Cited by 102Mobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

kBot: Knowledge-Enabled Personalized Chatbot for Asthma Self-Management

Open access
Kadariya, Dipesh, Venkataramanan, Revathy, Yip, Hong Yung, Kalra, Maninder, Thirunarayanan, Krishnaprasad, Sheth, Amit·2019·2019 IEEE International Conference on Smart Computing (SMARTCOMP)

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/smartcomp.2019.00043

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 measured
  • Three 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 measured
  • Two 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 measured
  • The 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 measured
  • Smartphones 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

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [10]Whittamore A, “ Technology can revolutionise supported self-management of asthma,” Primary Care Respiratory Society UK, vol. 4, 2017.

Cited by

Sorted by citations

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
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review