Identifying health information needs of senior online communities users: a text mining approach

Qian, Yuxing; Gui, Wenxuan

doi:10.1108/ajim-02-2020-0057

Cited by 15 publications

(15 citation statements)

References 41 publications

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“…Previous studies have mainly focused on online health information behaviour among healthy and young populations (Chen et al, 2017;Ghiasi, 2021;Li et al, 2020;Qian et al, 2019), and some instruments have been developed to evaluate health informationseeking behaviour among these groups of people, such as the Information-seeking Behaviour Scale and Information-seeking Behaviour Scale for Undergraduates (Chen et al, 2021;Timmers & Glas, 2010). However, people with cancer are different from healthy people and young people in terms of having worse physical conditions, older age distribution and higher demand for health and disease information.…”

Section: Backg Rou N Dmentioning

confidence: 99%

Development and preliminary testing of a questionnaire to measure mobile health information‐seeking behaviour in people with cancer

Zhang

Hong

et al. 2023

Nursing Open

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Aim:The aim of the study was to develop and psychologically test the mobile health information-seeking behaviour (MHISB) questionnaire in people with cancer.Design: Instrument development. Methods:The study was conducted in three phases in a southeastern city of China from May 2017 to April 2018. In phase one, an item pool was constructed based on a literature review and semistructured interviews. In phase two, expert evaluation and cognitive interviews were used to evaluate the content validity of the questionnaire. In phase three, a cross-sectional study was conducted with people with cancer.Cronbach's α was calculated for reliability analysis. Validity evaluation included content validity and construct validity. Results:The developed MHISB questionnaire has four dimensions (informationseeking frequency, information-seeking self-efficacy, health information evaluation and information-seeking willingness) and 25 items. Psychometric findings were satisfactory and supported the questionnaire's reliability. Conclusions:The construction process of the MHISB questionnaire was scientific and feasible. The MHISB questionnaire had acceptable validity and reliability, and it requires further improvement in future studies.

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Section: Backg Rou N Dmentioning

confidence: 99%

Development and preliminary testing of a questionnaire to measure mobile health information‐seeking behaviour in people with cancer

Zhang

Hong

et al. 2023

Nursing Open

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“…Others collected older adults' opinions on how technology could improve their intake (Łukasik et al, 2018) or synthesized literature (Scott et al, 2018;Takemoto et al, 2018). One study evaluated online communities and found posts included concerns about vitamins, fats, and protein (Qian & Gui, 2020).…”

Section: Nutrition Focus Characterizationmentioning

confidence: 99%

“…Like tablet hardware, this popular software is becoming increasingly important. Only one study addressed social media use (Qian & Gui, 2020); however, the new generation of aging adults is social media aware. Social media is a promising area to introduce future older adults to videos, 3D experiences, and new learning.…”

Section: Future Research and Limitationsmentioning

confidence: 99%

Technology Use in Managing the Nutrition Health of Older Adults: A Scoping Review

Lobuono,

Milovich

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Technology improvements for health care may enable nutritional health management for older adults. Research has yet to map the types of technology utilized to manage nutrition. This scoping review includes research in technology and nutrition to: (1) explain how technology is used to manage the nutrition needs of older adults; (2) describe the types of technology used to manage nutrition. The literature period was 21 years, but 86 percent of the papers retained were published within the past five years. The most common type of technology used is software, which is used to: (1) track, plan, and execute nutrition management and (2) assess technology use. The findings show that software for older adults lacks standardization. The internet of things is a promising area for research, and personal devices emphasize the tablet computer. The results suggest that managing older adult nutrition through technology is not yet a formable research area.

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“…Therefore, machine learning-based methods such as text mining and topic recognition have gradually become the most common methods to study users' information needs. Qian and Gui [19] applied word frequency analysis to identify the information needs of users in the elderly health community, which is mainly divided into 4 themes: coping with aging, diet and nutrition, physical exercise, and mental health. Based on the coword analysis and the latent Dirichlet allocation topic model, Wang et al [15] found that during the COVID-19 pandemic, Chinese online medical and health community users had the highest demand for information about disease symptoms [15].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

Cheng¹,

Lin²

2023

JMIR Form Res

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Background Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users’ questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users’ information needs. Objective Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users’ needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users’ needs in online medical and health communities so that users can perform more targeted information retrieval. Methods Using the Chinese online medical and health community “Qiuyi” as an example, we crawled questions posted by users in the “Cardiovascular Disease” section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users’ information needs label as the second-level label. Finally, by constructing a GCN model, users’ questions were automatically classified, thus realizing the multilevel classification of users’ needs. Results Based on the empirical research of questions posted by users in the “Cardiovascular Disease” section of Qiuyi, the hierarchical classification of users’ questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naïve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users’ needs, which in comparison with the multilevel classification model exhibited a great improvement. Conclusions A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users’ information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications.

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Identifying health information needs of senior online communities users: a text mining approach

Cited by 15 publications

References 41 publications

Development and preliminary testing of a questionnaire to measure mobile health information‐seeking behaviour in people with cancer

Development and preliminary testing of a questionnaire to measure mobile health information‐seeking behaviour in people with cancer

Technology Use in Managing the Nutrition Health of Older Adults: A Scoping Review

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

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