ArticlePDF Available

Special issue on digital health interventions in chronic medical conditions: Editorial

Authors:
  • University of Ulm, Institute of Psychology and Education
  • VU University Medical Center/AMC

Abstract and Figures

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 findings and discusses next steps needed to further the field 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) Efficacy and (cost-)effectiveness, 3) Dissemination and implementation research: reach and engagement as well as 4) next generation of digital interventions.
Content may be subject to copyright.
Internet Interventions 28 (2022) 100457
Available online 20 September 2021
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-
nc-nd/4.0/).
Special issue on digital health interventions in chronic medical conditions: Editorial
ARTICLE INFO
Keywords
Digital health interventions
Chronic medical diseases
Diabetes
Cancer
Pain
Heart disease
ABSTRACT
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) Efcacy 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
(H¨
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
specic 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 classied 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 efcacy 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 reect 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. Efcacy and (Cost-)Effectiveness
3. Dissemination and Implementation Research: Reach and
Engagement
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 specic
needs, challenges and preferences of specic patient groups as they may
vary across conditions and severity stage of disease. For example people
Contents lists available at ScienceDirect
Internet Interventions
journal homepage: www.elsevier.com/locate/invent
https://doi.org/10.1016/j.invent.2021.100457
Internet Interventions 28 (2022) 100457
2
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 exemplied 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-specic 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. Efcacy 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 efcacy 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 efcacy 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
populations.
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 specic 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 benet 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
veried 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
engagement
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. scientic 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
Needs
Assessment and
Intervenon
Development
Disseminaon
and
Implementaon
Efficacy and
(Cost)
Effecveness
Next Generaon
Digital Health
Intervenons
Paent and Health Care Provider Perspecve
Intervenon Development Guidelines
Pace and Efficiency
Theory Basis
Regulatories
Efficacy RCTs
(Cost-) Effecveness RCTs
Tesng different Implementaon Models
Differenal Indicaon and Mechanisms of Change
Dismantling and Addive RCT design
Reach Engagement Real-World Effecveness
Connous Quality Assurance
Just-in-Time Intervenons
Persuasive Design
opmized Intervenons
AI-based Chatbots
Complex (blended)
Internet Intervenons
Chronic Medical Condions Across the Lifespan
Health Care Promoon | Prevenon | Treatment Aercare | Recurrence Prevenon | Life Companions | Palliave Care
Fig. 1. Digital health interventions in people with chronic medical conditions.
H. Baumeister et al.
Internet Interventions 28 (2022) 100457
3
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
programs.
5. Conclusion
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.
References
Baumeister, H., Nowoczin, L., Lin, J., Seifferth, H., Seufert, J., Laubner, K., Ebert, D.D.,
2014. Impact of an acceptance facilitating intervention on diabetes patients
acceptance of Internet-based interventions for depression: a randomized controlled
trial. Diabetes Res. Clin. Pract. 105. https://doi.org/10.1016/j.diabres.2014.04.031.
Baumeister, H., Seifferth, H., Lin, J., Nowoczin, L., Luking, M., Ebert, D., 2015. Impact of
an acceptance facilitating intervention on patientsacceptance of Internet-based
pain interventions: a randomized controlled trial. Clin. J. Pain 31, 528535. https://
doi.org/10.1097/AJP.0000000000000118PM.
Baumeister, H., Kraft, R., Baumel, A., Pryss, R., Messner, E.-M., 2019. Persuasive e-
Health Design for Behavior Change, pp. 261276. https://doi.org/10.1007/978-3-
030-31620-4_17.
Baumeister, H., Terhorst, Y., Gr¨
assle, C., Freudenstein, M., Nübling, R., Ebert, D.D., 2020.
Impact of an acceptance facilitating intervention on psychotherapistsacceptance of
blended therapy. PLoS One 15, e0236995. https://doi.org/10.1371/journal.
pone.0236995.
Baumeister, H., Paganini, S., Sander, L.B., Lin, J., Schlicker, S., Terhorst, Y.,
Moshagen, M., Bengel, J., Lehr, D., Ebert, D.D., 2021. Effectiveness of a guided
Internet- and Mobile-based intervention for patients with chronic back pain and
depression (WARD-BP): a multicenter, pragmatic randomized controlled trial. Psy-
chother. Psychosom. 90, 255268. https://doi.org/10.1159/000511881.
Baumel, A., Edan, S., Kane, J.M., 2019. Is there a trial bias impacting user engagement
with unguided e-mental health interventions? A systematic comparison of published
reports and real-world usage of the same programs. Transl. Behav. Med. 9,
10201033. https://doi.org/10.1093/tbm/ibz147.
Bendig, E., Bauereiß, N., Ebert, D.D., Snoek, F., Andersson, G., Baumeister, H., 2018.
Internet- and mobile based psychological interventions in people with chronic
medical conditions. Dtsch. Aerzteblatt Int. 115, 659665. https://doi.org/10.3238/
arztebl.2018.0659.
Bendig, E., Erb, B., Schulze-Thuesing, L., Baumeister, H., 2019. Next generation: chatbots
in clinical psychology and psychotherapy to foster mental health a scoping review.
Verhaltenstherapie 115. https://doi.org/10.1159/000499492.
Bendig, E., Bauereiß, N., Buntrock, C., Habibovi´
c, M., Ebert, D.D., Baumeister, H., 2021.
Lessons learned from an attempted randomized-controlled feasibility trial on
WIDeCAD- an internet-based depression treatment for people living with coronary
artery disease (CAD). Internet Interv. 24, 100375. https://doi.org/10.1016/j.
invent.2021.100375.
Blaney, C., Hitchon, C.A., Marrie, R.A., Mackenzie, C., Holens, P., El-Gabalawy, R., 2021.
Support for a non-therapist assisted, Internet-based cognitive-behavioral therapy
(iCBT) intervention for mental health in rheumatoid arthritis patients. Internet
Interv. 24, 100385. https://doi.org/10.1016/j.invent.2021.100385.
Bonnert, M., S¨
arnholm, J., Andersson, E., Bergstr¨
om, S.E., Lalouni, M., Lundholm, C.,
Serlachius, E., Almqvist, C., 2021. Targeting excessive avoidance behavior to reduce
anxiety related to asthma: a feasibility study of an exposure-based treatment deliv-
ered online. Internet Interv. 25, 100415. https://doi.org/10.1016/j.
invent.2021.100415.
Breitborde, N.J.K., Srihari, V.H., Pollard, J.M., Addington, D.N., Woods, S.W., 2010.
Mediators and moderators in early intervention research. Early Interv. Psychiatry 4,
143152. https://doi.org/10.1111/j.1751-7893.2010.00177.x.
Capurro, D., Ganzinger, M., Perez-Lu, J., Knaup, P., 2014. Effectiveness of ehealth in-
terventions and information needs in palliative care: a systematic literature review.
J. Med. Internet Res. 16, e2812 https://doi.org/10.2196/jmir.2812.
Carolan-Olah, M., Vasilevski, V., Nagle, C., Stepto, N., 2021. Overview of a new eHealth
intervention to promote healthy eating and exercise in pregnancy: initial user re-
sponses and acceptability. Internet Interv. 25, 100393. https://doi.org/10.1016/j.
invent.2021.100393.
Corbin, J., Strauss, A., 1985. Managing chronic illness at home: three lines of work. Qual.
Sociol. 8, 224247. https://doi.org/10.1007/BF00989485.
Domhardt, M., Cuijpers, P., Ebert, D.D., Baumeister, H., 2021a. More light? Opportu-
nities and pitfalls in digitalized psychotherapy process research. Front. Psychol. 12,
863. https://doi.org/10.3389/fpsyg.2021.544129.
Domhardt, M., Schr¨
oder, A., Geirhos, A., Steubl, L., Baumeister, H., 2021b. Efcacy of
digital health interventions in youth with chronic medical conditions: a meta-anal-
ysis. Internet Interv. https://doi.org/10.1016/j.invent.2021.100373.
Ebert, D.D., Van Daele, T., Nordgreen, T., Karekla, M., Compare, A., Zarbo, C.,
Brugnera, A., Øverland, S., Trebbi, G., Jensen, K.L., Kaehlke, F., Baumeister, H.,
2018. Internet-and mobile-based psychological interventions: applications, efcacy,
and potential for improving mental health. A report of the EFPA E-Health Taskforce.
Eur. Psychol. 23, 167187. https://doi.org/10.1027/1016-9040/a000318.
Edwards-Stewart, A., Alexander, C., Armstrong, C.M., Hoyt, T., ODonohue, W., 2019.
Mobile applications for client use: ethical and legal considerations. Psychol. Serv. 16,
281285. https://doi.org/10.1037/ser0000321.
Etzelmueller, A., Vis, C., Karyotaki, E., Baumeister, H., Titov, N., Berking, M.,
Cuijpers, P., Riper, H., Ebert, D.D., 2020. Effects of internet-based cognitive
behavioral therapy in routine care for adults in treatment for depression and anxiety:
systematic review and meta-analysis. J. Med. Internet Res. https://doi.org/10.2196/
18100.
Fleming, T., Bavin, L., Lucassen, M., Stasiak, K., Hopkins, S., Merry, S., 2018. Beyond the
trial: systematic review of real-world uptake and engagement with digital self-help
interventions for depression, low mood, or anxiety. J. Med. Internet Res. 20, e9275
https://doi.org/10.2196/jmir.9275.
Gaebel, W., Lukies, R., Kerst, A., Stricker, J., Zielasek, J., Diekmann, S., Trost, N.,
Gouzoulis-Mayfrank, E., Bonroy, B., Cullen, K., Desie, K., Ewalds Mulliez, A.P.,
Gerlinger, G., Günther, K., Hiemstra, H.J., McDaid, S., Murphy, C., Sander, J.,
Sebbane, D., Roelandt, J.L., Thorpe, L., Topolska, D., Van Assche, E., Van Daele, T.,
Van den Broeck, L., Versluis, C., Vlijter, O., 2021. Upscaling e-mental health in
Europe: a six-country qualitative analysis and policy recommendations from the
eMEN project. Eur. Arch. Psychiatry Clin. Neurosci. 271, 10051016. https://doi.
org/10.1007/s00406-020-01133-y.
Geirhos, A., Lunkenheimer, F., Holl, R.W., Minden, K., Schmitt, A., Temming, S.,
Baumeister, H., Domhardt, M., 2021. Involving patientsperspective in the devel-
opment of an internet- and mobile-based CBT intervention for adolescents with
chronic medical conditions: ndings from a qualitative study. Internet Interv. 24,
100383. https://doi.org/10.1016/j.invent.2021.100383.
Harnas, S.J., Knoop, H., Booij, S.H., Braamse, A.M.J., 2021. Personalizing cognitive
behavioral therapy for cancer-related fatigue using ecological momentary assess-
ments followed by automated individual time series analyses: a case report series.
Internet Interv. 25, 100430. https://doi.org/10.1016/j.invent.2021.100430.
H¨
arter, M., Baumeister, H., Reuter, K., Jacobi, F., H¨
oer, M., Bengel, J., Wittchen, H.-U.,
2007. Increased 12-month prevalence rates of mental disorders in patients with
chronic somatic diseases. Psychother. Psychosom. 76 https://doi.org/10.1159/
000107563.
Holfelder, M., Mulansky, L., Schlee, W., Baumeister, H., Schobel, J., Greger, H., Hoff, A.,
Pryss, R., 2021. Medical device regulation efforts for mHealth apps during the
H. Baumeister et al.
Internet Interventions 28 (2022) 100457
4
COVID-19 pandemic - an experience report of Corona Check and Corona Health. J 4,
206222. https://doi.org/10.3390/j4020017.
Karekla, M., Kasinopoulos, O., Dias Neto, D., Ebert, D.D., Van Daele, T., Nordgreen, T.,
H¨
ofer, S., Oeverland, S., Jensen, K.L., 2019. Special issue: adjustment to chronic
illness original articles and reviews. Best practices and recommendations for digital
interventions to improve engagement and adherence in chronic illness sufferers. Eur.
Psychol. 24, 4967. https://doi.org/10.1027/1016-9040/a000349.
K¨
onigbauer, J., Letsch, J., Doebler, P., Ebert, D., Baumeister, H., 2017. Internet- and
mobile-based depression interventions for people with diagnosed depression: a
systematic review and meta-analysis. J. Affect. Disord. https://doi.org/10.1016/j.
jad.2017.07.021.
Lin, J., Klatt, L.-I., McCracken, L.M., Baumeister, H., 2018. Psychological exibility
mediates the effect of an online-based acceptance and commitment therapy for
chronic pain: an investigation of change processes. Pain 159, 663672. https://doi.
org/10.1097/j.pain.0000000000001134.
Mellergård, E., Johnsson, P., Eek, F., 2021. Developing a web-based support using self-
afrmation to motivate lifestyle changes in type 2 diabetes: a qualitative study
assessing patient perspectives on self-management and views on a digital lifestyle
intervention. Internet Interv. 24 https://doi.org/10.1016/j.invent.2021.100384.
Michie, S., Yardley, L., West, R., Patrick, K., Greaves, F., 2017. Developing and evalu-
ating digital interventions to promote behavior change in health and health care:
recommendations resulting from an international workshop. J. Med. Internet Res.
https://doi.org/10.2196/jmir.7126.
Muijs, L.T., de Wit, M., Knoop, H., Snoek, F.J., 2021. Feasibility and user experience of
the unguided web-based self-help app ‘MyDiaMateaimed to prevent and reduce
psychological distress and fatigue in adults with diabetes. Internet Interv. 25,
100414. https://doi.org/10.1016/j.invent.2021.100414.
Nahum-Shani, I., Smith, S.N., Spring, B.J., Collins, L.M., Witkiewitz, K., Tewari, A.,
Murphy, S.A., 2017. Just-in-time adaptive interventions (JITAIs) in mobile health:
key components and design principles for ongoing health behavior support. Ann.
Behav. Med. 52, 446462. https://doi.org/10.1007/s12160-016-9830-8.
Nap-van der Vlist, M.M., Houtveen, J., Dalmeijer, G.W., Grootenhuis, M.A., van der
Ent, C.K., van Grotel, M., Swart, J.F., van Montfrans, J.M., van de Putte, E.M.,
Nijhof, S.L., 2021. Internet and smartphone-based ecological momentary assessment
and personalized advice (PROfeel) in adolescents with chronic conditions: a feasi-
bility study. Internet Interv. 25, 100395. https://doi.org/10.1016/j.
invent.2021.100395.
Nobis, S., Lehr, D., Ebert, D.D., Baumeister, H., Snoek, F., Riper, H., Berking, M., 2015.
Efcacy of a web-based intervention with mobile phone support in treating
depressive symptoms in adults with type 1 and type 2 diabetes: a randomized
controlled trial. Diabetes Care 38, 776783. https://doi.org/10.2337/dc14-1728PM.
Nobis, S., Ebert, D.D., Lehr, D., Smit, F., Buntrock, C., Berking, M., Baumeister, H.,
Snoek, F., Funk, B., Riper, H., 2018. Web-based intervention for depressive symp-
toms in adults with types 1 and 2 diabetes mellitus: a health economic evaluation.
Br. J. Psychiatry 212. https://doi.org/10.1192/bjp.2018.10.
Petrie, K.J., Jones, A.S.K., 2019. Coping with chronic illness. In: Cambridge Handbook of
Psychology, Health and Medicine, Third edition, pp. 110113. https://doi.org/
10.2307/3423436.
Probst, T., Baumeister, H., McCracken, L., Lin, J., 2018. Baseline psychological inexi-
bility moderates the outcome pain interference in a randomized controlled trial on
Internet-based Acceptance and Commitment Therapy for chronic pain. J. Clin. Med.
8, 24. https://doi.org/10.3390/jcm8010024.
Pryss, R., Kraft, R., Baumeister, H., Winkler, J., Probst, T., Reichert, M., Langguth, B.,
Spiliopoulou, M., Schlee, W., 2019. Using Chatbots to Support Medical and Psy-
chological Treatment Procedures: Challenges, Opportunities, Technologies, Refer-
ence Architecture, pp. 249260. https://doi.org/10.1007/978-3-030-31620-4_16.
Sander, L.B., Paganini, S., Terhorst, Y., Schlicker, S., Lin, J., Spanhel, K., Buntrock, C.,
Ebert, D.D., Baumeister, H., 2020. Effectiveness of a guided web-based self-help
intervention to prevent depression in patients with persistent back pain: the PROD-
BP randomized clinical trial. JAMA Psychiatry. https://doi.org/10.1001/
jamapsychiatry.2020.1021.
Terhorst, Y., Messner, E.M., Schultchen, D., Paganini, S., Portenhauser, A., Eder, A.S.,
Bauer, M., Papenhoff, M., Baumeister, H., Sander, L.B., 2021. Systematic evaluation
of content and quality of English and German pain apps in European app stores.
Internet Interv. 24, 100376. https://doi.org/10.1016/j.invent.2021.100376.
Terpstra, J.A., van der Vaart, R., Ding, H.J., Klppenburg, M., Evers, A.W., 2021. Guided
Internet-based cognitive-behavioral therapy for patients with rheumatic conditions:
a systematic review. Internet Interv. 26, 100444 https://doi.org/10.1016/j.
invent.2021.100444.
Titov, N., Dear, B., Nielssen, O., Staples, L., Hadjistavropoulos, H., Nugent, M.,
Adlam, K., Nordgreen, T., Bruvik, K.H., Hovland, A., Repål, A., Mathiasen, K.,
Kraepelien, M., Blom, K., Svanborg, C., Lindefors, N., Kaldo, V., 2018. ICBT in
routine care: a descriptive analysis of successful clinics in ve countries. Internet
Interv. https://doi.org/10.1016/j.invent.2018.07.006.
van Bastelaar, K.M., Pouwer, F., Cuijpers, P., Twisk, J.W., Snoek, F.J., 2008. Web-based
cognitive behavioural therapy (W-CBT) for diabetes patients with co-morbid
depression: design of a randomised controlled trial. BMC Psychiatry 8, 9. https://doi.
org/10.1186/1471-244X-8-9 U6.
van der Hout, A., van Uden-Kraan, C.F., Holtmaat, K., Jansen, F., Lissenberg-Witte, B.I.,
Nieuwenhuijzen, G.A.P., Hardillo, J.A., Baatenburg de Jong, R.J., Tiren-Verbeet, N.
L., Sommeijer, D.W., de Heer, K., Schaar, C.G., Sedee, R.J.E., Bosscha, K., van den
Brekel, M.W.M., Petersen, J.F., Westerman, M., Honings, J., Takes, R.P.,
Houtenbos, I., van den Broek, W.T., de Bree, R., Jansen, P., Eerenstein, S.E.J.,
Leemans, C.R., Zijlstra, J.M., Cuijpers, P., van de Poll-Franse, L.V., Verdonck-de
Leeuw, I.M., 2021. Reasons for not reaching or using web-based self-management
applications, and the use and evaluation of Oncokompas among cancer survivors, in
the context of a randomised controlled trial. Internet Interv. 25, 100429. https://doi.
org/10.1016/j.invent.2021.100429.
Verkleij, M., Georgiopoulos, A.M., Friedman, D., 2021. Development and evaluation of
an internet-based cognitive behavioral therapy intervention for anxiety and
depression in adults with cystic brosis (eHealth CF-CBT): an international collab-
oration. Internet Interv. 24, 100372. https://doi.org/10.1016/j.
invent.2021.100372.
Wasserman, J., Palmer, R.C., Gomez, M.M., Berzon, R., Ibrahim, S.A., Ayanian, J.Z.,
2019. Advancing health services research to eliminate health care disparities. Am. J.
Public Health 109, S64S69. https://doi.org/10.2105/AJPH.2018.304922.
Harald Baumeister
a
,
*
, David D. Ebert
b
, Frank Snoek
c
a
Department of Clinical Psychology and Psychotherapy, Institute of
Psychology and Education, Ulm University, Germany
b
Psychology and Digital Mental Health Care, Department of Sport and
Health Sciences, Technical University of Munich, Germany
c
Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the
Netherlands
*
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: harald.baumeister@uni-ulm.de (H. Baumeister).
H. Baumeister et al.
... Digital health is defined as information communication technology that supports health through electronic and mobile health solutions and using big data, Diagnostics 2022, 12, 1730 2 of 12 computational genomics, and artificial intelligence [20]. Digital health may improve population health by increasing access to medical services and uptake of interventions [20][21][22][23][24]. Mobile applications, short messaging service (SMS), wearable devices, social media, and interactive websites have been used as intervention methods [14,16,17,[25][26][27]. ...
... Lifestyle interventions need to be conducted according to the different patient characteristics, and self-care should be encouraged to prevent and manage MetS. Digital health-based lifestyle interventions may be key to achieving lifestyle changes and improving health [22,23,29]. People need to be able to adhere to lifestyle behaviors in order to make consistent and long-lasting changes. ...
Article
Full-text available
Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1–2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥ 3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.
Article
Full-text available
Context Rheumatic conditions have a large impact on both patients and society. Many patients experience adjustment problems, such as symptoms of anxiety and depression and sleep problems, contributing to high healthcare costs. Internet-based cognitive-behavioral therapy (iCBT) has shown to support patients with somatic conditions in coping with their disease, with therapist-guided iCBT usually showing larger effects than unguided iCBT. However, the specific relevance of guided iCBT for rheumatic conditions has not been reviewed yet, which could have important implications for implementation. Objectives The objective of our review was to give an overview of evaluations of guided iCBT for rheumatic conditions, including physical, psychological, and impact on daily life outcomes. Methods This review is registered with PROSPERO with registration number CRD42020154911. The review followed PRISMA guidelines and included an assessment of risk of bias. PubMed, PsycINFO, Embase, Cochrane Library, Web of Science, and Emcare were searched until 5 October 2020. Inclusion criteria were: patients ≥18 years old with a rheumatic condition, randomized controlled trial, accessible full-text English article, original data, inclusion of psychological, and/or physical and/or impact outcomes, and therapist-guided iCBT. Study and sample characteristics, as well as clinical variables were extracted. Results A systematic search identified 6089 studies, of which 8 trials were included, comprising of 1707 participants in total. Significant medium to large between-group effects were found for psychological outcomes (depression, anxiety, catastrophizing, self-efficacy) and impact on daily life outcomes (impact on daily life, quality of life), whilst results for physical outcomes (pain intensity, fatigue) were mixed. Conclusion Whilst more research is warranted, for instance regarding physical outcomes, cost-effectiveness, safety of the intervention, and moderators of iCBT success, our results show that guided iCBT could be an important addition to medical treatment for rheumatic conditions. Guided iCBT can improve psychological and impact on daily life outcomes in patients with rheumatic conditions, which is promising for iCBT implementation in clinical practice.
Article
Full-text available
Introduction A common approach to personalizing psychological interventions is the allocation of treatment modules to individual patients based on cut-off scores on questionnaires, which are mostly based on group studies. However, this way, intraindividual variation and temporal dynamics are not taken into account. Automated individual time series analyses are a possible solution, since these can identify the factors influencing the targeted symptom in a specific individual, and associated modules can be allocated accordingly. The aim of this study was to illustrate how automated individual time series analyses can be applied to personalize cognitive behavioral therapy for cancer-related fatigue in cancer survivors and how this procedure differs from allocating modules based on questionnaires. Methods This study was a case report series (n = 3). Patients completed ecological momentary assessments at the start of therapy, and after three treatment modules (approximately 14 weeks). Assessments were analyzed with AutoVAR, an R package that automates the process of finding optimal vector autoregressive models. The results informed the treatment plan. Results Three cases were described. From the ecological momentary assessments and automated time series analyses three individual treatment plans were constructed, in which the most important predictor for cancer-related fatigue was treated first. For two patients, this led to the treatment ending after the follow-up ecological momentary assessments. One patient continued treatment until six months, the standard treatment time in regular treatment. All three treatment plans differed from the treatment plans informed by questionnaire scores. Discussion This study is one of the first to apply time series analyses in systematically personalizing psychological treatment. An important strength of this approach is that it can be used for every modular cognitive behavioral intervention where each treatment module addresses specific maintaining factors. If personalized CBT is more efficacious than standard, non-personalized CBT remains to be determined in controlled studies comparing it to usual care.
Article
Full-text available
Introduction The web-based self-management application Oncokompas was developed to support cancer survivors to monitor health-related quality of life and symptoms (Measure) and to provide tailored information (Learn) and supportive care options (Act). In a previously reported randomised controlled trial (RCT), 68% of 655 recruited survivors were eligible, and of those 45% participated in the RCT. Among participants of the RCT that were randomised to the intervention group, 52% used Oncokompas as intended. The aim of this study was to explore reasons for not participating in the RCT, and reasons for not using Oncokompas among non-users, and the use and evaluation of Oncokompas among users. Methods Reasons for not participating were assessed with a study-specific questionnaire among 243 survivors who declined participation. Usage was investigated among 320 participants randomised to the intervention group of the RCT via system data and a study-specific questionnaire that was assessed during the 1 week follow-up (T1) assessment. Results Main reasons for not participating were not interested in participation in scientific research (40%) and not interested in scientific research and Oncokompas (28%). Main reasons for not being interested in Oncokompas were wanting to leave the period of being ill behind (29%), no symptom burden (23%), or lacking internet skills (18%). Out of the 320 participants in the intervention group 167 (52%) used Oncokompas as intended. Among 72 non-users, main reasons for not using Oncokompas were no symptom burden (32%) or lack of time (26%). Among 248 survivors that activated their account, satisfaction and user-friendliness were rated with a 7 (scale 0–10). Within 3 (IQR 1–4) sessions, users selected 32 (IQR 6–37) topics. Main reasons for not using healthcare options in Act were that the information in Learn was already sufficient (44%) or no supportive care needs (32%). Discussion Main reasons for not reaching or using Oncokompas were no symptom burden, no supportive care needs, or lack of time. Users selected many cancer-generic and tumour-specific topics to address, indicating added value of the wide range of available topics.
Article
Full-text available
There is an established relationship between anxiety and asthma, which is associated with poor health outcomes. Most previous cognitive behavior therapies (CBT) have focused on comorbid panic disorder whereas anxiety related to asthma may rather be illness-specific. The feasibility of an online CBT targeting avoidance behavior in anxiety related to asthma was evaluated, using a pretest-posttest design. Thirty participants with self-reported anxiety related to asthma were offered an eight-week treatment with therapist support. Mean adherence was good (80% of content), and most participants (89%) reported adequate relief after treatment. Catastrophizing about asthma (CAS), assessed at 2 months after treatment, improved significantly with a large effect size (Cohen's d = 1.52). All secondary outcomes, including asthma control, avoidance behavior, fear of asthma symptoms and quality of life, improved significantly with moderate to large effect sizes (d: 0.40–1.44). All improvements were stable at 4 months follow up. Weekly ratings showed that a decrease in avoidance behavior predicted a decrease in CAS the following week throughout the treatment period. We conclude that CBT targeting avoidance behavior is a feasible treatment for anxiety related to asthma. The results justify investigation of efficacy and mechanisms of change in a randomized controlled trial. ClinicalTrials.gov, ID: NCT03486756.
Research
Full-text available
Citation: Holfelder, M.; Mulansky, L.; Schlee, W.; Baumeister, H.; Schobel, J.; Greger, H.; Hoff, A.; Pryss, R. Medical Abstract: Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a "medical app". However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team-especially software engineers-are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.
Article
Full-text available
Introduction Psychological distress and fatigue are common in persons with diabetes, adversely affecting quality of life and complicating diabetes self-management. Offering diabetes-specific self-guided cognitive behavioral therapy (CBT) may be helpful for persons with diabetes and mild symptoms of psychological distress and fatigue. We are the first to test the feasibility and user experiences of a web-based self-help app called ‘MyDiaMate’ in adults with type 1 and type 2 diabetes. Methods and materials MyDiaMate was developed in close collaboration with persons with diabetes and professionals, building on elements from existing (guided) diabetes-specific CBT interventions. The study was advertised, offering free access to the app for adults with diabetes for a period of three months. Feasibility and user experiences were tested in a non-randomized study with pre- and post- measurements and interviews in a small sample.. In addition usage of the app was studied using log-data.. Results In total N = 55 adults with diabetes signed up for the study. Mean age was M = 42.7 (SD = 15.6), mostly women (n = 39, 70.9%), higher educated (n = 36, 65.5%), and diagnosed with type 1 diabetes (n = 37, 67.3%). About half reported current or a history of psychological complaints. All the participants completed baseline assessments, and n = 32 participants (58%) completed the follow-up questionnaire. Main reasons for participating in the study were: to preserve or improve mental fitness (40.6%), curiosity (25.0%) and wanting to contribute to research (34.4%). No major technical issues were encountered in accessing or using the app. The app was opened at least once by n = 51 participants, median use of the modules was 28 min (1–80) within a period of 1 to 92 days (median = 10). Almost all participants (n = 50, 98.0%) opened the basic module ‘Diabetes in balance’, of whom 32 (62.7%) completed this module. ‘My mood’ and ‘My energy’ were opened by n = 40 (78.4%) and n = 32 (62.7%) participants, respectively, and completed by n = 21 (52.5%) and n = 9 (28.1%) of the participants. Of all participants, 40.6% would recommend the app to others living with diabetes. Conclusions This study confirmed the feasibility of MyDiaMate as a diabetes-specific self-guided app for adults wishing to preserve or improve their psychological health. While user experiences were overall positive, further tailoring the content to individual needs and preferences could enhance uptake, usage and appreciation. Future research should explore its effectiveness in a randomized controlled trial.
Article
Full-text available
Objective Growing up with a chronic disease comes with challenges, such as coping with fatigue. Many adolescents are severely fatigued, though its associated factors exhibit considerable interpersonal and longitudinal variation. We assessed whether PROfeel, a combination of a smartphone-based ecological momentary assessment (EMA) method using the internet, followed by a face-to-face dialogue and personalized advice for improvement of symptoms or tailor treatment based on a dynamic network analysis report, was feasible and useful. Study design Feasibility study in fatigued outpatient adolescents 12–18 years of age with cystic fibrosis, autoimmune disease, post-cancer treatment, or with medically unexplained fatigue. Participants were assessed at baseline to personalize EMA questions. EMA was conducted via smartphone notifications five times per day for approximately six weeks. Hereby, data was collected via the internet. The EMA results were translated into a personalized report, discussed with the participant, and subsequently translated into a personalized advice. Afterwards, semi-structured interviews on feasibility and usefulness were held. Results Fifty-seven adolescents were assessed (mean age 16.2y ± 1.6, 16% male). Adolescents deemed the smartphone-based EMA feasible, with the app being used for an average of 49 days. Forty-two percent of the notifications were answered and 85% of the participants would recommend the app to other adolescents. The personalized report was deemed useful and comprehensible and 95% recognized themselves in the personalized report, with 64% rating improved insight in their symptoms and subsequent steps towards an approach to reduce one's fatigue as good or very good. Conclusions PROfeel was found to be highly feasible and useful for fatigued adolescents with a chronic condition. This innovative method has clinical relevance through bringing a patient's daily life into the clinical conversation.
Article
Full-text available
Purpose The purpose of this project was to develop and evaluate an eHealth intervention to promote healthy lifestyle for pregnant women. The setting was a low socio-economic and multi-ethnic area in Melbourne, Australia. Methods This paper briefly describes the development of the eHealth intervention, which was aimed at a low level of literacy, and the evaluation of the intervention by pregnant women. A basic descriptive survey was undertaken to evaluate user friendliness, usefulness and acceptability of the intervention. Results The intervention was developed by a team of experts and forty pregnant women participated in the evaluation. Results indicated that participants found the intervention informative, useful and easy to navigate. They also identified some minor areas for improvement which will be addressed prior to proceeding to a formal controlled evaluation. Conclusion Results from this evaluation are encouraging and suggest that women found the intervention convenient, trustworthy and engaging. Most enjoyed navigating the website information. As such, it is likely to prove a useful support for delivering dietary and exercise information to pregnant women in the local low socio-economic area. Further formal evaluation will test the efficacy of the website in improving diet and exercise outcomes during pregnancy.
Article
Full-text available
Aims The aim of the present study was to explore patients' experiences of diabetes self-management and views on a digital lifestyle intervention using self-affirmation to motivate lifestyle changes. Methods Semi-structured interviews focusing on needs, attitudes, and barriers to diabetes self-management were conducted with 22 individuals with type 2 diabetes recruited from the All New Diabetics in Scania (ANDIS) cohort. The interviews were followed by three additional study visits, where participants gave feedback on computer-based assignments based on self-affirmation. Interviews and feedback were qualitatively analyzed using thematic analysis. Results Participants described a range of barriers to diabetes self-management, and a varying sense of urgency and distress related to diabetes management. A need for accessible, reliable, and relevant information was reported, as well as a sense that required lifestyle changes was incompatible with current life situation. Further, the use of self-affirmation was described as relevant, motivating and engaging. Conclusions Barriers to diabetes self-management need to be addressed when supporting diabetes self-management, e.g. through carefully matching the support to the patient's readiness to change, supporting patient autonomy and focusing on long-term changes. Using self-affirmation may raise acceptability of a digital lifestyle intervention and help connect diabetes self-management with overall life context, by guiding the patient to focus on personal relevance.
Article
Full-text available
Background Anxiety is common in patients with rheumatoid arthritis (RA) and associated with worse RA outcomes. This study assessed the feasibility and preliminary health impacts (mental and physical) of a non-therapist assisted, online mental health intervention targeting anxiety in this population. Methods Participants with confirmed RA and elevated anxiety symptoms were enrolled into the Worry and Sadness program, an Internet-based cognitive-behavioral therapy (iCBT) intervention for anxiety and depression shown to be effective in the general population. Validated self-report measures of anxiety, depression, pain interference, fatigue, physical health-related quality of life, functional status, and patient-reported disease severity were collected at baseline, post-intervention, and at three-month follow-up. Emotional distress scores were tracked between lessons. Participants provided qualitative feedback in writing post-intervention. Results We analyzed the responses of 34 participants; the majority was female (86%) and the mean age was 57 (SD = 13). Of these, 80% (n = 28) completed the study in its entirety. Among these completers, 94.1% described the program as worthwhile. We found statistically significant improvements in anxiety, depression and fatigue from baseline to three-month follow-up, with small to large effect sizes (d = 0.39–0.81). Post-hoc analyses revealed that statistically significant change occurred between baseline and post-intervention for anxiety and depression and was maintained at three-month follow-up, whereas statistically significant change occurred between baseline and three-month follow-up for fatigue. Statistically significant reductions in emotional distress occurred across the program, with a large effect size (d = 1.16) between the first and last lesson.