Digestive Diseases and Sciences

, Volume 63, Issue 9, pp 2220–2230 | Cite as

Disease Activity Patterns Recorded Using a Mobile Monitoring System Are Associated with Clinical Outcomes of Patients with Crohn’s Disease

  • Eun Soo Kim
  • Sung Kook Kim
  • Byung Ik JangEmail author
  • Kyeong Ok Kim
  • Eun Young Kim
  • Yoo Jin Lee
  • Hyun Seok Lee
  • Sang Gyu Kwak
  • the Crohn’s and Colitis Association in Daegu-Gyeongbuk (CCAiD)
Original Article



Usefulness of a mobile monitoring system for Crohn’s disease (CD) has not been evaluated. We aimed to determine whether disease activity patterns depicted using a web-based symptom diary for CD could indicate disease clinical outcomes.


Patients with CD from tertiary hospitals were prospectively invited to record their symptoms using a smartphone at least once a week. Disease activity patterns for at least 2 months were statistically classified into good and poor groups based on two factors in two consecutive time frames; the degree of score variation (maximum–minimum) in each frame and the trend (upward, stationary, or downward) of patterns indicated by the difference in the mean activity scores between two time frames.


Overall, 220 (82.7%) and 46 (17.3%) patients were included in good and poor groups, respectively. Poor group was significantly more associated with disease-related hospitalization (p = 0.004), unscheduled hospital visits (p = 0.005), and bowel surgery (p < 0.001) during the follow-up period than good group. In the multivariate analysis, poor patterns [odds ratio (OR) 2.62, p = 0.006], stricturing (OR 4.19, p < 0.001) or penetrating behavior (OR 2.27, p = 0.012), and young age at diagnosis (OR 1.06, p = 0.019) were independently associated with disease-related hospitalization. Poor patterns (OR 4.06, p = 0.006) and an ileal location (OR 5.79, p = 0.032) remained independent risk factors for unscheduled visits. Poor patterns (OR 15.2, p < 0.001) and stricturing behavior (OR 9.77, p = 0.004) were independent risk factors for bowel surgery.


The disease activity patterns depicted using a web-based symptom diary were useful indicators of poor clinical outcomes in patients with CD.


Crohn’s disease Disease activity pattern Smartphone 



This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A02062168) and by the research promoting grant from the Keimyung University Dongsan Medical Center in 2011.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflicts of interest.

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  1. 1.
    Veloso FT, Ferreira JT, Barros L, Almeida S. Clinical outcome of Crohn’s disease: analysis according to the vienna classification and clinical activity. Inflamm Bowel Dis. 2001;7:306–313.CrossRefPubMedGoogle Scholar
  2. 2.
    Thia KT, Sandborn WJ, Harmsen WS, Zinsmeister AR, Loftus EV Jr. Risk factors associated with progression to intestinal complications of Crohn’s disease in a population-based cohort. Gastroenterology. 2010;139:1147–1155.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Sandborn WJ, Feagan BG, Hanauer SB, et al. A review of activity indices and efficacy endpoints for clinical trials of medical therapy in adults with Crohn’s disease. Gastroenterology. 2002;122:512–530.CrossRefPubMedGoogle Scholar
  4. 4.
    Harvey RF, Bradshaw JM. A simple index of Crohn’s-disease activity. Lancet. 1980;1:514.CrossRefPubMedGoogle Scholar
  5. 5.
    Pariente B, Cosnes J, Danese S, et al. Development of the Crohn’s disease digestive damage score, the Lemann score. Inflamm Bowel Dis. 2011;17:1415–1422.CrossRefPubMedGoogle Scholar
  6. 6.
    Maiolo C, Mohamed EI, Fiorani CM, De Lorenzo A. Home telemonitoring for patients with severe respiratory illness: the Italian experience. J Telemed Telecare. 2003;9:67–71.CrossRefPubMedGoogle Scholar
  7. 7.
    Gomez EJ, Hernando ME, Garcia A, et al. Telemedicine as a tool for intensive management of diabetes: the DIABTel experience. Comput Methods Progr Biomed. 2002;69:163–177.CrossRefGoogle Scholar
  8. 8.
    Evangelista LS, Stromberg A, Westlake C, Ter-Galstanyan A, Anderson N, Dracup K. Developing a Web-based education and counseling program for heart failure patients. Prog Cardiovasc Nurs. 2006;21:196–201.CrossRefPubMedGoogle Scholar
  9. 9.
    Ajay VS, Jindal D, Roy A, et al. Development of a smartphone-enabled hypertension and diabetes mellitus management package to facilitate evidence-based care delivery in primary healthcare facilities in India: The mPower Heart Project. J Am Heart Assoc. 2016;5:e004343.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    McConnell MV, Shcherbina A, Pavlovic A, et al. Feasibility of obtaining measures of lifestyle from a smartphone app: the MyHeart counts cardiovascular health study. JAMA Cardiol. 2017;2:67–76.CrossRefPubMedGoogle Scholar
  11. 11.
    Chan NY, Choy CC. Screening for atrial fibrillation in 13 122 Hong Kong citizens with smartphone electrocardiogram. Heart. 2017;103:24–31.CrossRefPubMedGoogle Scholar
  12. 12.
    Capecci M, Pepa L, Verdini F, Ceravolo MG. A smartphone-based architecture to detect and quantify freezing of gait in Parkinson’s disease. Gait Posture. 2016;50:28–33.CrossRefPubMedGoogle Scholar
  13. 13.
    Van Deen WK, van der Meulen-de Jong AE, Parekh NK, et al. Development and validation of an inflammatory bowel diseases monitoring index for use with mobile health technologies. Clin Gastroenterol Hepatol. 2016;14:1742–1750e7.CrossRefPubMedGoogle Scholar
  14. 14.
    Kim ES, Park KS, Cho KB, et al. Development of a web-based, self-reporting symptom diary for Crohn’s disease, and its correlation with the Crohn’s disease activity index: web-based, self-reporting symptom diary for Crohn’s disease. J Crohns Colitis. 2017;11:1449–1455.CrossRefPubMedGoogle Scholar
  15. 15.
    Solberg IC, Vatn MH, Hoie O, et al. Clinical course in Crohn’s disease: results of a Norwegian population-based ten-year follow-up study. Clin Gastroenterol Hepatol. 2007;5:1430–1438.CrossRefPubMedGoogle Scholar
  16. 16.
    Bernstein CN, Loftus EV Jr, Ng SC, et al. Hospitalisations and surgery in Crohn’s disease. Gut. 2012;61:622–629.CrossRefPubMedGoogle Scholar
  17. 17.
    Ramos-Rivers C, Regueiro M, Vargas EJ, et al. Association between telephone activity and features of patients with inflammatory bowel disease. Clin Gastroenterol Hepatol. 2014;12:986–994.CrossRefPubMedGoogle Scholar
  18. 18.
    Sulz MC, Siebert U, Arvandi M, et al. Predictors for hospitalization and outpatient visits in patients with inflammatory bowel disease: results from the Swiss Inflammatory Bowel Disease Cohort Study. Eur J Gastroenterol Hepatol. 2013;25:790–797.CrossRefPubMedGoogle Scholar
  19. 19.
    Romberg-Camps MJ, Dagnelie PC, Kester AD, et al. Influence of phenotype at diagnosis and of other potential prognostic factors on the course of inflammatory bowel disease. Am J Gastroenterol. 2009;104:371–383.CrossRefPubMedGoogle Scholar
  20. 20.
    Moran GW, Dubeau MF, Kaplan GG, et al. Phenotypic features of Crohn’s disease associated with failure of medical treatment. Clin Gastroenterol Hepatol. 2014;12:434–442.CrossRefPubMedGoogle Scholar
  21. 21.
    Beaugerie L, Seksik P, Nion-Larmurier I, Gendre JP, Cosnes J. Predictors of Crohn’s disease. Gastroenterology. 2006;130:650–656.CrossRefPubMedGoogle Scholar
  22. 22.
    Lunney PC, Kariyawasam VC, Wang RR, et al. Smoking prevalence and its influence on disease course and surgery in Crohn’s disease and ulcerative colitis. Aliment Pharmacol Ther. 2015;42:61–70.CrossRefPubMedGoogle Scholar
  23. 23.
    Ng WK, Wong SH, Ng SC. Changing epidemiological trends of inflammatory bowel disease in Asia. Intest Res. 2016;14:111–119.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Hwang SW, Seo H, Kim GU, et al. Underestimation of smoking rates in an East Asian population with Crohn’s disease. Gut Liver. 2017;11:73–78.CrossRefPubMedGoogle Scholar
  25. 25.
    Ma J, Zhu J, Li N, et al. Severe and differential underestimation of self-reported smoking prevalence in Chinese adolescents. Int J Behav Med. 2014;21:662–666.CrossRefPubMedGoogle Scholar
  26. 26.
    Williet N, Sandborn WJ, Peyrin-Biroulet L. Patient-reported outcomes as primary end points in clinical trials of inflammatory bowel disease. Clin Gastroenterol Hepatol. 2014;12:1246–1256.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Eun Soo Kim
    • 1
    • 4
  • Sung Kook Kim
    • 1
  • Byung Ik Jang
    • 2
    Email author
  • Kyeong Ok Kim
    • 2
  • Eun Young Kim
    • 3
  • Yoo Jin Lee
    • 4
  • Hyun Seok Lee
    • 1
  • Sang Gyu Kwak
    • 5
  • the Crohn’s and Colitis Association in Daegu-Gyeongbuk (CCAiD)
  1. 1.Division of Gastroenterology, Department of Internal Medicine, School of MedicineKyungpook National UniversityDaeguKorea
  2. 2.Division of Gastroenterology and Hepatology, Department of Internal MedicineYeungnam University College of MedicineDaeguKorea
  3. 3.Division of Gastroenterology, Department of Internal MedicineCatholic University of Daegu School of MedicineDaeguKorea
  4. 4.Division of Gastroenterology, Department of Internal MedicineKeimyung University School of MedicineDaeguKorea
  5. 5.Department of Medical StatisticsCatholic University of Daegu School of MedicineDaeguKorea

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