Diabetes Prevention and Weight Loss with a Fully Automated Behavioral Intervention by Email, Web, and Mobile Phone: A Randomized Controlled Trial Among Persons with Prediabetes

Gladys Block, Kristen Mj Azar, Robert J Romanelli, Torin J Block, Donald Hopkins, Heather A Carpenter, Marina S Dolginsky, Mark L Hudes, Latha P Palaniappan, Clifford H Block, Gladys Block, Kristen Mj Azar, Robert J Romanelli, Torin J Block, Donald Hopkins, Heather A Carpenter, Marina S Dolginsky, Mark L Hudes, Latha P Palaniappan, Clifford H Block

Abstract

Background: One-third of US adults, 86 million people, have prediabetes. Two-thirds of adults are overweight or obese and at risk for diabetes. Effective and affordable interventions are needed that can reach these 86 million, and others at high risk, to reduce their progression to diagnosed diabetes.

Objective: The aim was to evaluate the effectiveness of a fully automated algorithm-driven behavioral intervention for diabetes prevention, Alive-PD, delivered via the Web, Internet, mobile phone, and automated phone calls.

Methods: Alive-PD provided tailored behavioral support for improvements in physical activity, eating habits, and factors such as weight loss, stress, and sleep. Weekly emails suggested small-step goals and linked to an individual Web page with tools for tracking, coaching, social support through virtual teams, competition, and health information. A mobile phone app and automated phone calls provided further support. The trial randomly assigned 339 persons to the Alive-PD intervention (n=163) or a 6-month wait-list usual-care control group (n=176). Participants were eligible if either fasting glucose or glycated hemoglobin A1c (HbA1c) was in the prediabetic range. Primary outcome measures were changes in fasting glucose and HbA1c at 6 months. Secondary outcome measures included clinic-measured changes in body weight, body mass index (BMI), waist circumference, triglyceride/high-density lipoprotein cholesterol (TG/HDL) ratio, and Framingham diabetes risk score. Analysis was by intention-to-treat.

Results: Participants' mean age was 55 (SD 8.9) years, mean BMI was 31.2 (SD 4.4) kg/m(2), and 68.7% (233/339) were male. Mean fasting glucose was in the prediabetic range (mean 109.9, SD 8.4 mg/dL), whereas the mean HbA1c was 5.6% (SD 0.3), in the normal range. In intention-to-treat analyses, Alive-PD participants achieved significantly greater reductions than controls in fasting glucose (mean -7.36 mg/dL, 95% CI -7.85 to -6.87 vs mean -2.19, 95% CI -2.64 to -1.73, P<.001), HbA1c (mean -0.26%, 95% CI -0.27 to -0.24 vs mean -0.18%, 95% CI -0.19 to -0.16, P<.001), and body weight (mean -3.26 kg, 95% CI -3.26 to -3.25 vs mean -1.26 kg, 95% CI -1.27 to -1.26, P<.001). Reductions in BMI, waist circumference, and TG/HDL were also significantly greater in Alive-PD participants than in the control group. At 6 months, the Alive-PD group reduced their Framingham 8-year diabetes risk from 16% to 11%, significantly more than the control group (P<.001). Participation and retention was good; intervention participants interacted with the program a median of 17 (IQR 14) of 24 weeks and 71.1% (116/163) were still interacting with the program in month 6.

Conclusions: Alive-PD improved glycemic control, body weight, BMI, waist circumference, TG/HDL ratio, and diabetes risk. As a fully automated system, the program has high potential for scalability and could potentially reach many of the 86 million US adults who have prediabetes as well as other at-risk groups.

Trial registration: Clinicaltrials.gov NCT01479062; https://ichgcp.net/clinical-trials-registry/NCT01479062 (Archived by WebCite at http://www.webcitation.org/6bt4V20NR).

Keywords: Internet; behavior change; intervention studies; nutrition; obesity; physical activity; prediabetes; prevention; smartphone; type 2 diabetes; weight loss.

Conflict of interest statement

Conflicts of Interest: GB, CHB, and TJB are the owners of Turnaround Health and NutritionQuest, the developers of Alive-PD. KA, RJR, LP, MD, DH, HAC, and MLH have no conflicts of interest.

Figures

Figure 1
Figure 1
Screenshot of Alive-PD personal home page.
Figure 2
Figure 2
Changes in primary and secondary endpoints over time. Solid line: control; dashed line: intervention; error bars: ± standard error. A: Change in HbA1c. B: Change in fasting glucose. C: Change in waist circumference. D: Change in weight. At 6 months, all measures were significantly different between control and intervention groups (P<.001).
Figure 3
Figure 3
Proportion achieving secondary endpoint thresholds at 6 months. Error bars not shown because all differences between control and intervention were P<.001. A: Percentage with ≥5% weight loss (complete data: n=156 control, n=136 intervention). B: Percentage who moved to normal fasting glucose (from ≥100 mg/dL to <100 mg/dL) (denominator: n=150 control, n=126 intervention). C: Percentage who moved from having metabolic syndrome to not having metabolic syndrome (denominator: n=110 control, n=86 intervention). D: Percentage whose BMI decreased by 1 kg/m2 (denominator: n=156 control, n=136 intervention).
Figure 4
Figure 4
Change in Framingham 8-year diabetes risk.

References

    1. American Diabetes Association. 2014. Jun 10, [2015-06-29]. Statistics About Diabetes
    1. Tabák AG, Herder C, Rathmann W, Brunner E, Kivimäki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012 Jun 16;379(9833):2279–90. doi: 10.1016/S0140-6736(12)60283-9.
    1. Dall TM, Yang W, Halder P, Pang B, Massoudi M, Wintfeld N, Semilla AP, Franz J, Hogan PF. The economic burden of elevated blood glucose levels in 2012: diagnosed and undiagnosed diabetes, gestational diabetes mellitus, and prediabetes. Diabetes Care. 2014 Dec;37(12):3172–9. doi: 10.2337/dc14-1036.
    1. Cefalu W, Petersen M, Ratner R. The alarming and rising costs of diabetes and prediabetes: a call for action! Diabetes Care. 2014 Dec;37(12):3137–8. doi: 10.2337/dc14-2329.
    1. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM, Diabetes Prevention Program Research Group Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002 Feb 7;346(6):393–403. doi: 10.1056/NEJMoa012512.
    1. Ackermann RT, Finch EA, Brizendine E, Zhou H, Marrero DG. Translating the Diabetes Prevention Program into the community. The DEPLOY Pilot Study. Am J Prev Med. 2008 Oct;35(4):357–63. doi: 10.1016/j.amepre.2008.06.035.
    1. Whittemore R, Melkus G, Wagner J, Dziura J, Northrup V, Grey M. Translating the diabetes prevention program to primary care: a pilot study. Nurs Res. 2009;58(1):2–12. doi: 10.1097/NNR.0b013e31818fcef3.
    1. Davis-Smith Y, Davis-Smith M, Boltri JM, Seale JP, Shellenberger S, Blalock T, Tobin B. Implementing a diabetes prevention program in a rural African-American church. J Natl Med Assoc. 2007 Apr;99(4):440–6.
    1. Ali M, Echouffo-Tcheugui J, Williamson D. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program? Health Aff (Millwood) 2012 Jan;31(1):67–75. doi: 10.1377/hlthaff.2011.1009.
    1. Lawlor M, Blackwell C, Isom S, Katula JA, Vitolins MZ, Morgan TM, Goff DC. Cost of a group translation of the Diabetes Prevention Program: Healthy Living Partnerships to Prevent Diabetes. Am J Prev Med. 2013 Apr;44(4 Suppl 4):S381–9. doi: 10.1016/j.amepre.2012.12.016.
    1. Vojta D, Koehler TB, Longjohn M, Lever JA, Caputo NF. A coordinated national model for diabetes prevention: linking health systems to an evidence-based community program. Am J Prev Med. 2013 Apr;44(4 Suppl 4):S301–6. doi: 10.1016/j.amepre.2012.12.018.
    1. Levine DM, Savarimuthu S, Squires A, Nicholson J, Jay M. Technology-assisted weight loss interventions in primary care: a systematic review. J Gen Intern Med. 2015 Jan;30(1):107–17. doi: 10.1007/s11606-014-2987-6.
    1. Hartmann-Boyce J, Jebb S, Fletcher B, Aveyard P. Self-help for weight loss in overweight and obese adults: systematic review and meta-analysis. Am J Public Health. 2015 Mar;105(3):e43–57. doi: 10.2105/AJPH.2014.302389.
    1. Hutchesson M, Rollo M, Krukowski R, Ells L, Harvey J, Morgan PJ, Callister R, Plotnikoff R, Collins CE. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. 2015 May;16(5):376–92. doi: 10.1111/obr.12268.
    1. McLaughlin T, Reaven G, Abbasi F, Lamendola C, Saad M, Waters D, Simon J, Krauss RM. Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? Am J Cardiol. 2005 Aug 1;96(3):399–404. doi: 10.1016/j.amjcard.2005.03.085.
    1. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart J, James WP, Loria CM, Smith SC, International Diabetes Federation Task Force on EpidemiologyPrevention. National Heart‚ Lung‚ and Blood Institute. American HA, World HF, International AS, International Association for the Study of Obesity Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009 Oct 20;120(16):1640–5. doi: 10.1161/CIRCULATIONAHA.109.192644.
    1. Framingham Heart Study. 2015. [2015-10-18]. Diabetes Risk Score .
    1. Davis NJ, Tomuta N, Schechter C, Isasi CR, Segal-Isaacson CJ, Stein D, Zonszein J, Wylie-Rosett J. Comparative study of the effects of a 1-year dietary intervention of a low-carbohydrate diet versus a low-fat diet on weight and glycemic control in type 2 diabetes. Diabetes Care. 2009 Jul;32(7):1147–52. doi: 10.2337/dc08-2108.
    1. Block G, Azar KM, Block TJ, Romanelli RJ, Carpenter H, Hopkins D, Palaniappan L, Block CH. A fully automated diabetes prevention program, Alive-PD: program design and randomized controlled trial protocol. JMIR Res Protoc. 2015;4(1):e3. doi: 10.2196/resprot.4046.
    1. Hsu W, Araneta MR, Kanaya A, Chiang J, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care. 2015 Jan;38(1):150–8. doi: 10.2337/dc14-2391.
    1. Centers for Disease Control and Prevention. 2015. [2015-06-29]. The CDC Diabetes Prevention Program (CDC DPP) Curriculum .
    1. Duhigg C. Power of Habit: Why We Do What We Do, and How to Change. London: Heinemann Educational Books; 2012.
    1. Hull CL. Essentials of Behavior. New Haven: Yale University Press; 1951.
    1. Dickinson A. Actions and habits: the development of behavioural autonomy. Phil Trans R Soc Lond B. 1985 Feb 13;308(1135):67–78. doi: 10.1098/rstb.1985.0010.
    1. Fogg GJ. Stanford Persuasive Tech Lab. 2014. [2015-06-29].
    1. Wansink B. Mindless Eating: Why We Eat More Than We Think. New York: Bantam; 2006.
    1. Brownell K, Marlatt G, Lichtenstein E, Wilson G. Understanding and preventing relapse. Am Psychol. 1986 Jul;41(7):765–82.
    1. Beck J. The Complete Beck Diet for Life: The 5-Stage Program for Permanent Weight Loss. Birmingham, AL: Oxmoor House; 2008.
    1. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991 Dec;50(2):179–211. doi: 10.1016/0749-5978(91)90020-T.
    1. Heshmat S. Eating Behavior and Obesity: Behavioral Economics Strategies for Health Professionals. New York: Springer Publishing Company; 2011.
    1. Cornum R, Matthews M, Seligman ME. Comprehensive soldier fitness: building resilience in a challenging institutional context. Am Psychol. 2011 Jan;66(1):4–9. doi: 10.1037/a0021420.
    1. Seligman ME, Railton P, Baumeister RF, Sripada C. Navigating into the future or driven by the past. Perspect Psychol Sci. 2013 Mar;8(2):119–41. doi: 10.1177/1745691612474317.
    1. Heckman J. Annals of Economic and Social Measurement, Volume 5, number 4. Cambridge, MA: The National Bureau of Economic Research; 1976. The common structure of statistical models of truncated, sample selection and limited dependent variables, and a simple estimator of such models; pp. 475–492.
    1. Allison P. Paper 312- 2012. [2015-10-18]. Handling Missing Data by Maximum Likelihood .
    1. Whittemore R. A systematic review of the translational research on the Diabetes Prevention Program. Transl Behav Med. 2011 Sep;1(3):480–91. doi: 10.1007/s13142-011-0062-y.
    1. Norris SL, Zhang X, Avenell A, Gregg E, Bowman B, Schmid CH, Lau J. Long-term effectiveness of weight-loss interventions in adults with pre-diabetes: a review. Am J Prev Med. 2005 Jan;28(1):126–39. doi: 10.1016/j.amepre.2004.08.006.
    1. Johnson M, Jones R, Freeman C, Woods HB, Gillett M, Goyder E, Payne N. Can diabetes prevention programmes be translated effectively into real-world settings and still deliver improved outcomes? A synthesis of evidence. Diabet Med. 2013 Jan;30(1):3–15. doi: 10.1111/dme.12018.
    1. Dunkley AJ, Bodicoat DH, Greaves CJ, Russell C, Yates T, Davies MJ, Khunti K. Diabetes prevention in the real world: effectiveness of pragmatic lifestyle interventions for the prevention of type 2 diabetes and of the impact of adherence to guideline recommendations: a systematic review and meta-analysis. Diabetes Care. 2014 Apr;37(4):922–33. doi: 10.2337/dc13-2195.
    1. McTigue KM, Conroy MB. Use of the internet in the treatment of obesity and prevention of type 2 diabetes in primary care. Proc Nutr Soc. 2013 Feb;72(1):98–108. doi: 10.1017/S0029665112002777.
    1. Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev. 2010 Apr;11(4):306–21. doi: 10.1111/j.1467-789X.2009.00646.x.
    1. Allen J, Stephens J, Patel A. Technology-assisted weight management interventions: systematic review of clinical trials. Telemed J E Health. 2014 Dec;20(12):1103–20. doi: 10.1089/tmj.2014.0030.
    1. Tang J, Abraham C, Greaves C, Yates T. Self-directed interventions to promote weight loss: a systematic review of reviews. J Med Internet Res. 2014;16(2):e58. doi: 10.2196/jmir.2857.
    1. Ma J, Yank V, Xiao L, Lavori PW, Wilson SR, Rosas LG, Stafford RS. Translating the Diabetes Prevention Program lifestyle intervention for weight loss into primary care: a randomized trial. JAMA Intern Med. 2013 Jan 28;173(2):113–21. doi: 10.1001/2013.jamainternmed.987.
    1. Thomas JG, Leahey TM, Wing RR. An automated internet behavioral weight-loss program by physician referral: a randomized controlled trial. Diabetes Care. 2015 Jan;38(1):9–15. doi: 10.2337/dc14-1474.
    1. Seo D, Niu J. Evaluation of Internet-based interventions on waist circumference reduction: a meta-analysis. J Med Internet Res. 2015;17(7):e181. doi: 10.2196/jmir.3921.
    1. Centers for Disease Control and Prevention. 2015. [2015-10-18]. Diabetes Prevention Recognition Program Standards and Operating Procedures .
    1. Centers for Disease Control and Prevention. 2015. [2015-08-04]. Registry of Recognized Programs, Virtual, Online or Combination of In-person/Online Programs .
    1. Khaylis A, Yiaslas T, Bergstrom J, Gore-Felton C. A review of efficacious technology-based weight-loss interventions: five key components. Telemed J E Health. 2010 Nov;16(9):931–8. doi: 10.1089/tmj.2010.0065.
    1. Winett R, Davy B, Marinik E, Savla J, Winett SG, Phillips SM, Lutes LD. Developing a new treatment paradigm for disease prevention and healthy aging. Transl Behav Med. 2014 Mar;4(1):117–23. doi: 10.1007/s13142-013-0225-0.
    1. Salas-Salvadó J, Martinez-González MÁ, Bulló M, Ros E. The role of diet in the prevention of type 2 diabetes. Nutr Metab Cardiovasc Dis. 2011 Sep;21 Suppl 2:B32–48. doi: 10.1016/j.numecd.2011.03.009.
    1. Risérus U, Willett W, Hu F. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009 Jan;48(1):44–51. doi: 10.1016/j.plipres.2008.10.002.
    1. Fox S, Rainie L. Pew Research Center. 2014. Feb 27, [2015-06-29]. The Web at 25 in the US

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться