***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 —We examined the association between depression , measured as either a continuous symptom severity score or a clinical disorder variable , with self-care behaviors in type 2 diabetes . RESEARCH DESIGN AND METHODS —We surveyed 879 type 2 diabetic patients from two primary care clinics using the Harvard Department of Psychiatry/National Depression Screening Day Scale ( H AND S ) , the Summary of Diabetes Self-Care Activities , and self-reported medication adherence . RESULTS —Of the patients , 19 % met the criteria for probable major depression ( H AND S score ≥9 ) , and an additional 66.5 % reported at least some depressive symptoms . After controlling for covariates , patients with probable major depression reported significantly fewer days ' adherent to diet , exercise , and glucose self-monitoring regimens  ( P < 0.01 ) and 2.3-fold increased odds of missing medication doses in the previous week ( 95 % CI 1.5–3.6 , P < 0.001 ) compared with all other respondents . Continuous depressive symptom severity scores were better predictors of nonadherence to diet , exercise , and medications than categorically defined probable major depression . Major depression was a better predictor of glucose monitoring . Among the two-thirds of patients not meeting the criteria for major depression ( H AND S score < 9 , n = 709 ) , increasing H AND S scores were incrementally associated with poorer self-care behaviors ( P < 0.01 ) . CONCLUSIONS —These findings challenge the conceptualization of depression as a categorical risk factor for nonadherence and suggest that even low levels of depressive symptomatology are associated with nonadherence to important aspects of diabetes self-care . Interventions aim ed at alleviating depressive symptoms , which are quite common , could result in significant improvements in diabetes self-care OBJECTIVE To determine medication adherence and predictors of suboptimal adherence in a community cohort of patients with diabetes and to test the hypothesis that adherence decreases with increased number of medicines prescribed . RESEARCH DESIGN AND METHODS A total of 128 r and omly selected patients with type 2 diabetes from a single community health center responded to a pharmacist-administered question naire regarding medication use . Survey data were linked to clinical data available from the electronic medical record . We assessed self-reported adherence rates for each diabetes-related medicine , barriers and attitudes regarding medication use , and HbA(1c ) , total cholesterol , and blood pressure levels . RESULTS Patients were taking a mean of 4.1 ( + /-1.9 ) diabetes-related medicines . The average 7-day adherence was 6.7 + /- 1.1 days . Total number of medicines prescribed was not correlated with medication adherence . Adherence was significantly lower for medicines not felt to be improving current or future health ( 6.1 vs. 6.9 days out of 7 , P < 0.001 ) . Among patients on three or more medicines , 71 % ( 15 of 21 patients ) with suboptimal adherence were perfectly adherent with all but one medicine . Side effects were the most commonly reported problem with medication use . Of 29 medicines causing side effects that interfered with adherence , 24 ( 83 % ) did so for > 1 month , and only 7 ( 24 % ) were reported to the patient 's primary care physician . CONCLUSIONS In this sample , patients reported very high medication adherence rates regardless of number of medicines prescribed . Among patients on multiple medicines , most patients with suboptimal adherence were perfectly adherent to all but one medicine . Unreported side effects and a lack of confidence in immediate or future benefits were significant predictors of suboptimal adherence . Physicians should not feel deterred from prescribing multiple agents in order to achieve adequate control of hyperglycemia , hypertension , and hyperlipidemia OBJECTIVE : To compare medication adherence calculated from four different data sources including a pill count and self-report obtained during a home medication history , as well as calculations based on refill frequency derived from a provincial prescription cl aims data base ( manual and electronic ) . DESIGN : Baseline medication adherence was collected as part of a prospect i ve , r and omized , controlled study . Mean medication adherence results obtained from the four data sources were compared using repeated- measures ANOVA followed by a Tukey 's multiple range test . SETTING : A pharmacy consultation service located at an interdisciplinary wellness center for noninstitutionalized elderly . PATIENTS : 65 years or older , noninstitutionalized , taking one or more prescribed or nonprescribed medications . Clients would either present to the wellness center or be referred by the Provincial Home Care program . RESULTS : When calculated from self-report or manual or electronic prescription cl aims data , mean percent adherence by drug was high and not statistically different ( 95.8 % ± 17.1 % , 107.6 % ± 40.3 % , and 94.6 % ± 24.0 % , respectively ) , whereas the pill count adherence was significantly lower at 74.0 % ± 41.5 % ( p < 0.0001 ) . CONCLUSIONS : An unexpected finding was that the pill count technique used in this study of elderly clients using chronic , repeat medications appeared to underestimate medication adherence . Numerous other limitations of pill count , self-report , and a province-wide prescription cl aims data base in estimating medication adherence are presented . When using medication adherence as a process measure , the research er and practitioner should be aware of the limitations unique to the data source they choose , and interpret data cautiously Background : Medication adherence is suboptimal , and clinicians and research ers struggle with identifying nonadherent patients . Various measures of medication adherence exist , but there is controversy regarding which measures provide acceptable data and how nonadherence should be defined . Objective : To assess agreement among patient self-report , pharmacy refill , and electronic adherence measures and compare the sensitivity and specificity of different cut-points for defining nonadherence . Methods : Data were analyzed from 2 similarly design ed r and omized controlled trials that assessed a  pharmacist 's intervention to improve medication adherence among patients with hypertension or heart failure . For each participant , adherence was measured by patient self-report , prescription refill records , and electronic lids on medication containers . Agreement among measures was assessed using Spearman 's correlation coefficient rho . Correlation coefficients were compared by patient characteristics using Fisher 's Z transformation , The sensitivity and specificity of different cut-points for defining nonadherence were calculated . Results : Median adherence was 84 % for self-report , 86 % for electronic , and 91 % for prescription refill adherence measurement . Refill and electronic adherence demonstrated the best agreement among measures ( rho = 0.48 ) . Age , depression , and other comorbid conditions influenced agreement among measures . Measures were generally in agreement , regardless of how nonadherence was defined . A cut-point of 80 % illustrated a fair balance between sensitivity and specificity for all measures . Conclusions : All measures provided similar estimates of overall adherence , although refill and electronic measures were in highest agreement . In selection of a measure , practitioners should consider population and disease characteristics , since measurement agreement could be influenced by these and other factors . The commonly used , clinical ly based cut-point of 80 % had a reasonable balance between sensitivity and specificity in studies of adherence in patients with heart failure or hypertension 
    Output:
    
RESULTS Demographic , disease-related and treatment-related , socio-economic and cultural factors were associated with medication adherence in the population s that were studied .