***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: Lowering blood pressure reduces cardiovascular risk , yet hypertension is poorly controlled in diabetic patients . In a pilot study we demonstrated that a home blood pressure telemonitoring system , which provided self-care messages on the smartphone of hypertensive diabetic patients immediately after each reading , improved blood pressure control . Messages were based on care paths defined by running averages of transmitted readings . The present study tests the system 's effectiveness in a r and omized , controlled trial in diabetic patients with uncontrolled systolic hypertension . Of 244 subjects screened for eligibility , 110 ( 45 % ) were r and omly allocated to the intervention ( n=55 ) or control ( n=55 ) group , and 105 ( 95.5 % ) completed the 1-year outcome visit . In the intention-to-treat analysis , mean daytime ambulatory systolic blood pressure , the primary end point , decreased significantly only in the intervention group by 9.1±15.6 mmHg ( SD ; P<0.0001 ) , and the mean between-group difference was 7.1±2.3 mmHg ( SE ; P<0.005 ) . Furthermore , 51 % of intervention subjects achieved the guideline recommended target of < 130/80 mmHg compared with 31 % of control subjects ( P<0.05 ) . These improvements were obtained without the use of more or different antihypertensive medications or additional clinic visits to physicians . Providing self-care support did not affect anxiety but worsened depression on the Hospital Anxiety and Depression Scale ( baseline , 4.1±3.76 ; exit , 5.2±4.30 ; P=0.014 ) . This study demonstrated that home blood pressure telemonitoring combined with automated self-care support reduced the blood pressure of diabetic patients with uncontrolled systolic hypertension  and improved hypertension control . Home blood pressure monitoring alone had no effect on blood pressure . Promoting patient self-care may have negative psychological effects BACKGROUND : Adolescents with chronic disease ( ACD ) must develop independent disease self-management and learn to communicate effectively with their health care team to transition from pediatric to adult-oriented health care systems . Disease-specific interventions have been implemented to aid specific ACD groups through transition . A generic approach might be effective and cost-saving . METHODS : Eighty-one ACD , aged 12 to 20 years , were recruited for a r and omized clinical trial evaluating an 8-month transition intervention ( MD2Me ) . MD2Me recipients received a 2-month intensive Web-based and text-delivered disease management and skill-based intervention followed by a 6-month review period . MD2Me recipients also had access to a texting algorithm for disease assessment and health care team contact . The intervention was applicable to adolescents with diverse chronic illnesses . Controls received mailed material s on general health topics . Disease management , health-related self-efficacy , and health assessment s were performed at baseline and at 2 and 8 months . Frequency of patient-initiated communications was recorded over the study period . Outcomes were analyzed according to assigned treatment group over time . RESULTS : MD2Me recipients demonstrated significant improvements in performance of disease management tasks , health-related self-efficacy , and patient-initiated communications compared with controls . CONCLUSIONS : Outcomes in ACD improved significantly among recipients of a generic , technology-based intervention . Technology can deliver transition interventions to adolescents with diverse chronic illnesses , and a generic approach offers a cost-effective means of positively influencing transition outcomes . Further research is needed to determine whether improved short-term outcomes translate into an improved transition for ACD 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 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 patients It is well-known that the United States is facing an obesity epidemic , and the long-term sequelae are costly ( 1 , 2 ) . Research ers continue to search for effective  weight-loss interventions that can be applied in outpatient setting s , but these are often time-consuming and re source -intensive , requiring repeated counseling ( 3 ) . It is no surprise that primary care providers often omit discussing weight loss with obese patients and rarely spend adequate time on counseling ( 4 , 5 ) . Smartphone applications ( apps ) may provide an alternative to re source -intensive weight-loss programs . In December 2013 , a survey by the Pew Research Center found that 58 % of Americans own smartphones and ownership is increasing among every demographic group , including low-income population s ( 6 ) . The nascent field of mobile health is rapidly exp and ing ; experts estimate that as many as 40000 health-related apps were available in 2012 , comprising a $ 718 million industry ( 7 ) . Many of these apps aim to help persons change behaviors to improve health , including weight loss , yet few have been rigorously evaluated . An effective app for reducing body weight could produce tremendous cost savings by preventing long-term complications , such as diabetes and cardiovascular events . To our knowledge , however , no studies have examined the effectiveness of delivering or prescribing an app for weight loss to patients in a clinical setting . We evaluated one of the most popular publicly available apps for  weight loss : MyFitnessPal ( MFP ) ( MyFitnessPal ) . MFP has received the highest possible rating , 5 out of 5 stars , from thous and s of review ers on the Apple and And roid ( Google ) app store Web sites . It has nearly 1 million likes on Facebook , and the company reports more than 50 million registered users . MFP incorporates elements of social cognitive theory , including self-monitoring , goal setting , and feedback . We sought to test the effect of providing this free , widely used smartphone app for weight loss to patients in their primary care clinic . Methods Design Overview mFit ( The Mobile Fitness Project ) was a r and omized , controlled trial in which participants were r and omly assigned to receive  usual primary care ( n= 107 ) or usual primary care plus the MFP app ( n= 105 ) ( Figure 1 ) . Assessment s were completed at baseline , 3 months , and 6 months between August 2012 and May 2013 . The institutional review board of the University of California , Los Angeles ( UCLA ) , approved the study , and all participants provided written informed consent . Figure 1 . Study flow diagram . * Among the 6 intervention group participants who withdrew , 3 reported not having enough time to use the app , 2 did not have time to return for follow-up , and 1 was no longer interested in participating in the study . Among the 8 control group participants who withdrew , 6 reported not having time to return for follow-up and 2 were no longer interested in participating in the study . Study data were collected on Apple iPads using Research Electronic Data Capture ( REDCap ) tools hosted at UCLA . REDCap is a secure , Web-based application design ed to support data capture for research studies , providing an intuitive interface , audit trails , and automated export ( 8) . Setting and Participants Participants were recruited from 2 UCLA primary care clinics that serve ethnically and socioeconomically diverse patient population s. Eligibility criteria included age 18 years or older , body mass index ( BMI ) of 25 kg/m2 or greater , and smartphone ownership . Participants also had to answer yes when asked , Are you interested in losing weight ? Exclusion criteria were current , planned , or previous pregnancy within 6 months ; receipt of hemodialysis ; life expectancy less than 6 months ; lack of interest in weight loss ; or current use of a smartphone app for weight loss . Screening and R and omization Patients were recruited during routine primary care visits at their respective clinics . The research team provided a script to medical assistants to use with any patients with BMI greater than 25 kg/m2 . Patients interested in enrollment were referred to the on-site research assistant , who screened , received consent from , and completed surveys with each patient . Participants were r and omly assigned in blocks by BMI of 25 to 30 kg/m2 and BMI greater than 30 kg/m2 to ensure roughly equal distribution of overweight and obese patients between the intervention and control groups . Our statistician used R ( R Foundation for Statistical Computing ) to generate the permuted block sequence . We printed the sequence and placed it in opaque envelopes . Research assistants helped intervention group participants download the MFP app onto their smartphone and showed them an instructional video developed by MFP . These participants also received a telephone call from the same research assistant 1 week after enrollment to assist with any technical problems with the app . Research assistants told control group patients to choose any activities you 'd like to lose weight , without specifying any particular interventions . Control group participants were aware that they were participating in a study of a weight-loss app but were blinded to the name of the app . To minimize contamination of the control group , providers and clinic staff were also blinded to the name of the app and to group assignment . At the 3-month follow-up visit , all participants received a 1-page educational h and out on healthy eating from www.myplate.gov . Participants received a $ 20 gift card for attending each follow-up visit . Each participant 's primary care provider was notified of their enrollment in the study . Blood pressure was measured at baseline , 3 months , and 6 months by trained research assistants using an automated monitor ( Dinamap , GE Medical Systems ) . Intervention We selected MFP as our intervention on the basis of 2 focus groups held with overweight primary care patients . Patients were asked about their impressions of various text messagebased programs and smartphone apps . Overall , there was much more interest in smartphone apps than text-based programs . A few participants stated they enjoyed using MFP , and a majority expressed great interest in trying it . Although we selected MFP as our intervention , there are many similar , publicly available apps that may be as popular as MFP . Some of these apps have been assessed in prior studies , but to our knowledge , none have been evaluated in a r and omized trial ( 9 ) . MFP was design ed 
    Output:
    
The majority of these interventions showed improvement on primary endpoints , such as HbA1c ;  mHealth technologies that interacted with both patients and providers were more likely to be effective .
There was little evidence about persistent use by patients , use by a patient ’s health care provider , or long-term effectiveness .