Digital Therapeutics for Healthy Longevity: A Roadmap

Tim Leistner

and Tobias Kowatsch

1,2,3 b

School of Medicine, University of St. Gallen, St. Gallen, Switzerland

Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland

Centre for Digital Health Interventions, Department of Management, Technology, and Economics,

ETH Zürich, Zurich, Switzerland

Keywords: Healthy Longevity, Digital Therapeutics, Scalability, Platform Business Model, Innovation, mHealth.

Abstract: Non-communicable diseases (NCDs), including common mental disorders, not only impose an enormous

health burden on individuals but also lead to substantial economic burdens for healthcare systems. Especially

individuals with lower socioeconomic status are affected by NCDs. Digital therapeutics (DTx) have the

potential to offer low-cost personalized interventions easing the burden of NCDs and addressing inequalities

in health. This position paper highlights the importance of preventive care and offers a roadmap toward DTx

for healthy longevity.

1 INTRODUCTION

Non-communicable diseases (NCDs) such as cancer,

cardiovascular diseases or diabetes, and common

mental disorders (CMDs) such as depression or

anxiety not only pose an enormous health burden on

individuals but also lead to substantial health

economic challenges for healthcare systems

(Jacobson et al., 2023; Vandenberghe & Albrecht,

2020). In 2012 NCDs were the leading cause of death

(WHO, 2014). Numbers increased to approximately

73% of all deaths by 2017 (Roth et al., 2018). NCDs

also lead to a major economic burden for NCD

households. Especially the population in lower-

income countries and China is affected by costs

associated with NCDs (Murphy et al., 2020). Also, in

developed countries like Switzerland NCDs account

for about 80% of the health costs (Wieser et al.,

2014). Unmodifiable risk factors for the development

of NCDs are age, genetics, and environmental factors.

Modifiable factors primarily relate to aspects of

lifestyle, for example, tobacco and excessive alcohol

consumption, low daily activity, food choices, and

lack of resilience. Especially individuals with low

socioeconomic status are substantially affected by

NCDs (Mackenbach et al., 2008), a highly relevant

socioeconomic inequity (Federal Office of Public

https://orcid.org/0000-0003-2808-8123

https://orcid.org/0000-0001-5939-4145

Health, 2016). At the same time, this group is

underrepresented in clinical trials (Davis et al., 2019;

Sharrocks et al., 2014). This fact leads to an important

problem as clinical trials that aim at developing novel

digital therapeutics (DTx), i.e., evidence-based

software for the prevention, management, and

treatment of disease (Digital Therapeutics Alliance,

2019), may not work for those individuals with a

lower socioeconomic status. Thus, DTx may not at all

lower the socioeconomic inequalities in health and

thus, will also not reduce the health and economic

burden of NCDs and CMDs (Carrilero et al., 2021;

Kowatsch, 2023).

To this end, we recommend a paradigm shift

where healthcare systems depart from curative care

and incentivize primary, secondary, and tertiary

prevention. We, therefore, highlight the importance

of scalable DTx for healthy longevity and that these

DTx are also effective in vulnerable individuals with

lower socioeconomic status.

Next, we outline a roadmap toward DTx for

healthy longevity. We provide a list of stakeholders

that may be important in pushing forward innovation

in this area with substantial societal impact. Then, the

building blocks of DTx and potential business

opportunities are discussed. Here, we provide two

examples of corresponding initiatives in Switzerland

616

Leistner, T. and Kowatsch, T.

Digital Therapeutics for Healthy Longevity: A Roadmap.

DOI: 10.5220/0011778500003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF, pages 616-622

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

and Singapore. We close this position paper with a

discussion and outline of future work.

2 ROADMAP

The WHO defines healthy aging as “the process of

developing and maintaining the functional ability that

enables well-being in older age” (WHO, 2015, p.

228). Healthy longevity extends this definition to all

stages of life: “Health is a state of complete physical,

mental and social well-being and not merely the

absence of disease or infirmity” (WHO, 2006, p. 1).

Therefore, a holistic approach to DTx is required, an

approach that does not neglect social or psychological

well-being. DTx for healthy longevity are defined as

DTx for primary, secondary, and tertiary prevention.

DTx for healthy longevity have, therefore, the overall

goal to maximize the average quality of life and

ensure that the increased lifespan is accompanied by

an increased health span (Beard et al., 2016; Chen et

al., 2018). For such DTx to be successfully deployed,

healthcare systems must support innovative

ecosystems, the development of novel DTx, and

business models.

2.1 Innovative Ecosystem

Developing DTx for healthy longevity requires an

innovative ecosystem of stakeholders that combine

complementary expertise from various disciplines.

We conducted expert interviews with medical

doctors, DTx experts, payers (health insurers), as well

as technology and innovation managers from both

academia and the healthcare industry.

The following stakeholders were identified to be

essential for such an innovative ecosystem:

policymakers, regulatory and public health bodies,

experts in biomedical ethics, public and private

hospitals, healthy and patient populations, patient

organizations, academic health institutions (e.g.,

medical and public health schools), digital health

start-ups, biomedical laboratories, physiotherapists,

nursing facilities, health and stress management

coaches, diet and nutrition experts, hospitality

facilities, and corporate health care units. These

stakeholders together can establish new DTx with a

holistic focus on mental, physical, and social health.

We also identified local initiatives and

educational programs at universities, like executive

education, to promote innovation in DTx for healthy

longevity and dedicated DTx innovation and

accelerator programs (e.g., the Dartmouth Innovation

Accelerator for Digital Health). Moreover, DTx

design and trial platforms are required, such as

Ethicadata.com, Mahalo.health, or MobileCoach.eu.

2.2 Digital Therapeutics

DTx for healthy longevity can trigger personalized

preventive care support at opportune moments

(receptive states) in case a vulnerable state is detected

or predicted (Nahum-Shani et al., 2023; Keller et al.,

2023). To this end, the three building blocks of DTx

cover states of vulnerability, states of receptivity, and

personalized support as depicted in Fig. 1. Moreover,

DTx also have the potential to improve themselves

with every human-DTx interaction, for example,

through reinforcement learning algorithms (Liao et

al., 2020). Each of the three building blocks is

discussed in the following sections, as well as a

design and evaluation framework for DTx.

Figure 1: Building blocks of a DTx (adapted from Keller et

al. 2023, Fig. 6.1, p. 67).

2.2.1 State of Vulnerability

First, for a DTx to decide on personalized support, a

specific state of vulnerability must be measured or

predicted. A vulnerable state is a ”person’s transient

tendency to experience adverse health outcomes or to

engage in maladaptive behaviors” (Nahum-Shani et

al., 2015, p. 3). Therefore, the first step in developing

an effective DTx is to measure or – if possible – even

predict adverse health outcomes or maladaptive

behaviors by using relevant sensor data streams or

patient-reported outcome data. Examples are the

measurement of nocturnal breathing signals for early

detection of Parkinson’s disease (Yang et al., 2022)

or acoustic cough detection to assess asthma control

or predict attacks (Tinschert et al., 2020). Digital

biomarkers can also be used in primary prevention.

State of vulnerability

e.g. stress at work via mouse

movements, high heart rate or

nocturnal breathing signals

State of receptivity

e.g. getting ready for bed

information based on contextual

factors of location and time

Personalized support

e.g. via conversational agents,

smart speakers or healthcare

robots

A learning system that adapts to

interpersonal differences and

optimizes the support

Digital Therapeutics for Healthy Longevity: A Roadmap

617

For example, it has been shown that stress at the

workplace can be measured via computer mouse

movements (Banholzer et al., 2021). More basic

biomarkers include step count, heart rate, and average

sleep duration and quality per night.

Recent improvements in consumer technology

(e.g., wearables, smartphones, smart speakers, or

smart TVs) further increase the quality of sensor data.

Accordingly, “mobile health technologies are

evolving from descriptive monitoring tools to digital

diagnostics and therapeutics” (Sim, 2019, p. 965);

this evolution of data streams requires more work

before digital biomarkers can improve patient

outcomes (Carovos et al., 2019).

2.2.2 State of Receptivity

New technologies also enable more effective means

of how and when to deliver support by DTx. Context-

aware notification management systems on

smartphones or smartwatches can increase the

response rate to notifications (Künzler et al., 2017,

2019; Mishra et al., 2021). For example, support is

better perceived when engaged in working, studying

or when getting ready for bed (Choi et al., 2019).

Contextual factors, such as geolocation or time, can

therefore influence states of receptivity. To this end,

effective DTx deliver personalized support at the

right time, i.e., the target is in a state of receptivity,

after a vulnerable state was measured or predicted.

However, such context-aware DTx may also raise

privacy and data security concerns which, in turn, can

represent substantial adoption barriers. It is, therefore,

of utmost importance to comply with any legal,

ethical, and regulatory requirements.

2.2.3 Personalized Support

Virtual or augmented reality, healthcare robots, or

smart speakers allow for a new way of delivering

personalized support with conversational agents

(CAs) such as chatbots or virtual assistants like

Amazon’s Alexa or Apple’s Siri (Kowatsch and

Fleisch, 2021). CAs are computer programs that

mimic human conversation via voice or text chat

(Bickmore & O’Leary, 2023). Humans can perceive

CAs as social actors (e.g., digital health assistants)

and build a working alliance (Kaveladze & Schueller,

2023). The latter is an important relationship quality

robustly linked to treatment outcomes (Del Re et al.,

2021). DTx that use conversational agents can reach

and engage individuals in a highly scalable manner

(Kowatsch et al., 2021). Barriers of implementing

CAs can be liability issues (Schlieter et al., 2022), for

example, patient safety was often overlooked in prior

work (Laranjo et al., 2018). Recent technological

advances in large language models for healthcare

promise safer and more accurate CAs in the future

(Singhal et al., 2022).

2.2.4 Design & Eval. Framework for DTx

As for the design of scalable and evidence-based

DTx, digital health companies, researchers, and/or

clinicians have the responsibility to validate and

evaluate the effectiveness of the DTx’ individual

building blocks (Kowatsch et al., 2019, Coravos et al.,

2019). Therefore, a design and evaluation framework

for DTx has been proposed (DEDHI) to support both

researchers and practitioners (Kowatsch et al., 2019).

DEDHI consists of four phases. Preparation,

optimization, evaluation, and implementation. It was

derived from the Multiphase Optimization Strategy

(Collins, 2018) and several other frameworks, e.g.,

Campbell et al. (2007). DEDHI specifies the goals

and tasks, technical maturity, evaluation criteria, and

implementation barriers for each phase.

2.3 Novel Business Models

Digital health companies that focus on prevention and

disease detection are underrepresented on the health

continuum with most businesses focused on disease

management (Cohen et al., 2020). And even though it

is “far better to prevent disease than to treat people

after they get sick” (Levine et al., 2019), only 3% of

total US healthcare expenditures are spent on

prevention (Pryor and Volpp, 2018). Against this

background and the health and economic burden of

NCDs and CMDs, there is a substantial need for

successful business models for prevention and aging

society (Coughlin, 2017). An exchange platform for

DTx may be a viable business model in this regard

(Wortmann et al., 2022). DTx for healthy longevity,

blended with various other online or online

preventive care services, can be offered and bought

by different types of consumers. For example, there

could be offerings like “Healthy Longevity Holidays”

with a blended DTx approach, were a target person

(or family) uses a DTx before and after the holidays

while getting personalized on-site coaching sessions

based on the data collected via the DTx during the

holiday period. In another, secondary prevention

example, national screening programs like Donna are

offered. Donna is a program for early detection of

breast cancer for women over 50. Women are

automatically scheduled for a mammography and

questionnaire every two years which significantly

reduces mortality (Warner, 2011). Finally,

Scale-IT-up 2023 - Workshop on Best Practices for Scaling-Up Digital Innovations in Healthcare

618

Germany’s DIGA concept represents an example for

tertiary prevention with DTx (FIDMD, 2020).

Doctors can prescribe this class of DTx while health

insurance companies take over the reimbursement.

The DTx deprexis, for example, can be prescribed as

an adjunctive treatment for people with depression. It

has been shown that this DTx results in better

treatment outcomes than human-delivered

psychotherapy alone (Berger et al., 2018). Another

example is the DTx zanadio for individuals with

obesity (Forkmann et al., 2022). The estimated

revenue of DIGAs in Germany was around 55 million

EUR in 2022 (McKinsey & Company, 2022). A

search in the PitchBook database for privately held

venture capital-backed companies in “longevity”

shows a rising number of deals and total capital raised

from 14 (€62.83 M) in 2017 to 49 (€333.57 M) in

2022 indicating the potential of DTx for healthy

longevity.

3 EXAMPLES

With two specific initiatives in Switzerland and

Singapore, we would like to illustrate the potential of

DTx for healthy longevity according to the roadmap

outlined above.

3.1 Switzerland

First, Switzerland was recently ranked as the most

innovative country (WIPO, 2022). The key to

innovation is university-industry collaboration and

matching preconditions (Rajalo et al., 2017). The

Switzerland Innovation initiative was invented to

improve collaboration by opening innovation parks, a

cluster for specific industries, e.g., in Basel for life

sciences. In 2022, the Switzerland Innovation Park

OST (SIP-OST) was founded in the canton of

St.Gallen to focus on health, industrial engineering,

and digitalization. This focus area enables

opportunities for innovation in DTx. With respect to

this position paper, SIP-OST is currently developing

a strategy for a DTx ecosystem with a focus on

healthy longevity. The region has access to clinical

and non-clinical populations with the cantonal

hospital of St.Gallen, the children's hospital, the

geriatric clinic, the rehabilitation clinic in Gais, and

the Hirslanden Clinic. For the development of

minimally invasive biosensors, SIP-OST can foster

the expertise of the Swiss Federal Laboratories for

Material Science and Technology (EMPA). For

example, EMPA developed textile-based ECG

electrodes for long-term monitoring (Weder et. Al,

2015) which is now the spin-off Nahtlos. Also, this

region is one of Switzerland's biggest IT hubs, and

has the infrastructure to build and scale DTx for

healthy longevity. The Center for Laboratory

Medicine, Labor Dr. Riesch, Labor Team W, or

Microsynth are some examples of laboratories

focusing on biomedical analyses. Synergies with

established organizations and competence centers

(e.g., the cantonal hospital and the Centre for Digital

Health Interventions) and the recently founded HSG

School of Medicine and HSG Institute of Computer

Science allow the development of scalable DTx. The

HSG Chairs of Information, Technology, and

Innovation Management, Entrepreneurship, Digital

Health Interventions, International Business Law and

Health Care Management enable the development of

new business models to bring new DTx for healthy

longevity to the market. A healthy start-up culture is

built with initiatives like Startfeld, START Global, or

Startup@HSG. In addition to the dovetailing of

technological, medical, and health-economic

expertise in Eastern Switzerland, it has a

geographically unique location between Lake

Constance and the Alps. This is particularly relevant

for primary, secondary, and tertiary prevention of

NCDs and CMDs, as a walk or a hike in nature

promotes physical and mental health (Hansen et al.,

2017). The rural areas also enable the development of

DTx projects and services with a strong focus on

remote monitoring and virtual clinics. The digital

health company OnlineDoctor, for example, connects

patients and dermatologists. Eastern Switzerland,

therefore, harbors a promising ecosystem to develop

innovative DTx for healthy longevity.

3.2 Singapore

Singapore will likely be the fifth oldest country

worldwide by 2050 by median age (United Nations,

2015). The recent adaptation to their aging society

will probably be an example for other nations on how

to transform the healthcare system to meet the need

of the global demographic shift. Healthier SG is a

healthcare transformation program that addresses the

aging of its population (Healthier SG, 2022). All

relevant stakeholders were involved in the

development of Healthier SG. At the center of

Healthier SG lies the challenge of changing personal

health-seeking behavior (Knittle et al., 2020) and

greater emphasis on preventive care instead of

curative care. Healthier SG designed a five-step

process to reach the program’s goals. First, everyone

in Singapore will choose a family doctor, who can

educate them on improving their health and who will

Digital Therapeutics for Healthy Longevity: A Roadmap

619

build a relationship with them. Second, personal

health plans will be developed depending on lifestyle

and screening. Annual check-ins as primary

prevention will be implemented. Third, local partners

help people to stay on track with their health plans

and, at the same time, promote social well-being.

Fourth, a national family doctor health program will

be implemented for over 60-year-olds in 2023. And

finally, and most importantly, policies will

incentivize preventive care, IT systems will be

developed to ensure a seamless sharing of data, and

healthcare manpower will be equipped with the

corresponding skills. Additionally, DTx like H365

promote daily activity by rewarding health points

which can be exchanged for vouchers.

4 DISCUSSION AND FUTURE

WORK

It is of utmost importance to lower social inequalities

in health. DTx for healthy longevity offer scalable

means to reach and engage vulnerable individuals. To

ensure the effectiveness of such DTx, principal

investigators of clinical studies should, therefore,

especially target and consider individuals with a

lower socioeconomic status. This can be achieved,

among other approaches, with the help of community

partnerships (Bonevski et al., 2014). It is now time to

give forethought to strong incentive structures and

evaluation methods to promote high-quality and

equitable DTx for healthy longevity. Clinicians,

(business) organizations, public bodies, and

regulators have the joint responsibility to design an

ecosystem where innovative DTx for an aging society

can strive. To come to these solutions, one must first

conduct iterative, interdisciplinary, and user-centered

healthy longevity studies. Feasibility, optimization,

evaluation, and implementation studies must be

conducted from a technical, medical, behavioral, and

health economic perspective. The resources required

depend strongly on which application scenario lies in

focus (e.g., a healthy vs. patient population).

Depending on that focus, appropriate partnerships

and funding sources can be selected.

As a next step, we will work on a globally

scalable DTx design and trial service based on

MobileCoach.eu. The objective of this service is to

accelerate the design of evidence-based DTx for

healthy longevity and with it, to reduce the

inequalities in health.

5 CONFLICT OF INTEREST

TK is affiliated with the Centre for Digital Health

Interventions (CDHI), a joint initiative of the Institute

for Implementation Science in Health Care,

University of Zurich; the Department of

Management, Technology, and Economics at Swiss

Federal Institute of Technology in Zürich; and the

Institute of Technology Management and School of

Medicine at the University of St Gallen. CDHI is

funded in part by the Swiss health insurer CSS. CSS

was not involved in this research. TK is also a co-

founder of Pathmate Technologies, a university spin-

off company that creates and delivers digital clinical

pathways. However, Pathmate Technologies was not

involved in this research.

REFERENCES

Banholzer, N., Feuerriegel, S., Fleisch, E., Bauer, G. F., &

Kowatsch, T. (2021). Computer mouse movements as

an indicator of work stress: longitudinal observational

field study. Journal of medical Internet

research, 23(4), e27121.

Beard, J. R., Officer, A., De Carvalho, I. A., Sadana, R.,

Pot, A. M., Michel, J. P., Lloyd-Sherlock, P., Epping-

Jordan, J. E., Peeters, G., Mahanani, W. R.,

Thiyyagarajan, J. A., & Chatterji, S. (2016). The World

report on ageing and health: a policy framework for

healthy ageing. The lancet, 387(10033), 2145-2154.

Berger, T., Krieger, T., Sude, K., Meyer, B., & Maercker,

A. (2018). Evaluating an e-mental health program

(“deprexis”) as adjunctive treatment tool in

psychotherapy for depression: Results of a pragmatic

randomized controlled trial. Journal of affective

disorders, 227, 455-462.

Bickmore, T., & O'Leary, T. (2023). Conversational agents

on smartphones and the web. In Digital Therapeutics

for Mental Health and Addiction (pp. 99-112).

Academic Press.

Bonevski, B., Randell, M., Paul, C., Chapman, K.,

Twyman, L., Bryant, J., Brozek, I., & Hughes, C.

(2014). Reaching the hard-to-reach: a systematic

review of strategies for improving health and medical

research with socially disadvantaged groups. BMC

medical research methodology, 14(1), 1-29.

Campbell, N. C., Murray, E., Darbyshire, J., Emery, J.,

Farmer, A., Griffiths, F., Guthrie, B, Lester, H., Wilson,

P. & Kinmonth, A. L. (2007). Designing and evaluating

complex interventions to improve health

care. Bmj, 334(7591), 455-459.

Carrilero, N., García‐Altés, A., Mendicuti, V. M., & Ruiz

García, B. (2021). Do governments care about

socioeconomic inequalities in health? Narrative review

of reports of EU‐15 countries. European Policy

Analysis, 7(2), 521-536.

Scale-IT-up 2023 - Workshop on Best Practices for Scaling-Up Digital Innovations in Healthcare

620

Chen, S., Kuhn, M., Prettner, K., & Bloom, D. E. (2018).

The macroeconomic burden of noncommunicable

diseases in the United States: Estimates and

projections. PloS one, 13(11), e0206702.

Choi, W., Park, S., Kim, D., Lim, Y. K., & Lee, U. (2019).

Multi-stage receptivity model for mobile just-in-time

health intervention. Proceedings of the ACM on

interactive, mobile, wearable and ubiquitous

technologies, 3(2), 1-26.

Cohen, A. B., Dorsey, E., Mathews, S. C., Bates, D. W., &

Safavi, K. (2020). A digital health industry cohort

across the health continuum. NPJ digital

medicine, 3(1), 1-10.

Collins, L. (2018). Optimization of behavioral,

biobehavioral, and biomedical interventions. Cham:

Springer International Publishing, 10(1007), 978-973.

Coravos, A., Khozin, S., & Mandl, K. D. (2019).

Developing and adopting safe and effective digital

biomarkers to improve patient outcomes. NPJ digital

medicine, 2(1), 1-5.

Coughlin, J. F. (2017). The longevity economy: Unlocking

the world's fastest-growing, most misunderstood

market. Hachette UK.

Davis, T. C., Arnold, C. L., Mills, G., & Miele, L. (2019).

A qualitative study exploring barriers and facilitators of

enrolling underrepresented populations in clinical trials

and biobanking. Frontiers in Cell and Developmental

Biology, 7, 74.

Del Re, A. C., Flückiger, C., Horvath, A. O., & Wampold,

B. E. (2021). Examining therapist effects in the

alliance–outcome relationship: A multilevel meta-

analysis. Journal of Consulting and Clinical

Psychology, 89(5), 371.

Digital Therapeutics Alliance. (2019). Digital Therapeutics

Definition and Core Principles. Digital Therapeutics

Alliance Fact Sheet.

Federal Office of Public Health. (2016). National Strategy

for the Prevention of Non-communicable Diseases

(NCD strategy). Federal Office of Public Health.

FIDMD. (2020). The Fast-Track Process for Digital Health

Applications (DiGA) according to Section 139e SGB V:

A Guide for Manufacturers, Service Providers and

Users. F. I. f. D. a. M. Devices.

Forkmann, K., Roth, L., & Mehl, N. (2022). Introducing

zanadio—A Digitalized, Multimodal Program to Treat

Obesity. Nutrients, 14(15), 3172.

Hansen, M. M., Jones, R., & Tocchini, K. (2017). Shinrin-

yoku (forest bathing) and nature therapy: A state-of-

the-art review. International journal of environmental

research and public health, 14(8), 851.

Healthier SG. (2022). White Paper on Healthier SG.

Forward SG.

Jacobson, N. C., Kowatsch, T., & Marsch, L. A. (Eds.).

(2023). Digital Therapeutics for Mental Health and

Addiction: The State of the Science and Vision for the

Future. Academic Press.

Kaveladze, B., & Schueller, S. M. (2023). A digital

therapeutic alliance in digital mental health. In Digital

Therapeutics for Mental Health and Addiction (pp. 87-

98). Academic Press.

Keller, R., v. Wangenheim, F., Mair, J. & Kowatsch, T.

(2023). Chapter 6 – Receptivity to mobile health

interventions. Academic Press.

Knittle, K., Heino, M., Marques, M. M., Stenius, M.,

Beattie, M., Ehbrecht, F., Hagger, S., Hardeman, W. &

Hankonen, N. (2020). The compendium of self-

enactable techniques to change and self-manage

motivation and behaviour v. 1.0. Nature Human

Behaviour, 4(2), 215-223.

Kowatsch T. (2023). How to Lower Socioeconomic

Inequalities in Health with Digital Therapeutics?

Healthification – A Short Animation. Workshop on Best

Practices for Scaling-Up Digital Innovations in

Healthcare - Scale-IT-up 2023, co-located with the

16th International Joint Conference on Biomedical

Engineering Systems and Technologies, 16-18

February 2023, Lisbon, Portugal.

Kowatsch, T., & Fleisch, E. (2021). Digital Health

Interventions. In Connected Business (pp. 71-95).

Springer, Cham.

Kowatsch, T., Otto, L., Harperink, S., Cotti, A., & Schlieter,

H. (2019). A design and evaluation framework

for digital health interventions. IT-Information

Technology, 61(5-6), 253-263.

Kowatsch, T., Schachner, T., Harperink, S., Barata, F.,

Dittler, U., Xiao, G., Stanger, C., v Wangenheim, F.,

Fleisch, E., Oswald, H. & Möller, A. (2021).

Conversational agents as mediating social actors in

chronic disease management involving health care

professionals, patients, and family members: multisite

single-arm feasibility study. Journal of medical

Internet research, 23(2), e25060.

Künzler, F., Kramer, J. N., & Kowatsch, T. (2017). Efficacy

of mobile context-aware notification management

systems: A systematic literature review and meta-

analysis. In 2017 IEEE 13th International Conference

on Wireless and Mobile Computing, Networking and

Communications (WiMob)(pp. 131-138). IEEE.

Künzler, F., Mishra, V., Kramer, J. N., Kotz, D., Fleisch,

E., & Kowatsch, T. (2019). Exploring the state-of-

receptivity for mhealth interventions. Proceedings of

the ACM on Interactive, Mobile, Wearable and

Ubiquitous Technologies, 3(4), 1-27.

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B.,

Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi,

F., Lau, A. Y. S. & Coiera, E. (2018). Conversational

agents in healthcare: a systematic review. Journal of the

American Medical Informatics Association, 25(9),

1248-1258.

Levine, S., Malone, E., Lekiachvili, A., & Briss, P. (2019).

Health care industry insights: why the use of preventive

services is still low. Preventing chronic disease, 16.

Liao, P., Greenewald, K., Klasnja, P., & Murphy, S. (2020).

Personalized heartsteps: A reinforcement learning

algorithm for optimizing physical activity. Proceedings

of the ACM on Interactive, Mobile, Wearable and

Ubiquitous Technologies, 4(1), 1-22.

Mackenbach, J. P., Stirbu, I., Roskam, A. J. R., Schaap, M.

M., Menvielle, G., Leinsalu, M., & Kunst, A. E. (2008).

Socioeconomic inequalities in health in 22 European

Digital Therapeutics for Healthy Longevity: A Roadmap

621

countries. New England journal of medicine, 358(23),

2468-2481.

McKinsey & Company. (2022). E-Health Monitor 2022

Deutschlands Weg in die digitale

Gesundheitsversorgung – Status quo und Perspektiven.

Medizinische Wissenschaftliche Verlagsgesellschaft.

Mishra, V., Künzler, F., Kramer, J. N., Fleisch, E.,

Kowatsch, T., & Kotz, D. (2021). Detecting receptivity

for mHealth interventions in the natural environment.

Proceedings of the ACM on interactive, mobile,

wearable and ubiquitous technologies, 5(2), 1-24.

Murphy, A., Palafox, B., Walli-Attaei, M., Powell-Jackson,

T., Rangarajan, S., Alhabib, K. F., Avezum, A., Calik,

K. B. T., Chifamba, J., Choudhury, T., Dagenais, G., L

Dans, A. , Gupta, R., Iqbal, R., Kaur, M., Kelishadi, R.,

Khatib, R., Kruger, I. M., Kutty, V. R., … & McKee,

M. (2020). The household economic burden of non-

communicable diseases in 18 countries. BMJ global

health, 5(2), e002040.

Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015).

Building health behavior models to guide the

development of just-in-time adaptive interventions: A

pragmatic framework. Health psychology, 34(S), 1209.

Nahum-Shani, I., Wetter, D. W., & Murphy, S. A. (2023).

Adapting just-in-time interventions to vulnerability

and receptivity: Conceptual and methodological

considerations. In Digital Therapeutics for Mental

Health and Addiction (pp. 77-87). Academic Press.

Pryor, K., & Volpp, K. (2018). Deployment of preventive

interventions—time for a paradigm shift. N Engl J

Med, 378(19), 1761-1763.

Rajalo, S., & Vadi, M. (2017). University-industry

innovation collaboration: Reconceptualization.

Technovation, 62, 42-54.

Roth, G. A., Abate, D., Abate, K. H., Abay, S. M., Abbafati,

C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela,

J., Abdelalim, A., Abdollahpour, I., Abdulkader, R. S.,

Abebe, H. T., Abebe, M., Abebe, Z., Abejie, A. N.,

Abera, S. F., Abil, O. Z., Abraha, H. N., … &

Borschmann, R. (2018). Global, regional, and national

age-sex-specific mortality for 282 causes of death in

195 countries and territories, 1980–2017: a systematic

analysis for the Global Burden of Disease Study

2017. The Lancet, 392(10159), 1736-1788.

Schlieter, H., Marsch, L. A., Whitehouse, D., Otto, L.,

Londral, A. R., Teepe, G. W., ... & Kowatsch, T.

(2022). Scale-up of Digital Innovations in Health Care:

Expert Commentary on Enablers and Barriers. Journal

of Medical Internet Research, 24(3), e24582.

Sharrocks, K., Spicer, J., Camidge, D. R., & Papa, S.

(2014). The impact of socioeconomic status on access

to cancer clinical trials. British journal of

cancer, 111(9), 1684-1687.

Sim, I. (2019). Mobile devices and health. New England

Journal of Medicine, 381(10), 956-968.

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J.,

Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H.,

Pfohl, S., Payne, P., Seneviratne, M., Gamble, P., Kelly,

C., Scharli, N., Chowdhery, A., Mansfield, A., Aguera

y Arcas, B. & Natarajan, V. (2022). Large Language

Models Encode Clinical Knowledge. arXiv preprint

arXiv:2212.13138.

Tinschert, P., Rassouli, F., Barata, F., Steurer-Stey, C.,

Fleisch, E., Puhan, M. A., Kowatsch T. & Brutsche, M.

H. (2020). Nocturnal cough and sleep quality to assess

asthma control and predict attacks. Journal of asthma

and allergy, 13, 669.

United Nations. (2015). Word population ageing Economic

& Social Affairs.

Vandenberghe, D., & Albrecht, J. (2020). The financial

burden of non-communicable diseases in the European

Union: a systematic review. European Journal of

Public Health, 30(4), 833-839.

Warner, E. (2011). Breast-cancer screening. New England

Journal of Medicine, 365(11), 1025-1032.

Weder, M., Hegemann, D., Amberg, M., Hess, M., Boesel,

L. F., Abächerli, R., Meyer, V. R. & Rossi, R. M.

(2015). Embroidered electrode with silver/titanium

coating for long-term ECG monitoring. Sensors, 15(1),

1750-1759.

WHO. (2006). Constitution of the World Health

Organization, Basic Documents. Forty.

WHO. (2014). Global status report on noncommunicable

diseases 2014. Word Health Organization.

WHO. (2015). World report on ageing and health. World

Health Organization.

Wieser, S. (2014). Die Kosten der nichtübertragbaren

Krankheiten in der Schweiz: Schlussbericht. Federal

Office of Public Health.

WIPO. (2022). Global Innovation Index 2022 World

Intellectual Property Organization. Geneva, 15

edition.

Wortmann, F., Jung, S., Bronner, W., & Gassmann, O.

(2022). The Platform Navigator: 88 Patterns to Design

and Implement Platform Business Models.

Yang, Y., Yuan, Y., Zhang, G., Wang, H., Chen, Y. C., Liu,

Y., Tarolli, G., Crepeau, D., Bukaryk, J., Junna, M. R.,

Videnovic, A., Ellis, T. D., Lipford, M. C., Dorsey, R.

& Katabi, D. (2022). Artificial intelligence-enabled

detection and assessment of Parkinson’s disease using

nocturnal breathing signals. Nature medicine, 28(10),

2207-2215.

Scale-IT-up 2023 - Workshop on Best Practices for Scaling-Up Digital Innovations in Healthcare

622