Facts and figures or prayers and hugs: how people with different health conditions support each other online
7 March 2016
e-health, findings, Health, health discussion forums, health forum, healthcare, information online, internet studies, machine learning, Methods, online forums, online health, online support groups, personalised Health and Care 2020, policy, Social Data Science, Wellbeing
Online support groups are being used increasingly by individuals who suffer from a wide range of medical conditions. OII DPhil Student Ulrike Deetjen‘s recent article with John Powell, Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis uses machine learning to examine the role of online support groups in the healthcare process. They categorise 40,000 online posts from one of the most well-used forums to show how users with different conditions receive different types of support.
Online support groups are one of the major ways in which the Internet has fundamentally changed how people experience health and health care. They provide a platform for health discussions formerly restricted by time and place, enable individuals to connect with others in similar situations, and facilitate open, anonymous communication.
Previous studies have identified that individuals primarily obtain two kinds of support from online support groups: informational (for example, advice on treatments, medication, symptom relief, and diet) and emotional (for example, receiving encouragement, being told they are in others’ prayers, receiving “hugs”, or being told that they are not alone). However, existing research has been limited as it has often used hand-coded qualitative approaches to contrast both forms of support, thereby only examining relatively few posts (<1,000) for one or two conditions.
In contrast, our research employed a machine-learning approach suitable for uncovering patterns in “big data”. Using this method a computer (which initially has no knowledge of online support groups) is given examples of informational and emotional posts (2,000 examples in our study). It then “learns” what words are associated with each category (emotional: prayers, sorry, hugs, glad, thoughts, deal, welcome, thank, god, loved, strength, alone, support, wonderful, sending; informational: effects, started, weight, blood, eating, drink, dose, night, recently, taking, side, using, twice, meal). The computer then uses this knowledge to assess new posts, and decide whether they contain more emotional or informational support.
With this approach we were able to determine the emotional or informational content of 40,000 posts across 14 different health conditions (breast cancer, prostate cancer, lung cancer, depression, schizophrenia, Alzheimer’s disease, multiple sclerosis, cystic fibrosis, fibromyalgia, heart failure, diabetes type 2, irritable bowel syndrome, asthma, and chronic obstructive pulmonary disease) on the international support group forum Dailystrength.org.
Our research revealed a slight overall tendency towards emotional posts (58% of posts were emotionally oriented). Across all diseases, those who write more also tend to write more emotional posts—we assume that as people become more involved and build relationships with other users they tend to provide more emotional support, instead of simply providing information in one-off interactions. At the same time, we also observed that older people write more informational posts. This may be explained by the fact that older people more generally use the Internet to find information, that they become experts in their chronic conditions over time, and that with increasing age health conditions may have less emotional impact as they are relatively more expected.
The demographic prevalence of the condition may also be enmeshed with the disease-related tendency to write informational or emotional posts. Our analysis suggests that content differs across the 14 conditions: mental health or brain-related conditions (such as depression, schizophrenia, and Alzheimer’s disease) feature more emotionally oriented posts, with around 80% of posts primarily containing emotional support. In contrast, nonterminal physical conditions (such as irritable bowel syndrome, diabetes, asthma) rather focus on informational support, with around 70% of posts providing advice about symptoms, treatments, and medication.
Finally, there was no gender difference across conditions with respect to the amount of posts that were informational versus emotional. That said, prostate cancer forums are oriented towards informational support, whereas breast cancer forums feature more emotional support. Apart from the generally different nature of both conditions, one explanation may lie in the nature of single-gender versus mixed-gender groups: an earlier meta-study found that women write more emotional content than men when talking among others of the same gender – but interestingly, in mixed-gender discussions, these differences nearly disappeared.
Our research helped to identify factors that determine whether online content is informational or emotional, and demonstrated how posts differ across conditions. In addition to theoretical insights about patient needs, this research will help practitioners to better understand the role of online support groups for different patients, and to provide advice to patients about the value of online support.
The results also suggest that online support groups should be integrated into the digital health strategies of the UK and other nations. At present the UK plan for “Personalised Health and Care 2020” is centred around digital services provided within the health system, and does not yet reflect the value of person-generated health data from online support groups to patients. Our research substantiates that it would benefit from considering the instrumental role that online support groups can play in the healthcare process.
Read the full paper: Deetjen, U. and J. A. Powell (2016) Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis. Journal of the American Medical Informatics Association. http://dx.doi.org/10.1093/jamia/ocv190
Ulrike Deetjen (née Rauer) is a doctoral student at the Oxford Internet Institute researching the influence of the Internet on healthcare provision and health outcomes.