Abstract
BACKGROUND: Telemedicine, the use of telecommunications to deliver health services, expertise and information, is a promising but unproven tool for improving the quality of diabetes care. We summarized the effectiveness of different methods of telemedicine for the management of diabetes compared with usual care.
METHODS: We searched MEDLINE, Embase and the Cochrane Central Register of Controlled Trials databases (to November 2015) and reference lists of existing systematic reviews for randomized controlled trials (RCTs) comparing telemedicine with usual care for adults with diabetes. Two independent reviewers selected the studies and assessed risk of bias in the studies. The primary outcome was glycated hemoglobin (HbA1C) reported at 3 time points (≤ 3 mo, 4–12 mo and > 12 mo). Other outcomes were quality of life, mortality and episodes of hypoglycemia. Trials were pooled using randomeffects meta-analysis, and heterogeneity was quantified using the I2 statistic.
RESULTS: From 3688 citations, we identified 111 eligible RCTs (n = 23 648). Telemedicine achieved significant but modest reductions in HbA1C in all 3 follow-up periods (difference in mean at ≤ 3 mo: −0.57%, 95% confidence interval [CI] −0.74% to −0.40% [39 trials]; at 4–12 mo: −0.28%, 95% CI −0.37% to −0.20% [87 trials]; and at > 12 mo: −0.26%, 95% CI −0.46% to −0.06% [5 trials]). Quantified heterogeneity (I2 statistic) was 75%, 69% and 58%, respectively. In meta-regression analyses, the effect of telemedicine on HbA1C appeared greatest in trials with higher HbA1C concentrations at baseline, in trials where providers used Web portals or text messaging to communicate with patients and in trials where telemedicine facilitated medication adjustment. Telemedicine had no convincing effect on quality of life, mortality or hypoglycemia.
INTERPRETATION: Compared with usual care, the addition of telemedicine, especially systems that allowed medication adjustments with or without text messaging or a Web portal, improved HbA1C but not other clinically relevant outcomes among patients with diabetes.
Diabetes is one of the most common chronic diseases worldwide and is associated with premature death and disability. Over the past 3 decades, the prevalence of diabetes has more than doubled globally1 and is projected to rise further from 382 million in 2013 to 592 million in 2035.2 Optimal glycemic control helps to prevent and reduce complications of diabetes, including cardiovascular disease, kidney disease, blindness, neuropathy and limb amputation.3,4 However, maintaining optimal glycemic control is challenging.5
Telemedicine is the use of telecommunications to deliver health services, including interactive, consultative and diagnostic services.6 Telemedicine interventions for diabetes can range from simple reminder systems via text messaging to complex Web interfaces through which patients can upload their glucose levels measured with a home meter and other pertinent data such as medications, dietary habits, activity level and medical history. Providers can review the data and provide feedback regarding medication adjustments and lifestyle modifications. Telemedicine has previously been shown to have clinical benefits for patients with severe asthma,7 chronic obstructive pulmonary disease,8 hypertension9 or chronic heart failure.10 It may also be helpful for providing care to people with diabetes, especially those unable to travel to health care facilities owing to large distances or disabilities. In particular, telemedicine may facilitate self-management, an important potential objective in diabetes care.11,12
Previous reviews describing the effect of telemedicine on the management of diabetes have been published.13–31 However, some focused on only specific types of telemedicine (e.g., telemonitoring20,23,26) or interventions delivered only by telephone.16,17,23,31 Given that this is a rapidly developing field, a large number of additional clinical trials have recently been published, which suggests the value of an updated review. We did a systematic review and quantitative synthesis of randomized controlled trials (RCTs) comparing the impact of different methods of telemedicine with usual care on glycated hemoglobin (HbA1C) and health-related quality of life in people with diabetes mellitus.
Methods
We performed a systematic review of RCTs that compared telemedicine with usual care for the management of diabetes (type 1 and type 2). The review was reported according to an accepted guideline. 32 We followed a written but unregistered protocol.
We included studies if they were RCTs (parallel, cluster or crossover); were published in English; enrolled adult patients with diabetes; compared telemedicine (some electronic form of provider-to-patient communication) with usual care; and reported the degree of metabolic control measured by HbA1C level. We excluded studies on gestational diabetes because of the different nature of the disease. We considered peer-reviewed full-text articles published until November 2015.
Literature search
The search strategy was designed by an expert librarian. We searched the following electronic databases through the Ovid interface: MEDLINE (1946–November 2015), Embase (1974–November 2015) and the Cochrane Central Register of Controlled Trials (November 2015). We also performed manual searches of the reference lists of existing systematic reviews. Because telemedicine is a broad term that can cover different interventions, we included all electronic forms of communication in our search. The search strategies are shown in Table A1 in Appendix 1 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150885/-/DC1). Results of the search were transferred to Endnote software and were checked for duplicates.
Study selection
Two reviewers (N.W. and L.F.) independently screened the titles and abstracts of all unique citations. Studies with “diabetes,” “type 1” or “type 2” in the title or abstract that studied any kind of telemedicine intervention were selected for full-text review. Two independent reviewers (L.F. and a research assistant) assessed them using an inclusion/exclusion form based on a priori selection criteria for eligibility. Disagreements between the reviewers were resolved by meeting with a third reviewer (N.W.).
Data extraction
We used a standardized method to extract and record relevant properties of each trial into a database. Data from eligible trials were extracted by 1 reviewer (L.F.) and checked by another reviewer (Y.L.) using a standardized extraction sheet. We resolved disagreements by discussion.
We extracted the following information from selected studies: trial characteristics (study name, year of publication, country, study design, duration and sample size); patient characteristics (age, sex, type of diabetes, diabetes duration, blood pressure, cholesterol, body mass index [BMI], smoking status and medications [insulin, oral hypoglycemic agents, lipid-lowering therapy]); telemedicine interventions; and outcomes.
We classified the telemedicine interventions by (a) form of communication from patient to provider, (b) form of communication from provider to patient, (c) type of provider (nurse, physician, allied health professional, clinical decision support system), (d) frequency of contact and (e) characteristics of any intervention. Forms of communication between provider and patient included telephone, smartphone application, email, text messaging (short message service [SMS]), Web portal (websites where patients upload blood glucose levels or other clinical data and share these with their health care providers, with or without provider-to-patient communication) and “smart” device or glucometer (any computerized device specifically developed to collect and transmit patients’ data to health care providers). Characteristics of any intervention included medication adjustment, exercise, general education about diabetes, blood pressure management and nutritional intervention.
Outcomes
The primary outcome was HbA1C level. Secondary outcomes were quality of life as measured by a validated instrument, mortality and incidence of hypoglycemia. Hypoglycemic events were classified as severe if they were reported as such or if they required assistance.
Risk-of-bias assessment
We assessed risk of bias using the Cochrane Collaboration’s tool33 and included other items (funding, intention to treat and interim analysis) also known to be associated with bias.34–40 Two reviewers (L.F. and a research assistant) assessed the trials independently and resolved any disagreements by meeting with a third reviewer (N.W.).
Data synthesis and analysis
We used Stata 13 (StataCorp) for all statistical analyses. We used the difference in means (MD) to pool continuous outcomes, and the risk ratio or the risk difference (when the events were rare) to pool dichotomous outcomes. Because of the differences expected between trials, we combined results using a random-effects model.41 We imputed missing standard deviations by substituting the baseline value from the same intervention group whenever possible; otherwise the median value from the systematic review was substituted.42 We pooled outcomes using 3 categories of time points (≤ 3 mo, 4–12 mo and > 12 mo). Dichotomous outcomes of HbA1C were pooled by the floored threshold value (e.g., < 6%, < 7%, < 8%, < 9%). We reported results from a quality-of-life instrument when data from at least 2 trials could be pooled. Heterogeneity was identified by visual inspection of the forest plots and by quantifying I2 statistic.43 We assessed publication bias using the Egger test44 and by visual inspection of the contour-enhanced funnel plot.45
We planned a priori to examine the association between population characteristics, intervention characteristics, risk-of-bias items (as specified earlier) and the effect of telemedicine on HbA1C for characteristics reported in 5 or more trials. We did univariable weighted (with the inverse of the trial variance) linear meta-regression to evaluate for effect modification on HbA1C at 4–12 months.46 In a post hoc analysis, we examined whether adjustment for potential confounders in the trial-level results modified the effect of telemedicine on HbA1C.
Results
Our literature search identified 3688 unique citations. After the screening of titles and abstracts, 517 potentially eligible studies were identified, of which 111 trials21,47–156 met our inclusion criteria (Figure 1). Disagreements occurred with 7% of the articles (κ value = 0.82).
Selection of trials for analysis. RCT = randomized controlled trial.
Characteristics of the trials are summarized in Table 1 (see end of article). Of the 111 included trials, 4 were published before 2000. Five were cluster RCTs, 3 were crossover trials, and the remainder were parallel RCTs. Forty-one trials (37%) were done in the United States, 14 (13%) in Korea and 7 (6%) each in Canada and Australia; 6 or fewer were done in each of the remaining countries.
Trial and population characteristics by type of diabetes
The median number of study participants was 114 (range 10–2378) (Table 1). The median mean age at baseline was 56 years, and the median mean BMI at baseline was 31. The range of metabolic control at baseline varied substantially between trials (mean HbA1C 6.4%–10.9%); however, the mean HbA1C level in 71 (64%) of the trials was 8% or greater at baseline.
The telemedicine interventions varied in a number of ways between the trials (Table 2 [see end of article]). Patients initiated communication with their health care providers in 3 ways: voice, text messaging and transmission of data. The trials used a large variety of platforms: Web portal (24%), customized “smart” device (14%), telephone for communication to provider (13%), smartphone application (8%), SMS (5%), email (3%), personal digital assistant (2%), automated voice reminder system (1%), computer software (1%), fax (1%), listserv (electronic mailing list to send group emails; 1%), customized patient-specific Web page (1%) or a call-me button (1%).
Telemedicine interventions
Health care providers initiated communication with patients in at least 4 ways: voice, text messaging, images and through clinical decision support systems. The platforms used were telephone (59%), clinical decision support system (32%; e.g., automated interactive voice [9%]), Web portal (22%), SMS (16%), email (7%), videoconference (4%), computer software (3%), customized “smart” device (3%), customized patient-specific Web page (2%), video message (2%), letter (2%), smartphone application (1%) or listserv (1%). Providers were nurses (37%), care managers (10%), diabetes educators (11%), physicians (29%), allied health professionals (17%; including dietitians, nutritionists, physiologists, exercise trainers, psychologists and pharmacists), clinical decision support systems (32%) and nonspecialized support (23%; including trained peers, members of research teams, counsellors and community health care workers).
Most (94%) of the interventions were interactive, whereby the patient could communicate with the provider, and the provider could communicate with the patient. Interactive telecommunication initiated by providers occurred in the following frequencies: at least daily (8%), weekly (26%), every 2 weeks (10%), monthly (16%) or less often (7%). Frequency of interaction was not reported in 33% of trials. Many of the interventions (45%) adjusted medication based on the data received. Other frequent components of the interventions included general diabetes education (76%), nutritional interventions (53%), exercise (49%) and blood pressure management (9%).
The risk-of-bias assessment of the trials is shown in Figure 2 and Table A2 in Appendix 1. Because blinding of participants is not feasible for telemedicine interventions, all trials were open label to the participants; thus, every trial included at least 1 element of risk of bias. However, we assessed for blinding of outcome assessors (present in 20% of trials). Seventy-eight trials (70%) reported and described an appropriate method of randomization, but only 30 (27%) reported an adequate allocation concealment process. The intention-to-treat principle was applied in 51 (46%) of the trials. Public funding was exclusively used in 57 trials (51%).
Summary of risk-of-bias assessment. See Table A2 in Appendix 1 for a detailed account of risk for each trial (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150885/-/DC1).
Effect on HbA1C
Thirty-nine trials (n = 3165) reported the effect of telemedicine on HbA1C at 3 months or less (Table 3 and Table A3 in Appendix 1). Eighty-seven trials (n = 15 524) reported HbA1C at 4–12 months, and 5 trials (n = 1896) reported HbA1C beyond 12 months. The MDs were all significant and favoured telemedicine, although there was large heterogeneity (≤ 3 mo: −0.57%, 95% confidence interval [CI] −0.74% to −0.40%, I2 = 75%; 4–12 mo: −0.28%, 95% CI −0.37% to −0.20%, I2 = 69% [Figure 3]; and > 12 mo: −0.26%, 95% CI −0.46% to −0.06%, I2 = 58%). Inspection of the effect sizes identified 3 outlier trials87,98,154 for which effects were larger than in the other trials. Exclusion of these 3 trials did not materially affect our results for the primary outcome (HbA1C at 4–12 mo), but it did reduce heterogeneity (−0.24%, 95% CI −0.31% to −0.16%, I2 = 58%). Findings were similar when control of HbA1C was dichotomized at various thresholds (6.4%–6.5%, 7%–7.5%, 8% or 9%) and when we pooled results from the last time points from every available trial (Table A3 in Appendix 1, and Appendix 2 [available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150885/-/DC1]).
Pooled estimates of the effect of telemedicine on outcomes
Differences in mean glycated hemoglobin levels at 4–12 months between telemedicine intervention groups and usual care groups. Values less than zero favour telemedicine. CI = confidence interval, MD = difference in means.
The contour funnel plot of HbA1C was asymmetrical, consistent with publication bias (more small studies favouring telemedicine) (Figure 4). The bias estimate from the regression analysis was significant (Egger test: bias −0.95, p = 0.02). When the 3 outlier trials were removed, the bias estimate was not significant (bias −0.68, p = 0.07).
Contour funnel plot using glycated hemoglobin levels at 4–12 months. Each trial’s precision (the inverse of the standard error of each study’s effect estimate) is plotted against each trials’s effect estimate. This funnel plot appears mildly asymmetric about the vertical dashed line (the fixed-effects pooled estimate). There are 3 statistical outliers that appear in the far right of the plot. The emptier left side of the inverted funnel may indicate small missing studies. Because most of these missing studies would be within the white region, they would be nonsignificant, which would indicate publication bias rather than some form of heterogeneity.
Meta-regression analysis
We explored a number of population and intervention characteristics using univariable meta-regression (Table 4). Both trial region and baseline HbA1C modified the effect of telemedicine on final HbA1C, but mean age, percent male, diabetes duration, BMI, insulin use, use of oral hypoglycemic therapy and diabetes type did not. European (n = 26) and North American trials (reference group, n = 47) reported similar MDs (difference in MD −0.08%, 95% CI −0.27% to 0.11%); however, trials from Asia (n = 9) reported significantly larger differences favouring telemedicine relative to North American trials (difference in MD −0.49%, 95% CI −0.77% to −0.22%).
Association between population characteristics, intervention characteristics, risk-of-bias items and the effect of telemedicine on HbA1C at 4–12 mo
Because most telemedicine platforms were used in fewer than 5 trials, it was not possible to use meta-regression to evaluate the relative merits of all platforms. Choice of patient-to-provider platform (smartphone application, Web portal, smart device, telephone) did not significantly modify the effect of telemedicine on HbA1C. However, choice of provider-to-patient platform (SMS text messaging, Web portal, clinical decision support system, telephone) significantly influenced the association between telemedicine and HbA1C, with both SMS text messaging and Web portal associated with greater benefit than telephone-based systems (difference in MD: SMS v. telephone −0.28%, 95% CI −0.52% to −0.05%; Web portal v. telephone −0.35%, 95% CI −0.56% to −0.14%). Interventions in which providers adjusted medication in response to data from patients were also associated with larger improvements in HbA1C (−0.23%, 95% CI −0.42% to −0.05%). Inclusion of interactive communication, exercise, general diabetes education, blood pressure management or nutritional interventions did not modify the benefit of telemedicine on HbA1C. Frequency of contact and type of provider did not significantly modify the association.
None of the items from the Cochrane risk-of-bias tool were significant effect modifiers, except for reporting loss to follow-up. Trials that partially reported loss to follow-up (i.e., no stated reasons for loss to follow-up, or loss was reported for the whole trial and not by group) showed a smaller difference in HbA1C than trials with fully reported loss to follow-up or trials that did not report loss to follow-up (difference in MD 0.30%, 95% CI 0.11% to 0.48%). Because there was no gradient of effect, there was no evidence that reporting versus not reporting loss to follow-up was a significant effect modifier.
Effect on quality of life and mortality
Few trials (27 trials) reported on quality of life. Among the 23 trials that reported an instrument used by at least one other trial, a total of 6 instruments were validated (Table 3). Telemedicine led to significant improvement in the Problem Areas in Diabetes score (MD at 4–12 mo: 2.86, 95% CI 1.74 to 3.97, I2 = 0%, 2 trials, n = 363). Three scores or subscores showed significant worsening (SF-36 physical functioning ≤ 3 mo: MD −3.98, 95% CI −0.62 to −7.34, I2 = 30%, 2 trials, n = 311; SF-36 social functioning ≤ 3 mo: MD −2.22, 95% CI −0.10 to −4.34, I2 = 0%, 2 trials, n = 311; and EQ-5D at 4–12 mo: MD −0.01, 95% CI −0.01 to −0.01, 2 trials, n = 743). There was no evidence of selective reporting of subscores for quality of life. However, the effect of telemedicine was not significant for most subscores, and the few statistically significant differences were likely not clinically relevant.157
We pooled the mental health and physical health component summaries of the SF-36 and SF-12 instruments from 7 trials (n = 1333): MD 0.55 (95% CI −0.83 to 1.92; I2 = 29%) and 0.06 (95% CI −1.01 to 1.13; I2 = 0%), respectively. We also pooled the global scores (after transformation to a 1–100 range, where 100 was optimal) from all 3 diabetes-specific instruments from 8 trials (14 within-trial subgroups, n = 1324): MD 0.86 (95% CI −0.73 to 2.45; I2 = 23%). Because all of these findings were nonsignificant,157 there was no evidence to suggest that telemedicine enhanced quality of life.
Eleven trials (n = 1361) reported all-cause mortality within 3 months, 42 trials (n = 7197) reported mortality at 4–12 months, and 4 trials (n = 2376) reported mortality beyond 12 months. The risk differences were all nonsignificant, without evidence of heterogeneity (≤ 3 mo: 0.2%, 95% CI −0.6% to 0.9%, I2 = 0%, 6 deaths; 4–12 mo: −0.2%, 95% CI −0.6% to 0.2%, I2 = 0%, 68 deaths; and > 12 mo: −0.3%, 95% CI −1.6% to 1.0%, I2 = 0%, 351 deaths).
Effect on hypoglycemia
Five trials (n = 462) reported participants with hypoglycemic episodes within 3 months, and 4 trials (n = 282) reported participants with hypoglycemia at 4–12 months (Table 3). One trial (n = 92) reported participants with severe hypoglycemia within 3 months, and 10 trials (n = 1259) reported participants with severe hypoglycemia at 4–12 months. There was no evidence that telemedicine reduced the risk of hypoglycemic episodes (risk difference for hypoglycemic episodes ≤ 3 mo: 0.0%, 95% CI −5.5% to 5.5%, I2 = 63%; and at 4–12 mo: 3.1%, 95% CI −7.9% to 14.2%, I2 = 47%). Risk differences for severe hypoglycemia were also not significant (≤ 3 mo: 0.0%, 95% CI −4.2% to 4.2%; and at 4–12 mo: −0.1%, 95% CI −1.0% to 0.8%, I2 = 0%).
Interpretation
Compared with usual care, the addition of telemedicine appeared to improve HbA1C significantly in people with either type 1 or 2 diabetes. Although there was substantial heterogeneity, the pooled analyses showed that telemedicine lowered HbA1C by 0.57% within 3 months and by 0.28% beyond 4 months. The lower apparent magnitude of benefit with longer follow-up may reflect reduced adherence to the intervention. Nonetheless, the effect on HbA1C appears clinically relevant and is comparable to improvements associated with some oral antidiabetic agents (0.5%–1.25%),158 psychosocial interventions (0.6%, 95% CI −1.2% to −0.1%)159 or quality improvement strategies (0.42%, 95% CI 0.29% to 0.54%)160 among patients with diabetes. However, we did not find good evidence that telemedicine reduced the risk of hypoglycemia, quality of life or mortality, although it is unlikely that benefits for the latter would have been observed given the short duration of the included trials. Although telemedicine may also improve patient satisfaction with care, we did not collect data to test this hypothesis, and thus this suggested benefit is speculative.
The meta-regression analyses suggested that telemedicine interventions that facilitated medication adjustments were more effective in improving glycemic control than interventions that did not allow such adjustements. This finding is consistent with medication adjustment by nurse or pharmacist (0.23%, 95% CI 0.05% to 0.42%) reported in a previous meta-regression analysis of quality improvement strategies, including case management. 160 Our findings suggest that text messaging and Web portals may be especially effective mechanisms for linking providers to patients with diabetes. The use of SMS text messaging may be feasible to communicate and motivate patients, which could result in positive outcomes.134 Although the trials we studied required providers to generate the text messages, it may prove feasible and less expensive to generate such messages by means of automated algorithms.92
There are various types of telemedicine interventions, including telehealth (clinical services provided at a distance6), telecare (often applied to non-clinical aspects of care such as mobility and safety27) and telemonitoring (remote collection and transmission of clinical data from patients to providers161). We primarily included trials in which patients received clinical feedback or communication from providers using some technology or devices. Therefore, we cannot differentiate trials that focused on telemonitoring or telecare in our review. Among the included trials, telemedicine interventions ranged from simple messages providing generic management suggestions for patients52,134 to more comprehensive interventions permitting videoconferencing with a nurse case manager, and remote monitoring of glucose and blood pressure with electronic data captured in the electronic medical record.133 This wide variation in interventions likely contributed to some of the observed heterogeneity, which was only partly explained by meta-regression.
Although our study is, to our knowledge, more comprehensive than previous studies of telemedicine in diabetes, our results are generally consistent with prior work showing beneficial effects of telemedicine on HbA1C. Compared with other systematic reviews, the relatively large number of studies that we identified allowed more detailed exploration of factors that may influence the magnitude of benefits on HbA1C. We were also able to show that effects on HbA1C diminished but were sustained over time and that benefits were more pronounced with more interactive interventions (e.g., Web portals and text messaging).
Limitations
Weaknesses of our systematic review include limitations of the constituent trials (small sample size, lack of blinding and relatively short duration). However, evidence suggests that lack of blinding would be less likely to affect an objectively assessed outcome such as HbA1C.162
Second, there was considerable variation in the types of telemedicine technology used, the type of care the control groups received and the populations studied. The variation may have contributed to the observed heterogeneity, and it may explain why some trials found positive effects of telemedicine and others found no benefit. However, we used meta-regression to identify which types of telemedicine interventions were particularly efficacious. The potential benefits of SMS text messaging and Web portals when used in conjunction with tailored (patient-specific) suggestions for medication adjustment suggest that these forms of intervention should be the highest priority for future uptake.
Third, as with all meta-regression analyses using summary data rather than individual participant data, our findings are vulnerable to the ecological fallacy (i.e., findings at the population level do not always translate correctly to individuals) and from limited statistical power.
Fourth, we did not collect data on the effects of telemedicine on satisfaction of care or its cost-effectiveness.163
Finally, we found some evidence of publication bias, which suggests that some small negative trials might exist, but they were not identified by our literature search. If this supposition were correct, it might lead to a slight overestimation of the efficacy of telemedicine interventions, but it would likely not affect our conclusion given that elimination of the outliers removed any significant publication bias.
Conclusion
Our systematic review showed that telemedicine may be a useful supplement to usual clinical care to control HbA1C, at least in the short term. Telemedicine interventions appeared to be most effective when they use a more interactive format, such as a Web portal or text messaging, to help patients with self-management.
Acknowledgements
The authors are grateful to Ghenette Houston for administrative support, and to Nasreen Ahmad and Sophanny Tiv for screening and data extraction.
Footnotes
Competing interests: Braden Manns has received a research grant from Baxter for work outside this study. No other competing interests were declared.
This article has been peer reviewed.
Contributors: Marcello Tonelli and Braden Manns contributed to the study conception. Labib Faruque, Arash Ehteshami-Afshar, Natasha Wiebe and Marcello Tonelli designed the study. Labib Faruque, Arash Ehteshami-Afshar, Natasha Wiebe, Neda Dianati-Maleki and Yuanchen Liu screened and extracted data. Natasha Wiebe performed the statistical analyses. All of the authors contributed to the interpretation of data. Labib Faruque, Arash Ehteshami-Afshar, Natasha Wiebe and Marcello Tonelli drafted the manuscript; all of the authors revised it critically for important intellectual content, approved the final version to be published and agreed to act as guarantors of the work.
Funding: This work was supported by a team grant to the Interdisciplinary Chronic Disease Collaboration from Alberta Innovates – Health Solutions. Marcello Tonelli and Brenda Hemmelgarn are supported by an Alberta Heritage Foundation for Medical Research Population Health Scholar Award. Brenda Hemmelgarn is supported by the Roy and Vi Baay Chair in Kidney Research. Braden Manns, Brenda Hemmelgarn and Marcello Tonelli are supported by an alternative funding partnership supported by Alberta Health and the Universities of Alberta and Calgary. The funding agencies had no role in study conception, study analysis or manuscript writing.
- Received October 31, 2016.
- Accepted July 12, 2016.
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