Benchmarking of mobile apps on heart failure

Cestari, Virna Ribeiro Feitosa; Florêncio, Raquel Sampaio; Garcês, Thiago Santos; Pessoa, Vera Lúcia Mendes de Paula; Moreira, Thereza Maria Magalhães

doi:10.1590/0034-7167-2020-1093

Cited by 8 publications

(6 citation statements)

References 26 publications

(36 reference statements)

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“…A análise dos dados foi orientada por estudos semelhantes, visando a avaliação do aplicativo móvel 18,23,24…”

Section: Análise Dos Dadosunclassified

Instrumento Misscare: avaliação de software mobile para a enfermagem

Rabin,

Silva,

Lima

et al. 2024

Braz. J. Hea. Rev.

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O presente estudo adotou como objetivo principal avaliar o aplicativo MISSCARE, pelas equipes de enfermagem que atendem em unidades oncológicas. Trata-se de um estudo de produção tecnológica, desenvolvido no estado do Rio Grande do Sul, entre agosto de 2021 a novembro de 2022. A amostra contou com 51 profissionais de saúde, entre enfermeiros e técnicos de enfermagem, que responderam o questionário de avaliação de sistemas “System Usability Scale'' (SUS), após utilizarem o aplicativo MISSCARE. Dentre os resultados, o instrumento “System Usability Scale” (SUS) obteve a média de 91,96 o que corresponde ao melhor escore imaginável e demonstra a boa usabilidade do aplicativo. O instrumento SUS mostrou-se com boa confiabilidade, pois segundo análise pelo coeficiente alfa de cronbach chegou-se ao valor de 0,81, o que é considerado alto. O índice de validade de conteúdo geral do construto ficou em 0.93 o que mostra uma excelente concordância entre as respostas. Sendo assim o aplicativo demonstrou boa adequação funcional, agilidade, utilidade, usabilidade e confiabilidade para aquilo a que se propõe. Por meio dos resultados obtidos, espera-se que o uso do aplicativo possa melhorar a qualidade do cuidado, ajudar os gestores a mapear a situação dos serviços de saúde e possibilitar a criação de estratégias a fim de sanar os problemas encontrados.

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“…A análise dos dados foi orientada por estudos semelhantes, visando a avaliação do aplicativo móvel 18,23,24…”

Section: Análise Dos Dadosunclassified

Instrumento Misscare: avaliação de software mobile para a enfermagem

Rabin,

Silva,

Lima

et al. 2024

Braz. J. Hea. Rev.

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“…25 Benchmarking is one of the methods to analyse a business or product. Benchmarking allows the assessment of performance and results of technology and product 26 and has been applied to mobile apps to identify apps about the topic of interest, and their usability 27 and guide the app development. 28 Benchmarking is selected because it is a 'standard to measure whose performance is against others in the industry field ' [pp.…”

Section: Qualitative Analysismentioning

confidence: 99%

The design and evaluation of a digital learning-based English chatbot as an online self-learning method

Lam

Kong

et al. 2023

International Journal of Engineering Business Management

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A majority of government and international organisations have chosen English as their official language since it is the only language in the world with the status of a global language and a world language. One of the key components of learning a language is oral communication; however, students frequently overlook the value of oral communication, which reduces the effectiveness of language acquisition. To enhance the motivation and effectiveness of non-native English speakers to learn and practise English, this study aims to develop an interactive self-learning and self-improvement approach. A well-designed and chatbot-assisted learning environment is designed, using a variety of concepts such as informal learning, language learning, mobile learning, and educational mobile application, and evaluated.

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“…Previous work has been done on benchmarking DHI system usability scores (SUS) across digital health apps [33] and for heart failure apps [34]. This study differs as it focuses on comparing DHIs using a broader selection of DHIs across health care domains and assessment areas (UX, PCA, and DP).…”

Section: Xsl • Fomentioning

confidence: 99%

Quality of Digital Health Interventions Across Different Health Care Domains: Secondary Data Analysis Study

Hyzy,

Bond,

Mulvenna

et al. 2023

JMIR Mhealth Uhealth

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Background There are more than 350,000 digital health interventions (DHIs) in the app stores. To ensure that they are effective and safe to use, they should be assessed for compliance with best practice standards. Objective The objective of this paper was to examine and compare the compliance of DHIs with best practice standards and adherence to user experience (UX), professional and clinical assurance (PCA), and data privacy (DP). Methods We collected assessment data from 1574 DHIs using the Organisation for the Review of Care and Health Apps Baseline Review (OBR) assessment tool. As part of the assessment, each DHI received a score out of 100 for each of the abovementioned areas (ie, UX, PCA, and DP). These 3 OBR scores are combined to make up the overall ORCHA score (a proxy for quality). Inferential statistics, probability distributions, Kruskal-Wallis, Wilcoxon rank sum test, Cliff delta, and Dunn tests were used to conduct the data analysis. Results We found that 57.3% (902/1574) of the DHIs had an Organisation for the Review of Care and Health Apps (ORCHA) score below the threshold of 65. The overall median OBR score (ORCHA score) for all DHIs was 61.5 (IQR 51.0-73.0) out of 100. A total of 46.2% (12/26) of DHI’s health care domains had a median equal to or above the ORCHA threshold score of 65. For the 3 assessment areas (UX, DP, and PCA), DHIs scored the highest for the UX assessment 75.2 (IQR 70.0-79.6), followed by DP 65.1 (IQR 55.0-73.4) and PCA 49.6 (IQR 31.9-76.1). UX scores had the least variance (SD 13.9), while PCA scores had the most (SD 24.8). Respiratory and urology DHIs were consistently highly ranked in the National Institute for Health and Care Excellence Evidence Standards Framework tiers B and C based on their ORCHA score. Conclusions There is a high level of variability in the ORCHA scores of DHIs across different health care domains. This suggests that there is an urgent need to improve compliance with best practices in some health care areas. Possible explanations for the observed differences might include varied market maturity and commercial interests within the different health care domains. More investment to support the development of higher-quality DHIs in areas such as ophthalmology, allergy, women’s health, sexual health, and dental care may be needed.

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Benchmarking of mobile apps on heart failure

Cited by 8 publications

References 26 publications

Instrumento Misscare: avaliação de software mobile para a enfermagem

Instrumento Misscare: avaliação de software mobile para a enfermagem

The design and evaluation of a digital learning-based English chatbot as an online self-learning method

Quality of Digital Health Interventions Across Different Health Care Domains: Secondary Data Analysis Study

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