Table 1.

Selected adherence measures according to study design

Adherence metric	Description	Evaluation use, advantages, and disadvantages	Examples
Direct
Observational	• Direct face-to-face observation of patient medication-taking behavior • Robust but subject to human circumvention • Expensive, burdensome to the health care provider • Not practical for routine clinical use (usually not feasible outside inpatient settings or clinical trials)	• Controlled settings that permit observation and where validation of adherence warrants expense: Oslo RDN Randomized Study (ClinTrialGov ID: NCT01673516) – candidates for renal denervation must first be verified as having true treatment-resistant hypertension through witnessed intake of the antihypertensive drugs as a means of reducing risk and cost. • When used in outpatient or uncontrolled clinical trial settings, may be subject to human circumvention or unwarranted cost	• Face-to-face observation
Blood measures of medication, metabolite, or biological marker	• Monitoring physical parameters provide indirect evidence of likelihood of adherence • Robust but subject to metabolic variation • “White coat syndrome” or “Hawthorne effect” may artificially inflate adherence measures • Not practical for routine clinical use (labor intensive and costly) • Point in time data that may be problematic for drugs with short half-lives	• Helpful in select circumstances such as a determinative measure for drugs with short half-lives • Appropriate in controlled settings that permit physical measures and where validation of adherence warrants expense – such as in clinical settings where drug level or effect informs whether a patient is very likely nonadherence (i.e., INR of 1.0 on a previously therapeutic dose of warfarin or drug level of zero for phenytoin)	• Drug assays of blood or urine • Use of drug markers with the target medication • Direct observation of patient use of medication
Indirect
Assessment of the patient’s clinical response and/or physiological markers	• Monitoring patient health outcomes/physiological status to determine adherence • No “White coat syndrome” or “Hawthorne effect” to artificially inflate adherence measures • Subject to metabolic variation and concomitant disease factors • When used as sole measure of adherence, results predictive but not likely to be conclusive	• Controlled settings that permit physiological response/outcome evaluation • Helpful for providing some information on whether patients may or may not be adherent in routine practice, such as blood pressure or blood glucose control	• Health outcomes • Physiological status
Self-reporting: patient questionnaires/diaries/structured interviews	• Inexpensive, relatively unobtrusive, provide patient perspectives and distinguish between intentional (process in which the patient actively decides not to use treatment or follow treatment recommendations)³⁶ and unintentional (passively inconsistent medication-taking behavior such as forgetfulness or carelessness).³⁶ Nonadherence has different underlying causes and therefore requires different interventions • Useful in identifying patients who need assistance with their medications, assessing patient concerns, and evaluating new programs • Some are disease specific • For routine clinical practice, should be brief, acceptable to patients, valid, reliable, have the ability to distinguish between different types of nonadherences and be able to be completed by or in conjunction with caregivers where necessary • The concordance of self-report and other measures of medication adherence varies widely based on the type of measures used. Measures asking respondents to report missed doses over-predict adherence subsequently monitored electronically, whereas global quantitative self- ratings were more concordant with adherence subsequently monitored electronically • When used as sole measure of adherence results may be unreliable – potentially predictive but not likely to be conclusive • “White coat syndrome” or “Hawthorne effect” and inaccurate patient recall may artificially inflate adherence measures	• Appropriate in clinical practice applications to distinguish between intentional and unintentional nonadherence	• The PAM.³⁷ • SEAMS • 100-point VAS • BMQ • MARS • HAQ • BARS
Medication event monitoring: electronic medication monitors/reminders	• Electronics incorporated into packaging that records events that are proxy for medication taking (i.e., package opening) • Able to provide pharmacodynamic information through identification of variable exposure to drugs created by diversely erratic execution of dosing regimens • Some products scientifically validated³⁸ • Provide insight into time of day and behavior patterns • Costly • “White coat syndrome” or “Hawthorne effect” may artificially inflate adherence measures	• Ideal in clinical trials • Potential for in-market use as part of clinician engagement strategy – lack of clarity regarding FDA regulatory status and enforcement discretion impacting market uptake	• MEMS • Helping Hand • GlowCaps • SIMpill • MERM
Medication monitoring: digital pill	• Electronics incorporated into pill emits impulse to record pill-taking event • Provide insight into time of day and behavior patterns • Costly • Patient acceptance issues • “White coat syndrome” or “Hawthorne effect” may artificially inflate adherence measures	• FDA regulated device – FDA 510 (k) premarket approval for ingestible sensors • “Hawthorne effect” may artificially inflate adherence measures	• Digital Pill (Proteus, e-techt)
Medication monitoring: pill counts (e.g., PT/PP)	• Counting missing pills as a proxy for medication taking • Results may be predictive but not likely to be conclusive • Easy to perform • Subject to human circumvention • Does not accurately capture the timing of medication taking • “White coat syndrome” or “Hawthorne effect” may artificially inflate adherence measures	• Commonly used in randomized, controlled clinical trials and when used as sole measure of adherence, results unreliable	• Pill count
PAMs (e.g., PDC, MPR)	• Objective estimates calculated from pharmacy data to assess the number of doses dispensed in relation to a dispensing period • Dispensing is a proxy for medication taking – built in upward bias that is especially pronounced when calculated over relatively short timeframes (<9 months) • Accessible and relatively low cost • Appropriate only for chronic/nonacute therapy • To be accurate, requires that patients obtain their medications from one closed pharmacy system such that all pharmacy records are consolidated • Does not currently capture primary nonadherence without comprehensive EHR interoperability • Mail order/automatic refills skew predictive value • Misses hospital-supplied medication and cash paying patients • Able to assess multiple medications • Provides no insight into if and when doses taken • Current “therapeutic gap” between pharmacy and physician may make patient clinical management application ineffective³⁹ • When used as sole measure of adherence, results predictive but not likely to be conclusive	• Quality Measures and Reimbursement – pharmacy “quality” evaluation • Identify primary nonadherent patients failing to initiate therapy disadvantages such as “requires the patient obtain their meds within a closed pharmacy system”, “mail order/auto refills skew predictive values”, “misses hospital-supplied meds” not listed under group trajectory model measures?	• PDC • MPR • PMN
Group trajectory model measures	• To gain a better perspective on PDC evaluation, add variables, such as income, education, and geography to evaluate a longer, more holistic perspective of adherence • Permits efficiency of pharmacy claims data use with a better understanding of adherence “patterns” • To be accurate, requires that patients obtain their medications from one closed pharmacy system such that all pharmacy records are consolidated • May facilitate targeting of interventions and may be useful to adjust for confounding by health-seeking behavior • Mail order/automatic refills skew predictive value • Misses hospital-supplied medication and cash paying patients • Limited to a snapshot of patient adherence at a point in time, for medications that treat chronic conditions • Current “therapeutic gap” between pharmacy and physician may make patient clinical management application ineffective • When used as sole measure of adherence, results predictive but not likely to be conclusive	• Ease of use of pharmacy data yielding more comprehensive evaluation of adherence patterns • Requires that patients obtain their medications within a closed pharmacy system to be accurate • Does not currently capture primary nonadherence without comprehensive EHR interoperability • Mail order/automatic refills skew predictive value • Misses hospital-supplied medication and cash paying patients	CVS/Brigham and Women’s Hospital recent research on a novel method, group- based trajectory models, for classifying patients by their long-term adherence.³² Will Shrank, Troy Brennan, Niteesh Choudry

Abbreviations: BARS, Brief Adherence Rating Scale; BMQ, brief medication questionnaire; CVS, convenience value and service; EHR, electronic health record; FDA, Food and Drug Administration; HAQ, health assessment questionnaire; INR, international normalized ratio; MARS, medication adherence rating scale; MEMS, Medication Event Monitoring Systems; MERM, medication event reminder monitor; MPR, medication possession ratio; PAM, patient activation measure; PDC, proportion of days covered; PMN, primary medication nonadherence; PP, number of pills prescribed; PT, number of pills taken; SEAMS, self-efficacy for appropriate medication use scale; VAS, visual analogue scale.