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      Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study

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        , MSc 1 , , PhD 2 , , PhD 1 , , , PhD 2
      (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      mHealth, diabetes, adoption, active utilization, personality traits, app

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          Abstract

          Background

          Mobile technology for health (mHealth) interventions are increasingly being used to help improve self-management among patients with diabetes; however, these interventions have not been adopted by a large number of patients and often have high dropout rates. Patient personality characteristics may play a critical role in app adoption and active utilization, but few studies have focused on addressing this question.

          Objective

          This study aims to address a gap in understanding of the relationship between personality traits and mHealth treatment for patients with diabetes. We tested the role of the five-factor model of personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) in mHealth adoption preference and active utilization.

          Methods

          We developed an mHealth app (DiaSocial) aimed to encourage diabetes self-management. We recruited 98 patients with diabetes—each patient freely chose whether to receive the standard care or the mHealth app intervention. Patient demographic information and patient personality characteristics were assessed at baseline. App usage data were collected to measure user utilization of the app. Patient health outcomes were assessed with lab measures of glycated hemoglobin (HbA 1c level). Logistic regression models and linear regression were employed to explore factors predicting the relationship between mHealth use (adoption and active utilization) and changes in health outcome.

          Results

          Of 98 study participants, 46 (47%) downloaded and used the app. Relatively younger patients with diabetes were 9% more likely to try and use the app ( P=.02, odds ratio [OR] 0.91, 95% CI 0.85-0.98) than older patients with diabetes were. Extraversion was negatively associated with adoption of the mHealth app ( P=.04, OR 0.71, 95% CI 0.51-0.98), and openness to experience was positively associated with adoption of the app ( P=.03, OR 1.73, 95% CI 1.07-2.80). Gender ( P=.43, OR 0.66, 95% CI 0.23-1.88), education (senior: P=.99, OR 1.00, 95% CI 0.32-3.11; higher: P=.21, OR 2.51, 95% CI 0.59-10.66), and baseline HbA 1c level ( P=.36, OR 0.79, 95% CI 0.47-1.31) were not associated with app adoption. Among those who adopted the app, a low education level (senior versus primary P=.003; higher versus primary P=.03) and a high level of openness to experience ( P=.048, OR 2.01, 95% CI 1.01-4.00) were associated with active app utilization. Active users showed a significantly greater decrease in HbA 1c level than other users (ΔHbA 1c=−0.64, P=.05).

          Conclusions

          This is one of the first studies to investigate how different personality traits influence the adoption and active utilization of an mHealth app among patients with diabetes. The research findings suggest that personality is a factor that should be considered when trying to identify patients who would benefit the most from apps for diabetes management.

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          Most cited references31

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          Who interacts on the Web?: The intersection of users’ personality and social media use

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            Standards of medical care for type 2 diabetes in China 2019

            The prevalence of diabetes in China has increased rapidly from 0.67% in 1980 to 10.4% in 2013, with the aging of the population and westernization of lifestyle. Since its foundation in 1991, the Chinese Diabetes Society (CDS) has been dedicated to improving academic exchange and the academic level of diabetes research in China. From 2003 to 2014, four versions of Chinese diabetes care guidelines have been published. The guidelines have played an important role in standardizing clinical practice and improving the status quo of diabetes prevention and control in China. Since September 2016, the CDS has invited experts in cardiovascular diseases, psychiatric diseases, nutrition, and traditional Chinese medicine to work with endocrinologists from the CDS to review the new clinical research evidence related to diabetes over the previous 4 years. Over a year of careful revision, this has resulted in the present, new version of guidelines for prevention and care of type 2 diabetes in China. The main contents include epidemiology of type 2 diabetes in China; diagnosis and classification of diabetes; primary, secondary, and tertiary diabetes prevention; diabetes education and management support; blood glucose monitoring; integrated control targets for type 2 diabetes and treatments for hyperglycaemia; medical nutrition therapy; exercise therapy for type 2 diabetes; smoking cessation; pharmacologic therapy for hyperglycaemia; metabolic surgery for type 2 diabetes; prevention and treatment of cardiovascular and cerebrovascular diseases in patients with type 2 diabetes; hypoglycaemia; chronic diabetic complications; special types of diabetes; metabolic syndrome; and diabetes and traditional Chinese medicine.
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              mHealth 2.0: Experiences, Possibilities, and Perspectives

              With more than 1 billion users having access to mobile broadband Internet and a rapidly growing mobile app market, all stakeholders involved have high hopes that this technology may improve health care. Expectations range from overcoming structural barriers to access in low-income countries to more effective, interactive treatment of chronic conditions. Before medical health practice supported by mobile devices ("mHealth") can scale up, a number of challenges need to be adequately addressed. From a psychological perspective, high attrition rates, digital divide of society, and intellectual capabilities of the users are key issues when implementing such technologies. Furthermore, apps addressing behavior change often lack a comprehensive concept, which is essential for an ongoing impact. From a clinical point of view, there is insufficient evidence to allow scaling up of mHealth interventions. In addition, new concepts are required to assess the efficacy and efficiency of interventions. Regarding technology interoperability, open standards and low-energy wireless protocols appear to be vital for successful implementation. There is an ongoing discussion in how far health care-related apps require a conformity assessment and how to best communicate quality standards to consumers. "Apps Peer-Review" and standard reporting via an "App synopsis" appear to be promising approaches to increase transparency for end users. With respect to development, more emphasis must be placed on context analysis to identify what generic functions of mobile information technology best meet the needs of stakeholders involved. Hence, interdisciplinary alliances and collaborative strategies are vital to achieve sustainable growth for "mHealth 2.0," the next generation mobile technology to support patient care.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                August 2020
                10 August 2020
                : 8
                : 8
                : e17709
                Affiliations
                [1 ] eHealth Research Institute School of Management Harbin Institute of Technology Harbin China
                [2 ] Center for Health Information & Decision Systems, Department of Decision, Operations, and Information Technologies Robert H Smith School of Business University of Maryland College Park, MD United States
                Author notes
                Corresponding Author: Xitong Guo xitongguo@ 123456hit.edu.cn
                Author information
                https://orcid.org/0000-0002-0791-7415
                https://orcid.org/0000-0003-1400-4515
                https://orcid.org/0000-0002-9569-0299
                https://orcid.org/0000-0002-2336-9682
                Article
                v8i8e17709
                10.2196/17709
                7445619
                32773382
                ef28ba4a-30d7-46a3-ba16-dc108238e15d
                ©Jingyuan Su, Michelle Dugas, Xitong Guo, Guodong (Gordon) Gao. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 10.08.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 6 January 2020
                : 10 March 2020
                : 4 May 2020
                : 20 May 2020
                Categories
                Original Paper
                Original Paper

                mhealth,diabetes,adoption,active utilization,personality traits,app

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