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Emerging Technologies: Data Systems and Devices| October 14 2020
Six Months of Hybrid Closed-Loop Versus Manual Insulin Delivery With Fingerprick Blood Glucose Monitoring in Adults With Type 1 Diabetes: A Randomized, Controlled Trial
Sybil A. McAuley
1Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
2Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
Melissa H. Lee
Barbora Paldus;
Barbora Paldus
Sara Vogrin;
Sara Vogrin
Martin I. de Bock;
Martin I. de Bock
3Department of Endocrinology and Diabetes, Perth Children's Hospital, Nedlands, Western Australia, Australia
4Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
5School of Paediatrics and Child Health, University of Western Australia, Nedlands, Western Australia, Australia
6Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
Mary B. Abraham;
Mary B. Abraham
Leon A. Bach;
Leon A. Bach
7Department of Endocrinology and Diabetes, The Alfred, Melbourne, Victoria, Australia
8Department of Medicine (Alfred Medical Research and Education Precinct), Monash University, Melbourne, Victoria, Australia
Morton G. Burt;
Morton G. Burt
9Southern Adelaide Diabetes and Endocrine Services, Flinders Medical Centre, Bedford Park, South Australia, Australia
10College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
Neale D. Cohen
11Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
Peter G. Colman;
Peter G. Colman
12Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, Victoria, Australia
Elizabeth A. Davis
Christel Hendrieckx;
Christel Hendrieckx
13School of Psychology, Deakin University, Geelong, Victoria, Australia
14Australian Centre for Behavioural Research in Diabetes, North Melbourne, Victoria, Australia
D. Jane Holmes-Walker
15Department of Diabetes and Endocrinology, Westmead Hospital, Westmead, New South Wales, Australia
16Sydney Medical School, University of Sydney, Camperdown, New South Wales, Australia
Joey Kaye;
Joey Kaye
17Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
Anthony C. Keech
18NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
Kavita Kumareswaran;
Kavita Kumareswaran
Richard J. MacIsaac
Roland W. McCallum;
Roland W. McCallum
19Department of Diabetes and Endocrinology, Royal Hobart Hospital, Hobart, Tasmania, Australia
Catriona M. Sims;
Catriona M. Sims
Jane Speight
Stephen N. Stranks;
Stephen N. Stranks
Vijaya Sundararajan;
Vijaya Sundararajan
20Department of Public Health, La Trobe University, Melbourne, Victoria, Australia
Steven Trawley
21The Cairnmillar Institute, Hawthorn East, Victoria, Australia
Glenn M. Ward;
Glenn M. Ward
Alicia J. Jenkins
Timothy W. Jones
David N. O'Neal
Corresponding author: David N. O'Neal, dno@unimelb.edu.au
Australian JDRF Closed-Loop Research Group
A complete list of the members of the Australian JDRF Closed-Loop Research Group can be found in the supplementary material online.
Diabetes Care 2020;43(12):3024–3033
https://doi.org/10.2337/dc20-1447
Sybil A. McAuley, Melissa H. Lee, Barbora Paldus, Sara Vogrin, Martin I. de Bock, Mary B. Abraham, Leon A. Bach, Morton G. Burt, Neale D. Cohen, Peter G. Colman, Elizabeth A. Davis, Christel Hendrieckx, D. Jane Holmes-Walker, Joey Kaye, Anthony C. Keech, Kavita Kumareswaran, Richard J. MacIsaac, Roland W. McCallum, Catriona M. Sims, Jane Speight, Stephen N. Stranks, Vijaya Sundararajan, Steven Trawley, Glenn M. Ward, Alicia J. Jenkins, Timothy W. Jones, David N. O'Neal, Australian JDRF Closed-Loop Research Group; Six Months of Hybrid Closed-Loop Versus Manual Insulin Delivery With Fingerprick Blood Glucose Monitoring in Adults With Type 1 Diabetes: A Randomized, Controlled Trial. Diabetes Care 1 December 2020; 43 (12): 3024–3033. https://doi.org/10.2337/dc20-1447
To investigate glycemic and psychosocial outcomes with hybrid closed-loop (HCL) versus user-determined insulin dosing with multiple daily injections (MDI) or insulin pump (i.e., standard therapy for most adults with type 1 diabetes).
Adults with type 1 diabetes using MDI or insulin pump without continuous glucose monitoring (CGM) were randomized to 26 weeks of HCL (Medtronic 670G) or continuation of current therapy. The primary outcome was masked CGM time in range (TIR; 70–180 mg/dL) during the final 3 weeks.
Participants were randomized to HCL (n = 61) or control (n = 59). Baseline mean (SD) age was 44.2 (11.7) years, HbA1c was 7.4% (0.9%) (57 [10] mmol/mol), 53% were women, and 51% used MDI. HCL TIR increased from (baseline) 55% (13%) to (26 weeks) 70% (10%) with the control group unchanged: (baseline) 55% (12%) and (26 weeks) 55% (13%) (difference 15% [95% CI 11, 19]; P < 0.0001). For HCL, HbA1c was lower (median [95% CI] difference −0.4% [−0.6, −0.2]; −4 mmol/mol [−7, −2]; P < 0.0001) and diabetes-specific positive well-being was higher (difference 1.2 [95% CI 0.4, 1.9]; P < 0.0048) without a deterioration in diabetes distress, perceived sleep quality, or cognition. Seventeen (9 device-related) versus 13 serious adverse events occurred in the HCL and control groups, respectively.
In adults with type 1 diabetes, 26 weeks of HCL improved TIR, HbA1c, and their sense of satisfaction from managing their diabetes compared with those continuing with user-determined insulin dosing and self-monitoring of blood glucose. For most people living with type 1 diabetes globally, this trial demonstrates that HCL is feasible, acceptable, and advantageous.
Despite recent significant therapeutic advances for type 1 diabetes, only modest improvements in glycemia, health, and quality of life have been achieved (1,2). Together with glucose monitoring, intensive insulin therapy via multiple daily injections (MDI) or continuous subcutaneous insulin infusion (pump) is a core strategy of current type 1 diabetes management. However, maintaining optimal glycemia with manual determination of insulin dosing remains challenging, and recommended targets are met by a minority of people with type 1 diabetes (3,4). Closed-loop (CL) systems with automated insulin delivery help address these challenges. Hybrid CL (HCL) systems involve automated basal delivery and a user-initiated component for (meal or high glucose correction) boluses (5). Meta-analyses of early CL trials suggest improved glycemia compared with manual insulin dose determination (6,7). CL research has greatly increased recently (8). Randomized trials have shown glucose benefits with HCL over sensor-augmented pump therapy for 3 and 6 months in outpatient settings (9,10). However, most previous HCL studies have incorporated sensor-augmented pumps as the comparator (6,7,9,10) and are of limited relevance to the majority of people with type 1 diabetes globally. Continuous glucose monitoring (CGM) uptake has been relatively low worldwide, being highest in the U.S., where ∼29% of adults with type 1 diabetes were CGM users in 2018 (2), and lower in other countries with limited reimbursement by national authorities or health insurance companies. For example, the Australasian Diabetes Data Network reported in 2019 that only 13% of adults used CGM (11). Consequently, most adults with type 1 diabetes managed with MDI or pump undertake home self-monitoring of blood glucose (SMBG).
Furthermore, studies to date have focused almost exclusively on metabolic outcomes in spite of recognition that psychological and cognitive factors are important in their own right and can also influence glycemia, with associations between increasing diabetes-specific distress and dysglycemia (12). In keeping, some studies have shown that use of diabetes technologies (including short-term use of HCL) is associated with reductions in diabetes distress (13,14). Among adults with type 1 diabetes, inadequate sleep quality has been associated with increased fear of hypoglycemia, greater nocturnal glycemic variation, and reduced insulin sensitivity (15,16), with experimentally induced sleep deprivation producing a prolonged state of cognitive impairment and hypoglycemic symptoms postrecovery (17). Therefore, HCL systems will be of greatest benefit if, in addition to improving glycemia, they also substantially reduce the psychological and cognitive burden of living with type 1 diabetes. Messer et al. (18) have conducted human-factors research in youth using Medtronic 670G. Corresponding research involving adults is lacking, with longer-term intervention trials needed focusing on both glycemia and holistic outcomes such as psychosocial well-being, sleep quality, and cognitive function.
The first commercial HCL device is the MiniMed 670G system, available since 2017 after U.S. Food and Drug Administration approval based upon the findings from an uncontrolled, pivotal study (19). Randomized, controlled trial evidence has not yet been published. Therefore, we performed a randomized, controlled trial comparing HCL with the MiniMed 670G system versus usual care (manually determined insulin delivery by MDI or pump without real-time CGM) over 26 weeks. We examined glucose outcomes, psychosocial outcomes, cognition, and subjective sleep quality.
We conducted an open-label, randomized, controlled, parallel-group clinical trial (Australian New Zealand Clinical Trials Registry ACTRN12617000520336) at seven tertiary hospitals in Australia comparing 26 weeks of HCL therapy with continuing standard diabetes care (without real-time CGM). The trial protocol (20) was approved by a central Human Research Ethics Committee (St Vincent's Hospital Melbourne), and governance was provided at each participating center. An independent Data Safety and Monitoring Board (DSMB) provided trial oversight.
The trial involved 16 study visits, including run-in and intervention periods. The run-in period was at least 5 weeks, with duration dependent on participants' individual preexisting carbohydrate counting and diabetes self-management knowledge determining their training requirements. During run-in, individualized education was provided by diabetes nurse educators and dietitians regarding diabetes self-management (including carbohydrate counting and use of an insulin bolus dose calculator). Participants using MDI were given an Accu-Chek Aviva Expert meter (Roche Diagnostics, Mannheim, Germany) with in-built bolus dose calculator and education regarding its use. Participants using pumps used the pump's bolus calculator. After education, participants undertook 3 weeks of baseline masked CGM, with a further 1–2 weeks if data were not available for ≥70% of the time. After run-in, participants were randomly assigned to use an HCL system (HCL group) or to continue their current diabetes therapy (control group) for a 26-week period.
The HCL group was trained in the use of the MiniMed 670G system (Medtronic, Northridge, CA) consisting of a glucose sensor (Enlite 3) and transmitter (GST3c MiniLink), coupled with an insulin pump incorporating a modified proportional integral derivative algorithm with insulin feedback (21). Target glucose is 120 mg/dL, with an optional increased temporary target of 150 mg/dL. Either insulin aspart or insulin lispro were used. Insulin boluses to manage meal-related insulin requirements and correction of elevated glucose levels were initiated by the participant, who entered an estimate of the amount of carbohydrate to be consumed into the pump and checked their capillary blood glucose level; an individualized insulin bolus was then advised by the pump, with delivery initiated by the participant. Control participants continued using their own personal insulin delivery device for 26 weeks, in conjunction with a bolus dose calculator (integrated within either their insulin pump or the glucose meter provided during run-in) to assist with meal-related dose estimation. All participants wore masked CGM (Guardian Sensor 3; Medtronic) to collect study outcome measurements at three time points: baseline prerandomization (for 3 weeks), midstudy (for 2 weeks), and study end (for 3 weeks). For participants randomized to HCL, the masked-CGM sensor was worn in addition to the 670G system's real-time CGM. Medtronic processed deidentified masked-CGM recordings using internal proprietary software and provided the raw data set to the research team for analysis. After completion of the final participant, Medtronic provided the deidentified CSV files of HCL pump downloads from randomization to study end to the research team for analysis.
Adverse events were collected throughout the trial. Reportable adverse events included serious adverse events, severe hypoglycemia (defined as hypoglycemia requiring assistance from another person to administer carbohydrate or glucagon or take other corrective actions), ketoacidosis, and adverse events occurring in association with a trial device.
Inclusion criteria were: aged between 25 and 75 years; a clinical diagnosis of type 1 diabetes for at least 1 year; HbA1c level ≤10.5% (≤91 mmol/mol); and MDI or insulin pump use. Purposive sampling ensured at least 40% of participants were on MDI or insulin pump. Exclusion criteria were: current real-time CGM use and use of any noninsulin glucose-lowering agent within the preceding 3 months. Complete eligibility criteria are provided in Supplementary Table 1.
Randomization of participants to HCL or control post–run-in was via a central electronic database, with 1:1 computer-generated group allocation using minimization with three stratification variables: proportion of time in glucose range 70–180 mg/dL prerandomization (≤50% or >50%); insulin delivery modality at enrollment (MDI or pump); and trial site. Clinical site investigators were responsible for trial implementation and medical care during the trial. Group allocation generation was designed by an investigator without trial involvement at the clinical sites. Allocation was masked to the trial statistical team and to all investigators undertaking data and laboratory analyses.
The primary outcome was the percentage of time that the masked CGM glucose measurements, at 23–26 weeks postrandomization, were in the target range of 70–180 mg/dL, termed time in range (TIR). Secondary glucose outcomes included: the proportion of time that glucose was above and below thresholds outside the target range, mean glucose, glucose SD, coefficient of variation (CV), and HbA1c. Glucose metrics were calculated separately for overall (0000–2359 h), daytime (0600–2359 h), and nighttime (0000–0559 h). HbA1c measurement used laboratory Bio-Rad D-100 analyzers certified by the NGSP as having traceability to the DCCT. These glucose outcomes are consistent with those recommended by current consensus guidelines (22,23). Intermediate-term glycemia (over ∼2 weeks) was measured by serum 1,5-anhydroglucitol (GlycoMark, Inc., New York, NY).
Nonglucose-related secondary outcomes included: clinical measures (change in total daily dose of insulin and basal/bolus proportions, change in insulin-to-carbohydrate ratio, and change in body weight); psychosocial measures (diabetes treatment satisfaction [Diabetes Treatment Satisfaction Questionnaires (DTSQs)], diabetes distress [Problem Area in Diabetes scale (PAID)], diabetes-specific positive well-being [four-item subscale of the W-BQ28], and diabetes-specific quality of life [DAWN2 Impact of Diabetes Profile (DIDP)]); cognitive functioning (Prospective and Retrospective Memory Questionnaire [PRMQ]), and subjective sleep quality (Pittsburgh Sleep Quality Index [PSQI]) (see published protocol [20]). Safety outcomes were frequency of severe hypoglycemia, diabetic ketoacidosis, adverse events related to the trial device, and any other untoward medical occurrence. Health economic outcomes, electrocardiograph profiles, and biomarkers of novel vascular risk factors will be reported separately.
A sample size of 120 randomized in a 1:1 ratio (HCL/control) provides 80% power with a type I error rate (two-sided) of 5% to reject the null hypothesis of no between-group difference in the proportion of TIR, assuming that the proportion of TIR with intervention would be at least 5% higher than in the control group, with a common SD of 9%, allowing for a dropout rate of 10%.
Data from all randomized participants were analyzed on an intention-to-treat basis. Missing data were replaced under a "missing at random" assumption and were multiply imputed using a multivariate normal regression imputation method, with imputations performed separately for each treatment arm. Sensitivity analyses of the primary outcome included changing the missing data replacement assumption to "missing completely at random" and to "missing not at random" as well as to the per-protocol population. The primary and main secondary outcomes are presented in this study. In addition, a prespecified subgroup analysis was undertaken according to baseline insulin delivery modality. Other secondary outcomes were analyzed separately after these main analyses were performed. Analyses of data for key outcomes were validated by an independent biostatistician.
To test for the effect of treatment allocation on outcomes, an ANCOVA was conducted, adjusting for treatment arm and baseline percentage time in target range. Where ANCOVA assumptions were violated, a rank-sum test was performed. Safety outcomes were analyzed as counts of events for each treatment group and the proportion of participants experiencing at least one event in each treatment group. The statistical analysis plan was published prior to the completion of the study visits (24). All P values are two-tailed. Analyses were performed with STATA version 15.1, R version 3.5.2, and R Studio version 1.2.5033.
Role of the Funding Sources
JDRF Australia provided input into the trial design. The funders of the trial had no role in data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all of the data in the trial and had final responsibility for the decision to submit for publication.
Trial participants (n = 150) were recruited between 26 April 2017 and 24 January 2019. Between 28 June 2017 and 2 May 2019, a total of 120 participants were randomized to the HCL group (n = 61) or the control group (n = 59). Sixty-four (53%) of the participants were women. Participants' ages ranged from 25 to 70 years, their duration of diabetes from 1 to 59 years, and their HbA1c at enrollment from 5.7 to 10.4% (39–90 mmol/mol). Insulin pumps were being used by 59 participants (49%). The baseline characteristics of the HCL and control groups were balanced (Table 1). In total, 110 randomized participants completed the trial (Supplementary Fig. 1). Missing data for each of the outcomes are presented in Supplementary Table 2.
Participant baseline characteristics
HCL group (n = 61) .
Control group (n = 59) .
Age, years 43.7 (11.7) 44.7 (11.8)
Women 33 (54) 31 (53)
Men 28 (46) 28 (47)
Socioeconomic status of area of residence in upper 50th percentile 48 (79) 52 (88)
BMI, kg/m2 26.8 (5.3) 26.0 (4.0)
Diabetes duration, years 24.0 (12.0) 24.1 (12.5)
Insulin delivery modality
Pump 31 (51) 28 (47)
MDI 30 (49) 31 (53)
Insulin TDD/weight (units/kg) 0.51 (0.41, 0.63) 0.54 (0.45, 0.66)
HbA1c at enrollment
% 7.8 (1.1) 7.7 (0.9)
mmol/mol 62 (12) 61 (10)
HbA1c at randomization
mmol/mol 57 (10) 58 (9)
C-peptide, pmol/mL 0.01 (0, 0.03) 0 (0, 0.01)
Carbohydrate counting 51 (84) 51 (86)
Microvascular complications: history 19 (31) 15 (25)
Macrovascular complications: history 4 (7) 6 (10)
Diabetic ketoacidosis: history of any event during the preceding 12 months 2 (3) 2 (3)
Severe hypoglycemia: history of any event during the preceding 12 months 8 (13) 6 (10)
Severe hypoglycemia: number of reported events during the preceding 12 months* 1.5 (1, 5) 2 (1, 4)
Hypoglycemia awareness (Gold score) 3 (2, 4) 2 (2, 4)
Severe diabetes distress (PAID score ≥40) 15 (25) 12 (20)
Inadequate sleep quality (PSQI >5) 34 (55) 31 (52)
Continuous data are presented as mean (SD) or median (IQR). Categorical data are presented as frequency (%).
TDD, total daily dose.
Within subgroup of participants who experienced any severe hypoglycemia event.
In the primary analysis of the entire trial population (Table 2), the mean (SD) percent TIR increased for those assigned to HCL from 55% (13%) at baseline to 70% (10%) at study end and remained unchanged in control participants, with 55% (12%) at baseline and 55% (13%) at study end (difference 15% [95% CI 11, 19]; P < 0.0001). There was no effect of sex on primary outcome. In the complete case population analysis (n = 100), using a missing-completely-at-random assumption, the between-arm difference in TIR at study end was 15% (14, 16) (P < 0.0001). Sensitivity analyses of the primary outcome showed similar results: the difference between arms after adjustment for stratification variables was 14.5% (10.7, 18.2), and with missing data imputed under a missing-not-at-random assumption, the differences were 16.9% (12.8, 21.0) and 12.7% (8.7, 16.8) for −1 SD and 1 SD, respectively.
Primary and secondary glucose and clinical and psychosocial outcomes
Baseline prerandomization .
Study end .
Difference HCL − control .
P value .
Percentage of time glucose 70–180 mg/dL* 55.2 (13.4) 54.5 (11.8) 69.9 (9.5) 54.7 (12.7) 14.8 (11.0, 18.5) <0.0001
Percentage of time glucose 70–140 mg/dL* 34.5 (11.1) 33.1 (11.9) 44.1 (8.5) 33.6 (12.0) 9.7 (6.3, 13.2) <0.0001
Percentage of time glucose <70 mg/dL† 4.6 (2.0, 7.6) 4.0 (2.3, 6.3) 1.8 (1.1, 3.4) 3.8 (2.9, 5.2) −2.0 (−2.5, −1.3) <0.0001
Percentage of time glucose >180 mg/dL* 39.2 (15.4) 40.7 (13.7) 27.6 (9.5) 40.3 (14.4) −12.0 (−16.1, −7.9) <0.0001
Percentage of time glucose >200 mg/dL† 11.6 (6.3, 20.3) 11.9 (8.9, 20.5) 5.7 (3.5, 8.3) 13.3 (9.8, 17.7) −7.5 (−5.6, −9.4) <0.0001
Percentage of time glucose >250 mg/dL† 3.7 (1.9, 7.4) 3.9 (1.7, 7.7) 1.3 (0.5, 2.8) 4.3 (2.8, 6.8) −2.9 (−2.1, −3.5) <0.0001
Mean glucose, mg/dL* 169 (27) 173 (25) 157 (14) 171 (23) −13 (−20, −7) 0.00014
Glucose SD* 67 (13) 68 (13) 54 (11) 67 (13) −13 (−16, −7) <0.0001
Glucose CV* 39.7 (6.0) 39.4 (5.0) 34.7 (4.5) 39.3 (5.4) −4.7 (−6.5, −2.9) <0.0001
Fasting capillary blood glucose* 167 (76) 166 (72) 155 (54) 171 (76) −18 (−29, −7) 0.0017
HbA1c, %* 7.4 (0.9) 7.5 (0.8) 7.0 (0.6) 7.4 (0.8) −0.4 (−0.6, −0.2) <0.0001
HbA1c, mmol/mol* 57 (10) 58 (9) 53 (7) 57 (9) −4 (−7, −2) <0.0001
1,5-Anhydroglucitol, μg/mL† 3.4 (2.2, 4.6) 3.0 (1.7, 5.4) 4.9 (3.4, 6.8) 3.3 (1.8, 5.2) 1.6 (0.7, 2.3) 0.00046
Insulin TDD, units† 38 (32, 52) 40 (31, 51) 38 (31, 46) 42 (34, 49) — —
Change in insulin TDD, units† — — −1.2 (−5.8, 3.1) −0.5 (−4.8, 2.9) −0.6 (−2.9, 2.5) 0.64
Insulin TDD/weight, units/kg† 0.51 (0.41, 0.63) 0.54 (0.45, 0.66) 0.50 (0.43, 0.57) 0.54 (0.48, 0.60) — —
Change in insulin TDD /weight, units/kg† — — −0.01 (−0.10, 0.03) −0.02 (−0.10, 0.04) −0.01 (−0.04, 0.03) 0.85
Basal insulin proportion, %* 51 (13) 50 (13) 46 (14) 52 (12) — —
Change in basal insulin proportion, %* — — −5.4 (16.9) 1.9 (8.2) −6.7 (−11.1, −2.3) 0.0034
ICR† 9.8 (7.3, 13.5) 9.7 (7.3, 12) 9.1 (6.8, 11.9) 8.7 (7.6, 10.5) — —
Change in ICR† ·· ·· −1.2 (−2.4, 0.0) 0.0 (−0.8, 0.0) −0.8 (−1.4, −0.1) 0.0078
Body weight, kg† 79.4 (66.7, 89.3) 74.4 (65.8, 86.0) 81.3 (68.4, 91.7) 74.3 (69.0, 83.6) — —
Change in body weight, kg† — — 0.6 (−1.9, 2.1) 0.7 (−0.7, 1.5) −0.1 (−1.1, 0.9) 0.77
Diabetes treatment satisfaction: DTSQs* 26.0 (5.1) 26.1 (5.1) 28.2 (5.9) 27.3 (5.1) 1.0 (−0.8, 2.7) 0.29
Diabetes distress: PAID‡ 20.0 (12.5, 38.8) 17.5 (11.9, 34.4) 16.7 (10.2, 27.4) 21.2 (9.5, 36.2) −17 (−33, 3) 0.10
Impact of diabetes on quality of life: DIDP* 4.7 (0.8) 4.7 (0.7) 4.5 (0.9) 4.8 (0.7) −0.3 (−0.6, 0.0) 0.023
Diabetes-specific positive well-being: four-item subscale of W-BQ28* 6.5 (2.4) 6.9 (2.4) 7.8 (2.4) 6.8 (2.6) 1.2 (0.4, 1.9) 0.0048
Prospective memory: PRMQ Prospective† 17 (13, 22) 17 (14, 22) 17 (14, 20) 18 (15, 24) −1.0 (−3.0, 0.0) 0.11
Retrospective memory: PRMQ Retrospective† 14 (11, 18) 13 (11, 17) 15 (11, 18) 15 (12, 17.5) 0.0 (−2.0, 2.0) 0.87
Perceived sleep quality: PSQI* 6.4 (3.2) 6.1 (3.2) 6.5 (3.1) 5.8 (3.0) 0.5 (−0.5, 1.5) 0.34
ICR, insulin-to-carbohydrate ratio; TDD, total daily dose.
Results presented as mean (SD) or mean difference (95% CI); analysis using ANCOVA with adjustment for baseline value.
Results presented as median (IQR) or median difference (95% CI); analysis using rank-sum test.
Results presented as median (IQR) or percentage difference (95% CI); analysis using ANCOVA with adjustment for baseline value with outcome log-transformed. Glucose levels are presented in milligrams per deciliter.
The treatment effect was evident at 3 months (Supplementary Table 3). All secondary glucose outcomes at 26 weeks favored the intervention group, with less time in each of the low and high glucose ranges, lower mean glucose, lower glucose SD and CV, lower HbA1c, and higher 1,5-anhydroglucitol levels compared with standard therapy (Table 2 and Fig. 1).
Glucose levels with HCL vs. control. A shows sensor glucose during the final 3 weeks of the trial. Lines denote median values, and shaded regions represent IQRs. Participants randomized to HCL therapy are shown by a red solid line; participants randomized to control (continuation of usual diabetes therapy) are shown by a blue dashed line. B shows HbA1c. Boxes represent HbA1c level at randomization, mid-study, and study end among participants assigned either HCL therapy (red) or control (blue). The lines represent the medians, and the bottom and top of each box represent the 25th and 75th percentiles.
Total daily insulin dose did not change between randomization and study end in either group, and there was no between-arm difference at study end. There was a larger mean (SD) decrease in proportional basal insulin use with HCL (HCL −5.4% [16.9%] vs. control 1.9% [8.2%]; P = 0.0034) and a larger increase in proportional bolus insulin use by study end (HCL 5.0% [17.0%] vs. control −1.6% [8.1%]; P = 0.0067). Body weight did not change between randomization and study end for either group. There was no between-arm difference in change in body weight at study end (P = 0.77).
Psychosocial, cognitive, and sleep quality outcomes are presented in Table 2. At 26 weeks, compared with the control group, HCL participants had better diabetes-specific positive well-being (four-item subscale of W-BQ28: 1.2 [0.4, 1.9]; P = 0.0048) and better diabetes-specific quality of life (DIDP: −0.3 [−0.6, 0.0]; P = 0.023). Diabetes treatment satisfaction, diabetes distress, prospective or retrospective cognitive functioning, and subjective sleep quality did not differ at study end.
The total number of study visits (in-person, e-mail, and phone) for HCL participants was greater than for the control group (median [interquartile range (IQR)] 31 [23, 38] vs. 19 [15, 24] visits; P < 0.001) as was time spent with HCL participants for study activities, education, and review of pump settings compared with control participants (27 [21, 32] vs. 14 [11, 19] hours; P < 0.001).
Compared with those already using pumps, participants using MDI at baseline required twice as long to achieve initial HCL activation (median [IQR] 28 [17, 35] days vs. 14 [9, 20] days, respectively). In the prespecified subgroup analyses by baseline insulin delivery modality (MDI or pump), both subgroups independently each had similar, significant glucose benefits from HCL (Fig. 2).
Subgroup analysis by baseline insulin delivery. Forest plot of differences in sensor glucose metrics at study end between participants assigned to HCL intervention vs. control, presented by insulin delivery at enrollment (MDI, aqua; pump, purple). Percent time glucose mg/dL. *Lines represent mean difference with adjustment for baseline values (95% CI). #Lines represent median difference (95% CI).
In the prespecified CGM analysis with daytime and night analyzed separately, the effect of HCL was evident for both periods with higher TIR, less time in hyperglycemia and hypoglycemia, and lower mean glucose, glucose SD, and CV. A slightly larger effect of HCL was seen at night than daytime for these parameters, other than mean glucose (Supplementary Fig. 2).
Analysis of CGM and insulin delivery data downloaded from the study pumps indicate that TIR increased to 70% postrandomization, which persisted until study end (Supplementary Fig. 4), and median (IQR) percent time CGM use was 84% (77, 89) with percent CGM time in HCL of 89% (84, 93). Supplementary Table 4 summarizes the CGM data pre- and post-HCL activation, showing that the increase in TIR occurred shortly following HCL activation. Overall, the median (IQR) percent time CGM use from first HCL activation to study end was 91% (84, 94), and percent CGM time in HCL was 94% (89, 97). For those assigned to HCL, the median number of calibrations per day was 3.5 (3, 4).
Safety outcomes were monitored by the DSMB; no trial intervention was undertaken. The number of serious adverse events after randomization by assigned group was 17 and 13 in the HCL and control groups, respectively (Table 3). The number of severe hypoglycemia events was eight and seven in the HCL and control groups, respectively. Overall, nine adverse events related to the trial device: four were episodes of severe hypoglycemia (two relating to the CL algorithm; one after a prandial bolus dose; and one overnight during open-loop insulin delivery with low-glucose suspend setting on); one was diabetic ketoacidosis; two were ketosis secondary to presumed insulin delivery line failure; one was ketosis after multiple pump suspensions; and one related to insulin infusion set dislodgement.
Safety outcomes: serious adverse events during randomized period
Any serious adverse event
Number of events 17 13
Number of participants (%) 13 (21) 9 (15)
Number of events per 100 person-years 56 44
Number of events* 8 7
Number of events related to study device 4 —
Number of participants (%) 6 (10) 3 (5)
Number of participants (%) 1 (2) 2 (3)
Any other untoward medical occurrence
Including number of events related to study device.
In this report of the first randomized, controlled trial with the MiniMed 670G, we found that 26 weeks of HCL improved CGM TIR by 15 percentage points compared with manual insulin dosing and SMBG for adults with type 1 diabetes. The comparator represents the current standard of resource provision for the majority of people with type 1 diabetes globally (25). The 15% difference in TIR translates to 3.6 additional hours per day within the healthy glucose range for HCL users compared with the control group. This TIR difference was achieved by reductions in time above and below predefined high and low glucose thresholds, respectively, with HCL improving all CGM parameters after the 26-week intervention. These improvements in CGM parameters were significant when analyzed over the entire 24 h, as well as during daytime and nighttime separately.
Analysis of downloaded pump data revealed that CGM use, HCL activation, and the TIR were durable. This contrasts with retrospective audits of U.S. clinic data in which significant discontinuation rates were observed (26–28). Differences in outcomes between our study and the aforementioned audits may relate to participant selection, the intensive education (including training on responding to alarms), level of health care professional and technical support, unrestricted access to consumables, and improvements in sensor configuration following the initial rollout of the technology.
The modest reduction in HbA1c of 0.4% (4 mmol/mol) at 26 weeks reflected the observed reduction in both hyper- and hypoglycemia, as well as near-target average prerandomization HbA1c. The improvement (increase) in 1,5-anhydroglucitol levels likely reflects diminished hyperglycemia, as the circulating levels of this naturally occurring metabolically inert polyol are lost via the urine with hyperglycemia. Importantly, differences in all measures of glycemia (CGM, 1,5-anydroglucitol, and HbA1c), which vary in location (interstitial fluid, serum, and hemoglobin, respectively) and time frame, are concordant and favor the HCL versus control group.
The TIR of 70% observed with HCL insulin delivery in this study was similar to the 72% TIR reported by the MiniMed 670G safety study (19), as well as that reported in 6-month studies by Kovatchev et al. (9) (with 64% TIR using a Roche Accu-Chek Spirit insulin pump or a Dexcom G4 or G5 sensor with inControl AP software) and Brown et al. (10) (with 71% TIR using a system comprising a Tandem Diabetes t:slim X2 insulin pump, a Dexcom G6 sensor, and the Control-IQ algorithm). The relatively greater improvement in overnight glycemia was also consistent with results of shorter-duration studies with various systems reported in two meta-analyses (6,7). This suggests that it is the conceptual approach of automated insulin delivery that has the major impact on glycemia rather than any specific algorithm or platform.
The control group in this trial reflects current resource provision for glucose management for the majority of adults living with type 1 diabetes. In Australia, where the trial was conducted, SMBG is the only subsidized monitoring option (regardless of mode of insulin delivery, with exception of specific groups [e.g., <21 years of age and pregnancy, which were exclusions in the present trial]). TIR improvement with HCL was independent of MDI or insulin pump use by participants at enrollment. The equivalent outcomes observed in each of these subgroups supports the benefit of HCL re-gardless of prior exposure to insulin pump therapy. Provision of detailed education during run-in regarding carbohydrate counting and diabetes self-management and the tools to facilitate prandial insulin dose calculation to all participants ensured that study outcomes were not influenced by differences in the ability to self-manage prandial insulin dosing. Our findings highlight that, with guidance and support, adults with type 1 diabetes who had less pretrial exposure to diabetes technologies experienced similar glycemic benefits with HCL to those who had previously used insulin pumps, which refutes prior assumptions made by clinicians as well as experiences of participants in a shorter trial, some of whom expressed that HCL could be "too big a step" (29,30).
Notably, even in a sample with near-target baseline HbA1c, the improvement in glycemia occurred with an increase in diabetes-specific positive well-being. Participants allocated HCL felt more positive about their diabetes and experienced a greater sense of satisfaction from managing the condition compared with those allocated to continue with MDI or insulin pump. The absence of an increase in diabetes treatment satisfaction or a reduction in diabetes distress in our trial may be explained by the counterbalancing of potential benefits with the burden of adding new technology to diabetes self-management and/or trial protocol requirements (26). This is consistent with some experiences reported in qualitative studies of HCL (30). While it is noteworthy that no deterioration in self-reported sleep quality was detected in the participants using HCL, this contrasts with the improved nighttime glycemic metrics observed in that group. It is possible that, as with the diabetes burden and treatment satisfaction measures, subjective sleep benefits were masked by sleep disruption from HCL therapy (31). The absence of any detectable increase in self-perceived memory failures among those managing HCL is encouraging, as these have been previously linked to forgetting medication among adults with type 1 diabetes (32). It is possible that longer studies, resulting in greater familiarity with HCL technology, could still result in improvements in the nonglycemic parameters discussed above.
Study strengths include a protocol with the control group reflecting current clinical practice, a holistic assessment of outcomes, and the robust implementation in clinical centers by health professionals engaged in the day-to-day clinical care of adults with type 1 diabetes. Protocol implementation was standardized across all seven sites. In this near-clinical setting, the trial withdrawal rate (8% postrandomization) was within the expected level. The primary outcome, measured with identical masked-CGM systems for both the intervention and control groups, ensured that collection of outcome data did not influence the participants' own responses to their glucose levels. CGM outcome measurements in the intervention group also did not impact the CL algorithm controlling insulin delivery, as a second independent sensor was in place (in addition to the sensor of the HCL system). Nonglucose secondary outcomes that are highly relevant to the health and lives of people with type 1 diabetes were robustly examined. Finally, the use of a commercially available HCL system enables rapid translation of study results into clinical practice.
There were limitations to the trial. As all study sites were tertiary diabetes centers, our study outcomes may not generalize to implementation of the technology by health care professionals with less type 1 diabetes technology experience or who are based outside specialized multidisciplinary team centers. Control group participants were not using real-time CGM, which can improve TIR and reduce hypoglycemia (33–35). However, it should be recognized that the most significant incremental health care cost relates to CGM provision and pump rather than incorporation of the algorithm itself into an insulin delivery system. This protocol included a single HCL system; the patient-reported outcomes reflect the users' experiences with this specific system. Outcomes are determined not only by device characteristics, but also by engagement and empowerment of the person using the device. While the implementation of the protocol was standardized, the impact of variation in the participants' type 1 diabetes education was not investigated formally.
A recent meta-analysis concluded that CL systems ranked best out of type 1 diabetes management technologies for TIR (36). However, we recognize that many people with type 1 diabetes may choose modes of insulin delivery (e.g., smart pens) and glucose monitoring (e.g., flash glucose monitoring) for reasons unrelated to glycemic management, such as ease of use, comfort, intrusiveness, and financial considerations (37). There is a need for CL technology, including the user interface, to evolve further to address these needs. Subsequent refinements to the Medtronic HCL platform have included broadened glucose and insulin delivery parameters permitting CL persistence with fewer alarms, automated correction boluses for hyperglycemia, lower glucose targets, and nonadjunctive use of sensor glucose (38), in addition to improvements in sensor performance. The ultimate CL goal is a system optimized to interface seamlessly with the user's needs while minimizing both the physical and psychological burden on the person with diabetes. There remains an ongoing requirement to evaluate future CL enhancements over longer periods and to include a range of holistic outcomes. Moreover, there is also a need for research to ensure that the model of care supporting implementation of this important technology uses resources effectively and enables people with type 1 diabetes to use these devices to their full potential so as to maximize human health.
Clinical trial reg. no. ACTRN12617000520336, www.anzctr.org.au
This article contains supplementary material online at https://doi.org/10.2337/figshare.13014374.
Acknowledgments. The authors thank the study volunteers for participation. The authors also thank the trial nurses, diabetes educators, and dietitians at the clinical sites for the support; lead site trial nurse Sue-Anne Wyatt (St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia); DSMB members John Santamaria (St Vincent's Hospital Melbourne), Chris Cowell (Westmead Children's Hospital, Westmead, New South Wales, Austrlia), Val Gebski (NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia), and Danny Liew (School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia); and central support by trial monitors Helen Pater and Sarah Mulray (NHMRC Clinical Trials Centre), 1,5-anhydroglucitol assays by Dr. Andrzej Januszewski (NHMRC Clinical Trials Centre), and database assistance by Dr. Adrienne Kirby (NHMRC Clinical Trials Centre).
Funding. This trial was funded by JDRF Australian Type 1 Diabetes Clinical Research Network (3-SRA-2016-351-M-B), a special initiative of the Australian Research Council, and the National Health and Medical Research Council of Australia (APP1099379). In-kind support was provided by Medtronic (HCL systems, masked CGM devices, and technical expertise with device issues) and Roche Diabetes Care (blood glucose meters for participants using MDI). S.A.M. is supported by a JDRF Research Award (5-ECR-2017-371-A-N). J.S. and C.H. are supported by core funding to the Australian Centre for Behavioural Research in Diabetes provided by the collaboration between Diabetes Victoria and Deakin University. A.J.J. is supported by a National Health and Medical Research Council Fellowship and is a Sydney Medical School Foundation Fellow. R.J.M. received research grants from the Rebecca L. Cooper Medical Research Foundation, St Vincent's Foundation, JDRF, Diabetes Australia Research Trust/Program, and the National Health and Medical Research Council of Australia. A.J.J. received research support from the National Health and Medical Research Council of Australia, JDRF Australia, and JDRF International.
JDRF Australia provided input into the trial design. The funders of the trial had no role in data collection, data analysis, data interpretation, or writing of the report.
Duality of Interest. S.A.M. reports support for research from Medtronic and speaker honoraria from Roche. M.H.L. reports speaker honoraria from Medtronic. B.P. reports speaker honoraria fees from Medtronic. L.A.B. reports grant funding from AstraZeneca. N.D.C. reports personal fees from Medtronic and Abbott Laboratories and grant funds from Ypsomed Group. J.K. reports speaker fees from Novo Nordisk and AstraZeneca and advisory board fees from Abbott Diabetes Care. R.J.M. reports research grants from Novo Nordisk, Servier, Medtronic, and Grey Innovation; received honoraria for lectures from Eli Lilly and Company, Novo Nordisk, Sanofi, AstraZeneca, Merck Sharp & Dohme, Norvartis, and Boehringer Ingelheim; received travel support from Novo Nordisk, Sanofi, and Boehringer Ingelheim; is on the advisory boards for Novo Nordisk, Sanofi, Boehringer Ingelheim and Lilly Diabetes Alliance, and AstraZeneca; and has been a principal investigator for industry-sponsored clinical trials run by Novo Nordisk, Bayer, Janssen-Cilag, and AbbVie Inc. J.S. has served on advisory boards for Medtronic, Roche Diabetes Care, and Sanofi Diabetes; her research group (Australian Centre for Behavioural Research in Diabetes) has received honoraria for this advisory board participation and has also received unrestricted educational grants and/or in-kind support from Abbott Diabetes Care, AstraZeneca, Medtronic, Roche Diabetes Care, and Sanofi Diabetes; has received sponsorship to attend educational meetings from Medtronic, Roche Diabetes Care, and Sanofi Diabetes; and has received consultancy income or speaker fees from Abbott Diabetes Care, AstraZeneca, Medtronic, Novo Nordisk, Roche Diabetes Care, and Sanofi Diabetes. S.T. reports nonfinancial support from Abbott Diabetes Care. A.J.J. has received research support from Medtronic, Sanofi, Abbott Laboratories, and Mylan and has served on advisory boards for Medtronic, Sanofi, and Abbott Diabetes Care. D.N.O. has served on advisory boards for Abbott Laboratories, Medtronic, Merck Sharp & Dohme, Novo Nordisk, Roche, and Sanofi; received research support from Medtronic, Novo Nordisk, Roche, Eli Lilly and Company, and Sanofi; and received travel support from Novo Nordisk and Merck Sharp & Dohme. No other potential conflicts of interest were reported.
Author Contributions. D.N.O. led the study. S.A.M., M.I.d.B., E.A.D., A.C.K., J.S., V.S., S.T., A.J.J., T.W.J., and D.N.O. codesigned the study. L.A.B., M.G.B., N.D.C., P.G.C., D.J.H.-W., J.K., R.J.M., S.N.S., and G.M.W. contributed to refining the protocol. S.A.M., M.H.L., B.P., L.A.B., M.G.B., N.D.C., P.G.C., D.J.H.-W., J.K., R.J.M., R.W.M., S.N.S., G.M.W., and D.N.O. were responsible for participant screening and enrollment and informed consent and providing medical care. S.A.M. wrote the first draft of the report with input from D.N.O. S.V. and V.S. contributed to data analysis. S.A.M., S.V., C.H., J.S., V.S., S.T., A.J.J., and D.N.O. contributed to interpretation of the results. All authors critically reviewed the report. D.N.O. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Prior Presentation. This work was presented at the 80th Scientific Sessions of the American Diabetes Association, 12–16 June 2020.
DCCT/EDIC Research Group
The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study at 30 years: overview
, et al.
State of type 1 diabetes management and outcomes from the T1D Exchange in 2016-2018
Diabetes Technol Ther
Tamborlane
Bergenstal
DuBose
T1D Exchange Clinic Network
The T1D Exchange clinic registry
J Clin Endocrinol Metab
McKnight
Glycaemic control of type 1 diabetes in clinical practice early in the 21st century: an international comparison
Diabet Med
Thabit
Coming of age: the artificial pancreas for type 1 diabetes
Weisman
Cardinez
Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials
Lancet Diabetes Endocrinol
Bekiari
Kitsios
Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis
Boughton
Automated insulin delivery in adults
Endocrinol Metab Clin North Am
Kovatchev
Raghinaru
, et al.;
iDCL Study Group
Randomized controlled trial of mobile closed-loop control
[published correction appears in Diabetes Care 2020;43:1366]
iDCL Trial Research Group
Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes
N Engl J Med
Australasian Diabetes Data Network
Accessed 12 June 2020. Available from https://www.addn.org.au/research
Hessler
Polonsky
Diabetes distress is linked with worsening diabetes management over time in adults with type 1 diabetes
Sharifi
De Bock
Jayawardene
Glycemia, treatment satisfaction, cognition, and sleep quality in adults and adolescents with type 1 diabetes when using a closed-loop system overnight versus sensor-augmented pump with low-glucose suspend function: a randomized crossover study
Ruedy
DIAMOND Study Group
The impact of continuous glucose monitoring on markers of quality of life in adults with type 1 diabetes: further findings from the DIAMOND randomized clinical trial
Martyn-Nemeth
Mihailescu
Poor sleep quality is associated with nocturnal glycaemic variability and fear of hypoglycaemia in adults with type 1 diabetes
J Adv Nurs
Cerghizan
Sleep quality and sleep duration, but not circadian parameters are associated with decreased insulin sensitivity in type 1 diabetes
Chronobiol Int
Zammitt
Frier
Effects of sleep deprivation on hypoglycemia-induced cognitive impairment and recovery in adults with type 1 diabetes
Vigers
Real world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months
Pediatr Diabetes
Weinzimer
Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes
McAuley
Sundararajan
Effect of 6 months of hybrid closed-loop insulin delivery in adults with type 1 diabetes: a randomised controlled trial protocol
Steil
Palerm
Kurtz
The effect of insulin feedback on closed loop glucose control
Maahs
Outcome measures for artificial pancreas clinical trials: a consensus report
International Hypoglycaemia Study Group
Glucose concentrations of less than 3.0 mmol/L (54 mg/dL) should be reported in clinical trials: a joint position statement of the American Diabetes Association and the European Association for the Study of Diabetes
Australian New Zealand Clinical Trials Registry
Adult Hybrid Closed Loop Study (HCL Adult) Statistical Analysis Plan, 2019. Accessed 13 April 2020. Available from https://www.anzctr.org.au/Steps11and12/372617-(Uploaded-28-10-2019-11-59-34)-Study-related%20document.pdf
Continuous glucose monitoring and global reimbursement: an update
Six months of hybrid closed loop in the real-world: an evaluation of children and young adults using the 670G system
Basina
One year clinical experience of the first commercial hybrid closed-loop system
Akturk
Champakanath
Brackett
Snell-Bergeon
Long-term real-life glycaemic outcomes with a hybrid closed-loop system compared with sensor-augmented pump therapy in patients with type 1 diabetes
Diabetes Obes Metab
Kimbell
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Health professionals' views about who would benefit from using a closed-loop system: a qualitative study
Hendrieckx
"It is definitely a game changer": a qualitative study of experiences with in-home overnight closed-loop technology among adults with type 1 diabetes
Farrington
Psychosocial impacts of hybrid closed-loop systems in the management of diabetes: a review
Trawley
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Speight
Prospective memory slips are associated with forgetting to take glucose-lowering therapies among adults with diabetes: results from the second Diabetes MILES - Australia (MILES-2) survey
Battelino
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The use and efficacy of continuous glucose monitoring in type 1 diabetes treated with insulin pump therapy: a randomised controlled trial
Riddlesworth
Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial
Continuous glucose monitoring vs conventional therapy for glycemic control in adults with type 1 diabetes treated with multiple daily insulin injections: the GOLD randomized clinical trial
Kiriakova
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Time in range for multiple technologies in type 1 diabetes: a systematic review and network meta-analysis
Tanenbaum
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© 2020 by the American Diabetes Association
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.


text:
Q: How to use mouseover to change border color of a css shape(circle)? I am trying to achieve this circular navigation menu
example : http://www.jerseywater.je/water/index.html#page_0/
I have done almost all of it!
 the problem now is how to change a border of a css shape with mouseover function
edit:
"that is when i hover some other shape the border color will change in the main circle"

#circle {
    width: 120px;
    height: 120px;
    border-radius: 70px;
    box-shadow: 6px 6px 5px #888888;
    -moz-border-radius: 70px;
    -webkit-border-radius: 70px;
    border: 4px solid #73AD21;
    position:absolute;
    top:50%;
    left:50%;
    margin-top:-60px;
    margin-left:-50px;
    background: transparent;
}

        Blah Blah

A: $(document).ready(function(){
    $( "#circle" )
  .mouseenter(function() {
    $( this ).css("border","black solid 1px");
  })
  .mouseleave(function() {
    $( this ).css("border","red solid 1px");
  });
});

Something like this: jsFiddle ?

A: #circle:hover {
  border-color: pink;
}

A: This is an example of what you may want to achieve:
http://codepen.io/TunderScripts/pen/ZBBdBM
html

 Look

Menu Item 1

Menu Item 2

Menu Item 3

Menu Item 4

css
* {
  box-sizing: border-box;
}

.container {
  width: 600px;
  height: 600px;
  border: 2px solid blue;
  position: relative;
  margin: 40px auto;
}

.main_circle {
  position: absolute;
  top: 50%;
  display: block;
  left: 50%;
  width: 80px;
  height: 80px;
  border-radius: 100%;
  border: 2px solid red;
  padding: 25px 20px;
  transform: translate( -50%, -50%) rotate(0deg);
  background: blue;
  z-index: 100;
}

.main_circle:before {
  content: "";
  height: 25px;
  width: 30px;
  display: block;
  border: 2px solid red;
  border-radius: 40px;
  position: absolute;
  float: left;
  top: -10px;
  z-index: 5;
}

.menu_items {
  position: absolute;
  height: 80px;
  width: 80px;
  border-radius: 100%;
  border: 2px solid green;
  padding: 17px;
  display: block;
}

.item1 {
  top: 20px;
  left: 250px;
}

.item2 {
  top: 260px;
  right: 20px;
}

.item3 {
  bottom: 20px;
  left: 250px;
}

.item4 {
  top: 260px;
  left: 20px;
}

and js
var menuItems = $('.menu_items').length;
var step = 360 / menuItems;

$('.menu_items').on('mouseover' , function(){
  var classList = $(this).attr('class');
  var identifier = classList.split(' ')[1].substr(4,1);

  var calc = (parseInt(identifier) - 1) * step;
  var transform = 'translate(-50%, -50%) rotate(' + calc + 'deg)';
  console.log(transform);
  $('.main_circle').css('transform', transform);
});

if you increase the number of menu-items, reposition them with css and the js will still work. Name the classes as I did with item#(number) and it should work. Have fun.

A: In the css use :hover eg:
#circle:hover {
    border: 10px solid black;
}

More details can be found here: http://www.w3schools.com/cssref/sel_hover.asp

redpajama_set_name: RedPajamaStackExchange


Due to the unique nature of our service, each ring is crafted using the appropriate technique for that specific piece. In order to provide such a detailed service, we have several workshops that specialise in different design and manufacturing processes. This allows us to utilise our technical knowledge to create your design into reality.
Each piece of jewellery designed by our clients, starts with a 3D print of your model in wax. In order for the printer to know what is is producing we need to produce a 3DM file.
The 3D printer uses a wax resin as printing material. Next, one or more wax sprues will be attached to your model. Then your model will be attached by the sprue to a wax 'tree'. The tree is then placed in a flask and covered in a fine plaster. When the plaster solidifies, it forms the mold for casting the metal. The plaster mold is then put in an oven and heated for several hours to the point where the wax is completely burned out.
Durham rose ensures each and every piece of jewelry they produce is completed to the highest standard by their highly skilled craftsmen. Each piece is hand polished paying an infinite amount of attention to detail in order to capture each intricate part of the design. All the stones are hand selected by a trained gemologist to complement each other ensuring a perfect match. Not only are the stones hand selected for their beauty and brilliance, but each stone is set by hand to ensure maximum security and precision. Here at Durham Rose only one piece will be crafted for one person ensuring we optimism the true meaning of the word bespoke.
Hallmarking is a legal requirement in the UK to ensure proof of authenticity. It guarantees that you are getting the exact purity of metal that you have purchased.
Here at Durham Rose we send your pieces to the trusted Sheffield Assay office for certification and hallmarking. Other locations for hallmarking are in Edinburgh, London and Birmingham each represented differently in the hallmark by distinguishing symbols.
Read more here about Hallmarking.
To add further sentiment to your piece of jewellery, you may wish to include some engraving on the inside of your band. This can be approved in a variety of fonts and we can even engrave your personal hand writing or an image. Most of our clients have a significant date or place that is completely unique to the occasion and we would be delighted in bringing this to life for you.
The majority of our engraving is laser engraved. This allows for precision in such as small area, however sometimes we need to rely on the steady hand and expertise of hand engraving, if the laser engraver can't focus for example. Laser engraving is a quick and effective process. Using a bonding agent you can customise your piece when heat from the laser bonds with the marking agent to the metal resulting in a permanent mark which we refer to as the engraving.
Before sending your bespoke piece of jewellery, we put it through quality control at our head office.
Your design consultant will personally inspect the piece to make sure it meets the highest of standards that Durham Rose is used to producing.
After this process has been completed, we now know your ring is ready ready to be packaged to perfection and delivered right to your door.
At Durham Rose we believe the packaging of your bespoke piece of jewellery is equally as important to the piece itself. The unveiling of your carefully handcrafted piece of jewellery is an experience in itself. We have selected a luxurious ring box to house your perfect piece and surrounded it with beautiful packaging tied together perfectly in our dusky plum ribbon.
We are aware great lengths have been taken into the planning of this gift, so we always send the piece in unbranded outer packaging as a way of disguising the content so as not to ruin the surprise.
Lets us guide you through the journey this incredible piece of jewelry went through before it became the piece of art you present to your loved one. We provide you with a book capturing your entire experience with us through to the start of your next adventure together. This will included a collect of images throughout the manufacturing process and we will print your selected proposal photographs to finish this gift off to perfection.


text:
Neil Etheridge: Walsall sign new goalkeeper on two-year deal
Last updated on 2 July 20152 July 2015 .From the section Football
Neil Etheridge started his youth career at Chelsea
League One side Walsall have made their second international signing in 24 hours by bringing in Philippines keeper Neil Etheridge on a two-year deal.
Following the acquisition of Cyprus defender Jason Demetriou, Etheridge, 25, fills the gap left by Richard O'Donnell's move to Wigan Athletic.
The ex-Chelsea trainee, who has made just 20 Football League appearances, was released by Charlton in May.
"We had a number of good options but Neil stood out," said boss Dean Smith.
Etheridge's international credentials
Londoner Neil Etheridge represented England at Schoolboy level before winning the first of his 44 caps for the Philippines in 2008 while still a Fulham Academy player. He has since also played for Charlton Athletic, Bristol Rovers, Crewe Alexandra and Oldham Athletic.
"He ticks all the boxes in age, room for development and experience at this level and above," said Smith.
He's an extremely driven young man who wants to be successful. These are the characteristics we're looking for.
"Neil will compete with Craig MacGillivray for a first-team place. They will both push each other on, exactly the kind of competition that we want."
Etheridge, who began his professional career at Fulham, added: "The faith that the gaffer and Neil Cutler (Walsall goalkeeping coach) have shown in me means a lot. Now is the time to get my head down and work hard.
"I have gained plenty of international experience in front of big crowds, but I've not played as many league games as I'd have liked. I intend to rectify that here."
Find all the latest football transfers on our dedicated page.
NFL Super Wildcard Monday: Dominant Rams establish big lead over misfiring Cardinals
From the section American Football

redpajama_set_name: RedPajamaCommonCrawl


Key dates for colleges, including moderation days, professional studying days and public holidays. ii There is a massive literature on this topic. Bob Smith, They Closed Their Schools: Prince Edward County, Virginia, 1951-1964 (Chapel Hill: College of North Carolina Press, 1965); James Latimer, The Rise and Fall of 'Huge Resistance," Richmond Occasions-Dispatch, Sept 22, 1996, A1, A9-A12; Ira M. Lechner, Huge Resistance: Virginia's Nice Leap Backward," Virginia Quarterly Evaluation, seventy four:4 (1998); Matthew Lassiter and Andrew Lewis, eds. The Moderates' Dilemma: Huge Resistance to School Desegregation in Virginia (Charlottesville: University Press of Virginia, 1998); George Lewis, Huge Resistance: The White Response to the Civil Rights Movement (Oxford: Hodder Arnold, 2006), pp. 52ff.
In discussions of changing Lee-Davis's title, I've witnessed lots of robust feelings. I consider such strong feelings come up precisely because present and former college students have come to see being a Confederate" as by some means an necessary a part of their historical past, tradition, and identification. I've argued all through this letter that a public faculty shouldn't play a job in promoting such an identity. But many individuals in the Lee-Davis neighborhood however do really feel that satisfaction" that they have been inspired to feel. You therefore can not look to the current and former student body to resolve this difficulty by way of voting or petitions. They are likely solely to ship the established order.
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Duplicate citation cleanup—not just "suppression"—is probably the main citation-related tactic that I'd continue to pay a substantial sum for. Otherwise, just manage your listings directly in Google My Business and Facebook, and if you can afford the cost and the sales headache, Yelp. Then call it a day and spend your SEO budget on more productive tactics (like review solicitation, Q&A monitoring, and the like).
Beyond Yelp and Facebook, Google seems to be moving more and more towards valuing vertical and local sources, most of which aren't part of standard listing networks. Companies like Yext and even my own former employer Moz, will probably need to adjust their business models and value propositions accordingly.


text:
Q: How to git svn fetch only branches/tags with certain patterns? I want to check out Boost library using git-svn, and I only want to checkout trunk and tags starting from version 1.35, i.e. tags/release/Boost_1_35 and up.  My config looks like this:
[svn-remote "svn"]
    ignore-paths = ^tags/release/(?i:(?!boost)|[^/]*(?:beta|rc)|boost_(?:0|1_[1-2]|1_3[0-4]))[^/]*/
    url = https://svn.boost.org/svn/boost
    fetch = trunk:refs/remotes/svn/trunk
    tags = tags/release/*:refs/remotes/svn/tags/*

However, git fetch still fetches tons of unrelated tags such as svn/tags/version_0-9-10 or svn/tags/version_0-9-10@44215.  I wonder what's the correct way of specifying ignore-paths to achieve this effect.
BTW: I've read How do I fetch/clone only a few branches using git-svn? but doesn't sounds a scalable solution.

A: I have managed to do so for certain branches, but since tags in svn are actually branches it should probably be the same:
After git svn init with "-t tags and -b branches" I have edited .git/config to be:
[svn-remote "svn"]
        ... url, fetch and stuff ...
        branches = branches/rel-2.1/*:refs/remotes/svn/branches/rel-2.1/*
        branches = branches/rel-2.2/*:refs/remotes/svn/branches/rel-2.2/*
        branches = branches/rel-2.3/*:refs/remotes/svn/branches/rel-2.3/*
        branches = branches/rel-2.4/*:refs/remotes/svn/branches/rel-2.4/*
        branches = branches/rel-2.5/*:refs/remotes/svn/branches/rel-2.5/*

I used a simple python code to generate the relevant lines and copied it to .git/config:
for x in range(1,6):
   print branches = "     branches/rel-2.{0}/*:refs/remotes/svn/branches/rel-2.{0}/*".format(x)

Not as scalable as regex, but that's the best I could find.


text:
Q: Throwing C++ exceptions outside static library? As a rule, exceptions must not propagate module boundaries as for example explained in Herb Sutters C++ Coding Standards (item 62). When compiled with different compilers or just compiler settings this might crash.
I can understand the issue in case e.g. of dynamic link libraries. But I wonder whether it also holds for static libraries. Is a static library a module in the sense of the above rule? If the library is compiled with other compiler settings (e.g. alignment) might the program crash, if an exception is thrown out of the static library and caught in the application?

A: Herb Sutters' description is also suitable for static library:

There is no ubiquitous binary standard for C++ exception handling.
  Don't allow exceptions to propagate between two pieces of code unless
  you control the compiler and compiler options used to build both
  sides; otherwise, the modules might not support compatible
  implementations for exception propagation. Typically, this boils down
  to: Don't let exceptions propagate across module/subsystem boundaries.

A: Generally, a static library has to be compiled by the same compiler and the same compiler settings (mostly) to be compatible with the deliverable (a dynamic library or an executable).
You can, then, throw exceptions outside the boundaries of a static library because it's not much different than a set of .obj files your compiler generated. And you obviously can throw exceptions between different .obj modules.
EDIT:
To sum up the comments:

*

*You can only use a static library if you're using the same compiler and compiler settings used to compile the library.

*You can throw exceptions between modules compiled with the same compiler and compiler settings.

*From 1) and 2) follows that you can throw exceptions from a static library because, if you're using it, that means you're using the same compiler and compiler settings, hence you can throw exceptions.

A: It depends on what Herb means by "module".  And the issues don't
only concern exceptions; they can concern anything using a C++
interface.
There is certainly no problem when exceptions cross translation
unit boundaries of sources compiled as part of the same
component.  Between components, if they are all part of the same
application, and you ensure that they are all compiled using the
same compiler, with the same compiler options, it may be safe,
although there can be problems when crossing between dynamic
libraries, depending on how the libraries are loaded.  (In
general, this is only a problem on Unix systems, where the
visibility of symbols in dynamically loaded components is
controlled by the options passed to the dynamic loader.)  As
a general rule: arrange to have all of your application compiled
with the same compiler and the same compiler options, and you
should have no real problems within the application (although
you may have to ensure that all dynamic components are
explicitly loaded, at least under Unix).  Between
"applications", where you are loading or being loaded by
"foreign" software, Herb's restrictions don't go far enough.  In
practice, the interface where you cross between the applications
must be defined in C.  And there may still be restrictions,
depending on how your code is loaded and what other dynamically
loaded components are being used.
Linking statically will remove the problems with regards to how
the library is loaded, but changes nothing else.


text:
Een alf (meervoud alven) is naar oud-Germaanse en oud-Noorse opvatting een geest in lucht, het water of waar dan ook in de natuur.

Een alf (Oudnoors álfar (nominatief enkelvoud: álfr) betekent elf) is in de Noorse mythologie een van de vruchtbare natuurgeesten of natuurkrachten, die al aan het werk waren voor er nog maar goden ontstonden. Zij werken dus mee aan de opbouw, het in stand houden, maar ook de afbraak, van de werelden.

Het waren bijvoorbeeld de alven die zorgden dat het levenssap van Ymir, de allereerste oerreus, bij de schepping werd omgezet tot stromen en zeeën.

Hoewel het concept in de bekende bronnen van de Edda nergens duidelijk wordt bepaald, komen alven in het algemeen naar voor als mooie vermenselijkte natuurwezens, de verpersoonlijking van vruchtbaarheid.

Nederland
In de Nederlandse volkskunde kent men het woord 'alf', verwant aan het woord 'elf'. Onderzoekers weten vaak niet wat zij zich bij een alf, althans in het Nederlands taalgebied, moeten voorstellen. In de volksoverlevering wordt de alf dikwijls verward met aardmannetjes of kabouters. In het zuiden van Nederland, met name Limburg, worden kabouters of aardmannetjes wel 'alvermannekes' of 'auvermannetjes' genoemd. Deze zijn verwant aan meer onstoffelijke wezens als alven en elfen.

Vroeger werd gedacht dat een alf een geest in de lucht was, in het water, of waar dan ook in de natuur. Er waren lichtalven (elfen) en donkeralven. In de 14e-eeuw schrijft ene 'broeder Geraert' in zijn werk 'Natuurkunde' dat alven duivelse wezens zijn: 'Duvele die sijn in die lucht, Coubouten, alven, nickers, maren. Jacob van Maerlant noemt het bedrog van alven 'elfsgedroch' als synoniem voor tovenarij.

Etymologie
Het woord álf (meervoud álfar) komt van dezelfde Proto-Indo-Europese wortel als waarvan het Latijnse woord albus (wit) van afgeleid is. Meestal wordt aangenomen dat het daarbij om een woord uit een voor-Indo-Europese substraattaal gaat.
Deze oorspronkelijke betekenis is onder meer karakteristiek voor de alven in de Noordse mythologie, die hun lichtverwantschap behielden als hun goddelijke status.
Alf is etymologisch verwant met het uit het Engels ontleende elf, maar heeft in tegenstelling tot elf de negatieve betekenis bewaard die in de Middeleeuwen aan deze wezens werd gegeven. De christelijke kerk zag ze namelijk als demonen.

Dualisme licht en donker
De IJslandse mythograaf en historicus Snorri Sturluson maakt eveneens gewag van twee categorieën alven:
 Lichtelfen (Oudnoors Ljòsálfar): Deze zijn helderder dan de zon, mooi om te zien. Ze wonen in Ljossalfheimr
 Donkerelfen (Oudnoors Dökkálfar) of Svartálfar: De donkere alven zijn zwarter dan pek. Ze komen overeen met dwergen en trollen. Hun verblijf Svartálfheim ligt in de onderwereld, en komt overeen met Nidavellir.

Het is kenmerkend voor de tijd waarin Snorri leefde dat dit onderscheid werd gemaakt. Ook de associatie met 'goed en kwaad' was toen niet meer ver weg, vergelijkbaar met het onderscheid tussen engelen en demonen uit de christenheid, Devas en Asuras uit het hindoeïsme, Seelie en Unseelie hoven van de Sidhe in de Keltische mythologie.

Vanwege hun lichtschuwheid worden donkere alven soms gedemoniseerd, alhoewel hun krachten in het duister aan het werk zijn, zoals uiteindelijk blijkt bij de Ragnarok waar zij zowel de lichtelfen als de Asen bijstaan. Ook hebben een aantal onder hen grote kunstwerken gecreëerd, zoals de Zonen van Ivaldi.

Identiteit en verblijf
Sommige auteurs speculeren dat Vanir en alven tot een eerdere Noordse Bronstijd religie in Scandinavië behoren en nadien werden vervangen door de Æsir (of Asen) als belangrijkste goden. Anderen zoals Georges Dumézil menen dat de Vanir (of Wanen) de goden van de gewone Noordse mensen waren en de Asen die van de priester- en krijgersstanden.

Vaak worden de alven in een adem genoemd met de Asen en worden ze met de goden geïdentificeerd.

In de Alvíssmál worden alven in ieder geval als onderscheiden van Wanen en van Asen beschouwd. Het kunnen 'de mindere goden' zijn geweest. Hun rijk Alfheim werd als tandgift aan de Wanengod Freyr, zelf de vruchtbaarheidsgod bij uitstek, toegewezen en veel van de 'helpers' van deze god waren waarschijnlijk alven (zoals Byggvir en Beyla, die hem als heer van de elfen toebehoorden) aangezien ze niet bij de goden werden gerekend. Twee andere dienaars van Freyr waren Fimafeng (later vermoord door Loki) en Eldir.

Oude status
Alven zijn alleszins sterk verbonden met de cultus van de vroege voorouders. De latere folklore laat verstaan dat het geloof in elfen heel algemeen verbreid was bij de Germaanse stammen, en niet enkel beperkt tot de oude Scandinavische.
In het algemeen kende men ook magische psychische krachten toe aan deze wezens. Zij zouden mensen rechtstreeks beïnvloeden en deels betoveren. Bepaalde dromen en vooral nachtmerries zouden van hen afkomstig zijn.

Men kan er over redetwisten in hoeverre de alven het Germaans equivalent zijn van de nymfen of sirenen in de Griekse en Romeinse mythologie, of van de vili en roesalki in de Slavische mythologie.

Elf
Mythisch wezen
Noordse mythologie

redpajama_set_name: RedPajamaWikipedia


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Sharing a bill has a few more steps. Enter an amount and description, and then select the people sharing the bill. This may include you. You can then select to divide the bill equally or you can manually enter the amount for each person.
Unfortunately, BillPin doesn't automatically calculate the remainder from the total amount if you manually enter specific amounts for some people. For example, if the dinner total for two people was $50 and your share was only $15, BillPin doesn't automatically calculate that your friend owes $35. You'll have to enter that number manually. We hope that feature gets added in an update.
BillPin is a free download at the Windows Phone Store. It is also available on iOS, Android, and the web. Will you be using this app? Give it a try and sound off in the comments!