Journal of Diabetes Research
Volume 2016 (2016), Article ID 6343927, 16 pages
http://dx.doi.org/10.1155/2016/6343927

Research Article

Demographical, Clinical, and Psychological Characteristics of Users and Nonusers of an Online Platform for T2DM Patients (e-VitaDM-3/ZODIAC-44)

Yvonne Roelofsen,¹ Michael van Vugt,² Steven H. Hendriks,¹ Kornelis J. J. van Hateren,¹ Klaas H. Groenier,1,3 Frank J. Snoek,² Nanne Kleefstra,1,4,5 Robbert Huijsman,⁶ and Henk J. G. Bilo1,4

¹Diabetes Centre, Isala, Zwolle, Netherlands
²Department of Medical Psychology, VU University Medical Center and Academic Medical Center (AMC), Amsterdam, Netherlands
³Department of General Practice, University of Groningen and University Medical Center Groningen, Groningen, Netherlands
⁴Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Groningen, Netherlands
⁵Langerhans Medical Research Group, Zwolle, Netherlands
⁶Institute of Healthcare Management & Policy, Erasmus University Rotterdam, Rotterdam, Netherlands

Received 7 January 2015; Accepted 30 April 2015

Academic Editor: Nitin Gupta

Copyright © 2016 Yvonne Roelofsen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Online platforms offer opportunities for support in changing lifestyle and taking responsibility for one’s health, but engaging patients with type 2 diabetes is challenging. Previous studies have shown that patients interested in platforms were more often male, younger, and higher educated. This study aims to investigate differences in clinical and psychological characteristics between users and nonusers of a newly developed platform. Methods. A prospective study started in the Drenthe region of Netherlands. Participants in the study concerning quality of care and quality of life were additionally invited to use the platform. Results. 633 patients were registered after they opted for platform use. Of these patients, 361 (57.0%) never logged on, 184 (29.1%) were labeled “curious” users, and 88 (13.9%) were identified as “active” users. Users had lower HbA1c levels and more often hypertension compared to nonusers, and reported higher quality of life, better well-being, lower diabetes-related distress, and better medication adherence. Discussion. Platform use was associated with more favorable clinical and psychological characteristics relative to nonuse. Those with greater severity of disease, lower mood, and progression of disease used the platform the least. Other approaches need to be developed to reach these patients. Furthermore, improving the platform could also help to reach them. This trial is registered with Clinicaltrials.gov NCT01570140.

1. Background

Type 2 diabetes mellitus (T2DM) in itself is associated with poorer health-related quality of life (HRQoL) [1]. People with T2DM are susceptible to develop long term complications, such as retinopathy, neuropathy, nephropathy, and chronic heart disease, which negatively influence HRQoL [2]. To prevent or delay development of these long term complications, adequate treatment modalities are necessary which mainly involve lifestyle changes and pharmacological treatment. Adherence to medication prescription and implementing life style changes are often better maintained and facilitated, when patients consider themselves more responsible for their treatment and have more knowledge regarding the causes and consequences of their disease. Improvements in knowledge about their disease can be described as promotion of health literacy. e-Health applications, such as web-portals, teleconsultation, and online care platforms, have the potential to support patients in changing lifestyle and taking more responsibility for their own health [3]. However, varying effects on clinical outcomes, quality of life, degree of self-care, perceived stress levels, patient satisfaction, and costs have been reported [4–10].

Previous studies showed that patients who were interested in using an online care platform were more often male, younger, and higher educated [11, 12]. However, within the subgroup of interested patients these differences were not found between actual users and nonusers [11]. In addition, other factors associated with higher portal enrollment and utilization are higher income, nonblack race, higher self-efficacy, and having better regulated diabetes [13]. Identifying the differences between platform users and nonusers could provide information to help target and support nonusers in becoming more active in their diabetes self-management.

The aim of the present, explorative study was to investigate possible differences in demographic, clinical, and psychological characteristics between users and nonusers of the platform e-Vita.

2. Methods

2.1. Study Design

We performed a cross-sectional analysis of baseline data of users and nonusers of the online patient platform e-Vita. Data was obtained from a prospective observational cohort study. Detailed information about the methods and design of the study as a whole can be found elsewhere [14].

2.2. Study Population and Setting

Forty-three out of 110 general practices in the Drenthe region of the Netherlands invited their T2DM patients for participation in a prospective observational cohort study concerning quality of care and HRQoL. Patients were also invited to use the online care platform e-Vita, in addition to their usual treatment. Patients interested in using the platform were registered by their practice nurse (PN) and received a user ID. In this ongoing study, participants were recruited from May 2012 onward. The current analysis includes patients recruited from May 2012 till March 2014.

2.3. Measurements

Demographic and clinical data were obtained from the personal health record systems of the general practitioners (GP), based on a core dataset of T2DM related information as advised by the Dutch Diabetes Federation and the Dutch College of General Practitioners [14]. All T2DM patients participating in the study filled in a range of validated questionnaires concerning perceived quality of life measured by the EuroQol Five Dimension (EQ-5D) Scale [15–17], emotional well-being measured by the World Health Organization Wellbeing Index 5-Item (WHO-5) questionnaire [18, 19], diabetes-related distress measured by the Problem Areas in Diabetes 5-Item (PAID-5) questionnaire [20], diabetes self-care behavior measured by 7 Dimensions of the Summary of Diabetes Self-Care Activities (SDSCA) questionnaire [21], and quality of received care measured by the Europep [22]. Suboptimal emotional well-being was defined by a raw score lower than 13 on the WHO-5 [23]. Additional questions about smoking habits, employment, and educational background were also included. To identify users and nonusers, registration data from the application software and log-files were used.

2.4. Description of e-Vita Platform

The e-Vita platform for T2DM patients (accessible through the login button on https://www.e-vita.nl/) [11, 14, 24] contains the following components: (1) an overview of health data concerning annual check-ups from 2009 onward, (2) educational modules meant to support care through self-management by setting person-specific goals and actions [25], (3) prompting patient self-monitoring of clinical values, (4) educational modules aimed at increasing diabetes knowledge, and (5) providing reliable information on T2DM in general.

2.5. Users and Nonusers

Information about login status and log-data were used to group patients into nonusers and users. All patients who logged in at least once were considered as users. Patients who had been online for at least two sessions with a minimum of five minutes per session were defined as “active” users; other patients were defined as “curious” users. A session included all logins to the platform within thirty minutes [24].

2.6. Statistical Analyses

Statistical analyses were performed using SPSS version 20 (IBM Corporation, Somers, NY, USA). Quantitative variables are described in means and standard deviations when normally distributed; otherwise medians and interquartile ranges are also described. Categorical variables are described in numbers and percentages. To identify differences in the domains of interest between the different groups of users, the Linear Mixed Models procedure was used, with groups of users being fixed factors (nonusers being the reference group), while adjusting for age and sex. Fisher’s exact test was used for categorical data. Differences were considered to be significant at a value of <0.05. In addition, results are adjusted for age and gender. Because of the explorative design of this study, no corrections for multiple testing were made [26]. Instead, the calculated values are only used as an indication of to what extent a difference could be interesting for further research.

2.7. Ethics

This study was approved by the Medical Ethical Review Committee of Isala, Zwolle, the Netherlands, and registered in Clinicaltrials.gov under number NCT01570140.

3. Results

In the period from May 2012 to March 2014, 3191 patients were invited to participate in the cohort study and to use the e-Vita platform. 633 patients were registered for care platform use. See Figure 1 for the patient flow.

Figure 1: Flowchart of patients and definitions.

Table 1 shows all differences and other notable characteristics for the comparison between nonusers, curious users, and active users of the platform. No differences were found in demographical characteristics between nonusers, curious users, and active users. HbA1c level of nonusers was higher compared to curious users () and to active users (). Curious and active users were more often known with hypertension compared to nonusers (). Curious users assessed the GP better on one question of the Europep compared to nonusers and active users (). Curious users scored higher on EQ-5D () and EQ-VAS (0.032) compared to nonusers, with no significant differences between curious users and active users or nonusers and active users. In addition, curious users’ WHO-5 score as well as their answers to the individual WHO-5 questions reported less depressive symptoms compared to nonusers and active users. Curious users scored lower on PAID-5 compared to nonusers (), with no significant differences between curious users versus active users and nonusers versus active users. Curious users performed better on one dimension of self-reported self-management activities (medication intake) compared to nonusers (), with no significant difference between curious users versus active users and nonusers versus active users. Table 2 shows the Cronbach’s alpha for all the multi-item scales.

Table 1: Differences and notable characteristics of nonusers, curious users, and active users.

n (%) or mean (SD) 
Demographic and clinical parameters
Nonusers ()
Missing
Curious users ()
Missing
Active users ()
Missing
Univariate 
value

Men
214 (59.3)
0 (0)
113 (61.4)
0 (0)
50 (56.8)
0 (0)
0.760

Age in years
62.1 ± 9.5 
63.0 (56.5–68.0)
0 (0)
61.8 ± 9.5 
62.0 (56.3–68.0)
0 (0)
62.0 ± 9.4 
63.0 (57.0–67.0)
0 (0)
0.935

Ethnicity

    Caucasian
292 (99.0)
66 (18.3)
143 (100)
41 (22.3)
65 (100)
23 (6.1)
0.706

    Other
3 (1.0)

0 (0)

0 (0)

0.382

Employment

    Fulltime/part-time working
99 (34.3)
72 (19.9)
61 (39.6)
30 (16.3)
20 (23.8)
4 (4.5)
0.063

    Retired
134 (46.4)

70 (45.5)

46 (54.8)

    Unemployed/housekeeper
38 (13.1)

20 (13.0)

9 (10.7)

    Incapacitated
18 (6.2)

3 (1.9)

9 (10.7)

Educational level

    None
0 (0)
73 (20.2)
1 (0.7)
31 (16.8)
0 (0)
4 (4.5)
0.125

    Primary school
24 (8.3)

9 (5.9)

4 (4.8)

    Low
127 (44.1)

52 (34.0)

27 (32.1)

    Intermediate
86 (29.9)

51 (33.3)

30 (35.7)

    High
51 (17.7)

40 (26.1)

23 (27.4)

T2DM duration in years
6.2 ± 4.6 
6.0 (2.0–9.0)
9 (2.5)
5.7 ± 4.4  
5.0 (2.0–8.0)
1 (0.5)
5.4 ± 4.4 
4.5 (2.0–8.0)
0 (0)
0.165

HbA1c in mmol/mol
50.6 ± 9.5 
50.0 (45.0–54.0)
3 (0.8)
48.7 ± 7.4 
48.0 (43.0–54.0)
0 (0)
47.0 ± 7.0 
46.0 (43.0–50.8)
0 (0)
0.001

BMI
29.8 ± 4.9 
29.0 (26.5–32.5)
3 (0.8)
30.0 ± 4.8 
29.3 (26.9–32.3)
0 (0)
29.9 ± 8.0 
28.0 (26.0–32.6)
2 (2.3)
0.921

Comorbidities/complications

    Hypertension
191 (84.1)
134 (37.1)
113 (93.4)
63 (34.2)
51 (92.7)
33 (37.5)
0.025

Items of Europep: patients who scored 4 (good) to 5 (excellent) 
What is your assessment of the general practitioner over the last 12 months with respect to the following?

    Making it easy for you to tell him or her about your problem
345 (93.5)
18 (4.7)
187 (97.9)
17 (8.2)
84 (93.3)
2 (2.2)
0.047

EQ-5D index-score
0.9 ± 0.2
72 (19.9)
0.9 ± 0.1
32 (17.4)
0.9 ± 0.2
4 (4.5)
0.030

EQ-VAS
74.7 ± 17.4 
80.0 (60.0–90.0)
74 (20.5)
79.3 ± 13.8 
80.0 (73.0–90.0)
35 (19.0)
76.9 ± 16.5 
80.0 (0.0–90.0)
5 (5.7)
0.032

WHO-5 score indicates suboptimal well-being, screening depression advised
36 (12.6)
76 (21.1)
8 (5.3)
33 (17.9)
9 (10.8)
1 (1.1)
0.018

WHO-5 answers advise screening depression
43 (15.5)
76 (21.1)
6 (4.0)
33 (17.9)
11 (13.3)
1 (1.1)
0.002

PAID-5 total score
2.8 ± 3.1 
2.0 (0.0–4.5)
76 (21.1)
1.8 ± 2.4 
1.0 (0.0–3.0)
32 (17.4)
2.2 ± 2.5 
1.0 (0.0–4.0)
1 (1.1)
0.016

SDSCA

    Medication in number of days
6.7 ± 1.0 
7.0 (7.0–7.0)
73 (20.2)
7.0 ± 0.2 
7.0 (7.0–7.0)
30 (16.3)
6.8 ± 0.8 
7.0 (7.0–7.0)
4 (4.5)
0.020

Table 2: Cronbach’s alpha for multi-item scales.

Multi-item scale
α

Europep

    Total
0.963

    Subscale general practice
0.966

    Subscale general practitioner
0.840

EQ-5D
0.652

WHO-5
0.872

PAID-5
0.867

SDSCA

    Total
0.517

    Subscale general diet
0.875

    Subscale specific diet
0¹

    Subscale total diet
0.446²

    Subscale exercise
0.663

    Subscale blood-glucose testing
0.912

    Subscale foot-care
0.593

Because of the negative intercorrelation between the two items Cronbach’s alpha is reported to be 0.

²The alpha for the subscale total diet is lower than that for the subscale general diet due to the low reliability of the subscale specific diet.

See Appendix A for tables with all characteristics as mentioned in the methods section for the comparison between nonusers and users. See Appendix B for tables with all characteristics for the comparison between nonusers, curious users, and active users.

The differences in characteristics between nonusers, curious users, and active users have also been adjusted for age and gender in a multivariate analysis. The results are shown in Table 3. values below 0.05 were found for differences regarding HbA1c between active users and nonusers (−3.624 mmol/mol) as well as between curious users and nonusers (−1.989 mmol/mol) and for differences between curious users and nonusers regarding EQ-5D (0.044), EQ-VAS (4.611), WHO-5 (3.609), PAID-5 (−0.929), and medication intake (0.236).

Table 3: Results of multivariate analysis, adjusted for age and gender.

-coefficient
95% CI
value

Lower bound
Upper bound

T2DM duration in years

    Intercept
0.018
−2.325
2.360
0.988

    Platform use

0.186

    Active users
−0.845
−1.876
0.186
0.108

    Curious users
−0.511
−1.300
0.277
0.203

    Nonusers
Ref. Cat.

    Male
−0.121
−0.832
0.589
0.738

    Female
Ref. Cat.

    Age
0.101
0.064
0.138
<0.0005

HbA1c in mmol/mol

    Intercept
53.431
48.931
57.931
<0.0005

    Platform use

<0.0005

    Active users
−3.624
−5.627
−1.621
<0.0005

    Curious users
−1.989
−3.516
−0.462
0.011

    Nonusers
Ref. Cat.

    Male
1.103
−0.270
2.477
0.115

    Female
Ref. Cat.

    Age
−0.055
−0.127
0.016
0.126

BMI

    Intercept
37.430
34.658
40.202
<0.0005

    Platform use

0.924

    Active users
0.079
−1.159
1.317
0.900

    Curious users
0.189
−0.747
1.124
0.692

    Nonusers
Ref. Cat.

    Male
−1.087
−1.931
−0.244
0.012

    Female
Ref. Cat.

    Age
−0.113
−0.156
−0.069
<0.0005

EQ-5D

    Intercept
0.866
0.773
0.958
<0.0005

    Platform use

0.022

    Active users
0.008
−0.031
0.047
0.674

    Curious users
0.044
0.013
0.076
0.006

    Nonusers
Ref. Cat.

    Male
0.056
0.027
0.085
<0.0005

    Female
Ref. Cat.

    Age
−0.001
−0.002
0.001
0.343

EQ-VAS

    Intercept
71.007
61.663
80.350
<0.0005

    Platform use

0.019

    Active users
2.291
−1.691
6.275
0.259

    Curious users
4.611
1.384
7.838
0.005

    Nonusers
Ref. Cat.

    Male
2.977
0.095
5.859
0.043

    Female
Ref. Cat.

    Age
0.030
−0.118
0.178
0.690

WHO-5

    Intercept
58.138
48.911
67.365
<0.0005

    Platform use

0.065

    Active users
−0.089
−4.008
3.829
0.964

    Curious users
3.609
0.446
6.773
0.025

    Nonusers
Ref. Cat.

    Male
5.766
2.932
8.600
<0.0005

    Female
Ref. Cat.

    Age
0.142
−0.004
0.289
0.057

PAID-5

    Intercept
5.129
3.520
6.737
<0.0005

    Platform use

0.004

    Active users
−0.511
−1.195
0.173
0.143

    Curious users
−0.929
−1.480
−0.378
0.001

    Nonusers
Ref. Cat.

    Male
−0.143
−0.639
0.353
0.571

    Female
Ref. Cat.

    Age
−0.037
−0.062
−0.011
0.005

SDSCA-medication

    Intercept
6.087
5.575
6.600
<0.0005

    Platform use

0.028

    Active users
0.081
−0.132
0.296
0.458

    Curious users
0.236
0.063
0.408
0.008

    Nonusers
Ref. Cat.

    Male
0.096
−0.058
0.250
0.222

    Female
Ref. Cat.

    Age
0.010
0.002
0.018
0.020

4. Discussion

In this exploratory study we found that only a small amount of clinical and psychological characteristics were associated with platform use. Curious users as well as active users had lower HbA1c compared to nonusers, which is in agreement with other studies [27, 28]. The more frequent presence of hypertension in curious and active users, however, contradicts with these studies. Curious users scored higher on EQ-5D and EQ-VAS and lower on PAID-5. Curious users scored also better on medication intake, which may reflect higher self-efficacy, in agreement with the study by Sarkar et al. [29]. After adjustment for age and gender, the difference in WHO-5 score between curious users and nonusers was also significant.

We observed that most of the patients, who were registered for platform use, never logged on. This could be influenced by (an insufficient) intrinsic motivation and (no) intention to change behaviours. Another explanation could be that patients do not see the platform as useful or as an added value to regular treatment. As an alternative explanation, login procedures might be too difficult and after trying for some time they might give up.

Previous research showed that web-portals and online care platforms are susceptible to implementation problems, low participation rates, and nonadherence, which, amongst others, can be caused by a mismatch in expectations between software developers, health care providers, and users [30–37]. Other reasons for limited use of care platforms or nonadherence rates are as follows: abundance of functionalities on a platform, no connection with the needs of patients, implementation by management only without active involvement of care providers, no embedding in the regular care process, no space for habituation, underestimation of the complexity of lifestyle changes in general [38], and barriers to easy access to a portal (e.g., complicated login procedures). Despite the use of focus groups for designing and testing, these reasons might also be applicable to the e-Vita platform and improvements could be made.

The current study has some limitations. A preselection of participants could in part have influenced results. Only patients who expressed their interest received a user-ID [14]; see also Figure 1. Relevant and significant differences might be more difficult to find.

Data were not complete for all patients, especially with regard to complications and risk factors (complete for 50–60%; see Tables 6 and 11). This may have led to an underestimation of presence of complications and risk factors. In addition, not all patients were seen by their GP or PN for the regular yearly check-up in the year 2012, which contributed to missing values in clinical parameters. Some questions about the assessment of the general practice and the general practitioner were poorly answered in general. A reason for this could be social desirability; patients may not like to be negative about their GP and prefer not answering these questions.

Although the online care platform e-Vita was designed for being suitable for all T2DM patients, a general assumption is that those with greater severity of disease, lower mood, progression of the disease, and complications would probably benefit most from an online care platform. However, when assessing the presented results, these patients use the platform the least.

Possibly, the current users were already more in control of their life and health and could therefore be more open to other forms of support, including e-Health facilities. Challenges to reach other patients remain manifold. A patients’ passive attitude may not be overcome by only providing e-facilities, since one’s interest and the sense of disease burden are low or even absent in the majority of the T2DM population. Factors as knowledge, motivation, and intention could be considered in future research.

Appendices

A. Results of Users and Nonusers of the Online Care Platform e-Vita

See Tables 4, 5, 6, 7, and 8.

Table 4: Demographic and clinical characteristics of users and nonusers.

Demographic and clinical parameters  
(%)/mean ± SD/median (25–75 quartiles)
Nonusers ()
Missing
Users ()
Missing
Univariate 
value

Men
214 (59.3)
0 (0)
163 (59.9)
0 (0)
0.95

Age in years
62.1 ± 9.5  
63.0 (56.5–68.0)
0 (0)
61.8 ± 9.4 
62.5 (57.0–68.0)
0 (0)
0.732

Ethnicity

    Caucasian
292 (99.0)
66 (18.3)
208 (100)
64 (2.5)
0.271

    Other
3 (1.0)

0 (0)

T2DM duration in years
6.2 ± 4.6 
6.0 (2.0–9.0)
9 (2.5)
5.6 ± 4.4 
5.0 (2.0–8.0)
1 (0.4)
0.068

HbA1c in mmol/mol
50.6 ± 9.5 
50.0 (45.0–54.0)
3 (0.8)
48.2 ± 7.3 
47.0 (43.0–53.0)
0 (0)
<0.0005

BMI
29.8 ± 4.9 
29.0 (26.5–32.5)
3 (0.8)
30.0 ± 6.0 
28.7 (26.3–32.4)
2 (0.7)
0.724

Systolic blood pressure in mmHG
135.6 ± 15.5
0 (0)
136.5 ± 16.0
4 (1.5)
0.463

Cholesterol in mmol/L
4.4 ± 1.0
4 (1.1)
4.4 ± 0.9
2 (0.7)
0.499

HDL in mmol/L
1.3 ± 0.4
4 (1.1)
1.3 ± 0.4
3 (1.1)
0.581

Cholesterol/HDL ratio
3.6 ± 1.1
160 (44.3)
3.6 ± 1.3
92 (33.8)
0.899

LDL in mmol/L
2.4 ± 0.9
12 (3.3)
2.3 ± 0.8
6 (2.2)
0.240

Triglycerides in mmol/L
1.7 ± 1.0 
1.5 (1.0–2.1)
7 (1.9)
1.8 ± 1.2 
1.5 (1.1–2.1)
2 (0.7)
0.482

Creatinine in µmol/L
78.6 ± 17.2 
77.0 (67.0–88.0)
6 (1.7)
79.9 ± 17.5 
79.0 (67.0–90.0)
1 (0.4)
0.359

Alb./creat. ratio in mg/mmol

    Men
2.0 ± 4.4 
0.7 (0.3–1.5)
23 (10.7)
1.9 ± 5.8 
0.5 (0.3–1.5)
25 (15.3)
0.853

    Women
1.6 ± 3.5 
0.7 (0.3–1.5)
31 (21.1)
0.9 ± 1.1 
0.6 (0.4–1.2)
18 (16.5)
0.070

MDRD in mL/min/1.73 m2
79.1 ± 49.0 
75.0 (61.0–88.0)
5 (1.4)
76.0 ± 16.6 
74.0 (61.0–87.0)
1 (0.4)
0.329

Smoking

    Yes
54 (15.1)
3 (0.8)
41 (15.1)
1 (0.4)
0.306

    Before
158 (44.1)

104 (38.4)

    No
146 (40.8)

126 (46.5)

Alcohol consumption in units/day

    0
166 (58.9)
79 (21.9)
139 (60.7)
43 (15.8)
0.870

    1
61 (21.6)

52 (22.7)

    2
39 (13.8)

30 (13.1)

    3
11 (3.9)

7 (3.1)

    4
4 (1.4)

1 (0.4)

    5
0 (0)

0 (0)

    6
1 (0.4)

0 (0)

Employment

    Fulltime/part-time working
99 (34.3)
72 (19.9)
81 (34.0)
34 (12.5)
0.909

    Retired
134 (46.4)

116 (48.7)

    Unemployed/ housekeeper
38 (13.1)

29 (12.2)

    Incapacitated
18 (6.2)

12 (5.0)

Educational level

    None
0 (0)
73 (20.2)
1 (0.4)
35 (12.9)
0.017

    Primary school
24 (8.3)

13 (5.5)

    Low
127 (44.1)

79 (33.3)

    Intermediate
86 (29.9)

81 (29.8)

    High
51 (17.7)

63 (23.2)

Table 5: Medication use of users and nonusers.

Medication prescription
(%)
Nonusers ()
Missing
Users ()
Missing
Univariate
value

Diabetes-related

    Oral treatment only
251 (71.3)
9 (2.5)
192 (71.9)
5 (1.8)
0.702

    Insulin treatment only
4 (1.1)
9 (2.5)
1 (0.4)
5 (1.8)
0.931

    Mix of oral and insulin treatment
40 (11.4)
9 (2.5)
23 (8.6)
5 (1.8)
0.248

    No medication
57 (16.2)
9 (2.5)
51 (19.1)
5 (1.8)
0.417

Comorbidity or complication related

    Calcium channel blockers
50 (14.2)
9 (2.5)
47 (17.6)
5 (1.8)
0.236

    Beta blockers
128 (36.4)
9 (2.5)
110 (41.2)
5 (1.8)
0.145

    Diuretics
121 (34.4)
9 (2.5)
94 (35.2)
5 (1.8)
0.870

    Ace and RAAS inhibitors
196 (55.7)
9 (2.5)
141 (52.8)
5 (1.8)
0.480

    Other blood pressure lowering medications
3 (0.9)
9 (2.5)
1 (0.4)
5 (1.8)
0.637

    Lipid lowering medication
280 (79.5)
9 (2.5)
213 (79.8)
5 (1.8)
0.847

Table 6: Complications and risk factors of users and nonusers.

Complications and risk factors
(%)
Nonusers ()
Missing
Users ()
Missing
Univariate
value

Cardiovascular, total
225 (96.2)
127 (35.2)
187 (98.4)
82 (30.1)
0.240

Cardiovascular, specific

    Angina pectoris
41 (21.7)
172 (47.6)
28 (19.7)
130 (47.8)
0.787

    Myocardial infarct
29 (15.3)
172 (47.6)
23 (16.0)
128 (47.1)
0.880

    Other/chronic ischemic heart diseases
34 (16.1)
150 (41.6)
24 (13.6)
96 (35.3)
0.569

    Hypertension
191 (84.1)
134 (37.1)
164 (93.2)
96 (35.3)
0.008

    TIA
12 (6.4)
174 (48.2)
7 (5.0)
133 (48.9)
0.642

    CVA
13 (7.0)
176 (48.8)
10 (7.1)
132 (48.5)
1.000

    Intermittent claudication
7 (3.3)
150 (41.6)
7 (4.0)
96 (35.3)
0.788

    Aortic aneurysms
4 (1.9)
150 (41.6)
2 (1.1)
96 (35.3)
0.693

    CABG
15 (5.1)
68 (18.8)
11 (5.4)
68 (25.0)
1.000

    PTCA
28 (9.6)
68 (18.8)
14 (6.8)
67 (24.6)
0.327

    Heart failure
14 (8.1)
189 (52.4)
10 (7.4)
136 (50.0)
0.834

Retinopathy
19 (9.3)
156 (43.2)
18 (10.2)
95 (34.9)
0.863

Renal impairment
35 (18.6)
173 (47.9)
26 (18.6)
132 (48.5)
1.000

Albuminuria

    Men
30 (14.5)
7 (3.3)
20 (12.6)
4 (2.5)
0.647

    Women
8 (5.8)
8 (5.4)
1 (1.0)
5 (4.6)
0.082

Neuropathy
49 (22.2)
140 (38.8)
39 (22.4)
98 (36.0)
1.000

Foot complication

    SIMMs 0
228 (77.6)
67 (18.6)
161 (76.7)
62 (22.8)
0.783

    SIMMs 1
57 (19.4)

40 (19.0)

    SIMMs 2 or 3
9 (3.1)

9 (4.3)

Psychiatric disorders
19 (9.0)
150 (41.6)
9 (5.1)
96 (35.3)
0.124

SIMMS refers to risk factors in the diabetic foot, the number is the stage which ranges from 0–3.

0: no loss of protective sensibility (PS) & Peripheral arterial disease (PAV).

1: loss of PS or PAV, with no signs of increased local pressure.

2: loss of PS in combination with and/or PAV and/or signs of local elevated pressure.

3: ulcer or amputation in history.

Table 7: Scores on quality of care (Europep) of users and nonusers.

Items of Europep: patients who scored 4 (good) or 5 (excellent) 
(%)
Nonusers ()
Missing
Users ()
Missing
Univariate
value

What is your assessment of the general practitioner over the last 12 months with respect to the following?

Making you feel you have time during consultation
337 (97.4)
15 (4.2)
256 (98.1)
11 (4.0)
0.622

Showing interest in your personal situation
324 (94.5)
18 (5.0)
246 (94.6)
12 (4.4)
0.864

Making it easy for you to tell him or her about your problem
323 (93.4)
15 (4.2)
245 (96.5)
18 (6.6)
0.110

Involving you in decisions about your medical care
311 (92.3)
24 (6.6)
239 (94.1)
18 (6.6)
0.290

Listening to you
322 (92.3)
12 (3.3)
243 (94.6)
15 (5.5)
0.270

Keeping your records and data confidential
310 (95.7)
37 (10.2)
236 (95.9)
26 (9.6)
0.846

Providing quick relief of your symptoms
272 (87.5)
50 (13.9)
201 (85.9)
38 (14.0)
0.635

Helping you to feel well so that you can perform your normal daily activities
265 (89.8)
66 (18.3)
196 (91.2)
57 (21.0)
0.483

Thoroughness of the approach to your problems
308 (91.4)
24 (6.6)
227 (89.7)
19 (7.0)
0.786

Your physical examination
292 (90.1)
37 (10.2)
222 (92.1)
31 (11.4)
0.327

Offering services for preventing diseases (screening, health checks, and immunizations)
286 (91.4)
48 (13.3)
225 (92.6)
29 (10.7)
0.655

Explaining the purpose of examinations, tests, and treatments
307 (93.0)
31 (8.6)
240 (93.8)
16 (5.9)
0.518

Telling you enough about your symptoms and/or illness
306 (92.2)
29 (8.0)
238 (93.3)
17 (6.3)
0.448

Helping you deal with emotions related to your health status
198 (86.8)
133 (36.8)
133 (84.7)
115 (42.3)
0.888

Helping understand why it is important to follow the GP’s advice
295 (89.7)
32 (8.9)
219 (89.4)
27 (9.9)
0.894

Knowing what has been done or told during previous contacts in the practice
270 (84.9)
43 (11.9)
219 (89.4)
27 (9.9)
0.071

Preparing you for what to expect from specialists, hospital care, and other care providers
199 (85.4)
128 (35.5)
156 (83.5)
85 (31.3)
0.513

What is your assessment of the general practice over the last 12 months with respect to the following?

The helpfulness of the practice staff (other than the doctor) to you
313 (93.4)
26 (7.2)
235 (92.9)
19 (7.0)
0.878

Getting an appointment to suit you
301 (88.5)
21 (5.8)
224 (86.5)
13 (4.8)
0.639

Getting through to the practice on telephone
249 (73.0)
20 (5.5)
180 (69.5)
13 (4.8)
0.662

Being able to talk to the general practitioner on the telephone
167 (70.5)
124 (34.3)
106 (63.1)
104 (38.2)
0.150

Waiting time in the waiting room
246 (71.3)
16 (4.4)
170 (65.4)
12 (4.4)
0.175

Providing quick services for urgent health problems
241 (90.3)
94 (26.0)
171 (86.8)
75 (27.6)
0.398

Table 8: Scores on quality of life (EQ-5D), well-being (WHO-5), diabetes-related distress (PAID-5), and self-care behavior (SDSCA).

EQ-5D, WHO-5, PAID-5, and SDSCA  
(%)/mean ± SD/median (25–75 quartiles)
Nonusers ()
Missing
Users
()
Missing
Univariate 
value

EQ-5D index-score
0.9 ± 0.2
72 (19.9)
0.9 ± 0.1
36 (13.2)
0.028

EQ-VAS
74.7 ± 17.4 
80.0 (60.0–90.0)
74 (20.5)
78.4 ± 14.9 
80.0 (71.0–90.0)
40 (14.7)
0.014

WHO-5 index-score
70.4 ± 17.9 
76.0 (60.0–80.0)
76 (21.1)
72.7 ± 14.2 
76.0 (68.0–80.0)
38 (14.0)
0.096

WHO-5 score indicates suboptimal well-being, screening depression advised
36 (12.6)
76 (21.1)
17 (7.3)
38 (14.0)
0.018

WHO-5 answers advise screening depression
43 (15.5)
76 (21.1)
17 (7.3)
38 (14.0)
0.004

PAID-5 total score
2.8 ± 3.1 
2.0 (0.0–4.5)
76 (21.1)
2.0 ± 2.5 
1.0 (0.0–3.0)
38 (14.0)
0.005

PAID-5 score indicates distress
15 (5.3)
76 (21.1)
6 (2.6)
38 (14.0)
0.058

SDSCA

    General diet in number of days
5.4 ± 1.8 
6.0 (5.0–7.0)
76 (21.1)
5.6 ± 1.8 
6.0 (5.0–7.0)
37 (13.6)
0.269

    Specific diet in number of days
5.6 ± 1.1 
5.7 (4.7–6.3)
73 (20.2)
5.7 ± 1.0 
6.0 (5.3–6.7)
34 (12.5)
0.056

    Exercise in number of days
4.0 ± 2.0 
4.0 (2.5–5.5)
72 (19.9)
4.0 ± 1.8 
4.0 (2.5–5.5)
34 (12.5)
0.919

    Blood-glucose in number of days
2.1 ± 2.2 
1.0 (0.0–4.0)
74 (20.5)
2.0 ± 2.2 
1.0 (0.5–3.5)
34 (12.5)
0.675

    Foot-care in number of days
1.9 ± 2.0 
1.5 (0.0–3.5)
72 (19.9)
1.9 ± 2.0 
1.0 (0.0–3.5)
34 (12.5)
0.695

    Medication in number of days
6.7 ± 1.0 
7.0 (7.0–7.0)
73 (20.2)
6.9 ± 0.5 
7.0 (7.0–7.0)
34 (12.5)
0.013

    Smoking
54 (25.1)
146 (40.4)
38 (22.8)
105 (38.6)
0.418

B. Results of Curious Users, Active Users, and Nonusers of the Online Care Platform e-Vita

See Tables 9, 10, 11, 12, and 13.

Table 9: Demographic and clinical characteristics of curious users, active users, and nonusers.

Demographic and clinical parameters  
(%)/mean ± SD/median (25–75 quartiles)
Nonusers
()
Missing
Curious users
()
Missing
Active users
()
Missing
Univariate 
value

Men
214 (59.3)
0 (0)
113 (61.4)
0 (0)
50 (56.8)
0 (0)
0.760

Age in years
62.1 ± 9.5 
63.0 (56.5–68.0)
0 (0)
61.8 ± 9.5 
62.0 (56.3–68.0)
0 (0)
62.0 ± 9.4 
63.0 (57.0–67.0)
0 (0)
0.935

Ethnicity

    Caucasian
292 (99.0)
66 (18.3)
143 (100)
41 (22.3)
65 (100)
23 (6.1)
0.706

    Other
3 (1.0)

0 (0)

0 (0)

0.382

T2DM duration in years
6.2 ± 4.6 
6.0 (2.0–9.0)
9 (2.5)
5.7 ± 4.4  
5.0 (2.0–8.0)
1 (0.5)
5.4 ± 4.4 
4.5 (2.0–8.0)
0 (0)
0.165

HbA1c in mmol/mol
50.6 ± 9.5 
50.0 (45.0–54.0)
3 (0.8)
48.7 ± 7.4 
48.0 (43.0–54.0)
0 (0)
47.0 ± 7.0 
46.0 (43.0–50.8)
0 (0)
0.001

BMI
29.8 ± 4.9 
29.0 (26.5–32.5)
3 (0.8)
30.0 ± 4.8 
29.3 (26.9–32.3)
0 (0)
29.9 ± 8.0 
28.0 (26.0–32.6)
2 (2.3)
0.921

Systolic blood pressure in mmHG
135.6 ± 15.5
0 (0)
137.2 ± 16.3
2 (1.1)
135.1 ± 15.3
2 (2.3)
0.463

Cholesterol in mmol/L
4.4 ± 1.0
4 (1.1)
4.4 ± 0.8
0 (0)
4.4 ± 0.9
2 (2.3)
0.775

HDL in mmol/L
1.3 ± 0.4
4 (1.1)
1.2 ± 0.3
1 (0.5)
1.3 ± 0.4
2 (2.3)
0.071

Cholesterol/HDL ratio
3.6 ± 1.1
160 (44.3)
3.7 ± 1.4
57 (31.0)
3.4 ± 1.0
35 (39.8)
0.185

LDL in mmol/L
2.4 ± 0.9
12 (3.3)
2.4 ± 0.8
3 (1.6)
2.3 ± 0.8
3 (3.4)
0.473

Triglycerides in mmol/L
1.7 ± 1.0 
1.5 (1.0–2.1)
7 (1.9)
1.8 ± 1.3 
1.5 (1.1–2.1)
0 (0)
1.7 ± 1.0 
1.4 (1.0–2.0)
2 (2.3)
0.531

Creatinine in µmol/L
78.6 ± 17.2 
77.0 (67.0–88.0)
6 (1.7)
80.9 ± 17.4 
79.0 (68.0–92.0)
1 (0.5)
77.8 ± 17.8 
75.5 (66.0–85.8)
0 (0)
0.259

Alb./creat. ratio in mg/mmol

    Men
2.0 ± 4.4 
0.7 (0.3–1.5)
23 (10.7)
2.2 ± 6.9 
0.5 (0.3–1.5)
18 (15.9)
1.3 ± 1.7 
0.7 (0.3–1.5)
7 (14.0)
0.636

    Women
1.6 ± 3.5 
0.7 (0.3–1.5)
31 (21.1)
1.1 ± 1.3 
0.7 (0.4–1.5)
8 (11.3)
0.6 ± 0.5 
0.6 (0.3–0.9)
10 (26.4)
0.155

MDRD in mL/min/1.73 m2
79.1 ± 49.0 
75.0 (61.0–88.0)
5 (1.4)
75.7 ± 16.2 
73.0 (61.0–87.0)
1 (0.5)
76.7 ± 17.4 
75.5 (61.0–89.0)
0 (0)
0.610

Smoking

    Yes
54 (15.1)
3 (0.8)
30 (16.4)
1 (0.5)
11 (12.5)
0 (0)
0.382

    Before
158 (44.1)

73 (39.7)

31 (35.2)

    No
146 (40.8)

80 (43.7)

46 (52.3)

Alcohol consumption in units/day

    0
166 (58.9)
79 (21.9)
98 (60.9)
23 (12.5)
41 (60.3)
20 (22.7)
0.646

    1
61 (21.6)

8 (23.6)

14 (20.6)

    2
39 (13.8)

17 (10.6)

13 (19.1)

    3
11 (3.9)

7 (4.3)

0 (0)

    4
4 (1.4)

1 (0.5)

0 (0)

    5
0 (0)

0 (0)

0 (0)

    6
1 (0.4)

0 (0)

0 (0)

Employment

    Fulltime/part-time working
99 (34.3)
72 (19.9)
61 (39.6)
30 (16.3)
20 (23.8)
4 (4.5)
0.063

    Retired
134 (46.4)

70 (45.5)

46 (54.8)

    Unemployed/housekeeper
38 (13.1)

20 (13.0)

9 (10.7)

    Incapacitated
18 (6.2)

3 (1.9)

9 (10.7)

Educational level

    None
0 (0)
73 (20.2)
1 (0.7)
31 (16.8)
0 (0)
4 (4.5)
0.125

    Primary school
24 (8.3)

9 (5.9)

4 (4.8)

    Low
127 (44.1)

52 (34.0)

27 (32.1)

    Intermediate
86 (29.9)

51 (33.3)

30 (35.7)

    High
51 (17.7)

40 (26.1)

23 (27.4)

Table 10: Medication prescription of curious users, active users, and nonusers.

Medication prescription
(%)
Nonusers
()
Missing
Curious users
()
Missing
Active users
()
Missing
Univariate
value

Diabetes-related

    Oral treatment only
251 (71.3)
9 (2.5)
128 (71.5)
5 (2.7)
64 (72.7)
0 (0)
1.000

    Insulin treatment only
4 (1.1)
9 (2.5)
1 (0.5)
5 (2.7)
0 (0)
0 (0)
0.899

    Mix of oral and insulin treatment
40 (11.4)
9 (2.5)
18 (10.1)
5 (2.7)
5 (5.7)
0 (0)
0.242

    No medication
57 (16.2)
9 (2.5)
32 (17.9)
5 (2.7)
19 (21.6)
0 (0)
0.521

Comorbidity or complication related

    Calcium channel blockers
50 (14.2)
9 (2.5)
31 (17.3)
5 (2.7)
16 (18.2)
0 (0)
0.415

    Beta blockers
128 (36.4)
9 (2.5)
74 (41.3)
5 (2.7)
36 (40.9)
0 (0)
0.324

    Diuretics
121 (34.4)
9 (2.5)
63 (35.2)
5 (2.7)
31 (35.2)
0 (0)
0.979

    Ace and RAAS inhibitors
196 (55.7)
9 (2.5)
94 (52.5)
5 (2.7)
47 (53.4)
0 (0)
0.738

    Other blood pressure lowering medications
3 (0.9)
9 (2.5)
0 (0)
5 (2.7)
1 (1.1)
0 (0)
0.357

    Lipid lowering medication
280 (79.5)
9 (2.5)
141 (78.8)
5 (2.7)
72 (81.8)
0 (0)
0.868

Table 11: Complications and risk factors of curious users, active users, and nonusers.

Complications and risk factors
(%)
Nonusers
()
Missing
Curious users
()
Missing
Active users
()
Missing
Univariate
value

Cardiovascular, total
225 (96.2)
127 (35.2)
128 (98.5)
54 (29.3)
59 (98.3)
28 (31.8)
0.506

Cardiovascular, specific

    Angina pectoris
41 (21.7)
172 (47.6)
21 (21.4)
86 (46.7)
7 (15.9)
44 (50.0)
0.698

    Myocardial infarct
29 (15.3)
172 (47.6)
15 (15.3)
86 (46.7)
8 (17.4)
42 (47.7)
0.932

    Other/chronic ischemic heart diseases
34 (16.1)
150 (41.6)
18 (14.9)
63 (34.2)
6 (10.9)
33 (37.5)
0.750

    Hypertension
191 (84.1)
134 (37.1)
113 (93.4)
63 (34.2)
51 (92.7)
33 (37.5)
0.025

    TIA
12 (6.4)
174 (48.2)
4 (4.2)
88 (47.8)
3 (7.0)
45 (51.1)
0.747

    CVA
13 (7.0)
176 (48.8)
6 (6.1)
86 (46.7)
4 (9.5)
46 (52.3)
0.745

    Intermittent claudication
7 (3.3)
150 (41.6)
4 (3.3)
63 (34.2)
3 (5.5)
33 (37.5)
0.689

     Aortic aneurysms
4 (1.9)
150 (41.6)
1 (0.8)
63 (34.2)
1 (1.8)
33 (37.5)
0.731

    CABG
15 (5.1)
68 (18.8)
7 (5.0)
43 (23.4)
4 (6.3)
25 (28.4)
0.916

    PTCA
28 (9.6)
68 (18.8)
9 (6.4)
43 (23.4)
5 (7.8)
24 (27.3)
0.588

    Heart failure
14 (8.1)
189 (52.4)
9 (9.4)
88 (47.8)
1 (2.5)
48 (54.5)
0.409

Retinopathy
19 (9.3)
156 (43.2)
14 (11.7)
64 (34.8)
4 (7.0)
31 (35.2)
0.640

Renal impairment
35 (18.6)
173 (47.9)
15 (15.5)
87 (47.3)
11 (25.6)
45 (51.1)
0.350

Albuminuria

    Men
30 (14.5)
7 (3.3)
14 (12.6)
2 (1.8)
6 (12.5)
2 (4.0)
0.908

    Women
8 (5.8)
8 (5.4)
1 (1.4)
1 (1.4)
0 (0)
4 (10.5)
0.226

Neuropathy
49 (22.2)
140 (38.8)
30 (24.6)
62 (33.7)
9 (17.3)
36 (40.9)
0.594

Foot complication

    SIMMs 0
228 (77.6)
67 (18.6)
105 (73.4)
41 (22.3)
56 (83.6)
21 (23.9)
0.524

    SIMMs 1
57 (19.4)

31 (21.7)

9 (13.4)

    SIMMs 2 or 3
9 (3.1)

7 (4.9)

2 (3.0)

Psychiatric disorders
19 (9.0)
150 (41.6)
7 (5.8)
63 (34.2)
2 (3.6)
33 (37.5)
0.317

Table 12: Scores on quality of care (Europep) of curious users, active users, and nonusers.

Items of Europep: patients who scored 4 (good) to 5 (excellent)
(%)
Nonusers ()
Missing
Curious users ()
Missing
Active users ()
Missing
Univariate
value

What is your assessment of the general practitioner over the last 12 months with respect to the following?

Making you feel that you have time during consultation
737 (95.1)
105 (11.9)
174 (99.4)
9 (4.9)
82 (95.3)
2 (2.3)
0.159

Showing interest in your personal situation
707 (91.6)
108 (12.3)
165 (94.8)
10 (5.4)
81 (94.2)
2 (2.3)
0.970

Making it easy for you to tell him or her about your problem
694 (92.2)
127 (14.4)
165 (98.2)
16 (8.7)
80 (93.0)
2 (2.3)
0.047

Involving you in decisions about your medical care
680 (90.7)
130 (14.8)
159 (93.5)
14 (7.6)
80 (95.2)
4 (4.5)
0.489

Listening to you
709 (92.0)
109 (12.4)
163 (94.8)
12 (6.5)
80 (94.1)
3 (3.4)
0.579

Keeping your records and data confidential
661 (93.8)
175 (19.9)
161 (97.0)
18 (9.8)
75 (93.8)
8 (9.1)
0.764

Providing quick relief of your symptoms
588 (83.9)
179 (20.3)
137 (86.2)
25 (13.6)
64 (85.3)
13 (14.8)
0.863

Helping you to feel well so that you can perform your normal daily activities
599 (89.7)
212 (24.1)
135 (91.8)
37 (20.1)
61 (89.7)
20 (22.7)
0.780

Thoroughness of the approach to your problems
675 (90.7)
136 (15.5)
153 (90.5)
15 (8.2)
74 (88.1)
4 (4.5)
0.769

Your physical examination
639 (90.8)
176 (20.0)
153 (92.7)
19 (10.3)
69 (90.8)
12 (13.6)
0.577

Offering services for preventing diseases (screening, health checks, and immunizations)
653 (92.5)
174 (19.8)
151 (91.5)
19 (10.3)
74 (94.9)
10 (11.4)
0.539

Explaining the purpose of examinations, tests, and treatments
677 (92.4)
147 (16.7)
163 (94.8)
12 (6.5)
77 (91.7)
4 (4.5)
0.402

Telling you enough about your symptoms and/or illness
662 (90.2)
146 (16.6)
158 (92.4)
13 (7.1)
80 (95.2)
4 (4.5)
0.579

Helping you deal with emotions related to your health status
424 (84.8)
380 (43.2)
89 (86.4)
81 (44.0)
44 (81.5)
34 (38.6)
0.659

Helping understand why it is important to follow the GP’s advice
631 (89.1)
172 (19.5)
146 (89.6)
21 (11.4)
73 (89.0)
6 (6.8)
0.965

Knowing what has been done or told during previous contacts in the practice
601 (86.8)
188 (21.4)
148 (90.2)
20 (10.9)
71 (87.7)
7 (8.0)
0.158

Preparing you for what to expect from specialists, hospital care, and other care providers
424 (82.8)
368 (41.8)
107 (8.3)
54 (29.3)
49 (86.0)
31 (35.2)
0.577

What is your assessment of the general practice over the last 12 months with respect to the following?

The helpfulness of the practice staff (other than the doctor) to you
313 (93.4)
26 (7.2)
158 (92.9)
14 (7.6)
77 (92.8)
5 (5.7)
0.953

Getting an appointment to suit you
301 (88.5)
21 (5.8)
152 (86.9)
9 (4.9)
72 (85.7)
4 (4.5)
0.867

Getting through to the practice on telephone
249 (73.0)
20 (5.5)
120 (68.6)
9 (4.9)
60 (71.4)
4 (4.5)
0.777

Being able to talk to the general practitioner on the telephone
167 (70.5)
124 (34.3)
71 (62.8)
71 (38.6)
35 (63.6)
33 (37.5)
0.306

Waiting time in the waiting room
246 (71.3)
16 (4.4)
119 (68.4)
10 (5.4)
51 (59.3)
2 (2.3)
0.148

Providing quick services for urgent health problems
241 (90.3)
94 (26.0)
118 (86.8)
48 (26.1)
53 (86.9)
27 (30.7)
0.658

Table 13: Scores on quality of life (EQ-5D), well-being (WHO-5), diabetes-related distress (PAID-5), and self-care behavior (SDSCA) of curious users, active users, and nonusers.

EQ-5D, WHO-5, PAID-5, and SDSCA 
(%)/mean ± SD/median (25–75 quartiles)
Nonusers
()
Missing
Curious users
()
Missing
Active users
()
Missing
Univariate 
value

EQ-5D index-score
0.9 ± 0.2
72 (19.9)
0.9 ± 0.1
32 (17.4)
0.9 ± 0.2
4 (4.5)
0.030

EQ-VAS
74.7 ± 17.4 
80.0 (60.0–90.0)
74 (20.5)
79.3 ± 13.8 
80.0 (73.0–90.0)
35 (19.0)
76.9 ± 16.5 
80.0 (0.0–90.0)
5 (5.7)
0.032

WHO-5 index-score
70.4 ± 17.9 
76.0 (60.0–80.0)
76 (21.1)
74.1 ± 12.7 
76.0 (68.0–80.0)
33 (17.9)
70.2 ± 16.5 
76.0 (60.0–80.0)
1 (1.1)
0.080

WHO-5 score indicates suboptimal well-being, screening depression advised
36 (12.6)
76 (21.1)
8 (5.3)
33 (17.9)
9 (10.8)
1 (1.1)
0.018

WHO-5 answers advise screening depression
43 (15.5)
76 (21.1)
6 (4.0)
33 (17.9)
11 (13.3)
1 (1.1)
0.002

PAID-5 total score
2.8 ± 3.1 
2.0 (0.0–4.5)
76 (21.1)
1.8 ± 2.4 
1.0 (0.0–3.0)
32 (17.4)
2.2 ± 2.5 
1.0 (0.0–4.0)
1 (1.1)
0.016

PAID-5 score indicates distress
15 (5.3)
76 (21.1)
4 (2.6)
32 (17.4)
2 (2.4)
1 (1.1)
0.183

SDSCA

    General diet in number of days
5.4 ± 1.8 
6.0 (5.0–7.0)
76 (21.1)
5.5 ± 1.9 
6.0 (5.0–7.0)
32 (17.4)
5.8 ± 1.7 
6.0 (5.5–7.0)
5 (5.7)
0.258

    Specific diet in number of days
5.6 ± 1.1 
5.7 (4.7–6.3)
73 (20.2)
5.7 ± 1.0 
6.0 (5.0–6.7)
30 (16.3)
5.7 ± 1.0 
6.0 (5.3–6.6)
4 (4.5)
0.160

    Exercise in number of days
4.0 ± 2.0 
4.0 (2.5–5.5)
72 (19.9)
4.1 ± 1.8 
4.0 (2.9–5.6)
30 (16.3)
3.8 ± 1.8 
3.8 (2.5–5.0)
4 (4.5)
0.612

    Blood-glucose in number of days
2.1 ± 2.2 
1.0 (0.0–4.0)
74 (20.5)
2.1 ± 2.4 
1.0 (0.0–4.0)
30 (16.3)
1.8 ± 1.8 
1.0 (0.5–2.3)
4 (4.5)
0.241

    Foot-care in number of days
1.9 ± 2.0 
1.5 (0.0–3.5)
72 (19.9)
1.9 ± 2.0 
1.0 (0.0–3.5)
30 (16.3)
1.8 ± 2.0 
1.0 (0.0–3.5)
4 (4.5)
0.924

    Medication in number of days
6.7 ± 1.0 
7.0 (7.0–7.0)
73 (20.2)
7.0 ± 0.2 
7.0 (7.0–7.0)
30 (16.3)
6.8 ± 0.8 
7.0 (7.0–7.0)
4 (4.5)
0.020

    Smoking
54 (25.1)
146 (40.4)
24 (21.8)
74 (40.2)
14 (24.6)
31 (35.2)
0.704

Abbreviations

EQ-5D: | EuroQol-5 Dimensions
EQ-VAS: | EuroQol Visual Analogue Scale
GFR: | Glomerular filtration rate
GP: | General practitioner
HRQoL: | Health-related quality of life
MDRD: | Modification of Diet in Renal Disease
PAID-5: | Problem Areas in Diabetes-5 questions
PN: | Practice nurse
SDSCA: | Summary of Diabetes Self-Care Activities
T2DM: | Type 2 diabetes mellitus
WHO-5: | WHO-Five Item Measure of Well-Being.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors wish to thank all the participating patients and primary health care workers in the Drenthe region of the Netherlands. Thanks also are due to Jurriaan Kok for his support and for managing the DM part of the e-Vita research program and all coordinators and supporters within the foundation Care Within Reach (in Dutch: stichting Zorg Binnen Bereik). The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this paper: the study has been funded by foundation Care Within Reach (in Dutch: stichting Zorg Binnen Bereik).

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