Mobile applications for people with diabetes published between 2010 and 2015

Issom, David-Zacharie; Woldaregay, Ashenafi Zebene; Chomutare, Taridzo; Bradway, Meghan; Årsand, Eirik; Hartvigsen, Gunnar

doi:10.2217/dmt.15.40

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…In the next step, we tried to provide a definition for each feature in order to determine the area of diabetes care that they address according to CCM components. Some features were well-defined and we extracted their definitions from the literature 7 24–29. However, the definition of some others was vague, and we tried to provide acceptable definitions based on reviewed apps.…”

Section: Methodsmentioning

confidence: 99%

Conformity of Diabetes Mobile apps with the Chronic Care Model

Salari

Kalhori

Ghazisaeidi

et al. 2019

BMJ Health Care Inform

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BackgroundDespite the growing use of mobile applications (apps) for chronic disease management, the evidence on the effectiveness of this technology on clinical and behavioural outcomes of the patients is scant. Many studies highlight the importance of the theoretical foundations of mobile-based interventions. One of the most widely accepted models for the management of chronic diseases, such as diabetes, is the Chronic Care Model (CCM). In this study, we investigated the conformity of the selected diabetes mobile apps with CCM.MethodWe searched online journal databases related to diabetes mobile apps to find common features. Then considering the components of the CCM as a reference model, features of some popular and top-ranking apps were compared with CCM.ResultsAmong 23 studied apps, 34 per cent of them had medium conformity and 66 per cent of these apps were in weak conformity. The self-management support component is covered by 100 per cent of them. Ninety-five per cent of apps have covered the proactive follow-up component.ConclusionsApp conformance with CCM is generally weak. App developers are recommended to give greater consideration to established theoretical models in their design and implementation.

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Section: Methodsmentioning

confidence: 99%

Conformity of Diabetes Mobile apps with the Chronic Care Model

Salari

Kalhori

Ghazisaeidi

et al. 2019

BMJ Health Care Inform

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“…Digital applications enable healthcare professionals, researchers and even peers, to be in contact with subjects more frequently and for longer periods of time. This leads to more representative data of a person's long-term diet coupled with an increased level of engagement, social support and feedback, allowing the delivery of more successful interventions [59,67,68]. By being more integrated in the lives of consumers, healthcare apps can assist with the translation of health education into application.…”

Section: Benefits Of Digital Healthcare Solutionsmentioning

confidence: 99%

“…Apps can provide nutrition education and allow for the setting of dietary goals. Many of these apps have been well received by consumers and facilitate improvements to the predictors of behavior change such as knowledge, self-efficacy and motivation [37,60,[67][68][69][70][71][72]. Being able to be in contact with a healthcare professional also provides a layer of support whenever required, and this has been shown to improve engagement and satisfaction with the lifestyle change [38,58,60,62,63].…”

Section: Benefits Of Digital Healthcare Solutionsmentioning

confidence: 99%

“…The lack of regulation for health and lifestyle apps may be a concern with regards to the quality and efficacy of future apps. Issom et al reviewed diabetes-focused mobile apps between 2010 and 2015, and found that only 8% of 53 publicly available apps were Food and Drug Administration (FDA)-approved; 4% were approved under the Health Insurance Portability and Accountability Act (HIPAA); and 6% obtained a CE marking which indicates conformity with health, safety and environmental protection standards [68]. Apps that focus solely on diet and diet recommendations are not considered mobile medical applications by the FDA and thus do not need to abide by their quality-control guidelines [77].…”

Section: Limitations Of Digital Healthcare Solutionsmentioning

confidence: 99%

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Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment

et al. 2020

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Obesity is a global health problem with wide-reaching economic and social implications. Nutrition surveillance systems are essential to understanding and addressing poor dietary practices. However, diets are incredibly diverse across populations and an accurate diagnosis of individualized nutritional issues is challenging. Current tools used in dietary assessment are cumbersome for users, and are only able to provide approximations of dietary information. Given the need for technological innovation, this paper reviews various novel digital methods for food volume estimation and explores the potential for adopting such technology in the Southeast Asian context. We discuss the current approaches to dietary assessment, as well as the potential opportunities that digital health can offer to the field. Recent advances in optics, computer vision and deep learning show promise in advancing the field of quantitative dietary assessment. The ease of access to the internet and the availability of smartphones with integrated cameras have expanded the toolsets available, and there is potential for automated food volume estimation to be developed and integrated as part of a digital dietary assessment tool. Such a tool may enable public health institutions to be able to gather an effective nutritional insight and combat the rising rates of obesity in the region.

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“…Regarding DM management, physical activity, healthy eating, medication adherence (e.g., insulin dosing), monitoring (e.g., blood glucose and weight tracking), education, and problem solving are the six essential behaviors for improving DM self-management [10]. Many mobile apps offer options to support these behaviors [11]. In 2009, the number of apps was 60; in 2011, it was 260; and in 2015, there were more than 1100 publicly available apps.…”

Section: Mobile Healthmentioning

confidence: 99%

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

et al. 2019

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The prevalence of diabetes is rising globally. Diabetes patients need continuous monitoring, and to achieve this objective, they have to be engaged in their healthcare management process. Mobile health (MH) is an information and communications technology trend to empower chronically ill patients in a smart environment. Discussing the current state of MH technologies is required in order to address their limitations. Existing review articles have evaluated the MH literature based on applicability and level of adoption by patients and healthcare providers. Most of these reviews asserted that MH apps and research have not reached a stable level yet. To the best of our knowledge, there is no clear description of solutions to these problems. In addition, no one has investigated and analyzed MH in its contextual environment in a detailed way. We conducted a comprehensive survey of MH research on diabetes management articles published between 2011 and September 27, 2017. In this survey, we discuss current challenges in MH, along with research gaps, opportunities, and trends. Our literature review searched three academic databases (ScienceDirect, IEEE Xplore, and SpringerLink). A total of 60 articles were analyzed, with 30% from ScienceDirect, 38% from IEEE Xplore, and 32% from SpringerLink. MH was analyzed in the context of the electronic health record (EHR) ecosystem. We consider dimensions such as clinical decision support systems, EHRs, cloud computing, semantic interoperability, wireless body area networks, and big data analytics. We propose specific metrics to analyze and evaluate MH from each of these dimensions. A comprehensive analysis of the literature from this viewpoint is valuable for both theoretical and developmental progress. This paper provides a critical analysis of challenges that have not been fully met and highlights directions for future research that could improve MH applicability. INDEX TERMS Mobile health, diabetes mellitus, electronic health record, cloud computing, clinical decision support system, big data analytics, wireless body area network, medical informatics.

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Mobile applications for people with diabetes published between 2010 and 2015

Cited by 9 publications

References 34 publications

Conformity of Diabetes Mobile apps with the Chronic Care Model

Conformity of Diabetes Mobile apps with the Chronic Care Model

Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment

Mobile Health Technologies for Diabetes Mellitus: Current State and Future Challenges

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