Tobias Kowatsch

Title

Prof. Dr.

Last Name

Kowatsch

First name

Tobias

tobias.kowatsch@unisg.ch

ORCID

0000-0001-5939-4145

Phone

+41 71 224 7244

Web Site

www.c4dhi.org

Now showing 1 - 9 of 9

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Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis
(JMIR Publications Inc., 2022-01-07)
Keller, Roman
;
Hartmann, Sven
;
Teepe, Gisbert Wilhelm
;
Lohse, Kim-Morgaine
;
Alattas, Aishah
Background: Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which these interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. Objective: Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. Methods: A systematic search was conducted using 2 venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies offering interventions for type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCIs’ websites, and data regarding intervention effectiveness were extracted. The Diabetes Prevention Recognition Program (DPRP) of the Center for Disease Control and Prevention in the United States was used to identify the recognition status. The DBCIs’ publications, websites, and mobile apps were reviewed with regard to the intervention characteristics. Results: The 16 top-funded companies offering DBCIs for type 2 diabetes received a total funding of US $2.4 billion as of June 15, 2021. Only 4 out of the 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). Further, 1 of those 4 RCTs showed a significant difference in glycated hemoglobin A1c (HbA1c) outcomes between the intervention and control groups. However, all the studies reported HbA1c improvements ranging from 0.2% to 1.9% over the course of 12 months. In addition, 6 interventions were fully recognized by the DPRP to deliver evidence-based programs, and 2 interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (13/16, 81%,), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (74/119, 62%) that could be used to tailor JITAIs. Conclusions: Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There is considerable variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions.
Type:journal article
Journal:Journal of Medical Internet Research
Volume:24
Issue:1
URL:https://doi.org/10.2196/33348
DOI:10.2196/33348
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109089
Scopus© Citations 38
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The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study
(JMIR Publications, 2022-04-27)
Marcia Nissen
;
Rüegger, Dominik
;
Stieger, Mirjam
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Flückiger, Christoph
;
Allemand, Mathias
Background: The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective: This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods: Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results: While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions: Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.
Type:journal article
Journal:Journal of Medical Internet Research
Volume:24
Issue:4
URL:https://www.jmir.org/2022/4/e32630
DOI:10.2196/32630
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108786
Scopus© Citations 69
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Human Cues in Self-help Lifestyle Interventions: an Experimental Field Study
(JMIR Publications, 2021-04-29)
Cohen Rodrigues, Talia
;
Reijnders, Thomas
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de Buisonjé, David
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Santhanam, Prabhakaran
;
Kowatsch, Tobias
Background: Self-help eHealth interventions are generally less effective than human-supported ones, as they suffer from a low level of adherence. Nevertheless, self-help interventions are useful in the prevention of non-communicable diseases, as they are easier and cheaper to widely implement. Adding humanness in the form of a text-based conversational agent (TCA) could provide a solution to non-adherence. In this study we investigate whether adding human cues to a TCA facilitates relationship-building with the agent, and makes interventions more attractive for people to adhere to. We will investigate the effects of two types of human cues, which are visual cues (eg, human avatar) and relational cues (eg, showing empathy). Objective: We aim to investigate if adding human cues to a TCA can help increase adherence to a self-help eHealth lifestyle intervention and explore the role of working alliance as a possible mediator of this relationship. Methods: Participants (N=121) followed a 3-week app-based physical activity intervention delivered by a TCA. Two types of human cues used by the TCA were manipulated, resulting in four experimental groups, which were (1) visual cues-group, (2) relational cues-group, (3) both visual and relational cues-group, and (4) no cues-group. Participants filled out the Working Alliance Inventory Short Revised form after the final day of the intervention. Adherence was measured as number of days participants responded to the messages of the TCA. Results: One-way ANOVA revealed a significant difference for adherence between conditions. Against our expectations, the groups with visual cues showed lower adherence compared to those with relational only or no cues (t(117) = -3.415, P = .001). No significant difference was found between the relational- and no cues-groups. Working alliance was not affected by cue-type, but showed to have a significant positive relationship with adherence (t(75) = 4.136, P < .001). Conclusions: We hypothesize that the negative effect of visual cues is due to a lack of transparency about the true nature of the coach. Visual resemblance of a human coach could have led to high expectations that could not be met by our digital coach. Furthermore, the inability of TCAs to use non-verbal communication could provide an explanation for the lack of effect of relational cues or the effect of cue-type on working alliance. We give suggestions for future studies to test these potential mechanisms. Clinical Trial: Pre-registration: OSF Registries, https://osf.io/mgw2s
Type:journal article
Journal:Journal of Medical Internet Research (JMIR) Preprints
Issue:29/04/2021:30057
URL:https://doi.org/10.2196/preprints.30057
DOI:10.2196/preprints.30057
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110460
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Personalization of Conversational Agent-Patient Interaction Styles for Chronic Disease Management: Two Consecutive Cross-sectional Questionnaire Studies
(JMIR Publications, 2021-05-26)
Gross, Christoph
;
Schachner, Theresa
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Hasl, Andrea
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Kohlbrenner, Dario
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Clarenbach, Christian F
Background: Conversational agents (CAs) for chronic disease management are receiving increasing attention in academia and the industry. However, long-term adherence to CAs is still a challenge and needs to be explored. Personalization of CAs has the potential to improve long-term adherence and, with it, user satisfaction, task efficiency, perceived benefits, and intended behavior change. Research on personalized CAs has already addressed different aspects, such as personalized recommendations and anthropomorphic cues. However, detailed information on interaction styles between patients and CAs in the role of medical health care professionals is scant. Such interaction styles play essential roles for patient satisfaction, treatment adherence, and outcome, as has been shown for physician-patient interactions. Currently, it is not clear (1) whether chronically ill patients prefer a CA with a paternalistic, informative, interpretive, or deliberative interaction style, and (2) which factors influence these preferences. Objective: We aimed to investigate the preferences of chronically ill patients for CA-delivered interaction styles. Methods: We conducted two studies. The first study included a paper-based approach and explored the preferences of chronic obstructive pulmonary disease (COPD) patients for paternalistic, informative, interpretive, and deliberative CA-delivered interaction styles. Based on these results, a second study assessed the effects of the paternalistic and deliberative interaction styles on the relationship quality between the CA and patients via hierarchical multiple linear regression analyses in an online experiment with COPD patients. Patients’ sociodemographic and disease-specific characteristics served as moderator variables. Results: Study 1 with 117 COPD patients revealed a preference for the deliberative (50/117) and informative (34/117) interaction styles across demographic characteristics. All patients who preferred the paternalistic style over the other interaction styles had more severe COPD (three patients, Global Initiative for Chronic Obstructive Lung Disease class 3 or 4). In Study 2 with 123 newly recruited COPD patients, younger participants and participants with a less recent COPD diagnosis scored higher on interaction-related outcomes when interacting with a CA that delivered the deliberative interaction style (interaction between age and CA type: relationship quality: b=−0.77, 95% CI −1.37 to −0.18; intention to continue interaction: b=−0.49, 95% CI −0.97 to −0.01; working alliance attachment bond: b=−0.65, 95% CI −1.26 to −0.04; working alliance goal agreement: b=−0.59, 95% CI −1.18 to −0.01; interaction between recency of COPD diagnosis and CA type: working alliance goal agreement: b=0.57, 95% CI 0.01 to 1.13). Conclusions: Our results indicate that age and a patient’s personal disease experience inform which CA interaction style the patient should be paired with to achieve increased interaction-related outcomes with the CA. These results allow the design of personalized health care CAs with the goal to increase long-term adherence to health-promoting behavior.
Type:journal article
Journal:Journal of Medical Internet Research
Volume:23
Issue:5:e26643
URL:https://doi.org/10.2196/26643
DOI:10.2196/26643
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110391
Scopus© Citations 20
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Conversational Agents as Mediating Social Actors in Chronic Disease Management Involving Healthcare Professionals, Patients, and Family Members: Intervention Design and Results from a Multi-site, Single-arm Feasibility Study
(JMIR Publications, 2021-02-17)
Kowatsch, Tobias
;
Schachner, Theresa
;
Harperink, Samira
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Barata, Filipe
;
Dittler, Ullrich
Background: Successful management of chronic diseases requires a trustful collaboration between health care professionals, patients, and family members. Scalable conversational agents, designed to assist health care professionals, may play a significant role in supporting this collaboration in a scalable way by reaching out to the everyday lives of patients and their family members. However, to date, it remains unclear whether conversational agents, in such a role, would be accepted and whether they can support this multistakeholder collaboration. Objective: With asthma in children representing a relevant target of chronic disease management, this study had the following objectives: (1) to describe the design of MAX, a conversational agent–delivered asthma intervention that supports health care professionals targeting child-parent teams in their everyday lives; and (2) to assess the (a) reach of MAX, (b) conversational agent–patient working alliance, (c) acceptance of MAX, (d) intervention completion rate, (e) cognitive and behavioral outcomes, and (f) human effort and responsiveness of health care professionals in primary and secondary care settings. Methods: MAX was designed to increase cognitive skills (ie, knowledge about asthma) and behavioral skills (ie, inhalation technique) in 10-15-year-olds with asthma, and enables support by a health professional and a family member. To this end, three design goals guided the development: (1) to build a conversational agent–patient working alliance; (2) to offer hybrid (human- and conversational agent–supported) ubiquitous coaching; and (3) to provide an intervention with high experiential value. An interdisciplinary team of computer scientists, asthma experts, and young patients with their parents developed the intervention collaboratively. The conversational agent communicates with health care professionals via email, with patients via a mobile chat app, and with a family member via SMS text messaging. A single-arm feasibility study in primary and secondary care settings was performed to assess MAX. Results: Results indicated an overall positive evaluation of MAX with respect to its reach (49.5%, 49/99 of recruited and eligible patient-family member teams participated), a strong patient-conversational agent working alliance, and high acceptance by all relevant stakeholders. Moreover, MAX led to improved cognitive and behavioral skills and an intervention completion rate of 75.5%. Family members supported the patients in 269 out of 275 (97.8%) coaching sessions. Most of the conversational turns (99.5%) were conducted between patients and the conversational agent as opposed to between patients and health care professionals, thus indicating the scalability of MAX. In addition, it took health care professionals less than 4 minutes to assess the inhalation technique and 3 days to deliver related feedback to the patients. Several suggestions for improvement were made. Conclusions: This study provides the first evidence that conversational agents, designed as mediating social actors involving health care professionals, patients, and family members, are not only accepted in such a “team player” role but also show potential to improve health-relevant outcomes in chronic disease management.
Type:journal article
Journal:Journal of Medical Internet Research (JMIR)
Volume:23
Issue:2:e25060
URL:https://doi.org/10.2196/25060
DOI:10.2196/25060
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110640
Scopus© Citations 80
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Elena+ Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol
(2021-10-21)
Ollier, Joseph
;
Neff, Simon
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Dworschak, Christine
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Sejdiji, Arber
;
Santhanam, Prabhakaran
Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.
Type:journal article
Journal:Frontiers in Public Health
Volume:9
Issue:1543
URL:https://www.frontiersin.org/article/10.3389/fpubh.2021.625640
DOI:10.3389/fpubh.2021.625640
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109801
Scopus© Citations 19
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Effects of a novel mobile health intervention compared to a multi-component behaviour changing program on body mass index, physical capacities and stress parameters in adolescents with obesity: a randomized controlled trial
(BioMed Central / Springer Nature, 2021-07-09)
Stasinaki, Aikaterini
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Büchter, Dirk
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Shih, Iris
;
Heldt, Katrin
;
Güsewell, Sabine
Background: Less than 2% of overweight children and adolescents in Switzerland can participate in multi-component behaviour changing interventions (BCI), due to costs and lack of time. Stress often hinders positive health outcomes in youth with obesity. Digital health interventions, with fewer on-site visits, promise health care access in remote regions; however, evidence for their effectiveness is scarce. Methods: This randomized controlled not blinded trial (1:1) was conducted in a childhood obesity center in Switzerland. Forty-one youth aged 10–18 years with body mass index (BMI) > P.90 with risk factors or co-morbidities or BMI > P.97 were recruited. During 5.5 months, the PathMate2 group (PM) received daily conversational agent counselling via mobile app, combined with standardized counselling (4 on-site visits). Controls (CON) participated in a BCI (7 on-site visits). We compared the outcomes of both groups after 5.5 (T1) and 12 (T2) months. Primary outcome was reduction in BMI-SDS (BMI standard deviation score: BMI adjusted for age and sex). Secondary outcomes were changes in body fat and muscle mass (bioelectrical impedance analysis), waist-to-height ratio, physical capacities (modified Dordel-Koch-Test), blood pressure and pulse. Additionally, we hypothesized that less stressed children would lose more weight. Thus, children performed biofeedback relaxation exercises while stress parameters (plasma cortisol, stress questionnaires) were evaluated. Results: At intervention start median BMI-SDS of all patients (18 PM, 13 CON) was 2.61 (obesity > + 2SD). BMI-SDS decreased significantly in CON at T1, but not at T2, and did not decrease in PM during the study. Muscle mass, strength and agility improved significantly in both groups at T2; only PM reduced significantly their body fat at T1 and T2. Average daily PM app usage rate was 71.5%. Cortisol serum levels decreased significantly after biofeedback but with no association between stress parameters and BMI-SDS. No side effects were observed. Conclusions: Equally to BCI, PathMate2 intervention resulted in significant and lasting improvements of physical capacities and body composition, but not in sustained BMI-SDS decrease. This youth-appealing mobile health intervention provides an interesting approach for youth with obesity who have limited access to health care. Biofeedback reduces acute stress and could be an innovative adjunct to usual care.
Type:journal article
Journal:BMC Pediatrics
Volume:21
Issue:308
URL:https://doi.org/10.1186/s12887-021-02781-2
DOI:10.1186/s12887-021-02781-2
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110225
Scopus© Citations 49
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Lena: a Voice-Based Conversational Agent for Remote Patient Monitoring in Chronic Obstructive Pulmonary Disease
(CEUR Workshop Proceedings (CEUR-WS.org), 2021-07-14)
Cleres, David
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Rassouli, Frank
;
Brutsche, Martin
;
Kowatsch, Tobias
;
Barata, Filipe
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. To manage the increasing number of COPD patients and reduce the social and economic burden of treatment, healthcare providers have sought to implement remote patient monitoring (RPM). Screen-based RPM applications, such as filling self-reports on the smartphone or computer, have been shown to increase the quality of life, reduce the frequency and severity of exacerbations, and increase physical activity in patients with COPD. These applications, however, are not without challenges for the elderly target population. They are often used on devices designed by and for a different age group, which makes filling out self-reports prone to error and induces fears of technology malfunctions. Voice-based conversational agents (VCAs) are available on more than 2.5 billion devices and are increasingly present in homes worldwide. Aside from their commercial success, VCAs are also credited with several functionalities, such as hands-free use, that make their adoption in healthcare attractive, especially for the elderly. In this work, we investigate the potential of VCAs for RPM of COPD. Specifically, we designed and evaluated Lena, a single-board computer-based VCA framed as a digital member of the medical team. Lena acts as RPM for the early prediction of COPD exacerbations by asking ten symptom-related questions to determine the patient’s daily health status. This paper presents the patients’ feedback after their interaction with Lena. Patients evaluated the acceptability of the system. Notably, all patients could imagine using the system once a day in the context of a larger study and wished to integrate Lena into their daily routine.
Type:conference paper
Journal:1st Workshop on Healthy Interfaces (HEALTHI), collocated with the 26th ACM Annual Conference on Intelligent User Interfaces (IUI) - Where HCI meets AI, Virtually Hosted by Texas A&M University, April 13-17, 2021, College Station, USA
Volume:2903
URL:http://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-4.pdf
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110217
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Face(book)ing the truth: initial lessons learned using Facebook advertisements for the chatbot-delivered Elena+ Care for COVID-19 intervention
(SCITEPRESS – Science and Technology Publications, 2021-02-11)
Ollier, Joseph
;
Santhanam, Prabhakaran
;
Kowatsch, Tobias
;
Pesquita, Cátia
;
Fred, Ana
Utilizing social media platforms to recruit participants for digital health interventions is becoming increasingly popular due to its ability to directly track advertising spend, number of app downloads and other metrics transparently. The following paper concerns the initial tests completed on the Facebook Ad Manager platform for the chatbot-delivered digital health intervention Elena+ Care for COVID-19. Eleven advertisements were run in the UK and Ireland during August/September 2020, with resulting downloads, post (i.e. advert) reactions, post shares and other advertisement engagement metrics tracked. Key findings from our advertising campaigns highlight that: (i) static images with text function better than carousel of images, (ii) Android users download and exhibit greater engagement behaviors than iOS users, and (iii) middle-aged and older women have the highest number of downloads and the most engaged behaviors (i.e. reacting to posts, sharing posts etc.). Lessons learned are discussed considering how other designers of digital health interventions may benefit and learn from our results when trialing and running their own ad campaigns. It is hoped that such discussions will be beneficial to other health practitioners seeking to scale-up their digital health interventions widely and reach individuals in need.
Type:book section
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110655

Tobias Kowatsch

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