Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study.

Journal: Medicina (Kaunas, Lithuania)
PMID: 38399616

Abstract

: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialists (CDCESs) in tracking the health status of patients with type 2 diabetes (T2D). This study aimed to evaluate the impact of this system on glycemic control and to identify suitable candidates for its use. : After obtaining written informed consent from all participants with T2D, the CDCESs conducted remote interviews with the patients using RoBoHoN. All participants completed a questionnaire immediately after the experiment. HbA1c was assessed at the time of the interview and two months later, and glycemic control status was categorized as either "Adequate" or "Inadequate" based on the target HbA1c levels outlined in the guidelines for adult and elderly patients with type 2 diabetes by the Japan Diabetes Society. Patients who changed their medication regimens within the two months following the interview were excluded from the study. : The clinical characteristics of the 28 eligible patients were as follows: 67.9 ± 14.8 years old, 23 men (69%), body mass index (24.7 ± 4.9 kg/m), and HbA1c levels 7.16 ± 1.11% at interview and two months later. Glycemic control status (GCS) was Adequate (A) to Inadequate (I): 1 case; I to A: 7 cases; A to A good: 14 cases; I to I: 6 cases (-value = 0.02862 by Chi-square test). Multiple regression analyses showed that Q1 (Did RoBoHoN speak clearly?) and Q7 (Was RoBoHoN's response natural?) significantly contributed to GCS, indicating that the naturalness of the responses did not impair the robot-assisted interviews. The results suggest that to improve the system in the future, it is more beneficial to focus on the content of the conversation rather than pursuing superficial naturalness in the responses. : This study demonstrated the efficacy of a robot-assisted diabetes management system that can contribute to improved glycemic control.

Authors

  • Kunimasa Yagi
    Department of Internal Medicine, Kanazawa Medical University Hospital, Ishikawa 920-0293, Japan.
  • Michiko Inagaki
    Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa 920-1192, Japan.
  • Yuya Asada
    Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa 920-1192, Japan.
  • Mako Komatsu
    School of Informatics, Kochi University of Technology, Kochi 780-8515, Japan.
  • Fuka Ogawa
    School of Informatics, Kochi University of Technology, Kochi 780-8515, Japan.
  • Tomomi Horiguchi
    Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa 920-1192, Japan.
  • Naoto Yamaaki
    Isobe Clinic, Ishikawa 290-0511, Japan.
  • Mikifumi Shikida
    School of Informatics, Kochi University of Technology, Kochi 780-8515, Japan.
  • Hideki Origasa
    Data Science and AI Innovation Research Promotion Center, Institute of Statistical Mathematics, Shiga University, Shiga 525-0034, Japan.
  • Shuichi Nishio
    Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka 565-0871, Japan.

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