***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 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 OBJECTIVE To test whether adding mobile application coaching and patient/provider web portals to community primary care compared with st and ard diabetes management would reduce glycated hemoglobin levels in patients with type 2 diabetes . RESEARCH DESIGN AND METHODS A cluster-r and omized clinical trial , the Mobile Diabetes Intervention Study , r and omly assigned 26 primary care practice s to one of three stepped treatment groups or a control group ( usual care ) . A total of 163 patients were enrolled and included in analysis . The primary outcome was change in glycated hemoglobin levels over a 1-year treatment period . Secondary outcomes were changes in patient-reported diabetes symptoms , diabetes distress , depression , and other clinical ( blood pressure ) and laboratory ( lipid ) values . Maximal treatment was a mobile- and web-based self-management patient coaching system and provider decision support . Patients received automated , real-time educational and behavioral messaging in response to individually analyzed blood glucose values , diabetes medications , and lifestyle behaviors communicated by mobile phone . Providers received quarterly reports summarizing patient ’s glycemic control , diabetes medication management , lifestyle behaviors , and evidence -based treatment options . RESULTS The mean declines in glycated hemoglobin were 1.9 % in the maximal treatment group and 0.7 % in the usual care group , a difference of 1.2 % ( P = 0.001 ) over 12 months . Appreciable differences were not observed between groups for patient-reported diabetes distress , depression , diabetes symptoms , or blood pressure and lipid levels ( all P > 0.05 ) . CONCLUSIONS The combination of behavioral mobile coaching with blood glucose data , lifestyle behaviors , and patient self-management data individually analyzed and presented with evidence -based guidelines to providers substantially reduced glycated hemoglobin levels over 1 year Background There is a strong will and need to find alternative models of health care delivery driven by the ever-increasing burden of chronic diseases . Objective The purpose of this 1-year trial was to study whether a structured mobile phone-based health coaching program , which was supported by a remote monitoring system , could be used to improve the health-related quality of life ( HRQL ) and /or the clinical measures of type 2 diabetes and heart disease patients . Methods A r and omized controlled trial was conducted among type 2 diabetes patients and heart disease patients of the South Karelia Social and Health Care District . Patients were recruited by sending invitations to r and omly selected patients using the electronic health records system . Health coaches called patients every 4 to 6 weeks and patients were encouraged to self-monitor their weight , blood pressure , blood glucose ( diabetics ) , and steps ( heart disease patients ) once per week . The primary outcome was HRQL measured by the Short Form ( 36 ) Health Survey ( SF-36 ) and glycosylated hemoglobin ( HbA1c ) among diabetic patients . The clinical measures assessed were blood pressure , weight , waist circumference , and lipid levels . Results A total of 267 heart patients and 250 diabetes patients started in the trial , of which 246 and 225 patients concluded the end-point assessment s , respectively . Withdrawal from the study was associated with the patients ’ unfamiliarity with mobile phones — of the 41 dropouts , 85 % ( 11/13 ) of the heart disease patients and 88 % ( 14/16 ) of the diabetes patients were familiar with mobile phones , whereas the corresponding percentages were 97.1 % ( 231/238 ) and 98.6 % ( 208/211 ) , respectively , among the rest of the patients ( P=.02 and P=.004 ) . Withdrawal was also associated with heart disease patients ’ comorbidities—40 % ( 8/20 ) of the dropouts had at least one comorbidity , whereas the corresponding percentage was 18.9 % ( 47/249 ) among the rest of the patients ( P=.02 ) . The intervention showed no statistically significant benefits over the current practice with regard to health-related quality of life — heart disease patients : beta=0.730 ( P=.36 ) for the physical component score and beta=-0.608 ( P=.62 ) for the mental component score ; diabetes patients : beta=0.875 ( P=.85 ) for the physical component score and beta=-0.770 ( P=.52 ) for the mental component score . There was a significant difference in waist circumference in the type 2 diabetes group ( beta=-1.711 , P=.01 ) . There were no differences in any other outcome variables . Conclusions A health coaching program supported with telemonitoring did not improve heart disease patients ' or diabetes patients '  quality of life or their clinical condition . There were indications that the intervention had a differential effect on heart patients and diabetes patients . Diabetes patients may be more prone to benefit from this kind of intervention . This should not be neglected when developing new ways for self-management of chronic diseases . Trial Registration Clinical Trials.gov NCT01310491 ; http:// clinical trials.gov/ct2/show/NCT01310491 ( Archived by WebCite at http://www.webcitation.org/6Z8l5FwAM ) BACKGROUND Drawing on previous web-based diabetes management programs based on the Chronic Care Model , we exp and ed an intervention to include care management through mobile phones and a game console web browser . METHODS The pilot intervention enrolled eight diabetes patients from the University of Washington in Seattle into a collaborative care program : connecting them to a care provider specializing in diabetes , providing access to their full electronic medical record , allowing wireless glucose uploads and e-mail with providers , and connecting them to the program 's web services through a game system . To evaluate the study , we conducted qualitative thematic analysis of semistructured interviews . RESULTS Participants expressed frustrations with using the cell phones and the game system in their everyday lives , but liked the wireless system for collaborating with a provider on uploaded glucoses and receiving automatic feedback on their blood sugar trends . A majority of participants also expressed that their participation in the trial increased their health awareness . DISCUSSION Mobile communication technologies showed promise within a web-based collaborative care program for type 2 diabetes . Future intervention design should focus on integrating easy-to-use applications within mobile technologies already familiar to patients and ensure the system allows for sufficient collaboration with a care provider Objective Few interventions have combined life-style and psychosocial approaches in the context of Type 2 diabetes management . The purpose of this study was to determine the effect of a multicomponent behavioral intervention on weight , glycemic control , renal function , and depressive symptoms in a sample of overweight/obese adults with Type 2 diabetes and marked depressive symptoms . Methods A sample of 111 adults with Type 2 diabetes were r and omly assigned to a 1-year intervention ( n = 57 ) or usual care ( n = 54 ) in a parallel groups design . Primary outcomes included weight , glycosylated hemoglobin , and Beck Depression Inventory II score . Estimated glomerular filtration rate served as a secondary outcome . All measures were assessed at baseline and 6 and 12 months after r and omization by assessors blind to r and omization . Latent growth modeling was used to examine intervention effects on each outcome . Results The intervention result ed in decreased weight ( mean [ M ] = 0.322 kg , st and ard error [ SE ] = 0.124 kg , p = .010 ) and glycosylated hemoglobin ( M = 0.066 % , SE = 0.028 % , p = .017 ) , and Beck Depression Inventory II scores ( M = 1.009 , SE = 0.226 , p < .001 ) , and improved estimated glomerular filtration rate ( M = 0.742 ml·min−1·1.73 m−2 , SE = 0.318 ml·min−1·1.73 m−2 , p = .020 ) each month during the first 6 months relative to usual care . Conclusions Multicomponent behavioral interventions  targeting weight loss and depressive symptoms as well as diet and physical activity are efficacious in the management of Type 2 diabetes . Trial Registration : This study is registered at Clinical trials.gov ID : NCT01739205 Aim To explain the subadditive efficacy typically observed with initial combination treatments for type 2 diabetes . Methods Individual subject data from 1186 patients with type 2 diabetes [ mean glycated haemoglobin ( HbA1c ) = 8.8 % ] treated with metformin , canagliflozin or canagliflozin + metformin were used . The baseline HbA1c versus ΔHbA1c relationships for monotherapy arms were determined using analysis of covariance and then used to predict efficacy in the combination arms by modelling how applying one treatment lowers the ‘ effective baseline HbA1c ’ for a second treatment . The model was further tested using data from several published combination studies . Results The mean ΔHbA1c levels were −1.25 , −1.33 , −1.37 , −1.77 and −1.81 % with metformin , canagliflozin 100 mg , canagliflozin 300 mg , canagliflozin 100 mg/metformin and canagliflozin 300 mg/metformin , respectively . Using the monotherapy results , the predicted efficacy for the canagliflozin/metformin arms was within 10 % of the observed values using the new model , whereas assuming simple additivity overpredicted efficacy in the combination arms by nearly 50 % . For 10 other published initial combination studies , predictions from the new model [ mean ( st and ard error ) predicted ΔHbA1c = 1.67 % ( 0.14 ) ] were much more consistent with observed values [ ΔHbA1c = 1.72 % ( 0.12 ) ] than predictions based on assuming additivity [ predicted ΔHbA1c = 2.19 % ( 0.21 ) ] . Conclusions The less‐than‐additive efficacy commonly seen with initial combination treatments for type 2 diabetes can be largely explained by the impact of baseline HbA1c on the efficacy of individual treatments . Novel formulas have been developed for predicting the efficacy of combination treatments based on the efficacy of individual treatments and the baseline HbA1c of the target patients Abstract Background : Overseeing proper insulin initiation and titration remains a challenging task in diabetes care . Recent advances in mobile technology have enabled new models of collaborative care between patients and healthcare providers ( HCPs ) . We hypothesized that the adoption of such technology could help individuals starting basal insulin achieve better glycemic control compared with st and ard clinical practice . Material s and Methods : This was a 12 ± 2-week r and omized controlled study with 40 individuals with type 2 diabetes who were starting basal insulin due to poor glycemic control . The control group ( n = 20 ) received st and ard face-to-face care and phone follow-up as needed in a tertiary center , whereas the intervention group ( n = 20 ) received care through the cloud-based diabetes management program where regular communications about glycemic control and insulin doses were conducted via patient self-tracking tools , shared decision-making interfaces , secure text messages , and virtual visits ( audio , video , and shared screen control ) instead of office visits . Results : By intention-to-treat analysis , the intervention group achieved a greater hemoglobin A1c decline compared with the control group ( 3.2 ± 1.5 % vs. 2.0 % ± 2.0 % ; P = 0.048 ) . The Diabetes Treatment Satisfaction Question naire showed a significant improvement in the intervention group compared with the control group ( an increase of 10.1 ± 11.7 vs. 2.1 ± 6.5 points ; P = 0.01 ) 
    Output:
    
No effects of mHealth app interventions were found on blood pressure , serum lipids , or weight .
Smartphone apps offered moderate benefits for T2DM self-management .