Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval

Connected and supported: a scoping review of how online communities provide social support for breast cancer survivors

PURPOSE

To (i) assess how and to what extent online communities are used among breast cancer survivors (BCS) as a source of social support, (ii) describe the kind of support BCS access through online communities, and (iii) explore how these communities foster social support for BCS that promotes well-being and reduces the challenges of survivorship.

METHODS

We conducted a scoping review. A professional librarian performed a comprehensive search in multiple databases from January 2010 to May 2023. The review process adhered to the Johana Briggs Institute's method guidelines and the PRISMA-ScR reporting system.

RESULTS

Fifteen studies were included. Participants used social media, cancer support communities, message boards, or websites for information and emotional support. Qualitative findings resulted in four themes: to reassure; to empower; to promote equity, diversity, and inclusion; and to demonstrate for BCS the drawbacks of online support.

CONCLUSIONS

We underscore that a variety of internet websites and social media platforms are valuable for and appreciated by BCS, especially as a source of social support and human connectedness. Our study raises the existing gap in cultural/ethnic representation in this field and shows that institutional and organizational efforts are needed to address gaps in information regarding access to social support for multiethnic BCS women.

IMPLICATIONS FOR CANCER SURVIVORS

This data synthesis will empower the BCS community by sharing how they can strengthen and support their peers and community via their participation in online communities that connect and support cancer survivors in healthcare spaces.

Information needs during cancer care: Qualitative research with locally advanced cervical cancer patients in Brazil, China, Germany, & the US

Objective

This study aimed to identify the information needs and factors for making informed treatment decisions among a diverse group of locally advanced cervical cancer (LACC) patients.

Methods

Semi-structured interviews were conducted with LACC patients of diverse demographic and socioeconomic backgrounds within two years of their cancer diagnosis. Trained moderators asked open-ended questions about patients' cancer journeys. Transcripts were analyzed using NVivo software to identify emergent themes.

Results

In 2022, 92 LACC patients in the United States (n = 26), Brazil (n = 25), China (n = 25), and Germany (n = 16) participated in the study. Physicians were valued sources of information, providing patients with details on prognosis, treatment options, and side effects. While most patients trusted their physicians, one-third sought a second opinion to validate their diagnosis or find a more trusted physician.Most patients conducted their own research on treatment options, side effects, causes of LACC, symptoms, and others' experiences. Challenges to information searches included understanding medical terms, finding relevant information, and evaluating source credibility.Overall, patients felt knowledgeable enough to participate in treatment decisions, either by accepting the recommended treatment or collaborating with their physicians. Nearly one-third of patients desired a more significant role in the decision-making process.

Conclusion

This study highlights the importance of physicians providing LACC patients comprehensive and understandable information, while involving them in the decision-making process. Understanding LACC patients' motivations to seek information and their willingness to actively engage in treatment decisions can lead to improved patient satisfaction in their cancer care.

Extracting the latent needs of dementia patients and caregivers from transcribed interviews in japanese: an initial assessment of the availability of morpheme selection as input data with Z-scores in machine learning

BACKGROUND

Given the increasing number of dementia patients worldwide, a new method was developed for machine learning models to identify the 'latent needs' of patients and caregivers to facilitate patient/public involvement in societal decision making.

METHODS

Japanese transcribed interviews with 53 dementia patients and caregivers were used. A new morpheme selection method using Z-scores was developed to identify trends in describing the latent needs. F-measures with and without the new method were compared using three machine learning models.

RESULTS

The F-measures with the new method were higher for the support vector machine (SVM) (F-measure of 0.81 with the new method and F-measure of 0.79 without the new method for patients) and Naive Bayes (F-measure of 0.69 with the new method and F-measure of 0.67 without the new method for caregivers and F-measure of 0.75 with the new method and F-measure of 0.73 without the new method for patients).

CONCLUSION

A new scheme based on Z-score adaptation for machine learning models was developed to predict the latent needs of dementia patients and their caregivers by extracting data from interviews in Japanese. However, this study alone cannot be used to assign significance to the adaptation of the new method because of no enough size of sample dataset. Such pre-selection with Z-score adaptation from text data in machine learning models should be considered with more modified suitable methods in the near future.

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

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.

Predicting Patient Satisfaction With Medications for Treating Opioid Use Disorder: Case Study Applying Natural Language Processing to Reviews of Methadone and Buprenorphine/Naloxone on Health-Related Social Media

Background

Medication-assisted treatment (MAT) is an effective method for treating opioid use disorder (OUD), which combines behavioral therapies with one of three Food and Drug Administration-approved medications: methadone, buprenorphine, and naloxone. While MAT has been shown to be effective initially, there is a need for more information from the patient perspective about the satisfaction with medications. Existing research focuses on patient satisfaction with the entirety of the treatment, making it difficult to determine the unique role of medication and overlooking the views of those who may lack access to treatment due to being uninsured or concerns over stigma. Studies focusing on patients' perspectives are also limited by the lack of scales that can efficiently collect self-reports across domains of concerns.

Objective

A broad survey of patients' viewpoints can be obtained through social media and drug review forums, which are then assessed using automated methods to discover factors associated with medication satisfaction. Because the text is unstructured, it may contain a mix of formal and informal language. The primary aim of this study was to use natural language processing methods on text posted on health-related social media to detect patients' satisfaction with two well-studied OUD medications: methadone and buprenorphine/naloxone.

Methods

We collected 4353 patient reviews of methadone and buprenorphine/naloxone from 2008 to 2021 posted on WebMD and Drugs.com. To build our predictive models for detecting patient satisfaction, we first employed different analyses to build four input feature sets using the vectorized text, topic models, duration of treatment, and biomedical concepts by applying MetaMap. We then developed six prediction models: logistic regression, Elastic Net, least absolute shrinkage and selection operator, random forest classifier, Ridge classifier, and extreme gradient boosting to predict patients' satisfaction. Lastly, we compared the prediction models' performance over different feature sets.

Results

Topics discovered included oral sensation, side effects, insurance, and doctor visits. Biomedical concepts included symptoms, drugs, and illnesses. The F-score of the predictive models across all methods ranged from 89.9% to 90.8%. The Ridge classifier model, a regression-based method, outperformed the other models.

Conclusions

Assessment of patients' satisfaction with opioid dependency treatment medication can be predicted using automated text analysis. Adding biomedical concepts such as symptoms, drug name, and illness, along with the duration of treatment and topic models, had the most benefits for improving the prediction performance of the Elastic Net model compared to other models. Some of the factors associated with patient satisfaction overlap with domains covered in medication satisfaction scales (eg, side effects) and qualitative patient reports (eg, doctors' visits), while others (insurance) are overlooked, thereby underscoring the value added from processing text on online health forums to better understand patient adherence.

Educational and Emotional Needs of Patients with Myelodysplastic Syndromes: An AI Analysis of Multi-Country Social Media

INTRODUCTION

Myelodysplastic syndromes (MDS) comprise a heterogeneous group of myeloid malignancies characterized by high symptom burden and limited treatment options. A central challenge to caring for patients with MDS is assessing their needs throughout the different phases of the disease. Patients and caregivers frequently consult online sources to address informational and emotional support needs.

METHODS

We conducted a social listening analysis of publicly available online forums to identify unmet needs of patients with MDS and their caregivers in the USA, the UK, Spain, Canada, France, and China. We used artificial intelligence (AI) and natural language processing (NLP) to group categories of posts into seven overarching motivations for online engagement (Clinical, Emotional, Treatments, Transplant, Education and Logistics, Physical, and Diet and Lifestyle).

RESULTS

Posts from the USA and China commonly discussed clinical topics such as MDS diagnosis, disease monitoring, and progression. Posts from Canada and France were frequently about treatments and treatment options. Emotional concerns were key drivers of posts from Canada, Spain, and the UK. Additionally, we also identified topics associated with negative language at key phases during the treatment experience where patients and caregivers exhibited increased online engagement, revealing educational and emotional support gaps at the time of diagnosis, when patients are deciding between treatment options, and when treatment options fail.

CONCLUSION

In this research, based on social media listening analyzed using AI and NLP, potential information gaps and unmet needs among patients with MDS were identified. Addressing these gaps through targeted patient education and guidance to emotional support options during these phases could reduce the disease burden and emotional distress experienced by patients with MDS.

A Digital Cancer Ecosystem to Deliver Health and Psychosocial Education as Preventive Intervention

Health education and psychosocial interventions prevent emotional distress, and the latter has been shown to have an impact on survival. In turn, digital health education interventions may help promote equity by reaching a higher number of cancer patients, both because they avoid journeys to the hospital, by and having a better efficiency. A total of 234 women recently diagnosed with breast cancer in a comprehensive cancer center used the digital ecosystem ICOnnecta’t from March 2019 to March 2021. ICOnnecta’t consists of four care levels, provided to patients according to their level of distress. The second level of this intervention consists of an educational campus, which was analyzed to track users’ interests and their information-seeking behavior. Overall, 99 out of 234 women (42.3%) used the educational campus. There were no significant differences in sociodemographic and clinical variables between the campus users and non-users. Among users, the median number of resources utilized per user was four (interquartile range: 2−9). Emotional and medical resources were the contents most frequently viewed and the audiovisual format the most consulted (p < 0.01). Resources were used mainly within the first three months from enrolment. Users who were guided to visit the virtual campus were more active than spontaneous users. Offering an early holistic health educational platform inside a digital cancer ecosystem, with health professionals involved, can reach more patients, promoting equity in the access of cancer information and prevention, from the very beginning of the disease.

Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions

Background

Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences.

Objective

This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application.

Methods

We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools.

Results

Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied.

Conclusions

This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

Machine learning in oncology—Perspectives in patient-reported outcome research

Background

Increasing data volumes in oncology pose new challenges for data analysis. Machine learning, a branch of artificial intelligence, can identify patterns even in very large and less structured datasets.

Objective

This article provides an overview of the possible applications for machine learning in oncology. Furthermore, the potential of machine learning in patient-reported outcome (PRO) research is discussed.

Materials and methods

We conducted a selective literature search (PubMed, MEDLINE, IEEE Xplore) and discuss current research.

Results

There are three primary applications for machine learning in oncology: (1) cancer detection or classification; (2) overall survival prediction or risk assessment; and (3) supporting therapy decision-making and prediction of treatment response. Generally, machine learning approaches in oncology PRO research are scarce and few studies integrate PRO data into machine learning models.

Discussion

Machine learning is a promising area of oncology, but few models have been transferred into clinical practice. The promise of personalized cancer therapy and shared decision-making through machine learning has yet to be realized. As an equally important emerging research area in oncology, PROs should also be incorporated into machine learning approaches. To gather the data necessary for this, broad implementation of PRO assessments in clinical practice, as well as the harmonization of existing datasets, is suggested.

Assessing how health information needs of individuals with colorectal cancer are met across the care continuum: an international cross-sectional survey

BACKGROUND

Studies evaluating health information needs in colorectal cancer (CRC) lack specificity in terms of study samples involving patients. We assessed how health information needs of individuals with CRC are met across the care continuum.

METHODS

We administered an international, online based survey. Participants were eligible for the study if they: 1) were 18 years of age or older; 2) received a diagnosis of CRC; and 3) were able to complete the online health survey in English, French, Spanish, or Mandarin. We grouped participants according to treatment status. The survey comprised sections: 1) demographic and cancer characteristics; 2) health information needs; and 3) health status and quality of life. We used multivariable regression models to identify factors associated with having health information needs met and evaluated impacts on health-related outcomes.

RESULTS

We analyzed survey responses from 1041 participants including 258 who were currently undergoing treatment and 783 who had completed treatment. Findings suggest that information needs regarding CRC treatments were largely met. However, we found unmet information needs regarding psychosocial impacts of CRC. This includes work/employment, mental health, sexual activity, and nutrition and diet. We did not identify significant predictors of having met health information needs, however, among participants undergoing treatment, those with colon cancer were more likely to have met health information needs regarding their treatments as compared to those with rectal cancer (0.125, 95% CI, 0.00 to 0.25, p-value = 0.051).

CONCLUSIONS

Our study provides a comprehensive assessment of health information needs among individuals with CRC across the care continuum.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Information Needs of Breast Cancer Patients: Theory-Generating Meta-Synthesis

BACKGROUND

Breast cancer has become one of the most frequently diagnosed carcinomas and the leading cause of cancer deaths. The substantial growth in the number of breast cancer patients has put great pressure on health services. Meanwhile, the information patients need has increased and become more complicated. Therefore, a comprehensive and in-depth understanding of their information needs is urgently needed to improve the quality of health care. However, previous studies related to the information needs of breast cancer patients have focused on different perspectives and have only contributed to individual results. A systematic review and synthesis of breast cancer patients' information needs is critical.

OBJECTIVE

This paper aims to systematically identify, evaluate, and synthesize existing primary qualitative research on the information needs of breast cancer patients.

METHODS

Web of Science, EBSCO, Scopus, ProQuest, PubMed, PsycINFO, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature were searched on February 12 and July 9, 2019, to collect relevant studies. A Google Scholar search, interpersonal network recommendations, and reference chaining were also conducted. Eligible studies included qualitative or mixed-methods studies focusing on the information needs (across the cancer continuum) of breast cancer patients or their social networks. Subsequently, a Critical Appraisals Skills Programme checklist was used to assess the quality of included research. The results, findings, and discussions were extracted. Data analysis was guided by the theory-generating meta-synthesis and grounded theory approach.

RESULTS

Three themes, 19 categories, and 55 concepts emerged: (1) incentives (physical abnormality, inquiry from others, subjective norm, and problems during appointments); (2) types of information needs (prevention, etiology, diagnosis, clinical manifestation, treatment, prognosis, impact and resumption of normal life, scientific research, and social assistance); (3) moderating variables (attitudes, health literacy, demographic characteristics, disease status, as well as political and cultural environment). The studies revealed that the information needs of breast cancer patients were triggered by different incentives. Subsequently, the patients sought a variety of information among different stages of the cancer journey. Five types of variables were also found to moderate the formation of information needs.

CONCLUSIONS

This study contributes to a thorough model of information needs among breast cancer patients and provides practical suggestions for health and information professionals.

Effects of a WhatsApp-Delivered Education Intervention to Enhance Breast Cancer Knowledge in Women: Mixed-Methods Study

Background

Breast cancer is the leading cause of cancer-related death in the female population. Health education interventions based on the use of mobile technologies enable the development of health self-care skills and have emerged as alternative strategies for the control of breast cancer. In previous studies, WhatsApp has stood out as a useful tool in health education strategies; however, it has not yet been applied for breast cancer education.

Objective

This study aimed to analyze the potential of WhatsApp as a health education tool used to improve women's knowledge on the risk reduction of breast cancer. It also aimed to understand how women feel sensitized within the WhatsApp group throughout the intervention and how they incorporate information posted to improve knowledge about early detection and risk reduction methods.

Methods

The study involved a pre-post health educational intervention with 35 women (aged 45-69 years) included in a WhatsApp group to share information (audio, video, text, and images) over 3 weeks on the early detection and risk reduction of breast cancer. Data were collected through questionnaires on topics related to risk reduction, as well as qualitative content analysis of group interactions. Effectiveness and feasibility were analyzed through conversations and the comparison of the scores obtained in the questionnaires before and after the intervention.

Results

A total of 293 messages were exchanged (moderator 120 and users 173). The average scores of the participants were 11.21 and 13.68 points before and after the educational intervention, respectively, with sufficient sample evidence that the difference was significant (P<.001). The intervention enabled women to improve their knowledge on all topics addressed, especially “myths and truths,” “incidence,” “clinical manifestations,” and “protective factors.” Some themes emerged from the interactions in the group, including group dynamics, general doubts, personal narratives, religious messages, daily news, and events.

Conclusions

The use of groups for women in WhatsApp for health education purposes seems to be a viable alternative in strategies on breast cancer control, especially as it provides a space for the exchange of experiences and disinhibition. However, the need for a moderator to answer the questions and the constant distractions by members of the group represent important limitations that should be considered when improving this strategy.

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning

Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query" (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Defining Essential Elements of Caregiver Support in Gynecologic Cancers Using the Modified Delphi Method

PURPOSE

Family caregivers are instrumental to patients with gynecologic cancer and can be deeply affected by the demands of caregiving. Our aims were as follows: (1) increase awareness of unmet needs of caregivers and (2) identify and prioritize the unmet needs of caregivers and essential support services to be provided in gynecologic cancer centers.

METHODS

From July 2017 to June 2018, a 16-person steering committee and 40 stakeholders, including caregivers, patients, and clinicians, participated in a modified Delphi approach to generate, review, and prioritize a set of essential elements for caregiver support. Stakeholders prioritized caregiver needs and brainstormed, discussed, and prioritized essential support services to meet those needs, using three rounds of a consensus-building protocol. Basic descriptive statistics were performed to feed means and rankings back to stakeholders before each round.

RESULTS

The top three caregiver needs were as follows: (1) to obtain information about their loved one's cancer, (2) how to provide support and comfort to their loved one, and (3) how to maintain their own emotional health and well-being. Fifteen essential elements of support for caregivers of patients with gynecologic cancer were identified and ranged from elements currently attainable (eg, information on managing symptoms, skilled-care training, a point person to help navigate the system) to more aspirational elements (eg, integrative services to promote caregiver well-being).

CONCLUSION

To ensure comprehensive quality care, clinicians and health care providers should strive to provide caregivers with the identified essential elements of support. Health care settings should work to incorporate caregiver needs into cancer care delivery.

Paying attention to relatives of cancer patients: What can we learn from their online writings?

OBJECTIVE

Relatives of cancer patients often play a crucial role in care, while their own needs generally receive scant attention. We investigate those topics on which relatives share information online.

METHODS

We coded user-generated content written by 185 relatives on a major Dutch cancer site (kanker.nl), into three main categories: 'Disease', 'Well-being' and 'Other subjects'. In addition, we analysed five websites (from five countries) for which content they provide that is relevant for relatives.

RESULTS

Our analysis showed that across cancer types, relatives share online information and emotions. Quantitative analysis showed that they mainly write about topics related to their own well-being (blog posters: 45% of the posts and group posters 64%). Blog posters found the disease-related topics more important than the group posters (45% and 29%).

CONCLUSIONS

This study has shown that relatives share different kinds of user-generatedcontent related to their own situation. This could be a valuable resource for further research into the needs of relatives, and a very useful source for identification of emotional and informational topics.

PRACTICE IMPLICATIONS

It is crucial that relatives are enabled to occupy their own space in the disease-and-treatment process appropriate to their needs and to help avoid caregiver burden.