Convology: an ontology for conversational agents in digital health

Web Semantics ◽  
2021 ◽  
pp. 7-21
Author(s):  
Mauro Dragoni ◽  
Giuseppe Rizzo ◽  
Matteo A. Senese
10.2196/25837 ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. e25837
Author(s):  
Maya Boustani ◽  
Stephanie Lunn ◽  
Ubbo Visser ◽  
Christine Lisetti

Background Digital health agents — embodied conversational agents designed specifically for health interventions — provide a promising alternative or supplement to behavioral health services by reducing barriers to access to care. Objective Our goals were to (1) develop an expressive, speech-enabled digital health agent operating in a 3-dimensional virtual environment to deliver a brief behavioral health intervention over the internet to reduce alcohol use and to (2) understand its acceptability, feasibility, and utility with its end users. Methods We developed an expressive, speech-enabled digital health agent with facial expressions and body gestures operating in a 3-dimensional virtual office and able to deliver a brief behavioral health intervention over the internet to reduce alcohol use. We then asked 51 alcohol users to report on the digital health agent acceptability, feasibility, and utility. Results The developed digital health agent uses speech recognition and a model of empathetic verbal and nonverbal behaviors to engage the user, and its performance enabled it to successfully deliver a brief behavioral health intervention over the internet to reduce alcohol use. Descriptive statistics indicated that participants had overwhelmingly positive experiences with the digital health agent, including engagement with the technology, acceptance, perceived utility, and intent to use the technology. Illustrative qualitative quotes provided further insight about the potential reach and impact of digital health agents in behavioral health care. Conclusions Web-delivered interventions delivered by expressive, speech-enabled digital health agents may provide an exciting complement or alternative to traditional one-on-one treatment. They may be especially helpful for hard-to-reach communities with behavioral workforce shortages.


2019 ◽  
Author(s):  
Jessica Chen ◽  
David Lyell ◽  
Liliana Laranjo ◽  
Farah Magrabi

BACKGROUND Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored. OBJECTIVE The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall. METHODS Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured. RESULTS Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (<i>Z</i>=–4.08, <i>P</i>&lt;.001) and simple tasks (<i>Z</i>=–2.24, <i>P</i>=.03). Complex tasks took significantly longer to complete (<i>Z</i>=–2.52, <i>P</i>=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (<i>Z</i>=–3.30, <i>P</i>=.001). However, there was no effect on errors. CONCLUSIONS Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.


2022 ◽  
Vol 9 ◽  
Author(s):  
Joseph Ollier ◽  
Marcia Nißen ◽  
Florian von Wangenheim

Background: Conversational agents (CAs) are a novel approach to delivering digital health interventions. In human interactions, terms of address often change depending on the context or relationship between interlocutors. In many languages, this encompasses T/V distinction—formal and informal forms of the second-person pronoun “You”—that conveys different levels of familiarity. Yet, few research articles have examined whether CAs' use of T/V distinction across language contexts affects users' evaluations of digital health applications.Methods: In an online experiment (N = 284), we manipulated a public health CA prototype to use either informal or formal T/V distinction forms in French (“tu” vs. “vous”) and German (“du” vs. “Sie”) language settings. A MANCOVA and post-hoc tests were performed to examine the effects of the independent variables (i.e., T/V distinction and Language) and the moderating role of users' demographic profile (i.e., Age and Gender) on eleven user evaluation variables. These were related to four themes: (i) Sociability, (ii) CA-User Collaboration, (iii) Service Evaluation, and (iv) Behavioral Intentions.Results: Results showed a four-way interaction between T/V Distinction, Language, Age, and Gender, influencing user evaluations across all outcome themes. For French speakers, when the informal “T form” (“Tu”) was used, higher user evaluation scores were generated for younger women and older men (e.g., the CA felt more humanlike or individuals were more likely to recommend the CA), whereas when the formal “V form” (“Vous”) was used, higher user evaluation scores were generated for younger men and older women. For German speakers, when the informal T form (“Du”) was used, younger users' evaluations were comparable regardless of Gender, however, as individuals' Age increased, the use of “Du” resulted in lower user evaluation scores, with this effect more pronounced in men. When using the formal V form (“Sie”), user evaluation scores were relatively stable, regardless of Gender, and only increasing slightly with Age.Conclusions: Results highlight how user CA evaluations vary based on the T/V distinction used and language setting, however, that even within a culturally homogenous language group, evaluations vary based on user demographics, thus highlighting the importance of personalizing CA language.


2021 ◽  
Author(s):  
Robyn Whittaker ◽  
Rosie Dobson ◽  
Katie Garner

BACKGROUND Despite significant progress in reducing tobacco use over the past two decades, tobacco still kills over 8 million people every year. Digital interventions such as text messaging have been found to help people quit smoking. Chatbots, or conversational agents, are newer digital tools that mimic instantaneous human conversation and therefore could extend the effectiveness of text messaging. OBJECTIVE This scoping review aims to assess the extent of research in the chatbot literature for smoking cessation and provide recommendations for future research in this area. METHODS Relevant studies were identified through searches conducted in MEDLINE, APA PsycINFO, Google Scholar and Scopus as well as an additional search on JMIR, Cochrane Library, Lancet Digital Health, and Digital Medicine. Studies were identified if they were conducted with tobacco smokers, were conducted between 2000 to 2021, were available in English and included a chatbot intervention. RESULTS Of the 323 studies identified, 10 studies were included in the review. Some studies noted an improvement in smoking cessation measures. However, the number of studies was limited and most had methodological or quality concerns. CONCLUSIONS More research is needed to make a firm conclusion of the efficacy of chatbots for smoking cessation. Researchers need to provide a more in-depth description of the chatbot functionality, mode of delivery, and theoretical underpinnings. CLINICALTRIAL na


2021 ◽  
Vol 30 (01) ◽  
pp. 191-199
Author(s):  
Tilman Dingler ◽  
Dominika Kwasnicka ◽  
Jing Wei ◽  
Enying Gong ◽  
Brian Oldenburg

Summary Objectives: To describe the use and promise of conversational agents in digital health—including health promotion andprevention—and how they can be combined with other new technologies to provide healthcare at home. Method: A narrative review of recent advances in technologies underpinning conversational agents and their use and potential for healthcare and improving health outcomes. Results: By responding to written and spoken language, conversational agents present a versatile, natural user interface and have the potential to make their services and applications more widely accessible. Historically, conversational interfaces for health applications have focused mainly on mental health, but with an increase in affordable devices and the modernization of health services, conversational agents are becoming more widely deployed across the health system. We present our work on context-aware voice assistants capable of proactively engaging users and delivering health information and services. The proactive voice agents we deploy, allow us to conduct experience sampling in people's homes and to collect information about the contexts in which users are interacting with them. Conclusion: In this article, we describe the state-of-the-art of these and other enabling technologies for speech and conversation and discuss ongoing research efforts to develop conversational agents that “live” with patients and customize their service offerings around their needs. These agents can function as ‘digital companions’ who will send reminders about medications and appointments, proactively check in to gather self-assessments, and follow up with patients on their treatment plans. Together with an unobtrusive and continuous collection of other health data, conversational agents can provide novel and deeply personalized access to digital health care, and they will continue to become an increasingly important part of the ecosystem for future healthcare delivery.


2020 ◽  
Author(s):  
Maya Boustani ◽  
Stephanie Lunn ◽  
Ubbo Visser ◽  
Christine Lisetti

BACKGROUND Digital health agents — embodied conversational agents designed specifically for health interventions — provide a promising alternative or supplement to behavioral health services by reducing barriers to access to care. OBJECTIVE Our goals were to (1) develop an expressive, speech-enabled digital health agent operating in a 3-dimensional virtual environment to deliver a brief behavioral health intervention over the internet to reduce alcohol use and to (2) understand its acceptability, feasibility, and utility with its end users. METHODS We developed an expressive, speech-enabled digital health agent with facial expressions and body gestures operating in a 3-dimensional virtual office and able to deliver a brief behavioral health intervention over the internet to reduce alcohol use. We then asked 51 alcohol users to report on the digital health agent acceptability, feasibility, and utility. RESULTS The developed digital health agent uses speech recognition and a model of empathetic verbal and nonverbal behaviors to engage the user, and its performance enabled it to successfully deliver a brief behavioral health intervention over the internet to reduce alcohol use. Descriptive statistics indicated that participants had overwhelmingly positive experiences with the digital health agent, including engagement with the technology, acceptance, perceived utility, and intent to use the technology. Illustrative qualitative quotes provided further insight about the potential reach and impact of digital health agents in behavioral health care. CONCLUSIONS Web-delivered interventions delivered by expressive, speech-enabled digital health agents may provide an exciting complement or alternative to traditional one-on-one treatment. They may be especially helpful for hard-to-reach communities with behavioral workforce shortages.


10.2196/14827 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e14827
Author(s):  
Jessica Chen ◽  
David Lyell ◽  
Liliana Laranjo ◽  
Farah Magrabi

Background Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored. Objective The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall. Methods Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured. Results Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=–4.08, P<.001) and simple tasks (Z=–2.24, P=.03). Complex tasks took significantly longer to complete (Z=–2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=–3.30, P=.001). However, there was no effect on errors. Conclusions Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.


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