Rethinking the frequency of between-visit monitoring for patients with diabetes

Medical Care

Volumn 52, Issue 6, 2014, Pages 511-518

  Piette, John D ^a,b,c   Aikens, James E ^c   Rosland, Ann M ^b,c   Sussman, Jeremy B ^b,c  

^a VA ANN ARBOR HEALTHCARE SYSTEM   (United States)

^b UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

care management; chronic disease; diabetes; Telemedicine

Indexed keywords

ANTIDIABETIC AGENT; GLUCOSE;

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; BLOOD GLUCOSE MONITORING; CONTROLLED STUDY; DIABETIC PATIENT; E-MAIL; FOOT CARE; GLUCOSE BLOOD LEVEL; HEALTH BEHAVIOR; HUMAN; HYPERGLYCEMIA; INTERACTIVE VOICE RESPONSE SYSTEM; MAJOR CLINICAL STUDY; MEDICAL HISTORY; MEDICAL INFORMATION; MEDICATION COMPLIANCE; MENTAL COMPOSITE SUMMARY SCORE; MORISKY MEDICATION ADHERENCE SCALE; NON INSULIN DEPENDENT DIABETES MELLITUS; PATIENT COMPLIANCE; PATIENT INFORMATION; PHYSICAL COMPOSITE SUMMERY SCORE; PREDICTION; PRIORITY JOURNAL; PROBLEM AREAS IN DIABETES SCALE; RATING SCALE; RISK FACTOR; SCORING SYSTEM; SELF CARE; SHORT FORM 12; TELEMONITORING; TELEPHONE INTERVIEW;

AGED; AGED, 80 AND OVER; BLOOD GLUCOSE SELF-MONITORING; COMMUNICATION; COMORBIDITY; DIABETES MELLITUS, TYPE 2; DIABETIC FOOT; FEMALE; HUMANS; HYPERGLYCEMIA; HYPOGLYCEMIA; HYPOGLYCEMIC AGENTS; LONGITUDINAL STUDIES; MALE; MEDICATION ADHERENCE; MIDDLE AGED; MONITORING, AMBULATORY; REMINDER SYSTEMS; SELF CARE; TELEPHONE;

EID: 84901285259     PISSN: 00257079     EISSN: 15371948     Source Type: Journal    
DOI: 10.1097/MLR.0000000000000131     Document Type: Article

References (33)

1. Funnell MM, Brown TL, C hilds BP, et al. National standards for diabetes self-management education. Diabetes Care. 2012;25(suppl 1): S101-S108.
2. DiClemente CC, Marinilli AS, Singh M, et al. The role of feedback in the process of health behavior change. Am J Health Behav. 2001;25: 217-227.
3. McClure JB. Are biomarkers useful treatment aids for promoting health behavior change Am J Prev Med. 2002;22:200-207.
4. Grant MD, Piotrowski ZH, Chappell R. Self-reported health and survival in the longitudinal study of aging, 1984-1986. J Clin Epidemiol. 1995;48:375-387. PMID: 7897459.
5. Aikens JE, Piette JD. Longitudinal association between medication adherence and glycaemic control in type 2 diabetes. Diab Med. 2013; 30:338-344.
6. Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: A systematic review. PLOS Med. 2013 ;10:e1001362+.
7. Piette JD Rosland AM Marinec NS et al Engagement with automated patient monitoring and self-management support calls: experience with a thousand chronically-ill patients Med Care. 2013 51 216-223 PMID: 23222527.
8. Searles J, Perrine M, Mundt J, et al. Self-report of drinking using touchtone telephone: extending the limits of reliable daily contact. J Stud Alcohol. 1995;56:375-382.
9. Piette JD, McPhee SJ, Weinberger M, et al. Use of automated telephone disease management calls in an ethnically diverse sample of low-income patients with diabetes. Diabetes Ca re. 1999;22:1302-1309.
10. Turvey CL, Willyard D, Hickman DH, et al. Telehealth screen for depression in a chronic illness care management program. Telemed J E Health. 2007;13:51-56.
11. Millard RW, Carver JR. Cross-sectional comparison of live and interactive voice recognition administration of the SF-12 health status survey. Am J Manag Care. 1999;5:153-159.
12. Piette JD, Datwani H, Gaudioso S, et al. Hypertension management using mobile technology and home blood pressure monitoring: results of a randomized trial in two low/middle income countries. Telemed J E Health. 2012; 18:613-620.
13. Graham J, Tomcavage J, Salek D, et al. Postdischarge monitoring using interactive voice response system reduces 30-day readmission rates in a case-managed Medicare population. Med Care. 2012;50:50-7s.
14. Estabrooks PA, Shoup JA, Gattshall M, et al. Automated telephone counseling for parents of overweight children: A randomized controlled trial. Am J Prev Med. 2009;36:35-42.
15. Mahoney DF, Tarlow BJ, Jones RN. Effects of an automated telephone support system on caregiver burden and anxiety: findings from the REACH for TLC intervention study. Gerontologist. 2003;43:556-567.
16. Lichtenstein MJ, Steele MA, Hoehn TP, et al. Visit frequency for essential hypertension: observed associations. J Fam Pract. 1989;28:667-672.
17. Schectman G, Barnas G, Laud P, et al. Prolonging the return visit interval in primary care. Am J med. 2005;118:393-399.
18. DeSalvo KB, Block JP, Muntner P, et al. Predictors of variation in office visit interval assignment. Int J Qual Health Care. 2003;15: 399-405.
19. DeSalvo KB, Bowdish BE, Alper AS, et al. Physician practice variation in assignment of return interval. Arch Intern Med. 2000;160:205-208.
20. Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006;29:725-731.
21. Piette JD, Sussman JB, Pfeiffer PN, et al. Maximizing the value of mobile health monitoring by avoiding redundant patient reports: prediction of depression-related symptoms and adherence problems in automated health ass essment services. J Med Internet Res. 2013; 13:e118.
22. Polonsky WH, Fisher L, Earles J, et al. Assessing psychosocial distress in diabetes: development of the diabetes distress scale. Diabetes Care. 2005;28:626-631.
23. Ware J Jr, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34:220-233.
24. Haas LMM, Beck J, Cox CE, et al. National standards for diabetes self-management education and support. Diabetes Care. 2013;36: S100-S108.
25. Hub er PJ. The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, CA: University of California Press; 1967:221-233.
26. White H. A heteroskedasticity-consisten covariance matrix estimator and a direct test for heteroskedasticity. Econometrica. 1980;48:817-830.
27. Rogers WH. Regression standard errors in clustered samples. Stata Techn Bull. 1993;13:19-23. Available at: http://www.stata-press.com/journals/ stbcontents/stb13.pdf.
28. Williams RL A note on robust variance estimation for clustercorrelated data Biometrics 2000 56 645-646.
29. Froot KA. Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data. J Financ Quant Anal. 1989;24:333-355.
30. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997;30:1145-1159.
31. Morisky DE, Ang A, Krousel-Wood M, et al. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens. 2008;10:348-354.
32. Steyerberg EW, Vickers AJ, Cook N R, et al. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology. 2010;21:128-138.
33. Michalski RS, Carbonell JG, Mitchell TM. Machine Learning: An Artificial Intelligence Approach. Volume Morgan Kaufman Publishers: Burlington, MA; 1986. Diseases and Kidney Diseases