Social Media Listening to Understand the Lived Experience of Individuals in Europe With Metastatic Breast Cancer: A Systematic Search and Content Analysis Study

Social media listening study to understand the journey and unmet needs of patients living with post-stroke spasticity

PURPOSE

Stroke survivors may develop spasticity (post-stroke spasticity [PSS]) that can challenge activity and participation. Recognising the needs and expectations of people with PSS is crucial for enhancing care. This study is the first to employ social media listening to explore the experiences, unmet needs, and quality of life (QoL) of people with PSS.

MATERIALS AND METHODS

A subset of 417 patient-centric PSS-related posts published on major social media platforms was identified for analysis from 31 600 retrieved.

RESULTS

Posts mainly discussed patient journey (centred around treatment options and management techniques), the impact of PSS on QoL, and patient interactions with healthcare practitioners (HCPs). Widely used treatment options had associated negative sentiments due to perceived inefficacy and lack of long-term effectiveness (botulinum neurotoxin) or side effects (oral anti-spasticity medications). Perceptions of treatment options and expected treatment outcomes influenced satisfaction with treatment and HCP interactions. Poor perceived treatment efficacy generally resulted in dissatisfaction with HCP interactions and seeking peer opinions online. Identified unmet needs focused on need for satisfactory treatment options, well-informed HCPs, and better patient education.

CONCLUSIONS

The study highlights the need for improved education for patients, caregivers, and HCPs regarding PSS and better communication between patients and HCPs to manage treatment expectations.

Detection of differences in physical symptoms between depressed and undepressed patients with breast cancer: a study using K-medoids clustering

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.

Social media to understand the endometriosis patient journey: Listening to influences driving treatment choices

BACKGROUND

Previous research has identified treatment attributes and outcomes for endometriosis patients, highlighting the need for tailored interventions to improve patient care. These studies emphasize the need to understand patient experiences, focusing on pain symptoms and factors that impact quality of life.

OBJECTIVES

This study aimed to provide an in-depth description of patient experiences with endometriosis related to treatment attributes and outcomes, including three key types of pain symptoms: Non-menstrual pelvic pain, dyspareunia, and dysmenorrhea, and non-pain factors such as out-of-pocket costs.

DESIGN

In this retrospective qualitative study, 47,745 public social media posts from the United States from December 2021 to December 2022 were analyzed.

METHODS

Boolean queries were created incorporating criteria to identify public posts referencing endometriosis and language indicative of patients expressed in the first-person point of view. Data were summarized via descriptive statistics.

RESULTS

Findings confirmed the relevance of non-menstrual pain, dyspareunia, and dysmenorrhea for treatment decision-making. Dysmenorrhea, described as excruciating and debilitating, was the most discussed symptom (10% of posts) followed by dyspareunia (3% of posts), with emotional and physical impacts detailed. Non-menstrual pain was specified in 1% of posts, including the follicular, ovulatory, and luteal phases of the menstrual cycle. Key themes that emerged organically included the impact of endometriosis on daily functioning, mental health, intimacy, fertility, and the role of online misinformation. Discussions detailed the struggle to balance symptom relief with long-term solutions, and frustration with diagnosis.

CONCLUSION

The current study highlights the difficulties patients experience with dysmenorrhea, dyspareunia, and non-menstrual pain and the challenges to treatment decisions. The relevance of social media for patient expression of their disease experience, the importance of recognizing the individualized needs of patients necessitating their active involvement in treatment decision-making, and the need for education about treatment options beyond surgery also emerged in the findings.

Using Natural Language Processing and Social Media Data to Understand the Lived Experience of People with Fibromyalgia

BACKGROUND AND OBJECTIVES

Fibromyalgia has many unmet needs relating to treatment, and the delivery of effective and evidence-based healthcare is lacking. We analyzed social media conversations to understand the patients' perspectives on the lived experience of fibromyalgia, factors reported to trigger flares of pain, and the treatments being discussed, identifying barriers and opportunities to improve healthcare delivery.

METHODS

A non-interventional retrospective analysis accessed detail-rich conversations about fibromyalgia patients' experiences with 714,000 documents, including a fibromyalgia language tag, which were curated between May 2019 and April 2021. Data were analyzed via qualitative and quantitative analyses.

RESULTS

Fibromyalgia conversations were found the most on Twitter and Reddit, and conversation trends remained stable over time. There were numerous environmental and modifiable triggers, ranging from the most frequent trigger of stress and anxiety to various foods. Arthritis and irritable bowel syndrome (IBS) were the most frequently associated comorbidities. Patients with fibromyalgia reported a wide range of symptoms, with pain being a cardinal feature. The massage, meditation and acupuncture domains were the most reported treatment modalities. Opportunities to improve healthcare delivered by medical providers were identified with current frustration relating to a lack of acknowledgement of their disease, minimization of symptoms and inadequately meeting their care needs.

CONCLUSIONS

We developed a comprehensive, large-scale study which emphasizes advanced natural language processing algorithm application in real-world research design. Through the extensive encapsulation of patient perspectives, we outlined the habitual symptoms, triggers and treatment modalities which provide a durable foundation for addressing gaps in healthcare provision.

A social media listening study of patients' experiences relating to metabolic dysfunction-associated steatotic liver disease: The LISTEN-MASLD study

INTRODUCTION AND OBJECTIVES

Patients increasingly use social media to share and access health-related information and experiences. This study employed social media listening to gain patient-centric insights into metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD).

MATERIALS AND METHOD

Publicly available social media data was collected between November 4th, 2020, and November 4th, 2022, about MASLD from eight countries: Brazil, China, France, Germany, Japan, South Korea, Spain, and the United Kingdom. The analysis involved capturing patient conversations on their journey stages (causes-risk factors, symptoms, diagnosis, and treatment), unmet needs, and impact on patient's quality of life (QoL) from various social media platforms to gain insights associated with MASLD.

RESULTS

A total of 1600 patient-centric posts were analyzed. The patient journey was the most prevalent topic (92 %) mentioned, followed by comorbidities (38 %) and interactions with HCPs (26 %, health care professionals). Key causes discussed included unhealthy diet (39 %) and overweight/obese (32 %), while the most mentioned symptoms were fatigue (20 %) and pain or abdominal discomfort (20 %). Ultrasound (n=170/553, 31 %) was the most used diagnosis, followed by blood tests (n=130/553, 24 %) and liver function tests (n=91/553, 16 %). Lifestyle management techniques were mainly the standard of care, followed by treatment (n=270/1061, 25 %) and follow-ups with HCPs (n=133/1061, 13 %). Over half (54 %) of the QoL discussion (n=104/192) focused on patients' comorbidities, and 27 % on disease severity, indicating that having MASLD in moderate to severe form with comorbidities significantly affects patients' quality of life. An emotional analysis revealed that patients were worried and frustrated about their condition but were also hopeful and determined to improve their health. Nearly 38 % of the posts mentioned that patients were emotionally affected by negative feelings, especially those with multiple comorbidities. Lack of access to knowledgeable HCPs and treatment options were the most frequently discussed unmet needs.

CONCLUSIONS

This analysis of NAFLD patient experiences introduces a unique approach for deriving insights into patients' experiences and their impact on QoL. These insights have the potential to complement conventional methods and foster patient-centric research.

Artificial intelligence-enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies

This article examines the opportunities and benefits of artificial intelligence (AI)-enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate the patient perspective to improve the quality, relevance, safety, and efficiency of drug development and evaluation. Gathering patient perspectives to support PFDD is aided by the participation of patient groups in communicating their treatment experiences, needs, preferences, and priorities through online platforms. SML is a method of gathering feedback directly from patients; however, distilling the quantity of data into actionable insights is challenging. AI-enabled methods, such as natural language processing (NLP), can facilitate data processing from SML studies. Herein, we describe a novel, trainable, AI-enabled, SML workflow that classifies posts made by patients or caregivers and uses NLP to provide data on their experiences. Our approach is an iterative process that balances human expert-led milestones and AI-enabled processes to support data preprocessing, patient and caregiver classification, and NLP methods to produce qualitative data. We explored the applicability of this workflow in 2 studies: 1 in patients with head and neck cancers and another in patients with esophageal cancer. Continuous refinement of AI-enabled algorithms was essential for collecting accurate and valuable results. This approach and workflow contribute to the establishment of well-defined standards of SML studies and advance the methodologic quality and rigor of researchers contributing to, conducting, and evaluating SML studies in a PFDD context.

Results of the Italian cross-sectional web-based survey "Nutrition and breast cancer, what would you like to know?" An attempt to collect and respond to patients' information needs, through social media

Introduction

Several studies have demonstrated that, following a breast cancer (BC) diagnosis, patients are eager to obtain information on cancer and nutrition, in order to ameliorate both their quality of life (QoL) and disease outcome. To avoid BC survivors to get wrong information from unreliable sources, healthcare providers need to be aware of patients' needs, to guide them toward optimal nutrition recommendations, aimed at preventing tumor recurrence and increasing survival rates.

Material and methods

The cross-sectional web-based survey "Nutrition and breast cancer, what would you like to know?" has been conceived and conducted, in Italy, between the 2nd and the 25th of June 2023. The link to the 19-items questionnaire, structured in 6 sections, was distributed via social media (Facebook and Instagram), newsletter, institutional websites, and printed flyers. Patients' responses were collected and analyzed, reporting absolute and relative frequencies.

Results

A total of 1616 participants (98.9% female and 1.1% male), with an average age of 47.5 years, answered the survey. Only subjects who declared having previously received a BC diagnosis (N=1159, 71.7%) were included in the present analysis. Overall, the respondents showed a wide interest in understanding whether nutrition might help to manage therapy side effects, as well as knowing how specific diets, foods, nutrients, and supplements could affect disease onset, progression and prognosis. Importantly, the need to receive evidence-based information from the "referring physician/specialist" and "nutritionist/dietitian" was expressed by 95.8% and 88.8% of them, respectively.

Discussion

In this study, we primarily aimed at intercepting nutrition information needs and sources of an Italian BC survivors' group. Based on that, we first organized a proactive digital intervention, to respond via Instagram live broadcasts to patients' "cancer and nutrition"-related questions. Secondly, we arranged a healthcare providers dedicated-workshop focused on the latest evidence-based knowledge on nutrition and BC. It is crucial, in fact, that once healthcare professionals capture patients' information needs, they can respond with appropriate nutritional guidance, counseling and education programs, while counteracting misleading and incorrect messages.

Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators

Patients, life science industry and regulatory authorities are united in their goal to reduce the disease burden of patients by closing remaining unmet needs. Patients have, however, not always been systematically and consistently involved in the drug development process. Recognizing this gap, regulatory bodies worldwide have initiated patient-focused drug development (PFDD) initiatives to foster a more systematic involvement of patients in the drug development process and to ensure that outcomes measured in clinical trials are truly relevant to patients and represent significant improvements to their quality of life. As a source of real-world evidence (RWE), social media has been consistently shown to capture the first-hand, spontaneous and unfiltered disease and treatment experience of patients and is acknowledged as a valid method for generating patient experience data by the Food and Drug Administration (FDA). While social media listening (SML) methods are increasingly applied to many diseases and use cases, a significant piece of uncertainty remains on how evidence derived from social media can be used in the drug development process and how it can impact regulatory decision making, including legal and ethical aspects. In this policy paper, we review the perspectives of three key stakeholder groups on the role of SML in drug development, namely patients, life science companies and regulators. We also carry out a systematic review of current practices and use cases for SML and, in particular, highlight benefits and drawbacks for the use of SML as a way to identify unmet needs of patients. While we find that the stakeholders are strongly aligned regarding the potential of social media for PFDD, we identify key areas in which regulatory guidance is needed to reduce uncertainty regarding the impact of SML as a source of patient experience data that has impact on regulatory decision making.

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

PURPOSE

Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative.

METHOD

To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model.

RESULT

The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores.

CONCLUSION

The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.

Understanding the Needs and Lived Experiences of Patients With Graft-Versus-Host Disease: Real-World European Public Social Media Listening Study

BACKGROUND

Graft-versus-host disease (GVHD) is the major cause of short- and long-term morbidity and mortality after allogeneic hematopoietic stem cell transplantation. Treatment options beyond corticosteroid therapy remain limited, and prolonged treatment often leads to impaired quality of life (QoL). A better understanding of the needs and experiences of patients with GVHD is required to improve patient care.

OBJECTIVE

The aim of this study is to explore different social media (SM) channels for gathering and analyzing the needs and experiences of patients and other stakeholders across 14 European countries.

METHODS

We conducted a retrospective analysis of SM data from the public domain. The Talkwalker social analytics tool collected data from open-access forums, blogs, and various social networking sites using predefined search strings. The raw data set derived from the aggregator tool was automatically screened for the relevancy of posts, generating the curated data set that was manually reviewed to identify posts that fell within the predefined inclusion and exclusion criteria. This final data set was then used for the deep-dive analysis.

RESULTS

A total of 9016 posts relating to GVHD were identified between April 2019 and April 2021. Deduplication and relevancy checks resulted in 325 insightful posts, with Twitter contributing 250 (77%) posts; blogs, 49 (15%) posts; forums, 13 (4%) posts; Facebook, 7 (2%) posts; and Instagram and YouTube, 4 (1%) posts. Patients with GVHD were the primary stakeholders, contributing 63% of all SM posts. In 234 posts, treatment was the most discussed stage of the patient journey (68%), followed by symptoms (33%), and diagnosis and tests (21%). Among treatment-related posts (n=159), steroid therapy was most frequently reported (54/159, 34%). Posts relating to treatment features (n=110) identified efficacy (45/110, 41%), side effects (38/110, 35%), and frequency and dosage (32/110, 29%), as the most frequently discussed features. Symptoms associated with GVHD were described in 24% (77/325) of posts, including skin-related conditions (49/77, 64%), dry eyes or vision change (13/77, 17%), pain and cramps (16/77, 21%), and fatigue or muscle weakness (12/77, 16%). The impacts of GVHD on QoL were discussed in 51% (165/325) of all posts, with the emotional, physical and functional, social, and financial impacts mentioned in 69% (114/165), 50% (82/165), 5% (8/165), and 2% (3/165) of these posts, respectively. Unmet needs were reported by patients or caregivers in 24% (77/325) of analyzed conversations, with treatment-related side effects being the most common (35/77, 45%) among these posts.

CONCLUSIONS

SM listening is a useful tool to identify medical needs. Treatment of GVHD, including treatment-related side effects, as well as its emotional and physical impact on QoL, are the major topics that GVHD stakeholders mention on SM. We encourage a structured discussion of these topics in interactions between health care providers and patients with GVHD.

TRIAL REGISTRATION

Not applicable.

Perspectives and Experiences of Patients With Thyroid Cancer at a Global Level: Retrospective Descriptive Study of Twitter Data

BACKGROUND

Twitter has become a popular platform for individuals to broadcast their daily experiences and opinions on a wide range of topics and emotions. Tweets from patients with cancer could offer insights into their needs. However, limited research has been conducted using Twitter data to understand the needs of patients with cancer despite the substantial amount of health-related data posted on the platform daily.

OBJECTIVE

This study aimed to uncover the potential of using Twitter data to understand the perspectives and experiences of patients with thyroid cancer at a global level.

METHODS

 This retrospective descriptive study collected tweets relevant to thyroid cancer in 2020 using the Twitter scraping tool. Only English-language tweets were included, and data preprocessing was performed to remove irrelevant tweets, duplicates, and retweets. Both tweets and Twitter users were manually classified into various groups based on the content. Each tweet underwent sentiment analysis and was classified as either positive, neutral, or negative.

RESULTS

A total of 13,135 tweets related to thyroid cancer were analyzed. The authors of the tweets included patients with thyroid cancer (3225 tweets, 24.6%), patient's families and friends (2449 tweets, 18.6%), medical journals and media (1733 tweets, 13.2%), health care professionals (1093 tweets, 8.3%), and medical health organizations (940 tweets, 7.2%), respectively. The most discussed topics related to living with cancer (3650 tweets, 27.8%), treatment (2891 tweets, 22%), diagnosis (1613 tweets, 12.3%), risk factors and prevention (1137 tweets, 8.7%), and research (953 tweets, 7.3%). An average of 36 tweets pertaining to thyroid cancer were posted daily. Notably, the release of a film addressing thyroid cancer and the public disclosure of a news reporter's personal diagnosis of thyroid cancer resulted in a significant escalation in the volume of tweets. From the sentiment analysis, 53.5% (7025/13,135) of tweets were classified as neutral statements and 32.7% (4299/13,135) of tweets expressed negative emotions. Tweets from patients with thyroid cancer had the highest proportion of negative emotion (1385/3225 tweets, 42.9%), particularly when discussing symptoms.

CONCLUSIONS

 This study provides new insights on using Twitter data as a valuable data source to understand the experiences of patients with thyroid cancer. Twitter may provide an opportunity to improve patient and physician engagement or apply as a potential research data source.

Perceived Unmet Needs in Patients and Caregivers Living With Advanced Bladder Cancer: An Infodemiology Study Using Data From Social Media in the United States (Preprint)

Background

Locally advanced or metastatic bladder cancer (BC), which is generally termed advanced BC (aBC), has a very poor prognosis, and in addition to its physical symptoms, it is associated with emotional and social challenges. However, few studies have assessed the unmet needs and burden of aBC from patient and caregiver perspectives. Infodemiology, that is, epidemiology based on internet health-related content, can help obtain more insights on patients’ and caregivers’ experiences with aBC.

Objective

The study aimed to identify the main discussion themes and the unmet needs of patients with aBC and their caregivers through a mixed methods analysis of social media posts.

Methods

Social media posts were collected between January 2015 and April 2021 from US geolocalized sites using specific keywords for aBC. Automatic natural language processing (regular expressions and machine learning) methods were used to filter out irrelevant content and identify verbatim posts from patients and caregivers. The verbatim posts were analyzed to identify main discussion themes using biterm topic modeling. Difficulties or unmet needs were further explored using qualitative research methods by 2 independent annotators until saturation of concepts.

Results

A total of 688 posts from 262 patients and 1214 posts from 679 caregivers discussing aBC were identified. Analysis of 340 randomly selected patient posts and 423 randomly selected caregiver posts uncovered 33 unique unmet need categories among patients and 36 among caregivers. The main unmet patient needs were related to challenges regarding adverse events (AEs; 28/95, 29%) and the psychological impact of aBC (20/95, 21%). Other patient unmet needs identified were prognosis or diagnosis errors (9/95, 9%) and the need for better management of aBC symptoms (9/95, 9%). The main unmet caregiver needs were related to the psychological impacts of aBC (46/177, 26.0%), the need for support groups and to share experiences between peers (28/177, 15.8%), and the fear and management of patient AEs (22/177, 12.4%).

Conclusions

The combination of manual and automatic methods allowed the extraction and analysis of several hundreds of social media posts from patients with aBC and their caregivers. The results highlighted the emotional burden of cancer for both patients and caregivers. Additional studies on patients with aBC and their caregivers are required to quantitatively explore the impact of this disease on quality of life.