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Bögemannetal. BMC Psychology (2023) 11:245
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BMC Psychology
Investigating two mobile just-in-time
adaptive interventions tofoster psychological
resilience: research protocol oftheDynaM-INT
study
S. A. Bögemann1*† , A. Riepenhausen2,3†, L. M. C. Puhlmann4,5†, S. Bar6, E. J. C. Hermsen1, J. Mituniewicz7,
Z. C. Reppmann2, A. Uściƚko7, J. M. C. van Leeuwen1, C. Wackerhagen2, K. S. L. Yuen4,8, M. Zerban8,
J. Weermeijer9, M. A. Marciniak10,11, N. Mor6,12, A. van Kraaij13, G. Köber14,15, S. Pooseh15, P. Koval16,
A. Arias‑Vásquez1, H. Binder14,15, W. De Raedt17, B. Kleim10,11, I. Myin‑Germeys9, K. Roelofs18,19, J. Timmer15,20,21,
O. Tüscher4,22, T. Hendler6,12,23,24†, D. Kobylińska7†, I. M. Veer25†, R. Kalisch4,8†, E. J. Hermans1† and H. Walter2,3†
Abstract
Background Stress‑related disorders such as anxiety and depression are highly prevalent and cause a tremendous
burden for affected individuals and society. In order to improve prevention strategies, knowledge regarding resilience
mechanisms and ways to boost them is highly needed. In the Dynamic Modelling of Resilience – interventional
multicenter study (DynaM‑INT), we will conduct a large‑scale feasibility and preliminary efficacy test for two mobile‑
and wearable‑based just‑in‑time adaptive interventions (JITAIs), designed to target putative resilience mechanisms.
Deep participant phenotyping at baseline serves to identify individual predictors for intervention success in terms
of target engagement and stress resilience.
Methods DynaM‑INT aims to recruit N = 250 healthy but vulnerable young adults in the transition phase
between adolescence and adulthood (18–27 years) across five research sites (Berlin, Mainz, Nijmegen, Tel Aviv,
and Warsaw). Participants are included if they report at least three negative burdensome past life events and show
increased levels of internalizing symptoms while not being affected by any major mental disorder. Participants are
characterized in a multimodal baseline phase, which includes neuropsychological tests, neuroimaging, bio‑samples,
sociodemographic and psychological questionnaires, a video‑recorded interview, as well as ecological momentary
assessments (EMA) and ecological physiological assessments (EPA).
Subsequently, participants are randomly assigned to one of two ecological momentary interventions (EMIs), target‑
ing either positive cognitive reappraisal or reward sensitivity. During the following intervention phase, participants’
stress responses are tracked using EMA and EPA, and JITAIs are triggered if an individually calibrated stress threshold
†Bögemann S. A., Riepenhausen A., and Puhlmann L. M. C., have shared first
authorship, while Hendler T., Kobylińska D., Veer I. M., Kalisch R., Hermans E. J.,
and Walter H. have shared last authorship.
*Correspondence:
S. A. Bögemann
sophie.bogemann@donders.ru.nl
Full list of author information is available at the end of the article
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Page 2 of 26
Bögemannetal. BMC Psychology (2023) 11:245
is crossed. In a three‑month‑long follow‑up phase, parts of the baseline characterization phase are repeated. Through‑
out the entire study, stressor exposure and mental health are regularly monitored to calculate stressor reactivity
as a proxy for outcome resilience. The online monitoring questionnaires and the repetition of the baseline question‑
naires also serve to assess target engagement.
Discussion The DynaM‑INT study intends to advance the field of resilience research by feasibility‑testing two new
mechanistically targeted JITAIs that aim at increasing individual stress resilience and identifying predictors for success‑
ful intervention response. Determining these predictors is an important step toward future randomized controlled
trials to establish the efficacy of these interventions.
Keywords Resilience, Stress, Resilience factors, Mental health, Longitudinal, Prospective, Ecological momentary
assessment, Ecological momentary intervention, Reappraisal, Mental imagery
Introduction
Background
Stress-related mental disorders such as depression and
anxiety disorders reside among the leading causes for
disability worldwide [1–3] and cause a considerable bur-
den to affected individuals, society, and the economy [4].
e general prevalence of mental disorders is particularly
high in late teens and young adults in their twenties [5],
with depression and anxiety showing a high rate of recur-
rence or persistence [6]. Although the link between stress
and mental disorders has been well known for quite some
time, the prevalence of stress-related disorders has not
decreased during the last years [7]. Next to a failure to
correctly implement clinical practice guidelines, one
likely cause is the lack of appropriate and accessible pre-
vention programs [7]. To inform prevention programs
and help identifying possible prevention targets, research
should ideally not only investigate contributing factors
and mechanisms related to vulnerability, dysfunction,
and psychopathology, but also investigate resilience, in
order to identify factors and mechanisms that help peo-
ple to stay healthy despite experiencing adversity [8].
Resilience can be defined as sustained or quickly
recovering good mental health during and after experi-
encing adversity [9, 10]. is definition of resilience as an
outcome rather than a trait reflects the difficulty to indi-
vidually predict good long-term mental health responses
to stressor exposure from a person’s stable features or
predispositions and acknowledges that staying men-
tally healthy appears to result from putatively dynamic
and complex processes allowing successful adaptation
to stressors [8, 10–14]. ese processes are not only
determined by individual predisposing factors (so-called
‘resilience factors’, e.g., a certain genotype, stable person-
ality traits, or beliefs) but also by characteristics specific
to the adverse events or circumstances and an inter-
play between the two, and they involve the activation
of protective mechanisms (‘resilience mechanisms’) at
the level of the individual or the environment. Defining
resilience as an outcome implies that resilience research
should make use of longitudinal study designs, assessing
adversity as well as mental health at several time points
to capture the dynamic nature of occurring stressors
and the possible subsequent changes in mental health
[8, 10]. Another necessary element of resilience studies
are assessments of resilience factors or mechanisms that
can be linked to the outcome and which should ideally
also be examined repeatedly, to thus uncover processes
of adaptation [8].
Although some resilience factors are quite stable and
will (mostly) not change much over the course of life (e.g.,
one’s genotype), other resilience factors are malleable and
can undergo change, for example, triggered by the experi-
ence of adversity itself (e.g., one’s individual repertoire of
emotion regulation strategies, which might increase after
learning a new strategy during a period of adversity).
Such individual adaptations have been termed allostatic
resilience processes, as opposed to homeostatic resilience
processes in which protective mechanisms are success-
fully engaged but an individual’s mode of operation in
coping with adversity is not lastingly altered [12]. Malle-
able resilience factors are thus natural targets for preven-
tion programs that aim to increase individual resilience
[10, 15]. Studies have investigated several interventions
designed to increase resilience, many of which focus on
cognitive-behavioral or mindfulness-based methods, or
a mix of both [16]. However, so far, many intervention
studies to foster resilience present substantial methodo-
logical deficiencies such as missing a clear definition and
operationalization of resilience, investigating effects of
the intervention on single resilience factors instead of on
outcome resilience, or the lack of baseline diagnostics or
long-term follow-ups [17].
The current study
e interventional study DynaM-INT of the EU Hori-
zon 2020 project consortium DynaMORE (‘Dynamic
Modelling of Resilience’ [18]) is designed to investigate
two mobile- and wearable-based just-in-time adaptive
interventions (JITAIs) aimed at fostering resilience and
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Page 3 of 26
Bögemannetal. BMC Psychology (2023) 11:245
to predict their success based on participants’ baseline
characteristics. e target sample consists of students
and apprentices between 18 and 27years. During this
period of life, several mental disorders appear for the
first time or even have their peak prevalence [19], and
students seem to be a particularly vulnerable group
for stress-related psychopathology [20–24]. Youth
and emerging adults are also among the groups that
were most strongly mentally affected by the COVID-
19 pandemic [25]. Insofar as early-onset stress-related
problems are often associated with life-long mental
vulnerability, investment in the mental health of emerg-
ing adults is likely to yield lasting gains and to be eco-
nomically particularly efficient [26]. To ensure that we
specifically include at-risk individuals, inclusion crite-
ria include the prior experience of at least three nega-
tive life events that are perceived as burdensome [27],
and a score in the mid-to-high range of the 28-item
version of the General Health Questionnaire (GHQ)
[28], a self-report instrument that captures internaliz-
ing symptomatology.
As a prospective-longitudinal resilience study, DynaM-
INT entails a multimodal baseline characterization phase
that focuses on potential resilience factors followed by
longitudinal, biweekly assessments of a small number of
hypothesized key resilience factors, considered poten-
tially malleable, as well as of experienced stressors (E)
and mental health problems (P) throughout the course of
the study (online monitoring questionnaires). See Fig.1
for a schematic overview of the study timeline.
Repeated E and P monitoring implements the Frequent
Stressor and Mental Health Monitoring (FRESHMO)
paradigm, which we have developed specifically for the
purpose of longitudinal resilience studies [12]. E and P
Fig. 1 Study timeline. The study involves a baseline characterization phase, an ecological momentary intervention phase, and a follow‑up phase.
On‑site assessments are done at the beginning of the baseline and follow‑up phases. In Berlin, Tel Aviv, and Warsaw, all baseline on‑site assessments
are conducted on one day, while in Mainz and Nijmegen, these baseline assessments are split into two days: M.I.N.I. interview and blood sampling
are done on day 1, all remaining procedures are performed on day 2. On both testing days in Mainz and Nijmegen, a urine drug test is conducted.
On‑site assessments are complemented by regular online monitoring of stressors, mental health problems, and selected resilience factors.
Abbreviations: EMA, ecological momentary assessment; EMI, ecological momentary intervention; EPA, ecological physiological assessment;
JITAI EMI, just‑in‑time ecological momentary intervention; M.I.N.I., Mini‑International Neuropsychiatric Interview
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Page 4 of 26
Bögemannetal. BMC Psychology (2023) 11:245
scores are used to calculate stressor reactivity (SR) scores,
the primary outcome variable and a proxy for outcome
resilience [12] using a residualization approach [29, 30].
Specifically, we regress individuals’ mental health prob-
lems P on their stressor exposure E, both across all moni-
toring time points, to determine our sample’s normative
E-P relationship. For any given individual timepoint, a
participant’s regression residual from this normative
E-P relationship reflects their SR relative to their current
stressor exposure and the sample’s normative reactiv-
ity. us, positive residuals indicate that the participant
experiences more mental health problems P than would
be expected given their stressor exposure E (higher SR)
at this time point, whereas negative residuals mean that a
participant has fewer mental health problems than would
be predicted at their given stressor exposure (lower SR)
at this time point. Within-participant SR score time-
courses will be calculated to investigate temporal fluc-
tuations in reactivity and set these into relation with the
interventions (see below) and with potential changes in
resilience factors resulting from the interventions [12].
e repeated assessment of several potential resilience
factors in the online monitoring questionnaires is com-
plemented by repetitions of parts of the baseline charac-
terization phase after six and eight months (‘follow-up
phase’; Fig.1).
Importantly, upon completion of the baseline char-
acterization phase, participants enter an ecological
momentary intervention (EMI) phase where they are
randomly assigned to one of two EMIs designed by our
consortium that aim to improve two distinct resilience
factors: ‘ReApp’, targeting positive cognitive reappraisal
of recent stressful or negative events [64], or ‘Imager’,
targeting reward sensitivity by positive mental imagery
[31,65]. e interventions are accompanied by ecological
momentary assessments (EMA) using smartphones and
ecological physiological assessments (EPA) using weara-
bles (wristbands) to assess mood and stress reaction pat-
terns in real time during real life and to allow triggering
of EMIs as JITAIs at times of high stress.
Specifically, after calibration of individual EMA and
EPA thresholds for stress responses on study devices as
part of baseline characterization (‘calibration week’, see
Fig.1), participants are first trained in using the assigned
intervention on their own phones without concurrent
EPA (‘training weeks’). en, participants are adminis-
tered three EMI ‘booster weeks’ on study devices dur-
ing which real-time EMA and EPA data is used to trigger
interventions specifically at moments when participants’
stress levels cross the individual threshold established
during the calibration week (that is, JITAI). e ration-
ale behind this approach is that these interventions are
thought to be most effective when participants apply the
previously learned cognitive strategies at moments when
they are needed most [32]. ese booster weeks happen
every four weeks over the period of three months in the
EMI phase. Between the booster weeks, participants are
encouraged to continue practicing the assigned interven-
tion (‘practice weeks’) on their own phones. Supplemen-
tary Figure S1 depicts the different assessments per week
type [see Additional file1].
Research questions
e study is primarily designed to identify baseline pre-
dictors of the effect of our JITAIs on stressor reactivity as
well as target engagement, in order to inform the design
of future randomized controlled trials testing the efficacy
of these interventions. To prepare predictor identifica-
tion, we will first evaluate intervention feasibility and effi-
cacy. We will evaluate feasibility by testing whether EMIs
with a JITAI element that uses mobile phones and wrist-
bands to trigger interventions specifically at times of high
stress can be conducted on a large scale, focusing on i)
technical implementation (feasibility research question
1, fQ1) as well as ii) participant adherence (fQ2) and iii)
participant experience (fQ3).
To preliminarily evaluate the efficacy, we will quantify
whether, relative to baseline, the interventions are accom-
panied by, i) reductions in SR scores (efficacy research
question 1, eQ1) and ii) increases in respective target
engagement (eQ2). For target engagement specifically, we
will assess changes in the use frequency of positive cog-
nitive reappraisal during and after the ReApp JITAI and
changes in reward sensitivity during and after the Imager
JITAI. ese patterns could be interpreted as further evi-
dence for intervention success [31, 64]. e efficacy tests
primarily use the biweekly assessed self-report measures
of stressor exposure, mental health, positive cognitive
reappraisal, and reward sensitivity.
Our efficacy tests will be further facilitated by the
possibility to compare DynaM-INT results to data
from the purely observational DynaM-OBS study [33],
to which DynaM-INT is the follow-up study. DynaM-
OBS uses the same type of baseline characterization and
repeated assessment of E, P, and resilience factors (spe-
cifically positive cognitive reappraisal) in a study sam-
ple and over a time period comparable to DynaM-INT.
DynaM-OBS thus provides us with an estimate of the
natural course of SR and target engagement measures
that can be used as a discovery sample and as a back-
ground against which the effects of the interventions
in DynaM-INT can be assessed. Note that DynaM-
OBS cannot be considered a formal control condition,
but may provide an informal effect estimate justifying
future randomized controlled trials (RCTs) with appro-
priate control conditions.
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Page 5 of 26
Bögemannetal. BMC Psychology (2023) 11:245
Following these evaluations of feasibility (fQ1-3) and
efficacy (eQ1-2), we will address our primary research
questions, namely, examining variables assessed in the
baseline characterization phase to identify those that
moderate (predict) the efficacy of either of the two inter-
ventions on i) stressor reactivity (primary research ques-
tion 1, pQ1) and ii) target engagement (pQ2). e exact
list of potential moderator variables to be examined,
besides initial levels of positive cognitive reappraisal
and reward sensitivity, will depend on the results of
the DynaM-OBS study. Specifically, in DynaM-INT we
will focus on predictors of low SR scores obtained from
DynaM-OBS. ese investigations aim to prepare future
RCTs intended to test the efficacy of these interventions
where baseline data serves to guide intervention adminis-
tration only to individuals that are likely to benefit from a
given intervention.
As a follow-up to our two primary research questions,
we will examine whether the anticipated reductions in
stressor reactivity are preceded or accompanied by the
anticipated increases in target engagement (secondary
research question, sQ1), which would suggest that the
interventions execute their effects via the targeted resil-
ience mechanisms.
A tertiary set of main research questions (tertiary
research question, tQ) to be answered with DynaM-INT
is related to Positive Appraisal Style eory of Resilience
(PASTOR) [10], the core theoretical framework of the
DynaM-INT study. Positive appraisal style (PAS) is the
tendency of an individual to appraise potential stressors
in a positive (i.e., non-negative) way while at the same
time avoiding delusionally positive appraisals. Positive
appraisers typically generate appraisals that range from
realistic to slightly unrealistically positive. Such a posi-
tive appraisal style is thought to enable the individual to
exhibit optimal, fine-tuned stress reactions that are suf-
ficient to cope with the stressor but that do not exceed-
ingly exhaust resources, which reduces the likelihood
of developing mental health problems in adverse life
situations. PASTOR claims that PAS is the key proxi-
mal resilience factor in that the effects of all other resil-
ience factors on outcome resilience are mediated by their
effects on PAS [10]. In PASTOR, positive cognitive reap-
praisal is one important sub-class of cognitive processes
that generate positive appraisals [10, 34], and it is there-
fore claimed that individuals who use positive cognitive
reappraisal more frequently and/or more efficiently are
likely to have higher PAS. us, positive cognitive reap-
praisal is an important component of PAS, which is why
it is here targeted by the ReApp EMI. By contrast, reward
sensitivity, as targeted by the Imager EMI, is a separate
potential resilience factor that is thought to promote
resilience insofar as it helps individuals generally apprais-
ing stressful situations in a more benign fashion, by
better integrating positive information into the overall
appraisal. Hence, eventually, one can assume that both
the ReApp and the Imager EMIs promote resilience by
promoting PAS.
PAS (like positive cognitive reappraisal and reward
sensitivity) is considered a malleable resilience factor.
Accordingly, in our study design, self-report measures
of PAS (like measures of the two EMI targets) are not
only taken in the questionnaire battery of the baseline
characterization phase but also when the characteriza-
tion is repeated at follow-up as well as in the biweekly
online monitoring questionnaires (see Fig. 1). is
allows us to ask whether the interventions are accom-
panied by increases in PAS relative to baseline (tQ1),
whether the anticipated reductions in stressor reactiv-
ity are preceded or accompanied by the anticipated
increases in PAS (tQ2), and whether the anticipated
increases in PAS are preceded or accompanied by the
anticipated increases in target engagement (tQ3). ese
findings would suggest that the interventions pro-
mote resilience by promoting PAS. Beyond interven-
tion effects, we will examine whether individuals with
high baseline PAS show less stressor reactivity (tQ4)
and whether changes in PAS throughout the course of
the study will be accompanied by inverse changes in
stressor reactivity (tQ5), irrespective of the treatment.
e research questions are summarized in Table 1;
additional exploratory research questions are outlined
in the analysis section. e DynaM-INT data set will be
made available to researchers to address other possible
research questions.
Methods
Study centers andstudy period
e multi-center study takes place in five research facili-
ties: Department of Psychiatry and Neurosciences at
Charité – Universitätsmedizin Berlin, Berlin, Germany;
Neuroimaging Center at Johannes Gutenberg Univer-
sity Medical Center in Mainz, Germany; Donders Cen-
tre for Cognitive Neuroimaging and Radboud university
medical center in Nijmegen, Netherlands; Sagol Brain
Institute at Tel Aviv University and Tel Aviv Sourasky
Medical Center, Tel Aviv, Israel, and Faculty of Psychol-
ogy at University of Warsaw, Warsaw, Poland. Data acqui-
sition started in April 2022. Completion of the baseline
characterization phase is expected in May 2023, comple-
tion of the intervention phase is expected in September
2023, and completion of the follow-up phase is expected
in December 2023.
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Page 6 of 26
Bögemannetal. BMC Psychology (2023) 11:245
Participants
In total, N = 250 healthy male and female participants
are planned to be recruited at the five study sites (N = 50
each). Where a study site cannot fulfil the recruitment
goal, other sites will attempt to compensate. Participants
need to be 18–27years, studying or in vocational train-
ing, have experienced at least three stressful life events
[27] that they perceived as burdensome before inclusion,
and report elevated levels of internalizing symptoms (a
score of ≥ 20 in the GHQ, 28-item version [28]). All inclu-
sion criteria are provided in Table2.
Design
As shown in Fig.1, the DynaM-INT study follows a pro-
spective-longitudinal design, consisting of an (online)
pre-screening for eligibility, a multimodal baseline char-
acterization phase (including neuropsychological tests,
neuroimaging, bio-samples, a sociodemographic and
psychological questionnaire battery, a video-recorded
interview), a calibration week where individual stress
thresholds are being determined based on ecological
momentary assessments (EMA) and ecological physi-
ological assessments (EPA)), an ecological momentary
intervention (EMI) phase (including two training weeks
where participants get familiar with one of two ran-
domly assigned interventions, three separated booster
weeks where JITAIs are triggered at times of high stress,
intermittent optional EMI practice weeks without JITAI,
and another video-recorded interview), and a follow-up
phase where parts of the baseline characterization phase
are repeated (including the psychological questionnaire
battery, bio-samples, and the video-recorded interview).
In addition, biweekly online monitoring questionnaires
are assessed throughout the course of the study. For an
extensive overview of all measures used and the days (d),
weeks (w) and months (M) from baseline at which they
are assessed (x), see Table3.
Procedures
Recruitment andscreening
Participants are recruited via e-mail distribution lists,
social media advertisements, flyers, digital blackboards,
and word-of-mouth. As a first step, potential participants
are asked to fill out an anonymous online screening sur-
vey on SoSci Survey [36] that checks for inclusion criteria
(Table2) via an automated algorithm. To be able to link
the pre-screening data to the study ID, potential partici-
pants generate an individual code that will be re-created
on-site upon inclusion. Eligible participants receive an
e-mail with the invitation to contact their study site to
schedule a phone call.
Further inclusion criteria regarding past and pre-
sent psychiatric diagnoses are assessed by trained staff
using the Mini-International Neuropsychiatric Inter-
view (M.I.N.I.) [35]. In Berlin, Tel Aviv, and Warsaw,
the M.I.N.I. is conducted on the phone and all records
are destroyed afterwards. Eligible participants are then
scheduled for the baseline characterization phase. In
Mainz and Nijmegen, participants receive an appoint-
ment for the first day of baseline assessments during
which the M.I.N.I. is conducted and participants who are
not eligible are treated as dropouts.
Table 1 List of research questions
In the DynaM-INT study, we attempt to answer multiple research questions, divided in feasibility and ecacy questions, as well as primary, secondary and tertiary
main research questions
Type Nr Research Question
Feasibility fQ1 Is JITAI using mobile phones and wristbands to trigger interventions specifically at times of high stress technically feasible?
fQ2 Do participants adhere to the JITAI?
fQ3 How do participants experience the JITAI?
Efficacy eQ1 Are the interventions accompanied by reductions in stressor reactivity relative to baseline?
eQ2 Are the interventions accompanied by increases in target engagement relative to baseline?
Primary pQ1 Can we identify predictors in the baseline characterization data for the effects of each of the two interventions on stressor reactivity?
pQ2 Can we identify predictors in the baseline characterization data for the effects of each of the two interventions on target engage‑
ment?
Secondary sQ1 Are the anticipated reductions in stressor reactivity preceded or accompanied by the anticipated increases in target engagement?
Tertiary tQ1 Are interventions accompanied by increases in PAS relative to baseline?
tQ2 Are the anticipated reductions in stressor reactivity preceded or accompanied by the anticipated increases in PAS?
tQ3 Are the anticipated increases in PAS preceded or accompanied by the anticipated increases in target engagement?
tQ4 Do individuals with high baseline PAS show less stressor reactivity?
tQ5 Are changes in PAS throughout the course of the study accompanied by inverse changes in stressor reactivity?
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Bögemannetal. BMC Psychology (2023) 11:245
Baseline characterization phase (month 1)
Participants are characterized in a multimodal baseline
characterization phase, consisting of on-site assessments,
as well as online questionnaires and assessments in daily
life. An overview of all procedural steps of the baseline
assessments can be found in Table4.
In Berlin, Tel Aviv, and Warsaw, all on-site baseline
assessments are conducted on one day (“day 1 + day 2”
in Table 4). In Mainz and Nijmegen, on-site baseline
assessments are split into two days: In Nijmegen, the
M.I.N.I., and blood sampling are done on day 1; in Mainz,
the M.I.N.I., blood sampling, and EMA/EPA briefing
are done on day 1. All remaining on-site assessments
are performed on day 2. In Berlin, Tel Aviv, and War-
saw, participants spend approximately 4h in the labo-
ratory during day 1. In Mainz and Nijmegen, they are
present for approximately 1 and 3h(s) on day 1 and 2,
respectively.
All participants receive written and verbal information
about the study and provide written informed consent at
the start of the baseline assessment. Next, (at the start of
both baseline days in the case of Mainz and Nijmegen),
participants undergo a urine-based drug screening test
(SureStep™ Multi-Drug One Step Screen Test Panel,
Innovacon Inc., USA) for amphetamine, barbiturates,
benzodiazepines, buprenorphine, clonazepam, cocaine,
fentanyl, heroin, ketamine, cannabis, methadone, meth-
amphetamine, methylenedioxymethamphetamine, mor-
phine, opiate oxycodone, phencyclidine, propoxyphene,
tramadol, and tricyclic antidepressants. After a negative
test, participants continued with the tests.
Neuropsychological tests Following inclusion, two neu-
ropsychological tests are conducted: the Trail Making
Test [37, 38], assessing visual attention and task switch-
ing speed, and the HAWIE Digit Symbol Test [39], meas-
uring processing speed.
Neuroimaging Participants receive a brief training of
the neuroimaging paradigms, during which the experi-
menter provides verbal explanations, asks questions, and
makes sure the participant understood the instructions,
while showing an on-screen presentation of the tasks.
Data acquisition parameters and the individual neuroim-
aging tasks are described in detail below.
Table 2 List of inclusion criteria and format in which they were assessed
Participants who are found eligible in criteria 1–10 in the anonymous online screening are invited to a phone interview (on-site interview in Mainz and Nijmegen) to
conrm/check eligibility for criteria 9–12. During their rst in-person appointment, participants receive written and verbal information about the study and provide
written informed consent (criterion 14). Inclusion criterion 10 only applies to the MRI subsample: participants who are not eligible for undergoing the MRI procedure
skip the neuroimaging procedures and take part in all other parts of the study. During the baseline day (both baseline days in the case of Mainz and Nijmegen see
Fig.1), a drug test is performed
Nr Criterion Format
1 Age between 18 and 27 Online
2 3 or more life events rated as burdening [27] Online
3 GHQ‑28 score of 20 or higher [28] Online
4 Body mass index between 18 and 27 Online
5 Currently studying or in vocational training Online
6 Proficiency in the official language of the country of study enrollment (minimum level of C1 in the Common European Frame‑
work of Reference for Languages) Online
7 Eligibility to participate in ecological physiological assessment using a wearable device (no skin disease in the wrist or chest
area and no medical condition that increases risk of infection through electrodes, no medication with phototoxic side effects) Online
8 The participant has a smartphone with iOS or Android operating system Online
9 No lifetime diagnosis of any severe mental or organic disorder that affects neurodevelopment due to its pathological mecha‑
nism or treatment (e.g., schizophrenia, bipolar disorder, anorexia/bulimia nervosa, attention deficit hyperactivity disorder,
autism spectrum disorder, meningitis, epilepsy, multiple sclerosis, stroke, brain cancer, brain concussion, or coma)
Online + interview
10 Eligibility for undergoing the functional magnetic resonance imaging protocol (normal or corrected‑to‑normal eyesight,
no hearing impairment, no claustrophobia, no non‑removable ferromagnetic metal in or at the body, not pregnant, no large
tattoo in head or neck area)
Online + interview
11 No diagnosis within 9 months before inclusion of any mental disorder other than a mild depressive episode (ICD F32.1),
tobacco abuse/dependence (ICD F12), or substance abuse, as assessed using the Mini‑International Neuropsychiatric Inter‑
view (M.I.N.I.)[36]
Interview
12 The participant has not participated in the previous DynaM‑OBS study or any study using an EMI similar to ReApp or Imager Interview
13 No consumption of any psychoactive drug or substance up to 4 weeks prior to the first psychological assessment and to the
MRI assessment Drug test
14 The participant has received all relevant information about the study, is able to obtain full insight and is fully contractually
capable, is willing and able to comply with the protocol and agrees to participate by giving written consent Interview
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Bögemannetal. BMC Psychology (2023) 11:245
Table 3 Overview of the measures used and the weeks (w) and months (M) from baseline, at which they are assessed (x: all sites; a: Mainz and Nijmegen; b: Berlin, Tel Aviv, and
Warsaw)
S Baseline
characterization phase
Ecological momentary intervention phase Follow-up phase
M1 M2 M3 M4 M5 M6 M7 M8
w0 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4
Inclusion Inclusion criteria x
M.I.N.I. Interview x
Drug screening a x
Mental
health
Online Questionnaires
GHQ‑28 General
Health Ques‑
tionnaire
x x x x x x x x x x x x x x x
PSS‑10 Perceived
Stress Scale
x x x
SCL‑90‑R Symptom
Checklist 90
Revised
x x x
WHO‑
DAS
WHO Dis‑
ability Assess‑
ment Scale
x x x
DBM Digital
Biomarkers
x x x x
Stressor
Expo-
sure
Online Questionnaires
MIMIS Mainz Inven‑
tory of Micro‑
stressors
x x x x x x x x x x x x x x
COV
Stress
Covid‑related
Stressors
x x x x x x x x x x x x x x
War
and Ter‑
ror
Stress
War‑ and Ter‑
ror‑related
Stressors
x x x x x x x x x x x x x x
LEQ Life Event
Question‑
naire
x x x x
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Bögemannetal. BMC Psychology (2023) 11:245
Table 3 (continued)
S Baseline
characterization phase
Ecological momentary intervention phase Follow-up phase
M1 M2 M3 M4 M5 M6 M7 M8
w0 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4
Potential
Resil-
ience
and Risk
Factors
Neuropsychological
battery
x
Neuroimaging battery x
Online Questionnaires
PASS‑
content
Perceived
Positive
Appraisal
Style Scale,
content‑
focused
xSxMxMxMxMxMxMxMxMxMxSxMxMxS
PASS‑
process
Perceived
Positive
Appraisal
Style Scale,
process‑
focused
xSxMxMxMxMxMxMxMxMxMxSxMxMxS
TEPS Temporal
Experiences
of Pleasure
xSxMxMxMxMxMxMxMxMxMxSxMxMxS
CPA Crisis‑related
Positive
Appraisals
x x x x x x x x x x x x x x
Other primary resilience
and risk factors
x x x
Secondary resilience
and risk factors
x x x
Sports and mental
activities
x x x
Bio-samples
EDTA
Blood
DNA/DNA‑
methylation
a b x
Proteomics a
Stool Microbiome x x
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Bögemannetal. BMC Psychology (2023) 11:245
Table 3 (continued)
S Baseline
characterization phase
Ecological momentary intervention phase Follow-up phase
M1 M2 M3 M4 M5 M6 M7 M8
w0 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4 w1 w2 w3 w4
Daily life
meas-
ures
Ambulatory Assessments
EPA Ecological
Physiological
Assessment
x x x x
EMA Ecological
Momentary
Assessment
x x* x* x x#x#x#x x#x#x#x x#x#x#
EMI Ecological
Momentary
Interventions
x* x* x#x#x#x#x#x#x#x#x#
JITA IEMI Just‑in‑time
Ecological
Momentary
Interventions
x x x
Online Questionnaire
uMARS User version
of the Mobile
Application
Rating Scale
question‑
naire
x
Resilience and risk factors (RFs) are grouped into primary and secondary RFs. Primary RFs are of main interest in the current study based on previous ndings and theoretical background of our consortium, while
secondary RFs are based on hypotheses drawn from the literature. RFs addressing target engagement (assessed with a subset of items in the PASS-process and the TEPS questionnaire) are assessed as traits or styles
(typical tendencies, habits, xS) during the extended online batteries in M1, M6, and M8 and as modes (response tendencies in the past days or weeks, xM) in the repeated online monitoring questionnaires. During training
(x*) and practice weeks (x#), EMIs are preceded by an EMA. During practice weeks (x#), EMIs are self-triggered/on-demand
Abbreviations: Mmonth, Sscreening phase, wweek
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When placed in the magnetic resonance imaging (MRI)
scanner, participants are provided with earplugs. ey
receive a 4-button Inline Fiber Optic Response Pad (Cur-
rent Design [40], in Berlin, Mainz, Nijmegen, and Tel
Aviv; in-house developed system in Warsaw) to their
right hand. ey are presented with the visual stimu-
lation of the tasks via a mirror placed on the head coil
that shows a monitor placed behind the scanner bore.
Before and after each task, the experimenter gives verbal
instructions and receives feedback from the participant
via an intercom system. e specific instructions are also
shown on the screen before each task. After scanning,
participants are asked to fill out an MRI exit interview
questionnaire, asking about experiences and potential
difficulties with the fMRI tasks, via SoSci Survey [36].
Participants who are not eligible for undergoing the
MRI procedure skip the neuroimaging procedures and
take part in all other parts of the study.
Bio‑samples From each participant, 9 ml of blood (in
Nijmegen: 10ml) is drawn into an EDTA tube (red mon-
ovette; Sarstedt, Nümbrecht, Germany) and stored as
whole-blood at -20°C or colder until assay of DNA and
DNA-methylation. In Mainz and Nijmegen, an additional
9ml (Mainz) or 10ml (Nijmegen) of blood are sampled
into EDTA tubes for proteomic analyses. To limit the
influence of metabolism or diurnal oscillations on prot-
eomics measurements, at these two sites blood is drawn
between 10:30 and 14:30 and participants are instructed
to arrive at least five hours sober. Blood samples for
Table 4 Procedure steps at baseline
Note that the M.I.N.I. interview is conducted twice in Berlin, Tel Aviv, and Warsaw because all records collected previous to informed consent only serve the purpose of
checking inclusion criteria and are immediately destroyed. Before each neuroimaging sequence, a eld map scan is acquired. The total duration of the imaging battery
is about 1h. Abbreviations: EMA, ecological momentary assessment; EPA, ecological physiology assessment; FLAIR—uid-attenuated inversion recovery;M.I.N.I., Mini-
International Neuropsychiatric Interview; T1, T1- weighted image
Procedure step Task/sample Self-ratings Duration
(mm:ss)
Phone screening M.I.N.I. interview (Berlin, Tel Aviv, & Warsaw)
Day 1
Informed consent (Mainz & Nijmegen)
On‑site screening M.I.N.I. interview (Mainz & Nijmegen)
Drug screen (Mainz & Nijmegen)
Bio‑samples Blood (Mainz & Nijmegen)
Post‑assessment Longitudinal schedule (Mainz & Nijmegen)
Online questionnaire briefing and DBM training (Mainz)
Emotional disturbances interview (Mainz & Nijmegen)
Day 2 Pre‑neuroimaging Drug screen
Informed consent (Berlin, Tel Aviv, & Warsaw)
M.I.N.I. interview (Berlin, Tel Aviv, & Warsaw)
Neuroimaging training
Neuropsychology Trail making test 01:30
Digit symbol test 01:30
Bio‑samples Blood (Berlin, Tel Aviv, & Warsaw)
Stool instruction
Neuroimaging battery Reward sensitivity task (MID) 08:26
T1 06:54
Reappraisal task Performance 13:06
Faces matching task 04:34
FLAIR 02:44
Resting state 07:10
Post‑neuroimaging MRI exit interview
EMA/EPA briefing
Online questionnaire briefing and DBM training (Berlin,
Nijmegen, Tel Aviv & Warsaw)
Longitudinal schedule (Berlin, Tel Aviv, & Warsaw)
Emotional Disturbances Interview
Day 3–8 Calibration week EMA/EPA data collection
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Bögemannetal. BMC Psychology (2023) 11:245
proteomics assay are centrifuged and serum is divided
into 8–16 aliquots (depending on volume), which are
stored at -80°C until assay. In Tel Aviv, one additional
tube (VACUETTE® TUBE 5ml CAT Serum Separator
Clot Activator) of blood is taken at each sampling time
point to derive CRP.
Stool samples are collected using an OMNIgene-gut
feces kit (OM-200, DNAgenotek). Participants receive a
test kit, an instruction sheet about the collection proce-
dure, the Bristol Stool Scale [41], and a verbal instruction.
ey are instructed to collect the stool sample as close
as possible to the return appointment, to take numer-
ous small samples from different locations in the stool
material, to fill out the Bristol Stool Scale, and to store
the sample at a dark place without direct sunlight until
returning it at the next appointment. Stool samples are
subsequently stored at -20°C until assay of gut microbi-
ome, or, in Nijmegen, directly shipped to the laboratory
processing the microbiome.
Post‑assessment procedures At the end of the baseline
day(s) (and each subsequent appointment), participants
are asked if they have experienced emotional distur-
bances triggered by any element of the preceding session
in a standardized interview, to ensure their well-being.
In case they report emotional disturbance and a need for
help, participants are directed to a site-specific clinician
associated with the study.
Online questionnaires Following the on-site baseline
day (Mainz and Nijmegen: baseline day 2), a sched-
ule with the participant’s dates for all questionnaires
is uploaded to SoSci Survery [36] to enable automatic
e-mail dispatch. e schedule consists of an extended
questionnaire battery, as well as shorter, biweekly moni-
toring questionnaires, used for the high-frequent lon-
gitudinal assessment of stressors and mental health
(FRESHMO paradigm) as well as of malleable resilience
factors (RFs) throughout the entire study [12]. RFs are
assessed as trait or style (the typical way or tendency
in which a person reacts to life experiences) during the
extended online batteries and as a mode (the extent to
which the RF was used or experienced in the past two
weeks [42]) during the biweekly monitoring question-
naires. Table5 provides an overview.
e extended questionnaire battery is administered as
part of the baseline characterization phase and is sent
out immediately. Participants are asked to finish the
online questionnaire battery within one week. Also, three
biweekly monitoring questionnaires form part of the
baseline characterization phase (see Fig.1). Participants
have two days to fill out those shorter questionnaires.
Video‑recorded interview Besides traditional self-report
instruments, the online questionnaire schedule contains
a self-developed, fully structured and video-recorded
interview asking participants about their experience of
mental health problems as well as recent and upcom-
ing emotional events. In each interview, participants
record short video segments of themselves answering the
respective questions. ese interviews provide audio-vis-
ual data to identify interview-based digital biomarkers of
mental health (DBMs). Details are given below.
Calibration week In the week following the on-site
baseline assessment day(s), EMA and EPA data is col-
lected. Participants use a study smartphone (Motorola
Moto E6 Play in Berlin, Mainz, Nijmegen, and Warsaw;
Xiaomi Redmi 7/7A in Tel Aviv) with the RADAR aRMT
app (adapted for the use in DynaM-INT) for EMA data
collection [51] and the Chill + wristband (developed by
IMEC [52]) for EPA data collection. Participants receive
a thorough explanation about the EMA and EPA devices,
applications, and procedures.
Each day during usual waking hours (between 7:30 and
22:30), questionnaires of around 2min length each are
sent at 10 different time points (“beeps”) via push notifi-
cations to the smartphone. Each notification is semi-ran-
domly scheduled to be sent out in a block of 90min. e
beep schedule is the same for all participants and is spec-
ified in Supplementary TableS1; EMA content can be
found in Supplementary Figure S2 [see Additional file1].
Each beep questionnaire remains online for 10min, and
participants receive a reminder notification 5min after
the initial beep notification.
EPA data is collected via the wristband for 16h per day.
e wristband also features a “stress” button that par-
ticipants are instructed to press when they experience a
stressful event. e calibration week lasts for six days.
All EMA data collected with the RADAR aRMT app
is immediately and automatically uploaded to a server at
the Donders Institute, where the initial feature extraction
takes place in real time. After completion of each EMA
questionnaire (via the RADAR aRMT app) participants
are redirected to the DynaMORE Chill + app (developed
by IMEC for the use in DynaM-INT) to upload 10 min
of EPA data acquired right before each EMA notifica-
tion to the server at the Donders Institute, where relevant
features are extracted and motion-related artifacts are
removed. A complete list of features is given in Supple-
mentary TableS2 [see Additional file1].
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Bögemannetal. BMC Psychology (2023) 11:245
Table 5 List of online self‑report questionnaires
Type Name (Abbreviation) Construct
Mental health
General Health Questionnaire (GHQ‑28) Symptoms of anxiety, depression, insomnia, social problems
as well as somatic symptoms. This inventory is designed to cap‑
ture the inability to carry out normal functions and the appear‑
ance of new and distressing phenomena in the general popula‑
tion, 28 items [28]
Perceived Stress Scale (PSS‑10) Degree to which participants appraise situations in their lives
as stressful, unpredictable, uncontrollable, and overloaded, 10
items [70, 71]
Revised Symptom Checklist 90 (SCL‑90‑R) Psychological distress in terms of nine primary symptom dimen‑
sions including somatization, obsessive–compulsive, interpersonal
sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid
ideation, and psychoticism, 90 items [72]
WHO Disability Assessment Schedule (WHODAS 2.0) Functioning and disability in accordance with the International
Classification of Functioning, Disability and Health, 12 items [73]
Stressor exposure
Mainz Inventory of Microstressors (MIMIS) 58 minor stressors of daily life (e.g., loss or displacement
of an object, interpersonal conflicts, bad weather, traffic). Partici‑
pants report whether the events have occurred and how straining
they were experienced on a 5‑point scale [44]
List of COVID‑related stressors (COV stress) A list of 23 stressors specific to the COVID‑pandemic (e.g.,
being at increased risk for an infection, loss of social contact,
having COVID symptoms, etc.), for which participants report
whether the situation occurred and how burdensome it was per‑
ceived on a 5‑point scale. The list was self‑developed in March
2020 for the DynaCORE studies on psychological resilience dur‑
ing the COVID‑pandemic [42, 74]
List of War‑ and Terror‑Related Stressors A self‑developed list of 15 stressors related to the war in Ukraine,
for which participants report whether the situation occurred
and how burdensome it was perceived on a 5‑point scale [63]
and a self‑developed list of 5 terror terror‑related stressors
for which participants report whether they experienced the situ‑
ation [63]
Life Events Questionnaire (LEQ) 27 stressful life events (e.g., death of a friend or family mem‑
ber, separation or divorce of the parents, illness or injury).
For each event, participants indicate whether and at what age
it has occurred and how positive or burdensome it has been
experienced [27]
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Table 5 (continued)
Type Name (Abbreviation) Construct
Questionnaires used to assess primary resilience and risk factors
Perceived Positive Appraisal Style Scale, content‑focused (PASS‑
content) Assessment of the perceived tendency to generate positive
appraisals in challenging situations. The instrument has 14 items;
answers are given on a 4‑point scale [45]
Perceived Positive Appraisal Style Scale, process‑focused (PASS‑
process) Assessment of the perceived tendency to employ positive
appraisal processes in challenging situations. The instrument
has 10 items; answers are given on a 5‑point scale [45]
Temporal Experience of Pleasure (TEPS) Reward sensitivity is assessed using four anticipatory items
from the TEPS [75]
List of crisis‑related positive appraisals A list of 6 self‑formulated positive appraisal contents specific
to the current crisis [63]
Brief Resilience Scale (BRS) The subjective ability to cope with and recover from stress, 10
items [76]
Cognitive Emotion Regulation Questionnaire (CERQ short) Different strategies of emotion regulation such as self‑blame,
other‑blame, rumination, catastrophizing, positive refocus‑
ing, planning, positive reappraisal, putting into perspective,
and acceptance, 18 items [90]
Coping Orientation to Problems Experienced questionnaire
(Brief COPE) Emotion regulation strategies such as self‑distraction, active
coping, denial, substance use, use of emotional support, use
of instrumental support, behavioral disengagement, venting,
positive reframing, planning, humor, acceptance, religion, and self‑
blame, 28 items [77]
General Self Efficacy Scale (GSE) Perceived ability to cope with a variety of difficult demands in life,
10 items [46]
Internal External Locus of Control‑4 (IE‑4) Degree to which individuals perceive themselves the out‑
comes of their behavior to be determined by their own actions
or by forces outside of their control 4 items, 4 items [47]
Life Orientation Test – Revised (LOT‑R) Dispositional optimism and pessimism, 10 items [48]
NEO‑Neuroticism Neuroticism scale of the NEO Five Factor Inventory (NEO‑FFI), 12
items [78]
Oslo 3 Item Social Support Scale (OSSS‑3) Degree to which participants perceive themselves as surrounded
by people who are close, concerned, and supportive [79]
Psychological Flexibility Questionnaire (PFQ) Subjective psychological flexibility, assessed via five factors includ‑
ing positive perception of change, characterization of the self
as flexible, self‑characterization as open and innovative, a percep‑
tion of reality as dynamic and changing, and a perception of real‑
ity as multifaceted, 20 items [80]
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Bögemannetal. BMC Psychology (2023) 11:245
Table 5 (continued)
Type Name (Abbreviation) Construct
Questionnaires used to assess secondary resilience and risk factors
Anxiety Sensitivity Index (ASI) Beliefs of negative implications of anxiety experiences, 18 items
[81]
Dimensional Anhedonia Rating Scale (DARS) Multiple facets of hedonic function such as desire, motivation,
effort, and consummatory pleasure across hedonic domains, 17
items [82]
Maltreatment and Abuse Chronology of Exposure (MACE) Abuse and neglect during development, 52 items [83]
Perceived Social Status Scale (PSS‑S) Subjective socioeconomic status by means of a drawing of a lad‑
der with 10 rungs, described to represent where people stand
in society. Participants are instructed to indicate the rung that best
represents where they stand on the ladder. Additionally, the same
question is asked for the dimensions of academic and occupa‑
tional status [84]
Ruminative Thought Style Questionnaire (RTS) Components of ruminative thinking including problem‑focused
thoughts, counterfactual thinking, repetitive thoughts, and antici‑
patory thoughts, 15 items [85]
Sensitivity to Punishment and Sensitivity to Reward Question‑
naire (SPSRQ) Tendency for aversive and appetitive behavior, 48 items [86]
State Trait Anxiety Inventory (STAI) Symptoms of anxiety as a state and as a general trait, respectively,
40 items [87]
Toronto Alexithymia Scale (TAS‑20) Deficiency in understanding, processing, or describing emotions,
20 items [88]
Questionnaire used to assess user experience of the intervention
User version of the Mobile Application Rating Scale (uMARS) A list of 10 questions, adapted from the user version of the user
version of the Mobile Application Rating Scale (uMARS) question‑
naire [89]
Resilience and risk factors (RFs) are grouped into primary and secondary RFs. Primary RFs are of main interest in the current study based on previous ndings and theoretical background of our consortium, while
secondary RFs are based on hypotheses drawn from the literature. Please note that only the original publications are cited here but not the validation studies of translated versions into the four study languages. An
overview of questionnaire validations, as well as the self-developed questionnaires are provided at [63]
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After the calibration week has finished, participants
come back to the lab to return study devices. All data
collected with the Chill + app is downloaded by the
researchers for additional offline feature extraction (of
the entire 6days × 16h EPA data). e baseline charac-
terization phase is completed by randomly assigning one
of the interventions to the participant based on a prede-
termined randomization sheet (computerized random
numbers to 1 of 2 EMIs).
Ecological momentary intervention phase (months 2–5)
e ecological momentary intervention (EMI) phase
consists of two training weeks, three booster weeks, and
nine encouraged practice weeks. See Fig.1. Also, online
monitoring questionnaires continue to be sent to partici-
pants in a biweekly manner throughout the entire EMI
phase. e video-recorded interview is repeated during
the month 3—week 4 monitoring questionnaire.
Training weeks Before the start of the two training
weeks (14days), participants receive a briefing on their
assigned intervention (ReApp or Imager EMI) via a video
call. Subsequently, they install the SEMA3 app [49] on
their own phone and enroll for the assigned EMI. e
purpose of the training weeks is to familiarize the partici-
pants with the assigned intervention and to initiate habit-
ual use of the cognitive techniques taught by the app.
Participants receive three daily EMIs via push notifica-
tions, scheduled throughout the day during pseudo-ran-
dom one-hour time windows (at 10:00, 14:30, and 19:00).
Participants have 20min to execute the EMI after they
receive the push notification. Researchers are automati-
cally notified by mail if compliance drops below 60%. In
that case, participants are contacted to resolve potential
problems. In addition, participants are asked to complete
one EMI before going to bed (on demand). Participants
are encouraged to manually start (additional) interven-
tions whenever they want to. EMIs are always preceded
by an EMA, which is identical to the EMAs performed
during calibration. EMI and EMA content is given in the
SEMA3 app during the training weeks.
Booster weeks Before the start of the first booster week,
participants receive a refresher briefing, either in person
when they pick up their devices, or via a video call. Dur-
ing the booster weeks, EMA and EPA data are collected
analogously to the calibration week, using the RADAR
aRMT app on study smartphones and Chill + wristbands.
Incoming EMA and EPA data are analyzed in real time
on a high-performance computing cluster at the Donders
Centre for Cognitive Neuroimaging in Nijmegen. If the
combination of extracted features exceeds the individual
threshold (set to a goal of triggering three interventions
per day, based on stressful situations from the calibra-
tion week), the assigned intervention is immediately
triggered via the RADAR-BASE platform. e interven-
tion arrives ~ 20min after the start of the EMA ques-
tionnaires. A maximum of four interventions are trig-
gered per day. resholds are adjusted on a daily basis to
accommodate signal drift.
Additionally, each day starts with a morning question-
naire and ends with an evening questionnaire also shown
in the RADAR aRMT app on the study smartphone,
given in Supplementary TableS2 [see Additional file1].
e evening questionnaire is followed by an additional
intervention, ensuring that all participants receive at least
one intervention per day. Participants are encouraged to
start additional interventions themselves whenever they
want to. Each booster week lasts for six days.
Practice weeks Participants are encouraged to use the
SEMA3 app on their own phone during the remain-
ing weeks of the EMI phase (i.e., during the weeks in
between booster weeks). During these encouraged prac-
tice weeks, participants do not receive notifications but
are instructed to complete EMIs whenever they want to.
Again, EMIs are always preceded by an EMA.
Follow‑up phase (months 6–8)
Online monitoring continues during the follow-up
phase and changes from biweekly to once a month dur-
ing months 7 and 8. e extended online questionnaire
battery is repeated during month 6—week 2 and month
8—week 4. Both assessments also include the video-
recorded interview. In month 6—week 2, user experi-
ence of the JITAI is assessed with an adapted version of
the user version of the Mobile Application Rating Scale
(uMARS) questionnaire [53]. Follow-up blood and stool
samples are also collected in month 6—week 2. See Fig.1.
Remuneration
Complete participation in all assessments is remuner-
ated with 340 EUR (in Tel Aviv 1200 NIS, in Warsaw
1200 PLN). Further, participants can win on average 10
EUR (40 NIS, 40 PLN) during the Monetary Incentive
Delay task in the neuroimaging battery. Participants
who finish all assessments are additionally included in
a lottery to win a 100 EUR / 400 NIS / 400 PLN voucher
on top (five vouchers in Berlin, Mainz, Nijmegen, and
Tel Aviv; one in Warsaw). To maintain compliance
throughout the longitudinal assessments, money is
disbursed in tranches at different time points through-
out the study, depicted in Supplementary TableS3 [see
Additional file1].
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Bögemannetal. BMC Psychology (2023) 11:245
Materials
Neuroimaging
MRI data acquisition In Berlin, Mainz, Nijmegen,
and Tel Aviv, brain imaging data are acquired on identi-
cal models of 3 T MAGNETOM Prisma systems (Sie-
mens Healthineers, Erlangen, Germany) with 32-chan-
nel head coils (Tel Aviv: 64-channel head coil) using the
following settings: Multiband gradient-echo echo planar
imaging (EPI) sequences (TR = 800 ms , TE = 37 ms, flip
angle = 52°, FOV = 208mm, voxel size = 2.0 × 2.0 × 2.0mm,
72 slices, MB acceleration factor = 8, phase-encoding
direction = PA) from the Center for Magnetic Resonance
Research, University of Minnesota, as adopted from the
Human Connectome Project, are used for blood oxygen-
level dependent (BOLD) fMRI [53]. Before each task,
a pair of blip-up/blip-down EPI sequences is acquired
(TR = 8000ms, TE = 66ms, flip angle = 90°, FOV = 208mm,
voxel size = 2.0 × 2.0 × 2.0 mm), one with an AP and one
with a PA phase-encoding direction. Furthermore, a
T1-MPRAGE sequence (TR = 2500 ms, TE = 2.22 ms, flip
angle = 8°, FOV = 256mm, voxel size = 0.8 × 0.8 × 0.8 mm)
and a FLAIR sequence (TR = 9000 ms , TE = 83 ms, flip
angle = 150°, FOV = 220mm, voxel size = 0.7 × 0.7 × 3.0mm)
are acquired.
In Warsaw, a 3T MAGNETO Trio system (Siemens, Ger-
many) is used until October 2022. ere, multiband gra-
dient-echo EPI sequences are acquired with the follow-
ing settings: TR = 1410 ms, TE = 30.4ms, flip angle = 56°,
FOV = 210mm, voxel size = 2.5 × 2.5 × 2.5mm, 60 slices,
MB acceleration factor = 3, phase-encoding direc-
tion = PA. Additionally, blip-up/blip-down EPI sequences
before each task (identical settings as other sites, except
for voxel size = 2.5 × 2.5 × 2.5 mm), a T1-MPRAGE
(TR = 1100 ms, TE = 3.32 ms, flip angle = 7°,
FOV = 256 mm, voxel size = 1.0 × 1.0 × 1.0 mm), and a
FLAIR sequence with identical settings as above are
acquired. In October 2022, Warsaw replaced the Trio sys-
tem with a 3T MAGNETOM Prisma system (Siemens
Healthineers, Erlangen, Germany) with 32-channel head
coils using the same settings as Berlin, Mainz, Nijmegen,
and Tel Aviv (described above).
Head movement is restricted by foam pads and tape on
the forehead. All task paradigms are presented using the
software Presentation® (Neurobehavioral systems [54])
on a monitor placed behind the scanner bore via a mirror
that is fixed on the head coil.
Reward sensitivity task An adapted version of the Mon-
etary Incentive Delay Task (MID) [55] is used to meas-
ure neural responses during anticipation and receipt of
rewards and losses [56]. Participants are told that they
can win or lose a small amount of money if they press
a button fast enough once a target stimulus (white star)
appears on the screen. Right before the target appears,
a cue that is presented for 2 s indicates whether they
can win (+ 3€/12NIS/12PLN, + 0.5€/2NIS/2PLN), lose
(-0.5€/2NIS/2PLN, -3€/12NIS/12PLN) or neither win nor
lose (0€/NIS/PLN) money during the following trial. e
cue is followed by a jittered anticipation phase of 2–2.5s,
after which participants need to press a button with their
index finger as soon as the target stimulus appears on
the screen. Each trial ends with a 2s numeric feedback
on subjects’ trial outcome as well as the overall gain. An
adaptive algorithm is applied that changes the duration of
target presentation for the participant within each condi-
tion based on their past performance to ensure that the
experience of reward does not differ between subjects
depending on their task performance. If the participant’s
hit rate is below 66%, the target duration is increased by
25ms; else, it is reduced by 25ms. Reaction times and hit
rates are collected as behavioral outcomes. A graphical
depiction of the task design is provided in Supplementary
Figure S3 [see Additional file1]. e reward sensitivity
task was used identically in the DynaM-OBS study [33]
and the Mainz Resilience Project (MARP) study [56, 57].
Note that the DynaM-OBS data set will be used to iden-
tify the reward-related behavioral and neural measures
from the task that are prospectively most strongly neg-
atively associated with participants’ SR scores during
that study [33]. ese will be used in DynaM-INT as
baseline indices of the targeted resilience factor reward
sensitivity, complementary to the questionnaire-based
self-report measures (see below). ey will be tested in
the main analyses of DynaM-INT as potential modera-
tors of intervention effects (primary research questions
on intervention success prediction, see Introduction
and Table1).
Situation‑focused volitional reappraisal task In the sit-
uation-focused volitional reappraisal task, assessing the
ability to use positive cognitive reappraisal (reappraisal
efficacy, reappraisal performance), participants are
instructed to positively reinterpret or just view photo-
graphs which are either negative, positive, or neutral and
to subsequently rate their affective state on a non-verbal
scale [56, 58]. Stimuli were selected from the Interna-
tional Affective Picture System (IAPS) [59] and EmoP-
ics [60] based on normative ratings regarding valence
and arousal. For details on the task design, see Supple-
mentary Figure S4 [see Additional file1]. e situation-
focused volitional reappraisal task was used identically in
the DynaM-OBS study [33]. Timing of the current task is
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Bögemannetal. BMC Psychology (2023) 11:245
identical to the MARP study [56, 57], but a different set
of IAPS/EmoPics stimuli [59, 60] is used.
Note that the same approach as above for the reward sen-
sitivity task will be used to decide which measures from
this task to include in the main analyses of DynaM-INT.
Implicit emotion processing task An adaptation of the
face matching task [61, 62] is used to assess the partici-
pants’ neural responses during implicit emotion pro-
cessing. In each trial, participants are presented with
one picture at the top and two pictures at the bottom
part of the screen, of which one is identical to the upper
one. ey are instructed to select the matching picture
from the bottom row by pressing a button. In the emo-
tion condition, the pictures are grayscale photographs
of Ekman faces [43] with angry or fearful expressions.
Faces are counterbalanced for sex and emotional valence.
In the control condition, the pictures contain geometric
shapes (circles, horizontal ellipses, and vertical ellipses).
Four blocks per condition, each consisting of one instruc-
tion (2s) and 6 trials (5s each), are alternately presented.
Details are given in Supplementary Figure S5 [see Addi-
tional file1]. e implicit emotion processing task was
used identically in the DynaM-OBS study [33].
Resting state A 7-min resting-state scan is acquired
during which participants are instructed to keep their
eyes open and focus on a fixation cross in the middle
of the screen. An identical resting-state scan was col-
lected in the DynaM-OBS study [33]. In the MARP
study [56, 57], a 6-min resting-state scan was included.
Online questionnaires
e assessment schedule of online questionnaires is out-
lined in Table3.
Items of the extended questionnaire battery assess
socio-demographic information at month 1 (study base-
line), and general health, stressor exposure, mental
health, as well as potential psycho-social resilience and
risk factors (collectively termed ‘RFs’) at months 1, 6 and
8. RFs included in the battery are assessed as relatively
stable styles or traits (i.e., the typical way or tendency in
which a person reacts to life experiences). e measures
included in the extended questionnaire battery at study
baseline will be employed as potential moderators of
intervention effects on the primary outcome variables,
SR scores and target engagement (see primary research
questions in Introduction and Table1).
e biweekly monitoring questionnaires adminis-
tered throughout the course of the study assess further
information on stressor exposure, mental health, and
central RFs necessary to calculate SR scores and target
engagement measures as the main outcome variables.
To build biweekly SR scores, these questionnaires con-
tain repeated measures of mental health problems (P),
assessed by the GHQ-28 [28], and on stressor exposure
(E), assessed primarily via a daily hassles list (MIMIS,
[44]). Further E measures assessed during the biweekly
monitoring, related for example to the COVID pandemic,
will be explored for their additional relevance when cal-
culating SR scores (see Table3).
Target engagement for ReApp is operationalized as the
self-reported use frequency of positive cognitive reap-
praisal (assessed with the items on acceptance, positive
reappraisal, putting into perspective, and distancing in the
PASS-process questionnaire) and for Imager as the self-
reported reward sensitivity (assessed using anticipatory
items of the TEPS questionnaire). While RFs included in
the extended questionnaire battery are assessed as rela-
tively stable styles, RFs included in the biweekly monitor-
ing were altered to be assessed as modes (i.e., the extent
to which the RF was used or experienced in the past
two weeks [42]). Complementary and secondary to the
biweekly mode assessments, target engagement will also
be determined from the corresponding style measures in
the extended questionnaire battery.
Finally, biweekly monitoring questionnaires also
include additional assessments of self-reported positive
appraisals (crisis-related positive appraisals and content-
focused perceived positive appraisal). ese are not pri-
mary measures of target engagement and rather used
in moderating analyses and to address tertiary research
questions.
Table 5 provides a detailed overview of all question-
naires used in the DynaM-INT study. Validated versions
of the questionnaires and their translations to the site-
specific languages are used whenever available. An over-
view of questionnaire validations for the different study
languages, as well as the self-developed questionnaires
can be found on OSF [63].
Video‑recorded interview
Each video-recorded interview comprises 13 questions
on current mental health problems and recent or future
experiences (40s per recorded answer). Eight questions
are based on the four subscales of the GHQ-28 [28] that
represent four symptom clusters of psychological distress
(somatic complaints, anxiety/insomnia, social dysfunc-
tion, and severe depression), with two interview ques-
tions per cluster. Four other questions ask about recent
positive and negative memories or future expectations,
respectively. One additional neutral question serves to
establish a baseline for participants’ facial expressivity
and vocal features.
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Bögemannetal. BMC Psychology (2023) 11:245
Using pretrained open-source algorithms, a compre-
hensive set of potential DBMs will be extracted from the
audio and video material, which roughly fall into four
categories: facial expressivity (e.g., positive and negative
emotions and overall expressivity), vocal features (e.g.,
voice pitch and shimmering), movement (e.g., gaze and
head movement), and speech content (e.g., the sentiment
of answers and word usage). A detailed description of the
interview and the analysis will be provided elsewhere.
Ecological momentary andphysiological assessments
Each EMA questionnaire includes in-the-moment self-
assessments of mood (affect), social context, physical
context, past event appraisal, and future event appraisal.
e morning questionnaire (~1min) contains questions
regarding the last night’s sleep and the phase of the men-
strual cycle. e evening questionnaire (~1min) contains
questions regarding the evaluation of the day, as well as
stress anticipation of the upcoming day. Supplementary
Figures S1 and S2 provide an overview of all assessed
EMA items [see Additional file1].
e Chill + collects four types of EPA-data: photo-
plethysmogram (PPG, containing infrared and green
PPG), galvanic skin response (GSR, containing a signal
capped at 2 microSiemens (μS) and one at 20μS), skin
temperature (ST) and accelerometer (ACC, in x, y and z
direction) data.
Feature extraction Real-time feature extraction and
analysis of EMA and EPA data for the purpose of stress-
level determination rely on two separate data streams.
e upload of EMA data to the Donders Centre for Cog-
nitive Neuroimaging in Nijmegen is implemented in the
RADAR-BASE platform. Feature extraction consists of
averaging (per EMA beep) all reversed positive affect
and all negative affect scores. Negative affect is based on
EMA items: “I feel irritated, anxious, insecure andsad”;
and positive affect is based on EMA items: “I feel happy,
satisfied and relaxed”.
e upload of the EPA data is implemented in the
DynaMORE chill + app, which enables a Bluetooth con-
nection between the phone and the Chill + device. e
DynaMORE chill + app collects 10min of data prior to
the EMA prompt time and sends it to the server hosted
by the Donders Centre for Cognitive Neuroimaging in
Nijmegen. e feature extraction algorithm considers
quality of incoming data, meaning that it will only calcu-
late features based on good quality. e 10min of data
are analyzed in one-minute windows. e results of those
separate windows are combined to obtain one value per
feature for each data subset of 10min. Features directly
used in the real-time decision algorithm (described
below) are the number of spontaneous skin conductance
responses, magnitude of spontaneous skin conductance
responses, maximum heart rate, and mean heart rate.
e number of Chill + button presses (indicating subjec-
tively reported stress moments) is also counted. Details
are given in Supplementary Table S2 [see Additional
file1].
reshold calculation e features from the calibra-
tion week during the baseline characterization phase are
used to calculate individual EMA/EPA baseline distribu-
tion parameters and thresholds for the JITAI triggering
during the later intervention phase (booster weeks). For
each of the included EMA and EPA features, individual-
ized means and standard deviations are calculated and
stored, which are later used to Z-score real-time data
for each feature (i.e., relative to the individual baseline
distribution).
All EMA features are Z-transformed and averaged into
an average EMA Z-score. All EPA features are Z-trans-
formed and averaged into an average EPA Z-score. We
then fit a linear regression between the total magnitude
of motion based on accelerometer data, and the aver-
aged Z-transformed EPA value. From this regression, the
slope and intercept are also stored to residualize the EPA
features with respect to motion during real-time analy-
sis in the intervention phase. Finally, EMA Z- scores and
motion-corrected average EPA Z-scores are averaged to
create a distribution of combined EMA/EPA Z-scores.
e initial triggering threshold for EMIs in the first
booster week is set at 60% of this distribution (i.e., this
value is exceeded in 40% of EMA/EPA beeps in the cali-
bration week), aiming at three interventions per day, with
an expected loss of 30% of beeps per day.
Real‑time decision algorithm EMA and EPA data col-
lected during the booster weeks in the intervention phase
is compared to individual baseline distribution param-
eters to decide whether an intervention is triggered at
that moment. For each new incoming set of EMA/EPA
data (i.e., each beep), relevant features are calculated and
standardized using the individual baseline distribution
parameters (mean and standard deviation of that feature
in the calibration week). Z-transformed EMA features
are then averaged, resulting in an EMA Z-score for that
beep. Z-transformed EPA features are also averaged and
then residualized with respect to motion based on the
total magnitude of motion obtained from the accelerom-
eters during the same 10-min EPA recording (and using
the regression parameters obtained from the calibration
week), resulting in the motion-corrected EPA Z-score.
Finally, the EMA Z-score and the motion-corrected EPA
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Bögemannetal. BMC Psychology (2023) 11:245
Z-score are averaged to result in the combined EMA/
EPA Z-score.
If there have been less than four interventions triggered
for that particular participant in that day, the combined
EMA/EPA Z-score is compared to the EMI triggering
threshold, which was initially derived from the calibra-
tion week data. If this Z-score exceeds the threshold, or
if there was a stress button press on the Chill + in the
10min preceding the EMA questionnaire, an interven-
tion will start. If for a given beep no (high quality) EPA
data is available, the decision will be based on EMA fea-
tures only.
reshold adjustment algorithm In addition to this
algorithm, which is run after each beep, another algo-
rithm which serves to dynamically adapt the trigger-
ing threshold is run each night. is second algorithm
keeps track of the number of interventions per day and
decreases the combined Z threshold at the end of the day
by 0.01 if there have been too few interventions (< 3), or
raises this threshold by 0.01 if there have been too many
(> 3).
Ecological momentary interventions
Intervention 1: ReApp e first intervention is target-
ing positive cognitive reappraisal. In this intervention,
participants are asked to think about negative events
they experienced or are about to experience in the close
future and positively reinterpret them by generating posi-
tive reappraisals (e.g., learning from the event, the event
has some unexpected positive aspects, advice that they
would give to a friend, advice that they would receive
from a friend). For details, see [64]. One intervention
takes about 2–3min.
Intervention 2: Imager e second intervention is tar-
geting reward sensitivity via the use of positive mental
imagery. In this intervention, participants are asked to
think about a pleasurable event that might happen to
them during that day and create a mental image of the
situation. For details, see [31, 65]. One intervention takes
about 2–3min.
Data analysis
To evaluate the above research questions, we will conduct
two sets of preparatory analyses (addressing feasibility
and efficacy), and three sets of main analyses (addressing
primary, secondary and tertiary research questions). See
Introduction and Table1.
Preparatory feasibility questions (fQ)
e first preparatory analysis addresses the feasibil-
ity of the just-in-time-adaptive EMIs that are triggered
at moments of high psychological and/or physiological
stress. We will consider the technical implementation
(fQ1) as well as participant’s adherence (fQ2) and expe-
rience (fQ3). ese analyses have a descriptive character
and may additionally inform exclusion criteria for the
main analysis.
To assess the technical implementation of our real-
time decision pipeline (fQ1), we will assess the percent-
age of completed EMA beeps that yielded successful
EPA uploads and feature extractions per booster week,
the number of minutes per EPA upload in those weeks,
and the percentage of triggered interventions per day in
each booster week. Further, we will compare the EMA
and EPA features of beeps that did and did not trigger
an intervention to investigate whether we indeed cap-
tured the most stressful moments of the day. Finally, we
will examine whether the threshold adjustment algo-
rithm works as expected, by comparing the percentage
of triggered interventions per week to the percentage of
interventions that would be triggered based on a fixed
threshold (i.e., without threshold adjustment algorithm).
To assess adherence (fQ2), we will determine the per-
centage of completed EMA questionnaires, the percent-
age of completed triggered interventions, the number of
completed self-triggered interventions, the total inter-
vention adherence (i.e., the total number of completed
triggered and self-triggered interventions), and the time
spent using the aRMT application. All adherence meas-
ures will be calculated for each booster week separately,
as well as summed for all booster weeks. e percentage
of completed EMA questionnaires will additionally be
calculated for the calibration week.
User experience (fQ3) is assessed with a shortened ver-
sion of the user version of the Mobile Application Rat-
ing Scale (uMARS) questionnaire [52], which is applied
as part of the second extended online questionnaire bat-
tery in month 6 in the beginning of the follow-up phase
(see Table 3) In addition to the general questions on
app usability, we will specifically focus on user experi-
ence Q1 (“What changes did you observe, for example, in
your mood, in your behavior etc., while using the app?”)
and Q2 (“Did the app help you use skills during relatively
stressful periods?”) for the feasibility research question.
Preparatory ecacy questions (eQ)
e second preparatory analyses address intervention
effects on participants’ individual stressor reactivity (SR)
scores (eQ1) and target engagement (eQ2). Estimat-
ing training efficacy forms the basis for our main analy-
ses of effect moderation (below) and will be achieved by
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Bögemannetal. BMC Psychology (2023) 11:245
comparing outcome scores during the training period
(the intervention phase) to the pre-training baseline (the
baseline characterization phase; see Fig.1).
We choose to examine the overall intervention phase as
the outcome phase because the mHealth literature sug-
gests different time-courses over which training effects
on health and wellbeing may emerge. For example, a
recent meta-analysis reports that only 8–12 week-long
resilience interventions already affect different measures
of resilience [66], but effects are not sustained at short-
term (< 3 months post intervention), medium-term
(3–6months post intervention), or long-term follow-ups
(> 6months post intervention). For other health and well-
being outcomes, there is evidence of incubation effects.
e same meta-analysis shows delay benefits for anxiety
and stress measures, which were not reduced post inter-
vention but at short-term follow-up. A meta-analysis of
mHealth interventions also reports increasing estimated
effect sizes on health outcomes with prolonged follow-
up (up to 9months) [67]. Considering that our resilience
operationalization via SR scores aims to improve on pre-
vious resilience measures [12] and involves residualized
mental health outcomes, effects in the present study may
follow either pattern. e use of novel EMIs with a JITAI
element in the present study adds further uncertainty.
Intervention effects on SR scores and target engage-
ment may thus emerge already after weeks or only after
months of training.
We will estimate intervention effects using linear
mixed models with repeated SR or target engagement
measures (as either modes or styles) as endpoints, com-
paring measurements that are part of the baseline to
those derived during the intervention training period.
Long-term follow-up measurements will be treated
separately. Our hypothesis is that participants develop
lower SR scores and higher target engagement during the
interventions.
Primary research questions (pQ)
Our primary analysis goal is to assess whether variables
(RFs) assessed at study baseline moderate (predict) the
effect of ReApp, Imager, or both interventions on SR
scores (pQ1) and target engagement measures (pQ2). We
will address the pQ1 and pQ2 hypotheses statistically by
evaluating the interaction between a given baseline varia-
ble and the respective intervention effect estimate, based
on the efficacy questions (eQ). Depending on the strength
of moderation, training effects may only be detected for a
subgroup of participants (see e.g., [64]), such that group-
level efficacy is not a prerequisite for addressing these
primary research questions. While many baseline vari-
ables qualify as potential moderators, the most important
ones are the self-reported use frequency of positive cog-
nitive reappraisal for the ReApp intervention, and self-
reported reward sensitivity for the Imager intervention
(see Online Questionnaires for definition of variables).
We hypothesize that lower baseline levels of these resil-
ience factors will be associated with stronger effects of
the respective intervention on SR scores (pQ1) and on
target engagement (pQ2).
Regarding the potential moderating influence of other
psychosocial and neurobiological RFs, the exact analy-
sis plan will depend on the results of the corresponding
analyses in our DynaM-OBS observational study [33],
which we use as a discovery sample to derive hypoth-
esized moderators and strength of hypotheses (e.g., sec-
ondary, tertiary, exploratory).
Given that the two EMIs have differing mechanistic
targets, we will first evaluate moderation effects sepa-
rately in each of the intervention groups. It is also pos-
sible that both interventions have unifying moderators,
such as PAS. Following separate analyses, if we observe
or hypothesize a joint mechanism (such as ultimate effect
mediation in both interventions by increases in PAS, see
Introduction), we will therefore pool participants over
both interventions for combined efficacy and modera-
tion analyses, maximizing analysis power. On the con-
trary, if we observe or hypothesize potentially differential
results, we may instead contrast the two interventions for
their main effects and effect moderation. As effect sizes
in intervention comparisons are typically relatively small
and result in power issues, we consider the latter analyses
exploratory.
e above-described linear mixed models represent
omnibus analyses of outcome measures across the entire
intervention training period. ey may thus be followed
by post-hoc contrasts of individual measurement time
points within the mixed-model framework, allowing us
to explore sensitive periods for intervention effects.
Supplemental analysis approaches Next to the above
outlined moderation analyses using interaction terms,
we will also examine simpler prospective associations
between baseline variables of interest and repeated SR
score measurements in separate regression models. We
aim to replicate associations found in DynaM-OBS [33],
and also to compare intervention-related associations in
DynaM-INT with associations in natural time-courses in
DynaM-OBS. Further analyses may involve DynaM-OBS
data[33] as an informal control condition against which
the effects of the interventions in DynaM-INT can be
assessed. Finally, we will also employ the DynaM-OBS
study[33] to explore the applicability of more complex
time-series analyses, and to examine the relationship
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Bögemannetal. BMC Psychology (2023) 11:245
between the different positive appraisal-related meas-
ures beyond positive cognitive reappraisal frequency
in DynaM-OBS and then try to replicate the result in
DynaM-INT.
Secondary research question (sQ1)
Our secondary research question is whether the antici-
pated reductions in stressor reactivity are preceded
or accompanied by the anticipated increases in target
engagement (sQ1), which would suggest that the inter-
ventions work via the targeted resilience mechanisms. To
address this question, we will employ linear mixed mod-
els for SR-target engagement covariance and lagged asso-
ciations, respectively. Again, DynaM-OBS [33] results
will be consulted to inform the modelling of more com-
plex time-series analyses for example between positive
cognitive reappraisal and SR, such as the size of the time
lag associations.
Tertiary research questions (tQ1‑tQ5)
e assessments in DynaM-INT employ various tests
potentially suitable to measure PAS. ese include the
following self-report instruments: Perceived Positive
Appraisal Style Scale – process-focused (PASS-process)
[45], Perceived Positive Appraisal Style Scale – content-
focused (PASS-content) [45], self-generated questions
on Crisis-related positive appraisals [63], an optimism
questionnaire [48], a control questionnaire [47], and a
self-efficacy questionnaire [46] (Table 5). For our ter-
tiary research questions, we will examine their relation
to stressor reactivity, target engagement, and potential
changes over the study period (tQ1-5) using measure-
ments from the relevant time points.
ese questionnaires are employed in the extended
questionnaire batteries administered at the baseline char-
acterization and follow-up phases. e PASS-process and
PASS-content are additionally included in the biweekly
online monitoring questionnaires. A non-questionnaire
test is the situation-focused volitional reappraisal fMRI
task, as administered in the baseline characterization
phase, which has also been employed in earlier studies,
including DynaM-OBS, serving to establish the PAS con-
struct and to test its relationship to resilience [33, 57, 68].
ese earlier data sets are being used to specify the opti-
mal PAS measure to be used in DynaM-INT before con-
ducting PAS-related analyses in this data set.
Additional analyses
Digital biomarkers from audiovisual recordings To
obtain more objective and sensitive indicators of partici-
pants’ mental health problems, we aim to identify digital
biomarkers of mental health (DBMs) from the audiovisual
data derived from participants’ video-recorded inter-
views. e interviews are completed at four timepoints
throughout the study. Using pre-trained open-source
algorithms, features that represent potential DBMs, such
as voice pitch, will be extracted from the recordings. Sub-
sequently, we will use machine learning-based analyses
such as feature selection to identify those features that
best align with self-reported GHQ scores in a data-driven
fashion. Next to convergent validity with the GHQ, we
will also consider discriminant validity to other question-
naires, test–retest reliability, and consistency across mul-
tiple analysis approaches.
In a second step, we aim to combine the identified fea-
tures to DBM-based P scores and use them to calculate
DBM-based SR scores, which can complement the pri-
mary, fully questionnaire-based SR as an additional out-
come in addressing the above hypotheses. For example,
we will investigate intervention effects on DBM-based SR
scores, whether the same RFs that predict questionnaire-
based SR also predict DBM-based SR, and whether those
RFs that are not measured via self-report questionnaires,
such as fMRI task-based activation or biological data
from the bio-samples, show stronger associations with
the DBM-based than questionnaire-based SR scores.
Next to using identified DBMs in a complementary out-
come measure, we will also explore how potential DBMs
relate to the main questionnaire-based SR as predictors
and whether any features relate to or predict intervention
success.
Discussion
With the DynaM-INT study, we are advancing the field
of resilience research by investigating two different just-
in-time adaptive interventions (JITAIs) that are targeted
at increasing putative resilience factors. e design
allows us to investigate the feasibility of just-in-time
EMIs, triggered at moments of high psychological and
physiological stress in real life. e multimodal baseline
characterization further enables us to identify predictors
for the effects of each of the interventions on stressor
reactivity and target engagement. At the same time,
the dense longitudinal measures allow us to investigate
whether the JITAIs are followed by reductions in stressor
reactivity and increases in target engagement over time.
e DynaM-INT study thereby aims to inform future
research about which parameters are important to con-
sider in future studies testing the efficacy of these inter-
ventions. Moreover, the DynaM-INT study yields a rich
database that can be shared with other researchers in the
field of resilience research.
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Page 23 of 26
Bögemannetal. BMC Psychology (2023) 11:245
Abbreviations
3T 3 Tesla
AP Anterior‑posterior
BOLD Blood oxygen level dependent
d Day
DBM Digital biomarkers of mental health derived from audio‑
visual recordings
DNA Deoxyribonucleic acid
DynaM‑INT Dynamic Modelling of Resilience‑Interventional Study
DynaM‑OBS Dynamic Modelling of Resilience‑Observational Study
E Experienced stressors
EMA Ecological momentary assessment
EMI Ecological momentary intervention
EPA Ecological physiological assessment
EPI Echoplanar imaging
FLAIR Fluid‑attenuated inversion recovery
(f )MRI (Functional) magnetic resonance imaging
FOV Field of view
FRESHMO Frequent stressor and mental health monitoring
GHQ General health questionnaire
HAWIE Hamburg Wechsler intelligence test for adults
h Hour
HR Heart rate
IAPS International affective picture system
ID Identifier
JITAI Just‑in‑time adaptive intervention
MB Multiband
MID Monetary incentive delay task
M.I.N.I. Mini‑International Neuropsychiatric Interview
ml Milliliter
M Month
ms Millisecond
NIS Israeli Shekel
nr Number
P Mental health problems
PA Posterior‑anterior
PAS Positive appraisal style
PASS‑content Perceived positive appraisal style scale, content‑focused
PASS‑process Perceived positive appraisal style scale, process‑focused
PASTOR Positive appraisal style theory of resilience
PLN Polish Złoty
R > NR Contrast regulate>no regulate
RF Resilience or risk factor
s Second
SR Stressor reactivity
T1‑MPRAGE Magnetization prepared rapid acquisition with gradient
echoes
TE Echo time
TR Repetition time
w Week
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40359‑ 023‑ 01249‑5.
Additional le1: TableS1. Beep schedule. Figure S1. Design of the differ‑
ent assessment weeks. Figure S2. EMA content. TableS2. Real‑time features.
Figure S3. Design of the reward sensitivity task. Figure S4. Design of the
Situation‑focused volitional reappraisal task. Figure S5. Design of the implicit
emotion processing task (Faces task). TableS3. Remuneration schedule.
Acknowledgements
We thank S. Stöber and N. Donner for their help with project administration. For
their help with study conduct, we thank the following people: At Charité: S. Blum, L.
Do, E. Hodapp, C. Rohr, E. Rossi, C. Sachs; in Mainz: H. Fiehn, L. Knirsch, D. Laurila‑Epe,
A. Peschel, J. Piloth, C. Schultheis, C. Walter, M. Weber; in Nijmegen: N. Aslan, J. Post‑
huma, M. Schepers; in Tel Aviv: I. David, S. Berman, O. Gafni, R. Horovich, S. Ben‑Dor,
D. Even‑Or, L. Lagziel; in Warsaw: S. Matys, N. Robak, J. Szurnicka, M. Wasylkowska.
Authors’ contributions
Drafting the manuscript: SAB, AR, LMCP, RK. Conception & study design: RK,
HW, IMV, AA‑V, BK, DK, EH, HB, IM‑G, JT, KR, KY, MM, OT, SP, TH, WdR. Acquisition
of data: SAB, AR, LMCP, SB, EJCH, ZCR, AU, MZ, JW, KY. Critical editing and revi‑
sion of the manuscript: All authors. All authors have approved the submitted
version of this manuscript. All authors have agreed to be personally account‑
able for the author’s own contributions and to ensure that questions related
to the accuracy or integrity of any part of the work, even ones in which the
author was not personally involved, are appropriately investigated, resolved,
and the resolution documented in the literature.
Funding
This project has received funding from the European Union’s Horizon 2020
research and innovation program under Grant Agreement numbers 777084
(DynaMORE project) and 101016127 (RESPOND project), Deutsche Forschun‑
gsgemeinschaft (DFG Grant CRC 1193, subprojects B01, C01, C04, Z03), the Ger‑
man Federal Ministry for Education and Research (BMBF) as part of the Network
for University Medicine under Grant number 01KX2021 (CEOsys and EViPan
projects), and the State of Rhineland‑Palatinate, Germany (MARP program, DRZ
program, Leibniz Institute for Resilience Research). The funding agencies had
no part in study design, collection, management, analysis, and interpretation of
data, writing of the report, and the decision to submit the report for publication.
Availability of data and materials
Self‑generated questionnaires are available at OSF [44].
Declarations
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki
and approved by the local ethics committees of all participating sites: The
ethics committee of Charité – Universitätsmedizin Berlin, Germany; the
medical ethical committee of Radboud university medical center (METC
Oost‑Nederland), Nijmegen, The Netherlands; the ethics committee of the
Tel Aviv University, Tel Aviv, Israel and the Helsinki committee of Tel Aviv
Souraski Medical Center; the ethics committee of the State Medical Board
of Rhineland‑ Palatinate, Mainz, Germany; the ethics committee for scien‑
tific research at Faculty of Psychology, University of Warsaw (Komisja Etyki
Badań Naukowych Wydziału Psychologii Uniwersytetu Warszawskiego),
Warsaw, Poland. All study participants provided written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests. RK has received
advisory honoraria from JoyVentures, Herzlia, Israel.
Author details
1 Donders Institute for Brain, Cognition and Behaviour, Radboud Univer‑
sity Medical Center, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands.
2 Research Division of Mind and Brain, Depar tment of Psychiatry and Neu‑
rosciences CCM, Charité ‑ Universitätsmedizin Berlin, Corporate Member
of Freie Universität Berlin, Humboldt‑Universität Zu Berlin, and Berlin Institute
of Health, Berlin, Germany. 3 Faculty of Philosophy, Berlin School of Mind
and Brain, Humboldt‑Universität Zu Berlin, Berlin, Germany. 4 Leibniz Institute
for Resilience Research (LIR), Mainz, Germany. 5 Max Planck Institute for Human
Cognitive and Brain Sciences, Leipzig, Germany. 6 Sagol Brain Institute, Tel Aviv
Sourasky Medical Center, Tel Aviv, Israel. 7 Faculty of Psychology, University
of Warsaw, Warsaw, Poland. 8 Neuroimaging Center (NIC), Focus Program
Translational Neuroscience (FTN), Johannes Gutenberg University Medi‑
cal Center, Mainz, Germany. 9 Center for Contextual Psychiatry, Department
of Neurosciences, KU Leuven, Louvain, Belgium. 10 Division of Experimental
Psychopathology and Psychotherapy, Department of Psychology, University
of Zurich, Zurich, Switzerland. 11 Department of Psychiatry, Psychotherapy
and Psychosomatics, Psychiatric University Hospital (PUK), University of Zurich,
Zurich, Switzerland. 12 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv,
Israel. 13 OnePlanet Research Center, Wageningen, The Netherlands. 14 Institute
of Medical Biometry and Statistics, Faculty of Medicine and Medical Center,
University of Freiburg, Freiburg, Germany. 15 Freiburg Center for Data Analysis
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 24 of 26
Bögemannetal. BMC Psychology (2023) 11:245
and Modelling, University of Freiburg, Freiburg, Germany. 16 Melbourne School
of Psychological Sciences, The University of Melbourne, Vic 3010, Australia.
17 Life Sciences Department, Imec, Louvain, Belgium. 18 Center for Cognitive
Neuroimaging, Donders Institute for Brain Cognition and Behaviour, Radboud
University, Nijmegen, The Netherlands. 19 Behavioural Science Institute, Rad‑
boud University, Nijmegen, The Netherlands. 20 Institute of Physics, University
of Freiburg, Freiburg, Germany. 21 Signalling Research Centres BIOSS and CIBSS,
University of Freiburg, Freiburg, Germany. 22 Department of Psychiatry
and Psychotherapy, Johannes Gutenberg University Medical Center, Mainz,
Germany. 23 School of Psychological Science, Tel Aviv University, Tel Aviv, Israel.
24 Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. 25 Depart‑
ment of Developmental Psychology, University of Amsterdam, Amsterdam,
The Netherlands.
Received: 26 May 2023 Accepted: 14 July 2023
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