among adults with type 2 diabetes ( t2 dm ) , suboptimal medication adherence is common ( 1,2 ) and is associated with higher healthcare costs and worse health outcomes ( 2,3 ) .
few interventions have been designed to promote adherence to diabetes medications , resulting in limited knowledge about what content should be included in interventions to improve this outcome ( 4 ) . to date , interventions that have effectively improved medication adherence in diabetes ( 510 ) have coupled content with reminder systems ( 6,7 ) and/or disseminated content using labor - intensive contacts with healthcare providers ( 5,6,810 ) .
although much has been gleaned from these interventions , it is unclear what content has been more or less effective .
the incorporation of theory - based behavior change content in interventions to improve medication adherence may offset the need for labor - intensive dissemination strategies , thereby increasing the likelihood of interventions being successfully implemented , adopted , and sustained .
furthermore , theories specifying modifiable and measurable determinants of behavior provide an opportunity to improve the mechanisms underlying behavioral performance , which allows for identifying why an intervention was more or less effective in changing behavior for future intervention efforts ( 11 )
. however , to our knowledge , no theoretical framework detailing the content necessary for medication adherence promotion in diabetes has been validated , and no results from diabetes medication adherence promotion interventions with theory - based content have been published to date . in hiv , interventions based on the information  motivation  behavioral skills ( imb )
the imb model of adherence has been validated with cross - sectional data collected from diverse samples of hiv - infected persons ( 1720 ) and then used to design interventions that have successfully improved both medication adherence ( 1316 ) and clinical outcomes ( 14,16 ) .
although medication adherence is just one of many recommended diabetes self - care behaviors , each recommended behavior ( e.g. , exercising and blood glucose testing ) is complex , with specific determinants that should be identified and accounted for in interventions .
the imb model is a behavior - specific theory that posits the performance of a behavior requires behavior - specific information , motivation , and behavioral skills .
while the imb model has predicted and promoted adherence to exercise and dietary recommendations among adults with t2 dm ( 21,22 ) , it has not yet been validated as a useful framework to promote diabetes medication adherence . according to the imb model of adherence ,
medication adherence is determined by the extent to which an individual is informed about his / her regimen , is motivated to adhere , and possesses the necessary behavioral skills to adhere in a variety of situations ( 12,1820 ) ( fig .
adherence information includes accurate knowledge about a regimen ( i.e. , how and when to take medications and what dose to take ) , potential side effects and drug interactions ( 12,1820 ) , and having accurate heuristics and theories that support consistent adherence ( as opposed to inaccurate heuristics such as  i only have to take medications when my blood sugar is high  ) ( 12 ) .
adherence motivation is a function of personal and social motivation to adhere ( 12 ) .
personal motivation to adhere reflects an individual s attitudes about adherence and is based on one s beliefs that medications are helpful , and not taking medications as prescribed would have undesirable consequences . social motivation to adhere rests on one s perceptions of social norms endorsing adherence and/or social and instrumental support for adherence .
adherence behavioral skills includes objective and perceived abilities or self - efficacy to obtain , store , have accessible , and self - cue the use of medications as directed across situations and despite challenges ( 12 ) .
solid lines indicate an effect between constructs , and the dashed line indicates a feedback loop in which health outcomes affect future levels of adherence information and motivation , which in turn affect adherence behavioral skills and subsequent diabetes medication adherence and health outcomes .
the imb model of adherence ( 12 ) suggests that adherence information and motivation often covary ( 17,19 ) ( i.e. , more knowledge may lead to increased motivation and motivated individuals may possess more knowledge ) , but adherence motivation may be present in the context of inaccurate or insufficient adherence information and vice versa ( 18,20 ) .
adherence information and motivation affect medication adherence primarily through the enactment of adherence behavioral skills used to initiate and maintain medication adherence ( 1820 ) , but may also directly affect behavior ( 17 ) when complex behavioral skills are not required for the performance of the behavior ( 12 ) . the model s primary outcome is adherence , but it also stipulates that consistent adherence should be associated with favorable health outcomes ( e.g. , glycemic control ) , which , in turn , contribute to high levels of adherence information and motivation overtime ( 12 ) ( fig .
many factors consistently associated with adults diabetes medication adherence ( 23 ) are included in the imb constructs .
information includes comprehension of the treatment regimen ; personal motivation includes perceptions of benefits and adverse effects of treatment ; and behavioral skills include patients ability to overcome cost - related barriers to adherence .
additional evidence suggests deficits in medication adherence  related information ( 24 ) , motivation [ i.e. , personal attitudes ( 24,25 ) and social support ( 26 ) ] , and behavioral skills [ i.e. , objective skills and self - efficacy ( 24,25 ) ] are associated with suboptimal adherence to diabetes medications and are more common among populations with lower socioeconomic status ( ses ) ( 24,27 ) .
additional factors associated with diabetes medication adherence include insulin use ( 28 ) , regimen complexity ( 23 ) , and psychological distress ( 23 ) .
however , each of these factors alone can not fully explain the presence , persistence , and complexity of nonadherence to diabetes medications .
thus , the imb model of adherence may provide a multivariate explanation of nonadherence and guide to promoting medication adherence among people with diabetes . because populations with low ses are at increased risk of nonadherence to diabetes medications ( 24,27 ) , we tested the imb model s predictions in a diverse , low ses sample from a federally qualified health center ( fqhc ) .
consistent with the imb model , we hypothesized that adherence - related information and motivation would determine adherence - related behavioral skills , which , in turn , would determine adherence behavior , and behavior would determine glycemic control .
we also tested whether insulin status and/or regimen complexity explained any additional variance in medication adherence or moderated associations between adherence - related information , motivation , or behavioral skills and adherence behavior .
we recruited adults ( 18 years old ) who were diagnosed with t2 dm and received outpatient care at an fqhc in nashville , tn , between june 2010 and november 2012 . trained research assistants ( ras ) worked with clinic personnel to consecutively recruit eligible patients who arrived for a clinic appointment , approaching patients in the clinic waiting room to describe the study and advertising the study on flyers in the clinic to prompt self - referrals .
eligible patients were english- or spanish - speaking adults who were self - administering prescribed medications for t2 dm .
all study materials were translated from english to spanish using the forward - backward technique by licensed translators ( 29 ) .
ras consulted with clinic personnel to identify and exclude patients who did not have a social security number required for compensation or had an intellectual disability , unintelligible speech , a lack of orientation to person / place / time , or a severe hearing impairment .
participants provided informed consent and completed an interviewer - administered survey in a private room before and/or after their clinic appointment . to accommodate high rates of limited literacy among patients treated at fqhcs in the southeastern u.s .
( 30 ) , ras read all items and response options aloud to ensure participants ability to read did not compromise their data .
ras also supplied a copy of each set of response options in large font , so participants could respond aloud and/or point to their response .
clinic nurses administered a point - of - care a1c test , and ras reviewed medical records .
demographic characteristics included self - reported age , sex , race , ethnicity , income , education , and insurance status .
clinical characteristics included self - reported duration of diagnosed diabetes in months and years and the number and type of prescribed diabetes medications obtained from the medical record .
we dichotomized insulin status ( prescribed insulin vs. oral agents only ) , and regimen complexity was indicated by the number of prescribed diabetes medications .
adherence information was assessed with the five - item diabetes medication knowledge questionnaire ( dmkq ) , which assesses respondents knowledge of the : 1 ) name ; 2 ) purpose ; 3 ) dosing schedule ; 4 ) side effects ; and 5 ) appropriate management of a missed dose for a single diabetes medication in their regimen ( 31 ) . to generate a more accurate estimate of adherence information ,
we administered the dmkq for each diabetes medication in a participant s regimen . in our sample , internal consistency reliability for the dmkq was unacceptable (  = 0.36 ) , so we examined pearson correlation coefficients between individual dmkq items and the measures of medication adherence ( described below ) to identify relevant adherence - related information .
significant dmkq items asked respondents about their dosing schedule ( i.e. , how and when to take a medication ) and handling of missed doses .
the internal consistency reliability of these items across diabetes medications was good (  = 0.80 ) , so we summed item scores for each medication and then averaged scores across medications to create a two - item dmkq composite .
adherence motivation was assessed with the medicines for diabetes questionnaire ( mdq ) , which measures respondents beliefs about taking diabetes medications ( 32 )
. the behavioral beliefs and normative beliefs subscales of the mdq were used to assess personal and social motivation to adhere , respectively .
the seven - item behavioral beliefs subscale asks respondents to report the degree to which they believe taking diabetes medications would  help me stay well  or  cause me to gain weight .  the three - item normative beliefs subscale asks respondents the degree to which their doctor / nurse , family / relatives , or partner / spouse would approve of them taking diabetes medications regularly . for both subscales ,
response options range from 1 , strongly disagree , to 5 , strongly agree , and are averaged to create a composite score ranging from 15 , with higher scores indicating greater adherence motivation .
adherence behavioral skills were assessed with the revised medication adherence self - efficacy scale ( mases - r ) , which is a reliable and valid measure of medication adherence self - efficacy ( 33 ) . the 13-item mases - r asks respondents to report their degree of confidence in taking their medications in various situations / circumstances such as  when you are traveling  or  whatever they cost .
 we added the word  diabetes  to each item to specify diabetes medications ( e.g. ,  how confident are you that you can take your diabetes medications   ) .
response options range from 1 , not at all sure , to 4 , extremely sure , and are averaged to create a composite score ranging from 14 , with higher scores indicating greater adherence behavioral skills .
diabetes medication adherence was assessed with two reliable and valid self - report measures : the summary of diabetes self - care activities medications subscale ( sdsca - ms ) ( 34 ) and the adherence to refills and medications scale for diabetes ( arms - d ) ( 35 ) .
although the sdsca - ms is the most widely used self - report measure of medication adherence in diabetes , the arms - d is more sensitive to nonadherence and more predictive of glycemic control ( 35 ) . including both measures
the two - item sdsca - ms asks respondents ,  on how many of the last 7 days did you  take this medication ?  and   take the correct number of pills / injections for this medication ?  requiring a 07 response for each item ( 34 ) .
we administered the sdsca - ms for each diabetes medication in a participant s regimen and then averaged these scores across medications to maintain the 07 scale , with higher scores indicating greater adherence .
the sdsca - ms composite had good interitem reliability across medications ( average r = 0.86 ) .
the 11-item arms - d asks respondents about their  daily experiences , on average  with taking their diabetes medications [ e.g. ,  how often do you forget to take your diabetes medicine(s ) when you feel sick ?  ] or refilling [ e.g. ,  how often do put off refilling your diabetes medicine(s ) because they cost too much money ?  ] ( 35 ) .
response options range from 1 , none of the time , to 4 , all of the time , and are summed to create a score ranging from 1144 .
we reverse - scored the arms - d so higher scores indicate greater adherence ( in the same direction as the sdsca - ms ) .
glycemic control was assessed with a nurse - administered valid and reliable point - of - care a1c ( % ) test ( 37 ) .
we used stata 12 to assess internal consistency reliability ( i.e. , cronbach  and interitem correlation ) , calculate summary statistics , and explore correlations .
we used amos 21 , a structural equation modeling ( sem ) program , to test statistical assumptions and estimate the models .
first , we assessed whether our data met the assumptions of maximum likelihood estimation sem .
multivariate nonnormality may result in an underestimate of data fit ( i.e. , inflated x values ) and biased ses ( 36 ) .
three data characteristics contribute to multivariate nonnormality ( 36 ) : 1 ) the distribution of any single variable is not normal ( i.e. , variables are skewed and kurtotic ) ; 2 ) the joint distribution of any pair of the variables is not bivariate normal [ i.e. , madia s coefficient > 1.96 ( 36 ) ] ; and 3 ) the presence of multivariate outliers [ i.e. , cases with an atypical pattern of scores ; mahalanobis distance p < 0.001 ( 36 ) ] .
data were also multivariate nonnormal ( mardia s coefficient , 29.3 ) , and removal of five multivariate outliers improved , but did not correct , multivariate nonnormality ( mardia s coefficient , 18.1 ) . therefore , we used bootstrapping to estimate robust ses and bias - corrected p values for parameter estimates and 95% cis for indirect effects ( 38 ) .
stable parameter estimates are indicated by low bias ( i.e. , bootstrapped and maximum likelihood estimates are similar ) and an unbiased good fitting model is indicated by a bollen - stine  with p > 0.05 calculated using the bootstrapped distribution ( 38 ) .
we used 5,000 bootstrapped samples ( 38 ) to estimate an imb model of diabetes medication adherence with five multivariate outliers removed .
hypotheses regarding structural relations between the imb model constructs were evaluated with an inspection of the direction and magnitude of the path coefficients ( direct effects ) and indirect effects , which indicate mediation .
significant indirect effects occur when the relationship between a predictor and an outcome is due to the predictor being associated with a third variable ( i.e. , all or part of the direct effect of a on c is due to a relationship between a and b ) .
model fit was tested with the comparative fit index ( 0.95 indicates good fit ) , the root mean square error of approximation ( 0.06 with ci 0.000.08 indicates good fit ) , and the standardized root mean square residual ( < 0.08 indicates acceptable fit , and 0 indicates perfect fit ) ( 36 ) .
agreement between multiple indices provides the best support a model has good data fit ( 36 ) . after evaluating the imb model of adherence
, we conducted a series of additional sems to examine the effects of insulin status and regimen complexity on the imb model relationships .
first , we added insulin status and , separately , regimen complexity to the model as predictors of adherence .
next , we evaluated each potential interaction with different models to assess whether insulin status or , separately , regimen complexity moderated associations between each of the imb constructs and adherence .
demographic characteristics included self - reported age , sex , race , ethnicity , income , education , and insurance status .
clinical characteristics included self - reported duration of diagnosed diabetes in months and years and the number and type of prescribed diabetes medications obtained from the medical record .
we dichotomized insulin status ( prescribed insulin vs. oral agents only ) , and regimen complexity was indicated by the number of prescribed diabetes medications .
adherence information was assessed with the five - item diabetes medication knowledge questionnaire ( dmkq ) , which assesses respondents knowledge of the : 1 ) name ; 2 ) purpose ; 3 ) dosing schedule ; 4 ) side effects ; and 5 ) appropriate management of a missed dose for a single diabetes medication in their regimen ( 31 ) . to generate a more accurate estimate of adherence information , we administered the dmkq for each diabetes medication in a participant s regimen . in our sample ,
internal consistency reliability for the dmkq was unacceptable (  = 0.36 ) , so we examined pearson correlation coefficients between individual dmkq items and the measures of medication adherence ( described below ) to identify relevant adherence - related information .
significant dmkq items asked respondents about their dosing schedule ( i.e. , how and when to take a medication ) and handling of missed doses .
the internal consistency reliability of these items across diabetes medications was good (  = 0.80 ) , so we summed item scores for each medication and then averaged scores across medications to create a two - item dmkq composite .
possible scores ranged from 03 with higher scores indicating greater adherence information . adherence motivation was assessed with the medicines for diabetes questionnaire ( mdq ) , which measures respondents beliefs about taking diabetes medications ( 32 ) .
the behavioral beliefs and normative beliefs subscales of the mdq were used to assess personal and social motivation to adhere , respectively .
the seven - item behavioral beliefs subscale asks respondents to report the degree to which they believe taking diabetes medications would  help me stay well  or  cause me to gain weight . 
the three - item normative beliefs subscale asks respondents the degree to which their doctor / nurse , family / relatives , or partner / spouse would approve of them taking diabetes medications regularly . for both subscales ,
response options range from 1 , strongly disagree , to 5 , strongly agree , and are averaged to create a composite score ranging from 15 , with higher scores indicating greater adherence motivation .
adherence behavioral skills were assessed with the revised medication adherence self - efficacy scale ( mases - r ) , which is a reliable and valid measure of medication adherence self - efficacy ( 33 ) . the 13-item mases - r asks respondents to report their degree of confidence in taking their medications in various situations / circumstances such as  when you are traveling  or  whatever they cost .  we added the word  diabetes  to each item to specify diabetes medications ( e.g. ,  how confident are you that you can take your diabetes medications   ) .
response options range from 1 , not at all sure , to 4 , extremely sure , and are averaged to create a composite score ranging from 14 , with higher scores indicating greater adherence behavioral skills .
diabetes medication adherence was assessed with two reliable and valid self - report measures : the summary of diabetes self - care activities medications subscale ( sdsca - ms ) ( 34 ) and the adherence to refills and medications scale for diabetes ( arms - d ) ( 35 ) .
although the sdsca - ms is the most widely used self - report measure of medication adherence in diabetes , the arms - d is more sensitive to nonadherence and more predictive of glycemic control ( 35 ) . including both measures reduces the influence of measurement error ( 36 ) .
the two - item sdsca - ms asks respondents ,  on how many of the last 7 days did you 
take this medication ?  and   take the correct number of pills / injections for this medication ?  requiring a 07 response for each item ( 34 ) .
we administered the sdsca - ms for each diabetes medication in a participant s regimen and then averaged these scores across medications to maintain the 07 scale , with higher scores indicating greater adherence .
the sdsca - ms composite had good interitem reliability across medications ( average r = 0.86 ) .
the 11-item arms - d asks respondents about their  daily experiences , on average  with taking their diabetes medications [ e.g. ,  how often do you forget to take your diabetes medicine(s ) when you feel sick ?  ] or refilling [ e.g. ,  how often do put off refilling your diabetes medicine(s ) because they cost too much money ?  ] ( 35 ) .
response options range from 1 , none of the time , to 4 , all of the time , and are summed to create a score ranging from 1144 .
we reverse - scored the arms - d so higher scores indicate greater adherence ( in the same direction as the sdsca - ms ) .
glycemic control was assessed with a nurse - administered valid and reliable point - of - care a1c ( % ) test ( 37 ) .
we used stata 12 to assess internal consistency reliability ( i.e. , cronbach  and interitem correlation ) , calculate summary statistics , and explore correlations .
we used amos 21 , a structural equation modeling ( sem ) program , to test statistical assumptions and estimate the models .
first , we assessed whether our data met the assumptions of maximum likelihood estimation sem .
multivariate nonnormality may result in an underestimate of data fit ( i.e. , inflated x values ) and biased ses ( 36 ) .
three data characteristics contribute to multivariate nonnormality ( 36 ) : 1 ) the distribution of any single variable is not normal ( i.e. , variables are skewed and kurtotic ) ; 2 ) the joint distribution of any pair of the variables is not bivariate normal [ i.e. , madia s coefficient > 1.96 ( 36 ) ] ; and 3 ) the presence of multivariate outliers [ i.e. , cases with an atypical pattern of scores ; mahalanobis distance p < 0.001 ( 36 ) ] .
data were also multivariate nonnormal ( mardia s coefficient , 29.3 ) , and removal of five multivariate outliers improved , but did not correct , multivariate nonnormality ( mardia s coefficient , 18.1 ) . therefore , we used bootstrapping to estimate robust ses and bias - corrected p values for parameter estimates and 95% cis for indirect effects ( 38 ) .
stable parameter estimates are indicated by low bias ( i.e. , bootstrapped and maximum likelihood estimates are similar ) and an unbiased good fitting model is indicated by a bollen - stine  with p > 0.05 calculated using the bootstrapped distribution ( 38 ) .
we used 5,000 bootstrapped samples ( 38 ) to estimate an imb model of diabetes medication adherence with five multivariate outliers removed .
hypotheses regarding structural relations between the imb model constructs were evaluated with an inspection of the direction and magnitude of the path coefficients ( direct effects ) and indirect effects , which indicate mediation .
significant indirect effects occur when the relationship between a predictor and an outcome is due to the predictor being associated with a third variable ( i.e. , all or part of the direct effect of a on c is due to a relationship between a and b ) .
model fit was tested with the comparative fit index ( 0.95 indicates good fit ) , the root mean square error of approximation ( 0.06 with ci 0.000.08 indicates good fit ) , and the standardized root mean square residual ( < 0.08 indicates acceptable fit , and 0 indicates perfect fit ) ( 36 ) .
agreement between multiple indices provides the best support a model has good data fit ( 36 ) . after evaluating the imb model of adherence
, we conducted a series of additional sems to examine the effects of insulin status and regimen complexity on the imb model relationships .
first , we added insulin status and , separately , regimen complexity to the model as predictors of adherence .
next , we evaluated each potential interaction with different models to assess whether insulin status or , separately , regimen complexity moderated associations between each of the imb constructs and adherence .
of the 588 patients with t2 dm who attended a clinic appointment during recruitment , ras approached 86.2% ( 507 ) . of these ,
as shown in table 1 , the sample of participants included in analyses ( with outliers removed ; n = 309 ) was diverse ( 53% were african american / black , and 8% reported hispanic ethnicity ) , and 11 interviews were conducted in spanish . nearly half ( 45% ) reported incomes < $ 10,000 , and 46% were uninsured . on average
, participants were prescribed 1.6  0.7 diabetes medications ( range 14 ) , and 47% were prescribed insulin .
table 2 presents descriptive statistics for each variable of interest and bivariate correlations between these variables .
participant characteristics ( n = 309 ) descriptive statistics and correlations between model variables ( n = 309 ) first , we estimated an imb model with the measures of personal and social motivation to adhere as indicators of a latent variable : adherence motivation . however , estimated variances on these factor loadings were negative , which is theoretically impossible and results from having only two indicator variables with small factor loadings ( i.e. , a heywood case ) ( 36 ) .
therefore , we estimated a model in which personal and social motivation to adhere were permitted to covary , but were independent representations of diverse aspects of adherence motivation .
2 ) had excellent data fit : comparative fit index , 1.00 ; root mean square error of approximation , 0.00 ( 90% ci 0.000.04 ) ; and standardized root mean square residual , 0.02 .
the parameter estimates were stable and unbiased by multivariate nonnormality ( bias ranged from 0.001 to 0.003 ) , and the bollen - stine  ( 8 , 309 ) , p = 1.00 indicated an unbiased model .
2 shows the parameter coefficients and the proportion of variance explained in each endogenous variable ( r ) .
an empirical test of the imb model of diabetes medication adherence using sem with standardized path coefficients and bootstrapped bias - corrected p values .
+ p < 0.08 ; * p  0.05 ; * * * p < 0.001 .
paths from information to behavioral skills and from personal motivation to behavioral skills were significant and in the predicted direction .
behavioral skills were significantly related to medication adherence , and also mediated the effects of information ( indirect effect 0.08 [ ci : 0.010.15 ] ) and personal motivation ( indirect effect 0.12 [ ci : 0.050.20 ] ) on adherence .
there was also a trend ( p < 0.08 ) toward a direct effect of information on adherence .
the imb constructs explained 41% of the variance in adherence , and adherence explained 9% of the variance in glycemic control .
social motivation was significantly related to adherence ( 0.13 ; p < 0.05 ) , indicating participants with greater perceived social norms for adherence reported worse adherence . given this unexpected
finding , we examined post hoc models with each item separately ( i.e. , doctor / nurse , family / relatives , and partner / spouse ) and found only the item about family / relatives was related to less adherence ( 0.15 ; p < 0.01 ) .
neither insulin status nor regimen complexity was associated with adherence ( not shown in fig .
2 ) , and no additional variance in adherence was explained by their inclusion in the model . moreover , none of the relationships between information , personal or social motivation , or behavioral skills and adherence were moderated by insulin status or regimen complexity .
the imb model of adherence ( 12 ) , which has been largely used to conceptualize hiv medication adherence ( 1720 ) , was applied to conceptualize the determinants of diabetes medication adherence among a low - income , diverse sample of adults with t2 dm . consistent with the imb model s predictions , adherence information and motivation were associated with adherence behavioral skills , which were , in turn , associated with diabetes medication adherence , and adherence was associated with glycemic control . in short ,
the imb model was well positioned to explain the sample s medication adherence behavior , accounting for 41% of the variability in this outcome .
furthermore , none of the associations between the imb constructs and adherence were moderated by insulin status or regimen complexity .
we used an objective assessment of adherence information and found that knowing how and when to take a medication and how to handle a missed dose were the types of information relevant to adherence .
consistent with other empirical tests of the imb model of adherence ( 1820 ) , the effect of information on adherence was mediated by behavioral skills .
we also found a trend toward a direct effect of information on adherence , which may be because we did not assess more complex knowledge ( e.g. , knowing how and when to get prescriptions refilled / reauthorized ) that may affect adherence through the enactment of behavioral skills , nor did we assess heuristics or implicit theories that may also influence adherence ( 12,18 ) . due to internal consistency concerns with our measure of information , only limited knowledge about medications was accounted for .
as others have found in the context of adherence to hiv medications ( 18,20 ) , information was not associated with personal or social motivation to adhere , suggesting the assessed adherence information was insufficient to motivate participants to adhere , and being motivated to adhere did not ensure participants had accurate and sufficient adherence information . personal motivation to adhere , which we operationalized with a measure of personal beliefs toward taking diabetes medications , was associated with more adherence behavioral skills as predicted .
we operationalized social motivation to adhere with a measure of social norms for adherence and found that having greater social norms for adherence was not associated with behavioral skills and was negatively associated with adherence .
the mdq normative beliefs subscale has not been validated and , while it assesses participants perceptions of others approval of adherence , it does not assess the degree to which participants desire such approval ( i.e. , valence ) .
post hoc analyses indicated the item inquiring about family / relatives approval for adherence was driving the significant negative association with adherence behavior .
this may be because positive family support ( e.g. ,  my family members help me remember to take my medications  )
often co - occurs with negative family support ( e.g. ,  my family members nag / argue with me about my medications  ) ( 26,39 ) , whereas only negative family support has been associated with less medication adherence ( 26 ) .
thus , the validity of the item inquiring about family members approval for adherence may be affected by the complex relationships between positive and negative family support and adherence .
future studies should measure : 1 ) respondents perceptions of social norms to adhere from meaningful others ; 2 ) respondents desire to please these referents ; and 3 ) referents positive and negative support for medication adherence .
adherence behavioral skills had a strong relationship with medication adherence and mediated the effects of information and motivation on adherence .
like others ( 1720 ) , we used a measure of self - efficacy to operationalize behavioral skills because of the impracticality of observing one s skills across situations and the high correlations between actual and perceived skills , but objective skills or factors that might serve as proxies ( e.g. , problem - solving ) for behavioral skills may operate differently .
the remaining 59% of unexplained variance in diabetes medication adherence may be attributed to unmeasured aspects of information , motivation , and behavioral skills and/or to unmeasured constructs previously associated with adherence .
however , several other constructs previously associated with adherence [ e.g. , access to healthcare and depressive symptoms ( 23 ) ] would be considered moderating factors in the imb model ( 12 ) .
others ( 23,28 ) have found associations between diabetes medication adherence and insulin status and regimen complexity , but we did not find these associations in our sample , nor did we find these to be moderating factors .
( 28 ) reported that patients prescribed insulin upon diagnosis were more likely to quit taking diabetes medications , but all of our participants were taking prescribed diabetes medications as a condition of enrollment and therefore may represent a different patient population .
relationships between regimen complexity and nonadherence have been found using dosing schedules to assess regimen complexity and ecological momentary assessment or pharmacy records to assess adherence ( 23 ) .
other factors that account for the remaining variance in glycemic control include ( but are not limited to ) adherence to other recommended self - care behaviors , the class and dose of antidiabetes medications , age , obesity , and diabetes - related complications ( 40 ) . in general , our use of self - report measures may have introduced recall and social desirability bias .
we selected these measures because they were the best available for assessing the imb constructs specific to diabetes medications ( rather than general measures of diabetes knowledge , motivation , and self - efficacy ) and captured previously identified barriers to diabetes medication adherence , but neither the dmkq nor the mdq have been formally validated .
furthermore , cross - sectional data prevent conclusions about true causal mediation and the ability to test the imb model s proposed feedback loop from health outcomes to information and motivation ( 12 ) .
our estimated model was theoretically justified , but , statistically , there are competing models ( including those with opposite directionality ) that would have generated comparable path coefficients and a good data fit
. moreover , the relationships between the imb constructs may be different in other patient populations and when using other measures of adherence
. different measures of the imb constructs may be necessary for patients managing multiple medications on different refill / reauthorization schedules and for those who have to self - adjust their insulin dose .
finally , the imb model ( 12 ) suggests potential moderators ( e.g. , unstable living situation or depression ) we did not explore .
our results suggest patients adherence - related information , motivation , and behavioral skills are important targets for interventions promoting adherence to diabetes medications , regardless of their medication regimen .
imb model - based intervention content might include strategies to ensure patients possess appropriate knowledge about their medication regimen ( i.e. , dosing schedules and handling missed doses ) and skills to obtain adherence information when needed ( e.g. , to access trustworthy sources and ask providers about medications ) .
motivation may be enhanced by targeting negative or false beliefs about adherence , juxtaposing consequences of nonadherence with a patients goals and interests , and teaching patients how to elicit support for adherence ( e.g. , asking for dose reminders when their routine changes ) .
intervention content that helps patients identify strategies to adhere in a variety of situations ( e.g. , setting alarm reminders to take / refill medications while traveling ) may increase adherence behavioral skills .
evaluations of imb model  based interventions can and should identify the extent to which intervention content improved patients adherence - related information , motivation , and behavioral skills and , in turn , medication adherence to inform future intervention efforts .
in general , our use of self - report measures may have introduced recall and social desirability bias .
we selected these measures because they were the best available for assessing the imb constructs specific to diabetes medications ( rather than general measures of diabetes knowledge , motivation , and self - efficacy ) and captured previously identified barriers to diabetes medication adherence , but neither the dmkq nor the mdq have been formally validated .
furthermore , cross - sectional data prevent conclusions about true causal mediation and the ability to test the imb model s proposed feedback loop from health outcomes to information and motivation ( 12 )
. our estimated model was theoretically justified , but , statistically , there are competing models ( including those with opposite directionality ) that would have generated comparable path coefficients and a good data fit
. moreover , the relationships between the imb constructs may be different in other patient populations and when using other measures of adherence .
different measures of the imb constructs may be necessary for patients managing multiple medications on different refill / reauthorization schedules and for those who have to self - adjust their insulin dose .
finally , the imb model ( 12 ) suggests potential moderators ( e.g. , unstable living situation or depression ) we did not explore .
our results suggest patients adherence - related information , motivation , and behavioral skills are important targets for interventions promoting adherence to diabetes medications , regardless of their medication regimen .
imb model - based intervention content might include strategies to ensure patients possess appropriate knowledge about their medication regimen ( i.e. , dosing schedules and handling missed doses ) and skills to obtain adherence information when needed ( e.g. , to access trustworthy sources and ask providers about medications ) .
motivation may be enhanced by targeting negative or false beliefs about adherence , juxtaposing consequences of nonadherence with a patients goals and interests , and teaching patients how to elicit support for adherence ( e.g. , asking for dose reminders when their routine changes ) .
intervention content that helps patients identify strategies to adhere in a variety of situations ( e.g. , setting alarm reminders to take / refill medications while traveling ) may increase adherence behavioral skills .
evaluations of imb model  based interventions can and should identify the extent to which intervention content improved patients adherence - related information , motivation , and behavioral skills and , in turn , medication adherence to inform future intervention efforts .
 objectivesuboptimal adherence to diabetes medications is prevalent and associated with unfavorable health outcomes , but it remains unclear what intervention content is necessary to effectively promote medication adherence in diabetes . 
 in other disease contexts , the information  motivation  behavioral skills ( imb ) model has effectively explained and promoted medication adherence and thus may have utility in explaining and promoting adherence to diabetes medications . 
 we tested the imb model s hypotheses in a sample of adults with type 2 diabetes.research design and methodsparticipants ( n = 314 ) completed an interviewer - administered survey and a1c test . 
 structural equation models tested the effects of diabetes medication adherence - related information , motivation , and behavioral skills on medication adherence and the effect of medication adherence on a1c.resultsthe imb elements explained 41% of the variance in adherence , and adherence explained 9% of the variance in a1c . 
 as predicted , behavioral skills had a direct effect on adherence (  = 0.59 ; p < 0.001 ) and mediated the effects of information ( indirect effect 0.08 [ 0.010.15 ] ) and motivation ( indirect effect 0.12 [ 0.050.20 ] ) on adherence . 
 medication adherence significantly predicted glycemic control (  = 0.30 ; p < 0.001 ) . 
 neither insulin status nor regimen complexity was associated with adherence , and neither moderated associations between the imb constructs and adherence.conclusionsthe results support the imb model s predictions and identify modifiable and intervenable determinants of diabetes medication adherence . 
 medication adherence promotion interventions may benefit from content targeting patients medication adherence - related information , motivation , and behavioral skills and assessing the degree to which change in these determinants leads to changes in medication adherence behavior . 