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The take-up of eHealth applications in general is still rather low and user attrition is often high. Only limited information is available about the use of eHealth technologies among specific patient groups.
The aim of this study was to explore the factors that influence the initial and long-term use of a Web-based application (DiabetesCoach) for supporting the self-care of patients with type 2 diabetes.
A mixed-methods research design was used for a process analysis of the actual usage of the Web application over a 2-year period and to identify user profiles. Research instruments included log files, interviews, usability tests, and a survey.
The DiabetesCoach was predominantly used for interactive features like online monitoring, personal data, and patient–nurse email contact. It was the continuous, personal feedback that particularly appealed to the patients; they felt more closely monitored by their nurse and encouraged to play a more active role in self-managing their disease. Despite the positive outcomes, usage of the Web application was hindered by low enrollment and nonusage attrition. The main barrier to enrollment had to do with a lack of access to the Internet (146/226, 65%). Although 68% (34/50) of the enrollees were continuous users, of whom 32% (16/50) could be defined as hardcore users (highly active), the remaining 32% (16/50) did not continue using the Web application for the full duration of the study period. Barriers to long-term use were primarily due to poor user-friendliness of the Web application (the absence of “push” factors or reminders) and selection of the “wrong” users; the well-regulated patients were not the ones who could benefit the most from system use because of a ceiling effect. Patients with a greater need for care seemed to be more engaged in long-term use; highly active users were significantly more often medication users than low/inactive users (
Innovations in health care will diffuse more rapidly when technology is employed that is simple to use and has applicable components for interactivity. This would foresee the patients’ need for continuous and personalized feedback, in particular for patients with a greater need for care. From this study several factors appear to influence increased use of eHealth technologies: (1) avoiding selective enrollment, (2) making use of participatory design methods, and (3) developing push factors for persistence. Further research should focus on the causal relationship between using the system’s features and actual usage, as such a view would provide important evidence on how specific technology features can engage and captivate users.
The prevalence of diabetes is rising quickly. Diabetes among adults—aged 20–79 years—affected 285 million adults in 2010 (6.4%) and is estimated to increase worldwide to 439 million adults by 2030 (7.7%) [
The introduction of the Internet into clinical practice has brought about many opportunities for self-care [
From previous studies we know that interactive eHealth technologies contribute positively to health care for patients with a chronic illness, realizing increased patient–provider communication, positive impact on metabolic control and behavior change, improved therapy adherence, and cost reductions [
Expanding the uptake of eHealth requires, first and foremost, a better understanding of the obstacles that prevent access (initial use) [
The aim of the study was to explore the factors that influenced the initial and long-term use of a Web-based application for supporting the self-care of patients with type 2 diabetes. A mixed-methods research design was applied to trace the usage over time (log files), along with the reasons for (non)usage (usability tests, interviews, and content analysis of email messages), and to identify user profiles (survey).
DiabetesCoach, a Web-based application for supporting self-care among patients with type 2 diabetes, was developed to encourage patients to play a more active role in their own care. The Web application is a low-tech solution for a large group of patients and was provided free of charge as a supplement to regular diabetes care. The application was developed by Medicinfo (Tilburg, Netherlands) in close collaboration with general practitioners, nurses, patients, behavioral scientists, and vendors (ie, health insurance companies). Initial development costs were relatively limited, and the running costs of the application were low. Therefore, a rise in use would not lead to an exponential rise in costs.
The following are the core features of the DiabetesCoach:
The patients’ self-monitored data were made available to the nurses with alerts signaling alarming metabolic values. Each nurse had access to each of her own patients’ DiabetesCoach details via a private account (protected by username and password). The Web application (not integrated with the nurse’s medical record) enabled nurses to set individual goals for their patients, add selected lifestyle programs, and highlights the appropriate chapter of the e-learning program. The patients received no particular instructions with regard to how often they should log on to DiabetesCoach. Patients measured metabolic values both at home and at the primary care practice during office visits. Nurses were allowed to have two extra consultation sessions per patient to compensate for the extra time needed to participate in the study. The information and guidelines provided in DiabetesCoach were in accordance with diabetes care standards and protocols in the Netherlands.
A primary health care foundation in the Netherlands consisting of 10 primary health care practices and a home care organization employing the diabetes nurses (n = 6) agreed to become partners in the pilot. Three primary health care practices volunteered to take part in the DiabetesCoach project.
The selection criteria for patient enrollment were (1) patients with type 2 diabetes mellitus (the primary focus was on fostering lifestyle changes), (2) patients being motivated to perform self-care activities, and (3) patients having access to the Internet and being sufficiently skilled to use the Internet. Through a recruitment letter, 350 patients were invited by the caregivers to use DiabetesCoach. Patients were informed about the purpose and possibilities of the Web application both through the letter and during the office visit. In total, 50 of the 350 invited patients (14%) enrolled in the project and filled out the informed consent forms.
Training sessions (offline) were set up for the enrollees. During the training sessions the participants received instructions on how to use the application, plus a user manual. Also, an email functionality was created for technical support.
We used a mixed-methods research design [
Directly after collecting the responses to the invitation letter, the nurses interviewed 226 of the 300 nonenrollees (patients who chose not to participate) during the office visit. Using an open-ended question the nurses asked the nonenrollees about their reasons for nonenrollment.
A
Making use of
We performed
One year after the initial use of the Web application, emails were sent to those patients (n = 20) who were not actively using the application by that time. Through an open-ended question patients were asked to report their reason for discontinueing use. We received six responses.
Research instruments and study characteristics
Research instruments | n | Purpose | Participants |
Interviews by nurses | 226 | Reasons for nonuse of the Web application | Nonenrolleesa |
Survey | 50 | Who uses the Web application? | Enrolleesb |
Log files | 50 | What features of the Web application are used? | Enrolleesb |
Long-term usage pattern (24 months) | |||
Profiles of continuous and discontinued users | |||
Usability tests | 20 | Reasons for use of the Web application | Enrolleesb |
Reasons for the decline in usage | |||
Email interviews | 6 | Reasons for the decline in usage | Enrolleesb |
Content analysis | 50 | What sort of information is communicated via the emails? | Enrolleesb |
a Primary care patients who chose
b Primary care patients who chose to participate in the DiabetesCoach project (n = 50).
Chronology of the data collection process.
We performed statistical analyses using SPSS version 16.0 (IBM Corporation, Somers, NY, USA). Standard descriptive statistics were performed, and chi-square tests (Fisher exact test) and
The researcher categorized collected responses of patients (n = 226, nonenrollees). Percentages of the answer categories were computed by multiple response analysis.
The coding process of the patient–nurse email messages was based on the grounding theory [
The usability test data were analyzed using deductive analysis. NN used standard approaches for qualitative data and took detailed notes during the sessions. Notes included the problems experienced during use of the Web application such as poor navigation structures, lack of triggers to use the system, technical errors, and problems with logging on to the system [
To identify the hardcore users we measured the actual use of the Web application by patients (n = 50) during the study period (24 months). Our measure of user activity was defined by three measures: (1)
To set the norm for
To set the norm for
All categories of the user profiles from highly active (7 patients) and low active (10 patients), to inactive users (3 patients) were represented in the usability tests.
Only 14% (n = 50) of the 350 patients responded positively to the invitation to use the Web application. Nurses interviewed 226 nonenrollees to gain insight into the barriers that inhibited their enrollment. The reasons given (n = 226) were lack of Internet (146/226, 65%), use will not have any added value (25/226, 11%), not in the mood to spend much time on the computer (23/226, 10%), not in the mood to be occupied with the disease (10/226, 4%), lack of skills to use the Internet (10/226, 4%), too busy or no time (4/226, 2%), or other, such as “patient is about to move to another town” (8/226, 4%). Obviously, patients experienced more external barriers to access (not having the equipment and lacking the right skills: 156/226, 69%) than internal motivational barriers (not willing to use it, no added value, too busy: 62/226, 27%).
The enrollees (n = 50) were aged between 43 and 80 (mean 61) years. Most were male (n = 37), of Dutch origin (40/43, 93%), with a high or medium level of education (
Patients mentioned three main reasons for using the Web application:
Enrollee characteristics
Characteristic | n | % | ||
|
||||
Low | 5 | 12 | ||
Medium | 22 | 51 | ||
High | 16 | 37 | ||
|
||||
Excellent | 0 | 0 | ||
Very good | 6 | 14 | ||
Good | 25 | 58 | ||
Fair | 12 | 28 | ||
Poor | 0 | 0 | ||
|
||||
None | 2 | 5 | ||
Diet | 4 | 9 | ||
Diet and tablets | 37 | 86 | ||
Diet, tablets, and insulin | 0 | 0 | ||
|
||||
0–2 | 12 | 29 | ||
3–6 | 16 | 38 | ||
>7 | 14 | 33 |
The log files revealed that the Web application was predominantly used for online monitoring (2216/6289, 35%; total hits of the core features of the Web application by patients during the study period: n = 6289), personal data (1648/6289, 26%) and patient–nurse email contact (1458/6289, 23%), and to a lesser extent for online education (473/6289, 8%), calendar (334/6289, 5%), personal lifestyle coach (160/6289, 3%), and the printing feature (108/6289, 2%). Patients were particularly interested in
The
In total, 323 email messages were sent during the study period. In the qualitative content analysis of the email messages, a total of 10 content categories were distinguished (see
Patients, on the other hand, communicated more than nurses about their state of health and how they were feeling. For example, they let their nurse know that they were doing well, as a confirmation or ratification of the treatment regimen. As such, email was primarily used to ensure the nurse was aware of what was going on. Nurses, for their part, responded by giving affective feedback such as expressions of empathy and compliments.
Email message content by content category quantified by statementa
Content categories | Total messages (n = 323) | Patients’ messages (n = 130) | Nurses’ messages (n = 193) | |||
n | % | n | % | n | % | |
Measurementsb | 104 | 32.2 | 42 | 32.3 | 64 | 33.2 |
Administrative communicationc | 101 | 31.3 | 25 | 19.2 | 77 | 39.9 |
Affective communicationd | 99 | 30.7 | 38 | 29.2 | 63 | 32.6 |
DiabetesCoach remarksd | 49 | 15.2 | 28 | 21.5 | 21 | 10.9 |
Medication usef | 42 | 13.0 | 12 | 9.2 | 31 | 16.1 |
Physical symptomsg | 29 | 9.0 | 19 | 14.6 | 10 | 5.2 |
Use of DiabetesCoach functionalitiesh | 24 | 7.4 | 3 | 2.3 | 21 | 10.9 |
Lifestyle supporti | 20 | 6.2 | 14 | 10.8 | 8 | 4.1 |
Current eventsj | 18 | 5.6 | 6 | 4.6 | 12 | 6.2 |
Otherk | 20 | 6.2 | 10 | 7.7 | 10 | 5.2 |
a Statement = a thematic unit (a unit of meaning within a message); one single message can contain one or more statements.
b Communication about clinical values such as blood sugar, blood pressure, weight, and cholesterol.
c Communication about referrals, appointment scheduling, etc.
d Expression of emotions such as compliments, relief, and worries, as well as social talk (warm wishes and thanks).
e Communication about (technical) problems with the use of the Web application.
f Communication about medication use.
g Communication about physical symptoms/health problems.
i Communication about DiabetesCoach functionalities, other than online monitoring, such as use of the lifestyle coach.
j Communication about new diabetes-related websites and courses.
k Communication not related to the use of the Web application.
Over the total study period (24 months) each patient visited the Web application on average 49 times (2464 hits/50 patients; mean number of log-ins). See
The features personal data, online monitoring, and email contact were all used regularly during the study period (
Long-term use of the web application by patients per practice.
Long-term use of the core features of the web application by patients.
Reasons for the decline in usage could be attributed to a
Perhaps if my diabetes nurse would provide some more help or pay some more attention to it, it might result in more interest.
I wouldn’t mind it being a bit more interactive; that you would get a signal to at least enter something every week and then to get some reply.
The most remarkable observation during the usability test was that the patients were unaware of the possibilities of the system, caused by uncommon navigation structures. In particular, the email feature was undiscovered, which could explain why the message overview was used more extensively than the actual sending of messages (
Furthermore, the email interviews revealed a ceiling effect; for some, using the application no longer had any added value. Patients with their blood sugar level under control had a less pronounced need to use a Web application for self-care support.
Medical checkups have been reduced to twice a year by mutual consultation with my general practitioner. A good result for me personally, but as a result there is very little for me to report.
Three groups of users could be distinguished:
(1)
Activity pattern: period of no activity <8 months (
Activity degree: 68%–100% (17–24 months use,
Number of log-ins: 45–191 (
(b)
Activity pattern: period of no activity <8 months
Activity degree: 29%–67% (7–16 months use)
Number of log-ins: 10–96.
(c)
Activity pattern: period of no activity ≥8 months
Activity degree: 0%–67% (0–16 months use)
Number of log-ins: 0–56.
When taking into account patient characteristics, the discontinued users did not differ substantially from the continuous users, although more of the discontinued users tended not to be taking medication (11/12, 92%).
We believe that more engagement in system use (being highly active) might result in better adherence to self-care activities. This is why we compared highly active users versus low/inactive users with respect to their characteristics and preferences.
We expected that patients with a greater need for care, such as the elderly, people on medication, and patients who had diabetes for a longer time, would benefit most from the technology and would therefore be more inclined to use the Web application. The results displayed in
Patient characteristics related to user activity
Characteristic | Highly active (n = 16) | Low/inactive (n = 34) |
|
||||
n | % | n | % | ||||
|
.60 | ||||||
Male | 12 | 75 | 25 | 73 | |||
Female | 4 | 25 | 9 | 26 | |||
|
.28 | ||||||
43–56 | 6 | 37 | 11 | 32 | |||
57–64 | 7 | 44 | 9 | 26 | |||
65–80 | 3 | 19 | 14 | 41 | |||
|
.94 | ||||||
Low | 2 | 13 | 3 | 11 | |||
Medium | 7 | 47 | 15 | 54 | |||
High | 6 | 40 | 10 | 36 | |||
|
.59 | ||||||
Very good | 3 | 20 | 3 | 11 | |||
Good | 8 | 53 | 17 | 61 | |||
Fair | 4 | 27 | 8 | 29 | |||
|
.005 | ||||||
Yes (tablets) | 6 | 40 | 1 | 4 | |||
No | 9 | 60 | 27 | 96 | |||
|
.03 | ||||||
0–2 | 2 | 13 | 10 | 37 | |||
3–6 | 5 | 33 | 11 | 41 | |||
>7 | 8 | 53 | 6 | 22 |
a
Ranking highly active group: (1) online monitoring, (2) email, (3) personal data.
Ranking low/inactive group: (1) personal data, (2) online monitoring, (3) email.
Highly active users seemed to have other goals than low/inactive users. Highly active users had a higher need for online monitoring, probably because they were more likely to be frequent medication users who regularly had to pass on their clinical values to their nurse. Particularly for these patients, online monitoring would be convenient (increased access). Low/inactive users, on the other hand, appreciated the ability to document personal details such as treatment plans and medication use (comparable with a personal health record).
The features online monitoring, email, and personal data appealed to both groups, yet the highly active users used all of the features more often, spread over a longer period of time (see
User activity related to the use of system features: ranking of the features
Personal dataa | Monitoring | Education | Calendar | Lifestyle coach | |||
|
|||||||
Total hits (2 years) | 781 | 1601 | 908 | 240 | 244 | 96 | |
Ranking | 20% | 41% | 24% | 6% | 6% | 3% | |
|
|||||||
Total hits (2 years) | 867 | 615 | 550 | 233 | 120 | 64 | |
Ranking | 35% | 25% | 23% | 10% | 5% | 3% |
a Ranking: 20.2% = 781 (total hits personal data)/3870 (total hits of all core features) × 100.
User activity related to the use of system features: mean number of hits
Personal |
Monitoring | Education | Calendar | Lifestyle |
|||
|
|||||||
Total hits (2 years) | 781 | 1601 | 908 | 240 | 244 | 96 | |
Mean hits per patienta | 49 | 100 | 57 | 15 | 15 | 6 | |
|
|||||||
Total hits (2 years) | 867 | 615 | 550 | 233 | 120 | 64 | |
Mean hits per patienta | 26 | 18 | 16 | 7 | 4 | 2 |
a Mean hits per patient: 49 = 781 (total hits personal data)/16 (number of highly active patients).
User activity of DiabetesCoach enrollees.
The aim of this study was to explore the factors that influenced the use of a Web-based application for supporting the self-care of patients with type 2 diabetes. The major advantages of using the Web application were improved access to care and enhanced patient–nurse communication. The features that appealed to the patients most, and with which they were often engaged, were online monitoring in combination with personal feedback through email and documentation of medication usage. These personalized and interactive features stimulated active participation by both the patient and the nurse. Patients felt better monitored by means of the continuously received feedback and were more motivated to take a more active role in self-managing their diabetes.
Unexpectedly, there was a high preference for the documentation of personal data referring to medication and treatment plans. The documentation feature is not interactive; no communication takes place. However, it is comparable, in a certain way, with a personal health record [
A great concern among eHealth technologies in general, and behavioral intervention programs in particular, is that they may reach those who need them the least (ceiling effect), or they fail to reach the ones with the greatest need for care, such as patients with chronic conditions (inverse care law) [
In the present study a ceiling effect (“I am doing well, so I do not need the technology”) caused attrition. According to Wangberg et al [
The results also illustrate the importance of providing automated reminders, a simple user interface, and personalized content by anticipating the needs of the individual patient. If the patient is not in need of education, then the other features should encourage the patient to use the system. The provision of features with various purposes would be more encouraging to use for a wider audience. Some users asked for the integration of monitoring, recording personal data, and logistics such as scheduling appointments. However, most of the features were presented as stand-alone applications.
To foster the widespread use of eHealth technologies like the DiabetesCoach, Internet use should be encouraged among the 65+ age range of the population; it is among the elderly that we have the largest growth potential [
Furthermore, we believe that the less motivated or relatively unhealthy patients could benefit the most from the use of eHealth technologies because of their greater need for care and their greater challenge for health improvement. Verheijden et al [
In order to understand and overcome technical flaws, users should be able to give feedback during usage so that the system can be fine-tuned to their needs and user profiles. Preferably, users should actively participate in the development of the content (health 2.0) [
To increase adherence, technology should have push factors for persistence such as feedback mechanisms and triggers [
Personalized feedback appeared to be one of the most promising features for long-term usage. In fact, two types of personalized feedback via email messages can be distinguished: personalized feedback from a caregiver via secure email and personalized feedback via automated messages and prompts. From the results of this study and the findings of Mohr et al [
Moreover, integrating the technology with existing clinical care could serve as a push factor. Stevens et al [
Besides, education should be provided in a more interactive way, for example through Web 2.0 tools that are built around user-generated or user-manipulated content, such as wikis, blogs, podcasts, and social networking sites [
The limitations of this study include the small and select sample of participants. Users were self-selected, as they were motivated to use the Web application. The patients and nurses who chose to participate in the project may possibly differ from other patient groups. Further research should be conducted, preferably with larger sample groups and among nonenrollees, to gain more thorough insights into the technology preferences of the different patient groups. Nevertheless, we believe that our results provide insights beyond the current literature into patients’ engagement in Web-based disease management programs. The use of a mixed-methods design [
In this study, attrition was not measured with the usual measures, such as Kaplan-Meier [
Our findings confirm the need for further research into usage patterns and user profiles [
We would like to thank the primary healthcare foundation Stichting Gezondheidscentrum Eindhoven (SGE) and the home care organization Zuidzorg for their cooperation in this study.
None declared
NN conceived the study and was the primary designer, although all of the authors contributed to the design. NN coordinated the collection and analysis of the data and wrote the original draft of the article. JvG, BB, SK and ES made substantial revisions and an analysis of the data. All of the co-authors contributed to and approved the final version of the manuscript.
Email message content categories.
Activity pattern of patients (in months).
User activity (based on activity pattern and activity degree).
Number of log-ins and number of hits per feature (per patient).
Number of hits on specific features by patients.