Mobile health applications for chronic diseases: A systematic review of features for lifestyle improvement

Debon, Raquel; Coleone, Joane Diomara; Bellei, Ericles Andrei; Marchi, Ana Carolina Bertoletti De

doi:10.1016/j.dsx.2019.07.016

Cited by 100 publications

(62 citation statements)

References 31 publications

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“…In the RecSysPA module, the normalization occurs using (6) for the element values of the target profile of the recommendations vE pr , and (7) for the values of profile elements that must be compared vE px , where vE pr is the element value of the target profile of the recommendation, and vE px is the element value of the other profiles. The elements of the target profile always assume 1 (one) for normalized values vE pr , while the elements' values of the other profiles vNE px receive a normalized value based on the value of the target profile.…”

Section: ) Physical Activity Results Index (Pari)mentioning

confidence: 99%

“…However, all features and functionalities of the eHealth platform do not guarantee improvement for the patients involved, since they generally do not know or do not comply with the system's information [6]. In addition to a data collection and storage mechanism, an eHealth system must have a tool that allows the search for healthy habits to contribute to the user's health [7]. This tool can incorporate different features [8], such as a recommender system, where relevant information about lifestyle choices are structured in a profile model that can provide personalized recommendations [9].…”

Section: Introductionmentioning

confidence: 99%

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A Physical Activity Recommender System for Patients With Arterial Hypertension

et al. 2020

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Recommender systems have been applied in several areas, including e-Health systems, which refers to information and health services enhanced through technology. However, most studies aim at imposing rules to improve lifestyle, rather than recommending nutrition and physical activities. In this context, this study aims to develop a system for recommending physical activities for hypertensive patients to create opportunities for the patients so they can search for and create a healthy lifestyle. To achieve this goal, we elaborated on a hypertensive user profile model, called HyperModel2PAR, and a physical activity recommender system for hypertensive patients, called HyperRecSysPA. The model resulting from this study is composed of 32 elements divided into three groups, which were used in the modeling of user profiles within the system for generating HyperRecSysPA recommendations. The developed system was validated by physicians who answered a specific questionnaire. As a result, ∼ 75% of the recommendations generated were approved. Therefore, this study has prospective contributions to the literature, since both models obtained conclusive results in the assessments performed.

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Section: ) Physical Activity Results Index (Pari)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Physical Activity Recommender System for Patients With Arterial Hypertension

et al. 2020

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“…It is our understanding that similar studies have rarely been performed in Chongqing, China. Research conducted in other counties has shown a positive role of mobile technology in accessibility to disease management [16,17], and some systematic reviews proved the potential of apps in improving health outcomes among patients living with chronic diseases [10,18]. With the increasing coverage of smartphones in recent years, apps related to medical care have attracted researchers’ attention in China [19,20], and also provide the possibility for the remote management of patients with chronic diseases, such as hypertension.…”

Section: Discussionmentioning

confidence: 99%

Home Blood Pressure Monitoring by a Mobile-Based Model in Chongqing, China: A Feasibility Study

Xiao

Lei

Zhang

et al. 2019

IJERPH

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Purpose: Increasing attention is being paid to the role of the intelligent self-management of hypertension under the context of increasing prevalence but limited medical resources. However, heterogeneity in interventions and outcome measures has hindered the interpretation of research evaluating mobile health technologies for hypertension control, and little study of such technology has been performed in China. Objective: This was a feasibility study aimed to understand patient and medical practitioners’ acceptance and experience of a mobile-phone based platform for the management of hypertensive patients. Methods: The model used behavioral incentives for daily blood pressure measurement and physician-facing prioritization of patients based on level of blood-pressure control. Patients were enrolled by purposive sampling. The platform was used for two-week blood pressure monitoring through WeChat, which simulated our future app. Qualitative interviews with patients and providers were conducted in time. Results: Twenty hypertensive patients and two providers were enrolled and used the platform throughout the two weeks. Patients reported daily home blood pressure monitoring to be simple, feasible and increased their health awareness. Specifically, patients self-reported that reminders, the daily frequency and time of monitoring, and positive reinforcement were important for maintaining adherence. Providers reported that they could manage patients more quickly and accurately, but reasonable feedback information was needed to avoid excessive increases in workload. Conclusion: The adoption of mobile-based technology to monitor patient’s blood pressure may provide a practical solution for managing patients in Chongqing, China. Patient health education and enhanced app functionality could improve patient compliance and satisfaction while reducing provider workload.

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“…Aplicativos m-Health (saúde móvel), têm desempenhado um papel central para treinamento, autogerenciamento, monitorização remota e coleta de dados de tratamento de doenças crônicas, como o Diabetes Mellitus (DM) [Debon et al 2019]. No entanto, [Wu et al 2017] argumentam que os aplicativos atuais podem não ser bons o suficiente para suportar o gerenciamento intensivo do DM Tipo 1, uma vez que eventos adversos, como hipoglicemia, estão sendo negligenciados e precisam ser melhor abordados em novos estudos.…”

Section: Caracterização Do Problema E Motivaçãounclassified

“…O certificado de registro de software foi concedido pelo Instituto Nacional da Propriedade Industrial (INPI) no processo BR512018001045-8. As produções secundárias incluem os artigos de [Cechetti et al 2019] e [Debon et al 2019].…”

Section: Autoria Contribuições E Produções Derivadasunclassified

Exploring mobile health applications for self-management of Diabetes Mellitus

Bellei¹

2020

Anais Estendidos Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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Na rotina de tratamento de Diabetes Mellitus (DM), o paciente precisa considerar diversos dados e fatores. Nessa perspectiva, esta dissertação apresenta um aplicativo para acompanhamento da interdependência entre os fatores de tratamento de DM Tipo 1 com uma abordagem de visualização de dados interativa. Inicialmente, foi realizada uma revisão sistemática de literatura para investigar as funcionalidades e as técnicas de fundamentação dos aplicativos existentes. Na sequência, um aplicativo foi prototipado, avaliado preliminarmente com 76 pacientes, e implementado junto de um painel de controle web. Por fim, foi conduzido um experimento piloto com 4 pacientes e uma avaliação de satisfação com 97 pacientes e 9 profissionais de saúde.

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Mobile health applications for chronic diseases: A systematic review of features for lifestyle improvement

Cited by 100 publications

References 31 publications

A Physical Activity Recommender System for Patients With Arterial Hypertension

A Physical Activity Recommender System for Patients With Arterial Hypertension

Home Blood Pressure Monitoring by a Mobile-Based Model in Chongqing, China: A Feasibility Study

Exploring mobile health applications for self-management of Diabetes Mellitus

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