kBot: Knowledge-Enabled Personalized Chatbot for Asthma Self-Management
Open accessAbstract
There is a well-recognized need for a shift to proactive asthma care given the impact asthma has on overall healthcare costs. The demand for continuous monitoring of patient's adherence to the medication care plan, assessment of environmental triggers, and management of asthma can be challenging in traditional clinical settings and taxing on clinical professionals. Recent years have seen a robust growth of general purpose conversational systems. However, they lack the capabilities to support applications such an individual's health, which requires the ability to contextualize, learn interactively, and provide the proper hyper-personalization needed to hold meaningful conversations. In this paper, we present kBot, a knowledge-enabled personalized chatbot system designed for health applications and adapted to help pediatric asthmatic patients (age 8 to 15) to better control their asthma. Its core functionalities include continuous monitoring of the patient's medication adherence and tracking of relevant health signals and environment data. kBot takes the form of an Android application with a frontend chat interface capable of conversing in both text and voice, and a backend cloud-based server application that handles data collection, processing, and dialogue management. It achieves contextualization by piecing together domain knowledge from online sources and inputs from our clinical partners. The personalization aspect is derived from patient answering questionnaires and day-to-day conversations. kBOT's preliminary evaluation focused on chatbot quality, technology acceptance, and system usability involved eight asthma clinicians and eight researchers. For both groups, kBot achieved an overall technology acceptance value of greater than 8 on the 11-point Likert scale and a mean System Usability Score (SUS) greater than 80.
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Half of the included studies (n=6) did not report any system measures.
“Half of the studies (n=6) did not report on any system measures [59,60,62,65-67]”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Evaluation Method section lists chatbot quality and system usability among kBot’s evaluation criteria. The Evaluation Result section reports scores for quality metrics and system usability (SUS), contrary to the attribution that this study reported no system measures. The paper does not address the citing review’s count of six studies. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThree studies reported technology acceptance through a System Usability Survey (SUS).
“A total of 3 studies [60,66,67] reported technology acceptance through a System Usability Survey (SUS).”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Evaluation Method identifies technology acceptance and system usability as separate criteria. The Evaluation Result reports technology acceptance from the first questionnaire and SUS scores from a second questionnaire; this paper did not measure technology acceptance through SUS. The cited paper also cannot establish the citing sentence’s total of three studies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTwo included studies used validated questionnaires only, 2 used adapted questionnaires only, and 1 used both validated and adapted questionnaires.
“Although 2 studies [60,69] used validated questionnaires only and 2 [62,67] used adapted questionnaires only, 1 study used both validated and adapted questionnaires [66].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Evaluation Method describes assessment criteria designed in consultation with a cognitive psychologist, and the Evaluation Result reports a first questionnaire and a second questionnaire identified as SUS (page numbers unavailable). This supports the attribution that kBot used two questionnaire sets, but our copy does not say that one was adapted or explicitly establish the validated-versus-adapted classification. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe majority of included studies (n=7) did not describe the user interface of their VCAs, whereas the remaining 5 papers did.
“The majority of the included studies (n=7) did not describe the user interface of their VCAs [60-62,64,68,70], whereas the remaining 5 papers did [59,65-67,69].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Methods > System Architecture section describes kBot’s user interface, including its Android chatroom, text and voice interaction, and rich-media content. That supports classifying this paper as one that describes its VCA interface. This paper does not establish how many other included studies described their interfaces, or the claimed seven-versus-five split. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSmartphones were the most used device to test the VCA (n=5), followed by smart speakers (n=3) and tablets (n=2).
“When considering the devices used to test the VCA, we found that smartphones were the most used (n=5) [61,65-67,69], followed by smart speakers (n=3) [62,64,68] and tablets (n=2) [59,60].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The citation is attached to the smartphone portion of the sentence. The Methods sections describe kBot as an Android client application and say evaluators tested it, but our copy does not identify the device they used as a smartphone rather than another Android device. The counts and other device categories are the citing authors’ synthesis, not findings attributed to this paper alone. (LLM, conf=0.76)
Experimental — accuracy not yet measured
The vast majority of VCAs (n=10) were not commercially available at the time of the systematic literature review.
“The vast majority of the VCAs (n=10) were not commercially available [59,60,62-69] at the time of this systematic literature review.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Discussion and Conclusion (page not provided) describes kBot as a prototype and says a pilot study is the next step. The Evaluation Method likewise reports only a preliminary assessment. These passages support treating kBot as a developmental VCA, but the paper does not state whether it was commercially available at the time of the review. The count of ten is the citing review’s aggregate, not a finding this individual paper can establish. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOnly 3 of the 7 studies that included a measure of technology acceptance through a questionnaire used a validated questionnaire, whereas the others adapted them.
“In particular, only 3 [60,66,69] of the 7 studies that included a measure of technology acceptance through a questionnaire [59,60,62,65-67,69] used a validated questionnaire, whereas the others adapted them.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The Evaluation Method and Result sections show that kBot measured technology acceptance through a questionnaire. They do not say whether that questionnaire was validated or adapted, and this paper cannot establish the citing document’s count of three validated questionnaires among seven studies. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTen of the selected papers developed and evaluated a VCA, whereas 2 aimed to report a criterion-based performance evaluation of existing commercial conversational agents.
“The majority (n=10) of the selected papers developed and evaluated VCA [59,60,62-69], whereas 2 [61,70] aimed to report a criterion-based performance evaluation of existing commercial conversational agents (eg, Google Assistant and Apple Siri).”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Among papers developing and evaluating a VCA, 6 assessed technology acceptance, 3 assessed system accuracy, and 1 assessed both.
“Among the papers developing and evaluating a VCA, 6 [59,60,62,65-67] assessed the technology acceptance of the VCA, whereas 3 [63,64,68] assessed the system accuracy. Only one [69] assessed both performance and acceptance.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Of the 12 studies, 7 reported technology acceptance findings, whereas 5 did not.
“Of the 12 studies, 7 [59,60,62,65-67,69] reported technology acceptance findings, whereas the others (n=5) did not [61,63,64,68,70].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Four papers did not explicitly specify the study design they used, whereas the other papers provided labels.
“A total of 4 papers [61,66,68,70] did not explicitly specify the study design they used, whereas the other papers provided labels.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The majority of the studies (n=10) did not report any behavioral measures, whereas 2 papers did.
“The majority of the studies (n=10) did not report any behavioral measures [59-63,65-67,69,70], whereas 2 papers [64,68] did.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The majority of included studies (n=10) did not provide measures of attitude toward the target health behavior.
“The majority of the included studies (n=10) did not provide measures of attitude toward the target health behavior [61-70].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Experimental — accuracy not yet measuredThe majority of included studies (n=8) did not report controlling for participants' previous experience with technology.
“The majority of the included studies (n=8) also did not report controlling for participants’ previous experience with technology [59-63,66,69,70].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Of the 12 included studies, 7 reported technology acceptance measures, whereas the remaining 5 did not.
“Of the 12 studies, 7 [59,60,62,65-67,69] reported technology acceptance measures, whereas the remaining studies [61,63,64,68,70] did not.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Other discussed conditions were hearing impairment [68], asthma [66], and Parkinson disease [65].
“Other discussed conditions were hearing impairment [68], asthma [66], and Parkinson disease [65].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Eight included studies named their VCA, whereas 2 did not specify any name.
“A total of 8 studies [60,62,63,65-69] named their VCA, whereas 2 studies [59,64] did not specify any name (Multimedia Appendix 5).”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The underlying architecture of the investigated VCAs was described in 7 included studies, whereas 3 papers did not provide this information.
“The underlying architecture of the investigated VCAs was described in 7 of the included studies [62,63,66-70], whereas 3 papers did not provide this information [61,64,65].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The other included studies targeted hearing-impaired individuals [68], smokers [61], patients with asthma [66], patients with glaucoma and diabetes [69], people with intellectual disability [64], and patients with chronic heart failure [63].
“The others targeted hearing-impaired individuals [68], smokers [61], patients with asthma [66], patients with glaucoma and diabetes [69], people with intellectual disability [64], and patients with chronic heart failure [63].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Three included studies provided a lookup of health information only, whereas 2 also provided targeted alerts and reminders.
“A total of 3 studies provided such a lookup of health information only [61,62,66], whereas 2 [62,66] also provided targeted alerts and reminders.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Monitoring activities were mainly implemented as active data capture and documentation (n=5), whereas 1 study also focused on self-monitoring of health or diagnostic data.
“Monitoring activities were mainly implemented as active data capture and documentation (n=5) [62,63,66-69], whereas 1 study [66] also focused on self-monitoring of health or diagnostic data.”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
Five VCAs exclusively focused on support, 3 exclusively focused on monitoring, and 4 investigated a VCA providing both monitoring and support.
“A total of 5 VCAs [59-61,64,70] exclusively focused on support, and 3 studies [63,65,68] exclusively focused on monitoring. In total, 4 studies investigated a VCA providing both monitoring and support [62,66,67,69].”
From Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review (2021) · cites this paper as [66]
The usability level (SUS score of 61.6) is comparable with the 2 psychological therapy chatbots with SUS scores of 63.6 and 57.0 but lower than that of asthma management (SUS score of about 83) and depression prevention chatbots (SUS score of approximately 75.7).
“This level of usability (SUS scores of 61.6) is comparable with the 2 psychological therapy chatbots with SUS scores of 63.6 and 57.0 [34] but lower than that of asthma management (SUS score of about 83) [35] and depression prevention chatbots (SUS score of approximately 75.7) [36].”
From Feasibility, Usability, and Effectiveness of a Machine Learning–Based Physical Activity Chatbot: Quasi-Experimental Study (2021) · cites this paper as [35]
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Cites
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“Half of the studies (n=6) did not report on any system measures [59,60,62,65-67], whereas the other half reported either speech recognition performance measures (n=4) [63,64,68,69] or criterion-based evaluation of the goodness of the VCA’s response (n=2) [61,70].”
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Let's Talk It Out: A Chatbot for Effective Study Habit Behavioral Change
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- Authors
- Kadariya, D., Venkataramanan, R., Yip, H. Y., Kalra, M., Thirunarayanan, K., & Sheth, A.
- Affiliations
- Wright State University (US)
- Dayton Children's Hospital (US)
- Venue
- 2019 IEEE International Conference on Smart Computing (SMARTCOMP)
- Publisher
- IEEE
- Published
- 2019-06
- Volume
- 2019
- Pages
- 138-143
- PMC
- PMC7432964
- OpenAlex
- W2966629505
- Cited by
- 102
- References
- 10
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 13 · 2023: 27 · 2022: 18 · 2021: 12 · 2020: 13 · 2019: 1
- FWCI
- 72.56
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE International Conference on Smart Computing (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Journal of Bioresource Management (submittedVersion, cc-by)
- Scholar Commons (University of South Carolina) (submittedVersion)
- CORE Scholar (Wright State University) (submittedVersion)
- Funders
- Mayo Clinic
- National Institute of Mental Health (R01MH105384)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD087132)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- asthma self-management, personalized chatbots, childhood asthma, medication adherence monitoring, environmental triggers, hyper-personalization, contextualization, technology acceptance, system usability, healthcare chatbots
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