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Quality of life and adherence to therapy in patients with chronic heart failure who were remotely monitored by chatbot compared to the standard follow-up group for 3 months
- Authors: Emelianov A.V.1, Zheleznykh E.A.1, Kozhevnikova M.V.1, Ageev A.A.1, Zektser V.Y.1, Belenkov Y.N.1
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Affiliations:
- I.M. Sechenov First Moscow State Medical University
- Issue: Vol 4, No 1S (2023)
- Pages: 53-56
- Section: Conference proceedings
- URL: https://jdigitaldiagnostics.com/DD/article/view/430343
- DOI: https://doi.org/10.17816/DD430343
- ID: 430343
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Abstract
BACKGROUND: Chronic heart failure (CHF) is one of the leading causes of death. Telemedicine and remote monitoring (RM) are a way to increase life expectancy and quality of life in patients with CHF. Methods based on messengers familiar to patients promote adherence and do not require additional training.
AIM: To compare quality of life and adherence to therapy in patients with CHF who were on RM using a chatbot compared to the standard follow-up (SFU) group for 3 months.
METHODS: Patients with CHF on optimal drug therapy discharged from the hospital were included in the study. Comparison groups were formed according to the method of observation, particularly, RM and SFU. A chatbot was set up for patients in the RM group. Monitoring was done using a seven-question survey sent daily. The signs of decompensation (red flags [RF]) were increased edema, dyspnea, body weight ≥2 kg per week, and changes in individual parameters of heart rate and blood pressure. If a RF was detected, telephone contact was made, and the therapy was corrected if necessary. Quality of life was assessed according to the Minnesota Quality of Life Questionnaire for patients with CHF (highest, 0 points; lowest, 105 points), and adherence was assessed using the Adherence Scale of the National Society for Evidence-based Pharmacotherapy.
RESULTS: A total of 60 patients were included in the study; 37 patients completed a 3-month follow-up. The RM group (n=17, 13 men, 76.5%; median age 61 [51; 62]) and comparison group (n=20, 14 men, 70%; mean age 64.9±8.9) were comparable according to the functional class (New York Heart Association), but differed in ejection fraction (42.8±13% versus 53.2±10.4% [p <0.05]). Adherence to the chat-bot was 67.2%. Adherence to therapy was not significantly different between the RM and SFU groups accounting for (17 [100%]) and (18 [90%], respectively, (p=0.62). In the RM group, RF was detected in 7 (41%) patients. Only one patient required correction of therapy. Patients in the RM group required no referral to a medical facility, whereas 2 patients in the SFU group required medical care. Quality of life was statistically significantly higher in the RM group, reaching 28.7±13.9 points compared to 37.7±17.9 points in the SFU group (p=0.04).
CONCLUSIONS: After 3 months, patients in the RM group were committed to the chatbot, with adherence to therapy comparable to the SFU group. Quality of life was statistically significantly higher in the RM group. Patients in the RM group did not go to medical facilities, in contrast to the SFU group. The limitations of the study were the small sample size and short follow-up period. The results require further research to obtain additional data.
Full Text
BACKGROUND: Chronic heart failure (CHF) is one of the leading causes of death. Telemedicine and remote monitoring (RM) are a way to increase life expectancy and quality of life in patients with CHF. Methods based on messengers familiar to patients promote adherence and do not require additional training.
AIM: To compare quality of life and adherence to therapy in patients with CHF who were on RM using a chatbot compared to the standard follow-up (SFU) group for 3 months.
METHODS: Patients with CHF on optimal drug therapy discharged from the hospital were included in the study. Comparison groups were formed according to the method of observation, particularly, RM and SFU. A chatbot was set up for patients in the RM group. Monitoring was done using a seven-question survey sent daily. The signs of decompensation (red flags [RF]) were increased edema, dyspnea, body weight ≥2 kg per week, and changes in individual parameters of heart rate and blood pressure. If a RF was detected, telephone contact was made, and the therapy was corrected if necessary. Quality of life was assessed according to the Minnesota Quality of Life Questionnaire for patients with CHF (highest, 0 points; lowest, 105 points), and adherence was assessed using the Adherence Scale of the National Society for Evidence-based Pharmacotherapy.
RESULTS: A total of 60 patients were included in the study; 37 patients completed a 3-month follow-up. The RM group (n=17, 13 men, 76.5%; median age 61 [51; 62]) and comparison group (n=20, 14 men, 70%; mean age 64.9±8.9) were comparable according to the functional class (New York Heart Association), but differed in ejection fraction (42.8±13% versus 53.2±10.4% [p <0.05]). Adherence to the chat-bot was 67.2%. Adherence to therapy was not significantly different between the RM and SFU groups accounting for (17 [100%]) and (18 [90%], respectively, (p=0.62). In the RM group, RF was detected in 7 (41%) patients. Only one patient required correction of therapy. Patients in the RM group required no referral to a medical facility, whereas 2 patients in the SFU group required medical care. Quality of life was statistically significantly higher in the RM group, reaching 28.7±13.9 points compared to 37.7±17.9 points in the SFU group (p=0.04).
CONCLUSIONS: After 3 months, patients in the RM group were committed to the chatbot, with adherence to therapy comparable to the SFU group. Quality of life was statistically significantly higher in the RM group. Patients in the RM group did not go to medical facilities, in contrast to the SFU group. The limitations of the study were the small sample size and short follow-up period. The results require further research to obtain additional data.
About the authors
Aleksei V. Emelianov
I.M. Sechenov First Moscow State Medical University
Email: emelyanow.alexei@yandex.ru
ORCID iD: 0000-0002-4748-8029
Russian Federation, Moscow
Elena A. Zheleznykh
I.M. Sechenov First Moscow State Medical University
Email: elenavlvp@gmail.com
ORCID iD: 0000-0002-2596-192X
Russian Federation, Moscow
Maria V. Kozhevnikova
I.M. Sechenov First Moscow State Medical University
Author for correspondence.
Email: kozhevnikova-m@inbox.ru
ORCID iD: 0000-0003-4778-7755
Russian Federation, Moscow
Anton A. Ageev
I.M. Sechenov First Moscow State Medical University
Email: antonageev1945@gmail.com
ORCID iD: 0000-0002-2161-2140
Russian Federation, Moscow
Vita Y. Zektser
I.M. Sechenov First Moscow State Medical University
Email: zektservita@mail.ru
ORCID iD: 0000-0002-8547-8673
Russian Federation, Moscow
Yuri N. Belenkov
I.M. Sechenov First Moscow State Medical University
Email: ynbelenkov@gmail.com
ORCID iD: 0000-0002-3014-6129
Russian Federation, Moscow
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