Rationale: Hyperglycaemia is an important cause of long-term macro-and microvascular complications in all patients with diabetes mellitus. However, only a small fraction of the patients with diabetes reaches the set target of glycemic control. Problems with adequate self-management usually underlie problems to maintain glycaemic control. Thus, patients need more support in order to reduce the burden and increase the effectiveness of their diabetes self-management. One way to do this is by using integrated technologies and personalized plans for diabetes care. For this purpose, the POWER2DM support system was developed to give patients insight into their condition and support diabetes patients and their health care professionals in setting and achieving self-management goals using predictive computer model simulations and behavioural action plans. Objective: To provide proof of concept that POWER2DM is safe and effective in improving glycaemic control, improving behavioural/psychosocial and lifestyle markers, and to assess the cost-effectiveness of the approach and to highlight any potential issues that may impede implementation. Study design: This is a pragmatic randomised controlled trial with 9 months follow-up in which patients will be randomised 1:1 to either Power2DM support (Power2DM group) or usual care (usual care group). There will be evaluation moments at baseline, after 11 weeks, 22 weeks and 37 weeks. Study population: 230 patients with diabetes (N=115 type 1 diabetes (T1D), N=115 type 2 diabetes (T2D)) recruited from out-patient clinics in the Netherlands (Leiden University Medical Centre and affiliating teaching hospitals N=115) and Córdoba, Spain (Reina Sofia University Hospital N=115). Intervention: The POWER2DM support group will receive access to the prototype 2 of the POWER2DM system. This system consists of two components: 1) the web-based Shared Decision Making Dashboard, used to set self-management goals together with a health care professional with the use of both short- and long-term predictive computer simulation models, and 2) the POWER2DM Self-Management Support System as a mobile application and webpage, used to support behavioural change in DM self-management. The system is fed with data from an activity tracker, a glucose monitor and manual data entry. Main study parameters/endpoints: Change in glucose regulation as measured by %HbA1c before and after the intervention compared between the intervention and control group.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Enrollment
222
This system consists of two components: 1) the web-based Shared Decision Making Dashboard, used to set self-management goals together with a health care professional with the use of both short- and long-term predictive computer simulation models, and 2) the POWER2DM Self-Management Support System as a mobile application and webpage, used to support behavioural change in DM self-management. The system is fed with data from an activity tracker, a glucose monitor and manual data entry. Power2DM support will be provided as an adjunct to usual care
Usual care as given to diabetes patients by the patient's diabetes care team.
Leiden University Medical Centre
Leiden, South Holland, Netherlands
Reina Sofia University Hospital
Córdoba, Spain
Change in glycemic control
Change in HbA1c before and after the intervention compared between the intervention and control group
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Amount hypoglycemia
Amount of hypoglycemia measured by time spent in hypoglycaemia before and after treatment in the Power2DM group compared to the usual care control group
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Hypo unawareness
Hypo unawareness as measured by Clarke's hypo unawareness questionnaire, before and after treatment in the Power2DM group compared to the usual care control group
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Incidence of adverse events
Incidence of adverse events occurring during the study period including serious hypoglycemic events
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Mean blood glucose (MBG)
As derived from continuous glucose measurements by a flash glucose monitoring (FGM) device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Standard deviation of MBG (SDBG)
As derived from continuous glucose measurements by a FGM device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Largest amplitude of glycemic excursions (LAGE)
As derived from continuous glucose measurements by a FGM device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Mean amplitude of glycemic excursions (MAGE)
As derived from continuous glucose measurements by a FGM device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Absolute means of daily differences (MODD)
As derived from continuous glucose measurements by a FGM device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Time spent in range
As derived from continuous glucose measurements by a FGM device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
ADVANCE Cardiovascular risk
The ADVANCE Cardiovascular Risk Engine, calculates the risk of major cardiovascular disease in patients with type 2 diabetes for the next 4 years (range 0-100%). This is defined as fatal or non-fatal myocardial infarction, stroke or cardiovascular death.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
ADVANCE Kidney disease Risk
The ADVANCE Kidney Risk Engine, calculates the risk of new-onset albuminuria and major kidney-related events in patients with type 2 diabetes for the next 5 years (range 0-100%). Major kidney-related events are defined as doubling of serum creatinin to \>2.26mg/dL, renal replacement therapy, or renal death.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Major Outcomes T1D
The Major Outcomes T1D risk score assess the 3, 5 and 7 year risk of a patient with type 1 diabetes on major outcomes (range 0-100%). These outcomes included major coronary heart disease, stroke, end-stage renal failure, amputations, blindness and all-cause death.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
UKPDS risk score
The UKPDS risk score calculated the risk a patient with type 2 diabetes will develop coronary heart disease, fatal coronary hear disease, stroke or fatal stroke (range 0-100%)
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Q score
The Q score is a single metric for a continuous glucose monitoring (CGM) profile which summarize the glucose profile using five factors: central tendency, hyperglycemia, hypoglycemia, intra- and inter daily variations.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Amount of steps
Average amount of steps per day over a week measured by a step counter
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Self-reported exercise time
Exercise time per week as reported in the POWER2DM system
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Frequency of self-monitoring of blood glucose (SMBG) measurements
Frequency of SMBG measurements as reported by the glucose measurement device
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Self-reported adherence to medication plan
Self-reported adherence to medication plan as reported in the POWER2DM system
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Weight
Weight in kilograms measured on a scale
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Body mass index (BMI)
BMI in kg/m2, computed from height and weight
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Diabetes Self-Management Questionnaire Revised (DSMQ-R)
Subscales: Glucose Management, Dietary Control, Physical Activity, Health Care Use Transformed scale scores can vary between 0-10, with higher scores indicating more effective self-care
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Patient utilities by EQ-5D
No subscales: EQ-5D provides a general health index with higher scores indicating better general health. QALYs will be calculated from EQ-5D scores.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Problem Areas in Diabetes (PAID)
The PAID provides a total diabetes distress score (0-100), with higher scores (\> 40) indicating more distress.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Mood/Well-being by WHO-5 and Patient Health Questionnaire (PHQ-9)
WHO-5 provides a total score (0-100) with higher scores indicating better wellbeing, PHQ-9 provides a total score (1-27) indicating a likelihood of depression, with higher scores indicating more depressive symptoms
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Technology Acceptance Questionnaire (TAQ)
The TAQ provides scores (1-7) on the following domains: Performance expectancy, Effort expectancy, Social influence, Facilitating conditions, Affect, Self-efficacy, Trust, Motivation and Behavioural intention. Higher scores indicate better acceptance of the system.
Time frame: 5 weeks and 37 weeks
Cost-effectiveness
Costs/quality adjusted life years (QALYs) Costs assessed via cost questionnaire and medication registry. QALYs based on patient utilities measured via EQ5D.
Time frame: Over 37 weeks
Stress by perceived Stress Scale (PSS)
The PSS provides a total perceived stress score (0-40), with higher scores indicating more perceived stress.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
Patient Assessment of Chronic Illness Care (PACIC)
The PACIC measures the patient's perception of the care that they receive.
Time frame: 0 weeks, 11 weeks, 22 weeks and 37 weeks
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