There were three reported  deaths out of 3578 participants .Computer-based  diabetes self-management interventions currently have limited effectiveness .
Current interventions do not show adequate evidence for improving depression , health-related quality of life or weight .
AUTHORS ' CONCLUSIONS Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control and the effect was larger in the mobile phone subgroup .
There is no evidence to show benefits in other biological outcomes or any cognitive , behavioural or emotional outcomes
BACKGROUND Diabetes is one of the commonest chronic medical conditions , affecting around 347 million adults worldwide .
Structured patient education programmes reduce the risk of diabetes-related complications four-fold .
Internet-based self-management programmes have been shown to be effective for a number of long-term conditions , but it is unclear what are the essential or effective components of such programmes .
If computer-based self-management interventions improve outcomes in type 2 diabetes , they could potentially provide a cost-effective option for reducing the burdens placed on patients and healthcare systems by this long-term condition .
OBJECTIVES To assess the effects on health status and health-related quality of life of computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus .
A Diabetes Report Card for the United States: Quality of Care in the 1990s
Context There are no recent national evaluations of diabetes care in the United States . Contribution Using data from two national surveys , this study documents a substantial gap between the recommended and actual care of diabetes in the United States between 1988 and 1995 . Many participants had hemoglobin A1c levels greater than 9.5 % ( 18.0 % ) , poorly controlled blood pressure ( 34.3 % ) , and elevated cholesterol levels ( 58.0 % ) . Implication s As a nation , the United States is falling short in caring for patients with diabetes . A periodic national report card may help us to gauge the success of future efforts to improve . The Editors Diabetes , a typical example of chronic disease , currently affects 16 million people in the United States , causes considerable morbidity and mortality , and costs the nation almost $ 100 billion per year ( 1 , 2 ) . Fortunately , several efficacious treatment strategies to prevent or delay diabetes complications have emerged during the past decade , including  control of glycemia , lipids , and hypertension ; early detection and treatment of diabetic retinopathy , nephropathy , and foot disease ;  therapy with aspirin and angiotensin-converting enzyme inhibitors ; and influenza and pneumococcal vaccines ( 3 - 12 ) . Many of these treatments are cost-effective ( 13 - 17 ) . However , their implementation in the United States remains suboptimal and varied ( 18 - 22 ) . There is considerable pressure on U.S. health care systems to improve this situation and to deliver high- quality care while controlling costs . Influential bodies , such as the Institute of Medicine , have recently emphasized the need for strategies to improve the current quality of all medical care in the United States ( 23 ) . Federal agencies are also increasingly responding with initiatives to address quality of care ( 24 , 25 ) . One powerful quality initiative in the United States is the Diabetes Quality Improvement Project ( DQIP ) , which is design ed to influence the care of millions of U.S. patients with diabetes ( 26 ) . The st and ard measures of quality of diabetes care proposed by DQIP were incorporated into the National Committee for Quality Assurance ( NCQA ) Health Plan Employer and Data Information Set . In contrast to guidelines or clinical goals for individual care , DQIP measures are design ed to assess the performance of health care systems for a population , and they offer a wayto make comparisons across health care systems . Several public and private health care systems , including the Indian Health Service ( 27 ) , the Veterans Administration , the U.S. Department of Defense , and numerous managed care organizations , have adopted these indicators . Currently , there is no national reference for assessing the quality of diabetes care using a st and ard set of measures . Such a reference could help health care systems using DQIP measures to compare their own performance against population norms rather than the norms of other health systems . This reference could be a benchmark for assessing population changes in quality of diabetes care after implementation of national quality improvement initiatives recommended by the Institute of Medicine and NCQA . Furthermore , the use of a st and ard set of measures to assess the quality of diabetes care at a national level can facilitate international comparison . For this report , we used U.S. national data to provide a reference and benchmark of the quality of diabetes care as measured by the DQIP indicators . Methods Surveys We used two federally funded , nationally representative population -based surveys , the Third U.S. National Health and Nutrition Examination Survey ( NHANES III ) ( 19881994 ) and the Behavioral Risk Factors Surveillance System ( BRFSS ) ( 1995 ) . Data from these two surveys were analyzed separately . Some measures came exclusively from NHANES III , and others came from BRFSS . We included adults 18 to 75 years of age who reported receiving a previous diagnosis of diabetes from a physician . Women with gestational diabetes were excluded . The two survey groups were similar in demographic characteristics , household income , health insurance coverage , and prevalence of diabetes . In NHANES III compared with BRFSS , more persons did not have a high school education ( 41.5 % vs. 27.1 % ) and fewer persons used insulin ( 30.9 % vs. 39.8 % ) ( Table 1 ) . Table 1 . Characteristics of Persons with Self-Reported Diabetes in the Third U.S. National Health and Nutrition Examination Survey ( 19881994 ) and the Behavioral Risk Factors Surveillance System1995 The methods for NHANES III are described elsewhere ( 28 ) . Briefly , the NHANES III survey used a nationally representative sample of the civilian noninstitutionalized population obtained through a complex multistage cluster sampling design , with oversampling of non-Hispanic black persons , Mexican-American persons , and elderly persons . During a household interview , data were collected on sociodemographic characteristics and medical and family history . Within 4 weeks , this was followed by a clinical examination at a mobile examination center . The procedures for blood collection and processing have also been described elsewhere ( 29 , 30 ) . Low-density lipoprotein ( LDL ) cholesterol level was calculated by using the Friedwald equation for persons who fasted more than 8 hours ( n = 302 ) . Data from NHANES III were self-reported ( demographic and clinical variables ) or were obtained during clinical examination ( hemoglobin A1c level , cholesterol level , triglyceride level , and blood pressure ) . The survey included 16 705 participants 18 to 75 years of age , 1026 of whom reported receiving a diagnosis of diabetes from a physician before the survey . The BRFSS is an annual r and om-digit telephone survey of state population based sample s of the civilian noninstitutionalized population of adults ( 18 years of age ) in each of the 50 states and the District of Columbia . Its purpose , methods , and data analyses have been described in detail elsewhere ( 31 ) . A diabetes-specific BRFSS module was used to collect data on clinical characteristics and preventive care practice s from respondents with diabetes . Data from BRFSS were self-reported ( dilated eye examination , lipid test , foot examination , and demographic and clinical variables ) . The survey included 103 929 participants 18 to 75 years of age , 3059 of whom completed the diabetes module and reported receiving a diagnosis of diabetes from a physician . We used the 1995 BRFSS so that the time frame would correspond with NHANES III . Quality -of-Care Measures We assessed the quality of diabetes care using indicators from the DQIP measurement set for which data were available . The DQIP began under the sponsorship of a coalition that included the American Diabetes Association , the Foundation for Accountability , the Health Care Financing Administration , and NCQA . The American Academy of Physicians , the American College of Physicians , the Veterans Administration , and the Centers for Disease Control and Prevention later joined the coalition . A committee of experts in diabetes care was responsible for developing the DQIP measure set . The DQIP classified the proposed indicators into three categories : accountability indicators , quality improvement indicators , and indicators under field-testing . The accountability measures , which are well grounded in evidence , have received consensus support from the scientific and medical community and have been extensively field-tested in a variety of health care setting s. These measures are used to compare health systems and plans or providers . The quality improvement measures are not considered appropriate for comparing systems and plans or providers but were recommended by the NCQA for assessing internal performance . Table 2 shows all of the analyzed quality indicators and their data sources . The DQIP measures are a combination of process and outcome measures . In this paper , we use DQIP terminology and DQIP accountability and quality improvement measures . Table 2 . Diabetes Quality Improvement Project Indicators and Related Data Sources Statistical Analysis All analyses were conducted by using SAS software ( SAS Institute , Inc. , Cary , North Carolina ) for data management and SUDAAN ( Research Triangle Institute , Research Triangle Park , North Carolina ) to account for the complex sampling scheme , the unequal probability of selection , the oversampling of certain demographic groups , and nonresponse factors ( 32 , 33 ) . Level of care was estimated by the percentage , with corresponding 95 % CIs , of respondents who reported each preventive care practice . We estimated prevalence by groups of sex , age , ethnicity , education , health insurance , type of treatment , and duration of diabetes .  Multiple logistic regression and computation of predictive margins were used to estimate the probability of receiving DQIP indicators when we controlled for all other independent variables . Predictive margins are a type of direct st and ardization in which the predicted values from the logistic regression models are averaged over the covariate distribution in the population ( 34 ) . Predictive margins and their st and ard errors from logistic regression models are provided by the current version of SUDAAN ( 33 ) . Taylor linearization is used in SUDAAN for calculating these st and ard errors . Odds ratios are usually used to display the result from logistic regression models . However , predictive margins are easier to interpret than odds ratios , and they do not require design ating one of the groups as the referent group . Results Diabetes Quality Improvement Project Accountability and Quality Improvement Measures Among adults 18 to 75 years of age with diabetes , 28.8 % had had  hemoglobin A1c levels  tested in the past year , and 18.0 % had a  hemoglobin A1c  level greater than 9.5 % .  Biannual lipid testing  was done in 85.3 % of participants , but only 42.0 % had an LDL cholesterol level less than 3.4 mmol/L ( < 130 mg/dL ) . Only 65.7 % had blood pressure less than 140/90 mm Hg , 63.3 % had an annual dilated eye examination ,
There were three reported  deaths out of 3578 participants .Computer-based  diabetes self-management interventions currently have limited effectiveness .
Current interventions do not show adequate evidence for improving depression , health-related quality of life or weight .
AUTHORS ' CONCLUSIONS Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control and the effect was larger in the mobile phone subgroup .
There is no evidence to show benefits in other biological outcomes or any cognitive , behavioural or emotional outcomes
BACKGROUND Diabetes is one of the commonest chronic medical conditions , affecting around 347 million adults worldwide .
Structured patient education programmes reduce the risk of diabetes-related complications four-fold .
Internet-based self-management programmes have been shown to be effective for a number of long-term conditions , but it is unclear what are the essential or effective components of such programmes .
If computer-based self-management interventions improve outcomes in type 2 diabetes , they could potentially provide a cost-effective option for reducing the burdens placed on patients and healthcare systems by this long-term condition .
OBJECTIVES To assess the effects on health status and health-related quality of life of computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus .