Implications of Remote Monitoring Technology in Optimizing Traditional Self-Monitoring of Blood Glucose in Adults with T2DM in primary care

doi:10.21203/rs.3.rs-672031/v1

Download PDF

Research article

Implications of Remote Monitoring Technology in Optimizing Traditional Self-Monitoring of Blood Glucose in Adults with T2DM in primary care

https://doi.org/10.21203/rs.3.rs-672031/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Self-monitoring of blood glucose (SMBG) has been shown to reduce hemoglobin A1C (HbA1C). Accordingly, guidelines recommend SMBG up to 4-10 times daily for adults with type 2 diabetes (T2DM) on insulin. For persons not on insulin, recommendations are equivocal. Newer technology-enabled blood glucose monitoring (BGM) devices can facilitate remote monitoring of glycemic data. New evidence generated by remote BGM may help to guide best practices for frequency and timing of finger-stick blood glucose (FSBG) monitoring in uncontrolled T2DM patients managed in primary care settings.

Objective: To evaluate the impact of SMBG utility and frequency on glycemic outcomes using a novel BGM system which auto-transfers near real-time FSBG data to a cloud-based dashboard using cellular networks.

Design: Secondary analysis of the intervention arm of a comparative non-randomized trial with propensity-matched chart controls.

Participants: Adults with T2DM and HbA1C >9% receiving care in five primary care practices in a healthcare system.

Interventions: Participation in a 3-month diabetes boot camp (DBC) using telemedicine and a novel BGM to support comprehensive diabetes care management.

Main Measures: The primary independent variable was frequency of FSBG. Secondary outcomes included frequency of FSBG by insulin status, distribution of FSBG checks by time of day, and hypoglycemia rates.

Endocrinology & Metabolism

blood glucose meter

self-monitoring of blood glucose

diabetes care management

remote glucose monitoring

In the United States, the increasing prevalence of type 2 diabetes mellitus (T2DM) drives a rising national health and economic burden, now estimated at $327 billion (1). Compelling evidence, mainly in patients on insulin therapy, supports the effectiveness of self-monitoring of blood glucose (SMBG) as a tool to enable successful diabetes care management, individualize glycemic control and prevent diabetes associated morbidity and mortality (2–6).

SMBG is a systematic approach to glucose monitoring which is key to the identification of glycemic patterns to guide adjustments in lifestyle and medications -referred to as pattern management- to support safe and effective attainment of glycemic targets among adults with T2DM (6). However, its role on the management of patients not on insulin or agents with hypoglycemic potential has been debated since previous evidence suggests it has no long standing clinical or statistically significant difference on glycemic control in this population (7, 8).

Traditionally, SMBG has been conducted via finger-stick blood glucose (FSBG) monitoring. More recently, continuous glucose monitoring (CGM) has proven to be an alternative and beneficial method of SMBG in type 1 diabetes (T1DM)(9); yet, its benefits in patients with T2DM are not as well established. A 2019 systematic review with meta-analysis reported that the use of CGM in T2DM was beneficial (10). Furthermore, although CGM use has increased in adults with T2DM, there are still multiple barriers to its widespread use in this population (11), and its uptake in primary care practices remains low (12, 13). Therefore, evidence to guide best practices for frequency and timing of FSBG monitoring in adults with uncontrolled T2DM receiving care in primary care settings is still needed.

Telemedicine uses telecommunications to diagnose and treat patients at a distance (14, 15). Connected health devices (CHDs) generate physiologic data that is transmitted to a smartphone, tablet, or the cloud (16). Telemonitoring uses CHDs to record patient’s physiologic data for review by health care providers remotely. Technology-enabled interventions, including those which use CHD to perform SMBG, have been proposed as tools to support improved T2DM care management goals (17). These technologies offer the potential to support a timely integration between SMBG and health care team services, thus enabling patients to safely reach glycemic control targets while potentially reducing acute care services utilization. Telemedicine, mobile app-based, and remote tele monitoring interventions for diabetes have been evaluated in systematic reviews, where they in aggregate modestly lower HbA1C levels (18–20). Additionally, a previous meta-analysis of remote SMBG interventions in adults with T2DM also found a modest impact on HbA1C (mean difference of -0.42%; 95% CI: − 0.56% to − 0.27%). Of note, the blood glucose meters (BGM) used in these studies required a patient-dependent data synchronization step through either: a) a hard connection to a modem, computer, or tablet; or b) a Bluetooth connection to a home modem or smartphone (21), and a personal internet connection or telephone line.

Interventions encompassing SMBG and telemedicine have been reported for both primary and specialty care settings (18–20). We previously conducted a technology enabled T2DM care management “Diabetes Boot Camp” (DBC) intervention which used a novel cellular-enabled finger stick BGM system to support intensified clinical care management and education in the primary care setting. This intervention led to a significant improvement on HbA1C from 11.2–8.1% for the intervention group as compared to 11.3–9.9% for controls (mean difference of -3.1 for the intervention group and − 1.6 between groups; p < 0.001)(22).

This secondary analysis presents an evaluation of the relationship between the frequency and timing of FSBGs, generated by the BGM during the DBC, and glycemic outcomes. Additionally, it provides insight into the use of a novel CHD that auto-pushed FSBG data to a cloud-based provider dashboard without Bluetooth connection, patient-dependent data synchronization steps, or accrual of data costs to the patient. While BGM in common use becomes increasingly connected and capable of user-friendly remote monitoring, use of traditional FSBG monitoring is still common, particularly in primary care practice. Learnings from this study may be used to inform the evidence base which guides the frequency and timing of FSBG monitoring in primary care settings among adults with T2DM regardless of whether traditional or connected BGM is utilized.

Study Population and Data Collection

In the primary study, conducted between November 2014 and November 2017, the DBC was offered to adults with uncontrolled T2DM (HbA1C ≥ 9%) receiving care in one of five primary care practices in a healthcare system. The intervention included an initial in person diabetes self-management education and support (DSMES) visit followed by 10 weeks of telemedicine visits by physician supervised nurse practitioners and Diabetes Care and Education Specialists (DCES) (22).

During the first in person visit, the participant was registered and trained on SMBG using an FDA cleared BGM (BioTel™ BGM System). This near real-time BGM was capable of automated upload of FSBG values to a cloud-based dashboard via cellular network. The participant simply performed a FSBG check in a procedure that is similar to that used with any standard BGM. There were no additional participant driven synchronization steps beyond performance of the FSBG check. Data transmission occurred immediately if the participant was within cellular range or was delayed then auto pushed once a cellular connection became available. The device manufacturer absorbed the cost of cellular network access and participants did not accrue any financial charges for cellular data use. Participants were asked to check at least two FSBG daily during the DBC, with testing initially requested before breakfast and 2 hours after dinner. Timing of the of FSBG checks was varied over the course of the intervention, as needed, to guide medication and lifestyle management decisions.

Follow up consisted of weekly DBC team review of the individual’s FSBGs on the BioTel™ dashboard. Views included FSBG averages, highest and lowest readings, BG pattern tables by time of day, adherence to the prescribed number of FSBG checks, and extreme highs (≥ 350mg/dL) and lows (≤ 70mg/dL), the latter of which also generated daily alerts. The DBC team provided tailored DSMES, feedback on BG results, and algorithm-driven medication adjustments (Fig. 1). All participants were informed that active DBC team surveillance occurred only intermittently during office hours and were instructed to contact their primary care provider or go to the emergency department in case of symptomatic glycemic extremes outside office hours.

Statistical Analysis

The primary study was powered to detect a difference of 0.5 in the change in HbA1c with SD = 2 with 80% power at alpha = 0.05 with a sample size of 128 in each group (paired t-test). The study reaches 100% power to detect this observed difference (1. 6, SD = 2.25, post hoc) with an alpha level of 0.01. For this sub-analysis, only the participants from the intervention arm of the parent study were included. The Biotel™ BGM system’s dashboard and the DBC’s team login events were available for analysis. The dashboard’s individual BG checks were used for descriptive analysis which generated the daily mean FSBG checks per participant, the distribution of FSBG checks by time of day, and the prevalence of hypoglycemic episodes (FSBG < 40, <54 and < 70mg/dL). Our primary outcome was change in HbA1C from baseline to DBC’s completion 3 months later. Our independent variable was average daily FSBG per participant. Covariates consisted of age, race, insurance status, and insulin treatment status at the end of DBC. Bivariate analyses used T Test or linear regression as appropriate. General linear models were used to assess for an association between average daily FSBG and change in HbA1C, while controlling for all covariates which trended toward significant in bivariate analyses (age, race, and insulin status). Chi square testing was used to assess for a variation in the distribution of FSBG check by time of day throughout DBC follow up (month 1 vs. month 2 vs. month 3).

A total of 366 participants completed the DBC intervention. Mean age was 56.1 years; 62.6% were female; 79.2% were Black; and 41.8% had Medicaid (Table 1).

Table 1
DBC Participants (n = 366)
Characteristic
Age, mean (SD)	56.7 (10.6)
Female, n (%)	225 (62)
Race
White, n (%)	49 (13)
African American, n (%)	296 (81)
Hispanic, n (%)	5 (1)
Insurance type, n (%) Commercial. n (%) Medicaid. n (%) Medicare. n (%) Private. n (%) Self-Pay. n (%)	6 (2) 154 (42) 64 (18) 134 (37) 8 (2)
Baseline HbA1C	11.2 (1.7)
Pharmacologic Regimen
Regimen Type	Baseline	DBC End (3 Months)
Any Insulin, n (%)	244 (68.0)	250 (69.4)
Basal-Bolus Insulin	145 (40.4)	145 (40.4)
Mono Basal	95(25.95)	104 (28.4)
Sulfonylurea	65 (17.8)	40 (10.9)
GLP-1	59 (16.1)	116 (31.7)

A total of 48,111 FSBG values were successfully transmitted to the BioTel™ BGM system cloud-based dashboard. Overall, DBC participants successfully transmitted an average of 1.5 BG values per day (Table 2 and Table 3). The DBC team accessed the Biotel™ dashboard an average of 3.9 times per patient per month. Regarding hypoglycemia, of the available FSBG values, 579 were < 70 mg/dL (1.2%), 133 were < 54 mg/dL (0.28%), and 89 were < 40 mg/dL (0.18%). In bivariate analyses, higher average daily FSBG checks, male sex (P = 0.02), and no insulin use (P = 0.01) were associated with greater improvement in HbA1C. In multivariate analyses, higher average daily FSBG checks (P = 0.0003) and no insulin use (P = 0.006) remained associated with greater improvement in HbA1C. Overall, the most common times of day for FSBG checks were pre-breakfast and post-dinner; and the least common, was overnight (Table 4). The distribution of FSBG checks by time of day changed over time with patients testing more frequently pre-breakfast in the third month of the intervention compared to the first month (p < 0.001).

Table 2
Finger-Stick Blood Glucose (FSBG) Testing Frequency
Total FSBG checks^a, mean (SD)	134 (66)
Daily FSBG checks^a, mean (SD)	1.49 (0.73)
SD: Standard deviation ^aAverage per participant during the 90 days of the intervention

Table 3
Frequency of Finger-Stick Blood Glucose Checks by Insulin Dosing Regimen
Regimen Type^a	n	Daily average FSBG checks^b Mean (SD)	p
Any Insulin	250	1.56 (0.73)	0.003
Non-Insulin	110	1.32 (0.71)	0.003
FSBG: Finger-stick blood glucose SD: Standard deviation ^aAt end of DBC (3 Months) ^bAverage per participant during the 90 days of Boot Camp

Table 4
Finger-Stick blood glucose checks distribution by time of day
Time Period	n (%)
00:00 to 06:00 (overnight)	3,399 (7.1)
06:01 to 09:00 (pre-breakfast)	10,954 (22.8)
09:01 to 12:00 (post-breakfast)	8,815 (18.3)
12:01 to 18:00 (post-lunch)	10,945 (22.8)
18:01 to 23:59 (post-dinner)	13,998 (29.1)

Despite the high prevalence of T2DM and its expected worsening burden of disease over the next 20 years (23), primary care providers still face numerous challenges to monitor glycemic trends and adequately guide the management of the more than 90% of patients with T2DM for whom they provide the majority of diabetes care management (12). The ACP (American College of Physicians) recommends routine SMBG for patients: on insulin; with evidence of hypoglycemic episodes; or, taking drugs likely to increase hypoglycemia while driving or operating machinery. Short-term monitoring is suggested when starting corticosteroids or to confirm suspected hypoglycemia. Neither the timing nor frequency of FSBGs are specified (24).

Anti-hyperglycemic regimen adjustment for T2DM relies on targeting HbA1C levels and data generated from SMBG through traditional FSBG checks and, more recently, by CGM. The use of CGM in T2DM, which has potential to overcome shortfalls in SMBG by finger stick, is slowly increasing, however, it has not yet been widely adopted, therefore FSBG remains the primary modality of SMBG for T2DM patients particularly in primary care settings (11–13). There are acknowledged limitations to the utility of traditional FSBG data. It examines discrete points in time which may miss critical periods relevant to optimization of glycemic control and does not quantify glycemic variability (13). Furthermore, FSBG monitoring can be underutilized or unreliably reported by patients (13, 25–27). As BGM systems in common use become CHDs, remote glucose monitoring will likely become a more common option in primary care settings, particularly as payers increasingly cover this service.

This secondary analysis examined the relationship between FSBG check frequency and HbA1C change in adults with uncontrolled T2DM in the primary care setting. Importantly, from a real-world primary care diabetes care management perspective, in this study the average daily frequency of FSBG checks was low, despite 40% of the participants being on insulin by the end of the intervention. Participants on average performed 1.5 FSBG checks daily. This is a considerably lower frequency than the guideline recommended 4–10 per day for patients on insulin regimens (28). More importantly, this finding was statistically significant regardless of whether the participant was on insulin or not by the end of the intervention. The robust improvement in HbA1C levels as a result of the intervention suggests that requesting a minimum of two FSBG checks daily (initially pre-breakfast and post-dinner) can generate actionable BG data if the values are reliably transmitted by a CHD and then used to guide glycemic regimen adjustments by the provider in collaboration with the patient, either on a recurring basis or prior to office visits.

From a technology perspective, SMBG via a novel, user-friendly CHD was coupled with telemedicine delivery of targeted DSMES and medication management using telephone calls, texting, and email to enable successful delivery of a diabetes care management intervention to adults with uncontrolled T2DM. Compared with prior studies using remote SMBG (20, 21), this intervention evaluated use of a system capable of auto-transmitting FSBG data to the cloud via cellular networks without any user-dependent synchronization steps and without a need for Bluetooth or a personal internet connection. We hypothesize that from the patient perspective, the lack of required steps beyond performing a FSBG using the device, as one would do with any regular BGM, was responsible to some degree for the success of the intervention. The technology also allowed the care team to access FSBG data on-demand which facilitated a timely response to glycemic extremes, expedient diabetes education and medication adjustments targeted to glycemic patterns and trends.

Although diabetes specific remote tele monitoring interventions are numerous, few rigorous studies have been reported. Moreover, the type of CHDs and telemonitoring technologies deployed are variable and dated, which makes positioning of our findings in the context of the existing literature challenging. Lim et al. reported a Bluetooth-enabled BGM capable of FSBG data transfer via synchronization to a home telephone modem (29). Similarly to this study, they reported a daily FSBG check frequency of 1.5 per day but a more modest HbA1C reduction (-0.9). More recently, Quinn et al. reported the results of a Bluetooth-enabled BGM which synchronizes with a smartphone or tablet app and transfers data wirelessly to a cloud-based dashboard if internet connection is available. They achieved a -1.2 reduction in HbA1C compared to controls; however, data on frequency of BG monitoring was not reported (30, 31).

The degree of improvement in glycemic control reported in prior studies has not been to the same degree as was found in this study. It is possible that the more modest improvements in glycemic control in prior studies were partially attributable to inability to effectively engage patients in the intervention, lack of end-user acceptance of the BGM technology and inadequate access to communications infrastructure (32), as well as significant complexity in BGM synchronization and data transmission processes. In the present intervention, there were no such participant-dependent steps for data transmission. This facilitated a user-friendly experience for both the participants and the DBC team while allowing them to exchange glycemic data seamlessly and reliably in order to guide time sensitive telemedicine care. Additionally, there was no data plan cost to the participants, removing a potential financial barrier to its use, particularly among vulnerable populations such as that served in this study.

While this report focuses on the use of FSBG monitoring via a novel CHD, SMBG can also be performed via CGM. A recent systematic review with meta-analysis on the use of CGM in T2DM compared to FSBG monitoring, demonstrated a statistically significantly greater lowering of HbA1c with CGM (10). However, the difference in the pooled mean HbA1C levels between T2DM patients on CGM versus FSBG monitoring was modest (mean difference in HbA1C: -0.25; 95% CI: -0.45 to -0.06)(10). While this may be important from a population health perspective, from the pragmatic perspective it would not necessarily be considered clinically significant and did not achieve the magnitude of HbA1C lowering demonstrated in the present study. Moreover, persons with T2DM may face multiple barriers for the adoption of CGM technology such as lack of insurance coverage, high out of pocket cost, patient-dependent data synchronization and transmission processes, device obtrusiveness (e.g. pain, irritation, discomfort or cosmetic disapproval) and alarm fatigue (33). When future strategies to enable primary care practices to monitor and respond to remote BGM systems can be integrated within usual office workflow, it will be important to factor in features of systems such as those in the one used in this study which might be considered “low-tech” in terms of usability from the patient perspective yet function fully as a “high-tech” CHD.

While the role of the primary care provider in managing patients with T2DM on insulin regimens is growing (12), there is still limited uptake of CGM in this setting (10, 13). The evidence generated in this study demonstrates that requesting two FSBG values daily and analyzing on average 1.5 daily FSBG results can yield adequate data to guide safe and effective anti-hyperglycemic regimen adjustments targeting optimization of blood glucose control on patients with T2DM regardless of their insulin use status.

This study had several limitations. The cohort was predominantly Black, reflecting the demographics of the population cared for in our system, and included adults with uncontrolled T2DM of ages ranging from the 40s to 60s. These features may limit generalizability to other racial, ethnic or younger/older age groups. It did, however, include diverse payers - Medicaid, Medicare and Private insurances- which would support its applicability across a variety of payers. While we put forward that this technology-enabled approach to T2DM care management was successful for a variety of reasons such as intensified clinical management, glycemic pattern driven education and streamlined SMBG, we did not attempt to identify the impact of individual components of this multifaceted intervention on outcomes. These areas will be the subject of future research efforts.

In summary, this uncontrolled T2DM clinical care management and education intervention generated evidence that adults asked to check two, and who performed an average of 1.5 FSBG daily, could safely and significantly improve their glycemic control regardless of whether or not they are on insulin. The most common timing of FSBG checks was initially pre-breakfast and post-dinner and was then varied as needed to facilitate pattern management. These findings represent an important primary care SMBG strategy regardless of insulin use among persons with T2DM either not willing or who face barriers to adopting CGM, or who cannot or are unwilling to perform FSBG checks more than twice daily. The elimination of additional steps to transmit BG data beyond those typically needed to perform a FSBG check and assuring no data cost to the user should be considered in efforts to promote uptake and adoption of future technology enabled BGM system interventions.

SMBG: Self-monitoring of blood glucose.
HbA1C: Hemoglobin A1C.
T2DM: Type 2 diabetes.
BGM: Blood glucose monitoring.
FSBG: Finger-stick blood glucose.
DBC: Diabetes boot camp.
CGM: Continuous glucose monitoring.
CHD: Connected health device.

Trial Registration number: NCT02925312

Ethics approval and consent to participate: The study was reviewed and approved by the MedStar Health Research Institute’s Institutional Review Board (IRB) and was conducted from November 2014 to February 2018. Participants during the first 16 months provided written informed consent. In July 2016, the study received a Quality Improvement designation by the IRB, so informed consent for research was no longer required.

Consent to publish: All the authors had access to all study data, as well as the final manuscript, and consented for final publication.

Availability of data and material: Data are available on reasonable request.

Competing interest: Ms. Kelly Gann is a full-time employee at Biotelemetry, the maker of the blood glucose management system used in the study. Drs. Montero, Toro-Tobón and Magee, and Ms. Nassar and Ms. Youssef report no conflicts of interest relevant to this research.

Funding: MedStar Health provided funding for the salary of the co-authors, and participated in the conception and design of the study and in review of the manuscript but did not participate in the decision to submit the manuscript for publication. BioTel (Telcare) provided starter blood glucose monitoring system kits to enrolled patients and trained study staff but did not participate in the design and conduct of the study or the writing and submission of the manuscript.

Authors' Contributions: Drs Magee and Montero, and Ms. Nassar and Youseef are responsible for the conception, design and data collection of the study. Drs Toro-Tobon, Montero and Magee are responsible for data analysis, interpretation and manuscript preparation. All the authors participated in the critical revision of the manuscript.

Acknowledgements: During the early phases of the DBC, Telcare/BioTel provided meters and strips to participants. A traditional supply chain model has subsequently been implemented to cover the cost of the meters and strips, depending upon the participant’s insurance plan.

American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care [Internet]. 2018 Mar 21 [cited 2020 Jul 30]; Available from: https://care.diabetesjournals.org/content/early/2018/03/20/dci18-0007.
Rowley WR, Bezold C, Arikan Y, Byrne E, Krohe S. Diabetes. Insights from Yesterday, Today, and Future Trends. Popul Health Manag 2016 Apr. 2030;28(1):6–12. 20(.
Rawshani A, Rawshani A, Franzén S, Sattar N, Eliasson B, Svensson A-M, et al. Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2018 Aug;16(7):633–44. 379(.
Saudek CD, Derr RL, Kalyani RR. Assessing glycemia in diabetes using self-monitoring blood glucose and hemoglobin A1c. JAMA. 2006 Apr 12;295(14):1688–97.
Machry RV, Rados DV, Gregório GR de, Rodrigues TC. Self-monitoring blood glucose improves glycemic control in type 2 diabetes without intensive treatment: A systematic review and meta-analysis. Diabetes Res Clin Pract. 2018 Aug;1:142:173–87.
Schnell O, Alawi H, Battelino T, Ceriello A, Diem P, Felton A-M, et al. Self-Monitoring of Blood Glucose in Type 2 Diabetes: Recent Studies. J Diabetes Sci Technol. 2013 Mar 1;7(2):478–88.
Malanda UL, Welschen LMC, Riphagen II, Dekker JM, Nijpels G, Bot SDM. Self-monitoring of blood glucose in patients with type 2 diabetes mellitus who are not using insulin. Cochrane Database Syst Rev. 2012 Jan;18:1:CD005060.
Young LA, Buse JB, Weaver MA, Vu MB, Mitchell CM, Blakeney T, et al. Glucose Self-monitoring in Non-Insulin-Treated Patients With Type 2 Diabetes in Primary Care Settings: A Randomized Trial. JAMA Intern Med. 2017 Jul 1;177(7):920–9.
Kravarusic J, Aleppo G. Diabetes Technology Use in Adults with Type 1 and Type 2 Diabetes. Endocrinol Metab Clin North Am. 2020 Mar;49(1):37–55.
Janapala RN, Jayaraj JS, Fathima N, Kashif T, Usman N, Dasari A, et al. Continuous Glucose Monitoring Versus Self-monitoring of Blood Glucose in Type 2 Diabetes Mellitus: A Systematic Review with Meta-analysis. Cureus [Internet]. [cited 2020 Jul 30];11(9). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822918/.
Kompala T, Neinstein A. A new era: increasing continuous glucose monitoring use in type 2 diabetes. Am J Manag Care. 2019;25(4 Spec No.):SP123–6..
Davidson JA. The Increasing Role of Primary Care Physicians in Caring for Patients With Type 2 Diabetes Mellitus. Mayo Clin Proc. 2010 Dec;85(12 Suppl):S3–4.
Shrivastav M, Gibson W, Shrivastav R, Elzea K, Khambatta C, Sonawane R, et al. Type 2 Diabetes Management in Primary Care: The Role of Retrospective, Professional Continuous Glucose Monitoring. Diabetes Spectr Publ Am Diabetes Assoc. 2018 Aug;31(3):279–87.
The Office the National Coordinator of Health Information Technology. Telemedicine and Telehealth. [Internet]. [cited 2020 Jan 15]. Available from:: https://www.healthit.gov/topic/health-it-initiatives/telemedicine-and-telehealth.
Klonoff DC. Telemedicine for Diabetes: Current and Future Trends. J Diabetes Sci Technol. 2015 Dec 17;10(1):3–5.
El Amrani L, Oude Engberink A, Ninot G, Hayot M, Carbonnel F. Connected Health Devices for Health Care in French General Medicine Practice: Cross-Sectional Study. JMIR MHealth UHealth [Internet]. 2017 Dec 21 [cited 2020 Jul 30];5(12). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5754567/.
McDonnell ME. Telemedicine in Complex Diabetes Management. Curr Diab Rep. 2018;24(7):42. 18(.
Flodgren G, Rachas A, Farmer AJ, Inzitari M, Shepperd S. Interactive telemedicine: effects on professional practice and health care outcomes. Cochrane Database Syst Rev [Internet]. 2015 Sep 7 [cited 2020 Jul 30];2015(9). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6473731/.
Faruque LI, Wiebe N, Ehteshami-Afshar A, Liu Y, Dianati-Maleki N, Hemmelgarn BR, et al. Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials. CMAJ Can Med Assoc J J Assoc Medicale Can. 2017 Mar;6(9):E341–64. 189(.
Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden K, et al. Mobile Health Applications for Self-Management of Diabetes [Internet]. Agency for Healthcare Research and Quality; 2018 May [cited 2020 Jul 30]. Available from: https://effectivehealthcare.ahrq.gov/topics/diabetes-mobile-devices/technical-brief.
Kim Y, Park J-E, Lee B-W, Jung C-H, Park D-A. Comparative effectiveness of telemonitoring versus usual care for type 2 diabetes: A systematic review and meta-analysis: J Telemed Telecare [Internet]. 2018 Jul 17 [cited 2020 Jul 30]; Available from: https://journals.sagepub.com/doi/10.1177/1357633X18782599.
Magee MF, Baker KM, Fernandez SJ, Huang C-C, Mete M, Montero AR, et al. Redesigning ambulatory care management for uncontrolled type 2 diabetes: a prospective cohort study of the impact of a Boot Camp model on outcomes. BMJ Open Diabetes Res Care. 2019 Nov 1;7(1):e000731.
International Diabetes Federation. Diabetes Atlas [Internet]. 7th ed. International Diabetes Federation; 2015 [cited 2020 Oct 28]. Available from: https://www.idf.org/e-library/epidemiology-research/diabetes-atlas/13-diabetes-atlas-seventh-edition.html.
Vijan S. Type 2 Diabetes. Ann Intern Med. 2019 Nov;5(9):ITC65–80. 171(.
Vincze G, Barner JC, Lopez D. Factors Associated With Adherence to Self-Monitoring of Blood Glucose Among Persons With Diabetes. Diabetes Educ. 2004 Jan;30(1)(1):112–25.
Kalergis M, Nadeau J, Pacaud D, Yared Z, Yale J-F Accuracy and Reliability of Reporting Self-monitoring of Blood Glucose Results in Adults With Type 1 and Type 2 Diabetes. Can J Diabetes. 2006 Jan 1;30(3):241–7.
Ajjan R, Slattery D, Wright E. Continuous Glucose Monitoring: A Brief Review for Primary Care Practitioners. Adv Ther. 2019;36(3):579–96.
Association AD. 7. Diabetes Technology: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021 Jan 1;44(Supplement 1):S85–99.
Lim S, Kang SM, Kim KM, Moon JH, Choi SH, Hwang H, et al. Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes. Acta Diabetol. 2016 Apr 1;53(2):189–98.
Quinn CC, Shardell MD, Terrin ML, Barr EA, Ballew SH, Gruber-Baldini AL. Cluster-Randomized Trial of a Mobile Phone Personalized Behavioral Intervention for Blood Glucose Control. Diabetes Care. 2011 Sep 1;34(9):1934–42.
Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol Ther. 2008 Jun;10(3):160–8.
Lee JY, Chan CKY, Chua SS, Ng CJ, Paraidathathu T, Lee KKC, et al. Telemonitoring and Team-Based Management of Glycemic Control on People with Type 2 Diabetes: a Cluster-Randomized Controlled Trial. J Gen Intern Med. 2020;35(1):87–94.
Engler R, Routh TL, Lucisano JY. Adoption Barriers for Continuous Glucose Monitoring and Their Potential Reduction With a Fully Implanted System: Results From Patient Preference Surveys. Clin Diabetes. 2018 Jan;36(1)(1):50–8.

CONSORTChecklistMonteroetal.pdf

Download PDF

Editorial decision: Minor revision
03 Aug, 2021
Review #1 received at journal
29 Jul, 2021
Review #2 received at journal
29 Jul, 2021
Reviewer #2 agreed at journal
17 Jul, 2021
Reviewer #1 agreed at journal
17 Jul, 2021
Reviewers invited by journal
04 Jul, 2021
Submission checks completed at journal
30 Jun, 2021
Editor assigned by journal
29 Jun, 2021
Editor invited by journal
28 Jun, 2021

You are reading this latest preprint version

Implications of Remote Monitoring Technology in Optimizing Traditional Self-Monitoring of Blood Glucose in Adults with T2DM in primary care

Status:

Version 1

Abstract

Figures

Background

Methods

Study Population and Data Collection

Statistical Analysis

Results

Discussion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1