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Real-time mechanism-based interventions for
daily alcohol challenges: Protocol for ecological
momentary assessment and intervention
Shuyan Liu
1,2
, Matthias Haucke
1
, Rika Groß
3
, Kay Schneider
1
,
Jaekyung Shin
1
, Fabian Arntz
4
, Patrick Bach
2,5
, Tobias Banaschewski
2,6
,
Christian Beste
7
, Lorenz Deserno
8,9
, Ulrich Ebner-Priemer
2,3,10
,
Tanja Endrass
9
, Marvin Ganz
3
, Ali Ghadami
3
, Marco Giurgiu
10
,
Andreas Heinz
1,2
, Falk Kiefer
5
, Reinhold Kliegl
4
, Bernd Lenz
2,5
, Marta
Anna Marciniak
11,12
, Andreas Meyer-Lindenberg
2,3
, Andreas
B. Neubauer
13
, Michael Rapp
2,4
, Michael N. Smolka
9
, Jens Strehle
14
,
Rainer Spanagel
2,15
, Gianna Spitta
1
, Heike Tost
2,3
, Henrik Walter
1,2
,
Hilmar Zech
8,9
, Dominic Reichert
16
and Markus Reichert
3,16,17
Abstract
Background: Advancing evidence-based, tailored interventions for substance use disorders (SUDs) requires understanding
temporal directionality while upholding ecological validity. Previous studies identified loneliness and craving as pivotal fac-
tors associated with alcohol consumption, yet the precise directionality of these relationships remains ambiguous.
Objective: This study aims to establish a smartphone-based real-life intervention platform that integrates momentary assess-
ment and intervention into everyday life. The platform will explore the temporal directionality of contextual influences on
alcohol use among individuals experiencing loneliness and craving.
Methods: We will target 180 individuals aged 18 to 70 in Germany who report loneliness, alcohol cravings, and meet risk or binge
drinking criteria (over 14 standard drinks per week or five drinks in a single day for males, and over seven drinks per week or four
1
Department of Psychiatry and Psychotherapy (Campus Charité Mitte),
Charité—Universitätsmedizin Berlin, Berlin, Germany
2
German Center for Mental Health (DZPG), Partner Sites Berlin/Potsdam
and Heidelberg/Mannheim/Ulm, Germany
3
Department of Psychiatry and Psychotherapy, Central Institute of Mental Health,
Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
4
Department of Sports and Health Sciences, University of Potsdam,
Potsdam, Germany
5
Department of Addictive Behavior and Addiction Medicine, Central
Institute of Mental Health (CIMH), Medical Faculty Mannheim, Heidelberg
University, Mannheim, Germany
6
Department of Child and Adolescent Psychiatry and Psychotherapy, Central
Institute of Mental Health, Medical Faculty Mannheim, Heidelberg
University, Mannheim, Germany
7
University Neuropsychology Center (UNC), TU Dresden, Dresden, Germany
8
Department of Child and Adolescent Psychiatry, Psychotherapy and
Psychosomatics, University Hospital and University Würzburg, Wurzburg,
Germany
9
Addiction Research, Institute for Clinical Psychology and Psychotherapy,
Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
10
Institute of Sports and Sports Science, Karlsruhe Institute of Technology,
Karlsruhe, Germany
11
Healthy Longevity Center, University of Zurich, Zurich, Switzerland
12
Department of Psychology, Education and Child Studies, Erasmus
University Rotterdam, Rotterdam, The Netherlands
13
RWTH Aachen University, Aachen, Germany
14
Center for Information Services and High Performance Computing (ZIH),
Technische Universität Dresden, Dresden, Germany
15
Institute of Psychopharmacology, Central Institute of Mental Health,
Medical Faculty Mannheim, Heidelberg University, Mannheim,
Germany
16
Department of eHealth and Sports Analytics, Faculty of Sport Science,
Ruhr-University Bochum, Bochum, Germany
17
Department for Sport and Exercise Science, Paris Lodron University
Salzburg, Salzburg, Austria
Corresponding authors:
Shuyan Liu, Department of Psychiatry and Psychotherapy, Charité—
Universitätsmedizin Berlin (Campus Charité Mitte), Charitéplatz 1, 10117,
Berlin, Germany.
Emails: siyan908@hotmail.com; shuyan.liu@charite.de
Markus Reichert, Department of eHealth and Sports Analytics, Faculty of
Sport Science, Ruhr-University Bochum, Bochum, Germany.
Email: markus.reichert@ruhr-uni-bochum.de
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org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is
attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Research protocol
DIGITAL HEALTH
Volume 11: 1–13
© The Author(s) 2025
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/20552076241311731
journals.sagepub.com/home/dhj
drinks in a single day for females). Using a Within-Person-Encouragement-Design and Just-In-Time-Adaptive-Interventions,
we will manipulate the contexts of loneliness and alcohol craving with cognitive reappraisal and physical activity interventions
against a control condition (working memory task).
Results: Recruitment started in June 2024, with data collection and processing expected by June 2027.
Conclusion: Our real-life intervention platform endeavors to serve as a robust tool for discerning the directionality of the
effects from time series data in everyday life influences on alcohol use for the future study. Ultimately, it will pave the
way for low-threshold prevention, clinical treatment, and therapy to target diverse contexts of everyday life in SUD.
Trial registration: German Clinical Trials Register DRKS00033133.
Keywords
Alcohol consumption, loneliness, craving, real-life assessment and intervention, cognitive reappraisal, physical activity
Submission date: 11 March 2024; Acceptance date: 18 December 2024
Introduction
The desire for social connection is one reason people
consume substances such as alcohol and cannabis.
1,2
Increased substance use can heighten the risk for substance
abuse and dependence. In Germany, up to 19% of the popu-
lation engage in hazardous drinking.
3
Previous studies have
identified loneliness and craving as significant factors asso-
ciated with alcohol consumption. However, the precise tem-
poral direction of these relationships—critical for
establishing causality—remains unclear.
4,5
To ascertain
the temporal direction of these contextual correlates for
real-life treatment targets, adopting an ecological approach
to momentary intervention is required. Therefore, the
objective of this study is to establish a real-life intervention
platform aimed at testing temporal directionality through
experimental manipulation in everyday life, particularly in
the context of loneliness and alcohol craving.
Loneliness, as the distressing experience of a discrep-
ancy between one’s desired and actual social connection,
6
correlates with adverse health behaviors such as increased
alcohol use and physical inactivity.
2
A Eurofound survey
conducted in 2016, prior to the pandemic, revealed that
12% of EU citizens experienced loneliness more than half
of the time. This figure doubled to 25% during the first
COVID-19 outbreak, with an additional 15.5% of indivi-
duals reporting feeling lonely more than half of the time.
7
Loneliness had a strong effect on alcohol consumption
among alcohol use disorder (AUD) participants across
age groups.
4
In particular, the sequential latent class regres-
sion models in our previous study showed that loneliness
predicted change in alcohol consumption patterns over
time in subsamples including adolescents and young
adults, and middle-aged men.
8
Craving, as “an intense desire or urge”for substances,
is described as a consequence of the conflict between the
need to consume substance and the desire to abstain; it
is recognized as one cardinal symptom of AUD and serves
as a major relapse predictor.
9
Craving is not only an estab-
lished marker for diagnoses on a cross-sectional and retro-
spective assessment level, but also evidenced to correlate
with substance use on a within-subject and momentary
level in everyday life: the more a person craves a substance
at a given moment, the higher the consumption is, and vice
versa.
1
Recent studies argue for craving to be a promising
causal marker with prognostic value and high potential as a
treatment target for interventions triggered by ecological
momentary assessment (EMA).
10
The EMA methodology (e.g. smartphone-based elec-
tronic diaries and wearables) enables to investigate intra-
and interindividual effects as well as reciprocal relation-
ships.
11,12
EMA involves repeatedly sampling of indivi-
duals’behaviors and experiences in real-time within their
natural environments. This method minimizes recall bias
and enhances ecological validity, while approximating tem-
poral dynamics (e.g. Granger causality).
13
However, EMA
canalsoimposeaburdenonparticipants,requiringthemto
respond to repeated questionnaires throughout the day,
which may disrupt ongoing activities and cause distress.
14
We can apply EMA to gather intensive longitudinal data
(ILD) on determinants and consequences of drug intake.
15
On the one hand, ILD is more ecologically valid, as it directly
measures the subjects’experience in their current environ-
ment; on the other hand, it allows to approximate temporal
associations. However, causal inferences can only be estab-
lished if: (1) variables of interest are correlated with each
other, (2) a temporal order is given, (3) hidden third variables
are controlled or explained; and (4) the direction of the effect is
indispensable (e.g. heightened levels of craving usually corres-
pond to increased alcohol consumption).
16
Accordingly,
experimental designs enable to test for causality through
2DIGITAL HEALTH
experimental manipulation under laboratory conditions and
control of potential hidden influences.
17
To approximate causality and test temporal directional-
ity underlying drug intake in everyday life, we need to
combine the advantages of laboratory and ambulatory
approaches. This combination allows us to achieve both
ecological validity and approximate causal temporal infer-
ences (i.e. an ecological approach to intervention). EMA
enables the collection of time series data, which can be
used to approximate the temporal necessity of causality,
known as Granger causality (i.e. where the cause precedes
the effect).
18
Just-In-Time-Adaptive-Interventions (JITAIs)
19
are recog-
nized as a first key step toward approximating causality
within subjects in everyday life.
20
In essence, JITAIs use real-
time analyses to determine momentary states (e.g. EMA of
loneliness and alcohol craving and device-based read-outs,
such as acceleration signals for physical activity detection),
and controlled or randomized intervention-assignments (e.g.
an instruction to engage in physical activity after a couple
of high-craving ratings). However, experimental manipula-
tions may be infeasible in everyday life due to practical or
ethical reasons (e.g. participants may be unable to interrupt
their task to follow intervention-instructions under high work-
related stress).
Here, the Within-Person-Encouragement-Design (WPED)
21
offers a viable solution through three key elements: (1)
smartphone-based, randomly assigned encouragement to
engage in an intervention as an instrumental variable
(Encouragement), (2) the intervention (i.e. cognitive
reappraisal, physical activity) itself (Treatment), (3) target vari-
ables (i.e. loneliness and alcohol craving) that are to be causally
influenced by the Treatment (Outcome). Based on an instru-
mental variable estimation approach, a causal treatment effect
on the outcome can be analyzed if a correlation between the
encouragement and the treatment is present and given the the-
oretical assumption that any encouragement effects on the
outcome are fully mediated by the treatment. The WPED com-
bines three major approaches: (1) multilevel analyses of within-
subject mechanisms, (2) experimental within-subject manipula-
tion of the “treatment”variable, and (3) randomized encourage-
ment as an instrumental variable to induce exogenous
experimental variation if consistent adherence to the “treat-
ment”is not realistic.
Both cognitive reappraisal and physical activity have
been associated with reduced loneliness and alcohol
craving.
22–26
At the behavioral and neurobiological levels,
both can improve cognition functions.
27,28
However, they
are very different in nature: on the one hand, cognitive
reappraisal is a collection of “structured, goal-directed,
and collaborative intervention strategies that focus on the
exploration, evaluation, and substitution of the maladaptive
thoughts, appraisals, and beliefs that maintain psycho-
logical disturbance.”
29
In the context of AUD, at low
engagement in reappraisal, greater startle reactivity to
uncertain threat was associated with greater problem
alcohol use.
30
On the other hand, physical activity describes a broad
wealth of human movements ranging from distinct types
of exercise (e.g. dancing, jogging as a structured physical
activity characterized by high energy expenditure and pro-
longed duration for a fitness goal) to different nonexercise
activities (NEAs; i.e. all other daily physical activities
including walking and stair-climbing, or sedentary breaks
such as short-term movement programs), with the latter
NEA especially qualifying for JITAI served by smartphone
applications.
31,32
The type, duration, and intensity of phys-
ical activity can be tailored toward individual preferences
and improvements of specific symptoms. Physical activity
is a promising adjunctive treatment for substance use dis-
order (SUD)
33
and benefits SUD patient care
34
; e.g. it has
been associated with reductions in craving across different
substances (e.g. tobacco, marijuana, alcohol), and espe-
cially in AUD, several studies indicate that physical activity
can decrease craving for alcohol.
35
In particular, the recent
literature assumes that “craving could be specifically tar-
geted by short and easily achievable physical exercise”
and short bouts of moderate physical activity such as
brisk walking are correlated with reduced craving.
36
Therefore, given both these proven benefits of cognitive
reappraisal and physical activity for SUD prevention and
treatment as outlined above, shown in Figure 1, and their
high suitability for momentary application in everyday
life (e.g. short yet effective cognitive reappraisal and phys-
ical activity programs guided via smartphone application),
they qualify as highly promising tools and interventions
in intensive longitudinal research for experimental manipu-
lation in everyday life to elucidate the real-life causality of
change among individuals with SUDs.
37,38
Depending on
Figure 1. Schematic overview of daily life causality testing in our
study. Systematic experimental manipulation in phases of
momentary loneliness/alcohol craving levels through the
incorporation of cognitive reappraisal (Intervention 1), physical
activity (Intervention 2), and working memory (control condition)
via interactive smartphone triggers will allow to test for the (non)
causal nature of real-life correlates (e.g. loneliness and alcohol
craving) of alcohol intake.
Liu et al. 3
the momentary settings (e.g. in work or leisure time) and
personal preferences as well as momentary cognitive cap-
acities, either the cognitive reappraisal or the physical activ-
ity intervention may be better suited to, for example, reduce
momentary loneliness and alcohol craving.
Therefore, the overarching aim of our study is to estab-
lish a real-life intervention platform that incorporates a
JITAI
39
into everyday life to test the temporal direction of
factors that lead to substance use. Our approach positions
us uniquely to: (1) understand the temporal directionality
of mechanisms of substance use in everyday life, (2) set
the evidence-basis for an increasing number of (digital)
mental health interventions designed for substance use
through separating disease causes from consequences, (3)
guide JITAIs
19
for the right type/amount/time of support
by adapting to individuals’changing internal and context-
ual states.
We hypothesize (H1) that cognitive reappraisal and
physical activity interventions will reduce momentary lone-
liness (H1a) and/or alcohol craving (H1b). In addition, we
assume (H2) that decreases in momentary loneliness and/
or alcohol craving will correspond to a decrease in real-life
alcohol consumption.
Methods
Participants and recruitment
The study will be conducted between June 2024 and June
2027 in Germany. It is a part of the transregio (TRR 265)
project “Addiction research consortium: Losing and regain-
ing control over drug intake”in Germany.
40
The TRR265
cohort targets 900 people with mainly mild to moderate
AUD and 150 age-matched controls. To test causality in
real-life mechanisms of problematic alcohol use, we will
target 180 risk and/or binge drinkers aged 18 to 70 who
report feeling lonely and are craving alcohol. Exclusion cri-
teria include chronic obstructive pulmonary disease, coron-
ary heart disease, heart failure, a body mass index of ≥35 or
≤18, use of assistive devices like walkers, and pregnancy.
The target sample will be recruited and screened both
from TRR 265 cohort and the general population in
Germany. With the assistance of the professional recruit-
ment agencies, we will deploy advertisements across mul-
tiple media channels, including newspapers, social media
platforms, notice boards, and within both inpatient and out-
patient clinics. Participants will receive a monetary reward
as compensation after completing the study. The amount of
compensation is determined by a percentage of the com-
pleted study items.
Risky alcohol use and binge drinking are defined by
the German Federal Centre for Health Education
(Bundeszentrale für gesundheitliche Aufklärung, BZgA),
41
and the German Ministry of Health.
42
For men, binge drinking
is defined as consuming five or more drinks on a single day,
while risk drinking is exceeding 14 drinks per week over
the past 30 days. For women, binge drinking is consuming
four or more drinks on a single day, and risk drinking is
exceeding seven drinks per week within the past 30 days.
For calculating alcohol consumption, we will ask participants
for their sex assigned at birth. Participants will be presented
with a list of common alcoholic beverages,
43
and will be
asked to report the number of each type of beverage they
consume on a weekly basis.
44
For screening purposes, high trait loneliness will be
assessed by using the short eight-item form UCLA
Loneliness Scale (ULS-8; cutoff scores ≥16).
45
High trait
alcohol craving will be assessed by using the five-item
Penn Alcohol Craving Scale (PACS-5; cutoff ≥mean
from a pilot sample of 50 participants).
46
We will also
assess their sociodemographic characteristics (i.e. age,
gender, education level, and income), alcohol consumption,
mental health status, and cognitive performance.
Ethics and informed consent
The study will be conducted in accordance with the
Helsinki Declaration of 1975 and the ethics committee’s
standards. The study was evaluated by the Ethics
Committee at Charité—Universitätsmedizin Berlin (refer-
ence number: EA1/270/22) and at Heidelberg University
(reference number: 2023-536). Written informed consent
will be obtained from all participants before they start the
study.
Sample size and power considerations
Our power estimation is based on simulation studies for the
WPED.
21
We expect a compliance rate exceeding 70% with
three (non)encouragements provided daily. Previous
research indicates that average compliance rates in similar
studies can reach up to 84%. However, many studies with
lower compliance rates often remain unpublished.
47
In add-
ition, earlier studies report compliance rates ranging from
50% to 90%.
48
Given this variability, we have adopted a
balanced assumption of a 70% compliance rate. In addition,
the dropout rate for technological-based psychological
intervention varies widely, from 2% to 83%.
49
We also
take a balanced approach by assuming a 20% dropout rate.
The study will involve 21 intervention days, resulting in
a maximum of 63 intervention data points per person. At a
total recruitment of 180 participants and a resulting sample
size of n=144 participants subsequent to a 20% drop-out
would result in a total of 6350 data points at a compliance
of 70% (144 participants ×21 intervention days ×3 (non)
encouragements ×0.7 compliance rate). According to
Schmiedek and Neubauer’s power simulation studies for
WPEDs,
21
our sample size will allow for sufficient power
to detect a small- to medium-sized average treatment
effect across a broad range of conditions.
4DIGITAL HEALTH
Design and procedure
To design the real-life intervention platform, we will
combine JITAIs as smartphone applications (apps) with
EMA (including physical activity tracking via acceler-
ometers and electronic diaries). For this purpose, we will
use the software movisensXS and TherapyBuilder (both
movisens GmbH, Karlsruhe) to incorporate two JITAIs
including cognitive reappraisal and physical activity, and
an active control condition involving a working memory
task.
50
In particular, we will generate motivators to break
up sedentary phases and to increase the number of steps
taken.
We will employ a WPED with counterbalancing. All
participants will take part in three conditions in a counterba-
lanced order, with each participant randomly assigned to
receive one of the following: Intervention 1, involving cog-
nitive reappraisal; Intervention 2, which includes physical
activity; or an active control condition featuring a gamified
working memory task. The duration of each condition will
be approximately 6 min. All participants will start with a
baseline EMA for seven consecutive days (eight times per
day), followed by a 21-day JITAI (three times per day),
while also receiving the usual EMA eight times per day.
They will end up with a follow-up EMA for seven consecu-
tive days (eight times per day; see Figure 2).
Two fixed-time EMAs will be administered daily: the
first in the morning at 08:00 and the last in the evening at
22:00. The morning EMA will include questions about
sleep quality.
51
During the evening EMA, participants
will complete a brief 3-min gamified response inhibition
task,
50
developed based on the widely used stop signal
task. This task is designed to assess potential changes in
cognitive functions over time and examine the mediation
effects of JITAI on enhancing cognitive function in relation
to problematic alcohol use. In addition to two fixed EMAs,
eight randomly timed EMAs will be send each day between
08:00 and 21:00, with at least a 60-min interval between
them. Participants can delay their responses to the EMAs
by up to 60 min to ensure data collection, especially if
they are in situations where using their phone is not feas-
ible. If a participant does not engage with the intervention
within the 60-min window, we will still send a question-
naire asking about their experienced loneliness and
craving, and reasons for skipping the intervention.
At the end of the 21-day JITAI study, we will evaluate
participants’perceptions of the interventions using the
Acceptability E-scale
52
the Digital Working Alliance
Inventory,
53
and a modified six-item Intervention-elicited
Reactance Scale.
54
Moreover, to evaluate the long-term
effects, we will conduct follow-up assessments three
times 1 month apart (i.e. 1 month, 2 months, and 3
Figure 2. Experimental design. After initial screening and baseline measurements, a 1-week preintervention ambulatory assessment
procedure will be followed by 3 weeks of WPED including JITAIs and subsequent 1-week postintervention EMA. Follow-up measurements
will be conducted 12 weeks after the EMA procedure. WPED: within-person-encouragement-design; JITAI:
Just-In-Time-Adaptive-Intervention; EMA: ecological momentary assessment.
Liu et al. 5
months later). Figure 3 provides a graphical overview of the
project.
Intervention 1 (cognitive reappraisal). Cognitive
reappraisal describes the process of actively challenging
(reappraising) maladaptive beliefs and thoughts associated
with distressing mental states, such as loneliness and drug
craving.
55–59
In an initial laboratory session lasting approxi-
mately 20–30 min, participants will receive an explanation
of cognitive reappraisal and be prompted to identify and
challenge thoughts (reappraise) associated with cravings
and loneliness. To illustrate the concept of cognitive
reappraisal, we developed a comic featuring male and
female characters grappling with loneliness and alcohol
cravings. This comic was created using the AI image gen-
erator Midjourney (see Figure 4). We have a subscription
to Midjourney and created Figure 4 on our own using the
Midjourney. Participants can access a video illustrating
the reappraisal techniques to refresh their understanding
of how reappraisal functions (https://www.trr265.org/en/
domäne-c05-video).
The JITAI will take approximately 6 min. It will begin
by prompting participants to describe the situations in
which they encountered feelings of loneliness or cravings.
Subsequently, they will be asked to articulate the thought
that most triggers their loneliness or craving. Finally, parti-
cipants will be tasked with documenting how they chal-
lenge this thought, employing four reappraisal
strategies.
60
(1) Considering disadvantages: What are the
disadvantages of thinking like this if I want to reach my
goals to drink less or not to feel lonely. (2) Checking
reality: Have I had any experiences to show that this
mental statement is not always true? (3) Use other people
as a reference point: What would someone who knows to
handle this situation well say if they knew I was trying to
reduce my drinking/not to feel lonely? (4) Imagine giving
advice: What advice could you give to a close person in a
similar situation? After each reappraisal, participants will
be asked to rate their conviction regarding the thought
related to loneliness or craving on a horizontal Visual
Analog Scale (VAS)
61,62
ranging from 0 (“not convinced
at all”)to10(“strongly convinced”). In addition, they
will be prompted to assess the difficulty of reappraising
the specific situation using a horizontal VAS ranging
from 0 (“strongly disagree”)to10(“strongly agree”).
Intervention 2 (physical activity). The physical activity
intervention will be designed to increase participants’step
count within a set timeframe. Previous studies have been
found that moderate-intensity exercise sessions lasting 5 to
15 min can significantly reduce alcohol cravings.
36,63–65
Accordingly, our physical activity intervention encourages
participants to take a brisk 6-min walk (or engage in indoor
walking). During this activity, the number of steps per
minute is tracked using a movisens Move4 wrist-sensor, pro-
viding participants with real-time feedback on their walking
pace. Control measures involve automatically assessing the
number of steps taken during exercise using the movisens
Move4 activity sensor.
Active control (a gamified working memory task). The
working memory is facilitated by the Great Brain
Experiment app,
66
which has been tested and validated in
Figure 3. Flow-chart of assessment and intervention.
6DIGITAL HEALTH
German.
50
The task is gamified to enhance participant
engagement and is designed based on a widely used
delay-match-to-sample task.
67
For more detailed descrip-
tion of the task, see Brown et al.
66
Randomized encouragement to engage in one of the
abovementioned interventions is triggered through
movisensXS, particularly in contexts where participants’
ratings indicate a high momentary level in at least one of
the following target situations: i) high loneliness only, ii)
high alcohol craving only, and iii) both high loneliness
and craving alcohol. We will limit the number of triggers
for interventions to a maximum of 3 per day. While
two-third of these situations (n=2), encouragement to
engage in an intervention will be provided, the remaining
situation (n=1) serve as control condition without encour-
agement. Loneliness and alcohol craving are detected in
real-time via analyses of the participants’e-diary ratings,
and physical activity is monitored via a sensor attached to
the participants’hip (movisens move-4), which is connected
to the smartphone via Bluetooth low energy for data trans-
fer and real-time analyses. To increase compliance, we will
implement a “buffer time”of up to 60 min (see Figure 5).
Outcome measures
Primary outcomes will be assessed at a momentary level,
with (momentary assessments conducted eight times per
day during the nonintervention phase and eight times per
day during the intervention phase:
1. The score of a single-item to measure the direct loneli-
ness
68
on a Likert scale ranging from 1 to 7 and the
three-item ULS-3 to measure the indirect loneliness in
English
69
and the respective validated German
version
70
on a VAS with the range 0 to 100.
2. The score of a single-item to measure direct craving
71
using a Likert scale ranging from 1 to 7 and the
five-item PACS to measure the indirect craving in
English
72
and the respective validated German
version
73
on a VAS with the range 0 to 100.
Secondary outcomes will be assessed at a momentary level,
with momentary assessments conducted eight times per day
during the nonintervention phase and eight times per day
during the intervention phase:
1. The score of a 6-item Multidimensional Mood
Questionnaire measured on a Likert scale ranging
from 1 to 6
74
2. The number and sort of standard drinks, German
version
43
3. The score of a modified version of the five-item Drug
Effects Questionnaire-5 to measure subjective response
to alcohol in English
75
and in German
76,77
using the
VAS ranging from 0 to 100
Figure 4. Illustrative screenshots from the comic elucidating cognitive reappraisal. This comic was created by us using the AI image
generator Midjourney.
Liu et al. 7
4. The score of two modified single-items to assess control
over drinking habits and feelings of loneliness
78
mea-
sured on a VAS ranging from 0 to 100
5. The score of a two-item scale to assess event appraisal
79
using a VAS which ranges from 0 to 100
6. The score of a three-item scale to measure momentary
social contacts.
80,81
This assessment includes the dur-
ation of social contacts in hours and minutes, as well
as the quality of these contacts, rated on a VAS from
0 to 100
7. Movement Acceleration Intensity (MAI) steps
and Euclidean Norm Minus One (ENMO)
82
assessed automatically via a movisens Move4
wrist-sensor.
83
Secondary outcomes measured on a daily basis (evening
assessment conducted once per day):
1. The score of a modified four-item scale to measure motiv-
ation for alcohol consumption (social, coping, enhance-
ment, and conformity)
43,84
on a Likert scale from 1 to 5
2. The score from three items inquiring about drug usage
other than alcohol, including usage and, if applicable,
dosage of other drugs as well as cigarettes. The specific
list includes: substances for sniffing (e.g. glue), tran-
quilizers or sedatives, stimulants (e.g. amphetamines,
methamphetamines), hallucinogens (e.g. drug mush-
rooms, crack cocaine, cocaine), relevin, heroin, narco-
tics, ecstasy, ketamine or phencyclidine, GB or liquid
ecstasy, anabolic steroids. This list is adapted from the
National Survey on Drug Use And Health by the U.S.
Department of Health and Human Services, Substance
Abuse and Mental Health Services Administration
85
3. The score of a modified two-item Patient Health
Questionnaire-2
86
on a Likert scale from 1 to 4
Figure 5. Overview of the within-person-encouragement-design specifications.
8DIGITAL HEALTH
4. The score of a modified two-item Generalized Anxiety
Disorder Scale-2
87
on a Likert scale from 1 to 4
5. The score on a single item measuring the stressfulness
of the day on a VAS ranging from 0 to 100 modified
form Preston and coworkers
88
6. The score of two 3-item Intention to Cope
Questionnaires, modified to address loneliness
89
and
craving
90
on a VAS from 0 to 100
7. The score of a 4-item Brief Impulsivity Scale
91
on a
Likert scale ranging from 1 to 5
8. The score of a single item considering limiting the
alcohol consumption on the following day using a
VAS from 0 to 100 modified from Howard and
coworkers
92
9. The score of a single item derived from the Reward
Probability Index
93
on a Likert scale ranging from 1
to 4
10. The score of a gamified stop-signal-task to measure
response inhibition
50
11. The score of a six-item scale to measure reappraisal
using a VAS which ranges from 0 to 100
94
To gain a deeper understanding of the interventions’effects,
we will monitor participants’use of reappraisal during the
physical activity intervention and vice versa. After each
encouragement condition, we will calculate both the MAI
and ENMO scores and assess reappraisal using the above-
mentioned six-item scale.
94
Upon the participants’return with the smartphone and
wearable devices, we will conduct a qualitative semistruc-
tured interview, which will be audiorecorded and later tran-
scribed. The following questions will be asked:
1. How feasible did you find the cognitive reappraisal,
physical activity, gamified working memory task?
2. How engaged were you in the cognitive reappraisal,
physical activity, gamified working memory task?
3. How acceptable did you find cognitive reappraisal,
physical activity, gamified working memory task?
4. What did you find most frustrating or annoying about
the app?
5. How could the study be improved?
6. What did you find most frustrating or annoying about
the study in general?
7. Did your participation in the study (cognitive
reappraisal, physical activity, gamified working
memory task) help reduce your alcohol consumption?
If so, how?
8. Did your participation in the study help reduce your
feelings of loneliness? If so, how?
9. Did your participation in the study help reduce your
craving for alcohol? If so, how?
10. How honestly did you complete the queries and inter-
ventions (cognitive reappraisal, physical activity,
gamified working memory task)?
Data analysis
We will analyze the resulting ILD using structural equation
and multilevel modeling (MLM) with R and Mplus. The
procedure including data preprocessing, analysis, and mod-
eling is more detailed below. To preprocess EMA data, we
will follow established state-of-the-art procedures detailed
in methods guidelines.
12
Physical activity data will be pre-
processed by computing established metrics such as MAI
and merged with the e-diary data (software Data Merger,
movisens GmbH, Karlsruhe).
To assess the effectiveness of our interventions (i.e. cog-
nitive reappraisal and physical activity) in reducing loneli-
ness and cravings, we will employ instrumental variable
estimation and build two-level structural equation models
for path analyses,
21
illustrated in Figure 5. On the first
level (Level 1), we will estimate repeated measurements
within subjects. In the path analyses, we will model (i)
direct effects of the instrumental variable (encouragement
for intervention) on the treatment (cognitive reappraisal
and physical activity); (ii) the treatment effect on the
target/outcome variables (i.e. loneliness or/and alcohol
craving); and (iii) the residual (co-)variances (σ
2
T
,σ
2
0
,ψ
T0
)
of the treatment (е
T
) and outcome (е
O
) on the within-subject
level. On the second level (Level 2), we will estimate
between subject differences in means, intercepts, and
regression coefficients as random effects. Beyond two-level
structural equation models, we will use established MLM
procedures to test for within-subject changes in alcohol
consumption and for pre–post differences in both mean
levels of alcohol-use triggers (i.e. loneliness or/and
alcohol craving).
For nonsystematic missing data,
47
we will impute the
missing values using the standard procedure.
95
Specifically, we will employ the R package Multiple
Imputation via Chained Equations to handle missing
data through an iterative predictive modeling
process.
14
For the MLM and multilevel structural equa-
tion modeling analysis, we will employ the default
FIML/MCM procedure in Mplus. In addition, we plan
to explore the effects of reappraisal and physical activ-
ity in comparison to a no-intervention condition within
a subsample of participants. The qualitative data will be
analyzed using thematic analysis with the software
MAXQDA. Our general approach involves identifying,
analyzing, and reporting key themes. Specifically, we
will read and reread the data to gain a deep understand-
ing, generate initial codes, and group these codes into
potential themes. We will then refine and review the
themes to ensure they accurately reflect the data. Each
theme will be clearly defined and named, and we will
integrate the themes with relevant data extracts for
reporting purposes.
Liu et al. 9
Expected results
We anticipate that our platform will demonstrate feasibility
in administering both cognitive reappraisal and physical
activity interventions, which are expected to reduce crav-
ings and loneliness. In addition, it will facilitate the integra-
tion of experimental manipulation into participants’daily
routines, enabling effective testing of causal relationships.
We expect that both cognitive reappraisal and physical
activity interventions will decrease momentary loneliness
(H1a) or/and alcohol craving (H1b). Furthermore, we
posit that decreases in momentary loneliness and alcohol
craving will be associated with a reduction in real-life
alcohol consumption (H2).
Discussion
In this study, we will integrate real-time mechanism-based
interventions for alcohol use while simultaneously collect-
ing data on behavioral patterns and physical activity
through a smartphone app and a wearable sensor. This
study marks a pioneering mechanistic investigation aimed
at understanding the underlying mechanisms of action of
interventions and behavioral processes among individuals
with problematic alcohol use in Germany. By employing
real-time data collection and momentary sensing techni-
ques, the study specifically targets the mechanisms under-
lying loneliness and craving in the context of alcohol
consumption.
JITAIs can be particularly valuable in the treatment of
AUD, especially given the significant gaps in face-to-face
treatment services.
96,97
Potential applications of this
JITAI include providing transitional support for individuals
with AUD awaiting formal psychotherapeutic treatment
and offering an alternative for those with milder AUD
who may not require intensive psychotherapy.
Despite its potential, our current study has several limita-
tions. Firstly, compared to an in-person intervention, we
have less control over participants’engagement with the
intervention. To address this issue, we plan to closely
monitor participant engagement and compliance through-
out the study. In addition, the timing of a JITAI is crucial
for triggering the intervention at the right moment.
19
There is a risk that we may not assess craving or loneliness
at the optimal times. Nonetheless, we must find a balance
between the intensity of our sampling and the burden
placed on participants.
The study will adopt multiple perspectives. First, it seeks
to assess generalizability to other SUDs in future studies.
Second, it aims to implement efficient digital interventions.
For example, we can implement a progress indicator—a
rotating display that shows the status of participants’inter-
vention progress in future work. Participants may accumu-
late points to reach modular intervention levels, displayed
via sophisticated graphs such as growing trees with an
increasing number of branches and leaves. Gamified pro-
gress indicators (scores and levels) can be implemented
via the TherapyDesigner and TherapyBuilder (both movi-
sens GmbH, Germany). Third, alternative causes and inter-
ventions for SUD will be explored. We can extend our
approach to possible alternative causes of SUD and respect-
ive intervention strategies in the general populations.
Importantly, insights into temporal directionality and
digital intervention adherence gained from the current
study will inform the development of the first evidence-
based tailored and expedient JITAIs in SUD. Fourth, trans-
ferability and scalability to other sociocultural contexts can
be explored in future studies. Ultimately, we will develop
an in-silico model of losing and regaining control in
alcohol use based on variational autoencoders and
provide evidence-based treatment options across cultures.
Conclusions
Our real-life intervention platform is designed as a robust
tool for discerning the directionality of effects from time
series data related to everyday influences on SUD. This
study may pave the way for low-threshold prevention, clin-
ical treatment, and therapy tailored to the diverse contexts
of SUD, thereby facilitating rapid advancements in SUD
healthcare. Moreover, our findings hold the potential to
establish an insights framework for developing effective
strategies to manage substance use risks in everyday life.
Acknowledgments:The authors would like to express their
gratitude to Johanna Marie Münker for her technical support.
Author contributions: SL and MR conceptualized the study. SL
and MH designed the cognitive reappraisal intervention and
comic. MR, RG, and DR designed the exercise intervention. SL
and MR wrote the first draft of the manuscript. MH, RG, KS,
PB, UEP, BL, MAM, MNS, RS, GS, and DR provided review
and feedback. All authors contributed to the manuscript and
approved the submitted version.
Data availability: Guarantor statement: SL and MR are willing to
take full responsibility for the article.
Declaration of conflicting interests: The authors declared no
potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Ethical approval: The study will be conducted in accordance with
the Helsinki Declaration of 1975 and the ethics committee’s
standards. The study was evaluated by the Ethics Committee at
Charité—Universitätsmedizin Berlin (reference number: EA1/
270/22) and at Heidelberg University (reference number:
2023-536).
10 DIGITAL HEALTH
Funding: This study is part of the Collaborative Research Center
TRR 265 (Losing and regaining control over drug intake –from
trajectories to mechanisms and interventions; Heinz et al 2020;
Spanagel et al 2024) and is funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation,
project number 402170461).
Informed consent: Written informed consent will be obtained
from all participants included in the study.
ORCID iDs: Shuyan Liu https://orcid.org/0000-0002-6948-
5734
Reinhold Kliegl https://orcid.org/0000-0002-0180-8488
Bernd Lenz https://orcid.org/0000-0001-6086-0924
Marta Anna Marciniak https://orcid.org/0000-0003-4301-3269
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