human
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVE To assess the effectiveness of structured blood glucose testing in poorly controlled , noninsulin-treated type 2 diabetes . RESEARCH DESIGN AND METHODS This 12-month , prospect i ve , cluster-r and omized , multicenter study recruited 483 poorly controlled ( A1C ≥7.5 % ) , insulin-naïve type 2 diabetic subjects from 34 primary care practice s in the U.S. Practice s were r and omized to an active control group ( ACG ) with enhanced usual care or a structured testing group ( STG ) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose ( SMBG ) . STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days . The primary end point was A1C level measured at 12 months . RESULTS The 12-month intent-to-treat analysis ( ACG , n = 227 ; STG , n = 256 ) showed significantly greater reductions in mean ( SE ) A1C in the STG compared with the ACG : −1.2 % ( 0.09 ) vs. −0.9 % ( 0.10 ) ; Δ = −0.3 % ; P = 0.04 . Per protocol analysis ( ACG , n = 161 ; STG , n = 130 ) showed even greater mean ( SE ) A1C reductions in the STG compared with the ACG : −1.3 % ( 0.11 ) vs. −0.8 % ( 0.11 ) ; Δ = −0.5 % ; P < 0.003 . Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients , regardless of the patient ’s initial baseline A1C level : 179 ( 75.5 % ) vs. 61 ( 28.0 % ) ; < 0.0001 . Both STG and ACG patients displayed significant ( P < 0.0001 ) improvements in general well-being ( GWB ) . CONCLUSIONS Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB OBJECTIVE This study was design ed to evaluate the impact of a teleassistance system on the metabolic control of type 2 diabetes patients . RESEARCH DESIGN AND METHODS We conducted a 1-year controlled parallel-group trial comparing patients r and omized ( 1 ) to an intervention group , assigned to a teleassistance system using real-time transmission of blood glucose results , with immediate reply when necessary , and telephone consultations , or ( 2 ) to a control group , being regularly followed-up at their healthcare center . Study subjects were type 2 diabetes patients > 30 years of age followed in the primary care setting . RESULTS A total of 328 type 2 diabetes patients were recruited from 35 family practice s in the province of Málaga , Spain . There was a reduction in hemoglobin A1c after 12 months from 7.62 + /- 1.60 % to 7.40 + /- 1.43 % ( P = 0.027 ) in the intervention group and from 7.44 + /- 1.31 % to 7.35 + /- 1.38 % ( P = 0.303 ) in the control group . The difference in the change between groups was not statistically significant . There was also a significant decrease in systolic and diastolic blood pressure , total cholesterol , low-density lipoprotein cholesterol , and body mass index in the intervention group . In the control group , the only significant decline was in low-density lipoprotein cholesterol . CONCLUSIONS A teleassistance system using real-time transmission of blood glucose results with an option to make telephone consultations is feasible in the primary care setting as a support tool for family physicians in their follow-up of type 2 diabetes patients BACKGROUND Telehome care has been proposed as a solution to the challenges of providing effective and affordable care for patients with diabetes . METHODS A total of 100 adult patients with type 2 diabetes-divided between insulin and noninsulin requiring-was enrolled in a r and omized , controlled trial aim ed at investigating the effects of telehome monitoring . The experimental group ( n = 50 ) received an in-home wireless glucose monitor and transmitter , whereas the control group ( n = 50 ) was instructed to follow the conventional arrangement . RESULTS There was an overall reduction in HbA1c values in both experimental and control groups after 6 months . A significant difference in HbA1c values between the groups was observed only among the noninsulin-requiring patients ( decline from 6.95 % ± 0.82 % to 6.66 % ± 0.86 % in IB vs. 7.21 % ± 2.02 % to 7.2 % ± 1.86 % in IIB ; p = 0.02 ) . The experimental group reported considerably less hyperglycemic and hypoglycemic events . The profile of the patient who benefited the most from telemonitoring consisted of older , more educated patient who had acquired the disease relatively recently , and who spends most of the time at home . The experimental group had higher overall scores on quality of life measures and sense of control over diabetes . There was a positive association between educational attainment and ability to use the telemonitoring system without help ( p = 0.045 ) . CONCLUSIONS Although not conclusive because of the small sample and short observation period , the study suggests that telehome monitoring is an effective tool in controlling type 2 diabetes in a primary care setting Background Type 2 diabetes mellitus is a worldwide challenge . Practice guidelines promote structured self-monitoring of blood glucose ( SMBG ) for informing health care providers about glycemic control and providing patient feedback to increase knowledge , self-efficacy , and behavior change .  Paired glucose testing — pairs of glucose results obtained before and after a meal or physical activity — is a method of structured SMBG . However , frequent access to glucose data to interpret values and recommend actions is challenging . A complete feedback loop— data collection and interpretation combined with feedback to modify treatment — has been associated with improved outcomes , yet there remains limited integration of SMBG feedback in diabetes management . Incorporating telehealth remote monitoring and asynchronous electronic health record ( EHR ) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management — employ the complete feedback loop to improve outcomes . Objective The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes . The primary aim was change in glycated hemoglobin (A1c)—a measure of overall glucose management — between groups after 6 months . The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities ( SDSCA ) , Diabetes Empowerment Scale , and Diabetes Knowledge Test . Methods A 2-group r and omized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits .  Participants were aged 30 - 70 years , not using insulin with A1c levels between 7.5 % and 10.9 % ( 58 - 96 mmol/mol ) . The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants , analyze actionable glucose data , and provide feedback . Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly . CDEs called participants monthly to discuss paired glucose testing trends and treatment changes . Separate mixed-effects models were used to analyze data . Results Participants ( N=90 ) were primarily white ( 64 % , 56/87 ) , mean age 58 ( SD 11 ) years , mean body mass index 34.1 ( SD 6.7 ) kg/m2 , with diabetes for mean 8.2 ( SD 5.4 ) years , and a mean A1c of 8.3 % ( SD 1.1 ; 67 mmol/mol ) . Both groups lowered A1c with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months ( SE 0.08 , t159=–2.87 , P=.005 ) . Change in medication ( SE 0.21 , t157=–3.37 , P=.009 ) was significantly associated with lower A1c level . The treatment group significantly improved on the SDSCA subscales carbohydrate spacing ( P=.04 ) , monitoring glucose ( P=.001 ) , and foot care ( P=.02 ) . Conclusions An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A1c levels compared to usual care . Trial Registration Clinical trials.gov NCT01715649 ; https://www . clinical trials.gov/ct2/show/NCT01715649 ( Archived by WebCite at http://www.webcitation.org/6ZinLl8D0 ) BACKGROUND Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions who experience barriers to access to care or a high burden of illness . METHODS The authors conducted a r and omized , controlled trial comparing telemedicine case management to usual care , with blinding of those obtaining outcome data , in 1,665 Medicare recipients with diabetes , aged 55 years or greater , and living in federally design ated medically underserved areas of New York State . The primary endpoints were HgbA1c , blood pressure , and low-density lipoprotein ( LDL ) cholesterol levels . RESULTS In the intervention group ( n = 844 ) , mean HgbA1c improved over one year from 7.35 % to 6.97 % and from 8.35 % to 7.42 % in the subgroup with baseline HgbA1c > or = 7 % ( n = 353 ) . In the usual care group ( n = 821 ) mean HgbA1c improved over one year from 7.42 % to 7.17 % . Adjusted net reductions ( one-year minus baseline mean values in each group , compared between groups ) favoring the intervention were as follows : HgbA1c , 0.18 % ( p = 0.006 ) , systolic and diastolic blood pressure , 3.4 ( p = 0.001 ) and 1.9 mm Hg ( p < 0.001 ) , and LDL cholesterol , 9.5 mg/dL ( p < 0.001 ) . In the subgroup with baseline HgbA1c > or = 7 % , net adjusted reduction in HgbA1c favoring the intervention group was 0.32 % ( p = 0.002 ) . Mean LDL cholesterol level in the intervention group at one year was 95.7 mg/dL. The intervention effects were similar in magnitude in the subgroups living in New York City and upstate New York . CONCLUSION Telemedicine case management improved glycemic control , blood pressure levels , and total and LDL cholesterol levels at one year of follow-up OBJECTIVE To improve quality and efficiency of care for elderly patients with type 2 diabetes , we introduced elderly-friendly strategies to the clinical decision support system (CDSS)-based ubiquitous healthcare ( u-healthcare ) service , which is an individualized health management system using advanced medical information technology . RESEARCH DESIGN AND METHODS We conducted a 6-month r and omized , controlled clinical trial involving 144 patients aged > 60 years . Participants were r and omly assigned to receive routine care ( control , n = 48 ) , to the self-monitored blood glucose ( SMBG  , n = 47 ) group , or to the u-healthcare group ( n = 49 ) . The primary end point was the proportion of patients achieving A1C < 7 % without hypoglycemia at 6 months . U-healthcare system refers to an individualized medical service in which medical instructions are given through the patient ’s mobile phone . Patients receive a glucometer with a public switched telephone network-connected cradle that automatically transfers test results to a hospital-based server . Once the data are transferred to the server , an automated system , the CDSS rule engine , generates and sends patient-specific messages by mobile phone . RESULTS After 6 months of follow-up , the mean A1C level was significantly decreased from 7.8 ± 1.3 % to 7.4 ± 1.0 % ( P < 0.001 ) in the u-healthcare group and from 7.9 ± 1.0 % to 7.7 ± 1.0 % ( P = 0.020 ) in the SMBG group , compared with 7.9 ± 0.8 % to 7.8 ± 1.0 % ( P = 0.274 ) in the control group . The proportion of patients with A1C < 7 % without hypoglycemia was 30.6 % in the u-healthcare group , 23.4 % in the SMBG group ( 23.4 % ) , and 14.0 % in the control group ( P < 0.05 ) . CONCLUSIONS The CDSS-based u-healthcare service achieved better glycemic control with less hypoglycemia than SMBG and routine care and may provide effective and safe diabetes management in the elderly diabetic 
    Output:
    
agent
Conclusions Telemonitoring interventions may be a better option than usual care in improving glycated haemoglobin control of patients with type 2 diabetes .
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVE To assess the effectiveness of structured blood glucose testing in poorly controlled , noninsulin-treated type 2 diabetes . RESEARCH DESIGN AND METHODS This 12-month , prospect i ve , cluster-r and omized , multicenter study recruited 483 poorly controlled ( A1C ≥7.5 % ) , insulin-naïve type 2 diabetic subjects from 34 primary care practice s in the U.S. Practice s were r and omized to an active control group ( ACG ) with enhanced usual care or a structured testing group ( STG ) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose ( SMBG ) . STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days . The primary end point was A1C level measured at 12 months . RESULTS The 12-month intent-to-treat analysis ( ACG , n = 227 ; STG , n = 256 ) showed significantly greater reductions in mean ( SE ) A1C in the STG compared with the ACG : −1.2 % ( 0.09 ) vs. −0.9 % ( 0.10 ) ; Δ = −0.3 % ; P = 0.04 . Per protocol analysis ( ACG , n = 161 ; STG , n = 130 ) showed even greater mean ( SE ) A1C reductions in the STG compared with the ACG : −1.3 % ( 0.11 ) vs. −0.8 % ( 0.11 ) ; Δ = −0.5 % ; P < 0.003 . Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients , regardless of the patient ’s initial baseline A1C level : 179 ( 75.5 % ) vs. 61 ( 28.0 % ) ; < 0.0001 . Both STG and ACG patients displayed significant ( P < 0.0001 ) improvements in general well-being ( GWB ) . CONCLUSIONS Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB OBJECTIVE This study was design ed to evaluate the impact of a teleassistance system on the metabolic control of type 2 diabetes patients . RESEARCH DESIGN AND METHODS We conducted a 1-year controlled parallel-group trial comparing patients r and omized ( 1 ) to an intervention group , assigned to a teleassistance system using real-time transmission of blood glucose results , with immediate reply when necessary , and telephone consultations , or ( 2 ) to a control group , being regularly followed-up at their healthcare center . Study subjects were type 2 diabetes patients > 30 years of age followed in the primary care setting . RESULTS A total of 328 type 2 diabetes patients were recruited from 35 family practice s in the province of Málaga , Spain . There was a reduction in hemoglobin A1c after 12 months from 7.62 + /- 1.60 % to 7.40 + /- 1.43 % ( P = 0.027 ) in the intervention group and from 7.44 + /- 1.31 % to 7.35 + /- 1.38 % ( P = 0.303 ) in the control group . The difference in the change between groups was not statistically significant . There was also a significant decrease in systolic and diastolic blood pressure , total cholesterol , low-density lipoprotein cholesterol , and body mass index in the intervention group . In the control group , the only significant decline was in low-density lipoprotein cholesterol . CONCLUSIONS A teleassistance system using real-time transmission of blood glucose results with an option to make telephone consultations is feasible in the primary care setting as a support tool for family physicians in their follow-up of type 2 diabetes patients BACKGROUND Telehome care has been proposed as a solution to the challenges of providing effective and affordable care for patients with diabetes . METHODS A total of 100 adult patients with type 2 diabetes-divided between insulin and noninsulin requiring-was enrolled in a r and omized , controlled trial aim ed at investigating the effects of telehome monitoring . The experimental group ( n = 50 ) received an in-home wireless glucose monitor and transmitter , whereas the control group ( n = 50 ) was instructed to follow the conventional arrangement . RESULTS There was an overall reduction in HbA1c values in both experimental and control groups after 6 months . A significant difference in HbA1c values between the groups was observed only among the noninsulin-requiring patients ( decline from 6.95 % ± 0.82 % to 6.66 % ± 0.86 % in IB vs. 7.21 % ± 2.02 % to 7.2 % ± 1.86 % in IIB ; p = 0.02 ) . The experimental group reported considerably less hyperglycemic and hypoglycemic events . The profile of the patient who benefited the most from telemonitoring consisted of older , more educated patient who had acquired the disease relatively recently , and who spends most of the time at home . The experimental group had higher overall scores on quality of life measures and sense of control over diabetes . There was a positive association between educational attainment and ability to use the telemonitoring system without help ( p = 0.045 ) . CONCLUSIONS Although not conclusive because of the small sample and short observation period , the study suggests that telehome monitoring is an effective tool in controlling type 2 diabetes in a primary care setting Background Type 2 diabetes mellitus is a worldwide challenge . Practice guidelines promote structured self-monitoring of blood glucose ( SMBG ) for informing health care providers about glycemic control and providing patient feedback to increase knowledge , self-efficacy , and behavior change .  Paired glucose testing — pairs of glucose results obtained before and after a meal or physical activity — is a method of structured SMBG . However , frequent access to glucose data to interpret values and recommend actions is challenging . A complete feedback loop— data collection and interpretation combined with feedback to modify treatment — has been associated with improved outcomes , yet there remains limited integration of SMBG feedback in diabetes management . Incorporating telehealth remote monitoring and asynchronous electronic health record ( EHR ) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management — employ the complete feedback loop to improve outcomes . Objective The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes . The primary aim was change in glycated hemoglobin (A1c)—a measure of overall glucose management — between groups after 6 months . The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities ( SDSCA ) , Diabetes Empowerment Scale , and Diabetes Knowledge Test . Methods A 2-group r and omized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits .  Participants were aged 30 - 70 years , not using insulin with A1c levels between 7.5 % and 10.9 % ( 58 - 96 mmol/mol ) . The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants , analyze actionable glucose data , and provide feedback . Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly . CDEs called participants monthly to discuss paired glucose testing trends and treatment changes . Separate mixed-effects models were used to analyze data . Results Participants ( N=90 ) were primarily white ( 64 % , 56/87 ) , mean age 58 ( SD 11 ) years , mean body mass index 34.1 ( SD 6.7 ) kg/m2 , with diabetes for mean 8.2 ( SD 5.4 ) years , and a mean A1c of 8.3 % ( SD 1.1 ; 67 mmol/mol ) . Both groups lowered A1c with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months ( SE 0.08 , t159=–2.87 , P=.005 ) . Change in medication ( SE 0.21 , t157=–3.37 , P=.009 ) was significantly associated with lower A1c level . The treatment group significantly improved on the SDSCA subscales carbohydrate spacing ( P=.04 ) , monitoring glucose ( P=.001 ) , and foot care ( P=.02 ) . Conclusions An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A1c levels compared to usual care . Trial Registration Clinical trials.gov NCT01715649 ; https://www . clinical trials.gov/ct2/show/NCT01715649 ( Archived by WebCite at http://www.webcitation.org/6ZinLl8D0 ) BACKGROUND Telemedicine is a promising but largely unproven technology for providing case management services to patients with chronic conditions who experience barriers to access to care or a high burden of illness . METHODS The authors conducted a r and omized , controlled trial comparing telemedicine case management to usual care , with blinding of those obtaining outcome data , in 1,665 Medicare recipients with diabetes , aged 55 years or greater , and living in federally design ated medically underserved areas of New York State . The primary endpoints were HgbA1c , blood pressure , and low-density lipoprotein ( LDL ) cholesterol levels . RESULTS In the intervention group ( n = 844 ) , mean HgbA1c improved over one year from 7.35 % to 6.97 % and from 8.35 % to 7.42 % in the subgroup with baseline HgbA1c > or = 7 % ( n = 353 ) . In the usual care group ( n = 821 ) mean HgbA1c improved over one year from 7.42 % to 7.17 % . Adjusted net reductions ( one-year minus baseline mean values in each group , compared between groups ) favoring the intervention were as follows : HgbA1c , 0.18 % ( p = 0.006 ) , systolic and diastolic blood pressure , 3.4 ( p = 0.001 ) and 1.9 mm Hg ( p < 0.001 ) , and LDL cholesterol , 9.5 mg/dL ( p < 0.001 ) . In the subgroup with baseline HgbA1c > or = 7 % , net adjusted reduction in HgbA1c favoring the intervention group was 0.32 % ( p = 0.002 ) . Mean LDL cholesterol level in the intervention group at one year was 95.7 mg/dL. The intervention effects were similar in magnitude in the subgroups living in New York City and upstate New York . CONCLUSION Telemedicine case management improved glycemic control , blood pressure levels , and total and LDL cholesterol levels at one year of follow-up OBJECTIVE To improve quality and efficiency of care for elderly patients with type 2 diabetes , we introduced elderly-friendly strategies to the clinical decision support system (CDSS)-based ubiquitous healthcare ( u-healthcare ) service , which is an individualized health management system using advanced medical information technology . RESEARCH DESIGN AND METHODS We conducted a 6-month r and omized , controlled clinical trial involving 144 patients aged > 60 years . Participants were r and omly assigned to receive routine care ( control , n = 48 ) , to the self-monitored blood glucose ( SMBG  , n = 47 ) group , or to the u-healthcare group ( n = 49 ) . The primary end point was the proportion of patients achieving A1C < 7 % without hypoglycemia at 6 months . U-healthcare system refers to an individualized medical service in which medical instructions are given through the patient ’s mobile phone . Patients receive a glucometer with a public switched telephone network-connected cradle that automatically transfers test results to a hospital-based server . Once the data are transferred to the server , an automated system , the CDSS rule engine , generates and sends patient-specific messages by mobile phone . RESULTS After 6 months of follow-up , the mean A1C level was significantly decreased from 7.8 ± 1.3 % to 7.4 ± 1.0 % ( P < 0.001 ) in the u-healthcare group and from 7.9 ± 1.0 % to 7.7 ± 1.0 % ( P = 0.020 ) in the SMBG group , compared with 7.9 ± 0.8 % to 7.8 ± 1.0 % ( P = 0.274 ) in the control group . The proportion of patients with A1C < 7 % without hypoglycemia was 30.6 % in the u-healthcare group , 23.4 % in the SMBG group ( 23.4 % ) , and 14.0 % in the control group ( P < 0.05 ) . CONCLUSIONS The CDSS-based u-healthcare service achieved better glycemic control with less hypoglycemia than SMBG and routine care and may provide effective and safe diabetes management in the elderly diabetic 
    Output:
    