Why adherence is a sensitive issue

It may be difficult to carry out at the point of care because of its length. However, this scale has been validated in various chronic conditions [ 4 , 49 ]. Reliability of this scale was measured by its internal consistency.

With coefficient alpha reliability at 0. MARS assesses both beliefs and barriers to medication adherence [ 38 ]. As a result, it is able to examine medication-taking behaviors and attitudes toward medication with higher validity and reliability values.

The internal consistency reliability of MARS is unclear [ 4 ]. Still, Thompson et al. It was designed and first validated for patients with schizophrenia [ 52 ]. Hence, this scale is limited to use in patients with chronic mental illness. An ideal medication adherence measure should present low cost and be user friendly, easy to carry out, highly reliable, flexible, and practical [ 13 , 15 ].

However, there is no single measure that can meet all these gold standards since each has its own drawbacks as described above. In a broad sense, subjective and objective measures are preferred in clinical and research settings, respectively, mainly due to cost effectiveness ratios. Self-report questionnaires, which have a reasonable predictive power, are more useful in a busy, resource-limited clinical setting with moderate to high literacy population.

Although pill count is an objective measure, the needs of staff and time have made it primarily used in routine clinical practice instead. While balancing accuracy and cost, pharmacy refill measures are more favorable for a large research population than using EMPs. Meanwhile, direct measures are seldom used since the intrusiveness and the cost are too high to be accepted by both patients and researchers.

Table 2 includes advantages, disadvantages, and the proposed target population s of the five types of medication adherence measures whilst Table 3 summarizes the function s , target population s , advantages, and disadvantage s specific to the discussed self-report questionnaires and scales. Multimeasure approach is often recommended in measuring medication adherence. Since there is no ideal medication adherence measure, it is appropriate to use more than one measure when researchers intend to have results that are close to reality.

Selecting two or more medication adherence measures might allow strengths of one method to help compensate putative weakness and to more accurately capture the information needed to determine adherence levels. A study using this approach which measured the adherence to HIV protease inhibitors in , Liu et al. An individual tool can only detect patients with low to moderate level of adherence. Other factors, such as white coat adherence, can lead to a false impression of medication adherence.

The use of a second measure can then help confirm the original findings. For instance, although MEMS is known for its high accuracy, adherence overestimation may still occur when using this method. Therefore, some studies use other measures in addition to MEMS, such as pill count, to attest the result and minimize discrepancies [ 36 , 37 ]. Moreover, different measure can identify different components of nonadherence. Subjective measures are more useful in determining the beliefs and barriers to adherence or predicting nonadherence.

Objective measures provide more accurate data on how patients perform in their medication regimes. A simple self-report survey has been used to predict the occurrence of low pharmacy refills in a high-risk elderly population to improve hypertensive management [ 47 ]. A meta-analysis also showed that this approach, including using a self-report method other than medical record reviews alone, can increase the sensitivity for nonadherence [ 54 ].

The concomitant use of both objective and subjective measures will, therefore, provide higher reliability and reveal more reasons of nonadherence, even in patients with high levels of adherence, and is currently recommended [ 55 ]. Nonetheless, increased complexity for analysis and interpretation should be acknowledged when using a multimeasure approach, such as different timeframes for measurements and different results produced [ 56 ].

Meanwhile, using multiple measures with the same sources of error, such as two subjective measures, does not help predict adherence level [ 57 ]. The cost and practicality of this approach in clinical setting may also be a hindrance. Therefore, while choosing which measures should be included, researchers should take potential errors, ability to overcome the precedent disadvantages, and practicality to be performed in the target population into consideration.

This is not a comprehensive review on all the existent medication adherence measures. Rather it is focused on the different types available and the most commonly used in different settings. The types of setting and population in the studies that are used as examples vary in different measures which can make comparisons cumbersome.

If researchers and healthcare professionals are looking for measures for a specific or rare condition, they should refer to studies that have a clearer validation. Moreover, this review is limited to researchers and health professionals conducting studies in English language. Many measures have been translated and validated in several languages over the years of development yet this review does not include them. There are worldwide ongoing public health reforms to minimize unnecessary healthcare expenditure and maximize public health outcome.

Improving medication adherence is a significant aspect in clinical practice and research. The lack of a universal guideline on medication adherence measures provides rooms for research on which measure, or which combination of measures, is the most appropriate for different target populations and health problems. Poor medication adherence is a key hindrance in combating the challenges of public health in both developed and developing countries.

For successful pharmacotherapy, healthcare professionals and researchers should utilize all available methods within their limits of practice to improve medication adherence. The authors declare that there is no conflict of interests regarding the publication of this paper.

This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Anna Giardini. Received 13 May Revised 31 Jul Accepted 05 Aug Published 11 Oct Introduction Adherence to medication is a crucial part of patient care and indispensable for reaching clinical goals. Overview For more than four decades, numerous researches on how to properly measure and quantify medication adherence have been conducted but none of them can be counted as the gold standard.

Direct Measures In addition to the classification of adherence measures as subjective and objective, many other studies labeled them as direct and indirect [ 7 , 15 , 21 , 22 ]. Measures Involving Secondary Database Analysis The data of secondary database includes the sequences and patterns derived from the curated primary data in systems such as electronic prescription service or pharmacy insurance claim.

Table 1. Equations of medication adherence measures involving secondary database analysis and pill count [ 15 , 19 , 27 , 28 ]. Table 2. Summary of the five types of medication adherence measure: target population s , advantages, and disadvantages. Table 3.

Summary of self-report questionnaire and scales: function s , target population s , advantages, and disadvantages. References E. Brown and J. Johnson, M. Williams, and E. Lavsa, A. Holzworth, and N. Svarstad, B. Chewning, B. Sleath, and C. McDonnell, M. Jacobs, H.

Monsanto, and J. Osterberg and T. Vrijens, S. Hughes et al. Fischer, M. Stedman, J. Lii et al. Solomon and S. Lehane and G. Sackett, R. Haynes, and E. Vermeire, H. Hearnshaw, P. Van Royen, and J.

Steiner and M. We will further explore differences across patients by age, category of adherence, and location. Treatment completed is defined as patient who has completed their treatment without evidence of failure but without any record of a negative smear or culture result in the last month of treatment or any time in previous occasion. Lost to follow-up is defined as a patient whose treatment has been interrupted for more than 2 months. Treatment failed is defined as a patient whose sputum smear or culture is positive at month 5 or later during treatment.

TB recurrence is defined as a patient with a positive culture result at any time during the 12 months follow-up period post treatment completion. Unfavourable outcome at 18 months post enrolment is defined as treatment failure, lost to follow-up and death during treatment, and recurrence after end of treatment.

Sample size calculations were conducted accounting for the clustered design [ 16 ]. If Internet connection is not available, a paper-based system is implemented and data captured into the web-based system once Internet connectivity is restored.

Documents containing personal identifiers will be kept in lockable cabinets that are only accessible by study staff. The database access is restricted with an encrypted password. All fields have automated system checks, which verify missing, range, and future dates. Any personal identifiers will be removed from the analytic dataset prior to the data analysis phase of the study. The data management team carries out a centralised statistical monitoring. Baseline characteristics data will summarised at individual level for all participants in the two arms.

The individual data will include age, gender, ethnicity, education, marital status, number of people they live with, special risk factors for TB, TB history, HIV status, antiretroviral status, CD4 count, and viral load. We will also report on TB treatment category, sputum results, smoking, alcohol and recreational drugs use, and TB related stigma at three time points which are at the start of taking TB treatment, completion of TB treatment, and end of 18 months follow-up.

The study is also collecting data on social harm during the course of treatment at the end of TB treatment and adverse events will be reported to the Ethics committees. To answer our main objective, we will measure adherence using data from the medication monitors.

A box opening any time during the day will be taken as a proxy for medication intake. Adherence will be calculated by dividing the number of days when pills were taken over number of days that pills were supposed to be taken to generate an adherence proportion for each individual enrolled. Analysis will be conducted at cluster-level due to the small number of clusters [ 16 ]. We will also conduct an adjusted analysis for the intervention effect, adjusting for imbalances of individual-level variables at baseline, using a two-stage approach [ 16 ].

We will fit a logistic regression model at the individual-level including any baseline variable which look imbalanced by study arm and variables which we anticipate to be associated with poor adherence.

The expected outcome for each individual is calculated and summed at the cluster-level. The log of cluster-level residual expected number of outcomes with the observed number of outcomes is compared by study arm using a t test.

We plan to also specify a limited number of subgroups and endpoint s they relate to. Subgroups will be measured at the cluster-level e. We will then estimate the intervention effect in each subgroup.

A full statistical analysis plan will be developed before study completion. For the secondary objectives, we will generate a binary variable indicating whether enrolled participants had successfully completed six months of TB treatment or not.

Unfavourable outcomes will be measured by generating a binary variable indicating whether enrolled participants had an unfavourable outcome 18 months after enrolment. A detailed statistical analysis plan will be written prior to any analyses. The trial investigators will have access to the final dataset. For the qualitative component, audio-recordings and process notes will be transcribed and where necessary translated prior to analysis.

Thematic analysis using deductive and inductive approaches will be used to describe the themes. A codebook will be created using the deductive themes and updated with any new themes that develop from the transcripts.

Saturation of themes will be assessed during study implementation by frequent review of the transcripts. For reliability, the original transcript and codebook will be sent to two independent reviewers. For economics evaluation, all time and motion data will be collected onto a paper form and captured onto an Excel workbook by trained research assistants.

The health economist will review the data on a regular basis and clarifications requests made promptly to avoid recall bias. Patient costing data will be collected on case report forms and entered into a password-protected database created in REDCap database. Although medication monitors have been used to measure adherence in HIV treatment patients, this study is among the few studies investigating adherence using medication monitors among drug-sensitive TB patients in sub-Saharan Africa [ 17 ].

The study is also one of the first to evaluate differentiated care following information from an adherence technology. The medication monitor offers a real time documentation of medication intakes, which allows ability to monitor medication adherence whenever required. This approach will also allow for a further discussion of why non-adherence occurred when participants return to facility for refills.

The differentiated care approach enables staff to follow-up on a weekly basis on participants that have missed doses allowing for timely action on missed doses unlike waiting for patients to return to facilities at end of the month as it is currently done in routine practice. The approach will also assist us in identifying people with TB who may need more support when on TB treatment. Other digital adherence technologies that have been explored for TB medication adherence include DOTS, which requires patients to give a missed call to a toll free number when the pills are removed from the medication blister pack when taking a dose.

The disadvantage of using DOTS is that treatment support before treatment intake is not provided but only done once a patient misses a dose [ 9 ]. Another technology that has been explored is video-supported treatment where patients take a video of themselves ingesting TB treatment and sending it to health care providers to view later [ 18 ].

The disadvantage with using this technology is that it requires that a patient have a smartphone and have access to Internet connection. Other criticisms have been that it is an intrusive and patronising method. Both these technologies require some effort from the patient to either give a missed call, send an SMS to the toll free number for DOTS, or take a video of themselves ingesting medication.

Furthermore, DOTS sleeves have to be customised to the different medication blister packs. The advantages of the medication monitor over the other technologies is that it has both visual and audio medication reminders and monthly refill reminders.

The monitor software also automatically sends text messages to patients if a patient does not take their medication at the scheduled time. All these features attempt to support patients when taking treatment [ 19 ]. With differentiated care based on adherence monitoring, one may be able to counter issues, related to fear of side effects, lack of disclosure, lack of support, etc. There are some operational challenges that need to be overcome to implement the study. Having a computer in the TB consultation rooms is not the current practice in South Africa and we will therefore provide electronic tablets loaded with data to our study team so this can be done.

The research team is operationalising the intervention and they require cell phones and airtime to be able to call patients who missed their doses, these will also be provided by the study.

The configuring of each device also requires access to an Internet connection. Most facilities in South African do not have free Internet access available and the study must therefore provide data to the study team. Reports that will be used to implement the differentiated care will not be automatically generated, so a dedicated person needs to download and generate them weekly. One of the study staff per province will be responsible for ensuring that this is done.

During the follow-up period that patients are still taking their TB treatment, we run a risk of some patients losing their medication monitors. If this occurs, the study will replace the medication monitors.

One limitation of the study is that intake notification through opening of the box does not mean that a patient has ingested their medication. We might get instances where the system has recorded an opening as an intake but the patient has not ingested the medication and vice versa. We will not perform plasma or urine drug concentration tests to check objectively on dosing.

However, there is evidence in HIV patient populations in South Africa that electronic adherence monitoring monitors are a good measure for adherence [ 20 ]. A second limitation is that most TB patients in South Africa have HIV co-infection but the current study is not monitoring medication adherence for both diseases.

It may be an even bleaker picture for people with diabetes, where one report mentions that about two thirds of people do not take their oral anti-diabetes medications properly Donnan et al, There are thought to be two types of non-adherence: unintentional and intentional Gray and Celino, Unintentional non-adherence "cannot take" is more about the practical problems of taking medicines, such as memory loss or difficulty opening packages, which may prevent someone taking medicines as planned.

Intentional non-adherence "will not take" , on the other hand, is based on a person's beliefs about the medicine, for example whether or not it will work. Obviously for some people it may be mixture of both intentional and unintentional non-adherence issues.

Use this link to get back to this page. Eighty recommendations concerned natural therapies and were excluded. Finally were included in the analysis. Thereof, were not fully adhered to Additional file 1 : Appendix Table C2.

Of evaluable reasons, reluctance was the most frequent, followed by disappearance of the underlying problem and barriers. Significant effects were only found if adherent patients included patients who diverged from prescribed therapy because of side effects.

In this case, in 3-year medication adherent patients HRQoL increased by 0. Outlier robust estimations barely changed estimates. Second, it was verified whether increased adherence was associated with improved HRQoL in those patients. It was found that side effects, forgetfulness, and being reluctant were the most important reasons for non-adherence to medication. Patients with side effects mostly discontinued therapy or changed the dose. On the one hand, this could have been the right decision and in concordance with the prescribing physician.

On the other hand, it could be hazardous. Based on the available data, this could not be evaluated. Forgetfulness was more often associated with paused applications than with discontinuation, and never with a change in dose. Therefore, patients sometimes forgot the application rather than the correct dosage. Reluctance was more often associated with changed doses than with discontinuation or other kinds of non-adherence.

Reasons for non-adherence to recommendations differed between the different types of recommendations. Mostly, visits to the doctor, improvements in nutrition, and improved control of vital signs and blood glucose were recommended. This might be problematic if abating symptoms, such as high blood glucose levels or angina pectoris, should still be treated [ 7 ].

The most important reason for non-adherence to improvements in control of vital signs and blood glucose was being reluctant, which can be problematic especially in post-MI patients [ 7 ]. Adherence to improvements in nutrition is usually considered to be important. For example achieving body fat goals is recommended in MI patients [ 7 ]. The finding that non-adherence to nutrition was mostly due to barriers is in line with previous research which found that patients find it hard to adhere to diet restrictions [ 33 ].

It was found that patients were more likely to visit a doctor than to improve self- and disease management, nutrition, or control of vital signs and blood glucose. It was also found that 3-year adherence to medication was more frequent than to recommendations.

Furthermore, non-adherence and reluctance were bigger problems in recommendations than in medications. Therefore, the findings of this research support existing evidence that patients have greater belief in medical therapy than in healthy lifestyle and disease management-related recommendations [ 33 ]. They also suggest that patients have more problems in explaining their reasons for non-adherence to recommendations than to medication, because patients did not report specific reasons for non-adherence to recommendations more frequently than to medication.

Results about the effect of adherence on HRQoL were sensitive to the definition of adherence. With the first definition, significant effects of adherence on HRQoL could not be identified. With the second definition of medication adherence side effects were allowed a significant increase of 0.

The result has shown to be robust to outliers. However, this finding is of limited generalizability because the study population was educated by the study-nurses and can therefore be assumed to be more qualified for autonomous therapy decisions than the general population [ 10 ]. An explanation for the positive effect of medication adherence, as defined in the second definition, on VAS-AL but not on VAS is the consideration of long-term effects and length of periods in different health states.

There are several explanations possible. However, nurses had a special evidence-based training for post-MI care, which should have insured high quality. It was found that adherence to medication but not to recommendations showed significant effects. This might indicate that in contrast to recommendations, medication was more effective with respect to VAS-AL.

Second, there might have been ceiling effects in recommendation and medication adherence, as the study population comprised only the intervention group and the intervention was designed to guarantee high adherence. Third, the power in the intervention group might not have been big enough to identify significant effects. Critical levels of adherence are rarely studied [ 34 ].

Besides, the utilized definition of medication adherence is similar to existing research. Furthermore, recommendations were only necessary if there was some kind of non-optimal behavior.

Therefore, recommendations are to some degree already an indicator of non-adherence. Lastly, even though additional analyses about the effect of VAS on adherence did not show significant results, reversed causality cannot be ruled out. Because a very specific intervention was analyzed, the results might only apply to patients after MI in similar interventions. An advantage of this study is the panel data, which allowed controlling for unobserved, time-invariant heterogeneity in the mixed effects models.

Panel data considered to be more valid to identify causal associations than cross-sectional data, as often utilized in surveys concerning the analysis of adherence [ 35 ].

Another advantage is the huge number of interviews and the prospective study design. Despite the problems discussed, this study contributes to the limited existing evidence about determinants of non-adherence and effects of adherence, especially to recommendations. Patient-reported reasons for non-adherence and recommendation types were considered to be determinants of adherence.

It was found that reasons for non-adherence differed with type of therapy healthy lifestyle vs. We found different reasons for non-adherence. For example reluctance side effects was the most important for non-adherence recommendations medication.

We also found that different recommendations were associated with different risks of non-adherence. The greatest risk of non-adherence to recommendations was associated with disease and self-management, visits no the doctor was significantly more likely to be adhered. Knowing about these determinants of non-adherence will help case-managers to take appropriate and stratified precautions to prevent non-adherence in dependence of the type of therapy.

The effect of adherence on HRQoL was sensitive to the definition of adherence. Patients who were adherent to medication within 3 years only gained 0. Therefore, it can be concluded that non-adherence to medication because of side effects was beneficial in the analyzed study population. However, the intervention group was schooled and can therefore be considered to be more qualified for therapy decisions than the general population.

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