Development and Evaluation of a Smartphone Application for Managing Gestational Diabetes Mellitus
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Objectives: The purpose of this study was to develop and evaluate an application (app) that provides tailored recommendations based on lifestyle and clinical data entered by the user. Methods: Knowledge and functions required for the gestational diabetes mellitus (GDM) management app were extracted from clinical practice guidelines and evaluated through an online survey. Common and tailored recommendations were developed and evaluated with a content validity index. Algorithms to link tailored recommendations with a patient’s data were developed and evaluated by experts. An Android-based app was developed and evaluated by comparing the process of data entry and recommendation retrieval and the usability of the app. After the app was revised, the user acceptance of the app was evaluated. Results: Six domains of knowledge and 14 functions were extracted. Seven common and 49 tailored recommendations were developed. Nine lifestyle and clinical data elements were modeled. Eight algorithms with 18 decision nodes presenting tailored recommendations based on patient’s data and 12 user interface screens were developed. All recommendations obtained from the use of app concurred with recommendations derived by algorithms. The average usability score was 69.5 out of 100. The user acceptance score with behavioral intention to use was 5.5, intrinsic motivation 4.3, the perceived ease of use score was 4.6, and the perceived usefulness score was 5.0 out of 7, respectively. Conclusions: The GDM management knowledge and tailored recommendations obtained in this study could be of help in managing GDM.
Abstract
Objectives: The purpose of this study was to develop and evaluate an application (app) that provides tailored recommendations based on lifestyle and clinical data entered by the user. Methods: Knowledge and functions required for the gestational diabetes mellitus (GDM) management app were extracted from clinical practice guidelines and evaluated through an online survey. Common and tailored recommendations were developed and evaluated with a content validity index. Algorithms to link tailored recommendations with a patient’s data were developed and evaluated by experts. An Android-based app was developed and evaluated by comparing the process of data entry and recommendation retrieval and the usability of the app. After the app was revised, the user acceptance of the app was evaluated. Results: Six domains of knowledge and 14 functions were extracted. Seven common and 49 tailored recommendations were developed. Nine lifestyle and clinical data elements were modeled. Eight algorithms with 18 decision nodes presenting tailored recommendations based on patient’s data and 12 user interface screens were developed. All recommendations obtained from the use of app concurred with recommendations derived by algorithms. The average usability score was 69.5 out of 100. The user acceptance score with behavioral intention to use was 5.5, intrinsic motivation 4.3, the perceived ease of use score was 4.6, and the perceived usefulness score was 5.0 out of 7, respectively. Conclusions: The GDM management knowledge and tailored recommendations obtained in this study could be of help in managing GDM.
참고문헌 (30)
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