Short-term and Long-term Efficacy of Oropharyngolaryngeal Strengthening Training on Voice Using a Mobile Healthcare Application in Elderly Women

Cho, Nam-Bin; Cho, Sung Rae; Choi, Seong Hee; You, Heecheon; Nam, Seok In; Kim, HyangHee

doi:10.12963/csd.21799

Cited by 8 publications

(6 citation statements)

References 44 publications

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“…, 2013) but also enhance the quality of diagnostic care, allowing mHealth's full potential to be realised.The national literacy rate as well as linguistic diversity are essential factors for management to consider. Only 72.1% of India's population is literate; this can create a communication barrier between a patient in one region and a doctor in another who may need further training (Zolfo et al ., 2010; Cho et al ., 2021). For sustainability of mHealth, intervallic forcasting is needed to manage social and cultural changes.The Internet and increased broadband connections are still the cornerstones of present mobile healthcare; good communication makes systems more practical and profitable in far-flung locales as shown by social cognitive theory (Xu et al.…”

Section: Discussionmentioning

confidence: 99%

“…The national literacy rate as well as linguistic diversity are essential factors for management to consider. Only 72.1% of India's population is literate; this can create a communication barrier between a patient in one region and a doctor in another who may need further training (Zolfo et al ., 2010; Cho et al ., 2021). For sustainability of mHealth, intervallic forcasting is needed to manage social and cultural changes.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

Swain

Muduli

Luthra³

2023

IJIEOM

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PurposeThe goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.Design/methodology/approachPotential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.FindingsThe study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.Practical implicationsPromoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.Originality/valueAt this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

Swain

Muduli

Luthra³

2023

IJIEOM

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“…Moreover, it simplifies the ordering of medicines using online payment. Cho et al [ 36 ] proposed a mobile healthcare application for investigating the effectiveness of oral and pharyngolaryngeal strength training on voice in older women. Watts et al [ 37 ] developed The Facial Remote Activity Monitoring Eyewear (FRAME) mobile application.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning‐Based Skin Diseases Classification using Smartphones

Oztel,

Yolcu Oztel,

Sahin

2023

Advanced Intelligent Systems

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Skin disease recognition is one of the essential topics in the medical industry. Detecting skin disease from appearance can be difficult due to the similar appearance of skin lesions. In some cases, such as the monkeypox virus, the illness must be quickly determined, and the patients must be isolated to reduce the spreading of the disease. This study aims to create a deep learning‐based automated intelligent mobile application to detect skin disease. First, different small‐size pretrained networks are trained for skin lesion image classification. Then, the most suitable network from the viewpoint of both performance and mobile compatibility is transformed into the TensorFlow Lite format. Finally, a mobile application is created on the Android platform that utilizes the smartphone's camera to obtain images and uses TensorFlow Lite to make predictions. The proposed system produces 74.27% classification accuracy for seven classes on a combined dataset. It produces comparable/better results compared to the literature. Owing to the proposed system, the patients can make a preliminary diagnosis of their lesions using their smartphones. Thus, risky patients can be encouraged to visit the hospital for a definitive diagnosis. In addition, the mobile application can avoid undue stress and false alarms.

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“…There are variety of mobile applications to improve healthcare [25][26][27][28]. Some of them are developed for identification of skin lesions.…”

Section: Mobile Applications For Identification Of Skin Lesion Imagesmentioning

confidence: 99%

Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application

2022

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Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device’s camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network’s training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.

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Short-term and Long-term Efficacy of Oropharyngolaryngeal Strengthening Training on Voice Using a Mobile Healthcare Application in Elderly Women

Cited by 8 publications

References 44 publications

Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

Analysis of barriers of mHealth adoption in the context of sustainable operational practices in health care supply chains

Deep Learning‐Based Skin Diseases Classification using Smartphones

Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application

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