Robin Huang1, Na Liu1, Mary Ann Nicdao2, Mary Mikaheal2, Tanya Baldacchino2, Annabelle Albeos2, Kathy Petoumenos3, Kamal Sud2,4,5, Jinman Kim1. 1. School of Computer Science, The University of Sydney, Camperdown, Australia. 2. Home Hemodialysis Unit, Regional Dialysis Centre, Blacktown Hospital, Sydney, Australia. 3. The Kirby Institute, UNSW Sydney, Sydney, NSW, Australia. 4. Department of Renal Medicine, Nepean Hospital, Sydney, Australia. 5. The University of Sydney Medical School, Sydney, Australia.
Abstract
OBJECTIVE: To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. MATERIALS AND METHODS: A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients' text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients' emotions (emoticon and sentiment) and TTD. RESULTS: Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13-5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22-2; P = .003) when compared to positive sentiments. DISCUSSION: The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. CONCLUSION: Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.
OBJECTIVE: To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. MATERIALS AND METHODS: A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients' text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients' emotions (emoticon and sentiment) and TTD. RESULTS: Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13-5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22-2; P = .003) when compared to positive sentiments. DISCUSSION: The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. CONCLUSION:Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.
Authors: Mark R Marshall; Carmel M Hawley; Peter G Kerr; Kevan R Polkinghorne; Roger J Marshall; John W M Agar; Stephen P McDonald Journal: Am J Kidney Dis Date: 2011-08-04 Impact factor: 8.860
Authors: Adam Darkins; Patricia Ryan; Rita Kobb; Linda Foster; Ellen Edmonson; Bonnie Wakefield; Anne E Lancaster Journal: Telemed J E Health Date: 2008-12 Impact factor: 3.536
Authors: Na Liu; Jinman Kim; Younhyun Jung; Adani Arisy; Mary Ann Nicdao; Mary Mikaheal; Tanya Baldacchino; Mohamed Khadra; Kamal Sud Journal: JMIR Hum Factors Date: 2017-08-29