.
57.

58.
59.

60.

61.

62.

63.

64.

65.
66.

67.

68.

69.
70.
71.

72.

Wang & Miller

MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure
modeling. Psychol Methods 1996 Jun;1(2):130-149. [doi: 10.1037/1082-989x.1.2.130]
Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives.
Struct Equ Model 1999 Jan;6(1):1-55. [doi: 10.1080/10705519909540118]
Henseler J, Dijkstra TK, Sarstedt M, Ringle CM, Diamantopoulos A, Straub DW, et al. Common beliefs and reality about
PLS: comments on Rönkkö and Evermann (2013). Organ Res Methods 2014 Apr 10;17(2):182-209. [doi:10.1177/1094428114526928]
Wright JH, Mishkind M, Eells TD, Chan SR. Computer-assisted cognitive-behavior therapy and mobile apps for depression
and anxiety. Curr Psychiatry Rep 2019 Jun 27;21(7):62. [doi: 10.1007/s11920-019-1031-2] [Medline: 31250242]
Josephine K, Josefine L, Philipp D, David E, Harald B. Internet- and mobile-based depression interventions for people withdiagnosed depression: a systematic review and meta-analysis. J Affect Disord 2017 Dec 01;223:28-40. [doi:10.1016/j.jad.2017.07.021] [Medline: 28715726]
Stawarz K, Preist C, Tallon D, Wiles N, Coyle D. User experience of cognitive behavioral therapy apps for depression: ananalysis of app functionality and user reviews. J Med Internet Res 2018 Jun 06;20(6):e10120 [FREE Full text] [doi:10.2196/10120] [Medline: 29875087]
Muñoz-Navarro R, Medrano LA, Limonero JT, González-Blanch C, Moriana JA, Ruiz-Rodríguez P, et al. The mediatingrole of emotion regulation in transdiagnostic cognitive behavioural therapy for emotional disorders in primary care: secondary
analyses of the PsicAP randomized controlled trial. J Affect Disord 2022 Apr 15;303:206-215 [FREE Full text] [doi:10.1016/j.jad.2022.01.029] [Medline: 34998804]
Gustafson DH, McTavish FM, Chih M, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support
recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry 2014 May;71(5):566-572 [FREE Full text] [doi:10.1001/jamapsychiatry.2013.4642] [Medline: 24671165]
Singh NB, Björling EA. A review of EMA assessment period reporting for mood variables in substance use research:
expanding existing EMA guidelines. Addict Behav 2019 Jul;94:133-146. [doi: 10.1016/j.addbeh.2019.01.033] [Medline:
30712774]
Linas BS, Genz A, Westergaard RP, Chang LW, Bollinger RC, Latkin C, et al. Ecological momentary assessment of illicit
drug use compared to biological and self-reported methods. JMIR Mhealth Uhealth 2016 Mar 15;4(1):e27 [FREE Full text][doi: 10.2196/mhealth.4470] [Medline: 26980400]
Tuna E, Bozo Ö. Exploring the link between emotional and behavioral dysregulation: a test of the emotional cascade model.
J Gen Psychol 2014;141(1):1-17. [doi: 10.1080/00221309.2013.834289] [Medline: 24838017]
McLaughlin KA, Aldao A, Wisco BE, Hilt LM. Rumination as a transdiagnostic factor underlying transitions between
internalizing symptoms and aggressive behavior in early adolescents. J Abnorm Psychol 2014 Feb;123(1):13-23 [FREEFull text] [doi: 10.1037/a0035358] [Medline: 24661155]
Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time
adaptive interventions: a pragmatic framework. Health Psychol 2015 Dec;34S:1209-1219 [FREE Full text] [doi:10.1037/hea0000306] [Medline: 26651462]
Kovács LN, Takacs ZK, Tóth Z, Simon E, Schmelowszky Á, Kökönyei G. Rumination in major depressive and bipolar
disorder - a meta-analysis. J Affect Disord 2020 Nov 01;276:1131-1141 [FREE Full text] [doi: 10.1016/j.jad.2020.07.131][Medline: 32777651]
Starr LR, Davila J. Excessive reassurance seeking, depression, and interpersonal rejection: a meta-analytic review. J Abnorm
Psychol 2008 Nov;117(4):762-775. [doi: 10.1037/a0013866] [Medline: 19025224]
McLaughlin KA, Nolen-Hoeksema S. Rumination as a transdiagnostic factor in depression and anxiety. Behav Res Ther
2011 Mar;49(3):186-193 [FREE Full text] [doi: 10.1016/j.brat.2010.12.006] [Medline: 21238951]
Hatzenbuehler ML, McLaughlin KA, Nolen-Hoeksema S. Emotion regulation and internalizing symptoms in a longitudinal
study of sexual minority and heterosexual adolescents. J Child Psychol Psychiatry 2008 Dec;49(12):1270-1278 [FREE Fulltext] [doi: 10.1111/j.1469-7610.2008.01924.x] [Medline: 18564066]
Cludius B, Mennin D, Ehring T. Emotion regulation as a transdiagnostic process. Emotion 2020 Feb;20(1):37-42. [doi:10.1037/emo0000646] [Medline: 31961175]

Abbreviations
CBT: cognitive behavioral therapy
CFI: comparative fit index
EMA: ecological momentary assessment
GIMME: Group Iterative Multiple Model Estimation
IRB: institutional review board
JITAI: just-in-time adaptive intervention
MCBT: mobile cognitive behavioral therapy
MDD: major depressive disorder
https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 20(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Wang & Miller

MRFCBT: mobile rumination-focused cognitive behavioral therapy
RCT: randomized controlled trial
REDCap: Research Electronic Data Capture
RFCBT: rumination-focused cognitive behavioral therapy
RMSEA: root mean square error of approximation
SDS: State Depression Scale
SRMR: standardized root mean square residual
uSEM: unified structural equation modeling

Edited by A Mavragani; submitted 13.02.22; peer-reviewed by H Kim, S Goldberg; comments to author 14.04.22; revised version
received 20.06.22; accepted 24.10.22; published 05.01.23
Please cite as:
Wang L, Miller L
Assessment and Disruption of Ruminative Episodes to Enhance Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive Interventionsin Clinical Depression: Pilot Randomized Controlled Trial
JMIR Form Res 2023;7:e37270
URL: https://formative.jmir.org/2023/1/e37270
doi: 10.2196/37270
PMID:©Liyuan Wang, Lynn Miller. Originally published in JMIR Formative Research (https://formative.jmir.org), 05.01.2023. Thisis an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR 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 and license information must be included.

https://formative.jmir.org/2023/1/e37270

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e37270 | p. 21(page number not for citation purposes)