Internet Interventions 28 (2022) 100457
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2214-7829/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
Special issue on digital health interventions in chronic medical conditions: Editorial
Digital health interventions
Chronic medical diseases
Chronic medical conditions are increasingly common and associated with a high burden for persons affected by
them. Digital health interventions might be a viable way to support persons with a chronic illness in their coping
and self-management. The present special issue's editorial on digital health interventions in chronic medical
conditions summarizes core ndings and discusses next steps needed to further the eld while avoiding to
reinvent the wheel, thereby elaborating on four topics extracted from the special issue's articles: 1) Needs
assessment and digital intervention development, 2) Efcacy and (cost-)effectiveness, 3) Dissemination and
implementation research: reach and engagement as well as 4) next generation of digital interventions.
Chronic medical conditions such as coronary heart disease, cancer,
diabetes, asthma and arthritis are increasingly common and associated
with a high burden for persons affected by them. As noted by Corbin and
Strauss (1985), managing a chronic illness involves ‘three lines of work’:
illness work, everyday life work, and biographical work. Persons with
chronic medical conditions are confronted with a multitude of disease
(self-)management challenges such as adhering to complex and often
ambivalent treatment plans over a long period of time, coping with
symptoms, functional limitations and treatment side-effects, as well as
dealing with social issues (e.g. stigma, role functioning) and an
increased risk for mental health problems such as depression and anxiety
arter et al., 2007; Petrie and Jones, 2019).
Digital health interventions might be a viable way to support persons
with a chronic illness in their coping and self-management and achieve
optimal health outcomes, both medical and psychological (Bendig et al.,
2018). Moreover, such approaches allow not only to be tailored to the
specic characteristics of individuals with medical conditions, but also
to be attuned to the needs of persons at different stages of the disease
trajectory, e.g. (co)-treatment, after-care and palliative care. While there
is already substantial evidence in the area of digital interventions in the
eld of mental health, there is still much to learn about the needs and
best way of providing digital interventions for people with medical
conditions (Ebert et al., 2018).
The present special issue provides a series of fourteen studies in
medical conditions, the majority of which can be classied as in the
early translational research stage of needs assessment and intervention
development (Blaney et al., 2021; Bonnert et al., 2021; Carolan-Olah
et al., 2021; Geirhos et al., 2021; Mellergård et al., 2021; Muijs et al.,
2021; Nap-van der Vlist et al., 2021; Verkleij et al., 2021). A few studies
report on the efcacy and effectiveness of digital interventions (Bendig
et al., 2021; Domhardt et al., 2021b; Terhorst et al., 2021; Terpstra et al.,
2021; van der Hout et al., 2021), while two studies focus on the next
development stage of digital interventions, aiming for personalisation
and adaptiveness (Harnas et al., 2021; Nap-van der Vlist et al., 2021).
The studies target a broad range of chronic medical conditions,
including diabetes (Geirhos et al., 2021; Mellergård et al., 2021; Muijs
et al., 2021), cancer (Harnas et al., 2021; Nap-van der Vlist et al., 2021;
van der Hout et al., 2021), arthritis and pain (Blaney et al., 2021;
Geirhos et al., 2021; Terhorst et al., 2021; Terpstra et al., 2021), cystic
brosis (Geirhos et al., 2021; Nap-van der Vlist et al., 2021; Verkleij
et al., 2021), asthma (Bonnert et al., 2021) and coronary artery disease
(Bendig et al., 2021) as well as chronic medical conditions in general
(Domhardt et al., 2021b) in adults as well as youth (Domhardt et al.,
2021b; Geirhos et al., 2021; Nap-van der Vlist et al., 2021).
This special issue provides us the opportunity to reect on the cur-
rent status of digital health in medical conditions in the broader context
of digital interventions and identify the next steps needed to further the
eld while avoiding to reinvent the wheel, with four extracted inter-
dependent categories (Fig. 1):
1. Needs Assessment and Digital Intervention Development
2. Efcacy and (Cost-)Effectiveness
3. Dissemination and Implementation Research: Reach and
4. Next Generation of Digital Interventions
1. Needs assessment and digital intervention development
The substantial number of papers on need assessment and inter-
vention development in this special issue appears indicative for the eld
of digital health interventions in people with chronic medical conditions
being still in its early stage. While there are clearly overarching psycho-
social themes across diseases, it is important to identify the specic
needs, challenges and preferences of specic patient groups as they may
vary across conditions and severity stage of disease. For example people
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Internet Interventions 28 (2022) 100457
with diabetes mellitus (Geirhos et al., 2021; Mellergård et al., 2021)
might be particularly interested in interventions supporting their long-
term daily struggle, while those with a progressive condition or in
palliative care phase (Capurro et al., 2014; Verkleij et al., 2021) may
have a demand for meaning-centered interventions, related to coping
with limited life-expectancy, dying or struggling with the help- and
hopelessness in a not yet always very supportive, personalised health
care system. As exemplied by Carolan-Olah et al. (2021), interventions
should also be attuned to the demands and preferences of the target
population, in socio-demographic and cultural terms, next to the
disease-specic needs. Interventions are to be optimized for people with
a medical condition with a migration background, low socioeconomic
status and different educational levels. In this context, it seems recom-
mendable to build interventions on existing knowledge gathered in the
past two decades of digital intervention development, that have led to
intervention development guidelines and recommendations from both a
clinical and a technological perspective (Edwards-Stewart et al., 2019;
Holfelder et al., 2021; Karekla et al., 2019; Michie et al., 2017).
2. Efcacy and (cost-)effectiveness
Once needs are assessed and the interventions developed, they usu-
ally pass the translational process of clinical testing with feasibility and
pilot trials followed by efcacy and (cost-)effectiveness trials. A few
aspects warrant special attention in order to make a difference in the
eld of digital interventions for people with chronic medical conditions.
First, interventions might not work the same across disease conditions
and at different stages of the disease and treatment. As one example,
Bendig et al. (2021) reported on a failed trial examining an intervention
for depression in people with coronary artery disease, while we do know
that this kind of intervention can work e.g. for people with diabetes
(Nobis et al., 2018; Nobis et al., 2015; van Bastelaar et al., 2008).
However, two other trials, one on the prevention of depression in pa-
tients with back pain (Sander et al., 2020) and one on the treatment of
depression in patients with back pain (Baumeister et al., 2021), using
almost the same intervention highlighted that we still need to take a
closer look at what works for whom at which stage of disease. While the
prevention trial, targeting individuals with chronic pain and subclinical
symptoms of depression, resulted in a more than impressive hazard ratio
of 0.48, i.e. onset of depression was halved within a period of twelve
months (Sander et al., 2020), the treatment trial, including only people
with a diagnosed Major Depression Disorder, provided a null-nding, at
least for the primary outcome (Baumeister et al., 2021), despite the fact
that the efcacy of digital depression interventions in the general pop-
ulation is well established (K¨
onigbauer et al., 2017). This nding also
supports the assumption that evidence generated in the general popu-
lation can not necessarily be simply generalized to chronic medical
Thus, digital interventions for depression in chronic medical condi-
tions can work and make a difference, but we cannot assume they work
for all people at any stage of disease with any intervention, regardless of
the specic active components and technological approaches. More
research is warranted to deepen our understanding of the differential
effectiveness of interventions by means of moderator analyses as esti-
mator for personalised intervention provision as well as studies on the
active components and the mechanisms of change (Breitborde et al.,
2010). As one example, it has been shown that people with chronic pain
are more likely to benet from an Acceptance and Commitment Therapy
(ACT) based pain intervention in case of initially lower psychological
exibility (Probst et al., 2018), and psychological exibility has been
veried as mechanism of change (Lin et al., 2018). Improving our
knowledge on the moderators, causal factors and mechanisms of change
can iteratively inform future intervention development to optimize
(digital) health care (Domhardt et al., 2021a).
3. Dissemination and implementation research: reach and
Dissemination and implementation research should accompany our
efforts to inform health care policy about the effectiveness and potential
of digital interventions in real world settings (Etzelmueller et al., 2020;
Gaebel et al., 2021; Titov et al., 2018). A main challenge in this context
is reach, i.e. reaching the target population at large and facilitating
acceptance towards digital health interventions (Baumeister et al., 2014;
Baumeister et al., 2015; Baumeister et al., 2020), as well as reaching
those not yet well covered by our health care systems, thereby aiming to
reduce health care disparities (Wasserman et al., 2019). A second topic
of interest is engagement, i.e. scientic evidence on how digital health
interventions are used in real word settings outside the research context.
Fleming et al. (2018) highlighted that completion or sustained use of
Paent and Health Care Provider Perspecve
Intervenon Development Guidelines
Pace and Eﬃciency
(Cost-) Eﬀecveness RCTs
Tesng diﬀerent Implementaon Models
Diﬀerenal Indicaon and Mechanisms of Change
Dismantling and Addive RCT design
Reach Engagement Real-World Eﬀecveness
Connous Quality Assurance
Chronic Medical Condions Across the Lifespan
Health Care Promoon | Prevenon | Treatment Aercare | Recurrence Prevenon | Life Companions | Palliave Care
Fig. 1. Digital health interventions in people with chronic medical conditions.
H. Baumeister et al.
Internet Interventions 28 (2022) 100457
self-help interventions for depression, low mood or anxiety provided in
real-world was only registered for 0.5% to 28.6% of health intervention
users. Similarly, Baumel et al. (2019) calculated that there is a research
bias regarding engagement estimates with intervention usage being four
times higher in trials compared with the same interventions in real-
world. Therefore, the report by van der Hout et al. (2021) in this spe-
cial issue on reasons for not reaching or engaging cancer patients
regarding the provided digital intervention is of utmost relevance,
underscoring the need to better understand ways of implementing evi-
dence based health care delivery models.
4. Next generation of digital health interventions
Information from the translational process of developing and eval-
uating digital health interventions for people with chronic medical
conditions can help to further improve interventions, thereby taking the
rapid technological innovation cycle into account. Next generation
digital health interventions for people with chronic medical conditions
will not only be informed by formative feedback from studies alongside
the translational process, but also by intervention designs aiming to
provide personalised and adaptive interventions such as Just-in-time-
adaptive interventions (i.e. personalised interventions provided at a
moment of opportunity; (Nahum-Shani et al., 2017)) or AI-based med-
ical and psychotherapeutic chatbots (Bendig et al., 2019; Pryss et al.,
2019). Similarly, next generation digital health interventions can and
should build more thoroughly on persuasive design principles in order to
improve intervention engagement - one of the well-established barriers
to successful intervention designs (Baumeister et al., 2019). The
complexity of this kind of technologically enhanced interventions is
nicely illustrated by Harnas et al. (2021) in this special issue, who
provide a case report series on personalizing cognitive behavioural
therapy for cancer-related fatigue by means of ecological momentary
assessments. Another challenge is the development of digital in-
terventions t for people with medical conditions as integral part of
often complex health care services models and disease management
The present special issue clearly illustrates the potential as well the
challenges that we face on the road to exploit the full potential of digital
intervention for people with a chronic medical condition. We commend
the many colleagues who have contributed to this special issue and
provided much needed evidence to add to this developing eld of
research. We hope it will stimulate future research within and across
medical conditions to provide answers on how digital interventions can
make a difference to help improve the lives of people with chronic
medical conditions – within and beyond established health care services.
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, David D. Ebert
, Frank Snoek
Department of Clinical Psychology and Psychotherapy, Institute of
Psychology and Education, Ulm University, Germany
Psychology and Digital Mental Health Care, Department of Sport and
Health Sciences, Technical University of Munich, Germany
Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the
Correspondence: Department of Clinical Psychology and
Psychotherapy, Institute of Psychology and Education, Ulm University,
Lise-Meitner-Straße 16, D-89081 Ulm, Germany.
E-mail address: firstname.lastname@example.org (H. Baumeister).
H. Baumeister et al.