This article has Open Peer Review reports available.
A web-based intervention to support self-management of patients with type 2 diabetes mellitus: effect on self-efficacy, self-care and diabetes distress
© Yu et al.; licensee BioMed Central Ltd. 2014
Received: 22 July 2014
Accepted: 27 November 2014
Published: 14 December 2014
Management of diabetes mellitus is complex and involves controlling multiple risk factors that may lead to complications. Given that patients provide most of their own diabetes care, patient self-management training is an important strategy for improving quality of care. Web-based interventions have the potential to bridge gaps in diabetes self-care and self-management. The objective of this study was to determine the effect of a web-based patient self-management intervention on psychological (self-efficacy, quality of life, self-care) and clinical (blood pressure, cholesterol, glycemic control, weight) outcomes.
For this cohort study we used repeated-measures modelling and qualitative individual interviews. We invited patients with type 2 diabetes to use a self-management website and asked them to complete questionnaires assessing self-efficacy (primary outcome) every three weeks for nine months before and nine months after they received access to the website. We collected clinical outcomes at three-month intervals over the same period. We conducted in-depth interviews at study conclusion to explore acceptability, strengths and weaknesses, and mediators of use of the website. We analyzed the data using a qualitative descriptive approach and inductive thematic analysis.
Eighty-one participants (mean age 57.2 years, standard deviation 12) were included in the analysis. The self-efficacy score did not improve significantly more than expected after nine months (absolute change 0.12; 95% confidence interval −0.028, 0.263; p = 0.11), nor did clinical outcomes. Website usage was limited (average 0.7 logins/month). Analysis of the interviews (n = 21) revealed four themes: 1) mediators of website use; 2) patterns of website use, including role of the blog in driving site traffic; 3) feedback on website; and 4) potential mechanisms for website effect.
A self-management website for patients with type 2 diabetes did not improve self-efficacy. Website use was limited. Although its perceived reliability, availability of a blog and emailed reminders drew people to the website, participants’ struggles with type 2 diabetes, competing priorities in their lives, and website accessibility were barriers to its use. Future interventions should aim to integrate the intervention seamlessly into the daily routine of end users such that it is not seen as yet another chore.
KeywordsDiabetes mellitus Online systems Patient self-management Self-efficacy Repeated measures modelling Qualitative methods
Management of diabetes mellitus is complex, and involves controlling multiple risk factors that may lead to complications. However, care gaps exist: the Behavioral Risk Factor Surveillance System has estimated that only 68% of patients with type 1 or type 2 diabetes had HbA1c measured at least twice in the previous year , despite a recommendation from the American Diabetes Association that it be measured at least two to four times per year . Given that patients provide most of their own diabetes care, patient self-management training is an important strategy for improving quality of care , particularly in the current era of patient-centred outcomes and comparative clinical effectiveness research . Patient self-management interventions have demonstrated benefits in terms of both quality of life  and glycemic control , but participation is low , effectiveness wanes over time , and access to trained professionals to support self-management is limited . Web-based self-management interventions are promising because they offer ease of access for patients who are computer-literate, and they can be scaled up with little cost . Web-based media have improved patient knowledge, the extent of behaviour change, and clinical outcomes for a range of conditions . However, principles of effective education, self-management support, and behaviour change have not been incorporated into current diabetes-related websites -. Reviews of existing diabetes websites showed that they presented didactic information of variable quality, they required advanced reading levels, and they followed a static, newspaper-format display, rather than harnessing the inherent advantages of websites, such as interactive technology, social support, and problem-solving assistance ,. A systematic review of electronic diabetes-related tools found that they had moderate but inconsistent effects on a variety of psychological and clinical outcomes, including HbA1c and weight; tools that were more interactive tools were associated with continued website use and greater clinical improvement . In addition, greater website use was correlated with greater clinical improvements: regular website users had greater reductions in HbA1c compared with intermittent users. Although this finding could be a consequence of the healthy user effect , addressing usability issues to increase the proportion of regular users may increase the effectiveness of interventions.
In a previous study, we developed an approach to address many of these limitations of existing web-based interventions . In the current study, we tested the impact of this approach on self-efficacy, quality of life, self-care, blood pressure, cholesterol, glycemic control, and exercise promotion amongst people with type 2 diabetes.
This study consisted of five phases: 1) development of the intervention, 2) feasibility testing, 3) usability testing; 4) refinement of the intervention, and 5) evaluation of the intervention using a cohort study and individual interviews. The study protocol and results of the first four phases are reported elsewhere ,. We report here the results of Phase 5.
Diabetes online companion: a web-based self-management intervention
The Diabetes Online Companion is a self-contained diabetes self-management website that was systematically developed according to self-efficacy theory. Self-efficacy refers to “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” . Randomized controlled trials have shown that diabetes self-management education programs incorporating principles of self-efficacy are associated with improvements in knowledge , health behaviours ,, self-efficacy -, HbA1c -, weight , and microvascular complications .
Our intervention incorporated evidence-based content and behaviour-change strategies and followed the principles of user-centred design . The website had four main components: 1) general information (static), 2) tailored information (interactive), 3) self-monitoring logs (interactive), and 4) a blog (interactive) (see Additional file 1 for sample screenshots). We posted a total of 53 blog posts over the intervention period, initially at a frequency of one per week. After four weeks of limited user activity, we increased the frequency of blog posts to two per week and added email prompts with each new posting. The topics, which covered medical content, diabetes-related news items, and practical issues, were selected on the basis of our feasibility and usability testing . In addition, participants received weekly email reminders to visit the site or complete their self-management trackers, as well as notices of any new content .
We conducted a single-arm pre-post cohort study. Consecutive series of individuals with diabetes were recruited from two family practice units and two endocrinology clinics in Toronto (one each from two academic health science centres). Those eligible for inclusion were aged ≥ 25 years with at least one of HbA1c > 7.0% (53 mmol/mol), systolic blood pressure > 130 mmHg, low-density-lipoprotein cholesterol (LDL-C) > 2.0 mmol/L, or body mass index (BMI) > 25 kg/m2. We excluded those who had Canadian Cardiovascular Society class 3 or 4 angina, did not speak English, were not available for follow-up, or had no regular access to the telephone and internet.
Website usage: We analyzed logs for the web server to assess the frequency and duration of specific components of the intervention . Specifically, we collected data for the following variables: duration of use by individual users, frequency of use, site penetration, most frequently accessed tools and pages, and patterns of use over time.
Patient-centred outcomes: We assessed self-efficacy, our primary outcome, with the Modified Grossman Self-efficacy for Diabetes Scale, which has moderate to high reliability (Cronbach’s alpha = 0.51 to 0.86; Additional file 1) ,. We selected self-efficacy because not only has it been validated in predicting and promoting patient behaviour change, but it also has been demonstrated to improve clinical outcomes ,,,. We assessed self-care behaviour with the Summary of Diabetes Self-Care Activities Measure – Revised  and diabetes-specific quality of life with the Diabetes Distress Scale . These patient-based outcomes were selected because they are relevant measures of knowledge use by patients.
Clinical outcomes: We collected data on HbA1c, systolic and diastolic blood pressure, LDL-C, and weight every three months. These outcomes were chosen to inform the sample size calculations in future trials.
We obtained data for age, sex, ethnicity, education, self-reported health literacy, employment, duration of diabetes, complications, smoking status, medications, HbA1c, systolic blood pressure, LDL-C, weight, current use of and comfort with a computer and the internet, self-care score, self-efficacy score, and quality-of-life score at baseline. Outcome data were collected by means of patient-completed questionnaires. For the pre- and post-implementation phases, aggregates of patient-completed questionnaires were obtained every three weeks for nine months through web-based surveys, resulting in 12 data points for each phase. Health literacy was measured by a three-item validated questionnaire completed by the patients ,. HbA1c and LDL-C were collected from medical records via chart audit. Systolic and diastolic blood pressures were measured by the research coordinator and were recorded as the average of three readings. Weight was also measured by the research coordinator. At the end of the study, each participant was asked to disclose whether he or she had used other web-based interventions and if so, whether those interventions employed text- or image-based didactic materials, interactive technology, or behavioural strategies. To assess for threats to validity from historical effects, we recorded secular events that might have affected our outcomes (such as diabetes-related news reports).
Sample size calculation
Using a range of correlations from 0.2 to 0.8, a significance level of 0.05, and a power of 80%, we calculated that a sample of at most 52 participants was required to detect a change of 0.5 units in self-efficacy score after the intervention (relative to the score before implementation). Differences of 0.1 to 0.5 in self-efficacy score have been correlated with metabolic control, eating behaviour, exercise behaviour, and other self-management behaviours ,. A formula for paired mean comparisons was applied , and the longitudinal nature of the study increased its power . A previous analysis reported a dropout rate of 20%–51% in studies of self-management ; we further adjusted the sample size to account for an expected dropout rate of 40%.
Linear mixed models were used to examine the effect of the intervention and time (intervention × time interaction) on self-efficacy, self-care, and diabetes distress. We selected these models to accommodate the complexities of typical longitudinal data sets for continuous outcomes; specifically, they allowed us to account properly for both within- and between-participant variability , and have been used in previous studies for similar analyses -. The models were also adjusted for age, sex, ethnicity, income (above or below Can $30, 000), education, employment, and health literacy, as each of these variables could affect the study outcomes ,,. The model examining the self-care outcome was also adjusted for interaction terms of the aforementioned variables with time. No additional interaction terms with time were included for other outcome models, because all additional interaction terms examined were non-significant. To avoid inflation of R2, all variables were specified a priori, and all interactions were tested simultaneously using a cut-off value of 0.30 . Models were assessed by means of residual plots.
To assess the potential effect of missing income data for three of the participants, a sensitivity analysis (imputing income as both high and low) was performed. Missing health literacy data for 15 of the participants were imputed using the mode of the distribution, because 95% of the remaining participants were health literate.
Linear mixed models were also used to examine the effect of the intervention and time (intervention × time interaction) on secondary outcomes. These models were adjusted for age, self-efficacy score, income, ethnicity, and insulin use (for HbA1c and weight only). We also compared the effect of the intervention between users and non-users of the website. Finally, we used descriptive statistics to analyze website usage. R software version 2.1.15 was used for all analyses .
Individual interviews were conducted 2 to 21 weeks after completion of quantitative data collection. We used a purposive sampling strategy to recruit participants with a range of experiences and characteristics  (sex, age, ethnicity, duration of diabetes, educational attainment, income) from the broader pool of cohort study participants. We developed a semi-structured interview guide to elicit participants’ views regarding the following website features: acceptability, usability, strengths and weaknesses of the intervention, facilitators and barriers to its use, user satisfaction, and sustainability of use (Additional file 1). We made the website available during each interview, in case the interviewee wanted to show the interviewer something on the website.
All interviews were audiotaped and transcribed verbatim . Transcripts were inductively analyzed to identify emergent categories and themes using a constant comparative approach . Coding was conducted independently by three team members with expertise in qualitative research methods (CHY, JAP, SH) . After coding an initial subset of interviews, a preliminary coding framework was developed on the basis of the emerging analysis, with discussion and consensus amongst the analysts ; the framework was then iteratively tested and refined with subsequent interviews . Thematic saturation was attained with 21 interviews . NVivo software (version 9) was used to assist with data management and retrieval. Techniques to ensure analytic rigour included use of multiple analysts, negative case analysis, and triangulation of the qualitative findings with the quantitative results ,,. Triangulation consisted of 1) examining the interview data through the lens of “effect on self-efficacy”, 2) corroborating qualitative findings with quantitative data, and 3) interrogating how the Diabetes Online Companion affected self-efficacy .
The study was approved by the Research Ethics Boards of St. Michael’s Hospital (reference number 09–091) and Sunnybrook Health Sciences Centre (reference number 177–2009). All participants gave written and verbal informed consent.
Demographic characteristics and baseline values of observational cohort and qualitative study
Observational cohort (%, n = 81)
Qualitative study (%, n = 21)
Duration of diabetes mellitus (years)
> 20 years
< High school
Annual income (Can$)†
15 000 to 29 999
30 000 to 59 999
60 000 to 89 999
Purpose of computer use*
Frequency of computer use*
< 1 time/week
≥ 1 time/day
Comfort with computer use
Did not respond
Frequency of Internet use for diabetes‡
< 1 time/week
≥ 1 time/day
Comfort with Internet use
Did not respond
Self-efficacy; mean (SD)
Self-care; mean (SD)
Diabetes distress; mean (SD)
HbA1c; mean (SD)
60.0 mmol/L (14.1)
54.9 mmol/L (10.7)
Systolic blood pressure (mm Hg); mean (SD)
Diastolic blood pressure (mm Hg); mean (SD)
LDL-C (mmol/L); mean (SD)
Weight (kg); mean (SD)
Overall, the most frequently accessed tools, for both first-time and return users, were the blog, followed by “My blood glucose log”, “My medication log”, and “My activity log”. Regarding site penetration, users viewed 6.6 pages per session, spending an average of 5 minutes 43 seconds on the site, and 1 minute 39 seconds per page.
Within the blog section of the website, there were a total of 569 page views by 35 participants over the study period, with peaks at week 10 (54 views), week 27 (43 views), and week 30 (53 views), corresponding to blog entries about the medication log, supplements and insulin, and foot and kidney care, respectively. A total of 13 comments responding to the blog postings were submitted by five participants. These comments took the following forms: 1) responding to the blog (agreement or disagreement); 2) requesting help with or providing feedback on the website; 3) requesting help with self-management; 4) offering assistance, empowerment, and their own solutions (including food recipes); 5) self-reporting behaviour change; 6) sharing responses to medication; and 7) warning others about interactions with health care providers.
Use of interactive and static tools
Overall, 47 (67%), 63 (90%), and 43 (60%) of 70 users visited static, interactive, and log pages, respectively, at least once. These users had a mean of 3.4, 4.5, and 9.3 visits/user to each of these page types, respectively.
Summary statistics of psychological and clinical outcomes (with 95% confidence interval), at implementation of intervention and 9 months later
Immediate change (at time of intervention)
Change in outcome at study end, over expected value
0.13 (0.06, 0.20)
0.12 (−0.028, 0.263)
0.26 (−0.17, 0.68)
0.44 (0.23, 0.63)
−2.29 (−3.76, −0.81)
−1.84 (−4.81, 1.12)
0.37 (−0.11, 0.85)
−0.055 (−1.13, 1.02)
0.37 (−3.00, 3.74)
−1.10 (−9.31, 7.12)
Blood pressure (mm Hg)
1.79 (−5.87, 9.26)
0.43 (−4.13, 4.98)
−8.22 (−19.18, 2.74)
−0.0006 (−0.26, 0.26)
0.14 (−0.55, 0.82)
Diabetes distress: Despite an immediate short-term decrease in diabetes distress score (−−2.29; 95% CI: −3.76, −0.81; p = 0.002), by nine months, this outcome had not decreased significantly over what was expected (effect: 1.84; 95% CI: −4.81, 1.12; p = 0.22; Figure 2 and Table 2).
There was no interaction with insulin use by time or intervention for any of these outcomes.
Self-care scores were positively correlated with age (0.04/year, 95% CI: 0.02, 0.06), p <0.001).
Diabetes distress varied with age and sex: younger female participants had greater diabetes distress.
When we conducted the sensitivity analysis with missing values for income assumed to be below $15,000 and missing values for health literacy assumed to be the mode, there were no changes in results for self-efficacy, self care, or diabetes distress.
Seventy-three of the participants were included in the analysis of clinical outcomes. The other eight participants were excluded because of missing data for HbA1c, blood pressure, LDL-C, or weight within 90 days of the self-efficacy data or because no data were obtained after implementation of the intervention. The intervention had no effect on HbA1c, blood pressure, LDL-C, or weight in either the unadjusted or the adjusted models (Table 2).
Comparison of users and non-users:
A total of 70 participants (86%) used the website at least once, whereas 11 (14%) did not use the site at all. At the nine-month follow-up after implementation of the intervention, there was no difference between users and non-users in terms of self-efficacy (0.15 vs. 0.13, p = 0.35) or self-care (0.18 vs. 0.13, p = 0.21). Users had a greater reduction in diabetes distress than did non-users (−4.7 vs. -0.9, p < 0.0001). There was no difference in the effect of using the intervention on any secondary outcomes, with the exception of diastolic blood pressure (users: +3.27 mm Hg; non-users: −1.6 mm Hg; p = 0.014; Additional file 1).
Themes identified and representative quotes from in-depth interviews
1) Barriers and facilitators of website use:
Barriers to website use
a) Competing health concerns
“Being 4 cancers in my life”. [3B53, 78-year old woman]
“it’s not just diabetes that I deal with”. [2B16, 37-year old woman]
b) Competing life concerns
“I think for me, health issues surround migraines and fatigue. By the time I was home and had any free time, there wasn’t a lot there. And then life style and other stresses the business and other stresses the business…” [2B16, 37-year old woman]
c) Lack of motivation
“It’s a me thing, as opposed to a site thing”. [2B09, 47-year old woman]
“Laziness”. [3B52, 65-year old woman]
d) Frustration with diabetes
“Part of it is, when you see the blood sugar is really high, I already know it’s high. I’m not taking the medication. So to log the fact that they are high, ends up making you more frustrated. So why do that”. [2B09, 47-year old woman]
e) Futility regarding diabetes
Sometimes I think no matter what I do it’s not going to matter because you read about this… they all end up on dialysis… they all, you know…” [2B09, 47-year old woman]
f) Login and password requirement
“I keep forgetting my password”. [3B27, 58-year old woman]
“The ability to change the password is good as well… Because I think that helps in returning, otherwise I would always go back and find your original email”. [2B16, 37-year old woman]
g) Limited computer and/or internet access
“I didn’t have too much time to look at it ’cause I don’t have a computer at home [3B39, 67-year old man]
“I wish I can have a mobile like app”. [3A10, 52-year old woman]
“on the road for work and limited web access”. [1A13, 47-year old man]
h) Onerous nature of data entry to use logs
“Um, but cause I did go in and I did try and do the tracking and I think cause I thought that was on an ongoing basis was the most useful part of it. But it was kind of a pain in the neck to use it… and kind of a pain in the ass getting where I wanted to go. I put some information and I wanted to delete it and I don’t know if I ever succeeded in getting rid of it”. [2B01, 48-year old man]
Facilitators of website use
i) Perceived reliability of information
“It has to be really, really tuned in or connected to the latest developments either at [hospital name] or the world… With tons of information by real authorities. If I had a diabetic questions, I thought you guys might answer it, I would consider this to be an authoritative source”. [2B01, 48-year old man]
j) Reminders, prompts of new content
“Actually love getting the reminders, I really do. In this day in age everybody is busy, but I’m really good about it. The day I get the reminder, I find time to go in, I read the blogs… As I said, if you told me there was something new under a certain heading, I would definitely go in. I just like the reminders, in this fast paced world you don’t always have time for things and the last thingyou think about is yourself. So I think the reminders are a great thing, and I always look”. [3B09, 61-year old woman]
k) Increasing comfort with internet use
“Slowly I got used to [the internet] and since that time I utilize the possibility to look after whatever is interesting to me”. [3B53, 78-year old woman]
“I starting writing a lot of emails to lawyers and the public trustee so I’m kind of used to no, more so than before, tracking stuff online. Kind of going paperless is neat”. [2B01, 48-year old man]
2) Patterns of use of the website, including role of blog in driving site traffic
a) Goal-directed use
“And that’s kind of how I use the tool. It’s not as much a “Do I go in daily and read a new section each day?” No, it’s when I have a specific concern that I hone in on it to a degree”. [2B09, 47-year old woman]
“Right, so there are a lot of places [in the website to which] you can go. But if those places don’t impact you… For example, if you don’t have high blood pressure, if it doesn’t impact you, you are less likely to go back to it. Then when it impacts you, then you know the resource is there”. [2B09, 47-year old woman]
b) Use in context of current concern
“Because the bottom of my toes is not sensitive and the side of my foot is not sensitive. So I looked up this website and tried to find out what could I do and is there any kind of help that I could find”. [3B52, 65-year old woman]
-Motivation for repeated use
“The foot complication is the one I usually look up”. [3B52, 65-year old woman]
-To gage urgency
“I would look something to see if I thought if it was urgent”. [3B13, 54-year old woman]
“Now, I have to say yesterday in desperation I actually tried to look up some medication information on this website…” [2B09, 47-year old woman]
c) Role of blog in driving site traffic:
“When new blogs coming up then I read it. Not always at once, but still eventually”. [3B53, 78-year old woman]
Dual roles of blog
“‘Ask the Expert’ should remain open all the time… And maybe it’s not just ‘Ask the Expert’; maybe it’s ‘Ask someone’. So you need an ‘Ask the Expert’ section and ‘Ask the Fellow Patient’ section”. [2B09, 47-year old woman]
i) Opportunity for anonymous request for information
“It provides an opportunity where people however anonymous they want to be and it might be something someone could be shy asking a heath care practitioner meanwhile go ahead and ask on something like that. So in that sense, I think that’s useful”. [2B16, 37-year old woman]
ii) Fostering a sense of community
“It’s interesting because people will come with a question through [the blog, such as] “Well, this worked for me”, or “This will work for everyone.” It is a sort of communal sense and conversation”. [3B53, 78-year old woman]
“So, I think learning to develop your support systems is extremely important for a diabetic. And that having a forum where even if you don’t have a lot of people in your life that you can talk to about this, but having a forum where maybe you can go on and have an online community can be very helpful. Provided that they understand that any information that they read has to be verified”. [3B13, 58 year-old woman]
“For example, if you are feeling yourself alone and loneliness is not a very good thing health-wise. It leads to depression and everything else and if you feel that you have to communicate, and then communicate here”. [3B49, 46-year old man]
-Though this was not a universal sentiment: others felt a lack of connection
“Sometimes with a blog it can be communal and it can be conversation, but it can also make people feel disconnected because: how comfortable are they in conversation?” [2B16, 37-year old woman]
iii) However, tension between desire for online community and fear of ridicule, timidity
“Because we are afraid that we are putting a stupid question”. [3B53, 78-year old woman]
“I found at times, responding to other posts, that [I felt] shy about responding. But I did go ahead and do that even though I thought at times that’s not necessarily my usual style”. [2B16 37-year old woman]
“It might take time to catch on, because people who are feeling shy about it may need to see what other people do with it for quite a while”. [2B16, 37-year old woman]
“I think some people are shy about how they write.” [2B16, 37-year old woman]
“I’ve just never been interactive…” [3B09, 61-year old woman]
iv) Other reasons for not contributing to the blog:
-Nothing new to contribute
“But I’m not a poster. I read, I learn, I just don’t put my 2 cents in. [I don’t post because] I would say the same thing – why would I bother?” [3B09, 61-year old woman]
-Reliability of content: Balance between evidence anecdote, trusted source versus patient centred
NO “I find for the most part it’s the blind leading the blind. I guess this one is being moderated but by and large you have a bunch of people who don’t know anything kind of spewing forth”. [3B13, 54-year old woman]
YES “However, the way this one is designed essentially it goes and it’s approved or reviewed before being posted in the first place. Which I think is a good style”. [2B16, 37-year old woman]
-Perceived relevance to specific demographic population
“I don’t know what the ages are but I’m thinking a younger person would be more familiar with communication in a way that is not face to face, more so”. [2B16, 37-year old woman]
3) General feedback on website characteristics:
a) Accurate and comprehensive
In fact, what I noticed is that their seemed to be more than what I would have anticipated… [2B01, 48-year old man]
So there are lots of things on the website that really did help. So things like how to control the blood pressure and how to do all those other… there is lots of things that help. [2B09, 47-year old woman]
b) Easy to navigate
“The website itself is easy to navigate. And I think that list is very good and I’ve found that every time I have looked, [the answer] could typically could fall into one of those categories. The answer might not be there, but I know where to start to look”. [2B09, 47-year old woman]
c) But could be more relevant:
-Want practical solutions
Maybe too many people are inundated with expert answers and not enough with real life answers. [3B09, 61-year old woman]
But that when you need it the most, that’s when I found it didn’t have everything I needed. It was still missing… the next piece; the next piece is what happens when you are in that mess? [3B49, 46-year old man]
Yeah, it combines that very practical app, ok so here is an endocrinologist talking about blah, blah, blah, new scientific in road into diabetes… ok, that’s great on an intellectual level… What does this mean on a practical level? For those that want to and can handle the scientific information, perfect. And those of us who can’t or don’t want to then there’s other things on the site. [3B13, 54-year old female]
What do you value about each type of information, the practical versus medical evidence based? 70 or 80 [percent of it practical] versus 30 and 20 for the medical. [3B53, 78-year old woman]
But nowhere does it tell you how to deal with it. So, what I’m looking for and what I was looking for in that peer support was other people in the same position who have found solutions to issues that typical websites don’t tell you [2B09, 47-year old woman]
4) Potential mechanism of impact of website on self-efficacy, behavior change and diabetes distress
a) Unanticipated pattern of use of DOC
“I actually I haven’t done it lately but when I first got this I searched a lot of stuff, I was interested so I did search. I printed it out I actually have some in a binder, which I have referred to on occasion. It’s nice to have something black and white that you can refer to. That’s why I like certain websites, because you can print it off and read it. I can search other references and I’ve done that. And I keep a binder and I refer to it when…” [3B09, 61-year old woman]
b) Unanticipated impact of reminder emails
“There were the reminders that you hadn’t visited the site in a while. [When I got those reminders], I said, I will answer it later…
This companion is a good reminder, I heard about it before but then let’s go back and check to make sure that I’m understanding properly…” [3B27, 58-year old woman]
“Constantly reminding me about the things that we need to be aware of. Most of us know but the Online Companion was a good reminder and got me thinking of things that I need to constantly do (some that we conveniently forget)”. [3B55, 58-year old woman]
c) Unanticipated role of DOC in “kickstarting” self-management behaviors
“Because of the recording of activity… I started… it guilted me into starting to record my activity and once I started recording it because also coming off of the Metformin, I started recording it there and then I switched to recording it in books and now I simply record it on the calendar”. [1A06, 62-year old woman]
1) Barriers and facilitators to use: Participants stated that they struggled with competing health and life concerns. They reported that it was “not just diabetes” that they dealt with (Table 3; 1a) and that they had to manage other concurrent medical conditions (Table 3; 1b). They spoke about their attempts to balance illness work with everyday life work; they found that after completing the latter, “there wasn’t a lot” of time or energy left for self-management of their disease, much less to use the website (Table 3; 1b). Some participants identified lack of motivation as “a me thing, as opposed to a site thing”, while others commented that “laziness” (Table 3; 1c) was a barrier to use.
Participants’ attitudes toward diabetes also coloured their approach to self-management and thus their use of the site. In particular, participants reported feeling frustrated with the uncontrolled nature of their disease, and the collection of self-monitoring information that showed a lack of metabolic control exacerbated this frustration (Table 3; 1d). Similarly, some participants said that they were sometimes overcome with a sense of futility. They perceived that regardless of their actions, some outcomes such as dialysis were inevitable (Table 3; 1e); hence, they saw no value in learning about the disease or in trying to self-manage the disease or use the website.
Eleven (52%) of the 21 interview participants said the requirement for a login and password prevented them from using the website because they often forgot their password (Table 3; 1f). Others were limited by poor computer or internet access and said they would prefer a mobile solution (Table 3; 1g). Finally, some participants noted that the onerous process for correcting error in log entries discouraged them from using the self-management tools (Table 3; 1h).
In contrast, other website characteristics appeared to encourage users to visit and return. The perceived reliability of the website’s information and the perception that it was an “authoritative source” drew users (Table 3; 1i). In addition, email reminders prompted them to return; such prompts seemed well-suited to what users characterized as a “fast-paced world” and served as effective reminders to make diabetes self-care a high priority (Table 3; 1j). Similarly, routinization of the online experience appeared to routinize use of the internet for certain aspects of health care. For example, participants reported that increasing their use of the internet and expanding their scope of internet activities created a new norm for internet usage, such that it became more commonplace for them to “look up whatever is interesting to me”, “track stuff online”, and embrace “going paperless” (Table 3; 1 k).
2) Patterns of website use, including role of the blog: Participants said that use of the website was driven by their individual context and circumstances. Rather than browsing at random, users said they were goal-directed: when they had a specific concern, they focused on that area of the website (Table 3; 2a). For example, one participant was initially motivated to visit (and subsequently continued to visit) the foot care section of the website because of her foot symptoms (Table 3; 2b). Participants also commented that they used the website to gauge the urgency of their concerns and to try to obtain immediate answers to their questions (Table 3; 2b).
We explored potential reasons for the unexpected finding that the blog was the most frequently accessed tool and appeared to drive website usage. Participants identified dual roles of the blog in providing a forum for both “expert” and “fellow patient” advice (Table 3; 2c). “Fellow patient” advice consisted of submitting suggested content or commenting on blog posts (i.e. participating in a discussion thread). For patients who were otherwise uncomfortable with asking questions of a health care provider, the blog afforded them the opportunity to obtain “expert” medical answers anonymously (Table 3; 2c,i). The provision of “fellow patient” advice was characterized as promoting a sense of community that some participants felt might combat their sense of isolation (Table 3; 2c,ii). However, this was not a universal sentiment, and some participants felt uncomfortable with and disconnected from the blog (Table 3; 2c,ii). In addition, there was a tension between a desire for online community and a fear of “looking foolish”. For example, some participants said they were afraid of “putting [up] a stupid question” or surmised that others were “shy about how they write” (Table 3; 2c,iii). Other reasons offered for not contributing to the blog included participants’ perceptions that they had nothing to offer, that the blog was not their preferred mode of communication, and that they preferred face-to-face communication (Table 3; 2c,iv). There were mixed views regarding the reliability of the blog content: one user commented that a blog represented “the blind leading the blind”, but others said they were reassured by the fact it was moderated by an expert (Table 3; 2c,iv).
3) General feedback on website characteristics: Participants commented on their impressions of the website overall and provided feedback concerning general features that were appreciated. Participants perceived that the website was accurate, comprehensive (Table 3; 3a), and easy to navigate (Table 3; 3b). While they appreciated the website’s provision of “evidence-based medical content”, they reported a desire for more practical solutions and “real life answers” (Table 3; 3c) and spoke about finding a balance between these two characteristics.
4) Mechanisms of effect of the website on self-efficacy, behaviour change, and diabetes distress: Despite apparently limited use of the website, the intervention appeared to have an effect on self-efficacy, behaviour change, and diabetes distress. Deeper exploration of the data regarding patterns of use and website features uncovered factors that might account for these quantitative findings. For example, rather than returning to the site to revisit and review items, some participants reported that they printed items of interest from the website and subsequently referred to these paper copies (Table 3; 4a). The use of reminder emails also had an effect. Participants reported that these emails not only prompted them to return to and log into the website, but also encouraged them in their own self-management (Table 3; 4b). Finally, one participant noted that the website had kick-started her self-management behaviours, by initially “guilting” her into recording them online. Although she did not subsequently login to the website to record these behaviours, she did continue to record them on paper.
We found that a self-management website for patients with type 2 diabetes led to no improvement in self-efficacy, diabetes distress, or clinical outcomes over the study period. However, there was an improvement in self-care (a secondary outcome), and the group that used the website experienced significantly lower diabetes distress than those who did not use it. Despite a user-centred design process and an increase in the frequency of blog posting from weekly to twice weekly, use of the website (as ascertained by login records) was limited. Our interviews revealed that both patient-related factors (e.g. competing health and life concerns, a sense of futility) and website-related factors (e.g. requirement for login, limited computer or internet access) limited use of the website. We also found that participants were motivated to access the website on the basis of their current needs and concerns, as well as new blog postings, with the blog fulfilling a need for both “expert” medical content and peer support and a sense of community.
These qualitative findings have confirmed the importance of website features such as the reliability and authoritativeness of information , as well as the use of blogs  and reminders  for continued engagement of users. Our findings also emphasize the need to provide a greater proportion of “practical” patient-centred content. A recent qualitative study analyzing 3005 diabetes-related blog posts showed that the most influential blogs were those written by patients and that only 10% of blogs cited biomedical literature , highlighting the dual needs for reliable, evidence-based content and engaging patient-based content.
Our data also suggest that mobile devices are a potential avenue through which to improve accessibility and use of a self-management site. A Cochrane review of computer-based diabetes self-management interventions identified 16 randomized controlled trials, which showed a small effect on glycemic control (−2.3 mmol/mol or −0.2%, p = 0.009), with the mobile phone subgroup experiencing a greater effect (−5.5 mmol/mol or −0.5%, p < 0.00001) . Given the increasing preference for mobile devices over desktop computers  for health information resources , mobile technology may overcome the barriers to website access and use that we identified, through greater integration with patients’ existing routine, such that self-management is no longer seen as additional “illness work”. However, as with web-based technology, a systematic approach to development, testing, implementation, and evaluation of mobile health technology is warranted. Although such technologies are proliferating, with over 736 applications related to diabetes alone, their usability and clinical effectiveness are variable , and concerns exist regarding their effectiveness and safety, as well as the security of personal health information . Our findings regarding user engagement with web-based technology echo those for mobile technology: an evaluation of 10 mobile diabetes applications emphasized the importance of user-centred design, an engaging interface, and context-driven use .
Competing health concerns were identified as a barrier to web-based self-management. Patients’ adherence to diabetes care is affected by multimorbidity (e.g. depression), which in turn directly affects self-management ability and competes for time and attention . For example, patients with a greater number of comorbidities placed a lower priority on diabetes and had worse diabetes self-management ability . Future interventions should consider strategies, such as shared decision-making and priority-setting, to empower patients with multiple comorbidities to optimize their self-care . For example, a patient may identify mood management as a priority, which is key to subsequent self-care. Thus, greater integration with the person’s cognitive, emotional, and health information-seeking preferences, daily living routine, and health context through the use of patient-based content, mobile devices, and individualized decision-making, may be further strategies to maximize website use and reduce intervention attrition.
Finally, our results may be extrapolated to other chronic diseases. In particular, our finding of the need for tailored content and peer support, balanced with concerns regarding information reliability and confidentiality, is applicable to other strategies for managing chronic disease. For example, a systematic review of the benefits and limitations of social media in the context of chronic disease identified benefits (increased interaction and social support, tailored and accessible information) and limitations (quality concerns and lack of reliability, confidentiality, and privacy)  to those we identified. Similarly, our finding of a reduction in diabetes distress in conjunction with no improvements in clinical outcomes echoes findings from intervention strategies targeting other chronic diseases. For example, another systematic review examining the effect of social media on psychological and physical outcomes in chronic disease found a relatively large body of evidence demonstrating psychological benefit (19 identified studies) but limited evidence for physical outcomes (4 identified studies) .
This study was limited by its non-randomized design. However, we employed a repeated-measures design that permitted reliable assessment of baseline self-efficacy. Although our primary outcome (self-efficacy) was a non-clinical outcome, it is a validated predictor of patient behaviour change and clinical outcomes ,,,. The infrequency of website use likely limited the effect of this intervention, but we obtained valuable insights regarding mediators of website use through our individual interviews. The qualitative evaluation was conducted by individuals who were also involved in developing the intervention, which created a potential for bias; however, we guarded against this bias by including individuals who were not involved in designing the website as members of the qualitative analytic team and by having three coders. As such, we were able to obtain and report critical feedback that participants openly shared. Study strengths include the use of multiple repeated measures, the use of validated outcomes, dual coding of all transcripts, and triangulation of the qualitative findings with the quantitative results ,,.
Increasing use of the World Wide Web by consumers for health information and ongoing revolutions in social media are strong indicators that consumers are welcoming and demanding a new era of technology in health care. However, full potential of this technology is hindered by limited uptake and high attrition rates. Use of the Diabetes Online Companion may be optimized by integrating a mobile interface, emphasizing “practical” patient-centred content such as a patient-led blog, and including a “prioritization” feature to help users with competing concerns. Our research findings have shed light on these limitations by identifying characteristics associated with website use and attrition and suggesting strategies to reduce website attrition as a way to potentially optimize clinical outcomes.
We thank Jovita Sundaramoorthy and Carolyn Gall-Casey of the Canadian Diabetes Association for their input and feedback related to project design. We also thank Kevin Thorpe for his invaluable input into the choice of methods for the statistical analysis.
The study was funded by the Canadian Institutes of Health Research (CIHR) Knowledge to Action Operating Grant (funding reference number KAL 94484). Dr. SE Straus is supported by a Tier 1 Canada Research Chair.
- Centers for Disease Control and Prevention: Diabetes Report Card 2012. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2012.Google Scholar
- American Diabetes Association: Standards of medical care in diabetes 2013. Diabetes Care. 2013, 36: S11-S66. 10.2337/dc13-S011.View ArticleGoogle Scholar
- Haas L, Maryniuk M, Beck J, Cox CE, Duker P, Edwards L, Fisher EB, Hanson L, Kent D, Kolb L, McLaughlin S, Orzeck E, Piette JD, Rhinehart AS, Rothman R, Sklaroff S, Tomky D, Youssef G: National standards for diabetes self-management education and supportdiabetes. Diabetes Care. 2014, Supplement 1: S144-S153. 10.2337/dc14-S144.View ArticleGoogle Scholar
- Selby JV, Beal AC, Frank L: The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda. J Am Med Assoc. 2012, 307: 1583-1584. 10.1001/jama.2012.500.View ArticleGoogle Scholar
- Cochrane J, Conn VS: Meta-analysis of quality of life outcomes following self-management training. Diabetes Educ. 2008, 34: 815-823. 10.1177/0145721708323640.View ArticleGoogle Scholar
- Minet L, Møller S, Vach W, Wagner L, Henriksen JE: Mediating the effect of self-care management intervention in type 2 diabetes: a meta-analysis of 47 randomised controlled trials. Patient Educ Couns. 2010, 80: 29-41. 10.1016/j.pec.2009.09.033.View ArticlePubMedGoogle Scholar
- Ruppert K, Uhler A, Siminerio L: Examining patient risk factors, comorbid conditions, participation, and physician referrals to a rural diabetes self-management education program. Diabetes Educ. 2010, 36: 603-612. 10.1177/0145721710369705.View ArticlePubMedGoogle Scholar
- Shaw K, Killeen M, Sullivan E, Bowman P: Disparities in diabetes self-management education for uninsured and underinsured adults. Diabetes Educ. 2011, 37: 813-819. 10.1177/0145721711424618.View ArticlePubMedGoogle Scholar
- Shekelle PG, Morton SC, Keeler EB: Costs and Benefits of Health Information Technology. 2006, Agency for Healthcare Research and Quality (US), RockvilleGoogle Scholar
- Yu CH, Bahniwal R, Laupacis A, Leung E, Orr MS, Straus SE: Systematic review and evaluation of web-accessible tools for management of diabetes and related cardiovascular risk factors by patients and healthcare providers. J Am Med Inform Assoc. 2012, 19: 514-522. 10.1136/amiajnl-2011-000307.View ArticlePubMedPubMed CentralGoogle Scholar
- Bull SS, Gaglio B, McKay HG, Glasgow RE: Harnessing the potential of the internet to promote chronic illness self-management: diabetes as an example of how well we are doing. Chronic Illness. 2005, 1: 143-155. 10.1177/17423953050010021101.View ArticlePubMedGoogle Scholar
- van Vugt M, de Wit M, Cleijne WH, Snoek FJ: Use of behavioral change techniques in web-based self-management programs for type 2 diabetes patients: systematic review. J Med Internet Res. 2013, 15: e279-10.2196/jmir.2800.View ArticlePubMedPubMed CentralGoogle Scholar
- Seidman JJ, Steinwachs D, Rubin HR: Design and testing of a tool for evaluating the quality of diabetes consumer-information Web sites. J Med Internet Res. 2003, 5: e30-10.2196/jmir.5.4.e30.View ArticlePubMedPubMed CentralGoogle Scholar
- Shrank WH, Patrick AR, Brookhart MA: Healthy user and related biases in observational studies of preventive interventions: a primer for physicians. J Gen Intern Med. 2011, 26: 546-550. 10.1007/s11606-010-1609-1.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu CH, Parsons J, Hall S, Newton D, Jovicic A, Lottridge D, Shah BR, Straus SE: User-centered design of a web-based self-management site for individuals with type 2 diabetes – providing a sense of control and community. BMC Med Inform Decis Mak. 2014, 14: 60-10.1186/1472-6947-14-60.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu C, Parsons J, Mamdani M, Lebovic G, Shah BR, Bhattacharyya O, Laupacis A, Straus SE: Designing and evaluating a web-based selfmanagement site for patients with type 2 diabetes - systematic website development and study protocol. BMC Med Inform Decis Mak. 2012, 12: 57-10.1186/1472-6947-12-57.View ArticlePubMedPubMed CentralGoogle Scholar
- Bandura A: Self-Efficacy: the Exercise of Control. 1997, W.H. Freeman, New YorkGoogle Scholar
- Glasgow RE, Toobert DJ, Hampson SE, Brown JE, Lewinsohn PM, Donnelly J: Improving self-care among older patients with type II diabetes: the “sixty something…” study. Patient Educ Couns. 1992, 19: 61-74. 10.1016/0738-3991(92)90102-O.View ArticlePubMedGoogle Scholar
- Reichard P, Toomingas B, Rosenqvist U: Changes in conceptions and attitudes during five years of intensified conventional insulin treatment in the stockholm diabetes intervention study (SDIS). Diabetes Educ. 1994, 20: 503-508. 10.1177/014572179402000607.View ArticlePubMedGoogle Scholar
- Anderson RM, Funnell MM, Butler PM, Arnold MS, Fitzgerald JT, Feste CC: Patient empowerment: results of a randomized controlled trial. Diabetes Care. 1995, 18: 943-949. 10.2337/diacare.18.7.943.View ArticlePubMedGoogle Scholar
- Trief PM, Teresi JA, Eimicke JP, Shea S, Weinstock RS: Improvement in diabetes self-efficacy and glycaemic control using telemedicine in a sample of older, ethnically diverse individuals who have diabetes: the IDEATel project. Age Ageing. 2009, 38: 219-225. 10.1093/ageing/afn299.View ArticlePubMedGoogle Scholar
- Aljasem LI, Peyrot M, Wissow L, Rubin RR: The impact of barriers and self-efficacy on self-care behaviors in type 2 diabetes. Diabetes Educ. 2001, 27: 393-404. 10.1177/014572170102700309.View ArticlePubMedGoogle Scholar
- Grossman HY, Brink S, Hauser S: Self-efficacy in adolescent girls and boys with insulin-dependent diabetes mellitus. Diabetes Care. 1987, 10: 324-329. 10.2337/diacare.10.3.324.View ArticlePubMedGoogle Scholar
- Nakahara R, Yoshiuchi K, Kumano H, Hara Y, Suematsu H, Kuboki T: Prospective study on influence of psychosocial factors on glycemic control in Japanese patients with type 2 diabetes. Psychosomatics. 2006, 47: 240-245. 10.1176/appi.psy.47.3.240.View ArticlePubMedGoogle Scholar
- Lorig KR, Ritter PL, Jacquez A: Outcomes of border health Spanish/English chronic disease self-management programs. Diabetes Educ. 2005, 31: 401-409. 10.1177/0145721705276574.View ArticlePubMedGoogle Scholar
- Toobert DJ, Hampson SE, Glasgow RE: The summary of diabetes self-care activities measure: results from 7 studies and a revised scale. Diabetes Care. 2000, 23: 943-950. 10.2337/diacare.23.7.943.View ArticlePubMedGoogle Scholar
- Polonsky WH, Fisher L, Earles J, Dudl RJ, Lees J, Mullan J, Jackson RA: Assessing psychosocial distress in diabetes: development of the Diabetes Distress Scale. Diabetes Care. 2005, 28: 626-631. 10.2337/diacare.28.3.626.View ArticlePubMedGoogle Scholar
- Chew LD, Bradley KA, Boyko EJ: Brief questions to identify patients with inadequate health literacy. Fam Med. 2004, 36: 588-594.PubMedGoogle Scholar
- Chew LD, Griffin JM, Partin MR: Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008, 23: 561-566. 10.1007/s11606-008-0520-5.View ArticlePubMedPubMed CentralGoogle Scholar
- Zar JH: Biostatistical Analysis. 1996, Upper Saddle River, N.J, Prentice HallGoogle Scholar
- Liu G, Liang KY: Sample size calculations for studies with correlated observations. Biometrics. 1997, 53: 937-947. 10.2307/2533554.View ArticlePubMedGoogle Scholar
- Norris SL, Engelgau MM, Narayan KM: Effectiveness of self-management training in type 2 diabetes: a systematic review of randomized controlled trials. Diabetes Care. 2001, 24: 561-587. 10.2337/diacare.24.3.561.View ArticlePubMedGoogle Scholar
- Diggle P, Heagerty P, Liang KY, Zeger S: Analysis of Longitudinal Data. 2002, Oxford University Press, Oxford, New YorkGoogle Scholar
- Verbeke G, Molenberghs G: Linear Mixed Models for Longitudinal Data. 2009, Springer, Verlag, New YorkGoogle Scholar
- Laird NM, Donnelly C, Ware JH: Review papers: longitudinal studies with continuous responses. Stat Methods Med Res. 1992, 1: 225-247. 10.1177/096228029200100302.View ArticlePubMedGoogle Scholar
- Cnaan A, Laird NM, Slasor P: Tutorial in biostatistics: using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med. 1997, 16: 2349-2380. 10.1002/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667>3.0.CO;2-E.View ArticlePubMedGoogle Scholar
- Sherrill DL, Holberg CJ, Enright PL, Lebowitz MD, Burrows B: Longitudinal analysis of the effects of smoking onset and cessation on pulmonary function. Am J Respir Crit Care Med. 1994, 149: 591-597. 10.1164/ajrccm.149.3.8118623.View ArticlePubMedGoogle Scholar
- Figaro MK, Elasy T, BeLue R: Exploring socioeconomic variations in diabetes control strategies: impact of outcome expectations. J Natl Med Assoc. 2009, 101: 18-23.View ArticlePubMedGoogle Scholar
- Osborn CY, Cavanaugh K, Wallston KA, Rothman RL: Self-efficacy links health literacy and numeracy to glycemic control. J Health Commun. 2010, 15: 146-158. 10.1080/10810730.2010.499980.View ArticlePubMedPubMed CentralGoogle Scholar
- Harrell FE, Lee KL, Mark DB: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996, 15: 361-387. 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.View ArticlePubMedGoogle Scholar
- R Team: R: A Language and Environment for Statistical Computing. 2006, R Foundation for Statistical Computing, ViennaGoogle Scholar
- Kuzel AJ: Sampling in Qualitative Inquiry. Doing Qualitative Research. Edited by: Crabtree BF, Miller W. 1999, Sage Publications, Thousand Oaks, 33-45.Google Scholar
- Poland BP: Transcription quality as an aspect of rigor in qualitative research. Qual Inq. 1995, 1: 290-310. 10.1177/107780049500100302.View ArticleGoogle Scholar
- Kvale S: InterViews: An Introduction to Qualitative Research Interviewing. 1996, Sage Publications, Thousand OaksGoogle Scholar
- Pope C, Ziebland S, Mays N: Qualitative research in health care: analysing qualitative data. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.View ArticlePubMedPubMed CentralGoogle Scholar
- Bryman A: Quantity and Quality. Social Research. 1988, Routledge, LondonGoogle Scholar
- Lemire M, Paré G, Sicotte C, Harvey C: Determinants of internet use as a preferred source of information on personal health. Int J Med Inform. 2008, 77: 723-734. 10.1016/j.ijmedinf.2008.03.002.View ArticlePubMedGoogle Scholar
- Nijland N, van Gemert-Pijnen JE, Kelders SM, Brandenburg BJ, Seydel ER: Factors influencing the use of a web-based application for supporting the self-care of patients with type 2 diabetes: a longitudinal study. J Med Internet Res. 2011, 13: e71-10.2196/jmir.1603.View ArticlePubMedPubMed CentralGoogle Scholar
- Gruzd A, Black FA, Le Yen TN, Amos K: Investigating biomedical research literature in the blogosphere: a case study of diabetes and glycated hemoglobin (HbA1c). J Med Libr Assoc. 2012, 100: 34-42. 10.3163/1536-5050.100.1.007.View ArticlePubMedPubMed CentralGoogle Scholar
- Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R, Wood B, Inniss JD, Murray E: Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus.Cochrane Database Syst Rev 2010, 3.,Google Scholar
- Smith A: A: 35% of American Adults Own a Smartphone: One Quarter of Smartphone Owners Use their Phone for Most of their Online Browsing. 2011, Pew Research Center, WashingtonGoogle Scholar
- Manhattan Research: U.S. mobile health audience jumps to 95 million adults. Cybercitizen Health® U.S. 2013. Available at http://manhattanresearch.com/News-and-Events/Press-Releases/mobile-health-95-million.
- Demidowich AP, Lu K, Tamler R, Bloomgarden Z: An evaluation of diabetes self-management applications for Android smartphones. J Telemed Telecare. 2012, 18: 235-238. 10.1258/jtt.2012.111002.View ArticlePubMedGoogle Scholar
- Eng DS: The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes. 2013, 14: 231-238. 10.1111/pedi.12034.View ArticlePubMedGoogle Scholar
- Årsand E, Frøisland DH, Skrøvseth SO, Chomutare T, Tatara N, Hartvigsen G, Tufano JT: Mobile health applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol. 2012, 6: 1197-1206. 10.1177/193229681200600525.View ArticlePubMedPubMed CentralGoogle Scholar
- Ciechanowski PS, Katon WJ, Russo JE: Depression and diabetes: impact of depressive symptoms on adherence, function, and costs. Arch Intern Med. 2000, 160: 3278-3285. 10.1001/archinte.160.21.3278.View ArticlePubMedGoogle Scholar
- Kerr E, Heisler M, Krein S, Kabeto M, Langa K, Weir D, Piette J: Beyond comorbidity counts: how do comorbidity type and severity influence diabetes patients’ treatment priorities and self-management?. J Gen Intern Med. 2007, 22: 1635-1640. 10.1007/s11606-007-0313-2.View ArticlePubMedPubMed CentralGoogle Scholar
- Joosten EA, DeFuentes-Merillas L, de Weert GH, Sensky T, van der Staak CP, de Jong CA: Systematic review of the effects of shared decision-making on patient satisfaction, treatment adherence and health status. Psychother Psychosom. 2008, 77: 219-226. 10.1159/000126073.View ArticlePubMedGoogle Scholar
- Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C: A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. 2013, 15: e85-10.2196/jmir.1933.View ArticlePubMedPubMed CentralGoogle Scholar
- Merolli M, Kathleen G, Martin-Sanchez F: Health outcomes and related effects of using social media in chronic disease management: A literature review and analysis of affordances. J Biomed Inform. 2013, 46: 957-969. 10.1016/j.jbi.2013.04.010.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.