Using Google Analytics with Health Information-Seeking Model to Evaluate the Design of Health Information Websites.

Health information websites can be useful for information seekers, and their design is crucial for the success of accessing the needed information. While web analytical tools (e.g. Google Analytics) used by such websites can provide descriptive measures of users, there is a disconnection between this data and the current understanding of health information-seeking behaviour. In this work, we leverage a theoretical model to interpret the Google Analytics data. Drawn on the visualisation of user behaviours based on this model, our research shows that better website design can be informed, and the evaluation of health websites can be performed on the basis of different user profiles.

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach.

BACKGROUND: After the COVID-19 pandemic, the conflict between limited mental health care resources and the rapidly growing number of patients has become more pronounced. It is necessary for psychologists to borrow artificial intelligence (AI)-based methods to analyze patients' satisfaction with drug treatment for those undergoing mental illness treatment. OBJECTIVE: Our goal was to construct highly accurate and transferable models for predicting the satisfaction of patients with mental illness with medication by analyzing their own experiences and comments related to medication intake. METHODS: We extracted 41,851 reviews in 20 categories of disorders related to mental illnesses from a large public data set of 161,297 reviews in 16,950 illness categories. To discover a more optimal structure of the natural language processing models, we proposed the Unified Interchangeable Model Fusion to decompose the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), support vector machine, and random forest (RF) models into 2 modules, the encoder and the classifier, and then reconstruct fused "encoder+classifer" models to accurately evaluate patients' satisfaction. The fused models were divided into 2 categories in terms of model structures, traditional machine learning-based models and neural network-based models. A new loss function was proposed for those neural network-based models to overcome overfitting and data imbalance. Finally, we fine-tuned the fused models and evaluated their performance comprehensively in terms of F1-score, accuracy, κ coefficient, and training time using 10-fold cross-validation. RESULTS: Through extensive experiments, the transformer bidirectional encoder+RF model outperformed the state-of-the-art BERT, MentalBERT, and other fused models. It became the optimal model for predicting the patients' satisfaction with drug treatment. It achieved an average graded F1-score of 0.872, an accuracy of 0.873, and a κ coefficient of 0.806. This model is suitable for high-standard users with sufficient computing resources. Alternatively, it turned out that the word-embedding encoder+RF model showed relatively good performance with an average graded F1-score of 0.801, an accuracy of 0.812, and a κ coefficient of 0.695 but with much less training time. It can be deployed in environments with limited computing resources. CONCLUSIONS: We analyzed the performance of support vector machine, RF, BERT, MentalBERT, and all fused models and identified the optimal models for different clinical scenarios. The findings can serve as evidence to support that the natural language processing methods can effectively assist psychologists in evaluating the satisfaction of patients with drug treatment programs and provide precise and standardized solutions. The Unified Interchangeable Model Fusion provides a different perspective on building AI models in mental health and has the potential to fuse the strengths of different components of the models into a single model, which may contribute to the development of AI in mental health.

Focus-RCNet: a lightweight recyclable waste classification algorithm based on focus and knowledge distillation.

Waste pollution is a significant environmental problem worldwide. With the continuous improvement in the living standards of the population and increasing richness of the consumption structure, the amount of domestic waste generated has increased dramatically, and there is an urgent need for further treatment. The rapid development of artificial intelligence has provided an effective solution for automated waste classification. However, the high computational power and complexity of algorithms make convolutional neural networks unsuitable for real-time embedded applications. In this paper, we propose a lightweight network architecture called Focus-RCNet, designed with reference to the sandglass structure of MobileNetV2, which uses deeply separable convolution to extract features from images. The Focus module is introduced to the field of recyclable waste image classification to reduce the dimensionality of features while retaining relevant information. To make the model focus more on waste image features while keeping the number of parameters small, we introduce the SimAM attention mechanism. In addition, knowledge distillation was used to further compress the number of parameters in the model. By training and testing on the TrashNet dataset, the Focus-RCNet model not only achieved an accuracy of 92[Formula: see text] but also showed high deployment mobility.

Participatory Design and Evaluation of the "Stem Cells Australia" Website for Delivering Complex Health Knowledge: Mixed Methods Study.

BACKGROUND: The internet has become a commonly used information source for people seeking to understand their health care options. However, inconsistent representation about what stem cell treatments are available and from whom, coupled with the lack of transparency about what has been shown to work or is even safe, can distract and mislead users. Given these challenges, there is a need to develop effective evidence-based tools for delivering information about health care options involving stem cells. OBJECTIVE: A need to redesign an existing website in Australia was identified to provide trustworthy information about stem cell research and to counter misinformation. Using a participatory design approach to generate an understanding of what information users need for stem cell treatments, the Stem Cells Australia website serves as a case study for the development and evaluation of websites delivering complex messages about science and health. METHODS: This study comprised 3 steps. First, a focus group and several one-on-one interviews with a purposive sample of users (n=12) were conducted to identify their needs and requirements. Second, we designed a new version of the website based on findings from the focus group and interviews. Finally, for evaluating the participatory design process, we collected 180 days of Google Analytics data for both the original and redesigned versions (90 days for each) and compared their differences using 2-sample z tests. RESULTS: The feedback from participants was grouped into 3 themes-needed and unwanted information, how and where to obtain information, and their information preferences. These were translated into requirements for rebuilding the website. The redesigned version reached users in other continents, despite the daily numbers of users (-61.2%; P<.001) and sessions (-61.7%; P<.001) decreasing. The redesigned version also showed substantial decrease in daily bounce rate (-97.2%; P<.001), significant increase in the daily average of page reads per session (+110.8%; P<.001), and long daily average for session duration (+22.9%; P=.045). Navigation flow analysis showed more traffic toward web pages related to health conditions in the redesigned version. CONCLUSIONS: Websites about stem cell research need to provide content for vulnerable global audiences. Participatory design that addresses knowledge gaps and information needs can produce better performance and engagement, which can be evaluated using Google Analytics, a common web analytics tool used by many websites. Learnings for improving the metrics regarding website identity, research updates, and clinical trials are concluded, which can inform the future design of websites seeking to engage users and provide reliable and accessible science and health information including but not limited to stem cell research and therapies.

Changes in Doctor-Patient Relationships in China during COVID-19: A Text Mining Analysis.

Doctor-patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients' attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.

Social Media Engagement in Two Governmental Schemes during the COVID-19 Pandemic in Macao.

Social media engagement is a vehicle for effective communication and engagement between governments and individuals, especially in crises such as the COVID-19 pandemic. Additionally, it can be used to communicate resilience measures and receive feedback. This research aims to investigate public social media engagement with resilience measures related to COVID-19 in Macao. We examined 1107 posts and 791 comments about the government's face mask supply and consumption voucher schemes on Facebook. Using the Crisis Lifecycle model, we partitioned the data and analyzed the content and engagement of related posts, as well as the word semantics in user comments. Our findings show that social media engagement in these resilience measures is high and positive in the early stages of the pandemic, suggesting social media's potential in mobilizing society, preserving social resilience, and serving as a two-way communication tool in public health emergencies.

Characterizing the Effects of Explosive Ordnance Disposal Operations on the Human Body While Wearing Heavy Personal Protective Equipment.

OBJECTIVE: To provide a comprehensive characterization of explosive ordnance disposal (EOD) personal protective equipment (PPE) by evaluating its effects on the human body, specifically the poses, tasks, and conditions under which EOD operations are performed. BACKGROUND: EOD PPE is designed to protect technicians from a blast. The required features of protection make EOD PPE heavy, bulky, poorly ventilated, and difficult to maneuver in. It is not clear how the EOD PPE wearer physiologically adapts to maintain physical and cognitive performance during EOD operations. METHOD: Fourteen participants performed EOD operations including mobility and inspection tasks with and without EOD PPE. Physiological measurement and kinematic data recording were used to record human physiological responses and performance. RESULTS: All physiological measures were significantly higher during the mobility and the inspection tasks when EOD PPE was worn. Participants spent significantly more time to complete the mobility tasks, whereas mixed results were found in the inspection tasks. Higher back muscle activations were seen in participants who performed object manipulation while wearing EOD PPE. CONCLUSION: EOD operations while wearing EOD PPE pose significant physical stress on the human body. The wearer's mobility is impacted by EOD PPE, resulting in decreased speed and higher muscle activations. APPLICATION: The testing and evaluation methodology in this study can be used to benchmark future EOD PPE designs. Identifying hazards posed by EOD PPE lays the groundwork for developing mitigation plans, such as exoskeletons, to reduce physical and cognitive stress caused by EOD PPE on the wearers without compromising their operational performance.

Engagement of Government Social Media on Facebook during the COVID-19 Pandemic in Macao.

Government social media is widely used for providing updates to and engaging with the public in the COVID-19 pandemic. While Facebook is one of the popular social media used by governments, there is only a scant of research on this platform. This paper aims to understand how government social media should be used and how its engagement changes in prodromal, acute and chronic stages of the pandemic. We collected 1664 posts and 10,805 comments from the Facebook pages of the Macao government from 1 January to 31 October 2020. Using word frequency and content analysis, the results suggest that the engagement was relatively low at the beginning and then surged in the acute stage, with a decreasing trend in the chronic stage. Information about public health measures maintained their engagement in all stages, whereas the engagement of other information was dropping over time. Government social media can be used for increasing vigilance and awareness in the prodromal stage; disseminating information and increasing transparency in the acute stage; and focusing on mental health support and recovery policies in the chronic stage. Additionally, it can be a tool for controlling rumors, providing regular updates and fostering community cohesion in public health crises.

Exploring the facilitators and barriers to using an online infertility risk prediction tool (FoRECAsT) for young women with breast cancer: a qualitative study protocol.

INTRODUCTION: As cancer treatments may impact on fertility, a high priority for young patients with breast cancer is access to evidence-based, personalised information for them and their healthcare providers to guide treatment and fertility-related decisions prior to cancer treatment. Current tools to predict fertility outcomes after breast cancer treatments are imprecise and do not offer individualised prediction. To address the gap, we are developing a novel personalised infertility risk prediction tool (FoRECAsT) for premenopausal patients with breast cancer that considers current reproductive status, planned chemotherapy and adjuvant endocrine therapy to determine likely post-treatment infertility. The aim of this study is to explore the feasibility of implementing this FoRECAsT tool into clinical practice by exploring the barriers and facilitators of its use among patients and healthcare providers. METHODS AND ANALYSIS: A cross-sectional exploratory study is being conducted using semistructured in-depth telephone interviews with 15-20 participants each from the following groups: (1) premenopausal patients with breast cancer younger than 40, diagnosed within last 5 years, (2) breast surgeons, (3) breast medical oncologists, (4) breast care nurses (5) fertility specialists and (6) fertility preservation nurses. Patients with breast cancer are being recruited from the joint Breast Service of three affiliated institutions of Victorian Comprehensive Cancer Centre in Melbourne, Australia-Peter MacCallum Cancer Centre, Royal Melbourne Hospital and Royal Women's Hospital, and clinicians are being recruited from across Australia. Interviews are being audio recorded, transcribed verbatim and imported into qualitative data analysis software to facilitate data management and analyses. ETHICS AND DISSEMINATION: The study protocol has been approved by Melbourne Health Human Research Ethics Committee, Australia (HREC number: 2017.163). Confidentiality and privacy are maintained at every stage of the study. Findings will be disseminated through peer-reviewed scholarly and scientific journals, national and international conference presentations, social media, broadcast media, print media, internet and various community/stakeholder engagement activities.

The Twitter Adventure of #MyHealthRecord: An Analysis of Different User Groups During the Opt-Out Period.

My Health Record (MHR), which is an online health summary for Australians, was changed from the opt-in to the opt-out model, and therefore sparked a vast discussion on Twitter. In order to understand the debate, the information dissemination and the levels of engagement, we have analysed tweets posted from July 2018 to February 2019. In this paper, we report on the findings of the patterns of discussion, the hashtags and the numbers of retweets and likes from different user categories. The results show that the discussion was highly political, and the tweets from the MHR official accounts had lower propagation and engagement than other user groups. This work highlights the implications of using social networking sites (SNSs) to promote large-scale mandatory electronic health record systems.

The Use of Web-Based Technologies in Health Research Participation: Qualitative Study of Consumer and Researcher Experiences.

BACKGROUND: Health consumers are often targeted for their involvement in health research including randomized controlled trials, focus groups, interviews, and surveys. However, as reported by many studies, recruitment and engagement of consumers in academic research remains challenging. In addition, there is scarce literature describing what consumers look for and want to achieve by participating in research. OBJECTIVE: Understanding and responding to the needs of consumers is crucial to the success of health research projects. In this study, we aim to understand consumers' needs and investigate the opportunities for addressing these needs with Web-based technologies, particularly in the use of Web-based research registers and social networking sites (SNSs). METHODS: We undertook a qualitative approach, interviewing both consumer and medical researchers in this study. With the help from an Australian-based organization supporting people with musculoskeletal conditions, we successfully interviewed 23 consumers and 10 researchers. All interviews were transcribed and analyzed with thematic analysis methodology. Data collection was stopped after the data themes reached saturation. RESULTS: We found that consumers perceive research as a learning opportunity and, therefore, expect high research transparency and regular updates. They also consider the sources of the information about research projects, the trust between consumers and researchers, and the mobility of consumers before participating in any research. Researchers need to be aware of such needs when designing a campaign for recruitment for their studies. On the other hand, researchers have attempted to establish a rapport with consumer participants, design research for consumers' needs, and use technologies to reach out to consumers. A systematic approach to integrating a variety of technologies is needed. CONCLUSIONS: On the basis of the feedback from both consumers and researchers, we propose 3 future directions to use Web-based technologies for addressing consumers' needs and engaging with consumers in health research: (1) researchers can make use of consumer registers and Web-based research portals, (2) SNSs and new media should be frequently used as an aid, and (3) new technologies should be adopted to remotely collect data and reduce administrative work for obtaining consumers' consent.

Online Health Seeking Behaviours: What Information Is Sought by Women Experiencing Miscarriage?

One in four pregnancies ends in miscarriage, a distressing event which can cause significant psychosocial impacts for many women, and yet often remains unseen and unspoken. Many would-be mothers turn to the internet for information and emotional support, and to share their experiences. In this paper, we present the results from 12 semi-structured interviews with women, investigating how and what online information they searched for at the time of miscarriage. We found that women are passive information seekers, searching for causes and preventive strategies to inform future pregnancies. Women want information presented in an easy to understand manner that is not overly clinical, and informed by credible sources. Women also seek psychological support and emotional relief through reading about others' experiences and sharing their stories online. The findings from this study provide a unique insight into the support and information needs of women, and will be used to guide the content, design and functionality of web-based technologies for women experiencing miscarriage.

Engaging Consumers with Musculoskeletal Conditions in Health Research: A User-Centred Perspective.

Consumers are frequently involved in different kinds of health research, such as clinical trials, focus groups, and surveys. As pointed out by different studies, recruiting and involving consumers to participate in academic research can be challenging. While different research and guidelines are provided to instruct researchers to recruit participants ethically, they seldom consider the needs and expectations of consumers. In this research, we interviewed 23 consumers with musculoskeletal conditions in Australia, to understand their needs and motivations for participating in research from a user-centred perspective. Based on these data, we systematically summarise consumers' feedback into four main themes: (1) Research as Learning Opportunity; (2) The Important Role of Communities and Health Professionals; (3) Research Transparency and Updates; and (4) Special Needs for People with MSK Conditions. As a result, a few recommendations are proposed and researchers should further consider these when designing consumer-based studies. Ultimately, with a better understanding of consumers, we hope that our research can enhance consumer engagement and improve their participation in health research.

Finding and Exploring Health Information with a Slider-Based User Interface.

Despite the fact that search engines are the primary channel to access online health information, there are better ways to find and explore health information on the web. Search engines are prone to problems when they are used to find health information. For instance, users have difficulties in expressing health scenarios with appropriate search keywords, search results are not optimised for medical queries, and the search process does not account for users' literacy levels and reading preferences. In this paper, we describe our approach to addressing these problems by introducing a novel design using a slider-based user interface for discovering health information without the need for precise search keywords. The user evaluation suggests that the interface is easy to use and able to assist users in the process of discovering new information. This study demonstrates the potential value of adopting slider controls in the user interface of health websites for navigation and information discovery.

Designing Health Websites Based on Users' Web-Based Information-Seeking Behaviors: A Mixed-Method Observational Study.

BACKGROUND: Laypeople increasingly use the Internet as a source of health information, but finding and discovering the right information remains problematic. These issues are partially due to the mismatch between the design of consumer health websites and the needs of health information seekers, particularly the lack of support for "exploring" health information. OBJECTIVE: The aim of this research was to create a design for consumer health websites by supporting different health information-seeking behaviors. We created a website called Better Health Explorer with the new design. Through the evaluation of this new design, we derive design implications for future implementations. METHODS: Better Health Explorer was designed using a user-centered approach. The design was implemented and assessed through a laboratory-based observational study. Participants tried to use Better Health Explorer and another live health website. Both websites contained the same content. A mixed-method approach was adopted to analyze multiple types of data collected in the experiment, including screen recordings, activity logs, Web browsing histories, and audiotaped interviews. RESULTS: Overall, 31 participants took part in the observational study. Our new design showed a positive result for improving the experience of health information seeking, by providing a wide range of information and an engaging environment. The results showed better knowledge acquisition, a higher number of page reads, and more query reformulations in both focused and exploratory search tasks. In addition, participants spent more time to discover health information with our design in exploratory search tasks, indicating higher engagement with the website. Finally, we identify 4 design considerations for designing consumer health websites and health information-seeking apps: (1) providing a dynamic information scope; (2) supporting serendipity; (3) considering trust implications; and (4) enhancing interactivity. CONCLUSIONS: Better Health Explorer provides strong support for the heterogeneous and shifting behaviors of health information seekers and eases the health information-seeking process. Our findings show the importance of understanding different health information-seeking behaviors and highlight the implications for designers of consumer health websites and health information-seeking apps.

Determination of methyl mercury in dental-unit wastewater.

OBJECTIVE: The objective of this investigation was to establish whether monomethyl mercury (MMHg) is present in dental-unit wastewater and if present, to determine the concentration relative to total mercury. METHODS: Wastewater samples were collected over an 18-month period from three locations: at the dental chair; at a 30-chair clinic, and at a 107-chair clinic. Total mercury determinations were completed using United States Environmental Protection Agency's (USEPA) method 1631. MMHg was measured utilizing modified USEPA method 1630. RESULTS: The total mercury levels were found to be: 45182.11 microg/l (n=13, SD=68562.42) for the chair-side samples, 5350.74 microg/l (n=12, SD=2672.94) for samples at the 30-chair clinic, and 13439.13 microg/l (n=13, SD=9898.91) for samples at the107-chair clinic. Monomethyl Hg levels averaged 0.90 microg/l (n=13, SD=0.87) for chair side samples, 8.26 (n=12, SD=7.74) for the 30-chair facility, and 26.77 microg/l (n=13, SD=34.50) for 107-chair facility. By way of comparison, the MMHg levels for the open ocean, lakes and rain are orders of magnitude lower than methyl mercury levels seen in dental wastewater (part per billion levels for dental wastewater samples compared to part per trillion levels for samples from the environment). SIGNIFICANCE: Environmentally important levels of MMHg were found to be present in dental-unit wastewater at concentrations orders of magnitude higher than seen in natural settings.