Skip to main content
PMC home page
Journal of Diabetes Research logoLink to Journal of Diabetes Research
. 2015 Nov 22;2016:6343927. doi: 10.1155/2016/6343927

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, Henk J G Bilo 1,4
PMCID: PMC4670670  PMID: 26682232

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 p 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 p 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.

Figure 1

Open in a new tab

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 (p = 0.038) and to active users (p = 0.001). Curious and active users were more often known with hypertension compared to nonusers (p = 0.025). Curious users assessed the GP better on one question of the Europep compared to nonusers and active users (p = 0.047). Curious users scored higher on EQ-5D (p = 0.030) 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 (p = 0.016), 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 (p = 0.020), 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 (n = 361) Missing Curious users (n = 184) Missing Active users (n = 88) Missing Univariate 
p 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
Open in a new tab

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
Open in a new tab

1Because 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. p 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.

b-coefficient 95% CI p 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
Open in a new tab

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.

Table 6.

Complications and risk factors of users and nonusers.

Complications and risk factors
n (%)		 Nonusers (n = 361) Missing Users (n = 272) Missing Univariate
p 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
Open in a new tab

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 11.

Complications and risk factors of curious users, active users, and nonusers.

Complications and risk factors
n (%)		 Nonusers
(n = 361)		 Missing Curious users
(n = 184)		 Missing Active users
(n = 88)		 Missing Univariate
p 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
Open in a new tab

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.

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).

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.

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  
n (%)/mean ± SD/median (25–75 quartiles)		 Nonusers (n = 361) Missing Users (n = 272) Missing Univariate 
p 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)    
Open in a new tab

Table 5.

Medication use of users and nonusers.

Medication prescription
n (%)		 Nonusers (n = 361) Missing Users (n = 272) Missing Univariate
p 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
Open in a new tab

Table 7.

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

Items of Europep: patients who scored 4 (good) or 5 (excellent) 
n (%)		 Nonusers (n = 361) Missing Users (n = 272) Missing Univariate
p 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
Open in a new tab

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  
n (%)/mean ± SD/median (25–75 quartiles)		 Nonusers (n = 361) Missing Users
(n = 272)		 Missing Univariate 
p 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
Open in a new tab

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  
n (%)/mean ± SD/median (25–75 quartiles)		 Nonusers
(n = 361)		 Missing Curious users
(n = 184)		 Missing Active users
(n = 88)		 Missing Univariate 
p 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)    
Open in a new tab

Table 10.

Medication prescription of curious users, active users, and nonusers.

Medication prescription
n (%)		 Nonusers
(n = 361)		 Missing Curious users
(n = 184)		 Missing Active users
(n = 88)		 Missing Univariate
p 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
Open in a new tab

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)
n (%)		 Nonusers (n = 361) Missing Curious users (n = 184) Missing Active users (n = 88) Missing Univariate
p 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
Open in a new tab

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 
n (%)/mean ± SD/median (25–75 quartiles)		 Nonusers
(n = 361)		 Missing Curious users
(n = 184)		 Missing Active users
(n = 88)		 Missing Univariate 
p 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
Open in a new tab

Conflict of Interests

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

References

  • 1.Rubin R. R., Peyrot M. Quality of life and diabetes. Diabetes/Metabolism Research and Reviews. 1999;15(3):205–218. doi: 10.1002/(SICI)1520-7560(199905/06)15:3&#x00026;lt;205::AID-DMRR29&#x0003e;3.0.CO;2-O. [DOI] [PubMed] [Google Scholar]
  • 2.Poortvliet M., Schrijvers C. T. M., Baan C. A. Diabetes in Nederland. Omvang, Risicofactoren en Gevolgen, nu en in de Toekomst. Bilthoven, The Netherlands: Rijksinstituut voor Volksgezondheid en Milieu (RIVM); 2007. (260322001). [Google Scholar]
  • 3.Tang P. C., Ash J. S., Bates D. W., Overhage J. M., Sands D. Z. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. Journal of the American Medical Informatics Association. 2006;13(2):121–126. doi: 10.1197/jamia.m2025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Verhoeven F., Tanja-Dijkstra K., Nijland N., Eysenbach G., Van Gemert-Pijnen L. Asynchronous and synchronous teleconsultation for diabetes care: a systematic literature review. Journal of Diabetes Science and Technology. 2010;4(3):666–684. doi: 10.1177/193229681000400323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Osborn C. Y., Mayberry L. S., Mulvaney S. A., Hess R. Patient web portals to improve diabetes outcomes: a systematic review. Current Diabetes Reports. 2010;10(6):422–435. doi: 10.1007/s11892-010-0151-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Black A. D., Car J., Pagliari C., et al. The impact of ehealth on the quality and safety of health care: a systematic overview. PLoS Medicine. 2011;8(1) doi: 10.1371/journal.pmed.1000387.e1000387 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Whitten P. S., Mair F. S., Haycox A., May C. R., Williams T. L., Hellmich S. Systematic review of cost effectiveness studies of telemedicine interventions. The British Medical Journal. 2002;324(7351):1434–1437. doi: 10.1136/bmj.324.7351.1434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Tenforde M., Jain A., Hickner J. The value of personal health records for chronic disease management: what do we know? Family Medicine. 2011;43(5):351–354. [PubMed] [Google Scholar]
  • 9.Ko H., Turner T., Jones C., Hill C. Patient-held medical records for patients with chronic disease: a systematic review. Quality and Safety in Health Care. 2010;19(5):p. e41. doi: 10.1136/qshc.2009.037531. [DOI] [PubMed] [Google Scholar]
  • 10.Zhai Y., Zhu W., Cai Y., Sun D., Zhao J. Clinical- and cost-effectiveness of telemedicine in type 2 diabetes mellitus: a systematic review and meta-analysis. Medicine. 2014;93(28, article e312) doi: 10.1097/md.0000000000000312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Roelofsen Y., Hendriks S. H., Sieverink F., et al. Differences between patients with type 2 diabetes mellitus interested and uninterested in the use of a patient platform (e-VitaDM-2/ZODIAC-41) Journal of Diabetes Science and Technology. 2014;8(2):230–237. doi: 10.1177/1932296814524496. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ronda M. C. M., Dijkhorst-Oei L.-T., Gorter K. J., Beulens J. W. J., Rutten G. E. H. M. Differences between diabetes patients who are interested or not in the use of a patient web portal. Diabetes Technology and Therapeutics. 2013;15(7):556–563. doi: 10.1089/dia.2013.0023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Amante D. J., Hogan T. P., Pagoto S. L., English T. M. A systematic review of electronic portal usage among patients with diabetes. Diabetes Technology & Therapeutics. 2014;16(11):784–793. doi: 10.1089/dia.2014.0078. [DOI] [PubMed] [Google Scholar]
  • 14.Roelofsen Y., Hendriks S. H., Sieverink F., et al. Design of the e-Vita diabetes mellitus study: effects and use of an interactive online care platform in patients with type 2 diabetes (e-VitaDM-1/ZODIAC-40) BMC Endocrine Disorders. 2014;14(1, article 22) doi: 10.1186/1472-6823-14-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Williams A. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208. doi: 10.1016/0168-8510(90)90421-9. [DOI] [PubMed] [Google Scholar]
  • 16.Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72. doi: 10.1016/0168-8510(96)00822-6. [DOI] [PubMed] [Google Scholar]
  • 17.Lamers L. M., Stalmeier P. F. M., McDonnell J., Krabbe P. F. M., Van Busschbach J. J. Measuring the quality of life in cost-utility analyses: the Dutch EQ-5D tariff. Nederlands Tijdschrift voor Geneeskunde. 2005;149(28):1574–1578. [PubMed] [Google Scholar]
  • 18.Bech P., Olsen L. R., Kjoller M., Rasmussen N. K. Measuring well-being rather than the absence of distress symptoms: a comparison of the SF-36 mental health subscale and the WHO-five well-being scale. International Journal of Methods in Psychiatric Research. 2003;12(2):85–91. doi: 10.1002/mpr.145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Bech P. Measuring the dimensions of psychological general well-being by the WHO-5. QOL Newsletter. 2004;32:15–16. [Google Scholar]
  • 20.McGuire B. E., Morrison T. G., Hermanns N., et al. Short-form measures of diabetes-related emotional distress: the Problem Areas in Diabetes Scale (PAID)-5 and PAID-1. Diabetologia. 2010;53(1):66–69. doi: 10.1007/s00125-009-1559-5. [DOI] [PubMed] [Google Scholar]
  • 21.Toobert D. J., Hampson S. E., Glasgow R. E. The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000;23(7):943–950. doi: 10.2337/diacare.23.7.943. [DOI] [PubMed] [Google Scholar]
  • 22.Grol R., Wensing M., Mainz J., et al. Patients in Europe evaluate general practice care: an international comparison. British Journal of General Practice. 2000;50(460):882–887. [PMC free article] [PubMed] [Google Scholar]
  • 23.WHO (Five) Well-Being Index (1998 Version) Hillerød, Denmark: Psychiatric Research Unit, WHO Collaborating Center for Mental Health, Frederiksborg General Hospital; 1998. [Google Scholar]
  • 24.Sieverink F., Kelders S. M., Braakman-Jansen L. M. A., van Gemert-Pijnen J. E. W. C. The added value of log file analyses of the use of a personal health record for patients with type 2 diabetes mellitus: preliminary results. Journal of Diabetes Science and Technology. 2014;8(2):247–255. doi: 10.1177/1932296814525696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.van Vugt M., de Wit M., Hendriks S. H., Roelofsen Y., Bilo H. J. G., Snoek F. J. Web-based self-management with and without coaching for type 2 diabetes patients in primary care: design of a randomized controlled trial. BMC Endocrine Disorders. 2013;13, article 53 doi: 10.1186/1472-6823-13-53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Bender R., Lange S. Adjusting for multiple testing—when and how? Journal of Clinical Epidemiology. 2001;54(4):343–349. doi: 10.1016/s0895-4356(00)00314-0. [DOI] [PubMed] [Google Scholar]
  • 27.Sarkar U., Karter A. J., Liu J. Y., et al. Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access. Journal of the American Medical Informatics Association. 2011;18(3):318–321. doi: 10.1136/jamia.2010.006015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Tenforde M., Nowacki A., Jain A., Hickner J. The association between personal health record use and diabetes quality measures. Journal of General Internal Medicine. 2012;27(4):420–424. doi: 10.1007/s11606-011-1889-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Sarkar U., Karter A. J., Liu J. Y., et al. The literacy divide: health literacy and the use of an internet-based patient portal in an integrated health system-results from the diabetes study of Northern California (DISTANCE) Journal of Health Communication. 2010;15(supplement 2):183–196. doi: 10.1080/10810730.2010.499988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Gustafson D. H., Wyatt J. C. Evaluation of ehealth systems and services. British Medical Journal. 2004;328(7449, article 1150) doi: 10.1136/bmj.328.7449.1150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Hesse B. W., Shneiderman B. eHealth research from the user’s perspective. The American Journal of Preventive Medicine. 2007;32(5) supplement:S97–S103. doi: 10.1016/j.amepre.2007.01.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Curry S. J. eHealth research and healthcare delivery beyond intervention effectiveness. The American Journal of Preventive Medicine. 2007;32(5, supplement):S127–S130. doi: 10.1016/j.amepre.2007.01.026. [DOI] [PubMed] [Google Scholar]
  • 33.Hjelm N. M. Benefits and drawbacks of telemedicine. Journal of Telemedicine and Telecare. 2005;11(2):60–70. doi: 10.1258/1357633053499886. [DOI] [PubMed] [Google Scholar]
  • 34.Flynn D., Gregory P., Makki H., Gabbay M. Expectations and experiences of eHealth in primary care: a qualitative practice-based investigation. International Journal of Medical Informatics. 2009;78(9):588–604. doi: 10.1016/j.ijmedinf.2009.03.008. [DOI] [PubMed] [Google Scholar]
  • 35.Eysenbach G. The law of attrition. Journal of Medical Internet Research. 2005;7(1, article e11) doi: 10.2196/jmir.7.1.e11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wangberg S. C., Bergmo T. S., Johnsen J.-A. K. Adherence in internet-based interventions. Patient Preference and Adherence. 2008;2:57–65. [PMC free article] [PubMed] [Google Scholar]
  • 37.Nijland N., van Gemert-Pijnen J. E. W. C., Kelders S. M., Brandenburg B. J., Seydel E. R. Factors influencing the use of a web-based application for supporting the self-care of patients with type 2 diabetes: a longitudinal study. Journal of Medical Internet Research. 2011;13(3):p. e71. doi: 10.2196/jmir.1603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Boshuizen D. Patiëntenportaal Werkt niet Vanzelf. Medisch Contact; 2014. [Google Scholar]

Articles from Journal of Diabetes Research are provided here courtesy of Wiley

RESOURCES