Social Media and Chronic Pain: What Do Patients Discuss?

What makes life go well? A network topic modeling analysis of well-being practices in adults with chronic pain

OBJECTIVE

This study leverages natural language processing techniques to identify specific practices older adults with chronic pain adopt to enhance well-being.

METHOD

We applied network topic modeling to open-ended survey responses from 683 adults (57% female) who reported experiencing chronic pain in the Midlife in the United States (MIDUS) study, analyzing responses to the question "What do you do to make your life go well?" Structural equation modeling was used to examine the relationships between identified topics and measures of pain interference and prescription pain medication use, adjusting for sociodemographics and well-being indicators.

RESULTS

The analyses revealed 12 key topics, including avoiding stress, maintaining social connections, and practicing spirituality and faith. Notably, maintaining social connections was negatively associated with pain interference (β = -0.14, SE = 0.05, P < .05) and prescription pain medication use (β = -0.11, SE = 0.04, P < .05).

CONCLUSION

The findings demonstrate the utility of network topic modeling in identifying complex psychosocial dimensions influencing chronic pain management, providing insights into the distinct role of well-being practices in shaping pain outcomes.

Social media users' perspectives of spinal cord stimulation: an analysis of data sourced from social media

OBJECTIVES

To identify Reddit users' viewpoints and inquiries about spinal cord stimulation (SCS) for chronic back pain using Reddit databases.

METHODS

We performed a qualitative analysis of public, anonymous threads and comments from targeted subreddits within the Reddit community. We used the Python Reddit API Wrapper to extract relevant data. A qualitative descriptive approach was employed, using natural language processing to identify and categorize common questions, concerns, and opinions shared by patients regarding SCS.

RESULTS

Our analysis included 112 posts and 448 comments. The tone of comments was neutral (n=231), followed by negative (n=121) and positive (n=96). 13 users actively encouraged other users to try the procedure, while 25 advised against it. The main topics of discussions revolved around pain relief expectations and adverse events. Almost half of users commenting about pain relief expectations reported experiencing considerably lower improvement than anticipated. Pocket pain, lead fracture/migration, infection risk, and scars were common topics of discussion among users. Furthermore, users shared strategies to mitigate postoperative discomfort and offered insights into device selection based on MRI conditionality, reprogramming need, and charging prerequisites.

CONCLUSION

Our Reddit analysis identified potential targets for enhanced dialog between physicians and patients around anticipated pain relief, complications, and postoperative care. Reddit and other social media platforms may offer valuable opportunities for healthcare professionals to improve engagement with patients.

Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum

BACKGROUND

Peer support for chronic pain is increasingly taking place on social media via social networking communities. Several theories on the development and maintenance of chronic pain highlight how rumination, catastrophizing, and negative social interactions can contribute to poor health outcomes. However, little is known regarding the role web-based health discussions play in the development of negative versus positive health attitudes relevant to chronic pain.

OBJECTIVE

This study aims to investigate how participation in online peer-to-peer support communities influenced pain expressions by examining how the sentiment of user language evolved in response to peer interactions.

METHODS

We collected the comment histories of 199 randomly sampled Reddit (Reddit, Inc) users who were active in a popular peer-to-peer chronic pain support community over 10 years. A total of 2 separate natural language processing methods were compared to calculate the sentiment of user comments on the forum (N=73,876). We then modeled the trajectories of users' language sentiment using mixed-effects growth curve modeling and measured the degree to which users affectively synchronized with their peers using bivariate wavelet analysis.

RESULTS

In comparison to a shuffled baseline, we found evidence that users entrained their language sentiment to match the language of community members they interacted with (t198=4.02; P<.001; Cohen d=0.40). This synchrony was most apparent in low-frequency sentiment changes unfolding over hundreds of interactions as opposed to reactionary changes occurring from comment to comment (F2,198=17.70; P<.001). We also observed a significant trend in sentiment across all users (β=-.02; P=.003), with users increasingly using more negative language as they continued to interact with the community. Notably, there was a significant interaction between affective synchrony and community tenure (β=.02; P=.02), such that greater affective synchrony was associated with negative sentiment trajectories among short-term users and positive sentiment trajectories among long-term users.

CONCLUSIONS

Our results are consistent with the social communication model of pain, which describes how social interactions can influence the expression of pain symptoms. The difference in long-term versus short-term affective synchrony observed between community members suggests a process of emotional coregulation and social learning. Participating in health discussions on Reddit appears to be associated with both negative and positive changes in sentiment depending on how individual users interacted with their peers. Thus, in addition to characterizing the sentiment dynamics existing within online chronic pain communities, our work provides insight into the potential benefits and drawbacks of relying on support communities organized on social media platforms.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain

PURPOSE OF REVIEW

This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

RECENT FINDINGS

In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Understanding chronic pain in the ubiquitous community: the role of open data

The combined use of social media, open data, and Artificial Intelligence has the potential to support practitioners and empower patients/citizens living with persistent pain, both as local and online communities. Given the wide availability of digital technology today, both practitioners and interested individuals can be connected with virtual communities and can support each other from the comfort of their homes. Digital means may represent new avenues for exploring the complexity of the pain experience. Online interactions of patients, data on effective treatments, and data collected by wearable devices may represent an incredible source of psychological, sociological, and physiological pain-related information. Digital means might provide several solutions that enhance inclusiveness and motivate patients to share personal experiences, limiting the sense of isolation in both rural and metropolitan areas. Building on the consensus of the usefulness of social media in enhancing the understanding of persistent pain and related subjective experiences via online communities and networks, we provide relevant scenarios where the effectiveness and efficiency of healthcare delivery might be improved by the adoption of the digital technologies mentioned above and repeated subsequently. The aim of this perspective paper is to explore the potential of open data, social media, and Artificial Intelligence in improving the prevention and management of persistent pain by adopting innovative non-biomedical approaches.

Facing Pain Together: A Randomized Controlled Trial of the Effects of Facebook Support Groups on Adults With Chronic Pain

Despite the popularity and affordances of social media groups for people with chronic conditions, there have been few controlled tests of the effects of these groups. This randomized controlled superiority trial examined the effects of Facebook groups on pain-related outcomes and tested whether a professional-led group leads to greater effects than a support group alone. We randomly assigned 119 adults with chronic pain to one of two Facebook group conditions: a standard condition (n = 60) in which participants were instructed to offer mutual support, or a professional-led condition (n = 59) in which the investigators disseminated empirically-supported, socially-oriented psychological interventions. Four groups were run (2 standard, 2 professional-led), each lasting 4 weeks and containing approximately 30 participants. Measures were administered at baseline, post-intervention, and 1-month follow-up. Across conditions, participants improved significantly in primary outcomes (pain severity and interference; medium-large effects) and a secondary outcome (depressive symptoms; small-medium effect), and they retained their outcomes 1 month after the groups ended. The 2 conditions did not differ on improvements. Overall, this study supports the use of social media groups as an additional tool to improve chronic pain-related outcomes. Our findings suggest that professional intervention may not have added value in these groups and that peer support alone may be driving improvements. Alternatively, the psychosocial intervention components used in the current study might have been ineffective, or more therapist direction may be warranted. Future research should examine when and how such guidance could enhance outcomes. PERSPECTIVE: Findings from this randomized trial support the use of both standard and professional-led Facebook groups as an accessible tool to enhance the lives of adults with chronic pain. This article provides direction for how to improve social media groups to optimize outcomes and satisfaction for more users.