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Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Mental health plays a key role across the cancer care continuum, from prognosis and active treatment to survivorship and palliative care. Digital health technologies offer an appealing, cost-effective tool to address psychological needs. This umbrella review aims to summarize and evaluate the available evidence on the efficacy of digital health interventions for improving mental health and psychosocial outcomes for populations with cancer. Literature searches were conducted in Embase, PsycINFO, PubMed, CINAHL, the Cochrane Library, and Web of Science from their inception to February 4, 2024. Systematic reviews (with or without meta-analysis) investigating the efficacy of digital health interventions for psychosocial variables in patients with cancer were included. Quality was assessed using the Assessing the Methodological Quality of Systematic Reviews-2 tool. In total, 78 systematic reviews were included in this review. Among diverse delivery modalities and types of digital interventions, websites and smartphone apps were the most commonly used. Depression was the most frequently addressed, followed by quality of life, anxiety, fatigue, and distress. The qualities of the reviews ranged from critically low to high. Generally, despite great heterogeneity in the strength and credibility of the evidence, digital health interventions were shown to be effective for mental health in patients with cancer. Taken together, digital health interventions show benefits for patients with cancer in improving mental health. Various gaps were identified, such as little research specifically focusing on older adult patients with cancer, a scarcity of reporting high-precision emotion management, and insufficient attention to other certain mood indicators. Further exploration of studies with standardized and rigorous approaches is required to inform practice. PROSPERO CRD42024565084; https://tinyurl.com/4cbxjeh9.

Background To detect the differences in physical symptoms between depressed and undepressed patients with breast cancer (BC), including common symptoms, co-occurring symptoms, and symptom clusters based on texts derived from social media and expressive writing. Methods A total of 1830 texts from social media and expressive writing were collected. The Chi-square test was used to compare the frequency of physical symptoms between depressed and undepressed patients with BC. Symptom lexicon of BC and K-medoids Clustering were used for mining physical symptoms and cluster analysis. Results The common physical symptoms reported by texts included general pains (59.38%), fatigue (26.60%), vomiting (24.82%), swelling of limbs (21.69%), difficulty sleeping (21.56%), nausea (16.78%), alopecia (15.14%), loss of appetite (13.78%), dizziness (11.60%), and concentration problems (11.19%). The frequency of difficulty sleeping (depressed 28.40%; undepressed 18.16%; P  = 0.002) in depressed patients was higher than undepressed patients with BC. High co-occurrence was observed in both commonly mentioned symptoms and those less commonly mentioned but frequently co-occurring with them. There were 5 symptom clusters identified in depressed patients and 6 symptom clusters in undepressed patients. Pain-related symptom cluster and gastrointestinal symptom cluster were both identified in the depressed and undepressed patients. The novel immune system impairment symptom cluster consisting of bleeding and fever was found in the undepressed patients. Conclusions This study found that difficulty sleeping was reported more frequently, and identified difficulty sleeping-pain symptom cluster in depressed patients. The novel immune system impairment symptom cluster in undepressed patients was detected. Healthcare providers can provide targeted care to depressed and undepressed patients based on these differences. These findings demonstrate that social media can provide new perspectives on symptom experiences. The combination of digital tools and traditional clinical tools for symptom management in follow-up has great potential in the future. Clinical trial number Not applicable.

The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes, experiences, and needs, which provides a new perspective and method for emotion recognition and management for patients with breast cancer (BC). However, at present, sentiment analysis in the field of BC is limited, and there is no emotional lexicon for this field. Therefore, it is necessary to construct an emotional lexicon that conforms to the characteristics of patients with BC so as to provide a new tool for accurate identification and analysis of the patients' emotions and a new method for their personalized emotion management. This study aimed to construct an emotional lexicon of patients with BC. Emotional words were obtained by merging the words in 2 general sentiment lexicons, the Chinese Linguistic Inquiry and Word Count (C-LIWC) and HowNet, and the words in text corpora acquired from patients with BC via Weibo, semistructured interviews, and expressive writing. The lexicon was constructed using manual annotation and classification under the guidance of Russell's valence-arousal space. Ekman's basic emotional categories, Lazarus' cognitive appraisal theory of emotion, and a qualitative text analysis based on the text corpora of patients with BC were combined to determine the fine-grained emotional categories of the lexicon we constructed. Precision, recall, and the F1-score were used to evaluate the lexicon's performance. The text corpora collected from patients in different stages of BC included 150 written materials, 17 interviews, and 6689 original posts and comments from Weibo, with a total of 1,923,593 Chinese characters. The emotional lexicon of patients with BC contained 9357 words and covered 8 fine-grained emotional categories: joy, anger, sadness, fear, disgust, surprise, somatic symptoms, and BC terminology. Experimental results showed that precision, recall, and the F1-score of positive emotional words were 98.42%, 99.73%, and 99.07%, respectively, and those of negative emotional words were 99.73%, 98.38%, and 99.05%, respectively, which all significantly outperformed the C-LIWC and HowNet. The emotional lexicon with fine-grained emotional categories conforms to the characteristics of patients with BC. Its performance related to identifying and classifying domain-specific emotional words in BC is better compared to the C-LIWC and HowNet. This lexicon not only provides a new tool for sentiment analysis in the field of BC but also provides a new perspective for recognizing the specific emotional state and needs of patients with BC and formulating tailored emotional management plans.

Background Social networks have become a vital space for breast cancer (BC) patients to share deeply personal emotions they might avoid expressing in real life. However, the unstructured and vast nature of these textual expressions poses challenges for manual analysis. To address this, our research team employed transfer learning methods to efficiently process and analyze large-scale text data for depression detection. Objective This study seeks to address the emotional struggles faced by women with BC, who often grapple with depression but lack accessible mental health support. This study aims to develop a transfer learning-based model to enable timely, non-invasive identification of depression through patients’ self-expressed texts, thereby offering a pathway to early intervention. Methods A mixed-methods framework integrated qualitative content analysis with deep learning. We recruited 300 BC patients (inpatients and online users). Depression status was assessed via the Self-rating Depression Scale (SDS), followed by collection and preprocessing of their self-expressed texts. Texts were manually annotated for depression scores/status, and formed a corpus. Content analysis was used to explore linguistic features. A BERT-based model pre-trained on a Weibo depression corpus was fine-tuned using clinical texts. Performance was evaluated via five-fold cross-validation, adversarial testing (word replacement, misspelling, deletion), and ablation studies. Model performance was evaluated using accuracy, precision, recall, and F 1 -score. The model was validated by robustness analysis and ablation studies. Results Participants were grouped into depressive (n = 88) and non-depressive (n = 212) cohorts, while financial burden ( P  = 0.025) and advanced cancer stage ( P  = 0.038) correlated with depression. Content analysis revealed significant differences in negative life attitudes ( P  < 0.05). The transfer learning model achieved 86.67% accuracy (F 1 -score = 0.79). The model demonstrated robustness to semantic noise but required spelling correction for clinical deployment. Conclusion This study established a culturally adapted detection framework. By combining social media pre-training and clinical fine-tuning, the model enables scalable, non-invasive depression screening, bridging cultural barriers to emotional disclosure. Future work should expand demographic diversity and integrate multimodal data for enhanced clinical utility.

Background Chemotherapy, whilst treating tumours, can also lead to numerous adverse reactions such as nausea and vomiting, fatigue and kidney toxicity, threatening the physical and mental health of patients. Simultaneously, misuse of chemotherapeutic drugs can seriously endanger patients' lives. Therefore, to maintain the safety of chemotherapy for cancer patients and to reduce the incidence of adverse reactions to chemotherapy, many guidelines state that a comprehensive assessment of the cancer patient should be conducted and documented before chemotherapy. This recommended procedure, however, has yet to be extensively embraced in Chinese hospitals. As such, this study aimed to standardise the content of pre-chemotherapy assessment for cancer patients in hospitals and to improve nurses' adherence to pre-chemotherapy assessment of cancer patients by conducting a national multi-site evidence implementation in China, hence protecting the safety of cancer patients undergoing chemotherapy and reducing the incidence of adverse reactions to chemotherapy in patients. Methods The national multi-site evidence implementation project was launched by a JBI Centre of Excellence in China and conducted using the JBI approach to evidence implementation. A pre- and post-audit approach was used to evaluate the effectiveness of the project. This project had seven phases: training, planning, baseline audit, evidence implementation, two rounds of follow-up audits (3 and 9 months after evidence implementation, respectively) and sustainability assessment. A live online broadcast allowed all participating hospitals to come together to provide a summary and feedback on the implementation of the project. Results Seventy-four hospitals from 32 cities in China participated in the project, four withdrew during the project's implementation, and 70 hospitals completed the project. The pre-and post-audit showed a significant improvement in the compliance rate of nurses performing pre-chemotherapy assessments for cancer patients. Patient satisfaction and chemotherapy safety were also improved through the project's implementation, and the participating nurses' enthusiasm and belief in implementing evidence into practice was increased. Conclusion The study demonstrated the feasibility of academic centres working with hospitals to promote the dissemination of evidence in clinical practice to accelerate knowledge translation. Further research is needed on the effectiveness of cross-regional and cross-organisational collaborations to facilitate evidence dissemination.

In this study, rice straw biochar modified with Co 3 O 4 -Fe 3 O 4 (RSBC@Co 3 O 4 -Fe 3 O 4 ) was successfully prepared via calcinating oxalate coprecipitation precursor and employed as a catalyst to activate peroxymonosulfate (PMS) for the treatment of Rhodamine B (RhB)-simulated wastewater. The results indicated that RSBC@Co 3 O 4 -Fe 3 O 4 exhibited high catalytic performance due to the synergy between Co 3 O 4 and Fe 3 O 4 doping into RSBC. Approximately 98% of RhB (180 mg/L) was degraded in the RSBC@Co 3 O 4 -Fe 3 O 4 /PMS system at initial pH 7 within 15 min. The degradation efficiency of RhB maintained over 90% after the fourth cycle, illustrating that RSBC@Co 3 O 4 -Fe 3 O 4 displayed excellent stability and reusability. The primary reactive oxygen species (ROS) answerable for the degradation of RhB were 1 O 2 , •OH, and SO 4 •− . Moreover, the intermediates involved in the degradation of RhB were identified and the possible degradation pathways were deduced. This work can provide a new approach to explore Co-based and BC-based catalysts for the degradation of organic pollutants.

In this study, rice straw biochar modified with Co O -Fe O (RSBC@Co O -Fe O ) was successfully prepared via calcinating oxalate coprecipitation precursor and employed as a catalyst to activate peroxymonosulfate (PMS) for the treatment of Rhodamine B (RhB)-simulated wastewater. The results indicated that RSBC@Co O -Fe O exhibited high catalytic performance due to the synergy between Co O and Fe O doping into RSBC. Approximately 98% of RhB (180 mg/L) was degraded in the RSBC@Co O -Fe O /PMS system at initial pH 7 within 15 min. The degradation efficiency of RhB maintained over 90% after the fourth cycle, illustrating that RSBC@Co O -Fe O displayed excellent stability and reusability. The primary reactive oxygen species (ROS) answerable for the degradation of RhB were O , •OH, and SO . Moreover, the intermediates involved in the degradation of RhB were identified and the possible degradation pathways were deduced. This work can provide a new approach to explore Co-based and BC-based catalysts for the degradation of organic pollutants.

The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001). Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.

Background Insect females undergo a huge transition in energy homeostasis after mating to compensate for nutrient investment during reproduction. To manage with this shift in metabolism, mated females experience extensive morphological, behavioral and physiological changes, including increased food intake and altered digestive processes. However, the mechanisms by which the digestive system responds to mating in females remain barely characterized. Here we performed transcriptomic analysis of the main digestive organ, the midgut, to investigate how gene expression varies with female mating status in Drosophila suzukii , a destructive and invasive soft fruit pest. Results We sequenced 15,275 unique genes with an average length of 1,467 bp. In total, 652 differentially expressed genes (DEGs) were detected between virgin and mated D. suzukii female midgut libraries. The DEGs were functionally annotated utilizing the GO and KEGG pathway annotation methods. Our results showed that the major GO terms associated with the DEGs from the virgin versus mated female midgut were largely appointed to the metabolic process, response to stimulus and immune system process. We obtained a mass of protein and lipid metabolism genes which were up-regulated and carbohydrate metabolism and immune-related genes which were down-regulated at different time points after mating in female midgut by qRT-PCR. These changes in metabolism and immunity may help supply the female with the nutrients and energy required to sustain egg production. Conclusion Our study characterizes the transcriptional mechanisms driven by mating in the D. suzukii female midgut. Identification and characterization of the DEGs between virgin and mated females midgut will not only be crucial to better understand molecular research related to intestine plasticity during reproduction, but may also provide abundant target genes for the development of effective and ecofriendly pest control strategies against this economically important species.