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

Authors: Yvonne Roelofsen; Michael van Vugt; Steven H. Hendriks; Kornelis J. J. van Hateren; Klaas H. Groenier; Frank J. Snoek; Nanne Kleefstra; Robbert Huijsman; Henk J. G. Bilo
Journal: Journal of Diabetes Research
 (2016)

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## 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.


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## Body

## 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 onhttps://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 ap 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 numberNCT01570140.


## 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 onhttps://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 ap 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 numberNCT01570140.


## 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 Figure1 for the patient flow.Figure 1
Flowchart of patients and definitions.Table1 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  ±  9.563.0  (56.5–68.0)
0  (0)
61.8  ±  9.562.0  (56.3–68.0)
0  (0)
62.0  ±  9.463.0  (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  ±  4.66.0  (2.0–9.0)
9  (2.5)
5.7  ±  4.45.0  (2.0–8.0)
1  (0.5)
5.4  ±  4.44.5  (2.0–8.0)
0  (0)
0.165
HbA1c in mmol/mol
50.6  ±  9.550.0  (45.0–54.0)
3  (0.8)
48.7  ±  7.448.0  (43.0–54.0)
0  (0)
47.0  ±  7.046.0  (43.0–50.8)
0  (0)
0.001
BMI
29.8  ±  4.929.0  (26.5–32.5)
3  (0.8)
30.0  ±  4.829.3  (26.9–32.3)
0  (0)
29.9  ±  8.028.0  (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  ±  17.480.0  (60.0–90.0)
74  (20.5)
79.3  ±  13.880.0  (73.0–90.0)
35  (19.0)
76.9  ±  16.580.0  (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  ±  3.12.0  (0.0–4.5)
76  (21.1)
1.8  ±  2.41.0  (0.0–3.0)
32  (17.4)
2.2  ±  2.51.0  (0.0–4.0)
1  (1.1)
0.016
SDSCA
Medication in number of days
6.7  ± 1.07.0  (7.0–7.0)
73  (20.2)
7.0  ±  0.27.0  (7.0–7.0)
30  (16.3)
6.8  ±  0.87.0  (7.0–7.0)
4  (4.5)
0.020Table 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
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 AppendixA 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 Table3. 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


## 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 Tables6 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.



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