Clinical relevance of smartphone apps for diabetes management: A global overview

Huang, Zhilian; Soljak, Michael; Boehm, Bernhard O.; Car, Josip

doi:10.1002/dmrr.2990

Cited by 52 publications

(38 citation statements)

References 21 publications

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“…There are limited information technology (IT) skills and training especially among older users [33]. Health apps are downloaded and used without guidance [34]. Therefore, to raise the level of acceptance among older users, allow for personal contact persons during the initial phase of use [24] and training.…”

Section: Facilitating Conditionsmentioning

confidence: 99%

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Petersen

Jacobs

Pather

2020

Lecture Notes in Computer Science

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The literature illustrates that technology will widen health disparity if its use is restricted to patients who are already motivated and demonstrate good self-management behaviours. Additionally, despite the availability of free mobile health (m-health) applications for diabetes self-management, usage is low. There are also limited studies of m-health acceptance in South Africa. This research is delineated to the Western Cape, South Africa. The populace suffers from increasing numbers of diabetic patients. Segments of the population also suffer from technological forms of exclusion, such as limited internet access. Therefore, the objective of this study was to identify challenges for user acceptance that discourages the use of m-health applications. This study analysed 130 semi-structured interviews, using thematic content analysis. Respondents were predominantly female with type 2 diabetes, older than 50, residing in the Western Cape. It used key constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The results confirmed that all four UTAUT constructs; performance expectancy ("the degree to which an individual believes that using the system will help him or her to attain gains in performance"), effort expectancy ("the degree of ease associated with the use of the system", social influence ("the degree to which an individual perceives that important others believe he or she should use the new system") and facilitating conditions ("the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the system"), explains the challenges for m-health acceptance in low socio-economic areas. Factors such as technology anxiety, resistance to change and a lack of trust in the use of devices for self-management need to be considered when implementing future interventions.

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Section: Facilitating Conditionsmentioning

confidence: 99%

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Petersen

Jacobs

Pather

2020

Lecture Notes in Computer Science

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“…As the number of people with diabetes continues to rise worldwide [ 1 ], the need to increase patients’ self-management skills is crucial to improve clinical outcomes and reduce health-related costs [ 2 , 3 ]. There is growing evidence that apps and digital interventions such as websites (web), social media, and other online services have a positive impact on diabetes self-management [ 4 - 12 ], suggesting that standard self-management could be supplemented by digital interventions to aid and improve patients’ skills [ 4 - 12 ]. While some apps and digital interventions have benefited patients, not all of them seem to be based on research, and some of these digital interventions could even compromise the safety of patients with diabetes [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review

Larbi¹,

Randine²,

Årsand³

et al. 2020

J Med Internet Res

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Background There is growing evidence that apps and digital interventions have a positive impact on diabetes self-management. Standard self-management for patients with diabetes could therefore be supplemented by apps and digital interventions to increase patients’ skills. Several initiatives, models, and frameworks suggest how health apps and digital interventions could be evaluated, but there are few standards for this. And although there are many methods for evaluating apps and digital interventions, a more specific approach might be needed for assessing digital diabetes self-management interventions. Objective This review aims to identify which methods and criteria are used to evaluate apps and digital interventions for diabetes self-management, and to describe how patients were involved in these evaluations. Methods We searched CINAHL, EMBASE, MEDLINE, and Web of Science for articles published from 2015 that referred to the evaluation of apps and digital interventions for diabetes self-management and involved patients in the evaluation. We then conducted a narrative qualitative synthesis of the findings, structured around the included studies’ quality, methods of evaluation, and evaluation criteria. Results Of 1681 articles identified, 31 fulfilled the inclusion criteria. A total of 7 articles were considered of high confidence in the evidence. Apps were the most commonly used platform for diabetes self-management (18/31, 58%), and type 2 diabetes (T2D) was the targeted health condition most studies focused on (12/31, 38%). Questionnaires, interviews, and user-group meetings were the most common methods of evaluation. Furthermore, the most evaluated criteria for apps and digital diabetes self-management interventions were cognitive impact, clinical impact, and usability. Feasibility and security and privacy were not evaluated by studies considered of high confidence in the evidence. Conclusions There were few studies with high confidence in the evidence that involved patients in the evaluation of apps and digital interventions for diabetes self-management. Additional evaluation criteria, such as sustainability and interoperability, should be focused on more in future studies to provide a better understanding of the effects and potential of apps and digital interventions for diabetes self-management.

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“…The SVM algorithm can analyze the data more comprehensively and summarize the law of development scientifically. The key steps are preparing data, searching rules, and revealing rules [26]. In the process of social development, data mining is the key content, which is widely used in the fields of electricity, medicine and agriculture.…”

Section: Establishment Of the Revenue Prediction Model For Svm Algorimentioning

confidence: 99%

Expected Income of New Currency in Blockchain Based on Data-Mining Technology

Xingzhou

Xin²

2020

Electronics

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In order to realize the understanding of expected returns after issuance of blockchain new currency initial coin offerings (ICO) and maximize investment returns, in this study, the Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithm is used to create a customer emotional dictionary of blockchain new currency, and collect users’ online comments based on blockchain currency before ICO. The Support Vector Machine (SVM) algorithm is used to construct an evaluation model, analyze and judge users’ comments, make accurate prediction of the expected return of ICO issuing new currency, improve investment operations, and maximize the return of investment. The results show that the combination of the SO-PMI and SVM algorithms can accurately evaluate the price after the issuance of new currency, and then realize the judgment of expected return and obtain the expected return of investment. It can be seen that the combination of algorithms based on data-mining technology is applied to the study of the expected return of new currency issuance in blockchain, which achieves the goal of revenue anticipation and greatly reduces the investment risk of new currency issuance in blockchain.

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Clinical relevance of smartphone apps for diabetes management: A global overview

Abstract: Our study showed that apps could play an important role in complementing multifaceted diabetes care, but should preferably be regulated, context specific, and more tailored to users' needs with clear guidance for patients and clinicians about the choices.

Cited by 52 publications

References 21 publications

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Barriers for User Acceptance of Mobile Health Applications for Diabetic Patients: Applying the UTAUT Model

Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review

Expected Income of New Currency in Blockchain Based on Data-Mining Technology

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