Abstract
Online health communities (OHCs) have become popular online environments for patients seeking and sharing treatment experiences. These platforms enable us to move beyond traditional sources of clinical information for learning about a patient’s long-term adherence to treatment. In spite of this opportunity, large-scale self-composed online free text brings challenges in processing and understanding patients’ health-related behaviors. Additionally, it has been shown that social support from trusted relationships (e.g., family and friends) positively influences treatment adherence in offline environments, but much less is known about the online setting. In this chapter, we showed that user activities in online health communities can be applied to learn about their medication adherence. Specifically, we focused on a 5-year hormonal therapy, a highly prevalent long-term treatment for breast cancer, with varying completion rates, in the breastcancer.org OHC. We characterized online user activities with emotion of self-disclosure and social interaction and focused on learning how these activities are associated with different adherence behaviors. To do so, we first designed a machine learning classifier to extract three types of adherence behaviors (taking, interruption, and completion of hormonal therapy), and then studied how emotions differ when patients mentioned different adherence behaviors. To examine the effect of social interaction, we relied on reciprocity (specifically in the form of reciprocal response to each other) to measure the social support between each other in this OHC. We first examined how reciprocity is related to time active in the OHC and the tones communicated by authors in their posts (e.g., emotions, writing styles, and social tendencies), and then assessed if such reciprocity is associated with treatment adherence. We found that patients in online health communities tend to exhibit fear with taking events, anger with interruption events, and joy (with a tinge of sadness and disgust) with completion events. We also found that the volume of the reciprocity is positively associated with completing the 5-year protocol, rather than the rate of the reciprocity or the fraction of the posts that received replies. We anticipate that our methodology can be applied to study treatment adherence for other diseases using online self-reported information.
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Notes
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It should be noted that vacation events for certain medications were not captured by any label in the initial annotation task. However, upon re-examination, we determined that this group of sentences was not labeled as non-relevant. This is notable because it means that we can still extract such instances from the set of relevant sentences through a deterministic rule-based method.
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References
Kathleen DFH, Pritchard I, Vora SR. Adjuvant endocrine therapy for non-metastatic, hormone receptor-positive breast cancer. Webpage. 2016. http://www.uptodate.com/contents/adjuvant-endocrine-therapy-for-non-metastatic-hormone-receptor-positive-breast-cancer. Accessed 1 Sept 2016.
Cancer among women. 2016. http://www.cdc.gov/cancer/dcpc/data/women.htm.
U.S. Breast Cancer Statistics. Webpage. 2016. http://www.breastcancer.org/symptoms/understand_bc/statistics. Accessed 1 Sept 2016.
Adjuvant therapy for breast cancer. Webpage. 2016. https://www.mskcc.org/cancer-care/patient-education/adjuvant-therapy-breast. Accessed 1 Sept 2016.
Murphy CC, Bartholomew LK, Carpentier MY, Bluethmann SM, et al. Adherence to adjuvant hormonal therapy among breast cancer survivors in clinical practice: a systematic review. Breast Cancer Res Treat. 2012;134(2):459–78.
Gotay C, Dunn J. Adherence to long-term adjuvant hormonal therapy for breast cancer. Expert Rev Pharmacoecon Outcomes Res. 2011;11(6):709–15.
Early Breast Cancer Trialists’ Collaborative Group and others. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: Patient-level meta-analysis of randomised trials. Lancet. 2011;378(9793):771–84.
Davies C, Pan H, Godwin J, Gray R, et al. Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet. 2013;381(9869):805–16.
Chlebowski RT, Kim J, Haque R. Adherence to endocrine therapy in breast cancer adjuvant and prevention settings. Cancer Prev Res. 2014;7(4):378–87.
Kuba S, Ishida M, Nakamura Y, Taguchi K, et al. Persistence and discontinuation of adjuvant endocrine therapy in women with breast cancer. Breast Cancer. 2016;23(1):128–33.
Brito C, Portela MC, de Vasconcellos MTL. Adherence to hormone therapy among women with breast cancer. BMC Cancer. 2014;14(1):1.
Schmidt N, Kostev K, Jockwig A, Kyvernitakis I, et al. Treatment persistence evaluation of tamoxifen and aromatase inhibitors in breast cancer patients in early and late stage disease. Int J Clin Pharmacol Ther. 2014;52(11):933–9.
Ziller V, Kalder M, Albert U-S, Holzhauer W, et al. Adherence to adjuvant endocrine therapy in postmenopausal women with breast cancer. Ann Oncol. 2009;20(3):431–6.
Oberguggenberger AS, Sztankay M, Beer B, Schubert B, Meraner V, et al. Adherence evaluation of endocrine treatment in breast cancer: methodological aspects. BMC Cancer. 2012;12(1):1.
Bhatta SS, Hou N, Moton ZN, Polite BN, et al. Factors associated with compliance to adjuvant hormone therapy in black and white women with breast cancer. Springerplus. 2013;2(1):1.
Beryl LL, Rendle KA, Halley MC, Gillespie KA, et al. Mapping the decision-making process for adjuvant endocrine therapy for breast cancer the role of decisional resolve. Med Decis Mak. 2016;37(1):79–90.
Makubate B, Donnan P, Dewar J, Thompson A, et al. Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality. Br J Cancer. 2013;108(7):1515–24.
Wuensch P, Hahne A, Haidinger R, Meißler K, et al. Discontinuation and non-adherence to endocrine therapy in breast cancer patients: is lack of communication the decisive factor? J Cancer Res Clin Oncol. 2015;141(1):55–60.
Wigertz A, Ahlgren J, Holmqvist M, Fornander T, et al. Adherence and discontinuation of adjuvant hormonal therapy in breast cancer patients: a population-based study. Breast Cancer Res Treat. 2012;133(1):367–73.
Yin Z, Fabbri D, Rosenbloom ST, Malin B. A scalable framework to detect personal health mentions on twitter. J Med Internet Res. 2015;17(6):e138.
Ryan T, Xenos S. Who uses Facebook? An investigation into the relationship between the big five, shyness, narcissism, loneliness, and Facebook usage. Comput Hum Behav. 2011;27(5):1658–64.
Attai DJ, Cowher MS, Al-Hamadani M, Schoger JM, et al. Twitter social media is an effective tool for breast cancer patient education and support: patient-reported outcomes by survey. J Med Internet Res. 2015;17(7):e188.
Yin Z, Xie W, Malin BA. Talking about my care: detecting mentions of hormonal therapy adherence behavior in an online breast cancer community. In: AMIA Annual Symposium Proceedings, vol. 2017. American Medical Informatics Association; 2017. p. 1868.
Yin Z, Malin B, Warner J, Hsueh P-Y, Chen C-H. The power of the patient voice: learning indicators of hormonal therapy adherence from an online breast cancer forum. In: Eleventh International AAAI Conference on Web and Social Media. 2017.
Yin Z, Song L, Malin B. Reciprocity and its association with treatment adherence in an online breast cancer forum. In: Computer-Based Medical Systems (CBMS), 2017 IEEE 30th International Symposium on. IEEE; 2017. p. 618–23.
IBM. Overview of the Watson tone analyzer service. Webpage. 2016. https://www.ibm.com/watson/developercloud/doc/tone-analyzer/. Accessed 9 Jan 2017.
Selfhout M, Denissen J, Branje S, Meeus W. In the eye of the beholder: perceived, actual, and peer-rated similarity in personality, communication, and friendship intensity during the acquaintanceship process. J Pers Soc Psychol. 2009;96(6):1152.
McCroskey JC, Daly JA, Sorensen G. Personality correlates of communication apprehension: a research note. Hum Commun Res. 1976;2(4):376–80.
Wu J, Lu ZK. Hormone therapy adherence and costs in women with breast cancer. Am J Pharm Benefits. 2013;5(2):65–70.
Neugut AI, Zhong X, Wright JD, Accordino M, et al. Nonadherence to medications for chronic conditions and nonadherence to adjuvant hormonal therapy in women with breast cancer. JAMA Oncol. 2016;2(10):1326–32.
Christensen AJ, Smith TW. Personality and patient adherence: correlates of the five-factor model in renal dialysis. J Behav Med. 1995;18(3):305–13.
Stilley CS, Sereika S, Muldoon MF, Ryan CM, et al. Psychological and cognitive function: predictors of adherence with cholesterol lowering treatment. Ann Behav Med. 2004;27(2):117–24.
O’Cleirigh C, Ironson G, Weiss A, Costa PT Jr. Conscientiousness predicts disease progression (cd4 number and viral load) in people living with HIV. Health Psychol. 2007;26(4):473.
Bruce JM, Hancock LM, Arnett P, Lynch S. Treatment adherence in multiple sclerosis: association with emotional status, personality, and cognition. J Behav Med. 2010;33(3):219–27.
Song X, Dent SF, Verma S, Clemons MJ, et al. Impact of personality traits on predictors of adherence to endocrine therapy. In: ASCO Annual Meeting Proceedings, vol. 33, no. 15 suppl; 2015. p. e20614.
Stanton AL, Petrie KJ, Partridge AH. Contributors to nonadherence and nonpersistence with endocrine therapy in breast cancer survivors recruited from an online research registry. Breast Cancer Res Treat. 2014;145(2):525–34.
Walker HE, Rosenberg SM, Stanton AL, Petrie KJ, et al. Perceptions, attributions, and emotions toward endocrine therapy in young women with breast cancer. J Adolesc Young Adult Oncol. 2016;5(1):16–23.
Vos SC, Buckner MM. Social media messages in an emerging health crisis: tweeting bird flu. J Health Commun. 2016;21(3):301–8.
Yun GW, David M, Park S, Joa CY, et al. Social media and flu: media twitter accounts as agenda setters. Int J Med Inform. 2016;91:67–73.
Elhadad N, Zhang S, Driscoll P, Brody S. Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions. In: Proceedings of the AMIA Annual Fall Symposium. 2014.
Weiss JB. Building an online community to support local cancer survivorship: combining informatics and participatory action research for collaborative design. Ph.D. dissertation, Vanderbilt University; 2009.
Frost J, Beekers N, Hengst B, Vendeloo R. Meeting cancer patient needs: designing a patient platform. In: CHI’12 Extended Abstracts on Human Factors in Computing Systems. 2012. p. 2381–6.
Yin Z, Chen Y, Fabbri D, Sun J, Malin B. # prayfordad: Learning the semantics behind why social media users disclose health information. In: Tenth International AAAI Conference on Web and Social Media. 2016.
Wang S, Li Y, Ferguson D, Zhai C. Sideeffectptm: An unsupervised topic model to mine adverse drug reactions from health forums. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. 2014. p. 321–30.
Zhang S, O’Carroll Bantum E, Owen J, Bakken S, et al. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. J Am Med Inform Assoc. 2017;24(2):451.
Marshall SA, Yang CC, Ping Q, Zhao M, et al. Symptom clusters in women with breast cancer: an analysis of data from social media and a research study. Qual Life Res. 2016;25(3):547–57.
Portier K, Greer GE, Rokach L, Ofek N, et al. Understanding topics and sentiment in an online cancer survivor community. J Natl Cancer Inst Monogr. 2013;47:195–8.
Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry. 2013;35(4):332–8.
Horvath KJ, Oakes JM, Rosser BS, et al. Feasibility, acceptability and preliminary efficacy of an online peer-to-peer social support art adherence intervention. AIDS Behav. 2013;17(6):2031–44.
Wang Y-C, Kraut R, Levine JM. “To stay or leave? The relationship of emotional and informational support to commitment in online health support groups. In: Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 2012. p. 833–42.
Jha M, Elhadad N. Cancer stage prediction based on patient online discourse. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. 2010. p. 64–71.
Freedman RA, Viswanath K, Vaz-Luis I, Keating NL. Learning from social media: utilizing advanced data extraction techniques to understand barriers to breast cancer treatment. Breast Cancer Res Treat. 2016;158(2):395–405.
Mao JJ, Chung A, Benton A, et al. Online discussion of drug side effects and discontinuation among breast cancer survivors. Pharmacoepidemiol Drug Saf. 2013;22(3):256–62.
Song L, Son J, Lin N. Social support. In: Scott J, Carrington P, editors. The Sage handbook of social network analysis. London: Sage; 2011. p. 116–28.
Marquez B, Anderson A, Wing RR, West DS, Newton RL, Meacham M, Hazuda HP, Peters A, Montez MG, Broyles ST, et al. The relationship of social support with treatment adherence and weight loss in Latinos with type 2 diabetes. Obesity. 2016;24(3):568–75.
Phillips FH, Barnes D. Social support and adherence for military veterans with hepatitis c. Clin Nurse Spec. 2015;30(1):38–44.
Nahum-Shani I, Bamberger PA, Bacharach SB. Social support and employee well-being the conditioning effect of perceived patterns of supportive exchange. J Health Soc Behav. 2011;52(1):123–39.
Fyrand L. Reciprocity: a predictor of mental health and continuity in elderly people’s relationships? A review. Curr Gerontol Geriatr Res. 2010;2010:340161.
Chandola T, Marmot M, Siegrist J. Failed reciprocity in close social relationships and health: findings from the whitehall ii study. J Psychosom Res. 2007;63(4):403–11.
Begum S, Aygun RS. Greedy hierarchical binary classifiers for multi-class classification of biological data. Netw Model Anal Health Inform Bioinform. 2014;3(1):1–15.
Farid DM, Zhang L, Rahman CM, et al. Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst Appl. 2014;41(4):1937–46.
Mikolov T, Dean J. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;2:3111–9.
Rehůřek R, Sojka P. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: ELRA; 2010. p. 45–50. http://is.muni.cz/publication/884893/en.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(Oct):2825–30.
Chirayil Subhash S. Personality analysing on watson cloud by tracking the digital footprints of the user. Ph.D. dissertation, National College of Ireland, Dublin; 2015.
Mostafa M, Crick T, Calderon AC, et al. Incorporating emotion and personality-based analysis in user-centered modelling. In: Research and Development in Intelligent Systems XXXIII: Incorporating Applications and Innovations in Intelligent Systems XXIV. Springer; 2016. p. 383–9.
Thies F, Wessel M, Rudolph J, et al. Personality matters: How signaling personality traits can influence the adoption and diffusion of crowdfunding campaigns. Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies, Tech. Rep.; 2016.
Finkel JR, Grenager T, Manning C. Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics; 2005. p. 363–70.
Cohen A. Fuzzy string matching in python. Webpage. 2016. https://github.com/seatgeek/fuzzywuzzy. Accessed 9 Jan 2017.
Barabasi A-L. The origin of bursts and heavy tails in human dynamics. Nature. 2005;435(7039):207–11.
Seidman G. Self-presentation and belonging on Facebook: how personality influences social media use and motivations. Personal Individ Differ. 2013;54(3):402–7.
Wilson K, Fornasier S, White KM. Psychological predictors of young adults’ use of social networking sites. Cyberpsychol Behav Soc Netw. 2010;13(2):173–7.
Karim NSA, Zamzuri NHA, Nor YM. Exploring the relationship between internet ethics in university students and the big five model of personality. Comput Educ. 2009;53(1):86–93.
Axelsson M, Brink E, Lotvall J. A personality and gender perspective on adherence and health-related quality of life in people with asthma and/or allergic rhinitis. J Am Assoc Nurse Pract. 2014;26(1):32–9.
McCrae RR, John OP. An introduction to the five-factor model and its applications. J Pers. 1992;60(2):175–215.
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This research was sponsored, in part, by grant IIS1418504 of the National Science Foundation.
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Yin, Z., Warner, J., Song, L., Hsueh, PY., Chen, CH., Malin, B. (2019). Learning Hormonal Therapy Medication Adherence from an Online Breast Cancer Forum. In: Bian, J., Guo, Y., He, Z., Hu, X. (eds) Social Web and Health Research. Springer, Cham. https://doi.org/10.1007/978-3-030-14714-3_12
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