MALE URINARY INCONTINENCE AND THE DIGITAL TECHNOLOGY: EVALUATION OF MOBILE APPLICATIONS AVAILABLE FOR DOWNLOAD

Objective: to evaluate the suitability and usefulness of mobile apps aimed at urinary incontinence rehabilitation in the male population.  Method: descriptive study, carried out with apps directed to the rehabilitation of male urinary incontinence. The apps were obtained from the Play Store and App Store. The search was conducted between May 3 and 10, 2021, in Minas Gerais, Brazil. The terms "urinary incontinence", "incontinencia urinaria", "urinary incontinence", and "Kegel" were used for selection. The applications were described and evaluated as established in the Applications Scoring System items. Results: Twenty-two apps were selected. Three were specific for men; three addressed exercises for pelvic muscle strengthening and voiding diary simultaneously; and five were compatible with both online stores. Conclusion: most of the available apps have limited functionality and information about male urinary incontinence. This study is expected to contribute to the development of more comprehensive and appropriate software for the male urinary incontinent population

INCONTINENCIA URINARIA MASCULINA Y TECNOLOGÍA DIGITAL: EVALUACIÓN DE APLICACIONES MÓVILVES DISPONÍVEIS PARA DOWNLOAD

Objetivo: evaluar la idoneidad y utilidad de las aplicaciones móviles para la rehabilitación de la incontinencia urinaria en la población masculina.   Método: estudio descriptivo, realizado con aplicaciones dirigidas a la rehabilitación de la incontinencia urinaria masculina. Las aplicaciones se obtuvieron de Play Store y App Store. La búsqueda se realizó entre el 3 y el 10 de mayo de 2021 en Minas Gerais, Brasil. Para la selección se utilizaron los términos “incontinência urinária”, “incontinencia urinaria”, “urinary incontinence” e “Kegel”. Las solicitudes fueron descritas y evaluadas según lo establecido en los ítems del Applications Scoring System.Resultados: Se seleccionaron 22 aplicaciones. Tres eran específicos para hombres; tres abordaban ejercicios para fortalecer la musculatura pélvica y miccional simultáneamente; y cinco eran compatibles con ambas salas virtuales. Conclusión: la mayoría de las aplicaciones disponibles tienen funcionalidades e información limitada sobre la incontinencia urinaria masculina. Se espera que este estudio contribuya al desarrollo de softwares más completos y adecuados para la población masculina con incontinencia urinaria

INCONTINÊNCIA URINÁRIA MASCULINA E A TECNOLOGIA DIGITAL: AVALIAÇÃO DE APLICATIVOS MÓVEIS DISPONÍVEIS PARA DOWNLOAD

Objetivo: avaliar a adequação e utilidade de aplicativos móveis voltados para reabilitação da incontinência urinária na população masculina.Método: estudo descritivo, realizado com aplicativos direcionados à reabilitação da incontinência urinária masculina. Os aplicativos foram obtidos na Play Store e App Store. A busca foi realizada entre 3 e 10 de maio de 2021, em Minas Gerais, Brasil. Foram utilizados os termos “incontinência urinária”, “incontinencia urinaria”, “urinary incontinence” e “Kegel” para seleção. Os aplicativos foram descritos e avaliados conforme estabelecido nos itens da Applications Scoring System.Resultados: Vinte e dois aplicativos foram selecionados. Três eram específicos para homens; três abordavam exercícios para fortalecimento da musculatura pélvica e diário miccional simultaneamente; e cinco eram compatíveis com ambas as lojas virtuais.Conclusão: a maioria dos aplicativos disponíveis possui funcionalidades e informações limitadas acerca da incontinência urinária masculina. Espera-se que esse estudo contribua para o desenvolvimento de softwares mais abrangentes e adequados à população masculina com incontinência urinária

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost