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Current Diabetes Reports
Published in: Current Diabetes Reports 8/2015

01-08-2015 | Psychosocial Aspects (KK Hood and S Jaser, Section Editors)

When Does Personalized Feedback Make A Difference? A Narrative Review of Recent Findings and Their Implications for Promoting Better Diabetes Self-Care

Authors: William H. Polonsky, Lawrence Fisher

Published in: Current Diabetes Reports | Issue 8/2015

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Abstract

Providing behavioral, biomarker, or disease risk feedback to patients is a key component of most behavioral interventions in diabetes, but it remains unclear what is necessary for such feedback to be truly engaging and effective. We sought to identify how personalized health-related feedback is most effectively designed and delivered, and how feedback may be tailored to meet the needs of individual patients with diabetes. To do so, we systematically reviewed recent findings concerning the effectiveness of feedback in eight health-related areas, including several specific to diabetes care (blood glucose monitoring and HbA1c) and others which touch on broader care dimensions (blood pressure, cholesterol, dietary intake, pedometer usage, self-weighing, and medical imaging). Five interdependent characteristics of health-related feedback were identified (clarity of the feedback message, personal meaningfulness of the feedback, frequency of feedback, guidance and support accompanying feedback, and interplay between feedback and patient characteristics) and applications for use in diabetes care were provided. Findings suggested that feedback will be most effective when it is easy for patients to understand and is personally meaningful, frequency of feedback is appropriate to the characteristics of the behavior/biomarker, guidance for using feedback is provided, and feedback is qualified by patient characteristics. We suggest that the effectiveness of feedback to promote better diabetes outcomes requires careful consideration of the feedback message, how it is delivered, and characteristics of the recipients.
Literature
1.
McClure JB. Are biomarkers a useful aid in smoking cessation? A review and analysis of the literature. Behav Med. 2001;27:37–47.PubMedCrossRef
2.
3.
Wing RR, Phelan S. Long-term weight loss maintenance. Am J Clin Nutr. 2005;82:222–5.
4.
Ford AL, Bergh C, Sabin MA, Hollinghurst S, Hunt LP, Shield JP. Treatment of childhood obesity by retraining eating behavior: randomized controlled trial. BMJ. 2010;340:5388–92.CrossRef
5.
Fitzsimons CF, Baker G, Gray SR, Nimmo MA, Mutrie N. Does physical activity counseling enhance the effects of a pedometer-based intervention over the long-term: 12-month findings from the Walking for Wellbeing in the West Study. BMJ Public Health. 2012;12:206–18.CrossRef
6.
Hollands GJ, Hankins M, Marteau TM. Visual feedback of individual’s medical imaging results for changing health behavior. Cochrane Database Systematic Reviews 2010.
7.
DiClemente CC, Marinilli AS, SIngh M, Bellino LE. The role of feedback in the process of health behavior change. Am J Health Behav. 2001;25:217–27.PubMedCrossRef
8.
Conroy MB, Kyeongra Y, Elci OU, Gabriel KP, Styn MA, Wang J, et al. Physical activity self-monitoring and weight loss: 6-month results of the SMART Trial. Med Sci Sports Exerc. 2011;43:1568–74.PubMedCentralPubMedCrossRef
9.
Carver CS, Scheier MF. Control theory: a useful conceptual framework for personality-social, clinical and health psychology. Psychol Bull. 1982;92:111–35.PubMedCrossRef
10.
Kanfer FH, Duerfeldt PH. Motivational properties of self-reinforcement. Elmsford: Pergamon Press; 1990.
11.
Bize R, Burnand B, Mueller Y, Comuz J. Biomedical risk assessments as an aid for smoking cessation. Cochrane Database Syst Rev. 2005;4, CD004705.PubMed
12.
Agarwal R, Bills JE, Hecht TJ, Light RP. Role of home blood pressure monitoring in overcoming therapeutic inertia and improving hypertension control: a systematic review and meta-analysis. Hypertension. 2011;57:29–38.PubMedCrossRef
13.
Marquez-Contreras E, Martell-Claros N, Gil-Guillen V, Figuera-Von M, Casado-Martinez J. Efficacy of a home blood pressure monitoring programme on therapeutic compliance in hypertension: the EAPACUM-HTA study. J Hypertens. 2006;24:169–75.PubMedCrossRef
14.
Vrijens B, Goetghebeur E. Comparing compliance patterns between randomized treatments. Controlled Clin Trials. 1997;18:187–203.PubMedCrossRef
15.
Ogedegbe G, Schoenthaler A. A systematic review of the effects of home blood pressure monitoring on medication adherence. J Clin Hypertens. 2006;8:176–81.
16.
Onzenoort HA, Verberk WJ, Kroon AA, Kessels AG, Nelemans PJ, van der Kuy PM, et al. Effect of self-measurement of blood pressure on adherence to treatment in patients with mild-to-moderate hypertension. J Hypertens. 2010;28:622–7.PubMedCrossRef
17.
Staessen JA, Hond ED, Celis H, Fagard R, Keary L, Vanderhoven G, et al. Antihypertensive treatment based on blood pressure management at home or in the physician’s office: a randomized controlled trial. JAMA. 2004;291:955–64.PubMedCrossRef
18.
Mahler H, Kulik J, Gerrard M, Gibbons F. Long-term effects of appearance-based interventions on sun protection behaviors. Health Psychol. 2007;26:350–60.PubMedCrossRef
19.
Chapin RB, Williams DC, Adair RF. Diabetes control improved when inner-city patients received graphic feedback about glycosylated hemoglobin levels. J Gen Intern Med. 2003;18:120–4.PubMedCentralPubMedCrossRef
20.
Levetan CS, Dawn KR, Robbins DC, Ratner RE. Impact of computer-generated personalized goals on HbA1C. Diabetes Care. 2002;25:2–8.PubMedCrossRef
21.
O’Connor PJ, Sperl-Hillen J, Johnson PE, Rush WA, Crain AL. Customized feedback to patients and providers failed to improve safety or quality of diabetes care. Diabetes Care. 2009;32:1158–63.PubMedCentralPubMedCrossRef
22.
Sherifali D, Greb JL, AMirthavasar G, Hunt D, Haynes RB, Harper W, et al. Effect of computer-generated tailored feedback on glycemic control in people with diabetes in the community. Diabetes Care. 2011;34:1794–8.PubMedCentralPubMedCrossRef
23.
Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health. JAMA. 2007;298:2296–304.PubMedCrossRef
24.
Tudor-Locke C, Lutes L. Why do pedometers work? A reflection upon the factors related to successfully increasing physical activity. Sports Med. 2009;39:981–93.PubMedCrossRef
25.
Tudor-Locke C, Corbin CB. Taking steps toward increased physical activity; using pedometers to measure and motivate. Pres Counc Phys Fit Sports. 2002;3:1–8.
26.
Kabutoya T, Ishikawa J, Hoshide S, Eguchi K, Shimada K, Kario K. A home blood pressure monitor equipped with a graphic function facilitates faster blood pressure control than conventional home blood pressure monitor. J Clin Hypertens. 2009;11:422–5.CrossRef
27.
Dowray S, Swartz JJ, Braxton D, Viera AJ. Potential effect of physical activity based menu labels on the calorie content of selected fast food meals. Appetite. 2013;62:173–81.PubMedCrossRef
28.
Polonsky WH, Fisher L. Right answer, but wrong question: SMBG can be clinically valuable for non-insulin users. Diabetes Care 2012;35.
29.
Parkes G, Greenhalgh T, Griffen M, Dent T. Effect on smoking quit rate of telling patients their lung age: the Step 2 quit randomized controlled trial. BMJ 2008.
30.
Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V. The constructive, destructive and reconstructive power of social norms. Psychol Sci. 2007;18:429–34.PubMedCrossRef
31.•
Rees G, Lamoureux EL, Nicolaou TE, Hodgson LA, Weinman J, Speight J. Feedback of personal retinal images appears to have a motivational impact in people with non-proliferative diabetic retinopathy and suboptimal HbA1C: findings of a pilot study. Diabetic Med. 2013;30:1122–5. In this small but intriguing pilot study, T1D patients with non-proliferative retinopathy who were exposed to personal retinal images demonstrated greater improvement in glycemic control than control patients over a 3-month period.PubMedCrossRef
32.
Gibbons F, Gerrard M, Lane DJ, Mahler H, Kulik J. Using UV photography to reduce use of tanning booths: a test of cognitive mediation. Health Psychol. 2005;24:358–63.PubMedCrossRef
33.
Shahab L, Hall S, Marteau T. Showing smokers with vascular disease images of the arteries to motivate cessation: a pilot study. Br J Health Psychol. 2007;12:275–83.PubMedCrossRef
34.•
Polonsky WH, Fisher L, Hessler DH, Edelman SV. What is so tough about self-monitoring of blood glucose? Perceived obstacles among patients with type 2 diabetes. Diabet Med. 2014;31:40–6. Three key obstacles to self-monitoring of blood glucose were identified—Burden, Avoidance, and Pointlessness. Higher levels of Avoidance and Pointlessness, but not Burden, were associated with less frequent self-monitoring use.PubMedCrossRef
35.
Wilson TD, Dunn DS, Kraft D, Lisle DJ. Introspection, attitude change and attitude-behavior consistency: the disruptive effects of explaining why we feel the way we do. In: Berkowitz L, editor. Advances in social psychology. New York: Academic Press; 1989. p. p. 287–343.
36.
Parkin CG, Hinnen D, Campbell RK, Geil P, Tetrick DL, Polonsky WH. Effective use of paired testing in type 2 diabetes: practical applications in clinical practice. Diabetes Educ. 2009;35:915–27.PubMedCrossRef
37.
Polonsky WH, Skinner CS. Perceived treatment efficacy: an overlooked opportunity in diabetes care. Clin Diabet. 2010;28:89–92.CrossRef
38.
Richardson CR, Newton TL, Abraham JJ, Sen A, Jimbo M, Swartz AM. A meta-analysis of pedometer-based walking interventions and weight loss. Ann Fam Med. 2008;6:69–77.PubMedCentralPubMedCrossRef
39.
Cagliero E, Levina EV, Nathan DM. Immediate feedback of HbA1C levels improves glycemic control in type 1 and insulin-dependent type 2 diabetic patients. Diabetes Care. 1999;22:1785–9.PubMedCrossRef
40.
Sheridan SL, Viera AJ, Krantz MJ, Ice CL, Steinman LE, Peters KE, et al. The effect of giving global risk information to adults: a systematic review. Arch Intern Med. 2010;170:230–9.PubMedCrossRef
41.
42.
Linde JA, Jeffery RW, French SA, Pronk NP, Boyle RG. Self-weighing in weight gain prevention and weight loss trials. Ann Behav Med. 2005;30:210–6.PubMedCrossRef
43.
Karter AJ, Parker MM, Moffet HH, Spence MM, Chan J, Ettner SL. Longitudinal study of new and prevalent use of self-monitoring of blood glucose. Diabetes Care. 2006;29:1757–63.PubMedCentralPubMedCrossRef
44.
Schutt M, Kern W, Krause U, Busch P, Dapp A. Is the frequency of self-monitoring of blood glucose related to long-term metabolic control? Multicenter analysis including 24,500 patients from 191 centers in Germany and Austria. Clin Endocrinol Diabetes. 2006;114:384–8.CrossRef
45.
Group JDRFCGMS. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med. 2008;359:1464–76.CrossRef
46.
Jakicic JM, Winters C, Lang W. Effect of exercise duration and intensity on weight loss in overweight, sedentary women: a randomized trial. JAMA. 2003;1999:1323–30.CrossRef
47.
Shay LE, Seibert D, Watts D, Sbrocco T, Pagliara C. Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight management program. Eat Behav. 2009;10:220–7.PubMedCentralPubMedCrossRef
48.
Baker G, Mutrie N, Lowry R. Using pedometers as motivational tools: are goals set in steps more effective than goals set in minutes for increased walking? J Health Promot Educ. 2008;46:21–6.CrossRef
49.
Fung CS, Wong WC, Wong CK, Lee A, Lam CL. Home blood pressure monitoring: a trial on the effect of a structured education program. Aust Fam Physician. 2013;42:233–8.PubMed
50.
Uhlig K, Balk EM, Patel K, Ip S, Kitsios GD, Obadab NO, Haynes SM, Rao M, Chang L, Gaylor J, Iovin RC: Self-measured blood pressure monitoring: comparative effectiveness. In Effective health care program: comparative effectiveness review. Rockville, MD, AHRQ, 2012.
51.
Pomerleau J, Lock K, Knai C, McKee M. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr. 2005;135:2486–95.PubMed
52.
Franciosi M, Lucisano G, Pellegrini F, Cantarello A, Consoli A, Cucco L. ROSES: role of self-monitoring of blood glucose and intensive education in patients with type 2 diabetes not receiving insulin: a pilot randomized clinical trial. Diabet Med. 2011;21:999–106.
53.
Duran A, Martin P, Runkle I, Perez N, Abad R, Fernandedz M, et al. Benefits of self-monitoring of blood glucose in the management of new-onset type 2 diabetes mellitus: The St Carlos Study, a prospective randomized clinic-based interventional study with parallel groups. J Diabet. 2010;2:203–11.CrossRef
54.
Shaw R, Fenwick E, Baker G, McAdam C, Fitzsimons CF, Mutrie N. “Pedometers cost buttons”: the feasibility of implementing a pedometer-based walking programme within the community. BMC Public Health. 2011;11:200–8.PubMedCentralPubMedCrossRef
55.
Logan AG, Dunai A, Mclassac WJ, Irvine MJ, Tisler A. Attitudes of primary care physicians and their patients about home blood pressure monitoring in Ontario. J Hypertens. 2008;26:446–52.PubMedCrossRef
56.
Bancej CM, Campbell N, McKay DW, Nichol M, Walker RL, Kaczorowski J. Home blood pressure monitoring among Canadian adults with hypertension: results from the 2009 survey on Living with Chronic Diseases in Canada. Can J Cardiol. 2010;26:152–7.CrossRef
57.
Levetan CS, Dawn KR, Murray JF, Popma JJ, Ratner RE, Robbins DC. Impact of computer-generated personalized goals on cholesterol lowering. Value Health. 2005;8:639–46.PubMedCrossRef
58.•
Polonsky WH, Hessler DH. What are the quality of life-related benefits and losses associated with real-time continuous glucose monitoring? A survey of current users. Diabetes Technol Ther. 2013;15:295–301. Diabetes-specific QOL benefits resulting from RT-CGM were found to be common; those benefits were associated with satisfaction with device accuracy and usability and trust in one’s ability to use RT-CGM data, suggesting that “perceived efficacy,” for both device and self, are key QOL determinants.PubMedCrossRef
59.
Heisler M, Bouknight RR, Hayward RA, Smith DM, Kerr EA. The relative importance of physician communication, participatory decision making, and patient understanding in diabetes self-management. J Gen Intern Med. 2000;17:243–52.CrossRef
60.
Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev. 2005;4, CD004274.PubMed
61.
Bosworth HB, Olsen MK, Oddone EZ. Improving blood pressure control by tailored feedback to patients and clinicians. Am Heart J. 2005;149:795–803.PubMedCrossRef
62.
Krueter MW, Skinner CS. Tailoring: what’s in a name? Health Educ Res. 2000;15:1–4.CrossRef
63.
64.
Fisher L, Hessler DH, Masharani U, Stryker LA. The impact of baseline patient characteristics on interventions to reduce diabetes distress: the role of personal conscientiousness and diabetes self-efficacy. Diabetic Med. 2014;31:739–46.PubMedCentralPubMedCrossRef
65.
66.
Butcher Z, Fairclough S, Stratton G, Richardson D. The effect of feedback and information on children’s pedometer step counts at school. Pediatr Exerc Sci. 2007;19:29–38.PubMed
67.
Chamberlain J, Dopita D, Gilgen E. Persistence of continuous glucose monitoring use in a community setting one year after purchase. Clin Diabetes. 2013;31:106–9.CrossRef
68.
Polonsky WH, Fisher L, Schikman C, Hinnen D, Parkin C, Jelovsky Z, et al. Structured self-monitoring of blood glucose significantly reduces A1C levels in poorly controlled, noninsulin-treated type 2 diabetes: results from the Structured Testing Program study. Diabetes Care. 2011;34:262–7.PubMedCentralPubMedCrossRef
69.
Fisher L, Dickinson WP. New technologies to support self-management support in diabetes: not just a bunch of cool apps. Diabetes Care. 2011;34:240–3.PubMedCentralPubMedCrossRef
Metadata
Title
When Does Personalized Feedback Make A Difference? A Narrative Review of Recent Findings and Their Implications for Promoting Better Diabetes Self-Care
Authors
William H. Polonsky
Lawrence Fisher
Publication date
01-08-2015
Publisher
Springer US
Published in
Current Diabetes Reports / Issue 8/2015
Print ISSN: 1534-4827
Electronic ISSN: 1539-0829
DOI
https://doi.org/10.1007/s11892-015-0620-7

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