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Artikel ; Online: Reliability of Commercial Voice Assistants' Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information.

Bérubé, Caterina / Kovacs, Zsolt Ferenc / Fleisch, Elgar / Kowatsch, Tobias

2021 Band 23, Heft 12, Seite(n) e32161

Abstract: Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients' access to health-related information through efficient and ... ...

Abstract	Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients' access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients' ability to make health-related decisions and manage their chronic conditions. Objective: This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD. Methods: We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer's disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3×3×6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated). Results: Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM. Conclusions: Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases.
Mesh-Begriff(e)	Humans ; Reproducibility of Results ; Self-Management ; Voice
Sprache	Englisch
Erscheinungsdatum	2021-12-20
Erscheinungsland	Canada
Dokumenttyp	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/32161
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial.

Bérubé, Caterina / Lehmann, Vera Franziska / Maritsch, Martin / Kraus, Mathias / Feuerriegel, Stefan / Wortmann, Felix / Züger, Thomas / Stettler, Christoph / Fleisch, Elgar / Kocaballi, A Baki / Kowatsch, Tobias

JMIR human factors

2024 Band 11, Seite(n) e42823

Abstract: Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous ... ...

Abstract	Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology-a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective: This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods: We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants' self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants' verbal feedback. Results: Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions: This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.
Mesh-Begriff(e)	Humans ; Blood Glucose ; Blood Glucose Self-Monitoring ; Diabetes Mellitus, Type 1/complications ; Feasibility Studies ; Hypoglycemia/diagnosis ; Perception
Chemische Substanzen	Blood Glucose
Sprache	Englisch
Erscheinungsdatum	2024-01-09
Erscheinungsland	Canada
Dokumenttyp	Journal Article
ISSN	2292-9495
ISSN (online)	2292-9495
DOI	10.2196/42823
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving.

Bérubé, Caterina / Maritsch, Martin / Lehmann, Vera Franziska / Kraus, Mathias / Feuerriegel, Stefan / Züger, Thomas / Wortmann, Felix / Stettler, Christoph / Fleisch, Elgar / Kocaballi, A Baki / Kowatsch, Tobias

JMIR human factors

2024 Band 11, Seite(n) e46967

Abstract: Background: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient ... ...

Abstract	Background: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. Objective: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. Methods: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. Results: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F Conclusions: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. Trial registration: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.
Mesh-Begriff(e)	Humans ; Arousal ; Automobiles ; Blood Glucose ; Diabetes Mellitus, Type 1 ; Hypoglycemia
Chemische Substanzen	Blood Glucose
Sprache	Englisch
Erscheinungsdatum	2024-04-18
Erscheinungsland	Canada
Dokumenttyp	Clinical Trial ; Journal Article
ISSN	2292-9495
ISSN (online)	2292-9495
DOI	10.2196/46967
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Smartwatches for non-invasive hypoglycaemia detection during cognitive and psychomotor stress.

Maritsch, Martin / Föll, Simon / Lehmann, Vera / Styger, Naïma / Bérubé, Caterina / Kraus, Mathias / Feuerriegel, Stefan / Kowatsch, Tobias / Züger, Thomas / Fleisch, Elgar / Wortmann, Felix / Stettler, Christoph

Diabetes, obesity & metabolism

2023 Band 26, Heft 3, Seite(n) 1133–1136

Mesh-Begriff(e)	Humans ; Hypoglycemia/diagnosis ; Blood Glucose ; Cognition ; Diabetes Mellitus, Type 1
Chemische Substanzen	Blood Glucose
Sprache	Englisch
Erscheinungsdatum	2023-12-12
Erscheinungsland	England
Dokumenttyp	Letter
ZDB-ID	1454944-x
ISSN	1463-1326 ; 1462-8902
ISSN (online)	1463-1326
ISSN	1462-8902
DOI	10.1111/dom.15402
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

Lehmann, Vera / Zueger, Thomas / Maritsch, Martin / Kraus, Mathias / Albrecht, Caroline / Bérubé, Caterina / Feuerriegel, Stefan / Wortmann, Felix / Kowatsch, Tobias / Styger, Naïma / Lagger, Sophie / Laimer, Markus / Fleisch, Elgar / Stettler, Christoph

Diabetes, obesity & metabolism

2023 Band 25, Heft 6, Seite(n) 1668–1676

Abstract: Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data.: Materials and methods: We first developed and tested our ML ...

Abstract	Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. Materials and methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Conclusions: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.
Mesh-Begriff(e)	Humans ; Hypoglycemia/chemically induced ; Hypoglycemia/diagnosis ; Diabetes Mellitus, Type 1/complications ; Diabetes Mellitus, Type 1/diagnosis ; Blood Glucose ; Insulin/adverse effects
Chemische Substanzen	Blood Glucose ; Insulin
Sprache	Englisch
Erscheinungsdatum	2023-03-06
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1454944-x
ISSN	1463-1326 ; 1462-8902
ISSN (online)	1463-1326
ISSN	1462-8902
DOI	10.1111/dom.15021
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review.

Bérubé, Caterina / Schachner, Theresa / Keller, Roman / Fleisch, Elgar / V Wangenheim, Florian / Barata, Filipe / Kowatsch, Tobias

Journal of medical Internet research

2021 Band 23, Heft 3, Seite(n) e25933

Abstract: Background: Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention ... ...

Abstract	Background: Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. Objective: This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. Methods: We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. Results: Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants' previous experience with technology. Finally, risk bias varied markedly. Conclusions: The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.
Mesh-Begriff(e)	Asthma ; Communication ; Health Behavior ; Humans ; Mental Health ; Smartphone
Sprache	Englisch
Erscheinungsdatum	2021-03-29
Erscheinungsland	Canada
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't ; Review ; Systematic Review
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/25933
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation

Feit, Anna Maria / Vordemann, Lukas / Park, Seonwook / Bérubé, Caterina / Hilliges, Otmar

2020

Abstract: This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task because gaze behavior varies greatly across individual users and tasks and ... ...

Abstract	This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task because gaze behavior varies greatly across individual users and tasks and groundtruth data is difficult to obtain. Thus, prior work has mostly focused on simpler target-search tasks or on establishing general interest, where gaze behavior is less complex. From the literature, we identify six metrics that capture different aspects of the gaze behavior during decision-making and combine them in a voting scheme. We empirically show, that this accounts for the large variations in gaze behavior and out-performs standalone metrics. Importantly, it offers an intuitive way to control the amount of detected information, which is crucial for different UI adaptation schemes to succeed. We show the applicability of our approach by developing a room-search application that changes the visual saliency of content detected as relevant. In an empirical study, we show that it detects up to 97% of relevant elements with respect to user self-reporting, which allows us to meaningfully adapt the interface, as confirmed by participants. Our approach is fast, does not need any explicit user input and can be applied independent of task and user. Comment: The first two authors contributed equally to this work
Schlagwörter	Computer Science - Human-Computer Interaction
Thema/Rubrik (Code)	004
Erscheinungsdatum	2020-07-26
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions

Bérubé, Caterina / Schachner, Theresa / Keller, Roman / Fleisch, Elgar / v Wangenheim, Florian / Barata, Filipe / Kowatsch, Tobias

Journal of Medical Internet Research, Vol 23, Iss 3, p e

Systematic Literature Review

2021 Band 25933

Abstract: BackgroundChronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and ... ...

Abstract	BackgroundChronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. ObjectiveThis study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. MethodsWe conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. ResultsOf 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a ...
Schlagwörter	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Thema/Rubrik (Code)	360
Sprache	Englisch
Erscheinungsdatum	2021-03-01T00:00:00Z
Verlag	JMIR Publications
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning

Maritsch, Martin / Bérubé, Caterina / Kraus, Mathias / Lehmann, Vera / Züger, Thomas / Feuerriegel, Stefan / Kowatsch, Tobias / Wortmann, Felix

2019

Abstract: The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and ... ...

Abstract	The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements. Comment: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers
Schlagwörter	Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction ; Statistics - Machine Learning
Thema/Rubrik (Code)	501
Erscheinungsdatum	2019-07-17
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Elena+ Care for COVID-19, a pandemic lifestyle care intervention

Ollier, Joseph / Neff, Simon / Dworschak, Christine / Sejdiji, Arber / Santhanam, Prabhakaran / Keller, Roman / Xiao, Grace / Asisof, Alina / Rügger, Dominik / Bérubé, Caterina / Tomas, Lena Hilfiker / Neff, Joël / Yao, Jiali / Alattas, Aishah / Varela-Mato, Veronica / Pitkethly, Amanda / Vara, Ma Dolores / Herrero, Rocío / Baños, Rosa Ma /

Parada, Carolina / Agatheswaran, Rajashree Sundaram / Villalobos, Victor / Keller, Olivia Clare / Chan, Wai Sze / Mishra, Varun

Frontiers in Public Health

Intervention design and study protocol

2021

Abstract: 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 ...

Titelübersetzung	Elena+ - Versorgung in Zeiten von COVID-19, eine Lebensstilintervention während der Pandemie: Interventionsdesign und Studienprotokoll
Abstract	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.
Schlagwörter	COVID-19 ; Coaching ; Computer Applications ; Computeranwendungen ; Conversational Agents ; Digital Interventions ; Digitale Interventionen ; Gesundheitsförderung ; Gesundheitsinformation ; Gesundheitsverhalten ; Health Behavior ; Health Information ; Health Promotion ; Konversationale Agenten ; Lebensstil ; Lifestyle ; Mental Health ; Pandemics ; Pandemie ; Program Evaluation ; Programmevaluation ; Psychische Gesundheit
Sprache	Englisch
Dokumenttyp	Artikel
ZDB-ID	2711781-9
ISSN	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2021.625640
Datenquelle	PSYNDEX

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