Identifying data elements and key features of a mobile-based self-care application for patients with COVID-19 in Iran

Mobile Health applications have shown different usages in the COVID-19 pandemic, which consisted of empowering patient’s awareness, promoting patient’s self-care, and self-monitor behaviors. The purpose of this study is to identify key features and capabilities of a mobile-based application for self-care and self-management of people with COVID-19 disease. This study was a descriptive-analytical study that was conducted in two main phases in 2020. In the first phase, a literature review study was performed. In the second phase, using the information obtained from the review of similar articles, a questionnaire was designed to validate identified requirements. Based on the results of the first phase, 53 data elements and technical key features for mobile-based self-care application for people with COVID-19 were identified. According to the statistical population, 11 data elements for demographic requirements, 11 data elements for clinical requirements, 15 data elements for self-care specifications, and 16 features for the technical capability of this app were determined. Most of the items were selected by infectious and internal medicine specialists (94%). This study supports that the use of mobile-based applications can play an important role in the management of this disease. Software design and development could help manage and improve patients’ health status.

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Malaria Geo-Localization with Mobile-Health Applications

SSRN Electronic Journal ◽

10.2139/ssrn.2708159 ◽

2015 ◽

Author(s):

Roberto Moro Visconti ◽

Alberto Larocca ◽

Michele Marconi

Keyword(s):

Mobile Health ◽

Mobile Health Applications ◽

Health Applications

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EFFECTIVNESS OF SPECIFIC MOBILE HEALTH APPLICATIONS (mHEALTH-APPS)in GESTATIONAL DIABTETES MELLITUS: A SYSTEMATIC REVIEW (Preprint)

10.2196/preprints.23016 ◽

2020 ◽

Author(s):

Claudia Eberle ◽

Maxine Löhnert

Keyword(s):

Mobile Health ◽

Cochrane Library ◽

Clinical Parameters ◽

Control Groups ◽

Manual Search ◽

Mobile Health Applications ◽

Short And Long Term ◽

Mhealth Apps ◽

Health Applications

BACKGROUND Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), Metabolic Syndrome (MetS) as well as cardiovascular disease (CD). Against this background mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM clearly. OBJECTIVE Since there is – to our knowledge – no systematic literature review published, which focusses on the effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes of mother and child, we conducted these much-needed analyses. METHODS Data sources: A systematic literature search in Medline (Pubmed), Cochrane Library, Embase, CINAHL and Web of Science was performed including full text publications since 2008 up to date. An additional manual search in references and Google Scholar was conducted subsequently. Study Eligibility Criteria: Women diagnosed with GDM using specific mHealth-Apps during pregnancy compared to control groups, which met main clinical parameters and outcomes in GDM management as well as maternity and offspring care. Study appraisal and synthesis methods: Study quality was assessed and rated “strong”, “moderate” or “weak” by using the Effective Public Health Practice Project (EPHPP) tool. Study results were strongly categorized by outcomes; an additional qualitative summary was assessed. Study selection: Overall, n= 114 studies were analyzed, n= 46 duplicates were removed, n=5 studies met the eligible criteria and n=1 study was assessed by manual search subsequently. In total, n=6 publications, analyzing n=408 GDM patients in the interventional and n=405 women diagnosed with GDM in the control groups, were included. These studies were divided into n=5 two-arm randomized controlled trials (RCT) and n=1 controlled clinical trial (CCT). RESULTS Distinct improvements in clinical parameters and outcomes, such as fasting blood glucoses (FBG), 2-hour postprandial blood glucoses (PBG), off target blood glucose measurements (OTBG), delivery modes and patient compliance were analyzed in GDM patients using specific mHealth-Apps compared to matched control groups. CONCLUSIONS mHealth-Apps clearly improve clinical outcomes in management of GDM effectively. More studies need to be done more in detail.

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Quality benchmarking of smartphone laboratory medicine applications: comparison of laboratory medicine specialists’ and non-laboratory medicine professionals’ evaluation

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2020-0869 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Snežana Jovičić ◽

Joanna Siodmiak ◽

Marta Duque Alcorta ◽

Maximillian Kittel ◽

Wytze Oosterhuis ◽

...

Keyword(s):

Information Quality ◽

Rating Scale ◽

Laboratory Medicine ◽

Test Results ◽

Content Quality ◽

Laboratory Test Results ◽

System Usability Scale ◽

Mobile Health Applications ◽

Fit For Purpose ◽

Health Applications

AbstractObjectivesThere are many mobile health applications (apps) now available and some that use in some way laboratory medicine data. Among them, patient-oriented are of the lowest content quality. The aim of this study was to compare the opinions of non-laboratory medicine professionals (NLMP) with those of laboratory medicine specialists (LMS) and define the benchmarks for quality assessment of laboratory medicine apps.MethodsTwenty-five volunteers from six European countries evaluated 16 selected patient-oriented apps. Participants were 20–60 years old, 44% were females, with different educational degrees, and no professional involvement in laboratory medicine. Each participant completed a questionnaire based on the Mobile Application Rating Scale (MARS) and the System Usability Scale, as previously used for rating the app quality by LMS. The responses from the two groups were compared using the Mann-Whitney U test and Spearman correlation.ResultsThe median total score of NLMP app evaluation was 2.73 out of 5 (IQR 0.95) compared to 3.78 (IQR 1.05) by the LMS. All scores were statistically significantly lower in the NLMP group (p<0.05), except for the item Information quality (p=0.1631). The suggested benchmarks for a useful appear: increasing awareness of the importance and delivering an understanding of persons’ own laboratory test results; understandable terminology; easy to use; appropriate graphic design, and trustworthy information.ConclusionsNLMP’ evaluation confirmed the low utility of currently available laboratory medicine apps. A reliable app should contain trustworthy and understandable information. The appearance of an app should be fit for purpose and easy to use.

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Mobile Health Applications to Support COVID-19 Self-management: Review and Evaluation

Applied Health Information Technology ◽

10.18502/ahit.v2i2.7992 ◽

2021 ◽

Author(s):

Sahar Khenarinezhad ◽

Ehsan Ghazanfari Savadkoohi ◽

Leila Shahmoradi

Keyword(s):

Mobile Health ◽

Mobile Apps ◽

Healthcare Providers ◽

Vital Signs ◽

Complete Information ◽

Self Management ◽

Management Measures ◽

Management Procedures ◽

Mobile Health Applications ◽

Health Applications

Aim: During the epidemic and with an increase in coronavirus (COVID-19) disease prevalence, emergency care is essential to help people stay informed and undertake self-management measures to protect their health. One of these self-management procedures is the use of mobile apps in health. Mobile health (mHealth) applications include mobile devices in collecting clinical health data, sharing healthcare information for practitioners and patients, real-time monitoring of patient vital signs, and the direct provision of care (via mobile telemedicine). Mobile apps are increasing to improve health, but before healthcare providers can recommend these applications to patients, they need to be sure the apps will help change patients' lifestyles. Method: A search was conducted systematically using the keywords "Covid-19," "Coronavirus," "Covid-19, and Self-management" at the "Apple App Store". Then we evaluated the apps according to MARS criteria in May 2020. Results: A total of 145 apps for COVID-19 self-management were identified, but only 32 apps met our inclusion criteria after being assessed. The overall mean MARS score was 2.9 out of 5, and more than half of the apps had a minimum acceptability score (range 2.5-3.9). The "who academy" app received the highest functionality score. Who Academy, Corona-Care and First Responder COVID-19 Guide had the highest scores for behavior change. Conclusion: Our findings showed that few apps meet the quality, content, and functionality criteria for Covid-19 self-management. Therefore, developers should use evidence-based medical guidelines in creating mobile health applications so that, they can provide comprehensive and complete information to both patients and healthcare provider.

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A Model for the National Mental Health Information Network

Applied Health Information Technology ◽

10.18502/ahit.v1i2.6166 ◽

2021 ◽

Author(s):

Reza Rabiei ◽

Farkhonde Aasdi ◽

Hamid Moghaddasi ◽

Mahdie Shojaei Baghini

Keyword(s):

Mental Health ◽

Health Information ◽

Accurate Information ◽

Information Network ◽

Second Phase ◽

Five Dimensions ◽

Mental Health Information ◽

Architectural Model ◽

National Mental Health ◽

Data Elements

Aim: Accurate information can be accessed in a timely manner through the Integrated Mental Health Information Network (MHIN). As Iran has no MHIN, this study was undertaken to propose an architectural model. Method: This research is a sequential mixed method. The organizational structure and database structure of the MHIN was identified, and the architectural model of the NMHIN was presented in two main phases. In the first phase, a quantitative study was conducted in a scoping review with an extensive review of the background, documents, information, and available resources about the mental health information network. In the second phase, to validate the proposed architecture, the Delphi technique was implemented. Questionnaires were distributed and collected both in person and by e-mail, and finally, the data were analyzed using SPSS-19. Results: The model of national MHIN was provided in five dimensions: MH entities, organizational ownership of databases, data elements of each database, linkage among databases, and exchangeable data elements among the databases. Conclusion: This model can be applied as a suitable platform to effectively and efficiently store and use mental health information. So, the available information can be used for providing mental health services more comfortably and appropriately. The results showed that connecting mental health entities can create a flow of information, coordinate MHIN activities, and improve performance, efficiency, and quality of mental health.

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