Using tools to assess patient adherence

  • Measures and survey tools, in conjunction with objective measures, are useful tools for characterizing patient health behavior
  • Effective use of these tools has the potential to improve adherence

Measures and survey tools are useful for characterizing patient health behavior

Quantitative methods are useful for characterizing health behavior and supporting patients in the self-management of their conditions. Surveys and questionnaires have the advantages of being easy to implement at the point of care and being acceptable to patients.1 They provide explicit examples and information on patient behavior that physicians can use to personalize care.

‘Self-reporting adherence assessment tools’ may either simply assess the frequency or type of non-adherence, such as the Medication Activation Questionnaire (MAQ)2 or Medication Adherence Report Scale (MARS),3 or go beyond this to assess the reasons for non- adherence.4 The Patient Activation Measure (PAM) is a reliable indicator of a patient’s willingness and ability to manage their health and care independently.5,6

Utilizing these tools can provide insights to their physician during a patient’s regular check-up.

The Medication Adherence Questionnaire can be used to assess patient adherence

Scales and surveys specifically quantifying medication adherence include self-reported questionnaires. These questionnaires are practical, flexible, and can help physicians identify levels of adherence as well as individual patient beliefs and concerns while providing real-time, relevant feedback.2

The Medication Adherence Questionnaire (MAQ), also known as the 4-item Morisky Medication Adherence Scale (MMAS-4) and Morisky Scale,2 is the most common adherence scale, and is short, simple to use, and validated across many different disease areas.  Questions for patients with hypertension might include:

  • Do you sometimes forget to take your hypertension medication?
  • When you feel like your hypertension is under control, do you sometimes stop taking your medicine?

The MAQ has the advantages of being relatively easy to administer, available in the English language, and is broadly applicable for patients with cardiovascular disease.7 It provides a useful basis for physicians to understand their patients’ adherence behavior and can inform routine clinical practice.

The Medication Adherence Report Scale assesses intentional and non-intentional adherence

The Medication Adherence Report Scale (MARS) is a 5 or 10-item self-report adherence scale which assesses both intentional and non-intentional adherence.3

The MARS-5 comprises items describing a range of non-adherent behaviors, with items phrased in a non-threatening and non-judgmental way to normalise adherence, and a response scale that allows more nuanced categorisation than a simple ‘yes/no’ or ‘high/low’ response (i.e. a patient is not simply categorised as adherent or non-adherent.3

The Patient Activation Measure is a reflection of the patient’s ability to manage their health

The Patient Activation Measure (PAM) reflects “the individual’s knowledge, skill and confidence in managing his/her own health and care”, and is a reliable indicator of a patient’s willingness and ability to manage their health and care independently.5,6

The PAM-13 questionnaire categorizes the patient into one of four progressively higher “stages of activation”, with higher activation stages indicating that the patient is more engaged with healthy behavior such as diet and exercise, and adherence to guidelines and treatments.5,6

Higher patient activation has been shown to be linked to positive clinical behavior and outcomes, including treatment adherence, and there is significant interest in being able to evaluate and influence the patient’s level of activation. The importance of the physician–patient relationship has been established: emotionally supportive and easily accessible providers who recognize patients as autonomous and motivate them to self-manage are associated with higher levels of patient activation.6

Limitations of existing self-reporting measurements of adherence

It has been suggested that a key limitation of long-standing measures is that they do not distinguish between the extent to which doses are missed and the reasons for missing doses. Measuring these separately can streamline the process:4

  • Measuring extent can help to identify patients with suboptimal levels of adherence
  • Measuring reasons can help identify targets for intervention if necessary

Such tools, including a self-reporting device conducting non-adherence cognitive interviews (to measure extent) and the Medication Adherence Self-Efficacy Scale (to measure reasons) with patients with hypertension, have been developed, with the aim of improving the measurement of self-reported non-adherence.4

Patients can be reluctant to ‘admit to’ non-adherence

Patients are often unwilling to admit to issues with adherence, especially when asked in a direct fashion; they may find this intimidating, and, therefore, reluctant to admit to not taking their medicines.8

An easy to use screener has been developed, called the Making Medicines Work For You screener, which enables patients to indicate whether they are experiencing any one or more of seven problems with their medicines (drawing on the COM-B framework), rather than asking them directly about their adherence. It includes items which assess Capability (I cannot manage so many medicines), Opportunity (I cannot afford either the time or money to get the medicines) and Motivation (I am not sure if the medicine is really helping me). A pilot study of patients with diabetes found that the screener could identify a range of medicines-related issues; 88% of the sample indicated at least one issue, contrasting with the relatively small numbers who typically disclose non-adherence.8

The SPUR (Social, Psychological, Usage, Rational) framework forms the basis for an interactive digital questionnaire that describes a patient’s behavioral risks of non-adherence and the factors behind their behavior. Tailored digital-adherence technologies such as this have the potential to provide more flexible and personalized support services to change behavior, with fewer demands on physician time.9

Objective measures are also useful tools for determining adherence

There are a variety of objective methods that can also be used to measure patient adherence to medications. These include:

  • Building of pharmacokinetic models to calculate the dose that the patient has ingested10
  • Electronic devices, such as the Medication Event Monitoring System (MEMS) have been used for several years to measure medication adherence, and are seen as a gold standard method to assess patients’ medication-taking behavior. The MEMS is an electronic device that consists of a MEMS cap with an electronic circuit that records opening of the MEMS pill bottle11
  • Prescription data in electronic medical records (EMRs), linked with pharmacy claims data, also provides an opportunity to examine prescription-fill rates and factors associated with it. A study in the U.S. found that significant proportions of patients (especially patients with no prior treatment history) did not fill prescriptions for anti-hypertensives12

Using questionnaires and screeners to boost adherence

Physicians can use the responses from questionnaires and screeners to provide the support needed to boost patient activation and improve adherence.2–6 A hypertensive patient who does not necessarily see themself as responsible for managing their condition may lack elementary knowledge about hypertension and the possible outcomes. Their physician can educate them on the condition and their role in managing it. Similarly, a patient with higher activation may have the necessary skills and knowledge to manage their hypertension and adhere to their treatment, but their adherence behavior may be derailed by stress or unexpected health events.13 In this case, the physician can positively influence their activation and adherence by making themselves available to address any questions or concerns the patient may have and helping them to feel supported should any unexpected changes arise.

References

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  2. Lam WY, et al. Medication Adherence Measures: An Overview. Biomed Res Int 2015;2015:217047.
  3. Chan AHY, et al. The Medication Adherence Report Scale: A measurement tool for eliciting patients’ reports of nonadherence. Br J Clin Pharmacol 2020;86:1281–88.
  4. Voils CI, et al. Initial validation of a self-report measure of the extent of and reasons for medication nonadherence. Med Care 2012;50(12):1013–19.
  5. Kinney RL, et al. The association between patient activation and medication adherence, hospitalization, and emergency room utilization in patients with chronic illnesses: a systematic review. Patient Educ Couns 2015;98(5):545–52.
  6. Graffigna G, et al. The role of Patient Health Engagement Model (PHE-model) in affecting patient activation and medication adherence: A structural equation model. PLoS One 2017;12(6):e0179865.
  7. Lavsa SM, et al. Selection of a validated scale for measuring medication adherence. J Am Pharm Assoc 2011;51(1):90–4.
  8. Weinman J, et al. Pilot Testing Of A Brief Pre-Consultation Screener For Improving The Identification And Discussion Of Medication Adherence In Routine Consultations. Patient Preference and Adherence 2019;13:1895–8.
  9. Dolgin K. The SPUR Model: A Framework for Considering Patient Behavior. Patient Preference and Adherence 2020;14:97–105.
  10. Özdemir V and Endrenyi L. A New Approach to Measure Adherence to Medicines Using Biomarkers and Sensors. OMICS 2019;23(7):334–7.
  11. Van Heuckelum M, et al. The effect of electronic monitoring feedback on medication adherence and clinical outcomes: A systematic review. PLoS ONE 2017;12(10):e0185453.
  12. Park Y, et al. Prescription fill rates for acute and chronic medications in claims-EMR linked data. Medicine (Baltimore) 2018;97(44):e13110.
  13. Hibbard JH, et al. Development of the Patient Activation Measure (PAM): Conceptualizing and Measuring Activation in Patients and Consumers. Health Serv Res 2004;39(4):1005–26.