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. 2019 May 10;4(2):e11017.
doi: 10.2196/11017.

Enhanced Self-Efficacy and Behavioral Changes Among Patients With Diabetes: Cloud-Based Mobile Health Platform and Mobile App Service

Dyna Yp Chao #  1 , Tom My Lin #  2 , Wen-Ya Ma  3
Affiliations

Enhanced Self-Efficacy and Behavioral Changes Among Patients With Diabetes: Cloud-Based Mobile Health Platform and Mobile App Service

Dyna Yp Chao et al. JMIR Diabetes. .

Abstract

Background: The prevalence of chronic disease is increasing rapidly. Health promotion models have shifted toward patient-centered care and self-efficacy. Devices and mobile app in the Internet of Things (IoT) have become critical self-management tools for collecting and analyzing personal data to improve individual health outcomes. However, the precise effects of Web-based interventions on self-efficacy and the related motivation factors behind individuals' behavioral changes have not been determined.

Objective: The objective of this study was to gain insight into patients' self-efficacy with newly diagnosed diabetes (type 2 diabetes mellitus) and analyze the association of patient-centered health promotion behavior and to examine the implications of the results for IoT and mobile health mobile app features.

Methods: The study used data from the electronic health database (n=3128). An experimental design (n=121) and randomized controlled trials were employed to determine patient preferences in the health promotion program (n=62) and mobile self-management education (n=28). The transtheoretical model was used as a framework for observing self-management behavior for the improvement of individual health, and the theory of planned behavior was used to evaluate personal goals, execution, outcome, and personal preferences. A mobile app was used to determine individualized health promotion interventions and to apply these interventions to improve patients' self-management and self-efficacy.

Results: Mobile questionnaires were administered for pre- and postintervention assessment through mobile app. A dynamic questionnaire allocation method was used to follow up and monitor patient behavioral changes in the subsequent 6 to 18 months. Participants at a high risk of problems related to blood pressure (systolic blood pressure ≥120 mm Hg) and body mass index (≥23 kg/m2) indicated high motivation to change and to achieve high scores in the self-care knowledge assessment (n=49, 95% CI -0.26% to -0.24%, P=.052). The associated clinical outcomes in the case group with the mobile-based intervention were slightly better than in the control group (glycated hemoglobin mean -1.25%, 95% CI 6.36 to 7.47, P=.002). In addition, 86% (42/49) of the participants improved their health knowledge through the mobile-based app and information and communications technology. The behavior-change compliance rate was higher among the women than among the men. In addition, the personal characteristics of steadiness and dominance corresponded with a higher compliance rate in the dietary and wellness intervention (83%, 81/98). Most participants (71%, 70/98) also increased their attention to healthy eating, being active, and monitoring their condition (30% 21/70, 21% 15/70, and 20% 14/70, respectively).

Conclusions: The overall compliance rate was discovered to be higher after the mobile app-based health intervention. Various intervention strategies based on patient characteristics, health care-related word-of-mouth communication, and social media may be used to increase self-efficacy and improve clinical outcomes. Additional research should be conducted to determine the most influential factors and the most effective adherence management techniques.

Keywords: health literacy; intervention; patient engagement; self-management; type 2 diabetes mellitus; word-of-mouth.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
360-Degree health management dashboard displaying three-dimensional triaxial data analysis of IPMF. x: disease, y: level, and z: patient impact factor. API: application programming interface; BOI: body of information; hi: health inventor; IoT: Internet of Things.
Figure 2
Figure 2
Study enrollment criteria and selection flow. BMI: body mass index; BW: body weight; HbA1c: glycated hemoglobin; KM: knowledge management; TTM: transtheoretical model.
Figure 3
Figure 3
Research design (initial stage, follow-up, and closure). A1C: HbA1c (glycated hemoglobin); AADE: American Association of Diabetes Educators; BG: blood glucose; BMI: body mass index; HDL: high-density lipoprotein; IoT: Internet of Things; LDL: low-density lipoprotein.
Figure 4
Figure 4
Patient-centered application framework and features. T2D: type 2 DM; TTM: transtheoretical model; WOM: word-of-mouth NWOM: negative word-of-mouth; PWOM: positive word-of-mouth; S-Concept: self-concept theory; SCT: social cognitive theory; SLT: social learning theory; TPB: teory of planned behavior.
Figure 5
Figure 5
Behavior maturity management (transtheoretical model): pre- and postassessment. IoT: Internet of Things.
Figure 6
Figure 6
Interactive patient pre- and postassessment.
Figure 7
Figure 7
The American Association of Diabetes Educators diabetes self-care behavior quiz.
Figure 8
Figure 8
Interactive physician and patient engagement dashboard.
Figure 9
Figure 9
The participant engagement frequency by Diabetes self-management of 7 categories. Interactive response (n=305).
Figure 10
Figure 10
Compliance of improved patients among those using the intervention. Dietary response (n=98).
Figure 11
Figure 11
Intervention and self-behavior change (by transtheoretical model view).
See this image and copyright information in PMC

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