a randomized trial to slow the progression of diabetes

Conditions: Diabetes Mellitus, Type 2

Summary: the research objective of this one-year study is to test whether an evidence-based, low-cost mobile diabetes management package (dmp), with or without an incentive program grounded in economic theory (m-power rewards), can effectively and cost-effectively improve health outcomes for adults with type 2 diabetes.

technological and economic advancement have created a major challenge to public health agencies in singapore and other first world countries: the challenge being how to get individuals to maintain a healthy lifestyle when that is no longer a requirement for economic prosperity (and in fact may be a hindrance). what is required is a low cost strategy that effectively manages risk factors for chronic disease without overwhelming the public healthcare infrastructure. although technology and economic advancement are clearly part of the problem, they may also be part of the solution. for patients with type 2 diabetes mellitus (t2dm), lifestyle modification can be highly effective at stemming the progression of the disease. effective interventions include modules that teach appropriate strategies for weight management, increased physical activity, better diet, routine glucose monitoring, and strict compliance to prescribed diabetes medications. these components can now be effectively delivered electronically. a recent review and meta-analysis of 13 smartphone applications for diabetes management found that these applications offered modest benefits, with a mean difference in hba1c of -0.40%. because of the high costs involved in treating people with chronic conditions, employers, insurers, and governments all have a financial incentive to contain the chronic disease epidemic. therefore, each has shown a willingness to invest in some level of prevention and treatment efforts. it is our contention that contingent rewards may be necessary to help people overcome their preferences for current over future consumption, often called present bias. behavioral economists recommend that, to overcome present bias, rewards should be tied to both short-term and long-term outcomes or behaviors, such as monitoring blood glucose and taking medications as prescribed (short term) and pre-defined outcomes such as achieving a target hba1c level over a specified duration (long term). based on a recent systematic review of incentive studies conducted by members of our team, an optimal rewards strategy has the potential to greatly increase the effectiveness of existing mobile diabetes applications. if shown to be effective and cost effective, the investigators also believe payers will subsidize such a strategy. specifically, the investigators propose to conduct a 52-week, three-arm randomized controlled trial to evaluate whether an evidence-based, low-cost mobile diabetes management package (dmp), with or without an incentive program grounded in economic theory, can effectively and cost-effectively improve outcomes for adults with diabetes. the control arm (arm 1) will receive usual care, whereas participants in the intervention arms will receive the dmp alone (arm 2) or the dmp with the m-power rewards incentive program (arm 3) in addition to their usual care. the investigators hypothesize that participants in arms 2 and 3 will show improved glycemic control, as measured by hba1c levels, at the month 12 primary endpoint compared to participants in the arm 1 control group. the investigators also hypothesize that arm 3 will have improved hba1c levels compared to arm 2 at month 12. similar hypotheses will be tested for secondary outcomes measured at month 6. in addition, the investigators will quantify the incremental cost-effectiveness of dmp with m-power rewards and the net cost implications of both from a third party payer's perspective. the investigators hypothesize that despite its higher implementation cost, effectiveness will be greater and net cost will be lower for arm 3 relative to the arm 1 control group due to the reduction in medical expenditures that result from improved glycaemic control.

Eligibility criteria:
all eligibility criteria will be self-declared. inclusion criteria: 1. diagnosed with t2dm with suboptimal diabetes control as defined by a hba1c level of between 7.5% and 11.0% (inclusive) at their most recent test taken within the past six calendar months. this hba1c inclusion criterion will be based on the patients' self-reported hba1c levels and test dates. 2. not on insulin. 3. on at least one oral glucose-lowering drug. 4. singapore citizen or permanent resident with no plans to relocate during the study period. 5. able to read, write, and communicate in english. 6. own a personal smartphone and be able to use it. exclusion criteria: 1. pregnant or lactating. 2. diagnosed with chronic kidney disease (stage 3b with egfr \<45ml/min) or undergoing dialysis for end-stage kidney failure. 3. diagnosed with liver cirrhosis. 4. diagnosed with cancer that required treatment in the past five years. 5. diagnosed with heart attack (i.e., acute myocardial infarction) within the past one year. 6. diagnosed with heart failure (i.e., congestive heart failure) 7. diagnosed with stroke or transient ischemic attacks. 8. undergone whole blood or red blood cell transfusion within the past three months. 9. diagnosed with severe anaemia (haemoglobin \<10g/dl) 10. diagnosed with sickle-cell disease 11. diagnosed with thalassemia major 12. undergone bariatric surgery or extensive bowel resection. 13. undergone lower limb amputation (including toe amputation). 14. taking systemic corticosteroids (including traditional chinese or malay medicine). 15. currently on doctor's advice against engaging in moderate-to-vigorous physical activity (i.e., brisk walking or more intense). 16. currently have a condition(s) that restricts engaging in moderate-to-vigorous physical activity (i.e., brisk walking or more intense).

Keywords: diabetes smartphone application behavior change weight monitoring physical activity blood glucose monitoring medication adherence financial incentive cost-effectiveness analysis